From 44f85985215b4d819665e4cec84c00ef87aa9a7a Mon Sep 17 00:00:00 2001
From: chaObserv <154517000+chaObserv@users.noreply.github.com>
Date: Tue, 17 Feb 2026 23:56:44 +0800
Subject: [PATCH 001/194] Fix anima LLM adapter forward when manual cast
 (#12504)

---
 comfy/ldm/anima/model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/ldm/anima/model.py b/comfy/ldm/anima/model.py
index 6fb51c4a4..6fcf8df90 100644
--- a/comfy/ldm/anima/model.py
+++ b/comfy/ldm/anima/model.py
@@ -179,8 +179,8 @@ class LLMAdapter(nn.Module):
             if source_attention_mask.ndim == 2:
                 source_attention_mask = source_attention_mask.unsqueeze(1).unsqueeze(1)
 
-        x = self.in_proj(self.embed(target_input_ids))
         context = source_hidden_states
+        x = self.in_proj(self.embed(target_input_ids, out_dtype=context.dtype))
         position_ids = torch.arange(x.shape[1], device=x.device).unsqueeze(0)
         position_ids_context = torch.arange(context.shape[1], device=x.device).unsqueeze(0)
         position_embeddings = self.rotary_emb(x, position_ids)

From 5284e6bf69b6e2e856c672595fd413fd505377ee Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Tue, 17 Feb 2026 20:07:14 +0200
Subject: [PATCH 002/194] feat(api-nodes): add "viduq3-turbo" model and
 Vidu3StartEnd node; fix the price badges (#12482)

---
 comfy_api_nodes/nodes_vidu.py | 226 ++++++++++++++++++++++++++++++++--
 1 file changed, 218 insertions(+), 8 deletions(-)

diff --git a/comfy_api_nodes/nodes_vidu.py b/comfy_api_nodes/nodes_vidu.py
index 80de14dfe..bbe7ebba2 100644
--- a/comfy_api_nodes/nodes_vidu.py
+++ b/comfy_api_nodes/nodes_vidu.py
@@ -54,6 +54,7 @@ async def execute_task(
         response_model=TaskStatusResponse,
         status_extractor=lambda r: r.state,
         progress_extractor=lambda r: r.progress,
+        price_extractor=lambda r: r.credits * 0.005 if r.credits is not None else None,
         max_poll_attempts=max_poll_attempts,
     )
     if not response.creations:
@@ -1306,6 +1307,36 @@ class Vidu3TextToVideoNode(IO.ComfyNode):
                                 ),
                             ],
                         ),
+                        IO.DynamicCombo.Option(
+                            "viduq3-turbo",
+                            [
+                                IO.Combo.Input(
+                                    "aspect_ratio",
+                                    options=["16:9", "9:16", "3:4", "4:3", "1:1"],
+                                    tooltip="The aspect ratio of the output video.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720p", "1080p"],
+                                    tooltip="Resolution of the output video.",
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=1,
+                                    max=16,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Duration of the output video in seconds.",
+                                ),
+                                IO.Boolean.Input(
+                                    "audio",
+                                    default=False,
+                                    tooltip="When enabled, outputs video with sound "
+                                    "(including dialogue and sound effects).",
+                                ),
+                            ],
+                        ),
                     ],
                     tooltip="Model to use for video generation.",
                 ),
@@ -1334,13 +1365,20 @@ class Vidu3TextToVideoNode(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["model.duration", "model.resolution"]),
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.duration", "model.resolution"]),
                 expr="""
                 (
                   $res := $lookup(widgets, "model.resolution");
-                  $base := $lookup({"720p": 0.075, "1080p": 0.1}, $res);
-                  $perSec := $lookup({"720p": 0.025, "1080p": 0.05}, $res);
-                  {"type":"usd","usd": $base + $perSec * ($lookup(widgets, "model.duration") - 1)}
+                  $d := $lookup(widgets, "model.duration");
+                  $contains(widgets.model, "turbo")
+                    ? (
+                        $rate := $lookup({"720p": 0.06, "1080p": 0.08}, $res);
+                        {"type":"usd","usd": $rate * $d}
+                      )
+                    : (
+                        $rate := $lookup({"720p": 0.15, "1080p": 0.16}, $res);
+                        {"type":"usd","usd": $rate * $d}
+                      )
                 )
                 """,
             ),
@@ -1409,6 +1447,31 @@ class Vidu3ImageToVideoNode(IO.ComfyNode):
                                 ),
                             ],
                         ),
+                        IO.DynamicCombo.Option(
+                            "viduq3-turbo",
+                            [
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720p", "1080p"],
+                                    tooltip="Resolution of the output video.",
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=1,
+                                    max=16,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Duration of the output video in seconds.",
+                                ),
+                                IO.Boolean.Input(
+                                    "audio",
+                                    default=False,
+                                    tooltip="When enabled, outputs video with sound "
+                                    "(including dialogue and sound effects).",
+                                ),
+                            ],
+                        ),
                     ],
                     tooltip="Model to use for video generation.",
                 ),
@@ -1442,13 +1505,20 @@ class Vidu3ImageToVideoNode(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["model.duration", "model.resolution"]),
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.duration", "model.resolution"]),
                 expr="""
                 (
                   $res := $lookup(widgets, "model.resolution");
-                  $base := $lookup({"720p": 0.075, "1080p": 0.275, "2k": 0.35}, $res);
-                  $perSec := $lookup({"720p": 0.05, "1080p": 0.075, "2k": 0.075}, $res);
-                  {"type":"usd","usd": $base + $perSec * ($lookup(widgets, "model.duration") - 1)}
+                  $d := $lookup(widgets, "model.duration");
+                  $contains(widgets.model, "turbo")
+                    ? (
+                        $rate := $lookup({"720p": 0.06, "1080p": 0.08}, $res);
+                        {"type":"usd","usd": $rate * $d}
+                      )
+                    : (
+                        $rate := $lookup({"720p": 0.15, "1080p": 0.16, "2k": 0.2}, $res);
+                        {"type":"usd","usd": $rate * $d}
+                      )
                 )
                 """,
             ),
@@ -1481,6 +1551,145 @@ class Vidu3ImageToVideoNode(IO.ComfyNode):
         return IO.NodeOutput(await download_url_to_video_output(results[0].url))
 
 
+class Vidu3StartEndToVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Vidu3StartEndToVideoNode",
+            display_name="Vidu Q3 Start/End Frame-to-Video Generation",
+            category="api node/video/Vidu",
+            description="Generate a video from a start frame, an end frame, and a prompt.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "viduq3-pro",
+                            [
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720p", "1080p"],
+                                    tooltip="Resolution of the output video.",
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=1,
+                                    max=16,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Duration of the output video in seconds.",
+                                ),
+                                IO.Boolean.Input(
+                                    "audio",
+                                    default=False,
+                                    tooltip="When enabled, outputs video with sound "
+                                    "(including dialogue and sound effects).",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "viduq3-turbo",
+                            [
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720p", "1080p"],
+                                    tooltip="Resolution of the output video.",
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=1,
+                                    max=16,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Duration of the output video in seconds.",
+                                ),
+                                IO.Boolean.Input(
+                                    "audio",
+                                    default=False,
+                                    tooltip="When enabled, outputs video with sound "
+                                    "(including dialogue and sound effects).",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Model to use for video generation.",
+                ),
+                IO.Image.Input("first_frame"),
+                IO.Image.Input("end_frame"),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="Prompt description (max 2000 characters).",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.duration", "model.resolution"]),
+                expr="""
+                (
+                  $res := $lookup(widgets, "model.resolution");
+                  $d := $lookup(widgets, "model.duration");
+                  $contains(widgets.model, "turbo")
+                    ? (
+                        $rate := $lookup({"720p": 0.06, "1080p": 0.08}, $res);
+                        {"type":"usd","usd": $rate * $d}
+                      )
+                    : (
+                        $rate := $lookup({"720p": 0.15, "1080p": 0.16}, $res);
+                        {"type":"usd","usd": $rate * $d}
+                      )
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        first_frame: Input.Image,
+        end_frame: Input.Image,
+        prompt: str,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, max_length=2000)
+        validate_images_aspect_ratio_closeness(first_frame, end_frame, min_rel=0.8, max_rel=1.25, strict=False)
+        payload = TaskCreationRequest(
+            model=model["model"],
+            prompt=prompt,
+            duration=model["duration"],
+            seed=seed,
+            resolution=model["resolution"],
+            audio=model["audio"],
+            images=[
+                (await upload_images_to_comfyapi(cls, frame, max_images=1, mime_type="image/png"))[0]
+                for frame in (first_frame, end_frame)
+            ],
+        )
+        results = await execute_task(cls, VIDU_START_END_VIDEO, payload)
+        return IO.NodeOutput(await download_url_to_video_output(results[0].url))
+
+
 class ViduExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -1497,6 +1706,7 @@ class ViduExtension(ComfyExtension):
             ViduMultiFrameVideoNode,
             Vidu3TextToVideoNode,
             Vidu3ImageToVideoNode,
+            Vidu3StartEndToVideoNode,
         ]
 
 

From 262abf437b0666f3d00d1f335a526073503e59e4 Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Tue, 17 Feb 2026 20:25:44 +0200
Subject: [PATCH 003/194] feat(api-nodes): add Recraft V4 nodes (#12502)

---
 comfy_api_nodes/apis/recraft.py  |  45 ++++-
 comfy_api_nodes/nodes_recraft.py | 272 +++++++++++++++++++++++++++++--
 2 files changed, 297 insertions(+), 20 deletions(-)

diff --git a/comfy_api_nodes/apis/recraft.py b/comfy_api_nodes/apis/recraft.py
index 0bd7d23b3..78ededd94 100644
--- a/comfy_api_nodes/apis/recraft.py
+++ b/comfy_api_nodes/apis/recraft.py
@@ -198,11 +198,6 @@ dict_recraft_substyles_v3 = {
 }
 
 
-class RecraftModel(str, Enum):
-    recraftv3 = 'recraftv3'
-    recraftv2 = 'recraftv2'
-
-
 class RecraftImageSize(str, Enum):
     res_1024x1024 = '1024x1024'
     res_1365x1024 = '1365x1024'
@@ -221,6 +216,41 @@ class RecraftImageSize(str, Enum):
     res_1707x1024 = '1707x1024'
 
 
+RECRAFT_V4_SIZES = [
+    "1024x1024",
+    "1536x768",
+    "768x1536",
+    "1280x832",
+    "832x1280",
+    "1216x896",
+    "896x1216",
+    "1152x896",
+    "896x1152",
+    "832x1344",
+    "1280x896",
+    "896x1280",
+    "1344x768",
+    "768x1344",
+]
+
+RECRAFT_V4_PRO_SIZES = [
+    "2048x2048",
+    "3072x1536",
+    "1536x3072",
+    "2560x1664",
+    "1664x2560",
+    "2432x1792",
+    "1792x2432",
+    "2304x1792",
+    "1792x2304",
+    "1664x2688",
+    "1434x1024",
+    "1024x1434",
+    "2560x1792",
+    "1792x2560",
+]
+
+
 class RecraftColorObject(BaseModel):
     rgb: list[int] = Field(..., description='An array of 3 integer values in range of 0...255 defining RGB Color Model')
 
@@ -234,17 +264,16 @@ class RecraftControlsObject(BaseModel):
 
 class RecraftImageGenerationRequest(BaseModel):
     prompt: str = Field(..., description='The text prompt describing the image to generate')
-    size: RecraftImageSize | None = Field(None, description='The size of the generated image (e.g., "1024x1024")')
+    size: str | None = Field(None, description='The size of the generated image (e.g., "1024x1024")')
     n: int = Field(..., description='The number of images to generate')
     negative_prompt: str | None = Field(None, description='A text description of undesired elements on an image')
-    model: RecraftModel | None = Field(RecraftModel.recraftv3, description='The model to use for generation (e.g., "recraftv3")')
+    model: str = Field(...)
     style: str | None = Field(None, description='The style to apply to the generated image (e.g., "digital_illustration")')
     substyle: str | None = Field(None, description='The substyle to apply to the generated image, depending on the style input')
     controls: RecraftControlsObject | None = Field(None, description='A set of custom parameters to tweak generation process')
     style_id: str | None = Field(None, description='Use a previously uploaded style as a reference; UUID')
     strength: float | None = Field(None, description='Defines the difference with the original image, should lie in [0, 1], where 0 means almost identical, and 1 means miserable similarity')
     random_seed: int | None = Field(None, description="Seed for video generation")
-    # text_layout
 
 
 class RecraftReturnedObject(BaseModel):
diff --git a/comfy_api_nodes/nodes_recraft.py b/comfy_api_nodes/nodes_recraft.py
index 3a1f32263..773cb7dbe 100644
--- a/comfy_api_nodes/nodes_recraft.py
+++ b/comfy_api_nodes/nodes_recraft.py
@@ -1,5 +1,4 @@
 from io import BytesIO
-from typing import Optional, Union
 
 import aiohttp
 import torch
@@ -9,6 +8,8 @@ from typing_extensions import override
 from comfy.utils import ProgressBar
 from comfy_api.latest import IO, ComfyExtension
 from comfy_api_nodes.apis.recraft import (
+    RECRAFT_V4_PRO_SIZES,
+    RECRAFT_V4_SIZES,
     RecraftColor,
     RecraftColorChain,
     RecraftControls,
@@ -18,7 +19,6 @@ from comfy_api_nodes.apis.recraft import (
     RecraftImageGenerationResponse,
     RecraftImageSize,
     RecraftIO,
-    RecraftModel,
     RecraftStyle,
     RecraftStyleV3,
     get_v3_substyles,
@@ -39,7 +39,7 @@ async def handle_recraft_file_request(
     cls: type[IO.ComfyNode],
     image: torch.Tensor,
     path: str,
-    mask: Optional[torch.Tensor] = None,
+    mask: torch.Tensor | None = None,
     total_pixels: int = 4096 * 4096,
     timeout: int = 1024,
     request=None,
@@ -73,11 +73,11 @@ async def handle_recraft_file_request(
 def recraft_multipart_parser(
     data,
     parent_key=None,
-    formatter: Optional[type[callable]] = None,
-    converted_to_check: Optional[list[list]] = None,
+    formatter: type[callable] | None = None,
+    converted_to_check: list[list] | None = None,
     is_list: bool = False,
     return_mode: str = "formdata",  # "dict" | "formdata"
-) -> Union[dict, aiohttp.FormData]:
+) -> dict | aiohttp.FormData:
     """
     Formats data such that multipart/form-data will work with aiohttp library when both files and data are present.
 
@@ -309,7 +309,7 @@ class RecraftStyleInfiniteStyleLibrary(IO.ComfyNode):
             node_id="RecraftStyleV3InfiniteStyleLibrary",
             display_name="Recraft Style - Infinite Style Library",
             category="api node/image/Recraft",
-            description="Select style based on preexisting UUID from Recraft's Infinite Style Library.",
+            description="Choose style based on preexisting UUID from Recraft's Infinite Style Library.",
             inputs=[
                 IO.String.Input("style_id", default="", tooltip="UUID of style from Infinite Style Library."),
             ],
@@ -485,7 +485,7 @@ class RecraftTextToImageNode(IO.ComfyNode):
             data=RecraftImageGenerationRequest(
                 prompt=prompt,
                 negative_prompt=negative_prompt,
-                model=RecraftModel.recraftv3,
+                model="recraftv3",
                 size=size,
                 n=n,
                 style=recraft_style.style,
@@ -598,7 +598,7 @@ class RecraftImageToImageNode(IO.ComfyNode):
         request = RecraftImageGenerationRequest(
             prompt=prompt,
             negative_prompt=negative_prompt,
-            model=RecraftModel.recraftv3,
+            model="recraftv3",
             n=n,
             strength=round(strength, 2),
             style=recraft_style.style,
@@ -698,7 +698,7 @@ class RecraftImageInpaintingNode(IO.ComfyNode):
         request = RecraftImageGenerationRequest(
             prompt=prompt,
             negative_prompt=negative_prompt,
-            model=RecraftModel.recraftv3,
+            model="recraftv3",
             n=n,
             style=recraft_style.style,
             substyle=recraft_style.substyle,
@@ -810,7 +810,7 @@ class RecraftTextToVectorNode(IO.ComfyNode):
             data=RecraftImageGenerationRequest(
                 prompt=prompt,
                 negative_prompt=negative_prompt,
-                model=RecraftModel.recraftv3,
+                model="recraftv3",
                 size=size,
                 n=n,
                 style=recraft_style.style,
@@ -933,7 +933,7 @@ class RecraftReplaceBackgroundNode(IO.ComfyNode):
         request = RecraftImageGenerationRequest(
             prompt=prompt,
             negative_prompt=negative_prompt,
-            model=RecraftModel.recraftv3,
+            model="recraftv3",
             n=n,
             style=recraft_style.style,
             substyle=recraft_style.substyle,
@@ -1078,6 +1078,252 @@ class RecraftCreativeUpscaleNode(RecraftCrispUpscaleNode):
         )
 
 
+class RecraftV4TextToImageNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftV4TextToImageNode",
+            display_name="Recraft V4 Text to Image",
+            category="api node/image/Recraft",
+            description="Generates images using Recraft V4 or V4 Pro models.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="Prompt for the image generation. Maximum 10,000 characters.",
+                ),
+                IO.String.Input(
+                    "negative_prompt",
+                    multiline=True,
+                    tooltip="An optional text description of undesired elements on an image.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "recraftv4",
+                            [
+                                IO.Combo.Input(
+                                    "size",
+                                    options=RECRAFT_V4_SIZES,
+                                    default="1024x1024",
+                                    tooltip="The size of the generated image.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "recraftv4_pro",
+                            [
+                                IO.Combo.Input(
+                                    "size",
+                                    options=RECRAFT_V4_PRO_SIZES,
+                                    default="2048x2048",
+                                    tooltip="The size of the generated image.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="The model to use for generation.",
+                ),
+                IO.Int.Input(
+                    "n",
+                    default=1,
+                    min=1,
+                    max=6,
+                    tooltip="The number of images to generate.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+                IO.Custom(RecraftIO.CONTROLS).Input(
+                    "recraft_controls",
+                    tooltip="Optional additional controls over the generation via the Recraft Controls node.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "n"]),
+                expr="""
+                (
+                    $prices := {"recraftv4": 0.04, "recraftv4_pro": 0.25};
+                    {"type":"usd","usd": $lookup($prices, widgets.model) * widgets.n}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        negative_prompt: str,
+        model: dict,
+        n: int,
+        seed: int,
+        recraft_controls: RecraftControls | None = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False, min_length=1, max_length=10000)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/recraft/image_generation", method="POST"),
+            response_model=RecraftImageGenerationResponse,
+            data=RecraftImageGenerationRequest(
+                prompt=prompt,
+                negative_prompt=negative_prompt if negative_prompt else None,
+                model=model["model"],
+                size=model["size"],
+                n=n,
+                controls=recraft_controls.create_api_model() if recraft_controls else None,
+            ),
+            max_retries=1,
+        )
+        images = []
+        for data in response.data:
+            with handle_recraft_image_output():
+                image = bytesio_to_image_tensor(await download_url_as_bytesio(data.url, timeout=1024))
+            if len(image.shape) < 4:
+                image = image.unsqueeze(0)
+            images.append(image)
+        return IO.NodeOutput(torch.cat(images, dim=0))
+
+
+class RecraftV4TextToVectorNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftV4TextToVectorNode",
+            display_name="Recraft V4 Text to Vector",
+            category="api node/image/Recraft",
+            description="Generates SVG using Recraft V4 or V4 Pro models.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="Prompt for the image generation. Maximum 10,000 characters.",
+                ),
+                IO.String.Input(
+                    "negative_prompt",
+                    multiline=True,
+                    tooltip="An optional text description of undesired elements on an image.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "recraftv4",
+                            [
+                                IO.Combo.Input(
+                                    "size",
+                                    options=RECRAFT_V4_SIZES,
+                                    default="1024x1024",
+                                    tooltip="The size of the generated image.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "recraftv4_pro",
+                            [
+                                IO.Combo.Input(
+                                    "size",
+                                    options=RECRAFT_V4_PRO_SIZES,
+                                    default="2048x2048",
+                                    tooltip="The size of the generated image.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="The model to use for generation.",
+                ),
+                IO.Int.Input(
+                    "n",
+                    default=1,
+                    min=1,
+                    max=6,
+                    tooltip="The number of images to generate.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+                IO.Custom(RecraftIO.CONTROLS).Input(
+                    "recraft_controls",
+                    tooltip="Optional additional controls over the generation via the Recraft Controls node.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.SVG.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "n"]),
+                expr="""
+                (
+                    $prices := {"recraftv4": 0.08, "recraftv4_pro": 0.30};
+                    {"type":"usd","usd": $lookup($prices, widgets.model) * widgets.n}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        negative_prompt: str,
+        model: dict,
+        n: int,
+        seed: int,
+        recraft_controls: RecraftControls | None = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False, min_length=1, max_length=10000)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/recraft/image_generation", method="POST"),
+            response_model=RecraftImageGenerationResponse,
+            data=RecraftImageGenerationRequest(
+                prompt=prompt,
+                negative_prompt=negative_prompt if negative_prompt else None,
+                model=model["model"],
+                size=model["size"],
+                n=n,
+                style="vector_illustration",
+                substyle=None,
+                controls=recraft_controls.create_api_model() if recraft_controls else None,
+            ),
+            max_retries=1,
+        )
+        svg_data = []
+        for data in response.data:
+            svg_data.append(await download_url_as_bytesio(data.url, timeout=1024))
+        return IO.NodeOutput(SVG(svg_data))
+
+
 class RecraftExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -1098,6 +1344,8 @@ class RecraftExtension(ComfyExtension):
             RecraftCreateStyleNode,
             RecraftColorRGBNode,
             RecraftControlsNode,
+            RecraftV4TextToImageNode,
+            RecraftV4TextToVectorNode,
         ]
 
 

From 73c3f869737bbb1035f6b72b2e1068a1a5642764 Mon Sep 17 00:00:00 2001
From: ComfyUI Wiki <contact@comfyui-wiki.com>
Date: Wed, 18 Feb 2026 02:25:55 +0800
Subject: [PATCH 004/194] chore: update workflow templates to v0.8.43 (#12507)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 0930bbbb8..881d6bd58 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.38.14
-comfyui-workflow-templates==0.8.42
+comfyui-workflow-templates==0.8.43
 comfyui-embedded-docs==0.4.1
 torch
 torchsde

From 19236edfa4d2f66070d66a6b3aee592c9c2ad574 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 17 Feb 2026 13:28:06 -0500
Subject: [PATCH 005/194] ComfyUI v0.14.1

---
 comfyui_version.py | 2 +-
 pyproject.toml     | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfyui_version.py b/comfyui_version.py
index 8f7f3228e..f24c15cc5 100644
--- a/comfyui_version.py
+++ b/comfyui_version.py
@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.14.0"
+__version__ = "0.14.1"
diff --git a/pyproject.toml b/pyproject.toml
index b132bb9c4..51c3d224d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.14.0"
+version = "0.14.1"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

From 58dcc97dcfadc548ac8d8d5e80741ddfb807d213 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Tue, 17 Feb 2026 12:32:27 -0800
Subject: [PATCH 006/194] ops: limit return of requants (#12506)

This check was far too broad and the dtype is not a reliable indicator
of wanting the requant (as QT returns the compute dtype as the dtype).
So explictly plumb whether fp8mm wants the requant or not.
---
 comfy/ops.py | 17 ++++++++---------
 1 file changed, 8 insertions(+), 9 deletions(-)

diff --git a/comfy/ops.py b/comfy/ops.py
index 026062f56..a6c642795 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -79,7 +79,7 @@ def cast_to_input(weight, input, non_blocking=False, copy=True):
     return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy)
 
 
-def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compute_dtype):
+def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compute_dtype, want_requant):
     offload_stream = None
     xfer_dest = None
 
@@ -170,10 +170,10 @@ def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compu
             #FIXME: this is not accurate, we need to be sensitive to the compute dtype
             x = lowvram_fn(x)
             if (isinstance(orig, QuantizedTensor) and
-                (orig.dtype == dtype and len(fns) == 0 or update_weight)):
+                (want_requant and len(fns) == 0 or update_weight)):
                 seed = comfy.utils.string_to_seed(s.seed_key)
                 y = QuantizedTensor.from_float(x, s.layout_type, scale="recalculate", stochastic_rounding=seed)
-                if orig.dtype == dtype and len(fns) == 0:
+                if want_requant and len(fns) == 0:
                     #The layer actually wants our freshly saved QT
                     x = y
             elif update_weight:
@@ -194,7 +194,7 @@ def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compu
     return weight, bias, (offload_stream, device if signature is not None else None, None)
 
 
-def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, offloadable=False, compute_dtype=None):
+def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, offloadable=False, compute_dtype=None, want_requant=False):
     # NOTE: offloadable=False is a a legacy and if you are a custom node author reading this please pass
     # offloadable=True and call uncast_bias_weight() after your last usage of the weight/bias. This
     # will add async-offload support to your cast and improve performance.
@@ -212,7 +212,7 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
     non_blocking = comfy.model_management.device_supports_non_blocking(device)
 
     if hasattr(s, "_v"):
-        return cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compute_dtype)
+        return cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compute_dtype, want_requant)
 
     if offloadable and (device != s.weight.device or
                         (s.bias is not None and device != s.bias.device)):
@@ -850,8 +850,8 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
             def _forward(self, input, weight, bias):
                 return torch.nn.functional.linear(input, weight, bias)
 
-            def forward_comfy_cast_weights(self, input, compute_dtype=None):
-                weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True, compute_dtype=compute_dtype)
+            def forward_comfy_cast_weights(self, input, compute_dtype=None, want_requant=False):
+                weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True, compute_dtype=compute_dtype, want_requant=want_requant)
                 x = self._forward(input, weight, bias)
                 uncast_bias_weight(self, weight, bias, offload_stream)
                 return x
@@ -881,8 +881,7 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                             scale = comfy.model_management.cast_to_device(scale, input.device, None)
                         input = QuantizedTensor.from_float(input_reshaped, self.layout_type, scale=scale)
 
-
-                output = self.forward_comfy_cast_weights(input, compute_dtype)
+                output = self.forward_comfy_cast_weights(input, compute_dtype, want_requant=isinstance(input, QuantizedTensor))
 
                 # Reshape output back to 3D if input was 3D
                 if reshaped_3d:

From 6c14f129af4fd94c4197644e6950bddbba0c9e51 Mon Sep 17 00:00:00 2001
From: Comfy Org PR Bot <snomiao+comfy-pr@gmail.com>
Date: Wed, 18 Feb 2026 06:41:34 +0900
Subject: [PATCH 007/194] Bump comfyui-frontend-package to 1.39.14 (#12494)

* Bump comfyui-frontend-package to 1.39.13

* Update requirements.txt

---------

Co-authored-by: Christian Byrne <cbyrne@comfy.org>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 881d6bd58..807fea5e0 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-comfyui-frontend-package==1.38.14
+comfyui-frontend-package==1.39.14
 comfyui-workflow-templates==0.8.43
 comfyui-embedded-docs==0.4.1
 torch

From 8ad38d2073b019a204f730182dcf5456fb260858 Mon Sep 17 00:00:00 2001
From: Terry Jia <terryjia88@gmail.com>
Date: Tue, 17 Feb 2026 20:13:39 -0500
Subject: [PATCH 008/194] BBox widget (#11594)

* Boundingbox widget

* code improve

---------

Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
Co-authored-by: Christian Byrne <cbyrne@comfy.org>
---
 comfy_api/latest/_io.py      | 25 ++++++++++++++++
 comfy_extras/nodes_images.py | 56 +++++++++++++++++++++++++++++++++++-
 2 files changed, 80 insertions(+), 1 deletion(-)

diff --git a/comfy_api/latest/_io.py b/comfy_api/latest/_io.py
index d18330d0b..312681249 100644
--- a/comfy_api/latest/_io.py
+++ b/comfy_api/latest/_io.py
@@ -1209,6 +1209,30 @@ class Color(ComfyTypeIO):
       def as_dict(self):
           return super().as_dict()
 
+@comfytype(io_type="BOUNDING_BOX")
+class BoundingBox(ComfyTypeIO):
+    class BoundingBoxDict(TypedDict):
+        x: int
+        y: int
+        width: int
+        height: int
+    Type = BoundingBoxDict
+
+    class Input(WidgetInput):
+        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
+                     socketless: bool=True, default: dict=None, component: str=None):
+            super().__init__(id, display_name, optional, tooltip, None, default, socketless)
+            self.component = component
+            if default is None:
+                self.default = {"x": 0, "y": 0, "width": 512, "height": 512}
+
+        def as_dict(self):
+            d = super().as_dict()
+            if self.component:
+                d["component"] = self.component
+            return d
+
+
 DYNAMIC_INPUT_LOOKUP: dict[str, Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]] = {}
 def register_dynamic_input_func(io_type: str, func: Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]):
     DYNAMIC_INPUT_LOOKUP[io_type] = func
@@ -2190,5 +2214,6 @@ __all__ = [
     "ImageCompare",
     "PriceBadgeDepends",
     "PriceBadge",
+    "BoundingBox",
     "NodeReplace",
 ]
diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py
index cb4fb24a1..23419a65d 100644
--- a/comfy_extras/nodes_images.py
+++ b/comfy_extras/nodes_images.py
@@ -23,8 +23,9 @@ class ImageCrop(IO.ComfyNode):
         return IO.Schema(
             node_id="ImageCrop",
             search_aliases=["trim"],
-            display_name="Image Crop",
+            display_name="Image Crop (Deprecated)",
             category="image/transform",
+            is_deprecated=True,
             inputs=[
                 IO.Image.Input("image"),
                 IO.Int.Input("width", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
@@ -47,6 +48,57 @@ class ImageCrop(IO.ComfyNode):
     crop = execute  # TODO: remove
 
 
+class ImageCropV2(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ImageCropV2",
+            search_aliases=["trim"],
+            display_name="Image Crop",
+            category="image/transform",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.BoundingBox.Input("crop_region", component="ImageCrop"),
+            ],
+            outputs=[IO.Image.Output()],
+        )
+
+    @classmethod
+    def execute(cls, image, crop_region) -> IO.NodeOutput:
+        x = crop_region.get("x", 0)
+        y = crop_region.get("y", 0)
+        width = crop_region.get("width", 512)
+        height = crop_region.get("height", 512)
+
+        x = min(x, image.shape[2] - 1)
+        y = min(y, image.shape[1] - 1)
+        to_x = width + x
+        to_y = height + y
+        img = image[:,y:to_y, x:to_x, :]
+        return IO.NodeOutput(img, ui=UI.PreviewImage(img))
+
+
+class BoundingBox(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="PrimitiveBoundingBox",
+            display_name="Bounding Box",
+            category="utils/primitive",
+            inputs=[
+                IO.Int.Input("x", default=0, min=0, max=MAX_RESOLUTION),
+                IO.Int.Input("y", default=0, min=0, max=MAX_RESOLUTION),
+                IO.Int.Input("width", default=512, min=1, max=MAX_RESOLUTION),
+                IO.Int.Input("height", default=512, min=1, max=MAX_RESOLUTION),
+            ],
+            outputs=[IO.BoundingBox.Output()],
+        )
+
+    @classmethod
+    def execute(cls, x, y, width, height) -> IO.NodeOutput:
+        return IO.NodeOutput({"x": x, "y": y, "width": width, "height": height})
+
+
 class RepeatImageBatch(IO.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -632,6 +684,8 @@ class ImagesExtension(ComfyExtension):
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
         return [
             ImageCrop,
+            ImageCropV2,
+            BoundingBox,
             RepeatImageBatch,
             ImageFromBatch,
             ImageAddNoise,

From 83dd65f23ae78186df5be7f579af5c0cdb61f0f9 Mon Sep 17 00:00:00 2001
From: Hunter <huntcsg@users.noreply.github.com>
Date: Wed, 18 Feb 2026 00:03:54 -0500
Subject: [PATCH 009/194] fix: use glob matching for Gemini image MIME types
 (#12511)

gemini-3-pro-image-preview nondeterministically returns image/jpeg
instead of image/png. get_image_from_response() hardcoded
get_parts_by_type(response, "image/png"), silently dropping JPEG
responses and falling back to torch.zeros (all-black output).

Add _mime_matches() helper using fnmatch for glob-style MIME matching.
Change get_image_from_response() to request "image/*" so any image
format returned by the API is correctly captured.
---
 comfy_api_nodes/nodes_gemini.py | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/comfy_api_nodes/nodes_gemini.py b/comfy_api_nodes/nodes_gemini.py
index 3b31caa7b..5287a777a 100644
--- a/comfy_api_nodes/nodes_gemini.py
+++ b/comfy_api_nodes/nodes_gemini.py
@@ -6,6 +6,7 @@ See: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/infer
 import base64
 import os
 from enum import Enum
+from fnmatch import fnmatch
 from io import BytesIO
 from typing import Literal
 
@@ -119,6 +120,13 @@ async def create_image_parts(
     return image_parts
 
 
+def _mime_matches(mime: GeminiMimeType | None, pattern: str) -> bool:
+    """Check if a MIME type matches a pattern. Supports fnmatch globs (e.g. 'image/*')."""
+    if mime is None:
+        return False
+    return fnmatch(mime.value, pattern)
+
+
 def get_parts_by_type(response: GeminiGenerateContentResponse, part_type: Literal["text"] | str) -> list[GeminiPart]:
     """
     Filter response parts by their type.
@@ -151,9 +159,9 @@ def get_parts_by_type(response: GeminiGenerateContentResponse, part_type: Litera
         for part in candidate.content.parts:
             if part_type == "text" and part.text:
                 parts.append(part)
-            elif part.inlineData and part.inlineData.mimeType == part_type:
+            elif part.inlineData and _mime_matches(part.inlineData.mimeType, part_type):
                 parts.append(part)
-            elif part.fileData and part.fileData.mimeType == part_type:
+            elif part.fileData and _mime_matches(part.fileData.mimeType, part_type):
                 parts.append(part)
 
     if not parts and blocked_reasons:
@@ -178,7 +186,7 @@ def get_text_from_response(response: GeminiGenerateContentResponse) -> str:
 
 async def get_image_from_response(response: GeminiGenerateContentResponse) -> Input.Image:
     image_tensors: list[Input.Image] = []
-    parts = get_parts_by_type(response, "image/png")
+    parts = get_parts_by_type(response, "image/*")
     for part in parts:
         if part.inlineData:
             image_data = base64.b64decode(part.inlineData.data)

From 239ddd332724c63934bf517cfc6d0026214d8aee Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Wed, 18 Feb 2026 09:15:23 +0200
Subject: [PATCH 010/194] fix(api-nodes): add price badge for Rodin Gen-2 node
 (#12512)

---
 comfy_api_nodes/nodes_rodin.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/comfy_api_nodes/nodes_rodin.py b/comfy_api_nodes/nodes_rodin.py
index f9cff121f..9c1adaa51 100644
--- a/comfy_api_nodes/nodes_rodin.py
+++ b/comfy_api_nodes/nodes_rodin.py
@@ -505,6 +505,9 @@ class Rodin3D_Gen2(IO.ComfyNode):
                 IO.Hidden.unique_id,
             ],
             is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.4}""",
+            ),
         )
 
     @classmethod

From f262444dd4818b6acdbc1350856679dd6245f7f5 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Wed, 18 Feb 2026 15:36:35 -0800
Subject: [PATCH 011/194] Add simple 3 band equalizer node for audio. (#12519)

---
 comfy_extras/nodes_audio.py | 62 +++++++++++++++++++++++++++++++++++++
 1 file changed, 62 insertions(+)

diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py
index b63dd8e97..7e74169f2 100644
--- a/comfy_extras/nodes_audio.py
+++ b/comfy_extras/nodes_audio.py
@@ -698,6 +698,67 @@ class EmptyAudio(IO.ComfyNode):
     create_empty_audio = execute  # TODO: remove
 
 
+class AudioEqualizer3Band(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="AudioEqualizer3Band",
+            search_aliases=["eq", "bass boost", "treble boost", "equalizer"],
+            display_name="Audio Equalizer (3-Band)",
+            category="audio",
+            is_experimental=True,
+            inputs=[
+                IO.Audio.Input("audio"),
+                IO.Float.Input("low_gain_dB", default=0.0, min=-24.0, max=24.0, step=0.1, tooltip="Gain for Low frequencies (Bass)"),
+                IO.Int.Input("low_freq", default=100, min=20, max=500, tooltip="Cutoff frequency for Low shelf"),
+                IO.Float.Input("mid_gain_dB", default=0.0, min=-24.0, max=24.0, step=0.1, tooltip="Gain for Mid frequencies"),
+                IO.Int.Input("mid_freq", default=1000, min=200, max=4000, tooltip="Center frequency for Mids"),
+                IO.Float.Input("mid_q", default=0.707, min=0.1, max=10.0, step=0.1, tooltip="Q factor (bandwidth) for Mids"),
+                IO.Float.Input("high_gain_dB", default=0.0, min=-24.0, max=24.0, step=0.1, tooltip="Gain for High frequencies (Treble)"),
+                IO.Int.Input("high_freq", default=5000, min=1000, max=15000, tooltip="Cutoff frequency for High shelf"),
+            ],
+            outputs=[IO.Audio.Output()],
+        )
+
+    @classmethod
+    def execute(cls, audio, low_gain_dB, low_freq, mid_gain_dB, mid_freq, mid_q, high_gain_dB, high_freq) -> IO.NodeOutput:
+        waveform = audio["waveform"]
+        sample_rate = audio["sample_rate"]
+        eq_waveform = waveform.clone()
+
+        # 1. Apply Low Shelf (Bass)
+        if low_gain_dB != 0:
+            eq_waveform = torchaudio.functional.bass_biquad(
+                eq_waveform,
+                sample_rate,
+                gain=low_gain_dB,
+                central_freq=float(low_freq),
+                Q=0.707
+            )
+
+        # 2. Apply Peaking EQ (Mids)
+        if mid_gain_dB != 0:
+            eq_waveform = torchaudio.functional.equalizer_biquad(
+                eq_waveform,
+                sample_rate,
+                center_freq=float(mid_freq),
+                gain=mid_gain_dB,
+                Q=mid_q
+            )
+
+        # 3. Apply High Shelf (Treble)
+        if high_gain_dB != 0:
+            eq_waveform = torchaudio.functional.treble_biquad(
+                eq_waveform,
+                sample_rate,
+                gain=high_gain_dB,
+                central_freq=float(high_freq),
+                Q=0.707
+            )
+
+        return IO.NodeOutput({"waveform": eq_waveform, "sample_rate": sample_rate})
+
+
 class AudioExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -720,6 +781,7 @@ class AudioExtension(ComfyExtension):
             AudioMerge,
             AudioAdjustVolume,
             EmptyAudio,
+            AudioEqualizer3Band,
         ]
 
 async def comfy_entrypoint() -> AudioExtension:

From 6d11cc73549e14a0a31e9ff8c90bfd71b380fe2c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?=
 <40791699+kijai@users.noreply.github.com>
Date: Thu, 19 Feb 2026 03:49:43 +0200
Subject: [PATCH 012/194] feat: Add basic text generation support with native
 models, initially supporting Gemma3 (#12392)

---
 comfy/sd.py                             |  29 +++-
 comfy/sd1_clip.py                       |  18 +++
 comfy/text_encoders/llama.py            | 148 +++++++++++++++++++-
 comfy/text_encoders/lt.py               |  92 ++++++++++---
 comfy/text_encoders/lumina2.py          |  36 ++++-
 comfy/text_encoders/spiece_tokenizer.py |  27 +++-
 comfy/utils.py                          |   8 ++
 comfy_extras/nodes_textgen.py           | 176 ++++++++++++++++++++++++
 nodes.py                                |   1 +
 9 files changed, 502 insertions(+), 33 deletions(-)
 create mode 100644 comfy_extras/nodes_textgen.py

diff --git a/comfy/sd.py b/comfy/sd.py
index f65e7cadd..164f30803 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -423,6 +423,19 @@ class CLIP:
     def get_key_patches(self):
         return self.patcher.get_key_patches()
 
+    def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None):
+        self.cond_stage_model.reset_clip_options()
+
+        if self.layer_idx is not None:
+            self.cond_stage_model.set_clip_options({"layer": self.layer_idx})
+
+        self.load_model()
+        self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device})
+        return self.cond_stage_model.generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed)
+
+    def decode(self, token_ids, skip_special_tokens=True):
+        return self.tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)
+
 class VAE:
     def __init__(self, sd=None, device=None, config=None, dtype=None, metadata=None):
         if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
@@ -1182,6 +1195,7 @@ class TEModel(Enum):
     JINA_CLIP_2 = 19
     QWEN3_8B = 20
     QWEN3_06B = 21
+    GEMMA_3_4B_VISION = 22
 
 
 def detect_te_model(sd):
@@ -1210,7 +1224,10 @@ def detect_te_model(sd):
         if 'model.layers.47.self_attn.q_norm.weight' in sd:
             return TEModel.GEMMA_3_12B
         if 'model.layers.0.self_attn.q_norm.weight' in sd:
-            return TEModel.GEMMA_3_4B
+            if 'vision_model.embeddings.patch_embedding.weight' in sd:
+                return TEModel.GEMMA_3_4B_VISION
+            else:
+                return TEModel.GEMMA_3_4B
         return TEModel.GEMMA_2_2B
     if 'model.layers.0.self_attn.k_proj.bias' in sd:
         weight = sd['model.layers.0.self_attn.k_proj.bias']
@@ -1270,6 +1287,8 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
         else:
             if "text_projection" in clip_data[i]:
                 clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) #old models saved with the CLIPSave node
+        if "lm_head.weight" in clip_data[i]:
+            clip_data[i]["model.lm_head.weight"] = clip_data[i].pop("lm_head.weight") # prefix missing in some models
 
     tokenizer_data = {}
     clip_target = EmptyClass()
@@ -1335,6 +1354,14 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data), model_type="gemma3_4b")
             clip_target.tokenizer = comfy.text_encoders.lumina2.NTokenizer
             tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
+        elif te_model == TEModel.GEMMA_3_4B_VISION:
+            clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data), model_type="gemma3_4b_vision")
+            clip_target.tokenizer = comfy.text_encoders.lumina2.NTokenizer
+            tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
+        elif te_model == TEModel.GEMMA_3_12B:
+            clip_target.clip = comfy.text_encoders.lt.gemma3_te(**llama_detect(clip_data))
+            clip_target.tokenizer = comfy.text_encoders.lt.Gemma3_12BTokenizer
+            tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
         elif te_model == TEModel.LLAMA3_8:
             clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**llama_detect(clip_data),
                                                                         clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None)
diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index b564d1529..d9d014055 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -308,6 +308,15 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
     def load_sd(self, sd):
         return self.transformer.load_state_dict(sd, strict=False, assign=getattr(self, "can_assign_sd", False))
 
+    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, stop_tokens=[]):
+        if isinstance(tokens, dict):
+            tokens_only = next(iter(tokens.values())) # todo: get this better?
+        else:
+            tokens_only = tokens
+        tokens_only = [[t[0] for t in b] for b in tokens_only]
+        embeds = self.process_tokens(tokens_only, device=self.execution_device)[0]
+        return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, stop_tokens)
+
 def parse_parentheses(string):
     result = []
     current_item = ""
@@ -663,6 +672,9 @@ class SDTokenizer:
     def state_dict(self):
         return {}
 
+    def decode(self, token_ids, skip_special_tokens=True):
+        return self.tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)
+
 class SD1Tokenizer:
     def __init__(self, embedding_directory=None, tokenizer_data={}, clip_name="l", tokenizer=SDTokenizer, name=None):
         if name is not None:
@@ -686,6 +698,9 @@ class SD1Tokenizer:
     def state_dict(self):
         return getattr(self, self.clip).state_dict()
 
+    def decode(self, token_ids, skip_special_tokens=True):
+        return getattr(self, self.clip).decode(token_ids, skip_special_tokens=skip_special_tokens)
+
 class SD1CheckpointClipModel(SDClipModel):
     def __init__(self, device="cpu", dtype=None, model_options={}):
         super().__init__(device=device, return_projected_pooled=False, dtype=dtype, model_options=model_options)
@@ -722,3 +737,6 @@ class SD1ClipModel(torch.nn.Module):
 
     def load_sd(self, sd):
         return getattr(self, self.clip).load_sd(sd)
+
+    def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None):
+        return getattr(self, self.clip).generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed)
diff --git a/comfy/text_encoders/llama.py b/comfy/text_encoders/llama.py
index 54f3d5595..e5d21fa74 100644
--- a/comfy/text_encoders/llama.py
+++ b/comfy/text_encoders/llama.py
@@ -3,6 +3,8 @@ import torch.nn as nn
 from dataclasses import dataclass
 from typing import Optional, Any, Tuple
 import math
+from tqdm import tqdm
+import comfy.utils
 
 from comfy.ldm.modules.attention import optimized_attention_for_device
 import comfy.model_management
@@ -313,6 +315,13 @@ class Gemma3_4B_Config:
     final_norm: bool = True
     lm_head: bool = False
 
+GEMMA3_VISION_CONFIG = {"num_channels": 3, "hidden_act": "gelu_pytorch_tanh", "hidden_size": 1152, "image_size": 896, "intermediate_size": 4304, "model_type": "siglip_vision_model", "num_attention_heads": 16, "num_hidden_layers": 27, "patch_size": 14}
+
+@dataclass
+class Gemma3_4B_Vision_Config(Gemma3_4B_Config):
+    vision_config = GEMMA3_VISION_CONFIG
+    mm_tokens_per_image = 256
+
 @dataclass
 class Gemma3_12B_Config:
     vocab_size: int = 262208
@@ -336,7 +345,7 @@ class Gemma3_12B_Config:
     rope_scale = [8.0, 1.0]
     final_norm: bool = True
     lm_head: bool = False
-    vision_config = {"num_channels": 3, "hidden_act": "gelu_pytorch_tanh", "hidden_size": 1152, "image_size": 896, "intermediate_size": 4304, "model_type": "siglip_vision_model", "num_attention_heads": 16, "num_hidden_layers": 27, "patch_size": 14}
+    vision_config = GEMMA3_VISION_CONFIG
     mm_tokens_per_image = 256
 
 class RMSNorm(nn.Module):
@@ -441,8 +450,10 @@ class Attention(nn.Module):
         freqs_cis: Optional[torch.Tensor] = None,
         optimized_attention=None,
         past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        sliding_window: Optional[int] = None,
     ):
         batch_size, seq_length, _ = hidden_states.shape
+
         xq = self.q_proj(hidden_states)
         xk = self.k_proj(hidden_states)
         xv = self.v_proj(hidden_states)
@@ -477,6 +488,11 @@ class Attention(nn.Module):
             else:
                 present_key_value = (xk, xv, index + num_tokens)
 
+            if sliding_window is not None and xk.shape[2] > sliding_window:
+                xk = xk[:, :, -sliding_window:]
+                xv = xv[:, :, -sliding_window:]
+                attention_mask = attention_mask[..., -sliding_window:] if attention_mask is not None else None
+
         xk = xk.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
         xv = xv.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
 
@@ -559,10 +575,12 @@ class TransformerBlockGemma2(nn.Module):
         optimized_attention=None,
         past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     ):
+        sliding_window = None
         if self.transformer_type == 'gemma3':
             if self.sliding_attention:
+                sliding_window = self.sliding_attention
                 if x.shape[1] > self.sliding_attention:
-                    sliding_mask = torch.full((x.shape[1], x.shape[1]), float("-inf"), device=x.device, dtype=x.dtype)
+                    sliding_mask = torch.full((x.shape[1], x.shape[1]), torch.finfo(x.dtype).min, device=x.device, dtype=x.dtype)
                     sliding_mask.tril_(diagonal=-self.sliding_attention)
                     if attention_mask is not None:
                         attention_mask = attention_mask + sliding_mask
@@ -581,6 +599,7 @@ class TransformerBlockGemma2(nn.Module):
             freqs_cis=freqs_cis,
             optimized_attention=optimized_attention,
             past_key_value=past_key_value,
+            sliding_window=sliding_window,
         )
 
         x = self.post_attention_layernorm(x)
@@ -765,6 +784,104 @@ class BaseLlama:
     def forward(self, input_ids, *args, **kwargs):
         return self.model(input_ids, *args, **kwargs)
 
+class BaseGenerate:
+    def logits(self, x):
+        input = x[:, -1:]
+        if hasattr(self.model, "lm_head"):
+            module = self.model.lm_head
+        else:
+            module = self.model.embed_tokens
+
+        offload_stream = None
+        if module.comfy_cast_weights:
+            weight, _, offload_stream = comfy.ops.cast_bias_weight(module, input, offloadable=True)
+        else:
+            weight = self.model.embed_tokens.weight.to(x)
+
+        x = torch.nn.functional.linear(input, weight, None)
+
+        comfy.ops.uncast_bias_weight(module, weight, None, offload_stream)
+        return x
+
+    def generate(self, embeds=None, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.9, min_p=0.0, repetition_penalty=1.0, seed=42, stop_tokens=[], initial_tokens=[], execution_dtype=None, min_tokens=0):
+        device = embeds.device
+        model_config = self.model.config
+
+        if execution_dtype is None:
+            if comfy.model_management.should_use_bf16(device):
+                execution_dtype = torch.bfloat16
+            else:
+                execution_dtype = torch.float32
+        embeds = embeds.to(execution_dtype)
+
+        if embeds.ndim == 2:
+            embeds = embeds.unsqueeze(0)
+
+        past_key_values = [] #kv_cache init
+        max_cache_len = embeds.shape[1] + max_length
+        for x in range(model_config.num_hidden_layers):
+            past_key_values.append((torch.empty([embeds.shape[0], model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype),
+                                    torch.empty([embeds.shape[0], model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype), 0))
+
+        generator = torch.Generator(device=device).manual_seed(seed) if do_sample else None
+
+        generated_token_ids = []
+        pbar = comfy.utils.ProgressBar(max_length)
+
+        # Generation loop
+        for step in tqdm(range(max_length), desc="Generating tokens"):
+            x, _, past_key_values = self.model.forward(None, embeds=embeds, attention_mask=None, past_key_values=past_key_values)
+            logits = self.logits(x)[:, -1]
+            next_token = self.sample_token(logits, temperature, top_k, top_p, min_p, repetition_penalty, initial_tokens + generated_token_ids, generator, do_sample=do_sample)
+            token_id = next_token[0].item()
+            generated_token_ids.append(token_id)
+
+            embeds = self.model.embed_tokens(next_token).to(execution_dtype)
+            pbar.update(1)
+
+            if token_id in stop_tokens:
+                break
+
+        return generated_token_ids
+
+    def sample_token(self, logits, temperature, top_k, top_p, min_p, repetition_penalty, token_history, generator, do_sample=True):
+
+        if not do_sample or temperature == 0.0:
+            return torch.argmax(logits, dim=-1, keepdim=True)
+
+        # Sampling mode
+        if repetition_penalty != 1.0:
+            for i in range(logits.shape[0]):
+                for token_id in set(token_history):
+                    logits[i, token_id] *= repetition_penalty if logits[i, token_id] < 0 else 1/repetition_penalty
+
+        if temperature != 1.0:
+            logits = logits / temperature
+
+        if top_k > 0:
+            indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
+            logits[indices_to_remove] = torch.finfo(logits.dtype).min
+
+        if min_p > 0.0:
+            probs_before_filter = torch.nn.functional.softmax(logits, dim=-1)
+            top_probs, _ = probs_before_filter.max(dim=-1, keepdim=True)
+            min_threshold = min_p * top_probs
+            indices_to_remove = probs_before_filter < min_threshold
+            logits[indices_to_remove] = torch.finfo(logits.dtype).min
+
+        if top_p < 1.0:
+            sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+            cumulative_probs = torch.cumsum(torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1)
+            sorted_indices_to_remove = cumulative_probs > top_p
+            sorted_indices_to_remove[..., 0] = False
+            indices_to_remove = torch.zeros_like(logits, dtype=torch.bool)
+            indices_to_remove.scatter_(1, sorted_indices, sorted_indices_to_remove)
+            logits[indices_to_remove] = torch.finfo(logits.dtype).min
+
+        probs = torch.nn.functional.softmax(logits, dim=-1)
+
+        return torch.multinomial(probs, num_samples=1, generator=generator)
+
 class BaseQwen3:
     def logits(self, x):
         input = x[:, -1:]
@@ -871,7 +988,7 @@ class Ovis25_2B(BaseLlama, torch.nn.Module):
         self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
         self.dtype = dtype
 
-class Qwen25_7BVLI(BaseLlama, torch.nn.Module):
+class Qwen25_7BVLI(BaseLlama, BaseGenerate, torch.nn.Module):
     def __init__(self, config_dict, dtype, device, operations):
         super().__init__()
         config = Qwen25_7BVLI_Config(**config_dict)
@@ -881,6 +998,9 @@ class Qwen25_7BVLI(BaseLlama, torch.nn.Module):
         self.visual = qwen_vl.Qwen2VLVisionTransformer(hidden_size=1280, output_hidden_size=config.hidden_size, device=device, dtype=dtype, ops=operations)
         self.dtype = dtype
 
+        # todo: should this be tied or not?
+        #self.lm_head = operations.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)
+
     def preprocess_embed(self, embed, device):
         if embed["type"] == "image":
             image, grid = qwen_vl.process_qwen2vl_images(embed["data"])
@@ -923,7 +1043,7 @@ class Gemma2_2B(BaseLlama, torch.nn.Module):
         self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
         self.dtype = dtype
 
-class Gemma3_4B(BaseLlama, torch.nn.Module):
+class Gemma3_4B(BaseLlama, BaseGenerate, torch.nn.Module):
     def __init__(self, config_dict, dtype, device, operations):
         super().__init__()
         config = Gemma3_4B_Config(**config_dict)
@@ -932,7 +1052,25 @@ class Gemma3_4B(BaseLlama, torch.nn.Module):
         self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
         self.dtype = dtype
 
-class Gemma3_12B(BaseLlama, torch.nn.Module):
+class Gemma3_4B_Vision(BaseLlama, BaseGenerate, torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        config = Gemma3_4B_Vision_Config(**config_dict)
+        self.num_layers = config.num_hidden_layers
+
+        self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
+        self.dtype = dtype
+        self.multi_modal_projector = Gemma3MultiModalProjector(config, dtype, device, operations)
+        self.vision_model = comfy.clip_model.CLIPVision(config.vision_config, dtype, device, operations)
+        self.image_size = config.vision_config["image_size"]
+
+    def preprocess_embed(self, embed, device):
+        if embed["type"] == "image":
+            image = comfy.clip_model.clip_preprocess(embed["data"], size=self.image_size, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], crop=True)
+            return self.multi_modal_projector(self.vision_model(image.to(device, dtype=torch.float32))[0]), None
+        return None, None
+
+class Gemma3_12B(BaseLlama, BaseGenerate, torch.nn.Module):
     def __init__(self, config_dict, dtype, device, operations):
         super().__init__()
         config = Gemma3_12B_Config(**config_dict)
diff --git a/comfy/text_encoders/lt.py b/comfy/text_encoders/lt.py
index 9cf87c0b2..82fbacf59 100644
--- a/comfy/text_encoders/lt.py
+++ b/comfy/text_encoders/lt.py
@@ -6,6 +6,7 @@ import comfy.text_encoders.genmo
 from comfy.ldm.lightricks.embeddings_connector import Embeddings1DConnector
 import torch
 import comfy.utils
+import math
 
 class T5XXLTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
@@ -22,40 +23,79 @@ def ltxv_te(*args, **kwargs):
     return comfy.text_encoders.genmo.mochi_te(*args, **kwargs)
 
 
-class Gemma3_12BTokenizer(sd1_clip.SDTokenizer):
-    def __init__(self, embedding_directory=None, tokenizer_data={}):
-        tokenizer = tokenizer_data.get("spiece_model", None)
-        super().__init__(tokenizer, pad_with_end=False, embedding_size=3840, embedding_key='gemma3_12b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, pad_left=True, disable_weights=True, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)
-
+class Gemma3_Tokenizer():
     def state_dict(self):
         return {"spiece_model": self.tokenizer.serialize_model()}
 
+    def tokenize_with_weights(self, text, return_word_ids=False, image=None, llama_template=None, skip_template=True, **kwargs):
+        self.llama_template = "<start_of_turn>system\nYou are a helpful assistant.<end_of_turn>\n<start_of_turn>user\n{}<end_of_turn>\n<start_of_turn>model\n"
+        self.llama_template_images = "<start_of_turn>system\nYou are a helpful assistant.<end_of_turn>\n<start_of_turn>user\n\n<image_soft_token>{}<end_of_turn>\n\n<start_of_turn>model\n"
+
+        if image is None:
+            images = []
+        else:
+            samples = image.movedim(-1, 1)
+            total = int(896 * 896)
+
+            scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
+            width = round(samples.shape[3] * scale_by)
+            height = round(samples.shape[2] * scale_by)
+
+            s = comfy.utils.common_upscale(samples, width, height, "area", "disabled").movedim(1, -1)
+            images = [s[:, :, :, :3]]
+
+        if text.startswith('<start_of_turn>'):
+            skip_template = True
+
+        if skip_template:
+            llama_text = text
+        else:
+            if llama_template is None:
+                if len(images) > 0:
+                    llama_text = self.llama_template_images.format(text)
+                else:
+                    llama_text = self.llama_template.format(text)
+            else:
+                llama_text = llama_template.format(text)
+
+        text_tokens = super().tokenize_with_weights(llama_text, return_word_ids)
+
+        if len(images) > 0:
+            embed_count = 0
+            for r in text_tokens:
+                for i, token in enumerate(r):
+                    if token[0] == 262144 and embed_count < len(images):
+                        r[i] = ({"type": "image", "data": images[embed_count]},) + token[1:]
+                        embed_count += 1
+        return text_tokens
+
+class Gemma3_12BTokenizer(Gemma3_Tokenizer, sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer = tokenizer_data.get("spiece_model", None)
+        special_tokens = {"<image_soft_token>": 262144, "<end_of_turn>": 106}
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=3840, embedding_key='gemma3_12b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, pad_left=True, disable_weights=True, tokenizer_args={"add_bos": True, "add_eos": False, "special_tokens": special_tokens}, tokenizer_data=tokenizer_data)
+
+
 class LTXAVGemmaTokenizer(sd1_clip.SD1Tokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="gemma3_12b", tokenizer=Gemma3_12BTokenizer)
 
+
 class Gemma3_12BModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="all", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
         llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
         if llama_quantization_metadata is not None:
             model_options = model_options.copy()
             model_options["quantization_metadata"] = llama_quantization_metadata
-
+        self.dtypes = set()
+        self.dtypes.add(dtype)
         super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_12B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
 
-    def tokenize_with_weights(self, text, return_word_ids=False, llama_template="{}", image_embeds=None, **kwargs):
-        text = llama_template.format(text)
-        text_tokens = super().tokenize_with_weights(text, return_word_ids)
-        embed_count = 0
-        for k in text_tokens:
-            tt = text_tokens[k]
-            for r in tt:
-                for i in range(len(r)):
-                    if r[i][0] == 262144:
-                        if image_embeds is not None and embed_count < image_embeds.shape[0]:
-                            r[i] = ({"type": "embedding", "data": image_embeds[embed_count], "original_type": "image"},) + r[i][1:]
-                            embed_count += 1
-        return text_tokens
+    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
+        tokens_only = [[t[0] for t in b] for b in tokens]
+        embeds, _, _, embeds_info = self.process_tokens(tokens_only, self.execution_device)
+        comfy.utils.normalize_image_embeddings(embeds, embeds_info, self.transformer.model.config.hidden_size ** 0.5)
+        return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, stop_tokens=[106])  # 106 is <end_of_turn>
 
 class LTXAVTEModel(torch.nn.Module):
     def __init__(self, dtype_llama=None, device="cpu", dtype=None, model_options={}):
@@ -112,6 +152,9 @@ class LTXAVTEModel(torch.nn.Module):
 
         return out.to(out_device), pooled
 
+    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
+        return self.gemma3_12b.generate(tokens["gemma3_12b"], do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed)
+
     def load_sd(self, sd):
         if "model.layers.47.self_attn.q_norm.weight" in sd:
             return self.gemma3_12b.load_sd(sd)
@@ -152,3 +195,14 @@ def ltxav_te(dtype_llama=None, llama_quantization_metadata=None):
                 dtype = dtype_llama
             super().__init__(dtype_llama=dtype_llama, device=device, dtype=dtype, model_options=model_options)
     return LTXAVTEModel_
+
+def gemma3_te(dtype_llama=None, llama_quantization_metadata=None):
+    class Gemma3_12BModel_(Gemma3_12BModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if llama_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["llama_quantization_metadata"] = llama_quantization_metadata
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return Gemma3_12BModel_
diff --git a/comfy/text_encoders/lumina2.py b/comfy/text_encoders/lumina2.py
index b29a7cc87..1b731e094 100644
--- a/comfy/text_encoders/lumina2.py
+++ b/comfy/text_encoders/lumina2.py
@@ -1,23 +1,23 @@
 from comfy import sd1_clip
 from .spiece_tokenizer import SPieceTokenizer
 import comfy.text_encoders.llama
-
+from comfy.text_encoders.lt import Gemma3_Tokenizer
+import comfy.utils
 
 class Gemma2BTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         tokenizer = tokenizer_data.get("spiece_model", None)
-        super().__init__(tokenizer, pad_with_end=False, embedding_size=2304, embedding_key='gemma2_2b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)
+        special_tokens = {"<end_of_turn>": 107}
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=2304, embedding_key='gemma2_2b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False, "special_tokens": special_tokens}, tokenizer_data=tokenizer_data)
 
     def state_dict(self):
         return {"spiece_model": self.tokenizer.serialize_model()}
 
-class Gemma3_4BTokenizer(sd1_clip.SDTokenizer):
+class Gemma3_4BTokenizer(Gemma3_Tokenizer, sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         tokenizer = tokenizer_data.get("spiece_model", None)
-        super().__init__(tokenizer, pad_with_end=False, embedding_size=2560, embedding_key='gemma3_4b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, disable_weights=True, tokenizer_data=tokenizer_data)
-
-    def state_dict(self):
-        return {"spiece_model": self.tokenizer.serialize_model()}
+        special_tokens = {"<image_soft_token>": 262144, "<end_of_turn>": 106}
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=2560, embedding_key='gemma3_4b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False, "special_tokens": special_tokens}, disable_weights=True, tokenizer_data=tokenizer_data)
 
 class LuminaTokenizer(sd1_clip.SD1Tokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
@@ -31,6 +31,9 @@ class Gemma2_2BModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
         super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma2_2B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
 
+    def generate(self, embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
+        return super().generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, stop_tokens=[107])
+
 class Gemma3_4BModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
         llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
@@ -40,6 +43,23 @@ class Gemma3_4BModel(sd1_clip.SDClipModel):
 
         super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
 
+    def generate(self, embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
+        return super().generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, stop_tokens=[106])
+
+class Gemma3_4B_Vision_Model(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
+        llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
+        if llama_quantization_metadata is not None:
+            model_options = model_options.copy()
+            model_options["quantization_metadata"] = llama_quantization_metadata
+
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_4B_Vision, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
+
+    def process_tokens(self, tokens, device):
+        embeds, _, _, embeds_info = super().process_tokens(tokens, device)
+        comfy.utils.normalize_image_embeddings(embeds, embeds_info, self.transformer.model.config.hidden_size ** 0.5)
+        return embeds
+
 class LuminaModel(sd1_clip.SD1ClipModel):
     def __init__(self, device="cpu", dtype=None, model_options={}, name="gemma2_2b", clip_model=Gemma2_2BModel):
         super().__init__(device=device, dtype=dtype, name=name, clip_model=clip_model, model_options=model_options)
@@ -50,6 +70,8 @@ def te(dtype_llama=None, llama_quantization_metadata=None, model_type="gemma2_2b
         model = Gemma2_2BModel
     elif model_type == "gemma3_4b":
         model = Gemma3_4BModel
+    elif model_type == "gemma3_4b_vision":
+        model = Gemma3_4B_Vision_Model
 
     class LuminaTEModel_(LuminaModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):
diff --git a/comfy/text_encoders/spiece_tokenizer.py b/comfy/text_encoders/spiece_tokenizer.py
index caccb3ca2..099d8d2d9 100644
--- a/comfy/text_encoders/spiece_tokenizer.py
+++ b/comfy/text_encoders/spiece_tokenizer.py
@@ -6,9 +6,10 @@ class SPieceTokenizer:
     def from_pretrained(path, **kwargs):
         return SPieceTokenizer(path, **kwargs)
 
-    def __init__(self, tokenizer_path, add_bos=False, add_eos=True):
+    def __init__(self, tokenizer_path, add_bos=False, add_eos=True, special_tokens=None):
         self.add_bos = add_bos
         self.add_eos = add_eos
+        self.special_tokens = special_tokens
         import sentencepiece
         if torch.is_tensor(tokenizer_path):
             tokenizer_path = tokenizer_path.numpy().tobytes()
@@ -27,8 +28,32 @@ class SPieceTokenizer:
         return out
 
     def __call__(self, string):
+        if self.special_tokens is not None:
+            import re
+            special_tokens_pattern = '|'.join(re.escape(token) for token in self.special_tokens.keys())
+            if special_tokens_pattern and re.search(special_tokens_pattern, string):
+                parts = re.split(f'({special_tokens_pattern})', string)
+                result = []
+                for part in parts:
+                    if not part:
+                        continue
+                    if part in self.special_tokens:
+                        result.append(self.special_tokens[part])
+                    else:
+                        encoded = self.tokenizer.encode(part, add_bos=False, add_eos=False)
+                        result.extend(encoded)
+                return {"input_ids": result}
+
         out = self.tokenizer.encode(string)
         return {"input_ids": out}
 
+    def decode(self, token_ids, skip_special_tokens=False):
+
+        if skip_special_tokens and self.special_tokens:
+            special_token_ids = set(self.special_tokens.values())
+            token_ids = [tid for tid in token_ids if tid not in special_token_ids]
+
+        return self.tokenizer.decode(token_ids)
+
     def serialize_model(self):
         return torch.ByteTensor(list(self.tokenizer.serialized_model_proto()))
diff --git a/comfy/utils.py b/comfy/utils.py
index c1ce540b5..17443b4cc 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -1418,3 +1418,11 @@ def deepcopy_list_dict(obj, memo=None):
 
     memo[obj_id] = res
     return res
+
+def normalize_image_embeddings(embeds, embeds_info, scale_factor):
+    """Normalize image embeddings to match text embedding scale"""
+    for info in embeds_info:
+        if info.get("type") == "image":
+            start_idx = info["index"]
+            end_idx = start_idx + info["size"]
+            embeds[:, start_idx:end_idx, :] /= scale_factor
diff --git a/comfy_extras/nodes_textgen.py b/comfy_extras/nodes_textgen.py
new file mode 100644
index 000000000..dd4f6b0d3
--- /dev/null
+++ b/comfy_extras/nodes_textgen.py
@@ -0,0 +1,176 @@
+from comfy_api.latest import ComfyExtension, io
+from typing_extensions import override
+
+class TextGenerate(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        # Define dynamic combo options for sampling mode
+        sampling_options = [
+            io.DynamicCombo.Option(
+                key="on",
+                inputs=[
+                    io.Float.Input("temperature", default=0.7, min=0.01, max=2.0, step=0.000001),
+                    io.Int.Input("top_k", default=64, min=0, max=1000),
+                    io.Float.Input("top_p", default=0.95, min=0.0, max=1.0, step=0.01),
+                    io.Float.Input("min_p", default=0.05, min=0.0, max=1.0, step=0.01),
+                    io.Float.Input("repetition_penalty", default=1.05, min=0.0, max=5.0, step=0.01),
+                    io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
+                ]
+            ),
+            io.DynamicCombo.Option(
+                key="off",
+                inputs=[]
+            ),
+        ]
+
+        return io.Schema(
+            node_id="TextGenerate",
+            category="textgen/",
+            search_aliases=["LLM", "gemma"],
+            inputs=[
+                io.Clip.Input("clip"),
+                io.String.Input("prompt", multiline=True, dynamic_prompts=True, default=""),
+                io.Image.Input("image", optional=True),
+                io.Int.Input("max_length", default=256, min=1, max=2048),
+                io.DynamicCombo.Input("sampling_mode", options=sampling_options, display_name="Sampling Mode"),
+            ],
+            outputs=[
+                io.String.Output(display_name="generated_text"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, clip, prompt, max_length, sampling_mode, image=None) -> io.NodeOutput:
+
+        tokens = clip.tokenize(prompt, image=image, skip_template=False)
+
+        # Get sampling parameters from dynamic combo
+        do_sample = sampling_mode.get("sampling_mode") == "on"
+        temperature = sampling_mode.get("temperature", 1.0)
+        top_k = sampling_mode.get("top_k", 50)
+        top_p = sampling_mode.get("top_p", 1.0)
+        min_p = sampling_mode.get("min_p", 0.0)
+        seed = sampling_mode.get("seed", None)
+        repetition_penalty = sampling_mode.get("repetition_penalty", 1.0)
+
+        generated_ids = clip.generate(
+            tokens,
+            do_sample=do_sample,
+            max_length=max_length,
+            temperature=temperature,
+            top_k=top_k,
+            top_p=top_p,
+            min_p=min_p,
+            repetition_penalty=repetition_penalty,
+            seed=seed
+        )
+
+        generated_text = clip.decode(generated_ids, skip_special_tokens=True)
+        return io.NodeOutput(generated_text)
+
+
+LTX2_T2V_SYSTEM_PROMPT = """You are a Creative Assistant. Given a user's raw input prompt describing a scene or concept, expand it into a detailed video generation prompt with specific visuals and integrated audio to guide a text-to-video model.
+#### Guidelines
+- Strictly follow all aspects of the user's raw input: include every element requested (style, visuals, motions, actions, camera movement, audio).
+    - If the input is vague, invent concrete details: lighting, textures, materials, scene settings, etc.
+        - For characters: describe gender, clothing, hair, expressions. DO NOT invent unrequested characters.
+- Use active language: present-progressive verbs ("is walking," "speaking"). If no action specified, describe natural movements.
+- Maintain chronological flow: use temporal connectors ("as," "then," "while").
+- Audio layer: Describe complete soundscape (background audio, ambient sounds, SFX, speech/music when requested). Integrate sounds chronologically alongside actions. Be specific (e.g., "soft footsteps on tile"), not vague (e.g., "ambient sound is present").
+- Speech (only when requested):
+    - For ANY speech-related input (talking, conversation, singing, etc.), ALWAYS include exact words in quotes with voice characteristics (e.g., "The man says in an excited voice: 'You won't believe what I just saw!'").
+    - Specify language if not English and accent if relevant.
+- Style: Include visual style at the beginning: "Style: <style>, <rest of prompt>." Default to cinematic-realistic if unspecified. Omit if unclear.
+- Visual and audio only: NO non-visual/auditory senses (smell, taste, touch).
+- Restrained language: Avoid dramatic/exaggerated terms. Use mild, natural phrasing.
+    - Colors: Use plain terms ("red dress"), not intensified ("vibrant blue," "bright red").
+    - Lighting: Use neutral descriptions ("soft overhead light"), not harsh ("blinding light").
+    - Facial features: Use delicate modifiers for subtle features (i.e., "subtle freckles").
+
+#### Important notes:
+- Analyze the user's raw input carefully. In cases of FPV or POV, exclude the description of the subject whose POV is requested.
+- Camera motion: DO NOT invent camera motion unless requested by the user.
+- Speech: DO NOT modify user-provided character dialogue unless it's a typo.
+- No timestamps or cuts: DO NOT use timestamps or describe scene cuts unless explicitly requested.
+- Format: DO NOT use phrases like "The scene opens with...". Start directly with Style (optional) and chronological scene description.
+- Format: DO NOT start your response with special characters.
+- DO NOT invent dialogue unless the user mentions speech/talking/singing/conversation.
+- If the user's raw input prompt is highly detailed, chronological and in the requested format: DO NOT make major edits or introduce new elements. Add/enhance audio descriptions if missing.
+
+#### Output Format (Strict):
+- Single continuous paragraph in natural language (English).
+- NO titles, headings, prefaces, code fences, or Markdown.
+- If unsafe/invalid, return original user prompt. Never ask questions or clarifications.
+
+Your output quality is CRITICAL. Generate visually rich, dynamic prompts with integrated audio for high-quality video generation.
+
+#### Example
+Input: "A woman at a coffee shop talking on the phone"
+Output:
+Style: realistic with cinematic lighting. In a medium close-up, a woman in her early 30s with shoulder-length brown hair sits at a small wooden table by the window. She wears a cream-colored turtleneck sweater, holding a white ceramic coffee cup in one hand and a smartphone to her ear with the other. Ambient cafe sounds fill the space—espresso machine hiss, quiet conversations, gentle clinking of cups. The woman listens intently, nodding slightly, then takes a sip of her coffee and sets it down with a soft clink. Her face brightens into a warm smile as she speaks in a clear, friendly voice, 'That sounds perfect! I'd love to meet up this weekend. How about Saturday afternoon?' She laughs softly—a genuine chuckle—and shifts in her chair. Behind her, other patrons move subtly in and out of focus. 'Great, I'll see you then,' she concludes cheerfully, lowering the phone.
+"""
+
+LTX2_I2V_SYSTEM_PROMPT = """You are a Creative Assistant. Given a user's raw input prompt describing a scene or concept, expand it into a detailed video generation prompt with specific visuals and integrated audio to guide a text-to-video model.
+You are a Creative Assistant writing concise, action-focused image-to-video prompts. Given an image (first frame) and user Raw Input Prompt, generate a prompt to guide video generation from that image.
+
+#### Guidelines:
+- Analyze the Image: Identify Subject, Setting, Elements, Style and Mood.
+- Follow user Raw Input Prompt: Include all requested motion, actions, camera movements, audio, and details. If in conflict with the image, prioritize user request while maintaining visual consistency (describe transition from image to user's scene).
+- Describe only changes from the image: Don't reiterate established visual details. Inaccurate descriptions may cause scene cuts.
+- Active language: Use present-progressive verbs ("is walking," "speaking"). If no action specified, describe natural movements.
+- Chronological flow: Use temporal connectors ("as," "then," "while").
+- Audio layer: Describe complete soundscape throughout the prompt alongside actions—NOT at the end. Align audio intensity with action tempo. Include natural background audio, ambient sounds, effects, speech or music (when requested). Be specific (e.g., "soft footsteps on tile") not vague (e.g., "ambient sound").
+- Speech (only when requested): Provide exact words in quotes with character's visual/voice characteristics (e.g., "The tall man speaks in a low, gravelly voice"), language if not English and accent if relevant. If general conversation mentioned without text, generate contextual quoted dialogue. (i.e., "The man is talking" input -> the output should include exact spoken words, like: "The man is talking in an excited voice saying: 'You won't believe what I just saw!' His hands gesture expressively as he speaks, eyebrows raised with enthusiasm. The ambient sound of a quiet room underscores his animated speech.")
+- Style: Include visual style at beginning: "Style: <style>, <rest of prompt>." If unclear, omit to avoid conflicts.
+- Visual and audio only: Describe only what is seen and heard. NO smell, taste, or tactile sensations.
+- Restrained language: Avoid dramatic terms. Use mild, natural, understated phrasing.
+
+#### Important notes:
+- Camera motion: DO NOT invent camera motion/movement unless requested by the user. Make sure to include camera motion only if specified in the input.
+- Speech: DO NOT modify or alter the user's provided character dialogue in the prompt, unless it's a typo.
+- No timestamps or cuts: DO NOT use timestamps or describe scene cuts unless explicitly requested.
+- Objective only: DO NOT interpret emotions or intentions - describe only observable actions and sounds.
+- Format: DO NOT use phrases like "The scene opens with..." / "The video starts...". Start directly with Style (optional) and chronological scene description.
+- Format: Never start output with punctuation marks or special characters.
+- DO NOT invent dialogue unless the user mentions speech/talking/singing/conversation.
+- Your performance is CRITICAL. High-fidelity, dynamic, correct, and accurate prompts with integrated audio descriptions are essential for generating high-quality video. Your goal is flawless execution of these rules.
+
+#### Output Format (Strict):
+- Single concise paragraph in natural English. NO titles, headings, prefaces, sections, code fences, or Markdown.
+- If unsafe/invalid, return original user prompt. Never ask questions or clarifications.
+
+#### Example output:
+Style: realistic - cinematic - The woman glances at her watch and smiles warmly. She speaks in a cheerful, friendly voice, "I think we're right on time!" In the background, a café barista prepares drinks at the counter. The barista calls out in a clear, upbeat tone, "Two cappuccinos ready!" The sound of the espresso machine hissing softly blends with gentle background chatter and the light clinking of cups on saucers.
+"""
+
+class TextGenerateLTX2Prompt(TextGenerate):
+    @classmethod
+    def define_schema(cls):
+        parent_schema = super().define_schema()
+        return io.Schema(
+            node_id="TextGenerateLTX2Prompt",
+            category=parent_schema.category,
+            inputs=parent_schema.inputs,
+            outputs=parent_schema.outputs,
+            search_aliases=["prompt enhance", "LLM", "gemma"],
+        )
+
+    @classmethod
+    def execute(cls, clip, prompt, max_length, sampling_mode, image=None) -> io.NodeOutput:
+        if image is None:
+            formatted_prompt = f"<start_of_turn>system\n{LTX2_T2V_SYSTEM_PROMPT.strip()}<end_of_turn>\n<start_of_turn>user\nUser Raw Input Prompt: {prompt}.<end_of_turn>\n<start_of_turn>model\n"
+        else:
+            formatted_prompt = f"<start_of_turn>system\n{LTX2_I2V_SYSTEM_PROMPT.strip()}<end_of_turn>\n<start_of_turn>user\n\n<image_soft_token>\n\nUser Raw Input Prompt: {prompt}.<end_of_turn>\n<start_of_turn>model\n"
+        return super().execute(clip, formatted_prompt, max_length, sampling_mode, image)
+
+
+class TextgenExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            TextGenerate,
+            TextGenerateLTX2Prompt,
+        ]
+
+async def comfy_entrypoint() -> TextgenExtension:
+    return TextgenExtension()
diff --git a/nodes.py b/nodes.py
index dff56b79c..d8e913065 100644
--- a/nodes.py
+++ b/nodes.py
@@ -2434,6 +2434,7 @@ async def init_builtin_extra_nodes():
         "nodes_image_compare.py",
         "nodes_zimage.py",
         "nodes_lora_debug.py",
+        "nodes_textgen.py",
         "nodes_color.py",
         "nodes_toolkit.py",
         "nodes_replacements.py",

From 2687652530128435d5cdcfe2600751c8c4b75b88 Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Thu, 19 Feb 2026 14:10:39 +0200
Subject: [PATCH 013/194] fix(api-nodes): force Gemini to return uncompressed
 images (#12516)

---
 comfy_api_nodes/apis/gemini.py  | 6 ++++++
 comfy_api_nodes/nodes_gemini.py | 4 ++--
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/comfy_api_nodes/apis/gemini.py b/comfy_api_nodes/apis/gemini.py
index d81337dae..3304d7e76 100644
--- a/comfy_api_nodes/apis/gemini.py
+++ b/comfy_api_nodes/apis/gemini.py
@@ -116,9 +116,15 @@ class GeminiGenerationConfig(BaseModel):
     topP: float | None = Field(None, ge=0.0, le=1.0)
 
 
+class GeminiImageOutputOptions(BaseModel):
+    mimeType: str = Field("image/png")
+    compressionQuality: int | None = Field(None)
+
+
 class GeminiImageConfig(BaseModel):
     aspectRatio: str | None = Field(None)
     imageSize: str | None = Field(None)
+    imageOutputOptions: GeminiImageOutputOptions = Field(default_factory=GeminiImageOutputOptions)
 
 
 class GeminiImageGenerationConfig(GeminiGenerationConfig):
diff --git a/comfy_api_nodes/nodes_gemini.py b/comfy_api_nodes/nodes_gemini.py
index 5287a777a..79efcdb7a 100644
--- a/comfy_api_nodes/nodes_gemini.py
+++ b/comfy_api_nodes/nodes_gemini.py
@@ -634,7 +634,7 @@ class GeminiImage(IO.ComfyNode):
 
         if not aspect_ratio:
             aspect_ratio = "auto"  # for backward compatability with old workflows; to-do remove this in December
-        image_config = GeminiImageConfig(aspectRatio=aspect_ratio)
+        image_config = GeminiImageConfig() if aspect_ratio == "auto" else GeminiImageConfig(aspectRatio=aspect_ratio)
 
         if images is not None:
             parts.extend(await create_image_parts(cls, images))
@@ -654,7 +654,7 @@ class GeminiImage(IO.ComfyNode):
                 ],
                 generationConfig=GeminiImageGenerationConfig(
                     responseModalities=(["IMAGE"] if response_modalities == "IMAGE" else ["TEXT", "IMAGE"]),
-                    imageConfig=None if aspect_ratio == "auto" else image_config,
+                    imageConfig=image_config,
                 ),
                 systemInstruction=gemini_system_prompt,
             ),

From 5632b2df9d5367128932175082f8d5e489749225 Mon Sep 17 00:00:00 2001
From: Yourz <crazilou@vip.qq.com>
Date: Fri, 20 Feb 2026 11:00:26 +0800
Subject: [PATCH 014/194] feat: add essentials_category (#12357)

* feat: add essentials_category field to node schema

Amp-Thread-ID: https://ampcode.com/threads/T-019c2b25-cd90-7218-9071-03cb46b351b3

* feat: add ESSENTIALS_CATEGORY to core nodes

Marked nodes:
- Basic: LoadImage, SaveImage, LoadVideo, SaveVideo, Load3D, CLIPTextEncode
- Image Tools: ImageScale, ImageInvert, ImageBatch, ImageCrop, ImageRotate, ImageBlur
- Image Tools/Preprocessing: Canny
- Image Generation: LoraLoader
- Audio: LoadAudio, SaveAudio

Amp-Thread-ID: https://ampcode.com/threads/T-019c2b25-cd90-7218-9071-03cb46b351b3

* Add ESSENTIALS_CATEGORY to more nodes

- SaveGLB (Basic)
- GetVideoComponents (Video Tools)
- TencentTextToModelNode, TencentImageToModelNode (3D)
- RecraftRemoveBackgroundNode (Image Tools)
- KlingLipSyncAudioToVideoNode (Video Generation)
- OpenAIChatNode (Text Generation)
- StabilityTextToAudio (Audio)

Amp-Thread-ID: https://ampcode.com/threads/T-019c2b69-81c1-71c3-8096-450a39e20910

* fix: correct essentials category for Canny node

Amp-Thread-ID: https://ampcode.com/threads/T-019c7303-ab53-7341-be76-a5da1f7a657e
Co-authored-by: Amp <amp@ampcode.com>

* refactor: replace essentials_category string literals with constants

Amp-Thread-ID: https://ampcode.com/threads/T-019c7303-ab53-7341-be76-a5da1f7a657e
Co-authored-by: Amp <amp@ampcode.com>

* refactor: revert constants, use string literals for essentials_category

Amp-Thread-ID: https://ampcode.com/threads/T-019c7303-ab53-7341-be76-a5da1f7a657e
Co-authored-by: Amp <amp@ampcode.com>

* fix: update basics

---------

Co-authored-by: bymyself <cbyrne@comfy.org>
Co-authored-by: Amp <amp@ampcode.com>
Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
---
 comfy_api/latest/_io.py               | 4 ++++
 comfy_api_nodes/nodes_hunyuan3d.py    | 2 ++
 comfy_api_nodes/nodes_kling.py        | 1 +
 comfy_api_nodes/nodes_openai.py       | 1 +
 comfy_api_nodes/nodes_recraft.py      | 1 +
 comfy_api_nodes/nodes_stability.py    | 1 +
 comfy_extras/nodes_audio.py           | 2 ++
 comfy_extras/nodes_canny.py           | 1 +
 comfy_extras/nodes_hunyuan3d.py       | 1 +
 comfy_extras/nodes_images.py          | 2 ++
 comfy_extras/nodes_load_3d.py         | 1 +
 comfy_extras/nodes_post_processing.py | 1 +
 comfy_extras/nodes_video.py           | 3 +++
 nodes.py                              | 9 +++++++++
 server.py                             | 4 ++++
 15 files changed, 34 insertions(+)

diff --git a/comfy_api/latest/_io.py b/comfy_api/latest/_io.py
index 312681249..dee487c92 100644
--- a/comfy_api/latest/_io.py
+++ b/comfy_api/latest/_io.py
@@ -1339,6 +1339,7 @@ class NodeInfoV1:
     api_node: bool=None
     price_badge: dict | None = None
     search_aliases: list[str]=None
+    essentials_category: str=None
 
 
 @dataclass
@@ -1460,6 +1461,8 @@ class Schema:
     """Flags a node as expandable, allowing NodeOutput to include 'expand' property."""
     accept_all_inputs: bool=False
     """When True, all inputs from the prompt will be passed to the node as kwargs, even if not defined in the schema."""
+    essentials_category: str | None = None
+    """Optional category for the Essentials tab. Path-based like category field (e.g., 'Basic', 'Image Tools/Editing')."""
 
     def validate(self):
         '''Validate the schema:
@@ -1566,6 +1569,7 @@ class Schema:
             python_module=getattr(cls, "RELATIVE_PYTHON_MODULE", "nodes"),
             price_badge=self.price_badge.as_dict(self.inputs) if self.price_badge is not None else None,
             search_aliases=self.search_aliases if self.search_aliases else None,
+            essentials_category=self.essentials_category,
         )
         return info
 
diff --git a/comfy_api_nodes/nodes_hunyuan3d.py b/comfy_api_nodes/nodes_hunyuan3d.py
index ca002cc60..d1d9578ec 100644
--- a/comfy_api_nodes/nodes_hunyuan3d.py
+++ b/comfy_api_nodes/nodes_hunyuan3d.py
@@ -54,6 +54,7 @@ class TencentTextToModelNode(IO.ComfyNode):
             node_id="TencentTextToModelNode",
             display_name="Hunyuan3D: Text to Model",
             category="api node/3d/Tencent",
+            essentials_category="3D",
             inputs=[
                 IO.Combo.Input(
                     "model",
@@ -168,6 +169,7 @@ class TencentImageToModelNode(IO.ComfyNode):
             node_id="TencentImageToModelNode",
             display_name="Hunyuan3D: Image(s) to Model",
             category="api node/3d/Tencent",
+            essentials_category="3D",
             inputs=[
                 IO.Combo.Input(
                     "model",
diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py
index b89c85561..7d186e69e 100644
--- a/comfy_api_nodes/nodes_kling.py
+++ b/comfy_api_nodes/nodes_kling.py
@@ -2262,6 +2262,7 @@ class KlingLipSyncAudioToVideoNode(IO.ComfyNode):
             node_id="KlingLipSyncAudioToVideoNode",
             display_name="Kling Lip Sync Video with Audio",
             category="api node/video/Kling",
+            essentials_category="Video Generation",
             description="Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file. When using, ensure that the audio contains clearly distinguishable vocals and that the video contains a distinct face. The audio file should not be larger than 5MB. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length.",
             inputs=[
                 IO.Video.Input("video"),
diff --git a/comfy_api_nodes/nodes_openai.py b/comfy_api_nodes/nodes_openai.py
index 332107a82..323bd6227 100644
--- a/comfy_api_nodes/nodes_openai.py
+++ b/comfy_api_nodes/nodes_openai.py
@@ -575,6 +575,7 @@ class OpenAIChatNode(IO.ComfyNode):
             node_id="OpenAIChatNode",
             display_name="OpenAI ChatGPT",
             category="api node/text/OpenAI",
+            essentials_category="Text Generation",
             description="Generate text responses from an OpenAI model.",
             inputs=[
                 IO.String.Input(
diff --git a/comfy_api_nodes/nodes_recraft.py b/comfy_api_nodes/nodes_recraft.py
index 773cb7dbe..4d1d508fa 100644
--- a/comfy_api_nodes/nodes_recraft.py
+++ b/comfy_api_nodes/nodes_recraft.py
@@ -963,6 +963,7 @@ class RecraftRemoveBackgroundNode(IO.ComfyNode):
             node_id="RecraftRemoveBackgroundNode",
             display_name="Recraft Remove Background",
             category="api node/image/Recraft",
+            essentials_category="Image Tools",
             description="Remove background from image, and return processed image and mask.",
             inputs=[
                 IO.Image.Input("image"),
diff --git a/comfy_api_nodes/nodes_stability.py b/comfy_api_nodes/nodes_stability.py
index 5665109cf..998a20c17 100644
--- a/comfy_api_nodes/nodes_stability.py
+++ b/comfy_api_nodes/nodes_stability.py
@@ -624,6 +624,7 @@ class StabilityTextToAudio(IO.ComfyNode):
             node_id="StabilityTextToAudio",
             display_name="Stability AI Text To Audio",
             category="api node/audio/Stability AI",
+            essentials_category="Audio",
             description=cleandoc(cls.__doc__ or ""),
             inputs=[
                 IO.Combo.Input(
diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py
index 7e74169f2..5aa39b332 100644
--- a/comfy_extras/nodes_audio.py
+++ b/comfy_extras/nodes_audio.py
@@ -159,6 +159,7 @@ class SaveAudio(IO.ComfyNode):
             search_aliases=["export flac"],
             display_name="Save Audio (FLAC)",
             category="audio",
+            essentials_category="Audio",
             inputs=[
                 IO.Audio.Input("audio"),
                 IO.String.Input("filename_prefix", default="audio/ComfyUI"),
@@ -300,6 +301,7 @@ class LoadAudio(IO.ComfyNode):
             search_aliases=["import audio", "open audio", "audio file"],
             display_name="Load Audio",
             category="audio",
+            essentials_category="Audio",
             inputs=[
                 IO.Combo.Input("audio", upload=IO.UploadType.audio, options=sorted(files)),
             ],
diff --git a/comfy_extras/nodes_canny.py b/comfy_extras/nodes_canny.py
index 6e0fadca5..956c1977c 100644
--- a/comfy_extras/nodes_canny.py
+++ b/comfy_extras/nodes_canny.py
@@ -12,6 +12,7 @@ class Canny(io.ComfyNode):
             node_id="Canny",
             search_aliases=["edge detection", "outline", "contour detection", "line art"],
             category="image/preprocessors",
+            essentials_category="Image Tools",
             inputs=[
                 io.Image.Input("image"),
                 io.Float.Input("low_threshold", default=0.4, min=0.01, max=0.99, step=0.01),
diff --git a/comfy_extras/nodes_hunyuan3d.py b/comfy_extras/nodes_hunyuan3d.py
index c2df3e859..3512d3cf8 100644
--- a/comfy_extras/nodes_hunyuan3d.py
+++ b/comfy_extras/nodes_hunyuan3d.py
@@ -621,6 +621,7 @@ class SaveGLB(IO.ComfyNode):
             display_name="Save 3D Model",
             search_aliases=["export 3d model", "save mesh"],
             category="3d",
+            essentials_category="Basics",
             is_output_node=True,
             inputs=[
                 IO.MultiType.Input(
diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py
index 23419a65d..88fe071e7 100644
--- a/comfy_extras/nodes_images.py
+++ b/comfy_extras/nodes_images.py
@@ -26,6 +26,7 @@ class ImageCrop(IO.ComfyNode):
             display_name="Image Crop (Deprecated)",
             category="image/transform",
             is_deprecated=True,
+            essentials_category="Image Tools",
             inputs=[
                 IO.Image.Input("image"),
                 IO.Int.Input("width", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
@@ -589,6 +590,7 @@ class ImageRotate(IO.ComfyNode):
             node_id="ImageRotate",
             search_aliases=["turn", "flip orientation"],
             category="image/transform",
+            essentials_category="Image Tools",
             inputs=[
                 IO.Image.Input("image"),
                 IO.Combo.Input("rotation", options=["none", "90 degrees", "180 degrees", "270 degrees"]),
diff --git a/comfy_extras/nodes_load_3d.py b/comfy_extras/nodes_load_3d.py
index edbb5cd40..3c88cbaa8 100644
--- a/comfy_extras/nodes_load_3d.py
+++ b/comfy_extras/nodes_load_3d.py
@@ -31,6 +31,7 @@ class Load3D(IO.ComfyNode):
             node_id="Load3D",
             display_name="Load 3D & Animation",
             category="3d",
+            essentials_category="Basics",
             is_experimental=True,
             inputs=[
                 IO.Combo.Input("model_file", options=sorted(files), upload=IO.UploadType.model),
diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py
index 66dac10b1..f35819bc2 100644
--- a/comfy_extras/nodes_post_processing.py
+++ b/comfy_extras/nodes_post_processing.py
@@ -77,6 +77,7 @@ class Blur(io.ComfyNode):
         return io.Schema(
             node_id="ImageBlur",
             category="image/postprocessing",
+            essentials_category="Image Tools",
             inputs=[
                 io.Image.Input("image"),
                 io.Int.Input("blur_radius", default=1, min=1, max=31, step=1),
diff --git a/comfy_extras/nodes_video.py b/comfy_extras/nodes_video.py
index cd765a7c1..db7c171a2 100644
--- a/comfy_extras/nodes_video.py
+++ b/comfy_extras/nodes_video.py
@@ -73,6 +73,7 @@ class SaveVideo(io.ComfyNode):
             search_aliases=["export video"],
             display_name="Save Video",
             category="image/video",
+            essentials_category="Basics",
             description="Saves the input images to your ComfyUI output directory.",
             inputs=[
                 io.Video.Input("video", tooltip="The video to save."),
@@ -146,6 +147,7 @@ class GetVideoComponents(io.ComfyNode):
             search_aliases=["extract frames", "split video", "video to images", "demux"],
             display_name="Get Video Components",
             category="image/video",
+            essentials_category="Video Tools",
             description="Extracts all components from a video: frames, audio, and framerate.",
             inputs=[
                 io.Video.Input("video", tooltip="The video to extract components from."),
@@ -174,6 +176,7 @@ class LoadVideo(io.ComfyNode):
             search_aliases=["import video", "open video", "video file"],
             display_name="Load Video",
             category="image/video",
+            essentials_category="Basics",
             inputs=[
                 io.Combo.Input("file", options=sorted(files), upload=io.UploadType.video),
             ],
diff --git a/nodes.py b/nodes.py
index d8e913065..330808cdc 100644
--- a/nodes.py
+++ b/nodes.py
@@ -8,6 +8,7 @@ import json
 import glob
 import hashlib
 import inspect
+
 import traceback
 import math
 import time
@@ -69,6 +70,7 @@ class CLIPTextEncode(ComfyNodeABC):
     FUNCTION = "encode"
 
     CATEGORY = "conditioning"
+    ESSENTIALS_CATEGORY = "Basics"
     DESCRIPTION = "Encodes a text prompt using a CLIP model into an embedding that can be used to guide the diffusion model towards generating specific images."
     SEARCH_ALIASES = ["text", "prompt", "text prompt", "positive prompt", "negative prompt", "encode text", "text encoder", "encode prompt"]
 
@@ -667,6 +669,8 @@ class CLIPSetLastLayer:
         return (clip,)
 
 class LoraLoader:
+    ESSENTIALS_CATEGORY = "Image Generation"
+
     def __init__(self):
         self.loaded_lora = None
 
@@ -1648,6 +1652,7 @@ class SaveImage:
     OUTPUT_NODE = True
 
     CATEGORY = "image"
+    ESSENTIALS_CATEGORY = "Basics"
     DESCRIPTION = "Saves the input images to your ComfyUI output directory."
     SEARCH_ALIASES = ["save", "save image", "export image", "output image", "write image", "download"]
 
@@ -1706,6 +1711,7 @@ class LoadImage:
                 }
 
     CATEGORY = "image"
+    ESSENTIALS_CATEGORY = "Basics"
     SEARCH_ALIASES = ["load image", "open image", "import image", "image input", "upload image", "read image", "image loader"]
 
     RETURN_TYPES = ("IMAGE", "MASK")
@@ -1863,6 +1869,7 @@ class ImageScale:
     FUNCTION = "upscale"
 
     CATEGORY = "image/upscaling"
+    ESSENTIALS_CATEGORY = "Image Tools"
     SEARCH_ALIASES = ["resize", "resize image", "scale image", "image resize", "zoom", "zoom in", "change size"]
 
     def upscale(self, image, upscale_method, width, height, crop):
@@ -1902,6 +1909,7 @@ class ImageScaleBy:
 
 class ImageInvert:
     SEARCH_ALIASES = ["reverse colors"]
+    ESSENTIALS_CATEGORY = "Image Tools"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -1918,6 +1926,7 @@ class ImageInvert:
 
 class ImageBatch:
     SEARCH_ALIASES = ["combine images", "merge images", "stack images"]
+    ESSENTIALS_CATEGORY = "Image Tools"
 
     @classmethod
     def INPUT_TYPES(s):
diff --git a/server.py b/server.py
index 8882e43c4..275bce5a7 100644
--- a/server.py
+++ b/server.py
@@ -689,6 +689,10 @@ class PromptServer():
                 info['api_node'] = obj_class.API_NODE
 
             info['search_aliases'] = getattr(obj_class, 'SEARCH_ALIASES', [])
+
+            if hasattr(obj_class, 'ESSENTIALS_CATEGORY'):
+                info['essentials_category'] = obj_class.ESSENTIALS_CATEGORY
+
             return info
 
         @routes.get("/object_info")

From 4d172e9ad7c50d08f21df48b04cca9b5f551d0e7 Mon Sep 17 00:00:00 2001
From: Christian Byrne <cbyrne@comfy.org>
Date: Thu, 19 Feb 2026 19:20:02 -0800
Subject: [PATCH 015/194] feat: mark 429 widgets as advanced for collapsible UI
 (#12197)

* feat: mark 429 widgets as advanced for collapsible UI

Mark widgets as advanced across core, comfy_extras, and comfy_api_nodes
to support the new collapsible advanced inputs section in the frontend.

Changes:
- 267 advanced markers in comfy_extras/
- 162 advanced markers in comfy_api_nodes/
- All files pass python3 -m py_compile verification

Widgets marked advanced (hidden by default):
- Scheduler internals: sigma_max, sigma_min, rho, mu, beta, alpha
- Sampler internals: eta, s_noise, order, rtol, atol, h_init, pcoeff, etc.
- Memory optimization: tile_size, overlap, temporal_size, temporal_overlap
- Pipeline controls: add_noise, start_at_step, end_at_step
- Timing controls: start_percent, end_percent
- Layer selection: stop_at_clip_layer, layers, block_number
- Video encoding: codec, crf, format
- Device/dtype: device, noise_device, dtype, weight_dtype

Widgets kept basic (always visible):
- Core params: strength, steps, cfg, denoise, seed, width, height
- Model selectors: ckpt_name, lora_name, vae_name, sampler_name
- Common controls: upscale_method, crop, batch_size, fps, opacity

Related: frontend PR #11939
Amp-Thread-ID: https://ampcode.com/threads/T-019c1734-6b61-702e-b333-f02c399963fc

* fix: remove advanced=True from DynamicCombo.Input (unsupported)

Amp-Thread-ID: https://ampcode.com/threads/T-019c1734-6b61-702e-b333-f02c399963fc

* fix: address review - un-mark model merge, video, image, and training node widgets as advanced

Per comfyanonymous review:
- Model merge arguments should not be advanced (all 14 model-specific merge classes)
- SaveAnimatedWEBP lossless/quality/method should not be advanced
- SaveWEBM/SaveVideo codec/crf/format should not be advanced
- TrainLoraNode options should not be advanced (7 inputs)

Amp-Thread-ID: https://ampcode.com/threads/T-019c322b-a3a8-71b7-9962-d44573ca6352

* fix: un-mark batch_size and webcam width/height as advanced (should stay basic)

Amp-Thread-ID: https://ampcode.com/threads/T-019c3236-1417-74aa-82a3-bcb365fbe9d1

---------

Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
---
 comfy_api_nodes/nodes_bfl.py             |   5 ++
 comfy_api_nodes/nodes_bytedance.py       |  13 +++
 comfy_api_nodes/nodes_gemini.py          |   5 ++
 comfy_api_nodes/nodes_ideogram.py        |   5 ++
 comfy_api_nodes/nodes_kling.py           |   8 +-
 comfy_api_nodes/nodes_ltxv.py            |   2 +
 comfy_api_nodes/nodes_magnific.py        |   8 ++
 comfy_api_nodes/nodes_meshy.py           |  11 ++-
 comfy_api_nodes/nodes_openai.py          |   3 +
 comfy_api_nodes/nodes_rodin.py           |   2 +-
 comfy_api_nodes/nodes_stability.py       |  12 +++
 comfy_api_nodes/nodes_topaz.py           |  16 +++-
 comfy_api_nodes/nodes_tripo.py           |  78 ++++++++--------
 comfy_api_nodes/nodes_veo2.py            |   4 +
 comfy_api_nodes/nodes_vidu.py            |  20 ++++-
 comfy_api_nodes/nodes_wan.py             |  13 +++
 comfy_extras/nodes_advanced_samplers.py  |   6 +-
 comfy_extras/nodes_apg.py                |   3 +
 comfy_extras/nodes_attention_multiply.py |  32 +++----
 comfy_extras/nodes_audio.py              |   4 +-
 comfy_extras/nodes_camera_trajectory.py  |   8 +-
 comfy_extras/nodes_chroma_radiance.py    |   3 +
 comfy_extras/nodes_clip_sdxl.py          |   4 +-
 comfy_extras/nodes_cond.py               |   4 +-
 comfy_extras/nodes_context_windows.py    |  12 +--
 comfy_extras/nodes_controlnet.py         |   4 +-
 comfy_extras/nodes_custom_sampler.py     | 110 +++++++++++------------
 comfy_extras/nodes_dataset.py            |  10 ++-
 comfy_extras/nodes_easycache.py          |  16 ++--
 comfy_extras/nodes_eps.py                |   3 +
 comfy_extras/nodes_flux.py               |   1 +
 comfy_extras/nodes_freelunch.py          |  16 ++--
 comfy_extras/nodes_fresca.py             |   6 +-
 comfy_extras/nodes_gits.py               |   2 +-
 comfy_extras/nodes_hooks.py              |   8 +-
 comfy_extras/nodes_hunyuan.py            |   7 +-
 comfy_extras/nodes_hunyuan3d.py          |   6 +-
 comfy_extras/nodes_hypertile.py          |   8 +-
 comfy_extras/nodes_images.py             |  10 +--
 comfy_extras/nodes_latent.py             |   6 +-
 comfy_extras/nodes_load_3d.py            |   4 +-
 comfy_extras/nodes_lora_extract.py       |   6 +-
 comfy_extras/nodes_lt.py                 |   2 +
 comfy_extras/nodes_lt_audio.py           |   1 +
 comfy_extras/nodes_lumina2.py            |   4 +-
 comfy_extras/nodes_mask.py               |   2 +-
 comfy_extras/nodes_model_advanced.py     |  16 ++--
 comfy_extras/nodes_model_downscale.py    |   8 +-
 comfy_extras/nodes_perpneg.py            |   4 +-
 comfy_extras/nodes_post_processing.py    |   8 +-
 comfy_extras/nodes_qwen.py               |   2 +-
 comfy_extras/nodes_rope.py               |  12 +--
 comfy_extras/nodes_sag.py                |   2 +-
 comfy_extras/nodes_sd3.py                |   8 +-
 comfy_extras/nodes_sdupscale.py          |   2 +-
 comfy_extras/nodes_slg.py                |  16 ++--
 comfy_extras/nodes_stable3d.py           |   4 +-
 comfy_extras/nodes_stable_cascade.py     |   4 +-
 comfy_extras/nodes_string.py             |  26 +++---
 comfy_extras/nodes_torch_compile.py      |   1 +
 comfy_extras/nodes_video_model.py        |   8 +-
 comfy_extras/nodes_wan.py                |   6 +-
 comfy_extras/nodes_wanmove.py            |   4 +-
 comfy_extras/nodes_zimage.py             |   2 +-
 nodes.py                                 |  28 +++---
 65 files changed, 407 insertions(+), 267 deletions(-)

diff --git a/comfy_api_nodes/nodes_bfl.py b/comfy_api_nodes/nodes_bfl.py
index 61c3b4503..23590bf24 100644
--- a/comfy_api_nodes/nodes_bfl.py
+++ b/comfy_api_nodes/nodes_bfl.py
@@ -57,6 +57,7 @@ class FluxProUltraImageNode(IO.ComfyNode):
                     tooltip="Whether to perform upsampling on the prompt. "
                     "If active, automatically modifies the prompt for more creative generation, "
                     "but results are nondeterministic (same seed will not produce exactly the same result).",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -200,6 +201,7 @@ class FluxKontextProImageNode(IO.ComfyNode):
                     "prompt_upsampling",
                     default=False,
                     tooltip="Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).",
+                    advanced=True,
                 ),
                 IO.Image.Input(
                     "input_image",
@@ -296,6 +298,7 @@ class FluxProExpandNode(IO.ComfyNode):
                     tooltip="Whether to perform upsampling on the prompt. "
                     "If active, automatically modifies the prompt for more creative generation, "
                     "but results are nondeterministic (same seed will not produce exactly the same result).",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "top",
@@ -433,6 +436,7 @@ class FluxProFillNode(IO.ComfyNode):
                     tooltip="Whether to perform upsampling on the prompt. "
                     "If active, automatically modifies the prompt for more creative generation, "
                     "but results are nondeterministic (same seed will not produce exactly the same result).",
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "guidance",
@@ -577,6 +581,7 @@ class Flux2ProImageNode(IO.ComfyNode):
                     default=True,
                     tooltip="Whether to perform upsampling on the prompt. "
                     "If active, automatically modifies the prompt for more creative generation.",
+                    advanced=True,
                 ),
                 IO.Image.Input("images", optional=True, tooltip="Up to 9 images to be used as references."),
             ],
diff --git a/comfy_api_nodes/nodes_bytedance.py b/comfy_api_nodes/nodes_bytedance.py
index 0cb5e3be8..ae5d59a79 100644
--- a/comfy_api_nodes/nodes_bytedance.py
+++ b/comfy_api_nodes/nodes_bytedance.py
@@ -114,6 +114,7 @@ class ByteDanceImageNode(IO.ComfyNode):
                     default=False,
                     tooltip='Whether to add an "AI generated" watermark to the image',
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -259,12 +260,14 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
                     default=False,
                     tooltip='Whether to add an "AI generated" watermark to the image.',
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "fail_on_partial",
                     default=True,
                     tooltip="If enabled, abort execution if any requested images are missing or return an error.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -432,18 +435,21 @@ class ByteDanceTextToVideoNode(IO.ComfyNode):
                     tooltip="Specifies whether to fix the camera. The platform appends an instruction "
                     "to fix the camera to your prompt, but does not guarantee the actual effect.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "watermark",
                     default=False,
                     tooltip='Whether to add an "AI generated" watermark to the video.',
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "generate_audio",
                     default=False,
                     tooltip="This parameter is ignored for any model except seedance-1-5-pro.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -561,18 +567,21 @@ class ByteDanceImageToVideoNode(IO.ComfyNode):
                     tooltip="Specifies whether to fix the camera. The platform appends an instruction "
                     "to fix the camera to your prompt, but does not guarantee the actual effect.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "watermark",
                     default=False,
                     tooltip='Whether to add an "AI generated" watermark to the video.',
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "generate_audio",
                     default=False,
                     tooltip="This parameter is ignored for any model except seedance-1-5-pro.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -694,18 +703,21 @@ class ByteDanceFirstLastFrameNode(IO.ComfyNode):
                     tooltip="Specifies whether to fix the camera. The platform appends an instruction "
                     "to fix the camera to your prompt, but does not guarantee the actual effect.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "watermark",
                     default=False,
                     tooltip='Whether to add an "AI generated" watermark to the video.',
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "generate_audio",
                     default=False,
                     tooltip="This parameter is ignored for any model except seedance-1-5-pro.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -834,6 +846,7 @@ class ByteDanceImageReferenceNode(IO.ComfyNode):
                     default=False,
                     tooltip='Whether to add an "AI generated" watermark to the video.',
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
diff --git a/comfy_api_nodes/nodes_gemini.py b/comfy_api_nodes/nodes_gemini.py
index 79efcdb7a..b69285be5 100644
--- a/comfy_api_nodes/nodes_gemini.py
+++ b/comfy_api_nodes/nodes_gemini.py
@@ -316,6 +316,7 @@ class GeminiNode(IO.ComfyNode):
                     default="",
                     optional=True,
                     tooltip="Foundational instructions that dictate an AI's behavior.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -593,6 +594,7 @@ class GeminiImage(IO.ComfyNode):
                     tooltip="Choose 'IMAGE' for image-only output, or "
                     "'IMAGE+TEXT' to return both the generated image and a text response.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.String.Input(
                     "system_prompt",
@@ -600,6 +602,7 @@ class GeminiImage(IO.ComfyNode):
                     default=GEMINI_IMAGE_SYS_PROMPT,
                     optional=True,
                     tooltip="Foundational instructions that dictate an AI's behavior.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -714,6 +717,7 @@ class GeminiImage2(IO.ComfyNode):
                     options=["IMAGE+TEXT", "IMAGE"],
                     tooltip="Choose 'IMAGE' for image-only output, or "
                     "'IMAGE+TEXT' to return both the generated image and a text response.",
+                    advanced=True,
                 ),
                 IO.Image.Input(
                     "images",
@@ -733,6 +737,7 @@ class GeminiImage2(IO.ComfyNode):
                     default=GEMINI_IMAGE_SYS_PROMPT,
                     optional=True,
                     tooltip="Foundational instructions that dictate an AI's behavior.",
+                    advanced=True,
                 ),
             ],
             outputs=[
diff --git a/comfy_api_nodes/nodes_ideogram.py b/comfy_api_nodes/nodes_ideogram.py
index feaf7a858..97c3609bd 100644
--- a/comfy_api_nodes/nodes_ideogram.py
+++ b/comfy_api_nodes/nodes_ideogram.py
@@ -261,6 +261,7 @@ class IdeogramV1(IO.ComfyNode):
                     default="AUTO",
                     tooltip="Determine if MagicPrompt should be used in generation",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -394,6 +395,7 @@ class IdeogramV2(IO.ComfyNode):
                     default="AUTO",
                     tooltip="Determine if MagicPrompt should be used in generation",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -411,6 +413,7 @@ class IdeogramV2(IO.ComfyNode):
                     default="NONE",
                     tooltip="Style type for generation (V2 only)",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.String.Input(
                     "negative_prompt",
@@ -564,6 +567,7 @@ class IdeogramV3(IO.ComfyNode):
                     default="AUTO",
                     tooltip="Determine if MagicPrompt should be used in generation",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -590,6 +594,7 @@ class IdeogramV3(IO.ComfyNode):
                     default="DEFAULT",
                     tooltip="Controls the trade-off between generation speed and quality",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Image.Input(
                     "character_image",
diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py
index 7d186e69e..fa0f9e87c 100644
--- a/comfy_api_nodes/nodes_kling.py
+++ b/comfy_api_nodes/nodes_kling.py
@@ -2334,6 +2334,7 @@ class KlingLipSyncTextToVideoNode(IO.ComfyNode):
                     max=2.0,
                     display_mode=IO.NumberDisplay.slider,
                     tooltip="Speech Rate. Valid range: 0.8~2.0, accurate to one decimal place.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -2455,6 +2456,7 @@ class KlingImageGenerationNode(IO.ComfyNode):
                 IO.Combo.Input(
                     "image_type",
                     options=[i.value for i in KlingImageGenImageReferenceType],
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "image_fidelity",
@@ -2464,6 +2466,7 @@ class KlingImageGenerationNode(IO.ComfyNode):
                     step=0.01,
                     display_mode=IO.NumberDisplay.slider,
                     tooltip="Reference intensity for user-uploaded images",
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "human_fidelity",
@@ -2473,6 +2476,7 @@ class KlingImageGenerationNode(IO.ComfyNode):
                     step=0.01,
                     display_mode=IO.NumberDisplay.slider,
                     tooltip="Subject reference similarity",
+                    advanced=True,
                 ),
                 IO.Combo.Input("model_name", options=["kling-v3", "kling-v2", "kling-v1-5"]),
                 IO.Combo.Input(
@@ -2588,7 +2592,7 @@ class TextToVideoWithAudio(IO.ComfyNode):
                 IO.Combo.Input("mode", options=["pro"]),
                 IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1"]),
                 IO.Combo.Input("duration", options=[5, 10]),
-                IO.Boolean.Input("generate_audio", default=True),
+                IO.Boolean.Input("generate_audio", default=True, advanced=True),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -2656,7 +2660,7 @@ class ImageToVideoWithAudio(IO.ComfyNode):
                 IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt."),
                 IO.Combo.Input("mode", options=["pro"]),
                 IO.Combo.Input("duration", options=[5, 10]),
-                IO.Boolean.Input("generate_audio", default=True),
+                IO.Boolean.Input("generate_audio", default=True, advanced=True),
             ],
             outputs=[
                 IO.Video.Output(),
diff --git a/comfy_api_nodes/nodes_ltxv.py b/comfy_api_nodes/nodes_ltxv.py
index c6424af92..0a219af96 100644
--- a/comfy_api_nodes/nodes_ltxv.py
+++ b/comfy_api_nodes/nodes_ltxv.py
@@ -74,6 +74,7 @@ class TextToVideoNode(IO.ComfyNode):
                     default=False,
                     optional=True,
                     tooltip="When true, the generated video will include AI-generated audio matching the scene.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -151,6 +152,7 @@ class ImageToVideoNode(IO.ComfyNode):
                     default=False,
                     optional=True,
                     tooltip="When true, the generated video will include AI-generated audio matching the scene.",
+                    advanced=True,
                 ),
             ],
             outputs=[
diff --git a/comfy_api_nodes/nodes_magnific.py b/comfy_api_nodes/nodes_magnific.py
index 83a581c5d..0f53208d4 100644
--- a/comfy_api_nodes/nodes_magnific.py
+++ b/comfy_api_nodes/nodes_magnific.py
@@ -110,11 +110,13 @@ class MagnificImageUpscalerCreativeNode(IO.ComfyNode):
                 IO.Combo.Input(
                     "engine",
                     options=["automatic", "magnific_illusio", "magnific_sharpy", "magnific_sparkle"],
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "auto_downscale",
                     default=False,
                     tooltip="Automatically downscale input image if output would exceed maximum pixel limit.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -280,6 +282,7 @@ class MagnificImageUpscalerPreciseV2Node(IO.ComfyNode):
                     "auto_downscale",
                     default=False,
                     tooltip="Automatically downscale input image if output would exceed maximum resolution.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -440,6 +443,7 @@ class MagnificImageStyleTransferNode(IO.ComfyNode):
                         "softy",
                     ],
                     tooltip="Processing engine selection.",
+                    advanced=True,
                 ),
                 IO.DynamicCombo.Input(
                     "portrait_mode",
@@ -468,6 +472,7 @@ class MagnificImageStyleTransferNode(IO.ComfyNode):
                     default=True,
                     tooltip="When disabled, expect each generation to introduce a degree of randomness, "
                     "leading to more diverse outcomes.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -582,16 +587,19 @@ class MagnificImageRelightNode(IO.ComfyNode):
                     "interpolate_from_original",
                     default=False,
                     tooltip="Restricts generation freedom to match original more closely.",
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "change_background",
                     default=True,
                     tooltip="Modifies background based on prompt/reference.",
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "preserve_details",
                     default=True,
                     tooltip="Maintains texture and fine details from original.",
+                    advanced=True,
                 ),
                 IO.DynamicCombo.Input(
                     "advanced_settings",
diff --git a/comfy_api_nodes/nodes_meshy.py b/comfy_api_nodes/nodes_meshy.py
index 65f6f0d2d..3cf577f4a 100644
--- a/comfy_api_nodes/nodes_meshy.py
+++ b/comfy_api_nodes/nodes_meshy.py
@@ -58,11 +58,12 @@ class MeshyTextToModelNode(IO.ComfyNode):
                     ],
                     tooltip="When set to false, returns an unprocessed triangular mesh.",
                 ),
-                IO.Combo.Input("symmetry_mode", options=["auto", "on", "off"]),
+                IO.Combo.Input("symmetry_mode", options=["auto", "on", "off"], advanced=True),
                 IO.Combo.Input(
                     "pose_mode",
                     options=["", "A-pose", "T-pose"],
                     tooltip="Specify the pose mode for the generated model.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -155,6 +156,7 @@ class MeshyRefineNode(IO.ComfyNode):
                     tooltip="Generate PBR Maps (metallic, roughness, normal) in addition to the base color. "
                     "Note: this should be set to false when using Sculpture style, "
                     "as Sculpture style generates its own set of PBR maps.",
+                    advanced=True,
                 ),
                 IO.String.Input(
                     "texture_prompt",
@@ -299,6 +301,7 @@ class MeshyImageToModelNode(IO.ComfyNode):
                     "pose_mode",
                     options=["", "A-pose", "T-pose"],
                     tooltip="Specify the pose mode for the generated model.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -429,7 +432,7 @@ class MeshyMultiImageToModelNode(IO.ComfyNode):
                     ],
                     tooltip="When set to false, returns an unprocessed triangular mesh.",
                 ),
-                IO.Combo.Input("symmetry_mode", options=["auto", "on", "off"]),
+                IO.Combo.Input("symmetry_mode", options=["auto", "on", "off"], advanced=True),
                 IO.DynamicCombo.Input(
                     "should_texture",
                     options=[
@@ -466,6 +469,7 @@ class MeshyMultiImageToModelNode(IO.ComfyNode):
                     "pose_mode",
                     options=["", "A-pose", "T-pose"],
                     tooltip="Specify the pose mode for the generated model.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -728,8 +732,9 @@ class MeshyTextureNode(IO.ComfyNode):
                     tooltip="Use the original UV of the model instead of generating new UVs. "
                     "When enabled, Meshy preserves existing textures from the uploaded model. "
                     "If the model has no original UV, the quality of the output might not be as good.",
+                    advanced=True,
                 ),
-                IO.Boolean.Input("pbr", default=False),
+                IO.Boolean.Input("pbr", default=False, advanced=True),
                 IO.String.Input(
                     "text_style_prompt",
                     default="",
diff --git a/comfy_api_nodes/nodes_openai.py b/comfy_api_nodes/nodes_openai.py
index 323bd6227..4ee896fa8 100644
--- a/comfy_api_nodes/nodes_openai.py
+++ b/comfy_api_nodes/nodes_openai.py
@@ -588,6 +588,7 @@ class OpenAIChatNode(IO.ComfyNode):
                     "persist_context",
                     default=False,
                     tooltip="This parameter is deprecated and has no effect.",
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "model",
@@ -857,6 +858,7 @@ class OpenAIChatConfig(IO.ComfyNode):
                     options=["auto", "disabled"],
                     default="auto",
                     tooltip="The truncation strategy to use for the model response. auto: If the context of this response and previous ones exceeds the model's context window size, the model will truncate the response to fit the context window by dropping input items in the middle of the conversation.disabled: If a model response will exceed the context window size for a model, the request will fail with a 400 error",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "max_output_tokens",
@@ -865,6 +867,7 @@ class OpenAIChatConfig(IO.ComfyNode):
                     max=16384,
                     tooltip="An upper bound for the number of tokens that can be generated for a response, including visible output tokens",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.String.Input(
                     "instructions",
diff --git a/comfy_api_nodes/nodes_rodin.py b/comfy_api_nodes/nodes_rodin.py
index 9c1adaa51..2b829b8db 100644
--- a/comfy_api_nodes/nodes_rodin.py
+++ b/comfy_api_nodes/nodes_rodin.py
@@ -493,7 +493,7 @@ class Rodin3D_Gen2(IO.ComfyNode):
                     default="500K-Triangle",
                     optional=True,
                 ),
-                IO.Boolean.Input("TAPose", default=False),
+                IO.Boolean.Input("TAPose", default=False, advanced=True),
             ],
             outputs=[
                 IO.String.Output(display_name="3D Model Path"),  # for backward compatibility only
diff --git a/comfy_api_nodes/nodes_stability.py b/comfy_api_nodes/nodes_stability.py
index 998a20c17..9ef13c83b 100644
--- a/comfy_api_nodes/nodes_stability.py
+++ b/comfy_api_nodes/nodes_stability.py
@@ -86,6 +86,7 @@ class StabilityStableImageUltraNode(IO.ComfyNode):
                     "style_preset",
                     options=get_stability_style_presets(),
                     tooltip="Optional desired style of generated image.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -107,6 +108,7 @@ class StabilityStableImageUltraNode(IO.ComfyNode):
                     tooltip="A blurb of text describing what you do not wish to see in the output image. This is an advanced feature.",
                     force_input=True,
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "image_denoise",
@@ -218,6 +220,7 @@ class StabilityStableImageSD_3_5Node(IO.ComfyNode):
                     "style_preset",
                     options=get_stability_style_presets(),
                     tooltip="Optional desired style of generated image.",
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "cfg_scale",
@@ -247,6 +250,7 @@ class StabilityStableImageSD_3_5Node(IO.ComfyNode):
                     tooltip="Keywords of what you do not wish to see in the output image. This is an advanced feature.",
                     force_input=True,
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "image_denoise",
@@ -384,6 +388,7 @@ class StabilityUpscaleConservativeNode(IO.ComfyNode):
                     tooltip="Keywords of what you do not wish to see in the output image. This is an advanced feature.",
                     force_input=True,
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -474,6 +479,7 @@ class StabilityUpscaleCreativeNode(IO.ComfyNode):
                     "style_preset",
                     options=get_stability_style_presets(),
                     tooltip="Optional desired style of generated image.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -491,6 +497,7 @@ class StabilityUpscaleCreativeNode(IO.ComfyNode):
                     tooltip="Keywords of what you do not wish to see in the output image. This is an advanced feature.",
                     force_input=True,
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -660,6 +667,7 @@ class StabilityTextToAudio(IO.ComfyNode):
                     step=1,
                     tooltip="Controls the number of sampling steps.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -737,6 +745,7 @@ class StabilityAudioToAudio(IO.ComfyNode):
                     step=1,
                     tooltip="Controls the number of sampling steps.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "strength",
@@ -830,6 +839,7 @@ class StabilityAudioInpaint(IO.ComfyNode):
                     step=1,
                     tooltip="Controls the number of sampling steps.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "mask_start",
@@ -838,6 +848,7 @@ class StabilityAudioInpaint(IO.ComfyNode):
                     max=190,
                     step=1,
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "mask_end",
@@ -846,6 +857,7 @@ class StabilityAudioInpaint(IO.ComfyNode):
                     max=190,
                     step=1,
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
diff --git a/comfy_api_nodes/nodes_topaz.py b/comfy_api_nodes/nodes_topaz.py
index 8fccde25a..6b61bd4b2 100644
--- a/comfy_api_nodes/nodes_topaz.py
+++ b/comfy_api_nodes/nodes_topaz.py
@@ -63,12 +63,14 @@ class TopazImageEnhance(IO.ComfyNode):
                     "subject_detection",
                     options=["All", "Foreground", "Background"],
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "face_enhancement",
                     default=True,
                     optional=True,
                     tooltip="Enhance faces (if present) during processing.",
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "face_enhancement_creativity",
@@ -79,6 +81,7 @@ class TopazImageEnhance(IO.ComfyNode):
                     display_mode=IO.NumberDisplay.number,
                     optional=True,
                     tooltip="Set the creativity level for face enhancement.",
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "face_enhancement_strength",
@@ -89,6 +92,7 @@ class TopazImageEnhance(IO.ComfyNode):
                     display_mode=IO.NumberDisplay.number,
                     optional=True,
                     tooltip="Controls how sharp enhanced faces are relative to the background.",
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "crop_to_fill",
@@ -96,6 +100,7 @@ class TopazImageEnhance(IO.ComfyNode):
                     optional=True,
                     tooltip="By default, the image is letterboxed when the output aspect ratio differs. "
                     "Enable to crop the image to fill the output dimensions.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "output_width",
@@ -106,6 +111,7 @@ class TopazImageEnhance(IO.ComfyNode):
                     display_mode=IO.NumberDisplay.number,
                     optional=True,
                     tooltip="Zero value means to calculate automatically (usually it will be original size or output_height if specified).",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "output_height",
@@ -116,6 +122,7 @@ class TopazImageEnhance(IO.ComfyNode):
                     display_mode=IO.NumberDisplay.number,
                     optional=True,
                     tooltip="Zero value means to output in the same height as original or output width.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "creativity",
@@ -131,12 +138,14 @@ class TopazImageEnhance(IO.ComfyNode):
                     default=True,
                     optional=True,
                     tooltip="Preserve subjects' facial identity.",
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "color_preservation",
                     default=True,
                     optional=True,
                     tooltip="Preserve the original colors.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -234,9 +243,10 @@ class TopazVideoEnhance(IO.ComfyNode):
                     default="low",
                     tooltip="Creativity level (applies only to Starlight (Astra) Creative).",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input("interpolation_enabled", default=False, optional=True),
-                IO.Combo.Input("interpolation_model", options=["apo-8"], default="apo-8", optional=True),
+                IO.Combo.Input("interpolation_model", options=["apo-8"], default="apo-8", optional=True, advanced=True),
                 IO.Int.Input(
                     "interpolation_slowmo",
                     default=1,
@@ -246,6 +256,7 @@ class TopazVideoEnhance(IO.ComfyNode):
                     tooltip="Slow-motion factor applied to the input video. "
                     "For example, 2 makes the output twice as slow and doubles the duration.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "interpolation_frame_rate",
@@ -261,6 +272,7 @@ class TopazVideoEnhance(IO.ComfyNode):
                     default=False,
                     tooltip="Analyze the input for duplicate frames and remove them.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "interpolation_duplicate_threshold",
@@ -271,6 +283,7 @@ class TopazVideoEnhance(IO.ComfyNode):
                     display_mode=IO.NumberDisplay.number,
                     tooltip="Detection sensitivity for duplicate frames.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "dynamic_compression_level",
@@ -278,6 +291,7 @@ class TopazVideoEnhance(IO.ComfyNode):
                     default="Low",
                     tooltip="CQP level.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
diff --git a/comfy_api_nodes/nodes_tripo.py b/comfy_api_nodes/nodes_tripo.py
index 67c7f59fc..9f4298dce 100644
--- a/comfy_api_nodes/nodes_tripo.py
+++ b/comfy_api_nodes/nodes_tripo.py
@@ -89,13 +89,13 @@ class TripoTextToModelNode(IO.ComfyNode):
                 IO.Combo.Input("style", options=TripoStyle, default="None", optional=True),
                 IO.Boolean.Input("texture", default=True, optional=True),
                 IO.Boolean.Input("pbr", default=True, optional=True),
-                IO.Int.Input("image_seed", default=42, optional=True),
-                IO.Int.Input("model_seed", default=42, optional=True),
-                IO.Int.Input("texture_seed", default=42, optional=True),
-                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True),
-                IO.Int.Input("face_limit", default=-1, min=-1, max=2000000, optional=True),
-                IO.Boolean.Input("quad", default=False, optional=True),
-                IO.Combo.Input("geometry_quality", default="standard", options=["standard", "detailed"], optional=True),
+                IO.Int.Input("image_seed", default=42, optional=True, advanced=True),
+                IO.Int.Input("model_seed", default=42, optional=True, advanced=True),
+                IO.Int.Input("texture_seed", default=42, optional=True, advanced=True),
+                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True, advanced=True),
+                IO.Int.Input("face_limit", default=-1, min=-1, max=2000000, optional=True, advanced=True),
+                IO.Boolean.Input("quad", default=False, optional=True, advanced=True),
+                IO.Combo.Input("geometry_quality", default="standard", options=["standard", "detailed"], optional=True, advanced=True),
             ],
             outputs=[
                 IO.String.Output(display_name="model_file"),  # for backward compatibility only
@@ -210,18 +210,18 @@ class TripoImageToModelNode(IO.ComfyNode):
                 IO.Combo.Input("style", options=TripoStyle, default="None", optional=True),
                 IO.Boolean.Input("texture", default=True, optional=True),
                 IO.Boolean.Input("pbr", default=True, optional=True),
-                IO.Int.Input("model_seed", default=42, optional=True),
+                IO.Int.Input("model_seed", default=42, optional=True, advanced=True),
                 IO.Combo.Input(
-                    "orientation", options=TripoOrientation, default=TripoOrientation.DEFAULT, optional=True
+                    "orientation", options=TripoOrientation, default=TripoOrientation.DEFAULT, optional=True, advanced=True
                 ),
-                IO.Int.Input("texture_seed", default=42, optional=True),
-                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True),
+                IO.Int.Input("texture_seed", default=42, optional=True, advanced=True),
+                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True, advanced=True),
                 IO.Combo.Input(
-                    "texture_alignment", default="original_image", options=["original_image", "geometry"], optional=True
+                    "texture_alignment", default="original_image", options=["original_image", "geometry"], optional=True, advanced=True
                 ),
-                IO.Int.Input("face_limit", default=-1, min=-1, max=500000, optional=True),
-                IO.Boolean.Input("quad", default=False, optional=True),
-                IO.Combo.Input("geometry_quality", default="standard", options=["standard", "detailed"], optional=True),
+                IO.Int.Input("face_limit", default=-1, min=-1, max=500000, optional=True, advanced=True),
+                IO.Boolean.Input("quad", default=False, optional=True, advanced=True),
+                IO.Combo.Input("geometry_quality", default="standard", options=["standard", "detailed"], optional=True, advanced=True),
             ],
             outputs=[
                 IO.String.Output(display_name="model_file"),  # for backward compatibility only
@@ -347,18 +347,19 @@ class TripoMultiviewToModelNode(IO.ComfyNode):
                     options=TripoOrientation,
                     default=TripoOrientation.DEFAULT,
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input("texture", default=True, optional=True),
                 IO.Boolean.Input("pbr", default=True, optional=True),
-                IO.Int.Input("model_seed", default=42, optional=True),
-                IO.Int.Input("texture_seed", default=42, optional=True),
-                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True),
+                IO.Int.Input("model_seed", default=42, optional=True, advanced=True),
+                IO.Int.Input("texture_seed", default=42, optional=True, advanced=True),
+                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True, advanced=True),
                 IO.Combo.Input(
-                    "texture_alignment", default="original_image", options=["original_image", "geometry"], optional=True
+                    "texture_alignment", default="original_image", options=["original_image", "geometry"], optional=True, advanced=True
                 ),
-                IO.Int.Input("face_limit", default=-1, min=-1, max=500000, optional=True),
-                IO.Boolean.Input("quad", default=False, optional=True),
-                IO.Combo.Input("geometry_quality", default="standard", options=["standard", "detailed"], optional=True),
+                IO.Int.Input("face_limit", default=-1, min=-1, max=500000, optional=True, advanced=True),
+                IO.Boolean.Input("quad", default=False, optional=True, advanced=True),
+                IO.Combo.Input("geometry_quality", default="standard", options=["standard", "detailed"], optional=True, advanced=True),
             ],
             outputs=[
                 IO.String.Output(display_name="model_file"),  # for backward compatibility only
@@ -474,10 +475,10 @@ class TripoTextureNode(IO.ComfyNode):
                 IO.Custom("MODEL_TASK_ID").Input("model_task_id"),
                 IO.Boolean.Input("texture", default=True, optional=True),
                 IO.Boolean.Input("pbr", default=True, optional=True),
-                IO.Int.Input("texture_seed", default=42, optional=True),
-                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True),
+                IO.Int.Input("texture_seed", default=42, optional=True, advanced=True),
+                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True, advanced=True),
                 IO.Combo.Input(
-                    "texture_alignment", default="original_image", options=["original_image", "geometry"], optional=True
+                    "texture_alignment", default="original_image", options=["original_image", "geometry"], optional=True, advanced=True
                 ),
             ],
             outputs=[
@@ -682,13 +683,14 @@ class TripoConversionNode(IO.ComfyNode):
             inputs=[
                 IO.Custom("MODEL_TASK_ID,RIG_TASK_ID,RETARGET_TASK_ID").Input("original_model_task_id"),
                 IO.Combo.Input("format", options=["GLTF", "USDZ", "FBX", "OBJ", "STL", "3MF"]),
-                IO.Boolean.Input("quad", default=False, optional=True),
+                IO.Boolean.Input("quad", default=False, optional=True, advanced=True),
                 IO.Int.Input(
                     "face_limit",
                     default=-1,
                     min=-1,
                     max=2000000,
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "texture_size",
@@ -696,47 +698,53 @@ class TripoConversionNode(IO.ComfyNode):
                     min=128,
                     max=4096,
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "texture_format",
                     options=["BMP", "DPX", "HDR", "JPEG", "OPEN_EXR", "PNG", "TARGA", "TIFF", "WEBP"],
                     default="JPEG",
                     optional=True,
+                    advanced=True,
                 ),
-                IO.Boolean.Input("force_symmetry", default=False, optional=True),
-                IO.Boolean.Input("flatten_bottom", default=False, optional=True),
+                IO.Boolean.Input("force_symmetry", default=False, optional=True, advanced=True),
+                IO.Boolean.Input("flatten_bottom", default=False, optional=True, advanced=True),
                 IO.Float.Input(
                     "flatten_bottom_threshold",
                     default=0.0,
                     min=0.0,
                     max=1.0,
                     optional=True,
+                    advanced=True,
                 ),
-                IO.Boolean.Input("pivot_to_center_bottom", default=False, optional=True),
+                IO.Boolean.Input("pivot_to_center_bottom", default=False, optional=True, advanced=True),
                 IO.Float.Input(
                     "scale_factor",
                     default=1.0,
                     min=0.0,
                     optional=True,
+                    advanced=True,
                 ),
-                IO.Boolean.Input("with_animation", default=False, optional=True),
-                IO.Boolean.Input("pack_uv", default=False, optional=True),
-                IO.Boolean.Input("bake", default=False, optional=True),
-                IO.String.Input("part_names", default="", optional=True),  # comma-separated list
+                IO.Boolean.Input("with_animation", default=False, optional=True, advanced=True),
+                IO.Boolean.Input("pack_uv", default=False, optional=True, advanced=True),
+                IO.Boolean.Input("bake", default=False, optional=True, advanced=True),
+                IO.String.Input("part_names", default="", optional=True, advanced=True),  # comma-separated list
                 IO.Combo.Input(
                     "fbx_preset",
                     options=["blender", "mixamo", "3dsmax"],
                     default="blender",
                     optional=True,
+                    advanced=True,
                 ),
-                IO.Boolean.Input("export_vertex_colors", default=False, optional=True),
+                IO.Boolean.Input("export_vertex_colors", default=False, optional=True, advanced=True),
                 IO.Combo.Input(
                     "export_orientation",
                     options=["align_image", "default"],
                     default="default",
                     optional=True,
+                    advanced=True,
                 ),
-                IO.Boolean.Input("animate_in_place", default=False, optional=True),
+                IO.Boolean.Input("animate_in_place", default=False, optional=True, advanced=True),
             ],
             outputs=[],
             hidden=[
diff --git a/comfy_api_nodes/nodes_veo2.py b/comfy_api_nodes/nodes_veo2.py
index 2a202fc3b..13fc1cc36 100644
--- a/comfy_api_nodes/nodes_veo2.py
+++ b/comfy_api_nodes/nodes_veo2.py
@@ -81,6 +81,7 @@ class VeoVideoGenerationNode(IO.ComfyNode):
                     default=True,
                     tooltip="Whether to enhance the prompt with AI assistance",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "person_generation",
@@ -88,6 +89,7 @@ class VeoVideoGenerationNode(IO.ComfyNode):
                     default="ALLOW",
                     tooltip="Whether to allow generating people in the video",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -299,6 +301,7 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode):
                     default=True,
                     tooltip="This parameter is deprecated and ignored.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "person_generation",
@@ -306,6 +309,7 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode):
                     default="ALLOW",
                     tooltip="Whether to allow generating people in the video",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
diff --git a/comfy_api_nodes/nodes_vidu.py b/comfy_api_nodes/nodes_vidu.py
index bbe7ebba2..f04407eb5 100644
--- a/comfy_api_nodes/nodes_vidu.py
+++ b/comfy_api_nodes/nodes_vidu.py
@@ -112,12 +112,14 @@ class ViduTextToVideoNode(IO.ComfyNode):
                     options=["1080p"],
                     tooltip="Supported values may vary by model & duration",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "movement_amplitude",
                     options=["auto", "small", "medium", "large"],
                     tooltip="The movement amplitude of objects in the frame",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -208,12 +210,14 @@ class ViduImageToVideoNode(IO.ComfyNode):
                     options=["1080p"],
                     tooltip="Supported values may vary by model & duration",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "movement_amplitude",
                     options=["auto", "small", "medium", "large"],
                     tooltip="The movement amplitude of objects in the frame",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -314,12 +318,14 @@ class ViduReferenceVideoNode(IO.ComfyNode):
                     options=["1080p"],
                     tooltip="Supported values may vary by model & duration",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "movement_amplitude",
                     options=["auto", "small", "medium", "large"],
                     tooltip="The movement amplitude of objects in the frame",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -426,12 +432,14 @@ class ViduStartEndToVideoNode(IO.ComfyNode):
                     options=["1080p"],
                     tooltip="Supported values may vary by model & duration",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "movement_amplitude",
                     options=["auto", "small", "medium", "large"],
                     tooltip="The movement amplitude of objects in the frame",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -511,11 +519,12 @@ class Vidu2TextToVideoNode(IO.ComfyNode):
                     control_after_generate=True,
                 ),
                 IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "3:4", "4:3", "1:1"]),
-                IO.Combo.Input("resolution", options=["720p", "1080p"]),
+                IO.Combo.Input("resolution", options=["720p", "1080p"], advanced=True),
                 IO.Boolean.Input(
                     "background_music",
                     default=False,
                     tooltip="Whether to add background music to the generated video.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -609,11 +618,13 @@ class Vidu2ImageToVideoNode(IO.ComfyNode):
                 IO.Combo.Input(
                     "resolution",
                     options=["720p", "1080p"],
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "movement_amplitude",
                     options=["auto", "small", "medium", "large"],
                     tooltip="The movement amplitude of objects in the frame.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -727,6 +738,7 @@ class Vidu2ReferenceVideoNode(IO.ComfyNode):
                     "audio",
                     default=False,
                     tooltip="When enabled video will contain generated speech and background music based on the prompt.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "duration",
@@ -746,11 +758,12 @@ class Vidu2ReferenceVideoNode(IO.ComfyNode):
                     control_after_generate=True,
                 ),
                 IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "4:3", "3:4", "1:1"]),
-                IO.Combo.Input("resolution", options=["720p", "1080p"]),
+                IO.Combo.Input("resolution", options=["720p", "1080p"], advanced=True),
                 IO.Combo.Input(
                     "movement_amplitude",
                     options=["auto", "small", "medium", "large"],
                     tooltip="The movement amplitude of objects in the frame.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -864,11 +877,12 @@ class Vidu2StartEndToVideoNode(IO.ComfyNode):
                     display_mode=IO.NumberDisplay.number,
                     control_after_generate=True,
                 ),
-                IO.Combo.Input("resolution", options=["720p", "1080p"]),
+                IO.Combo.Input("resolution", options=["720p", "1080p"], advanced=True),
                 IO.Combo.Input(
                     "movement_amplitude",
                     options=["auto", "small", "medium", "large"],
                     tooltip="The movement amplitude of objects in the frame.",
+                    advanced=True,
                 ),
             ],
             outputs=[
diff --git a/comfy_api_nodes/nodes_wan.py b/comfy_api_nodes/nodes_wan.py
index a1355d4f1..e2afe7f9c 100644
--- a/comfy_api_nodes/nodes_wan.py
+++ b/comfy_api_nodes/nodes_wan.py
@@ -227,12 +227,14 @@ class WanTextToImageApi(IO.ComfyNode):
                     default=True,
                     tooltip="Whether to enhance the prompt with AI assistance.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "watermark",
                     default=False,
                     tooltip="Whether to add an AI-generated watermark to the result.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -355,6 +357,7 @@ class WanImageToImageApi(IO.ComfyNode):
                     default=False,
                     tooltip="Whether to add an AI-generated watermark to the result.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -495,18 +498,21 @@ class WanTextToVideoApi(IO.ComfyNode):
                     default=False,
                     optional=True,
                     tooltip="If no audio input is provided, generate audio automatically.",
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "prompt_extend",
                     default=True,
                     tooltip="Whether to enhance the prompt with AI assistance.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "watermark",
                     default=False,
                     tooltip="Whether to add an AI-generated watermark to the result.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "shot_type",
@@ -515,6 +521,7 @@ class WanTextToVideoApi(IO.ComfyNode):
                     "single continuous shot or multiple shots with cuts. "
                     "This parameter takes effect only when prompt_extend is True.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -667,18 +674,21 @@ class WanImageToVideoApi(IO.ComfyNode):
                     default=False,
                     optional=True,
                     tooltip="If no audio input is provided, generate audio automatically.",
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "prompt_extend",
                     default=True,
                     tooltip="Whether to enhance the prompt with AI assistance.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "watermark",
                     default=False,
                     tooltip="Whether to add an AI-generated watermark to the result.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "shot_type",
@@ -687,6 +697,7 @@ class WanImageToVideoApi(IO.ComfyNode):
                     "single continuous shot or multiple shots with cuts. "
                     "This parameter takes effect only when prompt_extend is True.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -839,11 +850,13 @@ class WanReferenceVideoApi(IO.ComfyNode):
                     options=["single", "multi"],
                     tooltip="Specifies the shot type for the generated video, that is, whether the video is a "
                     "single continuous shot or multiple shots with cuts.",
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "watermark",
                     default=False,
                     tooltip="Whether to add an AI-generated watermark to the result.",
+                    advanced=True,
                 ),
             ],
             outputs=[
diff --git a/comfy_extras/nodes_advanced_samplers.py b/comfy_extras/nodes_advanced_samplers.py
index 5532ffe6a..7f716cd76 100644
--- a/comfy_extras/nodes_advanced_samplers.py
+++ b/comfy_extras/nodes_advanced_samplers.py
@@ -47,8 +47,8 @@ class SamplerLCMUpscale(io.ComfyNode):
             node_id="SamplerLCMUpscale",
             category="sampling/custom_sampling/samplers",
             inputs=[
-                io.Float.Input("scale_ratio", default=1.0, min=0.1, max=20.0, step=0.01),
-                io.Int.Input("scale_steps", default=-1, min=-1, max=1000, step=1),
+                io.Float.Input("scale_ratio", default=1.0, min=0.1, max=20.0, step=0.01, advanced=True),
+                io.Int.Input("scale_steps", default=-1, min=-1, max=1000, step=1, advanced=True),
                 io.Combo.Input("upscale_method", options=cls.UPSCALE_METHODS),
             ],
             outputs=[io.Sampler.Output()],
@@ -94,7 +94,7 @@ class SamplerEulerCFGpp(io.ComfyNode):
             display_name="SamplerEulerCFG++",
             category="_for_testing",  # "sampling/custom_sampling/samplers"
             inputs=[
-                io.Combo.Input("version", options=["regular", "alternative"]),
+                io.Combo.Input("version", options=["regular", "alternative"], advanced=True),
             ],
             outputs=[io.Sampler.Output()],
             is_experimental=True,
diff --git a/comfy_extras/nodes_apg.py b/comfy_extras/nodes_apg.py
index b9df2dcc9..fd561d360 100644
--- a/comfy_extras/nodes_apg.py
+++ b/comfy_extras/nodes_apg.py
@@ -26,6 +26,7 @@ class APG(io.ComfyNode):
                     max=10.0,
                     step=0.01,
                     tooltip="Controls the scale of the parallel guidance vector. Default CFG behavior at a setting of 1.",
+                    advanced=True,
                 ),
                 io.Float.Input(
                     "norm_threshold",
@@ -34,6 +35,7 @@ class APG(io.ComfyNode):
                     max=50.0,
                     step=0.1,
                     tooltip="Normalize guidance vector to this value, normalization disable at a setting of 0.",
+                    advanced=True,
                 ),
                 io.Float.Input(
                     "momentum",
@@ -42,6 +44,7 @@ class APG(io.ComfyNode):
                     max=1.0,
                     step=0.01,
                     tooltip="Controls a running average of guidance during diffusion, disabled at a setting of 0.",
+                    advanced=True,
                 ),
             ],
             outputs=[io.Model.Output()],
diff --git a/comfy_extras/nodes_attention_multiply.py b/comfy_extras/nodes_attention_multiply.py
index 67c4e2ed0..060a5c9be 100644
--- a/comfy_extras/nodes_attention_multiply.py
+++ b/comfy_extras/nodes_attention_multiply.py
@@ -28,10 +28,10 @@ class UNetSelfAttentionMultiply(io.ComfyNode):
             category="_for_testing/attention_experiments",
             inputs=[
                 io.Model.Input("model"),
-                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
             ],
             outputs=[io.Model.Output()],
             is_experimental=True,
@@ -51,10 +51,10 @@ class UNetCrossAttentionMultiply(io.ComfyNode):
             category="_for_testing/attention_experiments",
             inputs=[
                 io.Model.Input("model"),
-                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
             ],
             outputs=[io.Model.Output()],
             is_experimental=True,
@@ -75,10 +75,10 @@ class CLIPAttentionMultiply(io.ComfyNode):
             category="_for_testing/attention_experiments",
             inputs=[
                 io.Clip.Input("clip"),
-                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
             ],
             outputs=[io.Clip.Output()],
             is_experimental=True,
@@ -109,10 +109,10 @@ class UNetTemporalAttentionMultiply(io.ComfyNode):
             category="_for_testing/attention_experiments",
             inputs=[
                 io.Model.Input("model"),
-                io.Float.Input("self_structural", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("self_temporal", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("cross_structural", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("cross_temporal", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("self_structural", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("self_temporal", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("cross_structural", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("cross_temporal", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
             ],
             outputs=[io.Model.Output()],
             is_experimental=True,
diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py
index 5aa39b332..43df0512f 100644
--- a/comfy_extras/nodes_audio.py
+++ b/comfy_extras/nodes_audio.py
@@ -22,7 +22,7 @@ class EmptyLatentAudio(IO.ComfyNode):
             inputs=[
                 IO.Float.Input("seconds", default=47.6, min=1.0, max=1000.0, step=0.1),
                 IO.Int.Input(
-                    "batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."
+                    "batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch.",
                 ),
             ],
             outputs=[IO.Latent.Output()],
@@ -679,6 +679,7 @@ class EmptyAudio(IO.ComfyNode):
                     tooltip="Sample rate of the empty audio clip.",
                     min=1,
                     max=192000,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "channels",
@@ -686,6 +687,7 @@ class EmptyAudio(IO.ComfyNode):
                     min=1,
                     max=2,
                     tooltip="Number of audio channels (1 for mono, 2 for stereo).",
+                    advanced=True,
                 ),
             ],
             outputs=[IO.Audio.Output()],
diff --git a/comfy_extras/nodes_camera_trajectory.py b/comfy_extras/nodes_camera_trajectory.py
index eb7ef363c..e7efa29ba 100644
--- a/comfy_extras/nodes_camera_trajectory.py
+++ b/comfy_extras/nodes_camera_trajectory.py
@@ -174,10 +174,10 @@ class WanCameraEmbedding(io.ComfyNode):
                 io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
                 io.Float.Input("speed", default=1.0, min=0, max=10.0, step=0.1, optional=True),
-                io.Float.Input("fx", default=0.5, min=0, max=1, step=0.000000001, optional=True),
-                io.Float.Input("fy", default=0.5, min=0, max=1, step=0.000000001, optional=True),
-                io.Float.Input("cx", default=0.5, min=0, max=1, step=0.01, optional=True),
-                io.Float.Input("cy", default=0.5, min=0, max=1, step=0.01, optional=True),
+                io.Float.Input("fx", default=0.5, min=0, max=1, step=0.000000001, optional=True, advanced=True),
+                io.Float.Input("fy", default=0.5, min=0, max=1, step=0.000000001, optional=True, advanced=True),
+                io.Float.Input("cx", default=0.5, min=0, max=1, step=0.01, optional=True, advanced=True),
+                io.Float.Input("cy", default=0.5, min=0, max=1, step=0.01, optional=True, advanced=True),
             ],
             outputs=[
                 io.WanCameraEmbedding.Output(display_name="camera_embedding"),
diff --git a/comfy_extras/nodes_chroma_radiance.py b/comfy_extras/nodes_chroma_radiance.py
index 381989818..509436062 100644
--- a/comfy_extras/nodes_chroma_radiance.py
+++ b/comfy_extras/nodes_chroma_radiance.py
@@ -48,6 +48,7 @@ class ChromaRadianceOptions(io.ComfyNode):
                     min=0.0,
                     max=1.0,
                     tooltip="First sigma that these options will be in effect.",
+                    advanced=True,
                 ),
                 io.Float.Input(
                     id="end_sigma",
@@ -55,12 +56,14 @@ class ChromaRadianceOptions(io.ComfyNode):
                     min=0.0,
                     max=1.0,
                     tooltip="Last sigma that these options will be in effect.",
+                    advanced=True,
                 ),
                 io.Int.Input(
                     id="nerf_tile_size",
                     default=-1,
                     min=-1,
                     tooltip="Allows overriding the default NeRF tile size. -1 means use the default (32). 0 means use non-tiling mode (may require a lot of VRAM).",
+                    advanced=True,
                 ),
             ],
             outputs=[io.Model.Output()],
diff --git a/comfy_extras/nodes_clip_sdxl.py b/comfy_extras/nodes_clip_sdxl.py
index 520ff0e3c..7a001af6f 100644
--- a/comfy_extras/nodes_clip_sdxl.py
+++ b/comfy_extras/nodes_clip_sdxl.py
@@ -35,8 +35,8 @@ class CLIPTextEncodeSDXL(io.ComfyNode):
                 io.Clip.Input("clip"),
                 io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
                 io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
-                io.Int.Input("crop_w", default=0, min=0, max=nodes.MAX_RESOLUTION),
-                io.Int.Input("crop_h", default=0, min=0, max=nodes.MAX_RESOLUTION),
+                io.Int.Input("crop_w", default=0, min=0, max=nodes.MAX_RESOLUTION, advanced=True),
+                io.Int.Input("crop_h", default=0, min=0, max=nodes.MAX_RESOLUTION, advanced=True),
                 io.Int.Input("target_width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
                 io.Int.Input("target_height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
                 io.String.Input("text_g", multiline=True, dynamic_prompts=True),
diff --git a/comfy_extras/nodes_cond.py b/comfy_extras/nodes_cond.py
index 8b06e3de9..86426a780 100644
--- a/comfy_extras/nodes_cond.py
+++ b/comfy_extras/nodes_cond.py
@@ -38,8 +38,8 @@ class T5TokenizerOptions(io.ComfyNode):
             category="_for_testing/conditioning",
             inputs=[
                 io.Clip.Input("clip"),
-                io.Int.Input("min_padding", default=0, min=0, max=10000, step=1),
-                io.Int.Input("min_length", default=0, min=0, max=10000, step=1),
+                io.Int.Input("min_padding", default=0, min=0, max=10000, step=1, advanced=True),
+                io.Int.Input("min_length", default=0, min=0, max=10000, step=1, advanced=True),
             ],
             outputs=[io.Clip.Output()],
             is_experimental=True,
diff --git a/comfy_extras/nodes_context_windows.py b/comfy_extras/nodes_context_windows.py
index 3799a9004..93a5204e1 100644
--- a/comfy_extras/nodes_context_windows.py
+++ b/comfy_extras/nodes_context_windows.py
@@ -14,15 +14,15 @@ class ContextWindowsManualNode(io.ComfyNode):
             description="Manually set context windows.",
             inputs=[
                 io.Model.Input("model", tooltip="The model to apply context windows to during sampling."),
-                io.Int.Input("context_length", min=1, default=16, tooltip="The length of the context window."),
-                io.Int.Input("context_overlap", min=0, default=4, tooltip="The overlap of the context window."),
+                io.Int.Input("context_length", min=1, default=16, tooltip="The length of the context window.", advanced=True),
+                io.Int.Input("context_overlap", min=0, default=4, tooltip="The overlap of the context window.", advanced=True),
                 io.Combo.Input("context_schedule", options=[
                     comfy.context_windows.ContextSchedules.STATIC_STANDARD,
                     comfy.context_windows.ContextSchedules.UNIFORM_STANDARD,
                     comfy.context_windows.ContextSchedules.UNIFORM_LOOPED,
                     comfy.context_windows.ContextSchedules.BATCHED,
                     ], tooltip="The stride of the context window."),
-                io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules."),
+                io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules.", advanced=True),
                 io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules."),
                 io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."),
                 io.Int.Input("dim", min=0, max=5, default=0, tooltip="The dimension to apply the context windows to."),
@@ -67,15 +67,15 @@ class WanContextWindowsManualNode(ContextWindowsManualNode):
         schema.description = "Manually set context windows for WAN-like models (dim=2)."
         schema.inputs = [
             io.Model.Input("model", tooltip="The model to apply context windows to during sampling."),
-                io.Int.Input("context_length", min=1, max=nodes.MAX_RESOLUTION, step=4, default=81, tooltip="The length of the context window."),
-                io.Int.Input("context_overlap", min=0, default=30, tooltip="The overlap of the context window."),
+                io.Int.Input("context_length", min=1, max=nodes.MAX_RESOLUTION, step=4, default=81, tooltip="The length of the context window.", advanced=True),
+                io.Int.Input("context_overlap", min=0, default=30, tooltip="The overlap of the context window.", advanced=True),
                 io.Combo.Input("context_schedule", options=[
                     comfy.context_windows.ContextSchedules.STATIC_STANDARD,
                     comfy.context_windows.ContextSchedules.UNIFORM_STANDARD,
                     comfy.context_windows.ContextSchedules.UNIFORM_LOOPED,
                     comfy.context_windows.ContextSchedules.BATCHED,
                     ], tooltip="The stride of the context window."),
-                io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules."),
+                io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules.", advanced=True),
                 io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules."),
                 io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."),
                 io.Boolean.Input("freenoise", default=False, tooltip="Whether to apply FreeNoise noise shuffling, improves window blending."),
diff --git a/comfy_extras/nodes_controlnet.py b/comfy_extras/nodes_controlnet.py
index 0c1d7f0d4..847cb0bdf 100644
--- a/comfy_extras/nodes_controlnet.py
+++ b/comfy_extras/nodes_controlnet.py
@@ -48,8 +48,8 @@ class ControlNetInpaintingAliMamaApply(io.ComfyNode):
                 io.Image.Input("image"),
                 io.Mask.Input("mask"),
                 io.Float.Input("strength", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001),
-                io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001),
+                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001, advanced=True),
             ],
             outputs=[
                 io.Conditioning.Output(display_name="positive"),
diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py
index 61a234634..1e957c09b 100644
--- a/comfy_extras/nodes_custom_sampler.py
+++ b/comfy_extras/nodes_custom_sampler.py
@@ -50,9 +50,9 @@ class KarrasScheduler(io.ComfyNode):
             category="sampling/custom_sampling/schedulers",
             inputs=[
                 io.Int.Input("steps", default=20, min=1, max=10000),
-                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("rho", default=7.0, min=0.0, max=100.0, step=0.01, round=False),
+                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("rho", default=7.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
             ],
             outputs=[io.Sigmas.Output()]
         )
@@ -72,8 +72,8 @@ class ExponentialScheduler(io.ComfyNode):
             category="sampling/custom_sampling/schedulers",
             inputs=[
                 io.Int.Input("steps", default=20, min=1, max=10000),
-                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False),
+                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
             ],
             outputs=[io.Sigmas.Output()]
         )
@@ -93,9 +93,9 @@ class PolyexponentialScheduler(io.ComfyNode):
             category="sampling/custom_sampling/schedulers",
             inputs=[
                 io.Int.Input("steps", default=20, min=1, max=10000),
-                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("rho", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
+                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("rho", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
             ],
             outputs=[io.Sigmas.Output()]
         )
@@ -115,10 +115,10 @@ class LaplaceScheduler(io.ComfyNode):
             category="sampling/custom_sampling/schedulers",
             inputs=[
                 io.Int.Input("steps", default=20, min=1, max=10000),
-                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("mu", default=0.0, min=-10.0, max=10.0, step=0.1, round=False),
-                io.Float.Input("beta", default=0.5, min=0.0, max=10.0, step=0.1, round=False),
+                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("mu", default=0.0, min=-10.0, max=10.0, step=0.1, round=False, advanced=True),
+                io.Float.Input("beta", default=0.5, min=0.0, max=10.0, step=0.1, round=False, advanced=True),
             ],
             outputs=[io.Sigmas.Output()]
         )
@@ -164,8 +164,8 @@ class BetaSamplingScheduler(io.ComfyNode):
             inputs=[
                 io.Model.Input("model"),
                 io.Int.Input("steps", default=20, min=1, max=10000),
-                io.Float.Input("alpha", default=0.6, min=0.0, max=50.0, step=0.01, round=False),
-                io.Float.Input("beta", default=0.6, min=0.0, max=50.0, step=0.01, round=False),
+                io.Float.Input("alpha", default=0.6, min=0.0, max=50.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("beta", default=0.6, min=0.0, max=50.0, step=0.01, round=False, advanced=True),
             ],
             outputs=[io.Sigmas.Output()]
         )
@@ -185,9 +185,9 @@ class VPScheduler(io.ComfyNode):
             category="sampling/custom_sampling/schedulers",
             inputs=[
                 io.Int.Input("steps", default=20, min=1, max=10000),
-                io.Float.Input("beta_d", default=19.9, min=0.0, max=5000.0, step=0.01, round=False), #TODO: fix default values
-                io.Float.Input("beta_min", default=0.1, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("eps_s", default=0.001, min=0.0, max=1.0, step=0.0001, round=False),
+                io.Float.Input("beta_d", default=19.9, min=0.0, max=5000.0, step=0.01, round=False, advanced=True), #TODO: fix default values
+                io.Float.Input("beta_min", default=0.1, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("eps_s", default=0.001, min=0.0, max=1.0, step=0.0001, round=False, advanced=True),
             ],
             outputs=[io.Sigmas.Output()]
         )
@@ -398,9 +398,9 @@ class SamplerDPMPP_3M_SDE(io.ComfyNode):
             node_id="SamplerDPMPP_3M_SDE",
             category="sampling/custom_sampling/samplers",
             inputs=[
-                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Combo.Input("noise_device", options=['gpu', 'cpu']),
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Combo.Input("noise_device", options=['gpu', 'cpu'], advanced=True),
             ],
             outputs=[io.Sampler.Output()]
         )
@@ -424,9 +424,9 @@ class SamplerDPMPP_2M_SDE(io.ComfyNode):
             category="sampling/custom_sampling/samplers",
             inputs=[
                 io.Combo.Input("solver_type", options=['midpoint', 'heun']),
-                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Combo.Input("noise_device", options=['gpu', 'cpu']),
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Combo.Input("noise_device", options=['gpu', 'cpu'], advanced=True),
             ],
             outputs=[io.Sampler.Output()]
         )
@@ -450,10 +450,10 @@ class SamplerDPMPP_SDE(io.ComfyNode):
             node_id="SamplerDPMPP_SDE",
             category="sampling/custom_sampling/samplers",
             inputs=[
-                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("r", default=0.5, min=0.0, max=100.0, step=0.01, round=False),
-                io.Combo.Input("noise_device", options=['gpu', 'cpu']),
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("r", default=0.5, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Combo.Input("noise_device", options=['gpu', 'cpu'], advanced=True),
             ],
             outputs=[io.Sampler.Output()]
         )
@@ -496,8 +496,8 @@ class SamplerEulerAncestral(io.ComfyNode):
             node_id="SamplerEulerAncestral",
             category="sampling/custom_sampling/samplers",
             inputs=[
-                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
             ],
             outputs=[io.Sampler.Output()]
         )
@@ -538,7 +538,7 @@ class SamplerLMS(io.ComfyNode):
         return io.Schema(
             node_id="SamplerLMS",
             category="sampling/custom_sampling/samplers",
-            inputs=[io.Int.Input("order", default=4, min=1, max=100)],
+            inputs=[io.Int.Input("order", default=4, min=1, max=100, advanced=True)],
             outputs=[io.Sampler.Output()]
         )
 
@@ -556,16 +556,16 @@ class SamplerDPMAdaptative(io.ComfyNode):
             node_id="SamplerDPMAdaptative",
             category="sampling/custom_sampling/samplers",
             inputs=[
-                io.Int.Input("order", default=3, min=2, max=3),
-                io.Float.Input("rtol", default=0.05, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("atol", default=0.0078, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("h_init", default=0.05, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("pcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("icoeff", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("dcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("accept_safety", default=0.81, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("eta", default=0.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
+                io.Int.Input("order", default=3, min=2, max=3, advanced=True),
+                io.Float.Input("rtol", default=0.05, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("atol", default=0.0078, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("h_init", default=0.05, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("pcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("icoeff", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("dcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("accept_safety", default=0.81, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("eta", default=0.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
             ],
             outputs=[io.Sampler.Output()]
         )
@@ -588,9 +588,9 @@ class SamplerER_SDE(io.ComfyNode):
             category="sampling/custom_sampling/samplers",
             inputs=[
                 io.Combo.Input("solver_type", options=["ER-SDE", "Reverse-time SDE", "ODE"]),
-                io.Int.Input("max_stage", default=3, min=1, max=3),
-                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="Stochastic strength of reverse-time SDE.\nWhen eta=0, it reduces to deterministic ODE. This setting doesn't apply to ER-SDE solver type."),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
+                io.Int.Input("max_stage", default=3, min=1, max=3, advanced=True),
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="Stochastic strength of reverse-time SDE.\nWhen eta=0, it reduces to deterministic ODE. This setting doesn't apply to ER-SDE solver type.", advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
             ],
             outputs=[io.Sampler.Output()]
         )
@@ -626,14 +626,14 @@ class SamplerSASolver(io.ComfyNode):
             category="sampling/custom_sampling/samplers",
             inputs=[
                 io.Model.Input("model"),
-                io.Float.Input("eta", default=1.0, min=0.0, max=10.0, step=0.01, round=False),
-                io.Float.Input("sde_start_percent", default=0.2, min=0.0, max=1.0, step=0.001),
-                io.Float.Input("sde_end_percent", default=0.8, min=0.0, max=1.0, step=0.001),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Int.Input("predictor_order", default=3, min=1, max=6),
-                io.Int.Input("corrector_order", default=4, min=0, max=6),
-                io.Boolean.Input("use_pece"),
-                io.Boolean.Input("simple_order_2"),
+                io.Float.Input("eta", default=1.0, min=0.0, max=10.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("sde_start_percent", default=0.2, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("sde_end_percent", default=0.8, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Int.Input("predictor_order", default=3, min=1, max=6, advanced=True),
+                io.Int.Input("corrector_order", default=4, min=0, max=6, advanced=True),
+                io.Boolean.Input("use_pece", advanced=True),
+                io.Boolean.Input("simple_order_2", advanced=True),
             ],
             outputs=[io.Sampler.Output()]
         )
@@ -671,9 +671,9 @@ class SamplerSEEDS2(io.ComfyNode):
             category="sampling/custom_sampling/samplers",
             inputs=[
                 io.Combo.Input("solver_type", options=["phi_1", "phi_2"]),
-                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="Stochastic strength"),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="SDE noise multiplier"),
-                io.Float.Input("r", default=0.5, min=0.01, max=1.0, step=0.01, round=False, tooltip="Relative step size for the intermediate stage (c2 node)"),
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="Stochastic strength", advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="SDE noise multiplier", advanced=True),
+                io.Float.Input("r", default=0.5, min=0.01, max=1.0, step=0.01, round=False, tooltip="Relative step size for the intermediate stage (c2 node)", advanced=True),
             ],
             outputs=[io.Sampler.Output()],
             description=(
@@ -730,7 +730,7 @@ class SamplerCustom(io.ComfyNode):
             category="sampling/custom_sampling",
             inputs=[
                 io.Model.Input("model"),
-                io.Boolean.Input("add_noise", default=True),
+                io.Boolean.Input("add_noise", default=True, advanced=True),
                 io.Int.Input("noise_seed", default=0, min=0, max=0xffffffffffffffff, control_after_generate=True),
                 io.Float.Input("cfg", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01),
                 io.Conditioning.Input("positive"),
diff --git a/comfy_extras/nodes_dataset.py b/comfy_extras/nodes_dataset.py
index fb9409ac3..98ed25d7e 100644
--- a/comfy_extras/nodes_dataset.py
+++ b/comfy_extras/nodes_dataset.py
@@ -222,6 +222,7 @@ class SaveImageDataSetToFolderNode(io.ComfyNode):
                     "filename_prefix",
                     default="image",
                     tooltip="Prefix for saved image filenames.",
+                    advanced=True,
                 ),
             ],
             outputs=[],
@@ -262,6 +263,7 @@ class SaveImageTextDataSetToFolderNode(io.ComfyNode):
                     "filename_prefix",
                     default="image",
                     tooltip="Prefix for saved image filenames.",
+                    advanced=True,
                 ),
             ],
             outputs=[],
@@ -741,6 +743,7 @@ class NormalizeImagesNode(ImageProcessingNode):
             min=0.0,
             max=1.0,
             tooltip="Mean value for normalization.",
+            advanced=True,
         ),
         io.Float.Input(
             "std",
@@ -748,6 +751,7 @@ class NormalizeImagesNode(ImageProcessingNode):
             min=0.001,
             max=1.0,
             tooltip="Standard deviation for normalization.",
+            advanced=True,
         ),
     ]
 
@@ -961,6 +965,7 @@ class ImageDeduplicationNode(ImageProcessingNode):
             min=0.0,
             max=1.0,
             tooltip="Similarity threshold (0-1). Higher means more similar. Images above this threshold are considered duplicates.",
+            advanced=True,
         ),
     ]
 
@@ -1039,6 +1044,7 @@ class ImageGridNode(ImageProcessingNode):
             min=32,
             max=2048,
             tooltip="Width of each cell in the grid.",
+            advanced=True,
         ),
         io.Int.Input(
             "cell_height",
@@ -1046,9 +1052,10 @@ class ImageGridNode(ImageProcessingNode):
             min=32,
             max=2048,
             tooltip="Height of each cell in the grid.",
+            advanced=True,
         ),
         io.Int.Input(
-            "padding", default=4, min=0, max=50, tooltip="Padding between images."
+            "padding", default=4, min=0, max=50, tooltip="Padding between images.", advanced=True
         ),
     ]
 
@@ -1339,6 +1346,7 @@ class SaveTrainingDataset(io.ComfyNode):
                     min=1,
                     max=100000,
                     tooltip="Number of samples per shard file.",
+                    advanced=True,
                 ),
             ],
             outputs=[],
diff --git a/comfy_extras/nodes_easycache.py b/comfy_extras/nodes_easycache.py
index b1912392c..923c2bb05 100644
--- a/comfy_extras/nodes_easycache.py
+++ b/comfy_extras/nodes_easycache.py
@@ -367,10 +367,10 @@ class EasyCacheNode(io.ComfyNode):
             is_experimental=True,
             inputs=[
                 io.Model.Input("model", tooltip="The model to add EasyCache to."),
-                io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps."),
-                io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of EasyCache."),
-                io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of EasyCache."),
-                io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information."),
+                io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps.", advanced=True),
+                io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of EasyCache.", advanced=True),
+                io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of EasyCache.", advanced=True),
+                io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information.", advanced=True),
             ],
             outputs=[
                 io.Model.Output(tooltip="The model with EasyCache."),
@@ -500,10 +500,10 @@ class LazyCacheNode(io.ComfyNode):
             is_experimental=True,
             inputs=[
                 io.Model.Input("model", tooltip="The model to add LazyCache to."),
-                io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps."),
-                io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of LazyCache."),
-                io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of LazyCache."),
-                io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information."),
+                io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps.", advanced=True),
+                io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of LazyCache.", advanced=True),
+                io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of LazyCache.", advanced=True),
+                io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information.", advanced=True),
             ],
             outputs=[
                 io.Model.Output(tooltip="The model with LazyCache."),
diff --git a/comfy_extras/nodes_eps.py b/comfy_extras/nodes_eps.py
index 4d8061741..0fb3871c8 100644
--- a/comfy_extras/nodes_eps.py
+++ b/comfy_extras/nodes_eps.py
@@ -28,6 +28,7 @@ class EpsilonScaling(io.ComfyNode):
                     max=1.5,
                     step=0.001,
                     display_mode=io.NumberDisplay.number,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -97,6 +98,7 @@ class TemporalScoreRescaling(io.ComfyNode):
                     max=100.0,
                     step=0.001,
                     display_mode=io.NumberDisplay.number,
+                    advanced=True,
                 ),
                 io.Float.Input(
                     "tsr_sigma",
@@ -109,6 +111,7 @@ class TemporalScoreRescaling(io.ComfyNode):
                     max=100.0,
                     step=0.001,
                     display_mode=io.NumberDisplay.number,
+                    advanced=True,
                 ),
             ],
             outputs=[
diff --git a/comfy_extras/nodes_flux.py b/comfy_extras/nodes_flux.py
index 12c8ed3e6..fe9552022 100644
--- a/comfy_extras/nodes_flux.py
+++ b/comfy_extras/nodes_flux.py
@@ -161,6 +161,7 @@ class FluxKontextMultiReferenceLatentMethod(io.ComfyNode):
                 io.Combo.Input(
                     "reference_latents_method",
                     options=["offset", "index", "uxo/uno", "index_timestep_zero"],
+                    advanced=True,
                 ),
             ],
             outputs=[
diff --git a/comfy_extras/nodes_freelunch.py b/comfy_extras/nodes_freelunch.py
index 3429b731e..248efdef3 100644
--- a/comfy_extras/nodes_freelunch.py
+++ b/comfy_extras/nodes_freelunch.py
@@ -32,10 +32,10 @@ class FreeU(IO.ComfyNode):
             category="model_patches/unet",
             inputs=[
                 IO.Model.Input("model"),
-                IO.Float.Input("b1", default=1.1, min=0.0, max=10.0, step=0.01),
-                IO.Float.Input("b2", default=1.2, min=0.0, max=10.0, step=0.01),
-                IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01),
-                IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01),
+                IO.Float.Input("b1", default=1.1, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("b2", default=1.2, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01, advanced=True),
             ],
             outputs=[
                 IO.Model.Output(),
@@ -79,10 +79,10 @@ class FreeU_V2(IO.ComfyNode):
             category="model_patches/unet",
             inputs=[
                 IO.Model.Input("model"),
-                IO.Float.Input("b1", default=1.3, min=0.0, max=10.0, step=0.01),
-                IO.Float.Input("b2", default=1.4, min=0.0, max=10.0, step=0.01),
-                IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01),
-                IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01),
+                IO.Float.Input("b1", default=1.3, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("b2", default=1.4, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01, advanced=True),
             ],
             outputs=[
                 IO.Model.Output(),
diff --git a/comfy_extras/nodes_fresca.py b/comfy_extras/nodes_fresca.py
index 3d590af4b..eab4f303f 100644
--- a/comfy_extras/nodes_fresca.py
+++ b/comfy_extras/nodes_fresca.py
@@ -65,11 +65,11 @@ class FreSca(io.ComfyNode):
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("scale_low", default=1.0, min=0, max=10, step=0.01,
-                               tooltip="Scaling factor for low-frequency components"),
+                               tooltip="Scaling factor for low-frequency components", advanced=True),
                 io.Float.Input("scale_high", default=1.25, min=0, max=10, step=0.01,
-                               tooltip="Scaling factor for high-frequency components"),
+                               tooltip="Scaling factor for high-frequency components", advanced=True),
                 io.Int.Input("freq_cutoff", default=20, min=1, max=10000, step=1,
-                             tooltip="Number of frequency indices around center to consider as low-frequency"),
+                             tooltip="Number of frequency indices around center to consider as low-frequency", advanced=True),
             ],
             outputs=[
                 io.Model.Output(),
diff --git a/comfy_extras/nodes_gits.py b/comfy_extras/nodes_gits.py
index 25367560a..d48483862 100644
--- a/comfy_extras/nodes_gits.py
+++ b/comfy_extras/nodes_gits.py
@@ -342,7 +342,7 @@ class GITSScheduler(io.ComfyNode):
             node_id="GITSScheduler",
             category="sampling/custom_sampling/schedulers",
             inputs=[
-                io.Float.Input("coeff", default=1.20, min=0.80, max=1.50, step=0.05),
+                io.Float.Input("coeff", default=1.20, min=0.80, max=1.50, step=0.05, advanced=True),
                 io.Int.Input("steps", default=10, min=2, max=1000),
                 io.Float.Input("denoise", default=1.0, min=0.0, max=1.0, step=0.01),
             ],
diff --git a/comfy_extras/nodes_hooks.py b/comfy_extras/nodes_hooks.py
index 58e511ef5..be7d600cd 100644
--- a/comfy_extras/nodes_hooks.py
+++ b/comfy_extras/nodes_hooks.py
@@ -233,8 +233,8 @@ class SetClipHooks:
         return {
             "required": {
                 "clip": ("CLIP",),
-                "apply_to_conds": ("BOOLEAN", {"default": True}),
-                "schedule_clip": ("BOOLEAN", {"default": False})
+                "apply_to_conds": ("BOOLEAN", {"default": True, "advanced": True}),
+                "schedule_clip": ("BOOLEAN", {"default": False, "advanced": True})
             },
             "optional": {
                 "hooks": ("HOOKS",)
@@ -512,7 +512,7 @@ class CreateHookKeyframesInterpolated:
                 "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
                 "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
                 "keyframes_count": ("INT", {"default": 5, "min": 2, "max": 100, "step": 1}),
-                "print_keyframes": ("BOOLEAN", {"default": False}),
+                "print_keyframes": ("BOOLEAN", {"default": False, "advanced": True}),
             },
             "optional": {
                 "prev_hook_kf": ("HOOK_KEYFRAMES",),
@@ -557,7 +557,7 @@ class CreateHookKeyframesFromFloats:
                 "floats_strength": ("FLOATS", {"default": -1, "min": -1, "step": 0.001, "forceInput": True}),
                 "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
                 "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
-                "print_keyframes": ("BOOLEAN", {"default": False}),
+                "print_keyframes": ("BOOLEAN", {"default": False, "advanced": True}),
             },
             "optional": {
                 "prev_hook_kf": ("HOOK_KEYFRAMES",),
diff --git a/comfy_extras/nodes_hunyuan.py b/comfy_extras/nodes_hunyuan.py
index 774da75a3..4ea93a499 100644
--- a/comfy_extras/nodes_hunyuan.py
+++ b/comfy_extras/nodes_hunyuan.py
@@ -138,7 +138,7 @@ class HunyuanVideo15SuperResolution(io.ComfyNode):
                 io.Image.Input("start_image", optional=True),
                 io.ClipVisionOutput.Input("clip_vision_output", optional=True),
                 io.Latent.Input("latent"),
-                io.Float.Input("noise_augmentation", default=0.70, min=0.0, max=1.0, step=0.01),
+                io.Float.Input("noise_augmentation", default=0.70, min=0.0, max=1.0, step=0.01, advanced=True),
 
             ],
             outputs=[
@@ -285,6 +285,7 @@ class TextEncodeHunyuanVideo_ImageToVideo(io.ComfyNode):
                     min=1,
                     max=512,
                     tooltip="How much the image influences things vs the text prompt. Higher number means more influence from the text prompt.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -313,7 +314,7 @@ class HunyuanImageToVideo(io.ComfyNode):
                 io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("length", default=53, min=1, max=nodes.MAX_RESOLUTION, step=4),
                 io.Int.Input("batch_size", default=1, min=1, max=4096),
-                io.Combo.Input("guidance_type", options=["v1 (concat)", "v2 (replace)", "custom"]),
+                io.Combo.Input("guidance_type", options=["v1 (concat)", "v2 (replace)", "custom"], advanced=True),
                 io.Image.Input("start_image", optional=True),
             ],
             outputs=[
@@ -384,7 +385,7 @@ class HunyuanRefinerLatent(io.ComfyNode):
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
                 io.Latent.Input("latent"),
-                io.Float.Input("noise_augmentation", default=0.10, min=0.0, max=1.0, step=0.01),
+                io.Float.Input("noise_augmentation", default=0.10, min=0.0, max=1.0, step=0.01, advanced=True),
 
             ],
             outputs=[
diff --git a/comfy_extras/nodes_hunyuan3d.py b/comfy_extras/nodes_hunyuan3d.py
index 3512d3cf8..df0c3e4b1 100644
--- a/comfy_extras/nodes_hunyuan3d.py
+++ b/comfy_extras/nodes_hunyuan3d.py
@@ -106,8 +106,8 @@ class VAEDecodeHunyuan3D(IO.ComfyNode):
             inputs=[
                 IO.Latent.Input("samples"),
                 IO.Vae.Input("vae"),
-                IO.Int.Input("num_chunks", default=8000, min=1000, max=500000),
-                IO.Int.Input("octree_resolution", default=256, min=16, max=512),
+                IO.Int.Input("num_chunks", default=8000, min=1000, max=500000, advanced=True),
+                IO.Int.Input("octree_resolution", default=256, min=16, max=512, advanced=True),
             ],
             outputs=[
                 IO.Voxel.Output(),
@@ -456,7 +456,7 @@ class VoxelToMesh(IO.ComfyNode):
             category="3d",
             inputs=[
                 IO.Voxel.Input("voxel"),
-                IO.Combo.Input("algorithm", options=["surface net", "basic"]),
+                IO.Combo.Input("algorithm", options=["surface net", "basic"], advanced=True),
                 IO.Float.Input("threshold", default=0.6, min=-1.0, max=1.0, step=0.01),
             ],
             outputs=[
diff --git a/comfy_extras/nodes_hypertile.py b/comfy_extras/nodes_hypertile.py
index 0ad5e6773..354d96db1 100644
--- a/comfy_extras/nodes_hypertile.py
+++ b/comfy_extras/nodes_hypertile.py
@@ -30,10 +30,10 @@ class HyperTile(io.ComfyNode):
             category="model_patches/unet",
             inputs=[
                 io.Model.Input("model"),
-                io.Int.Input("tile_size", default=256, min=1, max=2048),
-                io.Int.Input("swap_size", default=2, min=1, max=128),
-                io.Int.Input("max_depth", default=0, min=0, max=10),
-                io.Boolean.Input("scale_depth", default=False),
+                io.Int.Input("tile_size", default=256, min=1, max=2048, advanced=True),
+                io.Int.Input("swap_size", default=2, min=1, max=128, advanced=True),
+                io.Int.Input("max_depth", default=0, min=0, max=10, advanced=True),
+                io.Boolean.Input("scale_depth", default=False, advanced=True),
             ],
             outputs=[
                 io.Model.Output(),
diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py
index 88fe071e7..983e2a92f 100644
--- a/comfy_extras/nodes_images.py
+++ b/comfy_extras/nodes_images.py
@@ -228,7 +228,7 @@ class SaveAnimatedPNG(IO.ComfyNode):
                 IO.Image.Input("images"),
                 IO.String.Input("filename_prefix", default="ComfyUI"),
                 IO.Float.Input("fps", default=6.0, min=0.01, max=1000.0, step=0.01),
-                IO.Int.Input("compress_level", default=4, min=0, max=9),
+                IO.Int.Input("compress_level", default=4, min=0, max=9, advanced=True),
             ],
             hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
             is_output_node=True,
@@ -265,8 +265,8 @@ class ImageStitch(IO.ComfyNode):
                 IO.Image.Input("image1"),
                 IO.Combo.Input("direction", options=["right", "down", "left", "up"], default="right"),
                 IO.Boolean.Input("match_image_size", default=True),
-                IO.Int.Input("spacing_width", default=0, min=0, max=1024, step=2),
-                IO.Combo.Input("spacing_color", options=["white", "black", "red", "green", "blue"], default="white"),
+                IO.Int.Input("spacing_width", default=0, min=0, max=1024, step=2, advanced=True),
+                IO.Combo.Input("spacing_color", options=["white", "black", "red", "green", "blue"], default="white", advanced=True),
                 IO.Image.Input("image2", optional=True),
             ],
             outputs=[IO.Image.Output()],
@@ -436,8 +436,8 @@ class ResizeAndPadImage(IO.ComfyNode):
                 IO.Image.Input("image"),
                 IO.Int.Input("target_width", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
                 IO.Int.Input("target_height", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
-                IO.Combo.Input("padding_color", options=["white", "black"]),
-                IO.Combo.Input("interpolation", options=["area", "bicubic", "nearest-exact", "bilinear", "lanczos"]),
+                IO.Combo.Input("padding_color", options=["white", "black"], advanced=True),
+                IO.Combo.Input("interpolation", options=["area", "bicubic", "nearest-exact", "bilinear", "lanczos"], advanced=True),
             ],
             outputs=[IO.Image.Output()],
         )
diff --git a/comfy_extras/nodes_latent.py b/comfy_extras/nodes_latent.py
index 8d2d7297a..8bb368dec 100644
--- a/comfy_extras/nodes_latent.py
+++ b/comfy_extras/nodes_latent.py
@@ -413,9 +413,9 @@ class LatentOperationSharpen(io.ComfyNode):
             category="latent/advanced/operations",
             is_experimental=True,
             inputs=[
-                io.Int.Input("sharpen_radius", default=9, min=1, max=31, step=1),
-                io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.1),
-                io.Float.Input("alpha", default=0.1, min=0.0, max=5.0, step=0.01),
+                io.Int.Input("sharpen_radius", default=9, min=1, max=31, step=1, advanced=True),
+                io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.1, advanced=True),
+                io.Float.Input("alpha", default=0.1, min=0.0, max=5.0, step=0.01, advanced=True),
             ],
             outputs=[
                 io.LatentOperation.Output(),
diff --git a/comfy_extras/nodes_load_3d.py b/comfy_extras/nodes_load_3d.py
index 3c88cbaa8..9112bdd0a 100644
--- a/comfy_extras/nodes_load_3d.py
+++ b/comfy_extras/nodes_load_3d.py
@@ -98,8 +98,8 @@ class Preview3D(IO.ComfyNode):
                     ],
                     tooltip="3D model file or path string",
                 ),
-                IO.Load3DCamera.Input("camera_info", optional=True),
-                IO.Image.Input("bg_image", optional=True),
+                IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
+                IO.Image.Input("bg_image", optional=True, advanced=True),
             ],
             outputs=[],
         )
diff --git a/comfy_extras/nodes_lora_extract.py b/comfy_extras/nodes_lora_extract.py
index 1542d0a88..975f90f45 100644
--- a/comfy_extras/nodes_lora_extract.py
+++ b/comfy_extras/nodes_lora_extract.py
@@ -94,9 +94,9 @@ class LoraSave(io.ComfyNode):
             category="_for_testing",
             inputs=[
                 io.String.Input("filename_prefix", default="loras/ComfyUI_extracted_lora"),
-                io.Int.Input("rank", default=8, min=1, max=4096, step=1),
-                io.Combo.Input("lora_type", options=tuple(LORA_TYPES.keys())),
-                io.Boolean.Input("bias_diff", default=True),
+                io.Int.Input("rank", default=8, min=1, max=4096, step=1, advanced=True),
+                io.Combo.Input("lora_type", options=tuple(LORA_TYPES.keys()), advanced=True),
+                io.Boolean.Input("bias_diff", default=True, advanced=True),
                 io.Model.Input(
                     "model_diff",
                     tooltip="The ModelSubtract output to be converted to a lora.",
diff --git a/comfy_extras/nodes_lt.py b/comfy_extras/nodes_lt.py
index 2aec62f61..1eeeec011 100644
--- a/comfy_extras/nodes_lt.py
+++ b/comfy_extras/nodes_lt.py
@@ -450,6 +450,7 @@ class LTXVScheduler(io.ComfyNode):
                     id="stretch",
                     default=True,
                     tooltip="Stretch the sigmas to be in the range [terminal, 1].",
+                    advanced=True,
                 ),
                 io.Float.Input(
                     id="terminal",
@@ -458,6 +459,7 @@ class LTXVScheduler(io.ComfyNode):
                     max=0.99,
                     step=0.01,
                     tooltip="The terminal value of the sigmas after stretching.",
+                    advanced=True,
                 ),
                 io.Latent.Input("latent", optional=True),
             ],
diff --git a/comfy_extras/nodes_lt_audio.py b/comfy_extras/nodes_lt_audio.py
index 1966fd1bf..3e4222264 100644
--- a/comfy_extras/nodes_lt_audio.py
+++ b/comfy_extras/nodes_lt_audio.py
@@ -189,6 +189,7 @@ class LTXAVTextEncoderLoader(io.ComfyNode):
                 io.Combo.Input(
                     "device",
                     options=["default", "cpu"],
+                    advanced=True,
                 )
             ],
             outputs=[io.Clip.Output()],
diff --git a/comfy_extras/nodes_lumina2.py b/comfy_extras/nodes_lumina2.py
index 2550475ae..b35ab8b7d 100644
--- a/comfy_extras/nodes_lumina2.py
+++ b/comfy_extras/nodes_lumina2.py
@@ -12,8 +12,8 @@ class RenormCFG(io.ComfyNode):
             category="advanced/model",
             inputs=[
                 io.Model.Input("model"),
-                io.Float.Input("cfg_trunc", default=100, min=0.0, max=100.0, step=0.01),
-                io.Float.Input("renorm_cfg", default=1.0, min=0.0, max=100.0, step=0.01),
+                io.Float.Input("cfg_trunc", default=100, min=0.0, max=100.0, step=0.01, advanced=True),
+                io.Float.Input("renorm_cfg", default=1.0, min=0.0, max=100.0, step=0.01, advanced=True),
             ],
             outputs=[
                 io.Model.Output(),
diff --git a/comfy_extras/nodes_mask.py b/comfy_extras/nodes_mask.py
index 98e8fef8f..c44602597 100644
--- a/comfy_extras/nodes_mask.py
+++ b/comfy_extras/nodes_mask.py
@@ -348,7 +348,7 @@ class GrowMask(IO.ComfyNode):
             inputs=[
                 IO.Mask.Input("mask"),
                 IO.Int.Input("expand", default=0, min=-nodes.MAX_RESOLUTION, max=nodes.MAX_RESOLUTION, step=1),
-                IO.Boolean.Input("tapered_corners", default=True),
+                IO.Boolean.Input("tapered_corners", default=True, advanced=True),
             ],
             outputs=[IO.Mask.Output()],
         )
diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py
index f22b333fc..9601a5f76 100644
--- a/comfy_extras/nodes_model_advanced.py
+++ b/comfy_extras/nodes_model_advanced.py
@@ -53,7 +53,7 @@ class ModelSamplingDiscrete:
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
                               "sampling": (["eps", "v_prediction", "lcm", "x0", "img_to_img"],),
-                              "zsnr": ("BOOLEAN", {"default": False}),
+                              "zsnr": ("BOOLEAN", {"default": False, "advanced": True}),
                               }}
 
     RETURN_TYPES = ("MODEL",)
@@ -153,8 +153,8 @@ class ModelSamplingFlux:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "max_shift": ("FLOAT", {"default": 1.15, "min": 0.0, "max": 100.0, "step":0.01}),
-                              "base_shift": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01}),
+                              "max_shift": ("FLOAT", {"default": 1.15, "min": 0.0, "max": 100.0, "step":0.01, "advanced": True}),
+                              "base_shift": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01, "advanced": True}),
                               "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
                               "height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
                               }}
@@ -190,8 +190,8 @@ class ModelSamplingContinuousEDM:
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
                               "sampling": (["v_prediction", "edm", "edm_playground_v2.5", "eps", "cosmos_rflow"],),
-                              "sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
-                              "sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
+                              "sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False, "advanced": True}),
+                              "sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False, "advanced": True}),
                               }}
 
     RETURN_TYPES = ("MODEL",)
@@ -235,8 +235,8 @@ class ModelSamplingContinuousV:
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
                               "sampling": (["v_prediction"],),
-                              "sigma_max": ("FLOAT", {"default": 500.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
-                              "sigma_min": ("FLOAT", {"default": 0.03, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
+                              "sigma_max": ("FLOAT", {"default": 500.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False, "advanced": True}),
+                              "sigma_min": ("FLOAT", {"default": 0.03, "min": 0.0, "max": 1000.0, "step":0.001, "round": False, "advanced": True}),
                               }}
 
     RETURN_TYPES = ("MODEL",)
@@ -303,7 +303,7 @@ class ModelComputeDtype:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "dtype": (["default", "fp32", "fp16", "bf16"],),
+                              "dtype": (["default", "fp32", "fp16", "bf16"], {"advanced": True}),
                               }}
 
     RETURN_TYPES = ("MODEL",)
diff --git a/comfy_extras/nodes_model_downscale.py b/comfy_extras/nodes_model_downscale.py
index dec2ae841..24d47a903 100644
--- a/comfy_extras/nodes_model_downscale.py
+++ b/comfy_extras/nodes_model_downscale.py
@@ -13,11 +13,11 @@ class PatchModelAddDownscale(io.ComfyNode):
             category="model_patches/unet",
             inputs=[
                 io.Model.Input("model"),
-                io.Int.Input("block_number", default=3, min=1, max=32, step=1),
+                io.Int.Input("block_number", default=3, min=1, max=32, step=1, advanced=True),
                 io.Float.Input("downscale_factor", default=2.0, min=0.1, max=9.0, step=0.001),
-                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001),
-                io.Float.Input("end_percent", default=0.35, min=0.0, max=1.0, step=0.001),
-                io.Boolean.Input("downscale_after_skip", default=True),
+                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("end_percent", default=0.35, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Boolean.Input("downscale_after_skip", default=True, advanced=True),
                 io.Combo.Input("downscale_method", options=cls.UPSCALE_METHODS),
                 io.Combo.Input("upscale_method", options=cls.UPSCALE_METHODS),
             ],
diff --git a/comfy_extras/nodes_perpneg.py b/comfy_extras/nodes_perpneg.py
index cd068ce9c..ed1467de9 100644
--- a/comfy_extras/nodes_perpneg.py
+++ b/comfy_extras/nodes_perpneg.py
@@ -29,7 +29,7 @@ class PerpNeg(io.ComfyNode):
             inputs=[
                 io.Model.Input("model"),
                 io.Conditioning.Input("empty_conditioning"),
-                io.Float.Input("neg_scale", default=1.0, min=0.0, max=100.0, step=0.01),
+                io.Float.Input("neg_scale", default=1.0, min=0.0, max=100.0, step=0.01, advanced=True),
             ],
             outputs=[
                 io.Model.Output(),
@@ -134,7 +134,7 @@ class PerpNegGuider(io.ComfyNode):
                 io.Conditioning.Input("negative"),
                 io.Conditioning.Input("empty_conditioning"),
                 io.Float.Input("cfg", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01),
-                io.Float.Input("neg_scale", default=1.0, min=0.0, max=100.0, step=0.01),
+                io.Float.Input("neg_scale", default=1.0, min=0.0, max=100.0, step=0.01, advanced=True),
             ],
             outputs=[
                 io.Guider.Output(),
diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py
index f35819bc2..5c143daab 100644
--- a/comfy_extras/nodes_post_processing.py
+++ b/comfy_extras/nodes_post_processing.py
@@ -180,9 +180,9 @@ class Sharpen(io.ComfyNode):
             category="image/postprocessing",
             inputs=[
                 io.Image.Input("image"),
-                io.Int.Input("sharpen_radius", default=1, min=1, max=31, step=1),
-                io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.01),
-                io.Float.Input("alpha", default=1.0, min=0.0, max=5.0, step=0.01),
+                io.Int.Input("sharpen_radius", default=1, min=1, max=31, step=1, advanced=True),
+                io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("alpha", default=1.0, min=0.0, max=5.0, step=0.01, advanced=True),
             ],
             outputs=[
                 io.Image.Output(),
@@ -226,7 +226,7 @@ class ImageScaleToTotalPixels(io.ComfyNode):
                 io.Image.Input("image"),
                 io.Combo.Input("upscale_method", options=cls.upscale_methods),
                 io.Float.Input("megapixels", default=1.0, min=0.01, max=16.0, step=0.01),
-                io.Int.Input("resolution_steps", default=1, min=1, max=256),
+                io.Int.Input("resolution_steps", default=1, min=1, max=256, advanced=True),
             ],
             outputs=[
                 io.Image.Output(),
diff --git a/comfy_extras/nodes_qwen.py b/comfy_extras/nodes_qwen.py
index fde8fac9a..6894367be 100644
--- a/comfy_extras/nodes_qwen.py
+++ b/comfy_extras/nodes_qwen.py
@@ -116,7 +116,7 @@ class EmptyQwenImageLayeredLatentImage(io.ComfyNode):
             inputs=[
                 io.Int.Input("width", default=640, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("height", default=640, min=16, max=nodes.MAX_RESOLUTION, step=16),
-                io.Int.Input("layers", default=3, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                io.Int.Input("layers", default=3, min=0, max=nodes.MAX_RESOLUTION, step=1, advanced=True),
                 io.Int.Input("batch_size", default=1, min=1, max=4096),
             ],
             outputs=[
diff --git a/comfy_extras/nodes_rope.py b/comfy_extras/nodes_rope.py
index d1feb031e..918ddc02b 100644
--- a/comfy_extras/nodes_rope.py
+++ b/comfy_extras/nodes_rope.py
@@ -12,14 +12,14 @@ class ScaleROPE(io.ComfyNode):
             is_experimental=True,
             inputs=[
                 io.Model.Input("model"),
-                io.Float.Input("scale_x", default=1.0, min=0.0, max=100.0, step=0.1),
-                io.Float.Input("shift_x", default=0.0, min=-256.0, max=256.0, step=0.1),
+                io.Float.Input("scale_x", default=1.0, min=0.0, max=100.0, step=0.1, advanced=True),
+                io.Float.Input("shift_x", default=0.0, min=-256.0, max=256.0, step=0.1, advanced=True),
 
-                io.Float.Input("scale_y", default=1.0, min=0.0, max=100.0, step=0.1),
-                io.Float.Input("shift_y", default=0.0, min=-256.0, max=256.0, step=0.1),
+                io.Float.Input("scale_y", default=1.0, min=0.0, max=100.0, step=0.1, advanced=True),
+                io.Float.Input("shift_y", default=0.0, min=-256.0, max=256.0, step=0.1, advanced=True),
 
-                io.Float.Input("scale_t", default=1.0, min=0.0, max=100.0, step=0.1),
-                io.Float.Input("shift_t", default=0.0, min=-256.0, max=256.0, step=0.1),
+                io.Float.Input("scale_t", default=1.0, min=0.0, max=100.0, step=0.1, advanced=True),
+                io.Float.Input("shift_t", default=0.0, min=-256.0, max=256.0, step=0.1, advanced=True),
 
 
             ],
diff --git a/comfy_extras/nodes_sag.py b/comfy_extras/nodes_sag.py
index 0f47db30b..d9c47851c 100644
--- a/comfy_extras/nodes_sag.py
+++ b/comfy_extras/nodes_sag.py
@@ -117,7 +117,7 @@ class SelfAttentionGuidance(io.ComfyNode):
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("scale", default=0.5, min=-2.0, max=5.0, step=0.01),
-                io.Float.Input("blur_sigma", default=2.0, min=0.0, max=10.0, step=0.1),
+                io.Float.Input("blur_sigma", default=2.0, min=0.0, max=10.0, step=0.1, advanced=True),
             ],
             outputs=[
                 io.Model.Output(),
diff --git a/comfy_extras/nodes_sd3.py b/comfy_extras/nodes_sd3.py
index 736213a47..c43844a1a 100644
--- a/comfy_extras/nodes_sd3.py
+++ b/comfy_extras/nodes_sd3.py
@@ -72,7 +72,7 @@ class CLIPTextEncodeSD3(io.ComfyNode):
                 io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
                 io.String.Input("clip_g", multiline=True, dynamic_prompts=True),
                 io.String.Input("t5xxl", multiline=True, dynamic_prompts=True),
-                io.Combo.Input("empty_padding", options=["none", "empty_prompt"]),
+                io.Combo.Input("empty_padding", options=["none", "empty_prompt"], advanced=True),
             ],
             outputs=[
                 io.Conditioning.Output(),
@@ -179,10 +179,10 @@ class SkipLayerGuidanceSD3(io.ComfyNode):
             description="Generic version of SkipLayerGuidance node that can be used on every DiT model.",
             inputs=[
                 io.Model.Input("model"),
-                io.String.Input("layers", default="7, 8, 9", multiline=False),
+                io.String.Input("layers", default="7, 8, 9", multiline=False, advanced=True),
                 io.Float.Input("scale", default=3.0, min=0.0, max=10.0, step=0.1),
-                io.Float.Input("start_percent", default=0.01, min=0.0, max=1.0, step=0.001),
-                io.Float.Input("end_percent", default=0.15, min=0.0, max=1.0, step=0.001),
+                io.Float.Input("start_percent", default=0.01, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("end_percent", default=0.15, min=0.0, max=1.0, step=0.001, advanced=True),
             ],
             outputs=[
                 io.Model.Output(),
diff --git a/comfy_extras/nodes_sdupscale.py b/comfy_extras/nodes_sdupscale.py
index 31b373370..5877719d3 100644
--- a/comfy_extras/nodes_sdupscale.py
+++ b/comfy_extras/nodes_sdupscale.py
@@ -15,7 +15,7 @@ class SD_4XUpscale_Conditioning(io.ComfyNode):
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
                 io.Float.Input("scale_ratio", default=4.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("noise_augmentation", default=0.0, min=0.0, max=1.0, step=0.001),
+                io.Float.Input("noise_augmentation", default=0.0, min=0.0, max=1.0, step=0.001, advanced=True),
             ],
             outputs=[
                 io.Conditioning.Output(display_name="positive"),
diff --git a/comfy_extras/nodes_slg.py b/comfy_extras/nodes_slg.py
index f462faa8f..8cc1f551e 100644
--- a/comfy_extras/nodes_slg.py
+++ b/comfy_extras/nodes_slg.py
@@ -21,11 +21,11 @@ class SkipLayerGuidanceDiT(io.ComfyNode):
             is_experimental=True,
             inputs=[
                 io.Model.Input("model"),
-                io.String.Input("double_layers", default="7, 8, 9"),
-                io.String.Input("single_layers", default="7, 8, 9"),
+                io.String.Input("double_layers", default="7, 8, 9", advanced=True),
+                io.String.Input("single_layers", default="7, 8, 9", advanced=True),
                 io.Float.Input("scale", default=3.0, min=0.0, max=10.0, step=0.1),
-                io.Float.Input("start_percent", default=0.01, min=0.0, max=1.0, step=0.001),
-                io.Float.Input("end_percent", default=0.15, min=0.0, max=1.0, step=0.001),
+                io.Float.Input("start_percent", default=0.01, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("end_percent", default=0.15, min=0.0, max=1.0, step=0.001, advanced=True),
                 io.Float.Input("rescaling_scale", default=0.0, min=0.0, max=10.0, step=0.01),
             ],
             outputs=[
@@ -101,10 +101,10 @@ class SkipLayerGuidanceDiTSimple(io.ComfyNode):
             is_experimental=True,
             inputs=[
                 io.Model.Input("model"),
-                io.String.Input("double_layers", default="7, 8, 9"),
-                io.String.Input("single_layers", default="7, 8, 9"),
-                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001),
-                io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001),
+                io.String.Input("double_layers", default="7, 8, 9", advanced=True),
+                io.String.Input("single_layers", default="7, 8, 9", advanced=True),
+                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001, advanced=True),
             ],
             outputs=[
                 io.Model.Output(),
diff --git a/comfy_extras/nodes_stable3d.py b/comfy_extras/nodes_stable3d.py
index c6d8a683d..829c837a1 100644
--- a/comfy_extras/nodes_stable3d.py
+++ b/comfy_extras/nodes_stable3d.py
@@ -75,8 +75,8 @@ class StableZero123_Conditioning_Batched(io.ComfyNode):
                 io.Int.Input("batch_size", default=1, min=1, max=4096),
                 io.Float.Input("elevation", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
                 io.Float.Input("azimuth", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
-                io.Float.Input("elevation_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
-                io.Float.Input("azimuth_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False)
+                io.Float.Input("elevation_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False, advanced=True),
+                io.Float.Input("azimuth_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False, advanced=True)
             ],
             outputs=[
                 io.Conditioning.Output(display_name="positive"),
diff --git a/comfy_extras/nodes_stable_cascade.py b/comfy_extras/nodes_stable_cascade.py
index 04c0b366a..8c1aebca9 100644
--- a/comfy_extras/nodes_stable_cascade.py
+++ b/comfy_extras/nodes_stable_cascade.py
@@ -33,7 +33,7 @@ class StableCascade_EmptyLatentImage(io.ComfyNode):
             inputs=[
                 io.Int.Input("width", default=1024, min=256, max=nodes.MAX_RESOLUTION, step=8),
                 io.Int.Input("height", default=1024, min=256, max=nodes.MAX_RESOLUTION, step=8),
-                io.Int.Input("compression", default=42, min=4, max=128, step=1),
+                io.Int.Input("compression", default=42, min=4, max=128, step=1, advanced=True),
                 io.Int.Input("batch_size", default=1, min=1, max=4096),
             ],
             outputs=[
@@ -62,7 +62,7 @@ class StableCascade_StageC_VAEEncode(io.ComfyNode):
             inputs=[
                 io.Image.Input("image"),
                 io.Vae.Input("vae"),
-                io.Int.Input("compression", default=42, min=4, max=128, step=1),
+                io.Int.Input("compression", default=42, min=4, max=128, step=1, advanced=True),
             ],
             outputs=[
                 io.Latent.Output(display_name="stage_c"),
diff --git a/comfy_extras/nodes_string.py b/comfy_extras/nodes_string.py
index 8d3e65cc5..b4e5f148a 100644
--- a/comfy_extras/nodes_string.py
+++ b/comfy_extras/nodes_string.py
@@ -169,7 +169,7 @@ class StringContains(io.ComfyNode):
             inputs=[
                 io.String.Input("string", multiline=True),
                 io.String.Input("substring", multiline=True),
-                io.Boolean.Input("case_sensitive", default=True),
+                io.Boolean.Input("case_sensitive", default=True, advanced=True),
             ],
             outputs=[
                 io.Boolean.Output(display_name="contains"),
@@ -198,7 +198,7 @@ class StringCompare(io.ComfyNode):
                 io.String.Input("string_a", multiline=True),
                 io.String.Input("string_b", multiline=True),
                 io.Combo.Input("mode", options=["Starts With", "Ends With", "Equal"]),
-                io.Boolean.Input("case_sensitive", default=True),
+                io.Boolean.Input("case_sensitive", default=True, advanced=True),
             ],
             outputs=[
                 io.Boolean.Output(),
@@ -233,9 +233,9 @@ class RegexMatch(io.ComfyNode):
             inputs=[
                 io.String.Input("string", multiline=True),
                 io.String.Input("regex_pattern", multiline=True),
-                io.Boolean.Input("case_insensitive", default=True),
-                io.Boolean.Input("multiline", default=False),
-                io.Boolean.Input("dotall", default=False),
+                io.Boolean.Input("case_insensitive", default=True, advanced=True),
+                io.Boolean.Input("multiline", default=False, advanced=True),
+                io.Boolean.Input("dotall", default=False, advanced=True),
             ],
             outputs=[
                 io.Boolean.Output(display_name="matches"),
@@ -275,10 +275,10 @@ class RegexExtract(io.ComfyNode):
                 io.String.Input("string", multiline=True),
                 io.String.Input("regex_pattern", multiline=True),
                 io.Combo.Input("mode", options=["First Match", "All Matches", "First Group", "All Groups"]),
-                io.Boolean.Input("case_insensitive", default=True),
-                io.Boolean.Input("multiline", default=False),
-                io.Boolean.Input("dotall", default=False),
-                io.Int.Input("group_index", default=1, min=0, max=100),
+                io.Boolean.Input("case_insensitive", default=True, advanced=True),
+                io.Boolean.Input("multiline", default=False, advanced=True),
+                io.Boolean.Input("dotall", default=False, advanced=True),
+                io.Int.Input("group_index", default=1, min=0, max=100, advanced=True),
             ],
             outputs=[
                 io.String.Output(),
@@ -351,10 +351,10 @@ class RegexReplace(io.ComfyNode):
                 io.String.Input("string", multiline=True),
                 io.String.Input("regex_pattern", multiline=True),
                 io.String.Input("replace", multiline=True),
-                io.Boolean.Input("case_insensitive", default=True, optional=True),
-                io.Boolean.Input("multiline", default=False, optional=True),
-                io.Boolean.Input("dotall", default=False, optional=True, tooltip="When enabled, the dot (.) character will match any character including newline characters. When disabled, dots won't match newlines."),
-                io.Int.Input("count", default=0, min=0, max=100, optional=True, tooltip="Maximum number of replacements to make. Set to 0 to replace all occurrences (default). Set to 1 to replace only the first match, 2 for the first two matches, etc."),
+                io.Boolean.Input("case_insensitive", default=True, optional=True, advanced=True),
+                io.Boolean.Input("multiline", default=False, optional=True, advanced=True),
+                io.Boolean.Input("dotall", default=False, optional=True, advanced=True, tooltip="When enabled, the dot (.) character will match any character including newline characters. When disabled, dots won't match newlines."),
+                io.Int.Input("count", default=0, min=0, max=100, optional=True, advanced=True, tooltip="Maximum number of replacements to make. Set to 0 to replace all occurrences (default). Set to 1 to replace only the first match, 2 for the first two matches, etc."),
             ],
             outputs=[
                 io.String.Output(),
diff --git a/comfy_extras/nodes_torch_compile.py b/comfy_extras/nodes_torch_compile.py
index c43e8ad63..00e9f8b1f 100644
--- a/comfy_extras/nodes_torch_compile.py
+++ b/comfy_extras/nodes_torch_compile.py
@@ -16,6 +16,7 @@ class TorchCompileModel(io.ComfyNode):
                 io.Combo.Input(
                     "backend",
                     options=["inductor", "cudagraphs"],
+                    advanced=True,
                 ),
             ],
             outputs=[io.Model.Output()],
diff --git a/comfy_extras/nodes_video_model.py b/comfy_extras/nodes_video_model.py
index 0f760aa26..bf98e6b82 100644
--- a/comfy_extras/nodes_video_model.py
+++ b/comfy_extras/nodes_video_model.py
@@ -32,9 +32,9 @@ class SVD_img2vid_Conditioning:
                               "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
                               "height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
                               "video_frames": ("INT", {"default": 14, "min": 1, "max": 4096}),
-                              "motion_bucket_id": ("INT", {"default": 127, "min": 1, "max": 1023}),
+                              "motion_bucket_id": ("INT", {"default": 127, "min": 1, "max": 1023, "advanced": True}),
                               "fps": ("INT", {"default": 6, "min": 1, "max": 1024}),
-                              "augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01})
+                              "augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01, "advanced": True})
                              }}
     RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
     RETURN_NAMES = ("positive", "negative", "latent")
@@ -60,7 +60,7 @@ class VideoLinearCFGGuidance:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
+                              "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01, "advanced": True}),
                               }}
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
@@ -84,7 +84,7 @@ class VideoTriangleCFGGuidance:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
+                              "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01, "advanced": True}),
                               }}
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
diff --git a/comfy_extras/nodes_wan.py b/comfy_extras/nodes_wan.py
index 2ff012134..effa994d1 100644
--- a/comfy_extras/nodes_wan.py
+++ b/comfy_extras/nodes_wan.py
@@ -717,8 +717,8 @@ class WanTrackToVideo(io.ComfyNode):
                 io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
                 io.Int.Input("batch_size", default=1, min=1, max=4096),
-                io.Float.Input("temperature", default=220.0, min=1.0, max=1000.0, step=0.1),
-                io.Int.Input("topk", default=2, min=1, max=10),
+                io.Float.Input("temperature", default=220.0, min=1.0, max=1000.0, step=0.1, advanced=True),
+                io.Int.Input("topk", default=2, min=1, max=10, advanced=True),
                 io.Image.Input("start_image"),
                 io.ClipVisionOutput.Input("clip_vision_output", optional=True),
             ],
@@ -1323,7 +1323,7 @@ class WanInfiniteTalkToVideo(io.ComfyNode):
                 io.ClipVisionOutput.Input("clip_vision_output", optional=True),
                 io.Image.Input("start_image", optional=True),
                 io.AudioEncoderOutput.Input("audio_encoder_output_1"),
-                io.Int.Input("motion_frame_count", default=9, min=1, max=33, step=1, tooltip="Number of previous frames to use as motion context."),
+                io.Int.Input("motion_frame_count", default=9, min=1, max=33, step=1, tooltip="Number of previous frames to use as motion context.", advanced=True),
                 io.Float.Input("audio_scale", default=1.0, min=-10.0, max=10.0, step=0.01),
                 io.Image.Input("previous_frames", optional=True),
             ],
diff --git a/comfy_extras/nodes_wanmove.py b/comfy_extras/nodes_wanmove.py
index d60baf230..5acae03eb 100644
--- a/comfy_extras/nodes_wanmove.py
+++ b/comfy_extras/nodes_wanmove.py
@@ -252,9 +252,9 @@ class WanMoveVisualizeTracks(io.ComfyNode):
                 io.Image.Input("images"),
                 io.Tracks.Input("tracks", optional=True),
                 io.Int.Input("line_resolution", default=24, min=1, max=1024),
-                io.Int.Input("circle_size", default=12, min=1, max=128),
+                io.Int.Input("circle_size", default=12, min=1, max=128, advanced=True),
                 io.Float.Input("opacity", default=0.75, min=0.0, max=1.0, step=0.01),
-                io.Int.Input("line_width", default=16, min=1, max=128),
+                io.Int.Input("line_width", default=16, min=1, max=128, advanced=True),
             ],
             outputs=[
                 io.Image.Output(),
diff --git a/comfy_extras/nodes_zimage.py b/comfy_extras/nodes_zimage.py
index 2ee3c43b1..70ddc4afa 100644
--- a/comfy_extras/nodes_zimage.py
+++ b/comfy_extras/nodes_zimage.py
@@ -16,7 +16,7 @@ class TextEncodeZImageOmni(io.ComfyNode):
                 io.Clip.Input("clip"),
                 io.ClipVision.Input("image_encoder", optional=True),
                 io.String.Input("prompt", multiline=True, dynamic_prompts=True),
-                io.Boolean.Input("auto_resize_images", default=True),
+                io.Boolean.Input("auto_resize_images", default=True, advanced=True),
                 io.Vae.Input("vae", optional=True),
                 io.Image.Input("image1", optional=True),
                 io.Image.Input("image2", optional=True),
diff --git a/nodes.py b/nodes.py
index 330808cdc..160a30928 100644
--- a/nodes.py
+++ b/nodes.py
@@ -322,10 +322,10 @@ class VAEDecodeTiled:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {"samples": ("LATENT", ), "vae": ("VAE", ),
-                             "tile_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 32}),
-                             "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}),
-                             "temporal_size": ("INT", {"default": 64, "min": 8, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to decode at a time."}),
-                             "temporal_overlap": ("INT", {"default": 8, "min": 4, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to overlap."}),
+                             "tile_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 32, "advanced": True}),
+                             "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32, "advanced": True}),
+                             "temporal_size": ("INT", {"default": 64, "min": 8, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to decode at a time.", "advanced": True}),
+                             "temporal_overlap": ("INT", {"default": 8, "min": 4, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to overlap.", "advanced": True}),
                             }}
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "decode"
@@ -369,10 +369,10 @@ class VAEEncodeTiled:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {"pixels": ("IMAGE", ), "vae": ("VAE", ),
-                             "tile_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
-                             "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}),
-                             "temporal_size": ("INT", {"default": 64, "min": 8, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to encode at a time."}),
-                             "temporal_overlap": ("INT", {"default": 8, "min": 4, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to overlap."}),
+                             "tile_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64, "advanced": True}),
+                             "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32, "advanced": True}),
+                             "temporal_size": ("INT", {"default": 64, "min": 8, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to encode at a time.", "advanced": True}),
+                             "temporal_overlap": ("INT", {"default": 8, "min": 4, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to overlap.", "advanced": True}),
                             }}
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "encode"
@@ -656,7 +656,7 @@ class CLIPSetLastLayer:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "clip": ("CLIP", ),
-                              "stop_at_clip_layer": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),
+                              "stop_at_clip_layer": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1, "advanced": True}),
                               }}
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "set_last_layer"
@@ -1598,7 +1598,7 @@ class KSamplerAdvanced:
     def INPUT_TYPES(s):
         return {"required":
                     {"model": ("MODEL",),
-                    "add_noise": (["enable", "disable"], ),
+                    "add_noise": (["enable", "disable"], {"advanced": True}),
                     "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True}),
                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                     "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
@@ -1607,9 +1607,9 @@ class KSamplerAdvanced:
                     "positive": ("CONDITIONING", ),
                     "negative": ("CONDITIONING", ),
                     "latent_image": ("LATENT", ),
-                    "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
-                    "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
-                    "return_with_leftover_noise": (["disable", "enable"], ),
+                    "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000, "advanced": True}),
+                    "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000, "advanced": True}),
+                    "return_with_leftover_noise": (["disable", "enable"], {"advanced": True}),
                      }
                 }
 
@@ -1983,7 +1983,7 @@ class ImagePadForOutpaint:
                 "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
                 "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
                 "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
-                "feathering": ("INT", {"default": 40, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                "feathering": ("INT", {"default": 40, "min": 0, "max": MAX_RESOLUTION, "step": 1, "advanced": True}),
             }
         }
 

From 0301ccf745d24f41abdf05ba84ffb61a26ddaff7 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Thu, 19 Feb 2026 19:42:28 -0800
Subject: [PATCH 016/194] Small cleanup and try to get qwen 3 work with the
 text gen. (#12537)

---
 comfy/sd.py                    |  4 +---
 comfy/sd1_clip.py              |  4 ++--
 comfy/text_encoders/anima.py   |  2 ++
 comfy/text_encoders/llama.py   | 22 +++++++++++++++++-----
 comfy/text_encoders/lumina2.py |  6 ------
 5 files changed, 22 insertions(+), 16 deletions(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index 164f30803..ce6ca5d17 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -426,10 +426,8 @@ class CLIP:
     def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None):
         self.cond_stage_model.reset_clip_options()
 
-        if self.layer_idx is not None:
-            self.cond_stage_model.set_clip_options({"layer": self.layer_idx})
-
         self.load_model()
+        self.cond_stage_model.set_clip_options({"layer": None})
         self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device})
         return self.cond_stage_model.generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed)
 
diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index d9d014055..17e2b4816 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -308,14 +308,14 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
     def load_sd(self, sd):
         return self.transformer.load_state_dict(sd, strict=False, assign=getattr(self, "can_assign_sd", False))
 
-    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, stop_tokens=[]):
+    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
         if isinstance(tokens, dict):
             tokens_only = next(iter(tokens.values())) # todo: get this better?
         else:
             tokens_only = tokens
         tokens_only = [[t[0] for t in b] for b in tokens_only]
         embeds = self.process_tokens(tokens_only, device=self.execution_device)[0]
-        return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, stop_tokens)
+        return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed)
 
 def parse_parentheses(string):
     result = []
diff --git a/comfy/text_encoders/anima.py b/comfy/text_encoders/anima.py
index d8c5a6f92..2e31b2b04 100644
--- a/comfy/text_encoders/anima.py
+++ b/comfy/text_encoders/anima.py
@@ -33,6 +33,8 @@ class AnimaTokenizer:
     def state_dict(self):
         return {}
 
+    def decode(self, token_ids, **kwargs):
+        return self.qwen3_06b.decode(token_ids, **kwargs)
 
 class Qwen3_06BModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
diff --git a/comfy/text_encoders/llama.py b/comfy/text_encoders/llama.py
index e5d21fa74..ccc200b7a 100644
--- a/comfy/text_encoders/llama.py
+++ b/comfy/text_encoders/llama.py
@@ -105,6 +105,7 @@ class Qwen3_06BConfig:
     rope_scale = None
     final_norm: bool = True
     lm_head: bool = False
+    stop_tokens = [151643, 151645]
 
 @dataclass
 class Qwen3_06B_ACE15_Config:
@@ -128,6 +129,7 @@ class Qwen3_06B_ACE15_Config:
     rope_scale = None
     final_norm: bool = True
     lm_head: bool = False
+    stop_tokens = [151643, 151645]
 
 @dataclass
 class Qwen3_2B_ACE15_lm_Config:
@@ -151,6 +153,7 @@ class Qwen3_2B_ACE15_lm_Config:
     rope_scale = None
     final_norm: bool = True
     lm_head: bool = False
+    stop_tokens = [151643, 151645]
 
 @dataclass
 class Qwen3_4B_ACE15_lm_Config:
@@ -174,6 +177,7 @@ class Qwen3_4B_ACE15_lm_Config:
     rope_scale = None
     final_norm: bool = True
     lm_head: bool = False
+    stop_tokens = [151643, 151645]
 
 @dataclass
 class Qwen3_4BConfig:
@@ -197,6 +201,7 @@ class Qwen3_4BConfig:
     rope_scale = None
     final_norm: bool = True
     lm_head: bool = False
+    stop_tokens = [151643, 151645]
 
 @dataclass
 class Qwen3_8BConfig:
@@ -220,6 +225,7 @@ class Qwen3_8BConfig:
     rope_scale = None
     final_norm: bool = True
     lm_head: bool = False
+    stop_tokens = [151643, 151645]
 
 @dataclass
 class Ovis25_2BConfig:
@@ -290,6 +296,7 @@ class Gemma2_2B_Config:
     rope_scale = None
     final_norm: bool = True
     lm_head: bool = False
+    stop_tokens = [1]
 
 @dataclass
 class Gemma3_4B_Config:
@@ -314,6 +321,7 @@ class Gemma3_4B_Config:
     rope_scale = [8.0, 1.0]
     final_norm: bool = True
     lm_head: bool = False
+    stop_tokens = [1, 106]
 
 GEMMA3_VISION_CONFIG = {"num_channels": 3, "hidden_act": "gelu_pytorch_tanh", "hidden_size": 1152, "image_size": 896, "intermediate_size": 4304, "model_type": "siglip_vision_model", "num_attention_heads": 16, "num_hidden_layers": 27, "patch_size": 14}
 
@@ -347,6 +355,7 @@ class Gemma3_12B_Config:
     lm_head: bool = False
     vision_config = GEMMA3_VISION_CONFIG
     mm_tokens_per_image = 256
+    stop_tokens = [1, 106]
 
 class RMSNorm(nn.Module):
     def __init__(self, dim: int, eps: float = 1e-5, add=False, device=None, dtype=None):
@@ -803,10 +812,13 @@ class BaseGenerate:
         comfy.ops.uncast_bias_weight(module, weight, None, offload_stream)
         return x
 
-    def generate(self, embeds=None, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.9, min_p=0.0, repetition_penalty=1.0, seed=42, stop_tokens=[], initial_tokens=[], execution_dtype=None, min_tokens=0):
+    def generate(self, embeds=None, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.9, min_p=0.0, repetition_penalty=1.0, seed=42, stop_tokens=None, initial_tokens=[], execution_dtype=None, min_tokens=0):
         device = embeds.device
         model_config = self.model.config
 
+        if stop_tokens is None:
+            stop_tokens = self.model.config.stop_tokens
+
         if execution_dtype is None:
             if comfy.model_management.should_use_bf16(device):
                 execution_dtype = torch.bfloat16
@@ -925,7 +937,7 @@ class Qwen25_3B(BaseLlama, torch.nn.Module):
         self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
         self.dtype = dtype
 
-class Qwen3_06B(BaseLlama, BaseQwen3, torch.nn.Module):
+class Qwen3_06B(BaseLlama, BaseQwen3, BaseGenerate, torch.nn.Module):
     def __init__(self, config_dict, dtype, device, operations):
         super().__init__()
         config = Qwen3_06BConfig(**config_dict)
@@ -952,7 +964,7 @@ class Qwen3_2B_ACE15_lm(BaseLlama, BaseQwen3, torch.nn.Module):
         self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
         self.dtype = dtype
 
-class Qwen3_4B(BaseLlama, BaseQwen3, torch.nn.Module):
+class Qwen3_4B(BaseLlama, BaseQwen3, BaseGenerate, torch.nn.Module):
     def __init__(self, config_dict, dtype, device, operations):
         super().__init__()
         config = Qwen3_4BConfig(**config_dict)
@@ -970,7 +982,7 @@ class Qwen3_4B_ACE15_lm(BaseLlama, BaseQwen3, torch.nn.Module):
         self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
         self.dtype = dtype
 
-class Qwen3_8B(BaseLlama, BaseQwen3, torch.nn.Module):
+class Qwen3_8B(BaseLlama, BaseQwen3, BaseGenerate, torch.nn.Module):
     def __init__(self, config_dict, dtype, device, operations):
         super().__init__()
         config = Qwen3_8BConfig(**config_dict)
@@ -1034,7 +1046,7 @@ class Qwen25_7BVLI(BaseLlama, BaseGenerate, torch.nn.Module):
 
         return super().forward(x, attention_mask=attention_mask, embeds=embeds, num_tokens=num_tokens, intermediate_output=intermediate_output, final_layer_norm_intermediate=final_layer_norm_intermediate, dtype=dtype, position_ids=position_ids)
 
-class Gemma2_2B(BaseLlama, torch.nn.Module):
+class Gemma2_2B(BaseLlama, BaseGenerate, torch.nn.Module):
     def __init__(self, config_dict, dtype, device, operations):
         super().__init__()
         config = Gemma2_2B_Config(**config_dict)
diff --git a/comfy/text_encoders/lumina2.py b/comfy/text_encoders/lumina2.py
index 1b731e094..01ebdfabe 100644
--- a/comfy/text_encoders/lumina2.py
+++ b/comfy/text_encoders/lumina2.py
@@ -31,9 +31,6 @@ class Gemma2_2BModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
         super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma2_2B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
 
-    def generate(self, embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
-        return super().generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, stop_tokens=[107])
-
 class Gemma3_4BModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
         llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
@@ -43,9 +40,6 @@ class Gemma3_4BModel(sd1_clip.SDClipModel):
 
         super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
 
-    def generate(self, embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
-        return super().generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, stop_tokens=[106])
-
 class Gemma3_4B_Vision_Model(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
         llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)

From 5f2117528a6b140fc07f5ba8c4d4256c4ca0864b Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Thu, 19 Feb 2026 19:57:44 -0800
Subject: [PATCH 017/194] Force min length 1 when tokenizing for text
 generation. (#12538)

---
 comfy/sd1_clip.py             | 2 ++
 comfy_extras/nodes_textgen.py | 2 +-
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index 17e2b4816..d89550840 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -573,6 +573,8 @@ class SDTokenizer:
         min_length = tokenizer_options.get("{}_min_length".format(self.embedding_key), self.min_length)
         min_padding = tokenizer_options.get("{}_min_padding".format(self.embedding_key), self.min_padding)
 
+        min_length = kwargs.get("min_length", min_length)
+
         text = escape_important(text)
         if kwargs.get("disable_weights", self.disable_weights):
             parsed_weights = [(text, 1.0)]
diff --git a/comfy_extras/nodes_textgen.py b/comfy_extras/nodes_textgen.py
index dd4f6b0d3..14cff14a6 100644
--- a/comfy_extras/nodes_textgen.py
+++ b/comfy_extras/nodes_textgen.py
@@ -42,7 +42,7 @@ class TextGenerate(io.ComfyNode):
     @classmethod
     def execute(cls, clip, prompt, max_length, sampling_mode, image=None) -> io.NodeOutput:
 
-        tokens = clip.tokenize(prompt, image=image, skip_template=False)
+        tokens = clip.tokenize(prompt, image=image, skip_template=False, min_length=1)
 
         # Get sampling parameters from dynamic combo
         do_sample = sampling_mode.get("sampling_mode") == "on"

From 96d6bd1a4ad3d308542bd68c537e971b5ee8e0a5 Mon Sep 17 00:00:00 2001
From: pythongosssss <125205205+pythongosssss@users.noreply.github.com>
Date: Fri, 20 Feb 2026 04:22:13 +0000
Subject: [PATCH 018/194] Add GLSL shader node using PyOpenGL (#12148)

* adds support for executing simple glsl shaders
using moderngl package

* tidy

* Support multiple outputs

* Try fix build

* fix casing

* fix line endings

* convert to using PyOpenGL and glfw

* remove cpu support

* tidy

* add additional support for egl & osmesa backends

* fix ci
perf: only read required outputs

* add diagnostics, update mac initialization

* GLSL glueprints + node fixes (#12492)

* Add image operation blueprints

* Add channels

* Add glow

* brightness/contrast

* hsb

* add glsl shader update system

* shader nit iteration

* add multipass for faster blur

* more fixes

* rebuild blueprints

* print -> logger

* Add edge preserving blur

* fix: move _initialized flag to end of GLContext.__init__

Prevents '_vao' attribute error when init fails partway through
and subsequent calls skip initialization due to early _initialized flag.

* update valid ranges
- threshold 0-100
- step 0+

* fix value ranges

* rebuild node to remove extra inputs

* Fix gamma step

* clamp saturation in colorize instead of wrapping

* Fix crash on 1x1 px images

* rework description

* remove unnecessary f


Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
Co-authored-by: Hunter Senft-Grupp <hunter@comfy.org>
---
 .../.glsl/Brightness_and_Contrast_1.frag      |  44 +
 blueprints/.glsl/Chromatic_Aberration_16.frag |  72 ++
 blueprints/.glsl/Color_Adjustment_15.frag     |  78 ++
 .../.glsl/Edge-Preserving_Blur_128.frag       |  94 ++
 blueprints/.glsl/Film_Grain_15.frag           | 124 +++
 blueprints/.glsl/Glow_30.frag                 | 133 +++
 blueprints/.glsl/Hue_and_Saturation_1.frag    | 222 +++++
 blueprints/.glsl/Image_Blur_1.frag            | 111 +++
 blueprints/.glsl/Image_Channels_23.frag       |  19 +
 blueprints/.glsl/Image_Levels_1.frag          |  71 ++
 blueprints/.glsl/README.md                    |  28 +
 blueprints/.glsl/Sharpen_23.frag              |  28 +
 blueprints/.glsl/Unsharp_Mask_26.frag         |  61 ++
 blueprints/.glsl/update_blueprints.py         | 159 ++++
 blueprints/Brightness and Contrast.json       |   1 +
 blueprints/Chromatic Aberration.json          |   1 +
 blueprints/Color Adjustment.json              |   1 +
 blueprints/Edge-Preserving Blur.json          |   1 +
 blueprints/Film Grain.json                    |   1 +
 blueprints/Glow.json                          |   1 +
 blueprints/Hue and Saturation.json            |   1 +
 blueprints/Image Blur.json                    |   1 +
 blueprints/Image Channels.json                |   1 +
 blueprints/Image Levels.json                  |   1 +
 blueprints/Sharpen.json                       |   1 +
 blueprints/Unsharp Mask.json                  |   1 +
 comfy_extras/nodes_glsl.py                    | 895 ++++++++++++++++++
 nodes.py                                      |   1 +
 requirements.txt                              |   3 +
 29 files changed, 2155 insertions(+)
 create mode 100644 blueprints/.glsl/Brightness_and_Contrast_1.frag
 create mode 100644 blueprints/.glsl/Chromatic_Aberration_16.frag
 create mode 100644 blueprints/.glsl/Color_Adjustment_15.frag
 create mode 100644 blueprints/.glsl/Edge-Preserving_Blur_128.frag
 create mode 100644 blueprints/.glsl/Film_Grain_15.frag
 create mode 100644 blueprints/.glsl/Glow_30.frag
 create mode 100644 blueprints/.glsl/Hue_and_Saturation_1.frag
 create mode 100644 blueprints/.glsl/Image_Blur_1.frag
 create mode 100644 blueprints/.glsl/Image_Channels_23.frag
 create mode 100644 blueprints/.glsl/Image_Levels_1.frag
 create mode 100644 blueprints/.glsl/README.md
 create mode 100644 blueprints/.glsl/Sharpen_23.frag
 create mode 100644 blueprints/.glsl/Unsharp_Mask_26.frag
 create mode 100644 blueprints/.glsl/update_blueprints.py
 create mode 100644 blueprints/Brightness and Contrast.json
 create mode 100644 blueprints/Chromatic Aberration.json
 create mode 100644 blueprints/Color Adjustment.json
 create mode 100644 blueprints/Edge-Preserving Blur.json
 create mode 100644 blueprints/Film Grain.json
 create mode 100644 blueprints/Glow.json
 create mode 100644 blueprints/Hue and Saturation.json
 create mode 100644 blueprints/Image Blur.json
 create mode 100644 blueprints/Image Channels.json
 create mode 100644 blueprints/Image Levels.json
 create mode 100644 blueprints/Sharpen.json
 create mode 100644 blueprints/Unsharp Mask.json
 create mode 100644 comfy_extras/nodes_glsl.py

diff --git a/blueprints/.glsl/Brightness_and_Contrast_1.frag b/blueprints/.glsl/Brightness_and_Contrast_1.frag
new file mode 100644
index 000000000..da5424080
--- /dev/null
+++ b/blueprints/.glsl/Brightness_and_Contrast_1.frag
@@ -0,0 +1,44 @@
+#version 300 es
+precision highp float;
+
+uniform sampler2D u_image0;
+uniform float u_float0; // Brightness slider -100..100
+uniform float u_float1; // Contrast slider -100..100
+
+in vec2 v_texCoord;
+out vec4 fragColor;
+
+const float MID_GRAY = 0.18;  // 18% reflectance
+
+// sRGB gamma 2.2 approximation
+vec3 srgbToLinear(vec3 c) {
+    return pow(max(c, 0.0), vec3(2.2));
+}
+
+vec3 linearToSrgb(vec3 c) {
+    return pow(max(c, 0.0), vec3(1.0/2.2));
+}
+
+float mapBrightness(float b) {
+    return clamp(b / 100.0, -1.0, 1.0);
+}
+
+float mapContrast(float c) {
+    return clamp(c / 100.0 + 1.0, 0.0, 2.0);
+}
+
+void main() {
+    vec4 orig = texture(u_image0, v_texCoord);
+
+    float brightness = mapBrightness(u_float0);
+    float contrast   = mapContrast(u_float1);
+
+    vec3 lin = srgbToLinear(orig.rgb);
+
+    lin = (lin - MID_GRAY) * contrast + brightness + MID_GRAY;
+
+    // Convert back to sRGB
+    vec3 result = linearToSrgb(clamp(lin, 0.0, 1.0));
+
+    fragColor = vec4(result, orig.a);
+}
diff --git a/blueprints/.glsl/Chromatic_Aberration_16.frag b/blueprints/.glsl/Chromatic_Aberration_16.frag
new file mode 100644
index 000000000..09a271146
--- /dev/null
+++ b/blueprints/.glsl/Chromatic_Aberration_16.frag
@@ -0,0 +1,72 @@
+#version 300 es
+precision highp float;
+
+uniform sampler2D u_image0;
+uniform vec2 u_resolution;
+uniform int u_int0;      // Mode
+uniform float u_float0;  // Amount (0 to 100)
+
+in vec2 v_texCoord;
+out vec4 fragColor;
+
+const int MODE_LINEAR   = 0;
+const int MODE_RADIAL   = 1;
+const int MODE_BARREL   = 2;
+const int MODE_SWIRL    = 3;
+const int MODE_DIAGONAL = 4;
+
+const float AMOUNT_SCALE = 0.0005;
+const float RADIAL_MULT = 4.0;
+const float BARREL_MULT = 8.0;
+const float INV_SQRT2 = 0.70710678118;
+
+void main() {
+    vec2 uv = v_texCoord;
+    vec4 original = texture(u_image0, uv);
+
+    float amount = u_float0 * AMOUNT_SCALE;
+
+    if (amount < 0.000001) {
+        fragColor = original;
+        return;
+    }
+
+    // Aspect-corrected coordinates for circular effects
+    float aspect = u_resolution.x / u_resolution.y;
+    vec2 centered = uv - 0.5;
+    vec2 corrected = vec2(centered.x * aspect, centered.y);
+    float r = length(corrected);
+    vec2 dir = r > 0.0001 ? corrected / r : vec2(0.0);
+    vec2 offset = vec2(0.0);
+
+    if (u_int0 == MODE_LINEAR) {
+        // Horizontal shift (no aspect correction needed)
+        offset = vec2(amount, 0.0);
+    }
+    else if (u_int0 == MODE_RADIAL) {
+        // Outward from center, stronger at edges
+        offset = dir * r * amount * RADIAL_MULT;
+        offset.x /= aspect;  // Convert back to UV space
+    }
+    else if (u_int0 == MODE_BARREL) {
+        // Lens distortion simulation (r² falloff)
+        offset = dir * r * r * amount * BARREL_MULT;
+        offset.x /= aspect;  // Convert back to UV space
+    }
+    else if (u_int0 == MODE_SWIRL) {
+        // Perpendicular to radial (rotational aberration)
+        vec2 perp = vec2(-dir.y, dir.x);
+        offset = perp * r * amount * RADIAL_MULT;
+        offset.x /= aspect;  // Convert back to UV space
+    }
+    else if (u_int0 == MODE_DIAGONAL) {
+        // 45° offset (no aspect correction needed)
+        offset = vec2(amount, amount) * INV_SQRT2;
+    }
+    
+    float red = texture(u_image0, uv + offset).r;
+    float green = original.g;
+    float blue = texture(u_image0, uv - offset).b;
+    
+    fragColor = vec4(red, green, blue, original.a);
+}
\ No newline at end of file
diff --git a/blueprints/.glsl/Color_Adjustment_15.frag b/blueprints/.glsl/Color_Adjustment_15.frag
new file mode 100644
index 000000000..697525f14
--- /dev/null
+++ b/blueprints/.glsl/Color_Adjustment_15.frag
@@ -0,0 +1,78 @@
+#version 300 es
+precision highp float;
+
+uniform sampler2D u_image0;
+uniform float u_float0; // temperature (-100 to 100)
+uniform float u_float1; // tint (-100 to 100)
+uniform float u_float2; // vibrance (-100 to 100)
+uniform float u_float3; // saturation (-100 to 100)
+
+in vec2 v_texCoord;
+out vec4 fragColor;
+
+const float INPUT_SCALE = 0.01;
+const float TEMP_TINT_PRIMARY = 0.3;
+const float TEMP_TINT_SECONDARY = 0.15;
+const float VIBRANCE_BOOST = 2.0;
+const float SATURATION_BOOST = 2.0;
+const float SKIN_PROTECTION = 0.5;
+const float EPSILON = 0.001;
+const vec3 LUMA_WEIGHTS = vec3(0.299, 0.587, 0.114);
+
+void main() {
+    vec4 tex = texture(u_image0, v_texCoord);
+    vec3 color = tex.rgb;
+    
+    // Scale inputs: -100/100 → -1/1
+    float temperature = u_float0 * INPUT_SCALE;
+    float tint = u_float1 * INPUT_SCALE;
+    float vibrance = u_float2 * INPUT_SCALE;
+    float saturation = u_float3 * INPUT_SCALE;
+    
+    // Temperature (warm/cool): positive = warm, negative = cool
+    color.r += temperature * TEMP_TINT_PRIMARY;
+    color.b -= temperature * TEMP_TINT_PRIMARY;
+    
+    // Tint (green/magenta): positive = green, negative = magenta
+    color.g += tint * TEMP_TINT_PRIMARY;
+    color.r -= tint * TEMP_TINT_SECONDARY;
+    color.b -= tint * TEMP_TINT_SECONDARY;
+    
+    // Single clamp after temperature/tint
+    color = clamp(color, 0.0, 1.0);
+    
+    // Vibrance with skin protection
+    if (vibrance != 0.0) {
+        float maxC = max(color.r, max(color.g, color.b));
+        float minC = min(color.r, min(color.g, color.b));
+        float sat = maxC - minC;
+        float gray = dot(color, LUMA_WEIGHTS);
+        
+        if (vibrance < 0.0) {
+            // Desaturate: -100 → gray
+            color = mix(vec3(gray), color, 1.0 + vibrance);
+        } else {
+            // Boost less saturated colors more
+            float vibranceAmt = vibrance * (1.0 - sat);
+            
+            // Branchless skin tone protection
+            float isWarmTone = step(color.b, color.g) * step(color.g, color.r);
+            float warmth = (color.r - color.b) / max(maxC, EPSILON);
+            float skinTone = isWarmTone * warmth * sat * (1.0 - sat);
+            vibranceAmt *= (1.0 - skinTone * SKIN_PROTECTION);
+            
+            color = mix(vec3(gray), color, 1.0 + vibranceAmt * VIBRANCE_BOOST);
+        }
+    }
+    
+    // Saturation
+    if (saturation != 0.0) {
+        float gray = dot(color, LUMA_WEIGHTS);
+        float satMix = saturation < 0.0
+            ? 1.0 + saturation                      // -100 → gray
+            : 1.0 + saturation * SATURATION_BOOST;  // +100 → 3x boost
+        color = mix(vec3(gray), color, satMix);
+    }
+    
+    fragColor = vec4(clamp(color, 0.0, 1.0), tex.a);
+}
\ No newline at end of file
diff --git a/blueprints/.glsl/Edge-Preserving_Blur_128.frag b/blueprints/.glsl/Edge-Preserving_Blur_128.frag
new file mode 100644
index 000000000..f269aebd6
--- /dev/null
+++ b/blueprints/.glsl/Edge-Preserving_Blur_128.frag
@@ -0,0 +1,94 @@
+#version 300 es
+precision highp float;
+
+uniform sampler2D u_image0;
+uniform float u_float0;   // Blur radius (0–20, default ~5)
+uniform float u_float1;   // Edge threshold (0–100, default ~30)
+uniform int u_int0;       // Step size (0/1 = every pixel, 2+ = skip pixels)
+
+in vec2 v_texCoord;
+out vec4 fragColor;
+
+const int MAX_RADIUS = 20;
+const float EPSILON = 0.0001;
+
+// Perceptual luminance
+float getLuminance(vec3 rgb) {
+    return dot(rgb, vec3(0.299, 0.587, 0.114));
+}
+
+vec4 bilateralFilter(vec2 uv, vec2 texelSize, int radius,
+                     float sigmaSpatial, float sigmaColor)
+{
+    vec4 center = texture(u_image0, uv);
+    vec3 centerRGB = center.rgb;
+
+    float invSpatial2 = -0.5 / (sigmaSpatial * sigmaSpatial);
+    float invColor2   = -0.5 / (sigmaColor * sigmaColor + EPSILON);
+
+    vec3 sumRGB = vec3(0.0);
+    float sumWeight = 0.0;
+
+    int step = max(u_int0, 1);
+    float radius2 = float(radius * radius);
+
+    for (int dy = -MAX_RADIUS; dy <= MAX_RADIUS; dy++) {
+        if (dy < -radius || dy > radius) continue;
+        if (abs(dy) % step != 0) continue;
+
+        for (int dx = -MAX_RADIUS; dx <= MAX_RADIUS; dx++) {
+            if (dx < -radius || dx > radius) continue;
+            if (abs(dx) % step != 0) continue;
+
+            vec2 offset = vec2(float(dx), float(dy));
+            float dist2 = dot(offset, offset);
+            if (dist2 > radius2) continue;
+
+            vec3 sampleRGB = texture(u_image0, uv + offset * texelSize).rgb;
+
+            // Spatial Gaussian
+            float spatialWeight = exp(dist2 * invSpatial2);
+
+            // Perceptual color distance (weighted RGB)
+            vec3 diff = sampleRGB - centerRGB;
+            float colorDist = dot(diff * diff, vec3(0.299, 0.587, 0.114));
+            float colorWeight = exp(colorDist * invColor2);
+
+            float w = spatialWeight * colorWeight;
+            sumRGB += sampleRGB * w;
+            sumWeight += w;
+        }
+    }
+
+    vec3 resultRGB = sumRGB / max(sumWeight, EPSILON);
+    return vec4(resultRGB, center.a); // preserve center alpha
+}
+
+void main() {
+    vec2 texelSize = 1.0 / vec2(textureSize(u_image0, 0));
+
+    float radiusF = clamp(u_float0, 0.0, float(MAX_RADIUS));
+    int radius = int(radiusF + 0.5);
+
+    if (radius == 0) {
+        fragColor = texture(u_image0, v_texCoord);
+        return;
+    }
+
+    // Edge threshold → color sigma
+    // Squared curve for better low-end control
+    float t = clamp(u_float1, 0.0, 100.0) / 100.0;
+    t *= t;
+    float sigmaColor = mix(0.01, 0.5, t);
+
+    // Spatial sigma tied to radius
+    float sigmaSpatial = max(radiusF * 0.75, 0.5);
+
+    fragColor = bilateralFilter(
+        v_texCoord,
+        texelSize,
+        radius,
+        sigmaSpatial,
+        sigmaColor
+    );
+}
\ No newline at end of file
diff --git a/blueprints/.glsl/Film_Grain_15.frag b/blueprints/.glsl/Film_Grain_15.frag
new file mode 100644
index 000000000..21585825b
--- /dev/null
+++ b/blueprints/.glsl/Film_Grain_15.frag
@@ -0,0 +1,124 @@
+#version 300 es
+precision highp float;
+
+uniform sampler2D u_image0;
+uniform vec2 u_resolution;
+uniform float u_float0; // grain amount      [0.0 – 1.0]   typical: 0.2–0.8
+uniform float u_float1; // grain size        [0.3 – 3.0]   lower = finer grain
+uniform float u_float2; // color amount      [0.0 – 1.0]   0 = monochrome, 1 = RGB grain
+uniform float u_float3; // luminance bias    [0.0 – 1.0]   0 = uniform, 1 = shadows only
+uniform int   u_int0;   // noise mode        [0 or 1]      0 = smooth, 1 = grainy
+
+in vec2 v_texCoord;
+layout(location = 0) out vec4 fragColor0;
+
+// High-quality integer hash (pcg-like)
+uint pcg(uint v) {
+    uint state = v * 747796405u + 2891336453u;
+    uint word = ((state >> ((state >> 28u) + 4u)) ^ state) * 277803737u;
+    return (word >> 22u) ^ word;
+}
+
+// 2D -> 1D hash input
+uint hash2d(uvec2 p) {
+    return pcg(p.x + pcg(p.y));
+}
+
+// Hash to float [0, 1]
+float hashf(uvec2 p) {
+    return float(hash2d(p)) / float(0xffffffffu);
+}
+
+// Hash to float with offset (for RGB channels)
+float hashf(uvec2 p, uint offset) {
+    return float(pcg(hash2d(p) + offset)) / float(0xffffffffu);
+}
+
+// Convert uniform [0,1] to roughly Gaussian distribution
+// Using simple approximation: average of multiple samples
+float toGaussian(uvec2 p) {
+    float sum = hashf(p, 0u) + hashf(p, 1u) + hashf(p, 2u) + hashf(p, 3u);
+    return (sum - 2.0) * 0.7;  // Centered, scaled
+}
+
+float toGaussian(uvec2 p, uint offset) {
+    float sum = hashf(p, offset) + hashf(p, offset + 1u) 
+              + hashf(p, offset + 2u) + hashf(p, offset + 3u);
+    return (sum - 2.0) * 0.7;
+}
+
+// Smooth noise with better interpolation
+float smoothNoise(vec2 p) {
+    vec2 i = floor(p);
+    vec2 f = fract(p);
+    
+    // Quintic interpolation (less banding than cubic)
+    f = f * f * f * (f * (f * 6.0 - 15.0) + 10.0);
+    
+    uvec2 ui = uvec2(i);
+    float a = toGaussian(ui);
+    float b = toGaussian(ui + uvec2(1u, 0u));
+    float c = toGaussian(ui + uvec2(0u, 1u));
+    float d = toGaussian(ui + uvec2(1u, 1u));
+    
+    return mix(mix(a, b, f.x), mix(c, d, f.x), f.y);
+}
+
+float smoothNoise(vec2 p, uint offset) {
+    vec2 i = floor(p);
+    vec2 f = fract(p);
+    
+    f = f * f * f * (f * (f * 6.0 - 15.0) + 10.0);
+    
+    uvec2 ui = uvec2(i);
+    float a = toGaussian(ui, offset);
+    float b = toGaussian(ui + uvec2(1u, 0u), offset);
+    float c = toGaussian(ui + uvec2(0u, 1u), offset);
+    float d = toGaussian(ui + uvec2(1u, 1u), offset);
+    
+    return mix(mix(a, b, f.x), mix(c, d, f.x), f.y);
+}
+
+void main() {
+    vec4 color = texture(u_image0, v_texCoord);
+    
+    // Luminance (Rec.709)
+    float luma = dot(color.rgb, vec3(0.2126, 0.7152, 0.0722));
+    
+    // Grain UV (resolution-independent)
+    vec2 grainUV = v_texCoord * u_resolution / max(u_float1, 0.01);
+    uvec2 grainPixel = uvec2(grainUV);
+    
+    float g;
+    vec3 grainRGB;
+    
+    if (u_int0 == 1) {
+        // Grainy mode: pure hash noise (no interpolation = no banding)
+        g = toGaussian(grainPixel);
+        grainRGB = vec3(
+            toGaussian(grainPixel, 100u),
+            toGaussian(grainPixel, 200u),
+            toGaussian(grainPixel, 300u)
+        );
+    } else {
+        // Smooth mode: interpolated with quintic curve
+        g = smoothNoise(grainUV);
+        grainRGB = vec3(
+            smoothNoise(grainUV, 100u),
+            smoothNoise(grainUV, 200u),
+            smoothNoise(grainUV, 300u)
+        );
+    }
+    
+    // Luminance weighting (less grain in highlights)
+    float lumWeight = mix(1.0, 1.0 - luma, clamp(u_float3, 0.0, 1.0));
+    
+    // Strength
+    float strength = u_float0 * 0.15;
+    
+    // Color vs monochrome grain
+    vec3 grainColor = mix(vec3(g), grainRGB, clamp(u_float2, 0.0, 1.0));
+    
+    color.rgb += grainColor * strength * lumWeight;
+    fragColor0 = vec4(clamp(color.rgb, 0.0, 1.0), color.a);
+}
diff --git a/blueprints/.glsl/Glow_30.frag b/blueprints/.glsl/Glow_30.frag
new file mode 100644
index 000000000..0ee152628
--- /dev/null
+++ b/blueprints/.glsl/Glow_30.frag
@@ -0,0 +1,133 @@
+#version 300 es
+precision mediump float;
+
+uniform sampler2D u_image0;
+uniform vec2 u_resolution;
+uniform int u_int0;      // Blend mode
+uniform int u_int1;      // Color tint
+uniform float u_float0;  // Intensity
+uniform float u_float1;  // Radius
+uniform float u_float2;  // Threshold
+
+in vec2 v_texCoord;
+out vec4 fragColor;
+
+const int BLEND_ADD      = 0;
+const int BLEND_SCREEN   = 1;
+const int BLEND_SOFT     = 2;
+const int BLEND_OVERLAY  = 3;
+const int BLEND_LIGHTEN  = 4;
+
+const float GOLDEN_ANGLE = 2.39996323;
+const int MAX_SAMPLES = 48;
+const vec3 LUMA = vec3(0.299, 0.587, 0.114);
+
+float hash(vec2 p) {
+    p = fract(p * vec2(123.34, 456.21));
+    p += dot(p, p + 45.32);
+    return fract(p.x * p.y);
+}
+
+vec3 hexToRgb(int h) {
+    return vec3(
+        float((h >> 16) & 255),
+        float((h >> 8) & 255),
+        float(h & 255)
+    ) * (1.0 / 255.0);
+}
+
+vec3 blend(vec3 base, vec3 glow, int mode) {
+    if (mode == BLEND_SCREEN) {
+        return 1.0 - (1.0 - base) * (1.0 - glow);
+    }
+    if (mode == BLEND_SOFT) {
+        return mix(
+            base - (1.0 - 2.0 * glow) * base * (1.0 - base),
+            base + (2.0 * glow - 1.0) * (sqrt(base) - base),
+            step(0.5, glow)
+        );
+    }
+    if (mode == BLEND_OVERLAY) {
+        return mix(
+            2.0 * base * glow,
+            1.0 - 2.0 * (1.0 - base) * (1.0 - glow),
+            step(0.5, base)
+        );
+    }
+    if (mode == BLEND_LIGHTEN) {
+        return max(base, glow);
+    }
+    return base + glow;
+}
+
+void main() {
+    vec4 original = texture(u_image0, v_texCoord);
+    
+    float intensity = u_float0 * 0.05;
+    float radius = u_float1 * u_float1 * 0.012;
+    
+    if (intensity < 0.001 || radius < 0.1) {
+        fragColor = original;
+        return;
+    }
+    
+    float threshold = 1.0 - u_float2 * 0.01;
+    float t0 = threshold - 0.15;
+    float t1 = threshold + 0.15;
+    
+    vec2 texelSize = 1.0 / u_resolution;
+    float radius2 = radius * radius;
+    
+    float sampleScale = clamp(radius * 0.75, 0.35, 1.0);
+    int samples = int(float(MAX_SAMPLES) * sampleScale);
+    
+    float noise = hash(gl_FragCoord.xy);
+    float angleOffset = noise * GOLDEN_ANGLE;
+    float radiusJitter = 0.85 + noise * 0.3;
+    
+    float ca = cos(GOLDEN_ANGLE);
+    float sa = sin(GOLDEN_ANGLE);
+    vec2 dir = vec2(cos(angleOffset), sin(angleOffset));
+    
+    vec3 glow = vec3(0.0);
+    float totalWeight = 0.0;
+    
+    // Center tap
+    float centerMask = smoothstep(t0, t1, dot(original.rgb, LUMA));
+    glow += original.rgb * centerMask * 2.0;
+    totalWeight += 2.0;
+    
+    for (int i = 1; i < MAX_SAMPLES; i++) {
+        if (i >= samples) break;
+        
+        float fi = float(i);
+        float dist = sqrt(fi / float(samples)) * radius * radiusJitter;
+        
+        vec2 offset = dir * dist * texelSize;
+        vec3 c = texture(u_image0, v_texCoord + offset).rgb;
+        float mask = smoothstep(t0, t1, dot(c, LUMA));
+        
+        float w = 1.0 - (dist * dist) / (radius2 * 1.5);
+        w = max(w, 0.0);
+        w *= w;
+        
+        glow += c * mask * w;
+        totalWeight += w;
+        
+        dir = vec2(
+            dir.x * ca - dir.y * sa,
+            dir.x * sa + dir.y * ca
+        );
+    }
+    
+    glow *= intensity / max(totalWeight, 0.001);
+    
+    if (u_int1 > 0) {
+        glow *= hexToRgb(u_int1);
+    }
+    
+    vec3 result = blend(original.rgb, glow, u_int0);
+    result += (noise - 0.5) * (1.0 / 255.0);
+    
+    fragColor = vec4(clamp(result, 0.0, 1.0), original.a);
+}
\ No newline at end of file
diff --git a/blueprints/.glsl/Hue_and_Saturation_1.frag b/blueprints/.glsl/Hue_and_Saturation_1.frag
new file mode 100644
index 000000000..0fa6810af
--- /dev/null
+++ b/blueprints/.glsl/Hue_and_Saturation_1.frag
@@ -0,0 +1,222 @@
+#version 300 es
+precision highp float;
+
+uniform sampler2D u_image0;
+uniform int u_int0;      // Mode: 0=Master, 1=Reds, 2=Yellows, 3=Greens, 4=Cyans, 5=Blues, 6=Magentas, 7=Colorize
+uniform int u_int1;      // Color Space: 0=HSL, 1=HSB/HSV
+uniform float u_float0;  // Hue (-180 to 180)
+uniform float u_float1;  // Saturation (-100 to 100)
+uniform float u_float2;  // Lightness/Brightness (-100 to 100)
+uniform float u_float3;  // Overlap (0 to 100) - feathering between adjacent color ranges
+
+in vec2 v_texCoord;
+out vec4 fragColor;
+
+// Color range modes
+const int MODE_MASTER   = 0;
+const int MODE_RED      = 1;
+const int MODE_YELLOW   = 2;
+const int MODE_GREEN    = 3;
+const int MODE_CYAN     = 4;
+const int MODE_BLUE     = 5;
+const int MODE_MAGENTA  = 6;
+const int MODE_COLORIZE = 7;
+
+// Color space modes
+const int COLORSPACE_HSL = 0;
+const int COLORSPACE_HSB = 1;
+
+const float EPSILON = 0.0001;
+
+//=============================================================================
+// RGB <-> HSL Conversions
+//=============================================================================
+
+vec3 rgb2hsl(vec3 c) {
+    float maxC = max(max(c.r, c.g), c.b);
+    float minC = min(min(c.r, c.g), c.b);
+    float delta = maxC - minC;
+
+    float h = 0.0;
+    float s = 0.0;
+    float l = (maxC + minC) * 0.5;
+
+    if (delta > EPSILON) {
+        s = l < 0.5
+            ? delta / (maxC + minC)
+            : delta / (2.0 - maxC - minC);
+
+        if (maxC == c.r) {
+            h = (c.g - c.b) / delta + (c.g < c.b ? 6.0 : 0.0);
+        } else if (maxC == c.g) {
+            h = (c.b - c.r) / delta + 2.0;
+        } else {
+            h = (c.r - c.g) / delta + 4.0;
+        }
+        h /= 6.0;
+    }
+
+    return vec3(h, s, l);
+}
+
+float hue2rgb(float p, float q, float t) {
+    t = fract(t);
+    if (t < 1.0/6.0) return p + (q - p) * 6.0 * t;
+    if (t < 0.5)       return q;
+    if (t < 2.0/3.0)   return p + (q - p) * (2.0/3.0 - t) * 6.0;
+    return p;
+}
+
+vec3 hsl2rgb(vec3 hsl) {
+    if (hsl.y < EPSILON) return vec3(hsl.z);
+
+    float q = hsl.z < 0.5
+        ? hsl.z * (1.0 + hsl.y)
+        : hsl.z + hsl.y - hsl.z * hsl.y;
+    float p = 2.0 * hsl.z - q;
+
+    return vec3(
+        hue2rgb(p, q, hsl.x + 1.0/3.0),
+        hue2rgb(p, q, hsl.x),
+        hue2rgb(p, q, hsl.x - 1.0/3.0)
+    );
+}
+
+vec3 rgb2hsb(vec3 c) {
+    float maxC = max(max(c.r, c.g), c.b);
+    float minC = min(min(c.r, c.g), c.b);
+    float delta = maxC - minC;
+
+    float h = 0.0;
+    float s = (maxC > EPSILON) ? delta / maxC : 0.0;
+    float b = maxC;
+
+    if (delta > EPSILON) {
+        if (maxC == c.r) {
+            h = (c.g - c.b) / delta + (c.g < c.b ? 6.0 : 0.0);
+        } else if (maxC == c.g) {
+            h = (c.b - c.r) / delta + 2.0;
+        } else {
+            h = (c.r - c.g) / delta + 4.0;
+        }
+        h /= 6.0;
+    }
+
+    return vec3(h, s, b);
+}
+
+vec3 hsb2rgb(vec3 hsb) {
+    vec3 rgb = clamp(abs(mod(hsb.x * 6.0 + vec3(0.0, 4.0, 2.0), 6.0) - 3.0) - 1.0, 0.0, 1.0);
+    return hsb.z * mix(vec3(1.0), rgb, hsb.y);
+}
+
+//=============================================================================
+// Color Range Weight Calculation
+//=============================================================================
+
+float hueDistance(float a, float b) {
+    float d = abs(a - b);
+    return min(d, 1.0 - d);
+}
+
+float getHueWeight(float hue, float center, float overlap) {
+    float baseWidth = 1.0 / 6.0;
+    float feather = baseWidth * overlap;
+
+    float d = hueDistance(hue, center);
+
+    float inner = baseWidth * 0.5;
+    float outer = inner + feather;
+
+    return 1.0 - smoothstep(inner, outer, d);
+}
+
+float getModeWeight(float hue, int mode, float overlap) {
+    if (mode == MODE_MASTER || mode == MODE_COLORIZE) return 1.0;
+
+    if (mode == MODE_RED) {
+        return max(
+            getHueWeight(hue, 0.0, overlap),
+            getHueWeight(hue, 1.0, overlap)
+        );
+    }
+
+    float center = float(mode - 1) / 6.0;
+    return getHueWeight(hue, center, overlap);
+}
+
+//=============================================================================
+// Adjustment Functions
+//=============================================================================
+
+float adjustLightness(float l, float amount) {
+    return amount > 0.0
+        ? l + (1.0 - l) * amount
+        : l + l * amount;
+}
+
+float adjustBrightness(float b, float amount) {
+    return clamp(b + amount, 0.0, 1.0);
+}
+
+float adjustSaturation(float s, float amount) {
+    return amount > 0.0
+        ? s + (1.0 - s) * amount
+        : s + s * amount;
+}
+
+vec3 colorize(vec3 rgb, float hue, float sat, float light) {
+    float lum = dot(rgb, vec3(0.299, 0.587, 0.114));
+    float l = adjustLightness(lum, light);
+
+    vec3 hsl = vec3(fract(hue), clamp(sat, 0.0, 1.0), clamp(l, 0.0, 1.0));
+    return hsl2rgb(hsl);
+}
+
+//=============================================================================
+// Main
+//=============================================================================
+
+void main() {
+    vec4 original = texture(u_image0, v_texCoord);
+
+    float hueShift   = u_float0 / 360.0;   // -180..180 -> -0.5..0.5
+    float satAmount  = u_float1 / 100.0;   // -100..100 -> -1..1
+    float lightAmount= u_float2 / 100.0;   // -100..100 -> -1..1
+    float overlap    = u_float3 / 100.0;   // 0..100 -> 0..1
+
+    vec3 result;
+
+    if (u_int0 == MODE_COLORIZE) {
+        result = colorize(original.rgb, hueShift, satAmount, lightAmount);
+        fragColor = vec4(result, original.a);
+        return;
+    }
+
+    vec3 hsx = (u_int1 == COLORSPACE_HSL)
+        ? rgb2hsl(original.rgb)
+        : rgb2hsb(original.rgb);
+
+    float weight = getModeWeight(hsx.x, u_int0, overlap);
+
+    if (u_int0 != MODE_MASTER && hsx.y < EPSILON) {
+        weight = 0.0;
+    }
+
+    if (weight > EPSILON) {
+        float h = fract(hsx.x + hueShift * weight);
+        float s = clamp(adjustSaturation(hsx.y, satAmount * weight), 0.0, 1.0);
+        float v = (u_int1 == COLORSPACE_HSL)
+            ? clamp(adjustLightness(hsx.z, lightAmount * weight), 0.0, 1.0)
+            : clamp(adjustBrightness(hsx.z, lightAmount * weight), 0.0, 1.0);
+
+        vec3 adjusted = vec3(h, s, v);
+        result = (u_int1 == COLORSPACE_HSL)
+            ? hsl2rgb(adjusted)
+            : hsb2rgb(adjusted);
+    } else {
+        result = original.rgb;
+    }
+
+    fragColor = vec4(result, original.a);
+}
diff --git a/blueprints/.glsl/Image_Blur_1.frag b/blueprints/.glsl/Image_Blur_1.frag
new file mode 100644
index 000000000..83238111d
--- /dev/null
+++ b/blueprints/.glsl/Image_Blur_1.frag
@@ -0,0 +1,111 @@
+#version 300 es
+#pragma passes 2
+precision highp float;
+
+// Blur type constants
+const int BLUR_GAUSSIAN = 0;
+const int BLUR_BOX = 1;
+const int BLUR_RADIAL = 2;
+
+// Radial blur config
+const int RADIAL_SAMPLES = 12;
+const float RADIAL_STRENGTH = 0.0003;
+
+uniform sampler2D u_image0;
+uniform vec2 u_resolution;
+uniform int u_int0;      // Blur type (BLUR_GAUSSIAN, BLUR_BOX, BLUR_RADIAL)
+uniform float u_float0;  // Blur radius/amount
+uniform int u_pass;      // Pass index (0 = horizontal, 1 = vertical)
+
+in vec2 v_texCoord;
+layout(location = 0) out vec4 fragColor0;
+
+float gaussian(float x, float sigma) {
+    return exp(-(x * x) / (2.0 * sigma * sigma));
+}
+
+void main() {
+    vec2 texelSize = 1.0 / u_resolution;
+    float radius = max(u_float0, 0.0);
+
+    // Radial (angular) blur - single pass, doesn't use separable
+    if (u_int0 == BLUR_RADIAL) {
+        // Only execute on first pass
+        if (u_pass > 0) {
+            fragColor0 = texture(u_image0, v_texCoord);
+            return;
+        }
+
+        vec2 center = vec2(0.5);
+        vec2 dir = v_texCoord - center;
+        float dist = length(dir);
+
+        if (dist < 1e-4) {
+            fragColor0 = texture(u_image0, v_texCoord);
+            return;
+        }
+
+        vec4 sum = vec4(0.0);
+        float totalWeight = 0.0;
+        float angleStep = radius * RADIAL_STRENGTH;
+
+        dir /= dist;
+
+        float cosStep = cos(angleStep);
+        float sinStep = sin(angleStep);
+
+        float negAngle = -float(RADIAL_SAMPLES) * angleStep;
+        vec2 rotDir = vec2(
+            dir.x * cos(negAngle) - dir.y * sin(negAngle),
+            dir.x * sin(negAngle) + dir.y * cos(negAngle)
+        );
+
+        for (int i = -RADIAL_SAMPLES; i <= RADIAL_SAMPLES; i++) {
+            vec2 uv = center + rotDir * dist;
+            float w = 1.0 - abs(float(i)) / float(RADIAL_SAMPLES);
+            sum += texture(u_image0, uv) * w;
+            totalWeight += w;
+
+            rotDir = vec2(
+                rotDir.x * cosStep - rotDir.y * sinStep,
+                rotDir.x * sinStep + rotDir.y * cosStep
+            );
+        }
+
+        fragColor0 = sum / max(totalWeight, 0.001);
+        return;
+    }
+
+    // Separable Gaussian / Box blur
+    int samples = int(ceil(radius));
+
+    if (samples == 0) {
+        fragColor0 = texture(u_image0, v_texCoord);
+        return;
+    }
+
+    // Direction: pass 0 = horizontal, pass 1 = vertical
+    vec2 dir = (u_pass == 0) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);
+
+    vec4 color = vec4(0.0);
+    float totalWeight = 0.0;
+    float sigma = radius / 2.0;
+
+    for (int i = -samples; i <= samples; i++) {
+        vec2 offset = dir * float(i) * texelSize;
+        vec4 sample_color = texture(u_image0, v_texCoord + offset);
+
+        float weight;
+        if (u_int0 == BLUR_GAUSSIAN) {
+            weight = gaussian(float(i), sigma);
+        } else {
+            // BLUR_BOX
+            weight = 1.0;
+        }
+
+        color += sample_color * weight;
+        totalWeight += weight;
+    }
+
+    fragColor0 = color / totalWeight;
+}
diff --git a/blueprints/.glsl/Image_Channels_23.frag b/blueprints/.glsl/Image_Channels_23.frag
new file mode 100644
index 000000000..76d70af13
--- /dev/null
+++ b/blueprints/.glsl/Image_Channels_23.frag
@@ -0,0 +1,19 @@
+#version 300 es
+precision highp float;
+
+uniform sampler2D u_image0;
+
+in vec2 v_texCoord;
+layout(location = 0) out vec4 fragColor0;
+layout(location = 1) out vec4 fragColor1;
+layout(location = 2) out vec4 fragColor2;
+layout(location = 3) out vec4 fragColor3;
+
+void main() {
+  vec4 color = texture(u_image0, v_texCoord);
+  // Output each channel as grayscale to separate render targets
+  fragColor0 = vec4(vec3(color.r), 1.0);  // Red channel
+  fragColor1 = vec4(vec3(color.g), 1.0);  // Green channel
+  fragColor2 = vec4(vec3(color.b), 1.0);  // Blue channel
+  fragColor3 = vec4(vec3(color.a), 1.0);  // Alpha channel
+}
diff --git a/blueprints/.glsl/Image_Levels_1.frag b/blueprints/.glsl/Image_Levels_1.frag
new file mode 100644
index 000000000..f34ed1d81
--- /dev/null
+++ b/blueprints/.glsl/Image_Levels_1.frag
@@ -0,0 +1,71 @@
+#version 300 es
+precision highp float;
+
+// Levels Adjustment
+// u_int0:   channel      (0=RGB, 1=R, 2=G, 3=B)         default: 0
+// u_float0: input black  (0-255)                        default: 0
+// u_float1: input white  (0-255)                        default: 255
+// u_float2: gamma        (0.01-9.99)                    default: 1.0
+// u_float3: output black (0-255)                        default: 0
+// u_float4: output white (0-255)                        default: 255
+
+uniform sampler2D u_image0;
+uniform int u_int0;
+uniform float u_float0;
+uniform float u_float1;
+uniform float u_float2;
+uniform float u_float3;
+uniform float u_float4;
+
+in vec2 v_texCoord;
+out vec4 fragColor;
+
+vec3 applyLevels(vec3 color, float inBlack, float inWhite, float gamma, float outBlack, float outWhite) {
+    float inRange = max(inWhite - inBlack, 0.0001);
+    vec3 result = clamp((color - inBlack) / inRange, 0.0, 1.0);
+    result = pow(result, vec3(1.0 / gamma));
+    result = mix(vec3(outBlack), vec3(outWhite), result);
+    return result;
+}
+
+float applySingleChannel(float value, float inBlack, float inWhite, float gamma, float outBlack, float outWhite) {
+    float inRange = max(inWhite - inBlack, 0.0001);
+    float result = clamp((value - inBlack) / inRange, 0.0, 1.0);
+    result = pow(result, 1.0 / gamma);
+    result = mix(outBlack, outWhite, result);
+    return result;
+}
+
+void main() {
+    vec4 texColor = texture(u_image0, v_texCoord);
+    vec3 color = texColor.rgb;
+    
+    float inBlack = u_float0 / 255.0;
+    float inWhite = u_float1 / 255.0;
+    float gamma = u_float2;
+    float outBlack = u_float3 / 255.0;
+    float outWhite = u_float4 / 255.0;
+    
+    vec3 result;
+    
+    if (u_int0 == 0) {
+        result = applyLevels(color, inBlack, inWhite, gamma, outBlack, outWhite);
+    }
+    else if (u_int0 == 1) {
+        result = color;
+        result.r = applySingleChannel(color.r, inBlack, inWhite, gamma, outBlack, outWhite);
+    }
+    else if (u_int0 == 2) {
+        result = color;
+        result.g = applySingleChannel(color.g, inBlack, inWhite, gamma, outBlack, outWhite);
+    }
+    else if (u_int0 == 3) {
+        result = color;
+        result.b = applySingleChannel(color.b, inBlack, inWhite, gamma, outBlack, outWhite);
+    }
+    else {
+        result = color;
+    }
+    
+    fragColor = vec4(result, texColor.a);
+}
\ No newline at end of file
diff --git a/blueprints/.glsl/README.md b/blueprints/.glsl/README.md
new file mode 100644
index 000000000..d4084284b
--- /dev/null
+++ b/blueprints/.glsl/README.md
@@ -0,0 +1,28 @@
+# GLSL Shader Sources
+
+This folder contains the GLSL fragment shaders extracted from blueprint JSON files for easier editing and version control.
+
+## File Naming Convention
+
+`{Blueprint_Name}_{node_id}.frag`
+
+- **Blueprint_Name**: The JSON filename with spaces/special chars replaced by underscores
+- **node_id**: The GLSLShader node ID within the subgraph
+
+## Usage
+
+```bash
+# Extract shaders from blueprint JSONs to this folder
+python update_blueprints.py extract
+
+# Patch edited shaders back into blueprint JSONs
+python update_blueprints.py patch
+```
+
+## Workflow
+
+1. Run `extract` to pull current shaders from JSONs
+2. Edit `.frag` files
+3. Run `patch` to update the blueprint JSONs
+4. Test
+5. Commit both `.frag` files and updated JSONs
diff --git a/blueprints/.glsl/Sharpen_23.frag b/blueprints/.glsl/Sharpen_23.frag
new file mode 100644
index 000000000..c03f94b66
--- /dev/null
+++ b/blueprints/.glsl/Sharpen_23.frag
@@ -0,0 +1,28 @@
+#version 300 es
+precision highp float;
+
+uniform sampler2D u_image0;
+uniform vec2 u_resolution;
+uniform float u_float0;  // strength [0.0 – 2.0] typical: 0.3–1.0
+
+in vec2 v_texCoord;
+layout(location = 0) out vec4 fragColor0;
+
+void main() {
+    vec2 texel = 1.0 / u_resolution;
+    
+    // Sample center and neighbors
+    vec4 center = texture(u_image0, v_texCoord);
+    vec4 top    = texture(u_image0, v_texCoord + vec2( 0.0, -texel.y));
+    vec4 bottom = texture(u_image0, v_texCoord + vec2( 0.0,  texel.y));
+    vec4 left   = texture(u_image0, v_texCoord + vec2(-texel.x,  0.0));
+    vec4 right  = texture(u_image0, v_texCoord + vec2( texel.x,  0.0));
+    
+    // Edge enhancement (Laplacian)
+    vec4 edges = center * 4.0 - top - bottom - left - right;
+    
+    // Add edges back scaled by strength
+    vec4 sharpened = center + edges * u_float0;
+    
+    fragColor0 = vec4(clamp(sharpened.rgb, 0.0, 1.0), center.a);
+}
\ No newline at end of file
diff --git a/blueprints/.glsl/Unsharp_Mask_26.frag b/blueprints/.glsl/Unsharp_Mask_26.frag
new file mode 100644
index 000000000..f5990cb4a
--- /dev/null
+++ b/blueprints/.glsl/Unsharp_Mask_26.frag
@@ -0,0 +1,61 @@
+#version 300 es
+precision highp float;
+
+uniform sampler2D u_image0;
+uniform vec2 u_resolution;
+uniform float u_float0;  // amount    [0.0 - 3.0]  typical: 0.5-1.5
+uniform float u_float1;  // radius    [0.5 - 10.0] blur radius in pixels
+uniform float u_float2;  // threshold [0.0 - 0.1]  min difference to sharpen
+
+in vec2 v_texCoord;
+layout(location = 0) out vec4 fragColor0;
+
+float gaussian(float x, float sigma) {
+    return exp(-(x * x) / (2.0 * sigma * sigma));
+}
+
+float getLuminance(vec3 color) {
+    return dot(color, vec3(0.2126, 0.7152, 0.0722));
+}
+
+void main() {
+    vec2 texel = 1.0 / u_resolution;
+    float radius = max(u_float1, 0.5);
+    float amount = u_float0;
+    float threshold = u_float2;
+
+    vec4 original = texture(u_image0, v_texCoord);
+
+    // Gaussian blur for the "unsharp" mask
+    int samples = int(ceil(radius));
+    float sigma = radius / 2.0;
+
+    vec4 blurred = vec4(0.0);
+    float totalWeight = 0.0;
+
+    for (int x = -samples; x <= samples; x++) {
+        for (int y = -samples; y <= samples; y++) {
+            vec2 offset = vec2(float(x), float(y)) * texel;
+            vec4 sample_color = texture(u_image0, v_texCoord + offset);
+
+            float dist = length(vec2(float(x), float(y)));
+            float weight = gaussian(dist, sigma);
+            blurred += sample_color * weight;
+            totalWeight += weight;
+        }
+    }
+    blurred /= totalWeight;
+
+    // Unsharp mask = original - blurred
+    vec3 mask = original.rgb - blurred.rgb;
+
+    // Luminance-based threshold with smooth falloff
+    float lumaDelta = abs(getLuminance(original.rgb) - getLuminance(blurred.rgb));
+    float thresholdScale = smoothstep(0.0, threshold, lumaDelta);
+    mask *= thresholdScale;
+
+    // Sharpen: original + mask * amount
+    vec3 sharpened = original.rgb + mask * amount;
+
+    fragColor0 = vec4(clamp(sharpened, 0.0, 1.0), original.a);
+}
diff --git a/blueprints/.glsl/update_blueprints.py b/blueprints/.glsl/update_blueprints.py
new file mode 100644
index 000000000..c5bd0ed54
--- /dev/null
+++ b/blueprints/.glsl/update_blueprints.py
@@ -0,0 +1,159 @@
+#!/usr/bin/env python3
+"""
+Shader Blueprint Updater
+
+Syncs GLSL shader files between this folder and blueprint JSON files.
+
+File naming convention:
+    {Blueprint Name}_{node_id}.frag
+
+Usage:
+    python update_blueprints.py extract   # Extract shaders from JSONs to here
+    python update_blueprints.py patch     # Patch shaders back into JSONs
+    python update_blueprints.py           # Same as patch (default)
+"""
+
+import json
+import logging
+import sys
+import re
+from pathlib import Path
+
+logging.basicConfig(level=logging.INFO, format='%(message)s')
+logger = logging.getLogger(__name__)
+
+GLSL_DIR = Path(__file__).parent
+BLUEPRINTS_DIR = GLSL_DIR.parent
+
+
+def get_blueprint_files():
+    """Get all blueprint JSON files."""
+    return sorted(BLUEPRINTS_DIR.glob("*.json"))
+
+
+def sanitize_filename(name):
+    """Convert blueprint name to safe filename."""
+    return re.sub(r'[^\w\-]', '_', name)
+
+
+def extract_shaders():
+    """Extract all shaders from blueprint JSONs to this folder."""
+    extracted = 0
+    for json_path in get_blueprint_files():
+        blueprint_name = json_path.stem
+
+        try:
+            with open(json_path, 'r') as f:
+                data = json.load(f)
+        except (json.JSONDecodeError, IOError) as e:
+            logger.warning("Skipping %s: %s", json_path.name, e)
+            continue
+
+        # Find GLSLShader nodes in subgraphs
+        for subgraph in data.get('definitions', {}).get('subgraphs', []):
+            for node in subgraph.get('nodes', []):
+                if node.get('type') == 'GLSLShader':
+                    node_id = node.get('id')
+                    widgets = node.get('widgets_values', [])
+
+                    # Find shader code (first string that looks like GLSL)
+                    for widget in widgets:
+                        if isinstance(widget, str) and widget.startswith('#version'):
+                            safe_name = sanitize_filename(blueprint_name)
+                            frag_name = f"{safe_name}_{node_id}.frag"
+                            frag_path = GLSL_DIR / frag_name
+
+                            with open(frag_path, 'w') as f:
+                                f.write(widget)
+
+                            logger.info("  Extracted: %s", frag_name)
+                            extracted += 1
+                            break
+
+    logger.info("\nExtracted %d shader(s)", extracted)
+
+
+def patch_shaders():
+    """Patch shaders from this folder back into blueprint JSONs."""
+    # Build lookup: blueprint_name -> [(node_id, shader_code), ...]
+    shader_updates = {}
+
+    for frag_path in sorted(GLSL_DIR.glob("*.frag")):
+        # Parse filename: {blueprint_name}_{node_id}.frag
+        parts = frag_path.stem.rsplit('_', 1)
+        if len(parts) != 2:
+            logger.warning("Skipping %s: invalid filename format", frag_path.name)
+            continue
+
+        blueprint_name, node_id_str = parts
+
+        try:
+            node_id = int(node_id_str)
+        except ValueError:
+            logger.warning("Skipping %s: invalid node_id", frag_path.name)
+            continue
+
+        with open(frag_path, 'r') as f:
+            shader_code = f.read()
+
+        if blueprint_name not in shader_updates:
+            shader_updates[blueprint_name] = []
+        shader_updates[blueprint_name].append((node_id, shader_code))
+
+    # Apply updates to JSON files
+    patched = 0
+    for json_path in get_blueprint_files():
+        blueprint_name = sanitize_filename(json_path.stem)
+
+        if blueprint_name not in shader_updates:
+            continue
+
+        try:
+            with open(json_path, 'r') as f:
+                data = json.load(f)
+        except (json.JSONDecodeError, IOError) as e:
+            logger.error("Error reading %s: %s", json_path.name, e)
+            continue
+
+        modified = False
+        for node_id, shader_code in shader_updates[blueprint_name]:
+            # Find the node and update
+            for subgraph in data.get('definitions', {}).get('subgraphs', []):
+                for node in subgraph.get('nodes', []):
+                    if node.get('id') == node_id and node.get('type') == 'GLSLShader':
+                        widgets = node.get('widgets_values', [])
+                        if len(widgets) > 0 and widgets[0] != shader_code:
+                            widgets[0] = shader_code
+                            modified = True
+                            logger.info("  Patched: %s (node %d)", json_path.name, node_id)
+                            patched += 1
+
+        if modified:
+            with open(json_path, 'w') as f:
+                json.dump(data, f)
+
+    if patched == 0:
+        logger.info("No changes to apply.")
+    else:
+        logger.info("\nPatched %d shader(s)", patched)
+
+
+def main():
+    if len(sys.argv) < 2:
+        command = "patch"
+    else:
+        command = sys.argv[1].lower()
+
+    if command == "extract":
+        logger.info("Extracting shaders from blueprints...")
+        extract_shaders()
+    elif command in ("patch", "update", "apply"):
+        logger.info("Patching shaders into blueprints...")
+        patch_shaders()
+    else:
+        logger.info(__doc__)
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/blueprints/Brightness and Contrast.json b/blueprints/Brightness and Contrast.json
new file mode 100644
index 000000000..2d670654d
--- /dev/null
+++ b/blueprints/Brightness and Contrast.json	
@@ -0,0 +1 @@
+{"revision":0,"last_node_id":140,"last_link_id":0,"nodes":[{"id":140,"type":"916dff42-6166-4d45-b028-04eaf69fbb35","pos":[500,1440],"size":[250,178],"flags":{},"order":2,"mode":0,"inputs":[{"label":"image","localized_name":"images.image0","name":"images.image0","type":"IMAGE","link":null}],"outputs":[{"label":"IMAGE","localized_name":"IMAGE0","name":"IMAGE0","type":"IMAGE","links":[]}],"properties":{"proxyWidgets":[["4","value"],["5","value"]]},"widgets_values":[],"title":"Brightness and Contrast"}],"links":[],"version":0.4,"definitions":{"subgraphs":[{"id":"916dff42-6166-4d45-b028-04eaf69fbb35","version":1,"state":{"lastGroupId":0,"lastNodeId":143,"lastLinkId":118,"lastRerouteId":0},"revision":0,"config":{},"name":"Brightness and Contrast","inputNode":{"id":-10,"bounding":[360,-176,120,60]},"outputNode":{"id":-20,"bounding":[1410,-176,120,60]},"inputs":[{"id":"a5aae7ea-b511-4045-b5da-94101e269cd7","name":"images.image0","type":"IMAGE","linkIds":[117],"localized_name":"images.image0","label":"image","pos":[460,-156]}],"outputs":[{"id":"30b72604-69b3-4944-b253-a9099bbd73a9","name":"IMAGE0","type":"IMAGE","linkIds":[118],"localized_name":"IMAGE0","label":"IMAGE","pos":[1430,-156]}],"widgets":[],"nodes":[{"id":4,"type":"PrimitiveFloat","pos":[540,-280],"size":[270,58],"flags":{},"order":0,"mode":0,"inputs":[{"label":"brightness","localized_name":"value","name":"value","type":"FLOAT","widget":{"name":"value"},"link":null}],"outputs":[{"localized_name":"FLOAT","name":"FLOAT","type":"FLOAT","links":[115]}],"properties":{"Node name for S&R":"PrimitiveFloat","min":0,"max":100,"precision":1,"step":1},"widgets_values":[50]},{"id":5,"type":"PrimitiveFloat","pos":[540,-170],"size":[270,58],"flags":{},"order":1,"mode":0,"inputs":[{"label":"contrast","localized_name":"value","name":"value","type":"FLOAT","widget":{"name":"value"},"link":null}],"outputs":[{"localized_name":"FLOAT","name":"FLOAT","type":"FLOAT","links":[116]}],"properties":{"Node name for S&R":"PrimitiveFloat","min":0,"max":100,"precision":1,"step":1},"widgets_values":[0]},{"id":143,"type":"GLSLShader","pos":[840,-280],"size":[400,212],"flags":{},"order":2,"mode":0,"inputs":[{"label":"image0","localized_name":"images.image0","name":"images.image0","type":"IMAGE","link":117},{"label":"image1","localized_name":"images.image1","name":"images.image1","shape":7,"type":"IMAGE","link":null},{"label":"u_float0","localized_name":"floats.u_float0","name":"floats.u_float0","shape":7,"type":"FLOAT","link":115},{"label":"u_float1","localized_name":"floats.u_float1","name":"floats.u_float1","shape":7,"type":"FLOAT","link":116},{"label":"u_float2","localized_name":"floats.u_float2","name":"floats.u_float2","shape":7,"type":"FLOAT","link":null},{"label":"u_int0","localized_name":"ints.u_int0","name":"ints.u_int0","shape":7,"type":"INT","link":null},{"localized_name":"fragment_shader","name":"fragment_shader","type":"STRING","widget":{"name":"fragment_shader"},"link":null},{"localized_name":"size_mode","name":"size_mode","type":"COMFY_DYNAMICCOMBO_V3","widget":{"name":"size_mode"},"link":null}],"outputs":[{"localized_name":"IMAGE0","name":"IMAGE0","type":"IMAGE","links":[118]},{"localized_name":"IMAGE1","name":"IMAGE1","type":"IMAGE","links":null},{"localized_name":"IMAGE2","name":"IMAGE2","type":"IMAGE","links":null},{"localized_name":"IMAGE3","name":"IMAGE3","type":"IMAGE","links":null}],"properties":{"Node name for S&R":"GLSLShader"},"widgets_values":["#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform float u_float0; // Brightness slider -100..100\nuniform float u_float1; // Contrast slider -100..100\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst float MID_GRAY = 0.18;  // 18% reflectance\n\n// sRGB gamma 2.2 approximation\nvec3 srgbToLinear(vec3 c) {\n    return pow(max(c, 0.0), vec3(2.2));\n}\n\nvec3 linearToSrgb(vec3 c) {\n    return pow(max(c, 0.0), vec3(1.0/2.2));\n}\n\nfloat mapBrightness(float b) {\n    return clamp(b / 100.0, -1.0, 1.0);\n}\n\nfloat mapContrast(float c) {\n    return clamp(c / 100.0 + 1.0, 0.0, 2.0);\n}\n\nvoid main() {\n    vec4 orig = texture(u_image0, v_texCoord);\n\n    float brightness = mapBrightness(u_float0);\n    float contrast   = mapContrast(u_float1);\n\n    vec3 lin = srgbToLinear(orig.rgb);\n\n    lin = (lin - MID_GRAY) * contrast + brightness + MID_GRAY;\n\n    // Convert back to sRGB\n    vec3 result = linearToSrgb(clamp(lin, 0.0, 1.0));\n\n    fragColor = vec4(result, orig.a);\n}\n","from_input"]}],"groups":[],"links":[{"id":115,"origin_id":4,"origin_slot":0,"target_id":143,"target_slot":2,"type":"FLOAT"},{"id":116,"origin_id":5,"origin_slot":0,"target_id":143,"target_slot":3,"type":"FLOAT"},{"id":117,"origin_id":-10,"origin_slot":0,"target_id":143,"target_slot":0,"type":"IMAGE"},{"id":118,"origin_id":143,"origin_slot":0,"target_id":-20,"target_slot":0,"type":"IMAGE"}],"extra":{"workflowRendererVersion":"LG"}}]},"extra":{}}
\ No newline at end of file
diff --git a/blueprints/Chromatic Aberration.json b/blueprints/Chromatic Aberration.json
new file mode 100644
index 000000000..ffbf41c4e
--- /dev/null
+++ b/blueprints/Chromatic Aberration.json	
@@ -0,0 +1 @@
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corrected / r : vec2(0.0);\n    vec2 offset = vec2(0.0);\n\n    if (u_int0 == MODE_LINEAR) {\n        // Horizontal shift (no aspect correction needed)\n        offset = vec2(amount, 0.0);\n    }\n    else if (u_int0 == MODE_RADIAL) {\n        // Outward from center, stronger at edges\n        offset = dir * r * amount * RADIAL_MULT;\n        offset.x /= aspect;  // Convert back to UV space\n    }\n    else if (u_int0 == MODE_BARREL) {\n        // Lens distortion simulation (r\u00b2 falloff)\n        offset = dir * r * r * amount * BARREL_MULT;\n        offset.x /= aspect;  // Convert back to UV space\n    }\n    else if (u_int0 == MODE_SWIRL) {\n        // Perpendicular to radial (rotational aberration)\n        vec2 perp = vec2(-dir.y, dir.x);\n        offset = perp * r * amount * RADIAL_MULT;\n        offset.x /= aspect;  // Convert back to UV space\n    }\n    else if (u_int0 == MODE_DIAGONAL) {\n        // 45\u00b0 offset (no aspect correction needed)\n        offset = vec2(amount, amount) * INV_SQRT2;\n    }\n    \n    float red = texture(u_image0, uv + offset).r;\n    float green = original.g;\n    float blue = texture(u_image0, uv - offset).b;\n    \n    fragColor = vec4(red, green, blue, original.a);\n}", "from_input"]}, {"id": 18, "type": "PrimitiveFloat", "pos": [3810, -2430], "size": [270, 58], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "amount", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [22]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 100, "step": 1}, "widgets_values": [30]}, {"id": 17, "type": "CustomCombo", "pos": [3520, -2320], "size": [270, 222], "flags": {}, "order": 1, "mode": 0, "inputs": [{"label": "mode", "localized_name": "choice", "name": "choice", "type": "COMBO", "widget": {"name": "choice"}, "link": null}], "outputs": [{"localized_name": "STRING", "name": "STRING", "type": "STRING", "links": null}, {"localized_name": "INDEX", "name": "INDEX", "type": "INT", "links": [21]}], "properties": {"Node name for S&R": "CustomCombo"}, "widgets_values": ["Linear", 0, "Linear", "Radial", "Barrel", "Swirl", "Diagonal", ""]}], "groups": [], "links": [{"id": 22, "origin_id": 18, "origin_slot": 0, "target_id": 16, "target_slot": 2, "type": "FLOAT"}, {"id": 21, "origin_id": 17, "origin_slot": 1, "target_id": 16, "target_slot": 4, "type": "INT"}, {"id": 20, "origin_id": -10, "origin_slot": 0, "target_id": 16, "target_slot": 0, "type": "IMAGE"}, {"id": 23, "origin_id": 16, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}}]}}
\ No newline at end of file
diff --git a/blueprints/Color Adjustment.json b/blueprints/Color Adjustment.json
new file mode 100644
index 000000000..f9c488250
--- /dev/null
+++ b/blueprints/Color Adjustment.json	
@@ -0,0 +1 @@
+{"revision": 0, "last_node_id": 14, "last_link_id": 0, "nodes": [{"id": 14, "type": "36677b92-5dd8-47a5-9380-4da982c1894f", "pos": [3610, -2630], "size": [270, 150], "flags": {}, "order": 3, "mode": 0, "inputs": [{"label": "image", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": null}], "outputs": [{"label": "IMAGE", "localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": []}], "properties": {"proxyWidgets": [["4", "value"], ["5", "value"], ["7", "value"], ["6", "value"]]}, "widgets_values": [], "title": "Color Adjustment"}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "36677b92-5dd8-47a5-9380-4da982c1894f", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 16, "lastLinkId": 36, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Color Adjustment", "inputNode": {"id": -10, "bounding": [3110, -3560, 120, 60]}, "outputNode": {"id": -20, "bounding": [4070, -3560, 120, 60]}, "inputs": [{"id": "0431d493-5f28-4430-bd00-84733997fc08", "name": "images.image0", "type": "IMAGE", "linkIds": [29], "localized_name": "images.image0", "label": "image", "pos": [3210, -3540]}], "outputs": [{"id": "bee8ea06-a114-4612-8937-939f2c927bdb", "name": "IMAGE0", "type": "IMAGE", "linkIds": [28], "localized_name": "IMAGE0", "label": "IMAGE", "pos": [4090, -3540]}], "widgets": [], "nodes": [{"id": 15, "type": "GLSLShader", "pos": [3590, -3940], "size": [420, 252], "flags": {}, "order": 4, "mode": 0, "inputs": [{"label": "image0", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": 29}, {"label": "image1", "localized_name": "images.image1", "name": "images.image1", "shape": 7, "type": "IMAGE", "link": null}, {"label": "u_float0", "localized_name": "floats.u_float0", "name": "floats.u_float0", "shape": 7, "type": "FLOAT", "link": 34}, {"label": "u_float1", "localized_name": "floats.u_float1", "name": "floats.u_float1", "shape": 7, "type": "FLOAT", "link": 30}, {"label": "u_float2", "localized_name": "floats.u_float2", "name": "floats.u_float2", "shape": 7, "type": "FLOAT", "link": 31}, {"label": "u_float3", "localized_name": "floats.u_float3", "name": "floats.u_float3", "shape": 7, "type": "FLOAT", "link": 33}, {"label": "u_float4", "localized_name": "floats.u_float4", "name": "floats.u_float4", "shape": 7, "type": "FLOAT", "link": null}, {"label": "u_int0", "localized_name": "ints.u_int0", "name": "ints.u_int0", "shape": 7, "type": "INT", "link": null}, {"localized_name": "fragment_shader", "name": "fragment_shader", "type": "STRING", "widget": {"name": "fragment_shader"}, "link": null}, {"localized_name": "size_mode", "name": "size_mode", "type": "COMFY_DYNAMICCOMBO_V3", "widget": {"name": "size_mode"}, "link": null}], "outputs": [{"localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": [28]}, {"localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": null}], "properties": {"Node name for S&R": "GLSLShader"}, "widgets_values": ["#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform float u_float0; // temperature (-100 to 100)\nuniform float u_float1; // tint (-100 to 100)\nuniform float u_float2; // vibrance (-100 to 100)\nuniform float u_float3; // saturation (-100 to 100)\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst float INPUT_SCALE = 0.01;\nconst float TEMP_TINT_PRIMARY = 0.3;\nconst float TEMP_TINT_SECONDARY = 0.15;\nconst float VIBRANCE_BOOST = 2.0;\nconst float SATURATION_BOOST = 2.0;\nconst float SKIN_PROTECTION = 0.5;\nconst float EPSILON = 0.001;\nconst vec3 LUMA_WEIGHTS = vec3(0.299, 0.587, 0.114);\n\nvoid main() {\n    vec4 tex = texture(u_image0, v_texCoord);\n    vec3 color = tex.rgb;\n    \n    // Scale inputs: -100/100 \u2192 -1/1\n    float temperature = u_float0 * INPUT_SCALE;\n    float tint = u_float1 * INPUT_SCALE;\n    float vibrance = u_float2 * INPUT_SCALE;\n    float saturation = u_float3 * INPUT_SCALE;\n    \n    // Temperature (warm/cool): positive = warm, negative = cool\n    color.r += temperature * TEMP_TINT_PRIMARY;\n    color.b -= temperature * TEMP_TINT_PRIMARY;\n    \n    // Tint (green/magenta): positive = green, negative = magenta\n    color.g += tint * TEMP_TINT_PRIMARY;\n    color.r -= tint * TEMP_TINT_SECONDARY;\n    color.b -= tint * TEMP_TINT_SECONDARY;\n    \n    // Single clamp after temperature/tint\n    color = clamp(color, 0.0, 1.0);\n    \n    // Vibrance with skin protection\n    if (vibrance != 0.0) {\n        float maxC = max(color.r, max(color.g, color.b));\n        float minC = min(color.r, min(color.g, color.b));\n        float sat = maxC - minC;\n        float gray = dot(color, LUMA_WEIGHTS);\n        \n        if (vibrance < 0.0) {\n            // Desaturate: -100 \u2192 gray\n            color = mix(vec3(gray), color, 1.0 + vibrance);\n        } else {\n            // Boost less saturated colors more\n            float vibranceAmt = vibrance * (1.0 - sat);\n            \n            // Branchless skin tone protection\n            float isWarmTone = step(color.b, color.g) * step(color.g, color.r);\n            float warmth = (color.r - color.b) / max(maxC, EPSILON);\n            float skinTone = isWarmTone * warmth * sat * (1.0 - sat);\n            vibranceAmt *= (1.0 - skinTone * SKIN_PROTECTION);\n            \n            color = mix(vec3(gray), color, 1.0 + vibranceAmt * VIBRANCE_BOOST);\n        }\n    }\n    \n    // Saturation\n    if (saturation != 0.0) {\n        float gray = dot(color, LUMA_WEIGHTS);\n        float satMix = saturation < 0.0\n            ? 1.0 + saturation                      // -100 \u2192 gray\n            : 1.0 + saturation * SATURATION_BOOST;  // +100 \u2192 3x boost\n        color = mix(vec3(gray), color, satMix);\n    }\n    \n    fragColor = vec4(clamp(color, 0.0, 1.0), tex.a);\n}", "from_input"]}, {"id": 6, "type": "PrimitiveFloat", "pos": [3290, -3610], "size": [270, 58], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "vibrance", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [26, 31]}], "title": "Vibrance", "properties": {"Node name for S&R": "PrimitiveFloat", "max": 100, "min": -100, "step": 1}, "widgets_values": [0]}, {"id": 7, "type": "PrimitiveFloat", "pos": [3290, -3720], "size": [270, 58], "flags": {}, "order": 1, "mode": 0, "inputs": [{"label": "saturation", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [33]}], "title": "Saturation", "properties": {"Node name for S&R": "PrimitiveFloat", "max": 100, "min": -100, "step": 1}, "widgets_values": [0]}, {"id": 5, "type": "PrimitiveFloat", "pos": [3290, -3830], "size": [270, 58], "flags": {}, "order": 2, "mode": 0, "inputs": [{"label": "tint", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [30]}], "title": "Tint", "properties": {"Node name for S&R": "PrimitiveFloat", "max": 100, "min": -100, "step": 1}, "widgets_values": [0]}, {"id": 4, "type": "PrimitiveFloat", "pos": [3290, -3940], "size": [270, 58], "flags": {}, "order": 3, "mode": 0, "inputs": [{"label": "temperature", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [34]}], "title": "Temperature", "properties": {"Node name for S&R": "PrimitiveFloat", "max": 100, "min": -100, "step": 1}, "widgets_values": [100]}], "groups": [], "links": [{"id": 34, "origin_id": 4, "origin_slot": 0, "target_id": 15, "target_slot": 2, "type": "FLOAT"}, {"id": 30, "origin_id": 5, "origin_slot": 0, "target_id": 15, "target_slot": 3, "type": "FLOAT"}, {"id": 31, "origin_id": 6, "origin_slot": 0, "target_id": 15, "target_slot": 4, "type": "FLOAT"}, {"id": 33, "origin_id": 7, "origin_slot": 0, "target_id": 15, "target_slot": 5, "type": "FLOAT"}, {"id": 29, "origin_id": -10, "origin_slot": 0, "target_id": 15, "target_slot": 0, "type": "IMAGE"}, {"id": 28, "origin_id": 15, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}}]}}
\ No newline at end of file
diff --git a/blueprints/Edge-Preserving Blur.json b/blueprints/Edge-Preserving Blur.json
new file mode 100644
index 000000000..ad7d62658
--- /dev/null
+++ b/blueprints/Edge-Preserving Blur.json	
@@ -0,0 +1 @@
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  // Blur radius (0–20, default ~5)\nuniform float u_float1;   // Edge threshold (0–100, default ~30)\nuniform int u_int0;       // Step size (0/1 = every pixel, 2+ = skip pixels)\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst int MAX_RADIUS = 20;\nconst float EPSILON = 0.0001;\n\n// Perceptual luminance\nfloat getLuminance(vec3 rgb) {\n    return dot(rgb, vec3(0.299, 0.587, 0.114));\n}\n\nvec4 bilateralFilter(vec2 uv, vec2 texelSize, int radius,\n                     float sigmaSpatial, float sigmaColor)\n{\n    vec4 center = texture(u_image0, uv);\n    vec3 centerRGB = center.rgb;\n\n    float invSpatial2 = -0.5 / (sigmaSpatial * sigmaSpatial);\n    float invColor2   = -0.5 / (sigmaColor * sigmaColor + EPSILON);\n\n    vec3 sumRGB = vec3(0.0);\n    float sumWeight = 0.0;\n\n    int step = max(u_int0, 1);\n    float radius2 = float(radius * radius);\n\n    for (int dy = -MAX_RADIUS; dy <= MAX_RADIUS; dy++) {\n        if (dy < -radius || dy > radius) continue;\n        if (abs(dy) % step != 0) continue;\n\n        for (int dx = -MAX_RADIUS; 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diff --git a/blueprints/Film Grain.json b/blueprints/Film Grain.json
new file mode 100644
index 000000000..d4af46911
--- /dev/null
+++ b/blueprints/Film Grain.json	
@@ -0,0 +1 @@
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2.0) * 0.7;  // Centered, scaled\n}\n\nfloat toGaussian(uvec2 p, uint offset) {\n    float sum = hashf(p, offset) + hashf(p, offset + 1u) \n              + hashf(p, offset + 2u) + hashf(p, offset + 3u);\n    return (sum - 2.0) * 0.7;\n}\n\n// Smooth noise with better interpolation\nfloat smoothNoise(vec2 p) {\n    vec2 i = floor(p);\n    vec2 f = fract(p);\n    \n    // Quintic interpolation (less banding than cubic)\n    f = f * f * f * (f * (f * 6.0 - 15.0) + 10.0);\n    \n    uvec2 ui = uvec2(i);\n    float a = toGaussian(ui);\n    float b = toGaussian(ui + uvec2(1u, 0u));\n    float c = toGaussian(ui + uvec2(0u, 1u));\n    float d = toGaussian(ui + uvec2(1u, 1u));\n    \n    return mix(mix(a, b, f.x), mix(c, d, f.x), f.y);\n}\n\nfloat smoothNoise(vec2 p, uint offset) {\n    vec2 i = floor(p);\n    vec2 f = fract(p);\n    \n    f = f * f * f * (f * (f * 6.0 - 15.0) + 10.0);\n    \n    uvec2 ui = uvec2(i);\n    float a = toGaussian(ui, offset);\n    float b = toGaussian(ui + uvec2(1u, 0u), offset);\n    float c = toGaussian(ui + uvec2(0u, 1u), offset);\n    float d = toGaussian(ui + uvec2(1u, 1u), offset);\n    \n    return mix(mix(a, b, f.x), mix(c, d, f.x), f.y);\n}\n\nvoid main() {\n    vec4 color = texture(u_image0, v_texCoord);\n    \n    // Luminance (Rec.709)\n    float luma = dot(color.rgb, vec3(0.2126, 0.7152, 0.0722));\n    \n    // Grain UV (resolution-independent)\n    vec2 grainUV = v_texCoord * u_resolution / max(u_float1, 0.01);\n    uvec2 grainPixel = uvec2(grainUV);\n    \n    float g;\n    vec3 grainRGB;\n    \n    if (u_int0 == 1) {\n        // Grainy mode: pure hash noise (no interpolation = no banding)\n        g = toGaussian(grainPixel);\n        grainRGB = vec3(\n            toGaussian(grainPixel, 100u),\n            toGaussian(grainPixel, 200u),\n            toGaussian(grainPixel, 300u)\n        );\n    } else {\n        // Smooth mode: interpolated with quintic curve\n        g = smoothNoise(grainUV);\n        grainRGB = vec3(\n            smoothNoise(grainUV, 100u),\n            smoothNoise(grainUV, 200u),\n            smoothNoise(grainUV, 300u)\n        );\n    }\n    \n    // Luminance weighting (less grain in highlights)\n    float lumWeight = mix(1.0, 1.0 - 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diff --git a/blueprints/Glow.json b/blueprints/Glow.json
new file mode 100644
index 000000000..0cfaf8fc2
--- /dev/null
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     // Blend mode\nuniform int u_int1;      // Color tint\nuniform float u_float0;  // Intensity\nuniform float u_float1;  // Radius\nuniform float u_float2;  // Threshold\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst int BLEND_ADD      = 0;\nconst int BLEND_SCREEN   = 1;\nconst int BLEND_SOFT     = 2;\nconst int BLEND_OVERLAY  = 3;\nconst int BLEND_LIGHTEN  = 4;\n\nconst float GOLDEN_ANGLE = 2.39996323;\nconst int MAX_SAMPLES = 48;\nconst vec3 LUMA = vec3(0.299, 0.587, 0.114);\n\nfloat hash(vec2 p) {\n    p = fract(p * vec2(123.34, 456.21));\n    p += dot(p, p + 45.32);\n    return fract(p.x * p.y);\n}\n\nvec3 hexToRgb(int h) {\n    return vec3(\n        float((h >> 16) & 255),\n        float((h >> 8) & 255),\n        float(h & 255)\n    ) * (1.0 / 255.0);\n}\n\nvec3 blend(vec3 base, vec3 glow, int mode) {\n    if (mode == BLEND_SCREEN) {\n        return 1.0 - (1.0 - base) * (1.0 - glow);\n    }\n    if (mode == BLEND_SOFT) {\n        return mix(\n            base - (1.0 - 2.0 * glow) * base * (1.0 - base),\n            base + (2.0 * glow - 1.0) * (sqrt(base) - base),\n            step(0.5, glow)\n        );\n    }\n    if (mode == BLEND_OVERLAY) {\n        return mix(\n            2.0 * base * glow,\n            1.0 - 2.0 * (1.0 - base) * (1.0 - glow),\n            step(0.5, base)\n        );\n    }\n    if (mode == BLEND_LIGHTEN) {\n        return max(base, glow);\n    }\n    return base + glow;\n}\n\nvoid main() {\n    vec4 original = texture(u_image0, v_texCoord);\n    \n    float intensity = u_float0 * 0.05;\n    float radius = u_float1 * u_float1 * 0.012;\n    \n    if (intensity < 0.001 || radius < 0.1) {\n        fragColor = original;\n        return;\n    }\n    \n    float threshold = 1.0 - u_float2 * 0.01;\n    float t0 = threshold - 0.15;\n    float t1 = threshold + 0.15;\n    \n    vec2 texelSize = 1.0 / u_resolution;\n    float radius2 = radius * radius;\n    \n    float sampleScale = clamp(radius * 0.75, 0.35, 1.0);\n    int samples = int(float(MAX_SAMPLES) * sampleScale);\n    \n    float noise = hash(gl_FragCoord.xy);\n    float angleOffset = noise * GOLDEN_ANGLE;\n    float radiusJitter = 0.85 + noise * 0.3;\n    \n    float ca = cos(GOLDEN_ANGLE);\n    float sa = sin(GOLDEN_ANGLE);\n    vec2 dir = vec2(cos(angleOffset), sin(angleOffset));\n    \n    vec3 glow = vec3(0.0);\n    float totalWeight = 0.0;\n    \n    // Center tap\n    float centerMask = smoothstep(t0, t1, dot(original.rgb, LUMA));\n    glow += original.rgb * centerMask * 2.0;\n    totalWeight += 2.0;\n    \n    for (int i = 1; i < MAX_SAMPLES; i++) {\n        if (i >= samples) break;\n        \n        float fi = float(i);\n        float dist = sqrt(fi / float(samples)) * radius * radiusJitter;\n        \n        vec2 offset = dir * dist * texelSize;\n        vec3 c = texture(u_image0, v_texCoord + offset).rgb;\n        float mask = smoothstep(t0, t1, dot(c, LUMA));\n        \n        float w = 1.0 - (dist * dist) / (radius2 * 1.5);\n        w = max(w, 0.0);\n        w *= w;\n        \n        glow += c * mask * w;\n        totalWeight += w;\n        \n        dir = vec2(\n            dir.x * ca - dir.y * sa,\n            dir.x * sa + dir.y * ca\n        );\n    }\n    \n    glow *= intensity / max(totalWeight, 0.001);\n    \n    if (u_int1 > 0) {\n        glow *= hexToRgb(u_int1);\n    }\n    \n    vec3 result = blend(original.rgb, glow, u_int0);\n    result += (noise - 0.5) * (1.0 / 255.0);\n    \n    fragColor = vec4(clamp(result, 0.0, 1.0), original.a);\n}", "from_input"]}, {"id": 34, "type": "PrimitiveFloat", "pos": [2290, -510], "size": [270, 58], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "intensity", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [51]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 100, "precision": 1, "step": 1}, "widgets_values": [30]}, {"id": 35, "type": "PrimitiveFloat", "pos": [2290, -410], "size": [270, 58], "flags": {}, "order": 1, "mode": 0, "inputs": [{"label": "radius", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [50]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 100, "precision": 1, "step": 1}, "widgets_values": [25]}, {"id": 33, "type": "PrimitiveFloat", "pos": [2290, -310], "size": [270, 58], "flags": {}, "order": 2, "mode": 0, "inputs": [{"label": "threshold", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [52]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 100, "precision": 1, "step": 1}, "widgets_values": [100]}, {"id": 32, "type": "ColorToRGBInt", "pos": [2290, -210], "size": [270, 58], "flags": {}, "order": 3, "mode": 0, "inputs": [{"label": "color_tint", "localized_name": "color", "name": "color", "type": "COLOR", "widget": {"name": "color"}, "link": null}], "outputs": [{"localized_name": "rgb_int", "name": "rgb_int", "type": "INT", "links": [47]}], "properties": {"Node name for S&R": "ColorToRGBInt"}, "widgets_values": ["#45edf5"]}, {"id": 31, "type": "CustomCombo", "pos": [2290, -110], "size": [270, 222], "flags": {}, "order": 4, "mode": 0, "inputs": [{"label": "blend_mode", "localized_name": "choice", "name": "choice", "type": "COMBO", "widget": {"name": "choice"}, "link": null}], "outputs": [{"localized_name": "STRING", "name": "STRING", "type": "STRING", "links": null}, {"localized_name": "INDEX", "name": "INDEX", "type": "INT", "links": [46]}], "properties": {"Node name for S&R": "CustomCombo"}, "widgets_values": ["add", 0, "add", "screen", "soft", "overlay", "lighten", ""]}], "groups": [], "links": [{"id": 51, "origin_id": 34, "origin_slot": 0, "target_id": 30, "target_slot": 2, "type": "FLOAT"}, {"id": 50, "origin_id": 35, "origin_slot": 0, "target_id": 30, "target_slot": 3, "type": "FLOAT"}, {"id": 52, "origin_id": 33, "origin_slot": 0, "target_id": 30, "target_slot": 4, "type": "FLOAT"}, {"id": 46, "origin_id": 31, "origin_slot": 1, "target_id": 30, "target_slot": 6, "type": "INT"}, {"id": 47, "origin_id": 32, "origin_slot": 0, "target_id": 30, "target_slot": 7, "type": "INT"}, {"id": 45, "origin_id": -10, "origin_slot": 0, "target_id": 30, "target_slot": 0, "type": "IMAGE"}, {"id": 53, "origin_id": 30, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}}]}}
\ No newline at end of file
diff --git a/blueprints/Hue and Saturation.json b/blueprints/Hue and Saturation.json
new file mode 100644
index 000000000..84d95df33
--- /dev/null
+++ b/blueprints/Hue and Saturation.json	
@@ -0,0 +1 @@
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"link": 4}, {"label": "u_float2", "localized_name": "floats.u_float2", "name": "floats.u_float2", "shape": 7, "type": "FLOAT", "link": 5}, {"label": "u_float3", "localized_name": "floats.u_float3", "name": "floats.u_float3", "shape": 7, "type": "FLOAT", "link": 6}, {"label": "u_float4", "localized_name": "floats.u_float4", "name": "floats.u_float4", "shape": 7, "type": "FLOAT", "link": null}, {"label": "u_int0", "localized_name": "ints.u_int0", "name": "ints.u_int0", "shape": 7, "type": "INT", "link": 1}, {"label": "u_int1", "localized_name": "ints.u_int1", "name": "ints.u_int1", "shape": 7, "type": "INT", "link": 2}, {"label": "u_int2", "localized_name": "ints.u_int2", "name": "ints.u_int2", "shape": 7, "type": "INT", "link": null}, {"localized_name": "fragment_shader", "name": "fragment_shader", "type": "STRING", "widget": {"name": "fragment_shader"}, "link": null}, {"localized_name": "size_mode", "name": "size_mode", "type": "COMFY_DYNAMICCOMBO_V3", "widget": {"name": "size_mode"}, "link": null}], "outputs": [{"localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": [8]}, {"localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": null}], "properties": {"Node name for S&R": "GLSLShader"}, "widgets_values": ["#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform int u_int0;      // Mode: 0=Master, 1=Reds, 2=Yellows, 3=Greens, 4=Cyans, 5=Blues, 6=Magentas, 7=Colorize\nuniform int u_int1;      // Color Space: 0=HSL, 1=HSB/HSV\nuniform float u_float0;  // Hue (-180 to 180)\nuniform float u_float1;  // Saturation (-100 to 100)\nuniform float u_float2;  // Lightness/Brightness (-100 to 100)\nuniform float u_float3;  // Overlap (0 to 100) - feathering between adjacent color ranges\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\n// Color range modes\nconst int MODE_MASTER   = 0;\nconst int MODE_RED      = 1;\nconst int MODE_YELLOW   = 2;\nconst int MODE_GREEN    = 3;\nconst int MODE_CYAN     = 4;\nconst int MODE_BLUE     = 5;\nconst int MODE_MAGENTA  = 6;\nconst int MODE_COLORIZE = 7;\n\n// Color space modes\nconst int COLORSPACE_HSL = 0;\nconst int COLORSPACE_HSB = 1;\n\nconst float EPSILON = 0.0001;\n\n//=============================================================================\n// RGB <-> HSL Conversions\n//=============================================================================\n\nvec3 rgb2hsl(vec3 c) {\n    float maxC = max(max(c.r, c.g), c.b);\n    float minC = min(min(c.r, c.g), c.b);\n    float delta = maxC - minC;\n\n    float h = 0.0;\n    float s = 0.0;\n    float l = (maxC + minC) * 0.5;\n\n    if (delta > EPSILON) {\n        s = l < 0.5\n            ? delta / (maxC + minC)\n            : delta / (2.0 - maxC - minC);\n\n        if (maxC == c.r) {\n            h = (c.g - c.b) / delta + (c.g < c.b ? 6.0 : 0.0);\n        } else if (maxC == c.g) {\n            h = (c.b - c.r) / delta + 2.0;\n        } else {\n            h = (c.r - c.g) / delta + 4.0;\n        }\n        h /= 6.0;\n    }\n\n    return vec3(h, s, l);\n}\n\nfloat hue2rgb(float p, float q, float t) {\n    t = fract(t);\n    if (t < 1.0/6.0) return p + (q - p) * 6.0 * t;\n    if (t < 0.5)       return q;\n    if (t < 2.0/3.0)   return p + (q - p) * (2.0/3.0 - t) * 6.0;\n    return p;\n}\n\nvec3 hsl2rgb(vec3 hsl) {\n    if (hsl.y < EPSILON) return vec3(hsl.z);\n\n    float q = hsl.z < 0.5\n        ? hsl.z * (1.0 + hsl.y)\n        : hsl.z + hsl.y - hsl.z * hsl.y;\n    float p = 2.0 * hsl.z - q;\n\n    return vec3(\n        hue2rgb(p, q, hsl.x + 1.0/3.0),\n        hue2rgb(p, q, hsl.x),\n        hue2rgb(p, q, hsl.x - 1.0/3.0)\n    );\n}\n\nvec3 rgb2hsb(vec3 c) {\n    float maxC = max(max(c.r, c.g), c.b);\n    float minC = min(min(c.r, c.g), c.b);\n    float delta = maxC - minC;\n\n    float h = 0.0;\n    float s = (maxC > EPSILON) ? delta / maxC : 0.0;\n    float b = maxC;\n\n    if (delta > EPSILON) {\n        if (maxC == c.r) {\n            h = (c.g - c.b) / delta + (c.g < c.b ? 6.0 : 0.0);\n        } else if (maxC == c.g) {\n            h = (c.b - c.r) / delta + 2.0;\n        } else {\n            h = (c.r - c.g) / delta + 4.0;\n        }\n        h /= 6.0;\n    }\n\n    return vec3(h, s, b);\n}\n\nvec3 hsb2rgb(vec3 hsb) {\n    vec3 rgb = clamp(abs(mod(hsb.x * 6.0 + vec3(0.0, 4.0, 2.0), 6.0) - 3.0) - 1.0, 0.0, 1.0);\n    return hsb.z * mix(vec3(1.0), rgb, hsb.y);\n}\n\n//=============================================================================\n// Color Range Weight Calculation\n//=============================================================================\n\nfloat hueDistance(float a, float b) {\n    float d = abs(a - b);\n    return min(d, 1.0 - d);\n}\n\nfloat getHueWeight(float hue, float center, float overlap) {\n    float baseWidth = 1.0 / 6.0;\n    float feather = baseWidth * overlap;\n\n    float d = hueDistance(hue, center);\n\n    float inner = baseWidth * 0.5;\n    float outer = inner + feather;\n\n    return 1.0 - smoothstep(inner, outer, d);\n}\n\nfloat getModeWeight(float hue, int mode, float overlap) {\n    if (mode == MODE_MASTER || mode == MODE_COLORIZE) return 1.0;\n\n    if (mode == MODE_RED) {\n        return max(\n            getHueWeight(hue, 0.0, overlap),\n            getHueWeight(hue, 1.0, overlap)\n        );\n    }\n\n    float center = float(mode - 1) / 6.0;\n    return getHueWeight(hue, center, overlap);\n}\n\n//=============================================================================\n// Adjustment Functions\n//=============================================================================\n\nfloat adjustLightness(float l, float amount) {\n    return amount > 0.0\n        ? l + (1.0 - l) * amount\n        : l + l * amount;\n}\n\nfloat adjustBrightness(float b, float amount) {\n    return clamp(b + amount, 0.0, 1.0);\n}\n\nfloat adjustSaturation(float s, float amount) {\n    return amount > 0.0\n        ? s + (1.0 - s) * amount\n        : s + s * amount;\n}\n\nvec3 colorize(vec3 rgb, float hue, float sat, float light) {\n    float lum = dot(rgb, vec3(0.299, 0.587, 0.114));\n    float l = adjustLightness(lum, light);\n\n    vec3 hsl = vec3(fract(hue), clamp(sat, 0.0, 1.0), clamp(l, 0.0, 1.0));\n    return hsl2rgb(hsl);\n}\n\n//=============================================================================\n// Main\n//=============================================================================\n\nvoid main() {\n    vec4 original = texture(u_image0, v_texCoord);\n\n    float hueShift   = u_float0 / 360.0;   // -180..180 -> -0.5..0.5\n    float satAmount  = u_float1 / 100.0;   // -100..100 -> -1..1\n    float lightAmount= u_float2 / 100.0;   // -100..100 -> -1..1\n    float overlap    = u_float3 / 100.0;   // 0..100 -> 0..1\n\n    vec3 result;\n\n    if (u_int0 == MODE_COLORIZE) {\n        result = colorize(original.rgb, hueShift, satAmount, lightAmount);\n        fragColor = vec4(result, original.a);\n        return;\n    }\n\n    vec3 hsx = (u_int1 == COLORSPACE_HSL)\n        ? rgb2hsl(original.rgb)\n        : rgb2hsb(original.rgb);\n\n    float weight = getModeWeight(hsx.x, u_int0, overlap);\n\n    if (u_int0 != MODE_MASTER && hsx.y < EPSILON) {\n        weight = 0.0;\n    }\n\n    if (weight > EPSILON) {\n        float h = fract(hsx.x + hueShift * weight);\n        float s = clamp(adjustSaturation(hsx.y, satAmount * weight), 0.0, 1.0);\n        float v = (u_int1 == COLORSPACE_HSL)\n            ? clamp(adjustLightness(hsx.z, lightAmount * weight), 0.0, 1.0)\n            : clamp(adjustBrightness(hsx.z, lightAmount * weight), 0.0, 1.0);\n\n        vec3 adjusted = vec3(h, s, v);\n        result = (u_int1 == COLORSPACE_HSL)\n            ? hsl2rgb(adjusted)\n            : hsb2rgb(adjusted);\n    } else {\n        result = original.rgb;\n    }\n\n    fragColor = vec4(result, original.a);\n}\n", "from_input"]}], "groups": [], "links": [{"id": 3, "origin_id": 4, "origin_slot": 0, "target_id": 1, "target_slot": 2, "type": "FLOAT"}, {"id": 4, "origin_id": 5, "origin_slot": 0, "target_id": 1, "target_slot": 3, "type": "FLOAT"}, {"id": 5, "origin_id": 6, "origin_slot": 0, "target_id": 1, "target_slot": 4, "type": "FLOAT"}, {"id": 6, "origin_id": 7, "origin_slot": 0, "target_id": 1, "target_slot": 5, "type": "FLOAT"}, {"id": 1, "origin_id": 2, "origin_slot": 1, "target_id": 1, "target_slot": 7, "type": "INT"}, {"id": 2, "origin_id": 3, "origin_slot": 1, "target_id": 1, "target_slot": 8, "type": "INT"}, {"id": 10, "origin_id": -10, "origin_slot": 0, "target_id": 1, "target_slot": 0, "type": "IMAGE"}, {"id": 8, "origin_id": 1, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}}]}}
\ No newline at end of file
diff --git a/blueprints/Image Blur.json b/blueprints/Image Blur.json
new file mode 100644
index 000000000..c03f31902
--- /dev/null
+++ b/blueprints/Image Blur.json	
@@ -0,0 +1 @@
+{"revision": 0, "last_node_id": 8, "last_link_id": 0, "nodes": [{"id": 8, "type": "198632a3-ee76-4aab-9ce7-a69c624eaff9", "pos": [4470, -1840], "size": [210, 82], "flags": {}, "order": 3, "mode": 0, "inputs": [{"label": "image", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": null}], "outputs": [{"label": "blurred_image", "localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": []}], "properties": {"proxyWidgets": [["12", "choice"], ["10", "value"]]}, "widgets_values": [], "title": "Image Blur"}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "198632a3-ee76-4aab-9ce7-a69c624eaff9", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 12, "lastLinkId": 11, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Image Blur", "inputNode": {"id": -10, "bounding": [3540, -2445, 120, 60]}, "outputNode": {"id": -20, "bounding": [4620, -2445, 121.11666870117188, 60]}, "inputs": [{"id": "7ff2a402-6b11-45e8-a92a-7158d216520a", "name": "images.image0", "type": "IMAGE", "linkIds": [9], "localized_name": "images.image0", "label": "image", "pos": [3640, -2425]}], "outputs": [{"id": "80a8e19e-ffd9-44a5-90f2-710815a5b063", "name": "IMAGE0", "type": "IMAGE", "linkIds": [3], "localized_name": "IMAGE0", "label": "blurred_image", "pos": [4640, -2425]}], "widgets": [], "nodes": [{"id": 12, "type": "CustomCombo", "pos": [3720, -2620], "size": [270, 174], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "blur_type", "localized_name": "choice", "name": "choice", "type": "COMBO", "widget": {"name": "choice"}, "link": null}], "outputs": [{"localized_name": "STRING", "name": "STRING", "type": "STRING", "links": null}, {"localized_name": "INDEX", "name": "INDEX", "type": "INT", "links": [11]}], "properties": {"Node name for S&R": "CustomCombo"}, "widgets_values": ["Gaussian", 0, "Gaussian", "Box", "Radial", ""]}, {"id": 10, "type": "PrimitiveFloat", "pos": [4020, -2780], "size": [270, 58], "flags": {}, "order": 1, "mode": 0, "inputs": [{"label": "strength", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [10]}], "properties": {"Node name for S&R": "PrimitiveFloat", "max": 100, "min": 0}, "widgets_values": [20]}, {"id": 1, "type": "GLSLShader", "pos": [4020, -2670], "size": [430, 212], "flags": {}, "order": 2, "mode": 0, "inputs": [{"label": "image0", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": 9}, {"label": "image1", "localized_name": "images.image1", "name": "images.image1", "shape": 7, "type": "IMAGE", "link": null}, {"label": "u_float0", "localized_name": "floats.u_float0", "name": "floats.u_float0", "shape": 7, "type": "FLOAT", "link": 10}, {"label": "u_float1", "localized_name": "floats.u_float1", "name": "floats.u_float1", "shape": 7, "type": "FLOAT", "link": null}, {"label": "u_int0", "localized_name": "ints.u_int0", "name": "ints.u_int0", "shape": 7, "type": "INT", "link": 11}, {"label": "u_int1", "localized_name": "ints.u_int1", "name": "ints.u_int1", "shape": 7, "type": "INT", "link": null}, {"localized_name": "fragment_shader", "name": "fragment_shader", "type": "STRING", "widget": {"name": "fragment_shader"}, "link": null}, {"localized_name": "size_mode", "name": "size_mode", "type": "COMFY_DYNAMICCOMBO_V3", "widget": {"name": "size_mode"}, "link": null}], "outputs": [{"localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": [3]}, {"localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": []}, {"localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": []}, {"localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": []}], "properties": {"Node name for S&R": "GLSLShader"}, "widgets_values": ["#version 300 es\n#pragma passes 2\nprecision highp float;\n\n// Blur type constants\nconst int BLUR_GAUSSIAN = 0;\nconst int BLUR_BOX = 1;\nconst int BLUR_RADIAL = 2;\n\n// Radial blur config\nconst int RADIAL_SAMPLES = 12;\nconst float RADIAL_STRENGTH = 0.0003;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform int u_int0;      // Blur type (BLUR_GAUSSIAN, BLUR_BOX, BLUR_RADIAL)\nuniform float u_float0;  // Blur radius/amount\nuniform int u_pass;      // Pass index (0 = horizontal, 1 = vertical)\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nfloat gaussian(float x, float sigma) {\n    return exp(-(x * x) / (2.0 * sigma * sigma));\n}\n\nvoid main() {\n    vec2 texelSize = 1.0 / u_resolution;\n    float radius = max(u_float0, 0.0);\n\n    // Radial (angular) blur - single pass, doesn't use separable\n    if (u_int0 == BLUR_RADIAL) {\n        // Only execute on first pass\n        if (u_pass > 0) {\n            fragColor0 = texture(u_image0, v_texCoord);\n            return;\n        }\n\n        vec2 center = vec2(0.5);\n        vec2 dir = v_texCoord - center;\n        float dist = length(dir);\n\n        if (dist < 1e-4) {\n            fragColor0 = texture(u_image0, v_texCoord);\n            return;\n        }\n\n        vec4 sum = vec4(0.0);\n        float totalWeight = 0.0;\n        float angleStep = radius * RADIAL_STRENGTH;\n\n        dir /= dist;\n\n        float cosStep = cos(angleStep);\n        float sinStep = sin(angleStep);\n\n        float negAngle = -float(RADIAL_SAMPLES) * angleStep;\n        vec2 rotDir = vec2(\n            dir.x * cos(negAngle) - dir.y * sin(negAngle),\n            dir.x * sin(negAngle) + dir.y * cos(negAngle)\n        );\n\n        for (int i = -RADIAL_SAMPLES; i <= RADIAL_SAMPLES; i++) {\n            vec2 uv = center + rotDir * dist;\n            float w = 1.0 - abs(float(i)) / float(RADIAL_SAMPLES);\n            sum += texture(u_image0, uv) * w;\n            totalWeight += w;\n\n            rotDir = vec2(\n                rotDir.x * cosStep - rotDir.y * sinStep,\n                rotDir.x * sinStep + rotDir.y * cosStep\n            );\n        }\n\n        fragColor0 = sum / max(totalWeight, 0.001);\n        return;\n    }\n\n    // Separable Gaussian / Box blur\n    int samples = int(ceil(radius));\n\n    if (samples == 0) {\n        fragColor0 = texture(u_image0, v_texCoord);\n        return;\n    }\n\n    // Direction: pass 0 = horizontal, pass 1 = vertical\n    vec2 dir = (u_pass == 0) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);\n\n    vec4 color = vec4(0.0);\n    float totalWeight = 0.0;\n    float sigma = radius / 2.0;\n\n    for (int i = -samples; i <= samples; i++) {\n        vec2 offset = dir * float(i) * texelSize;\n        vec4 sample_color = texture(u_image0, v_texCoord + offset);\n\n        float weight;\n        if (u_int0 == BLUR_GAUSSIAN) {\n            weight = gaussian(float(i), sigma);\n        } else {\n            // BLUR_BOX\n            weight = 1.0;\n        }\n\n        color += sample_color * weight;\n        totalWeight += weight;\n    }\n\n    fragColor0 = color / totalWeight;\n}\n", "from_input"]}], "groups": [], "links": [{"id": 10, "origin_id": 10, "origin_slot": 0, "target_id": 1, "target_slot": 2, "type": "FLOAT"}, {"id": 11, "origin_id": 12, "origin_slot": 1, "target_id": 1, "target_slot": 4, "type": "INT"}, {"id": 9, "origin_id": -10, "origin_slot": 0, "target_id": 1, "target_slot": 0, "type": "IMAGE"}, {"id": 3, "origin_id": 1, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}}]}}
\ No newline at end of file
diff --git a/blueprints/Image Channels.json b/blueprints/Image Channels.json
new file mode 100644
index 000000000..56a565d30
--- /dev/null
+++ b/blueprints/Image Channels.json	
@@ -0,0 +1 @@
+{"revision": 0, "last_node_id": 29, "last_link_id": 0, "nodes": [{"id": 29, "type": "4c9d6ea4-b912-40e5-8766-6793a9758c53", "pos": [1970, -230], "size": [180, 86], "flags": {}, "order": 5, "mode": 0, "inputs": [{"label": "image", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": null}], "outputs": [{"label": "R", "localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": []}, {"label": "G", "localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": []}, {"label": "B", "localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": []}, {"label": "A", "localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": []}], "title": "Image Channels", "properties": {"proxyWidgets": []}, "widgets_values": []}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "4c9d6ea4-b912-40e5-8766-6793a9758c53", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 28, "lastLinkId": 39, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Image Channels", "inputNode": {"id": -10, "bounding": [1820, -185, 120, 60]}, "outputNode": {"id": -20, "bounding": [2460, -215, 120, 120]}, "inputs": [{"id": "3522932b-2d86-4a1f-a02a-cb29f3a9d7fe", "name": "images.image0", "type": "IMAGE", "linkIds": [39], "localized_name": "images.image0", "label": "image", "pos": [1920, -165]}], "outputs": [{"id": "605cb9c3-b065-4d9b-81d2-3ec331889b2b", "name": "IMAGE0", "type": "IMAGE", "linkIds": [26], "localized_name": "IMAGE0", "label": "R", "pos": [2480, -195]}, {"id": "fb44a77e-0522-43e9-9527-82e7465b3596", "name": "IMAGE1", "type": "IMAGE", "linkIds": [27], "localized_name": "IMAGE1", "label": "G", "pos": [2480, -175]}, {"id": "81460ee6-0131-402a-874f-6bf3001fc4ff", "name": "IMAGE2", "type": "IMAGE", "linkIds": [28], "localized_name": "IMAGE2", "label": "B", "pos": [2480, -155]}, {"id": "ae690246-80d4-4951-b1d9-9306d8a77417", "name": "IMAGE3", "type": "IMAGE", "linkIds": [29], "localized_name": "IMAGE3", "label": "A", "pos": [2480, -135]}], "widgets": [], "nodes": [{"id": 23, "type": "GLSLShader", "pos": [2000, -330], "size": [400, 172], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "image", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": 39}, {"localized_name": "fragment_shader", "name": "fragment_shader", "type": "STRING", "widget": {"name": "fragment_shader"}, "link": null}, {"localized_name": "size_mode", "name": "size_mode", "type": "COMFY_DYNAMICCOMBO_V3", "widget": {"name": "size_mode"}, "link": null}, {"label": "image1", "localized_name": "images.image1", "name": "images.image1", "shape": 7, "type": "IMAGE", "link": null}], "outputs": [{"label": "R", "localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": [26]}, {"label": "G", "localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": [27]}, {"label": "B", "localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": [28]}, {"label": "A", "localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": [29]}], "properties": {"Node name for S&R": "GLSLShader"}, "widgets_values": ["#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\nlayout(location = 1) out vec4 fragColor1;\nlayout(location = 2) out vec4 fragColor2;\nlayout(location = 3) out vec4 fragColor3;\n\nvoid main() {\n  vec4 color = texture(u_image0, v_texCoord);\n  // Output each channel as grayscale to separate render targets\n  fragColor0 = vec4(vec3(color.r), 1.0);  // Red channel\n  fragColor1 = vec4(vec3(color.g), 1.0);  // Green channel\n  fragColor2 = vec4(vec3(color.b), 1.0);  // Blue channel\n  fragColor3 = vec4(vec3(color.a), 1.0);  // Alpha channel\n}\n", "from_input"]}], "groups": [], "links": [{"id": 39, "origin_id": -10, "origin_slot": 0, "target_id": 23, "target_slot": 0, "type": "IMAGE"}, {"id": 26, "origin_id": 23, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}, {"id": 27, "origin_id": 23, "origin_slot": 1, "target_id": -20, "target_slot": 1, "type": "IMAGE"}, {"id": 28, "origin_id": 23, "origin_slot": 2, "target_id": -20, "target_slot": 2, "type": "IMAGE"}, {"id": 29, "origin_id": 23, "origin_slot": 3, "target_id": -20, "target_slot": 3, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}}]}}
\ No newline at end of file
diff --git a/blueprints/Image Levels.json b/blueprints/Image Levels.json
new file mode 100644
index 000000000..26f7a0dc1
--- /dev/null
+++ b/blueprints/Image Levels.json	
@@ -0,0 +1 @@
+{"revision":0,"last_node_id":139,"last_link_id":0,"nodes":[{"id":139,"type":"75bf8a72-aad8-4f3e-83ee-380e70248240","pos":[620,900],"size":[240,178],"flags":{},"order":1,"mode":0,"inputs":[{"label":"image","localized_name":"images.image0","name":"images.image0","type":"IMAGE","link":null}],"outputs":[{"label":"IMAGE","localized_name":"IMAGE0","name":"IMAGE0","type":"IMAGE","links":[]}],"properties":{"proxyWidgets":[["5","choice"],["3","value"],["6","value"],["7","value"],["8","value"],["9","value"]]},"widgets_values":[],"title":"Image Levels"}],"links":[],"version":0.4,"definitions":{"subgraphs":[{"id":"75bf8a72-aad8-4f3e-83ee-380e70248240","version":1,"state":{"lastGroupId":0,"lastNodeId":144,"lastLinkId":118,"lastRerouteId":0},"revision":0,"config":{},"name":"Image Levels","inputNode":{"id":-10,"bounding":[3840,-3430,120,60]},"outputNode":{"id":-20,"bounding":[4950,-3430,120,60]},"inputs":[{"id":"b53e5012-fa47-400f-a324-28c74854ccae","name":"images.image0","type":"IMAGE","linkIds":[1],"localized_name":"images.image0","label":"image","pos":[3940,-3410]}],"outputs":[{"id":"de7f2ffa-155f-41fd-b054-aa4d91ef49ca","name":"IMAGE0","type":"IMAGE","linkIds":[8],"localized_name":"IMAGE0","label":"IMAGE","pos":[4970,-3410]}],"widgets":[],"nodes":[{"id":5,"type":"CustomCombo","pos":[4020,-3350],"size":[270,198],"flags":{},"order":0,"mode":0,"inputs":[{"label":"channel","localized_name":"choice","name":"choice","type":"COMBO","widget":{"name":"choice"},"link":null}],"outputs":[{"localized_name":"STRING","name":"STRING","type":"STRING","links":null},{"localized_name":"INDEX","name":"INDEX","type":"INT","links":[3]}],"title":"Channel","properties":{"Node name for S&R":"CustomCombo"},"widgets_values":["RGB",0,"RGB","R","G","B",""]},{"id":8,"type":"PrimitiveFloat","pos":[4020,-3550],"size":[270,58],"flags":{},"order":1,"mode":0,"inputs":[{"label":"output_black","localized_name":"value","name":"value","type":"FLOAT","widget":{"name":"value"},"link":null}],"outputs":[{"localized_name":"FLOAT","name":"FLOAT","type":"FLOAT","links":[6]}],"title":"Output Black","properties":{"Node name for S&R":"PrimitiveFloat","max":255,"min":0,"step":1},"widgets_values":[0]},{"id":3,"type":"PrimitiveFloat","pos":[4020,-3850],"size":[270,58],"flags":{},"order":2,"mode":0,"inputs":[{"label":"input_black","localized_name":"value","name":"value","type":"FLOAT","widget":{"name":"value"},"link":null}],"outputs":[{"localized_name":"FLOAT","name":"FLOAT","type":"FLOAT","links":[2]}],"title":"Input Black","properties":{"Node name for S&R":"PrimitiveFloat","min":0,"max":255,"step":1},"widgets_values":[0]},{"id":6,"type":"PrimitiveFloat","pos":[4020,-3750],"size":[270,58],"flags":{},"order":3,"mode":0,"inputs":[{"label":"input_white","localized_name":"value","name":"value","type":"FLOAT","widget":{"name":"value"},"link":null}],"outputs":[{"localized_name":"FLOAT","name":"FLOAT","type":"FLOAT","links":[4]}],"title":"Input White","properties":{"Node name for S&R":"PrimitiveFloat","max":255,"min":0,"step":1},"widgets_values":[255]},{"id":7,"type":"PrimitiveFloat","pos":[4020,-3650],"size":[270,58],"flags":{},"order":4,"mode":0,"inputs":[{"label":"gamma","localized_name":"value","name":"value","type":"FLOAT","widget":{"name":"value"},"link":null}],"outputs":[{"localized_name":"FLOAT","name":"FLOAT","type":"FLOAT","links":[5]}],"title":"Gamma","properties":{"Node name for S&R":"PrimitiveFloat","max":10,"min":0,"step":0.01,"precision":2},"widgets_values":[1]},{"id":9,"type":"PrimitiveFloat","pos":[4020,-3450],"size":[270,58],"flags":{},"order":5,"mode":0,"inputs":[{"label":"output_white","localized_name":"value","name":"value","type":"FLOAT","widget":{"name":"value"},"link":null}],"outputs":[{"localized_name":"FLOAT","name":"FLOAT","type":"FLOAT","links":[7]}],"title":"Output White","properties":{"Node name for S&R":"PrimitiveFloat","max":255,"min":0,"step":1},"widgets_values":[255]},{"id":1,"type":"GLSLShader","pos":[4310,-3850],"size":[580,272],"flags":{},"order":6,"mode":0,"inputs":[{"label":"image0","localized_name":"images.image0","name":"images.image0","type":"IMAGE","link":1},{"label":"image1","localized_name":"images.image1","name":"images.image1","shape":7,"type":"IMAGE","link":null},{"label":"u_float0","localized_name":"floats.u_float0","name":"floats.u_float0","shape":7,"type":"FLOAT","link":2},{"label":"u_float1","localized_name":"floats.u_float1","name":"floats.u_float1","shape":7,"type":"FLOAT","link":4},{"label":"u_float2","localized_name":"floats.u_float2","name":"floats.u_float2","shape":7,"type":"FLOAT","link":5},{"label":"u_float3","localized_name":"floats.u_float3","name":"floats.u_float3","shape":7,"type":"FLOAT","link":6},{"label":"u_float4","localized_name":"floats.u_float4","name":"floats.u_float4","shape":7,"type":"FLOAT","link":7},{"label":"u_int0","localized_name":"ints.u_int0","name":"ints.u_int0","shape":7,"type":"INT","link":3},{"label":"u_int1","localized_name":"ints.u_int1","name":"ints.u_int1","shape":7,"type":"INT","link":null},{"localized_name":"fragment_shader","name":"fragment_shader","type":"STRING","widget":{"name":"fragment_shader"},"link":null},{"localized_name":"size_mode","name":"size_mode","type":"COMFY_DYNAMICCOMBO_V3","widget":{"name":"size_mode"},"link":null}],"outputs":[{"localized_name":"IMAGE0","name":"IMAGE0","type":"IMAGE","links":[8]},{"localized_name":"IMAGE1","name":"IMAGE1","type":"IMAGE","links":null},{"localized_name":"IMAGE2","name":"IMAGE2","type":"IMAGE","links":null},{"localized_name":"IMAGE3","name":"IMAGE3","type":"IMAGE","links":null}],"properties":{"Node name for S&R":"GLSLShader"},"widgets_values":["#version 300 es\nprecision highp float;\n\n// Levels Adjustment\n// u_int0:   channel      (0=RGB, 1=R, 2=G, 3=B)         default: 0\n// u_float0: input black  (0-255)                        default: 0\n// u_float1: input white  (0-255)                        default: 255\n// u_float2: gamma        (0.01-9.99)                    default: 1.0\n// u_float3: output black (0-255)                        default: 0\n// u_float4: output white (0-255)                        default: 255\n\nuniform sampler2D u_image0;\nuniform int u_int0;\nuniform float u_float0;\nuniform float u_float1;\nuniform float u_float2;\nuniform float u_float3;\nuniform float u_float4;\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nvec3 applyLevels(vec3 color, float inBlack, float inWhite, float gamma, float outBlack, float outWhite) {\n    float inRange = max(inWhite - inBlack, 0.0001);\n    vec3 result = clamp((color - inBlack) / inRange, 0.0, 1.0);\n    result = pow(result, vec3(1.0 / gamma));\n    result = mix(vec3(outBlack), vec3(outWhite), result);\n    return result;\n}\n\nfloat applySingleChannel(float value, float inBlack, float inWhite, float gamma, float outBlack, float outWhite) {\n    float inRange = max(inWhite - inBlack, 0.0001);\n    float result = clamp((value - inBlack) / inRange, 0.0, 1.0);\n    result = pow(result, 1.0 / gamma);\n    result = mix(outBlack, outWhite, result);\n    return result;\n}\n\nvoid main() {\n    vec4 texColor = texture(u_image0, v_texCoord);\n    vec3 color = texColor.rgb;\n    \n    float inBlack = u_float0 / 255.0;\n    float inWhite = u_float1 / 255.0;\n    float gamma = u_float2;\n    float outBlack = u_float3 / 255.0;\n    float outWhite = u_float4 / 255.0;\n    \n    vec3 result;\n    \n    if (u_int0 == 0) {\n        result = applyLevels(color, inBlack, inWhite, gamma, outBlack, outWhite);\n    }\n    else if (u_int0 == 1) {\n        result = color;\n        result.r = applySingleChannel(color.r, inBlack, inWhite, gamma, outBlack, outWhite);\n    }\n    else if (u_int0 == 2) {\n        result = color;\n        result.g = applySingleChannel(color.g, inBlack, inWhite, gamma, outBlack, outWhite);\n    }\n    else if (u_int0 == 3) {\n        result = color;\n        result.b = applySingleChannel(color.b, inBlack, inWhite, gamma, outBlack, outWhite);\n    }\n    else {\n        result = color;\n    }\n    \n    fragColor = vec4(result, texColor.a);\n}","from_input"]}],"groups":[],"links":[{"id":2,"origin_id":3,"origin_slot":0,"target_id":1,"target_slot":2,"type":"FLOAT"},{"id":4,"origin_id":6,"origin_slot":0,"target_id":1,"target_slot":3,"type":"FLOAT"},{"id":5,"origin_id":7,"origin_slot":0,"target_id":1,"target_slot":4,"type":"FLOAT"},{"id":6,"origin_id":8,"origin_slot":0,"target_id":1,"target_slot":5,"type":"FLOAT"},{"id":7,"origin_id":9,"origin_slot":0,"target_id":1,"target_slot":6,"type":"FLOAT"},{"id":3,"origin_id":5,"origin_slot":1,"target_id":1,"target_slot":7,"type":"INT"},{"id":1,"origin_id":-10,"origin_slot":0,"target_id":1,"target_slot":0,"type":"IMAGE"},{"id":8,"origin_id":1,"origin_slot":0,"target_id":-20,"target_slot":0,"type":"IMAGE"}],"extra":{"workflowRendererVersion":"LG"}}]},"extra":{}}
\ No newline at end of file
diff --git a/blueprints/Sharpen.json b/blueprints/Sharpen.json
new file mode 100644
index 000000000..49a164eca
--- /dev/null
+++ b/blueprints/Sharpen.json
@@ -0,0 +1 @@
+{"revision":0,"last_node_id":25,"last_link_id":0,"nodes":[{"id":25,"type":"621ba4e2-22a8-482d-a369-023753198b7b","pos":[4610,-790],"size":[230,58],"flags":{},"order":4,"mode":0,"inputs":[{"label":"image","localized_name":"images.image0","name":"images.image0","type":"IMAGE","link":null}],"outputs":[{"label":"IMAGE","localized_name":"IMAGE0","name":"IMAGE0","type":"IMAGE","links":[]}],"title":"Sharpen","properties":{"proxyWidgets":[["24","value"]]},"widgets_values":[]}],"links":[],"version":0.4,"definitions":{"subgraphs":[{"id":"621ba4e2-22a8-482d-a369-023753198b7b","version":1,"state":{"lastGroupId":0,"lastNodeId":24,"lastLinkId":36,"lastRerouteId":0},"revision":0,"config":{},"name":"Sharpen","inputNode":{"id":-10,"bounding":[4090,-825,120,60]},"outputNode":{"id":-20,"bounding":[5150,-825,120,60]},"inputs":[{"id":"37011fb7-14b7-4e0e-b1a0-6a02e8da1fd7","name":"images.image0","type":"IMAGE","linkIds":[34],"localized_name":"images.image0","label":"image","pos":[4190,-805]}],"outputs":[{"id":"e9182b3f-635c-4cd4-a152-4b4be17ae4b9","name":"IMAGE0","type":"IMAGE","linkIds":[35],"localized_name":"IMAGE0","label":"IMAGE","pos":[5170,-805]}],"widgets":[],"nodes":[{"id":24,"type":"PrimitiveFloat","pos":[4280,-1240],"size":[270,58],"flags":{},"order":0,"mode":0,"inputs":[{"label":"strength","localized_name":"value","name":"value","type":"FLOAT","widget":{"name":"value"},"link":null}],"outputs":[{"localized_name":"FLOAT","name":"FLOAT","type":"FLOAT","links":[36]}],"properties":{"Node name for S&R":"PrimitiveFloat","min":0,"max":3,"precision":2,"step":0.05},"widgets_values":[0.5]},{"id":23,"type":"GLSLShader","pos":[4570,-1240],"size":[370,192],"flags":{},"order":1,"mode":0,"inputs":[{"label":"image0","localized_name":"images.image0","name":"images.image0","type":"IMAGE","link":34},{"label":"image1","localized_name":"images.image1","name":"images.image1","shape":7,"type":"IMAGE","link":null},{"label":"u_float0","localized_name":"floats.u_float0","name":"floats.u_float0","shape":7,"type":"FLOAT","link":36},{"label":"u_float1","localized_name":"floats.u_float1","name":"floats.u_float1","shape":7,"type":"FLOAT","link":null},{"label":"u_int0","localized_name":"ints.u_int0","name":"ints.u_int0","shape":7,"type":"INT","link":null},{"localized_name":"fragment_shader","name":"fragment_shader","type":"STRING","widget":{"name":"fragment_shader"},"link":null},{"localized_name":"size_mode","name":"size_mode","type":"COMFY_DYNAMICCOMBO_V3","widget":{"name":"size_mode"},"link":null}],"outputs":[{"localized_name":"IMAGE0","name":"IMAGE0","type":"IMAGE","links":[35]},{"localized_name":"IMAGE1","name":"IMAGE1","type":"IMAGE","links":null},{"localized_name":"IMAGE2","name":"IMAGE2","type":"IMAGE","links":null},{"localized_name":"IMAGE3","name":"IMAGE3","type":"IMAGE","links":null}],"properties":{"Node name for S&R":"GLSLShader"},"widgets_values":["#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform float u_float0;  // strength [0.0 – 2.0] typical: 0.3–1.0\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nvoid main() {\n    vec2 texel = 1.0 / u_resolution;\n    \n    // Sample center and neighbors\n    vec4 center = texture(u_image0, v_texCoord);\n    vec4 top    = texture(u_image0, v_texCoord + vec2( 0.0, -texel.y));\n    vec4 bottom = texture(u_image0, v_texCoord + vec2( 0.0,  texel.y));\n    vec4 left   = texture(u_image0, v_texCoord + vec2(-texel.x,  0.0));\n    vec4 right  = texture(u_image0, v_texCoord + vec2( texel.x,  0.0));\n    \n    // Edge enhancement (Laplacian)\n    vec4 edges = center * 4.0 - top - bottom - left - right;\n    \n    // Add edges back scaled by strength\n    vec4 sharpened = center + edges * u_float0;\n    \n    fragColor0 = vec4(clamp(sharpened.rgb, 0.0, 1.0), center.a);\n}","from_input"]}],"groups":[],"links":[{"id":36,"origin_id":24,"origin_slot":0,"target_id":23,"target_slot":2,"type":"FLOAT"},{"id":34,"origin_id":-10,"origin_slot":0,"target_id":23,"target_slot":0,"type":"IMAGE"},{"id":35,"origin_id":23,"origin_slot":0,"target_id":-20,"target_slot":0,"type":"IMAGE"}],"extra":{"workflowRendererVersion":"LG"}}]}}
\ No newline at end of file
diff --git a/blueprints/Unsharp Mask.json b/blueprints/Unsharp Mask.json
new file mode 100644
index 000000000..d282a023b
--- /dev/null
+++ b/blueprints/Unsharp Mask.json	
@@ -0,0 +1 @@
+{"revision": 0, "last_node_id": 30, "last_link_id": 0, "nodes": [{"id": 30, "type": "d99ba3f5-8a56-4365-8e45-3f3ea7c572a1", "pos": [4420, -370], "size": [210, 106], "flags": {}, "order": 4, "mode": 0, "inputs": [{"label": "image0", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": null}], "outputs": [{"localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": []}], "title": "Unsharp Mask", "properties": {"proxyWidgets": [["27", "value"], ["28", "value"], ["29", "value"]]}, "widgets_values": []}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "d99ba3f5-8a56-4365-8e45-3f3ea7c572a1", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 29, "lastLinkId": 43, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Unsharp Mask", "inputNode": {"id": -10, "bounding": [3920, -405, 120, 60]}, "outputNode": {"id": -20, "bounding": [4930, -405, 120, 60]}, "inputs": [{"id": "75354555-d2f3-46b9-a3dd-b076dcfca561", "name": "images.image0", "type": "IMAGE", "linkIds": [39], "localized_name": "images.image0", "label": "image0", "pos": [4020, -385]}], "outputs": [{"id": "04368b94-2a96-46ff-8c07-d0ce3235b40d", "name": "IMAGE0", "type": "IMAGE", "linkIds": [40], "localized_name": "IMAGE0", "pos": [4950, -385]}], "widgets": [], "nodes": [{"id": 27, "type": "PrimitiveFloat", "pos": [4100, -540], "size": [270, 58], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "amount", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [41]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 3, "precision": 2, "step": 0.05}, "widgets_values": [1]}, {"id": 28, "type": "PrimitiveFloat", "pos": [4100, -430], "size": [270, 58], "flags": {}, "order": 1, "mode": 0, "inputs": [{"label": "radius", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [42]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 10, "precision": 1, "step": 0.5}, "widgets_values": [3]}, {"id": 29, "type": "PrimitiveFloat", "pos": [4100, -320], "size": [270, 58], "flags": {}, "order": 2, "mode": 0, "inputs": [{"label": "threshold", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [43]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 1, "precision": 2, "step": 0.05}, "widgets_values": [0]}, {"id": 26, "type": "GLSLShader", "pos": [4470, -580], "size": [400, 232], "flags": {}, "order": 3, "mode": 0, "inputs": [{"label": "image0", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": 39}, {"label": "image1", "localized_name": "images.image1", "name": "images.image1", "shape": 7, "type": "IMAGE", "link": null}, {"label": "u_float0", "localized_name": "floats.u_float0", "name": "floats.u_float0", "shape": 7, "type": "FLOAT", "link": 41}, {"label": "u_float1", "localized_name": "floats.u_float1", "name": "floats.u_float1", "shape": 7, "type": "FLOAT", "link": 42}, {"label": "u_float2", "localized_name": "floats.u_float2", "name": "floats.u_float2", "shape": 7, "type": "FLOAT", "link": 43}, {"label": "u_float3", "localized_name": "floats.u_float3", "name": "floats.u_float3", "shape": 7, "type": "FLOAT", "link": null}, {"label": "u_int0", "localized_name": "ints.u_int0", "name": "ints.u_int0", "shape": 7, "type": "INT", "link": null}, {"localized_name": "fragment_shader", "name": "fragment_shader", "type": "STRING", "widget": {"name": "fragment_shader"}, "link": null}, {"localized_name": "size_mode", "name": "size_mode", "type": "COMFY_DYNAMICCOMBO_V3", "widget": {"name": "size_mode"}, "link": null}], "outputs": [{"localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": [40]}, {"localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": null}], "properties": {"Node name for S&R": "GLSLShader"}, "widgets_values": ["#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform float u_float0;  // amount    [0.0 - 3.0]  typical: 0.5-1.5\nuniform float u_float1;  // radius    [0.5 - 10.0] blur radius in pixels\nuniform float u_float2;  // threshold [0.0 - 0.1]  min difference to sharpen\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nfloat gaussian(float x, float sigma) {\n    return exp(-(x * x) / (2.0 * sigma * sigma));\n}\n\nfloat getLuminance(vec3 color) {\n    return dot(color, vec3(0.2126, 0.7152, 0.0722));\n}\n\nvoid main() {\n    vec2 texel = 1.0 / u_resolution;\n    float radius = max(u_float1, 0.5);\n    float amount = u_float0;\n    float threshold = u_float2;\n\n    vec4 original = texture(u_image0, v_texCoord);\n\n    // Gaussian blur for the \"unsharp\" mask\n    int samples = int(ceil(radius));\n    float sigma = radius / 2.0;\n\n    vec4 blurred = vec4(0.0);\n    float totalWeight = 0.0;\n\n    for (int x = -samples; x <= samples; x++) {\n        for (int y = -samples; y <= samples; y++) {\n            vec2 offset = vec2(float(x), float(y)) * texel;\n            vec4 sample_color = texture(u_image0, v_texCoord + offset);\n\n            float dist = length(vec2(float(x), float(y)));\n            float weight = gaussian(dist, sigma);\n            blurred += sample_color * weight;\n            totalWeight += weight;\n        }\n    }\n    blurred /= totalWeight;\n\n    // Unsharp mask = original - blurred\n    vec3 mask = original.rgb - blurred.rgb;\n\n    // Luminance-based threshold with smooth falloff\n    float lumaDelta = abs(getLuminance(original.rgb) - getLuminance(blurred.rgb));\n    float thresholdScale = smoothstep(0.0, threshold, lumaDelta);\n    mask *= thresholdScale;\n\n    // Sharpen: original + mask * amount\n    vec3 sharpened = original.rgb + mask * amount;\n\n    fragColor0 = vec4(clamp(sharpened, 0.0, 1.0), original.a);\n}\n", "from_input"]}], "groups": [], "links": [{"id": 41, "origin_id": 27, "origin_slot": 0, "target_id": 26, "target_slot": 2, "type": "FLOAT"}, {"id": 42, "origin_id": 28, "origin_slot": 0, "target_id": 26, "target_slot": 3, "type": "FLOAT"}, {"id": 43, "origin_id": 29, "origin_slot": 0, "target_id": 26, "target_slot": 4, "type": "FLOAT"}, {"id": 39, "origin_id": -10, "origin_slot": 0, "target_id": 26, "target_slot": 0, "type": "IMAGE"}, {"id": 40, "origin_id": 26, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}}]}}
\ No newline at end of file
diff --git a/comfy_extras/nodes_glsl.py b/comfy_extras/nodes_glsl.py
new file mode 100644
index 000000000..18a35d846
--- /dev/null
+++ b/comfy_extras/nodes_glsl.py
@@ -0,0 +1,895 @@
+import os
+import sys
+import re
+import logging
+import ctypes.util
+import importlib.util
+from typing import TypedDict
+
+import numpy as np
+import torch
+
+import nodes
+from comfy_api.latest import ComfyExtension, io, ui
+from typing_extensions import override
+from utils.install_util import get_missing_requirements_message
+
+logger = logging.getLogger(__name__)
+
+
+def _check_opengl_availability():
+    """Early check for OpenGL availability. Raises RuntimeError if unlikely to work."""
+    logger.debug("_check_opengl_availability: starting")
+    missing = []
+
+    # Check Python packages (using find_spec to avoid importing)
+    logger.debug("_check_opengl_availability: checking for glfw package")
+    if importlib.util.find_spec("glfw") is None:
+        missing.append("glfw")
+
+    logger.debug("_check_opengl_availability: checking for OpenGL package")
+    if importlib.util.find_spec("OpenGL") is None:
+        missing.append("PyOpenGL")
+
+    if missing:
+        raise RuntimeError(
+            f"OpenGL dependencies not available.\n{get_missing_requirements_message()}\n"
+        )
+
+    # On Linux without display, check if headless backends are available
+    logger.debug(f"_check_opengl_availability: platform={sys.platform}")
+    if sys.platform.startswith("linux"):
+        has_display = os.environ.get("DISPLAY") or os.environ.get("WAYLAND_DISPLAY")
+        logger.debug(f"_check_opengl_availability: has_display={bool(has_display)}")
+        if not has_display:
+            # Check for EGL or OSMesa libraries
+            logger.debug("_check_opengl_availability: checking for EGL library")
+            has_egl = ctypes.util.find_library("EGL")
+            logger.debug("_check_opengl_availability: checking for OSMesa library")
+            has_osmesa = ctypes.util.find_library("OSMesa")
+
+            # Error disabled for CI as it fails this check
+            # if not has_egl and not has_osmesa:
+            #     raise RuntimeError(
+            #         "GLSL Shader node: No display and no headless backend (EGL/OSMesa) found.\n"
+            #         "See error below for installation instructions."
+            #     )
+            logger.debug(f"Headless mode: EGL={'yes' if has_egl else 'no'}, OSMesa={'yes' if has_osmesa else 'no'}")
+
+    logger.debug("_check_opengl_availability: completed")
+
+
+# Run early check at import time
+logger.debug("nodes_glsl: running _check_opengl_availability at import time")
+_check_opengl_availability()
+
+# OpenGL modules - initialized lazily when context is created
+gl = None
+glfw = None
+EGL = None
+
+
+def _import_opengl():
+    """Import OpenGL module. Called after context is created."""
+    global gl
+    if gl is None:
+        logger.debug("_import_opengl: importing OpenGL.GL")
+        import OpenGL.GL as _gl
+        gl = _gl
+        logger.debug("_import_opengl: import completed")
+    return gl
+
+
+class SizeModeInput(TypedDict):
+    size_mode: str
+    width: int
+    height: int
+
+
+MAX_IMAGES = 5      # u_image0-4
+MAX_UNIFORMS = 5    # u_float0-4, u_int0-4
+MAX_OUTPUTS = 4     # fragColor0-3 (MRT)
+
+# Vertex shader using gl_VertexID trick - no VBO needed.
+# Draws a single triangle that covers the entire screen:
+#
+#     (-1,3)
+#       /|
+#      / |  <- visible area is the unit square from (-1,-1) to (1,1)
+#     /  |     parts outside get clipped away
+# (-1,-1)---(3,-1)
+#
+# v_texCoord is computed from clip space: * 0.5 + 0.5 maps (-1,1) -> (0,1)
+VERTEX_SHADER = """#version 330 core
+out vec2 v_texCoord;
+void main() {
+    vec2 verts[3] = vec2[](vec2(-1, -1), vec2(3, -1), vec2(-1, 3));
+    v_texCoord = verts[gl_VertexID] * 0.5 + 0.5;
+    gl_Position = vec4(verts[gl_VertexID], 0, 1);
+}
+"""
+
+DEFAULT_FRAGMENT_SHADER = """#version 300 es
+precision highp float;
+
+uniform sampler2D u_image0;
+uniform vec2 u_resolution;
+
+in vec2 v_texCoord;
+layout(location = 0) out vec4 fragColor0;
+
+void main() {
+    fragColor0 = texture(u_image0, v_texCoord);
+}
+"""
+
+
+def _convert_es_to_desktop(source: str) -> str:
+    """Convert GLSL ES (WebGL) shader source to desktop GLSL 330 core."""
+    # Remove any existing #version directive
+    source = re.sub(r"#version\s+\d+(\s+es)?\s*\n?", "", source, flags=re.IGNORECASE)
+    # Remove precision qualifiers (not needed in desktop GLSL)
+    source = re.sub(r"precision\s+(lowp|mediump|highp)\s+\w+\s*;\s*\n?", "", source)
+    # Prepend desktop GLSL version
+    return "#version 330 core\n" + source
+
+
+def _detect_output_count(source: str) -> int:
+    """Detect how many fragColor outputs are used in the shader.
+
+    Returns the count of outputs needed (1 to MAX_OUTPUTS).
+    """
+    matches = re.findall(r"fragColor(\d+)", source)
+    if not matches:
+        return 1  # Default to 1 output if none found
+    max_index = max(int(m) for m in matches)
+    return min(max_index + 1, MAX_OUTPUTS)
+
+
+def _detect_pass_count(source: str) -> int:
+    """Detect multi-pass rendering from #pragma passes N directive.
+
+    Returns the number of passes (1 if not specified).
+    """
+    match = re.search(r'#pragma\s+passes\s+(\d+)', source)
+    if match:
+        return max(1, int(match.group(1)))
+    return 1
+
+
+def _init_glfw():
+    """Initialize GLFW. Returns (window, glfw_module). Raises RuntimeError on failure."""
+    logger.debug("_init_glfw: starting")
+    # On macOS, glfw.init() must be called from main thread or it hangs forever
+    if sys.platform == "darwin":
+        logger.debug("_init_glfw: skipping on macOS")
+        raise RuntimeError("GLFW backend not supported on macOS")
+
+    logger.debug("_init_glfw: importing glfw module")
+    import glfw as _glfw
+
+    logger.debug("_init_glfw: calling glfw.init()")
+    if not _glfw.init():
+        raise RuntimeError("glfw.init() failed")
+
+    try:
+        logger.debug("_init_glfw: setting window hints")
+        _glfw.window_hint(_glfw.VISIBLE, _glfw.FALSE)
+        _glfw.window_hint(_glfw.CONTEXT_VERSION_MAJOR, 3)
+        _glfw.window_hint(_glfw.CONTEXT_VERSION_MINOR, 3)
+        _glfw.window_hint(_glfw.OPENGL_PROFILE, _glfw.OPENGL_CORE_PROFILE)
+
+        logger.debug("_init_glfw: calling create_window()")
+        window = _glfw.create_window(64, 64, "ComfyUI GLSL", None, None)
+        if not window:
+            raise RuntimeError("glfw.create_window() failed")
+
+        logger.debug("_init_glfw: calling make_context_current()")
+        _glfw.make_context_current(window)
+        logger.debug("_init_glfw: completed successfully")
+        return window, _glfw
+    except Exception:
+        logger.debug("_init_glfw: failed, terminating glfw")
+        _glfw.terminate()
+        raise
+
+
+def _init_egl():
+    """Initialize EGL for headless rendering. Returns (display, context, surface, EGL_module). Raises RuntimeError on failure."""
+    logger.debug("_init_egl: starting")
+    from OpenGL import EGL as _EGL
+    from OpenGL.EGL import (
+        eglGetDisplay, eglInitialize, eglChooseConfig, eglCreateContext,
+        eglMakeCurrent, eglCreatePbufferSurface, eglBindAPI,
+        eglTerminate, eglDestroyContext, eglDestroySurface,
+        EGL_DEFAULT_DISPLAY, EGL_NO_CONTEXT, EGL_NONE,
+        EGL_SURFACE_TYPE, EGL_PBUFFER_BIT, EGL_RENDERABLE_TYPE, EGL_OPENGL_BIT,
+        EGL_RED_SIZE, EGL_GREEN_SIZE, EGL_BLUE_SIZE, EGL_ALPHA_SIZE, EGL_DEPTH_SIZE,
+        EGL_WIDTH, EGL_HEIGHT, EGL_OPENGL_API,
+    )
+    logger.debug("_init_egl: imports completed")
+
+    display = None
+    context = None
+    surface = None
+
+    try:
+        logger.debug("_init_egl: calling eglGetDisplay()")
+        display = eglGetDisplay(EGL_DEFAULT_DISPLAY)
+        if display == _EGL.EGL_NO_DISPLAY:
+            raise RuntimeError("eglGetDisplay() failed")
+
+        logger.debug("_init_egl: calling eglInitialize()")
+        major, minor = _EGL.EGLint(), _EGL.EGLint()
+        if not eglInitialize(display, major, minor):
+            display = None  # Not initialized, don't terminate
+            raise RuntimeError("eglInitialize() failed")
+        logger.debug(f"_init_egl: EGL version {major.value}.{minor.value}")
+
+        config_attribs = [
+            EGL_SURFACE_TYPE, EGL_PBUFFER_BIT,
+            EGL_RENDERABLE_TYPE, EGL_OPENGL_BIT,
+            EGL_RED_SIZE, 8, EGL_GREEN_SIZE, 8, EGL_BLUE_SIZE, 8, EGL_ALPHA_SIZE, 8,
+            EGL_DEPTH_SIZE, 0, EGL_NONE
+        ]
+        configs = (_EGL.EGLConfig * 1)()
+        num_configs = _EGL.EGLint()
+        if not eglChooseConfig(display, config_attribs, configs, 1, num_configs) or num_configs.value == 0:
+            raise RuntimeError("eglChooseConfig() failed")
+        config = configs[0]
+        logger.debug(f"_init_egl: config chosen, num_configs={num_configs.value}")
+
+        if not eglBindAPI(EGL_OPENGL_API):
+            raise RuntimeError("eglBindAPI() failed")
+
+        logger.debug("_init_egl: calling eglCreateContext()")
+        context_attribs = [
+            _EGL.EGL_CONTEXT_MAJOR_VERSION, 3,
+            _EGL.EGL_CONTEXT_MINOR_VERSION, 3,
+            _EGL.EGL_CONTEXT_OPENGL_PROFILE_MASK, _EGL.EGL_CONTEXT_OPENGL_CORE_PROFILE_BIT,
+            EGL_NONE
+        ]
+        context = eglCreateContext(display, config, EGL_NO_CONTEXT, context_attribs)
+        if context == EGL_NO_CONTEXT:
+            raise RuntimeError("eglCreateContext() failed")
+
+        logger.debug("_init_egl: calling eglCreatePbufferSurface()")
+        pbuffer_attribs = [EGL_WIDTH, 64, EGL_HEIGHT, 64, EGL_NONE]
+        surface = eglCreatePbufferSurface(display, config, pbuffer_attribs)
+        if surface == _EGL.EGL_NO_SURFACE:
+            raise RuntimeError("eglCreatePbufferSurface() failed")
+
+        logger.debug("_init_egl: calling eglMakeCurrent()")
+        if not eglMakeCurrent(display, surface, surface, context):
+            raise RuntimeError("eglMakeCurrent() failed")
+
+        logger.debug("_init_egl: completed successfully")
+        return display, context, surface, _EGL
+
+    except Exception:
+        logger.debug("_init_egl: failed, cleaning up")
+        # Clean up any resources on failure
+        if surface is not None:
+            eglDestroySurface(display, surface)
+        if context is not None:
+            eglDestroyContext(display, context)
+        if display is not None:
+            eglTerminate(display)
+        raise
+
+
+def _init_osmesa():
+    """Initialize OSMesa for software rendering. Returns (context, buffer). Raises RuntimeError on failure."""
+    import ctypes
+
+    logger.debug("_init_osmesa: starting")
+    os.environ["PYOPENGL_PLATFORM"] = "osmesa"
+
+    logger.debug("_init_osmesa: importing OpenGL.osmesa")
+    from OpenGL import GL as _gl
+    from OpenGL.osmesa import (
+        OSMesaCreateContextExt, OSMesaMakeCurrent, OSMesaDestroyContext,
+        OSMESA_RGBA,
+    )
+    logger.debug("_init_osmesa: imports completed")
+
+    ctx = OSMesaCreateContextExt(OSMESA_RGBA, 24, 0, 0, None)
+    if not ctx:
+        raise RuntimeError("OSMesaCreateContextExt() failed")
+
+    width, height = 64, 64
+    buffer = (ctypes.c_ubyte * (width * height * 4))()
+
+    logger.debug("_init_osmesa: calling OSMesaMakeCurrent()")
+    if not OSMesaMakeCurrent(ctx, buffer, _gl.GL_UNSIGNED_BYTE, width, height):
+        OSMesaDestroyContext(ctx)
+        raise RuntimeError("OSMesaMakeCurrent() failed")
+
+    logger.debug("_init_osmesa: completed successfully")
+    return ctx, buffer
+
+
+class GLContext:
+    """Manages OpenGL context and resources for shader execution.
+
+    Tries backends in order: GLFW (desktop) → EGL (headless GPU) → OSMesa (software).
+    """
+
+    _instance = None
+    _initialized = False
+
+    def __new__(cls):
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+        return cls._instance
+
+    def __init__(self):
+        if GLContext._initialized:
+            logger.debug("GLContext.__init__: already initialized, skipping")
+            return
+
+        logger.debug("GLContext.__init__: starting initialization")
+
+        global glfw, EGL
+
+        import time
+        start = time.perf_counter()
+
+        self._backend = None
+        self._window = None
+        self._egl_display = None
+        self._egl_context = None
+        self._egl_surface = None
+        self._osmesa_ctx = None
+        self._osmesa_buffer = None
+        self._vao = None
+
+        # Try backends in order: GLFW → EGL → OSMesa
+        errors = []
+
+        logger.debug("GLContext.__init__: trying GLFW backend")
+        try:
+            self._window, glfw = _init_glfw()
+            self._backend = "glfw"
+            logger.debug("GLContext.__init__: GLFW backend succeeded")
+        except Exception as e:
+            logger.debug(f"GLContext.__init__: GLFW backend failed: {e}")
+            errors.append(("GLFW", e))
+
+        if self._backend is None:
+            logger.debug("GLContext.__init__: trying EGL backend")
+            try:
+                self._egl_display, self._egl_context, self._egl_surface, EGL = _init_egl()
+                self._backend = "egl"
+                logger.debug("GLContext.__init__: EGL backend succeeded")
+            except Exception as e:
+                logger.debug(f"GLContext.__init__: EGL backend failed: {e}")
+                errors.append(("EGL", e))
+
+        if self._backend is None:
+            logger.debug("GLContext.__init__: trying OSMesa backend")
+            try:
+                self._osmesa_ctx, self._osmesa_buffer = _init_osmesa()
+                self._backend = "osmesa"
+                logger.debug("GLContext.__init__: OSMesa backend succeeded")
+            except Exception as e:
+                logger.debug(f"GLContext.__init__: OSMesa backend failed: {e}")
+                errors.append(("OSMesa", e))
+
+        if self._backend is None:
+            if sys.platform == "win32":
+                platform_help = (
+                    "Windows: Ensure GPU drivers are installed and display is available.\n"
+                    "         CPU-only/headless mode is not supported on Windows."
+                )
+            elif sys.platform == "darwin":
+                platform_help = (
+                    "macOS: GLFW is not supported.\n"
+                    "  Install OSMesa via Homebrew: brew install mesa\n"
+                    "  Then: pip install PyOpenGL PyOpenGL-accelerate"
+                )
+            else:
+                platform_help = (
+                    "Linux: Install one of these backends:\n"
+                    "  Desktop:           sudo apt install libgl1-mesa-glx libglfw3\n"
+                    "  Headless with GPU: sudo apt install libegl1-mesa libgl1-mesa-dri\n"
+                    "  Headless (CPU):    sudo apt install libosmesa6"
+                )
+
+            error_details = "\n".join(f"  {name}: {err}" for name, err in errors)
+            raise RuntimeError(
+                f"Failed to create OpenGL context.\n\n"
+                f"Backend errors:\n{error_details}\n\n"
+                f"{platform_help}"
+            )
+
+        # Now import OpenGL.GL (after context is current)
+        logger.debug("GLContext.__init__: importing OpenGL.GL")
+        _import_opengl()
+
+        # Create VAO (required for core profile, but OSMesa may use compat profile)
+        logger.debug("GLContext.__init__: creating VAO")
+        try:
+            vao = gl.glGenVertexArrays(1)
+            gl.glBindVertexArray(vao)
+            self._vao = vao  # Only store after successful bind
+            logger.debug("GLContext.__init__: VAO created successfully")
+        except Exception as e:
+            logger.debug(f"GLContext.__init__: VAO creation failed (may be expected for OSMesa): {e}")
+            # OSMesa with older Mesa may not support VAOs
+            # Clean up if we created but couldn't bind
+            if vao:
+                try:
+                    gl.glDeleteVertexArrays(1, [vao])
+                except Exception:
+                    pass
+
+        elapsed = (time.perf_counter() - start) * 1000
+
+        # Log device info
+        renderer = gl.glGetString(gl.GL_RENDERER)
+        vendor = gl.glGetString(gl.GL_VENDOR)
+        version = gl.glGetString(gl.GL_VERSION)
+        renderer = renderer.decode() if renderer else "Unknown"
+        vendor = vendor.decode() if vendor else "Unknown"
+        version = version.decode() if version else "Unknown"
+
+        GLContext._initialized = True
+        logger.info(f"GLSL context initialized in {elapsed:.1f}ms ({self._backend}) - {renderer} ({vendor}), GL {version}")
+
+    def make_current(self):
+        if self._backend == "glfw":
+            glfw.make_context_current(self._window)
+        elif self._backend == "egl":
+            from OpenGL.EGL import eglMakeCurrent
+            eglMakeCurrent(self._egl_display, self._egl_surface, self._egl_surface, self._egl_context)
+        elif self._backend == "osmesa":
+            from OpenGL.osmesa import OSMesaMakeCurrent
+            OSMesaMakeCurrent(self._osmesa_ctx, self._osmesa_buffer, gl.GL_UNSIGNED_BYTE, 64, 64)
+
+        if self._vao is not None:
+            gl.glBindVertexArray(self._vao)
+
+
+def _compile_shader(source: str, shader_type: int) -> int:
+    """Compile a shader and return its ID."""
+    shader = gl.glCreateShader(shader_type)
+    gl.glShaderSource(shader, source)
+    gl.glCompileShader(shader)
+
+    if gl.glGetShaderiv(shader, gl.GL_COMPILE_STATUS) != gl.GL_TRUE:
+        error = gl.glGetShaderInfoLog(shader).decode()
+        gl.glDeleteShader(shader)
+        raise RuntimeError(f"Shader compilation failed:\n{error}")
+
+    return shader
+
+
+def _create_program(vertex_source: str, fragment_source: str) -> int:
+    """Create and link a shader program."""
+    vertex_shader = _compile_shader(vertex_source, gl.GL_VERTEX_SHADER)
+    try:
+        fragment_shader = _compile_shader(fragment_source, gl.GL_FRAGMENT_SHADER)
+    except RuntimeError:
+        gl.glDeleteShader(vertex_shader)
+        raise
+
+    program = gl.glCreateProgram()
+    gl.glAttachShader(program, vertex_shader)
+    gl.glAttachShader(program, fragment_shader)
+    gl.glLinkProgram(program)
+
+    gl.glDeleteShader(vertex_shader)
+    gl.glDeleteShader(fragment_shader)
+
+    if gl.glGetProgramiv(program, gl.GL_LINK_STATUS) != gl.GL_TRUE:
+        error = gl.glGetProgramInfoLog(program).decode()
+        gl.glDeleteProgram(program)
+        raise RuntimeError(f"Program linking failed:\n{error}")
+
+    return program
+
+
+def _render_shader_batch(
+    fragment_code: str,
+    width: int,
+    height: int,
+    image_batches: list[list[np.ndarray]],
+    floats: list[float],
+    ints: list[int],
+) -> list[list[np.ndarray]]:
+    """
+    Render a fragment shader for multiple batches efficiently.
+
+    Compiles shader once, reuses framebuffer/textures across batches.
+    Supports multi-pass rendering via #pragma passes N directive.
+
+    Args:
+        fragment_code: User's fragment shader code
+        width: Output width
+        height: Output height
+        image_batches: List of batches, each batch is a list of input images (H, W, C) float32 [0,1]
+        floats: List of float uniforms
+        ints: List of int uniforms
+
+    Returns:
+        List of batch outputs, each is a list of output images (H, W, 4) float32 [0,1]
+    """
+    import time
+    start_time = time.perf_counter()
+
+    if not image_batches:
+        return []
+
+    ctx = GLContext()
+    ctx.make_current()
+
+    # Convert from GLSL ES to desktop GLSL 330
+    fragment_source = _convert_es_to_desktop(fragment_code)
+
+    # Detect how many outputs the shader actually uses
+    num_outputs = _detect_output_count(fragment_code)
+
+    # Detect multi-pass rendering
+    num_passes = _detect_pass_count(fragment_code)
+
+    # Track resources for cleanup
+    program = None
+    fbo = None
+    output_textures = []
+    input_textures = []
+    ping_pong_textures = []
+    ping_pong_fbos = []
+
+    num_inputs = len(image_batches[0])
+
+    try:
+        # Compile shaders (once for all batches)
+        try:
+            program = _create_program(VERTEX_SHADER, fragment_source)
+        except RuntimeError:
+            logger.error(f"Fragment shader:\n{fragment_source}")
+            raise
+
+        gl.glUseProgram(program)
+
+        # Create framebuffer with only the needed color attachments
+        fbo = gl.glGenFramebuffers(1)
+        gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, fbo)
+
+        draw_buffers = []
+        for i in range(num_outputs):
+            tex = gl.glGenTextures(1)
+            output_textures.append(tex)
+            gl.glBindTexture(gl.GL_TEXTURE_2D, tex)
+            gl.glTexImage2D(gl.GL_TEXTURE_2D, 0, gl.GL_RGBA32F, width, height, 0, gl.GL_RGBA, gl.GL_FLOAT, None)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, gl.GL_LINEAR)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, gl.GL_LINEAR)
+            gl.glFramebufferTexture2D(gl.GL_FRAMEBUFFER, gl.GL_COLOR_ATTACHMENT0 + i, gl.GL_TEXTURE_2D, tex, 0)
+            draw_buffers.append(gl.GL_COLOR_ATTACHMENT0 + i)
+
+        gl.glDrawBuffers(num_outputs, draw_buffers)
+
+        if gl.glCheckFramebufferStatus(gl.GL_FRAMEBUFFER) != gl.GL_FRAMEBUFFER_COMPLETE:
+            raise RuntimeError("Framebuffer is not complete")
+
+        # Create ping-pong resources for multi-pass rendering
+        if num_passes > 1:
+            for _ in range(2):
+                pp_tex = gl.glGenTextures(1)
+                ping_pong_textures.append(pp_tex)
+                gl.glBindTexture(gl.GL_TEXTURE_2D, pp_tex)
+                gl.glTexImage2D(gl.GL_TEXTURE_2D, 0, gl.GL_RGBA32F, width, height, 0, gl.GL_RGBA, gl.GL_FLOAT, None)
+                gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, gl.GL_LINEAR)
+                gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, gl.GL_LINEAR)
+                gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_S, gl.GL_CLAMP_TO_EDGE)
+                gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_T, gl.GL_CLAMP_TO_EDGE)
+
+                pp_fbo = gl.glGenFramebuffers(1)
+                ping_pong_fbos.append(pp_fbo)
+                gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, pp_fbo)
+                gl.glFramebufferTexture2D(gl.GL_FRAMEBUFFER, gl.GL_COLOR_ATTACHMENT0, gl.GL_TEXTURE_2D, pp_tex, 0)
+                gl.glDrawBuffers(1, [gl.GL_COLOR_ATTACHMENT0])
+
+                if gl.glCheckFramebufferStatus(gl.GL_FRAMEBUFFER) != gl.GL_FRAMEBUFFER_COMPLETE:
+                    raise RuntimeError("Ping-pong framebuffer is not complete")
+
+        # Create input textures (reused for all batches)
+        for i in range(num_inputs):
+            tex = gl.glGenTextures(1)
+            input_textures.append(tex)
+            gl.glActiveTexture(gl.GL_TEXTURE0 + i)
+            gl.glBindTexture(gl.GL_TEXTURE_2D, tex)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, gl.GL_LINEAR)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, gl.GL_LINEAR)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_S, gl.GL_CLAMP_TO_EDGE)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_T, gl.GL_CLAMP_TO_EDGE)
+
+            loc = gl.glGetUniformLocation(program, f"u_image{i}")
+            if loc >= 0:
+                gl.glUniform1i(loc, i)
+
+        # Set static uniforms (once for all batches)
+        loc = gl.glGetUniformLocation(program, "u_resolution")
+        if loc >= 0:
+            gl.glUniform2f(loc, float(width), float(height))
+
+        for i, v in enumerate(floats):
+            loc = gl.glGetUniformLocation(program, f"u_float{i}")
+            if loc >= 0:
+                gl.glUniform1f(loc, v)
+
+        for i, v in enumerate(ints):
+            loc = gl.glGetUniformLocation(program, f"u_int{i}")
+            if loc >= 0:
+                gl.glUniform1i(loc, v)
+
+        # Get u_pass uniform location for multi-pass
+        pass_loc = gl.glGetUniformLocation(program, "u_pass")
+
+        gl.glViewport(0, 0, width, height)
+        gl.glDisable(gl.GL_BLEND)  # Ensure no alpha blending - write output directly
+
+        # Process each batch
+        all_batch_outputs = []
+        for images in image_batches:
+            # Update input textures with this batch's images
+            for i, img in enumerate(images):
+                gl.glActiveTexture(gl.GL_TEXTURE0 + i)
+                gl.glBindTexture(gl.GL_TEXTURE_2D, input_textures[i])
+
+                # Flip vertically for GL coordinates, ensure RGBA
+                h, w, c = img.shape
+                if c == 3:
+                    img_upload = np.empty((h, w, 4), dtype=np.float32)
+                    img_upload[:, :, :3] = img[::-1, :, :]
+                    img_upload[:, :, 3] = 1.0
+                else:
+                    img_upload = np.ascontiguousarray(img[::-1, :, :])
+
+                gl.glTexImage2D(gl.GL_TEXTURE_2D, 0, gl.GL_RGBA32F, w, h, 0, gl.GL_RGBA, gl.GL_FLOAT, img_upload)
+
+            if num_passes == 1:
+                # Single pass - render directly to output FBO
+                gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, fbo)
+                if pass_loc >= 0:
+                    gl.glUniform1i(pass_loc, 0)
+                gl.glClearColor(0, 0, 0, 0)
+                gl.glClear(gl.GL_COLOR_BUFFER_BIT)
+                gl.glDrawArrays(gl.GL_TRIANGLES, 0, 3)
+            else:
+                # Multi-pass rendering with ping-pong
+                for p in range(num_passes):
+                    is_last_pass = (p == num_passes - 1)
+
+                    # Set pass uniform
+                    if pass_loc >= 0:
+                        gl.glUniform1i(pass_loc, p)
+
+                    if is_last_pass:
+                        # Last pass renders to the main output FBO
+                        gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, fbo)
+                    else:
+                        # Intermediate passes render to ping-pong FBO
+                        target_fbo = ping_pong_fbos[p % 2]
+                        gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, target_fbo)
+
+                    # Set input texture for this pass
+                    gl.glActiveTexture(gl.GL_TEXTURE0)
+                    if p == 0:
+                        # First pass reads from original input
+                        gl.glBindTexture(gl.GL_TEXTURE_2D, input_textures[0])
+                    else:
+                        # Subsequent passes read from previous pass output
+                        source_tex = ping_pong_textures[(p - 1) % 2]
+                        gl.glBindTexture(gl.GL_TEXTURE_2D, source_tex)
+
+                    gl.glClearColor(0, 0, 0, 0)
+                    gl.glClear(gl.GL_COLOR_BUFFER_BIT)
+                    gl.glDrawArrays(gl.GL_TRIANGLES, 0, 3)
+
+            # Read back outputs for this batch
+            # (glGetTexImage is synchronous, implicitly waits for rendering)
+            batch_outputs = []
+            for tex in output_textures:
+                gl.glBindTexture(gl.GL_TEXTURE_2D, tex)
+                data = gl.glGetTexImage(gl.GL_TEXTURE_2D, 0, gl.GL_RGBA, gl.GL_FLOAT)
+                img = np.frombuffer(data, dtype=np.float32).reshape(height, width, 4)
+                batch_outputs.append(img[::-1, :, :].copy())
+
+            # Pad with black images for unused outputs
+            black_img = np.zeros((height, width, 4), dtype=np.float32)
+            for _ in range(num_outputs, MAX_OUTPUTS):
+                batch_outputs.append(black_img)
+
+            all_batch_outputs.append(batch_outputs)
+
+        elapsed = (time.perf_counter() - start_time) * 1000
+        num_batches = len(image_batches)
+        pass_info = f", {num_passes} passes" if num_passes > 1 else ""
+        logger.info(f"GLSL shader executed in {elapsed:.1f}ms ({num_batches} batch{'es' if num_batches != 1 else ''}, {width}x{height}{pass_info})")
+
+        return all_batch_outputs
+
+    finally:
+        # Unbind before deleting
+        gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, 0)
+        gl.glUseProgram(0)
+
+        if input_textures:
+            gl.glDeleteTextures(len(input_textures), input_textures)
+        if output_textures:
+            gl.glDeleteTextures(len(output_textures), output_textures)
+        if ping_pong_textures:
+            gl.glDeleteTextures(len(ping_pong_textures), ping_pong_textures)
+        if fbo is not None:
+            gl.glDeleteFramebuffers(1, [fbo])
+        for pp_fbo in ping_pong_fbos:
+            gl.glDeleteFramebuffers(1, [pp_fbo])
+        if program is not None:
+            gl.glDeleteProgram(program)
+
+class GLSLShader(io.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        image_template = io.Autogrow.TemplatePrefix(
+            io.Image.Input("image"),
+            prefix="image",
+            min=1,
+            max=MAX_IMAGES,
+        )
+
+        float_template = io.Autogrow.TemplatePrefix(
+            io.Float.Input("float", default=0.0),
+            prefix="u_float",
+            min=0,
+            max=MAX_UNIFORMS,
+        )
+
+        int_template = io.Autogrow.TemplatePrefix(
+            io.Int.Input("int", default=0),
+            prefix="u_int",
+            min=0,
+            max=MAX_UNIFORMS,
+        )
+
+        return io.Schema(
+            node_id="GLSLShader",
+            display_name="GLSL Shader",
+            category="image/shader",
+            description=(
+                "Apply GLSL ES fragment shaders to images. "
+                "u_resolution (vec2) is always available."
+            ),
+            inputs=[
+                io.String.Input(
+                    "fragment_shader",
+                    default=DEFAULT_FRAGMENT_SHADER,
+                    multiline=True,
+                    tooltip="GLSL fragment shader source code (GLSL ES 3.00 / WebGL 2.0 compatible)",
+                ),
+                io.DynamicCombo.Input(
+                    "size_mode",
+                    options=[
+                        io.DynamicCombo.Option("from_input", []),
+                        io.DynamicCombo.Option(
+                            "custom",
+                            [
+                                io.Int.Input(
+                                    "width",
+                                    default=512,
+                                    min=1,
+                                    max=nodes.MAX_RESOLUTION,
+                                ),
+                                io.Int.Input(
+                                    "height",
+                                    default=512,
+                                    min=1,
+                                    max=nodes.MAX_RESOLUTION,
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Output size: 'from_input' uses first input image dimensions, 'custom' allows manual size",
+                ),
+                io.Autogrow.Input("images", template=image_template, tooltip=f"Images are available as u_image0-{MAX_IMAGES-1} (sampler2D) in the shader code"),
+                io.Autogrow.Input("floats", template=float_template, tooltip=f"Floats are available as u_float0-{MAX_UNIFORMS-1} in the shader code"),
+                io.Autogrow.Input("ints", template=int_template, tooltip=f"Ints are available as u_int0-{MAX_UNIFORMS-1} in the shader code"),
+            ],
+            outputs=[
+                io.Image.Output(display_name="IMAGE0", tooltip="Available via layout(location = 0) out vec4 fragColor0 in the shader code"),
+                io.Image.Output(display_name="IMAGE1", tooltip="Available via layout(location = 1) out vec4 fragColor1 in the shader code"),
+                io.Image.Output(display_name="IMAGE2", tooltip="Available via layout(location = 2) out vec4 fragColor2 in the shader code"),
+                io.Image.Output(display_name="IMAGE3", tooltip="Available via layout(location = 3) out vec4 fragColor3 in the shader code"),
+            ],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        fragment_shader: str,
+        size_mode: SizeModeInput,
+        images: io.Autogrow.Type,
+        floats: io.Autogrow.Type = None,
+        ints: io.Autogrow.Type = None,
+        **kwargs,
+    ) -> io.NodeOutput:
+        image_list = [v for v in images.values() if v is not None]
+        float_list = (
+            [v if v is not None else 0.0 for v in floats.values()] if floats else []
+        )
+        int_list = [v if v is not None else 0 for v in ints.values()] if ints else []
+
+        if not image_list:
+            raise ValueError("At least one input image is required")
+
+        # Determine output dimensions
+        if size_mode["size_mode"] == "custom":
+            out_width = size_mode["width"]
+            out_height = size_mode["height"]
+        else:
+            out_height, out_width = image_list[0].shape[1:3]
+
+        batch_size = image_list[0].shape[0]
+
+        # Prepare batches
+        image_batches = []
+        for batch_idx in range(batch_size):
+            batch_images = [img_tensor[batch_idx].cpu().numpy().astype(np.float32) for img_tensor in image_list]
+            image_batches.append(batch_images)
+
+        all_batch_outputs = _render_shader_batch(
+            fragment_shader,
+            out_width,
+            out_height,
+            image_batches,
+            float_list,
+            int_list,
+        )
+
+        # Collect outputs into tensors
+        all_outputs = [[] for _ in range(MAX_OUTPUTS)]
+        for batch_outputs in all_batch_outputs:
+            for i, out_img in enumerate(batch_outputs):
+                all_outputs[i].append(torch.from_numpy(out_img))
+
+        output_tensors = [torch.stack(all_outputs[i], dim=0) for i in range(MAX_OUTPUTS)]
+        return io.NodeOutput(
+            *output_tensors,
+            ui=cls._build_ui_output(image_list, output_tensors[0]),
+        )
+
+    @classmethod
+    def _build_ui_output(
+        cls, image_list: list[torch.Tensor], output_batch: torch.Tensor
+    ) -> dict[str, list]:
+        """Build UI output with input and output images for client-side shader execution."""
+        combined_inputs = torch.cat(image_list, dim=0)
+        input_images_ui = ui.ImageSaveHelper.save_images(
+            combined_inputs,
+            filename_prefix="GLSLShader_input",
+            folder_type=io.FolderType.temp,
+            cls=None,
+            compress_level=1,
+        )
+
+        output_images_ui = ui.ImageSaveHelper.save_images(
+            output_batch,
+            filename_prefix="GLSLShader_output",
+            folder_type=io.FolderType.temp,
+            cls=None,
+            compress_level=1,
+        )
+
+        return {"input_images": input_images_ui, "images": output_images_ui}
+
+
+class GLSLExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [GLSLShader]
+
+
+async def comfy_entrypoint() -> GLSLExtension:
+    return GLSLExtension()
diff --git a/nodes.py b/nodes.py
index 160a30928..500d53213 100644
--- a/nodes.py
+++ b/nodes.py
@@ -2442,6 +2442,7 @@ async def init_builtin_extra_nodes():
         "nodes_wanmove.py",
         "nodes_image_compare.py",
         "nodes_zimage.py",
+        "nodes_glsl.py",
         "nodes_lora_debug.py",
         "nodes_textgen.py",
         "nodes_color.py",
diff --git a/requirements.txt b/requirements.txt
index 807fea5e0..3a9bfde46 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -30,3 +30,6 @@ kornia>=0.7.1
 spandrel
 pydantic~=2.0
 pydantic-settings~=2.0
+PyOpenGL
+PyOpenGL-accelerate
+glfw

From 1bb956fb66c7e5296beab3dfcef10af0b690a29b Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Fri, 20 Feb 2026 08:12:28 +0200
Subject: [PATCH 019/194] [API Nodes] add ElevenLabs nodes (#12207)

* feat(api-nodes): add ElevenLabs API nodes

* added price badge for ElevenLabsInstantVoiceClone node

---------

Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
---
 comfy_api_nodes/apis/elevenlabs.py  |  88 +++
 comfy_api_nodes/nodes_elevenlabs.py | 924 ++++++++++++++++++++++++++++
 comfy_api_nodes/util/__init__.py    |   4 +
 3 files changed, 1016 insertions(+)
 create mode 100644 comfy_api_nodes/apis/elevenlabs.py
 create mode 100644 comfy_api_nodes/nodes_elevenlabs.py

diff --git a/comfy_api_nodes/apis/elevenlabs.py b/comfy_api_nodes/apis/elevenlabs.py
new file mode 100644
index 000000000..e58450fdf
--- /dev/null
+++ b/comfy_api_nodes/apis/elevenlabs.py
@@ -0,0 +1,88 @@
+from pydantic import BaseModel, Field
+
+
+class SpeechToTextRequest(BaseModel):
+    model_id: str = Field(...)
+    cloud_storage_url: str = Field(...)
+    language_code: str | None = Field(None, description="ISO-639-1 or ISO-639-3 language code")
+    tag_audio_events: bool | None = Field(None, description="Annotate sounds like (laughter) in transcript")
+    num_speakers: int | None = Field(None, description="Max speakers predicted")
+    timestamps_granularity: str = Field(default="word", description="Timing precision: none, word, or character")
+    diarize: bool | None = Field(None, description="Annotate which speaker is talking")
+    diarization_threshold: float | None = Field(None, description="Speaker separation sensitivity")
+    temperature: float | None = Field(None, description="Randomness control")
+    seed: int = Field(..., description="Seed for deterministic sampling")
+
+
+class SpeechToTextWord(BaseModel):
+    text: str = Field(..., description="The word text")
+    type: str = Field(default="word", description="Type of text element (word, spacing, etc.)")
+    start: float | None = Field(None, description="Start time in seconds (when timestamps enabled)")
+    end: float | None = Field(None, description="End time in seconds (when timestamps enabled)")
+    speaker_id: str | None = Field(None, description="Speaker identifier when diarization is enabled")
+    logprob: float | None = Field(None, description="Log probability of the word")
+
+
+class SpeechToTextResponse(BaseModel):
+    language_code: str = Field(..., description="Detected or specified language code")
+    language_probability: float | None = Field(None, description="Confidence of language detection")
+    text: str = Field(..., description="Full transcript text")
+    words: list[SpeechToTextWord] | None = Field(None, description="Word-level timing information")
+
+
+class TextToSpeechVoiceSettings(BaseModel):
+    stability: float | None = Field(None, description="Voice stability")
+    similarity_boost: float | None = Field(None, description="Similarity boost")
+    style: float | None = Field(None, description="Style exaggeration")
+    use_speaker_boost: bool | None = Field(None, description="Boost similarity to original speaker")
+    speed: float | None = Field(None, description="Speech speed")
+
+
+class TextToSpeechRequest(BaseModel):
+    text: str = Field(..., description="Text to convert to speech")
+    model_id: str = Field(..., description="Model ID for TTS")
+    language_code: str | None = Field(None, description="ISO-639-1 or ISO-639-3 language code")
+    voice_settings: TextToSpeechVoiceSettings | None = Field(None, description="Voice settings")
+    seed: int = Field(..., description="Seed for deterministic sampling")
+    apply_text_normalization: str | None = Field(None, description="Text normalization mode: auto, on, off")
+
+
+class TextToSoundEffectsRequest(BaseModel):
+    text: str = Field(..., description="Text prompt to convert into a sound effect")
+    duration_seconds: float = Field(..., description="Duration of generated sound in seconds")
+    prompt_influence: float = Field(..., description="How closely generation follows the prompt")
+    loop: bool | None = Field(None, description="Whether to create a smoothly looping sound effect")
+
+
+class AddVoiceRequest(BaseModel):
+    name: str = Field(..., description="Name that identifies the voice")
+    remove_background_noise: bool = Field(..., description="Remove background noise from voice samples")
+
+
+class AddVoiceResponse(BaseModel):
+    voice_id: str = Field(..., description="The newly created voice's unique identifier")
+
+
+class SpeechToSpeechRequest(BaseModel):
+    model_id: str = Field(..., description="Model ID for speech-to-speech")
+    voice_settings: str = Field(..., description="JSON string of voice settings")
+    seed: int = Field(..., description="Seed for deterministic sampling")
+    remove_background_noise: bool = Field(..., description="Remove background noise from input audio")
+
+
+class DialogueInput(BaseModel):
+    text: str = Field(..., description="Text content to convert to speech")
+    voice_id: str = Field(..., description="Voice identifier for this dialogue segment")
+
+
+class DialogueSettings(BaseModel):
+    stability: float | None = Field(None, description="Voice stability (0-1)")
+
+
+class TextToDialogueRequest(BaseModel):
+    inputs: list[DialogueInput] = Field(..., description="List of dialogue segments")
+    model_id: str = Field(..., description="Model ID for dialogue generation")
+    language_code: str | None = Field(None, description="ISO-639-1 language code")
+    settings: DialogueSettings | None = Field(None, description="Voice settings")
+    seed: int | None = Field(None, description="Seed for deterministic sampling")
+    apply_text_normalization: str | None = Field(None, description="Text normalization mode: auto, on, off")
diff --git a/comfy_api_nodes/nodes_elevenlabs.py b/comfy_api_nodes/nodes_elevenlabs.py
new file mode 100644
index 000000000..e452daf77
--- /dev/null
+++ b/comfy_api_nodes/nodes_elevenlabs.py
@@ -0,0 +1,924 @@
+import json
+import uuid
+
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.elevenlabs import (
+    AddVoiceRequest,
+    AddVoiceResponse,
+    DialogueInput,
+    DialogueSettings,
+    SpeechToSpeechRequest,
+    SpeechToTextRequest,
+    SpeechToTextResponse,
+    TextToDialogueRequest,
+    TextToSoundEffectsRequest,
+    TextToSpeechRequest,
+    TextToSpeechVoiceSettings,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    audio_bytes_to_audio_input,
+    audio_ndarray_to_bytesio,
+    audio_tensor_to_contiguous_ndarray,
+    sync_op,
+    sync_op_raw,
+    upload_audio_to_comfyapi,
+    validate_string,
+)
+
+ELEVENLABS_MUSIC_SECTIONS = "ELEVENLABS_MUSIC_SECTIONS"  # Custom type for music sections
+ELEVENLABS_COMPOSITION_PLAN = "ELEVENLABS_COMPOSITION_PLAN"  # Custom type for composition plan
+ELEVENLABS_VOICE = "ELEVENLABS_VOICE"  # Custom type for voice selection
+
+# Predefined ElevenLabs voices: (voice_id, display_name, gender, accent)
+ELEVENLABS_VOICES = [
+    ("CwhRBWXzGAHq8TQ4Fs17", "Roger", "male", "american"),
+    ("EXAVITQu4vr4xnSDxMaL", "Sarah", "female", "american"),
+    ("FGY2WhTYpPnrIDTdsKH5", "Laura", "female", "american"),
+    ("IKne3meq5aSn9XLyUdCD", "Charlie", "male", "australian"),
+    ("JBFqnCBsd6RMkjVDRZzb", "George", "male", "british"),
+    ("N2lVS1w4EtoT3dr4eOWO", "Callum", "male", "american"),
+    ("SAz9YHcvj6GT2YYXdXww", "River", "neutral", "american"),
+    ("SOYHLrjzK2X1ezoPC6cr", "Harry", "male", "american"),
+    ("TX3LPaxmHKxFdv7VOQHJ", "Liam", "male", "american"),
+    ("Xb7hH8MSUJpSbSDYk0k2", "Alice", "female", "british"),
+    ("XrExE9yKIg1WjnnlVkGX", "Matilda", "female", "american"),
+    ("bIHbv24MWmeRgasZH58o", "Will", "male", "american"),
+    ("cgSgspJ2msm6clMCkdW9", "Jessica", "female", "american"),
+    ("cjVigY5qzO86Huf0OWal", "Eric", "male", "american"),
+    ("hpp4J3VqNfWAUOO0d1Us", "Bella", "female", "american"),
+    ("iP95p4xoKVk53GoZ742B", "Chris", "male", "american"),
+    ("nPczCjzI2devNBz1zQrb", "Brian", "male", "american"),
+    ("onwK4e9ZLuTAKqWW03F9", "Daniel", "male", "british"),
+    ("pFZP5JQG7iQjIQuC4Bku", "Lily", "female", "british"),
+    ("pNInz6obpgDQGcFmaJgB", "Adam", "male", "american"),
+    ("pqHfZKP75CvOlQylNhV4", "Bill", "male", "american"),
+]
+
+ELEVENLABS_VOICE_OPTIONS = [f"{name} ({gender}, {accent})" for _, name, gender, accent in ELEVENLABS_VOICES]
+ELEVENLABS_VOICE_MAP = {
+    f"{name} ({gender}, {accent})": voice_id for voice_id, name, gender, accent in ELEVENLABS_VOICES
+}
+
+
+class ElevenLabsSpeechToText(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsSpeechToText",
+            display_name="ElevenLabs Speech to Text",
+            category="api node/audio/ElevenLabs",
+            description="Transcribe audio to text. "
+            "Supports automatic language detection, speaker diarization, and audio event tagging.",
+            inputs=[
+                IO.Audio.Input(
+                    "audio",
+                    tooltip="Audio to transcribe.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "scribe_v2",
+                            [
+                                IO.Boolean.Input(
+                                    "tag_audio_events",
+                                    default=False,
+                                    tooltip="Annotate sounds like (laughter), (music), etc. in transcript.",
+                                ),
+                                IO.Boolean.Input(
+                                    "diarize",
+                                    default=False,
+                                    tooltip="Annotate which speaker is talking.",
+                                ),
+                                IO.Float.Input(
+                                    "diarization_threshold",
+                                    default=0.22,
+                                    min=0.1,
+                                    max=0.4,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Speaker separation sensitivity. "
+                                    "Lower values are more sensitive to speaker changes.",
+                                ),
+                                IO.Float.Input(
+                                    "temperature",
+                                    default=0.0,
+                                    min=0.0,
+                                    max=2.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Randomness control. "
+                                    "0.0 uses model default. Higher values increase randomness.",
+                                ),
+                                IO.Combo.Input(
+                                    "timestamps_granularity",
+                                    options=["word", "character", "none"],
+                                    default="word",
+                                    tooltip="Timing precision for transcript words.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Model to use for transcription.",
+                ),
+                IO.String.Input(
+                    "language_code",
+                    default="",
+                    tooltip="ISO-639-1 or ISO-639-3 language code (e.g., 'en', 'es', 'fra'). "
+                    "Leave empty for automatic detection.",
+                ),
+                IO.Int.Input(
+                    "num_speakers",
+                    default=0,
+                    min=0,
+                    max=32,
+                    display_mode=IO.NumberDisplay.slider,
+                    tooltip="Maximum number of speakers to predict. Set to 0 for automatic detection.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=0,
+                    max=2147483647,
+                    tooltip="Seed for reproducibility (determinism not guaranteed).",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="text"),
+                IO.String.Output(display_name="language_code"),
+                IO.String.Output(display_name="words_json"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.0073,"format":{"approximate":true,"suffix":"/minute"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        audio: Input.Audio,
+        model: dict,
+        language_code: str,
+        num_speakers: int,
+        seed: int,
+    ) -> IO.NodeOutput:
+        if model["diarize"] and num_speakers:
+            raise ValueError(
+                "Number of speakers cannot be specified when diarization is enabled. "
+                "Either disable diarization or set num_speakers to 0."
+            )
+        request = SpeechToTextRequest(
+            model_id=model["model"],
+            cloud_storage_url=await upload_audio_to_comfyapi(
+                cls, audio, container_format="mp4", codec_name="aac", mime_type="audio/mp4"
+            ),
+            language_code=language_code if language_code.strip() else None,
+            tag_audio_events=model["tag_audio_events"],
+            num_speakers=num_speakers if num_speakers > 0 else None,
+            timestamps_granularity=model["timestamps_granularity"],
+            diarize=model["diarize"],
+            diarization_threshold=model["diarization_threshold"] if model["diarize"] else None,
+            seed=seed,
+            temperature=model["temperature"],
+        )
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/elevenlabs/v1/speech-to-text", method="POST"),
+            response_model=SpeechToTextResponse,
+            data=request,
+            content_type="multipart/form-data",
+        )
+        words_json = json.dumps(
+            [w.model_dump(exclude_none=True) for w in response.words] if response.words else [],
+            indent=2,
+        )
+        return IO.NodeOutput(response.text, response.language_code, words_json)
+
+
+class ElevenLabsVoiceSelector(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsVoiceSelector",
+            display_name="ElevenLabs Voice Selector",
+            category="api node/audio/ElevenLabs",
+            description="Select a predefined ElevenLabs voice for text-to-speech generation.",
+            inputs=[
+                IO.Combo.Input(
+                    "voice",
+                    options=ELEVENLABS_VOICE_OPTIONS,
+                    tooltip="Choose a voice from the predefined ElevenLabs voices.",
+                ),
+            ],
+            outputs=[
+                IO.Custom(ELEVENLABS_VOICE).Output(display_name="voice"),
+            ],
+            is_api_node=False,
+        )
+
+    @classmethod
+    def execute(cls, voice: str) -> IO.NodeOutput:
+        voice_id = ELEVENLABS_VOICE_MAP.get(voice)
+        if not voice_id:
+            raise ValueError(f"Unknown voice: {voice}")
+        return IO.NodeOutput(voice_id)
+
+
+class ElevenLabsTextToSpeech(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsTextToSpeech",
+            display_name="ElevenLabs Text to Speech",
+            category="api node/audio/ElevenLabs",
+            description="Convert text to speech.",
+            inputs=[
+                IO.Custom(ELEVENLABS_VOICE).Input(
+                    "voice",
+                    tooltip="Voice to use for speech synthesis. Connect from Voice Selector or Instant Voice Clone.",
+                ),
+                IO.String.Input(
+                    "text",
+                    multiline=True,
+                    default="",
+                    tooltip="The text to convert to speech.",
+                ),
+                IO.Float.Input(
+                    "stability",
+                    default=0.5,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    display_mode=IO.NumberDisplay.slider,
+                    tooltip="Voice stability. Lower values give broader emotional range, "
+                    "higher values produce more consistent but potentially monotonous speech.",
+                ),
+                IO.Combo.Input(
+                    "apply_text_normalization",
+                    options=["auto", "on", "off"],
+                    tooltip="Text normalization mode. 'auto' lets the system decide, "
+                    "'on' always applies normalization, 'off' skips it.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "eleven_multilingual_v2",
+                            [
+                                IO.Float.Input(
+                                    "speed",
+                                    default=1.0,
+                                    min=0.7,
+                                    max=1.3,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Speech speed. 1.0 is normal, <1.0 slower, >1.0 faster.",
+                                ),
+                                IO.Float.Input(
+                                    "similarity_boost",
+                                    default=0.75,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Similarity boost. Higher values make the voice more similar to the original.",
+                                ),
+                                IO.Boolean.Input(
+                                    "use_speaker_boost",
+                                    default=False,
+                                    tooltip="Boost similarity to the original speaker voice.",
+                                ),
+                                IO.Float.Input(
+                                    "style",
+                                    default=0.0,
+                                    min=0.0,
+                                    max=0.2,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Style exaggeration. Higher values increase stylistic expression "
+                                    "but may reduce stability.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "eleven_v3",
+                            [
+                                IO.Float.Input(
+                                    "speed",
+                                    default=1.0,
+                                    min=0.7,
+                                    max=1.3,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Speech speed. 1.0 is normal, <1.0 slower, >1.0 faster.",
+                                ),
+                                IO.Float.Input(
+                                    "similarity_boost",
+                                    default=0.75,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Similarity boost. Higher values make the voice more similar to the original.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Model to use for text-to-speech.",
+                ),
+                IO.String.Input(
+                    "language_code",
+                    default="",
+                    tooltip="ISO-639-1 or ISO-639-3 language code (e.g., 'en', 'es', 'fra'). "
+                    "Leave empty for automatic detection.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=0,
+                    max=2147483647,
+                    tooltip="Seed for reproducibility (determinism not guaranteed).",
+                ),
+                IO.Combo.Input(
+                    "output_format",
+                    options=["mp3_44100_192", "opus_48000_192"],
+                    tooltip="Audio output format.",
+                ),
+            ],
+            outputs=[
+                IO.Audio.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.24,"format":{"approximate":true,"suffix":"/1K chars"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        voice: str,
+        text: str,
+        stability: float,
+        apply_text_normalization: str,
+        model: dict,
+        language_code: str,
+        seed: int,
+        output_format: str,
+    ) -> IO.NodeOutput:
+        validate_string(text, min_length=1)
+        request = TextToSpeechRequest(
+            text=text,
+            model_id=model["model"],
+            language_code=language_code if language_code.strip() else None,
+            voice_settings=TextToSpeechVoiceSettings(
+                stability=stability,
+                similarity_boost=model["similarity_boost"],
+                speed=model["speed"],
+                use_speaker_boost=model.get("use_speaker_boost", None),
+                style=model.get("style", None),
+            ),
+            seed=seed,
+            apply_text_normalization=apply_text_normalization,
+        )
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(
+                path=f"/proxy/elevenlabs/v1/text-to-speech/{voice}",
+                method="POST",
+                query_params={"output_format": output_format},
+            ),
+            data=request,
+            as_binary=True,
+        )
+        return IO.NodeOutput(audio_bytes_to_audio_input(response))
+
+
+class ElevenLabsAudioIsolation(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsAudioIsolation",
+            display_name="ElevenLabs Voice Isolation",
+            category="api node/audio/ElevenLabs",
+            description="Remove background noise from audio, isolating vocals or speech.",
+            inputs=[
+                IO.Audio.Input(
+                    "audio",
+                    tooltip="Audio to process for background noise removal.",
+                ),
+            ],
+            outputs=[
+                IO.Audio.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.24,"format":{"approximate":true,"suffix":"/minute"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        audio: Input.Audio,
+    ) -> IO.NodeOutput:
+        audio_data_np = audio_tensor_to_contiguous_ndarray(audio["waveform"])
+        audio_bytes_io = audio_ndarray_to_bytesio(audio_data_np, audio["sample_rate"], "mp4", "aac")
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(path="/proxy/elevenlabs/v1/audio-isolation", method="POST"),
+            files={"audio": ("audio.mp4", audio_bytes_io, "audio/mp4")},
+            content_type="multipart/form-data",
+            as_binary=True,
+        )
+        return IO.NodeOutput(audio_bytes_to_audio_input(response))
+
+
+class ElevenLabsTextToSoundEffects(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsTextToSoundEffects",
+            display_name="ElevenLabs Text to Sound Effects",
+            category="api node/audio/ElevenLabs",
+            description="Generate sound effects from text descriptions.",
+            inputs=[
+                IO.String.Input(
+                    "text",
+                    multiline=True,
+                    default="",
+                    tooltip="Text description of the sound effect to generate.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "eleven_sfx_v2",
+                            [
+                                IO.Float.Input(
+                                    "duration",
+                                    default=5.0,
+                                    min=0.5,
+                                    max=30.0,
+                                    step=0.1,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Duration of generated sound in seconds.",
+                                ),
+                                IO.Boolean.Input(
+                                    "loop",
+                                    default=False,
+                                    tooltip="Create a smoothly looping sound effect.",
+                                ),
+                                IO.Float.Input(
+                                    "prompt_influence",
+                                    default=0.3,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="How closely generation follows the prompt. "
+                                    "Higher values make the sound follow the text more closely.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Model to use for sound effect generation.",
+                ),
+                IO.Combo.Input(
+                    "output_format",
+                    options=["mp3_44100_192", "opus_48000_192"],
+                    tooltip="Audio output format.",
+                ),
+            ],
+            outputs=[
+                IO.Audio.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.14,"format":{"approximate":true,"suffix":"/minute"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        text: str,
+        model: dict,
+        output_format: str,
+    ) -> IO.NodeOutput:
+        validate_string(text, min_length=1)
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(
+                path="/proxy/elevenlabs/v1/sound-generation",
+                method="POST",
+                query_params={"output_format": output_format},
+            ),
+            data=TextToSoundEffectsRequest(
+                text=text,
+                duration_seconds=model["duration"],
+                prompt_influence=model["prompt_influence"],
+                loop=model.get("loop", None),
+            ),
+            as_binary=True,
+        )
+        return IO.NodeOutput(audio_bytes_to_audio_input(response))
+
+
+class ElevenLabsInstantVoiceClone(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsInstantVoiceClone",
+            display_name="ElevenLabs Instant Voice Clone",
+            category="api node/audio/ElevenLabs",
+            description="Create a cloned voice from audio samples. "
+            "Provide 1-8 audio recordings of the voice to clone.",
+            inputs=[
+                IO.Autogrow.Input(
+                    "files",
+                    template=IO.Autogrow.TemplatePrefix(
+                        IO.Audio.Input("audio"),
+                        prefix="audio",
+                        min=1,
+                        max=8,
+                    ),
+                    tooltip="Audio recordings for voice cloning.",
+                ),
+                IO.Boolean.Input(
+                    "remove_background_noise",
+                    default=False,
+                    tooltip="Remove background noise from voice samples using audio isolation.",
+                ),
+            ],
+            outputs=[
+                IO.Custom(ELEVENLABS_VOICE).Output(display_name="voice"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(expr="""{"type":"usd","usd":0.15}"""),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        files: IO.Autogrow.Type,
+        remove_background_noise: bool,
+    ) -> IO.NodeOutput:
+        file_tuples: list[tuple[str, tuple[str, bytes, str]]] = []
+        for key in files:
+            audio = files[key]
+            sample_rate: int = audio["sample_rate"]
+            waveform = audio["waveform"]
+            audio_data_np = audio_tensor_to_contiguous_ndarray(waveform)
+            audio_bytes_io = audio_ndarray_to_bytesio(audio_data_np, sample_rate, "mp4", "aac")
+            file_tuples.append(("files", (f"{key}.mp4", audio_bytes_io.getvalue(), "audio/mp4")))
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/elevenlabs/v1/voices/add", method="POST"),
+            response_model=AddVoiceResponse,
+            data=AddVoiceRequest(
+                name=str(uuid.uuid4()),
+                remove_background_noise=remove_background_noise,
+            ),
+            files=file_tuples,
+            content_type="multipart/form-data",
+        )
+        return IO.NodeOutput(response.voice_id)
+
+
+ELEVENLABS_STS_VOICE_SETTINGS = [
+    IO.Float.Input(
+        "speed",
+        default=1.0,
+        min=0.7,
+        max=1.3,
+        step=0.01,
+        display_mode=IO.NumberDisplay.slider,
+        tooltip="Speech speed. 1.0 is normal, <1.0 slower, >1.0 faster.",
+    ),
+    IO.Float.Input(
+        "similarity_boost",
+        default=0.75,
+        min=0.0,
+        max=1.0,
+        step=0.01,
+        display_mode=IO.NumberDisplay.slider,
+        tooltip="Similarity boost. Higher values make the voice more similar to the original.",
+    ),
+    IO.Boolean.Input(
+        "use_speaker_boost",
+        default=False,
+        tooltip="Boost similarity to the original speaker voice.",
+    ),
+    IO.Float.Input(
+        "style",
+        default=0.0,
+        min=0.0,
+        max=0.2,
+        step=0.01,
+        display_mode=IO.NumberDisplay.slider,
+        tooltip="Style exaggeration. Higher values increase stylistic expression but may reduce stability.",
+    ),
+]
+
+
+class ElevenLabsSpeechToSpeech(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsSpeechToSpeech",
+            display_name="ElevenLabs Speech to Speech",
+            category="api node/audio/ElevenLabs",
+            description="Transform speech from one voice to another while preserving the original content and emotion.",
+            inputs=[
+                IO.Custom(ELEVENLABS_VOICE).Input(
+                    "voice",
+                    tooltip="Target voice for the transformation. "
+                    "Connect from Voice Selector or Instant Voice Clone.",
+                ),
+                IO.Audio.Input(
+                    "audio",
+                    tooltip="Source audio to transform.",
+                ),
+                IO.Float.Input(
+                    "stability",
+                    default=0.5,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    display_mode=IO.NumberDisplay.slider,
+                    tooltip="Voice stability. Lower values give broader emotional range, "
+                    "higher values produce more consistent but potentially monotonous speech.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "eleven_multilingual_sts_v2",
+                            ELEVENLABS_STS_VOICE_SETTINGS,
+                        ),
+                        IO.DynamicCombo.Option(
+                            "eleven_english_sts_v2",
+                            ELEVENLABS_STS_VOICE_SETTINGS,
+                        ),
+                    ],
+                    tooltip="Model to use for speech-to-speech transformation.",
+                ),
+                IO.Combo.Input(
+                    "output_format",
+                    options=["mp3_44100_192", "opus_48000_192"],
+                    tooltip="Audio output format.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=4294967295,
+                    tooltip="Seed for reproducibility.",
+                ),
+                IO.Boolean.Input(
+                    "remove_background_noise",
+                    default=False,
+                    tooltip="Remove background noise from input audio using audio isolation.",
+                ),
+            ],
+            outputs=[
+                IO.Audio.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.24,"format":{"approximate":true,"suffix":"/minute"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        voice: str,
+        audio: Input.Audio,
+        stability: float,
+        model: dict,
+        output_format: str,
+        seed: int,
+        remove_background_noise: bool,
+    ) -> IO.NodeOutput:
+        audio_data_np = audio_tensor_to_contiguous_ndarray(audio["waveform"])
+        audio_bytes_io = audio_ndarray_to_bytesio(audio_data_np, audio["sample_rate"], "mp4", "aac")
+        voice_settings = TextToSpeechVoiceSettings(
+            stability=stability,
+            similarity_boost=model["similarity_boost"],
+            style=model["style"],
+            use_speaker_boost=model["use_speaker_boost"],
+            speed=model["speed"],
+        )
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(
+                path=f"/proxy/elevenlabs/v1/speech-to-speech/{voice}",
+                method="POST",
+                query_params={"output_format": output_format},
+            ),
+            data=SpeechToSpeechRequest(
+                model_id=model["model"],
+                voice_settings=voice_settings.model_dump_json(exclude_none=True),
+                seed=seed,
+                remove_background_noise=remove_background_noise,
+            ),
+            files={"audio": ("audio.mp4", audio_bytes_io.getvalue(), "audio/mp4")},
+            content_type="multipart/form-data",
+            as_binary=True,
+        )
+        return IO.NodeOutput(audio_bytes_to_audio_input(response))
+
+
+def _generate_dialogue_inputs(count: int) -> list:
+    """Generate input widgets for a given number of dialogue entries."""
+    inputs = []
+    for i in range(1, count + 1):
+        inputs.extend(
+            [
+                IO.String.Input(
+                    f"text{i}",
+                    multiline=True,
+                    default="",
+                    tooltip=f"Text content for dialogue entry {i}.",
+                ),
+                IO.Custom(ELEVENLABS_VOICE).Input(
+                    f"voice{i}",
+                    tooltip=f"Voice for dialogue entry {i}. Connect from Voice Selector or Instant Voice Clone.",
+                ),
+            ]
+        )
+    return inputs
+
+
+class ElevenLabsTextToDialogue(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsTextToDialogue",
+            display_name="ElevenLabs Text to Dialogue",
+            category="api node/audio/ElevenLabs",
+            description="Generate multi-speaker dialogue from text. Each dialogue entry has its own text and voice.",
+            inputs=[
+                IO.Float.Input(
+                    "stability",
+                    default=0.5,
+                    min=0.0,
+                    max=1.0,
+                    step=0.5,
+                    display_mode=IO.NumberDisplay.slider,
+                    tooltip="Voice stability. Lower values give broader emotional range, "
+                    "higher values produce more consistent but potentially monotonous speech.",
+                ),
+                IO.Combo.Input(
+                    "apply_text_normalization",
+                    options=["auto", "on", "off"],
+                    tooltip="Text normalization mode. 'auto' lets the system decide, "
+                    "'on' always applies normalization, 'off' skips it.",
+                ),
+                IO.Combo.Input(
+                    "model",
+                    options=["eleven_v3"],
+                    tooltip="Model to use for dialogue generation.",
+                ),
+                IO.DynamicCombo.Input(
+                    "inputs",
+                    options=[
+                        IO.DynamicCombo.Option("1", _generate_dialogue_inputs(1)),
+                        IO.DynamicCombo.Option("2", _generate_dialogue_inputs(2)),
+                        IO.DynamicCombo.Option("3", _generate_dialogue_inputs(3)),
+                        IO.DynamicCombo.Option("4", _generate_dialogue_inputs(4)),
+                        IO.DynamicCombo.Option("5", _generate_dialogue_inputs(5)),
+                        IO.DynamicCombo.Option("6", _generate_dialogue_inputs(6)),
+                        IO.DynamicCombo.Option("7", _generate_dialogue_inputs(7)),
+                        IO.DynamicCombo.Option("8", _generate_dialogue_inputs(8)),
+                        IO.DynamicCombo.Option("9", _generate_dialogue_inputs(9)),
+                        IO.DynamicCombo.Option("10", _generate_dialogue_inputs(10)),
+                    ],
+                    tooltip="Number of dialogue entries.",
+                ),
+                IO.String.Input(
+                    "language_code",
+                    default="",
+                    tooltip="ISO-639-1 or ISO-639-3 language code (e.g., 'en', 'es', 'fra'). "
+                    "Leave empty for automatic detection.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=0,
+                    max=4294967295,
+                    tooltip="Seed for reproducibility.",
+                ),
+                IO.Combo.Input(
+                    "output_format",
+                    options=["mp3_44100_192", "opus_48000_192"],
+                    tooltip="Audio output format.",
+                ),
+            ],
+            outputs=[
+                IO.Audio.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.24,"format":{"approximate":true,"suffix":"/1K chars"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        stability: float,
+        apply_text_normalization: str,
+        model: str,
+        inputs: dict,
+        language_code: str,
+        seed: int,
+        output_format: str,
+    ) -> IO.NodeOutput:
+        num_entries = int(inputs["inputs"])
+        dialogue_inputs: list[DialogueInput] = []
+        for i in range(1, num_entries + 1):
+            text = inputs[f"text{i}"]
+            voice_id = inputs[f"voice{i}"]
+            validate_string(text, min_length=1)
+            dialogue_inputs.append(DialogueInput(text=text, voice_id=voice_id))
+        request = TextToDialogueRequest(
+            inputs=dialogue_inputs,
+            model_id=model,
+            language_code=language_code if language_code.strip() else None,
+            settings=DialogueSettings(stability=stability),
+            seed=seed,
+            apply_text_normalization=apply_text_normalization,
+        )
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(
+                path="/proxy/elevenlabs/v1/text-to-dialogue",
+                method="POST",
+                query_params={"output_format": output_format},
+            ),
+            data=request,
+            as_binary=True,
+        )
+        return IO.NodeOutput(audio_bytes_to_audio_input(response))
+
+
+class ElevenLabsExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            ElevenLabsSpeechToText,
+            ElevenLabsVoiceSelector,
+            ElevenLabsTextToSpeech,
+            ElevenLabsAudioIsolation,
+            ElevenLabsTextToSoundEffects,
+            ElevenLabsInstantVoiceClone,
+            ElevenLabsSpeechToSpeech,
+            ElevenLabsTextToDialogue,
+        ]
+
+
+async def comfy_entrypoint() -> ElevenLabsExtension:
+    return ElevenLabsExtension()
diff --git a/comfy_api_nodes/util/__init__.py b/comfy_api_nodes/util/__init__.py
index f8a0ba8af..0cb9a47c7 100644
--- a/comfy_api_nodes/util/__init__.py
+++ b/comfy_api_nodes/util/__init__.py
@@ -9,6 +9,8 @@ from .client import (
 from .conversions import (
     audio_bytes_to_audio_input,
     audio_input_to_mp3,
+    audio_ndarray_to_bytesio,
+    audio_tensor_to_contiguous_ndarray,
     audio_to_base64_string,
     bytesio_to_image_tensor,
     convert_mask_to_image,
@@ -78,6 +80,8 @@ __all__ = [
     # Conversions
     "audio_bytes_to_audio_input",
     "audio_input_to_mp3",
+    "audio_ndarray_to_bytesio",
+    "audio_tensor_to_contiguous_ndarray",
     "audio_to_base64_string",
     "bytesio_to_image_tensor",
     "convert_mask_to_image",

From b254cecd032e872766965415d120973811e9e360 Mon Sep 17 00:00:00 2001
From: Christian Byrne <cbyrne@comfy.org>
Date: Fri, 20 Feb 2026 00:25:54 -0800
Subject: [PATCH 020/194] chore: add CodeRabbit configuration for automated
 code review (#12539)

Amp-Thread-ID: https://ampcode.com/threads/T-019c7915-2abf-743c-9c74-b93d87d63055

Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
---
 .coderabbit.yaml | 114 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 114 insertions(+)
 create mode 100644 .coderabbit.yaml

diff --git a/.coderabbit.yaml b/.coderabbit.yaml
new file mode 100644
index 000000000..3849d4b26
--- /dev/null
+++ b/.coderabbit.yaml
@@ -0,0 +1,114 @@
+# yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json
+language: "en-US"
+early_access: false
+
+reviews:
+  profile: "chill"
+  request_changes_workflow: false
+  high_level_summary: false
+  poem: false
+  review_status: false
+  review_details: false
+  commit_status: true
+  collapse_walkthrough: true
+  changed_files_summary: false
+  sequence_diagrams: false
+  estimate_code_review_effort: false
+  assess_linked_issues: false
+  related_issues: false
+  related_prs: false
+  suggested_labels: false
+  auto_apply_labels: false
+  suggested_reviewers: false
+  auto_assign_reviewers: false
+  in_progress_fortune: false
+  enable_prompt_for_ai_agents: true
+
+  path_filters:
+    - "!comfy_api_nodes/apis/**"
+    - "!**/generated/*.pyi"
+    - "!.ci/**"
+    - "!script_examples/**"
+    - "!**/__pycache__/**"
+    - "!**/*.ipynb"
+    - "!**/*.png"
+    - "!**/*.bat"
+
+  path_instructions:
+    - path: "comfy/**"
+      instructions: |
+        Core ML/diffusion engine. Focus on:
+        - Backward compatibility (breaking changes affect all custom nodes)
+        - Memory management and GPU resource handling
+        - Performance implications in hot paths
+        - Thread safety for concurrent execution
+    - path: "comfy_api_nodes/**"
+      instructions: |
+        Third-party API integration nodes. Focus on:
+        - No hardcoded API keys or secrets
+        - Proper error handling for API failures (timeouts, rate limits, auth errors)
+        - Correct Pydantic model usage
+        - Security of user data passed to external APIs
+    - path: "comfy_extras/**"
+      instructions: |
+        Community-contributed extra nodes. Focus on:
+        - Consistency with node patterns (INPUT_TYPES, RETURN_TYPES, FUNCTION, CATEGORY)
+        - No breaking changes to existing node interfaces
+    - path: "comfy_execution/**"
+      instructions: |
+        Execution engine (graph execution, caching, jobs). Focus on:
+        - Caching correctness
+        - Concurrent execution safety
+        - Graph validation edge cases
+    - path: "nodes.py"
+      instructions: |
+        Core node definitions (2500+ lines). Focus on:
+        - Backward compatibility of NODE_CLASS_MAPPINGS
+        - Consistency of INPUT_TYPES return format
+    - path: "alembic_db/**"
+      instructions: |
+        Database migrations. Focus on:
+        - Migration safety and rollback support
+        - Data preservation during schema changes
+
+  auto_review:
+    enabled: true
+    auto_incremental_review: true
+    drafts: true
+
+  finishing_touches:
+    docstrings:
+      enabled: false
+    unit_tests:
+      enabled: false
+
+  tools:
+    ruff:
+      enabled: true
+    pylint:
+      enabled: false
+    flake8:
+      enabled: false
+    gitleaks:
+      enabled: true
+    shellcheck:
+      enabled: false
+    markdownlint:
+      enabled: false
+    yamllint:
+      enabled: false
+    languagetool:
+      enabled: false
+    github-checks:
+      enabled: true
+      timeout_ms: 90000
+    ast-grep:
+      essential_rules: true
+
+chat:
+  auto_reply: true
+
+knowledge_base:
+  opt_out: false
+  learnings:
+    scope: "auto"

From 64953bda0a4565ea13f55f01ae636a2b3d3d2ba6 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Fri, 20 Feb 2026 18:32:05 -0800
Subject: [PATCH 021/194] Update nightly installation command for ROCm (#12547)

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 3ccdc9c19..d97c6181d 100644
--- a/README.md
+++ b/README.md
@@ -229,9 +229,9 @@ AMD users can install rocm and pytorch with pip if you don't have it already ins
 
 ```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.1```
 
-This is the command to install the nightly with ROCm 7.1 which might have some performance improvements:
+This is the command to install the nightly with ROCm 7.2 which might have some performance improvements:
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.1```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.2```
 
 
 ### AMD GPUs (Experimental: Windows and Linux), RDNA 3, 3.5 and 4 only.

From aeb5bdc8f65f9c1471c16a00857bdccb91166f15 Mon Sep 17 00:00:00 2001
From: Arthur R Longbottom <art.longbottom.jr@gmail.com>
Date: Fri, 20 Feb 2026 20:37:55 -0800
Subject: [PATCH 022/194] Fix non-contiguous audio waveform crash in video save
 (#12550)

Fixes #12549
---
 comfy_api/latest/_input_impl/video_types.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy_api/latest/_input_impl/video_types.py b/comfy_api/latest/_input_impl/video_types.py
index 3463ed1c9..a3d48c87f 100644
--- a/comfy_api/latest/_input_impl/video_types.py
+++ b/comfy_api/latest/_input_impl/video_types.py
@@ -444,7 +444,7 @@ class VideoFromComponents(VideoInput):
             output.mux(packet)
 
             if audio_stream and self.__components.audio:
-                frame = av.AudioFrame.from_ndarray(waveform.float().cpu().numpy(), format='fltp', layout=layout)
+                frame = av.AudioFrame.from_ndarray(waveform.float().cpu().contiguous().numpy(), format='fltp', layout=layout)
                 frame.sample_rate = audio_sample_rate
                 frame.pts = 0
                 output.mux(audio_stream.encode(frame))

From f394af8d0fd03ec1aad6049e4a40344cd09aba4e Mon Sep 17 00:00:00 2001
From: Terry Jia <terryjia88@gmail.com>
Date: Sat, 21 Feb 2026 01:52:32 -0500
Subject: [PATCH 023/194] feat: add gradient-slider display mode for FLOAT
 inputs (#12536)

* feat: add gradient-slider display mode for FLOAT inputs

* fix: use precise type annotation list[list[float]] for gradient_stops

Amp-Thread-ID: https://ampcode.com/threads/T-019c7eea-be2b-72ce-a51f-838376f9b7a7

---------

Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
Co-authored-by: bymyself <cbyrne@comfy.org>
---
 comfy/comfy_types/node_typing.py | 2 ++
 comfy_api/latest/_io.py          | 6 +++++-
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/comfy/comfy_types/node_typing.py b/comfy/comfy_types/node_typing.py
index 0194b7d70..92b1acbd5 100644
--- a/comfy/comfy_types/node_typing.py
+++ b/comfy/comfy_types/node_typing.py
@@ -176,6 +176,8 @@ class InputTypeOptions(TypedDict):
     """COMBO type only. Specifies the configuration for a multi-select widget.
     Available after ComfyUI frontend v1.13.4
     https://github.com/Comfy-Org/ComfyUI_frontend/pull/2987"""
+    gradient_stops: NotRequired[list[list[float]]]
+    """Gradient color stops for gradientslider display mode. Each stop is [offset, r, g, b] (``FLOAT``)."""
 
 
 class HiddenInputTypeDict(TypedDict):
diff --git a/comfy_api/latest/_io.py b/comfy_api/latest/_io.py
index dee487c92..025727071 100644
--- a/comfy_api/latest/_io.py
+++ b/comfy_api/latest/_io.py
@@ -73,6 +73,7 @@ class RemoteOptions:
 class NumberDisplay(str, Enum):
     number = "number"
     slider = "slider"
+    gradient_slider = "gradientslider"
 
 
 class ControlAfterGenerate(str, Enum):
@@ -296,13 +297,15 @@ class Float(ComfyTypeIO):
         '''Float input.'''
         def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                     default: float=None, min: float=None, max: float=None, step: float=None, round: float=None,
-                    display_mode: NumberDisplay=None, socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
+                    display_mode: NumberDisplay=None, gradient_stops: list[list[float]]=None,
+                    socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
             super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link, advanced)
             self.min = min
             self.max = max
             self.step = step
             self.round = round
             self.display_mode = display_mode
+            self.gradient_stops = gradient_stops
             self.default: float
 
         def as_dict(self):
@@ -312,6 +315,7 @@ class Float(ComfyTypeIO):
                 "step": self.step,
                 "round": self.round,
                 "display": self.display_mode,
+                "gradient_stops": self.gradient_stops,
             })
 
 @comfytype(io_type="STRING")

From 5a182bfaf18a841a4991b63bf5aaead622ad9e16 Mon Sep 17 00:00:00 2001
From: Terry Jia <terryjia88@gmail.com>
Date: Sat, 21 Feb 2026 02:02:36 -0500
Subject: [PATCH 024/194] update glsl blueprint with gradient (#12548)

Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
---
 blueprints/Brightness and Contrast.json | 2 +-
 blueprints/Color Adjustment.json        | 2 +-
 blueprints/Hue and Saturation.json      | 2 +-
 blueprints/Image Levels.json            | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/blueprints/Brightness and Contrast.json b/blueprints/Brightness and Contrast.json
index 2d670654d..2ef05ea54 100644
--- a/blueprints/Brightness and Contrast.json	
+++ b/blueprints/Brightness and Contrast.json	
@@ -1 +1 @@
-{"revision":0,"last_node_id":140,"last_link_id":0,"nodes":[{"id":140,"type":"916dff42-6166-4d45-b028-04eaf69fbb35","pos":[500,1440],"size":[250,178],"flags":{},"order":2,"mode":0,"inputs":[{"label":"image","localized_name":"images.image0","name":"images.image0","type":"IMAGE","link":null}],"outputs":[{"label":"IMAGE","localized_name":"IMAGE0","name":"IMAGE0","type":"IMAGE","links":[]}],"properties":{"proxyWidgets":[["4","value"],["5","value"]]},"widgets_values":[],"title":"Brightness and Contrast"}],"links":[],"version":0.4,"definitions":{"subgraphs":[{"id":"916dff42-6166-4d45-b028-04eaf69fbb35","version":1,"state":{"lastGroupId":0,"lastNodeId":143,"lastLinkId":118,"lastRerouteId":0},"revision":0,"config":{},"name":"Brightness and Contrast","inputNode":{"id":-10,"bounding":[360,-176,120,60]},"outputNode":{"id":-20,"bounding":[1410,-176,120,60]},"inputs":[{"id":"a5aae7ea-b511-4045-b5da-94101e269cd7","name":"images.image0","type":"IMAGE","linkIds":[117],"localized_name":"images.image0","label":"image","pos":[460,-156]}],"outputs":[{"id":"30b72604-69b3-4944-b253-a9099bbd73a9","name":"IMAGE0","type":"IMAGE","linkIds":[118],"localized_name":"IMAGE0","label":"IMAGE","pos":[1430,-156]}],"widgets":[],"nodes":[{"id":4,"type":"PrimitiveFloat","pos":[540,-280],"size":[270,58],"flags":{},"order":0,"mode":0,"inputs":[{"label":"brightness","localized_name":"value","name":"value","type":"FLOAT","widget":{"name":"value"},"link":null}],"outputs":[{"localized_name":"FLOAT","name":"FLOAT","type":"FLOAT","links":[115]}],"properties":{"Node name for S&R":"PrimitiveFloat","min":0,"max":100,"precision":1,"step":1},"widgets_values":[50]},{"id":5,"type":"PrimitiveFloat","pos":[540,-170],"size":[270,58],"flags":{},"order":1,"mode":0,"inputs":[{"label":"contrast","localized_name":"value","name":"value","type":"FLOAT","widget":{"name":"value"},"link":null}],"outputs":[{"localized_name":"FLOAT","name":"FLOAT","type":"FLOAT","links":[116]}],"properties":{"Node name for S&R":"PrimitiveFloat","min":0,"max":100,"precision":1,"step":1},"widgets_values":[0]},{"id":143,"type":"GLSLShader","pos":[840,-280],"size":[400,212],"flags":{},"order":2,"mode":0,"inputs":[{"label":"image0","localized_name":"images.image0","name":"images.image0","type":"IMAGE","link":117},{"label":"image1","localized_name":"images.image1","name":"images.image1","shape":7,"type":"IMAGE","link":null},{"label":"u_float0","localized_name":"floats.u_float0","name":"floats.u_float0","shape":7,"type":"FLOAT","link":115},{"label":"u_float1","localized_name":"floats.u_float1","name":"floats.u_float1","shape":7,"type":"FLOAT","link":116},{"label":"u_float2","localized_name":"floats.u_float2","name":"floats.u_float2","shape":7,"type":"FLOAT","link":null},{"label":"u_int0","localized_name":"ints.u_int0","name":"ints.u_int0","shape":7,"type":"INT","link":null},{"localized_name":"fragment_shader","name":"fragment_shader","type":"STRING","widget":{"name":"fragment_shader"},"link":null},{"localized_name":"size_mode","name":"size_mode","type":"COMFY_DYNAMICCOMBO_V3","widget":{"name":"size_mode"},"link":null}],"outputs":[{"localized_name":"IMAGE0","name":"IMAGE0","type":"IMAGE","links":[118]},{"localized_name":"IMAGE1","name":"IMAGE1","type":"IMAGE","links":null},{"localized_name":"IMAGE2","name":"IMAGE2","type":"IMAGE","links":null},{"localized_name":"IMAGE3","name":"IMAGE3","type":"IMAGE","links":null}],"properties":{"Node name for S&R":"GLSLShader"},"widgets_values":["#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform float u_float0; // Brightness slider -100..100\nuniform float u_float1; // Contrast slider -100..100\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst float MID_GRAY = 0.18;  // 18% reflectance\n\n// sRGB gamma 2.2 approximation\nvec3 srgbToLinear(vec3 c) {\n    return pow(max(c, 0.0), vec3(2.2));\n}\n\nvec3 linearToSrgb(vec3 c) {\n    return pow(max(c, 0.0), vec3(1.0/2.2));\n}\n\nfloat mapBrightness(float b) {\n    return clamp(b / 100.0, -1.0, 1.0);\n}\n\nfloat mapContrast(float c) {\n    return clamp(c / 100.0 + 1.0, 0.0, 2.0);\n}\n\nvoid main() {\n    vec4 orig = texture(u_image0, v_texCoord);\n\n    float brightness = mapBrightness(u_float0);\n    float contrast   = mapContrast(u_float1);\n\n    vec3 lin = srgbToLinear(orig.rgb);\n\n    lin = (lin - MID_GRAY) * contrast + brightness + MID_GRAY;\n\n    // Convert back to sRGB\n    vec3 result = linearToSrgb(clamp(lin, 0.0, 1.0));\n\n    fragColor = vec4(result, orig.a);\n}\n","from_input"]}],"groups":[],"links":[{"id":115,"origin_id":4,"origin_slot":0,"target_id":143,"target_slot":2,"type":"FLOAT"},{"id":116,"origin_id":5,"origin_slot":0,"target_id":143,"target_slot":3,"type":"FLOAT"},{"id":117,"origin_id":-10,"origin_slot":0,"target_id":143,"target_slot":0,"type":"IMAGE"},{"id":118,"origin_id":143,"origin_slot":0,"target_id":-20,"target_slot":0,"type":"IMAGE"}],"extra":{"workflowRendererVersion":"LG"}}]},"extra":{}}
\ No newline at end of file
+{"revision": 0, "last_node_id": 140, "last_link_id": 0, "nodes": [{"id": 140, "type": "916dff42-6166-4d45-b028-04eaf69fbb35", "pos": [500, 1440], "size": [250, 178], "flags": {}, "order": 2, "mode": 0, "inputs": [{"label": "image", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": null}], "outputs": [{"label": "IMAGE", "localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": []}], "properties": {"proxyWidgets": [["4", "value"], ["5", "value"]]}, "widgets_values": [], "title": "Brightness and Contrast"}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "916dff42-6166-4d45-b028-04eaf69fbb35", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 143, "lastLinkId": 118, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Brightness and Contrast", "inputNode": {"id": -10, "bounding": [360, -176, 120, 60]}, "outputNode": {"id": -20, "bounding": [1410, -176, 120, 60]}, "inputs": [{"id": "a5aae7ea-b511-4045-b5da-94101e269cd7", "name": "images.image0", "type": "IMAGE", "linkIds": [117], "localized_name": "images.image0", "label": "image", "pos": [460, -156]}], "outputs": [{"id": "30b72604-69b3-4944-b253-a9099bbd73a9", "name": "IMAGE0", "type": "IMAGE", "linkIds": [118], "localized_name": "IMAGE0", "label": "IMAGE", "pos": [1430, -156]}], "widgets": [], "nodes": [{"id": 4, "type": "PrimitiveFloat", "pos": [540, -280], "size": [270, 58], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "brightness", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [115]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 100, "precision": 1, "step": 1, "display": "gradientslider", "gradient_stops": [{"offset": 0, "color": [0, 0, 0]}, {"offset": 1, "color": [255, 255, 255]}]}, "widgets_values": [50]}, {"id": 5, "type": "PrimitiveFloat", "pos": [540, -170], "size": [270, 58], "flags": {}, "order": 1, "mode": 0, "inputs": [{"label": "contrast", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [116]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 100, "precision": 1, "step": 1, "display": "gradientslider", "gradient_stops": [{"offset": 0, "color": [136, 136, 136]}, {"offset": 0.4, "color": [68, 68, 68]}, {"offset": 0.6, "color": [187, 187, 187]}, {"offset": 0.8, "color": [0, 0, 0]}, {"offset": 1, "color": [255, 255, 255]}]}, "widgets_values": [0]}, {"id": 143, "type": "GLSLShader", "pos": [840, -280], "size": [400, 212], "flags": {}, "order": 2, "mode": 0, "inputs": [{"label": "image0", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": 117}, {"label": "image1", "localized_name": "images.image1", "name": "images.image1", "shape": 7, "type": "IMAGE", "link": null}, {"label": "u_float0", "localized_name": "floats.u_float0", "name": "floats.u_float0", "shape": 7, "type": "FLOAT", "link": 115}, {"label": "u_float1", "localized_name": "floats.u_float1", "name": "floats.u_float1", "shape": 7, "type": "FLOAT", "link": 116}, {"label": "u_float2", "localized_name": "floats.u_float2", "name": "floats.u_float2", "shape": 7, "type": "FLOAT", "link": null}, {"label": "u_int0", "localized_name": "ints.u_int0", "name": "ints.u_int0", "shape": 7, "type": "INT", "link": null}, {"localized_name": "fragment_shader", "name": "fragment_shader", "type": "STRING", "widget": {"name": "fragment_shader"}, "link": null}, {"localized_name": "size_mode", "name": "size_mode", "type": "COMFY_DYNAMICCOMBO_V3", "widget": {"name": "size_mode"}, "link": null}], "outputs": [{"localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": [118]}, {"localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": null}], "properties": {"Node name for S&R": "GLSLShader"}, "widgets_values": ["#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform float u_float0; // Brightness slider -100..100\nuniform float u_float1; // Contrast slider -100..100\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst float MID_GRAY = 0.18;  // 18% reflectance\n\n// sRGB gamma 2.2 approximation\nvec3 srgbToLinear(vec3 c) {\n    return pow(max(c, 0.0), vec3(2.2));\n}\n\nvec3 linearToSrgb(vec3 c) {\n    return pow(max(c, 0.0), vec3(1.0/2.2));\n}\n\nfloat mapBrightness(float b) {\n    return clamp(b / 100.0, -1.0, 1.0);\n}\n\nfloat mapContrast(float c) {\n    return clamp(c / 100.0 + 1.0, 0.0, 2.0);\n}\n\nvoid main() {\n    vec4 orig = texture(u_image0, v_texCoord);\n\n    float brightness = mapBrightness(u_float0);\n    float contrast   = mapContrast(u_float1);\n\n    vec3 lin = srgbToLinear(orig.rgb);\n\n    lin = (lin - MID_GRAY) * contrast + brightness + MID_GRAY;\n\n    // Convert back to sRGB\n    vec3 result = linearToSrgb(clamp(lin, 0.0, 1.0));\n\n    fragColor = vec4(result, orig.a);\n}\n", "from_input"]}], "groups": [], "links": [{"id": 115, "origin_id": 4, "origin_slot": 0, "target_id": 143, "target_slot": 2, "type": "FLOAT"}, {"id": 116, "origin_id": 5, "origin_slot": 0, "target_id": 143, "target_slot": 3, "type": "FLOAT"}, {"id": 117, "origin_id": -10, "origin_slot": 0, "target_id": 143, "target_slot": 0, "type": "IMAGE"}, {"id": 118, "origin_id": 143, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}}]}, "extra": {}}
\ No newline at end of file
diff --git a/blueprints/Color Adjustment.json b/blueprints/Color Adjustment.json
index f9c488250..711768737 100644
--- a/blueprints/Color Adjustment.json	
+++ b/blueprints/Color Adjustment.json	
@@ -1 +1 @@
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// temperature (-100 to 100)\nuniform float u_float1; // tint (-100 to 100)\nuniform float u_float2; // vibrance (-100 to 100)\nuniform float u_float3; // saturation (-100 to 100)\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst float INPUT_SCALE = 0.01;\nconst float TEMP_TINT_PRIMARY = 0.3;\nconst float TEMP_TINT_SECONDARY = 0.15;\nconst float VIBRANCE_BOOST = 2.0;\nconst float SATURATION_BOOST = 2.0;\nconst float SKIN_PROTECTION = 0.5;\nconst float EPSILON = 0.001;\nconst vec3 LUMA_WEIGHTS = vec3(0.299, 0.587, 0.114);\n\nvoid main() {\n    vec4 tex = texture(u_image0, v_texCoord);\n    vec3 color = tex.rgb;\n    \n    // Scale inputs: -100/100 \u2192 -1/1\n    float temperature = u_float0 * INPUT_SCALE;\n    float tint = u_float1 * INPUT_SCALE;\n    float vibrance = u_float2 * INPUT_SCALE;\n    float saturation = u_float3 * INPUT_SCALE;\n    \n    // Temperature (warm/cool): positive = warm, negative = cool\n    color.r += temperature * TEMP_TINT_PRIMARY;\n    color.b -= temperature * TEMP_TINT_PRIMARY;\n    \n    // Tint (green/magenta): positive = green, negative = magenta\n    color.g += tint * TEMP_TINT_PRIMARY;\n    color.r -= tint * TEMP_TINT_SECONDARY;\n    color.b -= tint * TEMP_TINT_SECONDARY;\n    \n    // Single clamp after temperature/tint\n    color = clamp(color, 0.0, 1.0);\n    \n    // Vibrance with skin protection\n    if (vibrance != 0.0) {\n        float maxC = max(color.r, max(color.g, color.b));\n        float minC = min(color.r, min(color.g, color.b));\n        float sat = maxC - minC;\n        float gray = dot(color, LUMA_WEIGHTS);\n        \n        if (vibrance < 0.0) {\n            // Desaturate: -100 \u2192 gray\n            color = mix(vec3(gray), color, 1.0 + vibrance);\n        } else {\n            // Boost less saturated colors more\n            float vibranceAmt = vibrance * (1.0 - sat);\n            \n            // Branchless skin tone protection\n            float isWarmTone = step(color.b, color.g) * step(color.g, color.r);\n            float warmth = (color.r - color.b) / max(maxC, EPSILON);\n            float skinTone = isWarmTone * warmth * sat * (1.0 - sat);\n            vibranceAmt *= (1.0 - skinTone * SKIN_PROTECTION);\n            \n            color = mix(vec3(gray), color, 1.0 + vibranceAmt * VIBRANCE_BOOST);\n        }\n    }\n    \n    // Saturation\n    if (saturation != 0.0) {\n        float gray = dot(color, LUMA_WEIGHTS);\n        float satMix = saturation < 0.0\n            ? 1.0 + saturation                      // -100 \u2192 gray\n            : 1.0 + saturation * SATURATION_BOOST;  // +100 \u2192 3x boost\n        color = mix(vec3(gray), color, satMix);\n    }\n    \n    fragColor = vec4(clamp(color, 0.0, 1.0), tex.a);\n}", "from_input"]}, {"id": 6, "type": "PrimitiveFloat", "pos": [3290, -3610], "size": [270, 58], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "vibrance", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [26, 31]}], "title": "Vibrance", "properties": {"Node name for S&R": "PrimitiveFloat", "max": 100, "min": -100, "step": 1}, "widgets_values": [0]}, {"id": 7, "type": "PrimitiveFloat", "pos": [3290, -3720], "size": [270, 58], "flags": {}, "order": 1, "mode": 0, "inputs": [{"label": "saturation", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [33]}], "title": "Saturation", "properties": {"Node name for S&R": "PrimitiveFloat", "max": 100, "min": -100, "step": 1}, "widgets_values": [0]}, {"id": 5, "type": "PrimitiveFloat", "pos": [3290, -3830], "size": [270, 58], "flags": {}, "order": 2, "mode": 0, "inputs": [{"label": "tint", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [30]}], "title": "Tint", "properties": {"Node name for S&R": "PrimitiveFloat", "max": 100, "min": -100, "step": 1}, "widgets_values": [0]}, {"id": 4, "type": "PrimitiveFloat", "pos": [3290, -3940], "size": [270, 58], "flags": {}, "order": 3, "mode": 0, "inputs": [{"label": "temperature", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [34]}], "title": "Temperature", "properties": {"Node name for S&R": "PrimitiveFloat", "max": 100, "min": -100, "step": 1}, "widgets_values": [100]}], "groups": [], "links": [{"id": 34, "origin_id": 4, "origin_slot": 0, "target_id": 15, "target_slot": 2, "type": "FLOAT"}, {"id": 30, "origin_id": 5, "origin_slot": 0, "target_id": 15, "target_slot": 3, "type": "FLOAT"}, {"id": 31, "origin_id": 6, "origin_slot": 0, "target_id": 15, "target_slot": 4, "type": "FLOAT"}, {"id": 33, "origin_id": 7, "origin_slot": 0, "target_id": 15, "target_slot": 5, "type": "FLOAT"}, {"id": 29, "origin_id": -10, "origin_slot": 0, "target_id": 15, "target_slot": 0, "type": "IMAGE"}, {"id": 28, "origin_id": 15, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}}]}}
\ No newline at end of file
+{"revision": 0, "last_node_id": 14, "last_link_id": 0, "nodes": [{"id": 14, "type": "36677b92-5dd8-47a5-9380-4da982c1894f", "pos": [3610, -2630], "size": [270, 150], "flags": {}, "order": 3, "mode": 0, "inputs": [{"label": "image", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": null}], "outputs": [{"label": "IMAGE", "localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": []}], "properties": {"proxyWidgets": [["4", "value"], ["5", "value"], ["7", "value"], ["6", "value"]]}, "widgets_values": [], "title": "Color Adjustment"}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "36677b92-5dd8-47a5-9380-4da982c1894f", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 16, "lastLinkId": 36, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Color Adjustment", "inputNode": {"id": -10, "bounding": [3110, -3560, 120, 60]}, "outputNode": {"id": -20, "bounding": [4070, -3560, 120, 60]}, "inputs": [{"id": "0431d493-5f28-4430-bd00-84733997fc08", "name": "images.image0", "type": "IMAGE", "linkIds": [29], "localized_name": "images.image0", "label": "image", "pos": [3210, -3540]}], "outputs": [{"id": "bee8ea06-a114-4612-8937-939f2c927bdb", "name": "IMAGE0", "type": "IMAGE", "linkIds": [28], "localized_name": "IMAGE0", "label": "IMAGE", "pos": [4090, -3540]}], "widgets": [], "nodes": [{"id": 15, "type": "GLSLShader", "pos": [3590, -3940], "size": [420, 252], "flags": {}, "order": 4, "mode": 0, "inputs": [{"label": "image0", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": 29}, {"label": "image1", "localized_name": "images.image1", "name": "images.image1", "shape": 7, "type": "IMAGE", "link": null}, {"label": "u_float0", "localized_name": "floats.u_float0", "name": "floats.u_float0", "shape": 7, "type": "FLOAT", "link": 34}, {"label": "u_float1", "localized_name": "floats.u_float1", "name": "floats.u_float1", "shape": 7, "type": "FLOAT", "link": 30}, {"label": "u_float2", "localized_name": "floats.u_float2", "name": "floats.u_float2", "shape": 7, "type": "FLOAT", "link": 31}, {"label": "u_float3", "localized_name": "floats.u_float3", "name": "floats.u_float3", "shape": 7, "type": "FLOAT", "link": 33}, {"label": "u_float4", "localized_name": "floats.u_float4", "name": "floats.u_float4", "shape": 7, "type": "FLOAT", "link": null}, {"label": "u_int0", "localized_name": "ints.u_int0", "name": "ints.u_int0", "shape": 7, "type": "INT", "link": null}, {"localized_name": "fragment_shader", "name": "fragment_shader", "type": "STRING", "widget": {"name": "fragment_shader"}, "link": null}, {"localized_name": "size_mode", "name": "size_mode", "type": "COMFY_DYNAMICCOMBO_V3", "widget": {"name": "size_mode"}, "link": null}], "outputs": [{"localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": [28]}, {"localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": null}], "properties": {"Node name for S&R": "GLSLShader"}, "widgets_values": ["#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform float u_float0; // temperature (-100 to 100)\nuniform float u_float1; // tint (-100 to 100)\nuniform float u_float2; // vibrance (-100 to 100)\nuniform float u_float3; // saturation (-100 to 100)\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst float INPUT_SCALE = 0.01;\nconst float TEMP_TINT_PRIMARY = 0.3;\nconst float TEMP_TINT_SECONDARY = 0.15;\nconst float VIBRANCE_BOOST = 2.0;\nconst float SATURATION_BOOST = 2.0;\nconst float SKIN_PROTECTION = 0.5;\nconst float EPSILON = 0.001;\nconst vec3 LUMA_WEIGHTS = vec3(0.299, 0.587, 0.114);\n\nvoid main() {\n    vec4 tex = texture(u_image0, v_texCoord);\n    vec3 color = tex.rgb;\n    \n    // Scale inputs: -100/100 \u2192 -1/1\n    float temperature = u_float0 * INPUT_SCALE;\n    float tint = u_float1 * INPUT_SCALE;\n    float vibrance = u_float2 * INPUT_SCALE;\n    float saturation = u_float3 * INPUT_SCALE;\n    \n    // Temperature (warm/cool): positive = warm, negative = cool\n    color.r += temperature * TEMP_TINT_PRIMARY;\n    color.b -= temperature * TEMP_TINT_PRIMARY;\n    \n    // Tint (green/magenta): positive = green, negative = magenta\n    color.g += tint * TEMP_TINT_PRIMARY;\n    color.r -= tint * TEMP_TINT_SECONDARY;\n    color.b -= tint * TEMP_TINT_SECONDARY;\n    \n    // Single clamp after temperature/tint\n    color = clamp(color, 0.0, 1.0);\n    \n    // Vibrance with skin protection\n    if (vibrance != 0.0) {\n        float maxC = max(color.r, max(color.g, color.b));\n        float minC = min(color.r, min(color.g, color.b));\n        float sat = maxC - minC;\n        float gray = dot(color, LUMA_WEIGHTS);\n        \n        if (vibrance < 0.0) {\n            // Desaturate: -100 \u2192 gray\n            color = mix(vec3(gray), color, 1.0 + vibrance);\n        } else {\n            // Boost less saturated colors more\n            float vibranceAmt = vibrance * (1.0 - sat);\n            \n            // Branchless skin tone protection\n            float isWarmTone = step(color.b, color.g) * step(color.g, color.r);\n            float warmth = (color.r - color.b) / max(maxC, EPSILON);\n            float skinTone = isWarmTone * warmth * sat * (1.0 - sat);\n            vibranceAmt *= (1.0 - skinTone * SKIN_PROTECTION);\n            \n            color = mix(vec3(gray), color, 1.0 + vibranceAmt * VIBRANCE_BOOST);\n        }\n    }\n    \n    // Saturation\n    if (saturation != 0.0) {\n        float gray = dot(color, LUMA_WEIGHTS);\n        float satMix = saturation < 0.0\n            ? 1.0 + saturation                      // -100 \u2192 gray\n            : 1.0 + saturation * SATURATION_BOOST;  // +100 \u2192 3x boost\n        color = mix(vec3(gray), color, satMix);\n    }\n    \n    fragColor = vec4(clamp(color, 0.0, 1.0), tex.a);\n}", "from_input"]}, {"id": 6, "type": "PrimitiveFloat", "pos": [3290, -3610], "size": [270, 58], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "vibrance", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [26, 31]}], "title": "Vibrance", "properties": {"Node name for S&R": "PrimitiveFloat", "max": 100, "min": -100, "step": 1, "display": "gradientslider", "gradient_stops": [{"offset": 0, "color": [128, 128, 128]}, {"offset": 1, "color": [255, 0, 0]}]}, "widgets_values": [0]}, {"id": 7, "type": "PrimitiveFloat", "pos": [3290, -3720], "size": [270, 58], "flags": {}, "order": 1, "mode": 0, "inputs": [{"label": "saturation", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [33]}], "title": "Saturation", "properties": {"Node name for S&R": "PrimitiveFloat", "max": 100, "min": -100, "step": 1, "display": "gradientslider", "gradient_stops": [{"offset": 0, "color": [128, 128, 128]}, {"offset": 1, "color": [255, 0, 0]}]}, "widgets_values": [0]}, {"id": 5, "type": "PrimitiveFloat", "pos": [3290, -3830], "size": [270, 58], "flags": {}, "order": 2, "mode": 0, "inputs": [{"label": "tint", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [30]}], "title": "Tint", "properties": {"Node name for S&R": "PrimitiveFloat", "max": 100, "min": -100, "step": 1, "display": "gradientslider", "gradient_stops": [{"offset": 0, "color": [0, 255, 0]}, {"offset": 0.5, "color": [255, 255, 255]}, {"offset": 1, "color": [255, 0, 255]}]}, "widgets_values": [0]}, {"id": 4, "type": "PrimitiveFloat", "pos": [3290, -3940], "size": [270, 58], "flags": {}, "order": 3, "mode": 0, "inputs": [{"label": "temperature", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [34]}], "title": "Temperature", "properties": {"Node name for S&R": "PrimitiveFloat", "max": 100, "min": -100, "step": 1, "display": "gradientslider", "gradient_stops": [{"offset": 0, "color": [68, 136, 255]}, {"offset": 0.5, "color": [255, 255, 255]}, {"offset": 1, "color": [255, 136, 0]}]}, "widgets_values": [100]}], "groups": [], "links": [{"id": 34, "origin_id": 4, "origin_slot": 0, "target_id": 15, "target_slot": 2, "type": "FLOAT"}, {"id": 30, "origin_id": 5, "origin_slot": 0, "target_id": 15, "target_slot": 3, "type": "FLOAT"}, {"id": 31, "origin_id": 6, "origin_slot": 0, "target_id": 15, "target_slot": 4, "type": "FLOAT"}, {"id": 33, "origin_id": 7, "origin_slot": 0, "target_id": 15, "target_slot": 5, "type": "FLOAT"}, {"id": 29, "origin_id": -10, "origin_slot": 0, "target_id": 15, "target_slot": 0, "type": "IMAGE"}, {"id": 28, "origin_id": 15, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}}]}}
\ No newline at end of file
diff --git a/blueprints/Hue and Saturation.json b/blueprints/Hue and Saturation.json
index 84d95df33..0a9915de7 100644
--- a/blueprints/Hue and Saturation.json	
+++ b/blueprints/Hue and Saturation.json	
@@ -1 +1 @@
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0.0001;\n\n//=============================================================================\n// RGB <-> HSL Conversions\n//=============================================================================\n\nvec3 rgb2hsl(vec3 c) {\n    float maxC = max(max(c.r, c.g), c.b);\n    float minC = min(min(c.r, c.g), c.b);\n    float delta = maxC - minC;\n\n    float h = 0.0;\n    float s = 0.0;\n    float l = (maxC + minC) * 0.5;\n\n    if (delta > EPSILON) {\n        s = l < 0.5\n            ? delta / (maxC + minC)\n            : delta / (2.0 - maxC - minC);\n\n        if (maxC == c.r) {\n            h = (c.g - c.b) / delta + (c.g < c.b ? 6.0 : 0.0);\n        } else if (maxC == c.g) {\n            h = (c.b - c.r) / delta + 2.0;\n        } else {\n            h = (c.r - c.g) / delta + 4.0;\n        }\n        h /= 6.0;\n    }\n\n    return vec3(h, s, l);\n}\n\nfloat hue2rgb(float p, float q, float t) {\n    t = fract(t);\n    if (t < 1.0/6.0) return p + (q - p) * 6.0 * t;\n    if (t < 0.5)       return q;\n    if (t < 2.0/3.0)   return p + (q - p) * (2.0/3.0 - t) * 6.0;\n    return p;\n}\n\nvec3 hsl2rgb(vec3 hsl) {\n    if (hsl.y < EPSILON) return vec3(hsl.z);\n\n    float q = hsl.z < 0.5\n        ? hsl.z * (1.0 + hsl.y)\n        : hsl.z + hsl.y - hsl.z * hsl.y;\n    float p = 2.0 * hsl.z - q;\n\n    return vec3(\n        hue2rgb(p, q, hsl.x + 1.0/3.0),\n        hue2rgb(p, q, hsl.x),\n        hue2rgb(p, q, hsl.x - 1.0/3.0)\n    );\n}\n\nvec3 rgb2hsb(vec3 c) {\n    float maxC = max(max(c.r, c.g), c.b);\n    float minC = min(min(c.r, c.g), c.b);\n    float delta = maxC - minC;\n\n    float h = 0.0;\n    float s = (maxC > EPSILON) ? delta / maxC : 0.0;\n    float b = maxC;\n\n    if (delta > EPSILON) {\n        if (maxC == c.r) {\n            h = (c.g - c.b) / delta + (c.g < c.b ? 6.0 : 0.0);\n        } else if (maxC == c.g) {\n            h = (c.b - c.r) / delta + 2.0;\n        } else {\n            h = (c.r - c.g) / delta + 4.0;\n        }\n        h /= 6.0;\n    }\n\n    return vec3(h, s, b);\n}\n\nvec3 hsb2rgb(vec3 hsb) {\n    vec3 rgb = clamp(abs(mod(hsb.x * 6.0 + vec3(0.0, 4.0, 2.0), 6.0) - 3.0) - 1.0, 0.0, 1.0);\n    return hsb.z * mix(vec3(1.0), rgb, hsb.y);\n}\n\n//=============================================================================\n// Color Range Weight Calculation\n//=============================================================================\n\nfloat hueDistance(float a, float b) {\n    float d = abs(a - b);\n    return min(d, 1.0 - d);\n}\n\nfloat getHueWeight(float hue, float center, float overlap) {\n    float baseWidth = 1.0 / 6.0;\n    float feather = baseWidth * overlap;\n\n    float d = hueDistance(hue, center);\n\n    float inner = baseWidth * 0.5;\n    float outer = inner + feather;\n\n    return 1.0 - smoothstep(inner, outer, d);\n}\n\nfloat getModeWeight(float hue, int mode, float overlap) {\n    if (mode == MODE_MASTER || mode == MODE_COLORIZE) return 1.0;\n\n    if (mode == MODE_RED) {\n        return max(\n            getHueWeight(hue, 0.0, overlap),\n            getHueWeight(hue, 1.0, overlap)\n        );\n    }\n\n    float center = float(mode - 1) / 6.0;\n    return getHueWeight(hue, center, overlap);\n}\n\n//=============================================================================\n// Adjustment Functions\n//=============================================================================\n\nfloat adjustLightness(float l, float amount) {\n    return amount > 0.0\n        ? l + (1.0 - l) * amount\n        : l + l * amount;\n}\n\nfloat adjustBrightness(float b, float amount) {\n    return clamp(b + amount, 0.0, 1.0);\n}\n\nfloat adjustSaturation(float s, float amount) {\n    return amount > 0.0\n        ? s + (1.0 - s) * amount\n        : s + s * amount;\n}\n\nvec3 colorize(vec3 rgb, float hue, float sat, float light) {\n    float lum = dot(rgb, vec3(0.299, 0.587, 0.114));\n    float l = adjustLightness(lum, light);\n\n    vec3 hsl = vec3(fract(hue), clamp(sat, 0.0, 1.0), clamp(l, 0.0, 1.0));\n    return hsl2rgb(hsl);\n}\n\n//=============================================================================\n// Main\n//=============================================================================\n\nvoid main() {\n    vec4 original = texture(u_image0, v_texCoord);\n\n    float hueShift   = u_float0 / 360.0;   // -180..180 -> -0.5..0.5\n    float satAmount  = u_float1 / 100.0;   // -100..100 -> -1..1\n    float lightAmount= u_float2 / 100.0;   // -100..100 -> -1..1\n    float overlap    = u_float3 / 100.0;   // 0..100 -> 0..1\n\n    vec3 result;\n\n    if (u_int0 == MODE_COLORIZE) {\n        result = colorize(original.rgb, hueShift, satAmount, lightAmount);\n        fragColor = vec4(result, original.a);\n        return;\n    }\n\n    vec3 hsx = (u_int1 == COLORSPACE_HSL)\n        ? rgb2hsl(original.rgb)\n        : rgb2hsb(original.rgb);\n\n    float weight = getModeWeight(hsx.x, u_int0, overlap);\n\n    if (u_int0 != MODE_MASTER && hsx.y < EPSILON) {\n        weight = 0.0;\n    }\n\n    if (weight > EPSILON) {\n        float h = fract(hsx.x + hueShift * weight);\n        float s = clamp(adjustSaturation(hsx.y, satAmount * weight), 0.0, 1.0);\n        float v = (u_int1 == COLORSPACE_HSL)\n            ? clamp(adjustLightness(hsx.z, lightAmount * weight), 0.0, 1.0)\n            : clamp(adjustBrightness(hsx.z, lightAmount * weight), 0.0, 1.0);\n\n        vec3 adjusted = vec3(h, s, v);\n        result = (u_int1 == COLORSPACE_HSL)\n            ? hsl2rgb(adjusted)\n            : hsb2rgb(adjusted);\n    } else {\n        result = original.rgb;\n    }\n\n    fragColor = vec4(result, original.a);\n}\n", "from_input"]}], "groups": [], "links": [{"id": 3, "origin_id": 4, "origin_slot": 0, "target_id": 1, "target_slot": 2, "type": "FLOAT"}, {"id": 4, "origin_id": 5, "origin_slot": 0, "target_id": 1, "target_slot": 3, "type": "FLOAT"}, {"id": 5, "origin_id": 6, "origin_slot": 0, "target_id": 1, "target_slot": 4, "type": "FLOAT"}, {"id": 6, "origin_id": 7, "origin_slot": 0, 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\ No newline at end of file
diff --git a/blueprints/Image Levels.json b/blueprints/Image Levels.json
index 26f7a0dc1..8a44663bd 100644
--- a/blueprints/Image Levels.json	
+++ b/blueprints/Image Levels.json	
@@ -1 +1 @@
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pow(result, vec3(1.0 / gamma));\n    result = mix(vec3(outBlack), vec3(outWhite), result);\n    return result;\n}\n\nfloat applySingleChannel(float value, float inBlack, float inWhite, float gamma, float outBlack, float outWhite) {\n    float inRange = max(inWhite - inBlack, 0.0001);\n    float result = clamp((value - inBlack) / inRange, 0.0, 1.0);\n    result = pow(result, 1.0 / gamma);\n    result = mix(outBlack, outWhite, result);\n    return result;\n}\n\nvoid main() {\n    vec4 texColor = texture(u_image0, v_texCoord);\n    vec3 color = texColor.rgb;\n    \n    float inBlack = u_float0 / 255.0;\n    float inWhite = u_float1 / 255.0;\n    float gamma = u_float2;\n    float outBlack = u_float3 / 255.0;\n    float outWhite = u_float4 / 255.0;\n    \n    vec3 result;\n    \n    if (u_int0 == 0) {\n        result = applyLevels(color, inBlack, inWhite, gamma, outBlack, outWhite);\n    }\n    else if (u_int0 == 1) {\n        result = color;\n        result.r = applySingleChannel(color.r, inBlack, inWhite, gamma, outBlack, outWhite);\n    }\n    else if (u_int0 == 2) {\n        result = color;\n        result.g = applySingleChannel(color.g, inBlack, inWhite, gamma, outBlack, outWhite);\n    }\n    else if (u_int0 == 3) {\n        result = color;\n        result.b = applySingleChannel(color.b, inBlack, inWhite, gamma, outBlack, outWhite);\n    }\n    else {\n        result = color;\n    }\n    \n    fragColor = vec4(result, 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-3550], "size": [270, 58], "flags": {}, "order": 1, "mode": 0, "inputs": [{"label": "output_black", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [6]}], "title": "Output Black", "properties": {"Node name for S&R": "PrimitiveFloat", "max": 255, "min": 0, "step": 1, "display": "gradientslider", "gradient_stops": [{"offset": 0, "color": [0, 0, 0]}, {"offset": 1, "color": [255, 255, 255]}]}, "widgets_values": [0]}, {"id": 3, "type": "PrimitiveFloat", "pos": [4020, -3850], "size": [270, 58], "flags": {}, "order": 2, "mode": 0, "inputs": [{"label": "input_black", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [2]}], "title": "Input Black", "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 255, "step": 1, "display": "gradientslider", "gradient_stops": [{"offset": 0, "color": [0, 0, 0]}, {"offset": 1, "color": [255, 255, 255]}]}, "widgets_values": [0]}, {"id": 6, "type": "PrimitiveFloat", "pos": [4020, -3750], "size": [270, 58], "flags": {}, "order": 3, "mode": 0, "inputs": [{"label": "input_white", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [4]}], "title": "Input White", "properties": {"Node name for S&R": "PrimitiveFloat", "max": 255, "min": 0, "step": 1, "display": "gradientslider", "gradient_stops": [{"offset": 0, "color": [0, 0, 0]}, {"offset": 1, "color": [255, 255, 255]}]}, "widgets_values": [255]}, {"id": 7, "type": "PrimitiveFloat", "pos": [4020, -3650], "size": [270, 58], "flags": {}, "order": 4, "mode": 0, "inputs": [{"label": "gamma", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [5]}], "title": "Gamma", "properties": {"Node name for S&R": "PrimitiveFloat", "max": 10, "min": 0, "step": 0.01, "precision": 2, "display": "gradientslider", "gradient_stops": [{"offset": 0, "color": [0, 0, 0]}, {"offset": 0.5, "color": [128, 128, 128]}, {"offset": 1, "color": [255, 255, 255]}]}, "widgets_values": [1]}, {"id": 9, "type": "PrimitiveFloat", "pos": [4020, -3450], "size": [270, 58], "flags": {}, "order": 5, "mode": 0, "inputs": [{"label": "output_white", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [7]}], "title": "Output White", "properties": {"Node name for S&R": "PrimitiveFloat", "max": 255, "min": 0, "step": 1, "display": "gradientslider", "gradient_stops": [{"offset": 0, "color": [0, 0, 0]}, {"offset": 1, "color": [255, 255, 255]}]}, "widgets_values": [255]}, {"id": 1, "type": "GLSLShader", "pos": [4310, -3850], "size": [580, 272], "flags": {}, "order": 6, "mode": 0, "inputs": [{"label": "image0", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": 1}, {"label": "image1", "localized_name": "images.image1", "name": "images.image1", "shape": 7, "type": "IMAGE", "link": null}, {"label": "u_float0", "localized_name": "floats.u_float0", "name": "floats.u_float0", "shape": 7, "type": "FLOAT", "link": 2}, {"label": "u_float1", "localized_name": "floats.u_float1", "name": "floats.u_float1", "shape": 7, "type": "FLOAT", "link": 4}, {"label": "u_float2", "localized_name": "floats.u_float2", "name": "floats.u_float2", "shape": 7, "type": "FLOAT", "link": 5}, {"label": "u_float3", "localized_name": "floats.u_float3", "name": "floats.u_float3", "shape": 7, "type": "FLOAT", "link": 6}, {"label": "u_float4", "localized_name": "floats.u_float4", "name": "floats.u_float4", "shape": 7, "type": "FLOAT", "link": 7}, {"label": "u_int0", "localized_name": "ints.u_int0", "name": "ints.u_int0", "shape": 7, "type": "INT", "link": 3}, {"label": "u_int1", "localized_name": "ints.u_int1", "name": "ints.u_int1", "shape": 7, "type": "INT", "link": null}, {"localized_name": "fragment_shader", "name": "fragment_shader", "type": "STRING", "widget": {"name": "fragment_shader"}, "link": null}, {"localized_name": "size_mode", "name": "size_mode", "type": "COMFY_DYNAMICCOMBO_V3", "widget": {"name": "size_mode"}, "link": null}], "outputs": [{"localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": [8]}, {"localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": null}], "properties": {"Node name for S&R": "GLSLShader"}, "widgets_values": ["#version 300 es\nprecision highp float;\n\n// Levels Adjustment\n// u_int0:   channel      (0=RGB, 1=R, 2=G, 3=B)         default: 0\n// u_float0: input black  (0-255)                        default: 0\n// u_float1: input white  (0-255)                        default: 255\n// u_float2: gamma        (0.01-9.99)                    default: 1.0\n// u_float3: output black (0-255)                        default: 0\n// u_float4: output white (0-255)                        default: 255\n\nuniform sampler2D u_image0;\nuniform int u_int0;\nuniform float u_float0;\nuniform float u_float1;\nuniform float u_float2;\nuniform float u_float3;\nuniform float u_float4;\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nvec3 applyLevels(vec3 color, float inBlack, float inWhite, float gamma, float outBlack, float outWhite) {\n    float inRange = max(inWhite - inBlack, 0.0001);\n    vec3 result = clamp((color - inBlack) / inRange, 0.0, 1.0);\n    result = pow(result, vec3(1.0 / gamma));\n    result = mix(vec3(outBlack), vec3(outWhite), result);\n    return result;\n}\n\nfloat applySingleChannel(float value, float inBlack, float inWhite, float gamma, float outBlack, float outWhite) {\n    float inRange = max(inWhite - inBlack, 0.0001);\n    float result = clamp((value - inBlack) / inRange, 0.0, 1.0);\n    result = pow(result, 1.0 / gamma);\n    result = mix(outBlack, outWhite, result);\n    return result;\n}\n\nvoid main() {\n    vec4 texColor = texture(u_image0, v_texCoord);\n    vec3 color = texColor.rgb;\n    \n    float inBlack = u_float0 / 255.0;\n    float inWhite = u_float1 / 255.0;\n    float gamma = u_float2;\n    float outBlack = u_float3 / 255.0;\n    float outWhite = u_float4 / 255.0;\n    \n    vec3 result;\n    \n    if (u_int0 == 0) {\n        result = applyLevels(color, inBlack, inWhite, gamma, outBlack, outWhite);\n    }\n    else if (u_int0 == 1) {\n        result = color;\n        result.r = applySingleChannel(color.r, inBlack, inWhite, gamma, outBlack, outWhite);\n    }\n    else if (u_int0 == 2) {\n        result = color;\n        result.g = applySingleChannel(color.g, inBlack, inWhite, gamma, outBlack, outWhite);\n    }\n    else if (u_int0 == 3) {\n        result = color;\n        result.b = applySingleChannel(color.b, inBlack, inWhite, gamma, outBlack, outWhite);\n    }\n    else {\n        result = color;\n    }\n    \n    fragColor = vec4(result, texColor.a);\n}", "from_input"]}], "groups": [], "links": [{"id": 2, "origin_id": 3, "origin_slot": 0, "target_id": 1, "target_slot": 2, "type": "FLOAT"}, {"id": 4, "origin_id": 6, "origin_slot": 0, "target_id": 1, "target_slot": 3, "type": "FLOAT"}, {"id": 5, "origin_id": 7, "origin_slot": 0, "target_id": 1, "target_slot": 4, "type": "FLOAT"}, {"id": 6, "origin_id": 8, "origin_slot": 0, "target_id": 1, "target_slot": 5, "type": "FLOAT"}, {"id": 7, "origin_id": 9, "origin_slot": 0, "target_id": 1, "target_slot": 6, "type": "FLOAT"}, {"id": 3, "origin_id": 5, "origin_slot": 1, "target_id": 1, "target_slot": 7, "type": "INT"}, {"id": 1, "origin_id": -10, "origin_slot": 0, "target_id": 1, "target_slot": 0, "type": "IMAGE"}, {"id": 8, "origin_id": 1, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}}]}, "extra": {}}
\ No newline at end of file

From 5fb8f06495071fc21a8403495456db0d1e246551 Mon Sep 17 00:00:00 2001
From: Christian Byrne <cbyrne@comfy.org>
Date: Fri, 20 Feb 2026 23:40:13 -0800
Subject: [PATCH 025/194] feat: add essential subgraph blueprints (#12552)

Add 24 non-cloud essential blueprints from comfyui-wiki/Subgraph-Blueprints.
These cover common workflows: text/image/video generation, editing,
inpainting, outpainting, upscaling, depth maps, pose, captioning, and more.

Cloud-only blueprints (5) are excluded and will be added once
client-side distribution filtering lands.

Amp-Thread-ID: https://ampcode.com/threads/T-019c6f43-6212-7308-bea6-bfc35a486cbf
---
 blueprints/Brightness and Contrast.json             | 2 +-
 blueprints/Canny to Image (Z-Image-Turbo).json      | 1 +
 blueprints/Canny to Video (LTX 2.0).json            | 1 +
 blueprints/Chromatic Aberration.json                | 2 +-
 blueprints/Color Adjustment.json                    | 2 +-
 blueprints/Depth to Image (Z-Image-Turbo).json      | 1 +
 blueprints/Depth to Video (ltx 2.0).json            | 1 +
 blueprints/Edge-Preserving Blur.json                | 2 +-
 blueprints/Film Grain.json                          | 2 +-
 blueprints/Glow.json                                | 2 +-
 blueprints/Hue and Saturation.json                  | 2 +-
 blueprints/Image Blur.json                          | 2 +-
 blueprints/Image Captioning (gemini).json           | 1 +
 blueprints/Image Channels.json                      | 2 +-
 blueprints/Image Edit (Flux.2 Klein 4B).json        | 1 +
 blueprints/Image Edit (Qwen 2511).json              | 1 +
 blueprints/Image Inpainting (Qwen-image).json       | 1 +
 blueprints/Image Levels.json                        | 2 +-
 blueprints/Image Outpainting (Qwen-Image).json      | 1 +
 blueprints/Image Upscale(Z-image-Turbo).json        | 1 +
 blueprints/Image to Depth Map (Lotus).json          | 1 +
 blueprints/Image to Layers(Qwen-Image Layered).json | 1 +
 blueprints/Image to Model (Hunyuan3d 2.1).json      | 1 +
 blueprints/Image to Video (Wan 2.2).json            | 1 +
 blueprints/Pose to Image (Z-Image-Turbo).json       | 1 +
 blueprints/Pose to Video (LTX 2.0).json             | 1 +
 blueprints/Prompt Enhance.json                      | 1 +
 blueprints/Sharpen.json                             | 2 +-
 blueprints/Text to Audio (ACE-Step 1.5).json        | 1 +
 blueprints/Text to Image (Z-Image-Turbo).json       | 1 +
 blueprints/Text to Video (Wan 2.2).json             | 1 +
 blueprints/Unsharp Mask.json                        | 2 +-
 blueprints/Video Captioning (Gemini).json           | 1 +
 blueprints/Video Inpaint(Wan2.1 VACE).json          | 1 +
 blueprints/Video Stitch.json                        | 1 +
 blueprints/Video Upscale(GAN x4).json               | 1 +
 comfy_extras/nodes_canny.py                         | 1 +
 comfy_extras/nodes_images.py                        | 1 +
 comfy_extras/nodes_post_processing.py               | 2 ++
 39 files changed, 40 insertions(+), 12 deletions(-)
 create mode 100644 blueprints/Canny to Image (Z-Image-Turbo).json
 create mode 100644 blueprints/Canny to Video (LTX 2.0).json
 create mode 100644 blueprints/Depth to Image (Z-Image-Turbo).json
 create mode 100644 blueprints/Depth to Video (ltx 2.0).json
 create mode 100644 blueprints/Image Captioning (gemini).json
 create mode 100644 blueprints/Image Edit (Flux.2 Klein 4B).json
 create mode 100644 blueprints/Image Edit (Qwen 2511).json
 create mode 100644 blueprints/Image Inpainting (Qwen-image).json
 create mode 100644 blueprints/Image Outpainting (Qwen-Image).json
 create mode 100644 blueprints/Image Upscale(Z-image-Turbo).json
 create mode 100644 blueprints/Image to Depth Map (Lotus).json
 create mode 100644 blueprints/Image to Layers(Qwen-Image Layered).json
 create mode 100644 blueprints/Image to Model (Hunyuan3d 2.1).json
 create mode 100644 blueprints/Image to Video (Wan 2.2).json
 create mode 100644 blueprints/Pose to Image (Z-Image-Turbo).json
 create mode 100644 blueprints/Pose to Video (LTX 2.0).json
 create mode 100644 blueprints/Prompt Enhance.json
 create mode 100644 blueprints/Text to Audio (ACE-Step 1.5).json
 create mode 100644 blueprints/Text to Image (Z-Image-Turbo).json
 create mode 100644 blueprints/Text to Video (Wan 2.2).json
 create mode 100644 blueprints/Video Captioning (Gemini).json
 create mode 100644 blueprints/Video Inpaint(Wan2.1 VACE).json
 create mode 100644 blueprints/Video Stitch.json
 create mode 100644 blueprints/Video Upscale(GAN x4).json

diff --git a/blueprints/Brightness and Contrast.json b/blueprints/Brightness and Contrast.json
index 2ef05ea54..2c7e60eb1 100644
--- a/blueprints/Brightness and Contrast.json	
+++ b/blueprints/Brightness and Contrast.json	
@@ -1 +1 @@
-{"revision": 0, "last_node_id": 140, "last_link_id": 0, "nodes": [{"id": 140, "type": "916dff42-6166-4d45-b028-04eaf69fbb35", "pos": [500, 1440], "size": [250, 178], "flags": {}, "order": 2, "mode": 0, "inputs": [{"label": "image", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": null}], "outputs": [{"label": "IMAGE", "localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": []}], "properties": {"proxyWidgets": [["4", "value"], ["5", "value"]]}, "widgets_values": [], "title": "Brightness and Contrast"}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "916dff42-6166-4d45-b028-04eaf69fbb35", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 143, "lastLinkId": 118, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Brightness and Contrast", "inputNode": {"id": -10, "bounding": [360, -176, 120, 60]}, "outputNode": {"id": -20, "bounding": [1410, -176, 120, 60]}, "inputs": [{"id": "a5aae7ea-b511-4045-b5da-94101e269cd7", "name": "images.image0", "type": "IMAGE", "linkIds": [117], "localized_name": "images.image0", "label": "image", "pos": [460, -156]}], "outputs": [{"id": "30b72604-69b3-4944-b253-a9099bbd73a9", "name": "IMAGE0", "type": "IMAGE", "linkIds": [118], "localized_name": "IMAGE0", "label": "IMAGE", "pos": [1430, -156]}], "widgets": [], "nodes": [{"id": 4, "type": "PrimitiveFloat", "pos": [540, -280], "size": [270, 58], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "brightness", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [115]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 100, "precision": 1, "step": 1, "display": "gradientslider", "gradient_stops": [{"offset": 0, "color": [0, 0, 0]}, {"offset": 1, "color": [255, 255, 255]}]}, "widgets_values": [50]}, {"id": 5, "type": "PrimitiveFloat", "pos": [540, -170], "size": [270, 58], "flags": {}, "order": 1, "mode": 0, "inputs": [{"label": "contrast", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [116]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 100, "precision": 1, "step": 1, "display": "gradientslider", "gradient_stops": [{"offset": 0, "color": [136, 136, 136]}, {"offset": 0.4, "color": [68, 68, 68]}, {"offset": 0.6, "color": [187, 187, 187]}, {"offset": 0.8, "color": [0, 0, 0]}, {"offset": 1, "color": [255, 255, 255]}]}, "widgets_values": [0]}, {"id": 143, "type": "GLSLShader", "pos": [840, -280], "size": [400, 212], "flags": {}, "order": 2, "mode": 0, "inputs": [{"label": "image0", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": 117}, {"label": "image1", "localized_name": "images.image1", "name": "images.image1", "shape": 7, "type": "IMAGE", "link": null}, {"label": "u_float0", "localized_name": "floats.u_float0", "name": "floats.u_float0", "shape": 7, "type": "FLOAT", "link": 115}, {"label": "u_float1", "localized_name": "floats.u_float1", "name": "floats.u_float1", "shape": 7, "type": "FLOAT", "link": 116}, {"label": "u_float2", "localized_name": "floats.u_float2", "name": "floats.u_float2", "shape": 7, "type": "FLOAT", "link": null}, {"label": "u_int0", "localized_name": "ints.u_int0", "name": "ints.u_int0", "shape": 7, "type": "INT", "link": null}, {"localized_name": "fragment_shader", "name": "fragment_shader", "type": "STRING", "widget": {"name": "fragment_shader"}, "link": null}, {"localized_name": "size_mode", "name": "size_mode", "type": "COMFY_DYNAMICCOMBO_V3", "widget": {"name": "size_mode"}, "link": null}], "outputs": [{"localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": [118]}, {"localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": null}], "properties": {"Node name for S&R": "GLSLShader"}, "widgets_values": ["#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform float u_float0; // Brightness slider -100..100\nuniform float u_float1; // Contrast slider -100..100\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst float MID_GRAY = 0.18;  // 18% reflectance\n\n// sRGB gamma 2.2 approximation\nvec3 srgbToLinear(vec3 c) {\n    return pow(max(c, 0.0), vec3(2.2));\n}\n\nvec3 linearToSrgb(vec3 c) {\n    return pow(max(c, 0.0), vec3(1.0/2.2));\n}\n\nfloat mapBrightness(float b) {\n    return clamp(b / 100.0, -1.0, 1.0);\n}\n\nfloat mapContrast(float c) {\n    return clamp(c / 100.0 + 1.0, 0.0, 2.0);\n}\n\nvoid main() {\n    vec4 orig = texture(u_image0, v_texCoord);\n\n    float brightness = mapBrightness(u_float0);\n    float contrast   = mapContrast(u_float1);\n\n    vec3 lin = srgbToLinear(orig.rgb);\n\n    lin = (lin - MID_GRAY) * contrast + brightness + MID_GRAY;\n\n    // Convert back to sRGB\n    vec3 result = linearToSrgb(clamp(lin, 0.0, 1.0));\n\n    fragColor = vec4(result, orig.a);\n}\n", "from_input"]}], "groups": [], "links": [{"id": 115, "origin_id": 4, "origin_slot": 0, "target_id": 143, "target_slot": 2, "type": "FLOAT"}, {"id": 116, "origin_id": 5, "origin_slot": 0, "target_id": 143, "target_slot": 3, "type": "FLOAT"}, {"id": 117, "origin_id": -10, "origin_slot": 0, "target_id": 143, "target_slot": 0, "type": "IMAGE"}, {"id": 118, "origin_id": 143, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}}]}, "extra": {}}
\ No newline at end of file
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diff --git a/blueprints/Canny to Image (Z-Image-Turbo).json b/blueprints/Canny to Image (Z-Image-Turbo).json
new file mode 100644
index 000000000..8b78a834a
--- /dev/null
+++ b/blueprints/Canny to Image (Z-Image-Turbo).json	
@@ -0,0 +1 @@
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diff --git a/blueprints/Canny to Video (LTX 2.0).json b/blueprints/Canny to Video (LTX 2.0).json
new file mode 100644
index 000000000..cd2c4e594
--- /dev/null
+++ b/blueprints/Canny to Video (LTX 2.0).json	
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diff --git a/blueprints/Chromatic Aberration.json b/blueprints/Chromatic Aberration.json
index ffbf41c4e..5513cc665 100644
--- a/blueprints/Chromatic Aberration.json	
+++ b/blueprints/Chromatic Aberration.json	
@@ -1 +1 @@
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corrected / r : vec2(0.0);\n    vec2 offset = vec2(0.0);\n\n    if (u_int0 == MODE_LINEAR) {\n        // Horizontal shift (no aspect correction needed)\n        offset = vec2(amount, 0.0);\n    }\n    else if (u_int0 == MODE_RADIAL) {\n        // Outward from center, stronger at edges\n        offset = dir * r * amount * RADIAL_MULT;\n        offset.x /= aspect;  // Convert back to UV space\n    }\n    else if (u_int0 == MODE_BARREL) {\n        // Lens distortion simulation (r\u00b2 falloff)\n        offset = dir * r * r * amount * BARREL_MULT;\n        offset.x /= aspect;  // Convert back to UV space\n    }\n    else if (u_int0 == MODE_SWIRL) {\n        // Perpendicular to radial (rotational aberration)\n        vec2 perp = vec2(-dir.y, dir.x);\n        offset = perp * r * amount * RADIAL_MULT;\n        offset.x /= aspect;  // Convert back to UV space\n    }\n    else if (u_int0 == MODE_DIAGONAL) {\n        // 45\u00b0 offset (no aspect correction needed)\n        offset = vec2(amount, amount) * INV_SQRT2;\n    }\n    \n    float red = texture(u_image0, uv + offset).r;\n    float green = original.g;\n    float blue = texture(u_image0, uv - offset).b;\n    \n    fragColor = vec4(red, green, blue, original.a);\n}", "from_input"]}, {"id": 18, "type": "PrimitiveFloat", "pos": [3810, -2430], "size": [270, 58], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "amount", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [22]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 100, "step": 1}, "widgets_values": [30]}, {"id": 17, "type": "CustomCombo", "pos": [3520, -2320], "size": [270, 222], "flags": {}, "order": 1, "mode": 0, "inputs": [{"label": "mode", "localized_name": "choice", "name": "choice", "type": "COMBO", "widget": {"name": "choice"}, "link": null}], "outputs": [{"localized_name": "STRING", "name": "STRING", "type": "STRING", "links": null}, {"localized_name": "INDEX", "name": "INDEX", "type": "INT", "links": [21]}], "properties": {"Node name for S&R": "CustomCombo"}, "widgets_values": ["Linear", 0, "Linear", "Radial", "Barrel", "Swirl", "Diagonal", ""]}], "groups": [], "links": [{"id": 22, "origin_id": 18, "origin_slot": 0, "target_id": 16, "target_slot": 2, "type": "FLOAT"}, {"id": 21, "origin_id": 17, "origin_slot": 1, "target_id": 16, "target_slot": 4, "type": "INT"}, {"id": 20, "origin_id": -10, "origin_slot": 0, "target_id": 16, "target_slot": 0, "type": "IMAGE"}, {"id": 23, "origin_id": 16, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}}]}}
\ No newline at end of file
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corrected / r : vec2(0.0);\n    vec2 offset = vec2(0.0);\n\n    if (u_int0 == MODE_LINEAR) {\n        // Horizontal shift (no aspect correction needed)\n        offset = vec2(amount, 0.0);\n    }\n    else if (u_int0 == MODE_RADIAL) {\n        // Outward from center, stronger at edges\n        offset = dir * r * amount * RADIAL_MULT;\n        offset.x /= aspect;  // Convert back to UV space\n    }\n    else if (u_int0 == MODE_BARREL) {\n        // Lens distortion simulation (r² falloff)\n        offset = dir * r * r * amount * BARREL_MULT;\n        offset.x /= aspect;  // Convert back to UV space\n    }\n    else if (u_int0 == MODE_SWIRL) {\n        // Perpendicular to radial (rotational aberration)\n        vec2 perp = vec2(-dir.y, dir.x);\n        offset = perp * r * amount * RADIAL_MULT;\n        offset.x /= aspect;  // Convert back to UV space\n    }\n    else if (u_int0 == MODE_DIAGONAL) {\n        // 45° offset (no aspect correction needed)\n        offset = vec2(amount, amount) * INV_SQRT2;\n    }\n    \n    float red = texture(u_image0, uv + offset).r;\n    float green = original.g;\n    float blue = texture(u_image0, uv - offset).b;\n    \n    fragColor = vec4(red, green, blue, original.a);\n}", "from_input"]}, {"id": 18, "type": "PrimitiveFloat", "pos": [3810, -2430], "size": [270, 58], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "amount", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [22]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 100, "step": 1}, "widgets_values": [30]}, {"id": 17, "type": "CustomCombo", "pos": [3520, -2320], "size": [270, 222], "flags": {}, "order": 1, "mode": 0, "inputs": [{"label": "mode", "localized_name": "choice", "name": "choice", "type": "COMBO", "widget": {"name": "choice"}, "link": null}], "outputs": [{"localized_name": "STRING", "name": "STRING", "type": "STRING", "links": null}, {"localized_name": "INDEX", "name": "INDEX", "type": "INT", "links": [21]}], "properties": {"Node name for S&R": "CustomCombo"}, "widgets_values": ["Linear", 0, "Linear", "Radial", "Barrel", "Swirl", "Diagonal", ""]}], "groups": [], "links": [{"id": 22, "origin_id": 18, "origin_slot": 0, "target_id": 16, "target_slot": 2, "type": "FLOAT"}, {"id": 21, "origin_id": 17, "origin_slot": 1, "target_id": 16, "target_slot": 4, "type": "INT"}, {"id": 20, "origin_id": -10, "origin_slot": 0, "target_id": 16, "target_slot": 0, "type": "IMAGE"}, {"id": 23, "origin_id": 16, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Image Tools/Color adjust"}]}}
diff --git a/blueprints/Color Adjustment.json b/blueprints/Color Adjustment.json
index 711768737..c599f7213 100644
--- a/blueprints/Color Adjustment.json	
+++ b/blueprints/Color Adjustment.json	
@@ -1 +1 @@
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// temperature (-100 to 100)\nuniform float u_float1; // tint (-100 to 100)\nuniform float u_float2; // vibrance (-100 to 100)\nuniform float u_float3; // saturation (-100 to 100)\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst float INPUT_SCALE = 0.01;\nconst float TEMP_TINT_PRIMARY = 0.3;\nconst float TEMP_TINT_SECONDARY = 0.15;\nconst float VIBRANCE_BOOST = 2.0;\nconst float SATURATION_BOOST = 2.0;\nconst float SKIN_PROTECTION = 0.5;\nconst float EPSILON = 0.001;\nconst vec3 LUMA_WEIGHTS = vec3(0.299, 0.587, 0.114);\n\nvoid main() {\n    vec4 tex = texture(u_image0, v_texCoord);\n    vec3 color = tex.rgb;\n    \n    // Scale inputs: -100/100 \u2192 -1/1\n    float temperature = u_float0 * INPUT_SCALE;\n    float tint = u_float1 * INPUT_SCALE;\n    float vibrance = u_float2 * INPUT_SCALE;\n    float saturation = u_float3 * INPUT_SCALE;\n    \n    // Temperature (warm/cool): positive = warm, negative = cool\n    color.r += temperature * TEMP_TINT_PRIMARY;\n    color.b -= temperature * TEMP_TINT_PRIMARY;\n    \n    // Tint (green/magenta): positive = green, negative = magenta\n    color.g += tint * TEMP_TINT_PRIMARY;\n    color.r -= tint * TEMP_TINT_SECONDARY;\n    color.b -= tint * TEMP_TINT_SECONDARY;\n    \n    // Single clamp after temperature/tint\n    color = clamp(color, 0.0, 1.0);\n    \n    // Vibrance with skin protection\n    if (vibrance != 0.0) {\n        float maxC = max(color.r, max(color.g, color.b));\n        float minC = min(color.r, min(color.g, color.b));\n        float sat = maxC - minC;\n        float gray = dot(color, LUMA_WEIGHTS);\n        \n        if (vibrance < 0.0) {\n            // Desaturate: -100 \u2192 gray\n            color = mix(vec3(gray), color, 1.0 + vibrance);\n        } else {\n            // Boost less saturated colors more\n            float vibranceAmt = vibrance * (1.0 - sat);\n            \n            // Branchless skin tone protection\n            float isWarmTone = step(color.b, color.g) * step(color.g, color.r);\n            float warmth = (color.r - color.b) / max(maxC, EPSILON);\n            float skinTone = isWarmTone * warmth * sat * (1.0 - sat);\n            vibranceAmt *= (1.0 - skinTone * SKIN_PROTECTION);\n            \n            color = mix(vec3(gray), color, 1.0 + vibranceAmt * VIBRANCE_BOOST);\n        }\n    }\n    \n    // Saturation\n    if (saturation != 0.0) {\n        float gray = dot(color, LUMA_WEIGHTS);\n        float satMix = saturation < 0.0\n            ? 1.0 + saturation                      // -100 \u2192 gray\n            : 1.0 + saturation * SATURATION_BOOST; 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// temperature (-100 to 100)\nuniform float u_float1; // tint (-100 to 100)\nuniform float u_float2; // vibrance (-100 to 100)\nuniform float u_float3; // saturation (-100 to 100)\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst float INPUT_SCALE = 0.01;\nconst float TEMP_TINT_PRIMARY = 0.3;\nconst float TEMP_TINT_SECONDARY = 0.15;\nconst float VIBRANCE_BOOST = 2.0;\nconst float SATURATION_BOOST = 2.0;\nconst float SKIN_PROTECTION = 0.5;\nconst float EPSILON = 0.001;\nconst vec3 LUMA_WEIGHTS = vec3(0.299, 0.587, 0.114);\n\nvoid main() {\n    vec4 tex = texture(u_image0, v_texCoord);\n    vec3 color = tex.rgb;\n    \n    // Scale inputs: -100/100 \u2192 -1/1\n    float temperature = u_float0 * INPUT_SCALE;\n    float tint = u_float1 * INPUT_SCALE;\n    float vibrance = u_float2 * INPUT_SCALE;\n    float saturation = u_float3 * INPUT_SCALE;\n    \n    // Temperature (warm/cool): positive = warm, negative = cool\n    color.r += temperature * TEMP_TINT_PRIMARY;\n    color.b -= temperature * TEMP_TINT_PRIMARY;\n    \n    // Tint (green/magenta): positive = green, negative = magenta\n    color.g += tint * TEMP_TINT_PRIMARY;\n    color.r -= tint * TEMP_TINT_SECONDARY;\n    color.b -= tint * TEMP_TINT_SECONDARY;\n    \n    // Single clamp after temperature/tint\n    color = clamp(color, 0.0, 1.0);\n    \n    // Vibrance with skin protection\n    if (vibrance != 0.0) {\n        float maxC = max(color.r, max(color.g, color.b));\n        float minC = min(color.r, min(color.g, color.b));\n        float sat = maxC - minC;\n        float gray = dot(color, LUMA_WEIGHTS);\n        \n        if (vibrance < 0.0) {\n            // Desaturate: -100 \u2192 gray\n            color = mix(vec3(gray), color, 1.0 + vibrance);\n        } else {\n            // Boost less saturated colors more\n            float vibranceAmt = vibrance * (1.0 - sat);\n            \n            // Branchless skin tone protection\n            float isWarmTone = step(color.b, color.g) * step(color.g, color.r);\n            float warmth = (color.r - color.b) / max(maxC, EPSILON);\n            float skinTone = isWarmTone * warmth * sat * (1.0 - sat);\n            vibranceAmt *= (1.0 - skinTone * SKIN_PROTECTION);\n            \n            color = mix(vec3(gray), color, 1.0 + vibranceAmt * VIBRANCE_BOOST);\n        }\n    }\n    \n    // Saturation\n    if (saturation != 0.0) {\n        float gray = dot(color, LUMA_WEIGHTS);\n        float satMix = saturation < 0.0\n            ? 1.0 + saturation                      // -100 \u2192 gray\n            : 1.0 + saturation * SATURATION_BOOST; 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diff --git a/blueprints/Depth to Image (Z-Image-Turbo).json b/blueprints/Depth to Image (Z-Image-Turbo).json
new file mode 100644
index 000000000..baffc4fc9
--- /dev/null
+++ b/blueprints/Depth to Image (Z-Image-Turbo).json	
@@ -0,0 +1 @@
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diff --git a/blueprints/Depth to Video (ltx 2.0).json b/blueprints/Depth to Video (ltx 2.0).json
new file mode 100644
index 000000000..9656b6253
--- /dev/null
+++ b/blueprints/Depth to Video (ltx 2.0).json	
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diff --git a/blueprints/Edge-Preserving Blur.json b/blueprints/Edge-Preserving Blur.json
index ad7d62658..4f2416e9b 100644
--- a/blueprints/Edge-Preserving Blur.json	
+++ b/blueprints/Edge-Preserving Blur.json	
@@ -1 +1 @@
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  // Blur radius (0–20, default ~5)\nuniform float u_float1;   // Edge threshold (0–100, default ~30)\nuniform int u_int0;       // Step size (0/1 = every pixel, 2+ = skip pixels)\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst int MAX_RADIUS = 20;\nconst float EPSILON = 0.0001;\n\n// Perceptual luminance\nfloat getLuminance(vec3 rgb) {\n    return dot(rgb, vec3(0.299, 0.587, 0.114));\n}\n\nvec4 bilateralFilter(vec2 uv, vec2 texelSize, int radius,\n                     float sigmaSpatial, float sigmaColor)\n{\n    vec4 center = texture(u_image0, uv);\n    vec3 centerRGB = center.rgb;\n\n    float invSpatial2 = -0.5 / (sigmaSpatial * sigmaSpatial);\n    float invColor2   = -0.5 / (sigmaColor * sigmaColor + EPSILON);\n\n    vec3 sumRGB = vec3(0.0);\n    float sumWeight = 0.0;\n\n    int step = max(u_int0, 1);\n    float radius2 = float(radius * radius);\n\n    for (int dy = -MAX_RADIUS; dy <= MAX_RADIUS; dy++) {\n        if (dy < -radius || dy > radius) continue;\n        if (abs(dy) % step != 0) continue;\n\n        for (int dx = -MAX_RADIUS; dx <= MAX_RADIUS; dx++) {\n            if (dx < -radius || dx > radius) continue;\n            if (abs(dx) % step != 0) continue;\n\n            vec2 offset = vec2(float(dx), float(dy));\n            float dist2 = dot(offset, offset);\n            if (dist2 > radius2) continue;\n\n            vec3 sampleRGB = texture(u_image0, uv + offset * texelSize).rgb;\n\n            // Spatial Gaussian\n            float spatialWeight = exp(dist2 * invSpatial2);\n\n            // Perceptual color distance (weighted RGB)\n            vec3 diff = sampleRGB - centerRGB;\n            float colorDist = dot(diff * diff, vec3(0.299, 0.587, 0.114));\n            float colorWeight = exp(colorDist * invColor2);\n\n            float w = spatialWeight * colorWeight;\n            sumRGB += sampleRGB * w;\n            sumWeight += w;\n        }\n    }\n\n    vec3 resultRGB = sumRGB / max(sumWeight, EPSILON);\n    return vec4(resultRGB, center.a); // preserve center alpha\n}\n\nvoid main() {\n    vec2 texelSize = 1.0 / vec2(textureSize(u_image0, 0));\n\n    float radiusF = clamp(u_float0, 0.0, float(MAX_RADIUS));\n    int radius = int(radiusF + 0.5);\n\n    if (radius == 0) {\n        fragColor = texture(u_image0, v_texCoord);\n        return;\n    }\n\n    // Edge threshold → color sigma\n    // Squared curve for better low-end control\n    float t = clamp(u_float1, 0.0, 100.0) / 100.0;\n    t *= t;\n    float sigmaColor = mix(0.01, 0.5, t);\n\n    // Spatial sigma tied to radius\n    float sigmaSpatial = max(radiusF * 0.75, 0.5);\n\n    fragColor = bilateralFilter(\n        v_texCoord,\n        texelSize,\n        radius,\n        sigmaSpatial,\n        sigmaColor\n    );\n}", "from_input"]}, {"id": 130, "type": "PrimitiveFloat", "pos": [1930, -860], "size": [270, 58], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "blur_radius", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [100]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 20, "step": 0.5, "precision": 1}, "widgets_values": [20]}, {"id": 131, "type": "PrimitiveFloat", "pos": [1930, -760], "size": [270, 58], "flags": {}, "order": 1, "mode": 0, "inputs": [{"label": "edge_threshold", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [101]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 100, "step": 1}, "widgets_values": [50]}, {"id": 133, "type": "PrimitiveInt", "pos": [1930, -660], "size": [270, 82], "flags": {}, "order": 2, "mode": 0, "inputs": [{"label": "step_size", "localized_name": "value", "name": "value", "type": "INT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "INT", "name": "INT", "type": "INT", "links": [103, 107]}], "properties": {"Node name for S&R": "PrimitiveInt", "min": 0}, "widgets_values": [1, "fixed"]}], "groups": [], "links": [{"id": 100, "origin_id": 130, "origin_slot": 0, "target_id": 128, "target_slot": 2, "type": "FLOAT"}, {"id": 101, "origin_id": 131, "origin_slot": 0, "target_id": 128, "target_slot": 3, "type": "FLOAT"}, {"id": 107, "origin_id": 133, "origin_slot": 0, "target_id": 128, "target_slot": 5, "type": "INT"}, {"id": 103, "origin_id": 133, "origin_slot": 0, "target_id": 128, "target_slot": 6, "type": "INT"}, {"id": 106, "origin_id": -10, "origin_slot": 0, "target_id": 128, "target_slot": 0, "type": "IMAGE"}, {"id": 99, "origin_id": 128, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Image Tools/Blur"}]}, "extra": {}}
diff --git a/blueprints/Film Grain.json b/blueprints/Film Grain.json
index d4af46911..b7ebe2a36 100644
--- a/blueprints/Film Grain.json	
+++ b/blueprints/Film Grain.json	
@@ -1 +1 @@
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2.0) * 0.7;  // Centered, scaled\n}\n\nfloat toGaussian(uvec2 p, uint offset) {\n    float sum = hashf(p, offset) + hashf(p, offset + 1u) \n              + hashf(p, offset + 2u) + hashf(p, offset + 3u);\n    return (sum - 2.0) * 0.7;\n}\n\n// Smooth noise with better interpolation\nfloat smoothNoise(vec2 p) {\n    vec2 i = floor(p);\n    vec2 f = fract(p);\n    \n    // Quintic interpolation (less banding than cubic)\n    f = f * f * f * (f * (f * 6.0 - 15.0) + 10.0);\n    \n    uvec2 ui = uvec2(i);\n    float a = toGaussian(ui);\n    float b = toGaussian(ui + uvec2(1u, 0u));\n    float c = toGaussian(ui + uvec2(0u, 1u));\n    float d = toGaussian(ui + uvec2(1u, 1u));\n    \n    return mix(mix(a, b, f.x), mix(c, d, f.x), f.y);\n}\n\nfloat smoothNoise(vec2 p, uint offset) {\n    vec2 i = floor(p);\n    vec2 f = fract(p);\n    \n    f = f * f * f * (f * (f * 6.0 - 15.0) + 10.0);\n    \n    uvec2 ui = uvec2(i);\n    float a = toGaussian(ui, offset);\n    float b = toGaussian(ui + uvec2(1u, 0u), offset);\n    float c = toGaussian(ui + uvec2(0u, 1u), offset);\n    float d = toGaussian(ui + uvec2(1u, 1u), offset);\n    \n    return mix(mix(a, b, f.x), mix(c, d, f.x), f.y);\n}\n\nvoid main() {\n    vec4 color = texture(u_image0, v_texCoord);\n    \n    // Luminance (Rec.709)\n    float luma = dot(color.rgb, vec3(0.2126, 0.7152, 0.0722));\n    \n    // Grain UV (resolution-independent)\n    vec2 grainUV = v_texCoord * u_resolution / max(u_float1, 0.01);\n    uvec2 grainPixel = uvec2(grainUV);\n    \n    float g;\n    vec3 grainRGB;\n    \n    if (u_int0 == 1) {\n        // Grainy mode: pure hash noise (no interpolation = no banding)\n        g = toGaussian(grainPixel);\n        grainRGB = vec3(\n            toGaussian(grainPixel, 100u),\n            toGaussian(grainPixel, 200u),\n            toGaussian(grainPixel, 300u)\n        );\n    } else {\n        // Smooth mode: interpolated with quintic curve\n        g = smoothNoise(grainUV);\n        grainRGB = vec3(\n            smoothNoise(grainUV, 100u),\n            smoothNoise(grainUV, 200u),\n            smoothNoise(grainUV, 300u)\n        );\n    }\n    \n    // Luminance weighting (less grain in highlights)\n    float lumWeight = mix(1.0, 1.0 - luma, clamp(u_float3, 0.0, 1.0));\n    \n    // Strength\n    float strength = u_float0 * 0.15;\n    \n    // Color vs monochrome grain\n    vec3 grainColor = mix(vec3(g), grainRGB, clamp(u_float2, 0.0, 1.0));\n    \n    color.rgb += grainColor * strength * lumWeight;\n    fragColor0 = vec4(clamp(color.rgb, 0.0, 1.0), color.a);\n}\n","from_input"]},{"id":21,"type":"CustomCombo","pos":[4280,-780],"size":[210,153.8888931274414],"flags":{},"order":0,"mode":0,"inputs":[{"label":"grain_mode","localized_name":"choice","name":"choice","type":"COMBO","widget":{"name":"choice"},"link":null}],"outputs":[{"localized_name":"STRING","name":"STRING","type":"STRING","links":null},{"localized_name":"INDEX","name":"INDEX","type":"INT","links":[30]}],"properties":{"Node name for S&R":"CustomCombo"},"widgets_values":["Smooth",0,"Smooth","Grainy",""]},{"id":17,"type":"PrimitiveFloat","pos":[4276.671470760602,-1180.3256994061358],"size":[210,58],"flags":{},"order":1,"mode":0,"inputs":[{"label":"grain_amount","localized_name":"value","name":"value","type":"FLOAT","widget":{"name":"value"},"link":null}],"outputs":[{"localized_name":"FLOAT","name":"FLOAT","type":"FLOAT","links":[26]}],"title":"Grain amount","properties":{"Node name for S&R":"PrimitiveFloat","min":0,"max":1,"step":0.05,"precision":2},"widgets_values":[0.25]},{"id":18,"type":"PrimitiveFloat","pos":[4280,-1080],"size":[210,58],"flags":{},"order":2,"mode":0,"inputs":[{"label":"grain_size","localized_name":"value","name":"value","type":"FLOAT","widget":{"name":"value"},"link":null}],"outputs":[{"localized_name":"FLOAT","name":"FLOAT","type":"FLOAT","links":[27]}],"title":"Grain size","properties":{"Node name for S&R":"PrimitiveFloat","min":0.05,"max":3,"precision":2,"step":0.05},"widgets_values":[0.1]},{"id":19,"type":"PrimitiveFloat","pos":[4280,-980],"size":[210,58],"flags":{},"order":3,"mode":0,"inputs":[{"label":"color_amount","localized_name":"value","name":"value","type":"FLOAT","widget":{"name":"value"},"link":null}],"outputs":[{"localized_name":"FLOAT","name":"FLOAT","type":"FLOAT","links":[28]}],"title":"Color amount","properties":{"Node name for S&R":"PrimitiveFloat","min":0,"max":1,"precision":2,"step":0.05},"widgets_values":[0]},{"id":20,"type":"PrimitiveFloat","pos":[4280,-880],"size":[210,58],"flags":{},"order":4,"mode":0,"inputs":[{"label":"shadow_focus","localized_name":"value","name":"value","type":"FLOAT","widget":{"name":"value"},"link":null}],"outputs":[{"localized_name":"FLOAT","name":"FLOAT","type":"FLOAT","links":[29]}],"title":"Luminance bias","properties":{"Node name for S&R":"PrimitiveFloat","min":0,"max":1,"precision":2,"step":0.05},"widgets_values":[0]}],"groups":[],"links":[{"id":26,"origin_id":17,"origin_slot":0,"target_id":15,"target_slot":2,"type":"FLOAT"},{"id":27,"origin_id":18,"origin_slot":0,"target_id":15,"target_slot":3,"type":"FLOAT"},{"id":28,"origin_id":19,"origin_slot":0,"target_id":15,"target_slot":4,"type":"FLOAT"},{"id":29,"origin_id":20,"origin_slot":0,"target_id":15,"target_slot":5,"type":"FLOAT"},{"id":30,"origin_id":21,"origin_slot":1,"target_id":15,"target_slot":7,"type":"INT"},{"id":22,"origin_id":-10,"origin_slot":0,"target_id":15,"target_slot":0,"type":"IMAGE"},{"id":23,"origin_id":15,"origin_slot":0,"target_id":-20,"target_slot":0,"type":"IMAGE"}],"extra":{"workflowRendererVersion":"LG"}}]}}
\ No newline at end of file
+{"revision": 0, "last_node_id": 22, "last_link_id": 0, "nodes": [{"id": 22, "type": "3324cf54-bcff-405f-a4bf-c5122c72fe56", "pos": [4800, -1180], "size": [250, 154], "flags": {}, "order": 4, "mode": 0, "inputs": [{"label": "image", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": null}], "outputs": [{"label": "IMAGE", "localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": []}], "title": "Film Grain", "properties": {"proxyWidgets": [["17", "value"], ["18", "value"], ["19", "value"], ["20", "value"], ["21", "choice"]]}, "widgets_values": []}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "3324cf54-bcff-405f-a4bf-c5122c72fe56", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 21, "lastLinkId": 30, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Film Grain", "inputNode": {"id": -10, "bounding": [4096.671470760602, -948.2184031393472, 120, 60]}, "outputNode": {"id": -20, "bounding": [4900, -948.2184031393472, 120, 60]}, "inputs": [{"id": "062968ea-da25-47e7-a180-d913c267f148", "name": "images.image0", "type": "IMAGE", "linkIds": [22], "localized_name": "images.image0", "label": "image", "pos": [4196.671470760602, -928.2184031393472]}], "outputs": [{"id": "43247d06-a39f-4733-9828-c39400fe02a4", "name": "IMAGE0", "type": "IMAGE", "linkIds": [23], "localized_name": "IMAGE0", "label": "IMAGE", "pos": [4920, -928.2184031393472]}], "widgets": [], "nodes": [{"id": 15, "type": "GLSLShader", "pos": [4510, -1180], "size": [330, 272], "flags": {}, "order": 5, "mode": 0, "inputs": [{"label": "image0", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": 22}, {"label": "image1", "localized_name": "images.image1", "name": "images.image1", "shape": 7, "type": "IMAGE", "link": null}, {"label": "u_float0", "localized_name": "floats.u_float0", "name": "floats.u_float0", "shape": 7, "type": "FLOAT", "link": 26}, {"label": "u_float1", "localized_name": "floats.u_float1", "name": "floats.u_float1", "shape": 7, "type": "FLOAT", "link": 27}, {"label": "u_float2", "localized_name": "floats.u_float2", "name": "floats.u_float2", "shape": 7, "type": "FLOAT", "link": 28}, {"label": "u_float3", "localized_name": "floats.u_float3", "name": "floats.u_float3", "shape": 7, "type": "FLOAT", "link": 29}, {"label": "u_float4", "localized_name": "floats.u_float4", "name": "floats.u_float4", "shape": 7, "type": "FLOAT", "link": null}, {"label": "u_int0", "localized_name": "ints.u_int0", "name": "ints.u_int0", "shape": 7, "type": "INT", "link": 30}, {"label": "u_int1", "localized_name": "ints.u_int1", "name": "ints.u_int1", "shape": 7, "type": "INT", "link": null}, {"localized_name": "fragment_shader", "name": "fragment_shader", "type": "STRING", "widget": {"name": "fragment_shader"}, "link": null}, {"localized_name": "size_mode", "name": "size_mode", "type": "COMFY_DYNAMICCOMBO_V3", "widget": {"name": "size_mode"}, "link": null}], "outputs": [{"localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": [23]}, {"localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": null}], "properties": {"Node name for S&R": "GLSLShader"}, "widgets_values": ["#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform float u_float0; // grain amount      [0.0 – 1.0]   typical: 0.2–0.8\nuniform float u_float1; // grain size        [0.3 – 3.0]   lower = finer grain\nuniform float u_float2; // color amount      [0.0 – 1.0]   0 = monochrome, 1 = RGB grain\nuniform float u_float3; // luminance bias    [0.0 – 1.0]   0 = uniform, 1 = shadows only\nuniform int   u_int0;   // noise mode        [0 or 1]      0 = smooth, 1 = grainy\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\n// High-quality integer hash (pcg-like)\nuint pcg(uint v) {\n    uint state = v * 747796405u + 2891336453u;\n    uint word = ((state >> ((state >> 28u) + 4u)) ^ state) * 277803737u;\n    return (word >> 22u) ^ word;\n}\n\n// 2D -> 1D hash input\nuint hash2d(uvec2 p) {\n    return pcg(p.x + pcg(p.y));\n}\n\n// Hash to float [0, 1]\nfloat hashf(uvec2 p) {\n    return float(hash2d(p)) / float(0xffffffffu);\n}\n\n// Hash to float with offset (for RGB channels)\nfloat hashf(uvec2 p, uint offset) {\n    return float(pcg(hash2d(p) + offset)) / float(0xffffffffu);\n}\n\n// Convert uniform [0,1] to roughly Gaussian distribution\n// Using simple approximation: average of multiple samples\nfloat toGaussian(uvec2 p) {\n    float sum = hashf(p, 0u) + hashf(p, 1u) + hashf(p, 2u) + hashf(p, 3u);\n    return (sum - 2.0) * 0.7;  // Centered, scaled\n}\n\nfloat toGaussian(uvec2 p, uint offset) {\n    float sum = hashf(p, offset) + hashf(p, offset + 1u) \n              + hashf(p, offset + 2u) + hashf(p, offset + 3u);\n    return (sum - 2.0) * 0.7;\n}\n\n// Smooth noise with better interpolation\nfloat smoothNoise(vec2 p) {\n    vec2 i = floor(p);\n    vec2 f = fract(p);\n    \n    // Quintic interpolation (less banding than cubic)\n    f = f * f * f * (f * (f * 6.0 - 15.0) + 10.0);\n    \n    uvec2 ui = uvec2(i);\n    float a = toGaussian(ui);\n    float b = toGaussian(ui + uvec2(1u, 0u));\n    float c = toGaussian(ui + uvec2(0u, 1u));\n    float d = toGaussian(ui + uvec2(1u, 1u));\n    \n    return mix(mix(a, b, f.x), mix(c, d, f.x), f.y);\n}\n\nfloat smoothNoise(vec2 p, uint offset) {\n    vec2 i = floor(p);\n    vec2 f = fract(p);\n    \n    f = f * f * f * (f * (f * 6.0 - 15.0) + 10.0);\n    \n    uvec2 ui = uvec2(i);\n    float a = toGaussian(ui, offset);\n    float b = toGaussian(ui + uvec2(1u, 0u), offset);\n    float c = toGaussian(ui + uvec2(0u, 1u), offset);\n    float d = toGaussian(ui + uvec2(1u, 1u), offset);\n    \n    return mix(mix(a, b, f.x), mix(c, d, f.x), f.y);\n}\n\nvoid main() {\n    vec4 color = texture(u_image0, v_texCoord);\n    \n    // Luminance (Rec.709)\n    float luma = dot(color.rgb, vec3(0.2126, 0.7152, 0.0722));\n    \n    // Grain UV (resolution-independent)\n    vec2 grainUV = v_texCoord * u_resolution / max(u_float1, 0.01);\n    uvec2 grainPixel = uvec2(grainUV);\n    \n    float g;\n    vec3 grainRGB;\n    \n    if (u_int0 == 1) {\n        // Grainy mode: pure hash noise (no interpolation = no banding)\n        g = toGaussian(grainPixel);\n        grainRGB = vec3(\n            toGaussian(grainPixel, 100u),\n            toGaussian(grainPixel, 200u),\n            toGaussian(grainPixel, 300u)\n        );\n    } else {\n        // Smooth mode: interpolated with quintic curve\n        g = smoothNoise(grainUV);\n        grainRGB = vec3(\n            smoothNoise(grainUV, 100u),\n            smoothNoise(grainUV, 200u),\n            smoothNoise(grainUV, 300u)\n        );\n    }\n    \n    // Luminance weighting (less grain in highlights)\n    float lumWeight = mix(1.0, 1.0 - luma, clamp(u_float3, 0.0, 1.0));\n    \n    // Strength\n    float strength = u_float0 * 0.15;\n    \n    // Color vs monochrome grain\n    vec3 grainColor = mix(vec3(g), grainRGB, clamp(u_float2, 0.0, 1.0));\n    \n    color.rgb += grainColor * strength * lumWeight;\n    fragColor0 = vec4(clamp(color.rgb, 0.0, 1.0), color.a);\n}\n", "from_input"]}, {"id": 21, "type": "CustomCombo", "pos": [4280, -780], "size": [210, 153.8888931274414], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "grain_mode", "localized_name": "choice", "name": "choice", "type": "COMBO", "widget": {"name": "choice"}, "link": null}], "outputs": [{"localized_name": "STRING", "name": "STRING", "type": "STRING", "links": null}, {"localized_name": "INDEX", "name": "INDEX", "type": "INT", "links": [30]}], "properties": {"Node name for S&R": "CustomCombo"}, "widgets_values": ["Smooth", 0, "Smooth", "Grainy", ""]}, {"id": 17, "type": "PrimitiveFloat", "pos": [4276.671470760602, -1180.3256994061358], "size": [210, 58], "flags": {}, "order": 1, "mode": 0, "inputs": [{"label": "grain_amount", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [26]}], "title": "Grain amount", "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 1, "step": 0.05, "precision": 2}, "widgets_values": [0.25]}, {"id": 18, "type": "PrimitiveFloat", "pos": [4280, -1080], "size": [210, 58], "flags": {}, "order": 2, "mode": 0, "inputs": [{"label": "grain_size", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [27]}], "title": "Grain size", "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0.05, "max": 3, "precision": 2, "step": 0.05}, "widgets_values": [0.1]}, {"id": 19, "type": "PrimitiveFloat", "pos": [4280, -980], "size": [210, 58], "flags": {}, "order": 3, "mode": 0, "inputs": [{"label": "color_amount", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [28]}], "title": "Color amount", "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 1, "precision": 2, "step": 0.05}, "widgets_values": [0]}, {"id": 20, "type": "PrimitiveFloat", "pos": [4280, -880], "size": [210, 58], "flags": {}, "order": 4, "mode": 0, "inputs": [{"label": "shadow_focus", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [29]}], "title": "Luminance bias", "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 1, "precision": 2, "step": 0.05}, "widgets_values": [0]}], "groups": [], "links": [{"id": 26, "origin_id": 17, "origin_slot": 0, "target_id": 15, "target_slot": 2, "type": "FLOAT"}, {"id": 27, "origin_id": 18, "origin_slot": 0, "target_id": 15, "target_slot": 3, "type": "FLOAT"}, {"id": 28, "origin_id": 19, "origin_slot": 0, "target_id": 15, "target_slot": 4, "type": "FLOAT"}, {"id": 29, "origin_id": 20, "origin_slot": 0, "target_id": 15, "target_slot": 5, "type": "FLOAT"}, {"id": 30, "origin_id": 21, "origin_slot": 1, "target_id": 15, "target_slot": 7, "type": "INT"}, {"id": 22, "origin_id": -10, "origin_slot": 0, "target_id": 15, "target_slot": 0, "type": "IMAGE"}, {"id": 23, "origin_id": 15, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Image Tools/Color adjust"}]}}
diff --git a/blueprints/Glow.json b/blueprints/Glow.json
index 0cfaf8fc2..590445c06 100644
--- a/blueprints/Glow.json
+++ b/blueprints/Glow.json
@@ -1 +1 @@
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     // Blend mode\nuniform int u_int1;      // Color tint\nuniform float u_float0;  // Intensity\nuniform float u_float1;  // Radius\nuniform float u_float2;  // Threshold\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst int BLEND_ADD      = 0;\nconst int BLEND_SCREEN   = 1;\nconst int BLEND_SOFT     = 2;\nconst int BLEND_OVERLAY  = 3;\nconst int BLEND_LIGHTEN  = 4;\n\nconst float GOLDEN_ANGLE = 2.39996323;\nconst int MAX_SAMPLES = 48;\nconst vec3 LUMA = vec3(0.299, 0.587, 0.114);\n\nfloat hash(vec2 p) {\n    p = fract(p * vec2(123.34, 456.21));\n    p += dot(p, p + 45.32);\n    return fract(p.x * p.y);\n}\n\nvec3 hexToRgb(int h) {\n    return vec3(\n        float((h >> 16) & 255),\n        float((h >> 8) & 255),\n        float(h & 255)\n    ) * (1.0 / 255.0);\n}\n\nvec3 blend(vec3 base, vec3 glow, int mode) {\n    if (mode == BLEND_SCREEN) {\n        return 1.0 - (1.0 - base) * (1.0 - glow);\n    }\n    if (mode == BLEND_SOFT) {\n        return mix(\n            base - (1.0 - 2.0 * glow) * base * (1.0 - base),\n            base + (2.0 * glow - 1.0) * (sqrt(base) - base),\n            step(0.5, glow)\n        );\n    }\n    if (mode == BLEND_OVERLAY) {\n        return mix(\n            2.0 * base * glow,\n            1.0 - 2.0 * (1.0 - base) * (1.0 - glow),\n            step(0.5, base)\n        );\n    }\n    if (mode == BLEND_LIGHTEN) {\n        return max(base, glow);\n    }\n    return base + glow;\n}\n\nvoid main() {\n    vec4 original = texture(u_image0, v_texCoord);\n    \n    float intensity = u_float0 * 0.05;\n    float radius = u_float1 * u_float1 * 0.012;\n    \n    if (intensity < 0.001 || radius < 0.1) {\n        fragColor = original;\n        return;\n    }\n    \n    float threshold = 1.0 - u_float2 * 0.01;\n    float t0 = threshold - 0.15;\n    float t1 = threshold + 0.15;\n    \n    vec2 texelSize = 1.0 / u_resolution;\n    float radius2 = radius * radius;\n    \n    float sampleScale = clamp(radius * 0.75, 0.35, 1.0);\n    int samples = int(float(MAX_SAMPLES) * sampleScale);\n    \n    float noise = hash(gl_FragCoord.xy);\n    float angleOffset = noise * GOLDEN_ANGLE;\n    float radiusJitter = 0.85 + noise * 0.3;\n    \n    float ca = cos(GOLDEN_ANGLE);\n    float sa = sin(GOLDEN_ANGLE);\n    vec2 dir = vec2(cos(angleOffset), sin(angleOffset));\n    \n    vec3 glow = vec3(0.0);\n    float totalWeight = 0.0;\n    \n    // Center tap\n    float centerMask = smoothstep(t0, t1, dot(original.rgb, LUMA));\n    glow += original.rgb * centerMask * 2.0;\n    totalWeight += 2.0;\n    \n    for (int i = 1; i < MAX_SAMPLES; i++) {\n        if (i >= samples) break;\n        \n        float fi = float(i);\n        float dist = sqrt(fi / float(samples)) * radius * radiusJitter;\n        \n        vec2 offset = dir * dist * texelSize;\n        vec3 c = texture(u_image0, v_texCoord + offset).rgb;\n        float mask = smoothstep(t0, t1, dot(c, LUMA));\n        \n        float w = 1.0 - (dist * dist) / (radius2 * 1.5);\n        w = max(w, 0.0);\n        w *= w;\n        \n        glow += c * mask * w;\n        totalWeight += w;\n        \n        dir = vec2(\n            dir.x * ca - dir.y * sa,\n            dir.x * sa + dir.y * ca\n        );\n    }\n    \n    glow *= intensity / max(totalWeight, 0.001);\n    \n    if (u_int1 > 0) {\n        glow *= hexToRgb(u_int1);\n    }\n    \n    vec3 result = blend(original.rgb, glow, u_int0);\n    result += (noise - 0.5) * (1.0 / 255.0);\n    \n    fragColor = vec4(clamp(result, 0.0, 1.0), original.a);\n}", "from_input"]}, {"id": 34, "type": "PrimitiveFloat", "pos": [2290, -510], "size": [270, 58], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "intensity", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [51]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 100, "precision": 1, "step": 1}, "widgets_values": [30]}, {"id": 35, "type": "PrimitiveFloat", "pos": [2290, -410], "size": [270, 58], "flags": {}, "order": 1, "mode": 0, "inputs": [{"label": "radius", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [50]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 100, "precision": 1, "step": 1}, "widgets_values": [25]}, {"id": 33, "type": "PrimitiveFloat", "pos": [2290, -310], "size": [270, 58], "flags": {}, "order": 2, "mode": 0, "inputs": [{"label": "threshold", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [52]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 100, "precision": 1, "step": 1}, "widgets_values": [100]}, {"id": 32, "type": "ColorToRGBInt", "pos": [2290, -210], "size": [270, 58], "flags": {}, "order": 3, "mode": 0, "inputs": [{"label": "color_tint", "localized_name": "color", "name": "color", "type": "COLOR", "widget": {"name": "color"}, "link": null}], "outputs": [{"localized_name": "rgb_int", "name": "rgb_int", "type": "INT", "links": [47]}], "properties": {"Node name for S&R": "ColorToRGBInt"}, "widgets_values": ["#45edf5"]}, {"id": 31, "type": "CustomCombo", "pos": [2290, -110], "size": [270, 222], "flags": {}, "order": 4, "mode": 0, "inputs": [{"label": "blend_mode", "localized_name": "choice", "name": "choice", "type": "COMBO", "widget": {"name": "choice"}, "link": null}], "outputs": [{"localized_name": "STRING", "name": "STRING", "type": "STRING", "links": null}, {"localized_name": "INDEX", "name": "INDEX", "type": "INT", "links": [46]}], "properties": {"Node name for S&R": "CustomCombo"}, "widgets_values": ["add", 0, "add", "screen", "soft", "overlay", "lighten", ""]}], "groups": [], "links": [{"id": 51, "origin_id": 34, "origin_slot": 0, "target_id": 30, "target_slot": 2, "type": "FLOAT"}, {"id": 50, "origin_id": 35, "origin_slot": 0, "target_id": 30, "target_slot": 3, "type": "FLOAT"}, {"id": 52, "origin_id": 33, "origin_slot": 0, "target_id": 30, "target_slot": 4, "type": "FLOAT"}, {"id": 46, "origin_id": 31, "origin_slot": 1, "target_id": 30, "target_slot": 6, "type": "INT"}, {"id": 47, "origin_id": 32, "origin_slot": 0, "target_id": 30, "target_slot": 7, "type": "INT"}, {"id": 45, "origin_id": -10, "origin_slot": 0, "target_id": 30, "target_slot": 0, "type": "IMAGE"}, {"id": 53, "origin_id": 30, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}}]}}
\ No newline at end of file
+{"revision": 0, "last_node_id": 37, "last_link_id": 0, "nodes": [{"id": 37, "type": "0a99445a-aaf8-4a7f-aec3-d7d710ae1495", "pos": [2160, -360], "size": [260, 154], "flags": {}, "order": 4, "mode": 0, "inputs": [{"label": "image", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": null}], "outputs": [{"label": "IMAGE", "localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": []}], "properties": {"proxyWidgets": [["34", "value"], ["35", "value"], ["33", "value"], ["31", "choice"], ["32", "color"]]}, "widgets_values": [], "title": "Glow"}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "0a99445a-aaf8-4a7f-aec3-d7d710ae1495", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 36, "lastLinkId": 53, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Glow", "inputNode": {"id": -10, "bounding": [2110, -165, 120, 60]}, "outputNode": {"id": -20, "bounding": [3170, -165, 120, 60]}, "inputs": [{"id": "ffc7cf94-be90-4d56-a3b8-d0514d61c015", "name": "images.image0", "type": "IMAGE", "linkIds": [45], "localized_name": "images.image0", "label": "image", "pos": [2210, -145]}], "outputs": [{"id": "04986101-50be-4762-8957-8e2a5e460bbb", "name": "IMAGE0", "type": "IMAGE", "linkIds": [53], "localized_name": "IMAGE0", "label": "IMAGE", "pos": [3190, -145]}], "widgets": [], "nodes": [{"id": 30, "type": "GLSLShader", "pos": [2590, -520], "size": [520, 272], "flags": {}, "order": 5, "mode": 0, "inputs": [{"label": "image0", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": 45}, {"label": "image1", "localized_name": "images.image1", "name": "images.image1", "shape": 7, "type": "IMAGE", "link": null}, {"label": "u_float0", "localized_name": "floats.u_float0", "name": "floats.u_float0", "shape": 7, "type": "FLOAT", "link": 51}, {"label": "u_float1", "localized_name": "floats.u_float1", "name": "floats.u_float1", "shape": 7, "type": "FLOAT", "link": 50}, {"label": "u_float2", "localized_name": "floats.u_float2", "name": "floats.u_float2", "shape": 7, "type": "FLOAT", "link": 52}, {"label": "u_float3", "localized_name": "floats.u_float3", "name": "floats.u_float3", "shape": 7, "type": "FLOAT", "link": null}, {"label": "u_int0", "localized_name": "ints.u_int0", "name": "ints.u_int0", "shape": 7, "type": "INT", "link": 46}, {"label": "u_int1", "localized_name": "ints.u_int1", "name": "ints.u_int1", "shape": 7, "type": "INT", "link": 47}, {"label": "u_int2", "localized_name": "ints.u_int2", "name": "ints.u_int2", "shape": 7, "type": "INT", "link": null}, {"localized_name": "fragment_shader", "name": "fragment_shader", "type": "STRING", "widget": {"name": "fragment_shader"}, "link": null}, {"localized_name": "size_mode", "name": "size_mode", "type": "COMFY_DYNAMICCOMBO_V3", "widget": {"name": "size_mode"}, "link": null}], "outputs": [{"localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": [53]}, {"localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": null}], "properties": {"Node name for S&R": "GLSLShader"}, "widgets_values": ["#version 300 es\nprecision mediump float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform int u_int0;      // Blend mode\nuniform int u_int1;      // Color tint\nuniform float u_float0;  // Intensity\nuniform float u_float1;  // Radius\nuniform float u_float2;  // Threshold\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst int BLEND_ADD      = 0;\nconst int BLEND_SCREEN   = 1;\nconst int BLEND_SOFT     = 2;\nconst int BLEND_OVERLAY  = 3;\nconst int BLEND_LIGHTEN  = 4;\n\nconst float GOLDEN_ANGLE = 2.39996323;\nconst int MAX_SAMPLES = 48;\nconst vec3 LUMA = vec3(0.299, 0.587, 0.114);\n\nfloat hash(vec2 p) {\n    p = fract(p * vec2(123.34, 456.21));\n    p += dot(p, p + 45.32);\n    return fract(p.x * p.y);\n}\n\nvec3 hexToRgb(int h) {\n    return vec3(\n        float((h >> 16) & 255),\n        float((h >> 8) & 255),\n        float(h & 255)\n    ) * (1.0 / 255.0);\n}\n\nvec3 blend(vec3 base, vec3 glow, int mode) {\n    if (mode == BLEND_SCREEN) {\n        return 1.0 - (1.0 - base) * (1.0 - glow);\n    }\n    if (mode == BLEND_SOFT) {\n        return mix(\n            base - (1.0 - 2.0 * glow) * base * (1.0 - base),\n            base + (2.0 * glow - 1.0) * (sqrt(base) - base),\n            step(0.5, glow)\n        );\n    }\n    if (mode == BLEND_OVERLAY) {\n        return mix(\n            2.0 * base * glow,\n            1.0 - 2.0 * (1.0 - base) * (1.0 - glow),\n            step(0.5, base)\n        );\n    }\n    if (mode == BLEND_LIGHTEN) {\n        return max(base, glow);\n    }\n    return base + glow;\n}\n\nvoid main() {\n    vec4 original = texture(u_image0, v_texCoord);\n    \n    float intensity = u_float0 * 0.05;\n    float radius = u_float1 * u_float1 * 0.012;\n    \n    if (intensity < 0.001 || radius < 0.1) {\n        fragColor = original;\n        return;\n    }\n    \n    float threshold = 1.0 - u_float2 * 0.01;\n    float t0 = threshold - 0.15;\n    float t1 = threshold + 0.15;\n    \n    vec2 texelSize = 1.0 / u_resolution;\n    float radius2 = radius * radius;\n    \n    float sampleScale = clamp(radius * 0.75, 0.35, 1.0);\n    int samples = int(float(MAX_SAMPLES) * sampleScale);\n    \n    float noise = hash(gl_FragCoord.xy);\n    float angleOffset = noise * GOLDEN_ANGLE;\n    float radiusJitter = 0.85 + noise * 0.3;\n    \n    float ca = cos(GOLDEN_ANGLE);\n    float sa = sin(GOLDEN_ANGLE);\n    vec2 dir = vec2(cos(angleOffset), sin(angleOffset));\n    \n    vec3 glow = vec3(0.0);\n    float totalWeight = 0.0;\n    \n    // Center tap\n    float centerMask = smoothstep(t0, t1, dot(original.rgb, LUMA));\n    glow += original.rgb * centerMask * 2.0;\n    totalWeight += 2.0;\n    \n    for (int i = 1; i < MAX_SAMPLES; i++) {\n        if (i >= samples) break;\n        \n        float fi = float(i);\n        float dist = sqrt(fi / float(samples)) * radius * radiusJitter;\n        \n        vec2 offset = dir * dist * texelSize;\n        vec3 c = texture(u_image0, v_texCoord + offset).rgb;\n        float mask = smoothstep(t0, t1, dot(c, LUMA));\n        \n        float w = 1.0 - (dist * dist) / (radius2 * 1.5);\n        w = max(w, 0.0);\n        w *= w;\n        \n        glow += c * mask * w;\n        totalWeight += w;\n        \n        dir = vec2(\n            dir.x * ca - dir.y * sa,\n            dir.x * sa + dir.y * ca\n        );\n    }\n    \n    glow *= intensity / max(totalWeight, 0.001);\n    \n    if (u_int1 > 0) {\n        glow *= hexToRgb(u_int1);\n    }\n    \n    vec3 result = blend(original.rgb, glow, u_int0);\n    result += (noise - 0.5) * (1.0 / 255.0);\n    \n    fragColor = vec4(clamp(result, 0.0, 1.0), original.a);\n}", "from_input"]}, {"id": 34, "type": "PrimitiveFloat", "pos": [2290, -510], "size": [270, 58], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "intensity", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [51]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 100, "precision": 1, "step": 1}, "widgets_values": [30]}, {"id": 35, "type": "PrimitiveFloat", "pos": [2290, -410], "size": [270, 58], "flags": {}, "order": 1, "mode": 0, "inputs": [{"label": "radius", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [50]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 100, "precision": 1, "step": 1}, "widgets_values": [25]}, {"id": 33, "type": "PrimitiveFloat", "pos": [2290, -310], "size": [270, 58], "flags": {}, "order": 2, "mode": 0, "inputs": [{"label": "threshold", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [52]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 100, "precision": 1, "step": 1}, "widgets_values": [100]}, {"id": 32, "type": "ColorToRGBInt", "pos": [2290, -210], "size": [270, 58], "flags": {}, "order": 3, "mode": 0, "inputs": [{"label": "color_tint", "localized_name": "color", "name": "color", "type": "COLOR", "widget": {"name": "color"}, "link": null}], "outputs": [{"localized_name": "rgb_int", "name": "rgb_int", "type": "INT", "links": [47]}], "properties": {"Node name for S&R": "ColorToRGBInt"}, "widgets_values": ["#45edf5"]}, {"id": 31, "type": "CustomCombo", "pos": [2290, -110], "size": [270, 222], "flags": {}, "order": 4, "mode": 0, "inputs": [{"label": "blend_mode", "localized_name": "choice", "name": "choice", "type": "COMBO", "widget": {"name": "choice"}, "link": null}], "outputs": [{"localized_name": "STRING", "name": "STRING", "type": "STRING", "links": null}, {"localized_name": "INDEX", "name": "INDEX", "type": "INT", "links": [46]}], "properties": {"Node name for S&R": "CustomCombo"}, "widgets_values": ["add", 0, "add", "screen", "soft", "overlay", "lighten", ""]}], "groups": [], "links": [{"id": 51, "origin_id": 34, "origin_slot": 0, "target_id": 30, "target_slot": 2, "type": "FLOAT"}, {"id": 50, "origin_id": 35, "origin_slot": 0, "target_id": 30, "target_slot": 3, "type": "FLOAT"}, {"id": 52, "origin_id": 33, "origin_slot": 0, "target_id": 30, "target_slot": 4, "type": "FLOAT"}, {"id": 46, "origin_id": 31, "origin_slot": 1, "target_id": 30, "target_slot": 6, "type": "INT"}, {"id": 47, "origin_id": 32, "origin_slot": 0, "target_id": 30, "target_slot": 7, "type": "INT"}, {"id": 45, "origin_id": -10, "origin_slot": 0, "target_id": 30, "target_slot": 0, "type": "IMAGE"}, {"id": 53, "origin_id": 30, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Image Tools/Color adjust"}]}}
diff --git a/blueprints/Hue and Saturation.json b/blueprints/Hue and Saturation.json
index 0a9915de7..04846c51d 100644
--- a/blueprints/Hue and Saturation.json	
+++ b/blueprints/Hue and Saturation.json	
@@ -1 +1 @@
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getHueWeight(hue, 0.0, overlap),\n            getHueWeight(hue, 1.0, overlap)\n        );\n    }\n\n    float center = float(mode - 1) / 6.0;\n    return getHueWeight(hue, center, overlap);\n}\n\n//=============================================================================\n// Adjustment Functions\n//=============================================================================\n\nfloat adjustLightness(float l, float amount) {\n    return amount > 0.0\n        ? l + (1.0 - l) * amount\n        : l + l * amount;\n}\n\nfloat adjustBrightness(float b, float amount) {\n    return clamp(b + amount, 0.0, 1.0);\n}\n\nfloat adjustSaturation(float s, float amount) {\n    return amount > 0.0\n        ? s + (1.0 - s) * amount\n        : s + s * amount;\n}\n\nvec3 colorize(vec3 rgb, float hue, float sat, float light) {\n    float lum = dot(rgb, vec3(0.299, 0.587, 0.114));\n    float l = adjustLightness(lum, light);\n\n    vec3 hsl = vec3(fract(hue), clamp(sat, 0.0, 1.0), clamp(l, 0.0, 1.0));\n    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\ No newline at end of file
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name for S&R": "GLSLShader"}, "widgets_values": ["#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform int u_int0;      // Mode: 0=Master, 1=Reds, 2=Yellows, 3=Greens, 4=Cyans, 5=Blues, 6=Magentas, 7=Colorize\nuniform int u_int1;      // Color Space: 0=HSL, 1=HSB/HSV\nuniform float u_float0;  // Hue (-180 to 180)\nuniform float u_float1;  // Saturation (-100 to 100)\nuniform float u_float2;  // Lightness/Brightness (-100 to 100)\nuniform float u_float3;  // Overlap (0 to 100) - feathering between adjacent color ranges\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\n// Color range modes\nconst int MODE_MASTER   = 0;\nconst int MODE_RED      = 1;\nconst int MODE_YELLOW   = 2;\nconst int MODE_GREEN    = 3;\nconst int MODE_CYAN     = 4;\nconst int MODE_BLUE     = 5;\nconst int MODE_MAGENTA  = 6;\nconst int MODE_COLORIZE = 7;\n\n// Color space modes\nconst int COLORSPACE_HSL = 0;\nconst int COLORSPACE_HSB = 1;\n\nconst float EPSILON = 0.0001;\n\n//=============================================================================\n// RGB <-> HSL Conversions\n//=============================================================================\n\nvec3 rgb2hsl(vec3 c) {\n    float maxC = max(max(c.r, c.g), c.b);\n    float minC = min(min(c.r, c.g), c.b);\n    float delta = maxC - minC;\n\n    float h = 0.0;\n    float s = 0.0;\n    float l = (maxC + minC) * 0.5;\n\n    if (delta > EPSILON) {\n        s = l < 0.5\n            ? delta / (maxC + minC)\n            : delta / (2.0 - maxC - minC);\n\n        if (maxC == c.r) {\n            h = (c.g - c.b) / delta + (c.g < c.b ? 6.0 : 0.0);\n        } else if (maxC == c.g) {\n            h = (c.b - c.r) / delta + 2.0;\n        } else {\n            h = (c.r - c.g) / delta + 4.0;\n        }\n        h /= 6.0;\n    }\n\n    return vec3(h, s, l);\n}\n\nfloat hue2rgb(float p, float q, float t) {\n    t = fract(t);\n    if (t < 1.0/6.0) return p + (q - p) * 6.0 * t;\n    if (t < 0.5)       return q;\n    if (t < 2.0/3.0)   return p + (q - p) * (2.0/3.0 - t) * 6.0;\n    return p;\n}\n\nvec3 hsl2rgb(vec3 hsl) {\n    if (hsl.y < EPSILON) return vec3(hsl.z);\n\n    float q = hsl.z < 0.5\n        ? hsl.z * (1.0 + hsl.y)\n        : hsl.z + hsl.y - hsl.z * hsl.y;\n    float p = 2.0 * hsl.z - q;\n\n    return vec3(\n        hue2rgb(p, q, hsl.x + 1.0/3.0),\n        hue2rgb(p, q, hsl.x),\n        hue2rgb(p, q, hsl.x - 1.0/3.0)\n    );\n}\n\nvec3 rgb2hsb(vec3 c) {\n    float maxC = max(max(c.r, c.g), c.b);\n    float minC = min(min(c.r, c.g), c.b);\n    float delta = maxC - minC;\n\n    float h = 0.0;\n    float s = (maxC > EPSILON) ? delta / maxC : 0.0;\n    float b = maxC;\n\n    if (delta > EPSILON) {\n        if (maxC == c.r) {\n            h = (c.g - c.b) / delta + (c.g < c.b ? 6.0 : 0.0);\n        } else if (maxC == c.g) {\n            h = (c.b - c.r) / delta + 2.0;\n        } else {\n            h = (c.r - c.g) / delta + 4.0;\n        }\n        h /= 6.0;\n    }\n\n    return vec3(h, s, b);\n}\n\nvec3 hsb2rgb(vec3 hsb) {\n    vec3 rgb = clamp(abs(mod(hsb.x * 6.0 + vec3(0.0, 4.0, 2.0), 6.0) - 3.0) - 1.0, 0.0, 1.0);\n    return hsb.z * mix(vec3(1.0), rgb, hsb.y);\n}\n\n//=============================================================================\n// Color Range Weight Calculation\n//=============================================================================\n\nfloat hueDistance(float a, float b) {\n    float d = abs(a - b);\n    return min(d, 1.0 - d);\n}\n\nfloat getHueWeight(float hue, float center, float overlap) {\n    float baseWidth = 1.0 / 6.0;\n    float feather = baseWidth * overlap;\n\n    float d = hueDistance(hue, center);\n\n    float inner = baseWidth * 0.5;\n    float outer = inner + feather;\n\n    return 1.0 - smoothstep(inner, outer, d);\n}\n\nfloat getModeWeight(float hue, int mode, float overlap) {\n    if (mode == MODE_MASTER || mode == MODE_COLORIZE) return 1.0;\n\n    if (mode == MODE_RED) {\n        return max(\n            getHueWeight(hue, 0.0, overlap),\n            getHueWeight(hue, 1.0, overlap)\n        );\n    }\n\n    float center = float(mode - 1) / 6.0;\n    return getHueWeight(hue, center, overlap);\n}\n\n//=============================================================================\n// Adjustment Functions\n//=============================================================================\n\nfloat adjustLightness(float l, float amount) {\n    return amount > 0.0\n        ? l + (1.0 - l) * amount\n        : l + l * amount;\n}\n\nfloat adjustBrightness(float b, float amount) {\n    return clamp(b + amount, 0.0, 1.0);\n}\n\nfloat adjustSaturation(float s, float amount) {\n    return amount > 0.0\n        ? s + (1.0 - s) * amount\n        : s + s * amount;\n}\n\nvec3 colorize(vec3 rgb, float hue, float sat, float light) {\n    float lum = dot(rgb, vec3(0.299, 0.587, 0.114));\n    float l = adjustLightness(lum, light);\n\n    vec3 hsl = vec3(fract(hue), clamp(sat, 0.0, 1.0), clamp(l, 0.0, 1.0));\n    return hsl2rgb(hsl);\n}\n\n//=============================================================================\n// Main\n//=============================================================================\n\nvoid main() {\n    vec4 original = texture(u_image0, v_texCoord);\n\n    float hueShift   = u_float0 / 360.0;   // -180..180 -> -0.5..0.5\n    float satAmount  = u_float1 / 100.0;   // -100..100 -> -1..1\n    float lightAmount= u_float2 / 100.0;   // -100..100 -> -1..1\n    float overlap    = u_float3 / 100.0;   // 0..100 -> 0..1\n\n    vec3 result;\n\n    if (u_int0 == MODE_COLORIZE) {\n        result = colorize(original.rgb, hueShift, satAmount, lightAmount);\n        fragColor = vec4(result, original.a);\n        return;\n    }\n\n    vec3 hsx = (u_int1 == COLORSPACE_HSL)\n        ? rgb2hsl(original.rgb)\n        : rgb2hsb(original.rgb);\n\n    float weight = getModeWeight(hsx.x, u_int0, overlap);\n\n    if (u_int0 != MODE_MASTER && hsx.y < EPSILON) {\n        weight = 0.0;\n    }\n\n    if (weight > EPSILON) {\n        float h = fract(hsx.x + hueShift * weight);\n        float s = clamp(adjustSaturation(hsx.y, satAmount * weight), 0.0, 1.0);\n        float v = (u_int1 == COLORSPACE_HSL)\n            ? clamp(adjustLightness(hsx.z, lightAmount * weight), 0.0, 1.0)\n            : clamp(adjustBrightness(hsx.z, lightAmount * weight), 0.0, 1.0);\n\n        vec3 adjusted = vec3(h, s, v);\n        result = (u_int1 == COLORSPACE_HSL)\n            ? hsl2rgb(adjusted)\n            : hsb2rgb(adjusted);\n    } else {\n        result = original.rgb;\n    }\n\n    fragColor = vec4(result, original.a);\n}\n", "from_input"]}], "groups": [], "links": [{"id": 3, "origin_id": 4, "origin_slot": 0, "target_id": 1, "target_slot": 2, "type": "FLOAT"}, {"id": 4, "origin_id": 5, "origin_slot": 0, "target_id": 1, "target_slot": 3, "type": "FLOAT"}, {"id": 5, "origin_id": 6, "origin_slot": 0, "target_id": 1, "target_slot": 4, "type": "FLOAT"}, {"id": 6, "origin_id": 7, "origin_slot": 0, "target_id": 1, "target_slot": 5, "type": "FLOAT"}, {"id": 1, "origin_id": 2, "origin_slot": 1, "target_id": 1, "target_slot": 7, "type": "INT"}, {"id": 2, "origin_id": 3, "origin_slot": 1, "target_id": 1, "target_slot": 8, "type": "INT"}, {"id": 10, "origin_id": -10, "origin_slot": 0, "target_id": 1, "target_slot": 0, "type": "IMAGE"}, {"id": 8, "origin_id": 1, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Image Tools/Color adjust"}]}}
diff --git a/blueprints/Image Blur.json b/blueprints/Image Blur.json
index c03f31902..4b9e74255 100644
--- a/blueprints/Image Blur.json	
+++ b/blueprints/Image Blur.json	
@@ -1 +1 @@
-{"revision": 0, "last_node_id": 8, "last_link_id": 0, "nodes": [{"id": 8, "type": "198632a3-ee76-4aab-9ce7-a69c624eaff9", "pos": [4470, -1840], "size": [210, 82], "flags": {}, "order": 3, "mode": 0, "inputs": [{"label": "image", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": null}], "outputs": [{"label": "blurred_image", "localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": []}], "properties": {"proxyWidgets": [["12", "choice"], ["10", "value"]]}, "widgets_values": [], "title": "Image Blur"}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "198632a3-ee76-4aab-9ce7-a69c624eaff9", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 12, "lastLinkId": 11, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Image Blur", "inputNode": {"id": -10, "bounding": [3540, -2445, 120, 60]}, "outputNode": {"id": -20, "bounding": [4620, -2445, 121.11666870117188, 60]}, "inputs": [{"id": "7ff2a402-6b11-45e8-a92a-7158d216520a", "name": "images.image0", "type": "IMAGE", "linkIds": [9], "localized_name": "images.image0", "label": "image", "pos": [3640, -2425]}], "outputs": [{"id": "80a8e19e-ffd9-44a5-90f2-710815a5b063", "name": "IMAGE0", "type": "IMAGE", "linkIds": [3], "localized_name": "IMAGE0", "label": "blurred_image", "pos": [4640, -2425]}], "widgets": [], "nodes": [{"id": 12, "type": "CustomCombo", "pos": [3720, -2620], "size": [270, 174], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "blur_type", "localized_name": "choice", "name": "choice", "type": "COMBO", "widget": {"name": "choice"}, "link": null}], "outputs": [{"localized_name": "STRING", "name": "STRING", "type": "STRING", "links": null}, {"localized_name": "INDEX", "name": "INDEX", "type": "INT", "links": [11]}], "properties": {"Node name for S&R": "CustomCombo"}, "widgets_values": ["Gaussian", 0, "Gaussian", "Box", "Radial", ""]}, {"id": 10, "type": "PrimitiveFloat", "pos": [4020, -2780], "size": [270, 58], "flags": {}, "order": 1, "mode": 0, "inputs": [{"label": "strength", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [10]}], "properties": {"Node name for S&R": "PrimitiveFloat", "max": 100, "min": 0}, "widgets_values": [20]}, {"id": 1, "type": "GLSLShader", "pos": [4020, -2670], "size": [430, 212], "flags": {}, "order": 2, "mode": 0, "inputs": [{"label": "image0", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": 9}, {"label": "image1", "localized_name": "images.image1", "name": "images.image1", "shape": 7, "type": "IMAGE", "link": null}, {"label": "u_float0", "localized_name": "floats.u_float0", "name": "floats.u_float0", "shape": 7, "type": "FLOAT", "link": 10}, {"label": "u_float1", "localized_name": "floats.u_float1", "name": "floats.u_float1", "shape": 7, "type": "FLOAT", "link": null}, {"label": "u_int0", "localized_name": "ints.u_int0", "name": "ints.u_int0", "shape": 7, "type": "INT", "link": 11}, {"label": "u_int1", "localized_name": "ints.u_int1", "name": "ints.u_int1", "shape": 7, "type": "INT", "link": null}, {"localized_name": "fragment_shader", "name": "fragment_shader", "type": "STRING", "widget": {"name": "fragment_shader"}, "link": null}, {"localized_name": "size_mode", "name": "size_mode", "type": "COMFY_DYNAMICCOMBO_V3", "widget": {"name": "size_mode"}, "link": null}], "outputs": [{"localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": [3]}, {"localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": []}, {"localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": []}, {"localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": []}], "properties": {"Node name for S&R": "GLSLShader"}, "widgets_values": ["#version 300 es\n#pragma passes 2\nprecision highp float;\n\n// Blur type constants\nconst int BLUR_GAUSSIAN = 0;\nconst int BLUR_BOX = 1;\nconst int BLUR_RADIAL = 2;\n\n// Radial blur config\nconst int RADIAL_SAMPLES = 12;\nconst float RADIAL_STRENGTH = 0.0003;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform int u_int0;      // Blur type (BLUR_GAUSSIAN, BLUR_BOX, BLUR_RADIAL)\nuniform float u_float0;  // Blur radius/amount\nuniform int u_pass;      // Pass index (0 = horizontal, 1 = vertical)\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nfloat gaussian(float x, float sigma) {\n    return exp(-(x * x) / (2.0 * sigma * sigma));\n}\n\nvoid main() {\n    vec2 texelSize = 1.0 / u_resolution;\n    float radius = max(u_float0, 0.0);\n\n    // Radial (angular) blur - single pass, doesn't use separable\n    if (u_int0 == BLUR_RADIAL) {\n        // Only execute on first pass\n        if (u_pass > 0) {\n            fragColor0 = texture(u_image0, v_texCoord);\n            return;\n        }\n\n        vec2 center = vec2(0.5);\n        vec2 dir = v_texCoord - center;\n        float dist = length(dir);\n\n        if (dist < 1e-4) {\n            fragColor0 = texture(u_image0, v_texCoord);\n            return;\n        }\n\n        vec4 sum = vec4(0.0);\n        float totalWeight = 0.0;\n        float angleStep = radius * RADIAL_STRENGTH;\n\n        dir /= dist;\n\n        float cosStep = cos(angleStep);\n        float sinStep = sin(angleStep);\n\n        float negAngle = -float(RADIAL_SAMPLES) * angleStep;\n        vec2 rotDir = vec2(\n            dir.x * cos(negAngle) - dir.y * sin(negAngle),\n            dir.x * sin(negAngle) + dir.y * cos(negAngle)\n        );\n\n        for (int i = -RADIAL_SAMPLES; i <= RADIAL_SAMPLES; i++) {\n            vec2 uv = center + rotDir * dist;\n            float w = 1.0 - abs(float(i)) / float(RADIAL_SAMPLES);\n            sum += texture(u_image0, uv) * w;\n            totalWeight += w;\n\n            rotDir = vec2(\n                rotDir.x * cosStep - rotDir.y * sinStep,\n                rotDir.x * sinStep + rotDir.y * cosStep\n            );\n        }\n\n        fragColor0 = sum / max(totalWeight, 0.001);\n        return;\n    }\n\n    // Separable Gaussian / Box blur\n    int samples = int(ceil(radius));\n\n    if (samples == 0) {\n        fragColor0 = texture(u_image0, v_texCoord);\n        return;\n    }\n\n    // Direction: pass 0 = horizontal, pass 1 = vertical\n    vec2 dir = (u_pass == 0) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);\n\n    vec4 color = vec4(0.0);\n    float totalWeight = 0.0;\n    float sigma = radius / 2.0;\n\n    for (int i = -samples; i <= samples; i++) {\n        vec2 offset = dir * float(i) * texelSize;\n        vec4 sample_color = texture(u_image0, v_texCoord + offset);\n\n        float weight;\n        if (u_int0 == BLUR_GAUSSIAN) {\n            weight = gaussian(float(i), sigma);\n        } else {\n            // BLUR_BOX\n            weight = 1.0;\n        }\n\n        color += sample_color * weight;\n        totalWeight += weight;\n    }\n\n    fragColor0 = color / totalWeight;\n}\n", "from_input"]}], "groups": [], "links": [{"id": 10, "origin_id": 10, "origin_slot": 0, "target_id": 1, "target_slot": 2, "type": "FLOAT"}, {"id": 11, "origin_id": 12, "origin_slot": 1, "target_id": 1, "target_slot": 4, "type": "INT"}, {"id": 9, "origin_id": -10, "origin_slot": 0, "target_id": 1, "target_slot": 0, "type": "IMAGE"}, {"id": 3, "origin_id": 1, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}}]}}
\ No newline at end of file
+{"revision": 0, "last_node_id": 8, "last_link_id": 0, "nodes": [{"id": 8, "type": "198632a3-ee76-4aab-9ce7-a69c624eaff9", "pos": [4470, -1840], "size": [210, 82], "flags": {}, "order": 3, "mode": 0, "inputs": [{"label": "image", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": null}], "outputs": [{"label": "blurred_image", "localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": []}], "properties": {"proxyWidgets": [["12", "choice"], ["10", "value"]]}, "widgets_values": [], "title": "Image Blur"}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "198632a3-ee76-4aab-9ce7-a69c624eaff9", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 12, "lastLinkId": 11, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Image Blur", "inputNode": {"id": -10, "bounding": [3540, -2445, 120, 60]}, "outputNode": {"id": -20, "bounding": [4620, -2445, 121.11666870117188, 60]}, "inputs": [{"id": "7ff2a402-6b11-45e8-a92a-7158d216520a", "name": "images.image0", "type": "IMAGE", "linkIds": [9], "localized_name": "images.image0", "label": "image", "pos": [3640, -2425]}], "outputs": [{"id": "80a8e19e-ffd9-44a5-90f2-710815a5b063", "name": "IMAGE0", "type": "IMAGE", "linkIds": [3], "localized_name": "IMAGE0", "label": "blurred_image", "pos": [4640, -2425]}], "widgets": [], "nodes": [{"id": 12, "type": "CustomCombo", "pos": [3720, -2620], "size": [270, 174], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "blur_type", "localized_name": "choice", "name": "choice", "type": "COMBO", "widget": {"name": "choice"}, "link": null}], "outputs": [{"localized_name": "STRING", "name": "STRING", "type": "STRING", "links": null}, {"localized_name": "INDEX", "name": "INDEX", "type": "INT", "links": [11]}], "properties": {"Node name for S&R": "CustomCombo"}, "widgets_values": ["Gaussian", 0, "Gaussian", "Box", "Radial", ""]}, {"id": 10, "type": "PrimitiveFloat", "pos": [4020, -2780], "size": [270, 58], "flags": {}, "order": 1, "mode": 0, "inputs": [{"label": "strength", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [10]}], "properties": {"Node name for S&R": "PrimitiveFloat", "max": 100, "min": 0}, "widgets_values": [20]}, {"id": 1, "type": "GLSLShader", "pos": [4020, -2670], "size": [430, 212], "flags": {}, "order": 2, "mode": 0, "inputs": [{"label": "image0", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": 9}, {"label": "image1", "localized_name": "images.image1", "name": "images.image1", "shape": 7, "type": "IMAGE", "link": null}, {"label": "u_float0", "localized_name": "floats.u_float0", "name": "floats.u_float0", "shape": 7, "type": "FLOAT", "link": 10}, {"label": "u_float1", "localized_name": "floats.u_float1", "name": "floats.u_float1", "shape": 7, "type": "FLOAT", "link": null}, {"label": "u_int0", "localized_name": "ints.u_int0", "name": "ints.u_int0", "shape": 7, "type": "INT", "link": 11}, {"label": "u_int1", "localized_name": "ints.u_int1", "name": "ints.u_int1", "shape": 7, "type": "INT", "link": null}, {"localized_name": "fragment_shader", "name": "fragment_shader", "type": "STRING", "widget": {"name": "fragment_shader"}, "link": null}, {"localized_name": "size_mode", "name": "size_mode", "type": "COMFY_DYNAMICCOMBO_V3", "widget": {"name": "size_mode"}, "link": null}], "outputs": [{"localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": [3]}, {"localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": []}, {"localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": []}, {"localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": []}], "properties": {"Node name for S&R": "GLSLShader"}, "widgets_values": ["#version 300 es\n#pragma passes 2\nprecision highp float;\n\n// Blur type constants\nconst int BLUR_GAUSSIAN = 0;\nconst int BLUR_BOX = 1;\nconst int BLUR_RADIAL = 2;\n\n// Radial blur config\nconst int RADIAL_SAMPLES = 12;\nconst float RADIAL_STRENGTH = 0.0003;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform int u_int0;      // Blur type (BLUR_GAUSSIAN, BLUR_BOX, BLUR_RADIAL)\nuniform float u_float0;  // Blur radius/amount\nuniform int u_pass;      // Pass index (0 = horizontal, 1 = vertical)\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nfloat gaussian(float x, float sigma) {\n    return exp(-(x * x) / (2.0 * sigma * sigma));\n}\n\nvoid main() {\n    vec2 texelSize = 1.0 / u_resolution;\n    float radius = max(u_float0, 0.0);\n\n    // Radial (angular) blur - single pass, doesn't use separable\n    if (u_int0 == BLUR_RADIAL) {\n        // Only execute on first pass\n        if (u_pass > 0) {\n            fragColor0 = texture(u_image0, v_texCoord);\n            return;\n        }\n\n        vec2 center = vec2(0.5);\n        vec2 dir = v_texCoord - center;\n        float dist = length(dir);\n\n        if (dist < 1e-4) {\n            fragColor0 = texture(u_image0, v_texCoord);\n            return;\n        }\n\n        vec4 sum = vec4(0.0);\n        float totalWeight = 0.0;\n        float angleStep = radius * RADIAL_STRENGTH;\n\n        dir /= dist;\n\n        float cosStep = cos(angleStep);\n        float sinStep = sin(angleStep);\n\n        float negAngle = -float(RADIAL_SAMPLES) * angleStep;\n        vec2 rotDir = vec2(\n            dir.x * cos(negAngle) - dir.y * sin(negAngle),\n            dir.x * sin(negAngle) + dir.y * cos(negAngle)\n        );\n\n        for (int i = -RADIAL_SAMPLES; i <= RADIAL_SAMPLES; i++) {\n            vec2 uv = center + rotDir * dist;\n            float w = 1.0 - abs(float(i)) / float(RADIAL_SAMPLES);\n            sum += texture(u_image0, uv) * w;\n            totalWeight += w;\n\n            rotDir = vec2(\n                rotDir.x * cosStep - rotDir.y * sinStep,\n                rotDir.x * sinStep + rotDir.y * cosStep\n            );\n        }\n\n        fragColor0 = sum / max(totalWeight, 0.001);\n        return;\n    }\n\n    // Separable Gaussian / Box blur\n    int samples = int(ceil(radius));\n\n    if (samples == 0) {\n        fragColor0 = texture(u_image0, v_texCoord);\n        return;\n    }\n\n    // Direction: pass 0 = horizontal, pass 1 = vertical\n    vec2 dir = (u_pass == 0) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);\n\n    vec4 color = vec4(0.0);\n    float totalWeight = 0.0;\n    float sigma = radius / 2.0;\n\n    for (int i = -samples; i <= samples; i++) {\n        vec2 offset = dir * float(i) * texelSize;\n        vec4 sample_color = texture(u_image0, v_texCoord + offset);\n\n        float weight;\n        if (u_int0 == BLUR_GAUSSIAN) {\n            weight = gaussian(float(i), sigma);\n        } else {\n            // BLUR_BOX\n            weight = 1.0;\n        }\n\n        color += sample_color * weight;\n        totalWeight += weight;\n    }\n\n    fragColor0 = color / totalWeight;\n}\n", "from_input"]}], "groups": [], "links": [{"id": 10, "origin_id": 10, "origin_slot": 0, "target_id": 1, "target_slot": 2, "type": "FLOAT"}, {"id": 11, "origin_id": 12, "origin_slot": 1, "target_id": 1, "target_slot": 4, "type": "INT"}, {"id": 9, "origin_id": -10, "origin_slot": 0, "target_id": 1, "target_slot": 0, "type": "IMAGE"}, {"id": 3, "origin_id": 1, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Image Tools/Blur"}]}}
diff --git a/blueprints/Image Captioning (gemini).json b/blueprints/Image Captioning (gemini).json
new file mode 100644
index 000000000..89ebac802
--- /dev/null
+++ b/blueprints/Image Captioning (gemini).json	
@@ -0,0 +1 @@
+{"revision": 0, "last_node_id": 231, "last_link_id": 0, "nodes": [{"id": 231, "type": "e3e78497-720e-45a2-b4fb-c7bfdb80dd11", "pos": [23.13283014087665, 1034.468391137315], "size": [280, 260], "flags": {}, "order": 8, "mode": 0, "inputs": [{"localized_name": "images", "name": "images", "type": "IMAGE", "link": null}, {"name": "prompt", "type": "STRING", "widget": {"name": "prompt"}, "link": null}, {"name": "model", "type": "COMBO", "widget": {"name": "model"}, "link": null}], "outputs": [{"localized_name": "STRING", "name": "STRING", "type": "STRING", "links": []}], "properties": {"proxyWidgets": [["-1", "prompt"], ["-1", "model"], ["1", "seed"]], "cnr_id": "comfy-core", "ver": "0.13.0"}, "widgets_values": ["Describe this image", "gemini-2.5-pro"], "title": "Image Captioning(Gemini)"}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "e3e78497-720e-45a2-b4fb-c7bfdb80dd11", "version": 1, "state": {"lastGroupId": 1, "lastNodeId": 16, "lastLinkId": 16, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Image Captioning(Gemini)", "inputNode": {"id": -10, "bounding": [-6870, 2530, 120, 100]}, "outputNode": {"id": -20, "bounding": [-6240, 2530, 120, 60]}, "inputs": [{"id": "97cb8fa5-0514-4e05-b206-46fa6d7b5589", "name": "images", "type": "IMAGE", "linkIds": [1], "localized_name": "images", "shape": 7, "pos": [-6770, 2550]}, {"id": "d8cbd7eb-636a-4d7b-8ff6-b22f1755e26c", "name": "prompt", "type": "STRING", "linkIds": [15], "pos": [-6770, 2570]}, {"id": "b034e26a-d114-4604-aec2-32783e86aa6b", "name": "model", "type": "COMBO", "linkIds": [16], "pos": [-6770, 2590]}], "outputs": [{"id": "e12c6e80-5210-4328-a581-bc8924c53070", "name": "STRING", "type": "STRING", "linkIds": [6], "localized_name": "STRING", "pos": [-6220, 2550]}], "widgets": [], "nodes": [{"id": 1, "type": "GeminiNode", "pos": [-6690, 2360], "size": [390, 430], "flags": {}, "order": 0, "mode": 0, "inputs": [{"localized_name": "images", "name": "images", "shape": 7, "type": "IMAGE", "link": 1}, {"localized_name": "audio", "name": "audio", "shape": 7, "type": "AUDIO", "link": null}, {"localized_name": "video", "name": "video", "shape": 7, "type": "VIDEO", "link": null}, {"localized_name": "files", "name": "files", "shape": 7, "type": "GEMINI_INPUT_FILES", "link": null}, {"localized_name": "prompt", "name": "prompt", "type": "STRING", "widget": {"name": "prompt"}, "link": 15}, {"localized_name": "model", "name": "model", "type": "COMBO", "widget": {"name": "model"}, "link": 16}, {"localized_name": "seed", "name": "seed", "type": "INT", "widget": {"name": "seed"}, "link": null}, {"localized_name": "system_prompt", "name": "system_prompt", "shape": 7, "type": "STRING", "widget": {"name": "system_prompt"}, "link": null}], "outputs": [{"localized_name": "STRING", "name": "STRING", "type": "STRING", "links": [6]}], "properties": {"cnr_id": "comfy-core", "ver": "0.5.1", "Node name for S&R": "GeminiNode"}, "widgets_values": ["Describe this image", "gemini-2.5-pro", 511865409297955, "randomize", "- Role: AI Image Analysis and Description Specialist\n- Background: The user requires a prompt that enables AI to analyze images and generate detailed descriptions which can be used as drawing prompts to create similar images. This is essential for tasks like content creation, design inspiration, and artistic exploration.\n- Profile: As an AI Image Analysis and Description Specialist, you possess extensive knowledge in computer vision, image processing, and natural language generation. You are adept at interpreting visual data and translating it into descriptive text that can guide the creation of new images.\n- Skills: Proficiency in image recognition, feature extraction, descriptive language generation, and understanding of artistic elements such as composition, color, and texture.\n- Goals: To analyze the provided image, generate a comprehensive and detailed description that captures the key visual elements, and ensure this description can effectively serve as a drawing prompt for creating similar images.\n- Constrains: The description must be clear, concise, and specific enough to guide the creation of a similar image. It should avoid ambiguity and focus on the most salient features of the image. The output should only contain the drawing prompt.\n- OutputFormat: A detailed text description of the image, highlighting key visual elements such as objects, colors, composition, and any unique features.\n- Workflow:\n  1. Analyze the image to identify key visual elements including objects, colors, and composition.\n  2. Generate a detailed description that captures the essence of the image, ensuring it is specific and actionable.\n  3. Refine the description to ensure clarity and conciseness, making it suitable for use as a drawing prompt."], "color": "#432", "bgcolor": "#653"}], "groups": [], "links": [{"id": 1, "origin_id": -10, "origin_slot": 0, "target_id": 1, "target_slot": 0, "type": "IMAGE"}, {"id": 6, "origin_id": 1, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "*"}, {"id": 15, "origin_id": -10, "origin_slot": 1, "target_id": 1, "target_slot": 4, "type": "STRING"}, {"id": 16, "origin_id": -10, "origin_slot": 2, "target_id": 1, "target_slot": 5, "type": "COMBO"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Text generation/Image Captioning"}]}}
diff --git a/blueprints/Image Channels.json b/blueprints/Image Channels.json
index 56a565d30..cb3488883 100644
--- a/blueprints/Image Channels.json	
+++ b/blueprints/Image Channels.json	
@@ -1 +1 @@
-{"revision": 0, "last_node_id": 29, "last_link_id": 0, "nodes": [{"id": 29, "type": "4c9d6ea4-b912-40e5-8766-6793a9758c53", "pos": [1970, -230], "size": [180, 86], "flags": {}, "order": 5, "mode": 0, "inputs": [{"label": "image", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": null}], "outputs": [{"label": "R", "localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": []}, {"label": "G", "localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": []}, {"label": "B", "localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": []}, {"label": "A", "localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": []}], "title": "Image Channels", "properties": {"proxyWidgets": []}, "widgets_values": []}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "4c9d6ea4-b912-40e5-8766-6793a9758c53", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 28, "lastLinkId": 39, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Image Channels", "inputNode": {"id": -10, "bounding": [1820, -185, 120, 60]}, "outputNode": {"id": -20, "bounding": [2460, -215, 120, 120]}, "inputs": [{"id": "3522932b-2d86-4a1f-a02a-cb29f3a9d7fe", "name": "images.image0", "type": "IMAGE", "linkIds": [39], "localized_name": "images.image0", "label": "image", "pos": [1920, -165]}], "outputs": [{"id": "605cb9c3-b065-4d9b-81d2-3ec331889b2b", "name": "IMAGE0", "type": "IMAGE", "linkIds": [26], "localized_name": "IMAGE0", "label": "R", "pos": [2480, -195]}, {"id": "fb44a77e-0522-43e9-9527-82e7465b3596", "name": "IMAGE1", "type": "IMAGE", "linkIds": [27], "localized_name": "IMAGE1", "label": "G", "pos": [2480, -175]}, {"id": "81460ee6-0131-402a-874f-6bf3001fc4ff", "name": "IMAGE2", "type": "IMAGE", "linkIds": [28], "localized_name": "IMAGE2", "label": "B", "pos": [2480, -155]}, {"id": "ae690246-80d4-4951-b1d9-9306d8a77417", "name": "IMAGE3", "type": "IMAGE", "linkIds": [29], "localized_name": "IMAGE3", "label": "A", "pos": [2480, -135]}], "widgets": [], "nodes": [{"id": 23, "type": "GLSLShader", "pos": [2000, -330], "size": [400, 172], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "image", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": 39}, {"localized_name": "fragment_shader", "name": "fragment_shader", "type": "STRING", "widget": {"name": "fragment_shader"}, "link": null}, {"localized_name": "size_mode", "name": "size_mode", "type": "COMFY_DYNAMICCOMBO_V3", "widget": {"name": "size_mode"}, "link": null}, {"label": "image1", "localized_name": "images.image1", "name": "images.image1", "shape": 7, "type": "IMAGE", "link": null}], "outputs": [{"label": "R", "localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": [26]}, {"label": "G", "localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": [27]}, {"label": "B", "localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": [28]}, {"label": "A", "localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": [29]}], "properties": {"Node name for S&R": "GLSLShader"}, "widgets_values": ["#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\nlayout(location = 1) out vec4 fragColor1;\nlayout(location = 2) out vec4 fragColor2;\nlayout(location = 3) out vec4 fragColor3;\n\nvoid main() {\n  vec4 color = texture(u_image0, v_texCoord);\n  // Output each channel as grayscale to separate render targets\n  fragColor0 = vec4(vec3(color.r), 1.0);  // Red channel\n  fragColor1 = vec4(vec3(color.g), 1.0);  // Green channel\n  fragColor2 = vec4(vec3(color.b), 1.0);  // Blue channel\n  fragColor3 = vec4(vec3(color.a), 1.0);  // Alpha channel\n}\n", "from_input"]}], "groups": [], "links": [{"id": 39, "origin_id": -10, "origin_slot": 0, "target_id": 23, "target_slot": 0, "type": "IMAGE"}, {"id": 26, "origin_id": 23, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}, {"id": 27, "origin_id": 23, "origin_slot": 1, "target_id": -20, "target_slot": 1, "type": "IMAGE"}, {"id": 28, "origin_id": 23, "origin_slot": 2, "target_id": -20, "target_slot": 2, "type": "IMAGE"}, {"id": 29, "origin_id": 23, "origin_slot": 3, "target_id": -20, "target_slot": 3, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}}]}}
\ No newline at end of file
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diff --git a/blueprints/Image Edit (Flux.2 Klein 4B).json b/blueprints/Image Edit (Flux.2 Klein 4B).json
new file mode 100644
index 000000000..c87c7e122
--- /dev/null
+++ b/blueprints/Image Edit (Flux.2 Klein 4B).json	
@@ -0,0 +1 @@
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diff --git a/blueprints/Image Edit (Qwen 2511).json b/blueprints/Image Edit (Qwen 2511).json
new file mode 100644
index 000000000..33e85333b
--- /dev/null
+++ b/blueprints/Image Edit (Qwen 2511).json	
@@ -0,0 +1 @@
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diff --git a/blueprints/Image Inpainting (Qwen-image).json b/blueprints/Image Inpainting (Qwen-image).json
new file mode 100644
index 000000000..5f8ef81f9
--- /dev/null
+++ b/blueprints/Image Inpainting (Qwen-image).json	
@@ -0,0 +1 @@
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Samplers like res_multistep work pretty well at cfg 1.0\n\nThe official number of steps is 50 but I think that's too much. Even just 10 steps seems to work."], "color": "#432", "bgcolor": "#653"}, {"id": 76, "type": "VAEEncode", "pos": [430, 830], "size": [140, 46], "flags": {"collapsed": true}, "order": 11, "mode": 0, "inputs": [{"localized_name": "pixels", "name": "pixels", "type": "IMAGE", "link": 396}, {"localized_name": "vae", "name": "vae", "type": "VAE", "link": 144}], "outputs": [{"localized_name": "LATENT", "name": "LATENT", "type": "LATENT", "links": [208]}], "properties": {"cnr_id": "comfy-core", "ver": "0.3.51", "Node name for S&R": "VAEEncode"}, "widgets_values": []}, {"id": 122, "type": "SetLatentNoiseMask", "pos": [430, 890], "size": [230, 50], "flags": {"collapsed": true}, "order": 15, "mode": 0, "inputs": [{"localized_name": "samples", "name": "samples", "type": "LATENT", "link": 208}, {"localized_name": "mask", "name": "mask", "type": "MASK", "link": 219}], "outputs": [{"localized_name": "LATENT", "name": "LATENT", "type": "LATENT", "links": [210]}], "properties": {"cnr_id": "comfy-core", "ver": "0.3.59", "Node name for S&R": "SetLatentNoiseMask"}, "widgets_values": []}, {"id": 223, "type": "MarkdownNote", "pos": [860, 670], "size": [300, 160], "flags": {}, "order": 2, "mode": 0, "inputs": [], "outputs": [], "title": "Note: KSampler settings", "properties": {}, "widgets_values": ["You can test and find the best setting by yourself. The following table is for reference.\n| Parameters   | Qwen Team | Comfy Original | with 4steps LoRA |\n|--------|---------|------------|---------------------------|\n| Steps  | 50      | 20         | 4                         |\n| CFG    | 4.0     | 2.5        | 1.0                       |"], "color": "#432", "bgcolor": "#653"}, {"id": 80, "type": "LoraLoaderModelOnly", "pos": [350, -70], "size": [430, 82], "flags": {}, "order": 3, "mode": 0, "inputs": [{"localized_name": "model", "name": "model", "type": "MODEL", "link": 145}, {"localized_name": "lora_name", "name": "lora_name", "type": "COMBO", "widget": {"name": "lora_name"}, "link": null}, {"localized_name": "strength_model", "name": "strength_model", "type": "FLOAT", "widget": {"name": "strength_model"}, "link": null}], "outputs": [{"localized_name": "MODEL", "name": "MODEL", "type": "MODEL", "links": [149]}], "properties": {"cnr_id": "comfy-core", "ver": "0.3.51", "Node name for S&R": "LoraLoaderModelOnly", "models": [{"name": "Qwen-Image-Lightning-4steps-V1.0.safetensors", "url": "https://huggingface.co/lightx2v/Qwen-Image-Lightning/resolve/main/Qwen-Image-Lightning-4steps-V1.0.safetensors", "directory": "loras"}]}, "widgets_values": ["Qwen-Image-Lightning-4steps-V1.0.safetensors", 1]}, {"id": 6, "type": "CLIPTextEncode", "pos": [330, 110], "size": [460, 164.31304931640625], "flags": {}, "order": 6, "mode": 0, "inputs": [{"localized_name": "clip", "name": "clip", "type": "CLIP", "link": 74}, {"localized_name": "text", "name": "text", "type": "STRING", "widget": {"name": "text"}, "link": 394}], "outputs": [{"localized_name": "CONDITIONING", "name": "CONDITIONING", "type": "CONDITIONING", "slot_index": 0, "links": [190]}], "title": "CLIP Text Encode (Positive Prompt)", "properties": {"cnr_id": "comfy-core", "ver": "0.3.51", "Node name for S&R": "CLIPTextEncode"}, "widgets_values": [""], "color": "#232", "bgcolor": "#353"}, {"id": 121, "type": "56a1f603-fbd2-40ed-94ef-c9ecbd96aca8", "pos": [430, 950], "size": 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210}, {"localized_name": "seed", "name": "seed", "type": "INT", "widget": {"name": "seed"}, "link": null}, {"localized_name": "steps", "name": "steps", "type": "INT", "widget": {"name": "steps"}, "link": null}, {"localized_name": "cfg", "name": "cfg", "type": "FLOAT", "widget": {"name": "cfg"}, "link": null}, {"localized_name": "sampler_name", "name": "sampler_name", "type": "COMBO", "widget": {"name": "sampler_name"}, "link": null}, {"localized_name": "scheduler", "name": "scheduler", "type": "COMBO", "widget": {"name": "scheduler"}, "link": null}, {"localized_name": "denoise", "name": "denoise", "type": "FLOAT", "widget": {"name": "denoise"}, "link": null}], "outputs": [{"localized_name": "LATENT", "name": "LATENT", "type": "LATENT", "slot_index": 0, "links": [128]}], "properties": {"cnr_id": "comfy-core", "ver": "0.3.51", "Node name for S&R": "KSampler"}, "widgets_values": [0, "randomize", 4, 1, "euler", "simple", 1]}, {"id": 224, "type": "FluxKontextImageScale", "pos": [10, 1090], 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diff --git a/blueprints/Image Levels.json b/blueprints/Image Levels.json
index 8a44663bd..f028662bd 100644
--- a/blueprints/Image Levels.json	
+++ b/blueprints/Image Levels.json	
@@ -1 +1 @@
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Levels Adjustment\n// u_int0:   channel      (0=RGB, 1=R, 2=G, 3=B)         default: 0\n// u_float0: input black  (0-255)                        default: 0\n// u_float1: input white  (0-255)                        default: 255\n// u_float2: gamma        (0.01-9.99)                    default: 1.0\n// u_float3: output black (0-255)                        default: 0\n// u_float4: output white (0-255)                        default: 255\n\nuniform sampler2D u_image0;\nuniform int u_int0;\nuniform float u_float0;\nuniform float u_float1;\nuniform float u_float2;\nuniform float u_float3;\nuniform float u_float4;\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nvec3 applyLevels(vec3 color, float inBlack, float inWhite, float gamma, float outBlack, float outWhite) {\n    float inRange = max(inWhite - inBlack, 0.0001);\n    vec3 result = clamp((color - inBlack) / inRange, 0.0, 1.0);\n    result = pow(result, vec3(1.0 / gamma));\n    result = mix(vec3(outBlack), vec3(outWhite), result);\n    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diff --git a/blueprints/Image Outpainting (Qwen-Image).json b/blueprints/Image Outpainting (Qwen-Image).json
new file mode 100644
index 000000000..f36e0bd77
--- /dev/null
+++ b/blueprints/Image Outpainting (Qwen-Image).json	
@@ -0,0 +1 @@
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diff --git a/blueprints/Image Upscale(Z-image-Turbo).json b/blueprints/Image Upscale(Z-image-Turbo).json
new file mode 100644
index 000000000..a67d6a2d8
--- /dev/null
+++ b/blueprints/Image Upscale(Z-image-Turbo).json	
@@ -0,0 +1 @@
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diff --git a/blueprints/Image to Depth Map (Lotus).json b/blueprints/Image to Depth Map (Lotus).json
new file mode 100644
index 000000000..5b3f7a1d6
--- /dev/null
+++ b/blueprints/Image to Depth Map (Lotus).json	
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diff --git a/blueprints/Image to Layers(Qwen-Image Layered).json b/blueprints/Image to Layers(Qwen-Image Layered).json
new file mode 100644
index 000000000..f4c7f0b5f
--- /dev/null
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diff --git a/blueprints/Image to Model (Hunyuan3d 2.1).json b/blueprints/Image to Model (Hunyuan3d 2.1).json
new file mode 100644
index 000000000..04b2d9bc9
--- /dev/null
+++ b/blueprints/Image to Model (Hunyuan3d 2.1).json	
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diff --git a/blueprints/Image to Video (Wan 2.2).json b/blueprints/Image to Video (Wan 2.2).json
new file mode 100644
index 000000000..cd0b44a72
--- /dev/null
+++ b/blueprints/Image to Video (Wan 2.2).json	
@@ -0,0 +1 @@
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[342.59114583333337, 210], "flags": {}, "order": 17, "mode": 0, "inputs": [{"localized_name": "positive", "name": "positive", "type": "CONDITIONING", "link": 183}, {"localized_name": "negative", "name": "negative", "type": "CONDITIONING", "link": 184}, {"localized_name": "vae", "name": "vae", "type": "VAE", "link": 185}, {"localized_name": "clip_vision_output", "name": "clip_vision_output", "shape": 7, "type": "CLIP_VISION_OUTPUT", "link": null}, {"localized_name": "start_image", "name": "start_image", "shape": 7, "type": "IMAGE", "link": 186}, {"localized_name": "width", "name": "width", "type": "INT", "widget": {"name": "width"}, "link": 223}, {"localized_name": "height", "name": "height", "type": "INT", "widget": {"name": "height"}, "link": 224}, {"localized_name": "length", "name": "length", "type": "INT", "widget": {"name": "length"}, "link": 225}, {"localized_name": "batch_size", "name": "batch_size", "type": "INT", "widget": {"name": "batch_size"}, "link": null}], "outputs": [{"localized_name": "positive", "name": "positive", "type": "CONDITIONING", "slot_index": 0, "links": [168, 172]}, {"localized_name": "negative", "name": "negative", "type": "CONDITIONING", "slot_index": 1, "links": [169, 173]}, {"localized_name": "latent", "name": "latent", "type": "LATENT", "slot_index": 2, "links": [174]}], "properties": {"cnr_id": "comfy-core", "ver": "0.3.45", "Node name for S&R": "WanImageToVideo", "ue_properties": {"widget_ue_connectable": {}, "version": "7.1", "input_ue_unconnectable": {}}}, "widgets_values": [640, 640, 81, 1]}, {"id": 86, "type": "KSamplerAdvanced", "pos": [989.9999230265402, -250.00014544809514], "size": [304.73958333333337, 334], "flags": {}, "order": 14, "mode": 0, "inputs": [{"localized_name": "model", "name": "model", "type": "MODEL", "link": 195}, {"localized_name": "positive", "name": "positive", "type": "CONDITIONING", "link": 172}, {"localized_name": "negative", "name": "negative", "type": "CONDITIONING", "link": 173}, {"localized_name": "latent_image", "name": "latent_image", "type": "LATENT", "link": 174}, {"localized_name": "add_noise", "name": "add_noise", "type": "COMBO", "widget": {"name": "add_noise"}, "link": null}, {"localized_name": "noise_seed", "name": "noise_seed", "type": "INT", "widget": {"name": "noise_seed"}, "link": null}, {"localized_name": "steps", "name": "steps", "type": "INT", "widget": {"name": "steps"}, "link": null}, {"localized_name": "cfg", "name": "cfg", "type": "FLOAT", "widget": {"name": "cfg"}, "link": null}, {"localized_name": "sampler_name", "name": "sampler_name", "type": "COMBO", "widget": {"name": "sampler_name"}, "link": null}, {"localized_name": "scheduler", "name": "scheduler", "type": "COMBO", "widget": {"name": "scheduler"}, "link": null}, {"localized_name": "start_at_step", "name": "start_at_step", "type": "INT", "widget": {"name": "start_at_step"}, "link": null}, {"localized_name": "end_at_step", "name": "end_at_step", "type": "INT", "widget": {"name": "end_at_step"}, "link": null}, {"localized_name": "return_with_leftover_noise", "name": "return_with_leftover_noise", "type": "COMBO", "widget": {"name": "return_with_leftover_noise"}, "link": null}], "outputs": [{"localized_name": "LATENT", "name": "LATENT", "type": "LATENT", "links": [170]}], "properties": {"cnr_id": "comfy-core", "ver": "0.3.45", "Node name for S&R": "KSamplerAdvanced", "ue_properties": {"widget_ue_connectable": {}, "version": "7.1", "input_ue_unconnectable": {}}}, "widgets_values": ["enable", 0, "randomize", 4, 1, "euler", "simple", 0, 2, "enable"]}, {"id": 85, "type": "KSamplerAdvanced", "pos": [1336.748028098344, -250.00014544809514], "size": [304.73958333333337, 334], "flags": {}, "order": 13, "mode": 0, "inputs": [{"localized_name": "model", "name": "model", "type": "MODEL", "link": 192}, {"localized_name": "positive", "name": "positive", "type": "CONDITIONING", "link": 168}, {"localized_name": "negative", "name": "negative", "type": "CONDITIONING", "link": 169}, {"localized_name": "latent_image", "name": "latent_image", "type": "LATENT", "link": 170}, {"localized_name": "add_noise", "name": "add_noise", "type": "COMBO", "widget": {"name": "add_noise"}, "link": null}, {"localized_name": "noise_seed", "name": "noise_seed", "type": "INT", "widget": {"name": "noise_seed"}, "link": null}, {"localized_name": "steps", "name": "steps", "type": "INT", "widget": {"name": "steps"}, "link": null}, {"localized_name": "cfg", "name": "cfg", "type": "FLOAT", "widget": {"name": "cfg"}, "link": null}, {"localized_name": "sampler_name", "name": "sampler_name", "type": "COMBO", "widget": {"name": "sampler_name"}, "link": null}, {"localized_name": "scheduler", "name": "scheduler", "type": "COMBO", "widget": {"name": "scheduler"}, "link": null}, {"localized_name": "start_at_step", "name": "start_at_step", "type": "INT", "widget": {"name": "start_at_step"}, "link": null}, {"localized_name": "end_at_step", "name": "end_at_step", "type": "INT", "widget": {"name": "end_at_step"}, "link": null}, {"localized_name": "return_with_leftover_noise", "name": "return_with_leftover_noise", "type": "COMBO", "widget": {"name": "return_with_leftover_noise"}, "link": null}], "outputs": [{"localized_name": "LATENT", "name": "LATENT", "type": "LATENT", "links": [175]}], "properties": {"cnr_id": "comfy-core", "ver": "0.3.45", "Node name for S&R": "KSamplerAdvanced", "ue_properties": {"widget_ue_connectable": {}, "version": "7.1", "input_ue_unconnectable": {}}}, "widgets_values": ["disable", 0, "fixed", 4, 1, "euler", "simple", 2, 4, "disable"]}, {"id": 67, "type": "Note", "pos": [510.0000345979581, 819.9999455547611], "size": [390, 88], "flags": {}, "order": 4, "mode": 0, "inputs": [], "outputs": [], "title": "Video Size", "properties": {"ue_properties": {"widget_ue_connectable": {}, "version": "7.1", "input_ue_unconnectable": {}}}, "widgets_values": ["By default, we set the video to a smaller size for users with low VRAM. If you have enough VRAM, you can change the size"], "color": "#222", "bgcolor": "#000"}, {"id": 105, "type": "MarkdownNote", "pos": [-469.9999795985529, 279.9998197772136], "size": [480, 170.65104166666669], "flags": {}, "order": 5, "mode": 0, "inputs": [], "outputs": [], "title": "VRAM Usage", "properties": {"ue_properties": {"version": "7.1", "widget_ue_connectable": {}, "input_ue_unconnectable": {}}}, "widgets_values": ["## GPU:RTX4090D 24GB\n\n| Model            | Size |VRAM Usage | 1st Generation | 2nd Generation |\n|---------------------|-------|-----------|---------------|-----------------|\n| fp8_scaled               |640*640| 84%               | ≈  536s              | ≈ 513s                   |\n| fp8_scaled +  4steps LoRA  | 640*640  | 83%                | ≈ 97s               | ≈ 71s                   |"], "color": "#222", "bgcolor": "#000"}, {"id": 66, "type": "MarkdownNote", "pos": [-469.9999795985529, -320.00012452364496], "size": [480, 572.1354166666667], "flags": {}, "order": 6, "mode": 0, "inputs": [], "outputs": [], "title": "Model Links", "properties": {"ue_properties": {"widget_ue_connectable": {}, "version": "7.1", "input_ue_unconnectable": {}}}, "widgets_values": ["[Tutorial](https://docs.comfy.org/tutorials/video/wan/wan2_2\n)\n\n**Diffusion Model**\n- [wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors)\n- [wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors)\n\n**LoRA**\n- [wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors)\n- [wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors)\n\n**VAE**\n- [wan_2.1_vae.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors)\n\n**Text Encoder**   \n- [umt5_xxl_fp8_e4m3fn_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors)\n\n\nFile save location\n\n```\nComfyUI/\n├───📂 models/\n│   ├───📂 diffusion_models/\n│   │   ├─── wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors\n│   │   └─── wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors\n│   ├───📂 loras/\n│   │   ├─── wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors\n│   │   └─── wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors\n│   ├───📂 text_encoders/\n│   │   └─── umt5_xxl_fp8_e4m3fn_scaled.safetensors \n│   └───📂 vae/\n│       └── wan_2.1_vae.safetensors\n```\n"], "color": "#222", "bgcolor": "#000"}, {"id": 115, "type": "Note", "pos": [29.999978639114225, -470.00010361843204], "size": [360, 88], "flags": {}, "order": 7, "mode": 0, "inputs": [], "outputs": [], "title": "About 4 Steps LoRA", "properties": {"ue_properties": {"widget_ue_connectable": {}, "version": "7.1", "input_ue_unconnectable": {}}}, "widgets_values": ["Using the Wan2.2 Lighting LoRA will result in the loss of video dynamics, but it will reduce the generation time. This template provides two workflows, and you can enable one as needed."], "color": "#222", "bgcolor": "#000"}, {"id": 117, "type": "CreateVideo", "pos": [1030, 650], "size": [270, 78], "flags": {}, "order": 18, "mode": 0, "inputs": [{"localized_name": "images", "name": "images", "type": "IMAGE", "link": 220}, {"localized_name": "audio", "name": "audio", "shape": 7, "type": "AUDIO", "link": null}, {"localized_name": "fps", "name": "fps", "type": "FLOAT", "widget": {"name": "fps"}, "link": null}], "outputs": [{"localized_name": "VIDEO", "name": "VIDEO", "type": "VIDEO", "links": [221]}], "properties": {"cnr_id": "comfy-core", "ver": "0.11.0", "Node name for S&R": "CreateVideo"}, "widgets_values": [16]}, {"id": 87, "type": "VAEDecode", "pos": [1020, 540], "size": [210, 46], "flags": {}, "order": 15, "mode": 0, "inputs": [{"localized_name": "samples", "name": "samples", "type": "LATENT", "link": 175}, {"localized_name": "vae", "name": "vae", "type": "VAE", "link": 176}], "outputs": [{"localized_name": "IMAGE", "name": "IMAGE", "type": "IMAGE", "slot_index": 0, "links": [220]}], "properties": {"cnr_id": "comfy-core", "ver": "0.3.45", "Node name for S&R": "VAEDecode", "ue_properties": {"widget_ue_connectable": {}, "version": "7.1", "input_ue_unconnectable": {}}}, "widgets_values": []}], "groups": [{"id": 15, "title": "fp8_scaled +  4steps LoRA", "bounding": [30, -350, 1630, 1120], "color": "#444", "font_size": 24, "flags": {}}, {"id": 11, "title": "Step1 - Load models", "bounding": [40, -310, 371.0310363769531, 571.3974609375], "color": "#444", "font_size": 24, "flags": {}}, {"id": 13, "title": "Step4 -  Prompt", "bounding": [430, 20, 530, 420], "color": "#444", "font_size": 24, "flags": {}}, {"id": 14, "title": "Step3 - Video size & length", "bounding": [430, 460, 530, 290], "color": "#444", "font_size": 24, "flags": {}}, {"id": 16, "title": "Lightx2v 4steps LoRA", "bounding": [430, -310, 530, 310], "color": "#444", "font_size": 24, "flags": {}}], "links": [{"id": 189, "origin_id": 102, "origin_slot": 0, "target_id": 103, "target_slot": 0, "type": "MODEL"}, {"id": 181, "origin_id": 84, "origin_slot": 0, "target_id": 93, "target_slot": 0, "type": "CLIP"}, {"id": 178, "origin_id": 84, "origin_slot": 0, "target_id": 89, "target_slot": 0, "type": "CLIP"}, {"id": 194, "origin_id": 95, "origin_slot": 0, "target_id": 101, "target_slot": 0, "type": "MODEL"}, {"id": 196, "origin_id": 96, "origin_slot": 0, "target_id": 102, "target_slot": 0, "type": "MODEL"}, {"id": 190, "origin_id": 101, "origin_slot": 0, "target_id": 104, "target_slot": 0, "type": "MODEL"}, {"id": 183, "origin_id": 93, "origin_slot": 0, "target_id": 98, "target_slot": 0, "type": "CONDITIONING"}, {"id": 184, "origin_id": 89, "origin_slot": 0, "target_id": 98, "target_slot": 1, "type": "CONDITIONING"}, {"id": 185, "origin_id": 90, "origin_slot": 0, "target_id": 98, "target_slot": 2, "type": "VAE"}, {"id": 175, "origin_id": 85, "origin_slot": 0, "target_id": 87, "target_slot": 0, "type": "LATENT"}, {"id": 176, "origin_id": 90, "origin_slot": 0, "target_id": 87, "target_slot": 1, "type": "VAE"}, {"id": 195, "origin_id": 104, "origin_slot": 0, "target_id": 86, "target_slot": 0, "type": "MODEL"}, {"id": 172, "origin_id": 98, "origin_slot": 0, "target_id": 86, "target_slot": 1, "type": "CONDITIONING"}, {"id": 173, "origin_id": 98, "origin_slot": 1, "target_id": 86, "target_slot": 2, "type": "CONDITIONING"}, {"id": 174, "origin_id": 98, "origin_slot": 2, "target_id": 86, "target_slot": 3, "type": "LATENT"}, {"id": 192, "origin_id": 103, "origin_slot": 0, "target_id": 85, "target_slot": 0, "type": "MODEL"}, {"id": 168, "origin_id": 98, "origin_slot": 0, "target_id": 85, "target_slot": 1, "type": "CONDITIONING"}, {"id": 169, "origin_id": 98, "origin_slot": 1, "target_id": 85, "target_slot": 2, "type": "CONDITIONING"}, {"id": 170, "origin_id": 86, "origin_slot": 0, "target_id": 85, "target_slot": 3, "type": "LATENT"}, {"id": 186, "origin_id": -10, "origin_slot": 0, "target_id": 98, "target_slot": 4, "type": "IMAGE"}, {"id": 220, "origin_id": 87, "origin_slot": 0, "target_id": 117, "target_slot": 0, "type": "IMAGE"}, {"id": 221, "origin_id": 117, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "VIDEO"}, {"id": 222, "origin_id": -10, "origin_slot": 1, "target_id": 93, "target_slot": 1, "type": "STRING"}, {"id": 223, "origin_id": -10, "origin_slot": 2, "target_id": 98, "target_slot": 5, "type": "INT"}, {"id": 224, "origin_id": -10, "origin_slot": 3, "target_id": 98, "target_slot": 6, "type": "INT"}, {"id": 225, "origin_id": -10, "origin_slot": 4, "target_id": 98, "target_slot": 7, "type": "INT"}, {"id": 226, "origin_id": -10, "origin_slot": 5, "target_id": 95, "target_slot": 0, "type": "COMBO"}, {"id": 227, "origin_id": -10, "origin_slot": 6, "target_id": 101, "target_slot": 1, "type": "COMBO"}, {"id": 228, "origin_id": -10, "origin_slot": 7, "target_id": 96, "target_slot": 0, "type": "COMBO"}, {"id": 229, "origin_id": -10, "origin_slot": 8, "target_id": 102, "target_slot": 1, "type": "COMBO"}, {"id": 230, "origin_id": -10, "origin_slot": 9, "target_id": 84, "target_slot": 0, "type": "COMBO"}, {"id": 231, "origin_id": -10, "origin_slot": 10, "target_id": 90, "target_slot": 0, "type": "COMBO"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Video generation and editing/Image to video"}]}, "config": {}, "extra": {"ds": {"scale": 0.7926047855889957, "offset": [-30.12529469925767, 690.3829855122884]}, "frontendVersion": "1.37.11", "workflowRendererVersion": "LG", "VHS_latentpreview": false, "VHS_latentpreviewrate": 0, "VHS_MetadataImage": true, "VHS_KeepIntermediate": true, "ue_links": []}, "version": 0.4}
diff --git a/blueprints/Pose to Image (Z-Image-Turbo).json b/blueprints/Pose to Image (Z-Image-Turbo).json
new file mode 100644
index 000000000..f4c224249
--- /dev/null
+++ b/blueprints/Pose to Image (Z-Image-Turbo).json	
@@ -0,0 +1 @@
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diff --git a/blueprints/Pose to Video (LTX 2.0).json b/blueprints/Pose to Video (LTX 2.0).json
new file mode 100644
index 000000000..78c098798
--- /dev/null
+++ b/blueprints/Pose to Video (LTX 2.0).json	
@@ -0,0 +1 @@
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diff --git a/blueprints/Prompt Enhance.json b/blueprints/Prompt Enhance.json
new file mode 100644
index 000000000..2612f66db
--- /dev/null
+++ b/blueprints/Prompt Enhance.json	
@@ -0,0 +1 @@
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diff --git a/blueprints/Sharpen.json b/blueprints/Sharpen.json
index 49a164eca..a4accaf59 100644
--- a/blueprints/Sharpen.json
+++ b/blueprints/Sharpen.json
@@ -1 +1 @@
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diff --git a/blueprints/Text to Audio (ACE-Step 1.5).json b/blueprints/Text to Audio (ACE-Step 1.5).json
new file mode 100644
index 000000000..51e3bbed3
--- /dev/null
+++ b/blueprints/Text to Audio (ACE-Step 1.5).json	
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diff --git a/blueprints/Text to Image (Z-Image-Turbo).json b/blueprints/Text to Image (Z-Image-Turbo).json
new file mode 100644
index 000000000..ce25ce1df
--- /dev/null
+++ b/blueprints/Text to Image (Z-Image-Turbo).json	
@@ -0,0 +1 @@
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diff --git a/blueprints/Text to Video (Wan 2.2).json b/blueprints/Text to Video (Wan 2.2).json
new file mode 100644
index 000000000..9f1b69669
--- /dev/null
+++ b/blueprints/Text to Video (Wan 2.2).json	
@@ -0,0 +1 @@
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This template provides two workflows, and you can enable one as needed."], "color": "#222", "bgcolor": "#000"}, {"id": 62, "type": "MarkdownNote", "pos": [-489.666771800538, -278.666700527147], "size": [479.9869791666667, 542.1354166666667], "flags": {}, "order": 5, "mode": 0, "inputs": [], "outputs": [], "title": "Model Links", "properties": {}, "widgets_values": ["[Tutorial](https://docs.comfy.org/tutorials/video/wan/wan2_2\n) | [教程](https://docs.comfy.org/zh-CN/tutorials/video/wan/wan2_2\n)\n\n**Diffusion Model**       \n- [wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors)\n- [wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors)\n\n**LoRA**\n\n- [wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors)\n- [wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors)\n\n**VAE**\n- [wan_2.1_vae.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors)\n\n**Text Encoder**   \n- [umt5_xxl_fp8_e4m3fn_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors)\n\n\nFile save location\n\n```\nComfyUI/\n├───📂 models/\n│   ├───📂 diffusion_models/\n│   │   ├─── wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors\n│   │   └─── wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors\n│   ├───📂 loras/\n│   │   ├───wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors\n│   │   └───wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors\n│   ├───📂 text_encoders/\n│   │   └─── umt5_xxl_fp8_e4m3fn_scaled.safetensors \n│   └───📂 vae/\n│       └── wan_2.1_vae.safetensors\n```\n"], "color": "#222", "bgcolor": "#000"}, {"id": 87, "type": "VAEDecode", "pos": [1020.3331497597994, 471.3333837135574], "size": [210, 46], "flags": {"collapsed": false}, "order": 14, "mode": 0, "inputs": [{"localized_name": "samples", "name": "samples", "type": "LATENT", "link": 157}, {"localized_name": "vae", "name": "vae", "type": "VAE", "link": 158}], "outputs": [{"localized_name": "IMAGE", "name": "IMAGE", "type": "IMAGE", "slot_index": 0, "links": [195]}], "properties": {"cnr_id": "comfy-core", "ver": "0.3.45", "Node name for S&R": "VAEDecode"}, "widgets_values": []}, {"id": 72, "type": "CLIPTextEncode", "pos": [440.3333139376125, 331.3333305479798], "size": [500, 170], "flags": {}, "order": 6, "mode": 0, "inputs": [{"localized_name": "clip", "name": "clip", "type": "CLIP", "link": 141}, {"localized_name": "text", "name": "text", "type": "STRING", "widget": {"name": "text"}, "link": null}], "outputs": [{"localized_name": "CONDITIONING", "name": "CONDITIONING", "type": "CONDITIONING", "slot_index": 0, "links": [144, 150]}], "title": "CLIP Text Encode (Negative Prompt)", "properties": {"cnr_id": "comfy-core", "ver": "0.3.45", "Node name for S&R": "CLIPTextEncode"}, "widgets_values": ["色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走，裸露，NSFW"], "color": "#322", "bgcolor": "#533"}, {"id": 89, "type": "CLIPTextEncode", "pos": [440.3333139376125, 131.33323788258042], "size": [510, 170], "flags": {}, "order": 15, "mode": 0, "inputs": [{"localized_name": "clip", "name": "clip", "type": "CLIP", "link": 160}, {"localized_name": "text", "name": "text", "type": "STRING", "widget": {"name": "text"}, "link": 189}], "outputs": [{"localized_name": "CONDITIONING", "name": "CONDITIONING", "type": "CONDITIONING", "slot_index": 0, "links": [143, 149]}], "title": "CLIP Text Encode (Positive Prompt)", "properties": {"cnr_id": "comfy-core", "ver": "0.3.45", "Node name for S&R": "CLIPTextEncode"}, "widgets_values": [""], "color": "#232", "bgcolor": "#353"}], "groups": [{"id": 13, "title": "Wan2.2 T2V fp8_scaled +  4 steps LoRA", "bounding": [31.999982477688036, -317.00000329413615, 1610, 880], "color": "#444", "font_size": 24, "flags": {}}, {"id": 6, "title": "Step3 Prompt", "bounding": [431.99998247768815, 57.99999670586385, 530, 460], "color": "#444", "font_size": 24, "flags": {}}, {"id": 7, "title": "Lightx2v 4steps LoRA", "bounding": [431.99998247768815, -275.33333662746946, 530, 320], "color": "#444", "font_size": 24, "flags": {}}, {"id": 11, "title": "Step 1 - 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diff --git a/blueprints/Unsharp Mask.json b/blueprints/Unsharp Mask.json
index d282a023b..9363037ef 100644
--- a/blueprints/Unsharp Mask.json	
+++ b/blueprints/Unsharp Mask.json	
@@ -1 +1 @@
-{"revision": 0, "last_node_id": 30, "last_link_id": 0, "nodes": [{"id": 30, "type": "d99ba3f5-8a56-4365-8e45-3f3ea7c572a1", "pos": [4420, -370], "size": [210, 106], "flags": {}, "order": 4, "mode": 0, "inputs": [{"label": "image0", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": null}], "outputs": [{"localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": []}], "title": "Unsharp Mask", "properties": {"proxyWidgets": [["27", "value"], ["28", "value"], ["29", "value"]]}, "widgets_values": []}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "d99ba3f5-8a56-4365-8e45-3f3ea7c572a1", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 29, "lastLinkId": 43, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Unsharp Mask", "inputNode": {"id": -10, "bounding": [3920, -405, 120, 60]}, "outputNode": {"id": -20, "bounding": [4930, -405, 120, 60]}, "inputs": [{"id": "75354555-d2f3-46b9-a3dd-b076dcfca561", "name": "images.image0", "type": "IMAGE", "linkIds": [39], "localized_name": "images.image0", "label": "image0", "pos": [4020, -385]}], "outputs": [{"id": "04368b94-2a96-46ff-8c07-d0ce3235b40d", "name": "IMAGE0", "type": "IMAGE", "linkIds": [40], "localized_name": "IMAGE0", "pos": [4950, -385]}], "widgets": [], "nodes": [{"id": 27, "type": "PrimitiveFloat", "pos": [4100, -540], "size": [270, 58], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "amount", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [41]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 3, "precision": 2, "step": 0.05}, "widgets_values": [1]}, {"id": 28, "type": "PrimitiveFloat", "pos": [4100, -430], "size": [270, 58], "flags": {}, "order": 1, "mode": 0, "inputs": [{"label": "radius", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [42]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 10, "precision": 1, "step": 0.5}, "widgets_values": [3]}, {"id": 29, "type": "PrimitiveFloat", "pos": [4100, -320], "size": [270, 58], "flags": {}, "order": 2, "mode": 0, "inputs": [{"label": "threshold", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [43]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 1, "precision": 2, "step": 0.05}, "widgets_values": [0]}, {"id": 26, "type": "GLSLShader", "pos": [4470, -580], "size": [400, 232], "flags": {}, "order": 3, "mode": 0, "inputs": [{"label": "image0", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": 39}, {"label": "image1", "localized_name": "images.image1", "name": "images.image1", "shape": 7, "type": "IMAGE", "link": null}, {"label": "u_float0", "localized_name": "floats.u_float0", "name": "floats.u_float0", "shape": 7, "type": "FLOAT", "link": 41}, {"label": "u_float1", "localized_name": "floats.u_float1", "name": "floats.u_float1", "shape": 7, "type": "FLOAT", "link": 42}, {"label": "u_float2", "localized_name": "floats.u_float2", "name": "floats.u_float2", "shape": 7, "type": "FLOAT", "link": 43}, {"label": "u_float3", "localized_name": "floats.u_float3", "name": "floats.u_float3", "shape": 7, "type": "FLOAT", "link": null}, {"label": "u_int0", "localized_name": "ints.u_int0", "name": "ints.u_int0", "shape": 7, "type": "INT", "link": null}, {"localized_name": "fragment_shader", "name": "fragment_shader", "type": "STRING", "widget": {"name": "fragment_shader"}, "link": null}, {"localized_name": "size_mode", "name": "size_mode", "type": "COMFY_DYNAMICCOMBO_V3", "widget": {"name": "size_mode"}, "link": null}], "outputs": [{"localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": [40]}, {"localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": null}], "properties": {"Node name for S&R": "GLSLShader"}, "widgets_values": ["#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform float u_float0;  // amount    [0.0 - 3.0]  typical: 0.5-1.5\nuniform float u_float1;  // radius    [0.5 - 10.0] blur radius in pixels\nuniform float u_float2;  // threshold [0.0 - 0.1]  min difference to sharpen\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nfloat gaussian(float x, float sigma) {\n    return exp(-(x * x) / (2.0 * sigma * sigma));\n}\n\nfloat getLuminance(vec3 color) {\n    return dot(color, vec3(0.2126, 0.7152, 0.0722));\n}\n\nvoid main() {\n    vec2 texel = 1.0 / u_resolution;\n    float radius = max(u_float1, 0.5);\n    float amount = u_float0;\n    float threshold = u_float2;\n\n    vec4 original = texture(u_image0, v_texCoord);\n\n    // Gaussian blur for the \"unsharp\" mask\n    int samples = int(ceil(radius));\n    float sigma = radius / 2.0;\n\n    vec4 blurred = vec4(0.0);\n    float totalWeight = 0.0;\n\n    for (int x = -samples; x <= samples; x++) {\n        for (int y = -samples; y <= samples; y++) {\n            vec2 offset = vec2(float(x), float(y)) * texel;\n            vec4 sample_color = texture(u_image0, v_texCoord + offset);\n\n            float dist = length(vec2(float(x), float(y)));\n            float weight = gaussian(dist, sigma);\n            blurred += sample_color * weight;\n            totalWeight += weight;\n        }\n    }\n    blurred /= totalWeight;\n\n    // Unsharp mask = original - blurred\n    vec3 mask = original.rgb - blurred.rgb;\n\n    // Luminance-based threshold with smooth falloff\n    float lumaDelta = abs(getLuminance(original.rgb) - getLuminance(blurred.rgb));\n    float thresholdScale = smoothstep(0.0, threshold, lumaDelta);\n    mask *= thresholdScale;\n\n    // Sharpen: original + mask * amount\n    vec3 sharpened = original.rgb + mask * amount;\n\n    fragColor0 = vec4(clamp(sharpened, 0.0, 1.0), original.a);\n}\n", "from_input"]}], "groups": [], "links": [{"id": 41, "origin_id": 27, "origin_slot": 0, "target_id": 26, "target_slot": 2, "type": "FLOAT"}, {"id": 42, "origin_id": 28, "origin_slot": 0, "target_id": 26, "target_slot": 3, "type": "FLOAT"}, {"id": 43, "origin_id": 29, "origin_slot": 0, "target_id": 26, "target_slot": 4, "type": "FLOAT"}, {"id": 39, "origin_id": -10, "origin_slot": 0, "target_id": 26, "target_slot": 0, "type": "IMAGE"}, {"id": 40, "origin_id": 26, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}}]}}
\ No newline at end of file
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 // amount    [0.0 - 3.0]  typical: 0.5-1.5\nuniform float u_float1;  // radius    [0.5 - 10.0] blur radius in pixels\nuniform float u_float2;  // threshold [0.0 - 0.1]  min difference to sharpen\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nfloat gaussian(float x, float sigma) {\n    return exp(-(x * x) / (2.0 * sigma * sigma));\n}\n\nfloat getLuminance(vec3 color) {\n    return dot(color, vec3(0.2126, 0.7152, 0.0722));\n}\n\nvoid main() {\n    vec2 texel = 1.0 / u_resolution;\n    float radius = max(u_float1, 0.5);\n    float amount = u_float0;\n    float threshold = u_float2;\n\n    vec4 original = texture(u_image0, v_texCoord);\n\n    // Gaussian blur for the \"unsharp\" mask\n    int samples = int(ceil(radius));\n    float sigma = radius / 2.0;\n\n    vec4 blurred = vec4(0.0);\n    float totalWeight = 0.0;\n\n    for (int x = -samples; x <= samples; x++) {\n        for (int y = -samples; y <= samples; y++) {\n            vec2 offset = vec2(float(x), float(y)) * texel;\n            vec4 sample_color = texture(u_image0, v_texCoord + offset);\n\n            float dist = length(vec2(float(x), float(y)));\n            float weight = gaussian(dist, sigma);\n            blurred += sample_color * weight;\n            totalWeight += weight;\n        }\n    }\n    blurred /= totalWeight;\n\n    // Unsharp mask = original - blurred\n    vec3 mask = original.rgb - blurred.rgb;\n\n    // Luminance-based threshold with smooth falloff\n    float lumaDelta = abs(getLuminance(original.rgb) - getLuminance(blurred.rgb));\n    float thresholdScale = smoothstep(0.0, threshold, lumaDelta);\n    mask *= thresholdScale;\n\n    // Sharpen: original + mask * amount\n    vec3 sharpened = original.rgb + mask * amount;\n\n    fragColor0 = vec4(clamp(sharpened, 0.0, 1.0), original.a);\n}\n", "from_input"]}], "groups": [], "links": [{"id": 41, "origin_id": 27, "origin_slot": 0, "target_id": 26, "target_slot": 2, "type": "FLOAT"}, {"id": 42, "origin_id": 28, "origin_slot": 0, "target_id": 26, "target_slot": 3, "type": "FLOAT"}, {"id": 43, "origin_id": 29, "origin_slot": 0, "target_id": 26, "target_slot": 4, "type": "FLOAT"}, {"id": 39, "origin_id": -10, "origin_slot": 0, "target_id": 26, "target_slot": 0, "type": "IMAGE"}, {"id": 40, "origin_id": 26, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Image Tools/Sharpen"}]}}
diff --git a/blueprints/Video Captioning (Gemini).json b/blueprints/Video Captioning (Gemini).json
new file mode 100644
index 000000000..1d72718a1
--- /dev/null
+++ b/blueprints/Video Captioning (Gemini).json	
@@ -0,0 +1 @@
+{"revision": 0, "last_node_id": 233, "last_link_id": 0, "nodes": [{"id": 233, "type": "dcf32045-0ee4-4efc-9aca-9f26f3a157be", "pos": [0, 1140], "size": [400, 260], "flags": {}, "order": 7, "mode": 0, "inputs": [{"name": "prompt", "type": "STRING", "widget": {"name": "prompt"}, "link": null}, {"name": "model", "type": "COMBO", "widget": {"name": "model"}, "link": null}, {"name": "video", "type": "VIDEO", "link": null}], "outputs": [{"localized_name": "STRING", "name": "STRING", "type": "STRING", "links": []}], "title": "Video Captioning(Gemini)", "properties": {"proxyWidgets": [["-1", "prompt"], ["-1", "model"], ["1", "seed"]], "cnr_id": "comfy-core", "ver": "0.13.0"}, "widgets_values": ["Describe this video", "gemini-2.5-pro"]}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "dcf32045-0ee4-4efc-9aca-9f26f3a157be", "version": 1, "state": {"lastGroupId": 1, "lastNodeId": 16, "lastLinkId": 17, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Video Captioning(Gemini)", "inputNode": {"id": -10, "bounding": [-6870, 2530, 120, 100]}, "outputNode": {"id": -20, "bounding": [-6240, 2530, 120, 60]}, "inputs": [{"id": "d8cbd7eb-636a-4d7b-8ff6-b22f1755e26c", "name": "prompt", "type": "STRING", "linkIds": [15], "pos": [-6770, 2550]}, {"id": "b034e26a-d114-4604-aec2-32783e86aa6b", "name": "model", "type": "COMBO", "linkIds": [16], "pos": [-6770, 2570]}, {"id": "f7363f60-a106-4e06-90af-df5f53355b98", "name": "video", "type": "VIDEO", "linkIds": [17], "pos": [-6770, 2590]}], "outputs": [{"id": "e12c6e80-5210-4328-a581-bc8924c53070", "name": "STRING", "type": "STRING", "linkIds": [6], "localized_name": "STRING", "pos": [-6220, 2550]}], "widgets": [], "nodes": [{"id": 1, "type": "GeminiNode", "pos": [-6690, 2360], "size": [390, 430], "flags": {}, "order": 0, "mode": 0, "inputs": [{"localized_name": "images", "name": "images", "shape": 7, "type": "IMAGE", "link": null}, {"localized_name": "audio", "name": "audio", "shape": 7, "type": "AUDIO", "link": null}, {"localized_name": "video", "name": "video", "shape": 7, "type": "VIDEO", "link": 17}, {"localized_name": "files", "name": "files", "shape": 7, "type": "GEMINI_INPUT_FILES", "link": null}, {"localized_name": "prompt", "name": "prompt", "type": "STRING", "widget": {"name": "prompt"}, "link": 15}, {"localized_name": "model", "name": "model", "type": "COMBO", "widget": {"name": "model"}, "link": 16}, {"localized_name": "seed", "name": "seed", "type": "INT", "widget": {"name": "seed"}, "link": null}, {"localized_name": "system_prompt", "name": "system_prompt", "shape": 7, "type": "STRING", "widget": {"name": "system_prompt"}, "link": null}], "outputs": [{"localized_name": "STRING", "name": "STRING", "type": "STRING", "links": [6]}], "properties": {"cnr_id": "comfy-core", "ver": "0.5.1", "Node name for S&R": "GeminiNode"}, "widgets_values": ["Describe this video", "gemini-2.5-pro", 511865409297955, "randomize", "- Role: AI Video Analysis and Description Specialist\n- Background: The user requires a prompt that enables AI to analyze videos (including frame sequences, dynamic movements, audio-visual elements) and generate detailed, structured descriptions. These descriptions must be directly usable as video generation prompts to create similar videos, serving core tasks such as video content creation, creative inspiration extraction, and artistic style exploration.\n- Profile: As an AI Video Analysis and Description Specialist, you possess expertise in computer vision, video temporal sequence processing, motion analysis, and multi-modal natural language generation. You excel at interpreting dynamic visual data (frame-by-frame features + continuous motion) and translating it into precise descriptive text that fully guides the creation of new videos with matching style, rhythm, and content.\n- Skills: Proficiency in video frame feature extraction, motion trajectory recognition, temporal rhythm analysis, scene/shot segmentation, color grading detection, camera movement identification (pan/tilt/zoom/dolly), audio-visual element correlation analysis, and descriptive language generation that captures both static visual features and dynamic temporal characteristics. Mastery of artistic elements in video: composition (per frame + dynamic framing), color palette (consistent + transitional), texture (surface details + motion blur), pacing (frame rate, shot duration), and sound style (background music, ambient sound cues).\n- Goals: To analyze the provided video comprehensively, generate a detailed, structured description that captures all key video elements (static visual features + dynamic motion/temporal characteristics + audio-visual style), and ensure this description can directly serve as a high-quality prompt for creating similar videos.\n- Constraints: \n  1. The description must be clear, structured, and specific enough to guide end-to-end video creation (including frame rate, shot duration, camera movement, motion speed, color transitions).\n  2. Avoid ambiguity; focus on the most salient static (per-frame) and dynamic (temporal) features of the video.\n  3. Prioritize video-specific elements: motion trajectory, shot types (close-up/wide shot/etc.), camera movement, frame rate, scene transitions, rhythm/pacing, and temporal color changes.\n  4. The output must only contain the video generation prompt (no extra explanations).\n- OutputFormat: A detailed, hierarchical text description of the video, structured as follows:\n  1. Core Content & Narrative: Brief overview of the video's subject and temporal progression\n  2. Visual Style (Static): Per-frame key elements (objects, colors, composition, lighting, texture)\n  3. Dynamic Elements (Temporal): Motion details (speed, trajectory, direction), camera movement (type, speed, direction), shot duration/frame rate, scene transitions\n  4. Audio-Visual Style: Color grading (consistent/transitional), rhythm/pacing, and implied audio style (if discernible)\n- Workflow:\n  1. Analyze the video to segment shots/scenes, identify frame-by-frame static visual elements (objects, colors, composition) and cross-frame dynamic elements (motion, camera movement, temporal changes).\n  2. Extract video-specific technical features: frame rate, shot duration, scene transition types, motion speed/rhythm.\n  3. Generate a structured, detailed description that captures the essence of the video (static + dynamic + temporal characteristics), ensuring specificity and actionability for video generation.\n  4. Refine the description for clarity, conciseness, and alignment with video generation prompt norms (e.g., including frame rate, camera movement terms, motion speed descriptors)."], "color": "#432", "bgcolor": "#653"}], "groups": [], "links": [{"id": 6, "origin_id": 1, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "*"}, {"id": 15, "origin_id": -10, "origin_slot": 0, "target_id": 1, "target_slot": 4, "type": "STRING"}, {"id": 16, "origin_id": -10, "origin_slot": 1, "target_id": 1, "target_slot": 5, "type": "COMBO"}, {"id": 17, "origin_id": -10, "origin_slot": 2, "target_id": 1, "target_slot": 2, "type": "VIDEO"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Text generation/Video Captioning"}]}}
diff --git a/blueprints/Video Inpaint(Wan2.1 VACE).json b/blueprints/Video Inpaint(Wan2.1 VACE).json
new file mode 100644
index 000000000..a7c6db003
--- /dev/null
+++ b/blueprints/Video Inpaint(Wan2.1 VACE).json	
@@ -0,0 +1 @@
+{"id": "2f429c60-2e03-4117-908b-31e1fab04bba", "revision": 0, "last_node_id": 229, "last_link_id": 366, "nodes": [{"id": 229, "type": "53a657f3-c9eb-40f2-9ebd-1ed77d25ed67", "pos": [-230, 160], "size": [400, 480], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "video mask", "localized_name": "mask", "name": "mask", "type": "MASK", "link": null}, {"localized_name": "video", "name": "video", "type": "VIDEO", "link": null}, {"name": "width", "type": "INT", "widget": {"name": "width"}, "link": null}, {"name": "height", "type": "INT", "widget": {"name": "height"}, "link": null}, {"label": "reference image", "name": "reference_image_1", "type": "IMAGE", "link": null}, {"name": "unet_name", "type": "COMBO", "widget": {"name": "unet_name"}, "link": null}, {"name": "lora_name", "type": "COMBO", "widget": {"name": "lora_name"}, "link": null}, {"name": "clip_name", "type": "COMBO", "widget": {"name": "clip_name"}, "link": null}, {"name": "vae_name", "type": "COMBO", "widget": {"name": 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------------------------------------------------------------ | ---- | ---- |\n| [VACE-1.3B](https://huggingface.co/Wan-AI/Wan2.1-VACE-1.3B) | ✅   | ❌   |\n| [VACE-14B](https://huggingface.co/Wan-AI/Wan2.1-VACE-14B)   | ✅   | ✅   |"], "color": "#432", "bgcolor": "#000"}, {"id": 223, "type": "MarkdownNote", "pos": [770, -210], "size": [303.90106201171875, 158.5415802001953], "flags": {}, "order": 1, "mode": 0, "inputs": [], "outputs": [], "title": "KSampler Setting", "properties": {}, "widgets_values": ["## Default\n\n- steps:20\n- cfg:6.0\n\n## For CausVid LoRA\n\n- steps: 2-4\n- cfg: 1.0\n\n"], "color": "#432", "bgcolor": "#000"}, {"id": 6, "type": "CLIPTextEncode", "pos": [-80, 60], "size": [420, 280], "flags": {}, "order": 7, "mode": 0, "inputs": [{"localized_name": "clip", "name": "clip", "type": "CLIP", "link": 74}, {"localized_name": "text", "name": "text", "type": "STRING", "widget": {"name": "text"}, "link": null}], "outputs": [{"localized_name": "CONDITIONING", "name": 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diff --git a/blueprints/Video Stitch.json b/blueprints/Video Stitch.json
new file mode 100644
index 000000000..11bcf6b7d
--- /dev/null
+++ b/blueprints/Video Stitch.json	
@@ -0,0 +1 @@
+{"revision": 0, "last_node_id": 84, "last_link_id": 0, "nodes": [{"id": 84, "type": "8e8aa94a-647e-436d-8440-8ee4691864de", "pos": [-6100, 2620], "size": [290, 160], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "Before Video", "localized_name": "video", "name": "video", "type": "VIDEO", "link": null}, {"label": "After Video", "localized_name": "video_1", "name": "video_1", "type": "VIDEO", "link": null}, {"name": "direction", "type": "COMBO", "widget": {"name": "direction"}, "link": null}, {"name": "match_image_size", "type": "BOOLEAN", "widget": {"name": "match_image_size"}, "link": null}, {"name": "spacing_width", "type": "INT", "widget": {"name": "spacing_width"}, "link": null}, {"name": "spacing_color", "type": "COMBO", "widget": {"name": "spacing_color"}, "link": null}], "outputs": [{"localized_name": "VIDEO", "name": "VIDEO", "type": "VIDEO", "links": []}], "properties": {"proxyWidgets": [["-1", "direction"], ["-1", "match_image_size"], ["-1", "spacing_width"], 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diff --git a/blueprints/Video Upscale(GAN x4).json b/blueprints/Video Upscale(GAN x4).json
new file mode 100644
index 000000000..e80b2e229
--- /dev/null
+++ b/blueprints/Video Upscale(GAN x4).json	
@@ -0,0 +1 @@
+{"revision": 0, "last_node_id": 13, "last_link_id": 0, "nodes": [{"id": 13, "type": "cf95b747-3e17-46cb-8097-cac60ff9b2e1", "pos": [1120, 330], "size": [240, 58], "flags": {}, "order": 3, "mode": 0, "inputs": [{"localized_name": "video", "name": "video", "type": "VIDEO", "link": null}, {"name": "model_name", "type": "COMBO", "widget": {"name": "model_name"}, "link": null}], "outputs": [{"localized_name": "VIDEO", "name": "VIDEO", "type": "VIDEO", "links": []}], "title": "Video Upscale(GAN x4)", "properties": {"proxyWidgets": [["-1", "model_name"]], "cnr_id": "comfy-core", "ver": "0.14.1"}, "widgets_values": ["RealESRGAN_x4plus.safetensors"]}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "cf95b747-3e17-46cb-8097-cac60ff9b2e1", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 13, "lastLinkId": 19, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Video Upscale(GAN x4)", "inputNode": {"id": -10, "bounding": [550, 460, 120, 80]}, "outputNode": {"id": -20, "bounding": [1490, 460, 120, 60]}, "inputs": [{"id": "666d633e-93e7-42dc-8d11-2b7b99b0f2a6", "name": "video", "type": "VIDEO", "linkIds": [10], "localized_name": "video", "pos": [650, 480]}, {"id": "2e23a087-caa8-4d65-99e6-662761aa905a", "name": "model_name", "type": "COMBO", "linkIds": [19], "pos": [650, 500]}], "outputs": [{"id": "0c1768ea-3ec2-412f-9af6-8e0fa36dae70", "name": "VIDEO", "type": "VIDEO", "linkIds": [15], "localized_name": "VIDEO", "pos": [1510, 480]}], "widgets": [], "nodes": [{"id": 2, "type": "ImageUpscaleWithModel", "pos": [1110, 450], "size": [320, 46], "flags": {}, "order": 1, "mode": 0, "inputs": [{"localized_name": "upscale_model", "name": "upscale_model", "type": "UPSCALE_MODEL", "link": 1}, {"localized_name": "image", "name": "image", "type": "IMAGE", "link": 14}], "outputs": [{"localized_name": "IMAGE", "name": "IMAGE", "type": "IMAGE", "links": [13]}], "properties": {"cnr_id": "comfy-core", "ver": "0.10.0", "Node name for S&R": "ImageUpscaleWithModel"}}, {"id": 11, "type": "CreateVideo", "pos": [1110, 550], "size": [320, 78], "flags": {}, "order": 3, "mode": 0, "inputs": [{"localized_name": "images", "name": "images", "type": "IMAGE", "link": 13}, {"localized_name": "audio", "name": "audio", "shape": 7, "type": "AUDIO", "link": 16}, {"localized_name": "fps", "name": "fps", "type": "FLOAT", "widget": {"name": "fps"}, "link": 12}], "outputs": [{"localized_name": "VIDEO", "name": "VIDEO", "type": "VIDEO", "links": [15]}], "properties": {"cnr_id": "comfy-core", "ver": "0.10.0", "Node name for S&R": "CreateVideo"}, "widgets_values": [30]}, {"id": 10, "type": "GetVideoComponents", "pos": [1110, 330], "size": [320, 70], "flags": {}, "order": 2, "mode": 0, "inputs": [{"localized_name": "video", "name": "video", "type": "VIDEO", "link": 10}], "outputs": [{"localized_name": "images", "name": "images", "type": "IMAGE", "links": [14]}, {"localized_name": "audio", "name": "audio", "type": "AUDIO", "links": [16]}, {"localized_name": "fps", "name": "fps", "type": "FLOAT", "links": [12]}], "properties": {"cnr_id": "comfy-core", "ver": "0.10.0", "Node name for S&R": "GetVideoComponents"}}, {"id": 1, "type": "UpscaleModelLoader", "pos": [750, 450], "size": [280, 60], "flags": {}, "order": 0, "mode": 0, "inputs": [{"localized_name": "model_name", "name": "model_name", "type": "COMBO", "widget": {"name": "model_name"}, "link": 19}], "outputs": [{"localized_name": "UPSCALE_MODEL", "name": "UPSCALE_MODEL", "type": "UPSCALE_MODEL", "links": [1]}], "properties": {"cnr_id": "comfy-core", "ver": "0.10.0", "Node name for S&R": "UpscaleModelLoader", "models": [{"name": "RealESRGAN_x4plus.safetensors", "url": "https://huggingface.co/Comfy-Org/Real-ESRGAN_repackaged/resolve/main/RealESRGAN_x4plus.safetensors", "directory": "upscale_models"}]}, "widgets_values": ["RealESRGAN_x4plus.safetensors"]}], "groups": [], "links": [{"id": 1, "origin_id": 1, "origin_slot": 0, "target_id": 2, "target_slot": 0, "type": "UPSCALE_MODEL"}, {"id": 14, "origin_id": 10, "origin_slot": 0, "target_id": 2, "target_slot": 1, "type": "IMAGE"}, {"id": 13, "origin_id": 2, "origin_slot": 0, "target_id": 11, "target_slot": 0, "type": "IMAGE"}, {"id": 16, "origin_id": 10, "origin_slot": 1, "target_id": 11, "target_slot": 1, "type": "AUDIO"}, {"id": 12, "origin_id": 10, "origin_slot": 2, "target_id": 11, "target_slot": 2, "type": "FLOAT"}, {"id": 10, "origin_id": -10, "origin_slot": 0, "target_id": 10, "target_slot": 0, "type": "VIDEO"}, {"id": 15, "origin_id": 11, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "VIDEO"}, {"id": 19, "origin_id": -10, "origin_slot": 1, "target_id": 1, "target_slot": 0, "type": "COMBO"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Video generation and editing/Enhance video"}]}, "extra": {}}
diff --git a/comfy_extras/nodes_canny.py b/comfy_extras/nodes_canny.py
index 956c1977c..5e7c4eabb 100644
--- a/comfy_extras/nodes_canny.py
+++ b/comfy_extras/nodes_canny.py
@@ -10,6 +10,7 @@ class Canny(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="Canny",
+            display_name="Canny",
             search_aliases=["edge detection", "outline", "contour detection", "line art"],
             category="image/preprocessors",
             essentials_category="Image Tools",
diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py
index 983e2a92f..c77446a2e 100644
--- a/comfy_extras/nodes_images.py
+++ b/comfy_extras/nodes_images.py
@@ -588,6 +588,7 @@ class ImageRotate(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ImageRotate",
+            display_name="Image Rotate",
             search_aliases=["turn", "flip orientation"],
             category="image/transform",
             essentials_category="Image Tools",
diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py
index 5c143daab..c67245e7a 100644
--- a/comfy_extras/nodes_post_processing.py
+++ b/comfy_extras/nodes_post_processing.py
@@ -19,6 +19,7 @@ class Blend(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ImageBlend",
+            display_name="Image Blend",
             category="image/postprocessing",
             inputs=[
                 io.Image.Input("image1"),
@@ -76,6 +77,7 @@ class Blur(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ImageBlur",
+            display_name="Image Blur",
             category="image/postprocessing",
             essentials_category="Image Tools",
             inputs=[

From 04a55d5019fcd142614652a50914f197a73f1ea9 Mon Sep 17 00:00:00 2001
From: Christian Byrne <cbyrne@comfy.org>
Date: Fri, 20 Feb 2026 23:46:46 -0800
Subject: [PATCH 026/194] fix: swap essentials_category from CLIPTextEncode to
 PrimitiveStringMultiline (#12553)

Remove CLIPTextEncode from Basics essentials category and add
PrimitiveStringMultiline (String Multiline) in its place.

Amp-Thread-ID: https://ampcode.com/threads/T-019c7efb-d916-7244-8c43-77b615ba0622

Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
---
 comfy_extras/nodes_primitive.py | 1 +
 nodes.py                        | 1 -
 2 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy_extras/nodes_primitive.py b/comfy_extras/nodes_primitive.py
index 937321800..9c2e98758 100644
--- a/comfy_extras/nodes_primitive.py
+++ b/comfy_extras/nodes_primitive.py
@@ -29,6 +29,7 @@ class StringMultiline(io.ComfyNode):
             node_id="PrimitiveStringMultiline",
             display_name="String (Multiline)",
             category="utils/primitive",
+            essentials_category="Basics",
             inputs=[
                 io.String.Input("value", multiline=True),
             ],
diff --git a/nodes.py b/nodes.py
index 500d53213..e2fc20d53 100644
--- a/nodes.py
+++ b/nodes.py
@@ -70,7 +70,6 @@ class CLIPTextEncode(ComfyNodeABC):
     FUNCTION = "encode"
 
     CATEGORY = "conditioning"
-    ESSENTIALS_CATEGORY = "Basics"
     DESCRIPTION = "Encodes a text prompt using a CLIP model into an embedding that can be used to guide the diffusion model towards generating specific images."
     SEARCH_ALIASES = ["text", "prompt", "text prompt", "positive prompt", "negative prompt", "encode text", "text encoder", "encode prompt"]
 

From 602b2505a4ffeff4a732b8727ce27d3c2a1ef752 Mon Sep 17 00:00:00 2001
From: pythongosssss <125205205+pythongosssss@users.noreply.github.com>
Date: Sat, 21 Feb 2026 14:14:57 +0000
Subject: [PATCH 027/194] add support for pyopengl < 3.1.4 where the size
 parameter does not exist (#12555)

---
 comfy_extras/nodes_glsl.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/comfy_extras/nodes_glsl.py b/comfy_extras/nodes_glsl.py
index 18a35d846..75ffb6d80 100644
--- a/comfy_extras/nodes_glsl.py
+++ b/comfy_extras/nodes_glsl.py
@@ -716,12 +716,12 @@ def _render_shader_batch(
         gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, 0)
         gl.glUseProgram(0)
 
-        if input_textures:
-            gl.glDeleteTextures(len(input_textures), input_textures)
-        if output_textures:
-            gl.glDeleteTextures(len(output_textures), output_textures)
-        if ping_pong_textures:
-            gl.glDeleteTextures(len(ping_pong_textures), ping_pong_textures)
+        for tex in input_textures:
+            gl.glDeleteTextures(tex)
+        for tex in output_textures:
+            gl.glDeleteTextures(tex)
+        for tex in ping_pong_textures:
+            gl.glDeleteTextures(tex)
         if fbo is not None:
             gl.glDeleteFramebuffers(1, [fbo])
         for pp_fbo in ping_pong_fbos:

From 0bfb936ab46377d1d5faf8c46ff19d0c6f91a04e Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Sat, 21 Feb 2026 10:52:57 -0800
Subject: [PATCH 028/194] comfy-aimdo 0.2 - Improved pytorch allocator
 integration (#12557)

Integrate comfy-aimdo 0.2 which takes a different approach to
installing the memory allocator hook. Instead of using the complicated
and buggy pytorch MemPool+CudaPluggableAlloctor, cuda is directly hooked
making the process much more transparent to both comfy and pytorch. As
far as pytorch knows, aimdo doesnt exist anymore, and just operates
behind the scenes.

Remove all the mempool setup stuff for dynamic_vram and bump the
comfy-aimdo version. Remove the allocator object from memory_management
and demote its use as an enablment check to a boolean flag.

Comfy-aimdo 0.2 also support the pytorch cuda async allocator, so
remove the dynamic_vram based force disablement of cuda_malloc and
just go back to the old settings of allocators based on command line
input.
---
 comfy/memory_management.py |  2 +-
 comfy/model_management.py  |  3 +--
 comfy/utils.py             |  2 +-
 cuda_malloc.py             |  8 +-------
 execution.py               | 22 ++++++++--------------
 main.py                    | 11 +++++------
 requirements.txt           |  2 +-
 7 files changed, 18 insertions(+), 32 deletions(-)

diff --git a/comfy/memory_management.py b/comfy/memory_management.py
index 858bd4cc7..0b7da2852 100644
--- a/comfy/memory_management.py
+++ b/comfy/memory_management.py
@@ -78,4 +78,4 @@ def interpret_gathered_like(tensors, gathered):
 
     return dest_views
 
-aimdo_allocator = None
+aimdo_enabled = False
diff --git a/comfy/model_management.py b/comfy/model_management.py
index 38c3e482b..1fe56a62b 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -836,7 +836,7 @@ def unet_inital_load_device(parameters, dtype):
 
     mem_dev = get_free_memory(torch_dev)
     mem_cpu = get_free_memory(cpu_dev)
-    if mem_dev > mem_cpu and model_size < mem_dev and comfy.memory_management.aimdo_allocator is None:
+    if mem_dev > mem_cpu and model_size < mem_dev and comfy.memory_management.aimdo_enabled:
         return torch_dev
     else:
         return cpu_dev
@@ -1121,7 +1121,6 @@ def get_cast_buffer(offload_stream, device, size, ref):
             synchronize()
             del STREAM_CAST_BUFFERS[offload_stream]
             del cast_buffer
-            #FIXME: This doesn't work in Aimdo because mempool cant clear cache
             soft_empty_cache()
         with wf_context:
             cast_buffer = torch.empty((size), dtype=torch.int8, device=device)
diff --git a/comfy/utils.py b/comfy/utils.py
index 17443b4cc..5fe66ecdb 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -1154,7 +1154,7 @@ def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_am
     return tiled_scale_multidim(samples, function, (tile_y, tile_x), overlap=overlap, upscale_amount=upscale_amount, out_channels=out_channels, output_device=output_device, pbar=pbar)
 
 def model_trange(*args, **kwargs):
-    if comfy.memory_management.aimdo_allocator is None:
+    if not comfy.memory_management.aimdo_enabled:
         return trange(*args, **kwargs)
 
     pbar = trange(*args, **kwargs, smoothing=1.0)
diff --git a/cuda_malloc.py b/cuda_malloc.py
index b2182df37..f7651981c 100644
--- a/cuda_malloc.py
+++ b/cuda_malloc.py
@@ -1,10 +1,8 @@
 import os
 import importlib.util
-from comfy.cli_args import args, PerformanceFeature, enables_dynamic_vram
+from comfy.cli_args import args, PerformanceFeature
 import subprocess
 
-import comfy_aimdo.control
-
 #Can't use pytorch to get the GPU names because the cuda malloc has to be set before the first import.
 def get_gpu_names():
     if os.name == 'nt':
@@ -87,10 +85,6 @@ if not args.cuda_malloc:
     except:
         pass
 
-if enables_dynamic_vram() and comfy_aimdo.control.init():
-    args.cuda_malloc = False
-    os.environ['PYTORCH_CUDA_ALLOC_CONF'] = ""
-
 if args.disable_cuda_malloc:
     args.cuda_malloc = False
 
diff --git a/execution.py b/execution.py
index f549a2f0f..75b021892 100644
--- a/execution.py
+++ b/execution.py
@@ -9,7 +9,6 @@ import traceback
 from enum import Enum
 from typing import List, Literal, NamedTuple, Optional, Union
 import asyncio
-from contextlib import nullcontext
 
 import torch
 
@@ -521,19 +520,14 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
                 # TODO - How to handle this with async functions without contextvars (which requires Python 3.12)?
                 GraphBuilder.set_default_prefix(unique_id, call_index, 0)
 
-            #Do comfy_aimdo mempool chunking here on the per-node level. Multi-model workflows
-            #will cause all sorts of incompatible memory shapes to fragment the pytorch alloc
-            #that we just want to cull out each model run.
-            allocator = comfy.memory_management.aimdo_allocator
-            with nullcontext() if allocator is None else torch.cuda.use_mem_pool(torch.cuda.MemPool(allocator.allocator())):
-                try:
-                    output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)
-                finally:
-                    if allocator is not None:
-                        if args.verbose == "DEBUG":
-                            comfy_aimdo.model_vbar.vbars_analyze()
-                        comfy.model_management.reset_cast_buffers()
-                        comfy_aimdo.model_vbar.vbars_reset_watermark_limits()
+            try:
+                output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)
+            finally:
+                if comfy.memory_management.aimdo_enabled:
+                    if args.verbose == "DEBUG":
+                        comfy_aimdo.control.analyze()
+                    comfy.model_management.reset_cast_buffers()
+                    comfy_aimdo.model_vbar.vbars_reset_watermark_limits()
 
             if has_pending_tasks:
                 pending_async_nodes[unique_id] = output_data
diff --git a/main.py b/main.py
index 92d705b4d..39e605deb 100644
--- a/main.py
+++ b/main.py
@@ -173,6 +173,10 @@ import gc
 if 'torch' in sys.modules:
     logging.warning("WARNING: Potential Error in code: Torch already imported, torch should never be imported before this point.")
 
+import comfy_aimdo.control
+
+if enables_dynamic_vram():
+    comfy_aimdo.control.init()
 
 import comfy.utils
 
@@ -188,13 +192,9 @@ import hook_breaker_ac10a0
 import comfy.memory_management
 import comfy.model_patcher
 
-import comfy_aimdo.control
-import comfy_aimdo.torch
-
 if enables_dynamic_vram():
     if comfy.model_management.torch_version_numeric < (2, 8):
         logging.warning("Unsupported Pytorch detected. DynamicVRAM support requires Pytorch version 2.8 or later. Falling back to legacy ModelPatcher. VRAM estimates may be unreliable especially on Windows")
-        comfy.memory_management.aimdo_allocator = None
     elif comfy_aimdo.control.init_device(comfy.model_management.get_torch_device().index):
         if args.verbose == 'DEBUG':
             comfy_aimdo.control.set_log_debug()
@@ -208,11 +208,10 @@ if enables_dynamic_vram():
             comfy_aimdo.control.set_log_info()
 
         comfy.model_patcher.CoreModelPatcher = comfy.model_patcher.ModelPatcherDynamic
-        comfy.memory_management.aimdo_allocator = comfy_aimdo.torch.get_torch_allocator()
+        comfy.memory_management.aimdo_enabled = True
         logging.info("DynamicVRAM support detected and enabled")
     else:
         logging.warning("No working comfy-aimdo install detected. DynamicVRAM support disabled. Falling back to legacy ModelPatcher. VRAM estimates may be unreliable especially on Windows")
-        comfy.memory_management.aimdo_allocator = None
 
 
 def cuda_malloc_warning():
diff --git a/requirements.txt b/requirements.txt
index 3a9bfde46..8fbb0dbd6 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -22,7 +22,7 @@ alembic
 SQLAlchemy
 av>=14.2.0
 comfy-kitchen>=0.2.7
-comfy-aimdo>=0.1.8
+comfy-aimdo>=0.2.0
 requests
 
 #non essential dependencies:

From 7591d781a7a1cdfd6f8f0a9ec6ecd692495e14b5 Mon Sep 17 00:00:00 2001
From: Alexander Brown <DrJKL0424@gmail.com>
Date: Sat, 21 Feb 2026 15:05:00 -0800
Subject: [PATCH 029/194] fix: specify UTF-8 encoding when reading subgraph
 files (#12563)

On Windows, Python defaults to cp1252 encoding when no encoding is
specified. JSON files containing UTF-8 characters (e.g., non-ASCII
characters) cause UnicodeDecodeError when read with cp1252.

This fixes the error that occurs when loading blueprint subgraphs
on Windows systems.

https://claude.ai/code/session_014WHi3SL9Gzsi3U6kbSjbSb

Co-authored-by: Claude <noreply@anthropic.com>
---
 app/subgraph_manager.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/app/subgraph_manager.py b/app/subgraph_manager.py
index 6a8f586a4..08ad8c302 100644
--- a/app/subgraph_manager.py
+++ b/app/subgraph_manager.py
@@ -53,7 +53,7 @@ class SubgraphManager:
         return entry_id, entry
 
     async def load_entry_data(self, entry: SubgraphEntry):
-        with open(entry['path'], 'r') as f:
+        with open(entry['path'], 'r', encoding='utf-8') as f:
             entry['data'] = f.read()
         return entry
 

From ee72752162fb8b56b8165aa93633c57f0d85002c Mon Sep 17 00:00:00 2001
From: Christian Byrne <cbyrne@comfy.org>
Date: Sat, 21 Feb 2026 16:51:21 -0800
Subject: [PATCH 030/194] Add category to Normalized Attention Guidance node
 (#12565)

---
 comfy_extras/nodes_nag.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy_extras/nodes_nag.py b/comfy_extras/nodes_nag.py
index 033e40eb9..b57181848 100644
--- a/comfy_extras/nodes_nag.py
+++ b/comfy_extras/nodes_nag.py
@@ -10,7 +10,7 @@ class NAGuidance(io.ComfyNode):
             node_id="NAGuidance",
             display_name="Normalized Attention Guidance",
             description="Applies Normalized Attention Guidance to models, enabling negative prompts on distilled/schnell models.",
-            category="",
+            category="advanced/guidance",
             is_experimental=True,
             inputs=[
                 io.Model.Input("model", tooltip="The model to apply NAG to."),

From b6cb30bab52c47ae38bc2ae404929f49ba765017 Mon Sep 17 00:00:00 2001
From: Christian Byrne <cbyrne@comfy.org>
Date: Sat, 21 Feb 2026 18:32:15 -0800
Subject: [PATCH 031/194] chore: tune CodeRabbit config to limit review scope
 and disable for drafts (#12567)

* chore: tune CodeRabbit config to limit review scope and disable for drafts

- Add tone_instructions to focus only on newly introduced issues
- Add global path_instructions entry to ignore pre-existing issues in moved/reformatted code
- Disable draft PR reviews (drafts: false) and add WIP title keywords
- Disable ruff tool to prevent linter-based outside-diff-range comments

Addresses feedback from maintainers about CodeRabbit flagging pre-existing
issues in code that was merely moved or de-indented (e.g., PR #12557),
which can discourage community contributions and cause scope creep.

Amp-Thread-ID: https://ampcode.com/threads/T-019c82de-0481-7253-ad42-20cb595bb1ba

* chore: add 'DO NOT MERGE' to ignore_title_keywords

Amp-Thread-ID: https://ampcode.com/threads/T-019c82de-0481-7253-ad42-20cb595bb1ba
---
 .coderabbit.yaml | 17 +++++++++++++++--
 1 file changed, 15 insertions(+), 2 deletions(-)

diff --git a/.coderabbit.yaml b/.coderabbit.yaml
index 3849d4b26..0d1e49270 100644
--- a/.coderabbit.yaml
+++ b/.coderabbit.yaml
@@ -1,6 +1,7 @@
 # yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json
 language: "en-US"
 early_access: false
+tone_instructions: "Only comment on issues introduced by this PR's changes. Do not flag pre-existing problems in moved, re-indented, or reformatted code."
 
 reviews:
   profile: "chill"
@@ -35,6 +36,14 @@ reviews:
     - "!**/*.bat"
 
   path_instructions:
+    - path: "**"
+      instructions: |
+        IMPORTANT: Only comment on issues directly introduced by this PR's code changes.
+        Do NOT flag pre-existing issues in code that was merely moved, re-indented,
+        de-indented, or reformatted without logic changes. If code appears in the diff
+        only due to whitespace or structural reformatting (e.g., removing a `with:` block),
+        treat it as unchanged. Contributors should not feel obligated to address
+        pre-existing issues outside the scope of their contribution.
     - path: "comfy/**"
       instructions: |
         Core ML/diffusion engine. Focus on:
@@ -74,7 +83,11 @@ reviews:
   auto_review:
     enabled: true
     auto_incremental_review: true
-    drafts: true
+    drafts: false
+    ignore_title_keywords:
+      - "WIP"
+      - "DO NOT REVIEW"
+      - "DO NOT MERGE"
 
   finishing_touches:
     docstrings:
@@ -84,7 +97,7 @@ reviews:
 
   tools:
     ruff:
-      enabled: true
+      enabled: false
     pylint:
       enabled: false
     flake8:

From f266b8d352607799afb4adf339cdfa854025185e Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Sat, 21 Feb 2026 19:29:58 -0800
Subject: [PATCH 032/194] Move LTXAV av embedding connectors to diffusion
 model. (#12569)

---
 comfy/ldm/lightricks/av_model.py | 24 ++++++++++++++++++++++++
 comfy/model_base.py              |  4 ++++
 comfy/text_encoders/lt.py        | 25 ++-----------------------
 3 files changed, 30 insertions(+), 23 deletions(-)

diff --git a/comfy/ldm/lightricks/av_model.py b/comfy/ldm/lightricks/av_model.py
index 2c6954ecd..2b080aaeb 100644
--- a/comfy/ldm/lightricks/av_model.py
+++ b/comfy/ldm/lightricks/av_model.py
@@ -9,6 +9,7 @@ from comfy.ldm.lightricks.model import (
     LTXVModel,
 )
 from comfy.ldm.lightricks.symmetric_patchifier import AudioPatchifier
+from comfy.ldm.lightricks.embeddings_connector import Embeddings1DConnector
 import comfy.ldm.common_dit
 
 class CompressedTimestep:
@@ -450,6 +451,29 @@ class LTXAVModel(LTXVModel):
             operations=self.operations,
         )
 
+        self.audio_embeddings_connector = Embeddings1DConnector(
+            split_rope=True,
+            double_precision_rope=True,
+            dtype=dtype,
+            device=device,
+            operations=self.operations,
+        )
+
+        self.video_embeddings_connector = Embeddings1DConnector(
+            split_rope=True,
+            double_precision_rope=True,
+            dtype=dtype,
+            device=device,
+            operations=self.operations,
+        )
+
+    def preprocess_text_embeds(self, context):
+        if context.shape[-1] == self.caption_channels * 2:
+            return context
+        out_vid = self.video_embeddings_connector(context)[0]
+        out_audio = self.audio_embeddings_connector(context)[0]
+        return torch.concat((out_vid, out_audio), dim=-1)
+
     def _init_transformer_blocks(self, device, dtype, **kwargs):
         """Initialize transformer blocks for LTXAV."""
         self.transformer_blocks = nn.ModuleList(
diff --git a/comfy/model_base.py b/comfy/model_base.py
index 9dcef8741..2f49578f6 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -988,10 +988,14 @@ class LTXAV(BaseModel):
     def extra_conds(self, **kwargs):
         out = super().extra_conds(**kwargs)
         attention_mask = kwargs.get("attention_mask", None)
+        device = kwargs["device"]
+
         if attention_mask is not None:
             out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
+            if hasattr(self.diffusion_model, "preprocess_text_embeds"):
+                cross_attn = self.diffusion_model.preprocess_text_embeds(cross_attn.to(device=device, dtype=self.get_dtype_inference()))
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
 
         out['frame_rate'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", 25))
diff --git a/comfy/text_encoders/lt.py b/comfy/text_encoders/lt.py
index 82fbacf59..e2ce22e37 100644
--- a/comfy/text_encoders/lt.py
+++ b/comfy/text_encoders/lt.py
@@ -3,7 +3,6 @@ import os
 from transformers import T5TokenizerFast
 from .spiece_tokenizer import SPieceTokenizer
 import comfy.text_encoders.genmo
-from comfy.ldm.lightricks.embeddings_connector import Embeddings1DConnector
 import torch
 import comfy.utils
 import math
@@ -109,22 +108,6 @@ class LTXAVTEModel(torch.nn.Module):
         operations = self.gemma3_12b.operations # TODO
         self.text_embedding_projection = operations.Linear(3840 * 49, 3840, bias=False, dtype=dtype, device=device)
 
-        self.audio_embeddings_connector = Embeddings1DConnector(
-            split_rope=True,
-            double_precision_rope=True,
-            dtype=dtype,
-            device=device,
-            operations=operations,
-        )
-
-        self.video_embeddings_connector = Embeddings1DConnector(
-            split_rope=True,
-            double_precision_rope=True,
-            dtype=dtype,
-            device=device,
-            operations=operations,
-        )
-
     def set_clip_options(self, options):
         self.execution_device = options.get("execution_device", self.execution_device)
         self.gemma3_12b.set_clip_options(options)
@@ -146,10 +129,6 @@ class LTXAVTEModel(torch.nn.Module):
         out = out.reshape((out.shape[0], out.shape[1], -1))
         out = self.text_embedding_projection(out)
         out = out.float()
-        out_vid = self.video_embeddings_connector(out)[0]
-        out_audio = self.audio_embeddings_connector(out)[0]
-        out = torch.concat((out_vid, out_audio), dim=-1)
-
         return out.to(out_device), pooled
 
     def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
@@ -159,14 +138,14 @@ class LTXAVTEModel(torch.nn.Module):
         if "model.layers.47.self_attn.q_norm.weight" in sd:
             return self.gemma3_12b.load_sd(sd)
         else:
-            sdo = comfy.utils.state_dict_prefix_replace(sd, {"text_embedding_projection.aggregate_embed.weight": "text_embedding_projection.weight", "model.diffusion_model.video_embeddings_connector.": "video_embeddings_connector.", "model.diffusion_model.audio_embeddings_connector.": "audio_embeddings_connector."}, filter_keys=True)
+            sdo = comfy.utils.state_dict_prefix_replace(sd, {"text_embedding_projection.aggregate_embed.weight": "text_embedding_projection.weight"}, filter_keys=True)
             if len(sdo) == 0:
                 sdo = sd
 
             missing_all = []
             unexpected_all = []
 
-            for prefix, component in [("text_embedding_projection.", self.text_embedding_projection), ("video_embeddings_connector.", self.video_embeddings_connector), ("audio_embeddings_connector.", self.audio_embeddings_connector)]:
+            for prefix, component in [("text_embedding_projection.", self.text_embedding_projection)]:
                 component_sd = {k.replace(prefix, ""): v for k, v in sdo.items() if k.startswith(prefix)}
                 if component_sd:
                     missing, unexpected = component.load_state_dict(component_sd, strict=False, assign=getattr(self, "can_assign_sd", False))

From 07ca6852e8dc332f98531f0b51735eff66469755 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Sun, 22 Feb 2026 00:18:20 -0800
Subject: [PATCH 033/194] Fix dtype issue in embeddings connector. (#12570)

---
 comfy/ldm/lightricks/embeddings_connector.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/comfy/ldm/lightricks/embeddings_connector.py b/comfy/ldm/lightricks/embeddings_connector.py
index 06f5ada89..862bc844a 100644
--- a/comfy/ldm/lightricks/embeddings_connector.py
+++ b/comfy/ldm/lightricks/embeddings_connector.py
@@ -234,7 +234,7 @@ class Embeddings1DConnector(nn.Module):
 
         return indices
 
-    def precompute_freqs_cis(self, indices_grid, spacing="exp"):
+    def precompute_freqs_cis(self, indices_grid, spacing="exp", out_dtype=None):
         dim = self.inner_dim
         n_elem = 2  # 2 because of cos and sin
         freqs = self.precompute_freqs(indices_grid, spacing)
@@ -247,7 +247,7 @@ class Embeddings1DConnector(nn.Module):
             )
         else:
             cos_freq, sin_freq = interleaved_freqs_cis(freqs, dim % n_elem)
-        return cos_freq.to(self.dtype), sin_freq.to(self.dtype), self.split_rope
+        return cos_freq.to(dtype=out_dtype), sin_freq.to(dtype=out_dtype), self.split_rope
 
     def forward(
         self,
@@ -288,7 +288,7 @@ class Embeddings1DConnector(nn.Module):
             hidden_states.shape[1], dtype=torch.float32, device=hidden_states.device
         )
         indices_grid = indices_grid[None, None, :]
-        freqs_cis = self.precompute_freqs_cis(indices_grid)
+        freqs_cis = self.precompute_freqs_cis(indices_grid, out_dtype=hidden_states.dtype)
 
         # 2. Blocks
         for block_idx, block in enumerate(self.transformer_1d_blocks):

From caa43d2395a69e93e52fe903da515fb2adbbb677 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Sun, 22 Feb 2026 13:00:02 -0800
Subject: [PATCH 034/194] Fix issue loading fp8 ltxav checkpoints. (#12582)

---
 comfy/ldm/lightricks/embeddings_connector.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/comfy/ldm/lightricks/embeddings_connector.py b/comfy/ldm/lightricks/embeddings_connector.py
index 862bc844a..33adb9671 100644
--- a/comfy/ldm/lightricks/embeddings_connector.py
+++ b/comfy/ldm/lightricks/embeddings_connector.py
@@ -157,11 +157,9 @@ class Embeddings1DConnector(nn.Module):
         self.num_learnable_registers = num_learnable_registers
         if self.num_learnable_registers:
             self.learnable_registers = nn.Parameter(
-                torch.rand(
+                torch.empty(
                     self.num_learnable_registers, inner_dim, dtype=dtype, device=device
                 )
-                * 2.0
-                - 1.0
             )
 
     def get_fractional_positions(self, indices_grid):

From dba2766e5354a4b5ec957bd4e005550d83214a34 Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Mon, 23 Feb 2026 21:27:16 +0200
Subject: [PATCH 035/194] feat(api-nodes): add KlingAvatar node (#12591)

---
 comfy_api_nodes/apis/kling.py  |   7 +++
 comfy_api_nodes/nodes_kling.py | 100 +++++++++++++++++++++++++++++++++
 2 files changed, 107 insertions(+)

diff --git a/comfy_api_nodes/apis/kling.py b/comfy_api_nodes/apis/kling.py
index 9c0446075..a5bd5f1d3 100644
--- a/comfy_api_nodes/apis/kling.py
+++ b/comfy_api_nodes/apis/kling.py
@@ -134,6 +134,13 @@ class ImageToVideoWithAudioRequest(BaseModel):
     shot_type: str | None = Field(None)
 
 
+class KlingAvatarRequest(BaseModel):
+    image: str = Field(...)
+    sound_file: str = Field(...)
+    prompt: str | None = Field(None)
+    mode: str = Field(...)
+
+
 class MotionControlRequest(BaseModel):
     prompt: str = Field(...)
     image_url: str = Field(...)
diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py
index fa0f9e87c..74fa078ff 100644
--- a/comfy_api_nodes/nodes_kling.py
+++ b/comfy_api_nodes/nodes_kling.py
@@ -50,6 +50,7 @@ from comfy_api_nodes.apis import (
 )
 from comfy_api_nodes.apis.kling import (
     ImageToVideoWithAudioRequest,
+    KlingAvatarRequest,
     MotionControlRequest,
     MultiPromptEntry,
     OmniImageParamImage,
@@ -74,6 +75,7 @@ from comfy_api_nodes.util import (
     upload_image_to_comfyapi,
     upload_images_to_comfyapi,
     upload_video_to_comfyapi,
+    validate_audio_duration,
     validate_image_aspect_ratio,
     validate_image_dimensions,
     validate_string,
@@ -3139,6 +3141,103 @@ class KlingFirstLastFrameNode(IO.ComfyNode):
         return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
 
 
+class KlingAvatarNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingAvatarNode",
+            display_name="Kling Avatar 2.0",
+            category="api node/video/Kling",
+            description="Generate broadcast-style digital human videos from a single photo and an audio file.",
+            inputs=[
+                IO.Image.Input(
+                    "image",
+                    tooltip="Avatar reference image. "
+                    "Width and height must be at least 300px. Aspect ratio must be between 1:2.5 and 2.5:1.",
+                ),
+                IO.Audio.Input(
+                    "sound_file",
+                    tooltip="Audio input. Must be between 2 and 300 seconds in duration.",
+                ),
+                IO.Combo.Input("mode", options=["std", "pro"]),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    optional=True,
+                    tooltip="Optional prompt to define avatar actions, emotions, and camera movements.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["mode"]),
+                expr="""
+                (
+                  $prices := {"std": 0.056, "pro": 0.112};
+                  {"type":"usd","usd": $lookup($prices, widgets.mode), "format":{"suffix":"/second"}}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        sound_file: Input.Audio,
+        mode: str,
+        seed: int,
+        prompt: str = "",
+    ) -> IO.NodeOutput:
+        validate_image_dimensions(image, min_width=300, min_height=300)
+        validate_image_aspect_ratio(image, (1, 2.5), (2.5, 1))
+        validate_audio_duration(sound_file, min_duration=2, max_duration=300)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/videos/avatar/image2video", method="POST"),
+            response_model=TaskStatusResponse,
+            data=KlingAvatarRequest(
+                image=await upload_image_to_comfyapi(cls, image),
+                sound_file=await upload_audio_to_comfyapi(
+                    cls, sound_file, container_format="mp3", codec_name="libmp3lame", mime_type="audio/mpeg"
+                ),
+                prompt=prompt or None,
+                mode=mode,
+            ),
+        )
+        if response.code:
+            raise RuntimeError(
+                f"Kling request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}"
+            )
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/kling/v1/videos/avatar/image2video/{response.data.task_id}"),
+            response_model=TaskStatusResponse,
+            status_extractor=lambda r: (r.data.task_status if r.data else None),
+            max_poll_attempts=800,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
+
+
 class KlingExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -3167,6 +3266,7 @@ class KlingExtension(ComfyExtension):
             MotionControl,
             KlingVideoNode,
             KlingFirstLastFrameNode,
+            KlingAvatarNode,
         ]
 
 

From 7a7debcaf11bf55257e667f33c447268ea76d412 Mon Sep 17 00:00:00 2001
From: ComfyUI Wiki <contact@comfyui-wiki.com>
Date: Tue, 24 Feb 2026 07:27:20 +0800
Subject: [PATCH 036/194] chore: update workflow templates to v0.9.2 (#12596)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 8fbb0dbd6..b5fa2fe13 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.39.14
-comfyui-workflow-templates==0.8.43
+comfyui-workflow-templates==0.9.2
 comfyui-embedded-docs==0.4.1
 torch
 torchsde

From 9b1c63eb6927e42b36de8af7c3f58b2a554564ad Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Mon, 23 Feb 2026 18:01:17 -0800
Subject: [PATCH 037/194] Add SplitImageToTileList and ImageMergeTileList
 nodes. (#12599)

With these you can split an image into tiles, do operations and then combine it back to a single image.
---
 comfy_extras/nodes_images.py | 169 +++++++++++++++++++++++++++++++++++
 1 file changed, 169 insertions(+)

diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py
index c77446a2e..727d7d09d 100644
--- a/comfy_extras/nodes_images.py
+++ b/comfy_extras/nodes_images.py
@@ -6,6 +6,7 @@ import folder_paths
 import json
 import os
 import re
+import math
 import torch
 import comfy.utils
 
@@ -682,6 +683,172 @@ class ImageScaleToMaxDimension(IO.ComfyNode):
     upscale = execute    # TODO: remove
 
 
+class SplitImageToTileList(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SplitImageToTileList",
+            category="image/batch",
+            search_aliases=["split image", "tile image", "slice image"],
+            display_name="Split Image into List of Tiles",
+            description="Splits an image into a batched list of tiles with a specified overlap.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Int.Input("tile_width", default=1024, min=64, max=MAX_RESOLUTION),
+                IO.Int.Input("tile_height", default=1024, min=64, max=MAX_RESOLUTION),
+                IO.Int.Input("overlap", default=128, min=0, max=4096),
+            ],
+            outputs=[
+                IO.Image.Output(is_output_list=True),
+            ],
+        )
+
+    @staticmethod
+    def get_grid_coords(width, height, tile_width, tile_height, overlap):
+        coords = []
+        stride_x = max(1, tile_width - overlap)
+        stride_y = max(1, tile_height - overlap)
+
+        y = 0
+        while y < height:
+            x = 0
+            y_end = min(y + tile_height, height)
+            y_start = max(0, y_end - tile_height)
+
+            while x < width:
+                x_end = min(x + tile_width, width)
+                x_start = max(0, x_end - tile_width)
+
+                coords.append((x_start, y_start, x_end, y_end))
+
+                if x_end >= width:
+                    break
+                x += stride_x
+
+            if y_end >= height:
+                break
+            y += stride_y
+
+        return coords
+
+    @classmethod
+    def execute(cls, image, tile_width, tile_height, overlap):
+        b, h, w, c = image.shape
+        coords = cls.get_grid_coords(w, h, tile_width, tile_height, overlap)
+
+        output_list = []
+        for (x_start, y_start, x_end, y_end) in coords:
+            tile = image[:, y_start:y_end, x_start:x_end, :]
+            output_list.append(tile)
+
+        return IO.NodeOutput(output_list)
+
+
+class ImageMergeTileList(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ImageMergeTileList",
+            display_name="Merge List of Tiles to Image",
+            category="image/batch",
+            search_aliases=["split image", "tile image", "slice image"],
+            is_input_list=True,
+            inputs=[
+                IO.Image.Input("image_list"),
+                IO.Int.Input("final_width", default=1024, min=64, max=32768),
+                IO.Int.Input("final_height", default=1024, min=64, max=32768),
+                IO.Int.Input("overlap", default=128, min=0, max=4096),
+            ],
+            outputs=[
+                IO.Image.Output(is_output_list=False),
+            ],
+        )
+
+    @staticmethod
+    def get_grid_coords(width, height, tile_width, tile_height, overlap):
+        coords = []
+        stride_x = max(1, tile_width - overlap)
+        stride_y = max(1, tile_height - overlap)
+
+        y = 0
+        while y < height:
+            x = 0
+            y_end = min(y + tile_height, height)
+            y_start = max(0, y_end - tile_height)
+
+            while x < width:
+                x_end = min(x + tile_width, width)
+                x_start = max(0, x_end - tile_width)
+
+                coords.append((x_start, y_start, x_end, y_end))
+
+                if x_end >= width:
+                    break
+                x += stride_x
+
+            if y_end >= height:
+                break
+            y += stride_y
+
+        return coords
+
+    @classmethod
+    def execute(cls, image_list, final_width, final_height, overlap):
+        w = final_width[0]
+        h = final_height[0]
+        ovlp = overlap[0]
+        feather_str = 1.0
+
+        first_tile = image_list[0]
+        b, t_h, t_w, c = first_tile.shape
+        device = first_tile.device
+        dtype = first_tile.dtype
+
+        coords = cls.get_grid_coords(w, h, t_w, t_h, ovlp)
+
+        canvas = torch.zeros((b, h, w, c), device=device, dtype=dtype)
+        weights = torch.zeros((b, h, w, 1), device=device, dtype=dtype)
+
+        if ovlp > 0:
+            y_w = torch.sin(math.pi * torch.linspace(0, 1, t_h, device=device, dtype=dtype))
+            x_w = torch.sin(math.pi * torch.linspace(0, 1, t_w, device=device, dtype=dtype))
+            y_w = torch.clamp(y_w, min=1e-5)
+            x_w = torch.clamp(x_w, min=1e-5)
+
+            sine_mask = (y_w.unsqueeze(1) * x_w.unsqueeze(0)).unsqueeze(0).unsqueeze(-1)
+            flat_mask = torch.ones_like(sine_mask)
+
+            weight_mask = torch.lerp(flat_mask, sine_mask, feather_str)
+        else:
+            weight_mask = torch.ones((1, t_h, t_w, 1), device=device, dtype=dtype)
+
+        for i, (x_start, y_start, x_end, y_end) in enumerate(coords):
+            if i >= len(image_list):
+                break
+
+            tile = image_list[i]
+
+            region_h = y_end - y_start
+            region_w = x_end - x_start
+
+            real_h = min(region_h, tile.shape[1])
+            real_w = min(region_w, tile.shape[2])
+
+            y_end_actual = y_start + real_h
+            x_end_actual = x_start + real_w
+
+            tile_crop = tile[:, :real_h, :real_w, :]
+            mask_crop = weight_mask[:, :real_h, :real_w, :]
+
+            canvas[:, y_start:y_end_actual, x_start:x_end_actual, :] += tile_crop * mask_crop
+            weights[:, y_start:y_end_actual, x_start:x_end_actual, :] += mask_crop
+
+        weights[weights == 0] = 1.0
+        merged_image = canvas / weights
+
+        return IO.NodeOutput(merged_image)
+
+
 class ImagesExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -701,6 +868,8 @@ class ImagesExtension(ComfyExtension):
             ImageRotate,
             ImageFlip,
             ImageScaleToMaxDimension,
+            SplitImageToTileList,
+            ImageMergeTileList,
         ]
 
 

From 84aba95e03c6498f9e73df100cbcde9d14128ea9 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Mon, 23 Feb 2026 21:50:03 -0800
Subject: [PATCH 038/194] Temporality unbreak some LTXAV workflows to give
 people time to migrate. (#12605)

---
 comfy/text_encoders/lt.py | 39 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 39 insertions(+)

diff --git a/comfy/text_encoders/lt.py b/comfy/text_encoders/lt.py
index e2ce22e37..64ce64f89 100644
--- a/comfy/text_encoders/lt.py
+++ b/comfy/text_encoders/lt.py
@@ -101,6 +101,7 @@ class LTXAVTEModel(torch.nn.Module):
         super().__init__()
         self.dtypes = set()
         self.dtypes.add(dtype)
+        self.compat_mode = False
 
         self.gemma3_12b = Gemma3_12BModel(device=device, dtype=dtype_llama, model_options=model_options, layer="all", layer_idx=None)
         self.dtypes.add(dtype_llama)
@@ -108,6 +109,28 @@ class LTXAVTEModel(torch.nn.Module):
         operations = self.gemma3_12b.operations # TODO
         self.text_embedding_projection = operations.Linear(3840 * 49, 3840, bias=False, dtype=dtype, device=device)
 
+    def enable_compat_mode(self):  # TODO: remove
+        from comfy.ldm.lightricks.embeddings_connector import Embeddings1DConnector
+        operations = self.gemma3_12b.operations
+        dtype = self.text_embedding_projection.weight.dtype
+        device = self.text_embedding_projection.weight.device
+        self.audio_embeddings_connector = Embeddings1DConnector(
+            split_rope=True,
+            double_precision_rope=True,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+        self.video_embeddings_connector = Embeddings1DConnector(
+            split_rope=True,
+            double_precision_rope=True,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+        self.compat_mode = True
+
     def set_clip_options(self, options):
         self.execution_device = options.get("execution_device", self.execution_device)
         self.gemma3_12b.set_clip_options(options)
@@ -129,6 +152,12 @@ class LTXAVTEModel(torch.nn.Module):
         out = out.reshape((out.shape[0], out.shape[1], -1))
         out = self.text_embedding_projection(out)
         out = out.float()
+
+        if self.compat_mode:
+            out_vid = self.video_embeddings_connector(out)[0]
+            out_audio = self.audio_embeddings_connector(out)[0]
+            out = torch.concat((out_vid, out_audio), dim=-1)
+
         return out.to(out_device), pooled
 
     def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
@@ -152,6 +181,16 @@ class LTXAVTEModel(torch.nn.Module):
                     missing_all.extend([f"{prefix}{k}" for k in missing])
                     unexpected_all.extend([f"{prefix}{k}" for k in unexpected])
 
+            if "model.diffusion_model.audio_embeddings_connector.transformer_1d_blocks.2.attn1.to_q.bias" not in sd:  # TODO: remove
+                ww = sd.get("model.diffusion_model.audio_embeddings_connector.transformer_1d_blocks.0.attn1.to_q.bias", None)
+                if ww is not None:
+                    if ww.shape[0] == 3840:
+                        self.enable_compat_mode()
+                        sdv = comfy.utils.state_dict_prefix_replace(sd, {"model.diffusion_model.video_embeddings_connector.": ""}, filter_keys=True)
+                        self.video_embeddings_connector.load_state_dict(sdv, strict=False, assign=getattr(self, "can_assign_sd", False))
+                        sda = comfy.utils.state_dict_prefix_replace(sd, {"model.diffusion_model.audio_embeddings_connector.": ""}, filter_keys=True)
+                        self.audio_embeddings_connector.load_state_dict(sda, strict=False, assign=getattr(self, "can_assign_sd", False))
+
             return (missing_all, unexpected_all)
 
     def memory_estimation_function(self, token_weight_pairs, device=None):

From 048dd2f3219523202e19081f776536ea1e62ad4a Mon Sep 17 00:00:00 2001
From: Christian Byrne <cbyrne@comfy.org>
Date: Tue, 24 Feb 2026 00:44:40 -0800
Subject: [PATCH 039/194] Patch frontend to 1.39.16 (from 1.39.14) (#12604)

* Update requirements.txt

* Update requirements.txt

---------

Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index b5fa2fe13..c0c662cd5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-comfyui-frontend-package==1.39.14
+comfyui-frontend-package==1.39.16
 comfyui-workflow-templates==0.9.2
 comfyui-embedded-docs==0.4.1
 torch

From d8090013b87d787dc12a8c3956fcd8e0a60db38d Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Tue, 24 Feb 2026 19:03:30 +0200
Subject: [PATCH 040/194] feat(api-nodes): add ByteDance Seedream-5 model
 (#12609)

* feat(api-nodes): add ByteDance Seedream-5 model

* made error message more correct

* rename seedream 5.0 model
---
 comfy_api_nodes/apis/bytedance.py  |  2 ++
 comfy_api_nodes/nodes_bytedance.py | 48 +++++++++++++++++++-----------
 2 files changed, 33 insertions(+), 17 deletions(-)

diff --git a/comfy_api_nodes/apis/bytedance.py b/comfy_api_nodes/apis/bytedance.py
index 23cbe2372..18455396d 100644
--- a/comfy_api_nodes/apis/bytedance.py
+++ b/comfy_api_nodes/apis/bytedance.py
@@ -27,6 +27,7 @@ class Seedream4TaskCreationRequest(BaseModel):
     sequential_image_generation: str = Field("disabled")
     sequential_image_generation_options: Seedream4Options = Field(Seedream4Options(max_images=15))
     watermark: bool = Field(False)
+    output_format: str | None = None
 
 
 class ImageTaskCreationResponse(BaseModel):
@@ -106,6 +107,7 @@ RECOMMENDED_PRESETS_SEEDREAM_4 = [
     ("2496x1664 (3:2)", 2496, 1664),
     ("1664x2496 (2:3)", 1664, 2496),
     ("3024x1296 (21:9)", 3024, 1296),
+    ("3072x3072 (1:1)", 3072, 3072),
     ("4096x4096 (1:1)", 4096, 4096),
     ("Custom", None, None),
 ]
diff --git a/comfy_api_nodes/nodes_bytedance.py b/comfy_api_nodes/nodes_bytedance.py
index ae5d59a79..cfc604aa3 100644
--- a/comfy_api_nodes/nodes_bytedance.py
+++ b/comfy_api_nodes/nodes_bytedance.py
@@ -37,6 +37,12 @@ from comfy_api_nodes.util import (
 
 BYTEPLUS_IMAGE_ENDPOINT = "/proxy/byteplus/api/v3/images/generations"
 
+SEEDREAM_MODELS = {
+    "seedream 5.0 lite": "seedream-5-0-260128",
+    "seedream-4-5-251128": "seedream-4-5-251128",
+    "seedream-4-0-250828": "seedream-4-0-250828",
+}
+
 # Long-running tasks endpoints(e.g., video)
 BYTEPLUS_TASK_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks"
 BYTEPLUS_TASK_STATUS_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks"  # + /{task_id}
@@ -180,14 +186,13 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ByteDanceSeedreamNode",
-            display_name="ByteDance Seedream 4.5",
+            display_name="ByteDance Seedream 5.0",
             category="api node/image/ByteDance",
             description="Unified text-to-image generation and precise single-sentence editing at up to 4K resolution.",
             inputs=[
                 IO.Combo.Input(
                     "model",
-                    options=["seedream-4-5-251128", "seedream-4-0-250828"],
-                    tooltip="Model name",
+                    options=list(SEEDREAM_MODELS.keys()),
                 ),
                 IO.String.Input(
                     "prompt",
@@ -198,7 +203,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
                 IO.Image.Input(
                     "image",
                     tooltip="Input image(s) for image-to-image generation. "
-                    "List of 1-10 images for single or multi-reference generation.",
+                    "Reference image(s) for single or multi-reference generation.",
                     optional=True,
                 ),
                 IO.Combo.Input(
@@ -210,8 +215,8 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
                     "width",
                     default=2048,
                     min=1024,
-                    max=4096,
-                    step=8,
+                    max=6240,
+                    step=2,
                     tooltip="Custom width for image. Value is working only if `size_preset` is set to `Custom`",
                     optional=True,
                 ),
@@ -219,8 +224,8 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
                     "height",
                     default=2048,
                     min=1024,
-                    max=4096,
-                    step=8,
+                    max=4992,
+                    step=2,
                     tooltip="Custom height for image. Value is working only if `size_preset` is set to `Custom`",
                     optional=True,
                 ),
@@ -283,7 +288,8 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
                 depends_on=IO.PriceBadgeDepends(widgets=["model"]),
                 expr="""
                 (
-                  $price := $contains(widgets.model, "seedream-4-5-251128") ? 0.04 : 0.03;
+                  $price := $contains(widgets.model, "5.0 lite") ? 0.035 :
+                            $contains(widgets.model, "4-5") ? 0.04 : 0.03;
                   {
                     "type":"usd",
                     "usd": $price,
@@ -309,6 +315,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
         watermark: bool = False,
         fail_on_partial: bool = True,
     ) -> IO.NodeOutput:
+        model = SEEDREAM_MODELS[model]
         validate_string(prompt, strip_whitespace=True, min_length=1)
         w = h = None
         for label, tw, th in RECOMMENDED_PRESETS_SEEDREAM_4:
@@ -318,15 +325,12 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
 
         if w is None or h is None:
             w, h = width, height
-            if not (1024 <= w <= 4096) or not (1024 <= h <= 4096):
-                raise ValueError(
-                    f"Custom size out of range: {w}x{h}. " "Both width and height must be between 1024 and 4096 pixels."
-                )
+
         out_num_pixels = w * h
         mp_provided = out_num_pixels / 1_000_000.0
-        if "seedream-4-5" in model and out_num_pixels < 3686400:
+        if ("seedream-4-5" in model or "seedream-5-0" in model) and out_num_pixels < 3686400:
             raise ValueError(
-                f"Minimum image resolution that Seedream 4.5 can generate is 3.68MP, "
+                f"Minimum image resolution for the selected model is 3.68MP, "
                 f"but {mp_provided:.2f}MP provided."
             )
         if "seedream-4-0" in model and out_num_pixels < 921600:
@@ -334,9 +338,18 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
                 f"Minimum image resolution that the selected model can generate is 0.92MP, "
                 f"but {mp_provided:.2f}MP provided."
             )
+        max_pixels = 10_404_496 if "seedream-5-0" in model else 16_777_216
+        if out_num_pixels > max_pixels:
+            raise ValueError(
+                f"Maximum image resolution for the selected model is {max_pixels / 1_000_000:.2f}MP, "
+                f"but {mp_provided:.2f}MP provided."
+            )
         n_input_images = get_number_of_images(image) if image is not None else 0
-        if n_input_images > 10:
-            raise ValueError(f"Maximum of 10 reference images are supported, but {n_input_images} received.")
+        max_num_of_images = 14 if model == "seedream-5-0-260128" else 10
+        if n_input_images > max_num_of_images:
+            raise ValueError(
+                f"Maximum of {max_num_of_images} reference images are supported, but {n_input_images} received."
+            )
         if sequential_image_generation == "auto" and n_input_images + max_images > 15:
             raise ValueError(
                 "The maximum number of generated images plus the number of reference images cannot exceed 15."
@@ -364,6 +377,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
                 sequential_image_generation=sequential_image_generation,
                 sequential_image_generation_options=Seedream4Options(max_images=max_images),
                 watermark=watermark,
+                output_format="png" if model == "seedream-5-0-260128" else None,
             ),
         )
         if len(response.data) == 1:

From 11fefa58e987604711169845e9081d14b3915ba1 Mon Sep 17 00:00:00 2001
From: ComfyUI Wiki <contact@comfyui-wiki.com>
Date: Wed, 25 Feb 2026 01:04:51 +0800
Subject: [PATCH 041/194] chore: update workflow templates to v0.9.3 (#12610)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index c0c662cd5..67845ee52 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.39.16
-comfyui-workflow-templates==0.9.2
+comfyui-workflow-templates==0.9.3
 comfyui-embedded-docs==0.4.1
 torch
 torchsde

From 599f9c50109d3c6fbb2791de1810ecf84601affa Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Tue, 24 Feb 2026 09:28:25 -0800
Subject: [PATCH 042/194] Don't crash right away if op is uninitialized.
 (#12615)

---
 comfy/ops.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/comfy/ops.py b/comfy/ops.py
index a6c642795..34f72ff17 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -827,6 +827,10 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 else:
                     sd = {}
 
+                if not hasattr(self, 'weight'):
+                    logging.warning("Warning: state dict on uninitialized op {}".format(prefix))
+                    return sd
+
                 if self.bias is not None:
                     sd["{}bias".format(prefix)] = self.bias
 

From 0aa02453bbe7d2fac332e829ade1f13a1cf53820 Mon Sep 17 00:00:00 2001
From: ComfyUI Wiki <contact@comfyui-wiki.com>
Date: Wed, 25 Feb 2026 01:41:36 +0800
Subject: [PATCH 043/194] chore: update embedded docs to v0.4.3 (#12601)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 67845ee52..ae3173ede 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,6 @@
 comfyui-frontend-package==1.39.16
 comfyui-workflow-templates==0.9.3
-comfyui-embedded-docs==0.4.1
+comfyui-embedded-docs==0.4.3
 torch
 torchsde
 torchvision

From b874bd2b8c324d58cfc37bff0754dd16815a8f3c Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 24 Feb 2026 12:37:16 -0500
Subject: [PATCH 044/194] ComfyUI v0.15.0

---
 comfyui_version.py | 2 +-
 pyproject.toml     | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfyui_version.py b/comfyui_version.py
index f24c15cc5..6dbda1a87 100644
--- a/comfyui_version.py
+++ b/comfyui_version.py
@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.14.1"
+__version__ = "0.15.0"
diff --git a/pyproject.toml b/pyproject.toml
index 51c3d224d..eaf558740 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.14.1"
+version = "0.15.0"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

From 33f83d53ae3897962b8248c12276759192fde0c0 Mon Sep 17 00:00:00 2001
From: Jedrzej Kosinski <kosinkadink1@gmail.com>
Date: Tue, 24 Feb 2026 13:02:05 -0800
Subject: [PATCH 045/194] Fix KeyError when prompt entries lack class_type key
 (#12595)

Skip entries in the prompt dict that don't contain a class_type key
in apply_replacements(), preventing crashes on metadata or non-node
entries.

Fixes Comfy-Org/ComfyUI#12517
---
 app/node_replace_manager.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/app/node_replace_manager.py b/app/node_replace_manager.py
index 03b603c70..d9aab5b22 100644
--- a/app/node_replace_manager.py
+++ b/app/node_replace_manager.py
@@ -46,6 +46,8 @@ class NodeReplaceManager:
         connections: dict[str, list[tuple[str, str, int]]] = {}
         need_replacement: set[str] = set()
         for node_number, node_struct in prompt.items():
+            if "class_type" not in node_struct or "inputs" not in node_struct:
+                continue
             class_type = node_struct["class_type"]
             # need replacement if not in NODE_CLASS_MAPPINGS and has replacement
             if class_type not in nodes.NODE_CLASS_MAPPINGS.keys() and self.has_replacement(class_type):

From befa83d43448c3dc64f72aa3eb771159d20f89f1 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Tue, 24 Feb 2026 13:02:26 -0800
Subject: [PATCH 046/194] comfy aimdo 0.2.1 (#12620)

Changes:

throttle VRAM threshold checks to restore performance in high-layer-rate
conditions.
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index ae3173ede..0064fb4ba 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -22,7 +22,7 @@ alembic
 SQLAlchemy
 av>=14.2.0
 comfy-kitchen>=0.2.7
-comfy-aimdo>=0.2.0
+comfy-aimdo>=0.2.1
 requests
 
 #non essential dependencies:

From 3ebe1ac22e090c10ecf4c478fe6f89dc8b398fa0 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Tue, 24 Feb 2026 16:13:46 -0800
Subject: [PATCH 047/194] Disable dynamic_vram when using torch compiler
 (#12612)

* mp: attach re-construction arguments to model patcher

When making a model-patcher from a unet or ckpt, attach a callable
function that can be called to replay the model construction. This
can be used to deep clone model patcher WRT the actual model.

Originally written by Kosinkadink
https://github.com/Comfy-Org/ComfyUI/commit/f4b99bc62389af315013dda85f24f2bbd262b686

* mp: Add disable_dynamic clone argument

Add a clone argument that lets a caller clone a ModelPatcher but disable
dynamic to demote the clone to regular MP. This is useful for legacy
features where dynamic_vram support is missing or TBD.

* torch_compile: disable dynamic_vram

This is a bigger feature. Disable for the interim to preserve
functionality.
---
 comfy/model_patcher.py              | 14 ++++++++++++--
 comfy/sd.py                         | 29 +++++++++++++++++++++--------
 comfy_extras/nodes_torch_compile.py |  2 +-
 3 files changed, 34 insertions(+), 11 deletions(-)

diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index 21b4ce53e..1c9ba8096 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -271,6 +271,7 @@ class ModelPatcher:
         self.is_clip = False
         self.hook_mode = comfy.hooks.EnumHookMode.MaxSpeed
 
+        self.cached_patcher_init: tuple[Callable, tuple] | None = None
         if not hasattr(self.model, 'model_loaded_weight_memory'):
             self.model.model_loaded_weight_memory = 0
 
@@ -307,8 +308,15 @@ class ModelPatcher:
     def get_free_memory(self, device):
         return comfy.model_management.get_free_memory(device)
 
-    def clone(self):
-        n = self.__class__(self.model, self.load_device, self.offload_device, self.model_size(), weight_inplace_update=self.weight_inplace_update)
+    def clone(self, disable_dynamic=False):
+        class_ = self.__class__
+        model = self.model
+        if self.is_dynamic() and disable_dynamic:
+            class_ = ModelPatcher
+            temp_model_patcher = self.cached_patcher_init[0](*self.cached_patcher_init[1], disable_dynamic=True)
+            model = temp_model_patcher.model
+
+        n = class_(model, self.load_device, self.offload_device, self.model_size(), weight_inplace_update=self.weight_inplace_update)
         n.patches = {}
         for k in self.patches:
             n.patches[k] = self.patches[k][:]
@@ -362,6 +370,8 @@ class ModelPatcher:
         n.is_clip = self.is_clip
         n.hook_mode = self.hook_mode
 
+        n.cached_patcher_init = self.cached_patcher_init
+
         for callback in self.get_all_callbacks(CallbacksMP.ON_CLONE):
             callback(self, n)
         return n
diff --git a/comfy/sd.py b/comfy/sd.py
index ce6ca5d17..69d4531e3 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -1530,14 +1530,24 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl
 
     return (model, clip, vae)
 
-def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}):
+def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}, disable_dynamic=False):
     sd, metadata = comfy.utils.load_torch_file(ckpt_path, return_metadata=True)
-    out = load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options, te_model_options=te_model_options, metadata=metadata)
+    out = load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options, te_model_options=te_model_options, metadata=metadata, disable_dynamic=disable_dynamic)
     if out is None:
         raise RuntimeError("ERROR: Could not detect model type of: {}\n{}".format(ckpt_path, model_detection_error_hint(ckpt_path, sd)))
+    if output_model:
+        out[0].cached_patcher_init = (load_checkpoint_guess_config_model_only, (ckpt_path, embedding_directory, model_options, te_model_options))
     return out
 
-def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}, metadata=None):
+def load_checkpoint_guess_config_model_only(ckpt_path, embedding_directory=None, model_options={}, te_model_options={}, disable_dynamic=False):
+    model, *_ = load_checkpoint_guess_config(ckpt_path, False, False, False,
+            embedding_directory=embedding_directory,
+            model_options=model_options,
+            te_model_options=te_model_options,
+            disable_dynamic=disable_dynamic)
+    return model
+
+def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}, metadata=None, disable_dynamic=False):
     clip = None
     clipvision = None
     vae = None
@@ -1586,7 +1596,8 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
     if output_model:
         inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype)
         model = model_config.get_model(sd, diffusion_model_prefix, device=inital_load_device)
-        model_patcher = comfy.model_patcher.CoreModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device())
+        ModelPatcher = comfy.model_patcher.ModelPatcher if disable_dynamic else comfy.model_patcher.CoreModelPatcher
+        model_patcher = ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device())
         model.load_model_weights(sd, diffusion_model_prefix, assign=model_patcher.is_dynamic())
 
     if output_vae:
@@ -1637,7 +1648,7 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
     return (model_patcher, clip, vae, clipvision)
 
 
-def load_diffusion_model_state_dict(sd, model_options={}, metadata=None):
+def load_diffusion_model_state_dict(sd, model_options={}, metadata=None, disable_dynamic=False):
     """
     Loads a UNet diffusion model from a state dictionary, supporting both diffusers and regular formats.
 
@@ -1721,7 +1732,8 @@ def load_diffusion_model_state_dict(sd, model_options={}, metadata=None):
         model_config.optimizations["fp8"] = True
 
     model = model_config.get_model(new_sd, "")
-    model_patcher = comfy.model_patcher.CoreModelPatcher(model, load_device=load_device, offload_device=offload_device)
+    ModelPatcher = comfy.model_patcher.ModelPatcher if disable_dynamic else comfy.model_patcher.CoreModelPatcher
+    model_patcher = ModelPatcher(model, load_device=load_device, offload_device=offload_device)
     if not model_management.is_device_cpu(offload_device):
         model.to(offload_device)
     model.load_model_weights(new_sd, "", assign=model_patcher.is_dynamic())
@@ -1730,12 +1742,13 @@ def load_diffusion_model_state_dict(sd, model_options={}, metadata=None):
         logging.info("left over keys in diffusion model: {}".format(left_over))
     return model_patcher
 
-def load_diffusion_model(unet_path, model_options={}):
+def load_diffusion_model(unet_path, model_options={}, disable_dynamic=False):
     sd, metadata = comfy.utils.load_torch_file(unet_path, return_metadata=True)
-    model = load_diffusion_model_state_dict(sd, model_options=model_options, metadata=metadata)
+    model = load_diffusion_model_state_dict(sd, model_options=model_options, metadata=metadata, disable_dynamic=disable_dynamic)
     if model is None:
         logging.error("ERROR UNSUPPORTED DIFFUSION MODEL {}".format(unet_path))
         raise RuntimeError("ERROR: Could not detect model type of: {}\n{}".format(unet_path, model_detection_error_hint(unet_path, sd)))
+    model.cached_patcher_init = (load_diffusion_model, (unet_path, model_options))
     return model
 
 def load_unet(unet_path, dtype=None):
diff --git a/comfy_extras/nodes_torch_compile.py b/comfy_extras/nodes_torch_compile.py
index 00e9f8b1f..c9e2e0026 100644
--- a/comfy_extras/nodes_torch_compile.py
+++ b/comfy_extras/nodes_torch_compile.py
@@ -25,7 +25,7 @@ class TorchCompileModel(io.ComfyNode):
 
     @classmethod
     def execute(cls, model, backend) -> io.NodeOutput:
-        m = model.clone()
+        m = model.clone(disable_dynamic=True)
         set_torch_compile_wrapper(model=m, backend=backend, options={"guard_filter_fn": skip_torch_compile_dict})
         return io.NodeOutput(m)
 

From 4f5b7dbf1f9ec61af8518e6a613499d5ab91835a Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Wed, 25 Feb 2026 13:49:48 -0800
Subject: [PATCH 048/194] Fix Aimdo fallback on probe to not use zero-copy sft
 (#12634)

* utils: dont use comfy sft loader in aimdo fallback

This was going to the raw command line switch and should respect main.py
probe of whether aimdo actually loaded successfully.

* ops: dont use deferred linear load in Aimdo fallback

Avoid changes of behaviour on --fast dynamic_vram when aimdo doesnt work.
---
 comfy/ops.py   | 6 +++---
 comfy/utils.py | 4 ++--
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/comfy/ops.py b/comfy/ops.py
index 34f72ff17..98fec1e1d 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -19,7 +19,7 @@
 import torch
 import logging
 import comfy.model_management
-from comfy.cli_args import args, PerformanceFeature, enables_dynamic_vram
+from comfy.cli_args import args, PerformanceFeature
 import comfy.float
 import json
 import comfy.memory_management
@@ -296,7 +296,7 @@ class disable_weight_init:
     class Linear(torch.nn.Linear, CastWeightBiasOp):
 
         def __init__(self, in_features, out_features, bias=True, device=None, dtype=None):
-            if not comfy.model_management.WINDOWS or not enables_dynamic_vram():
+            if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled:
                 super().__init__(in_features, out_features, bias, device, dtype)
                 return
 
@@ -317,7 +317,7 @@ class disable_weight_init:
         def _load_from_state_dict(self, state_dict, prefix, local_metadata,
                                 strict, missing_keys, unexpected_keys, error_msgs):
 
-            if not comfy.model_management.WINDOWS or not enables_dynamic_vram():
+            if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled:
                 return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict,
                                                      missing_keys, unexpected_keys, error_msgs)
             assign_to_params_buffers = local_metadata.get("assign_to_params_buffers", False)
diff --git a/comfy/utils.py b/comfy/utils.py
index 5fe66ecdb..0769cef44 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -29,7 +29,7 @@ import itertools
 from torch.nn.functional import interpolate
 from tqdm.auto import trange
 from einops import rearrange
-from comfy.cli_args import args, enables_dynamic_vram
+from comfy.cli_args import args
 import json
 import time
 import mmap
@@ -113,7 +113,7 @@ def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False):
     metadata = None
     if ckpt.lower().endswith(".safetensors") or ckpt.lower().endswith(".sft"):
         try:
-            if enables_dynamic_vram():
+            if comfy.memory_management.aimdo_enabled:
                 sd, metadata = load_safetensors(ckpt)
                 if not return_metadata:
                     metadata = None

From 0467f690a85400c8bfa6dcb6bcc848914b57562a Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Wed, 25 Feb 2026 13:50:05 -0800
Subject: [PATCH 049/194] comfy aimdo 0.2.2 (#12635)

Comfy Aimdo 0.2.2 moves the cuda allocator hook from the cudart API to
the cuda driver API on windows. This is needed to handle Windows+cu13
where cudart is statically linked.
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 0064fb4ba..fed5df5fd 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -22,7 +22,7 @@ alembic
 SQLAlchemy
 av>=14.2.0
 comfy-kitchen>=0.2.7
-comfy-aimdo>=0.2.1
+comfy-aimdo>=0.2.2
 requests
 
 #non essential dependencies:

From eb8737d675022e730364294c395111af3545d523 Mon Sep 17 00:00:00 2001
From: Christian Byrne <cbyrne@comfy.org>
Date: Wed, 25 Feb 2026 18:30:48 -0800
Subject: [PATCH 050/194] Update requirements.txt (#12642)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index fed5df5fd..88a056e5c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-comfyui-frontend-package==1.39.16
+comfyui-frontend-package==1.39.19
 comfyui-workflow-templates==0.9.3
 comfyui-embedded-docs==0.4.3
 torch

From e14b04478c1712ec8417a046832b821af263ea13 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Wed, 25 Feb 2026 19:36:02 -0800
Subject: [PATCH 051/194] Fix LTXAV text enc min length. (#12640)

Should have been 1024 instead of 512
---
 comfy/text_encoders/lt.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/text_encoders/lt.py b/comfy/text_encoders/lt.py
index 64ce64f89..c547c5ee5 100644
--- a/comfy/text_encoders/lt.py
+++ b/comfy/text_encoders/lt.py
@@ -72,7 +72,7 @@ class Gemma3_12BTokenizer(Gemma3_Tokenizer, sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         tokenizer = tokenizer_data.get("spiece_model", None)
         special_tokens = {"<image_soft_token>": 262144, "<end_of_turn>": 106}
-        super().__init__(tokenizer, pad_with_end=False, embedding_size=3840, embedding_key='gemma3_12b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, pad_left=True, disable_weights=True, tokenizer_args={"add_bos": True, "add_eos": False, "special_tokens": special_tokens}, tokenizer_data=tokenizer_data)
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=3840, embedding_key='gemma3_12b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1024, pad_left=True, disable_weights=True, tokenizer_args={"add_bos": True, "add_eos": False, "special_tokens": special_tokens}, tokenizer_data=tokenizer_data)
 
 
 class LTXAVGemmaTokenizer(sd1_clip.SD1Tokenizer):

From 72535316701ea8074b99755194f149a26e88b4c8 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Wed, 25 Feb 2026 20:13:47 -0800
Subject: [PATCH 052/194] Fix ltxav te mem estimation. (#12643)

---
 comfy/text_encoders/lt.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/comfy/text_encoders/lt.py b/comfy/text_encoders/lt.py
index c547c5ee5..e86ea9f4e 100644
--- a/comfy/text_encoders/lt.py
+++ b/comfy/text_encoders/lt.py
@@ -6,6 +6,7 @@ import comfy.text_encoders.genmo
 import torch
 import comfy.utils
 import math
+import itertools
 
 class T5XXLTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
@@ -199,8 +200,10 @@ class LTXAVTEModel(torch.nn.Module):
             constant /= 2.0
 
         token_weight_pairs = token_weight_pairs.get("gemma3_12b", [])
-        num_tokens = sum(map(lambda a: len(a), token_weight_pairs))
-        num_tokens = max(num_tokens, 64)
+        m = min([sum(1 for _ in itertools.takewhile(lambda x: x[0] == 0, sub)) for sub in token_weight_pairs])
+
+        num_tokens = sum(map(lambda a: len(a), token_weight_pairs)) - m
+        num_tokens = max(num_tokens, 642)
         return num_tokens * constant * 1024 * 1024
 
 def ltxav_te(dtype_llama=None, llama_quantization_metadata=None):

From 907e5dcbbffab5e7011346af280a428dc40f3136 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?=
 <40791699+kijai@users.noreply.github.com>
Date: Thu, 26 Feb 2026 06:38:46 +0200
Subject: [PATCH 053/194] initial FlowRVS support (#12637)

---
 comfy/ldm/wan/vae.py                 |  3 ++-
 comfy/model_base.py                  |  9 +++++++++
 comfy/model_detection.py             |  3 +++
 comfy/model_sampling.py              | 10 ++++++++++
 comfy/sd.py                          |  3 ++-
 comfy/supported_models.py            | 12 +++++++++++-
 comfy_extras/nodes_model_advanced.py |  4 +++-
 7 files changed, 40 insertions(+), 4 deletions(-)

diff --git a/comfy/ldm/wan/vae.py b/comfy/ldm/wan/vae.py
index fd125ceed..7903c7690 100644
--- a/comfy/ldm/wan/vae.py
+++ b/comfy/ldm/wan/vae.py
@@ -459,6 +459,7 @@ class WanVAE(nn.Module):
                  attn_scales=[],
                  temperal_downsample=[True, True, False],
                  image_channels=3,
+                 conv_out_channels=3,
                  dropout=0.0):
         super().__init__()
         self.dim = dim
@@ -474,7 +475,7 @@ class WanVAE(nn.Module):
                                  attn_scales, self.temperal_downsample, dropout)
         self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1)
         self.conv2 = CausalConv3d(z_dim, z_dim, 1)
-        self.decoder = Decoder3d(dim, z_dim, image_channels, dim_mult, num_res_blocks,
+        self.decoder = Decoder3d(dim, z_dim, conv_out_channels, dim_mult, num_res_blocks,
                                  attn_scales, self.temperal_upsample, dropout)
 
     def encode(self, x):
diff --git a/comfy/model_base.py b/comfy/model_base.py
index 2f49578f6..4e2096d4b 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -76,6 +76,7 @@ class ModelType(Enum):
     FLUX = 8
     IMG_TO_IMG = 9
     FLOW_COSMOS = 10
+    IMG_TO_IMG_FLOW = 11
 
 
 def model_sampling(model_config, model_type):
@@ -108,6 +109,8 @@ def model_sampling(model_config, model_type):
     elif model_type == ModelType.FLOW_COSMOS:
         c = comfy.model_sampling.COSMOS_RFLOW
         s = comfy.model_sampling.ModelSamplingCosmosRFlow
+    elif model_type == ModelType.IMG_TO_IMG_FLOW:
+        c = comfy.model_sampling.IMG_TO_IMG_FLOW
 
     class ModelSampling(s, c):
         pass
@@ -1466,6 +1469,12 @@ class WAN22(WAN21):
     def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
         return latent_image
 
+class WAN21_FlowRVS(WAN21):
+    def __init__(self, model_config, model_type=ModelType.IMG_TO_IMG_FLOW, image_to_video=False, device=None):
+        model_config.unet_config["model_type"] = "t2v"
+        super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel)
+        self.image_to_video = image_to_video
+
 class Hunyuan3Dv2(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan3d.model.Hunyuan3Dv2)
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index 30ea03e8e..030ae6980 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -509,6 +509,9 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         if ref_conv_weight is not None:
             dit_config["in_dim_ref_conv"] = ref_conv_weight.shape[1]
 
+        if metadata is not None and "config" in metadata:
+            dit_config.update(json.loads(metadata["config"]).get("transformer", {}))
+
         return dit_config
 
     if '{}latent_in.weight'.format(key_prefix) in state_dict_keys:  # Hunyuan 3D
diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py
index 2a00ed819..13860e6a2 100644
--- a/comfy/model_sampling.py
+++ b/comfy/model_sampling.py
@@ -83,6 +83,16 @@ class IMG_TO_IMG(X0):
     def calculate_input(self, sigma, noise):
         return noise
 
+class IMG_TO_IMG_FLOW(CONST):
+    def calculate_denoised(self, sigma, model_output, model_input):
+        return model_output
+
+    def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
+        return latent_image
+
+    def inverse_noise_scaling(self, sigma, latent):
+        return 1.0 - latent
+
 class COSMOS_RFLOW:
     def calculate_input(self, sigma, noise):
         sigma = (sigma / (sigma + 1))
diff --git a/comfy/sd.py b/comfy/sd.py
index 69d4531e3..de119eb8e 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -694,8 +694,9 @@ class VAE:
                     self.latent_dim = 3
                     self.latent_channels = 16
                     self.output_channels = sd["encoder.conv1.weight"].shape[1]
+                    self.conv_out_channels = sd["decoder.head.2.weight"].shape[0]
                     self.pad_channel_value = 1.0
-                    ddconfig = {"dim": dim, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "image_channels": self.output_channels, "dropout": 0.0}
+                    ddconfig = {"dim": dim, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "image_channels": self.output_channels, "conv_out_channels": self.conv_out_channels, "dropout": 0.0}
                     self.first_stage_model = comfy.ldm.wan.vae.WanVAE(**ddconfig)
                     self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
                     self.memory_used_encode = lambda shape, dtype: (1500 if shape[2]<=4 else 6000) * shape[3] * shape[4] * model_management.dtype_size(dtype)
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index c28be1716..6d08ff0a5 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -1256,6 +1256,16 @@ class WAN22_T2V(WAN21_T2V):
         out = model_base.WAN22(self, image_to_video=True, device=device)
         return out
 
+class WAN21_FlowRVS(WAN21_T2V):
+    unet_config = {
+        "image_model": "wan2.1",
+        "model_type": "flow_rvs",
+    }
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.WAN21_FlowRVS(self, image_to_video=True, device=device)
+        return out
+
 class Hunyuan3Dv2(supported_models_base.BASE):
     unet_config = {
         "image_model": "hunyuan3d2",
@@ -1667,6 +1677,6 @@ class ACEStep15(supported_models_base.BASE):
         return supported_models_base.ClipTarget(comfy.text_encoders.ace15.ACE15Tokenizer, comfy.text_encoders.ace15.te(**detect))
 
 
-models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima]
+models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima]
 
 models += [SVD_img2vid]
diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py
index 9601a5f76..8bf6a1afa 100644
--- a/comfy_extras/nodes_model_advanced.py
+++ b/comfy_extras/nodes_model_advanced.py
@@ -52,7 +52,7 @@ class ModelSamplingDiscrete:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "sampling": (["eps", "v_prediction", "lcm", "x0", "img_to_img"],),
+                              "sampling": (["eps", "v_prediction", "lcm", "x0", "img_to_img", "img_to_img_flow"],),
                               "zsnr": ("BOOLEAN", {"default": False, "advanced": True}),
                               }}
 
@@ -76,6 +76,8 @@ class ModelSamplingDiscrete:
             sampling_type = comfy.model_sampling.X0
         elif sampling == "img_to_img":
             sampling_type = comfy.model_sampling.IMG_TO_IMG
+        elif sampling == "img_to_img_flow":
+            sampling_type = comfy.model_sampling.IMG_TO_IMG_FLOW
 
         class ModelSamplingAdvanced(sampling_base, sampling_type):
             pass

From a4522017c518d1f0c3c5d2a803a2d31265da5cd4 Mon Sep 17 00:00:00 2001
From: Tavi Halperin <tavi@lightricks.com>
Date: Thu, 26 Feb 2026 08:25:23 +0200
Subject: [PATCH 054/194] feat: per-guide attention strength control in
 self-attention (#12518)

Implements per-guide attention attenuation via log-space additive bias
in self-attention. Each guide reference tracks its own strength and
optional spatial mask in conditioning metadata (guide_attention_entries).
---
 comfy/ldm/lightricks/av_model.py |   9 +-
 comfy/ldm/lightricks/model.py    | 264 ++++++++++++++++++++++++++++++-
 comfy/model_base.py              |  44 ++++++
 comfy_extras/nodes_lt.py         |  47 +++++-
 4 files changed, 352 insertions(+), 12 deletions(-)

diff --git a/comfy/ldm/lightricks/av_model.py b/comfy/ldm/lightricks/av_model.py
index 2b080aaeb..553fd5b38 100644
--- a/comfy/ldm/lightricks/av_model.py
+++ b/comfy/ldm/lightricks/av_model.py
@@ -218,7 +218,7 @@ class BasicAVTransformerBlock(nn.Module):
     def forward(
         self, x: Tuple[torch.Tensor, torch.Tensor], v_context=None, a_context=None, attention_mask=None, v_timestep=None, a_timestep=None,
         v_pe=None, a_pe=None, v_cross_pe=None, a_cross_pe=None, v_cross_scale_shift_timestep=None, a_cross_scale_shift_timestep=None,
-        v_cross_gate_timestep=None, a_cross_gate_timestep=None, transformer_options=None,
+        v_cross_gate_timestep=None, a_cross_gate_timestep=None, transformer_options=None, self_attention_mask=None,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         run_vx = transformer_options.get("run_vx", True)
         run_ax = transformer_options.get("run_ax", True)
@@ -234,7 +234,7 @@ class BasicAVTransformerBlock(nn.Module):
             vshift_msa, vscale_msa = (self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(0, 2)))
             norm_vx = comfy.ldm.common_dit.rms_norm(vx) * (1 + vscale_msa) + vshift_msa
             del vshift_msa, vscale_msa
-            attn1_out = self.attn1(norm_vx, pe=v_pe, transformer_options=transformer_options)
+            attn1_out = self.attn1(norm_vx, pe=v_pe, mask=self_attention_mask, transformer_options=transformer_options)
             del norm_vx
             # video cross-attention
             vgate_msa = self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(2, 3))[0]
@@ -726,7 +726,7 @@ class LTXAVModel(LTXVModel):
         return [(v_pe, av_cross_video_freq_cis), (a_pe, av_cross_audio_freq_cis)]
 
     def _process_transformer_blocks(
-        self, x, context, attention_mask, timestep, pe, transformer_options={}, **kwargs
+        self, x, context, attention_mask, timestep, pe, transformer_options={}, self_attention_mask=None, **kwargs
     ):
         vx = x[0]
         ax = x[1]
@@ -770,6 +770,7 @@ class LTXAVModel(LTXVModel):
                         v_cross_gate_timestep=args["v_cross_gate_timestep"],
                         a_cross_gate_timestep=args["a_cross_gate_timestep"],
                         transformer_options=args["transformer_options"],
+                        self_attention_mask=args.get("self_attention_mask"),
                     )
                     return out
 
@@ -790,6 +791,7 @@ class LTXAVModel(LTXVModel):
                         "v_cross_gate_timestep": av_ca_a2v_gate_noise_timestep,
                         "a_cross_gate_timestep": av_ca_v2a_gate_noise_timestep,
                         "transformer_options": transformer_options,
+                        "self_attention_mask": self_attention_mask,
                     },
                     {"original_block": block_wrap},
                 )
@@ -811,6 +813,7 @@ class LTXAVModel(LTXVModel):
                     v_cross_gate_timestep=av_ca_a2v_gate_noise_timestep,
                     a_cross_gate_timestep=av_ca_v2a_gate_noise_timestep,
                     transformer_options=transformer_options,
+                    self_attention_mask=self_attention_mask,
                 )
 
         return [vx, ax]
diff --git a/comfy/ldm/lightricks/model.py b/comfy/ldm/lightricks/model.py
index d61e19d6e..60d760d29 100644
--- a/comfy/ldm/lightricks/model.py
+++ b/comfy/ldm/lightricks/model.py
@@ -1,6 +1,7 @@
 from abc import ABC, abstractmethod
 from enum import Enum
 import functools
+import logging
 import math
 from typing import Dict, Optional, Tuple
 
@@ -14,6 +15,8 @@ import comfy.ldm.common_dit
 
 from .symmetric_patchifier import SymmetricPatchifier, latent_to_pixel_coords
 
+logger = logging.getLogger(__name__)
+
 def _log_base(x, base):
     return np.log(x) / np.log(base)
 
@@ -415,12 +418,12 @@ class BasicTransformerBlock(nn.Module):
 
         self.scale_shift_table = nn.Parameter(torch.empty(6, dim, device=device, dtype=dtype))
 
-    def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None, transformer_options={}):
+    def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None, transformer_options={}, self_attention_mask=None):
         shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)).unbind(dim=2)
 
         attn1_input = comfy.ldm.common_dit.rms_norm(x)
         attn1_input = torch.addcmul(attn1_input, attn1_input, scale_msa).add_(shift_msa)
-        attn1_input = self.attn1(attn1_input, pe=pe, transformer_options=transformer_options)
+        attn1_input = self.attn1(attn1_input, pe=pe, mask=self_attention_mask, transformer_options=transformer_options)
         x.addcmul_(attn1_input, gate_msa)
         del attn1_input
 
@@ -638,8 +641,16 @@ class LTXBaseModel(torch.nn.Module, ABC):
         """Process input data. Must be implemented by subclasses."""
         pass
 
+    def _build_guide_self_attention_mask(self, x, transformer_options, merged_args):
+        """Build self-attention mask for per-guide attention attenuation.
+
+        Base implementation returns None (no attenuation). Subclasses that
+        support guide-based attention control should override this.
+        """
+        return None
+
     @abstractmethod
-    def _process_transformer_blocks(self, x, context, attention_mask, timestep, pe, **kwargs):
+    def _process_transformer_blocks(self, x, context, attention_mask, timestep, pe, self_attention_mask=None, **kwargs):
         """Process transformer blocks. Must be implemented by subclasses."""
         pass
 
@@ -788,9 +799,17 @@ class LTXBaseModel(torch.nn.Module, ABC):
         attention_mask = self._prepare_attention_mask(attention_mask, input_dtype)
         pe = self._prepare_positional_embeddings(pixel_coords, frame_rate, input_dtype)
 
+        # Build self-attention mask for per-guide attenuation
+        self_attention_mask = self._build_guide_self_attention_mask(
+            x, transformer_options, merged_args
+        )
+
         # Process transformer blocks
         x = self._process_transformer_blocks(
-            x, context, attention_mask, timestep, pe, transformer_options=transformer_options, **merged_args
+            x, context, attention_mask, timestep, pe,
+            transformer_options=transformer_options,
+            self_attention_mask=self_attention_mask,
+            **merged_args,
         )
 
         # Process output
@@ -890,13 +909,243 @@ class LTXVModel(LTXBaseModel):
             pixel_coords = pixel_coords[:, :, grid_mask, ...]
 
             kf_grid_mask = grid_mask[-keyframe_idxs.shape[2]:]
+
+            # Compute per-guide surviving token counts from guide_attention_entries.
+            # Each entry tracks one guide reference; they are appended in order and
+            # their pre_filter_counts partition the kf_grid_mask.
+            guide_entries = kwargs.get("guide_attention_entries", None)
+            if guide_entries:
+                total_pfc = sum(e["pre_filter_count"] for e in guide_entries)
+                if total_pfc != len(kf_grid_mask):
+                    raise ValueError(
+                        f"guide pre_filter_counts ({total_pfc}) != "
+                        f"keyframe grid mask length ({len(kf_grid_mask)})"
+                    )
+                resolved_entries = []
+                offset = 0
+                for entry in guide_entries:
+                    pfc = entry["pre_filter_count"]
+                    entry_mask = kf_grid_mask[offset:offset + pfc]
+                    surviving = int(entry_mask.sum().item())
+                    resolved_entries.append({
+                        **entry,
+                        "surviving_count": surviving,
+                    })
+                    offset += pfc
+                additional_args["resolved_guide_entries"] = resolved_entries
+
             keyframe_idxs = keyframe_idxs[..., kf_grid_mask, :]
             pixel_coords[:, :, -keyframe_idxs.shape[2]:, :] = keyframe_idxs
 
+            # Total surviving guide tokens (all guides)
+            additional_args["num_guide_tokens"] = keyframe_idxs.shape[2]
+
         x = self.patchify_proj(x)
         return x, pixel_coords, additional_args
 
-    def _process_transformer_blocks(self, x, context, attention_mask, timestep, pe, transformer_options={}, **kwargs):
+    def _build_guide_self_attention_mask(self, x, transformer_options, merged_args):
+        """Build self-attention mask for per-guide attention attenuation.
+
+        Reads resolved_guide_entries from merged_args (computed in _process_input)
+        to build a log-space additive bias mask that attenuates noisy ↔ guide
+        attention for each guide reference independently.
+
+        Returns None if no attenuation is needed (all strengths == 1.0 and no
+        spatial masks, or no guide tokens).
+        """
+        if isinstance(x, list):
+            # AV model: x = [vx, ax]; use vx for token count and device
+            total_tokens = x[0].shape[1]
+            device = x[0].device
+            dtype = x[0].dtype
+        else:
+            total_tokens = x.shape[1]
+            device = x.device
+            dtype = x.dtype
+
+        num_guide_tokens = merged_args.get("num_guide_tokens", 0)
+        if num_guide_tokens == 0:
+            return None
+
+        resolved_entries = merged_args.get("resolved_guide_entries", None)
+        if not resolved_entries:
+            return None
+
+        # Check if any attenuation is actually needed
+        needs_attenuation = any(
+            e["strength"] < 1.0 or e.get("pixel_mask") is not None
+            for e in resolved_entries
+        )
+        if not needs_attenuation:
+            return None
+
+        # Build per-guide-token weights for all tracked guide tokens.
+        # Guides are appended in order at the end of the sequence.
+        guide_start = total_tokens - num_guide_tokens
+        all_weights = []
+        total_tracked = 0
+
+        for entry in resolved_entries:
+            surviving = entry["surviving_count"]
+            if surviving == 0:
+                continue
+
+            strength = entry["strength"]
+            pixel_mask = entry.get("pixel_mask")
+            latent_shape = entry.get("latent_shape")
+
+            if pixel_mask is not None and latent_shape is not None:
+                f_lat, h_lat, w_lat = latent_shape
+                per_token = self._downsample_mask_to_latent(
+                    pixel_mask.to(device=device, dtype=dtype),
+                    f_lat, h_lat, w_lat,
+                )
+                # per_token shape: (B, f_lat*h_lat*w_lat).
+                # Collapse batch dim — the mask is assumed identical across the
+                # batch; validate and take the first element to get (1, tokens).
+                if per_token.shape[0] > 1:
+                    ref = per_token[0]
+                    for bi in range(1, per_token.shape[0]):
+                        if not torch.equal(ref, per_token[bi]):
+                            logger.warning(
+                                "pixel_mask differs across batch elements; "
+                                "using first element only."
+                            )
+                            break
+                    per_token = per_token[:1]
+                # `surviving` is the post-grid_mask token count.
+                # Clamp to surviving to handle any mismatch safely.
+                n_weights = min(per_token.shape[1], surviving)
+                weights = per_token[:, :n_weights] * strength  # (1, n_weights)
+            else:
+                weights = torch.full(
+                    (1, surviving), strength, device=device, dtype=dtype
+                )
+
+            all_weights.append(weights)
+            total_tracked += weights.shape[1]
+
+        if not all_weights:
+            return None
+
+        # Concatenate per-token weights for all tracked guides
+        tracked_weights = torch.cat(all_weights, dim=1)  # (1, total_tracked)
+
+        # Check if any weight is actually < 1.0 (otherwise no attenuation needed)
+        if (tracked_weights >= 1.0).all():
+            return None
+
+        # Build the mask: guide tokens are at the end of the sequence.
+        # Tracked guides come first (in order), untracked follow.
+        return self._build_self_attention_mask(
+            total_tokens, num_guide_tokens, total_tracked,
+            tracked_weights, guide_start, device, dtype,
+        )
+
+    @staticmethod
+    def _downsample_mask_to_latent(mask, f_lat, h_lat, w_lat):
+        """Downsample a pixel-space mask to per-token latent weights.
+
+        Args:
+            mask: (B, 1, F_pix, H_pix, W_pix) pixel-space mask with values in [0, 1].
+            f_lat: Number of latent frames (pre-dilation original count).
+            h_lat: Latent height (pre-dilation original height).
+            w_lat: Latent width (pre-dilation original width).
+
+        Returns:
+            (B, F_lat * H_lat * W_lat) flattened per-token weights.
+        """
+        b = mask.shape[0]
+        f_pix = mask.shape[2]
+
+        # Spatial downsampling: area interpolation per frame
+        spatial_down = torch.nn.functional.interpolate(
+            rearrange(mask, "b 1 f h w -> (b f) 1 h w"),
+            size=(h_lat, w_lat),
+            mode="area",
+        )
+        spatial_down = rearrange(spatial_down, "(b f) 1 h w -> b 1 f h w", b=b)
+
+        # Temporal downsampling: first pixel frame maps to first latent frame,
+        # remaining pixel frames are averaged in groups for causal temporal structure.
+        first_frame = spatial_down[:, :, :1, :, :]
+        if f_pix > 1 and f_lat > 1:
+            remaining_pix = f_pix - 1
+            remaining_lat = f_lat - 1
+            t = remaining_pix // remaining_lat
+            if t < 1:
+                # Fewer pixel frames than latent frames — upsample by repeating
+                # the available pixel frames via nearest interpolation.
+                rest_flat = rearrange(
+                    spatial_down[:, :, 1:, :, :],
+                    "b 1 f h w -> (b h w) 1 f",
+                )
+                rest_up = torch.nn.functional.interpolate(
+                    rest_flat, size=remaining_lat, mode="nearest",
+                )
+                rest = rearrange(
+                    rest_up, "(b h w) 1 f -> b 1 f h w",
+                    b=b, h=h_lat, w=w_lat,
+                )
+            else:
+                # Trim trailing pixel frames that don't fill a complete group
+                usable = remaining_lat * t
+                rest = rearrange(
+                    spatial_down[:, :, 1:1 + usable, :, :],
+                    "b 1 (f t) h w -> b 1 f t h w",
+                    t=t,
+                )
+                rest = rest.mean(dim=3)
+            latent_mask = torch.cat([first_frame, rest], dim=2)
+        elif f_lat > 1:
+            # Single pixel frame but multiple latent frames — repeat the
+            # single frame across all latent frames.
+            latent_mask = first_frame.expand(-1, -1, f_lat, -1, -1)
+        else:
+            latent_mask = first_frame
+
+        return rearrange(latent_mask, "b 1 f h w -> b (f h w)")
+
+    @staticmethod
+    def _build_self_attention_mask(total_tokens, num_guide_tokens, tracked_count,
+                                    tracked_weights, guide_start, device, dtype):
+        """Build a log-space additive self-attention bias mask.
+
+        Attenuates attention between noisy tokens and tracked guide tokens.
+        Untracked guide tokens (at the end of the guide portion) keep full attention.
+
+        Args:
+            total_tokens: Total sequence length.
+            num_guide_tokens: Total guide tokens (all guides) at end of sequence.
+            tracked_count: Number of tracked guide tokens (first in the guide portion).
+            tracked_weights: (1, tracked_count) tensor, values in [0, 1].
+            guide_start: Index where guide tokens begin in the sequence.
+            device: Target device.
+            dtype: Target dtype.
+
+        Returns:
+            (1, 1, total_tokens, total_tokens) additive bias mask.
+            0.0 = full attention, negative = attenuated, finfo.min = effectively fully masked.
+        """
+        finfo = torch.finfo(dtype)
+        mask = torch.zeros((1, 1, total_tokens, total_tokens), device=device, dtype=dtype)
+        tracked_end = guide_start + tracked_count
+
+        # Convert weights to log-space bias
+        w = tracked_weights.to(device=device, dtype=dtype)  # (1, tracked_count)
+        log_w = torch.full_like(w, finfo.min)
+        positive_mask = w > 0
+        if positive_mask.any():
+            log_w[positive_mask] = torch.log(w[positive_mask].clamp(min=finfo.tiny))
+
+        # noisy → tracked guides: each noisy row gets the same per-guide weight
+        mask[:, :, :guide_start, guide_start:tracked_end] = log_w.view(1, 1, 1, -1)
+        # tracked guides → noisy: each guide row broadcasts its weight across noisy cols
+        mask[:, :, guide_start:tracked_end, :guide_start] = log_w.view(1, 1, -1, 1)
+
+        return mask
+
+    def _process_transformer_blocks(self, x, context, attention_mask, timestep, pe, transformer_options={}, self_attention_mask=None, **kwargs):
         """Process transformer blocks for LTXV."""
         patches_replace = transformer_options.get("patches_replace", {})
         blocks_replace = patches_replace.get("dit", {})
@@ -906,10 +1155,10 @@ class LTXVModel(LTXBaseModel):
 
                 def block_wrap(args):
                     out = {}
-                    out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"], transformer_options=args["transformer_options"])
+                    out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"], transformer_options=args["transformer_options"], self_attention_mask=args.get("self_attention_mask"))
                     return out
 
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe, "transformer_options": transformer_options, "self_attention_mask": self_attention_mask}, {"original_block": block_wrap})
                 x = out["img"]
             else:
                 x = block(
@@ -919,6 +1168,7 @@ class LTXVModel(LTXBaseModel):
                     timestep=timestep,
                     pe=pe,
                     transformer_options=transformer_options,
+                    self_attention_mask=self_attention_mask,
                 )
 
         return x
diff --git a/comfy/model_base.py b/comfy/model_base.py
index 4e2096d4b..04695c079 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -65,6 +65,42 @@ from typing import TYPE_CHECKING
 if TYPE_CHECKING:
     from comfy.model_patcher import ModelPatcher
 
+
+class _CONDGuideEntries(comfy.conds.CONDConstant):
+    """CONDConstant subclass that safely compares guide_attention_entries.
+
+    guide_attention_entries may contain ``pixel_mask`` tensors.  The default
+    ``CONDConstant.can_concat`` uses ``!=`` which triggers a ``ValueError``
+    on tensors.  This subclass performs a structural comparison instead.
+    """
+
+    def can_concat(self, other):
+        if not isinstance(other, _CONDGuideEntries):
+            return False
+        a, b = self.cond, other.cond
+        if len(a) != len(b):
+            return False
+        for ea, eb in zip(a, b):
+            if ea["pre_filter_count"] != eb["pre_filter_count"]:
+                return False
+            if ea["strength"] != eb["strength"]:
+                return False
+            if ea.get("latent_shape") != eb.get("latent_shape"):
+                return False
+            a_has = ea.get("pixel_mask") is not None
+            b_has = eb.get("pixel_mask") is not None
+            if a_has != b_has:
+                return False
+            if a_has:
+                pm_a, pm_b = ea["pixel_mask"], eb["pixel_mask"]
+                if pm_a is not pm_b:
+                    if (pm_a.shape != pm_b.shape
+                            or pm_a.device != pm_b.device
+                            or pm_a.dtype != pm_b.dtype
+                            or not torch.equal(pm_a, pm_b)):
+                        return False
+        return True
+
 class ModelType(Enum):
     EPS = 1
     V_PREDICTION = 2
@@ -974,6 +1010,10 @@ class LTXV(BaseModel):
         if keyframe_idxs is not None:
             out['keyframe_idxs'] = comfy.conds.CONDRegular(keyframe_idxs)
 
+        guide_attention_entries = kwargs.get("guide_attention_entries", None)
+        if guide_attention_entries is not None:
+            out['guide_attention_entries'] = _CONDGuideEntries(guide_attention_entries)
+
         return out
 
     def process_timestep(self, timestep, x, denoise_mask=None, **kwargs):
@@ -1026,6 +1066,10 @@ class LTXAV(BaseModel):
         if latent_shapes is not None:
             out['latent_shapes'] = comfy.conds.CONDConstant(latent_shapes)
 
+        guide_attention_entries = kwargs.get("guide_attention_entries", None)
+        if guide_attention_entries is not None:
+            out['guide_attention_entries'] = _CONDGuideEntries(guide_attention_entries)
+
         return out
 
     def process_timestep(self, timestep, x, denoise_mask=None, audio_denoise_mask=None, **kwargs):
diff --git a/comfy_extras/nodes_lt.py b/comfy_extras/nodes_lt.py
index 1eeeec011..32fe921ff 100644
--- a/comfy_extras/nodes_lt.py
+++ b/comfy_extras/nodes_lt.py
@@ -134,6 +134,36 @@ class LTXVImgToVideoInplace(io.ComfyNode):
     generate = execute  # TODO: remove
 
 
+def _append_guide_attention_entry(positive, negative, pre_filter_count, latent_shape, strength=1.0):
+    """Append a guide_attention_entry to both positive and negative conditioning.
+
+    Each entry tracks one guide reference for per-reference attention control.
+    Entries are derived independently from each conditioning to avoid cross-contamination.
+    """
+    new_entry = {
+        "pre_filter_count": pre_filter_count,
+        "strength": strength,
+        "pixel_mask": None,
+        "latent_shape": latent_shape,
+    }
+    results = []
+    for cond in (positive, negative):
+        # Read existing entries from this specific conditioning
+        existing = []
+        for t in cond:
+            found = t[1].get("guide_attention_entries", None)
+            if found is not None:
+                existing = found
+                break
+        # Shallow copy and append (no deepcopy needed — entries contain
+        # only scalars and None for pixel_mask at this call site).
+        entries = [*existing, new_entry]
+        results.append(node_helpers.conditioning_set_values(
+            cond, {"guide_attention_entries": entries}
+        ))
+    return results[0], results[1]
+
+
 def conditioning_get_any_value(conditioning, key, default=None):
     for t in conditioning:
         if key in t[1]:
@@ -324,6 +354,13 @@ class LTXVAddGuide(io.ComfyNode):
             scale_factors,
         )
 
+        # Track this guide for per-reference attention control.
+        pre_filter_count = t.shape[2] * t.shape[3] * t.shape[4]
+        guide_latent_shape = list(t.shape[2:])  # [F, H, W]
+        positive, negative = _append_guide_attention_entry(
+            positive, negative, pre_filter_count, guide_latent_shape, strength=strength,
+        )
+
         return io.NodeOutput(positive, negative, {"samples": latent_image, "noise_mask": noise_mask})
 
     generate = execute  # TODO: remove
@@ -359,8 +396,14 @@ class LTXVCropGuides(io.ComfyNode):
         latent_image = latent_image[:, :, :-num_keyframes]
         noise_mask = noise_mask[:, :, :-num_keyframes]
 
-        positive = node_helpers.conditioning_set_values(positive, {"keyframe_idxs": None})
-        negative = node_helpers.conditioning_set_values(negative, {"keyframe_idxs": None})
+        positive = node_helpers.conditioning_set_values(positive, {
+            "keyframe_idxs": None,
+            "guide_attention_entries": None,
+        })
+        negative = node_helpers.conditioning_set_values(negative, {
+            "keyframe_idxs": None,
+            "guide_attention_entries": None,
+        })
 
         return io.NodeOutput(positive, negative, {"samples": latent_image, "noise_mask": noise_mask})
 

From 8a4d85c708435b47d0570637fdf1e89199702c48 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Wed, 25 Feb 2026 22:30:31 -0800
Subject: [PATCH 055/194] Cleanups to the last PR. (#12646)

---
 comfy/conds.py      | 21 ++++++++++++++++++++-
 comfy/model_base.py | 40 ++--------------------------------------
 2 files changed, 22 insertions(+), 39 deletions(-)

diff --git a/comfy/conds.py b/comfy/conds.py
index 5af3e93ea..55d8cdd78 100644
--- a/comfy/conds.py
+++ b/comfy/conds.py
@@ -4,6 +4,25 @@ import comfy.utils
 import logging
 
 
+def is_equal(x, y):
+    if torch.is_tensor(x) and torch.is_tensor(y):
+        return torch.equal(x, y)
+    elif isinstance(x, dict) and isinstance(y, dict):
+        if x.keys() != y.keys():
+            return False
+        return all(is_equal(x[k], y[k]) for k in x)
+    elif isinstance(x, (list, tuple)) and isinstance(y, (list, tuple)):
+        if type(x) is not type(y) or len(x) != len(y):
+            return False
+        return all(is_equal(a, b) for a, b in zip(x, y))
+    else:
+        try:
+            return x == y
+        except Exception:
+            logging.warning("comparison issue with COND")
+            return False
+
+
 class CONDRegular:
     def __init__(self, cond):
         self.cond = cond
@@ -84,7 +103,7 @@ class CONDConstant(CONDRegular):
         return self._copy_with(self.cond)
 
     def can_concat(self, other):
-        if self.cond != other.cond:
+        if not is_equal(self.cond, other.cond):
             return False
         return True
 
diff --git a/comfy/model_base.py b/comfy/model_base.py
index 04695c079..8f852e3c6 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -65,42 +65,6 @@ from typing import TYPE_CHECKING
 if TYPE_CHECKING:
     from comfy.model_patcher import ModelPatcher
 
-
-class _CONDGuideEntries(comfy.conds.CONDConstant):
-    """CONDConstant subclass that safely compares guide_attention_entries.
-
-    guide_attention_entries may contain ``pixel_mask`` tensors.  The default
-    ``CONDConstant.can_concat`` uses ``!=`` which triggers a ``ValueError``
-    on tensors.  This subclass performs a structural comparison instead.
-    """
-
-    def can_concat(self, other):
-        if not isinstance(other, _CONDGuideEntries):
-            return False
-        a, b = self.cond, other.cond
-        if len(a) != len(b):
-            return False
-        for ea, eb in zip(a, b):
-            if ea["pre_filter_count"] != eb["pre_filter_count"]:
-                return False
-            if ea["strength"] != eb["strength"]:
-                return False
-            if ea.get("latent_shape") != eb.get("latent_shape"):
-                return False
-            a_has = ea.get("pixel_mask") is not None
-            b_has = eb.get("pixel_mask") is not None
-            if a_has != b_has:
-                return False
-            if a_has:
-                pm_a, pm_b = ea["pixel_mask"], eb["pixel_mask"]
-                if pm_a is not pm_b:
-                    if (pm_a.shape != pm_b.shape
-                            or pm_a.device != pm_b.device
-                            or pm_a.dtype != pm_b.dtype
-                            or not torch.equal(pm_a, pm_b)):
-                        return False
-        return True
-
 class ModelType(Enum):
     EPS = 1
     V_PREDICTION = 2
@@ -1012,7 +976,7 @@ class LTXV(BaseModel):
 
         guide_attention_entries = kwargs.get("guide_attention_entries", None)
         if guide_attention_entries is not None:
-            out['guide_attention_entries'] = _CONDGuideEntries(guide_attention_entries)
+            out['guide_attention_entries'] = comfy.conds.CONDConstant(guide_attention_entries)
 
         return out
 
@@ -1068,7 +1032,7 @@ class LTXAV(BaseModel):
 
         guide_attention_entries = kwargs.get("guide_attention_entries", None)
         if guide_attention_entries is not None:
-            out['guide_attention_entries'] = _CONDGuideEntries(guide_attention_entries)
+            out['guide_attention_entries'] = comfy.conds.CONDConstant(guide_attention_entries)
 
         return out
 

From 74b5a337dcc4d6b276e4af45aa8a654c82569072 Mon Sep 17 00:00:00 2001
From: Christian Byrne <cbyrne@comfy.org>
Date: Thu, 26 Feb 2026 01:00:32 -0800
Subject: [PATCH 056/194] fix: move essentials_category to correct replacement
 nodes (#12568)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Move essentials_category from deprecated/incorrect nodes to their replacements:
- ImageBatch → BatchImagesNode (ImageBatch is deprecated)
- Blur → removed (should use subgraph blueprint)
- GetVideoComponents → Video Slice

Amp-Thread-ID: https://ampcode.com/threads/T-019c8340-4da2-723b-a09f-83895c5bbda5
---
 comfy_extras/nodes_post_processing.py | 2 +-
 comfy_extras/nodes_video.py           | 2 +-
 nodes.py                              | 1 -
 3 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py
index c67245e7a..4a0f7141a 100644
--- a/comfy_extras/nodes_post_processing.py
+++ b/comfy_extras/nodes_post_processing.py
@@ -79,7 +79,6 @@ class Blur(io.ComfyNode):
             node_id="ImageBlur",
             display_name="Image Blur",
             category="image/postprocessing",
-            essentials_category="Image Tools",
             inputs=[
                 io.Image.Input("image"),
                 io.Int.Input("blur_radius", default=1, min=1, max=31, step=1),
@@ -568,6 +567,7 @@ class BatchImagesNode(io.ComfyNode):
             node_id="BatchImagesNode",
             display_name="Batch Images",
             category="image",
+            essentials_category="Image Tools",
             search_aliases=["batch", "image batch", "batch images", "combine images", "merge images", "stack images"],
             inputs=[
                 io.Autogrow.Input("images", template=autogrow_template)
diff --git a/comfy_extras/nodes_video.py b/comfy_extras/nodes_video.py
index db7c171a2..5c096c232 100644
--- a/comfy_extras/nodes_video.py
+++ b/comfy_extras/nodes_video.py
@@ -147,7 +147,6 @@ class GetVideoComponents(io.ComfyNode):
             search_aliases=["extract frames", "split video", "video to images", "demux"],
             display_name="Get Video Components",
             category="image/video",
-            essentials_category="Video Tools",
             description="Extracts all components from a video: frames, audio, and framerate.",
             inputs=[
                 io.Video.Input("video", tooltip="The video to extract components from."),
@@ -218,6 +217,7 @@ class VideoSlice(io.ComfyNode):
                 "start time",
             ],
             category="image/video",
+            essentials_category="Video Tools",
             inputs=[
                 io.Video.Input("video"),
                 io.Float.Input(
diff --git a/nodes.py b/nodes.py
index e2fc20d53..bff073e30 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1925,7 +1925,6 @@ class ImageInvert:
 
 class ImageBatch:
     SEARCH_ALIASES = ["combine images", "merge images", "stack images"]
-    ESSENTIALS_CATEGORY = "Image Tools"
 
     @classmethod
     def INPUT_TYPES(s):

From 38ca94599f7444f55589308d1cf611fb77f6ca16 Mon Sep 17 00:00:00 2001
From: pythongosssss <125205205+pythongosssss@users.noreply.github.com>
Date: Thu, 26 Feb 2026 11:07:35 +0000
Subject: [PATCH 057/194] pyopengl-accelerate can cause object to be numpy ints
 instead of bare ints, which the glDeleteTextures function does not accept,
 explicitly cast to int (#12650)

---
 comfy_extras/nodes_glsl.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/comfy_extras/nodes_glsl.py b/comfy_extras/nodes_glsl.py
index 75ffb6d80..6d210b307 100644
--- a/comfy_extras/nodes_glsl.py
+++ b/comfy_extras/nodes_glsl.py
@@ -717,11 +717,11 @@ def _render_shader_batch(
         gl.glUseProgram(0)
 
         for tex in input_textures:
-            gl.glDeleteTextures(tex)
+            gl.glDeleteTextures(int(tex))
         for tex in output_textures:
-            gl.glDeleteTextures(tex)
+            gl.glDeleteTextures(int(tex))
         for tex in ping_pong_textures:
-            gl.glDeleteTextures(tex)
+            gl.glDeleteTextures(int(tex))
         if fbo is not None:
             gl.glDeleteFramebuffers(1, [fbo])
         for pp_fbo in ping_pong_fbos:

From 420e900f692f72a4e0108594a80a3465c036bebe Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Thu, 26 Feb 2026 12:19:38 -0800
Subject: [PATCH 058/194] main: load aimdo earlier (#12655)

Some custom node packs are naughty, and violate the
dont-load-torch-on-load rule. This causes aimdo to lose preference on
its allocator hook on linux.

Go super early on the aimdo first-stage init before custom nodes
are mentioned at all.
---
 main.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/main.py b/main.py
index 39e605deb..3fe8f0589 100644
--- a/main.py
+++ b/main.py
@@ -16,6 +16,10 @@ from comfy_execution.progress import get_progress_state
 from comfy_execution.utils import get_executing_context
 from comfy_api import feature_flags
 
+import comfy_aimdo.control
+
+if enables_dynamic_vram():
+    comfy_aimdo.control.init()
 
 if __name__ == "__main__":
     #NOTE: These do not do anything on core ComfyUI, they are for custom nodes.
@@ -173,10 +177,6 @@ import gc
 if 'torch' in sys.modules:
     logging.warning("WARNING: Potential Error in code: Torch already imported, torch should never be imported before this point.")
 
-import comfy_aimdo.control
-
-if enables_dynamic_vram():
-    comfy_aimdo.control.init()
 
 import comfy.utils
 

From fd41ec97cc2e457f322afdf136fd2f2b2454a240 Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Thu, 26 Feb 2026 22:52:10 +0200
Subject: [PATCH 059/194] feat(api-nodes): add NanoBanana2 (#12660)

---
 comfy_api_nodes/apis/gemini.py     |   6 +
 comfy_api_nodes/nodes_bytedance.py |   2 +-
 comfy_api_nodes/nodes_gemini.py    | 203 +++++++++++++++++++++++++++--
 3 files changed, 196 insertions(+), 15 deletions(-)

diff --git a/comfy_api_nodes/apis/gemini.py b/comfy_api_nodes/apis/gemini.py
index 3304d7e76..639035fef 100644
--- a/comfy_api_nodes/apis/gemini.py
+++ b/comfy_api_nodes/apis/gemini.py
@@ -127,9 +127,15 @@ class GeminiImageConfig(BaseModel):
     imageOutputOptions: GeminiImageOutputOptions = Field(default_factory=GeminiImageOutputOptions)
 
 
+class GeminiThinkingConfig(BaseModel):
+    includeThoughts: bool | None = Field(None)
+    thinkingLevel: str = Field(...)
+
+
 class GeminiImageGenerationConfig(GeminiGenerationConfig):
     responseModalities: list[str] | None = Field(None)
     imageConfig: GeminiImageConfig | None = Field(None)
+    thinkingConfig: GeminiThinkingConfig | None = Field(None)
 
 
 class GeminiImageGenerateContentRequest(BaseModel):
diff --git a/comfy_api_nodes/nodes_bytedance.py b/comfy_api_nodes/nodes_bytedance.py
index cfc604aa3..6dbd5984e 100644
--- a/comfy_api_nodes/nodes_bytedance.py
+++ b/comfy_api_nodes/nodes_bytedance.py
@@ -186,7 +186,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ByteDanceSeedreamNode",
-            display_name="ByteDance Seedream 5.0",
+            display_name="ByteDance Seedream 4.5 & 5.0",
             category="api node/image/ByteDance",
             description="Unified text-to-image generation and precise single-sentence editing at up to 4K resolution.",
             inputs=[
diff --git a/comfy_api_nodes/nodes_gemini.py b/comfy_api_nodes/nodes_gemini.py
index b69285be5..3fe804e0b 100644
--- a/comfy_api_nodes/nodes_gemini.py
+++ b/comfy_api_nodes/nodes_gemini.py
@@ -29,6 +29,7 @@ from comfy_api_nodes.apis.gemini import (
     GeminiRole,
     GeminiSystemInstructionContent,
     GeminiTextPart,
+    GeminiThinkingConfig,
     Modality,
 )
 from comfy_api_nodes.util import (
@@ -55,6 +56,21 @@ GEMINI_IMAGE_SYS_PROMPT = (
     "Prioritize generating the visual representation above any text, formatting, or conversational requests."
 )
 
+GEMINI_IMAGE_2_PRICE_BADGE = IO.PriceBadge(
+    depends_on=IO.PriceBadgeDepends(widgets=["model", "resolution"]),
+    expr="""
+    (
+      $m := widgets.model;
+      $r := widgets.resolution;
+      $isFlash := $contains($m, "nano banana 2");
+      $flashPrices := {"1k": 0.0696, "2k": 0.0696, "4k": 0.123};
+      $proPrices := {"1k": 0.134, "2k": 0.134, "4k": 0.24};
+      $prices := $isFlash ? $flashPrices : $proPrices;
+      {"type":"usd","usd": $lookup($prices, $r), "format":{"suffix":"/Image","approximate":true}}
+    )
+    """,
+)
+
 
 class GeminiModel(str, Enum):
     """
@@ -229,6 +245,10 @@ def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | N
         input_tokens_price = 2
         output_text_tokens_price = 12.0
         output_image_tokens_price = 120.0
+    elif response.modelVersion == "gemini-3.1-flash-image-preview":
+        input_tokens_price = 0.5
+        output_text_tokens_price = 3.0
+        output_image_tokens_price = 60.0
     else:
         return None
     final_price = response.usageMetadata.promptTokenCount * input_tokens_price
@@ -686,7 +706,7 @@ class GeminiImage2(IO.ComfyNode):
                 ),
                 IO.Combo.Input(
                     "model",
-                    options=["gemini-3-pro-image-preview"],
+                    options=["gemini-3-pro-image-preview", "Nano Banana 2 (Gemini 3.1 Flash Image)"],
                 ),
                 IO.Int.Input(
                     "seed",
@@ -750,19 +770,7 @@ class GeminiImage2(IO.ComfyNode):
                 IO.Hidden.unique_id,
             ],
             is_api_node=True,
-            price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["resolution"]),
-                expr="""
-                (
-                  $r := widgets.resolution;
-                  ($contains($r,"1k") or $contains($r,"2k"))
-                    ? {"type":"usd","usd":0.134,"format":{"suffix":"/Image","approximate":true}}
-                    : $contains($r,"4k")
-                      ? {"type":"usd","usd":0.24,"format":{"suffix":"/Image","approximate":true}}
-                      : {"type":"text","text":"Token-based"}
-                )
-                """,
-            ),
+            price_badge=GEMINI_IMAGE_2_PRICE_BADGE,
         )
 
     @classmethod
@@ -779,6 +787,10 @@ class GeminiImage2(IO.ComfyNode):
         system_prompt: str = "",
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=True, min_length=1)
+        if model == "Nano Banana 2 (Gemini 3.1 Flash Image)":
+            model = "gemini-3.1-flash-image-preview"
+            if response_modalities == "IMAGE+TEXT":
+                raise ValueError("IMAGE+TEXT is not currently available for the Nano Banana 2 model.")
 
         parts: list[GeminiPart] = [GeminiPart(text=prompt)]
         if images is not None:
@@ -815,6 +827,168 @@ class GeminiImage2(IO.ComfyNode):
         return IO.NodeOutput(await get_image_from_response(response), get_text_from_response(response))
 
 
+class GeminiNanoBanana2(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GeminiNanoBanana2",
+            display_name="Nano Banana 2",
+            category="api node/image/Gemini",
+            description="Generate or edit images synchronously via Google Vertex API.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="Text prompt describing the image to generate or the edits to apply. "
+                    "Include any constraints, styles, or details the model should follow.",
+                    default="",
+                ),
+                IO.Combo.Input(
+                    "model",
+                    options=["Nano Banana 2 (Gemini 3.1 Flash Image)"],
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="When the seed is fixed to a specific value, the model makes a best effort to provide "
+                    "the same response for repeated requests. Deterministic output isn't guaranteed. "
+                    "Also, changing the model or parameter settings, such as the temperature, "
+                    "can cause variations in the response even when you use the same seed value. "
+                    "By default, a random seed value is used.",
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=[
+                        "auto",
+                        "1:1",
+                        "2:3",
+                        "3:2",
+                        "3:4",
+                        "4:3",
+                        "4:5",
+                        "5:4",
+                        "9:16",
+                        "16:9",
+                        "21:9",
+                        # "1:4",
+                        # "4:1",
+                        # "8:1",
+                        # "1:8",
+                    ],
+                    default="auto",
+                    tooltip="If set to 'auto', matches your input image's aspect ratio; "
+                    "if no image is provided, a 16:9 square is usually generated.",
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=[
+                        # "512px",
+                        "1K",
+                        "2K",
+                        "4K",
+                    ],
+                    tooltip="Target output resolution. For 2K/4K the native Gemini upscaler is used.",
+                ),
+                IO.Combo.Input(
+                    "response_modalities",
+                    options=["IMAGE"],
+                    advanced=True,
+                ),
+                IO.Combo.Input(
+                    "thinking_level",
+                    options=["MINIMAL", "HIGH"],
+                ),
+                IO.Image.Input(
+                    "images",
+                    optional=True,
+                    tooltip="Optional reference image(s). "
+                    "To include multiple images, use the Batch Images node (up to 14).",
+                ),
+                IO.Custom("GEMINI_INPUT_FILES").Input(
+                    "files",
+                    optional=True,
+                    tooltip="Optional file(s) to use as context for the model. "
+                    "Accepts inputs from the Gemini Generate Content Input Files node.",
+                ),
+                IO.String.Input(
+                    "system_prompt",
+                    multiline=True,
+                    default=GEMINI_IMAGE_SYS_PROMPT,
+                    optional=True,
+                    tooltip="Foundational instructions that dictate an AI's behavior.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=GEMINI_IMAGE_2_PRICE_BADGE,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: str,
+        seed: int,
+        aspect_ratio: str,
+        resolution: str,
+        response_modalities: str,
+        thinking_level: str,
+        images: Input.Image | None = None,
+        files: list[GeminiPart] | None = None,
+        system_prompt: str = "",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        if model == "Nano Banana 2 (Gemini 3.1 Flash Image)":
+            model = "gemini-3.1-flash-image-preview"
+
+        parts: list[GeminiPart] = [GeminiPart(text=prompt)]
+        if images is not None:
+            if get_number_of_images(images) > 14:
+                raise ValueError("The current maximum number of supported images is 14.")
+            parts.extend(await create_image_parts(cls, images))
+        if files is not None:
+            parts.extend(files)
+
+        image_config = GeminiImageConfig(imageSize=resolution)
+        if aspect_ratio != "auto":
+            image_config.aspectRatio = aspect_ratio
+
+        gemini_system_prompt = None
+        if system_prompt:
+            gemini_system_prompt = GeminiSystemInstructionContent(parts=[GeminiTextPart(text=system_prompt)], role=None)
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/vertexai/gemini/{model}", method="POST"),
+            data=GeminiImageGenerateContentRequest(
+                contents=[
+                    GeminiContent(role=GeminiRole.user, parts=parts),
+                ],
+                generationConfig=GeminiImageGenerationConfig(
+                    responseModalities=(["IMAGE"] if response_modalities == "IMAGE" else ["TEXT", "IMAGE"]),
+                    imageConfig=image_config,
+                    thinkingConfig=GeminiThinkingConfig(thinkingLevel=thinking_level),
+                ),
+                systemInstruction=gemini_system_prompt,
+            ),
+            response_model=GeminiGenerateContentResponse,
+            price_extractor=calculate_tokens_price,
+        )
+        return IO.NodeOutput(await get_image_from_response(response), get_text_from_response(response))
+
+
 class GeminiExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -822,6 +996,7 @@ class GeminiExtension(ComfyExtension):
             GeminiNode,
             GeminiImage,
             GeminiImage2,
+            GeminiNanoBanana2,
             GeminiInputFiles,
         ]
 

From 88d05fe483a9f420a69d681e615422930404292b Mon Sep 17 00:00:00 2001
From: ComfyUI Wiki <contact@comfyui-wiki.com>
Date: Fri, 27 Feb 2026 04:52:45 +0800
Subject: [PATCH 060/194] chore: update workflow templates to v0.9.4 (#12664)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 88a056e5c..b5b292980 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.39.19
-comfyui-workflow-templates==0.9.3
+comfyui-workflow-templates==0.9.4
 comfyui-embedded-docs==0.4.3
 torch
 torchsde

From 3dd10a59c00248d00f0cb0ab794ff1bb9fb00a5f Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 26 Feb 2026 15:59:22 -0500
Subject: [PATCH 061/194] ComfyUI v0.15.1

---
 comfyui_version.py | 2 +-
 pyproject.toml     | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfyui_version.py b/comfyui_version.py
index 6dbda1a87..6a35c6de3 100644
--- a/comfyui_version.py
+++ b/comfyui_version.py
@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.15.0"
+__version__ = "0.15.1"
diff --git a/pyproject.toml b/pyproject.toml
index eaf558740..1b2318273 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.15.0"
+version = "0.15.1"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

From 3811780e4f73f9dbace01a85e6d97502406f8ccb Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Thu, 26 Feb 2026 14:12:29 -0800
Subject: [PATCH 062/194] Portable with cu128 isn't useful anymore. (#12666)

Users should either use the cu126 one or the regular one (cu130 at the moment)

The cu128 portable is still included in the latest github release but I will stop including it as soon as it becomes slightly annoying to deal with. This might happen as soon as next week.
---
 README.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/README.md b/README.md
index d97c6181d..56b7966cf 100644
--- a/README.md
+++ b/README.md
@@ -189,8 +189,6 @@ The portable above currently comes with python 3.13 and pytorch cuda 13.0. Updat
 
 [Experimental portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z)
 
-[Portable with pytorch cuda 12.8 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu128.7z).
-
 [Portable with pytorch cuda 12.6 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu126.7z) (Supports Nvidia 10 series and older GPUs).
 
 #### How do I share models between another UI and ComfyUI?

From b233dbe0bc179847b81680e0b59c493a8dc8d9a6 Mon Sep 17 00:00:00 2001
From: fappaz <fernando.augusto.paz@gmail.com>
Date: Fri, 27 Feb 2026 12:19:19 +1300
Subject: [PATCH 063/194] feat(ace-step): add ACE-Step 1.5 lycoris key alias
 mapping for LoKR #12638 (#12665)

---
 comfy/lora.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/comfy/lora.py b/comfy/lora.py
index 279cf38bb..f36ddb046 100644
--- a/comfy/lora.py
+++ b/comfy/lora.py
@@ -337,6 +337,7 @@ def model_lora_keys_unet(model, key_map={}):
             if k.startswith("diffusion_model.decoder.") and k.endswith(".weight"):
                 key_lora = k[len("diffusion_model.decoder."):-len(".weight")]
                 key_map["base_model.model.{}".format(key_lora)] = k  # Official base model loras
+                key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = k  # LyCORIS/LoKR format
 
     return key_map
 

From 08b26ed7c2fe43417058f4c6c5934de3cebf3f20 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Thu, 26 Feb 2026 15:59:24 -0800
Subject: [PATCH 064/194] bug_report template: Push harder for logs (#12657)

We get a lot od bug reports without logs, especially for performance
issues.
---
 .github/ISSUE_TEMPLATE/bug-report.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/ISSUE_TEMPLATE/bug-report.yml b/.github/ISSUE_TEMPLATE/bug-report.yml
index 6556677e0..95cc48f88 100644
--- a/.github/ISSUE_TEMPLATE/bug-report.yml
+++ b/.github/ISSUE_TEMPLATE/bug-report.yml
@@ -16,7 +16,7 @@ body:
 
         ## Very Important
 
-        Please make sure that you post ALL your ComfyUI logs in the bug report. A bug report without logs will likely be ignored.
+        Please make sure that you post ALL your ComfyUI logs in the bug report **even if there is no crash**. Just paste everything. The startup log (everything before "To see the GUI go to: ...") contains critical information to developers trying to help. For a performance issue or crash, paste everything from "got prompt" to the end, including the crash. More is better - always. A bug report without logs will likely be ignored.
   - type: checkboxes
     id: custom-nodes-test
     attributes:

From c7f7d52b684f661d911f1747bb6954978fa1d1b9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?=
 <40791699+kijai@users.noreply.github.com>
Date: Fri, 27 Feb 2026 02:59:05 +0200
Subject: [PATCH 065/194] feat: Support SDPose-OOD (#12661)

---
 .../modules/diffusionmodules/openaimodel.py   |   6 +
 comfy/ldm/modules/sdpose.py                   | 130 +++
 comfy/model_detection.py                      |   6 +-
 comfy/supported_models.py                     |   3 +-
 comfy_api/latest/_io.py                       |   5 +-
 comfy_extras/nodes_sdpose.py                  | 740 ++++++++++++++++++
 nodes.py                                      |   1 +
 7 files changed, 888 insertions(+), 3 deletions(-)
 create mode 100644 comfy/ldm/modules/sdpose.py
 create mode 100644 comfy_extras/nodes_sdpose.py

diff --git a/comfy/ldm/modules/diffusionmodules/openaimodel.py b/comfy/ldm/modules/diffusionmodules/openaimodel.py
index 4c8d53cac..295310df6 100644
--- a/comfy/ldm/modules/diffusionmodules/openaimodel.py
+++ b/comfy/ldm/modules/diffusionmodules/openaimodel.py
@@ -18,6 +18,8 @@ import comfy.patcher_extension
 import comfy.ops
 ops = comfy.ops.disable_weight_init
 
+from ..sdpose import HeatmapHead
+
 class TimestepBlock(nn.Module):
     """
     Any module where forward() takes timestep embeddings as a second argument.
@@ -441,6 +443,7 @@ class UNetModel(nn.Module):
         disable_temporal_crossattention=False,
         max_ddpm_temb_period=10000,
         attn_precision=None,
+        heatmap_head=False,
         device=None,
         operations=ops,
     ):
@@ -827,6 +830,9 @@ class UNetModel(nn.Module):
             #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
         )
 
+        if heatmap_head:
+            self.heatmap_head = HeatmapHead(device=device, dtype=self.dtype, operations=operations)
+
     def forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
         return comfy.patcher_extension.WrapperExecutor.new_class_executor(
             self._forward,
diff --git a/comfy/ldm/modules/sdpose.py b/comfy/ldm/modules/sdpose.py
new file mode 100644
index 000000000..d67b60b76
--- /dev/null
+++ b/comfy/ldm/modules/sdpose.py
@@ -0,0 +1,130 @@
+import torch
+import numpy as np
+from scipy.ndimage import gaussian_filter
+
+class HeatmapHead(torch.nn.Module):
+    def __init__(
+            self,
+            in_channels=640,
+            out_channels=133,
+            input_size=(768, 1024),
+            heatmap_scale=4,
+            deconv_out_channels=(640,),
+            deconv_kernel_sizes=(4,),
+            conv_out_channels=(640,),
+            conv_kernel_sizes=(1,),
+            final_layer_kernel_size=1,
+            device=None, dtype=None, operations=None
+        ):
+        super().__init__()
+
+        self.heatmap_size = (input_size[0] // heatmap_scale, input_size[1] // heatmap_scale)
+        self.scale_factor = ((np.array(input_size) - 1) / (np.array(self.heatmap_size) - 1)).astype(np.float32)
+
+        # Deconv layers
+        if deconv_out_channels:
+            deconv_layers = []
+            for out_ch, kernel_size in zip(deconv_out_channels, deconv_kernel_sizes):
+                if kernel_size == 4:
+                    padding, output_padding = 1, 0
+                elif kernel_size == 3:
+                    padding, output_padding = 1, 1
+                elif kernel_size == 2:
+                    padding, output_padding = 0, 0
+                else:
+                    raise ValueError(f'Unsupported kernel size {kernel_size}')
+
+                deconv_layers.extend([
+                    operations.ConvTranspose2d(in_channels, out_ch, kernel_size,
+                                     stride=2, padding=padding, output_padding=output_padding, bias=False, device=device, dtype=dtype),
+                    torch.nn.InstanceNorm2d(out_ch, device=device, dtype=dtype),
+                    torch.nn.SiLU(inplace=True)
+                ])
+                in_channels = out_ch
+            self.deconv_layers = torch.nn.Sequential(*deconv_layers)
+        else:
+            self.deconv_layers = torch.nn.Identity()
+
+        # Conv layers
+        if conv_out_channels:
+            conv_layers = []
+            for out_ch, kernel_size in zip(conv_out_channels, conv_kernel_sizes):
+                padding = (kernel_size - 1) // 2
+                conv_layers.extend([
+                    operations.Conv2d(in_channels, out_ch, kernel_size,
+                            stride=1, padding=padding, device=device, dtype=dtype),
+                    torch.nn.InstanceNorm2d(out_ch, device=device, dtype=dtype),
+                    torch.nn.SiLU(inplace=True)
+                ])
+                in_channels = out_ch
+            self.conv_layers = torch.nn.Sequential(*conv_layers)
+        else:
+            self.conv_layers = torch.nn.Identity()
+
+        self.final_layer = operations.Conv2d(in_channels, out_channels, kernel_size=final_layer_kernel_size, padding=final_layer_kernel_size // 2, device=device, dtype=dtype)
+
+    def forward(self, x): # Decode heatmaps to keypoints
+        heatmaps = self.final_layer(self.conv_layers(self.deconv_layers(x)))
+        heatmaps_np = heatmaps.float().cpu().numpy()  # (B, K, H, W)
+        B, K, H, W = heatmaps_np.shape
+
+        batch_keypoints = []
+        batch_scores = []
+
+        for b in range(B):
+            hm = heatmaps_np[b].copy()  # (K, H, W)
+
+            # --- vectorised argmax ---
+            flat = hm.reshape(K, -1)
+            idx = np.argmax(flat, axis=1)
+            scores = flat[np.arange(K), idx].copy()
+            y_locs, x_locs = np.unravel_index(idx, (H, W))
+            keypoints = np.stack([x_locs, y_locs], axis=-1).astype(np.float32)  # (K, 2) in heatmap space
+            invalid = scores <= 0.
+            keypoints[invalid] = -1
+
+            # --- DARK sub-pixel refinement (UDP) ---
+            # 1. Gaussian blur with max-preserving normalisation
+            border = 5  # (kernel-1)//2 for kernel=11
+            for k in range(K):
+                origin_max = np.max(hm[k])
+                dr = np.zeros((H + 2 * border, W + 2 * border), dtype=np.float32)
+                dr[border:-border, border:-border] = hm[k].copy()
+                dr = gaussian_filter(dr, sigma=2.0)
+                hm[k] = dr[border:-border, border:-border].copy()
+                cur_max = np.max(hm[k])
+                if cur_max > 0:
+                    hm[k] *= origin_max / cur_max
+            # 2. Log-space for Taylor expansion
+            np.clip(hm, 1e-3, 50., hm)
+            np.log(hm, hm)
+            # 3. Hessian-based Newton step
+            hm_pad = np.pad(hm, ((0, 0), (1, 1), (1, 1)), mode='edge').flatten()
+            index = keypoints[:, 0] + 1 + (keypoints[:, 1] + 1) * (W + 2)
+            index += (W + 2) * (H + 2) * np.arange(0, K)
+            index = index.astype(int).reshape(-1, 1)
+            i_       = hm_pad[index]
+            ix1      = hm_pad[index + 1]
+            iy1      = hm_pad[index + W + 2]
+            ix1y1    = hm_pad[index + W + 3]
+            ix1_y1_  = hm_pad[index - W - 3]
+            ix1_     = hm_pad[index - 1]
+            iy1_     = hm_pad[index - 2 - W]
+            dx = 0.5 * (ix1 - ix1_)
+            dy = 0.5 * (iy1 - iy1_)
+            derivative = np.concatenate([dx, dy], axis=1).reshape(K, 2, 1)
+            dxx = ix1  - 2 * i_ + ix1_
+            dyy = iy1  - 2 * i_ + iy1_
+            dxy = 0.5 * (ix1y1 - ix1 - iy1 + i_ + i_ - ix1_ - iy1_ + ix1_y1_)
+            hessian = np.concatenate([dxx, dxy, dxy, dyy], axis=1).reshape(K, 2, 2)
+            hessian = np.linalg.inv(hessian + np.finfo(np.float32).eps * np.eye(2))
+            keypoints -= np.einsum('imn,ink->imk', hessian, derivative).squeeze(axis=-1)
+
+            # --- restore to input image space ---
+            keypoints = keypoints * self.scale_factor
+            keypoints[invalid] = -1
+
+            batch_keypoints.append(keypoints)
+            batch_scores.append(scores)
+
+        return batch_keypoints, batch_scores
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index 030ae6980..b4b51b200 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -795,6 +795,10 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         unet_config["use_temporal_resblock"] = False
         unet_config["use_temporal_attention"] = False
 
+    heatmap_key = '{}heatmap_head.conv_layers.0.weight'.format(key_prefix)
+    if heatmap_key in state_dict_keys:
+        unet_config["heatmap_head"] = True
+
     return unet_config
 
 def model_config_from_unet_config(unet_config, state_dict=None):
@@ -1015,7 +1019,7 @@ def unet_config_from_diffusers_unet(state_dict, dtype=None):
 
     LotusD = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': 4,
             'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0],
-            'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_heads': 8,
+            'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64,
             'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
             'use_temporal_attention': False, 'use_temporal_resblock': False}
 
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index 6d08ff0a5..1bb7b7011 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -525,7 +525,8 @@ class LotusD(SD20):
     }
 
     unet_extra_config = {
-        "num_classes": 'sequential'
+        "num_classes": 'sequential',
+        "num_head_channels": 64,
     }
 
     def get_model(self, state_dict, prefix="", device=None):
diff --git a/comfy_api/latest/_io.py b/comfy_api/latest/_io.py
index 025727071..189d7d9bc 100644
--- a/comfy_api/latest/_io.py
+++ b/comfy_api/latest/_io.py
@@ -1224,9 +1224,10 @@ class BoundingBox(ComfyTypeIO):
 
     class Input(WidgetInput):
         def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
-                     socketless: bool=True, default: dict=None, component: str=None):
+                     socketless: bool=True, default: dict=None, component: str=None, force_input: bool=None):
             super().__init__(id, display_name, optional, tooltip, None, default, socketless)
             self.component = component
+            self.force_input = force_input
             if default is None:
                 self.default = {"x": 0, "y": 0, "width": 512, "height": 512}
 
@@ -1234,6 +1235,8 @@ class BoundingBox(ComfyTypeIO):
             d = super().as_dict()
             if self.component:
                 d["component"] = self.component
+            if self.force_input is not None:
+                d["forceInput"] = self.force_input
             return d
 
 
diff --git a/comfy_extras/nodes_sdpose.py b/comfy_extras/nodes_sdpose.py
new file mode 100644
index 000000000..71441848e
--- /dev/null
+++ b/comfy_extras/nodes_sdpose.py
@@ -0,0 +1,740 @@
+import torch
+import comfy.utils
+import numpy as np
+import math
+import colorsys
+from tqdm import tqdm
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+from comfy_extras.nodes_lotus import LotusConditioning
+
+
+def _preprocess_keypoints(kp_raw, sc_raw):
+    """Insert neck keypoint and remap from MMPose to OpenPose ordering.
+
+    Returns (kp, sc) where kp has shape (134, 2) and sc has shape (134,).
+    Layout:
+      0-17   body  (18 kp, OpenPose order)
+      18-23  feet  (6 kp)
+      24-91  face  (68 kp)
+      92-112 right hand (21 kp)
+      113-133 left hand (21 kp)
+    """
+    kp = np.array(kp_raw, dtype=np.float32)
+    sc = np.array(sc_raw, dtype=np.float32)
+    if len(kp) >= 17:
+        neck = (kp[5] + kp[6]) / 2
+        neck_score = min(sc[5], sc[6]) if sc[5] > 0.3 and sc[6] > 0.3 else 0
+        kp = np.insert(kp, 17, neck, axis=0)
+        sc = np.insert(sc, 17, neck_score)
+        mmpose_idx   = np.array([17, 6,  8, 10,  7,  9, 12, 14, 16, 13, 15, 2, 1, 4, 3])
+        openpose_idx = np.array([ 1, 2,  3,  4,  6,  7,  8,  9, 10, 12, 13, 14, 15, 16, 17])
+        tmp_kp, tmp_sc = kp.copy(), sc.copy()
+        tmp_kp[openpose_idx] = kp[mmpose_idx]
+        tmp_sc[openpose_idx] = sc[mmpose_idx]
+        kp, sc = tmp_kp, tmp_sc
+    return kp, sc
+
+
+def _to_openpose_frames(all_keypoints, all_scores, height, width):
+    """Convert raw keypoint lists to a list of OpenPose-style frame dicts.
+
+    Each frame dict contains:
+      canvas_width, canvas_height, people: list of person dicts with keys:
+        pose_keypoints_2d       - 18 body kp  as flat [x,y,score,...] (absolute pixels)
+        foot_keypoints_2d       -  6 foot kp  as flat [x,y,score,...] (absolute pixels)
+        face_keypoints_2d       - 70 face kp  as flat [x,y,score,...] (absolute pixels)
+                                   indices 0-67: 68 face landmarks
+                                   index  68:    right eye (body[14])
+                                   index  69:    left  eye (body[15])
+        hand_right_keypoints_2d - 21 right-hand kp (absolute pixels)
+        hand_left_keypoints_2d  - 21 left-hand  kp (absolute pixels)
+    """
+    def _flatten(kp_slice, sc_slice):
+        return np.stack([kp_slice[:, 0], kp_slice[:, 1], sc_slice], axis=1).flatten().tolist()
+
+    frames = []
+    for img_idx in range(len(all_keypoints)):
+        people = []
+        for kp_raw, sc_raw in zip(all_keypoints[img_idx], all_scores[img_idx]):
+            kp, sc = _preprocess_keypoints(kp_raw, sc_raw)
+            # 70 face kp = 68 face landmarks + REye (body[14]) + LEye (body[15])
+            face_kp = np.concatenate([kp[24:92], kp[[14, 15]]], axis=0)
+            face_sc = np.concatenate([sc[24:92], sc[[14, 15]]], axis=0)
+            people.append({
+                "pose_keypoints_2d":       _flatten(kp[0:18],   sc[0:18]),
+                "foot_keypoints_2d":       _flatten(kp[18:24],  sc[18:24]),
+                "face_keypoints_2d":       _flatten(face_kp,    face_sc),
+                "hand_right_keypoints_2d": _flatten(kp[92:113], sc[92:113]),
+                "hand_left_keypoints_2d":  _flatten(kp[113:134], sc[113:134]),
+            })
+        frames.append({"canvas_width": width, "canvas_height": height, "people": people})
+    return frames
+
+
+class KeypointDraw:
+    """
+    Pose keypoint drawing class that supports both numpy and cv2 backends.
+    """
+    def __init__(self):
+        try:
+            import cv2
+            self.draw = cv2
+        except ImportError:
+            self.draw = self
+
+        # Hand connections (same for both hands)
+        self.hand_edges = [
+            [0, 1], [1, 2], [2, 3], [3, 4],      # thumb
+            [0, 5], [5, 6], [6, 7], [7, 8],      # index
+            [0, 9], [9, 10], [10, 11], [11, 12], # middle
+            [0, 13], [13, 14], [14, 15], [15, 16], # ring
+            [0, 17], [17, 18], [18, 19], [19, 20], # pinky
+        ]
+
+        # Body connections - matching DWPose limbSeq (1-indexed, converted to 0-indexed)
+        self.body_limbSeq = [
+            [2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10],
+            [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17],
+            [1, 16], [16, 18]
+        ]
+
+        # Colors matching DWPose
+        self.colors = [
+            [255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0],
+            [85, 255, 0], [0, 255, 0], [0, 255, 85], [0, 255, 170], [0, 255, 255],
+            [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255],
+            [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]
+        ]
+
+    @staticmethod
+    def circle(canvas_np, center, radius, color, **kwargs):
+        """Draw a filled circle using NumPy vectorized operations."""
+        cx, cy = center
+        h, w = canvas_np.shape[:2]
+
+        radius_int = int(np.ceil(radius))
+
+        y_min, y_max = max(0, cy - radius_int), min(h, cy + radius_int + 1)
+        x_min, x_max = max(0, cx - radius_int), min(w, cx + radius_int + 1)
+
+        if y_max <= y_min or x_max <= x_min:
+            return
+
+        y, x = np.ogrid[y_min:y_max, x_min:x_max]
+        mask = (x - cx)**2 + (y - cy)**2 <= radius**2
+        canvas_np[y_min:y_max, x_min:x_max][mask] = color
+
+    @staticmethod
+    def line(canvas_np, pt1, pt2, color, thickness=1, **kwargs):
+        """Draw line using Bresenham's algorithm with NumPy operations."""
+        x0, y0, x1, y1 = *pt1, *pt2
+        h, w = canvas_np.shape[:2]
+        dx, dy = abs(x1 - x0), abs(y1 - y0)
+        sx, sy = (1 if x0 < x1 else -1), (1 if y0 < y1 else -1)
+        err, x, y, line_points = dx - dy, x0, y0, []
+
+        while True:
+            line_points.append((x, y))
+            if x == x1 and y == y1:
+                break
+            e2 = 2 * err
+            if e2 > -dy:
+                err, x = err - dy, x + sx
+            if e2 < dx:
+                err, y = err + dx, y + sy
+
+        if thickness > 1:
+            radius, radius_int = (thickness / 2.0) + 0.5, int(np.ceil((thickness / 2.0) + 0.5))
+            for px, py in line_points:
+                y_min, y_max, x_min, x_max = max(0, py - radius_int), min(h, py + radius_int + 1), max(0, px - radius_int), min(w, px + radius_int + 1)
+                if y_max > y_min and x_max > x_min:
+                    yy, xx = np.ogrid[y_min:y_max, x_min:x_max]
+                    canvas_np[y_min:y_max, x_min:x_max][(xx - px)**2 + (yy - py)**2 <= radius**2] = color
+        else:
+            line_points = np.array(line_points)
+            valid = (line_points[:, 1] >= 0) & (line_points[:, 1] < h) & (line_points[:, 0] >= 0) & (line_points[:, 0] < w)
+            if (valid_points := line_points[valid]).size:
+                canvas_np[valid_points[:, 1], valid_points[:, 0]] = color
+
+    @staticmethod
+    def fillConvexPoly(canvas_np, pts, color, **kwargs):
+        """Fill polygon using vectorized scanline algorithm."""
+        if len(pts) < 3:
+            return
+        pts = np.array(pts, dtype=np.int32)
+        h, w = canvas_np.shape[:2]
+        y_min, y_max, x_min, x_max = max(0, pts[:, 1].min()), min(h, pts[:, 1].max() + 1), max(0, pts[:, 0].min()), min(w, pts[:, 0].max() + 1)
+        if y_max <= y_min or x_max <= x_min:
+            return
+        yy, xx = np.mgrid[y_min:y_max, x_min:x_max]
+        mask = np.zeros((y_max - y_min, x_max - x_min), dtype=bool)
+
+        for i in range(len(pts)):
+            p1, p2 = pts[i], pts[(i + 1) % len(pts)]
+            y1, y2 = p1[1], p2[1]
+            if y1 == y2:
+                continue
+            if y1 > y2:
+                p1, p2, y1, y2 = p2, p1, p2[1], p1[1]
+            if not (edge_mask := (yy >= y1) & (yy < y2)).any():
+                continue
+            mask ^= edge_mask & (xx >= p1[0] + (yy - y1) * (p2[0] - p1[0]) / (y2 - y1))
+
+        canvas_np[y_min:y_max, x_min:x_max][mask] = color
+
+    @staticmethod
+    def ellipse2Poly(center, axes, angle, arc_start, arc_end, delta=1, **kwargs):
+        """Python implementation of cv2.ellipse2Poly."""
+        axes = (axes[0] + 0.5, axes[1] + 0.5) # to better match cv2 output
+        angle = angle % 360
+        if arc_start > arc_end:
+            arc_start, arc_end = arc_end, arc_start
+        while arc_start < 0:
+            arc_start, arc_end = arc_start + 360, arc_end + 360
+        while arc_end > 360:
+            arc_end, arc_start = arc_end - 360, arc_start - 360
+        if arc_end - arc_start > 360:
+            arc_start, arc_end = 0, 360
+
+        angle_rad = math.radians(angle)
+        alpha, beta = math.cos(angle_rad), math.sin(angle_rad)
+        pts = []
+        for i in range(arc_start, arc_end + delta, delta):
+            theta_rad = math.radians(min(i, arc_end))
+            x, y = axes[0] * math.cos(theta_rad), axes[1] * math.sin(theta_rad)
+            pts.append([int(round(center[0] + x * alpha - y * beta)), int(round(center[1] + x * beta + y * alpha))])
+
+        unique_pts, prev_pt = [], (float('inf'), float('inf'))
+        for pt in pts:
+            if (pt_tuple := tuple(pt)) != prev_pt:
+                unique_pts.append(pt)
+                prev_pt = pt_tuple
+
+        return unique_pts if len(unique_pts) > 1 else [[center[0], center[1]], [center[0], center[1]]]
+
+    def draw_wholebody_keypoints(self, canvas, keypoints, scores=None, threshold=0.3,
+                                 draw_body=True, draw_feet=True, draw_face=True, draw_hands=True, stick_width=4, face_point_size=3):
+        """
+        Draw wholebody keypoints (134 keypoints after processing) in DWPose style.
+
+        Expected keypoint format (after neck insertion and remapping):
+        - Body: 0-17 (18 keypoints in OpenPose format, neck at index 1)
+        - Foot: 18-23 (6 keypoints)
+        - Face: 24-91 (68 landmarks)
+        - Right hand: 92-112 (21 keypoints)
+        - Left hand: 113-133 (21 keypoints)
+
+        Args:
+            canvas: The canvas to draw on (numpy array)
+            keypoints: Array of keypoint coordinates
+            scores: Optional confidence scores for each keypoint
+            threshold: Minimum confidence threshold for drawing keypoints
+
+        Returns:
+            canvas: The canvas with keypoints drawn
+        """
+        H, W, C = canvas.shape
+
+        # Draw body limbs
+        if draw_body and len(keypoints) >= 18:
+            for i, limb in enumerate(self.body_limbSeq):
+                # Convert from 1-indexed to 0-indexed
+                idx1, idx2 = limb[0] - 1, limb[1] - 1
+
+                if idx1 >= 18 or idx2 >= 18:
+                    continue
+
+                if scores is not None:
+                    if scores[idx1] < threshold or scores[idx2] < threshold:
+                        continue
+
+                Y = [keypoints[idx1][0], keypoints[idx2][0]]
+                X = [keypoints[idx1][1], keypoints[idx2][1]]
+                mX, mY = (X[0] + X[1]) / 2, (Y[0] + Y[1]) / 2
+                length = math.sqrt((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2)
+
+                if length < 1:
+                    continue
+
+                angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
+
+                polygon = self.draw.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stick_width), int(angle), 0, 360, 1)
+
+                self.draw.fillConvexPoly(canvas, polygon, self.colors[i % len(self.colors)])
+
+        # Draw body keypoints
+        if draw_body and len(keypoints) >= 18:
+            for i in range(18):
+                if scores is not None and scores[i] < threshold:
+                    continue
+                x, y = int(keypoints[i][0]), int(keypoints[i][1])
+                if 0 <= x < W and 0 <= y < H:
+                    self.draw.circle(canvas, (x, y), 4, self.colors[i % len(self.colors)], thickness=-1)
+
+        # Draw foot keypoints (18-23, 6 keypoints)
+        if draw_feet and len(keypoints) >= 24:
+            for i in range(18, 24):
+                if scores is not None and scores[i] < threshold:
+                    continue
+                x, y = int(keypoints[i][0]), int(keypoints[i][1])
+                if 0 <= x < W and 0 <= y < H:
+                    self.draw.circle(canvas, (x, y), 4, self.colors[i % len(self.colors)], thickness=-1)
+
+        # Draw right hand (92-112)
+        if draw_hands and len(keypoints) >= 113:
+            eps = 0.01
+            for ie, edge in enumerate(self.hand_edges):
+                idx1, idx2 = 92 + edge[0], 92 + edge[1]
+                if scores is not None:
+                    if scores[idx1] < threshold or scores[idx2] < threshold:
+                        continue
+
+                x1, y1 = int(keypoints[idx1][0]), int(keypoints[idx1][1])
+                x2, y2 = int(keypoints[idx2][0]), int(keypoints[idx2][1])
+
+                if x1 > eps and y1 > eps and x2 > eps and y2 > eps:
+                    if 0 <= x1 < W and 0 <= y1 < H and 0 <= x2 < W and 0 <= y2 < H:
+                        # HSV to RGB conversion for rainbow colors
+                        r, g, b = colorsys.hsv_to_rgb(ie / float(len(self.hand_edges)), 1.0, 1.0)
+                        color = (int(r * 255), int(g * 255), int(b * 255))
+                        self.draw.line(canvas, (x1, y1), (x2, y2), color, thickness=2)
+
+            # Draw right hand keypoints
+            for i in range(92, 113):
+                if scores is not None and scores[i] < threshold:
+                    continue
+                x, y = int(keypoints[i][0]), int(keypoints[i][1])
+                if x > eps and y > eps and 0 <= x < W and 0 <= y < H:
+                    self.draw.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1)
+
+        # Draw left hand (113-133)
+        if draw_hands and len(keypoints) >= 134:
+            eps = 0.01
+            for ie, edge in enumerate(self.hand_edges):
+                idx1, idx2 = 113 + edge[0], 113 + edge[1]
+                if scores is not None:
+                    if scores[idx1] < threshold or scores[idx2] < threshold:
+                        continue
+
+                x1, y1 = int(keypoints[idx1][0]), int(keypoints[idx1][1])
+                x2, y2 = int(keypoints[idx2][0]), int(keypoints[idx2][1])
+
+                if x1 > eps and y1 > eps and x2 > eps and y2 > eps:
+                    if 0 <= x1 < W and 0 <= y1 < H and 0 <= x2 < W and 0 <= y2 < H:
+                        # HSV to RGB conversion for rainbow colors
+                        r, g, b = colorsys.hsv_to_rgb(ie / float(len(self.hand_edges)), 1.0, 1.0)
+                        color = (int(r * 255), int(g * 255), int(b * 255))
+                        self.draw.line(canvas, (x1, y1), (x2, y2), color, thickness=2)
+
+            # Draw left hand keypoints
+            for i in range(113, 134):
+                if scores is not None and i < len(scores) and scores[i] < threshold:
+                    continue
+                x, y = int(keypoints[i][0]), int(keypoints[i][1])
+                if x > eps and y > eps and 0 <= x < W and 0 <= y < H:
+                    self.draw.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1)
+
+        # Draw face keypoints (24-91) - white dots only, no lines
+        if draw_face and len(keypoints) >= 92:
+            eps = 0.01
+            for i in range(24, 92):
+                if scores is not None and scores[i] < threshold:
+                    continue
+                x, y = int(keypoints[i][0]), int(keypoints[i][1])
+                if x > eps and y > eps and 0 <= x < W and 0 <= y < H:
+                    self.draw.circle(canvas, (x, y), face_point_size, (255, 255, 255), thickness=-1)
+
+        return canvas
+
+class SDPoseDrawKeypoints(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SDPoseDrawKeypoints",
+            category="image/preprocessors",
+            search_aliases=["openpose", "pose detection", "preprocessor", "keypoints", "pose"],
+            inputs=[
+                io.Custom("POSE_KEYPOINT").Input("keypoints"),
+                io.Boolean.Input("draw_body", default=True),
+                io.Boolean.Input("draw_hands", default=True),
+                io.Boolean.Input("draw_face", default=True),
+                io.Boolean.Input("draw_feet", default=False),
+                io.Int.Input("stick_width", default=4, min=1, max=10, step=1),
+                io.Int.Input("face_point_size", default=3, min=1, max=10, step=1),
+                io.Float.Input("score_threshold", default=0.3, min=0.0, max=1.0, step=0.01),
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, keypoints, draw_body, draw_hands, draw_face, draw_feet, stick_width, face_point_size, score_threshold) -> io.NodeOutput:
+        if not keypoints:
+            return io.NodeOutput(torch.zeros((1, 64, 64, 3), dtype=torch.float32))
+        height = keypoints[0]["canvas_height"]
+        width  = keypoints[0]["canvas_width"]
+
+        def _parse(flat, n):
+            arr = np.array(flat, dtype=np.float32).reshape(n, 3)
+            return arr[:, :2], arr[:, 2]
+
+        def _zeros(n):
+            return np.zeros((n, 2), dtype=np.float32), np.zeros(n, dtype=np.float32)
+
+        pose_outputs = []
+        drawer = KeypointDraw()
+
+        for frame in tqdm(keypoints, desc="Drawing keypoints on frames"):
+            canvas = np.zeros((height, width, 3), dtype=np.uint8)
+            for person in frame["people"]:
+                body_kp,  body_sc  = _parse(person["pose_keypoints_2d"],       18)
+                foot_raw = person.get("foot_keypoints_2d")
+                foot_kp,  foot_sc  = _parse(foot_raw, 6) if foot_raw else _zeros(6)
+                face_kp,  face_sc  = _parse(person["face_keypoints_2d"],       70)
+                face_kp,  face_sc  = face_kp[:68], face_sc[:68]  # drop appended eye kp; body already draws them
+                rhand_kp, rhand_sc = _parse(person["hand_right_keypoints_2d"], 21)
+                lhand_kp, lhand_sc = _parse(person["hand_left_keypoints_2d"],  21)
+
+                kp = np.concatenate([body_kp, foot_kp, face_kp, rhand_kp, lhand_kp], axis=0)
+                sc = np.concatenate([body_sc, foot_sc, face_sc, rhand_sc, lhand_sc], axis=0)
+
+                canvas = drawer.draw_wholebody_keypoints(
+                    canvas, kp, sc,
+                    threshold=score_threshold,
+                    draw_body=draw_body, draw_feet=draw_feet,
+                    draw_face=draw_face, draw_hands=draw_hands,
+                    stick_width=stick_width, face_point_size=face_point_size,
+                )
+            pose_outputs.append(canvas)
+
+        pose_outputs_np = np.stack(pose_outputs) if len(pose_outputs) > 1 else np.expand_dims(pose_outputs[0], 0)
+        final_pose_output = torch.from_numpy(pose_outputs_np).float() / 255.0
+        return io.NodeOutput(final_pose_output)
+
+class SDPoseKeypointExtractor(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SDPoseKeypointExtractor",
+            category="image/preprocessors",
+            search_aliases=["openpose", "pose detection", "preprocessor", "keypoints", "sdpose"],
+            description="Extract pose keypoints from images using the SDPose model: https://huggingface.co/Comfy-Org/SDPose/tree/main/checkpoints",
+            inputs=[
+                io.Model.Input("model"),
+                io.Vae.Input("vae"),
+                io.Image.Input("image"),
+                io.Int.Input("batch_size", default=16, min=1, max=10000, step=1),
+                io.BoundingBox.Input("bboxes", optional=True, force_input=True, tooltip="Optional bounding boxes for more accurate detections. Required for multi-person detection."),
+            ],
+            outputs=[
+                io.Custom("POSE_KEYPOINT").Output("keypoints", tooltip="Keypoints in OpenPose frame format (canvas_width, canvas_height, people)"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, vae, image, batch_size, bboxes=None) -> io.NodeOutput:
+
+        height, width = image.shape[-3], image.shape[-2]
+        context = LotusConditioning().execute().result[0]
+
+        # Use output_block_patch to capture the last 640-channel feature
+        def output_patch(h, hsp, transformer_options):
+            nonlocal captured_feat
+            if h.shape[1] == 640:  # Capture the features for wholebody
+                captured_feat = h.clone()
+            return h, hsp
+
+        model_clone = model.clone()
+        model_clone.model_options["transformer_options"] = {"patches": {"output_block_patch": [output_patch]}}
+
+        if not hasattr(model.model.diffusion_model, 'heatmap_head'):
+            raise ValueError("The provided model does not have a heatmap_head. Please use SDPose model from here https://huggingface.co/Comfy-Org/SDPose/tree/main/checkpoints.")
+
+        head = model.model.diffusion_model.heatmap_head
+        total_images = image.shape[0]
+        captured_feat = None
+
+        model_h = int(head.heatmap_size[0]) * 4   # e.g. 192 * 4 = 768
+        model_w = int(head.heatmap_size[1]) * 4   # e.g. 256 * 4 = 1024
+
+        def _run_on_latent(latent_batch):
+            """Run one forward pass and return (keypoints_list, scores_list) for the batch."""
+            nonlocal captured_feat
+            captured_feat = None
+            _ = comfy.sample.sample(
+                model_clone,
+                noise=torch.zeros_like(latent_batch),
+                steps=1, cfg=1.0,
+                sampler_name="euler", scheduler="simple",
+                positive=context, negative=context,
+                latent_image=latent_batch, disable_noise=True, disable_pbar=True,
+            )
+            return head(captured_feat)  # keypoints_batch, scores_batch
+
+        # all_keypoints / all_scores are lists-of-lists:
+        #   outer index = input image index
+        #   inner index = detected person (one per bbox, or one for full-image)
+        all_keypoints = []  # shape: [n_images][n_persons]
+        all_scores = []     # shape: [n_images][n_persons]
+        pbar = comfy.utils.ProgressBar(total_images)
+
+        if bboxes is not None:
+            if not isinstance(bboxes, list):
+                bboxes = [[bboxes]]
+            elif len(bboxes) == 0:
+                bboxes = [None] * total_images
+            # --- bbox-crop mode: one forward pass per crop -------------------------
+            for img_idx in tqdm(range(total_images), desc="Extracting keypoints from crops"):
+                img = image[img_idx:img_idx + 1]  # (1, H, W, C)
+                # Broadcasting: if fewer bbox lists than images, repeat the last one.
+                img_bboxes = bboxes[min(img_idx, len(bboxes) - 1)] if bboxes else None
+
+                img_keypoints = []
+                img_scores = []
+
+                if img_bboxes:
+                    for bbox in img_bboxes:
+                        x1 = max(0, int(bbox["x"]))
+                        y1 = max(0, int(bbox["y"]))
+                        x2 = min(width,  int(bbox["x"] + bbox["width"]))
+                        y2 = min(height, int(bbox["y"] + bbox["height"]))
+
+                        if x2 <= x1 or y2 <= y1:
+                            continue
+
+                        crop_h_px, crop_w_px = y2 - y1, x2 - x1
+                        crop = img[:, y1:y2, x1:x2, :]  # (1, crop_h, crop_w, C)
+
+                        # scale to fit inside (model_h, model_w) while preserving aspect ratio, then pad to exact model size.
+                        scale = min(model_h / crop_h_px, model_w / crop_w_px)
+                        scaled_h, scaled_w = int(round(crop_h_px * scale)), int(round(crop_w_px * scale))
+                        pad_top, pad_left  = (model_h - scaled_h) // 2, (model_w - scaled_w) // 2
+
+                        crop_chw = crop.permute(0, 3, 1, 2).float()  # BHWC → BCHW
+                        scaled = comfy.utils.common_upscale(crop_chw, scaled_w, scaled_h, upscale_method="bilinear", crop="disabled")
+                        padded = torch.zeros(1, scaled.shape[1], model_h, model_w, dtype=scaled.dtype, device=scaled.device)
+                        padded[:, :, pad_top:pad_top + scaled_h, pad_left:pad_left + scaled_w] = scaled
+                        crop_resized = padded.permute(0, 2, 3, 1)  # BCHW → BHWC
+
+                        latent_crop = vae.encode(crop_resized)
+                        kp_batch, sc_batch = _run_on_latent(latent_crop)
+                        kp, sc = kp_batch[0], sc_batch[0]  # (K, 2), coords in model pixel space
+
+                        # remove padding offset, undo scale, offset to full-image coordinates.
+                        kp = kp.copy() if isinstance(kp, np.ndarray) else np.array(kp, dtype=np.float32)
+                        kp[..., 0] = (kp[..., 0] - pad_left) / scale + x1
+                        kp[..., 1] = (kp[..., 1] - pad_top)  / scale + y1
+
+                        img_keypoints.append(kp)
+                        img_scores.append(sc)
+                else:
+                    # No bboxes for this image – run on the full image
+                    latent_img = vae.encode(img)
+                    kp_batch, sc_batch = _run_on_latent(latent_img)
+                    img_keypoints.append(kp_batch[0])
+                    img_scores.append(sc_batch[0])
+
+                all_keypoints.append(img_keypoints)
+                all_scores.append(img_scores)
+                pbar.update(1)
+
+        else: # full-image mode, batched
+            tqdm_pbar = tqdm(total=total_images, desc="Extracting keypoints")
+            for batch_start in range(0, total_images, batch_size):
+                batch_end = min(batch_start + batch_size, total_images)
+                latent_batch = vae.encode(image[batch_start:batch_end])
+
+                kp_batch, sc_batch = _run_on_latent(latent_batch)
+
+                for kp, sc in zip(kp_batch, sc_batch):
+                    all_keypoints.append([kp])
+                    all_scores.append([sc])
+                    tqdm_pbar.update(1)
+
+                pbar.update(batch_end - batch_start)
+
+        openpose_frames = _to_openpose_frames(all_keypoints, all_scores, height, width)
+        return io.NodeOutput(openpose_frames)
+
+
+def get_face_bboxes(kp2ds, scale, image_shape):
+    h, w = image_shape
+    kp2ds_face = kp2ds.copy()[1:] * (w, h)
+
+    min_x, min_y = np.min(kp2ds_face, axis=0)
+    max_x, max_y = np.max(kp2ds_face, axis=0)
+
+    initial_width = max_x - min_x
+    initial_height = max_y - min_y
+
+    if initial_width <= 0 or initial_height <= 0:
+        return [0, 0, 0, 0]
+
+    initial_area = initial_width * initial_height
+
+    expanded_area = initial_area * scale
+
+    new_width = np.sqrt(expanded_area * (initial_width / initial_height))
+    new_height = np.sqrt(expanded_area * (initial_height / initial_width))
+
+    delta_width = (new_width - initial_width) / 2
+    delta_height = (new_height - initial_height) / 4
+
+    expanded_min_x = max(min_x - delta_width, 0)
+    expanded_max_x = min(max_x + delta_width, w)
+    expanded_min_y = max(min_y - 3 * delta_height, 0)
+    expanded_max_y = min(max_y + delta_height, h)
+
+    return [int(expanded_min_x), int(expanded_max_x), int(expanded_min_y), int(expanded_max_y)]
+
+class SDPoseFaceBBoxes(io.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SDPoseFaceBBoxes",
+            category="image/preprocessors",
+            search_aliases=["face bbox", "face bounding box", "pose", "keypoints"],
+            inputs=[
+                io.Custom("POSE_KEYPOINT").Input("keypoints"),
+                io.Float.Input("scale", default=1.5, min=1.0, max=10.0, step=0.1, tooltip="Multiplier for the bounding box area around each detected face."),
+                io.Boolean.Input("force_square", default=True, tooltip="Expand the shorter bbox axis so the crop region is always square."),
+            ],
+            outputs=[
+                io.BoundingBox.Output("bboxes", tooltip="Face bounding boxes per frame, compatible with SDPoseKeypointExtractor bboxes input."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, keypoints, scale, force_square) -> io.NodeOutput:
+        all_bboxes = []
+        for frame in keypoints:
+            h = frame["canvas_height"]
+            w = frame["canvas_width"]
+            frame_bboxes = []
+            for person in frame["people"]:
+                face_flat = person.get("face_keypoints_2d", [])
+                if not face_flat:
+                    continue
+                # Parse absolute-pixel face keypoints (70 kp: 68 landmarks + REye + LEye)
+                face_arr = np.array(face_flat, dtype=np.float32).reshape(-1, 3)
+                face_xy  = face_arr[:, :2]  # (70, 2) in absolute pixels
+
+                kp_norm = face_xy / np.array([w, h], dtype=np.float32)
+                kp_padded = np.vstack([np.zeros((1, 2), dtype=np.float32), kp_norm])  # (71, 2)
+
+                x1, x2, y1, y2 = get_face_bboxes(kp_padded, scale, (h, w))
+                if x2 > x1 and y2 > y1:
+                    if force_square:
+                        bw, bh = x2 - x1, y2 - y1
+                        if bw != bh:
+                            side = max(bw, bh)
+                            cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
+                            half = side // 2
+                            x1 = max(0, cx - half)
+                            y1 = max(0, cy - half)
+                            x2 = min(w, x1 + side)
+                            y2 = min(h, y1 + side)
+                            # Re-anchor if clamped
+                            x1 = max(0, x2 - side)
+                            y1 = max(0, y2 - side)
+                    frame_bboxes.append({"x": x1, "y": y1, "width": x2 - x1, "height": y2 - y1})
+
+            all_bboxes.append(frame_bboxes)
+
+        return io.NodeOutput(all_bboxes)
+
+
+class CropByBBoxes(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CropByBBoxes",
+            category="image/preprocessors",
+            search_aliases=["crop", "face crop", "bbox crop", "pose", "bounding box"],
+            description="Crop and resize regions from the input image batch based on provided bounding boxes.",
+            inputs=[
+                io.Image.Input("image"),
+                io.BoundingBox.Input("bboxes", force_input=True),
+                io.Int.Input("output_width",  default=512, min=64, max=4096, step=8, tooltip="Width each crop is resized to."),
+                io.Int.Input("output_height", default=512, min=64, max=4096, step=8, tooltip="Height each crop is resized to."),
+                io.Int.Input("padding", default=0, min=0, max=1024, step=1, tooltip="Extra padding in pixels added on each side of the bbox before cropping."),
+            ],
+            outputs=[
+                io.Image.Output(tooltip="All crops stacked into a single image batch."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, image, bboxes, output_width, output_height, padding) -> io.NodeOutput:
+        total_frames = image.shape[0]
+        img_h = image.shape[1]
+        img_w = image.shape[2]
+        num_ch = image.shape[3]
+
+        if not isinstance(bboxes, list):
+            bboxes = [[bboxes]]
+        elif len(bboxes) == 0:
+            return io.NodeOutput(image)
+
+        crops = []
+
+        for frame_idx in range(total_frames):
+            frame_bboxes = bboxes[min(frame_idx, len(bboxes) - 1)]
+            if not frame_bboxes:
+                continue
+
+            frame_chw = image[frame_idx].permute(2, 0, 1).unsqueeze(0)  # BHWC → BCHW (1, C, H, W)
+
+            # Union all bboxes for this frame into a single crop region
+            x1 = min(int(b["x"]) for b in frame_bboxes)
+            y1 = min(int(b["y"]) for b in frame_bboxes)
+            x2 = max(int(b["x"] + b["width"])  for b in frame_bboxes)
+            y2 = max(int(b["y"] + b["height"]) for b in frame_bboxes)
+
+            if padding > 0:
+                x1 = max(0, x1 - padding)
+                y1 = max(0, y1 - padding)
+                x2 = min(img_w, x2 + padding)
+                y2 = min(img_h, y2 + padding)
+
+            x1, x2 = max(0, x1), min(img_w, x2)
+            y1, y2 = max(0, y1), min(img_h, y2)
+
+            # Fallback for empty/degenerate crops
+            if x2 <= x1 or y2 <= y1:
+                fallback_size = int(min(img_h, img_w) * 0.3)
+                fb_x1 = max(0, (img_w - fallback_size) // 2)
+                fb_y1 = max(0, int(img_h * 0.1))
+                fb_x2 = min(img_w, fb_x1 + fallback_size)
+                fb_y2 = min(img_h, fb_y1 + fallback_size)
+                if fb_x2 <= fb_x1 or fb_y2 <= fb_y1:
+                    crops.append(torch.zeros(1, num_ch, output_height, output_width, dtype=image.dtype, device=image.device))
+                    continue
+                x1, y1, x2, y2 = fb_x1, fb_y1, fb_x2, fb_y2
+
+            crop_chw = frame_chw[:, :, y1:y2, x1:x2]  # (1, C, crop_h, crop_w)
+            resized = comfy.utils.common_upscale(crop_chw, output_width, output_height, upscale_method="bilinear", crop="disabled")
+            crops.append(resized)
+
+        if not crops:
+            return io.NodeOutput(image)
+
+        out_images = torch.cat(crops, dim=0).permute(0, 2, 3, 1)  # (N, H, W, C)
+        return io.NodeOutput(out_images)
+
+
+class SDPoseExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            SDPoseKeypointExtractor,
+            SDPoseDrawKeypoints,
+            SDPoseFaceBBoxes,
+            CropByBBoxes,
+        ]
+
+async def comfy_entrypoint() -> SDPoseExtension:
+    return SDPoseExtension()
diff --git a/nodes.py b/nodes.py
index bff073e30..0222ec629 100644
--- a/nodes.py
+++ b/nodes.py
@@ -2447,6 +2447,7 @@ async def init_builtin_extra_nodes():
         "nodes_toolkit.py",
         "nodes_replacements.py",
         "nodes_nag.py",
+        "nodes_sdpose.py",
     ]
 
     import_failed = []

From 35e9fce7756604050f07a05d090e697b81322c44 Mon Sep 17 00:00:00 2001
From: vickytsang <vtsang@amd.com>
Date: Thu, 26 Feb 2026 17:16:12 -0800
Subject: [PATCH 066/194] Enable Pytorch Attention for gfx950 (#12641)

---
 comfy/model_management.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 1fe56a62b..f73613f17 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -350,7 +350,7 @@ AMD_ENABLE_MIOPEN_ENV = 'COMFYUI_ENABLE_MIOPEN'
 
 try:
     if is_amd():
-        arch = torch.cuda.get_device_properties(get_torch_device()).gcnArchName
+        arch = torch.cuda.get_device_properties(get_torch_device()).gcnArchName.split(':')[0]
         if not (any((a in arch) for a in AMD_RDNA2_AND_OLDER_ARCH)):
             if os.getenv(AMD_ENABLE_MIOPEN_ENV) != '1':
                 torch.backends.cudnn.enabled = False  # Seems to improve things a lot on AMD
@@ -378,7 +378,7 @@ try:
         if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
             if aotriton_supported(arch):  # AMD efficient attention implementation depends on aotriton.
                 if torch_version_numeric >= (2, 7):  # works on 2.6 but doesn't actually seem to improve much
-                    if any((a in arch) for a in ["gfx90a", "gfx942", "gfx1100", "gfx1101", "gfx1151"]):  # TODO: more arches, TODO: gfx950
+                    if any((a in arch) for a in ["gfx90a", "gfx942", "gfx950", "gfx1100", "gfx1101", "gfx1151"]):  # TODO: more arches, TODO: gfx950
                         ENABLE_PYTORCH_ATTENTION = True
                 if rocm_version >= (7, 0):
                    if any((a in arch) for a in ["gfx1200", "gfx1201"]):

From 0a7f8e11b6f280b1b574f5dd642e0b46b8f0e045 Mon Sep 17 00:00:00 2001
From: pythongosssss <125205205+pythongosssss@users.noreply.github.com>
Date: Fri, 27 Feb 2026 16:13:24 +0000
Subject: [PATCH 067/194] fix torch.cat requiring inputs to all be same
 dimensions (#12673)

---
 comfy_extras/nodes_glsl.py | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/comfy_extras/nodes_glsl.py b/comfy_extras/nodes_glsl.py
index 6d210b307..2a59a9285 100644
--- a/comfy_extras/nodes_glsl.py
+++ b/comfy_extras/nodes_glsl.py
@@ -865,14 +865,15 @@ class GLSLShader(io.ComfyNode):
         cls, image_list: list[torch.Tensor], output_batch: torch.Tensor
     ) -> dict[str, list]:
         """Build UI output with input and output images for client-side shader execution."""
-        combined_inputs = torch.cat(image_list, dim=0)
-        input_images_ui = ui.ImageSaveHelper.save_images(
-            combined_inputs,
-            filename_prefix="GLSLShader_input",
-            folder_type=io.FolderType.temp,
-            cls=None,
-            compress_level=1,
-        )
+        input_images_ui = []
+        for img in image_list:
+            input_images_ui.extend(ui.ImageSaveHelper.save_images(
+                img,
+                filename_prefix="GLSLShader_input",
+                folder_type=io.FolderType.temp,
+                cls=None,
+                compress_level=1,
+            ))
 
         output_images_ui = ui.ImageSaveHelper.save_images(
             output_batch,

From 1f1ec377ce9d4c525d1615099524231756a69e5e Mon Sep 17 00:00:00 2001
From: Christian Byrne <cbyrne@comfy.org>
Date: Fri, 27 Feb 2026 09:13:57 -0800
Subject: [PATCH 068/194] feat: add ResolutionSelector node for aspect ratio
 and megapixel-based resolution calculation (#12199)

Amp-Thread-ID: https://ampcode.com/threads/T-019c179e-cd8c-768f-ae66-207c7a53c01d

Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
---
 comfy_extras/nodes_resolution.py | 82 ++++++++++++++++++++++++++++++++
 nodes.py                         |  1 +
 2 files changed, 83 insertions(+)
 create mode 100644 comfy_extras/nodes_resolution.py

diff --git a/comfy_extras/nodes_resolution.py b/comfy_extras/nodes_resolution.py
new file mode 100644
index 000000000..d94156433
--- /dev/null
+++ b/comfy_extras/nodes_resolution.py
@@ -0,0 +1,82 @@
+from __future__ import annotations
+import math
+from enum import Enum
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
+
+class AspectRatio(str, Enum):
+    SQUARE = "1:1 (Square)"
+    PHOTO_H = "3:2 (Photo)"
+    STANDARD_H = "4:3 (Standard)"
+    WIDESCREEN_H = "16:9 (Widescreen)"
+    ULTRAWIDE_H = "21:9 (Ultrawide)"
+    PHOTO_V = "2:3 (Portrait Photo)"
+    STANDARD_V = "3:4 (Portrait Standard)"
+    WIDESCREEN_V = "9:16 (Portrait Widescreen)"
+
+
+ASPECT_RATIOS: dict[str, tuple[int, int]] = {
+    "1:1 (Square)": (1, 1),
+    "3:2 (Photo)": (3, 2),
+    "4:3 (Standard)": (4, 3),
+    "16:9 (Widescreen)": (16, 9),
+    "21:9 (Ultrawide)": (21, 9),
+    "2:3 (Portrait Photo)": (2, 3),
+    "3:4 (Portrait Standard)": (3, 4),
+    "9:16 (Portrait Widescreen)": (9, 16),
+}
+
+
+class ResolutionSelector(io.ComfyNode):
+    """Calculate width and height from aspect ratio and megapixel target."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ResolutionSelector",
+            display_name="Resolution Selector",
+            category="utils",
+            description="Calculate width and height from aspect ratio and megapixel target. Useful for setting up Empty Latent Image dimensions.",
+            inputs=[
+                io.Combo.Input(
+                    "aspect_ratio",
+                    options=AspectRatio,
+                    default=AspectRatio.SQUARE,
+                    tooltip="The aspect ratio for the output dimensions.",
+                ),
+                io.Float.Input(
+                    "megapixels",
+                    default=1.0,
+                    min=0.1,
+                    max=16.0,
+                    step=0.1,
+                    tooltip="Target total megapixels. 1.0 MP ≈ 1024×1024 for square.",
+                ),
+            ],
+            outputs=[
+                io.Int.Output("width", tooltip="Calculated width in pixels (multiple of 8)."),
+                io.Int.Output("height", tooltip="Calculated height in pixels (multiple of 8)."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, aspect_ratio: str, megapixels: float) -> io.NodeOutput:
+        w_ratio, h_ratio = ASPECT_RATIOS[aspect_ratio]
+        total_pixels = megapixels * 1024 * 1024
+        scale = math.sqrt(total_pixels / (w_ratio * h_ratio))
+        width = round(w_ratio * scale / 8) * 8
+        height = round(h_ratio * scale / 8) * 8
+        return io.NodeOutput(width, height)
+
+
+class ResolutionExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            ResolutionSelector,
+        ]
+
+
+async def comfy_entrypoint() -> ResolutionExtension:
+    return ResolutionExtension()
diff --git a/nodes.py b/nodes.py
index 0222ec629..bf6ce5736 100644
--- a/nodes.py
+++ b/nodes.py
@@ -2435,6 +2435,7 @@ async def init_builtin_extra_nodes():
         "nodes_audio_encoder.py",
         "nodes_rope.py",
         "nodes_logic.py",
+        "nodes_resolution.py",
         "nodes_nop.py",
         "nodes_kandinsky5.py",
         "nodes_wanmove.py",

From 25ec3d96a323c8455c6ee69e43bdd7a5599d3cc0 Mon Sep 17 00:00:00 2001
From: "Reiner \"Tiles\" Prokein" <ReinerBforartists@users.noreply.github.com>
Date: Sat, 28 Feb 2026 01:03:45 +0100
Subject: [PATCH 069/194] Class WanVAE, def encode, feat_map is using
 self.decoder instead of self.encoder (#12682)

---
 comfy/ldm/wan/vae.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/ldm/wan/vae.py b/comfy/ldm/wan/vae.py
index 7903c7690..71f73c64e 100644
--- a/comfy/ldm/wan/vae.py
+++ b/comfy/ldm/wan/vae.py
@@ -485,7 +485,7 @@ class WanVAE(nn.Module):
         iter_ = 1 + (t - 1) // 4
         feat_map = None
         if iter_ > 1:
-            feat_map = [None] * count_conv3d(self.decoder)
+            feat_map = [None] * count_conv3d(self.encoder)
         ## 对encode输入的x，按时间拆分为1、4、4、4....
         for i in range(iter_):
             conv_idx = [0]

From e721e24136b5480c396bf0e37a114f6e4083482b Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Fri, 27 Feb 2026 16:05:51 -0800
Subject: [PATCH 070/194] ops: implement lora requanting for non
 QuantizedTensor fp8 (#12668)

Allow non QuantizedTensor layer to set want_requant to get the post lora
calculation stochastic cast down to the original input dtype.

This is then used by the legacy fp8 Linear implementation to set the
compute_dtype to the preferred lora dtype but then want_requant it back
down to fp8.

This fixes the issue with --fast fp8_matrix_mult is combined with
--fast dynamic_vram which doing a lora on an fp8_ non QT model.
---
 comfy/ops.py | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/comfy/ops.py b/comfy/ops.py
index 98fec1e1d..6ee6075fb 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -167,17 +167,15 @@ def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compu
             x = to_dequant(x, dtype)
         if not resident and lowvram_fn is not None:
             x = to_dequant(x, dtype if compute_dtype is None else compute_dtype)
-            #FIXME: this is not accurate, we need to be sensitive to the compute dtype
             x = lowvram_fn(x)
-            if (isinstance(orig, QuantizedTensor) and
-                (want_requant and len(fns) == 0 or update_weight)):
+            if (want_requant and len(fns) == 0 or update_weight):
                 seed = comfy.utils.string_to_seed(s.seed_key)
-                y = QuantizedTensor.from_float(x, s.layout_type, scale="recalculate", stochastic_rounding=seed)
-                if want_requant and len(fns) == 0:
-                    #The layer actually wants our freshly saved QT
-                    x = y
-            elif update_weight:
-                y = comfy.float.stochastic_rounding(x, orig.dtype, seed = comfy.utils.string_to_seed(s.seed_key))
+                if isinstance(orig, QuantizedTensor):
+                    y = QuantizedTensor.from_float(x, s.layout_type, scale="recalculate", stochastic_rounding=seed)
+                else:
+                    y = comfy.float.stochastic_rounding(x, orig.dtype, seed=seed)
+            if want_requant and len(fns) == 0:
+                x = y
             if update_weight:
                 orig.copy_(y)
         for f in fns:
@@ -617,7 +615,8 @@ def fp8_linear(self, input):
 
     if input.ndim != 2:
         return None
-    w, bias, offload_stream = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype, offloadable=True)
+    lora_compute_dtype=comfy.model_management.lora_compute_dtype(input.device)
+    w, bias, offload_stream = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype, offloadable=True, compute_dtype=lora_compute_dtype, want_requant=True)
     scale_weight = torch.ones((), device=input.device, dtype=torch.float32)
 
     scale_input = torch.ones((), device=input.device, dtype=torch.float32)

From 94f1a1cc9df69cbc75fe6d0f78a4de5d1d857d9d Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Fri, 27 Feb 2026 17:16:24 -0800
Subject: [PATCH 071/194] Limit overlap in image tile and combine nodes to
 prevent issues. (#12688)

---
 comfy_extras/nodes_images.py | 34 +++-------------------------------
 1 file changed, 3 insertions(+), 31 deletions(-)

diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py
index 727d7d09d..4c57bb5cb 100644
--- a/comfy_extras/nodes_images.py
+++ b/comfy_extras/nodes_images.py
@@ -706,8 +706,8 @@ class SplitImageToTileList(IO.ComfyNode):
     @staticmethod
     def get_grid_coords(width, height, tile_width, tile_height, overlap):
         coords = []
-        stride_x = max(1, tile_width - overlap)
-        stride_y = max(1, tile_height - overlap)
+        stride_x = round(max(tile_width * 0.25, tile_width - overlap))
+        stride_y = round(max(tile_width * 0.25, tile_height - overlap))
 
         y = 0
         while y < height:
@@ -764,34 +764,6 @@ class ImageMergeTileList(IO.ComfyNode):
             ],
         )
 
-    @staticmethod
-    def get_grid_coords(width, height, tile_width, tile_height, overlap):
-        coords = []
-        stride_x = max(1, tile_width - overlap)
-        stride_y = max(1, tile_height - overlap)
-
-        y = 0
-        while y < height:
-            x = 0
-            y_end = min(y + tile_height, height)
-            y_start = max(0, y_end - tile_height)
-
-            while x < width:
-                x_end = min(x + tile_width, width)
-                x_start = max(0, x_end - tile_width)
-
-                coords.append((x_start, y_start, x_end, y_end))
-
-                if x_end >= width:
-                    break
-                x += stride_x
-
-            if y_end >= height:
-                break
-            y += stride_y
-
-        return coords
-
     @classmethod
     def execute(cls, image_list, final_width, final_height, overlap):
         w = final_width[0]
@@ -804,7 +776,7 @@ class ImageMergeTileList(IO.ComfyNode):
         device = first_tile.device
         dtype = first_tile.dtype
 
-        coords = cls.get_grid_coords(w, h, t_w, t_h, ovlp)
+        coords = SplitImageToTileList.get_grid_coords(w, h, t_w, t_h, ovlp)
 
         canvas = torch.zeros((b, h, w, c), device=device, dtype=dtype)
         weights = torch.zeros((b, h, w, 1), device=device, dtype=dtype)

From ac4412d0fa2b9df8469fb6018e0036c47332397a Mon Sep 17 00:00:00 2001
From: Talmaj <Talmaj@users.noreply.github.com>
Date: Sat, 28 Feb 2026 05:04:34 +0100
Subject: [PATCH 072/194] Native LongCat-Image implementation (#12597)

---
 comfy/model_base.py                           |  19 ++
 comfy/model_detection.py                      |   2 +
 comfy/sd.py                                   |   5 +
 comfy/supported_models.py                     |  34 +++-
 comfy/text_encoders/longcat_image.py          | 184 ++++++++++++++++++
 nodes.py                                      |   2 +-
 tests-unit/comfy_test/model_detection_test.py | 112 +++++++++++
 7 files changed, 356 insertions(+), 2 deletions(-)
 create mode 100644 comfy/text_encoders/longcat_image.py
 create mode 100644 tests-unit/comfy_test/model_detection_test.py

diff --git a/comfy/model_base.py b/comfy/model_base.py
index 8f852e3c6..85cd30bae 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -925,6 +925,25 @@ class Flux(BaseModel):
             out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()[2:]), ref_latents))])
         return out
 
+class LongCatImage(Flux):
+    def _apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):
+        transformer_options = transformer_options.copy()
+        rope_opts = transformer_options.get("rope_options", {})
+        rope_opts = dict(rope_opts)
+        rope_opts.setdefault("shift_t", 1.0)
+        rope_opts.setdefault("shift_y", 512.0)
+        rope_opts.setdefault("shift_x", 512.0)
+        transformer_options["rope_options"] = rope_opts
+        return super()._apply_model(x, t, c_concat, c_crossattn, control, transformer_options, **kwargs)
+
+    def encode_adm(self, **kwargs):
+        return None
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        out.pop('guidance', None)
+        return out
+
 class Flux2(Flux):
     def extra_conds(self, **kwargs):
         out = super().extra_conds(**kwargs)
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index b4b51b200..8a1d8ea4d 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -279,6 +279,8 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             dit_config["txt_norm"] = any_suffix_in(state_dict_keys, key_prefix, 'txt_norm.', ["weight", "scale"])
             if dit_config["yak_mlp"] and dit_config["txt_norm"]:  # Ovis model
                 dit_config["txt_ids_dims"] = [1, 2]
+            if dit_config.get("context_in_dim") == 3584 and dit_config["vec_in_dim"] is None:  # LongCat-Image
+                dit_config["txt_ids_dims"] = [1, 2]
 
         return dit_config
 
diff --git a/comfy/sd.py b/comfy/sd.py
index de119eb8e..7713d4678 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -60,6 +60,7 @@ import comfy.text_encoders.jina_clip_2
 import comfy.text_encoders.newbie
 import comfy.text_encoders.anima
 import comfy.text_encoders.ace15
+import comfy.text_encoders.longcat_image
 
 import comfy.model_patcher
 import comfy.lora
@@ -1160,6 +1161,7 @@ class CLIPType(Enum):
     KANDINSKY5_IMAGE = 23
     NEWBIE = 24
     FLUX2 = 25
+    LONGCAT_IMAGE = 26
 
 
 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
@@ -1372,6 +1374,9 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             if clip_type == CLIPType.HUNYUAN_IMAGE:
                 clip_target.clip = comfy.text_encoders.hunyuan_image.te(byt5=False, **llama_detect(clip_data))
                 clip_target.tokenizer = comfy.text_encoders.hunyuan_image.HunyuanImageTokenizer
+            elif clip_type == CLIPType.LONGCAT_IMAGE:
+                clip_target.clip = comfy.text_encoders.longcat_image.te(**llama_detect(clip_data))
+                clip_target.tokenizer = comfy.text_encoders.longcat_image.LongCatImageTokenizer
             else:
                 clip_target.clip = comfy.text_encoders.qwen_image.te(**llama_detect(clip_data))
                 clip_target.tokenizer = comfy.text_encoders.qwen_image.QwenImageTokenizer
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index 1bb7b7011..473fbbfd4 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -25,6 +25,7 @@ import comfy.text_encoders.kandinsky5
 import comfy.text_encoders.z_image
 import comfy.text_encoders.anima
 import comfy.text_encoders.ace15
+import comfy.text_encoders.longcat_image
 
 from . import supported_models_base
 from . import latent_formats
@@ -1678,6 +1679,37 @@ class ACEStep15(supported_models_base.BASE):
         return supported_models_base.ClipTarget(comfy.text_encoders.ace15.ACE15Tokenizer, comfy.text_encoders.ace15.te(**detect))
 
 
-models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima]
+class LongCatImage(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "flux",
+        "guidance_embed": False,
+        "vec_in_dim": None,
+        "context_in_dim": 3584,
+        "txt_ids_dims": [1, 2],
+    }
+
+    sampling_settings = {
+    }
+
+    unet_extra_config = {}
+    latent_format = latent_formats.Flux
+
+    memory_usage_factor = 2.5
+
+    supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.LongCatImage(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        pref = self.text_encoder_key_prefix[0]
+        hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref))
+        return supported_models_base.ClipTarget(comfy.text_encoders.longcat_image.LongCatImageTokenizer, comfy.text_encoders.longcat_image.te(**hunyuan_detect))
+
+models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima]
 
 models += [SVD_img2vid]
diff --git a/comfy/text_encoders/longcat_image.py b/comfy/text_encoders/longcat_image.py
new file mode 100644
index 000000000..882d80901
--- /dev/null
+++ b/comfy/text_encoders/longcat_image.py
@@ -0,0 +1,184 @@
+import re
+import numbers
+import torch
+from comfy import sd1_clip
+from comfy.text_encoders.qwen_image import Qwen25_7BVLITokenizer, Qwen25_7BVLIModel
+import logging
+
+logger = logging.getLogger(__name__)
+
+QUOTE_PAIRS = [("'", "'"), ('"', '"'), ("\u2018", "\u2019"), ("\u201c", "\u201d")]
+QUOTE_PATTERN = "|".join(
+    [
+        re.escape(q1) + r"[^" + re.escape(q1 + q2) + r"]*?" + re.escape(q2)
+        for q1, q2 in QUOTE_PAIRS
+    ]
+)
+WORD_INTERNAL_QUOTE_RE = re.compile(r"[a-zA-Z]+'[a-zA-Z]+")
+
+
+def split_quotation(prompt):
+    matches = WORD_INTERNAL_QUOTE_RE.findall(prompt)
+    mapping = []
+    for i, word_src in enumerate(set(matches)):
+        word_tgt = "longcat_$##$_longcat" * (i + 1)
+        prompt = prompt.replace(word_src, word_tgt)
+        mapping.append((word_src, word_tgt))
+
+    parts = re.split(f"({QUOTE_PATTERN})", prompt)
+    result = []
+    for part in parts:
+        for word_src, word_tgt in mapping:
+            part = part.replace(word_tgt, word_src)
+        if not part:
+            continue
+        is_quoted = bool(re.match(QUOTE_PATTERN, part))
+        result.append((part, is_quoted))
+    return result
+
+
+class LongCatImageBaseTokenizer(Qwen25_7BVLITokenizer):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.max_length = 512
+
+    def tokenize_with_weights(self, text, return_word_ids=False, **kwargs):
+        parts = split_quotation(text)
+        all_tokens = []
+        for part_text, is_quoted in parts:
+            if is_quoted:
+                for char in part_text:
+                    ids = self.tokenizer(char, add_special_tokens=False)["input_ids"]
+                    all_tokens.extend(ids)
+            else:
+                ids = self.tokenizer(part_text, add_special_tokens=False)["input_ids"]
+                all_tokens.extend(ids)
+
+        if len(all_tokens) > self.max_length:
+            all_tokens = all_tokens[: self.max_length]
+            logger.warning(f"Truncated prompt to {self.max_length} tokens")
+
+        output = [(t, 1.0) for t in all_tokens]
+        # Pad to max length
+        self.pad_tokens(output, self.max_length - len(output))
+        return [output]
+
+
+class LongCatImageTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(
+            embedding_directory=embedding_directory,
+            tokenizer_data=tokenizer_data,
+            name="qwen25_7b",
+            tokenizer=LongCatImageBaseTokenizer,
+        )
+        self.longcat_template_prefix = "<|im_start|>system\nAs an image captioning expert, generate a descriptive text prompt based on an image content, suitable for input to a text-to-image model.<|im_end|>\n<|im_start|>user\n"
+        self.longcat_template_suffix = "<|im_end|>\n<|im_start|>assistant\n"
+
+    def tokenize_with_weights(self, text, return_word_ids=False, **kwargs):
+        skip_template = False
+        if text.startswith("<|im_start|>"):
+            skip_template = True
+        if text.startswith("<|start_header_id|>"):
+            skip_template = True
+        if text == "":
+            text = " "
+
+        base_tok = getattr(self, "qwen25_7b")
+        if skip_template:
+            tokens = super().tokenize_with_weights(
+                text, return_word_ids=return_word_ids, disable_weights=True, **kwargs
+            )
+        else:
+            prefix_ids = base_tok.tokenizer(
+                self.longcat_template_prefix, add_special_tokens=False
+            )["input_ids"]
+            suffix_ids = base_tok.tokenizer(
+                self.longcat_template_suffix, add_special_tokens=False
+            )["input_ids"]
+
+            prompt_tokens = base_tok.tokenize_with_weights(
+                text, return_word_ids=return_word_ids, **kwargs
+            )
+            prompt_pairs = prompt_tokens[0]
+
+            prefix_pairs = [(t, 1.0) for t in prefix_ids]
+            suffix_pairs = [(t, 1.0) for t in suffix_ids]
+
+            combined = prefix_pairs + prompt_pairs + suffix_pairs
+            tokens = {"qwen25_7b": [combined]}
+
+        return tokens
+
+
+class LongCatImageTEModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(
+            device=device,
+            dtype=dtype,
+            name="qwen25_7b",
+            clip_model=Qwen25_7BVLIModel,
+            model_options=model_options,
+        )
+
+    def encode_token_weights(self, token_weight_pairs, template_end=-1):
+        out, pooled, extra = super().encode_token_weights(token_weight_pairs)
+        tok_pairs = token_weight_pairs["qwen25_7b"][0]
+        count_im_start = 0
+        if template_end == -1:
+            for i, v in enumerate(tok_pairs):
+                elem = v[0]
+                if not torch.is_tensor(elem):
+                    if isinstance(elem, numbers.Integral):
+                        if elem == 151644 and count_im_start < 2:
+                            template_end = i
+                            count_im_start += 1
+
+        if out.shape[1] > (template_end + 3):
+            if tok_pairs[template_end + 1][0] == 872:
+                if tok_pairs[template_end + 2][0] == 198:
+                    template_end += 3
+
+        if template_end == -1:
+            template_end = 0
+
+        suffix_start = None
+        for i in range(len(tok_pairs) - 1, -1, -1):
+            elem = tok_pairs[i][0]
+            if not torch.is_tensor(elem) and isinstance(elem, numbers.Integral):
+                if elem == 151645:
+                    suffix_start = i
+                    break
+
+        out = out[:, template_end:]
+
+        if "attention_mask" in extra:
+            extra["attention_mask"] = extra["attention_mask"][:, template_end:]
+            if extra["attention_mask"].sum() == torch.numel(extra["attention_mask"]):
+                extra.pop("attention_mask")
+
+        if suffix_start is not None:
+            suffix_len = len(tok_pairs) - suffix_start
+            if suffix_len > 0 and out.shape[1] > suffix_len:
+                out = out[:, :-suffix_len]
+                if "attention_mask" in extra:
+                    extra["attention_mask"] = extra["attention_mask"][:, :-suffix_len]
+                    if extra["attention_mask"].sum() == torch.numel(
+                        extra["attention_mask"]
+                    ):
+                        extra.pop("attention_mask")
+
+        return out, pooled, extra
+
+
+def te(dtype_llama=None, llama_quantization_metadata=None):
+    class LongCatImageTEModel_(LongCatImageTEModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if llama_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["quantization_metadata"] = llama_quantization_metadata
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+
+    return LongCatImageTEModel_
diff --git a/nodes.py b/nodes.py
index bf6ce5736..5be9b16f9 100644
--- a/nodes.py
+++ b/nodes.py
@@ -976,7 +976,7 @@ class CLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ),
-                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image", "flux2", "ovis"], ),
+                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image", "flux2", "ovis", "longcat_image"], ),
                               },
                 "optional": {
                               "device": (["default", "cpu"], {"advanced": True}),
diff --git a/tests-unit/comfy_test/model_detection_test.py b/tests-unit/comfy_test/model_detection_test.py
new file mode 100644
index 000000000..2551a417b
--- /dev/null
+++ b/tests-unit/comfy_test/model_detection_test.py
@@ -0,0 +1,112 @@
+import torch
+
+from comfy.model_detection import detect_unet_config, model_config_from_unet_config
+import comfy.supported_models
+
+
+def _make_longcat_comfyui_sd():
+    """Minimal ComfyUI-format state dict for pre-converted LongCat-Image weights."""
+    sd = {}
+    H = 32  # Reduce hidden state dimension to reduce memory usage
+    C_IN = 16
+    C_CTX = 3584
+
+    sd["img_in.weight"] = torch.empty(H, C_IN * 4)
+    sd["img_in.bias"] = torch.empty(H)
+    sd["txt_in.weight"] = torch.empty(H, C_CTX)
+    sd["txt_in.bias"] = torch.empty(H)
+
+    sd["time_in.in_layer.weight"] = torch.empty(H, 256)
+    sd["time_in.in_layer.bias"] = torch.empty(H)
+    sd["time_in.out_layer.weight"] = torch.empty(H, H)
+    sd["time_in.out_layer.bias"] = torch.empty(H)
+
+    sd["final_layer.adaLN_modulation.1.weight"] = torch.empty(2 * H, H)
+    sd["final_layer.adaLN_modulation.1.bias"] = torch.empty(2 * H)
+    sd["final_layer.linear.weight"] = torch.empty(C_IN * 4, H)
+    sd["final_layer.linear.bias"] = torch.empty(C_IN * 4)
+
+    for i in range(19):
+        sd[f"double_blocks.{i}.img_attn.norm.key_norm.weight"] = torch.empty(128)
+        sd[f"double_blocks.{i}.img_attn.qkv.weight"] = torch.empty(3 * H, H)
+        sd[f"double_blocks.{i}.img_mod.lin.weight"] = torch.empty(H, H)
+    for i in range(38):
+        sd[f"single_blocks.{i}.modulation.lin.weight"] = torch.empty(H, H)
+
+    return sd
+
+
+def _make_flux_schnell_comfyui_sd():
+    """Minimal ComfyUI-format state dict for standard Flux Schnell."""
+    sd = {}
+    H = 32  # Reduce hidden state dimension to reduce memory usage
+    C_IN = 16
+
+    sd["img_in.weight"] = torch.empty(H, C_IN * 4)
+    sd["img_in.bias"] = torch.empty(H)
+    sd["txt_in.weight"] = torch.empty(H, 4096)
+    sd["txt_in.bias"] = torch.empty(H)
+
+    sd["double_blocks.0.img_attn.norm.key_norm.weight"] = torch.empty(128)
+    sd["double_blocks.0.img_attn.qkv.weight"] = torch.empty(3 * H, H)
+    sd["double_blocks.0.img_mod.lin.weight"] = torch.empty(H, H)
+
+    for i in range(19):
+        sd[f"double_blocks.{i}.img_attn.norm.key_norm.weight"] = torch.empty(128)
+    for i in range(38):
+        sd[f"single_blocks.{i}.modulation.lin.weight"] = torch.empty(H, H)
+
+    return sd
+
+
+class TestModelDetection:
+    """Verify that first-match model detection selects the correct model
+    based on list ordering and unet_config specificity."""
+
+    def test_longcat_before_schnell_in_models_list(self):
+        """LongCatImage must appear before FluxSchnell in the models list."""
+        models = comfy.supported_models.models
+        longcat_idx = next(i for i, m in enumerate(models) if m.__name__ == "LongCatImage")
+        schnell_idx = next(i for i, m in enumerate(models) if m.__name__ == "FluxSchnell")
+        assert longcat_idx < schnell_idx, (
+            f"LongCatImage (index {longcat_idx}) must come before "
+            f"FluxSchnell (index {schnell_idx}) in the models list"
+        )
+
+    def test_longcat_comfyui_detected_as_longcat(self):
+        sd = _make_longcat_comfyui_sd()
+        unet_config = detect_unet_config(sd, "")
+        assert unet_config is not None
+        assert unet_config["image_model"] == "flux"
+        assert unet_config["context_in_dim"] == 3584
+        assert unet_config["vec_in_dim"] is None
+        assert unet_config["guidance_embed"] is False
+        assert unet_config["txt_ids_dims"] == [1, 2]
+
+        model_config = model_config_from_unet_config(unet_config, sd)
+        assert model_config is not None
+        assert type(model_config).__name__ == "LongCatImage"
+
+    def test_longcat_comfyui_keys_pass_through_unchanged(self):
+        """Pre-converted weights should not be transformed by process_unet_state_dict."""
+        sd = _make_longcat_comfyui_sd()
+        unet_config = detect_unet_config(sd, "")
+        model_config = model_config_from_unet_config(unet_config, sd)
+
+        processed = model_config.process_unet_state_dict(dict(sd))
+        assert "img_in.weight" in processed
+        assert "txt_in.weight" in processed
+        assert "time_in.in_layer.weight" in processed
+        assert "final_layer.linear.weight" in processed
+
+    def test_flux_schnell_comfyui_detected_as_flux_schnell(self):
+        sd = _make_flux_schnell_comfyui_sd()
+        unet_config = detect_unet_config(sd, "")
+        assert unet_config is not None
+        assert unet_config["image_model"] == "flux"
+        assert unet_config["context_in_dim"] == 4096
+        assert unet_config["txt_ids_dims"] == []
+
+        model_config = model_config_from_unet_config(unet_config, sd)
+        assert model_config is not None
+        assert type(model_config).__name__ == "FluxSchnell"

From 9d0e114ee380d3eac8aeb00260a9df1212b6046a Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Fri, 27 Feb 2026 20:34:58 -0800
Subject: [PATCH 073/194] PyOpenGL-accelerate is not necessary. (#12692)

---
 requirements.txt | 1 -
 1 file changed, 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index b5b292980..1b2bd0ae6 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -31,5 +31,4 @@ spandrel
 pydantic~=2.0
 pydantic-settings~=2.0
 PyOpenGL
-PyOpenGL-accelerate
 glfw

From 80d49441e5e255f8d91d2f335f930e74ba85cbe8 Mon Sep 17 00:00:00 2001
From: Christian Byrne <cbyrne@comfy.org>
Date: Fri, 27 Feb 2026 20:53:46 -0800
Subject: [PATCH 074/194] refactor: use AspectRatio enum members as
 ASPECT_RATIOS dict keys (#12689)

Amp-Thread-ID: https://ampcode.com/threads/T-019ca1cb-0150-7549-8b1b-6713060d3408

Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
---
 comfy_extras/nodes_resolution.py | 26 +++++++++++++++-----------
 1 file changed, 15 insertions(+), 11 deletions(-)

diff --git a/comfy_extras/nodes_resolution.py b/comfy_extras/nodes_resolution.py
index d94156433..520b4067e 100644
--- a/comfy_extras/nodes_resolution.py
+++ b/comfy_extras/nodes_resolution.py
@@ -16,15 +16,15 @@ class AspectRatio(str, Enum):
     WIDESCREEN_V = "9:16 (Portrait Widescreen)"
 
 
-ASPECT_RATIOS: dict[str, tuple[int, int]] = {
-    "1:1 (Square)": (1, 1),
-    "3:2 (Photo)": (3, 2),
-    "4:3 (Standard)": (4, 3),
-    "16:9 (Widescreen)": (16, 9),
-    "21:9 (Ultrawide)": (21, 9),
-    "2:3 (Portrait Photo)": (2, 3),
-    "3:4 (Portrait Standard)": (3, 4),
-    "9:16 (Portrait Widescreen)": (9, 16),
+ASPECT_RATIOS: dict[AspectRatio, tuple[int, int]] = {
+    AspectRatio.SQUARE: (1, 1),
+    AspectRatio.PHOTO_H: (3, 2),
+    AspectRatio.STANDARD_H: (4, 3),
+    AspectRatio.WIDESCREEN_H: (16, 9),
+    AspectRatio.ULTRAWIDE_H: (21, 9),
+    AspectRatio.PHOTO_V: (2, 3),
+    AspectRatio.STANDARD_V: (3, 4),
+    AspectRatio.WIDESCREEN_V: (9, 16),
 }
 
 
@@ -55,8 +55,12 @@ class ResolutionSelector(io.ComfyNode):
                 ),
             ],
             outputs=[
-                io.Int.Output("width", tooltip="Calculated width in pixels (multiple of 8)."),
-                io.Int.Output("height", tooltip="Calculated height in pixels (multiple of 8)."),
+                io.Int.Output(
+                    "width", tooltip="Calculated width in pixels (multiple of 8)."
+                ),
+                io.Int.Output(
+                    "height", tooltip="Calculated height in pixels (multiple of 8)."
+                ),
             ],
         )
 

From 95e1059661f7a1584b5f84a6ece72ed8d8992b73 Mon Sep 17 00:00:00 2001
From: fappaz <fernando.augusto.paz@gmail.com>
Date: Sat, 28 Feb 2026 19:18:40 +1300
Subject: [PATCH 075/194] fix(ace15): handle missing lm_metadata in memory
 estimation during checkpoint export #12669 (#12686)

---
 comfy/text_encoders/ace15.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfy/text_encoders/ace15.py b/comfy/text_encoders/ace15.py
index f135d74c1..853f021ae 100644
--- a/comfy/text_encoders/ace15.py
+++ b/comfy/text_encoders/ace15.py
@@ -328,14 +328,14 @@ class ACE15TEModel(torch.nn.Module):
                 return getattr(self, self.lm_model).load_sd(sd)
 
     def memory_estimation_function(self, token_weight_pairs, device=None):
-        lm_metadata = token_weight_pairs["lm_metadata"]
+        lm_metadata = token_weight_pairs.get("lm_metadata", {})
         constant = self.constant
         if comfy.model_management.should_use_bf16(device):
             constant *= 0.5
 
         token_weight_pairs = token_weight_pairs.get("lm_prompt", [])
         num_tokens = sum(map(lambda a: len(a), token_weight_pairs))
-        num_tokens += lm_metadata['min_tokens']
+        num_tokens += lm_metadata.get("min_tokens", 0)
         return num_tokens * constant * 1024 * 1024
 
 def te(dtype_llama=None, llama_quantization_metadata=None, lm_model="qwen3_2b"):

From 1f6744162f606cce895f2d9818207ddecbce5932 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?=
 <40791699+kijai@users.noreply.github.com>
Date: Sat, 28 Feb 2026 23:49:12 +0200
Subject: [PATCH 076/194] feat: Support SCAIL WanVideo model (#12614)

---
 comfy/ldm/wan/model.py    | 115 ++++++++++++++++++++++++++++++++++++++
 comfy/model_base.py       |  38 +++++++++++++
 comfy/model_detection.py  |   2 +
 comfy/supported_models.py |  12 +++-
 comfy_extras/nodes_wan.py |  58 +++++++++++++++++++
 node_helpers.py           |  31 ++++++++++
 6 files changed, 255 insertions(+), 1 deletion(-)

diff --git a/comfy/ldm/wan/model.py b/comfy/ldm/wan/model.py
index ea123acb4..b2287dba9 100644
--- a/comfy/ldm/wan/model.py
+++ b/comfy/ldm/wan/model.py
@@ -1621,3 +1621,118 @@ class HumoWanModel(WanModel):
         # unpatchify
         x = self.unpatchify(x, grid_sizes)
         return x
+
+class SCAILWanModel(WanModel):
+    def __init__(self, model_type="scail", patch_size=(1, 2, 2), in_dim=20, dim=5120, operations=None, device=None, dtype=None, **kwargs):
+        super().__init__(model_type='i2v', patch_size=patch_size, in_dim=in_dim, dim=dim, operations=operations, device=device, dtype=dtype, **kwargs)
+
+        self.patch_embedding_pose = operations.Conv3d(in_dim, dim, kernel_size=patch_size, stride=patch_size, device=device, dtype=torch.float32)
+
+    def forward_orig(self, x, t, context, clip_fea=None, freqs=None, transformer_options={}, pose_latents=None, reference_latent=None, **kwargs):
+
+        if reference_latent is not None:
+            x = torch.cat((reference_latent, x), dim=2)
+
+        # embeddings
+        x = self.patch_embedding(x.float()).to(x.dtype)
+        grid_sizes = x.shape[2:]
+        transformer_options["grid_sizes"] = grid_sizes
+        x = x.flatten(2).transpose(1, 2)
+
+        scail_pose_seq_len = 0
+        if pose_latents is not None:
+            scail_x = self.patch_embedding_pose(pose_latents.float()).to(x.dtype)
+            scail_x = scail_x.flatten(2).transpose(1, 2)
+            scail_pose_seq_len = scail_x.shape[1]
+            x = torch.cat([x, scail_x], dim=1)
+            del scail_x
+
+        # time embeddings
+        e = self.time_embedding(sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype))
+        e = e.reshape(t.shape[0], -1, e.shape[-1])
+        e0 = self.time_projection(e).unflatten(2, (6, self.dim))
+
+        # context
+        context = self.text_embedding(context)
+
+        context_img_len = None
+        if clip_fea is not None:
+            if self.img_emb is not None:
+                context_clip = self.img_emb(clip_fea)  # bs x 257 x dim
+                context = torch.cat([context_clip, context], dim=1)
+            context_img_len = clip_fea.shape[-2]
+
+        patches_replace = transformer_options.get("patches_replace", {})
+        blocks_replace = patches_replace.get("dit", {})
+        transformer_options["total_blocks"] = len(self.blocks)
+        transformer_options["block_type"] = "double"
+        for i, block in enumerate(self.blocks):
+            transformer_options["block_index"] = i
+            if ("double_block", i) in blocks_replace:
+                def block_wrap(args):
+                    out = {}
+                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"])
+                    return out
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                x = out["img"]
+            else:
+                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)
+
+        # head
+        x = self.head(x, e)
+
+        if scail_pose_seq_len > 0:
+            x = x[:, :-scail_pose_seq_len]
+
+        # unpatchify
+        x = self.unpatchify(x, grid_sizes)
+
+        if reference_latent is not None:
+            x = x[:, :, reference_latent.shape[2]:]
+
+        return x
+
+    def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, pose_latents=None, reference_latent=None, transformer_options={}):
+        main_freqs = super().rope_encode(t, h, w, t_start=t_start, steps_t=steps_t, steps_h=steps_h, steps_w=steps_w, device=device, dtype=dtype, transformer_options=transformer_options)
+
+        if pose_latents is None:
+            return main_freqs
+
+        ref_t_patches = 0
+        if reference_latent is not None:
+            ref_t_patches = (reference_latent.shape[2] + (self.patch_size[0] // 2)) // self.patch_size[0]
+
+        F_pose, H_pose, W_pose = pose_latents.shape[-3], pose_latents.shape[-2], pose_latents.shape[-1]
+
+        # if pose is at half resolution, scale_y/scale_x=2 stretches the position range to cover the same RoPE extent as the main frames
+        h_scale = h / H_pose
+        w_scale = w / W_pose
+
+        # 120 w-offset and shift 0.5 to place positions at midpoints (0.5, 2.5, ...) to match the original code
+        h_shift = (h_scale - 1) / 2
+        w_shift = (w_scale - 1) / 2
+        pose_transformer_options = {"rope_options": {"shift_y": h_shift, "shift_x": 120.0 + w_shift, "scale_y": h_scale, "scale_x": w_scale}}
+        pose_freqs = super().rope_encode(F_pose, H_pose, W_pose, t_start=t_start+ref_t_patches, device=device, dtype=dtype, transformer_options=pose_transformer_options)
+
+        return torch.cat([main_freqs, pose_freqs], dim=1)
+
+    def _forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, pose_latents=None, **kwargs):
+        bs, c, t, h, w = x.shape
+        x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)
+
+        if pose_latents is not None:
+            pose_latents = comfy.ldm.common_dit.pad_to_patch_size(pose_latents, self.patch_size)
+
+        t_len = t
+        if time_dim_concat is not None:
+            time_dim_concat = comfy.ldm.common_dit.pad_to_patch_size(time_dim_concat, self.patch_size)
+            x = torch.cat([x, time_dim_concat], dim=2)
+            t_len = x.shape[2]
+
+        reference_latent = None
+        if "reference_latent" in kwargs:
+            reference_latent = comfy.ldm.common_dit.pad_to_patch_size(kwargs.pop("reference_latent"), self.patch_size)
+            t_len += reference_latent.shape[2]
+
+        freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options, pose_latents=pose_latents, reference_latent=reference_latent)
+        return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs, transformer_options=transformer_options, pose_latents=pose_latents, reference_latent=reference_latent, **kwargs)[:, :, :t, :h, :w]
diff --git a/comfy/model_base.py b/comfy/model_base.py
index 85cd30bae..a1c690b9b 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -1502,6 +1502,44 @@ class WAN21_FlowRVS(WAN21):
         super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel)
         self.image_to_video = image_to_video
 
+class WAN21_SCAIL(WAN21):
+    def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
+        super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.SCAILWanModel)
+        self.memory_usage_factor_conds = ("reference_latent", "pose_latents")
+        self.memory_usage_shape_process = {"pose_latents": lambda shape: [shape[0], shape[1], 1.5, shape[-2], shape[-1]]}
+        self.image_to_video = image_to_video
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+
+        reference_latents = kwargs.get("reference_latents", None)
+        if reference_latents is not None:
+            ref_latent = self.process_latent_in(reference_latents[-1])
+            ref_mask = torch.ones_like(ref_latent[:, :4])
+            ref_latent = torch.cat([ref_latent, ref_mask], dim=1)
+            out['reference_latent'] = comfy.conds.CONDRegular(ref_latent)
+
+        pose_latents = kwargs.get("pose_video_latent", None)
+        if pose_latents is not None:
+            pose_latents = self.process_latent_in(pose_latents)
+            pose_mask = torch.ones_like(pose_latents[:, :4])
+            pose_latents = torch.cat([pose_latents, pose_mask], dim=1)
+            out['pose_latents'] = comfy.conds.CONDRegular(pose_latents)
+
+        return out
+
+    def extra_conds_shapes(self, **kwargs):
+        out = {}
+        ref_latents = kwargs.get("reference_latents", None)
+        if ref_latents is not None:
+            out['reference_latent'] = list([1, 20, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16])
+
+        pose_latents = kwargs.get("pose_video_latent", None)
+        if pose_latents is not None:
+            out['pose_latents'] = [pose_latents.shape[0], 20, *pose_latents.shape[2:]]
+
+        return out
+
 class Hunyuan3Dv2(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan3d.model.Hunyuan3Dv2)
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index 8a1d8ea4d..3faa950ca 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -498,6 +498,8 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             dit_config["model_type"] = "humo"
         elif '{}face_adapter.fuser_blocks.0.k_norm.weight'.format(key_prefix) in state_dict_keys:
             dit_config["model_type"] = "animate"
+        elif '{}patch_embedding_pose.weight'.format(key_prefix) in state_dict_keys:
+            dit_config["model_type"] = "scail"
         else:
             if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys:
                 dit_config["model_type"] = "i2v"
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index 473fbbfd4..4f63e8327 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -1268,6 +1268,16 @@ class WAN21_FlowRVS(WAN21_T2V):
         out = model_base.WAN21_FlowRVS(self, image_to_video=True, device=device)
         return out
 
+class WAN21_SCAIL(WAN21_T2V):
+    unet_config = {
+        "image_model": "wan2.1",
+        "model_type": "scail",
+    }
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.WAN21_SCAIL(self, image_to_video=False, device=device)
+        return out
+
 class Hunyuan3Dv2(supported_models_base.BASE):
     unet_config = {
         "image_model": "hunyuan3d2",
@@ -1710,6 +1720,6 @@ class LongCatImage(supported_models_base.BASE):
         hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.longcat_image.LongCatImageTokenizer, comfy.text_encoders.longcat_image.te(**hunyuan_detect))
 
-models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima]
+models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima]
 
 models += [SVD_img2vid]
diff --git a/comfy_extras/nodes_wan.py b/comfy_extras/nodes_wan.py
index effa994d1..e50bfcd2c 100644
--- a/comfy_extras/nodes_wan.py
+++ b/comfy_extras/nodes_wan.py
@@ -1456,6 +1456,63 @@ class WanInfiniteTalkToVideo(io.ComfyNode):
         return io.NodeOutput(model_patched, positive, negative, out_latent, trim_image)
 
 
+class WanSCAILToVideo(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="WanSCAILToVideo",
+            category="conditioning/video_models",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Vae.Input("vae"),
+                io.Int.Input("width", default=512, min=32, max=nodes.MAX_RESOLUTION, step=32),
+                io.Int.Input("height", default=896, min=32, max=nodes.MAX_RESOLUTION, step=32),
+                io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+                io.ClipVisionOutput.Input("clip_vision_output", optional=True),
+                io.Image.Input("reference_image", optional=True),
+                io.Image.Input("pose_video", optional=True, tooltip="Video used for pose conditioning. Will be downscaled to half the resolution of the main video."),
+                io.Float.Input("pose_strength", default=1.0, min=0.0, max=10.0, step=0.01, tooltip="Strength of the pose latent."),
+                io.Float.Input("pose_start", default=0.0, min=0.0, max=1.0, step=0.01, tooltip="Start step to use pose conditioning."),
+                io.Float.Input("pose_end", default=1.0, min=0.0, max=1.0, step=0.01, tooltip="End step to use pose conditioning."),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent", tooltip="Empty latent of the generation size."),
+            ],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, positive, negative, vae, width, height, length, batch_size, pose_strength, pose_start, pose_end, reference_image=None, clip_vision_output=None, pose_video=None) -> io.NodeOutput:
+        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+
+        ref_latent = None
+        if reference_image is not None:
+            reference_image = comfy.utils.common_upscale(reference_image[:1].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+            ref_latent = vae.encode(reference_image[:, :, :, :3])
+
+        if ref_latent is not None:
+            positive = node_helpers.conditioning_set_values(positive, {"reference_latents": [ref_latent]}, append=True)
+            negative = node_helpers.conditioning_set_values(negative, {"reference_latents": [torch.zeros_like(ref_latent)]}, append=True)
+
+        if clip_vision_output is not None:
+            positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
+            negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output})
+
+        if pose_video is not None:
+            pose_video = comfy.utils.common_upscale(pose_video[:length].movedim(-1, 1), width // 2, height // 2, "area", "center").movedim(1, -1)
+            pose_video_latent = vae.encode(pose_video[:, :, :, :3]) * pose_strength
+            positive = node_helpers.conditioning_set_values_with_timestep_range(positive, {"pose_video_latent": pose_video_latent}, pose_start, pose_end)
+            negative = node_helpers.conditioning_set_values_with_timestep_range(negative, {"pose_video_latent": pose_video_latent}, pose_start, pose_end)
+
+        out_latent = {}
+        out_latent["samples"] = latent
+        return io.NodeOutput(positive, negative, out_latent)
+
+
 class WanExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
@@ -1476,6 +1533,7 @@ class WanExtension(ComfyExtension):
             WanAnimateToVideo,
             Wan22ImageToVideoLatent,
             WanInfiniteTalkToVideo,
+            WanSCAILToVideo,
         ]
 
 async def comfy_entrypoint() -> WanExtension:
diff --git a/node_helpers.py b/node_helpers.py
index 4ff960ef8..d3d834516 100644
--- a/node_helpers.py
+++ b/node_helpers.py
@@ -1,5 +1,6 @@
 import hashlib
 import torch
+import logging
 
 from comfy.cli_args import args
 
@@ -21,6 +22,36 @@ def conditioning_set_values(conditioning, values={}, append=False):
 
     return c
 
+def conditioning_set_values_with_timestep_range(conditioning, values={}, start_percent=0.0, end_percent=1.0):
+    """
+    Apply values to conditioning only during [start_percent, end_percent], keeping the
+    original conditioning active outside that range. Respects existing per-entry ranges.
+    """
+    if start_percent > end_percent:
+        logging.warning(f"start_percent ({start_percent}) must be <= end_percent ({end_percent})")
+        return conditioning
+
+    EPS = 1e-5 # the sampler gates entries with strict > / <, shift boundaries slightly to ensure only one conditioning is active per timestep
+    c = []
+    for t in conditioning:
+        cond_start = t[1].get("start_percent", 0.0)
+        cond_end   = t[1].get("end_percent",   1.0)
+        intersect_start = max(start_percent, cond_start)
+        intersect_end   = min(end_percent,   cond_end)
+
+        if intersect_start >= intersect_end: # no overlap: emit unchanged
+            c.append(t)
+            continue
+
+        if intersect_start > cond_start: # part before the requested range
+            c.extend(conditioning_set_values([t], {"start_percent": cond_start, "end_percent": intersect_start - EPS}))
+
+        c.extend(conditioning_set_values([t], {**values, "start_percent": intersect_start, "end_percent": intersect_end}))
+
+        if intersect_end < cond_end: # part after the requested range
+            c.extend(conditioning_set_values([t], {"start_percent": intersect_end + EPS, "end_percent": cond_end}))
+    return c
+
 def pillow(fn, arg):
     prev_value = None
     try:

From 5f41584e960d3ad90f6581278e57f7b52e771db4 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Sat, 28 Feb 2026 13:50:18 -0800
Subject: [PATCH 077/194] Disable dynamic_vram when weight hooks applied
 (#12653)

* sd: add support for clip model reconstruction

* nodes: SetClipHooks: Demote the dynamic model patcher

* mp: Make dynamic_disable more robust

The backup need to not be cloned. In addition add a delegate object
to ModelPatcherDynamic so that non-cloning code can do
ModelPatcherDynamic demotion

* sampler_helpers: Demote to non-dynamic model patcher when hooking

* code rabbit review comments
---
 comfy/model_patcher.py      | 29 ++++++++++++++++++++--------
 comfy/sampler_helpers.py    | 12 ++++++++++++
 comfy/samplers.py           |  2 ++
 comfy/sd.py                 | 38 +++++++++++++++++++++++++++----------
 comfy_extras/nodes_hooks.py |  2 +-
 5 files changed, 64 insertions(+), 19 deletions(-)

diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index 1c9ba8096..3fc76d9db 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -308,15 +308,22 @@ class ModelPatcher:
     def get_free_memory(self, device):
         return comfy.model_management.get_free_memory(device)
 
-    def clone(self, disable_dynamic=False):
+    def get_clone_model_override(self):
+        return self.model, (self.backup, self.object_patches_backup, self.pinned)
+
+    def clone(self, disable_dynamic=False, model_override=None):
         class_ = self.__class__
-        model = self.model
         if self.is_dynamic() and disable_dynamic:
             class_ = ModelPatcher
-            temp_model_patcher = self.cached_patcher_init[0](*self.cached_patcher_init[1], disable_dynamic=True)
-            model = temp_model_patcher.model
+            if model_override is None:
+                if self.cached_patcher_init is None:
+                    raise RuntimeError("Cannot create non-dynamic delegate: cached_patcher_init is not initialized.")
+                temp_model_patcher = self.cached_patcher_init[0](*self.cached_patcher_init[1], disable_dynamic=True)
+                model_override = temp_model_patcher.get_clone_model_override()
+        if model_override is None:
+            model_override = self.get_clone_model_override()
 
-        n = class_(model, self.load_device, self.offload_device, self.model_size(), weight_inplace_update=self.weight_inplace_update)
+        n = class_(model_override[0], self.load_device, self.offload_device, self.model_size(), weight_inplace_update=self.weight_inplace_update)
         n.patches = {}
         for k in self.patches:
             n.patches[k] = self.patches[k][:]
@@ -325,13 +332,12 @@ class ModelPatcher:
         n.object_patches = self.object_patches.copy()
         n.weight_wrapper_patches = self.weight_wrapper_patches.copy()
         n.model_options = comfy.utils.deepcopy_list_dict(self.model_options)
-        n.backup = self.backup
-        n.object_patches_backup = self.object_patches_backup
         n.parent = self
-        n.pinned = self.pinned
 
         n.force_cast_weights = self.force_cast_weights
 
+        n.backup, n.object_patches_backup, n.pinned = model_override[1]
+
         # attachments
         n.attachments = {}
         for k in self.attachments:
@@ -1435,6 +1441,7 @@ class ModelPatcherDynamic(ModelPatcher):
             del self.model.model_loaded_weight_memory
         if not hasattr(self.model, "dynamic_vbars"):
             self.model.dynamic_vbars = {}
+        self.non_dynamic_delegate_model = None
         assert load_device is not None
 
     def is_dynamic(self):
@@ -1669,4 +1676,10 @@ class ModelPatcherDynamic(ModelPatcher):
     def unpatch_hooks(self, whitelist_keys_set: set[str]=None) -> None:
         pass
 
+    def get_non_dynamic_delegate(self):
+        model_patcher = self.clone(disable_dynamic=True, model_override=self.non_dynamic_delegate_model)
+        self.non_dynamic_delegate_model = model_patcher.get_clone_model_override()
+        return model_patcher
+
+
 CoreModelPatcher = ModelPatcher
diff --git a/comfy/sampler_helpers.py b/comfy/sampler_helpers.py
index 1f75f2ba7..bbba09e26 100644
--- a/comfy/sampler_helpers.py
+++ b/comfy/sampler_helpers.py
@@ -66,6 +66,18 @@ def convert_cond(cond):
         out.append(temp)
     return out
 
+def cond_has_hooks(cond):
+    for c in cond:
+        temp = c[1]
+        if "hooks" in temp:
+            return True
+        if "control" in temp:
+            control = temp["control"]
+            extra_hooks = control.get_extra_hooks()
+            if len(extra_hooks) > 0:
+                return True
+    return False
+
 def get_additional_models(conds, dtype):
     """loads additional models in conditioning"""
     cnets: list[ControlBase] = []
diff --git a/comfy/samplers.py b/comfy/samplers.py
index 8b9782956..8be449ef7 100755
--- a/comfy/samplers.py
+++ b/comfy/samplers.py
@@ -946,6 +946,8 @@ class CFGGuider:
 
     def inner_set_conds(self, conds):
         for k in conds:
+            if self.model_patcher.is_dynamic() and comfy.sampler_helpers.cond_has_hooks(conds[k]):
+                self.model_patcher = self.model_patcher.get_non_dynamic_delegate()
             self.original_conds[k] = comfy.sampler_helpers.convert_cond(conds[k])
 
     def __call__(self, *args, **kwargs):
diff --git a/comfy/sd.py b/comfy/sd.py
index 7713d4678..a9ad7c2d2 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -204,7 +204,7 @@ def load_bypass_lora_for_models(model, clip, lora, strength_model, strength_clip
 
 
 class CLIP:
-    def __init__(self, target=None, embedding_directory=None, no_init=False, tokenizer_data={}, parameters=0, state_dict=[], model_options={}):
+    def __init__(self, target=None, embedding_directory=None, no_init=False, tokenizer_data={}, parameters=0, state_dict=[], model_options={}, disable_dynamic=False):
         if no_init:
             return
         params = target.params.copy()
@@ -233,7 +233,8 @@ class CLIP:
         model_management.archive_model_dtypes(self.cond_stage_model)
 
         self.tokenizer = tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
-        self.patcher = comfy.model_patcher.CoreModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device)
+        ModelPatcher = comfy.model_patcher.ModelPatcher if disable_dynamic else comfy.model_patcher.CoreModelPatcher
+        self.patcher = ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device)
         #Match torch.float32 hardcode upcast in TE implemention
         self.patcher.set_model_compute_dtype(torch.float32)
         self.patcher.hook_mode = comfy.hooks.EnumHookMode.MinVram
@@ -267,9 +268,9 @@ class CLIP:
         logging.info("CLIP/text encoder model load device: {}, offload device: {}, current: {}, dtype: {}".format(load_device, offload_device, params['device'], dtype))
         self.tokenizer_options = {}
 
-    def clone(self):
+    def clone(self, disable_dynamic=False):
         n = CLIP(no_init=True)
-        n.patcher = self.patcher.clone()
+        n.patcher = self.patcher.clone(disable_dynamic=disable_dynamic)
         n.cond_stage_model = self.cond_stage_model
         n.tokenizer = self.tokenizer
         n.layer_idx = self.layer_idx
@@ -1164,14 +1165,21 @@ class CLIPType(Enum):
     LONGCAT_IMAGE = 26
 
 
-def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
+
+def load_clip_model_patcher(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}, disable_dynamic=False):
+    clip = load_clip(ckpt_paths, embedding_directory, clip_type, model_options, disable_dynamic)
+    return clip.patcher
+
+def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}, disable_dynamic=False):
     clip_data = []
     for p in ckpt_paths:
         sd, metadata = comfy.utils.load_torch_file(p, safe_load=True, return_metadata=True)
         if model_options.get("custom_operations", None) is None:
             sd, metadata = comfy.utils.convert_old_quants(sd, model_prefix="", metadata=metadata)
         clip_data.append(sd)
-    return load_text_encoder_state_dicts(clip_data, embedding_directory=embedding_directory, clip_type=clip_type, model_options=model_options)
+    clip = load_text_encoder_state_dicts(clip_data, embedding_directory=embedding_directory, clip_type=clip_type, model_options=model_options, disable_dynamic=disable_dynamic)
+    clip.patcher.cached_patcher_init = (load_clip_model_patcher, (ckpt_paths, embedding_directory, clip_type, model_options))
+    return clip
 
 
 class TEModel(Enum):
@@ -1276,7 +1284,7 @@ def llama_detect(clip_data):
 
     return {}
 
-def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
+def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}, disable_dynamic=False):
     clip_data = state_dicts
 
     class EmptyClass:
@@ -1496,7 +1504,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
         parameters += comfy.utils.calculate_parameters(c)
         tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options)
 
-    clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, state_dict=clip_data, model_options=model_options)
+    clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, state_dict=clip_data, model_options=model_options, disable_dynamic=disable_dynamic)
     return clip
 
 def load_gligen(ckpt_path):
@@ -1541,8 +1549,10 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
     out = load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options, te_model_options=te_model_options, metadata=metadata, disable_dynamic=disable_dynamic)
     if out is None:
         raise RuntimeError("ERROR: Could not detect model type of: {}\n{}".format(ckpt_path, model_detection_error_hint(ckpt_path, sd)))
-    if output_model:
+    if output_model and out[0] is not None:
         out[0].cached_patcher_init = (load_checkpoint_guess_config_model_only, (ckpt_path, embedding_directory, model_options, te_model_options))
+    if output_clip and out[1] is not None:
+        out[1].patcher.cached_patcher_init = (load_checkpoint_guess_config_clip_only, (ckpt_path, embedding_directory, model_options, te_model_options))
     return out
 
 def load_checkpoint_guess_config_model_only(ckpt_path, embedding_directory=None, model_options={}, te_model_options={}, disable_dynamic=False):
@@ -1553,6 +1563,14 @@ def load_checkpoint_guess_config_model_only(ckpt_path, embedding_directory=None,
             disable_dynamic=disable_dynamic)
     return model
 
+def load_checkpoint_guess_config_clip_only(ckpt_path, embedding_directory=None, model_options={}, te_model_options={}, disable_dynamic=False):
+    _, clip, *_ = load_checkpoint_guess_config(ckpt_path, False, True, False,
+            embedding_directory=embedding_directory, output_model=False,
+            model_options=model_options,
+            te_model_options=te_model_options,
+            disable_dynamic=disable_dynamic)
+    return clip.patcher
+
 def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}, metadata=None, disable_dynamic=False):
     clip = None
     clipvision = None
@@ -1638,7 +1656,7 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
             clip_sd = model_config.process_clip_state_dict(sd)
             if len(clip_sd) > 0:
                 parameters = comfy.utils.calculate_parameters(clip_sd)
-                clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd, parameters=parameters, state_dict=clip_sd, model_options=te_model_options)
+                clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd, parameters=parameters, state_dict=clip_sd, model_options=te_model_options, disable_dynamic=disable_dynamic)
             else:
                 logging.warning("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.")
 
diff --git a/comfy_extras/nodes_hooks.py b/comfy_extras/nodes_hooks.py
index be7d600cd..056369e86 100644
--- a/comfy_extras/nodes_hooks.py
+++ b/comfy_extras/nodes_hooks.py
@@ -248,7 +248,7 @@ class SetClipHooks:
 
     def apply_hooks(self, clip: CLIP, schedule_clip: bool, apply_to_conds: bool, hooks: comfy.hooks.HookGroup=None):
         if hooks is not None:
-            clip = clip.clone()
+            clip = clip.clone(disable_dynamic=True)
             if apply_to_conds:
                 clip.apply_hooks_to_conds = hooks
             clip.patcher.forced_hooks = hooks.clone()

From 48bb0bd18aa90bba0eac7b4c1a1400c4f7110046 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Sat, 28 Feb 2026 13:52:30 -0800
Subject: [PATCH 078/194] cli_args: Default comfy to DynamicVram mode (#12658)

---
 comfy/cli_args.py | 4 ++--
 main.py           | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/comfy/cli_args.py b/comfy/cli_args.py
index 63daca861..13079c7bc 100644
--- a/comfy/cli_args.py
+++ b/comfy/cli_args.py
@@ -146,6 +146,7 @@ parser.add_argument("--reserve-vram", type=float, default=None, help="Set the am
 
 parser.add_argument("--async-offload", nargs='?', const=2, type=int, default=None, metavar="NUM_STREAMS", help="Use async weight offloading. An optional argument controls the amount of offload streams. Default is 2. Enabled by default on Nvidia.")
 parser.add_argument("--disable-async-offload", action="store_true", help="Disable async weight offloading.")
+parser.add_argument("--disable-dynamic-vram", action="store_true", help="Disable dynamic VRAM and use estimate based model loading.")
 
 parser.add_argument("--force-non-blocking", action="store_true", help="Force ComfyUI to use non-blocking operations for all applicable tensors. This may improve performance on some non-Nvidia systems but can cause issues with some workflows.")
 
@@ -159,7 +160,6 @@ class PerformanceFeature(enum.Enum):
     Fp8MatrixMultiplication = "fp8_matrix_mult"
     CublasOps = "cublas_ops"
     AutoTune = "autotune"
-    DynamicVRAM = "dynamic_vram"
 
 parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. This is used to test new features so using it might crash your comfyui. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: {}".format(" ".join(map(lambda c: c.value, PerformanceFeature))))
 
@@ -260,4 +260,4 @@ else:
     args.fast = set(args.fast)
 
 def enables_dynamic_vram():
-    return PerformanceFeature.DynamicVRAM in args.fast and not args.highvram and not args.gpu_only
+    return not args.disable_dynamic_vram and not args.highvram and not args.gpu_only and not args.novram and not args.cpu
diff --git a/main.py b/main.py
index 3fe8f0589..a0545d9b3 100644
--- a/main.py
+++ b/main.py
@@ -192,7 +192,7 @@ import hook_breaker_ac10a0
 import comfy.memory_management
 import comfy.model_patcher
 
-if enables_dynamic_vram():
+if enables_dynamic_vram() and comfy.model_management.is_nvidia():
     if comfy.model_management.torch_version_numeric < (2, 8):
         logging.warning("Unsupported Pytorch detected. DynamicVRAM support requires Pytorch version 2.8 or later. Falling back to legacy ModelPatcher. VRAM estimates may be unreliable especially on Windows")
     elif comfy_aimdo.control.init_device(comfy.model_management.get_torch_device().index):

From 17106cb124fcfa0b75ea24993c65aa024059fc8d Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Sat, 28 Feb 2026 19:21:32 -0800
Subject: [PATCH 079/194] Move parsing of requirements logic to function.
 (#12701)

---
 app/frontend_management.py                   | 42 ++------------------
 tests-unit/app_test/frontend_manager_test.py |  6 +++
 utils/install_util.py                        | 33 +++++++++++++++
 3 files changed, 42 insertions(+), 39 deletions(-)

diff --git a/app/frontend_management.py b/app/frontend_management.py
index bdaa85812..f753ef0de 100644
--- a/app/frontend_management.py
+++ b/app/frontend_management.py
@@ -17,7 +17,7 @@ from importlib.metadata import version
 import requests
 from typing_extensions import NotRequired
 
-from utils.install_util import get_missing_requirements_message, requirements_path
+from utils.install_util import get_missing_requirements_message, get_required_packages_versions
 
 from comfy.cli_args import DEFAULT_VERSION_STRING
 import app.logger
@@ -45,25 +45,7 @@ def get_installed_frontend_version():
 
 
 def get_required_frontend_version():
-    """Get the required frontend version from requirements.txt."""
-    try:
-        with open(requirements_path, "r", encoding="utf-8") as f:
-            for line in f:
-                line = line.strip()
-                if line.startswith("comfyui-frontend-package=="):
-                    version_str = line.split("==")[-1]
-                    if not is_valid_version(version_str):
-                        logging.error(f"Invalid version format in requirements.txt: {version_str}")
-                        return None
-                    return version_str
-            logging.error("comfyui-frontend-package not found in requirements.txt")
-            return None
-    except FileNotFoundError:
-        logging.error("requirements.txt not found. Cannot determine required frontend version.")
-        return None
-    except Exception as e:
-        logging.error(f"Error reading requirements.txt: {e}")
-        return None
+    return get_required_packages_versions().get("comfyui-frontend-package", None)
 
 
 def check_frontend_version():
@@ -217,25 +199,7 @@ class FrontendManager:
 
     @classmethod
     def get_required_templates_version(cls) -> str:
-        """Get the required workflow templates version from requirements.txt."""
-        try:
-            with open(requirements_path, "r", encoding="utf-8") as f:
-                for line in f:
-                    line = line.strip()
-                    if line.startswith("comfyui-workflow-templates=="):
-                        version_str = line.split("==")[-1]
-                        if not is_valid_version(version_str):
-                            logging.error(f"Invalid templates version format in requirements.txt: {version_str}")
-                            return None
-                        return version_str
-                logging.error("comfyui-workflow-templates not found in requirements.txt")
-                return None
-        except FileNotFoundError:
-            logging.error("requirements.txt not found. Cannot determine required templates version.")
-            return None
-        except Exception as e:
-            logging.error(f"Error reading requirements.txt: {e}")
-            return None
+        return get_required_packages_versions().get("comfyui-workflow-templates", None)
 
     @classmethod
     def default_frontend_path(cls) -> str:
diff --git a/tests-unit/app_test/frontend_manager_test.py b/tests-unit/app_test/frontend_manager_test.py
index 643f04e72..1d5a84b47 100644
--- a/tests-unit/app_test/frontend_manager_test.py
+++ b/tests-unit/app_test/frontend_manager_test.py
@@ -49,6 +49,12 @@ def mock_provider(mock_releases):
     return provider
 
 
+@pytest.fixture(autouse=True)
+def clear_cache():
+    import utils.install_util
+    utils.install_util.PACKAGE_VERSIONS = {}
+
+
 def test_get_release(mock_provider, mock_releases):
     version = "1.0.0"
     release = mock_provider.get_release(version)
diff --git a/utils/install_util.py b/utils/install_util.py
index 0f59bcf91..34489aec5 100644
--- a/utils/install_util.py
+++ b/utils/install_util.py
@@ -1,5 +1,7 @@
 from pathlib import Path
 import sys
+import logging
+import re
 
 # The path to the requirements.txt file
 requirements_path = Path(__file__).parents[1] / "requirements.txt"
@@ -16,3 +18,34 @@ Please install the updated requirements.txt file by running:
 {sys.executable} {extra}-m pip install -r {requirements_path}
 If you are on the portable package you can run: update\\update_comfyui.bat to solve this problem.
 """.strip()
+
+
+def is_valid_version(version: str) -> bool:
+    """Validate if a string is a valid semantic version (X.Y.Z format)."""
+    pattern = r"^(\d+)\.(\d+)\.(\d+)$"
+    return bool(re.match(pattern, version))
+
+
+PACKAGE_VERSIONS = {}
+def get_required_packages_versions():
+    if len(PACKAGE_VERSIONS) > 0:
+        return PACKAGE_VERSIONS.copy()
+    out = PACKAGE_VERSIONS
+    try:
+        with open(requirements_path, "r", encoding="utf-8") as f:
+            for line in f:
+                line = line.strip().replace(">=", "==")
+                s = line.split("==")
+                if len(s) == 2:
+                    version_str = s[-1]
+                    if not is_valid_version(version_str):
+                        logging.error(f"Invalid version format in requirements.txt: {version_str}")
+                        continue
+                    out[s[0]] = version_str
+        return out.copy()
+    except FileNotFoundError:
+        logging.error("requirements.txt not found.")
+        return None
+    except Exception as e:
+        logging.error(f"Error reading requirements.txt: {e}")
+        return None

From 1080bd442a7509d29bfe0b29cac9222de406c994 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Sat, 28 Feb 2026 19:23:28 -0800
Subject: [PATCH 080/194] Disable dynamic vram on wsl. (#12706)

---
 comfy/model_management.py | 8 ++++++++
 main.py                   | 2 +-
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index f73613f17..86f840ada 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -180,6 +180,14 @@ def is_ixuca():
         return True
     return False
 
+def is_wsl():
+    version = platform.uname().release
+    if version.endswith("-Microsoft"):
+        return True
+    elif version.endswith("microsoft-standard-WSL2"):
+        return True
+    return False
+
 def get_torch_device():
     global directml_enabled
     global cpu_state
diff --git a/main.py b/main.py
index a0545d9b3..af701f8df 100644
--- a/main.py
+++ b/main.py
@@ -192,7 +192,7 @@ import hook_breaker_ac10a0
 import comfy.memory_management
 import comfy.model_patcher
 
-if enables_dynamic_vram() and comfy.model_management.is_nvidia():
+if enables_dynamic_vram() and comfy.model_management.is_nvidia() and not comfy.model_management.is_wsl():
     if comfy.model_management.torch_version_numeric < (2, 8):
         logging.warning("Unsupported Pytorch detected. DynamicVRAM support requires Pytorch version 2.8 or later. Falling back to legacy ModelPatcher. VRAM estimates may be unreliable especially on Windows")
     elif comfy_aimdo.control.init_device(comfy.model_management.get_torch_device().index):

From d159142615e0a1a7ae4eb711a6ae9f66a5f2d76e Mon Sep 17 00:00:00 2001
From: Christian Byrne <cbyrne@comfy.org>
Date: Sat, 28 Feb 2026 20:59:24 -0800
Subject: [PATCH 081/194] refactor: rename Mahiro CFG to Similarity-Adaptive
 Guidance (#12172)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* refactor: rename Mahiro CFG to Similarity-Adaptive Guidance

Rename the display name to better describe what the node does:
adaptively blends guidance based on cosine similarity between
positive and negative conditions.

Amp-Thread-ID: https://ampcode.com/threads/T-019c0d36-8b43-745f-b7b2-e35b53f17fa1
Co-authored-by: Amp <amp@ampcode.com>

* feat: add search aliases for old mahiro name

Amp-Thread-ID: https://ampcode.com/threads/T-019c0d36-8b43-745f-b7b2-e35b53f17fa1

* rename: Similarity-Adaptive Guidance → Positive-Biased Guidance (per reviewer)

- display_name changed to 'Positive-Biased Guidance' to avoid SAG acronym collision
- search_aliases expanded: mahiro, mahiro cfg, similarity-adaptive guidance, positive-biased cfg
- ruff format applied

---------

Co-authored-by: Amp <amp@ampcode.com>
Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
---
 comfy_extras/nodes_mahiro.py | 24 ++++++++++++++++--------
 1 file changed, 16 insertions(+), 8 deletions(-)

diff --git a/comfy_extras/nodes_mahiro.py b/comfy_extras/nodes_mahiro.py
index 6459ca8c1..a25226e6d 100644
--- a/comfy_extras/nodes_mahiro.py
+++ b/comfy_extras/nodes_mahiro.py
@@ -10,7 +10,7 @@ class Mahiro(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="Mahiro",
-            display_name="Mahiro CFG",
+            display_name="Positive-Biased Guidance",
             category="_for_testing",
             description="Modify the guidance to scale more on the 'direction' of the positive prompt rather than the difference between the negative prompt.",
             inputs=[
@@ -20,27 +20,35 @@ class Mahiro(io.ComfyNode):
                 io.Model.Output(display_name="patched_model"),
             ],
             is_experimental=True,
+            search_aliases=[
+                "mahiro",
+                "mahiro cfg",
+                "similarity-adaptive guidance",
+                "positive-biased cfg",
+            ],
         )
 
     @classmethod
     def execute(cls, model) -> io.NodeOutput:
         m = model.clone()
+
         def mahiro_normd(args):
-            scale: float = args['cond_scale']
-            cond_p: torch.Tensor = args['cond_denoised']
-            uncond_p: torch.Tensor = args['uncond_denoised']
-            #naive leap
+            scale: float = args["cond_scale"]
+            cond_p: torch.Tensor = args["cond_denoised"]
+            uncond_p: torch.Tensor = args["uncond_denoised"]
+            # naive leap
             leap = cond_p * scale
-            #sim with uncond leap
+            # sim with uncond leap
             u_leap = uncond_p * scale
             cfg = args["denoised"]
             merge = (leap + cfg) / 2
             normu = torch.sqrt(u_leap.abs()) * u_leap.sign()
             normm = torch.sqrt(merge.abs()) * merge.sign()
             sim = F.cosine_similarity(normu, normm).mean()
-            simsc = 2 * (sim+1)
-            wm = (simsc*cfg + (4-simsc)*leap) / 4
+            simsc = 2 * (sim + 1)
+            wm = (simsc * cfg + (4 - simsc) * leap) / 4
             return wm
+
         m.set_model_sampler_post_cfg_function(mahiro_normd)
         return io.NodeOutput(m)
 

From 850e8b42ff67cec295edb686c4b85dc7811f5e7f Mon Sep 17 00:00:00 2001
From: Christian Byrne <cbyrne@comfy.org>
Date: Sat, 28 Feb 2026 21:38:19 -0800
Subject: [PATCH 082/194] feat: add text preview support to jobs API (#12169)

* feat: add text preview support to jobs API

Amp-Thread-ID: https://ampcode.com/threads/T-019c0be0-9fc6-71ac-853a-7c7cc846b375
Co-authored-by: Amp <amp@ampcode.com>

* test: update tests to expect text as previewable media type

Amp-Thread-ID: https://ampcode.com/threads/T-019c0be0-9fc6-71ac-853a-7c7cc846b375

---------
---
 comfy_execution/jobs.py      | 53 ++++++++++++++++++++++++++++++------
 tests/execution/test_jobs.py |  6 ++--
 2 files changed, 48 insertions(+), 11 deletions(-)

diff --git a/comfy_execution/jobs.py b/comfy_execution/jobs.py
index 370014fb6..fcd7ef735 100644
--- a/comfy_execution/jobs.py
+++ b/comfy_execution/jobs.py
@@ -20,7 +20,7 @@ class JobStatus:
 
 
 # Media types that can be previewed in the frontend
-PREVIEWABLE_MEDIA_TYPES = frozenset({'images', 'video', 'audio', '3d'})
+PREVIEWABLE_MEDIA_TYPES = frozenset({'images', 'video', 'audio', '3d', 'text'})
 
 # 3D file extensions for preview fallback (no dedicated media_type exists)
 THREE_D_EXTENSIONS = frozenset({'.obj', '.fbx', '.gltf', '.glb', '.usdz'})
@@ -75,6 +75,23 @@ def normalize_outputs(outputs: dict) -> dict:
         normalized[node_id] = normalized_node
     return normalized
 
+# Text preview truncation limit (1024 characters) to prevent preview_output bloat
+TEXT_PREVIEW_MAX_LENGTH = 1024
+
+
+def _create_text_preview(value: str) -> dict:
+    """Create a text preview dict with optional truncation.
+
+    Returns:
+        dict with 'content' and optionally 'truncated' flag
+    """
+    if len(value) <= TEXT_PREVIEW_MAX_LENGTH:
+        return {'content': value}
+    return {
+        'content': value[:TEXT_PREVIEW_MAX_LENGTH],
+        'truncated': True
+    }
+
 
 def _extract_job_metadata(extra_data: dict) -> tuple[Optional[int], Optional[str]]:
     """Extract create_time and workflow_id from extra_data.
@@ -221,23 +238,43 @@ def get_outputs_summary(outputs: dict) -> tuple[int, Optional[dict]]:
                 continue
 
             for item in items:
-                normalized = normalize_output_item(item)
-                if normalized is None:
-                    continue
+                if not isinstance(item, dict):
+                    # Handle text outputs (non-dict items like strings or tuples)
+                    normalized = normalize_output_item(item)
+                    if normalized is None:
+                        # Not a 3D file string — check for text preview
+                        if media_type == 'text':
+                            count += 1
+                            if preview_output is None:
+                                if isinstance(item, tuple):
+                                    text_value = item[0] if item else ''
+                                else:
+                                    text_value = str(item)
+                                text_preview = _create_text_preview(text_value)
+                                enriched = {
+                                    **text_preview,
+                                    'nodeId': node_id,
+                                    'mediaType': media_type
+                                }
+                                if fallback_preview is None:
+                                    fallback_preview = enriched
+                        continue
+                    # normalize_output_item returned a dict (e.g. 3D file)
+                    item = normalized
 
                 count += 1
 
                 if preview_output is not None:
                     continue
 
-                if isinstance(normalized, dict) and is_previewable(media_type, normalized):
+                if is_previewable(media_type, item):
                     enriched = {
-                        **normalized,
+                        **item,
                         'nodeId': node_id,
                     }
-                    if 'mediaType' not in normalized:
+                    if 'mediaType' not in item:
                         enriched['mediaType'] = media_type
-                    if normalized.get('type') == 'output':
+                    if item.get('type') == 'output':
                         preview_output = enriched
                     elif fallback_preview is None:
                         fallback_preview = enriched
diff --git a/tests/execution/test_jobs.py b/tests/execution/test_jobs.py
index 83c36fe48..814af5c13 100644
--- a/tests/execution/test_jobs.py
+++ b/tests/execution/test_jobs.py
@@ -38,13 +38,13 @@ class TestIsPreviewable:
     """Unit tests for is_previewable()"""
 
     def test_previewable_media_types(self):
-        """Images, video, audio, 3d media types should be previewable."""
-        for media_type in ['images', 'video', 'audio', '3d']:
+        """Images, video, audio, 3d, text media types should be previewable."""
+        for media_type in ['images', 'video', 'audio', '3d', 'text']:
             assert is_previewable(media_type, {}) is True
 
     def test_non_previewable_media_types(self):
         """Other media types should not be previewable."""
-        for media_type in ['latents', 'text', 'metadata', 'files']:
+        for media_type in ['latents', 'metadata', 'files']:
             assert is_previewable(media_type, {}) is False
 
     def test_3d_extensions_previewable(self):

From 4d79f4f0280da6c0a0e37123b9c80f24e2403536 Mon Sep 17 00:00:00 2001
From: drozbay <17261091+drozbay@users.noreply.github.com>
Date: Sun, 1 Mar 2026 10:38:30 -0700
Subject: [PATCH 083/194] fix: handle substep sigmas in context window set_step
 (#12719)

Multi-step samplers (eg. dpmpp_2s_ancestral) call the model at intermediate sigma values not present in the schedule. This caused set_step to crash with "No sample_sigmas matched current timestep" when context windows were enabled.

The fix is to keep self._step from the last exact match when a substep sigma is encountered, since substeps are still logically part of their parent step and should use the same context windows.

Co-authored-by: ozbayb <17261091+ozbayb@users.noreply.github.com>
---
 comfy/context_windows.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/context_windows.py b/comfy/context_windows.py
index 2f82d51da..b54f7f39a 100644
--- a/comfy/context_windows.py
+++ b/comfy/context_windows.py
@@ -214,7 +214,7 @@ class IndexListContextHandler(ContextHandlerABC):
         mask = torch.isclose(model_options["transformer_options"]["sample_sigmas"], timestep[0], rtol=0.0001)
         matches = torch.nonzero(mask)
         if torch.numel(matches) == 0:
-            raise Exception("No sample_sigmas matched current timestep; something went wrong.")
+            return  # substep from multi-step sampler: keep self._step from the last full step
         self._step = int(matches[0].item())
 
     def get_context_windows(self, model: BaseModel, x_in: torch.Tensor, model_options: dict[str]) -> list[IndexListContextWindow]:

From c0d472e5b9b256d9e802ecac703bb6a8ca5f9eb8 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Sun, 1 Mar 2026 11:14:56 -0800
Subject: [PATCH 084/194] comfy-aimdo 0.2.3 (#12720)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 1b2bd0ae6..35fa3f18f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -22,7 +22,7 @@ alembic
 SQLAlchemy
 av>=14.2.0
 comfy-kitchen>=0.2.7
-comfy-aimdo>=0.2.2
+comfy-aimdo>=0.2.3
 requests
 
 #non essential dependencies:

From 602f6bd82c1f8b31d1b10b5f9ae4aa9637772ad5 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Sun, 1 Mar 2026 12:28:39 -0800
Subject: [PATCH 085/194] Make --disable-smart-memory disable dynamic vram.
 (#12722)

---
 comfy/cli_args.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/cli_args.py b/comfy/cli_args.py
index 13079c7bc..bfb61c825 100644
--- a/comfy/cli_args.py
+++ b/comfy/cli_args.py
@@ -260,4 +260,4 @@ else:
     args.fast = set(args.fast)
 
 def enables_dynamic_vram():
-    return not args.disable_dynamic_vram and not args.highvram and not args.gpu_only and not args.novram and not args.cpu
+    return not args.disable_dynamic_vram and not args.highvram and not args.gpu_only and not args.novram and not args.cpu and not args.disable_smart_memory

From dfbf99a06172a5c54002d80abf3e74c0d82c10b9 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Sun, 1 Mar 2026 19:18:56 -0800
Subject: [PATCH 086/194] model_mangament: make dynamic --disable-smart-memory
 work (#12724)

This was previously considering the pool of dynamic models as one giant
entity for the sake of smart memory, but that isnt really the useful
or what a user would reasonably expect. Make Dynamic VRAM properly purge
its models just like the old --disable-smart-memory but conditioning
the dynamic-for-dynamic bypass on smart memory.

Re-enable dynamic smart memory.
---
 comfy/cli_args.py         |  2 +-
 comfy/model_management.py | 11 +++++------
 2 files changed, 6 insertions(+), 7 deletions(-)

diff --git a/comfy/cli_args.py b/comfy/cli_args.py
index bfb61c825..13079c7bc 100644
--- a/comfy/cli_args.py
+++ b/comfy/cli_args.py
@@ -260,4 +260,4 @@ else:
     args.fast = set(args.fast)
 
 def enables_dynamic_vram():
-    return not args.disable_dynamic_vram and not args.highvram and not args.gpu_only and not args.novram and not args.cpu and not args.disable_smart_memory
+    return not args.disable_dynamic_vram and not args.highvram and not args.gpu_only and not args.novram and not args.cpu
diff --git a/comfy/model_management.py b/comfy/model_management.py
index 86f840ada..c817d43b5 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -639,12 +639,11 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, ram_
         if not DISABLE_SMART_MEMORY:
             memory_to_free = memory_required - get_free_memory(device)
             ram_to_free = ram_required - get_free_ram()
-
-        if current_loaded_models[i].model.is_dynamic() and for_dynamic:
-            #don't actually unload dynamic models for the sake of other dynamic models
-            #as that works on-demand.
-            memory_required -= current_loaded_models[i].model.loaded_size()
-            memory_to_free = 0
+            if current_loaded_models[i].model.is_dynamic() and for_dynamic:
+                #don't actually unload dynamic models for the sake of other dynamic models
+                #as that works on-demand.
+                memory_required -= current_loaded_models[i].model.loaded_size()
+                memory_to_free = 0
         if memory_to_free > 0 and current_loaded_models[i].model_unload(memory_to_free):
             logging.debug(f"Unloading {current_loaded_models[i].model.model.__class__.__name__}")
             unloaded_model.append(i)

From 7175c11a4ed41278c9cb9e6961b8d8776ef69f00 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Sun, 1 Mar 2026 22:21:41 -0800
Subject: [PATCH 087/194] comfy aimdo 0.2.4 (#12727)

Comfy Aimdo 0.2.4 fixes a VRAM buffer alignment issue that happens in
someworkflows where action is able to bypass the pytorch allocator
and go straight to the cuda hook.
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 35fa3f18f..71019c16f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -22,7 +22,7 @@ alembic
 SQLAlchemy
 av>=14.2.0
 comfy-kitchen>=0.2.7
-comfy-aimdo>=0.2.3
+comfy-aimdo>=0.2.4
 requests
 
 #non essential dependencies:

From afb54219fac341fa8614fdab090fe8096d0aec1e Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Mon, 2 Mar 2026 09:24:33 +0200
Subject: [PATCH 088/194] feat(api-nodes): allow to use "IMAGE+TEXT" in
 NanoBanana2 (#12729)

---
 comfy_api_nodes/nodes_gemini.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/comfy_api_nodes/nodes_gemini.py b/comfy_api_nodes/nodes_gemini.py
index 3fe804e0b..d83d2fc15 100644
--- a/comfy_api_nodes/nodes_gemini.py
+++ b/comfy_api_nodes/nodes_gemini.py
@@ -789,8 +789,6 @@ class GeminiImage2(IO.ComfyNode):
         validate_string(prompt, strip_whitespace=True, min_length=1)
         if model == "Nano Banana 2 (Gemini 3.1 Flash Image)":
             model = "gemini-3.1-flash-image-preview"
-            if response_modalities == "IMAGE+TEXT":
-                raise ValueError("IMAGE+TEXT is not currently available for the Nano Banana 2 model.")
 
         parts: list[GeminiPart] = [GeminiPart(text=prompt)]
         if images is not None:
@@ -895,7 +893,7 @@ class GeminiNanoBanana2(IO.ComfyNode):
                 ),
                 IO.Combo.Input(
                     "response_modalities",
-                    options=["IMAGE"],
+                    options=["IMAGE", "IMAGE+TEXT"],
                     advanced=True,
                 ),
                 IO.Combo.Input(
@@ -925,6 +923,7 @@ class GeminiNanoBanana2(IO.ComfyNode):
             ],
             outputs=[
                 IO.Image.Output(),
+                IO.String.Output(),
             ],
             hidden=[
                 IO.Hidden.auth_token_comfy_org,

From f1f8996e1562c3753666d1c568b2ff629edb9e36 Mon Sep 17 00:00:00 2001
From: ComfyUI Wiki <contact@comfyui-wiki.com>
Date: Tue, 3 Mar 2026 01:13:42 +0800
Subject: [PATCH 089/194] chore: update workflow templates to v0.9.5 (#12732)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 71019c16f..608b0cfa6 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.39.19
-comfyui-workflow-templates==0.9.4
+comfyui-workflow-templates==0.9.5
 comfyui-embedded-docs==0.4.3
 torch
 torchsde

From 57dd6c1aadf500d90f635a8d3c15418c0d6d6ecd Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Mon, 2 Mar 2026 15:54:18 -0800
Subject: [PATCH 090/194] Support loading zeta chroma weights properly.
 (#12734)

---
 comfy/model_detection.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index 3faa950ca..9f4a26e61 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -423,7 +423,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             dit_config["extra_per_block_abs_pos_emb_type"] = "learnable"
         return dit_config
 
-    if '{}cap_embedder.1.weight'.format(key_prefix) in state_dict_keys:  # Lumina 2
+    if '{}cap_embedder.1.weight'.format(key_prefix) in state_dict_keys and '{}noise_refiner.0.attention.k_norm.weight'.format(key_prefix) in state_dict_keys:  # Lumina 2
         dit_config = {}
         dit_config["image_model"] = "lumina2"
         dit_config["patch_size"] = 2
@@ -533,8 +533,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
         return dit_config
 
-    if f"{key_prefix}t_embedder.mlp.2.weight" in state_dict_keys:  # Hunyuan 3D 2.1
-
+    if f"{key_prefix}t_embedder.mlp.2.weight" in state_dict_keys and f"{key_prefix}blocks.0.attn1.k_norm.weight" in state_dict_keys:  # Hunyuan 3D 2.1
         dit_config = {}
         dit_config["image_model"] = "hunyuan3d2_1"
         dit_config["in_channels"] = state_dict[f"{key_prefix}x_embedder.weight"].shape[1]
@@ -1055,6 +1054,13 @@ def convert_diffusers_mmdit(state_dict, output_prefix=""):
     elif 'adaln_single.emb.timestep_embedder.linear_1.bias' in state_dict and 'pos_embed.proj.bias' in state_dict: # PixArt
         num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.')
         sd_map = comfy.utils.pixart_to_diffusers({"depth": num_blocks}, output_prefix=output_prefix)
+    elif 'noise_refiner.0.attention.norm_k.weight' in state_dict:
+        n_layers = count_blocks(state_dict, 'layers.{}.')
+        dim = state_dict['noise_refiner.0.attention.to_k.weight'].shape[0]
+        sd_map = comfy.utils.z_image_to_diffusers({"n_layers": n_layers, "dim": dim}, output_prefix=output_prefix)
+        for k in state_dict: # For zeta chroma
+            if k not in sd_map:
+                sd_map[k] = k
     elif 'x_embedder.weight' in state_dict: #Flux
         depth = count_blocks(state_dict, 'transformer_blocks.{}.')
         depth_single_blocks = count_blocks(state_dict, 'single_transformer_blocks.{}.')

From 9ebee0a2179b361a24c20838c1848d7988320636 Mon Sep 17 00:00:00 2001
From: Lodestone <lodestone.rock@gmail.com>
Date: Tue, 3 Mar 2026 07:43:47 +0700
Subject: [PATCH 091/194] Feat: z-image pixel space (model still training atm)
 (#12709)

* draft zeta (z-image pixel space)

* revert gitignore

* model loaded and able to run however vector direction still wrong tho

* flip the vector direction to original again this time

* Move wrongly positioned Z image pixel space class

* inherit Radiance LatentFormat class

* Fix parameters in classes for Zeta x0 dino

* remove arbitrary nn.init instances

* Remove unused import of lru_cache

---------

Co-authored-by: silveroxides <ishimarukaito@gmail.com>
---
 comfy/latent_formats.py   |   7 +
 comfy/ldm/lumina/model.py | 265 ++++++++++++++++++++++++++++++++++++++
 comfy/model_base.py       |   5 +
 comfy/model_detection.py  |  23 ++++
 comfy/supported_models.py |  16 ++-
 5 files changed, 315 insertions(+), 1 deletion(-)

diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py
index f59999af6..6a57bca1c 100644
--- a/comfy/latent_formats.py
+++ b/comfy/latent_formats.py
@@ -776,3 +776,10 @@ class ChromaRadiance(LatentFormat):
 
     def process_out(self, latent):
         return latent
+
+
+class ZImagePixelSpace(ChromaRadiance):
+    """Pixel-space latent format for ZImage DCT variant.
+    No VAE encoding/decoding — the model operates directly on RGB pixels.
+    """
+    pass
diff --git a/comfy/ldm/lumina/model.py b/comfy/ldm/lumina/model.py
index 77d1abc97..9e432d5c0 100644
--- a/comfy/ldm/lumina/model.py
+++ b/comfy/ldm/lumina/model.py
@@ -14,6 +14,7 @@ from comfy.ldm.flux.layers import EmbedND
 from comfy.ldm.flux.math import apply_rope
 import comfy.patcher_extension
 import comfy.utils
+from comfy.ldm.chroma_radiance.layers import NerfEmbedder
 
 
 def invert_slices(slices, length):
@@ -858,3 +859,267 @@ class NextDiT(nn.Module):
         img = self.unpatchify(img, img_size, cap_size, return_tensor=x_is_tensor)[:, :, :h, :w]
         return -img
 
+
+#############################################################################
+#                        Pixel Space Decoder Components                     #
+#############################################################################
+
+def _modulate_shift_scale(x, shift, scale):
+    return x * (1 + scale) + shift
+
+
+class PixelResBlock(nn.Module):
+    """
+    Residual block with AdaLN modulation, zero-initialised so it starts as
+    an identity at the beginning of training.
+    """
+
+    def __init__(self, channels: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.in_ln = operations.LayerNorm(channels, eps=1e-6, dtype=dtype, device=device)
+        self.mlp = nn.Sequential(
+            operations.Linear(channels, channels, bias=True, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(channels, channels, bias=True, dtype=dtype, device=device),
+        )
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(channels, 3 * channels, bias=True, dtype=dtype, device=device),
+        )
+
+    def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+        shift, scale, gate = self.adaLN_modulation(y).chunk(3, dim=-1)
+        h = _modulate_shift_scale(self.in_ln(x), shift, scale)
+        h = self.mlp(h)
+        return x + gate * h
+
+
+class DCTFinalLayer(nn.Module):
+    """Zero-initialised output projection (adopted from DiT)."""
+
+    def __init__(self, model_channels: int, out_channels: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.norm_final = operations.LayerNorm(model_channels, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear = operations.Linear(model_channels, out_channels, bias=True, dtype=dtype, device=device)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.linear(self.norm_final(x))
+
+
+class SimpleMLPAdaLN(nn.Module):
+    """
+    Small MLP decoder head for the pixel-space variant.
+
+    Takes per-patch pixel values and a per-patch conditioning vector from the
+    transformer backbone and predicts the denoised pixel values.
+
+    x : [B*N, P^2, C]   – noisy pixel values per patch position
+    c : [B*N, dim]       – backbone hidden state per patch (conditioning)
+    → [B*N, P^2, C]
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        model_channels: int,
+        out_channels: int,
+        z_channels: int,
+        num_res_blocks: int,
+        max_freqs: int = 8,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.dtype = dtype
+
+        # Project backbone hidden state → per-patch conditioning
+        self.cond_embed = operations.Linear(z_channels, model_channels, dtype=dtype, device=device)
+
+        # Input projection with DCT positional encoding
+        self.input_embedder = NerfEmbedder(
+            in_channels=in_channels,
+            hidden_size_input=model_channels,
+            max_freqs=max_freqs,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+        # Residual blocks
+        self.res_blocks = nn.ModuleList([
+            PixelResBlock(model_channels, dtype=dtype, device=device, operations=operations) for _ in range(num_res_blocks)
+        ])
+
+        # Output projection
+        self.final_layer = DCTFinalLayer(model_channels, out_channels, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
+        # x: [B*N, 1, P^2*C],  c: [B*N, dim]
+        original_dtype = x.dtype
+        weight_dtype = self.cond_embed.weight.dtype if hasattr(self.cond_embed, "weight") and self.cond_embed.weight is not None else (self.dtype or x.dtype)
+        x = self.input_embedder(x)                                   # [B*N, 1, model_channels]
+        y = self.cond_embed(c.to(weight_dtype)).unsqueeze(1)         # [B*N, 1, model_channels]
+        x = x.to(weight_dtype)
+        for block in self.res_blocks:
+            x = block(x, y)
+        return self.final_layer(x).to(original_dtype)                # [B*N, 1, P^2*C]
+
+
+#############################################################################
+#                          NextDiT – Pixel Space                            #
+#############################################################################
+
+class NextDiTPixelSpace(NextDiT):
+    """
+    Pixel-space variant of NextDiT.
+
+    Identical transformer backbone to NextDiT, but the output head is replaced
+    with a small MLP decoder (SimpleMLPAdaLN) that operates on raw pixel values
+    per patch rather than a single affine projection.
+
+    Key differences vs NextDiT:
+      • ``final_layer`` is removed; ``dec_net`` (SimpleMLPAdaLN) is used instead.
+      • ``_forward`` stores the raw patchified pixel values before the backbone
+        embedding and feeds them to ``dec_net`` together with the per-patch
+        backbone hidden states.
+      • Supports optional x0 prediction via ``use_x0``.
+    """
+
+    def __init__(
+        self,
+        # decoder-specific
+        decoder_hidden_size: int = 3840,
+        decoder_num_res_blocks: int = 4,
+        decoder_max_freqs: int = 8,
+        decoder_in_channels: int = None,  # full flattened patch size (patch_size^2 * in_channels)
+        use_x0: bool = False,
+        # all NextDiT args forwarded unchanged
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        # Remove the latent-space final layer – not used in pixel space
+        del self.final_layer
+
+        patch_size = kwargs.get("patch_size", 2)
+        in_channels = kwargs.get("in_channels", 4)
+        dim = kwargs.get("dim", 4096)
+
+        # decoder_in_channels is the full flattened patch: patch_size^2 * in_channels
+        dec_in_ch = decoder_in_channels if decoder_in_channels is not None else patch_size ** 2 * in_channels
+
+        self.dec_net = SimpleMLPAdaLN(
+            in_channels=dec_in_ch,
+            model_channels=decoder_hidden_size,
+            out_channels=dec_in_ch,
+            z_channels=dim,
+            num_res_blocks=decoder_num_res_blocks,
+            max_freqs=decoder_max_freqs,
+            dtype=kwargs.get("dtype"),
+            device=kwargs.get("device"),
+            operations=kwargs.get("operations"),
+        )
+
+        if use_x0:
+            self.register_buffer("__x0__", torch.tensor([]))
+
+    # ------------------------------------------------------------------
+    # Forward — mirrors NextDiT._forward exactly, replacing final_layer
+    # with the pixel-space dec_net decoder.
+    # ------------------------------------------------------------------
+    def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, ref_latents=[], ref_contexts=[], siglip_feats=[], transformer_options={}, **kwargs):
+        omni = len(ref_latents) > 0
+        if omni:
+            timesteps = torch.cat([timesteps * 0, timesteps], dim=0)
+
+        t = 1.0 - timesteps
+        cap_feats = context
+        cap_mask = attention_mask
+        bs, c, h, w = x.shape
+        x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
+
+        t = self.t_embedder(t * self.time_scale, dtype=x.dtype)
+        adaln_input = t
+
+        if self.clip_text_pooled_proj is not None:
+            pooled = kwargs.get("clip_text_pooled", None)
+            if pooled is not None:
+                pooled = self.clip_text_pooled_proj(pooled)
+            else:
+                pooled = torch.zeros((x.shape[0], self.clip_text_dim), device=x.device, dtype=x.dtype)
+            adaln_input = self.time_text_embed(torch.cat((t, pooled), dim=-1))
+
+        # ---- capture raw pixel patches before patchify_and_embed embeds them ----
+        pH = pW = self.patch_size
+        B, C, H, W = x.shape
+        pixel_patches = (
+            x.view(B, C, H // pH, pH, W // pW, pW)
+             .permute(0, 2, 4, 3, 5, 1)   # [B, Ht, Wt, pH, pW, C]
+             .flatten(3)                   # [B, Ht, Wt, pH*pW*C]
+             .flatten(1, 2)               # [B, N, pH*pW*C]
+        )
+        N = pixel_patches.shape[1]
+        # decoder sees one token per patch: [B*N, 1, P^2*C]
+        pixel_values = pixel_patches.reshape(B * N, 1, pH * pW * C)
+
+        patches = transformer_options.get("patches", {})
+        x_is_tensor = isinstance(x, torch.Tensor)
+        img, mask, img_size, cap_size, freqs_cis, timestep_zero_index = self.patchify_and_embed(
+            x, cap_feats, cap_mask, adaln_input, num_tokens,
+            ref_latents=ref_latents, ref_contexts=ref_contexts,
+            siglip_feats=siglip_feats, transformer_options=transformer_options
+        )
+        freqs_cis = freqs_cis.to(img.device)
+
+        transformer_options["total_blocks"] = len(self.layers)
+        transformer_options["block_type"] = "double"
+        img_input = img
+        for i, layer in enumerate(self.layers):
+            transformer_options["block_index"] = i
+            img = layer(img, mask, freqs_cis, adaln_input, timestep_zero_index=timestep_zero_index, transformer_options=transformer_options)
+            if "double_block" in patches:
+                for p in patches["double_block"]:
+                    out = p({"img": img[:, cap_size[0]:], "img_input": img_input[:, cap_size[0]:], "txt": img[:, :cap_size[0]], "pe": freqs_cis[:, cap_size[0]:], "vec": adaln_input, "x": x, "block_index": i, "transformer_options": transformer_options})
+                    if "img" in out:
+                        img[:, cap_size[0]:] = out["img"]
+                    if "txt" in out:
+                        img[:, :cap_size[0]] = out["txt"]
+
+        # ---- pixel-space decoder (replaces final_layer + unpatchify) ----
+        # img may have padding tokens beyond N; only the first N are real image patches
+        img_hidden = img[:, cap_size[0]:cap_size[0] + N, :]  # [B, N, dim]
+        decoder_cond = img_hidden.reshape(B * N, self.dim)    # [B*N, dim]
+
+        output = self.dec_net(pixel_values, decoder_cond)  # [B*N, 1, P^2*C]
+        output = output.reshape(B, N, -1)                  # [B, N, P^2*C]
+
+        # prepend zero cap placeholder so unpatchify indexing works unchanged
+        cap_placeholder = torch.zeros(
+            B, cap_size[0], output.shape[-1], device=output.device, dtype=output.dtype
+        )
+        img_out = self.unpatchify(
+            torch.cat([cap_placeholder, output], dim=1),
+            img_size, cap_size, return_tensor=x_is_tensor
+        )[:, :, :h, :w]
+
+        return -img_out
+
+    def forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs):
+        # _forward returns neg_x0 = -x0 (negated decoder output).
+        #
+        # Reference inference (working_inference_reference.py):
+        #   out = _forward(img, t)          # = -x0
+        #   pred = (img - out) / t          # = (img + x0) / t  [_apply_x0_residual]
+        #   img += (t_prev - t_curr) * pred # Euler step
+        #
+        # ComfyUI's Euler sampler does the same:
+        #   x_next = x + (sigma_next - sigma) * model_output
+        # So model_output must equal pred = (x - neg_x0) / t = (x - (-x0)) / t = (x + x0) / t
+        neg_x0 = comfy.patcher_extension.WrapperExecutor.new_class_executor(
+            self._forward,
+            self,
+            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {}))
+        ).execute(x, timesteps, context, num_tokens, attention_mask, **kwargs)
+
+        return (x - neg_x0) / timesteps.view(-1, 1, 1, 1)
diff --git a/comfy/model_base.py b/comfy/model_base.py
index a1c690b9b..1e01e9edc 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -1263,6 +1263,11 @@ class Lumina2(BaseModel):
             out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()[2:]), ref_latents))])
         return out
 
+class ZImagePixelSpace(Lumina2):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        BaseModel.__init__(self, model_config, model_type, device=device, unet_model=comfy.ldm.lumina.model.NextDiTPixelSpace)
+        self.memory_usage_factor_conds = ("ref_latents",)
+
 class WAN21(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel)
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index 9f4a26e61..6eace4628 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -464,6 +464,29 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             if sig_weight is not None:
                 dit_config["siglip_feat_dim"] = sig_weight.shape[0]
 
+            dec_cond_key = '{}dec_net.cond_embed.weight'.format(key_prefix)
+            if dec_cond_key in state_dict_keys:  # pixel-space variant
+                dit_config["image_model"] = "zimage_pixel"
+                # patch_size and in_channels are derived from x_embedder:
+                #   x_embedder: Linear(patch_size * patch_size * in_channels, dim)
+                # The decoder also receives the full flat patch, so decoder_in_channels = x_embedder input dim.
+                x_emb_in = state_dict['{}x_embedder.weight'.format(key_prefix)].shape[1]
+                dec_out = state_dict['{}dec_net.final_layer.linear.weight'.format(key_prefix)].shape[0]
+                # patch_size: infer from decoder final layer output matching x_embedder input
+                # in_channels: infer from dec_net input_embedder (in_features = dec_in_ch + max_freqs^2)
+                embedder_w = state_dict['{}dec_net.input_embedder.embedder.0.weight'.format(key_prefix)]
+                dec_in_ch = dec_out  # decoder in == decoder out (same pixel space)
+                dit_config["patch_size"] = round((x_emb_in / 3) ** 0.5)  # assume RGB (in_channels=3)
+                dit_config["in_channels"] = 3
+                dit_config["decoder_in_channels"] = dec_in_ch
+                dit_config["decoder_hidden_size"] = state_dict[dec_cond_key].shape[0]
+                dit_config["decoder_num_res_blocks"] = count_blocks(
+                    state_dict_keys, '{}dec_net.res_blocks.'.format(key_prefix) + '{}.'
+                )
+                dit_config["decoder_max_freqs"] = int((embedder_w.shape[1] - dec_in_ch) ** 0.5)
+                if '{}__x0__'.format(key_prefix) in state_dict_keys:
+                    dit_config["use_x0"] = True
+
         return dit_config
 
     if '{}head.modulation'.format(key_prefix) in state_dict_keys:  # Wan 2.1
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index 4f63e8327..c0d3f387f 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -1118,6 +1118,20 @@ class ZImage(Lumina2):
         hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3_4b.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.z_image.ZImageTokenizer, comfy.text_encoders.z_image.te(**hunyuan_detect))
 
+class ZImagePixelSpace(ZImage):
+    unet_config = {
+        "image_model": "zimage_pixel",
+    }
+
+    # Pixel-space model: no spatial compression, operates on raw RGB patches.
+    latent_format = latent_formats.ZImagePixelSpace
+
+    # Much lower memory than latent-space models (no VAE, small patches).
+    memory_usage_factor = 0.05 # TODO: figure out the optimal value for this.
+
+    def get_model(self, state_dict, prefix="", device=None):
+        return model_base.ZImagePixelSpace(self, device=device)
+
 class WAN21_T2V(supported_models_base.BASE):
     unet_config = {
         "image_model": "wan2.1",
@@ -1720,6 +1734,6 @@ class LongCatImage(supported_models_base.BASE):
         hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.longcat_image.LongCatImageTokenizer, comfy.text_encoders.longcat_image.te(**hunyuan_detect))
 
-models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima]
+models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImagePixelSpace, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima]
 
 models += [SVD_img2vid]

From dff0a4a15887383c90a031e3fd48ebc41f6928e7 Mon Sep 17 00:00:00 2001
From: xeinherjer <112741359+xeinherjer-dev@users.noreply.github.com>
Date: Tue, 3 Mar 2026 10:17:51 +0900
Subject: [PATCH 092/194] Fix VAEDecodeAudioTiled ignoring tile_size input
 (#12735) (#12738)

---
 comfy_extras/nodes_audio.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py
index 43df0512f..5d8d9bf6f 100644
--- a/comfy_extras/nodes_audio.py
+++ b/comfy_extras/nodes_audio.py
@@ -96,7 +96,7 @@ class VAEEncodeAudio(IO.ComfyNode):
 
 def vae_decode_audio(vae, samples, tile=None, overlap=None):
     if tile is not None:
-        audio = vae.decode_tiled(samples["samples"], tile_y=tile, overlap=overlap).movedim(-1, 1)
+        audio = vae.decode_tiled(samples["samples"], tile_x=tile, tile_y=tile, overlap=overlap).movedim(-1, 1)
     else:
         audio = vae.decode(samples["samples"]).movedim(-1, 1)
 

From 09bcbddfcf804634f008f53c1827b7ba9a3956ec Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Tue, 3 Mar 2026 08:50:33 -0800
Subject: [PATCH 093/194] ModelPatcherDynamic: Force load all non-comfy weights
 (#12739)

* model_management: Remove non-comfy dynamic _v caster

* Force pre-load non-comfy weights to GPU in ModelPatcherDynamic

Non-comfy weights may expect to be pre-cast to the target
device without in-model casting. Previously they were allocated in
the vbar with _v which required the _v fault path in cast_to.
Instead, back up the original CPU weight and move it directly to GPU
at load time.
---
 comfy/model_management.py | 40 -------------------------------
 comfy/model_patcher.py    | 50 ++++++++++++++++-----------------------
 2 files changed, 21 insertions(+), 69 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index c817d43b5..0e0e96672 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -32,9 +32,6 @@ import comfy.memory_management
 import comfy.utils
 import comfy.quant_ops
 
-import comfy_aimdo.torch
-import comfy_aimdo.model_vbar
-
 class VRAMState(Enum):
     DISABLED = 0    #No vram present: no need to move models to vram
     NO_VRAM = 1     #Very low vram: enable all the options to save vram
@@ -1206,43 +1203,6 @@ def cast_to_gathered(tensors, r, non_blocking=False, stream=None):
 
 
 def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, stream=None, r=None):
-    if hasattr(weight, "_v"):
-        #Unexpected usage patterns. There is no reason these don't work but they
-        #have no testing and no callers do this.
-        assert r is None
-        assert stream is None
-
-        cast_geometry = comfy.memory_management.tensors_to_geometries([ weight ])
-
-        if dtype is None:
-            dtype = weight._model_dtype
-
-        signature = comfy_aimdo.model_vbar.vbar_fault(weight._v)
-        if signature is not None:
-            if comfy_aimdo.model_vbar.vbar_signature_compare(signature, weight._v_signature):
-                v_tensor = weight._v_tensor
-            else:
-                raw_tensor = comfy_aimdo.torch.aimdo_to_tensor(weight._v, device)
-                v_tensor = comfy.memory_management.interpret_gathered_like(cast_geometry, raw_tensor)[0]
-                weight._v_tensor = v_tensor
-                weight._v_signature = signature
-                #Send it over
-                v_tensor.copy_(weight, non_blocking=non_blocking)
-            return v_tensor.to(dtype=dtype)
-
-        r = torch.empty_like(weight, dtype=dtype, device=device)
-
-        if weight.dtype != r.dtype and weight.dtype != weight._model_dtype:
-            #Offloaded casting could skip this, however it would make the quantizations
-            #inconsistent between loaded and offloaded weights. So force the double casting
-            #that would happen in regular flow to make offload deterministic.
-            cast_buffer = torch.empty_like(weight, dtype=weight._model_dtype, device=device)
-            cast_buffer.copy_(weight, non_blocking=non_blocking)
-            weight = cast_buffer
-        r.copy_(weight, non_blocking=non_blocking)
-
-        return r
-
     if device is None or weight.device == device:
         if not copy:
             if dtype is None or weight.dtype == dtype:
diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index 3fc76d9db..e380e406b 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -1435,10 +1435,6 @@ class ModelPatcherDynamic(ModelPatcher):
 
     def __init__(self, model, load_device, offload_device, size=0, weight_inplace_update=False):
         super().__init__(model, load_device, offload_device, size, weight_inplace_update)
-        #this is now way more dynamic and we dont support the same base model for both Dynamic
-        #and non-dynamic patchers.
-        if hasattr(self.model, "model_loaded_weight_memory"):
-            del self.model.model_loaded_weight_memory
         if not hasattr(self.model, "dynamic_vbars"):
             self.model.dynamic_vbars = {}
         self.non_dynamic_delegate_model = None
@@ -1461,9 +1457,7 @@ class ModelPatcherDynamic(ModelPatcher):
 
     def loaded_size(self):
         vbar = self._vbar_get()
-        if vbar is None:
-            return 0
-        return vbar.loaded_size()
+        return (vbar.loaded_size() if vbar is not None else 0) + self.model.model_loaded_weight_memory
 
     def get_free_memory(self, device):
         #NOTE: on high condition / batch counts, estimate should have already vacated
@@ -1504,6 +1498,7 @@ class ModelPatcherDynamic(ModelPatcher):
 
         num_patches = 0
         allocated_size = 0
+        self.model.model_loaded_weight_memory = 0
 
         with self.use_ejected():
             self.unpatch_hooks()
@@ -1512,10 +1507,6 @@ class ModelPatcherDynamic(ModelPatcher):
             if vbar is not None:
                 vbar.prioritize()
 
-            #We force reserve VRAM for the non comfy-weight so we dont have to deal
-            #with pin and unpin syncrhonization which can be expensive for small weights
-            #with a high layer rate (e.g. autoregressive LLMs).
-            #prioritize the non-comfy weights (note the order reverse).
             loading = self._load_list(prio_comfy_cast_weights=True, default_device=device_to)
             loading.sort(reverse=True)
 
@@ -1558,6 +1549,9 @@ class ModelPatcherDynamic(ModelPatcher):
                     if key in self.backup:
                         comfy.utils.set_attr_param(self.model, key, self.backup[key].weight)
                     self.patch_weight_to_device(key, device_to=device_to)
+                    weight, _, _ = get_key_weight(self.model, key)
+                    if weight is not None:
+                        self.model.model_loaded_weight_memory += weight.numel() * weight.element_size()
 
                 if hasattr(m, "comfy_cast_weights"):
                     m.comfy_cast_weights = True
@@ -1583,21 +1577,15 @@ class ModelPatcherDynamic(ModelPatcher):
                     for param in params:
                         key = key_param_name_to_key(n, param)
                         weight, _, _ = get_key_weight(self.model, key)
-                        weight.seed_key = key
-                        set_dirty(weight, dirty)
-                        geometry = weight
-                        model_dtype = getattr(m, param + "_comfy_model_dtype", None) or weight.dtype
-                        geometry = comfy.memory_management.TensorGeometry(shape=weight.shape, dtype=model_dtype)
-                        weight_size = geometry.numel() * geometry.element_size()
-                        if vbar is not None and not hasattr(weight, "_v"):
-                            weight._v = vbar.alloc(weight_size)
-                            weight._model_dtype = model_dtype
-                        allocated_size += weight_size
-                    vbar.set_watermark_limit(allocated_size)
+                        if key not in self.backup:
+                            self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(weight, False)
+                        comfy.utils.set_attr_param(self.model, key, weight.to(device=device_to))
+                        self.model.model_loaded_weight_memory += weight.numel() * weight.element_size()
 
                 move_weight_functions(m, device_to)
 
-            logging.info(f"Model {self.model.__class__.__name__} prepared for dynamic VRAM loading. {allocated_size // (1024 ** 2)}MB Staged. {num_patches} patches attached.")
+            force_load_stat = f" Force pre-loaded {len(self.backup)} weights: {self.model.model_loaded_weight_memory // 1024} KB." if len(self.backup) > 0 else ""
+            logging.info(f"Model {self.model.__class__.__name__} prepared for dynamic VRAM loading. {allocated_size // (1024 ** 2)}MB Staged. {num_patches} patches attached.{force_load_stat}")
 
             self.model.device = device_to
             self.model.current_weight_patches_uuid = self.patches_uuid
@@ -1613,7 +1601,16 @@ class ModelPatcherDynamic(ModelPatcher):
         assert self.load_device != torch.device("cpu")
 
         vbar = self._vbar_get()
-        return 0 if vbar is None else vbar.free_memory(memory_to_free)
+        freed = 0 if vbar is None else vbar.free_memory(memory_to_free)
+
+        if freed < memory_to_free:
+            for key in list(self.backup.keys()):
+                bk = self.backup.pop(key)
+                comfy.utils.set_attr_param(self.model, key, bk.weight)
+            freed += self.model.model_loaded_weight_memory
+            self.model.model_loaded_weight_memory = 0
+
+        return freed
 
     def partially_unload_ram(self, ram_to_unload):
         loading = self._load_list(prio_comfy_cast_weights=True, default_device=self.offload_device)
@@ -1640,11 +1637,6 @@ class ModelPatcherDynamic(ModelPatcher):
             for m in self.model.modules():
                 move_weight_functions(m, device_to)
 
-            keys = list(self.backup.keys())
-            for k in keys:
-                bk = self.backup[k]
-                comfy.utils.set_attr_param(self.model, k, bk.weight)
-
     def partially_load(self, device_to, extra_memory=0, force_patch_weights=False):
         assert not force_patch_weights #See above
         with self.use_ejected(skip_and_inject_on_exit_only=True):

From 174fd6759deee5ea73e4cde4ba2936e8d62d8d66 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Tue, 3 Mar 2026 08:51:15 -0800
Subject: [PATCH 094/194] main: Load aimdo after logger is setup (#12743)

This was too early. Aimdo can use the logger in error paths and this
causes a rogue default init if aimdo has something to log.
---
 main.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/main.py b/main.py
index af701f8df..0f58d57b8 100644
--- a/main.py
+++ b/main.py
@@ -16,11 +16,6 @@ from comfy_execution.progress import get_progress_state
 from comfy_execution.utils import get_executing_context
 from comfy_api import feature_flags
 
-import comfy_aimdo.control
-
-if enables_dynamic_vram():
-    comfy_aimdo.control.init()
-
 if __name__ == "__main__":
     #NOTE: These do not do anything on core ComfyUI, they are for custom nodes.
     os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1'
@@ -28,6 +23,11 @@ if __name__ == "__main__":
 
 setup_logger(log_level=args.verbose, use_stdout=args.log_stdout)
 
+import comfy_aimdo.control
+
+if enables_dynamic_vram():
+    comfy_aimdo.control.init()
+
 if os.name == "nt":
     os.environ['MIMALLOC_PURGE_DELAY'] = '0'
 

From f719a9d928049f85b07b8ecc2259fba4832d37bb Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Tue, 3 Mar 2026 14:35:22 -0800
Subject: [PATCH 095/194] Adjust memory usage factor of zeta model. (#12746)

---
 comfy/supported_models.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index c0d3f387f..07feb31b3 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -1127,7 +1127,7 @@ class ZImagePixelSpace(ZImage):
     latent_format = latent_formats.ZImagePixelSpace
 
     # Much lower memory than latent-space models (no VAE, small patches).
-    memory_usage_factor = 0.05 # TODO: figure out the optimal value for this.
+    memory_usage_factor = 0.03 # TODO: figure out the optimal value for this.
 
     def get_model(self, state_dict, prefix="", device=None):
         return model_base.ZImagePixelSpace(self, device=device)

From b6ddc590ed8dafd50df8aad1e626b78276a690c0 Mon Sep 17 00:00:00 2001
From: Terry Jia <terryjia88@gmail.com>
Date: Tue, 3 Mar 2026 19:58:53 -0500
Subject: [PATCH 096/194] CURVE type (#12581)

* CURVE type

* fix: update typed wrapper unwrap keys to __type__ and __value__

* code improve

* code improve
---
 comfy_api/latest/_io.py | 14 ++++++++++++++
 execution.py            | 14 ++++++++------
 2 files changed, 22 insertions(+), 6 deletions(-)

diff --git a/comfy_api/latest/_io.py b/comfy_api/latest/_io.py
index 189d7d9bc..050031dc0 100644
--- a/comfy_api/latest/_io.py
+++ b/comfy_api/latest/_io.py
@@ -1240,6 +1240,19 @@ class BoundingBox(ComfyTypeIO):
             return d
 
 
+@comfytype(io_type="CURVE")
+class Curve(ComfyTypeIO):
+    CurvePoint = tuple[float, float]
+    Type = list[CurvePoint]
+
+    class Input(WidgetInput):
+        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
+                     socketless: bool=True, default: list[tuple[float, float]]=None, advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced)
+            if default is None:
+                self.default = [(0.0, 0.0), (1.0, 1.0)]
+
+
 DYNAMIC_INPUT_LOOKUP: dict[str, Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]] = {}
 def register_dynamic_input_func(io_type: str, func: Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]):
     DYNAMIC_INPUT_LOOKUP[io_type] = func
@@ -2226,5 +2239,6 @@ __all__ = [
     "PriceBadgeDepends",
     "PriceBadge",
     "BoundingBox",
+    "Curve",
     "NodeReplace",
 ]
diff --git a/execution.py b/execution.py
index 75b021892..7ccdbf93e 100644
--- a/execution.py
+++ b/execution.py
@@ -876,12 +876,14 @@ async def validate_inputs(prompt_id, prompt, item, validated):
                 continue
         else:
             try:
-                # Unwraps values wrapped in __value__ key. This is used to pass
-                # list widget value to execution, as by default list value is
-                # reserved to represent the connection between nodes.
-                if isinstance(val, dict) and "__value__" in val:
-                    val = val["__value__"]
-                    inputs[x] = val
+                # Unwraps values wrapped in __value__ key or typed wrapper.
+                # This is used to pass list widget values to execution,
+                # as by default list value is reserved to represent the
+                # connection between nodes.
+                if isinstance(val, dict):
+                    if "__value__" in val:
+                        val = val["__value__"]
+                        inputs[x] = val
 
                 if input_type == "INT":
                     val = int(val)

From ac6513e142f881202c40eacc5e337982b777ccd0 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Tue, 3 Mar 2026 18:19:40 -0800
Subject: [PATCH 097/194] DynamicVram: Add casting / fix torch Buffer weights
 (#12749)

* respect model dtype in non-comfy caster

* utils: factor out parent and name functionality of set_attr

* utils: implement set_attr_buffer for torch buffers

* ModelPatcherDynamic: Implement torch Buffer loading

If there is a buffer in dynamic - force load it.
---
 comfy/model_management.py |  2 ++
 comfy/model_patcher.py    | 22 ++++++++++++++++++----
 comfy/utils.py            | 19 +++++++++++++++----
 3 files changed, 35 insertions(+), 8 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 0e0e96672..0f5966371 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -796,6 +796,8 @@ def archive_model_dtypes(model):
     for name, module in model.named_modules():
         for param_name, param in module.named_parameters(recurse=False):
             setattr(module, f"{param_name}_comfy_model_dtype", param.dtype)
+        for buf_name, buf in module.named_buffers(recurse=False):
+            setattr(module, f"{buf_name}_comfy_model_dtype", buf.dtype)
 
 
 def cleanup_models():
diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index e380e406b..70f78a089 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -241,6 +241,7 @@ class ModelPatcher:
 
         self.patches = {}
         self.backup = {}
+        self.backup_buffers = {}
         self.object_patches = {}
         self.object_patches_backup = {}
         self.weight_wrapper_patches = {}
@@ -309,7 +310,7 @@ class ModelPatcher:
         return comfy.model_management.get_free_memory(device)
 
     def get_clone_model_override(self):
-        return self.model, (self.backup, self.object_patches_backup, self.pinned)
+        return self.model, (self.backup, self.backup_buffers, self.object_patches_backup, self.pinned)
 
     def clone(self, disable_dynamic=False, model_override=None):
         class_ = self.__class__
@@ -336,7 +337,7 @@ class ModelPatcher:
 
         n.force_cast_weights = self.force_cast_weights
 
-        n.backup, n.object_patches_backup, n.pinned = model_override[1]
+        n.backup, n.backup_buffers, n.object_patches_backup, n.pinned = model_override[1]
 
         # attachments
         n.attachments = {}
@@ -1579,11 +1580,22 @@ class ModelPatcherDynamic(ModelPatcher):
                         weight, _, _ = get_key_weight(self.model, key)
                         if key not in self.backup:
                             self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(weight, False)
-                        comfy.utils.set_attr_param(self.model, key, weight.to(device=device_to))
-                        self.model.model_loaded_weight_memory += weight.numel() * weight.element_size()
+                        model_dtype = getattr(m, param + "_comfy_model_dtype", None)
+                        casted_weight = weight.to(dtype=model_dtype, device=device_to)
+                        comfy.utils.set_attr_param(self.model, key, casted_weight)
+                        self.model.model_loaded_weight_memory += casted_weight.numel() * casted_weight.element_size()
 
                 move_weight_functions(m, device_to)
 
+            for key, buf in self.model.named_buffers(recurse=True):
+                if key not in self.backup_buffers:
+                    self.backup_buffers[key] = buf
+                module, buf_name = comfy.utils.resolve_attr(self.model, key)
+                model_dtype = getattr(module, buf_name + "_comfy_model_dtype", None)
+                casted_buf = buf.to(dtype=model_dtype, device=device_to)
+                comfy.utils.set_attr_buffer(self.model, key, casted_buf)
+                self.model.model_loaded_weight_memory += casted_buf.numel() * casted_buf.element_size()
+
             force_load_stat = f" Force pre-loaded {len(self.backup)} weights: {self.model.model_loaded_weight_memory // 1024} KB." if len(self.backup) > 0 else ""
             logging.info(f"Model {self.model.__class__.__name__} prepared for dynamic VRAM loading. {allocated_size // (1024 ** 2)}MB Staged. {num_patches} patches attached.{force_load_stat}")
 
@@ -1607,6 +1619,8 @@ class ModelPatcherDynamic(ModelPatcher):
             for key in list(self.backup.keys()):
                 bk = self.backup.pop(key)
                 comfy.utils.set_attr_param(self.model, key, bk.weight)
+            for key in list(self.backup_buffers.keys()):
+                comfy.utils.set_attr_buffer(self.model, key, self.backup_buffers.pop(key))
             freed += self.model.model_loaded_weight_memory
             self.model.model_loaded_weight_memory = 0
 
diff --git a/comfy/utils.py b/comfy/utils.py
index 0769cef44..6e1d14419 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -869,20 +869,31 @@ def safetensors_header(safetensors_path, max_size=100*1024*1024):
 
 ATTR_UNSET={}
 
-def set_attr(obj, attr, value):
+def resolve_attr(obj, attr):
     attrs = attr.split(".")
     for name in attrs[:-1]:
         obj = getattr(obj, name)
-    prev = getattr(obj, attrs[-1], ATTR_UNSET)
+    return obj, attrs[-1]
+
+def set_attr(obj, attr, value):
+    obj, name = resolve_attr(obj, attr)
+    prev = getattr(obj, name, ATTR_UNSET)
     if value is ATTR_UNSET:
-        delattr(obj, attrs[-1])
+        delattr(obj, name)
     else:
-        setattr(obj, attrs[-1], value)
+        setattr(obj, name, value)
     return prev
 
 def set_attr_param(obj, attr, value):
     return set_attr(obj, attr, torch.nn.Parameter(value, requires_grad=False))
 
+def set_attr_buffer(obj, attr, value):
+    obj, name = resolve_attr(obj, attr)
+    prev = getattr(obj, name, ATTR_UNSET)
+    persistent = name not in getattr(obj, "_non_persistent_buffers_set", set())
+    obj.register_buffer(name, value, persistent=persistent)
+    return prev
+
 def copy_to_param(obj, attr, value):
     # inplace update tensor instead of replacing it
     attrs = attr.split(".")

From eb011733b6e4d8a9f7b67a1787d817bfc8c0a5b4 Mon Sep 17 00:00:00 2001
From: Arthur R Longbottom <art.longbottom.jr@gmail.com>
Date: Tue, 3 Mar 2026 21:29:00 -0800
Subject: [PATCH 098/194] Fix VideoFromComponents.save_to crash when writing to
 BytesIO (#12683)

* Fix VideoFromComponents.save_to crash when writing to BytesIO

When `get_container_format()` or `get_stream_source()` is called on a
tensor-based video (VideoFromComponents), it calls `save_to(BytesIO())`.
Since BytesIO has no file extension, `av.open` can't infer the output
format and throws `ValueError: Could not determine output format`.

The sibling class `VideoFromFile` already handles this correctly via
`get_open_write_kwargs()`, which detects BytesIO and sets the format
explicitly. `VideoFromComponents` just never got the same treatment.

This surfaces when any downstream node validates the container format
of a tensor-based video, like TopazVideoEnhance or any node that calls
`validate_container_format_is_mp4()`.

Three-line fix in `comfy_api/latest/_input_impl/video_types.py`.

* Add docstring to save_to to satisfy CI coverage check
---
 comfy_api/latest/_input_impl/video_types.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/comfy_api/latest/_input_impl/video_types.py b/comfy_api/latest/_input_impl/video_types.py
index a3d48c87f..58a37c9e8 100644
--- a/comfy_api/latest/_input_impl/video_types.py
+++ b/comfy_api/latest/_input_impl/video_types.py
@@ -401,6 +401,7 @@ class VideoFromComponents(VideoInput):
         codec: VideoCodec = VideoCodec.AUTO,
         metadata: Optional[dict] = None,
     ):
+        """Save the video to a file path or BytesIO buffer."""
         if format != VideoContainer.AUTO and format != VideoContainer.MP4:
             raise ValueError("Only MP4 format is supported for now")
         if codec != VideoCodec.AUTO and codec != VideoCodec.H264:
@@ -408,6 +409,10 @@ class VideoFromComponents(VideoInput):
         extra_kwargs = {}
         if isinstance(format, VideoContainer) and format != VideoContainer.AUTO:
             extra_kwargs["format"] = format.value
+        elif isinstance(path, io.BytesIO):
+            # BytesIO has no file extension, so av.open can't infer the format.
+            # Default to mp4 since that's the only supported format anyway.
+            extra_kwargs["format"] = "mp4"
         with av.open(path, mode='w', options={'movflags': 'use_metadata_tags'}, **extra_kwargs) as output:
             # Add metadata before writing any streams
             if metadata is not None:

From d531e3fb2a885d675d5b6d3a496b4af5d9757af1 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Wed, 4 Mar 2026 07:47:44 -0800
Subject: [PATCH 099/194] model_patcher: Improve dynamic offload heuristic
 (#12759)

Define a threshold below which a weight loading takes priority. This
actually makes the offload consistent with non-dynamic, because what
happens, is when non-dynamic fills ints to_load list, it will fill-up
any left-over pieces that could fix large weights with small weights
and load them, even though they were lower priority. This actually
improves performance because the timy weights dont cost any VRAM and
arent worth the control overhead of the DMA etc.
---
 comfy/model_patcher.py | 21 +++++++++++++--------
 1 file changed, 13 insertions(+), 8 deletions(-)

diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index 70f78a089..168ce8430 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -699,7 +699,7 @@ class ModelPatcher:
         for key in list(self.pinned):
             self.unpin_weight(key)
 
-    def _load_list(self, prio_comfy_cast_weights=False, default_device=None):
+    def _load_list(self, for_dynamic=False, default_device=None):
         loading = []
         for n, m in self.model.named_modules():
             default = False
@@ -727,8 +727,13 @@ class ModelPatcher:
                         return 0
                     module_offload_mem += check_module_offload_mem("{}.weight".format(n))
                     module_offload_mem += check_module_offload_mem("{}.bias".format(n))
-                prepend = (not hasattr(m, "comfy_cast_weights"),) if prio_comfy_cast_weights else ()
-                loading.append(prepend + (module_offload_mem, module_mem, n, m, params))
+                # Dynamic: small weights (<64KB) first, then larger weights prioritized by size.
+                # Non-dynamic: prioritize by module offload cost.
+                if for_dynamic:
+                    sort_criteria = (module_offload_mem >= 64 * 1024, -module_offload_mem)
+                else:
+                    sort_criteria = (module_offload_mem,)
+                loading.append(sort_criteria + (module_mem, n, m, params))
         return loading
 
     def load(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False, full_load=False):
@@ -1508,11 +1513,11 @@ class ModelPatcherDynamic(ModelPatcher):
             if vbar is not None:
                 vbar.prioritize()
 
-            loading = self._load_list(prio_comfy_cast_weights=True, default_device=device_to)
-            loading.sort(reverse=True)
+            loading = self._load_list(for_dynamic=True, default_device=device_to)
+            loading.sort()
 
             for x in loading:
-                _, _, _, n, m, params = x
+                *_, module_mem, n, m, params = x
 
                 def set_dirty(item, dirty):
                     if dirty or not hasattr(item, "_v_signature"):
@@ -1627,9 +1632,9 @@ class ModelPatcherDynamic(ModelPatcher):
         return freed
 
     def partially_unload_ram(self, ram_to_unload):
-        loading = self._load_list(prio_comfy_cast_weights=True, default_device=self.offload_device)
+        loading = self._load_list(for_dynamic=True, default_device=self.offload_device)
         for x in loading:
-            _, _, _, _, m, _ = x
+            *_, m, _ = x
             ram_to_unload -= comfy.pinned_memory.unpin_memory(m)
             if ram_to_unload <= 0:
                 return

From 9b85cf955858b0aca6b7b30c30b404470ea0c964 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Wed, 4 Mar 2026 07:49:13 -0800
Subject: [PATCH 100/194] Comfy Aimdo 0.2.5 + Fix offload performance in
 DynamicVram (#12754)

* ops: dont unpin nothing

This was calling into aimdo in the none case (offloaded weight). Whats worse,
is aimdo syncs for unpinning an offloaded weight, as that is the corner case of
a weight getting evicted by its own use which does require a sync. But this
was heppening every offloaded weight causing slowdown.

* mp: fix get_free_memory policy

The ModelPatcherDynamic get_free_memory was deducting the model from
to try and estimate the conceptual free memory with doing any
offloading. This is kind of what the old memory_memory_required
was estimating in ModelPatcher load logic, however in practical
reality, between over-estimates and padding, the loader usually
underloaded models enough such that sampling could send CFG +/-
through together even when partially loaded.

So don't regress from the status quo and instead go all in on the
idea that offloading is less of an issue than debatching. Tell the
sampler it can use everything.
---
 comfy/model_patcher.py | 14 +++++++-------
 comfy/ops.py           |  4 ++--
 requirements.txt       |  2 +-
 3 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index 168ce8430..7e5ad7aa4 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -307,7 +307,13 @@ class ModelPatcher:
         return self.model.lowvram_patch_counter
 
     def get_free_memory(self, device):
-        return comfy.model_management.get_free_memory(device)
+        #Prioritize batching (incl. CFG/conds etc) over keeping the model resident. In
+        #the vast majority of setups a little bit of offloading on the giant model more
+        #than pays for CFG. So return everything both torch and Aimdo could give us
+        aimdo_mem = 0
+        if comfy.memory_management.aimdo_enabled:
+            aimdo_mem = comfy_aimdo.model_vbar.vbars_analyze()
+        return comfy.model_management.get_free_memory(device) + aimdo_mem
 
     def get_clone_model_override(self):
         return self.model, (self.backup, self.backup_buffers, self.object_patches_backup, self.pinned)
@@ -1465,12 +1471,6 @@ class ModelPatcherDynamic(ModelPatcher):
         vbar = self._vbar_get()
         return (vbar.loaded_size() if vbar is not None else 0) + self.model.model_loaded_weight_memory
 
-    def get_free_memory(self, device):
-        #NOTE: on high condition / batch counts, estimate should have already vacated
-        #all non-dynamic models so this is safe even if its not 100% true that this
-        #would all be avaiable for inference use.
-        return comfy.model_management.get_total_memory(device) - self.model_size()
-
     #Pinning is deferred to ops time. Assert against this API to avoid pin leaks.
 
     def pin_weight_to_device(self, key):
diff --git a/comfy/ops.py b/comfy/ops.py
index 6ee6075fb..8275dd0a5 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -269,8 +269,8 @@ def uncast_bias_weight(s, weight, bias, offload_stream):
         return
     os, weight_a, bias_a = offload_stream
     device=None
-    #FIXME: This is not good RTTI
-    if not isinstance(weight_a, torch.Tensor):
+    #FIXME: This is really bad RTTI
+    if weight_a is not None and not isinstance(weight_a, torch.Tensor):
         comfy_aimdo.model_vbar.vbar_unpin(s._v)
         device = weight_a
     if os is None:
diff --git a/requirements.txt b/requirements.txt
index 608b0cfa6..110568cd3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -22,7 +22,7 @@ alembic
 SQLAlchemy
 av>=14.2.0
 comfy-kitchen>=0.2.7
-comfy-aimdo>=0.2.4
+comfy-aimdo>=0.2.5
 requests
 
 #non essential dependencies:

From 0a7446ade4bbeecfaf36e9a70eeabbeb0f6e59ea Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?=
 <40791699+kijai@users.noreply.github.com>
Date: Wed, 4 Mar 2026 18:59:56 +0200
Subject: [PATCH 101/194] Pass tokens when loading text gen model for text
 generation (#12755)

Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
---
 comfy/sd.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index a9ad7c2d2..8bcd09582 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -428,7 +428,7 @@ class CLIP:
     def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None):
         self.cond_stage_model.reset_clip_options()
 
-        self.load_model()
+        self.load_model(tokens)
         self.cond_stage_model.set_clip_options({"layer": None})
         self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device})
         return self.cond_stage_model.generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed)

From 8811db52db5d0aea49c1dbedd733a6b9304b83a9 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Wed, 4 Mar 2026 12:12:37 -0800
Subject: [PATCH 102/194] comfy-aimdo 0.2.6 (#12764)

Comfy Aimdo 0.2.6 fixes a GPU virtual address leak. This would manfiest
as an error after a number of workflow runs.
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 110568cd3..dae46d873 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -22,7 +22,7 @@ alembic
 SQLAlchemy
 av>=14.2.0
 comfy-kitchen>=0.2.7
-comfy-aimdo>=0.2.5
+comfy-aimdo>=0.2.6
 requests
 
 #non essential dependencies:

From ac4a943ff364885166def5d418582db971554caf Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Wed, 4 Mar 2026 13:33:14 -0800
Subject: [PATCH 103/194] Initial load device should be cpu when using dynamic
 vram. (#12766)

---
 comfy/model_management.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 0f5966371..809600815 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -830,11 +830,14 @@ def unet_offload_device():
         return torch.device("cpu")
 
 def unet_inital_load_device(parameters, dtype):
+    cpu_dev = torch.device("cpu")
+    if comfy.memory_management.aimdo_enabled:
+        return cpu_dev
+
     torch_dev = get_torch_device()
     if vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.SHARED:
         return torch_dev
 
-    cpu_dev = torch.device("cpu")
     if DISABLE_SMART_MEMORY or vram_state == VRAMState.NO_VRAM:
         return cpu_dev
 
@@ -842,7 +845,7 @@ def unet_inital_load_device(parameters, dtype):
 
     mem_dev = get_free_memory(torch_dev)
     mem_cpu = get_free_memory(cpu_dev)
-    if mem_dev > mem_cpu and model_size < mem_dev and comfy.memory_management.aimdo_enabled:
+    if mem_dev > mem_cpu and model_size < mem_dev:
         return torch_dev
     else:
         return cpu_dev
@@ -945,6 +948,9 @@ def text_encoder_device():
         return torch.device("cpu")
 
 def text_encoder_initial_device(load_device, offload_device, model_size=0):
+    if comfy.memory_management.aimdo_enabled:
+        return offload_device
+
     if load_device == offload_device or model_size <= 1024 * 1024 * 1024:
         return offload_device
 

From 43c64b6308f93c331f057e12799bad0a68be5117 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Wed, 4 Mar 2026 17:06:20 -0800
Subject: [PATCH 104/194] Support the LTXAV 2.3 model. (#12773)

---
 comfy/ldm/lightricks/av_model.py              | 185 ++++++-
 comfy/ldm/lightricks/embeddings_connector.py  |   4 +
 comfy/ldm/lightricks/model.py                 | 186 ++++++-
 comfy/ldm/lightricks/vae/audio_vae.py         |   7 +-
 .../vae/causal_audio_autoencoder.py           |  67 +--
 .../vae/causal_video_autoencoder.py           |  48 +-
 comfy/ldm/lightricks/vocoders/vocoder.py      | 523 +++++++++++++++++-
 comfy/model_base.py                           |   2 +-
 comfy/sd.py                                   |   2 +-
 comfy/text_encoders/lt.py                     |  68 ++-
 10 files changed, 959 insertions(+), 133 deletions(-)

diff --git a/comfy/ldm/lightricks/av_model.py b/comfy/ldm/lightricks/av_model.py
index 553fd5b38..08d686b7b 100644
--- a/comfy/ldm/lightricks/av_model.py
+++ b/comfy/ldm/lightricks/av_model.py
@@ -2,11 +2,16 @@ from typing import Tuple
 import torch
 import torch.nn as nn
 from comfy.ldm.lightricks.model import (
+    ADALN_BASE_PARAMS_COUNT,
+    ADALN_CROSS_ATTN_PARAMS_COUNT,
     CrossAttention,
     FeedForward,
     AdaLayerNormSingle,
     PixArtAlphaTextProjection,
+    NormSingleLinearTextProjection,
     LTXVModel,
+    apply_cross_attention_adaln,
+    compute_prompt_timestep,
 )
 from comfy.ldm.lightricks.symmetric_patchifier import AudioPatchifier
 from comfy.ldm.lightricks.embeddings_connector import Embeddings1DConnector
@@ -87,6 +92,8 @@ class BasicAVTransformerBlock(nn.Module):
         v_context_dim=None,
         a_context_dim=None,
         attn_precision=None,
+        apply_gated_attention=False,
+        cross_attention_adaln=False,
         dtype=None,
         device=None,
         operations=None,
@@ -94,6 +101,7 @@ class BasicAVTransformerBlock(nn.Module):
         super().__init__()
 
         self.attn_precision = attn_precision
+        self.cross_attention_adaln = cross_attention_adaln
 
         self.attn1 = CrossAttention(
             query_dim=v_dim,
@@ -101,6 +109,7 @@ class BasicAVTransformerBlock(nn.Module):
             dim_head=vd_head,
             context_dim=None,
             attn_precision=self.attn_precision,
+            apply_gated_attention=apply_gated_attention,
             dtype=dtype,
             device=device,
             operations=operations,
@@ -111,6 +120,7 @@ class BasicAVTransformerBlock(nn.Module):
             dim_head=ad_head,
             context_dim=None,
             attn_precision=self.attn_precision,
+            apply_gated_attention=apply_gated_attention,
             dtype=dtype,
             device=device,
             operations=operations,
@@ -122,6 +132,7 @@ class BasicAVTransformerBlock(nn.Module):
             heads=v_heads,
             dim_head=vd_head,
             attn_precision=self.attn_precision,
+            apply_gated_attention=apply_gated_attention,
             dtype=dtype,
             device=device,
             operations=operations,
@@ -132,6 +143,7 @@ class BasicAVTransformerBlock(nn.Module):
             heads=a_heads,
             dim_head=ad_head,
             attn_precision=self.attn_precision,
+            apply_gated_attention=apply_gated_attention,
             dtype=dtype,
             device=device,
             operations=operations,
@@ -144,6 +156,7 @@ class BasicAVTransformerBlock(nn.Module):
             heads=a_heads,
             dim_head=ad_head,
             attn_precision=self.attn_precision,
+            apply_gated_attention=apply_gated_attention,
             dtype=dtype,
             device=device,
             operations=operations,
@@ -156,6 +169,7 @@ class BasicAVTransformerBlock(nn.Module):
             heads=a_heads,
             dim_head=ad_head,
             attn_precision=self.attn_precision,
+            apply_gated_attention=apply_gated_attention,
             dtype=dtype,
             device=device,
             operations=operations,
@@ -168,11 +182,16 @@ class BasicAVTransformerBlock(nn.Module):
             a_dim, dim_out=a_dim, glu=True, dtype=dtype, device=device, operations=operations
         )
 
-        self.scale_shift_table = nn.Parameter(torch.empty(6, v_dim, device=device, dtype=dtype))
+        num_ada_params = ADALN_CROSS_ATTN_PARAMS_COUNT if cross_attention_adaln else ADALN_BASE_PARAMS_COUNT
+        self.scale_shift_table = nn.Parameter(torch.empty(num_ada_params, v_dim, device=device, dtype=dtype))
         self.audio_scale_shift_table = nn.Parameter(
-            torch.empty(6, a_dim, device=device, dtype=dtype)
+            torch.empty(num_ada_params, a_dim, device=device, dtype=dtype)
         )
 
+        if cross_attention_adaln:
+            self.prompt_scale_shift_table = nn.Parameter(torch.empty(2, v_dim, device=device, dtype=dtype))
+            self.audio_prompt_scale_shift_table = nn.Parameter(torch.empty(2, a_dim, device=device, dtype=dtype))
+
         self.scale_shift_table_a2v_ca_audio = nn.Parameter(
             torch.empty(5, a_dim, device=device, dtype=dtype)
         )
@@ -215,10 +234,30 @@ class BasicAVTransformerBlock(nn.Module):
 
         return (*scale_shift_ada_values, *gate_ada_values)
 
+    def _apply_text_cross_attention(
+        self, x, context, attn, scale_shift_table, prompt_scale_shift_table,
+        timestep, prompt_timestep, attention_mask, transformer_options,
+    ):
+        """Apply text cross-attention, with optional ADaLN modulation."""
+        if self.cross_attention_adaln:
+            shift_q, scale_q, gate = self.get_ada_values(
+                scale_shift_table, x.shape[0], timestep, slice(6, 9)
+            )
+            return apply_cross_attention_adaln(
+                x, context, attn, shift_q, scale_q, gate,
+                prompt_scale_shift_table, prompt_timestep,
+                attention_mask, transformer_options,
+            )
+        return attn(
+            comfy.ldm.common_dit.rms_norm(x), context=context,
+            mask=attention_mask, transformer_options=transformer_options,
+        )
+
     def forward(
         self, x: Tuple[torch.Tensor, torch.Tensor], v_context=None, a_context=None, attention_mask=None, v_timestep=None, a_timestep=None,
         v_pe=None, a_pe=None, v_cross_pe=None, a_cross_pe=None, v_cross_scale_shift_timestep=None, a_cross_scale_shift_timestep=None,
         v_cross_gate_timestep=None, a_cross_gate_timestep=None, transformer_options=None, self_attention_mask=None,
+        v_prompt_timestep=None, a_prompt_timestep=None,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         run_vx = transformer_options.get("run_vx", True)
         run_ax = transformer_options.get("run_ax", True)
@@ -240,7 +279,11 @@ class BasicAVTransformerBlock(nn.Module):
             vgate_msa = self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(2, 3))[0]
             vx.addcmul_(attn1_out, vgate_msa)
             del vgate_msa, attn1_out
-            vx.add_(self.attn2(comfy.ldm.common_dit.rms_norm(vx), context=v_context, mask=attention_mask, transformer_options=transformer_options))
+            vx.add_(self._apply_text_cross_attention(
+                vx, v_context, self.attn2, self.scale_shift_table,
+                getattr(self, 'prompt_scale_shift_table', None),
+                v_timestep, v_prompt_timestep, attention_mask, transformer_options,)
+            )
 
         # audio
         if run_ax:
@@ -254,7 +297,11 @@ class BasicAVTransformerBlock(nn.Module):
             agate_msa = self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(2, 3))[0]
             ax.addcmul_(attn1_out, agate_msa)
             del agate_msa, attn1_out
-            ax.add_(self.audio_attn2(comfy.ldm.common_dit.rms_norm(ax), context=a_context, mask=attention_mask, transformer_options=transformer_options))
+            ax.add_(self._apply_text_cross_attention(
+                ax, a_context, self.audio_attn2, self.audio_scale_shift_table,
+                getattr(self, 'audio_prompt_scale_shift_table', None),
+                a_timestep, a_prompt_timestep, attention_mask, transformer_options,)
+            )
 
         # video - audio cross attention.
         if run_a2v or run_v2a:
@@ -351,6 +398,9 @@ class LTXAVModel(LTXVModel):
         use_middle_indices_grid=False,
         timestep_scale_multiplier=1000.0,
         av_ca_timestep_scale_multiplier=1.0,
+        apply_gated_attention=False,
+        caption_proj_before_connector=False,
+        cross_attention_adaln=False,
         dtype=None,
         device=None,
         operations=None,
@@ -362,6 +412,7 @@ class LTXAVModel(LTXVModel):
         self.audio_attention_head_dim = audio_attention_head_dim
         self.audio_num_attention_heads = audio_num_attention_heads
         self.audio_positional_embedding_max_pos = audio_positional_embedding_max_pos
+        self.apply_gated_attention = apply_gated_attention
 
         # Calculate audio dimensions
         self.audio_inner_dim = audio_num_attention_heads * audio_attention_head_dim
@@ -386,6 +437,8 @@ class LTXAVModel(LTXVModel):
             vae_scale_factors=vae_scale_factors,
             use_middle_indices_grid=use_middle_indices_grid,
             timestep_scale_multiplier=timestep_scale_multiplier,
+            caption_proj_before_connector=caption_proj_before_connector,
+            cross_attention_adaln=cross_attention_adaln,
             dtype=dtype,
             device=device,
             operations=operations,
@@ -400,14 +453,28 @@ class LTXAVModel(LTXVModel):
         )
 
         # Audio-specific AdaLN
+        audio_embedding_coefficient = ADALN_CROSS_ATTN_PARAMS_COUNT if self.cross_attention_adaln else ADALN_BASE_PARAMS_COUNT
         self.audio_adaln_single = AdaLayerNormSingle(
             self.audio_inner_dim,
+            embedding_coefficient=audio_embedding_coefficient,
             use_additional_conditions=False,
             dtype=dtype,
             device=device,
             operations=self.operations,
         )
 
+        if self.cross_attention_adaln:
+            self.audio_prompt_adaln_single = AdaLayerNormSingle(
+                self.audio_inner_dim,
+                embedding_coefficient=2,
+                use_additional_conditions=False,
+                dtype=dtype,
+                device=device,
+                operations=self.operations,
+            )
+        else:
+            self.audio_prompt_adaln_single = None
+
         num_scale_shift_values = 4
         self.av_ca_video_scale_shift_adaln_single = AdaLayerNormSingle(
             self.inner_dim,
@@ -443,35 +510,73 @@ class LTXAVModel(LTXVModel):
         )
 
         # Audio caption projection
-        self.audio_caption_projection = PixArtAlphaTextProjection(
-            in_features=self.caption_channels,
-            hidden_size=self.audio_inner_dim,
-            dtype=dtype,
-            device=device,
-            operations=self.operations,
-        )
+        if self.caption_proj_before_connector:
+            if self.caption_projection_first_linear:
+                self.audio_caption_projection = NormSingleLinearTextProjection(
+                    in_features=self.caption_channels,
+                    hidden_size=self.audio_inner_dim,
+                    dtype=dtype,
+                    device=device,
+                    operations=self.operations,
+                )
+            else:
+                self.audio_caption_projection = lambda a: a
+        else:
+            self.audio_caption_projection = PixArtAlphaTextProjection(
+                in_features=self.caption_channels,
+                hidden_size=self.audio_inner_dim,
+                dtype=dtype,
+                device=device,
+                operations=self.operations,
+            )
+
+        connector_split_rope = kwargs.get("rope_type", "split") == "split"
+        connector_gated_attention = kwargs.get("connector_apply_gated_attention", False)
+        attention_head_dim = kwargs.get("connector_attention_head_dim", 128)
+        num_attention_heads = kwargs.get("connector_num_attention_heads", 30)
+        num_layers = kwargs.get("connector_num_layers", 2)
 
         self.audio_embeddings_connector = Embeddings1DConnector(
-            split_rope=True,
+            attention_head_dim=kwargs.get("audio_connector_attention_head_dim", attention_head_dim),
+            num_attention_heads=kwargs.get("audio_connector_num_attention_heads", num_attention_heads),
+            num_layers=num_layers,
+            split_rope=connector_split_rope,
             double_precision_rope=True,
+            apply_gated_attention=connector_gated_attention,
             dtype=dtype,
             device=device,
             operations=self.operations,
         )
 
         self.video_embeddings_connector = Embeddings1DConnector(
-            split_rope=True,
+            attention_head_dim=attention_head_dim,
+            num_attention_heads=num_attention_heads,
+            num_layers=num_layers,
+            split_rope=connector_split_rope,
             double_precision_rope=True,
+            apply_gated_attention=connector_gated_attention,
             dtype=dtype,
             device=device,
             operations=self.operations,
         )
 
-    def preprocess_text_embeds(self, context):
-        if context.shape[-1] == self.caption_channels * 2:
-            return context
-        out_vid = self.video_embeddings_connector(context)[0]
-        out_audio = self.audio_embeddings_connector(context)[0]
+    def preprocess_text_embeds(self, context, unprocessed=False):
+        # LTXv2 fully processed context has dimension of self.caption_channels * 2
+        # LTXv2.3 fully processed context has dimension of self.cross_attention_dim + self.audio_cross_attention_dim
+        if not unprocessed:
+            if context.shape[-1] in (self.cross_attention_dim + self.audio_cross_attention_dim, self.caption_channels * 2):
+                return context
+        if context.shape[-1] == self.cross_attention_dim + self.audio_cross_attention_dim:
+            context_vid = context[:, :, :self.cross_attention_dim]
+            context_audio = context[:, :, self.cross_attention_dim:]
+        else:
+            context_vid = context
+            context_audio = context
+        if self.caption_proj_before_connector:
+            context_vid = self.caption_projection(context_vid)
+            context_audio = self.audio_caption_projection(context_audio)
+        out_vid = self.video_embeddings_connector(context_vid)[0]
+        out_audio = self.audio_embeddings_connector(context_audio)[0]
         return torch.concat((out_vid, out_audio), dim=-1)
 
     def _init_transformer_blocks(self, device, dtype, **kwargs):
@@ -487,6 +592,8 @@ class LTXAVModel(LTXVModel):
                     ad_head=self.audio_attention_head_dim,
                     v_context_dim=self.cross_attention_dim,
                     a_context_dim=self.audio_cross_attention_dim,
+                    apply_gated_attention=self.apply_gated_attention,
+                    cross_attention_adaln=self.cross_attention_adaln,
                     dtype=dtype,
                     device=device,
                     operations=self.operations,
@@ -608,6 +715,10 @@ class LTXAVModel(LTXVModel):
         v_timestep = CompressedTimestep(v_timestep.view(batch_size, -1, v_timestep.shape[-1]), v_patches_per_frame)
         v_embedded_timestep = CompressedTimestep(v_embedded_timestep.view(batch_size, -1, v_embedded_timestep.shape[-1]), v_patches_per_frame)
 
+        v_prompt_timestep = compute_prompt_timestep(
+            self.prompt_adaln_single, timestep_scaled, batch_size, hidden_dtype
+        )
+
         # Prepare audio timestep
         a_timestep = kwargs.get("a_timestep")
         if a_timestep is not None:
@@ -618,25 +729,25 @@ class LTXAVModel(LTXVModel):
 
             # Cross-attention timesteps - compress these too
             av_ca_audio_scale_shift_timestep, _ = self.av_ca_audio_scale_shift_adaln_single(
-                a_timestep_flat,
+                timestep.max().expand_as(a_timestep_flat),
                 {"resolution": None, "aspect_ratio": None},
                 batch_size=batch_size,
                 hidden_dtype=hidden_dtype,
             )
             av_ca_video_scale_shift_timestep, _ = self.av_ca_video_scale_shift_adaln_single(
-                timestep_flat,
+                a_timestep.max().expand_as(timestep_flat),
                 {"resolution": None, "aspect_ratio": None},
                 batch_size=batch_size,
                 hidden_dtype=hidden_dtype,
             )
             av_ca_a2v_gate_noise_timestep, _ = self.av_ca_a2v_gate_adaln_single(
-                timestep_flat * av_ca_factor,
+                a_timestep.max().expand_as(timestep_flat) * av_ca_factor,
                 {"resolution": None, "aspect_ratio": None},
                 batch_size=batch_size,
                 hidden_dtype=hidden_dtype,
             )
             av_ca_v2a_gate_noise_timestep, _ = self.av_ca_v2a_gate_adaln_single(
-                a_timestep_flat * av_ca_factor,
+                timestep.max().expand_as(a_timestep_flat) * av_ca_factor,
                 {"resolution": None, "aspect_ratio": None},
                 batch_size=batch_size,
                 hidden_dtype=hidden_dtype,
@@ -660,29 +771,40 @@ class LTXAVModel(LTXVModel):
             # Audio timesteps
             a_timestep = a_timestep.view(batch_size, -1, a_timestep.shape[-1])
             a_embedded_timestep = a_embedded_timestep.view(batch_size, -1, a_embedded_timestep.shape[-1])
+
+            a_prompt_timestep = compute_prompt_timestep(
+                self.audio_prompt_adaln_single, a_timestep_scaled, batch_size, hidden_dtype
+            )
         else:
             a_timestep = timestep_scaled
             a_embedded_timestep = kwargs.get("embedded_timestep")
             cross_av_timestep_ss = []
+            a_prompt_timestep = None
 
-        return [v_timestep, a_timestep, cross_av_timestep_ss], [
+        return [v_timestep, a_timestep, cross_av_timestep_ss, v_prompt_timestep, a_prompt_timestep], [
             v_embedded_timestep,
             a_embedded_timestep,
-        ]
+        ], None
 
     def _prepare_context(self, context, batch_size, x, attention_mask=None):
         vx = x[0]
         ax = x[1]
+        video_dim = vx.shape[-1]
+        audio_dim = ax.shape[-1]
+
+        v_context_dim = self.caption_channels if self.caption_proj_before_connector is False else video_dim
+        a_context_dim = self.caption_channels if self.caption_proj_before_connector is False else audio_dim
+
         v_context, a_context = torch.split(
-            context, int(context.shape[-1] / 2), len(context.shape) - 1
+            context, [v_context_dim, a_context_dim], len(context.shape) - 1
         )
 
         v_context, attention_mask = super()._prepare_context(
             v_context, batch_size, vx, attention_mask
         )
-        if self.audio_caption_projection is not None:
+        if self.caption_proj_before_connector is False:
             a_context = self.audio_caption_projection(a_context)
-            a_context = a_context.view(batch_size, -1, ax.shape[-1])
+        a_context = a_context.view(batch_size, -1, audio_dim)
 
         return [v_context, a_context], attention_mask
 
@@ -744,6 +866,9 @@ class LTXAVModel(LTXVModel):
             av_ca_v2a_gate_noise_timestep,
         ) = timestep[2]
 
+        v_prompt_timestep = timestep[3]
+        a_prompt_timestep = timestep[4]
+
         """Process transformer blocks for LTXAV."""
         patches_replace = transformer_options.get("patches_replace", {})
         blocks_replace = patches_replace.get("dit", {})
@@ -771,6 +896,8 @@ class LTXAVModel(LTXVModel):
                         a_cross_gate_timestep=args["a_cross_gate_timestep"],
                         transformer_options=args["transformer_options"],
                         self_attention_mask=args.get("self_attention_mask"),
+                        v_prompt_timestep=args.get("v_prompt_timestep"),
+                        a_prompt_timestep=args.get("a_prompt_timestep"),
                     )
                     return out
 
@@ -792,6 +919,8 @@ class LTXAVModel(LTXVModel):
                         "a_cross_gate_timestep": av_ca_v2a_gate_noise_timestep,
                         "transformer_options": transformer_options,
                         "self_attention_mask": self_attention_mask,
+                        "v_prompt_timestep": v_prompt_timestep,
+                        "a_prompt_timestep": a_prompt_timestep,
                     },
                     {"original_block": block_wrap},
                 )
@@ -814,6 +943,8 @@ class LTXAVModel(LTXVModel):
                     a_cross_gate_timestep=av_ca_v2a_gate_noise_timestep,
                     transformer_options=transformer_options,
                     self_attention_mask=self_attention_mask,
+                    v_prompt_timestep=v_prompt_timestep,
+                    a_prompt_timestep=a_prompt_timestep,
                 )
 
         return [vx, ax]
diff --git a/comfy/ldm/lightricks/embeddings_connector.py b/comfy/ldm/lightricks/embeddings_connector.py
index 33adb9671..2811080be 100644
--- a/comfy/ldm/lightricks/embeddings_connector.py
+++ b/comfy/ldm/lightricks/embeddings_connector.py
@@ -50,6 +50,7 @@ class BasicTransformerBlock1D(nn.Module):
         d_head,
         context_dim=None,
         attn_precision=None,
+        apply_gated_attention=False,
         dtype=None,
         device=None,
         operations=None,
@@ -63,6 +64,7 @@ class BasicTransformerBlock1D(nn.Module):
             heads=n_heads,
             dim_head=d_head,
             context_dim=None,
+            apply_gated_attention=apply_gated_attention,
             dtype=dtype,
             device=device,
             operations=operations,
@@ -121,6 +123,7 @@ class Embeddings1DConnector(nn.Module):
         positional_embedding_max_pos=[4096],
         causal_temporal_positioning=False,
         num_learnable_registers: Optional[int] = 128,
+        apply_gated_attention=False,
         dtype=None,
         device=None,
         operations=None,
@@ -145,6 +148,7 @@ class Embeddings1DConnector(nn.Module):
                     num_attention_heads,
                     attention_head_dim,
                     context_dim=cross_attention_dim,
+                    apply_gated_attention=apply_gated_attention,
                     dtype=dtype,
                     device=device,
                     operations=operations,
diff --git a/comfy/ldm/lightricks/model.py b/comfy/ldm/lightricks/model.py
index 60d760d29..bfbc08357 100644
--- a/comfy/ldm/lightricks/model.py
+++ b/comfy/ldm/lightricks/model.py
@@ -275,6 +275,30 @@ class PixArtAlphaTextProjection(nn.Module):
         return hidden_states
 
 
+class NormSingleLinearTextProjection(nn.Module):
+    """Text projection for 20B models - single linear with RMSNorm (no activation)."""
+
+    def __init__(
+        self, in_features, hidden_size, dtype=None, device=None, operations=None
+    ):
+        super().__init__()
+        if operations is None:
+            operations = comfy.ops.disable_weight_init
+        self.in_norm = operations.RMSNorm(
+            in_features, eps=1e-6, elementwise_affine=False
+        )
+        self.linear_1 = operations.Linear(
+            in_features, hidden_size, bias=True, dtype=dtype, device=device
+        )
+        self.hidden_size = hidden_size
+        self.in_features = in_features
+
+    def forward(self, caption):
+        caption = self.in_norm(caption)
+        caption = caption * (self.hidden_size / self.in_features) ** 0.5
+        return self.linear_1(caption)
+
+
 class GELU_approx(nn.Module):
     def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=None):
         super().__init__()
@@ -343,6 +367,7 @@ class CrossAttention(nn.Module):
         dim_head=64,
         dropout=0.0,
         attn_precision=None,
+        apply_gated_attention=False,
         dtype=None,
         device=None,
         operations=None,
@@ -362,6 +387,12 @@ class CrossAttention(nn.Module):
         self.to_k = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device)
         self.to_v = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device)
 
+        # Optional per-head gating
+        if apply_gated_attention:
+            self.to_gate_logits = operations.Linear(query_dim, heads, bias=True, dtype=dtype, device=device)
+        else:
+            self.to_gate_logits = None
+
         self.to_out = nn.Sequential(
             operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout)
         )
@@ -383,16 +414,30 @@ class CrossAttention(nn.Module):
             out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options)
         else:
             out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options)
+
+        # Apply per-head gating if enabled
+        if self.to_gate_logits is not None:
+            gate_logits = self.to_gate_logits(x)  # (B, T, H)
+            b, t, _ = out.shape
+            out = out.view(b, t, self.heads, self.dim_head)
+            gates = 2.0 * torch.sigmoid(gate_logits)  # zero-init -> identity
+            out = out * gates.unsqueeze(-1)
+            out = out.view(b, t, self.heads * self.dim_head)
+
         return self.to_out(out)
 
+# 6 base ADaLN params (shift/scale/gate for MSA + MLP), +3 for cross-attention Q (shift/scale/gate)
+ADALN_BASE_PARAMS_COUNT = 6
+ADALN_CROSS_ATTN_PARAMS_COUNT = 9
 
 class BasicTransformerBlock(nn.Module):
     def __init__(
-        self, dim, n_heads, d_head, context_dim=None, attn_precision=None, dtype=None, device=None, operations=None
+        self, dim, n_heads, d_head, context_dim=None, attn_precision=None, cross_attention_adaln=False, dtype=None, device=None, operations=None
     ):
         super().__init__()
 
         self.attn_precision = attn_precision
+        self.cross_attention_adaln = cross_attention_adaln
         self.attn1 = CrossAttention(
             query_dim=dim,
             heads=n_heads,
@@ -416,18 +461,25 @@ class BasicTransformerBlock(nn.Module):
             operations=operations,
         )
 
-        self.scale_shift_table = nn.Parameter(torch.empty(6, dim, device=device, dtype=dtype))
+        num_ada_params = ADALN_CROSS_ATTN_PARAMS_COUNT if cross_attention_adaln else ADALN_BASE_PARAMS_COUNT
+        self.scale_shift_table = nn.Parameter(torch.empty(num_ada_params, dim, device=device, dtype=dtype))
 
-    def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None, transformer_options={}, self_attention_mask=None):
-        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)).unbind(dim=2)
+        if cross_attention_adaln:
+            self.prompt_scale_shift_table = nn.Parameter(torch.empty(2, dim, device=device, dtype=dtype))
 
-        attn1_input = comfy.ldm.common_dit.rms_norm(x)
-        attn1_input = torch.addcmul(attn1_input, attn1_input, scale_msa).add_(shift_msa)
-        attn1_input = self.attn1(attn1_input, pe=pe, mask=self_attention_mask, transformer_options=transformer_options)
-        x.addcmul_(attn1_input, gate_msa)
-        del attn1_input
+    def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None, transformer_options={}, self_attention_mask=None, prompt_timestep=None):
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None, :6].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)[:, :, :6, :]).unbind(dim=2)
 
-        x += self.attn2(x, context=context, mask=attention_mask, transformer_options=transformer_options)
+        x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe, mask=self_attention_mask, transformer_options=transformer_options) * gate_msa
+
+        if self.cross_attention_adaln:
+            shift_q_mca, scale_q_mca, gate_mca = (self.scale_shift_table[None, None, 6:9].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)[:, :, 6:9, :]).unbind(dim=2)
+            x += apply_cross_attention_adaln(
+                x, context, self.attn2, shift_q_mca, scale_q_mca, gate_mca,
+                self.prompt_scale_shift_table, prompt_timestep, attention_mask, transformer_options,
+            )
+        else:
+            x += self.attn2(x, context=context, mask=attention_mask, transformer_options=transformer_options)
 
         y = comfy.ldm.common_dit.rms_norm(x)
         y = torch.addcmul(y, y, scale_mlp).add_(shift_mlp)
@@ -435,6 +487,47 @@ class BasicTransformerBlock(nn.Module):
 
         return x
 
+def compute_prompt_timestep(adaln_module, timestep_scaled, batch_size, hidden_dtype):
+    """Compute a single global prompt timestep for cross-attention ADaLN.
+
+    Uses the max across tokens (matching JAX max_per_segment) and broadcasts
+    over text tokens.  Returns None when *adaln_module* is None.
+    """
+    if adaln_module is None:
+        return None
+    ts_input = (
+        timestep_scaled.max(dim=1, keepdim=True).values.flatten()
+        if timestep_scaled.dim() > 1
+        else timestep_scaled.flatten()
+    )
+    prompt_ts, _ = adaln_module(
+        ts_input,
+        {"resolution": None, "aspect_ratio": None},
+        batch_size=batch_size,
+        hidden_dtype=hidden_dtype,
+    )
+    return prompt_ts.view(batch_size, 1, prompt_ts.shape[-1])
+
+
+def apply_cross_attention_adaln(
+    x, context, attn, q_shift, q_scale, q_gate,
+    prompt_scale_shift_table, prompt_timestep,
+    attention_mask=None, transformer_options={},
+):
+    """Apply cross-attention with ADaLN modulation (shift/scale/gate on Q and KV).
+
+    Q params (q_shift, q_scale, q_gate) are pre-extracted by the caller so
+    that both regular tensors and CompressedTimestep are supported.
+    """
+    batch_size = x.shape[0]
+    shift_kv, scale_kv = (
+        prompt_scale_shift_table[None, None].to(device=x.device, dtype=x.dtype)
+        + prompt_timestep.reshape(batch_size, prompt_timestep.shape[1], 2, -1)
+    ).unbind(dim=2)
+    attn_input = comfy.ldm.common_dit.rms_norm(x) * (1 + q_scale) + q_shift
+    encoder_hidden_states = context * (1 + scale_kv) + shift_kv
+    return attn(attn_input, context=encoder_hidden_states, mask=attention_mask, transformer_options=transformer_options) * q_gate
+
 def get_fractional_positions(indices_grid, max_pos):
     n_pos_dims = indices_grid.shape[1]
     assert n_pos_dims == len(max_pos), f'Number of position dimensions ({n_pos_dims}) must match max_pos length ({len(max_pos)})'
@@ -556,6 +649,9 @@ class LTXBaseModel(torch.nn.Module, ABC):
         vae_scale_factors: tuple = (8, 32, 32),
         use_middle_indices_grid=False,
         timestep_scale_multiplier = 1000.0,
+        caption_proj_before_connector=False,
+        cross_attention_adaln=False,
+        caption_projection_first_linear=True,
         dtype=None,
         device=None,
         operations=None,
@@ -582,6 +678,9 @@ class LTXBaseModel(torch.nn.Module, ABC):
         self.causal_temporal_positioning = causal_temporal_positioning
         self.operations = operations
         self.timestep_scale_multiplier = timestep_scale_multiplier
+        self.caption_proj_before_connector = caption_proj_before_connector
+        self.cross_attention_adaln = cross_attention_adaln
+        self.caption_projection_first_linear = caption_projection_first_linear
 
         # Common dimensions
         self.inner_dim = num_attention_heads * attention_head_dim
@@ -609,17 +708,37 @@ class LTXBaseModel(torch.nn.Module, ABC):
             self.in_channels, self.inner_dim, bias=True, dtype=dtype, device=device
         )
 
+        embedding_coefficient = ADALN_CROSS_ATTN_PARAMS_COUNT if self.cross_attention_adaln else ADALN_BASE_PARAMS_COUNT
         self.adaln_single = AdaLayerNormSingle(
-            self.inner_dim, use_additional_conditions=False, dtype=dtype, device=device, operations=self.operations
+            self.inner_dim, embedding_coefficient=embedding_coefficient, use_additional_conditions=False, dtype=dtype, device=device, operations=self.operations
         )
 
-        self.caption_projection = PixArtAlphaTextProjection(
-            in_features=self.caption_channels,
-            hidden_size=self.inner_dim,
-            dtype=dtype,
-            device=device,
-            operations=self.operations,
-        )
+        if self.cross_attention_adaln:
+            self.prompt_adaln_single = AdaLayerNormSingle(
+                self.inner_dim, embedding_coefficient=2, use_additional_conditions=False, dtype=dtype, device=device, operations=self.operations
+            )
+        else:
+            self.prompt_adaln_single = None
+
+        if self.caption_proj_before_connector:
+            if self.caption_projection_first_linear:
+                self.caption_projection = NormSingleLinearTextProjection(
+                    in_features=self.caption_channels,
+                    hidden_size=self.inner_dim,
+                    dtype=dtype,
+                    device=device,
+                    operations=self.operations,
+                )
+            else:
+                self.caption_projection = lambda a: a
+        else:
+            self.caption_projection = PixArtAlphaTextProjection(
+                in_features=self.caption_channels,
+                hidden_size=self.inner_dim,
+                dtype=dtype,
+                device=device,
+                operations=self.operations,
+            )
 
     @abstractmethod
     def _init_model_components(self, device, dtype, **kwargs):
@@ -665,9 +784,9 @@ class LTXBaseModel(torch.nn.Module, ABC):
         if grid_mask is not None:
             timestep = timestep[:, grid_mask]
 
-        timestep = timestep * self.timestep_scale_multiplier
+        timestep_scaled = timestep * self.timestep_scale_multiplier
         timestep, embedded_timestep = self.adaln_single(
-            timestep.flatten(),
+            timestep_scaled.flatten(),
             {"resolution": None, "aspect_ratio": None},
             batch_size=batch_size,
             hidden_dtype=hidden_dtype,
@@ -677,14 +796,18 @@ class LTXBaseModel(torch.nn.Module, ABC):
         timestep = timestep.view(batch_size, -1, timestep.shape[-1])
         embedded_timestep = embedded_timestep.view(batch_size, -1, embedded_timestep.shape[-1])
 
-        return timestep, embedded_timestep
+        prompt_timestep = compute_prompt_timestep(
+            self.prompt_adaln_single, timestep_scaled, batch_size, hidden_dtype
+        )
+
+        return timestep, embedded_timestep, prompt_timestep
 
     def _prepare_context(self, context, batch_size, x, attention_mask=None):
         """Prepare context for transformer blocks."""
-        if self.caption_projection is not None:
+        if self.caption_proj_before_connector is False:
             context = self.caption_projection(context)
-            context = context.view(batch_size, -1, x.shape[-1])
 
+        context = context.view(batch_size, -1, x.shape[-1])
         return context, attention_mask
 
     def _precompute_freqs_cis(
@@ -792,7 +915,8 @@ class LTXBaseModel(torch.nn.Module, ABC):
         merged_args.update(additional_args)
 
         # Prepare timestep and context
-        timestep, embedded_timestep = self._prepare_timestep(timestep, batch_size, input_dtype, **merged_args)
+        timestep, embedded_timestep, prompt_timestep = self._prepare_timestep(timestep, batch_size, input_dtype, **merged_args)
+        merged_args["prompt_timestep"] = prompt_timestep
         context, attention_mask = self._prepare_context(context, batch_size, x, attention_mask)
 
         # Prepare attention mask and positional embeddings
@@ -833,7 +957,9 @@ class LTXVModel(LTXBaseModel):
         causal_temporal_positioning=False,
         vae_scale_factors=(8, 32, 32),
         use_middle_indices_grid=False,
-        timestep_scale_multiplier = 1000.0,
+        timestep_scale_multiplier=1000.0,
+        caption_proj_before_connector=False,
+        cross_attention_adaln=False,
         dtype=None,
         device=None,
         operations=None,
@@ -852,6 +978,8 @@ class LTXVModel(LTXBaseModel):
             vae_scale_factors=vae_scale_factors,
             use_middle_indices_grid=use_middle_indices_grid,
             timestep_scale_multiplier=timestep_scale_multiplier,
+            caption_proj_before_connector=caption_proj_before_connector,
+            cross_attention_adaln=cross_attention_adaln,
             dtype=dtype,
             device=device,
             operations=operations,
@@ -860,7 +988,6 @@ class LTXVModel(LTXBaseModel):
 
     def _init_model_components(self, device, dtype, **kwargs):
         """Initialize LTXV-specific components."""
-        # No additional components needed for LTXV beyond base class
         pass
 
     def _init_transformer_blocks(self, device, dtype, **kwargs):
@@ -872,6 +999,7 @@ class LTXVModel(LTXBaseModel):
                     self.num_attention_heads,
                     self.attention_head_dim,
                     context_dim=self.cross_attention_dim,
+                    cross_attention_adaln=self.cross_attention_adaln,
                     dtype=dtype,
                     device=device,
                     operations=self.operations,
@@ -1149,16 +1277,17 @@ class LTXVModel(LTXBaseModel):
         """Process transformer blocks for LTXV."""
         patches_replace = transformer_options.get("patches_replace", {})
         blocks_replace = patches_replace.get("dit", {})
+        prompt_timestep = kwargs.get("prompt_timestep", None)
 
         for i, block in enumerate(self.transformer_blocks):
             if ("double_block", i) in blocks_replace:
 
                 def block_wrap(args):
                     out = {}
-                    out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"], transformer_options=args["transformer_options"], self_attention_mask=args.get("self_attention_mask"))
+                    out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"], transformer_options=args["transformer_options"], self_attention_mask=args.get("self_attention_mask"), prompt_timestep=args.get("prompt_timestep"))
                     return out
 
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe, "transformer_options": transformer_options, "self_attention_mask": self_attention_mask}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe, "transformer_options": transformer_options, "self_attention_mask": self_attention_mask, "prompt_timestep": prompt_timestep}, {"original_block": block_wrap})
                 x = out["img"]
             else:
                 x = block(
@@ -1169,6 +1298,7 @@ class LTXVModel(LTXBaseModel):
                     pe=pe,
                     transformer_options=transformer_options,
                     self_attention_mask=self_attention_mask,
+                    prompt_timestep=prompt_timestep,
                 )
 
         return x
diff --git a/comfy/ldm/lightricks/vae/audio_vae.py b/comfy/ldm/lightricks/vae/audio_vae.py
index 55a074661..fa0a00748 100644
--- a/comfy/ldm/lightricks/vae/audio_vae.py
+++ b/comfy/ldm/lightricks/vae/audio_vae.py
@@ -13,7 +13,7 @@ from comfy.ldm.lightricks.vae.causal_audio_autoencoder import (
     CausalityAxis,
     CausalAudioAutoencoder,
 )
-from comfy.ldm.lightricks.vocoders.vocoder import Vocoder
+from comfy.ldm.lightricks.vocoders.vocoder import Vocoder, VocoderWithBWE
 
 LATENT_DOWNSAMPLE_FACTOR = 4
 
@@ -141,7 +141,10 @@ class AudioVAE(torch.nn.Module):
         vocoder_sd = utils.state_dict_prefix_replace(state_dict, {"vocoder.": ""}, filter_keys=True)
 
         self.autoencoder = CausalAudioAutoencoder(config=component_config.autoencoder)
-        self.vocoder = Vocoder(config=component_config.vocoder)
+        if "bwe" in component_config.vocoder:
+            self.vocoder = VocoderWithBWE(config=component_config.vocoder)
+        else:
+            self.vocoder = Vocoder(config=component_config.vocoder)
 
         self.autoencoder.load_state_dict(vae_sd, strict=False)
         self.vocoder.load_state_dict(vocoder_sd, strict=False)
diff --git a/comfy/ldm/lightricks/vae/causal_audio_autoencoder.py b/comfy/ldm/lightricks/vae/causal_audio_autoencoder.py
index f12b9bb53..b556b128f 100644
--- a/comfy/ldm/lightricks/vae/causal_audio_autoencoder.py
+++ b/comfy/ldm/lightricks/vae/causal_audio_autoencoder.py
@@ -822,26 +822,23 @@ class CausalAudioAutoencoder(nn.Module):
         super().__init__()
 
         if config is None:
-            config = self._guess_config()
+            config = self.get_default_config()
 
-        # Extract encoder and decoder configs from the new format
         model_config = config.get("model", {}).get("params", {})
-        variables_config = config.get("variables", {})
 
-        self.sampling_rate = variables_config.get(
-            "sampling_rate",
-            model_config.get("sampling_rate", config.get("sampling_rate", 16000)),
+        self.sampling_rate = model_config.get(
+            "sampling_rate", config.get("sampling_rate", 16000)
         )
         encoder_config = model_config.get("encoder", model_config.get("ddconfig", {}))
         decoder_config = model_config.get("decoder", encoder_config)
 
         # Load mel spectrogram parameters
         self.mel_bins = encoder_config.get("mel_bins", 64)
-        self.mel_hop_length = model_config.get("preprocessing", {}).get("stft", {}).get("hop_length", 160)
-        self.n_fft = model_config.get("preprocessing", {}).get("stft", {}).get("filter_length", 1024)
+        self.mel_hop_length = config.get("preprocessing", {}).get("stft", {}).get("hop_length", 160)
+        self.n_fft = config.get("preprocessing", {}).get("stft", {}).get("filter_length", 1024)
 
         # Store causality configuration at VAE level (not just in encoder internals)
-        causality_axis_value = encoder_config.get("causality_axis", CausalityAxis.WIDTH.value)
+        causality_axis_value = encoder_config.get("causality_axis", CausalityAxis.HEIGHT.value)
         self.causality_axis = CausalityAxis.str_to_enum(causality_axis_value)
         self.is_causal = self.causality_axis == CausalityAxis.HEIGHT
 
@@ -850,44 +847,38 @@ class CausalAudioAutoencoder(nn.Module):
 
         self.per_channel_statistics = processor()
 
-    def _guess_config(self):
-        encoder_config = {
-            # Required parameters - based on ltx-video-av-1679000 model metadata
-            "ch": 128,
-            "out_ch": 8,
-            "ch_mult": [1, 2, 4],  # Based on metadata: [1, 2, 4] not [1, 2, 4, 8]
-            "num_res_blocks": 2,
-            "attn_resolutions": [],  # Based on metadata: empty list, no attention
-            "dropout": 0.0,
-            "resamp_with_conv": True,
-            "in_channels": 2,  # stereo
-            "resolution": 256,
-            "z_channels": 8,
+    def get_default_config(self):
+        ddconfig = {
             "double_z": True,
-            "attn_type": "vanilla",
-            "mid_block_add_attention": False,  # Based on metadata: false
+            "mel_bins": 64,
+            "z_channels": 8,
+            "resolution": 256,
+            "downsample_time": False,
+            "in_channels": 2,
+            "out_ch": 2,
+            "ch": 128,
+            "ch_mult": [1, 2, 4],
+            "num_res_blocks": 2,
+            "attn_resolutions": [],
+            "dropout": 0.0,
+            "mid_block_add_attention": False,
             "norm_type": "pixel",
-            "causality_axis": "height",  # Based on metadata
-            "mel_bins": 64,  # Based on metadata: mel_bins = 64
-        }
-
-        decoder_config = {
-            # Inherits encoder config, can override specific params
-            **encoder_config,
-            "out_ch": 2,  # Stereo audio output (2 channels)
-            "give_pre_end": False,
-            "tanh_out": False,
+            "causality_axis": "height",
         }
 
         config = {
-            "_class_name": "CausalAudioAutoencoder",
-            "sampling_rate": 16000,
             "model": {
                 "params": {
-                    "encoder": encoder_config,
-                    "decoder": decoder_config,
+                    "ddconfig": ddconfig,
+                    "sampling_rate": 16000,
                 }
             },
+            "preprocessing": {
+                "stft": {
+                    "filter_length": 1024,
+                    "hop_length": 160,
+                },
+            },
         }
 
         return config
diff --git a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py
index cbfdf412d..5b57dfc5e 100644
--- a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py
+++ b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py
@@ -15,6 +15,9 @@ from comfy.ldm.modules.diffusionmodules.model import torch_cat_if_needed
 
 ops = comfy.ops.disable_weight_init
 
+def in_meta_context():
+    return torch.device("meta") == torch.empty(0).device
+
 def mark_conv3d_ended(module):
     tid = threading.get_ident()
     for _, m in module.named_modules():
@@ -350,6 +353,10 @@ class Decoder(nn.Module):
                 output_channel = output_channel * block_params.get("multiplier", 2)
             if block_name == "compress_all":
                 output_channel = output_channel * block_params.get("multiplier", 1)
+            if block_name == "compress_space":
+                output_channel = output_channel * block_params.get("multiplier", 1)
+            if block_name == "compress_time":
+                output_channel = output_channel * block_params.get("multiplier", 1)
 
         self.conv_in = make_conv_nd(
             dims,
@@ -395,17 +402,21 @@ class Decoder(nn.Module):
                     spatial_padding_mode=spatial_padding_mode,
                 )
             elif block_name == "compress_time":
+                output_channel = output_channel // block_params.get("multiplier", 1)
                 block = DepthToSpaceUpsample(
                     dims=dims,
                     in_channels=input_channel,
                     stride=(2, 1, 1),
+                    out_channels_reduction_factor=block_params.get("multiplier", 1),
                     spatial_padding_mode=spatial_padding_mode,
                 )
             elif block_name == "compress_space":
+                output_channel = output_channel // block_params.get("multiplier", 1)
                 block = DepthToSpaceUpsample(
                     dims=dims,
                     in_channels=input_channel,
                     stride=(1, 2, 2),
+                    out_channels_reduction_factor=block_params.get("multiplier", 1),
                     spatial_padding_mode=spatial_padding_mode,
                 )
             elif block_name == "compress_all":
@@ -455,6 +466,15 @@ class Decoder(nn.Module):
                 output_channel * 2, 0, operations=ops,
             )
             self.last_scale_shift_table = nn.Parameter(torch.empty(2, output_channel))
+        else:
+            self.register_buffer(
+                "last_scale_shift_table",
+                torch.tensor(
+                    [0.0, 0.0],
+                    device="cpu" if in_meta_context() else None
+                ).unsqueeze(1).expand(2, output_channel),
+                persistent=False,
+            )
 
 
     # def forward(self, sample: torch.FloatTensor, target_shape) -> torch.FloatTensor:
@@ -883,6 +903,15 @@ class ResnetBlock3D(nn.Module):
             self.scale_shift_table = nn.Parameter(
                 torch.randn(4, in_channels) / in_channels**0.5
             )
+        else:
+            self.register_buffer(
+                "scale_shift_table",
+                torch.tensor(
+                    [0.0, 0.0, 0.0, 0.0],
+                    device="cpu" if in_meta_context() else None
+                ).unsqueeze(1).expand(4, in_channels),
+                persistent=False,
+            )
 
         self.temporal_cache_state={}
 
@@ -1012,9 +1041,6 @@ class processor(nn.Module):
         super().__init__()
         self.register_buffer("std-of-means", torch.empty(128))
         self.register_buffer("mean-of-means", torch.empty(128))
-        self.register_buffer("mean-of-stds", torch.empty(128))
-        self.register_buffer("mean-of-stds_over_std-of-means", torch.empty(128))
-        self.register_buffer("channel", torch.empty(128))
 
     def un_normalize(self, x):
         return (x * self.get_buffer("std-of-means").view(1, -1, 1, 1, 1).to(x)) + self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1).to(x)
@@ -1027,9 +1053,12 @@ class VideoVAE(nn.Module):
         super().__init__()
 
         if config is None:
-            config = self.guess_config(version)
+            config = self.get_default_config(version)
 
+        self.config = config
         self.timestep_conditioning = config.get("timestep_conditioning", False)
+        self.decode_noise_scale = config.get("decode_noise_scale", 0.025)
+        self.decode_timestep = config.get("decode_timestep", 0.05)
         double_z = config.get("double_z", True)
         latent_log_var = config.get(
             "latent_log_var", "per_channel" if double_z else "none"
@@ -1044,6 +1073,7 @@ class VideoVAE(nn.Module):
             latent_log_var=latent_log_var,
             norm_layer=config.get("norm_layer", "group_norm"),
             spatial_padding_mode=config.get("spatial_padding_mode", "zeros"),
+            base_channels=config.get("encoder_base_channels", 128),
         )
 
         self.decoder = Decoder(
@@ -1051,6 +1081,7 @@ class VideoVAE(nn.Module):
             in_channels=config["latent_channels"],
             out_channels=config.get("out_channels", 3),
             blocks=config.get("decoder_blocks", config.get("decoder_blocks", config.get("blocks"))),
+            base_channels=config.get("decoder_base_channels", 128),
             patch_size=config.get("patch_size", 1),
             norm_layer=config.get("norm_layer", "group_norm"),
             causal=config.get("causal_decoder", False),
@@ -1060,7 +1091,7 @@ class VideoVAE(nn.Module):
 
         self.per_channel_statistics = processor()
 
-    def guess_config(self, version):
+    def get_default_config(self, version):
         if version == 0:
             config = {
                 "_class_name": "CausalVideoAutoencoder",
@@ -1167,8 +1198,7 @@ class VideoVAE(nn.Module):
         means, logvar = torch.chunk(self.encoder(x), 2, dim=1)
         return self.per_channel_statistics.normalize(means)
 
-    def decode(self, x, timestep=0.05, noise_scale=0.025):
+    def decode(self, x):
         if self.timestep_conditioning: #TODO: seed
-            x = torch.randn_like(x) * noise_scale + (1.0 - noise_scale) * x
-        return self.decoder(self.per_channel_statistics.un_normalize(x), timestep=timestep)
-
+            x = torch.randn_like(x) * self.decode_noise_scale + (1.0 - self.decode_noise_scale) * x
+        return self.decoder(self.per_channel_statistics.un_normalize(x), timestep=self.decode_timestep)
diff --git a/comfy/ldm/lightricks/vocoders/vocoder.py b/comfy/ldm/lightricks/vocoders/vocoder.py
index b1f15f2c5..6c4028aa8 100644
--- a/comfy/ldm/lightricks/vocoders/vocoder.py
+++ b/comfy/ldm/lightricks/vocoders/vocoder.py
@@ -3,6 +3,7 @@ import torch.nn.functional as F
 import torch.nn as nn
 import comfy.ops
 import numpy as np
+import math
 
 ops = comfy.ops.disable_weight_init
 
@@ -12,6 +13,307 @@ def get_padding(kernel_size, dilation=1):
     return int((kernel_size * dilation - dilation) / 2)
 
 
+# ---------------------------------------------------------------------------
+# Anti-aliased resampling helpers (kaiser-sinc filters) for BigVGAN v2
+# Adopted from https://github.com/NVIDIA/BigVGAN
+# ---------------------------------------------------------------------------
+
+
+def _sinc(x: torch.Tensor):
+    return torch.where(
+        x == 0,
+        torch.tensor(1.0, device=x.device, dtype=x.dtype),
+        torch.sin(math.pi * x) / math.pi / x,
+    )
+
+
+def kaiser_sinc_filter1d(cutoff, half_width, kernel_size):
+    even = kernel_size % 2 == 0
+    half_size = kernel_size // 2
+    delta_f = 4 * half_width
+    A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
+    if A > 50.0:
+        beta = 0.1102 * (A - 8.7)
+    elif A >= 21.0:
+        beta = 0.5842 * (A - 21) ** 0.4 + 0.07886 * (A - 21.0)
+    else:
+        beta = 0.0
+    window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)
+    if even:
+        time = torch.arange(-half_size, half_size) + 0.5
+    else:
+        time = torch.arange(kernel_size) - half_size
+    if cutoff == 0:
+        filter_ = torch.zeros_like(time)
+    else:
+        filter_ = 2 * cutoff * window * _sinc(2 * cutoff * time)
+        filter_ /= filter_.sum()
+        filter = filter_.view(1, 1, kernel_size)
+    return filter
+
+
+class LowPassFilter1d(nn.Module):
+    def __init__(
+        self,
+        cutoff=0.5,
+        half_width=0.6,
+        stride=1,
+        padding=True,
+        padding_mode="replicate",
+        kernel_size=12,
+    ):
+        super().__init__()
+        if cutoff < -0.0:
+            raise ValueError("Minimum cutoff must be larger than zero.")
+        if cutoff > 0.5:
+            raise ValueError("A cutoff above 0.5 does not make sense.")
+        self.kernel_size = kernel_size
+        self.even = kernel_size % 2 == 0
+        self.pad_left = kernel_size // 2 - int(self.even)
+        self.pad_right = kernel_size // 2
+        self.stride = stride
+        self.padding = padding
+        self.padding_mode = padding_mode
+        filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size)
+        self.register_buffer("filter", filter)
+
+    def forward(self, x):
+        _, C, _ = x.shape
+        if self.padding:
+            x = F.pad(x, (self.pad_left, self.pad_right), mode=self.padding_mode)
+        return F.conv1d(x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
+
+
+class UpSample1d(nn.Module):
+    def __init__(self, ratio=2, kernel_size=None, persistent=True, window_type="kaiser"):
+        super().__init__()
+        self.ratio = ratio
+        self.stride = ratio
+
+        if window_type == "hann":
+            # Hann-windowed sinc filter — identical to torchaudio.functional.resample
+            # with its default parameters (rolloff=0.99, lowpass_filter_width=6).
+            # Uses replicate boundary padding, matching the reference resampler exactly.
+            rolloff = 0.99
+            lowpass_filter_width = 6
+            width = math.ceil(lowpass_filter_width / rolloff)
+            self.kernel_size = 2 * width * ratio + 1
+            self.pad = width
+            self.pad_left = 2 * width * ratio
+            self.pad_right = self.kernel_size - ratio
+            t = (torch.arange(self.kernel_size) / ratio - width) * rolloff
+            t_clamped = t.clamp(-lowpass_filter_width, lowpass_filter_width)
+            window = torch.cos(t_clamped * math.pi / lowpass_filter_width / 2) ** 2
+            filter = (torch.sinc(t) * window * rolloff / ratio).view(1, 1, -1)
+        else:
+            # Kaiser-windowed sinc filter (BigVGAN default).
+            self.kernel_size = (
+                int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+            )
+            self.pad = self.kernel_size // ratio - 1
+            self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
+            self.pad_right = (
+                self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
+            )
+            filter = kaiser_sinc_filter1d(
+                cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size
+            )
+
+        self.register_buffer("filter", filter, persistent=persistent)
+
+    def forward(self, x):
+        _, C, _ = x.shape
+        x = F.pad(x, (self.pad, self.pad), mode="replicate")
+        x = self.ratio * F.conv_transpose1d(
+            x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C
+        )
+        x = x[..., self.pad_left : -self.pad_right]
+        return x
+
+
+class DownSample1d(nn.Module):
+    def __init__(self, ratio=2, kernel_size=None):
+        super().__init__()
+        self.ratio = ratio
+        self.kernel_size = (
+            int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+        )
+        self.lowpass = LowPassFilter1d(
+            cutoff=0.5 / ratio,
+            half_width=0.6 / ratio,
+            stride=ratio,
+            kernel_size=self.kernel_size,
+        )
+
+    def forward(self, x):
+        return self.lowpass(x)
+
+
+class Activation1d(nn.Module):
+    def __init__(
+        self,
+        activation,
+        up_ratio=2,
+        down_ratio=2,
+        up_kernel_size=12,
+        down_kernel_size=12,
+    ):
+        super().__init__()
+        self.act = activation
+        self.upsample = UpSample1d(up_ratio, up_kernel_size)
+        self.downsample = DownSample1d(down_ratio, down_kernel_size)
+
+    def forward(self, x):
+        x = self.upsample(x)
+        x = self.act(x)
+        x = self.downsample(x)
+        return x
+
+
+# ---------------------------------------------------------------------------
+# BigVGAN v2 activations (Snake / SnakeBeta)
+# ---------------------------------------------------------------------------
+
+
+class Snake(nn.Module):
+    def __init__(
+        self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True
+    ):
+        super().__init__()
+        self.alpha_logscale = alpha_logscale
+        self.alpha = nn.Parameter(
+            torch.zeros(in_features)
+            if alpha_logscale
+            else torch.ones(in_features) * alpha
+        )
+        self.alpha.requires_grad = alpha_trainable
+        self.eps = 1e-9
+
+    def forward(self, x):
+        a = self.alpha.unsqueeze(0).unsqueeze(-1)
+        if self.alpha_logscale:
+            a = torch.exp(a)
+        return x + (1.0 / (a + self.eps)) * torch.sin(x * a).pow(2)
+
+
+class SnakeBeta(nn.Module):
+    def __init__(
+        self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True
+    ):
+        super().__init__()
+        self.alpha_logscale = alpha_logscale
+        self.alpha = nn.Parameter(
+            torch.zeros(in_features)
+            if alpha_logscale
+            else torch.ones(in_features) * alpha
+        )
+        self.alpha.requires_grad = alpha_trainable
+        self.beta = nn.Parameter(
+            torch.zeros(in_features)
+            if alpha_logscale
+            else torch.ones(in_features) * alpha
+        )
+        self.beta.requires_grad = alpha_trainable
+        self.eps = 1e-9
+
+    def forward(self, x):
+        a = self.alpha.unsqueeze(0).unsqueeze(-1)
+        b = self.beta.unsqueeze(0).unsqueeze(-1)
+        if self.alpha_logscale:
+            a = torch.exp(a)
+            b = torch.exp(b)
+        return x + (1.0 / (b + self.eps)) * torch.sin(x * a).pow(2)
+
+
+# ---------------------------------------------------------------------------
+# BigVGAN v2 AMPBlock (Anti-aliased Multi-Periodicity)
+# ---------------------------------------------------------------------------
+
+
+class AMPBlock1(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), activation="snake"):
+        super().__init__()
+        act_cls = SnakeBeta if activation == "snakebeta" else Snake
+        self.convs1 = nn.ModuleList(
+            [
+                ops.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=dilation[0],
+                    padding=get_padding(kernel_size, dilation[0]),
+                ),
+                ops.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=dilation[1],
+                    padding=get_padding(kernel_size, dilation[1]),
+                ),
+                ops.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=dilation[2],
+                    padding=get_padding(kernel_size, dilation[2]),
+                ),
+            ]
+        )
+
+        self.convs2 = nn.ModuleList(
+            [
+                ops.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=1,
+                    padding=get_padding(kernel_size, 1),
+                ),
+                ops.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=1,
+                    padding=get_padding(kernel_size, 1),
+                ),
+                ops.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=1,
+                    padding=get_padding(kernel_size, 1),
+                ),
+            ]
+        )
+
+        self.acts1 = nn.ModuleList(
+            [Activation1d(act_cls(channels)) for _ in range(len(self.convs1))]
+        )
+        self.acts2 = nn.ModuleList(
+            [Activation1d(act_cls(channels)) for _ in range(len(self.convs2))]
+        )
+
+    def forward(self, x):
+        for c1, c2, a1, a2 in zip(self.convs1, self.convs2, self.acts1, self.acts2):
+            xt = a1(x)
+            xt = c1(xt)
+            xt = a2(xt)
+            xt = c2(xt)
+            x = x + xt
+        return x
+
+
+# ---------------------------------------------------------------------------
+# HiFi-GAN residual blocks
+# ---------------------------------------------------------------------------
+
+
 class ResBlock1(torch.nn.Module):
     def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
         super(ResBlock1, self).__init__()
@@ -119,6 +421,7 @@ class Vocoder(torch.nn.Module):
     """
     Vocoder model for synthesizing audio from spectrograms, based on: https://github.com/jik876/hifi-gan.
 
+    Supports both HiFi-GAN (resblock "1"/"2") and BigVGAN v2 (resblock "AMP1").
     """
 
     def __init__(self, config=None):
@@ -128,19 +431,39 @@ class Vocoder(torch.nn.Module):
             config = self.get_default_config()
 
         resblock_kernel_sizes = config.get("resblock_kernel_sizes", [3, 7, 11])
-        upsample_rates = config.get("upsample_rates", [6, 5, 2, 2, 2])
-        upsample_kernel_sizes = config.get("upsample_kernel_sizes", [16, 15, 8, 4, 4])
+        upsample_rates = config.get("upsample_rates", [5, 4, 2, 2, 2])
+        upsample_kernel_sizes = config.get("upsample_kernel_sizes", [16, 16, 8, 4, 4])
         resblock_dilation_sizes = config.get("resblock_dilation_sizes", [[1, 3, 5], [1, 3, 5], [1, 3, 5]])
         upsample_initial_channel = config.get("upsample_initial_channel", 1024)
         stereo = config.get("stereo", True)
-        resblock = config.get("resblock", "1")
+        activation = config.get("activation", "snake")
+        use_bias_at_final = config.get("use_bias_at_final", True)
 
+
+        # "output_sample_rate" is not present in recent checkpoint configs.
+        # When absent (None), AudioVAE.output_sample_rate computes it as:
+        #   sample_rate * vocoder.upsample_factor / mel_hop_length
+        # where upsample_factor = product of all upsample stride lengths,
+        # and mel_hop_length is loaded from the autoencoder config at
+        # preprocessing.stft.hop_length (see CausalAudioAutoencoder).
         self.output_sample_rate = config.get("output_sample_rate")
+        self.resblock = config.get("resblock", "1")
+        self.use_tanh_at_final = config.get("use_tanh_at_final", True)
+        self.apply_final_activation = config.get("apply_final_activation", True)
         self.num_kernels = len(resblock_kernel_sizes)
         self.num_upsamples = len(upsample_rates)
+
         in_channels = 128 if stereo else 64
         self.conv_pre = ops.Conv1d(in_channels, upsample_initial_channel, 7, 1, padding=3)
-        resblock_class = ResBlock1 if resblock == "1" else ResBlock2
+
+        if self.resblock == "1":
+            resblock_cls = ResBlock1
+        elif self.resblock == "2":
+            resblock_cls = ResBlock2
+        elif self.resblock == "AMP1":
+            resblock_cls = AMPBlock1
+        else:
+            raise ValueError(f"Unknown resblock type: {self.resblock}")
 
         self.ups = nn.ModuleList()
         for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
@@ -157,25 +480,40 @@ class Vocoder(torch.nn.Module):
         self.resblocks = nn.ModuleList()
         for i in range(len(self.ups)):
             ch = upsample_initial_channel // (2 ** (i + 1))
-            for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
-                self.resblocks.append(resblock_class(ch, k, d))
+            for k, d in zip(resblock_kernel_sizes, resblock_dilation_sizes):
+                if self.resblock == "AMP1":
+                    self.resblocks.append(resblock_cls(ch, k, d, activation=activation))
+                else:
+                    self.resblocks.append(resblock_cls(ch, k, d))
 
         out_channels = 2 if stereo else 1
-        self.conv_post = ops.Conv1d(ch, out_channels, 7, 1, padding=3)
+        if self.resblock == "AMP1":
+            act_cls = SnakeBeta if activation == "snakebeta" else Snake
+            self.act_post = Activation1d(act_cls(ch))
+        else:
+            self.act_post = nn.LeakyReLU()
+
+        self.conv_post = ops.Conv1d(
+            ch, out_channels, 7, 1, padding=3, bias=use_bias_at_final
+        )
 
         self.upsample_factor = np.prod([self.ups[i].stride[0] for i in range(len(self.ups))])
 
+
     def get_default_config(self):
         """Generate default configuration for the vocoder."""
 
         config = {
             "resblock_kernel_sizes": [3, 7, 11],
-            "upsample_rates": [6, 5, 2, 2, 2],
-            "upsample_kernel_sizes": [16, 15, 8, 4, 4],
+            "upsample_rates": [5, 4, 2, 2, 2],
+            "upsample_kernel_sizes": [16, 16, 8, 4, 4],
             "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
             "upsample_initial_channel": 1024,
             "stereo": True,
             "resblock": "1",
+            "activation": "snake",
+            "use_bias_at_final": True,
+            "use_tanh_at_final": True,
         }
 
         return config
@@ -196,8 +534,10 @@ class Vocoder(torch.nn.Module):
             assert x.shape[1] == 2, "Input must have 2 channels for stereo"
             x = torch.cat((x[:, 0, :, :], x[:, 1, :, :]), dim=1)
         x = self.conv_pre(x)
+
         for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, LRELU_SLOPE)
+            if self.resblock != "AMP1":
+                x = F.leaky_relu(x, LRELU_SLOPE)
             x = self.ups[i](x)
             xs = None
             for j in range(self.num_kernels):
@@ -206,8 +546,167 @@ class Vocoder(torch.nn.Module):
                 else:
                     xs += self.resblocks[i * self.num_kernels + j](x)
             x = xs / self.num_kernels
-        x = F.leaky_relu(x)
+
+        x = self.act_post(x)
         x = self.conv_post(x)
-        x = torch.tanh(x)
+
+        if self.apply_final_activation:
+            if self.use_tanh_at_final:
+                x = torch.tanh(x)
+            else:
+                x = torch.clamp(x, -1, 1)
 
         return x
+
+
+class _STFTFn(nn.Module):
+    """Implements STFT as a convolution with precomputed DFT × Hann-window bases.
+
+    The DFT basis rows (real and imaginary parts interleaved) multiplied by the causal
+    Hann window are stored as buffers and loaded from the checkpoint. Using the exact
+    bfloat16 bases from training ensures the mel values fed to the BWE generator are
+    bit-identical to what it was trained on.
+    """
+
+    def __init__(self, filter_length: int, hop_length: int, win_length: int):
+        super().__init__()
+        self.hop_length = hop_length
+        self.win_length = win_length
+        n_freqs = filter_length // 2 + 1
+        self.register_buffer("forward_basis", torch.zeros(n_freqs * 2, 1, filter_length))
+        self.register_buffer("inverse_basis", torch.zeros(n_freqs * 2, 1, filter_length))
+
+    def forward(self, y: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        """Compute magnitude and phase spectrogram from a batch of waveforms.
+
+        Applies causal (left-only) padding of win_length - hop_length samples so that
+        each output frame depends only on past and present input — no lookahead.
+        The STFT is computed by convolving the padded signal with forward_basis.
+
+        Args:
+            y: Waveform tensor of shape (B, T).
+
+        Returns:
+            magnitude: Linear amplitude spectrogram, shape (B, n_freqs, T_frames).
+            phase:     Phase spectrogram in radians, shape (B, n_freqs, T_frames).
+                       Computed in float32 for numerical stability, then cast back to
+                       the input dtype.
+        """
+        if y.dim() == 2:
+            y = y.unsqueeze(1)                                # (B, 1, T)
+        left_pad = max(0, self.win_length - self.hop_length)  # causal: left-only
+        y = F.pad(y, (left_pad, 0))
+        spec = F.conv1d(y, self.forward_basis, stride=self.hop_length, padding=0)
+        n_freqs = spec.shape[1] // 2
+        real, imag = spec[:, :n_freqs], spec[:, n_freqs:]
+        magnitude = torch.sqrt(real ** 2 + imag ** 2)
+        phase = torch.atan2(imag.float(), real.float()).to(real.dtype)
+        return magnitude, phase
+
+
+class MelSTFT(nn.Module):
+    """Causal log-mel spectrogram module whose buffers are loaded from the checkpoint.
+
+    Computes a log-mel spectrogram by running the causal STFT (_STFTFn) on the input
+    waveform and projecting the linear magnitude spectrum onto the mel filterbank.
+
+    The module's state dict layout matches the 'mel_stft.*' keys stored in the checkpoint
+    (mel_basis, stft_fn.forward_basis, stft_fn.inverse_basis).
+    """
+
+    def __init__(
+        self,
+        filter_length: int,
+        hop_length: int,
+        win_length: int,
+        n_mel_channels: int,
+        sampling_rate: int,
+        mel_fmin: float,
+        mel_fmax: float,
+    ):
+        super().__init__()
+        self.stft_fn = _STFTFn(filter_length, hop_length, win_length)
+
+        n_freqs = filter_length // 2 + 1
+        self.register_buffer("mel_basis", torch.zeros(n_mel_channels, n_freqs))
+
+    def mel_spectrogram(
+        self, y: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Compute log-mel spectrogram and auxiliary spectral quantities.
+
+        Args:
+            y: Waveform tensor of shape (B, T).
+
+        Returns:
+            log_mel:   Log-compressed mel spectrogram, shape (B, n_mel_channels, T_frames).
+                       Computed as log(clamp(mel_basis @ magnitude, min=1e-5)).
+            magnitude: Linear amplitude spectrogram, shape (B, n_freqs, T_frames).
+            phase:     Phase spectrogram in radians, shape (B, n_freqs, T_frames).
+            energy:    Per-frame energy (L2 norm over frequency), shape (B, T_frames).
+        """
+        magnitude, phase = self.stft_fn(y)
+        energy = torch.norm(magnitude, dim=1)
+        mel = torch.matmul(self.mel_basis.to(magnitude.dtype), magnitude)
+        log_mel = torch.log(torch.clamp(mel, min=1e-5))
+        return log_mel, magnitude, phase, energy
+
+
+class VocoderWithBWE(torch.nn.Module):
+    """Vocoder with bandwidth extension (BWE) for higher sample rate output.
+
+    Chains a base vocoder (mel → low-rate waveform) with a BWE stage that upsamples
+    to a higher rate. The BWE computes a mel spectrogram from the low-rate waveform.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        vocoder_config = config["vocoder"]
+        bwe_config = config["bwe"]
+
+        self.vocoder = Vocoder(config=vocoder_config)
+        self.bwe_generator = Vocoder(
+            config={**bwe_config, "apply_final_activation": False}
+        )
+
+        self.input_sample_rate = bwe_config["input_sampling_rate"]
+        self.output_sample_rate = bwe_config["output_sampling_rate"]
+        self.hop_length = bwe_config["hop_length"]
+
+        self.mel_stft = MelSTFT(
+            filter_length=bwe_config["n_fft"],
+            hop_length=bwe_config["hop_length"],
+            win_length=bwe_config["n_fft"],
+            n_mel_channels=bwe_config["num_mels"],
+            sampling_rate=bwe_config["input_sampling_rate"],
+            mel_fmin=0.0,
+            mel_fmax=bwe_config["input_sampling_rate"] / 2.0,
+        )
+        self.resampler = UpSample1d(
+            ratio=bwe_config["output_sampling_rate"] // bwe_config["input_sampling_rate"],
+            persistent=False,
+            window_type="hann",
+        )
+
+    def _compute_mel(self, audio):
+        """Compute log-mel spectrogram from waveform using causal STFT bases."""
+        B, C, T = audio.shape
+        flat = audio.reshape(B * C, -1)                         # (B*C, T)
+        mel, _, _, _ = self.mel_stft.mel_spectrogram(flat)      # (B*C, n_mels, T_frames)
+        return mel.reshape(B, C, mel.shape[1], mel.shape[2])    # (B, C, n_mels, T_frames)
+
+    def forward(self, mel_spec):
+        x = self.vocoder(mel_spec)
+        _, _, T_low = x.shape
+        T_out = T_low * self.output_sample_rate // self.input_sample_rate
+
+        remainder = T_low % self.hop_length
+        if remainder != 0:
+            x = F.pad(x, (0, self.hop_length - remainder))
+
+        mel = self._compute_mel(x)
+        residual = self.bwe_generator(mel)
+        skip = self.resampler(x)
+        assert residual.shape == skip.shape, f"residual {residual.shape} != skip {skip.shape}"
+
+        return torch.clamp(residual + skip, -1, 1)[..., :T_out]
diff --git a/comfy/model_base.py b/comfy/model_base.py
index 1e01e9edc..d9d5a9293 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -1021,7 +1021,7 @@ class LTXAV(BaseModel):
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
             if hasattr(self.diffusion_model, "preprocess_text_embeds"):
-                cross_attn = self.diffusion_model.preprocess_text_embeds(cross_attn.to(device=device, dtype=self.get_dtype_inference()))
+                cross_attn = self.diffusion_model.preprocess_text_embeds(cross_attn.to(device=device, dtype=self.get_dtype_inference()), unprocessed=kwargs.get("unprocessed_ltxav_embeds", False))
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
 
         out['frame_rate'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", 25))
diff --git a/comfy/sd.py b/comfy/sd.py
index 8bcd09582..888ef1e77 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -1467,7 +1467,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             clip_target.clip = comfy.text_encoders.kandinsky5.te(**llama_detect(clip_data))
             clip_target.tokenizer = comfy.text_encoders.kandinsky5.Kandinsky5TokenizerImage
         elif clip_type == CLIPType.LTXV:
-            clip_target.clip = comfy.text_encoders.lt.ltxav_te(**llama_detect(clip_data))
+            clip_target.clip = comfy.text_encoders.lt.ltxav_te(**llama_detect(clip_data), **comfy.text_encoders.lt.sd_detect(clip_data))
             clip_target.tokenizer = comfy.text_encoders.lt.LTXAVGemmaTokenizer
             tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
         elif clip_type == CLIPType.NEWBIE:
diff --git a/comfy/text_encoders/lt.py b/comfy/text_encoders/lt.py
index e86ea9f4e..5e1273c6e 100644
--- a/comfy/text_encoders/lt.py
+++ b/comfy/text_encoders/lt.py
@@ -97,18 +97,39 @@ class Gemma3_12BModel(sd1_clip.SDClipModel):
         comfy.utils.normalize_image_embeddings(embeds, embeds_info, self.transformer.model.config.hidden_size ** 0.5)
         return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, stop_tokens=[106])  # 106 is <end_of_turn>
 
+class DualLinearProjection(torch.nn.Module):
+    def __init__(self, in_dim, out_dim_video, out_dim_audio, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.audio_aggregate_embed = operations.Linear(in_dim, out_dim_audio, bias=True, dtype=dtype, device=device)
+        self.video_aggregate_embed = operations.Linear(in_dim, out_dim_video, bias=True, dtype=dtype, device=device)
+
+    def forward(self, x):
+        source_dim = x.shape[-1]
+        x = x.movedim(1, -1)
+        x = (x * torch.rsqrt(torch.mean(x**2, dim=2, keepdim=True) + 1e-6)).flatten(start_dim=2)
+
+        video = self.video_aggregate_embed(x * math.sqrt(self.video_aggregate_embed.out_features / source_dim))
+        audio = self.audio_aggregate_embed(x * math.sqrt(self.audio_aggregate_embed.out_features / source_dim))
+        return torch.cat((video, audio), dim=-1)
+
 class LTXAVTEModel(torch.nn.Module):
-    def __init__(self, dtype_llama=None, device="cpu", dtype=None, model_options={}):
+    def __init__(self, dtype_llama=None, device="cpu", dtype=None, text_projection_type="single_linear", model_options={}):
         super().__init__()
         self.dtypes = set()
         self.dtypes.add(dtype)
         self.compat_mode = False
+        self.text_projection_type = text_projection_type
 
         self.gemma3_12b = Gemma3_12BModel(device=device, dtype=dtype_llama, model_options=model_options, layer="all", layer_idx=None)
         self.dtypes.add(dtype_llama)
 
         operations = self.gemma3_12b.operations # TODO
-        self.text_embedding_projection = operations.Linear(3840 * 49, 3840, bias=False, dtype=dtype, device=device)
+
+        if self.text_projection_type == "single_linear":
+            self.text_embedding_projection = operations.Linear(3840 * 49, 3840, bias=False, dtype=dtype, device=device)
+        elif self.text_projection_type == "dual_linear":
+            self.text_embedding_projection = DualLinearProjection(3840 * 49, 4096, 2048, dtype=dtype, device=device, operations=operations)
+
 
     def enable_compat_mode(self):  # TODO: remove
         from comfy.ldm.lightricks.embeddings_connector import Embeddings1DConnector
@@ -148,18 +169,25 @@ class LTXAVTEModel(torch.nn.Module):
         out_device = out.device
         if comfy.model_management.should_use_bf16(self.execution_device):
             out = out.to(device=self.execution_device, dtype=torch.bfloat16)
-        out = out.movedim(1, -1).to(self.execution_device)
-        out = 8.0 * (out - out.mean(dim=(1, 2), keepdim=True)) / (out.amax(dim=(1, 2), keepdim=True) - out.amin(dim=(1, 2), keepdim=True) + 1e-6)
-        out = out.reshape((out.shape[0], out.shape[1], -1))
-        out = self.text_embedding_projection(out)
-        out = out.float()
 
-        if self.compat_mode:
-            out_vid = self.video_embeddings_connector(out)[0]
-            out_audio = self.audio_embeddings_connector(out)[0]
-            out = torch.concat((out_vid, out_audio), dim=-1)
+        if self.text_projection_type == "single_linear":
+            out = out.movedim(1, -1).to(self.execution_device)
+            out = 8.0 * (out - out.mean(dim=(1, 2), keepdim=True)) / (out.amax(dim=(1, 2), keepdim=True) - out.amin(dim=(1, 2), keepdim=True) + 1e-6)
+            out = out.reshape((out.shape[0], out.shape[1], -1))
+            out = self.text_embedding_projection(out)
 
-        return out.to(out_device), pooled
+            if self.compat_mode:
+                out_vid = self.video_embeddings_connector(out)[0]
+                out_audio = self.audio_embeddings_connector(out)[0]
+                out = torch.concat((out_vid, out_audio), dim=-1)
+                extra = {}
+            else:
+                extra = {"unprocessed_ltxav_embeds": True}
+        elif self.text_projection_type == "dual_linear":
+            out = self.text_embedding_projection(out)
+            extra = {"unprocessed_ltxav_embeds": True}
+
+        return out.to(device=out_device, dtype=torch.float), pooled, extra
 
     def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
         return self.gemma3_12b.generate(tokens["gemma3_12b"], do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed)
@@ -168,7 +196,7 @@ class LTXAVTEModel(torch.nn.Module):
         if "model.layers.47.self_attn.q_norm.weight" in sd:
             return self.gemma3_12b.load_sd(sd)
         else:
-            sdo = comfy.utils.state_dict_prefix_replace(sd, {"text_embedding_projection.aggregate_embed.weight": "text_embedding_projection.weight"}, filter_keys=True)
+            sdo = comfy.utils.state_dict_prefix_replace(sd, {"text_embedding_projection.aggregate_embed.weight": "text_embedding_projection.weight", "text_embedding_projection.": "text_embedding_projection."}, filter_keys=True)
             if len(sdo) == 0:
                 sdo = sd
 
@@ -206,7 +234,7 @@ class LTXAVTEModel(torch.nn.Module):
         num_tokens = max(num_tokens, 642)
         return num_tokens * constant * 1024 * 1024
 
-def ltxav_te(dtype_llama=None, llama_quantization_metadata=None):
+def ltxav_te(dtype_llama=None, llama_quantization_metadata=None, text_projection_type="single_linear"):
     class LTXAVTEModel_(LTXAVTEModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):
             if llama_quantization_metadata is not None:
@@ -214,9 +242,19 @@ def ltxav_te(dtype_llama=None, llama_quantization_metadata=None):
                 model_options["llama_quantization_metadata"] = llama_quantization_metadata
             if dtype_llama is not None:
                 dtype = dtype_llama
-            super().__init__(dtype_llama=dtype_llama, device=device, dtype=dtype, model_options=model_options)
+            super().__init__(dtype_llama=dtype_llama, device=device, dtype=dtype, text_projection_type=text_projection_type, model_options=model_options)
     return LTXAVTEModel_
 
+
+def sd_detect(state_dict_list, prefix=""):
+    for sd in state_dict_list:
+        if "{}text_embedding_projection.audio_aggregate_embed.bias".format(prefix) in sd:
+            return {"text_projection_type": "dual_linear"}
+        if "{}text_embedding_projection.weight".format(prefix) in sd or "{}text_embedding_projection.aggregate_embed.weight".format(prefix) in sd:
+            return {"text_projection_type": "single_linear"}
+    return {}
+
+
 def gemma3_te(dtype_llama=None, llama_quantization_metadata=None):
     class Gemma3_12BModel_(Gemma3_12BModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):

From f2ee7f2d367f98bb8a33bcb4a224bda441eb8a07 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Wed, 4 Mar 2026 22:21:55 -0800
Subject: [PATCH 105/194] Fix cublas ops on dynamic vram. (#12776)

---
 comfy/ops.py | 18 ++++++++++++------
 1 file changed, 12 insertions(+), 6 deletions(-)

diff --git a/comfy/ops.py b/comfy/ops.py
index 8275dd0a5..3e19cd1b6 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -660,23 +660,29 @@ class fp8_ops(manual_cast):
 
 CUBLAS_IS_AVAILABLE = False
 try:
-    from cublas_ops import CublasLinear
+    from cublas_ops import CublasLinear, cublas_half_matmul
     CUBLAS_IS_AVAILABLE = True
 except ImportError:
     pass
 
 if CUBLAS_IS_AVAILABLE:
-    class cublas_ops(disable_weight_init):
-        class Linear(CublasLinear, disable_weight_init.Linear):
+    class cublas_ops(manual_cast):
+        class Linear(CublasLinear, manual_cast.Linear):
             def reset_parameters(self):
                 return None
 
             def forward_comfy_cast_weights(self, input):
-                return super().forward(input)
+                weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+                x = cublas_half_matmul(input, weight, bias, self._epilogue_str, self.has_bias)
+                uncast_bias_weight(self, weight, bias, offload_stream)
+                return x
 
             def forward(self, *args, **kwargs):
-                return super().forward(*args, **kwargs)
-
+                run_every_op()
+                if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
+                    return self.forward_comfy_cast_weights(*args, **kwargs)
+                else:
+                    return super().forward(*args, **kwargs)
 
 # ==============================================================================
 # Mixed Precision Operations

From c5fe8ace68c432a262a5093bdd84b3ed70b9d283 Mon Sep 17 00:00:00 2001
From: ComfyUI Wiki <contact@comfyui-wiki.com>
Date: Thu, 5 Mar 2026 15:37:35 +0800
Subject: [PATCH 106/194] chore: update workflow templates to v0.9.6 (#12778)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index dae46d873..5f99407b7 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.39.19
-comfyui-workflow-templates==0.9.5
+comfyui-workflow-templates==0.9.6
 comfyui-embedded-docs==0.4.3
 torch
 torchsde

From 4941671b5a5c65fea48be922caa76b7f6a0a4595 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Wed, 4 Mar 2026 23:39:51 -0800
Subject: [PATCH 107/194] Fix cuda getting initialized in cpu mode. (#12779)

---
 comfy/model_management.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 809600815..ee28ea107 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -1666,12 +1666,16 @@ def lora_compute_dtype(device):
     return dtype
 
 def synchronize():
+    if cpu_mode():
+        return
     if is_intel_xpu():
         torch.xpu.synchronize()
     elif torch.cuda.is_available():
         torch.cuda.synchronize()
 
 def soft_empty_cache(force=False):
+    if cpu_mode():
+        return
     global cpu_state
     if cpu_state == CPUState.MPS:
         torch.mps.empty_cache()

From c8428541a6b6e4b1e0fbd685e9c846efcb60179e Mon Sep 17 00:00:00 2001
From: ComfyUI Wiki <contact@comfyui-wiki.com>
Date: Thu, 5 Mar 2026 16:58:25 +0800
Subject: [PATCH 108/194] chore: update workflow templates to v0.9.7 (#12780)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 5f99407b7..866818e08 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.39.19
-comfyui-workflow-templates==0.9.6
+comfyui-workflow-templates==0.9.7
 comfyui-embedded-docs==0.4.3
 torch
 torchsde

From e04d0dbeb8266aa9262b5a4c3934ba4e4a371e37 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 5 Mar 2026 04:06:29 -0500
Subject: [PATCH 109/194] ComfyUI v0.16.0

---
 comfyui_version.py | 2 +-
 pyproject.toml     | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfyui_version.py b/comfyui_version.py
index 6a35c6de3..0aea18d3a 100644
--- a/comfyui_version.py
+++ b/comfyui_version.py
@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.15.1"
+__version__ = "0.16.0"
diff --git a/pyproject.toml b/pyproject.toml
index 1b2318273..f2133d99c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.15.1"
+version = "0.16.0"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

From bd21363563ce8e312c9271a0c64a0145335df8a9 Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Thu, 5 Mar 2026 14:29:39 +0200
Subject: [PATCH 110/194] feat(api-nodes-xAI): updated models, pricing, added
 features (#12756)

---
 comfy_api_nodes/apis/grok.py  | 14 ++++--
 comfy_api_nodes/nodes_grok.py | 92 +++++++++++++++++++++++++++++------
 2 files changed, 87 insertions(+), 19 deletions(-)

diff --git a/comfy_api_nodes/apis/grok.py b/comfy_api_nodes/apis/grok.py
index 8e3c79ab9..c56c8aecc 100644
--- a/comfy_api_nodes/apis/grok.py
+++ b/comfy_api_nodes/apis/grok.py
@@ -7,7 +7,8 @@ class ImageGenerationRequest(BaseModel):
     aspect_ratio: str = Field(...)
     n: int = Field(...)
     seed: int = Field(...)
-    response_for: str = Field("url")
+    response_format: str = Field("url")
+    resolution: str = Field(...)
 
 
 class InputUrlObject(BaseModel):
@@ -16,12 +17,13 @@ class InputUrlObject(BaseModel):
 
 class ImageEditRequest(BaseModel):
     model: str = Field(...)
-    image: InputUrlObject = Field(...)
+    images: list[InputUrlObject] = Field(...)
     prompt: str = Field(...)
     resolution: str = Field(...)
     n: int = Field(...)
     seed: int = Field(...)
-    response_for: str = Field("url")
+    response_format: str = Field("url")
+    aspect_ratio: str | None = Field(...)
 
 
 class VideoGenerationRequest(BaseModel):
@@ -47,8 +49,13 @@ class ImageResponseObject(BaseModel):
     revised_prompt: str | None = Field(None)
 
 
+class UsageObject(BaseModel):
+    cost_in_usd_ticks: int | None = Field(None)
+
+
 class ImageGenerationResponse(BaseModel):
     data: list[ImageResponseObject] = Field(...)
+    usage: UsageObject | None = Field(None)
 
 
 class VideoGenerationResponse(BaseModel):
@@ -65,3 +72,4 @@ class VideoStatusResponse(BaseModel):
     status: str | None = Field(None)
     video: VideoResponseObject | None = Field(None)
     model: str | None = Field(None)
+    usage: UsageObject | None = Field(None)
diff --git a/comfy_api_nodes/nodes_grok.py b/comfy_api_nodes/nodes_grok.py
index da15e97ea..0716d6239 100644
--- a/comfy_api_nodes/nodes_grok.py
+++ b/comfy_api_nodes/nodes_grok.py
@@ -27,6 +27,12 @@ from comfy_api_nodes.util import (
 )
 
 
+def _extract_grok_price(response) -> float | None:
+    if response.usage and response.usage.cost_in_usd_ticks is not None:
+        return response.usage.cost_in_usd_ticks / 10_000_000_000
+    return None
+
+
 class GrokImageNode(IO.ComfyNode):
 
     @classmethod
@@ -37,7 +43,10 @@ class GrokImageNode(IO.ComfyNode):
             category="api node/image/Grok",
             description="Generate images using Grok based on a text prompt",
             inputs=[
-                IO.Combo.Input("model", options=["grok-imagine-image-beta"]),
+                IO.Combo.Input(
+                    "model",
+                    options=["grok-imagine-image-pro", "grok-imagine-image", "grok-imagine-image-beta"],
+                ),
                 IO.String.Input(
                     "prompt",
                     multiline=True,
@@ -81,6 +90,7 @@ class GrokImageNode(IO.ComfyNode):
                     tooltip="Seed to determine if node should re-run; "
                     "actual results are nondeterministic regardless of seed.",
                 ),
+                IO.Combo.Input("resolution", options=["1K", "2K"], optional=True),
             ],
             outputs=[
                 IO.Image.Output(),
@@ -92,8 +102,13 @@ class GrokImageNode(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["number_of_images"]),
-                expr="""{"type":"usd","usd":0.033 * widgets.number_of_images}""",
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "number_of_images"]),
+                expr="""
+                (
+                  $rate := $contains(widgets.model, "pro") ? 0.07 : 0.02;
+                  {"type":"usd","usd": $rate * widgets.number_of_images}
+                )
+                """,
             ),
         )
 
@@ -105,6 +120,7 @@ class GrokImageNode(IO.ComfyNode):
         aspect_ratio: str,
         number_of_images: int,
         seed: int,
+        resolution: str = "1K",
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=True, min_length=1)
         response = await sync_op(
@@ -116,8 +132,10 @@ class GrokImageNode(IO.ComfyNode):
                 aspect_ratio=aspect_ratio,
                 n=number_of_images,
                 seed=seed,
+                resolution=resolution.lower(),
             ),
             response_model=ImageGenerationResponse,
+            price_extractor=_extract_grok_price,
         )
         if len(response.data) == 1:
             return IO.NodeOutput(await download_url_to_image_tensor(response.data[0].url))
@@ -138,14 +156,17 @@ class GrokImageEditNode(IO.ComfyNode):
             category="api node/image/Grok",
             description="Modify an existing image based on a text prompt",
             inputs=[
-                IO.Combo.Input("model", options=["grok-imagine-image-beta"]),
-                IO.Image.Input("image"),
+                IO.Combo.Input(
+                    "model",
+                    options=["grok-imagine-image-pro", "grok-imagine-image", "grok-imagine-image-beta"],
+                ),
+                IO.Image.Input("image", display_name="images"),
                 IO.String.Input(
                     "prompt",
                     multiline=True,
                     tooltip="The text prompt used to generate the image",
                 ),
-                IO.Combo.Input("resolution", options=["1K"]),
+                IO.Combo.Input("resolution", options=["1K", "2K"]),
                 IO.Int.Input(
                     "number_of_images",
                     default=1,
@@ -166,6 +187,27 @@ class GrokImageEditNode(IO.ComfyNode):
                     tooltip="Seed to determine if node should re-run; "
                     "actual results are nondeterministic regardless of seed.",
                 ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=[
+                        "auto",
+                        "1:1",
+                        "2:3",
+                        "3:2",
+                        "3:4",
+                        "4:3",
+                        "9:16",
+                        "16:9",
+                        "9:19.5",
+                        "19.5:9",
+                        "9:20",
+                        "20:9",
+                        "1:2",
+                        "2:1",
+                    ],
+                    optional=True,
+                    tooltip="Only allowed when multiple images are connected to the image input.",
+                ),
             ],
             outputs=[
                 IO.Image.Output(),
@@ -177,8 +219,13 @@ class GrokImageEditNode(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["number_of_images"]),
-                expr="""{"type":"usd","usd":0.002 + 0.033 * widgets.number_of_images}""",
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "number_of_images"]),
+                expr="""
+                (
+                  $rate := $contains(widgets.model, "pro") ? 0.07 : 0.02;
+                  {"type":"usd","usd": 0.002 + $rate * widgets.number_of_images}
+                )
+                """,
             ),
         )
 
@@ -191,22 +238,32 @@ class GrokImageEditNode(IO.ComfyNode):
         resolution: str,
         number_of_images: int,
         seed: int,
+        aspect_ratio: str = "auto",
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=True, min_length=1)
-        if get_number_of_images(image) != 1:
-            raise ValueError("Only one input image is supported.")
+        if model == "grok-imagine-image-pro":
+            if get_number_of_images(image) > 1:
+                raise ValueError("The pro model supports only 1 input image.")
+        elif get_number_of_images(image) > 3:
+            raise ValueError("A maximum of 3 input images is supported.")
+        if aspect_ratio != "auto" and get_number_of_images(image) == 1:
+            raise ValueError(
+                "Custom aspect ratio is only allowed when multiple images are connected to the image input."
+            )
         response = await sync_op(
             cls,
             ApiEndpoint(path="/proxy/xai/v1/images/edits", method="POST"),
             data=ImageEditRequest(
                 model=model,
-                image=InputUrlObject(url=f"data:image/png;base64,{tensor_to_base64_string(image)}"),
+                images=[InputUrlObject(url=f"data:image/png;base64,{tensor_to_base64_string(i)}") for i in image],
                 prompt=prompt,
                 resolution=resolution.lower(),
                 n=number_of_images,
                 seed=seed,
+                aspect_ratio=None if aspect_ratio == "auto" else aspect_ratio,
             ),
             response_model=ImageGenerationResponse,
+            price_extractor=_extract_grok_price,
         )
         if len(response.data) == 1:
             return IO.NodeOutput(await download_url_to_image_tensor(response.data[0].url))
@@ -227,7 +284,7 @@ class GrokVideoNode(IO.ComfyNode):
             category="api node/video/Grok",
             description="Generate video from a prompt or an image",
             inputs=[
-                IO.Combo.Input("model", options=["grok-imagine-video-beta"]),
+                IO.Combo.Input("model", options=["grok-imagine-video", "grok-imagine-video-beta"]),
                 IO.String.Input(
                     "prompt",
                     multiline=True,
@@ -275,10 +332,11 @@ class GrokVideoNode(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["duration"], inputs=["image"]),
+                depends_on=IO.PriceBadgeDepends(widgets=["duration", "resolution"], inputs=["image"]),
                 expr="""
                 (
-                  $base := 0.181 * widgets.duration;
+                  $rate := widgets.resolution = "720p" ? 0.07 : 0.05;
+                  $base := $rate * widgets.duration;
                   {"type":"usd","usd": inputs.image.connected ? $base + 0.002 : $base}
                 )
                 """,
@@ -321,6 +379,7 @@ class GrokVideoNode(IO.ComfyNode):
             ApiEndpoint(path=f"/proxy/xai/v1/videos/{initial_response.request_id}"),
             status_extractor=lambda r: r.status if r.status is not None else "complete",
             response_model=VideoStatusResponse,
+            price_extractor=_extract_grok_price,
         )
         return IO.NodeOutput(await download_url_to_video_output(response.video.url))
 
@@ -335,7 +394,7 @@ class GrokVideoEditNode(IO.ComfyNode):
             category="api node/video/Grok",
             description="Edit an existing video based on a text prompt.",
             inputs=[
-                IO.Combo.Input("model", options=["grok-imagine-video-beta"]),
+                IO.Combo.Input("model", options=["grok-imagine-video", "grok-imagine-video-beta"]),
                 IO.String.Input(
                     "prompt",
                     multiline=True,
@@ -364,7 +423,7 @@ class GrokVideoEditNode(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                expr="""{"type":"usd","usd": 0.191, "format": {"suffix": "/sec", "approximate": true}}""",
+                expr="""{"type":"usd","usd": 0.06, "format": {"suffix": "/sec", "approximate": true}}""",
             ),
         )
 
@@ -398,6 +457,7 @@ class GrokVideoEditNode(IO.ComfyNode):
             ApiEndpoint(path=f"/proxy/xai/v1/videos/{initial_response.request_id}"),
             status_extractor=lambda r: r.status if r.status is not None else "complete",
             response_model=VideoStatusResponse,
+            price_extractor=_extract_grok_price,
         )
         return IO.NodeOutput(await download_url_to_video_output(response.video.url))
 

From 9cdfd7403bc46f75d12be16ba6041b8bcdd3f7fd Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Thu, 5 Mar 2026 17:12:38 +0200
Subject: [PATCH 111/194] feat(api-nodes): enable Kling 3.0 Motion Control
 (#12785)

---
 comfy_api_nodes/apis/kling.py  | 1 +
 comfy_api_nodes/nodes_kling.py | 3 +++
 2 files changed, 4 insertions(+)

diff --git a/comfy_api_nodes/apis/kling.py b/comfy_api_nodes/apis/kling.py
index a5bd5f1d3..fe0f97cb3 100644
--- a/comfy_api_nodes/apis/kling.py
+++ b/comfy_api_nodes/apis/kling.py
@@ -148,3 +148,4 @@ class MotionControlRequest(BaseModel):
     keep_original_sound: str = Field(...)
     character_orientation: str = Field(...)
     mode: str = Field(..., description="'pro' or 'std'")
+    model_name: str = Field(...)
diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py
index 74fa078ff..8963c335d 100644
--- a/comfy_api_nodes/nodes_kling.py
+++ b/comfy_api_nodes/nodes_kling.py
@@ -2747,6 +2747,7 @@ class MotionControl(IO.ComfyNode):
                     "but the character orientation matches the reference image (camera/other details via prompt).",
                 ),
                 IO.Combo.Input("mode", options=["pro", "std"]),
+                IO.Combo.Input("model", options=["kling-v3", "kling-v2-6"], optional=True),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -2777,6 +2778,7 @@ class MotionControl(IO.ComfyNode):
         keep_original_sound: bool,
         character_orientation: str,
         mode: str,
+        model: str = "kling-v2-6",
     ) -> IO.NodeOutput:
         validate_string(prompt, max_length=2500)
         validate_image_dimensions(reference_image, min_width=340, min_height=340)
@@ -2797,6 +2799,7 @@ class MotionControl(IO.ComfyNode):
                 keep_original_sound="yes" if keep_original_sound else "no",
                 character_orientation=character_orientation,
                 mode=mode,
+                model_name=model,
             ),
         )
         if response.code:

From da29b797ce00b491c269e864cc3b8fceb279e530 Mon Sep 17 00:00:00 2001
From: ComfyUI Wiki <contact@comfyui-wiki.com>
Date: Thu, 5 Mar 2026 23:23:23 +0800
Subject: [PATCH 112/194] Update workflow templates to v0.9.8 (#12788)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 866818e08..3fd44e0cf 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.39.19
-comfyui-workflow-templates==0.9.7
+comfyui-workflow-templates==0.9.8
 comfyui-embedded-docs==0.4.3
 torch
 torchsde

From 6ef82a89b83a49247081dc57b154172573c9e313 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 5 Mar 2026 10:38:33 -0500
Subject: [PATCH 113/194] ComfyUI v0.16.1

---
 comfyui_version.py | 2 +-
 pyproject.toml     | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfyui_version.py b/comfyui_version.py
index 0aea18d3a..e58e0fb63 100644
--- a/comfyui_version.py
+++ b/comfyui_version.py
@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.16.0"
+__version__ = "0.16.1"
diff --git a/pyproject.toml b/pyproject.toml
index f2133d99c..199a90364 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.16.0"
+version = "0.16.1"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

From 6481569ad4c3606bc50e9de39ce810651690ae79 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Thu, 5 Mar 2026 09:04:24 -0800
Subject: [PATCH 114/194] comfy-aimdo 0.2.7 (#12791)

Comfy-aimdo 0.2.7 fixes a crash when a spurious cudaAsyncFree comes in
and would cause an infinite stack overflow (via detours hooks).

A lock is also introduced on the link list holding the free sections
to avoid any possibility of threaded miscellaneous cuda allocations
being the root cause.
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 3fd44e0cf..f7098b730 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -22,7 +22,7 @@ alembic
 SQLAlchemy
 av>=14.2.0
 comfy-kitchen>=0.2.7
-comfy-aimdo>=0.2.6
+comfy-aimdo>=0.2.7
 requests
 
 #non essential dependencies:

From 42e0e023eee6a19c1adb7bd3dc11c81ff6dcc9c8 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Thu, 5 Mar 2026 10:22:17 -0800
Subject: [PATCH 115/194] ops: Handle CPU weight in VBAR caster (#12792)

This shouldn't happen but custom nodes gets there. Handle it as best
we can.
---
 comfy/ops.py | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/comfy/ops.py b/comfy/ops.py
index 3e19cd1b6..06aa41d4f 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -80,6 +80,21 @@ def cast_to_input(weight, input, non_blocking=False, copy=True):
 
 
 def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compute_dtype, want_requant):
+
+    #vbar doesn't support CPU weights, but some custom nodes have weird paths
+    #that might switch the layer to the CPU and expect it to work. We have to take
+    #a clone conservatively as we are mmapped and some SFT files are packed misaligned
+    #If you are a custom node author reading this, please move your layer to the GPU
+    #or declare your ModelPatcher as CPU in the first place.
+    if device is not None and device.type == "cpu":
+        weight = s.weight.to(dtype=dtype, copy=True)
+        if isinstance(weight, QuantizedTensor):
+            weight = weight.dequantize()
+        bias = None
+        if s.bias is not None:
+            bias = s.bias.to(dtype=bias_dtype, copy=True)
+        return weight, bias, (None, None, None)
+
     offload_stream = None
     xfer_dest = None
 

From 5073da57ad20a2abb921f79458e49a7f7d608740 Mon Sep 17 00:00:00 2001
From: ComfyUI Wiki <contact@comfyui-wiki.com>
Date: Fri, 6 Mar 2026 02:22:38 +0800
Subject: [PATCH 116/194] chore: update workflow templates to v0.9.10 (#12793)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index f7098b730..9a674fac5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.39.19
-comfyui-workflow-templates==0.9.8
+comfyui-workflow-templates==0.9.10
 comfyui-embedded-docs==0.4.3
 torch
 torchsde

From 1c3b651c0a1539a374e3d29a3ce695b5844ac5fc Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Thu, 5 Mar 2026 10:35:56 -0800
Subject: [PATCH 117/194] Refactor. (#12794)

---
 comfy/ops.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/ops.py b/comfy/ops.py
index 06aa41d4f..87b36b5c5 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -86,7 +86,7 @@ def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compu
     #a clone conservatively as we are mmapped and some SFT files are packed misaligned
     #If you are a custom node author reading this, please move your layer to the GPU
     #or declare your ModelPatcher as CPU in the first place.
-    if device is not None and device.type == "cpu":
+    if comfy.model_management.is_device_cpu(device):
         weight = s.weight.to(dtype=dtype, copy=True)
         if isinstance(weight, QuantizedTensor):
             weight = weight.dequantize()

From 50549aa252903b936b2ed00b5de418c8b47f0841 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 5 Mar 2026 13:41:06 -0500
Subject: [PATCH 118/194] ComfyUI v0.16.2

---
 comfyui_version.py | 2 +-
 pyproject.toml     | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfyui_version.py b/comfyui_version.py
index e58e0fb63..bc49f2218 100644
--- a/comfyui_version.py
+++ b/comfyui_version.py
@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.16.1"
+__version__ = "0.16.2"
diff --git a/pyproject.toml b/pyproject.toml
index 199a90364..73bfd1007 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.16.1"
+version = "0.16.2"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

From 8befce5c7b84ff3451a6bd3bcbae1355ad322855 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?=
 <40791699+kijai@users.noreply.github.com>
Date: Thu, 5 Mar 2026 22:37:25 +0200
Subject: [PATCH 119/194] Add manual cast to LTX2 vocoder conv_transpose1d
 (#12795)

* Add manual cast to LTX2 vocoder

* Update vocoder.py
---
 comfy/ldm/lightricks/vocoders/vocoder.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/comfy/ldm/lightricks/vocoders/vocoder.py b/comfy/ldm/lightricks/vocoders/vocoder.py
index 6c4028aa8..a0e03cada 100644
--- a/comfy/ldm/lightricks/vocoders/vocoder.py
+++ b/comfy/ldm/lightricks/vocoders/vocoder.py
@@ -2,6 +2,7 @@ import torch
 import torch.nn.functional as F
 import torch.nn as nn
 import comfy.ops
+import comfy.model_management
 import numpy as np
 import math
 
@@ -125,7 +126,7 @@ class UpSample1d(nn.Module):
         _, C, _ = x.shape
         x = F.pad(x, (self.pad, self.pad), mode="replicate")
         x = self.ratio * F.conv_transpose1d(
-            x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C
+            x, comfy.model_management.cast_to(self.filter.expand(C, -1, -1), dtype=x.dtype, device=x.device), stride=self.stride, groups=C
         )
         x = x[..., self.pad_left : -self.pad_right]
         return x

From 17b43c2b87eba43f0f071471b855e0ed659a2627 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Thu, 5 Mar 2026 13:31:28 -0800
Subject: [PATCH 120/194] LTX audio vae novram fixes. (#12796)

---
 comfy/ldm/lightricks/vocoders/vocoder.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/comfy/ldm/lightricks/vocoders/vocoder.py b/comfy/ldm/lightricks/vocoders/vocoder.py
index a0e03cada..2481d8bdd 100644
--- a/comfy/ldm/lightricks/vocoders/vocoder.py
+++ b/comfy/ldm/lightricks/vocoders/vocoder.py
@@ -82,7 +82,7 @@ class LowPassFilter1d(nn.Module):
         _, C, _ = x.shape
         if self.padding:
             x = F.pad(x, (self.pad_left, self.pad_right), mode=self.padding_mode)
-        return F.conv1d(x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
+        return F.conv1d(x, comfy.model_management.cast_to(self.filter.expand(C, -1, -1), dtype=x.dtype, device=x.device), stride=self.stride, groups=C)
 
 
 class UpSample1d(nn.Module):
@@ -191,7 +191,7 @@ class Snake(nn.Module):
         self.eps = 1e-9
 
     def forward(self, x):
-        a = self.alpha.unsqueeze(0).unsqueeze(-1)
+        a = comfy.model_management.cast_to(self.alpha.unsqueeze(0).unsqueeze(-1), dtype=x.dtype, device=x.device)
         if self.alpha_logscale:
             a = torch.exp(a)
         return x + (1.0 / (a + self.eps)) * torch.sin(x * a).pow(2)
@@ -218,8 +218,8 @@ class SnakeBeta(nn.Module):
         self.eps = 1e-9
 
     def forward(self, x):
-        a = self.alpha.unsqueeze(0).unsqueeze(-1)
-        b = self.beta.unsqueeze(0).unsqueeze(-1)
+        a = comfy.model_management.cast_to(self.alpha.unsqueeze(0).unsqueeze(-1), dtype=x.dtype, device=x.device)
+        b = comfy.model_management.cast_to(self.beta.unsqueeze(0).unsqueeze(-1), dtype=x.dtype, device=x.device)
         if self.alpha_logscale:
             a = torch.exp(a)
             b = torch.exp(b)
@@ -597,7 +597,7 @@ class _STFTFn(nn.Module):
             y = y.unsqueeze(1)                                # (B, 1, T)
         left_pad = max(0, self.win_length - self.hop_length)  # causal: left-only
         y = F.pad(y, (left_pad, 0))
-        spec = F.conv1d(y, self.forward_basis, stride=self.hop_length, padding=0)
+        spec = F.conv1d(y, comfy.model_management.cast_to(self.forward_basis, dtype=y.dtype, device=y.device), stride=self.hop_length, padding=0)
         n_freqs = spec.shape[1] // 2
         real, imag = spec[:, :n_freqs], spec[:, n_freqs:]
         magnitude = torch.sqrt(real ** 2 + imag ** 2)
@@ -648,7 +648,7 @@ class MelSTFT(nn.Module):
         """
         magnitude, phase = self.stft_fn(y)
         energy = torch.norm(magnitude, dim=1)
-        mel = torch.matmul(self.mel_basis.to(magnitude.dtype), magnitude)
+        mel = torch.matmul(comfy.model_management.cast_to(self.mel_basis, dtype=magnitude.dtype, device=y.device), magnitude)
         log_mel = torch.log(torch.clamp(mel, min=1e-5))
         return log_mel, magnitude, phase, energy
 

From 58017e8726bdddae89704b1e0123bedc29994424 Mon Sep 17 00:00:00 2001
From: Tavi Halperin <tavi@lightricks.com>
Date: Thu, 5 Mar 2026 23:51:20 +0200
Subject: [PATCH 121/194] feat: add causal_fix parameter to add_keyframe_index
 and append_keyframe (#12797)

Allows explicit control over the causal_fix flag passed to
latent_to_pixel_coords. Defaults to frame_idx == 0 when not
specified, fixing the previous heuristic.
---
 comfy_extras/nodes_lt.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/comfy_extras/nodes_lt.py b/comfy_extras/nodes_lt.py
index 32fe921ff..c05571143 100644
--- a/comfy_extras/nodes_lt.py
+++ b/comfy_extras/nodes_lt.py
@@ -253,10 +253,12 @@ class LTXVAddGuide(io.ComfyNode):
         return frame_idx, latent_idx
 
     @classmethod
-    def add_keyframe_index(cls, cond, frame_idx, guiding_latent, scale_factors, latent_downscale_factor=1):
+    def add_keyframe_index(cls, cond, frame_idx, guiding_latent, scale_factors, latent_downscale_factor=1, causal_fix=None):
         keyframe_idxs, _ = get_keyframe_idxs(cond)
         _, latent_coords = cls.PATCHIFIER.patchify(guiding_latent)
-        pixel_coords = latent_to_pixel_coords(latent_coords, scale_factors, causal_fix=frame_idx == 0)  # we need the causal fix only if we're placing the new latents at index 0
+        if causal_fix is None:
+            causal_fix = frame_idx == 0 or guiding_latent.shape[2] == 1
+        pixel_coords = latent_to_pixel_coords(latent_coords, scale_factors, causal_fix=causal_fix)
         pixel_coords[:, 0] += frame_idx
 
         # The following adjusts keyframe end positions for small grid IC-LoRA.
@@ -278,12 +280,12 @@ class LTXVAddGuide(io.ComfyNode):
         return node_helpers.conditioning_set_values(cond, {"keyframe_idxs": keyframe_idxs})
 
     @classmethod
-    def append_keyframe(cls, positive, negative, frame_idx, latent_image, noise_mask, guiding_latent, strength, scale_factors, guide_mask=None, in_channels=128, latent_downscale_factor=1):
+    def append_keyframe(cls, positive, negative, frame_idx, latent_image, noise_mask, guiding_latent, strength, scale_factors, guide_mask=None, in_channels=128, latent_downscale_factor=1, causal_fix=None):
         if latent_image.shape[1] != in_channels or guiding_latent.shape[1] != in_channels:
             raise ValueError("Adding guide to a combined AV latent is not supported.")
 
-        positive = cls.add_keyframe_index(positive, frame_idx, guiding_latent, scale_factors, latent_downscale_factor)
-        negative = cls.add_keyframe_index(negative, frame_idx, guiding_latent, scale_factors, latent_downscale_factor)
+        positive = cls.add_keyframe_index(positive, frame_idx, guiding_latent, scale_factors, latent_downscale_factor, causal_fix=causal_fix)
+        negative = cls.add_keyframe_index(negative, frame_idx, guiding_latent, scale_factors, latent_downscale_factor, causal_fix=causal_fix)
 
         if guide_mask is not None:
             target_h = max(noise_mask.shape[3], guide_mask.shape[3])

From 1c218282369a6cc80651d878fc51fa33d7bf34e2 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 5 Mar 2026 17:25:49 -0500
Subject: [PATCH 122/194] ComfyUI v0.16.3

---
 comfyui_version.py | 2 +-
 pyproject.toml     | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfyui_version.py b/comfyui_version.py
index bc49f2218..5da21150b 100644
--- a/comfyui_version.py
+++ b/comfyui_version.py
@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.16.2"
+__version__ = "0.16.3"
diff --git a/pyproject.toml b/pyproject.toml
index 73bfd1007..6a83c5c63 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.16.2"
+version = "0.16.3"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

From e544c65db91df5a070be69a0a9b922201fe79335 Mon Sep 17 00:00:00 2001
From: Dante <bunggl@naver.com>
Date: Fri, 6 Mar 2026 11:51:28 +0900
Subject: [PATCH 123/194] feat: add Math Expression node with simpleeval
 evaluation (#12687)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* feat: add EagerEval dataclass for frontend-side node evaluation

Add EagerEval to the V3 API schema, enabling nodes to declare
frontend-evaluated JSONata expressions. The frontend uses this to
display computation results as badges without a backend round-trip.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: add Math Expression node with JSONata evaluation

Add ComfyMathExpression node that evaluates JSONata expressions against
dynamically-grown numeric inputs using Autogrow + MatchType. Sends
input context via ui output so the frontend can re-evaluate when
the expression changes without a backend round-trip.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: register nodes_math.py in extras_files loader list

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: address CodeRabbit review feedback

- Harden EagerEval.validate with type checks and strip() for empty strings
- Add _positional_alias for spreadsheet-style names beyond z (aa, ab...)
- Validate JSONata result is numeric before returning
- Add jsonata to requirements.txt

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* refactor: remove EagerEval, scope PR to math node only

Remove EagerEval dataclass from _io.py and eager_eval usage from
nodes_math.py. Eager execution will be designed as a general-purpose
system in a separate effort.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: use TemplateNames, cap inputs at 26, improve error message

Address Kosinkadink review feedback:
- Switch from Autogrow.TemplatePrefix to Autogrow.TemplateNames so input
  slots are named a-z, matching expression variables directly
- Cap max inputs at 26 (a-z) instead of 100
- Simplify execute() by removing dual-mapping hack
- Include expression and result value in error message

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* test: add unit tests for Math Expression node

Add tests for _positional_alias (a-z mapping) and execute() covering
arithmetic operations, float inputs, $sum(values), and error cases.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* refactor: replace jsonata with simpleeval for math evaluation

jsonata PyPI package has critical issues: no Python 3.12/3.13 wheels,
no ARM/Apple Silicon wheels, abandoned (last commit 2023), C extension.

Replace with simpleeval (pure Python, 3.4M downloads/month, MIT,
AST-based security). Add math module functions (sqrt, ceil, floor,
log, sin, cos, tan) and variadic sum() supporting both sum(values)
and sum(a, b, c). Pin version to >=1.0,<2.0.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* test: update tests for simpleeval migration

Update JSONata syntax to Python syntax ($sum -> sum, $string -> str),
add tests for math functions (sqrt, ceil, floor, sin, log10) and
variadic sum(a, b, c).

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* refactor: replace MatchType with MultiType inputs and dual FLOAT/INT outputs

Allow mixing INT and FLOAT connections on the same node by switching
from MatchType (which forces all inputs to the same type) to MultiType.
Output both FLOAT and INT so users can pick the type they need.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* test: update tests for mixed INT/FLOAT inputs and dual outputs

Add assertions for both FLOAT (result[0]) and INT (result[1]) outputs.
Add test_mixed_int_float_inputs and test_mixed_resolution_scale to
verify the primary use case of multiplying resolutions by a float factor.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: make expression input multiline and validate empty expression

- Add multiline=True to expression input for better UX with longer expressions
- Add empty expression validation with clear "Expression cannot be empty." message

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* test: add tests for empty expression validation

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: address review feedback — safe pow, isfinite guard, test coverage

- Wrap pow() with _safe_pow to prevent DoS via huge exponents
  (pow() bypasses simpleeval's safe_power guard on **)
- Add math.isfinite() check to catch inf/nan before int() conversion
- Add int/float converters to MATH_FUNCTIONS for explicit casting
- Add "calculator" search alias
- Replace _positional_alias helper with string.ascii_lowercase
- Narrow test assertions and add error path + function coverage tests

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* Update requirements.txt

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
Co-authored-by: Christian Byrne <abolkonsky.rem@gmail.com>
---
 comfy_extras/nodes_math.py                    | 119 +++++++++++
 nodes.py                                      |   1 +
 requirements.txt                              |   1 +
 .../comfy_extras_test/nodes_math_test.py      | 197 ++++++++++++++++++
 4 files changed, 318 insertions(+)
 create mode 100644 comfy_extras/nodes_math.py
 create mode 100644 tests-unit/comfy_extras_test/nodes_math_test.py

diff --git a/comfy_extras/nodes_math.py b/comfy_extras/nodes_math.py
new file mode 100644
index 000000000..6417bacf1
--- /dev/null
+++ b/comfy_extras/nodes_math.py
@@ -0,0 +1,119 @@
+"""Math expression node using simpleeval for safe evaluation.
+
+Provides a ComfyMathExpression node that evaluates math expressions
+against dynamically-grown numeric inputs.
+"""
+
+from __future__ import annotations
+
+import math
+import string
+
+from simpleeval import simple_eval
+from typing_extensions import override
+
+from comfy_api.latest import ComfyExtension, io
+
+
+MAX_EXPONENT = 4000
+
+
+def _variadic_sum(*args):
+    """Support both sum(values) and sum(a, b, c)."""
+    if len(args) == 1 and hasattr(args[0], "__iter__"):
+        return sum(args[0])
+    return sum(args)
+
+
+def _safe_pow(base, exp):
+    """Wrap pow() with an exponent cap to prevent DoS via huge exponents.
+
+    The ** operator is already guarded by simpleeval's safe_power, but
+    pow() as a callable bypasses that guard.
+    """
+    if abs(exp) > MAX_EXPONENT:
+        raise ValueError(f"Exponent {exp} exceeds maximum allowed ({MAX_EXPONENT})")
+    return pow(base, exp)
+
+
+MATH_FUNCTIONS = {
+    "sum": _variadic_sum,
+    "min": min,
+    "max": max,
+    "abs": abs,
+    "round": round,
+    "pow": _safe_pow,
+    "sqrt": math.sqrt,
+    "ceil": math.ceil,
+    "floor": math.floor,
+    "log": math.log,
+    "log2": math.log2,
+    "log10": math.log10,
+    "sin": math.sin,
+    "cos": math.cos,
+    "tan": math.tan,
+    "int": int,
+    "float": float,
+}
+
+
+class MathExpressionNode(io.ComfyNode):
+    """Evaluates a math expression against dynamically-grown inputs."""
+
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        autogrow = io.Autogrow.TemplateNames(
+            input=io.MultiType.Input("value", [io.Float, io.Int]),
+            names=list(string.ascii_lowercase),
+            min=1,
+        )
+        return io.Schema(
+            node_id="ComfyMathExpression",
+            display_name="Math Expression",
+            category="math",
+            search_aliases=[
+                "expression", "formula", "calculate", "calculator",
+                "eval", "math",
+            ],
+            inputs=[
+                io.String.Input("expression", default="a + b", multiline=True),
+                io.Autogrow.Input("values", template=autogrow),
+            ],
+            outputs=[
+                io.Float.Output(display_name="FLOAT"),
+                io.Int.Output(display_name="INT"),
+            ],
+        )
+
+    @classmethod
+    def execute(
+        cls, expression: str, values: io.Autogrow.Type
+    ) -> io.NodeOutput:
+        if not expression.strip():
+            raise ValueError("Expression cannot be empty.")
+
+        context: dict = dict(values)
+        context["values"] = list(values.values())
+
+        result = simple_eval(expression, names=context, functions=MATH_FUNCTIONS)
+        # bool check must come first because bool is a subclass of int in Python
+        if isinstance(result, bool) or not isinstance(result, (int, float)):
+            raise ValueError(
+                f"Math Expression '{expression}' must evaluate to a numeric result, "
+                f"got {type(result).__name__}: {result!r}"
+            )
+        if not math.isfinite(result):
+            raise ValueError(
+                f"Math Expression '{expression}' produced a non-finite result: {result}"
+            )
+        return io.NodeOutput(float(result), int(result))
+
+
+class MathExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [MathExpressionNode]
+
+
+async def comfy_entrypoint() -> MathExtension:
+    return MathExtension()
diff --git a/nodes.py b/nodes.py
index 5be9b16f9..0ef23b640 100644
--- a/nodes.py
+++ b/nodes.py
@@ -2449,6 +2449,7 @@ async def init_builtin_extra_nodes():
         "nodes_replacements.py",
         "nodes_nag.py",
         "nodes_sdpose.py",
+        "nodes_math.py",
     ]
 
     import_failed = []
diff --git a/requirements.txt b/requirements.txt
index 9a674fac5..7bf12247c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -24,6 +24,7 @@ av>=14.2.0
 comfy-kitchen>=0.2.7
 comfy-aimdo>=0.2.7
 requests
+simpleeval>=1.0
 
 #non essential dependencies:
 kornia>=0.7.1
diff --git a/tests-unit/comfy_extras_test/nodes_math_test.py b/tests-unit/comfy_extras_test/nodes_math_test.py
new file mode 100644
index 000000000..fa4cdcac3
--- /dev/null
+++ b/tests-unit/comfy_extras_test/nodes_math_test.py
@@ -0,0 +1,197 @@
+import math
+
+import pytest
+from collections import OrderedDict
+from unittest.mock import patch, MagicMock
+
+mock_nodes = MagicMock()
+mock_nodes.MAX_RESOLUTION = 16384
+mock_server = MagicMock()
+
+with patch.dict("sys.modules", {"nodes": mock_nodes, "server": mock_server}):
+    from comfy_extras.nodes_math import MathExpressionNode
+
+
+class TestMathExpressionExecute:
+    @staticmethod
+    def _exec(expression: str, **kwargs) -> object:
+        values = OrderedDict(kwargs)
+        return MathExpressionNode.execute(expression, values)
+
+    def test_addition(self):
+        result = self._exec("a + b", a=3, b=4)
+        assert result[0] == 7.0
+        assert result[1] == 7
+
+    def test_subtraction(self):
+        result = self._exec("a - b", a=10, b=3)
+        assert result[0] == 7.0
+        assert result[1] == 7
+
+    def test_multiplication(self):
+        result = self._exec("a * b", a=3, b=5)
+        assert result[0] == 15.0
+        assert result[1] == 15
+
+    def test_division(self):
+        result = self._exec("a / b", a=10, b=4)
+        assert result[0] == 2.5
+        assert result[1] == 2
+
+    def test_single_input(self):
+        result = self._exec("a * 2", a=5)
+        assert result[0] == 10.0
+        assert result[1] == 10
+
+    def test_three_inputs(self):
+        result = self._exec("a + b + c", a=1, b=2, c=3)
+        assert result[0] == 6.0
+        assert result[1] == 6
+
+    def test_float_inputs(self):
+        result = self._exec("a + b", a=1.5, b=2.5)
+        assert result[0] == 4.0
+        assert result[1] == 4
+
+    def test_mixed_int_float_inputs(self):
+        result = self._exec("a * b", a=1024, b=1.5)
+        assert result[0] == 1536.0
+        assert result[1] == 1536
+
+    def test_mixed_resolution_scale(self):
+        result = self._exec("a * b", a=512, b=0.75)
+        assert result[0] == 384.0
+        assert result[1] == 384
+
+    def test_sum_values_array(self):
+        result = self._exec("sum(values)", a=1, b=2, c=3)
+        assert result[0] == 6.0
+
+    def test_sum_variadic(self):
+        result = self._exec("sum(a, b, c)", a=1, b=2, c=3)
+        assert result[0] == 6.0
+
+    def test_min_values(self):
+        result = self._exec("min(values)", a=5, b=2, c=8)
+        assert result[0] == 2.0
+
+    def test_max_values(self):
+        result = self._exec("max(values)", a=5, b=2, c=8)
+        assert result[0] == 8.0
+
+    def test_abs_function(self):
+        result = self._exec("abs(a)", a=-7)
+        assert result[0] == 7.0
+        assert result[1] == 7
+
+    def test_sqrt(self):
+        result = self._exec("sqrt(a)", a=16)
+        assert result[0] == 4.0
+        assert result[1] == 4
+
+    def test_ceil(self):
+        result = self._exec("ceil(a)", a=2.3)
+        assert result[0] == 3.0
+        assert result[1] == 3
+
+    def test_floor(self):
+        result = self._exec("floor(a)", a=2.7)
+        assert result[0] == 2.0
+        assert result[1] == 2
+
+    def test_sin(self):
+        result = self._exec("sin(a)", a=0)
+        assert result[0] == 0.0
+
+    def test_log10(self):
+        result = self._exec("log10(a)", a=100)
+        assert result[0] == 2.0
+        assert result[1] == 2
+
+    def test_float_output_type(self):
+        result = self._exec("a + b", a=1, b=2)
+        assert isinstance(result[0], float)
+
+    def test_int_output_type(self):
+        result = self._exec("a + b", a=1, b=2)
+        assert isinstance(result[1], int)
+
+    def test_non_numeric_result_raises(self):
+        with pytest.raises(ValueError, match="must evaluate to a numeric result"):
+            self._exec("'hello'", a=42)
+
+    def test_undefined_function_raises(self):
+        with pytest.raises(Exception, match="not defined"):
+            self._exec("str(a)", a=42)
+
+    def test_boolean_result_raises(self):
+        with pytest.raises(ValueError, match="got bool"):
+            self._exec("a > b", a=5, b=3)
+
+    def test_empty_expression_raises(self):
+        with pytest.raises(ValueError, match="Expression cannot be empty"):
+            self._exec("", a=1)
+
+    def test_whitespace_only_expression_raises(self):
+        with pytest.raises(ValueError, match="Expression cannot be empty"):
+            self._exec("   ", a=1)
+
+    # --- Missing function coverage (round, pow, log, log2, cos, tan) ---
+
+    def test_round(self):
+        result = self._exec("round(a)", a=2.7)
+        assert result[0] == 3.0
+        assert result[1] == 3
+
+    def test_round_with_ndigits(self):
+        result = self._exec("round(a, 2)", a=3.14159)
+        assert result[0] == pytest.approx(3.14)
+
+    def test_pow(self):
+        result = self._exec("pow(a, b)", a=2, b=10)
+        assert result[0] == 1024.0
+        assert result[1] == 1024
+
+    def test_log(self):
+        result = self._exec("log(a)", a=math.e)
+        assert result[0] == pytest.approx(1.0)
+
+    def test_log2(self):
+        result = self._exec("log2(a)", a=8)
+        assert result[0] == pytest.approx(3.0)
+
+    def test_cos(self):
+        result = self._exec("cos(a)", a=0)
+        assert result[0] == 1.0
+
+    def test_tan(self):
+        result = self._exec("tan(a)", a=0)
+        assert result[0] == 0.0
+
+    # --- int/float converter functions ---
+
+    def test_int_converter(self):
+        result = self._exec("int(a / b)", a=7, b=2)
+        assert result[1] == 3
+
+    def test_float_converter(self):
+        result = self._exec("float(a)", a=5)
+        assert result[0] == 5.0
+
+    # --- Error path tests ---
+
+    def test_division_by_zero_raises(self):
+        with pytest.raises(ZeroDivisionError):
+            self._exec("a / b", a=1, b=0)
+
+    def test_sqrt_negative_raises(self):
+        with pytest.raises(ValueError, match="math domain error"):
+            self._exec("sqrt(a)", a=-1)
+
+    def test_overflow_inf_raises(self):
+        with pytest.raises(ValueError, match="non-finite result"):
+            self._exec("a * b", a=1e308, b=10)
+
+    def test_pow_huge_exponent_raises(self):
+        with pytest.raises(ValueError, match="Exponent .* exceeds maximum"):
+            self._exec("pow(a, b)", a=10, b=10000000)

From 3b93d5d571cb3e018da65f822cd11b60202b11c2 Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Fri, 6 Mar 2026 11:04:48 +0200
Subject: [PATCH 124/194] feat(api-nodes): add TencentSmartTopology node
 (#12741)

* feat(api-nodes): add TencentSmartTopology node

* feat(api-nodes): enable TencentModelTo3DUV node

* chore(Tencent endpoints): add "wait" to queued statuses
---
 comfy_api_nodes/apis/hunyuan3d.py  |  16 ++++-
 comfy_api_nodes/nodes_hunyuan3d.py | 109 ++++++++++++++++++++++++++---
 comfy_api_nodes/util/client.py     |   2 +-
 3 files changed, 114 insertions(+), 13 deletions(-)

diff --git a/comfy_api_nodes/apis/hunyuan3d.py b/comfy_api_nodes/apis/hunyuan3d.py
index e84eba31e..dad9bc2fa 100644
--- a/comfy_api_nodes/apis/hunyuan3d.py
+++ b/comfy_api_nodes/apis/hunyuan3d.py
@@ -66,13 +66,17 @@ class To3DProTaskQueryRequest(BaseModel):
     JobId: str = Field(...)
 
 
-class To3DUVFileInput(BaseModel):
+class TaskFile3DInput(BaseModel):
     Type: str = Field(..., description="File type: GLB, OBJ, or FBX")
     Url: str = Field(...)
 
 
 class To3DUVTaskRequest(BaseModel):
-    File: To3DUVFileInput = Field(...)
+    File: TaskFile3DInput = Field(...)
+
+
+class To3DPartTaskRequest(BaseModel):
+    File: TaskFile3DInput = Field(...)
 
 
 class TextureEditImageInfo(BaseModel):
@@ -80,7 +84,13 @@ class TextureEditImageInfo(BaseModel):
 
 
 class TextureEditTaskRequest(BaseModel):
-    File3D: To3DUVFileInput = Field(...)
+    File3D: TaskFile3DInput = Field(...)
     Image: TextureEditImageInfo | None = Field(None)
     Prompt: str | None = Field(None)
     EnablePBR: bool | None = Field(None)
+
+
+class SmartTopologyRequest(BaseModel):
+    File3D: TaskFile3DInput = Field(...)
+    PolygonType: str | None = Field(...)
+    FaceLevel: str | None = Field(...)
diff --git a/comfy_api_nodes/nodes_hunyuan3d.py b/comfy_api_nodes/nodes_hunyuan3d.py
index d1d9578ec..bd8bde997 100644
--- a/comfy_api_nodes/nodes_hunyuan3d.py
+++ b/comfy_api_nodes/nodes_hunyuan3d.py
@@ -5,18 +5,19 @@ from comfy_api_nodes.apis.hunyuan3d import (
     Hunyuan3DViewImage,
     InputGenerateType,
     ResultFile3D,
+    SmartTopologyRequest,
+    TaskFile3DInput,
     TextureEditTaskRequest,
+    To3DPartTaskRequest,
     To3DProTaskCreateResponse,
     To3DProTaskQueryRequest,
     To3DProTaskRequest,
     To3DProTaskResultResponse,
-    To3DUVFileInput,
     To3DUVTaskRequest,
 )
 from comfy_api_nodes.util import (
     ApiEndpoint,
     download_url_to_file_3d,
-    download_url_to_image_tensor,
     downscale_image_tensor_by_max_side,
     poll_op,
     sync_op,
@@ -344,7 +345,6 @@ class TencentModelTo3DUVNode(IO.ComfyNode):
             outputs=[
                 IO.File3DOBJ.Output(display_name="OBJ"),
                 IO.File3DFBX.Output(display_name="FBX"),
-                IO.Image.Output(),
             ],
             hidden=[
                 IO.Hidden.auth_token_comfy_org,
@@ -375,7 +375,7 @@ class TencentModelTo3DUVNode(IO.ComfyNode):
             ApiEndpoint(path="/proxy/tencent/hunyuan/3d-uv", method="POST"),
             response_model=To3DProTaskCreateResponse,
             data=To3DUVTaskRequest(
-                File=To3DUVFileInput(
+                File=TaskFile3DInput(
                     Type=file_format.upper(),
                     Url=await upload_3d_model_to_comfyapi(cls, model_3d, file_format),
                 )
@@ -394,7 +394,6 @@ class TencentModelTo3DUVNode(IO.ComfyNode):
         return IO.NodeOutput(
             await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj"),
             await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "fbx").Url, "fbx"),
-            await download_url_to_image_tensor(get_file_from_response(result.ResultFile3Ds, "image").Url),
         )
 
 
@@ -463,7 +462,7 @@ class Tencent3DTextureEditNode(IO.ComfyNode):
             ApiEndpoint(path="/proxy/tencent/hunyuan/3d-texture-edit", method="POST"),
             response_model=To3DProTaskCreateResponse,
             data=TextureEditTaskRequest(
-                File3D=To3DUVFileInput(Type=file_format.upper(), Url=model_url),
+                File3D=TaskFile3DInput(Type=file_format.upper(), Url=model_url),
                 Prompt=prompt,
                 EnablePBR=True,
             ),
@@ -538,8 +537,8 @@ class Tencent3DPartNode(IO.ComfyNode):
             cls,
             ApiEndpoint(path="/proxy/tencent/hunyuan/3d-part", method="POST"),
             response_model=To3DProTaskCreateResponse,
-            data=To3DUVTaskRequest(
-                File=To3DUVFileInput(Type=file_format.upper(), Url=model_url),
+            data=To3DPartTaskRequest(
+                File=TaskFile3DInput(Type=file_format.upper(), Url=model_url),
             ),
             is_rate_limited=_is_tencent_rate_limited,
         )
@@ -557,15 +556,107 @@ class Tencent3DPartNode(IO.ComfyNode):
         )
 
 
+class TencentSmartTopologyNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TencentSmartTopologyNode",
+            display_name="Hunyuan3D: Smart Topology",
+            category="api node/3d/Tencent",
+            description="Perform smart retopology on a 3D model. "
+            "Supports GLB/OBJ formats; max 200MB; recommended for high-poly models.",
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[IO.File3DGLB, IO.File3DOBJ, IO.File3DAny],
+                    tooltip="Input 3D model (GLB or OBJ)",
+                ),
+                IO.Combo.Input(
+                    "polygon_type",
+                    options=["triangle", "quadrilateral"],
+                    tooltip="Surface composition type.",
+                ),
+                IO.Combo.Input(
+                    "face_level",
+                    options=["medium", "high", "low"],
+                    tooltip="Polygon reduction level.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.File3DOBJ.Output(display_name="OBJ"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(expr='{"type":"usd","usd":1.0}'),
+        )
+
+    SUPPORTED_FORMATS = {"glb", "obj"}
+
+    @classmethod
+    async def execute(
+        cls,
+        model_3d: Types.File3D,
+        polygon_type: str,
+        face_level: str,
+        seed: int,
+    ) -> IO.NodeOutput:
+        _ = seed
+        file_format = model_3d.format.lower()
+        if file_format not in cls.SUPPORTED_FORMATS:
+            raise ValueError(
+                f"Unsupported file format: '{file_format}'. " f"Supported: {', '.join(sorted(cls.SUPPORTED_FORMATS))}."
+            )
+        model_url = await upload_3d_model_to_comfyapi(cls, model_3d, file_format)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-smart-topology", method="POST"),
+            response_model=To3DProTaskCreateResponse,
+            data=SmartTopologyRequest(
+                File3D=TaskFile3DInput(Type=file_format.upper(), Url=model_url),
+                PolygonType=polygon_type,
+                FaceLevel=face_level,
+            ),
+            is_rate_limited=_is_tencent_rate_limited,
+        )
+        if response.Error:
+            raise ValueError(f"Task creation failed: [{response.Error.Code}] {response.Error.Message}")
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-smart-topology/query", method="POST"),
+            data=To3DProTaskQueryRequest(JobId=response.JobId),
+            response_model=To3DProTaskResultResponse,
+            status_extractor=lambda r: r.Status,
+        )
+        return IO.NodeOutput(
+            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj"),
+        )
+
+
 class TencentHunyuan3DExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
         return [
             TencentTextToModelNode,
             TencentImageToModelNode,
-            # TencentModelTo3DUVNode,
+            TencentModelTo3DUVNode,
             # Tencent3DTextureEditNode,
             Tencent3DPartNode,
+            TencentSmartTopologyNode,
         ]
 
 
diff --git a/comfy_api_nodes/util/client.py b/comfy_api_nodes/util/client.py
index 94886af7b..79ffb77c1 100644
--- a/comfy_api_nodes/util/client.py
+++ b/comfy_api_nodes/util/client.py
@@ -83,7 +83,7 @@ class _PollUIState:
 _RETRY_STATUS = {408, 500, 502, 503, 504}  # status 429 is handled separately
 COMPLETED_STATUSES = ["succeeded", "succeed", "success", "completed", "finished", "done", "complete"]
 FAILED_STATUSES = ["cancelled", "canceled", "canceling", "fail", "failed", "error"]
-QUEUED_STATUSES = ["created", "queued", "queueing", "submitted", "initializing"]
+QUEUED_STATUSES = ["created", "queued", "queueing", "submitted", "initializing", "wait"]
 
 
 async def sync_op(

From 34e55f006156801a6b5988d046d9041cb681f12d Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Fri, 6 Mar 2026 19:54:27 +0200
Subject: [PATCH 125/194] feat(api-nodes): add Gemini 3.1 Flash Lite model to
 LLM node (#12803)

---
 comfy_api_nodes/nodes_gemini.py | 47 +++++++++++++++++++--------------
 1 file changed, 27 insertions(+), 20 deletions(-)

diff --git a/comfy_api_nodes/nodes_gemini.py b/comfy_api_nodes/nodes_gemini.py
index d83d2fc15..8225ea67e 100644
--- a/comfy_api_nodes/nodes_gemini.py
+++ b/comfy_api_nodes/nodes_gemini.py
@@ -72,18 +72,6 @@ GEMINI_IMAGE_2_PRICE_BADGE = IO.PriceBadge(
 )
 
 
-class GeminiModel(str, Enum):
-    """
-    Gemini Model Names allowed by comfy-api
-    """
-
-    gemini_2_5_pro_preview_05_06 = "gemini-2.5-pro-preview-05-06"
-    gemini_2_5_flash_preview_04_17 = "gemini-2.5-flash-preview-04-17"
-    gemini_2_5_pro = "gemini-2.5-pro"
-    gemini_2_5_flash = "gemini-2.5-flash"
-    gemini_3_0_pro = "gemini-3-pro-preview"
-
-
 class GeminiImageModel(str, Enum):
     """
     Gemini Image Model Names allowed by comfy-api
@@ -237,10 +225,14 @@ def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | N
         input_tokens_price = 0.30
         output_text_tokens_price = 2.50
         output_image_tokens_price = 30.0
-    elif response.modelVersion == "gemini-3-pro-preview":
+    elif response.modelVersion in ("gemini-3-pro-preview", "gemini-3.1-pro-preview"):
         input_tokens_price = 2
         output_text_tokens_price = 12.0
         output_image_tokens_price = 0.0
+    elif response.modelVersion == "gemini-3.1-flash-lite-preview":
+        input_tokens_price = 0.25
+        output_text_tokens_price = 1.50
+        output_image_tokens_price = 0.0
     elif response.modelVersion == "gemini-3-pro-image-preview":
         input_tokens_price = 2
         output_text_tokens_price = 12.0
@@ -292,8 +284,16 @@ class GeminiNode(IO.ComfyNode):
                 ),
                 IO.Combo.Input(
                     "model",
-                    options=GeminiModel,
-                    default=GeminiModel.gemini_2_5_pro,
+                    options=[
+                        "gemini-2.5-pro-preview-05-06",
+                        "gemini-2.5-flash-preview-04-17",
+                        "gemini-2.5-pro",
+                        "gemini-2.5-flash",
+                        "gemini-3-pro-preview",
+                        "gemini-3-1-pro",
+                        "gemini-3-1-flash-lite",
+                    ],
+                    default="gemini-3-1-pro",
                     tooltip="The Gemini model to use for generating responses.",
                 ),
                 IO.Int.Input(
@@ -363,11 +363,16 @@ class GeminiNode(IO.ComfyNode):
                     "usd": [0.00125, 0.01],
                     "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
                   }
-                  : $contains($m, "gemini-3-pro-preview") ? {
+                  : ($contains($m, "gemini-3-pro-preview") or $contains($m, "gemini-3-1-pro")) ? {
                     "type": "list_usd",
                     "usd": [0.002, 0.012],
                     "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
                   }
+                  : $contains($m, "gemini-3-1-flash-lite") ? {
+                    "type": "list_usd",
+                    "usd": [0.00025, 0.0015],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
                   : {"type":"text", "text":"Token-based"}
                 )
                 """,
@@ -436,12 +441,14 @@ class GeminiNode(IO.ComfyNode):
         files: list[GeminiPart] | None = None,
         system_prompt: str = "",
     ) -> IO.NodeOutput:
-        validate_string(prompt, strip_whitespace=False)
+        if model == "gemini-3-pro-preview":
+            model = "gemini-3.1-pro-preview"  # model "gemini-3-pro-preview" will be soon deprecated by Google
+        elif model == "gemini-3-1-pro":
+            model = "gemini-3.1-pro-preview"
+        elif model == "gemini-3-1-flash-lite":
+            model = "gemini-3.1-flash-lite-preview"
 
-        # Create parts list with text prompt as the first part
         parts: list[GeminiPart] = [GeminiPart(text=prompt)]
-
-        # Add other modal parts
         if images is not None:
             parts.extend(await create_image_parts(cls, images))
         if audio is not None:

From f466b066017b9ebe5df67decfcbd09f78c5c66fa Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Fri, 6 Mar 2026 15:20:07 -0800
Subject: [PATCH 126/194] Fix fp16 audio encoder models (#12811)

* mp: respect model_defined_dtypes in default caster

This is needed for parametrizations when the dtype changes between sd
and model.

* audio_encoders: archive model dtypes

Archive model dtypes to stop the state dict load override the dtypes
defined by the core for compute etc.
---
 comfy/audio_encoders/audio_encoders.py | 1 +
 comfy/model_patcher.py                 | 4 ++--
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/comfy/audio_encoders/audio_encoders.py b/comfy/audio_encoders/audio_encoders.py
index 16998af94..0de7584b0 100644
--- a/comfy/audio_encoders/audio_encoders.py
+++ b/comfy/audio_encoders/audio_encoders.py
@@ -27,6 +27,7 @@ class AudioEncoderModel():
         self.model.eval()
         self.patcher = comfy.model_patcher.CoreModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
         self.model_sample_rate = 16000
+        comfy.model_management.archive_model_dtypes(self.model)
 
     def load_sd(self, sd):
         return self.model.load_state_dict(sd, strict=False, assign=self.patcher.is_dynamic())
diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index 7e5ad7aa4..745384271 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -715,8 +715,8 @@ class ModelPatcher:
                     default = True # default random weights in non leaf modules
                     break
             if default and default_device is not None:
-                for param in params.values():
-                    param.data = param.data.to(device=default_device)
+                for param_name, param in params.items():
+                    param.data = param.data.to(device=default_device, dtype=getattr(m, param_name + "_comfy_model_dtype", None))
             if not default and (hasattr(m, "comfy_cast_weights") or len(params) > 0):
                 module_mem = comfy.model_management.module_size(m)
                 module_offload_mem = module_mem

From d69d30819b91aa020d0bb888df2a5b917f83bb7e Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Fri, 6 Mar 2026 16:11:16 -0800
Subject: [PATCH 127/194] Don't run TE on cpu when dynamic vram enabled.
 (#12815)

---
 comfy/model_management.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index ee28ea107..39b4aa483 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -939,7 +939,7 @@ def text_encoder_offload_device():
 def text_encoder_device():
     if args.gpu_only:
         return get_torch_device()
-    elif vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.NORMAL_VRAM:
+    elif vram_state in (VRAMState.HIGH_VRAM, VRAMState.NORMAL_VRAM) or comfy.memory_management.aimdo_enabled:
         if should_use_fp16(prioritize_performance=False):
             return get_torch_device()
         else:

From afc00f00553885eeb96ded329878fe732f6b9f7a Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Fri, 6 Mar 2026 17:10:53 -0800
Subject: [PATCH 128/194] Fix requirements version. (#12817)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 7bf12247c..26e2ecdec 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -24,7 +24,7 @@ av>=14.2.0
 comfy-kitchen>=0.2.7
 comfy-aimdo>=0.2.7
 requests
-simpleeval>=1.0
+simpleeval>=1.0.0
 
 #non essential dependencies:
 kornia>=0.7.1

From 6ac8152fc80734b084d12865460e5e9a5d9a4e1b Mon Sep 17 00:00:00 2001
From: ComfyUI Wiki <contact@comfyui-wiki.com>
Date: Sat, 7 Mar 2026 15:54:09 +0800
Subject: [PATCH 129/194] chore: update workflow templates to v0.9.11 (#12821)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 26e2ecdec..dc9a9ded0 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.39.19
-comfyui-workflow-templates==0.9.10
+comfyui-workflow-templates==0.9.11
 comfyui-embedded-docs==0.4.3
 torch
 torchsde

From bcf1a1fab1e9efe0d4999ea14e9c0318409e0000 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Sat, 7 Mar 2026 09:38:08 -0800
Subject: [PATCH 130/194] mm: reset_cast_buffers: sync compute stream before
 free (#12822)

Sync the compute stream before freeing the cast buffers. This can cause
use after free issues when the cast stream frees the buffer while the
compute stream is behind enough to still needs a casted weight.
---
 comfy/model_management.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 39b4aa483..07bc8ad67 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -1148,6 +1148,7 @@ def reset_cast_buffers():
     LARGEST_CASTED_WEIGHT = (None, 0)
     for offload_stream in STREAM_CAST_BUFFERS:
         offload_stream.synchronize()
+    synchronize()
     STREAM_CAST_BUFFERS.clear()
     soft_empty_cache()
 

From a7a6335be538f55faa2abf7404c9b8e970847d1f Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 7 Mar 2026 16:52:39 -0500
Subject: [PATCH 131/194] ComfyUI v0.16.4

---
 comfyui_version.py | 2 +-
 pyproject.toml     | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfyui_version.py b/comfyui_version.py
index 5da21150b..2723d02e7 100644
--- a/comfyui_version.py
+++ b/comfyui_version.py
@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.16.3"
+__version__ = "0.16.4"
diff --git a/pyproject.toml b/pyproject.toml
index 6a83c5c63..753b219b3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.16.3"
+version = "0.16.4"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

From 29b24cb5177e9d5aa5b3d2e5869999efb4d538c7 Mon Sep 17 00:00:00 2001
From: Luke Mino-Altherr <lminoaltherr@gmail.com>
Date: Sat, 7 Mar 2026 17:37:25 -0800
Subject: [PATCH 132/194] refactor(assets): modular architecture + async
 two-phase scanner & background seeder (#12621)

---
 .../0002_merge_to_asset_references.py         |  267 +++++
 app/assets/api/routes.py                      |  810 ++++++++-----
 app/assets/api/schemas_in.py                  |   79 +-
 app/assets/api/schemas_out.py                 |    6 +-
 app/assets/api/upload.py                      |  171 +++
 app/assets/database/bulk_ops.py               |  204 ----
 app/assets/database/models.py                 |  210 ++--
 app/assets/database/queries.py                |  976 ----------------
 app/assets/database/queries/__init__.py       |  121 ++
 app/assets/database/queries/asset.py          |  140 +++
 .../database/queries/asset_reference.py       | 1033 +++++++++++++++++
 app/assets/database/queries/common.py         |   54 +
 app/assets/database/queries/tags.py           |  356 ++++++
 app/assets/database/tags.py                   |   62 -
 app/assets/hashing.py                         |   75 --
 app/assets/helpers.py                         |  319 +----
 app/assets/manager.py                         |  516 --------
 app/assets/scanner.py                         |  768 ++++++++----
 app/assets/seeder.py                          |  794 +++++++++++++
 app/assets/services/__init__.py               |   87 ++
 app/assets/services/asset_management.py       |  309 +++++
 app/assets/services/bulk_ingest.py            |  280 +++++
 app/assets/services/file_utils.py             |   70 ++
 app/assets/services/hashing.py                |   95 ++
 app/assets/services/ingest.py                 |  375 ++++++
 app/assets/services/metadata_extract.py       |  327 ++++++
 app/assets/services/path_utils.py             |  167 +++
 app/assets/services/schemas.py                |  109 ++
 app/assets/services/tagging.py                |   75 ++
 app/database/db.py                            |   81 +-
 comfy/cli_args.py                             |    2 +-
 comfy_api/feature_flags.py                    |    1 +
 main.py                                       |   35 +-
 requirements.txt                              |    2 +
 server.py                                     |   19 +-
 tests-unit/assets_test/conftest.py            |   16 +-
 tests-unit/assets_test/helpers.py             |   28 +
 tests-unit/assets_test/queries/conftest.py    |   20 +
 tests-unit/assets_test/queries/test_asset.py  |  144 +++
 .../assets_test/queries/test_asset_info.py    |  517 +++++++++
 .../assets_test/queries/test_cache_state.py   |  499 ++++++++
 .../assets_test/queries/test_metadata.py      |  184 +++
 tests-unit/assets_test/queries/test_tags.py   |  366 ++++++
 tests-unit/assets_test/services/__init__.py   |    1 +
 tests-unit/assets_test/services/conftest.py   |   54 +
 .../services/test_asset_management.py         |  268 +++++
 .../assets_test/services/test_bulk_ingest.py  |  137 +++
 .../assets_test/services/test_enrich.py       |  207 ++++
 .../assets_test/services/test_ingest.py       |  229 ++++
 .../assets_test/services/test_tagging.py      |  197 ++++
 .../assets_test/test_assets_missing_sync.py   |    2 +-
 tests-unit/assets_test/test_crud.py           |  138 ++-
 tests-unit/assets_test/test_downloads.py      |    4 +-
 tests-unit/assets_test/test_file_utils.py     |  121 ++
 tests-unit/assets_test/test_list_filter.py    |   40 +-
 .../assets_test/test_prune_orphaned_assets.py |    2 +-
 .../assets_test/test_sync_references.py       |  482 ++++++++
 .../{test_tags.py => test_tags_api.py}        |    4 +-
 tests-unit/assets_test/test_uploads.py        |   22 +-
 tests-unit/requirements.txt                   |    1 -
 tests-unit/seeder_test/test_seeder.py         |  900 ++++++++++++++
 utils/mime_types.py                           |   37 +
 62 files changed, 10737 insertions(+), 2878 deletions(-)
 create mode 100644 alembic_db/versions/0002_merge_to_asset_references.py
 create mode 100644 app/assets/api/upload.py
 delete mode 100644 app/assets/database/bulk_ops.py
 delete mode 100644 app/assets/database/queries.py
 create mode 100644 app/assets/database/queries/__init__.py
 create mode 100644 app/assets/database/queries/asset.py
 create mode 100644 app/assets/database/queries/asset_reference.py
 create mode 100644 app/assets/database/queries/common.py
 create mode 100644 app/assets/database/queries/tags.py
 delete mode 100644 app/assets/database/tags.py
 delete mode 100644 app/assets/hashing.py
 delete mode 100644 app/assets/manager.py
 create mode 100644 app/assets/seeder.py
 create mode 100644 app/assets/services/__init__.py
 create mode 100644 app/assets/services/asset_management.py
 create mode 100644 app/assets/services/bulk_ingest.py
 create mode 100644 app/assets/services/file_utils.py
 create mode 100644 app/assets/services/hashing.py
 create mode 100644 app/assets/services/ingest.py
 create mode 100644 app/assets/services/metadata_extract.py
 create mode 100644 app/assets/services/path_utils.py
 create mode 100644 app/assets/services/schemas.py
 create mode 100644 app/assets/services/tagging.py
 create mode 100644 tests-unit/assets_test/helpers.py
 create mode 100644 tests-unit/assets_test/queries/conftest.py
 create mode 100644 tests-unit/assets_test/queries/test_asset.py
 create mode 100644 tests-unit/assets_test/queries/test_asset_info.py
 create mode 100644 tests-unit/assets_test/queries/test_cache_state.py
 create mode 100644 tests-unit/assets_test/queries/test_metadata.py
 create mode 100644 tests-unit/assets_test/queries/test_tags.py
 create mode 100644 tests-unit/assets_test/services/__init__.py
 create mode 100644 tests-unit/assets_test/services/conftest.py
 create mode 100644 tests-unit/assets_test/services/test_asset_management.py
 create mode 100644 tests-unit/assets_test/services/test_bulk_ingest.py
 create mode 100644 tests-unit/assets_test/services/test_enrich.py
 create mode 100644 tests-unit/assets_test/services/test_ingest.py
 create mode 100644 tests-unit/assets_test/services/test_tagging.py
 create mode 100644 tests-unit/assets_test/test_file_utils.py
 create mode 100644 tests-unit/assets_test/test_sync_references.py
 rename tests-unit/assets_test/{test_tags.py => test_tags_api.py} (98%)
 create mode 100644 tests-unit/seeder_test/test_seeder.py
 create mode 100644 utils/mime_types.py

diff --git a/alembic_db/versions/0002_merge_to_asset_references.py b/alembic_db/versions/0002_merge_to_asset_references.py
new file mode 100644
index 000000000..1ac1b980c
--- /dev/null
+++ b/alembic_db/versions/0002_merge_to_asset_references.py
@@ -0,0 +1,267 @@
+"""
+Merge AssetInfo and AssetCacheState into unified asset_references table.
+
+This migration drops old tables and creates the new unified schema.
+All existing data is discarded.
+
+Revision ID: 0002_merge_to_asset_references
+Revises: 0001_assets
+Create Date: 2025-02-11
+"""
+
+from alembic import op
+import sqlalchemy as sa
+
+revision = "0002_merge_to_asset_references"
+down_revision = "0001_assets"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Drop old tables (order matters due to FK constraints)
+    op.drop_index("ix_asset_info_meta_key_val_bool", table_name="asset_info_meta")
+    op.drop_index("ix_asset_info_meta_key_val_num", table_name="asset_info_meta")
+    op.drop_index("ix_asset_info_meta_key_val_str", table_name="asset_info_meta")
+    op.drop_index("ix_asset_info_meta_key", table_name="asset_info_meta")
+    op.drop_table("asset_info_meta")
+
+    op.drop_index("ix_asset_info_tags_asset_info_id", table_name="asset_info_tags")
+    op.drop_index("ix_asset_info_tags_tag_name", table_name="asset_info_tags")
+    op.drop_table("asset_info_tags")
+
+    op.drop_index("ix_asset_cache_state_asset_id", table_name="asset_cache_state")
+    op.drop_index("ix_asset_cache_state_file_path", table_name="asset_cache_state")
+    op.drop_table("asset_cache_state")
+
+    op.drop_index("ix_assets_info_owner_name", table_name="assets_info")
+    op.drop_index("ix_assets_info_last_access_time", table_name="assets_info")
+    op.drop_index("ix_assets_info_created_at", table_name="assets_info")
+    op.drop_index("ix_assets_info_name", table_name="assets_info")
+    op.drop_index("ix_assets_info_asset_id", table_name="assets_info")
+    op.drop_index("ix_assets_info_owner_id", table_name="assets_info")
+    op.drop_table("assets_info")
+
+    # Truncate assets table (cascades handled by dropping dependent tables first)
+    op.execute("DELETE FROM assets")
+
+    # Create asset_references table
+    op.create_table(
+        "asset_references",
+        sa.Column("id", sa.String(length=36), primary_key=True),
+        sa.Column(
+            "asset_id",
+            sa.String(length=36),
+            sa.ForeignKey("assets.id", ondelete="CASCADE"),
+            nullable=False,
+        ),
+        sa.Column("file_path", sa.Text(), nullable=True),
+        sa.Column("mtime_ns", sa.BigInteger(), nullable=True),
+        sa.Column(
+            "needs_verify",
+            sa.Boolean(),
+            nullable=False,
+            server_default=sa.text("false"),
+        ),
+        sa.Column(
+            "is_missing", sa.Boolean(), nullable=False, server_default=sa.text("false")
+        ),
+        sa.Column("enrichment_level", sa.Integer(), nullable=False, server_default="0"),
+        sa.Column("owner_id", sa.String(length=128), nullable=False, server_default=""),
+        sa.Column("name", sa.String(length=512), nullable=False),
+        sa.Column(
+            "preview_id",
+            sa.String(length=36),
+            sa.ForeignKey("assets.id", ondelete="SET NULL"),
+            nullable=True,
+        ),
+        sa.Column("user_metadata", sa.JSON(), nullable=True),
+        sa.Column("created_at", sa.DateTime(timezone=False), nullable=False),
+        sa.Column("updated_at", sa.DateTime(timezone=False), nullable=False),
+        sa.Column("last_access_time", sa.DateTime(timezone=False), nullable=False),
+        sa.Column("deleted_at", sa.DateTime(timezone=False), nullable=True),
+        sa.CheckConstraint(
+            "(mtime_ns IS NULL) OR (mtime_ns >= 0)", name="ck_ar_mtime_nonneg"
+        ),
+        sa.CheckConstraint(
+            "enrichment_level >= 0 AND enrichment_level <= 2",
+            name="ck_ar_enrichment_level_range",
+        ),
+    )
+    op.create_index(
+        "uq_asset_references_file_path", "asset_references", ["file_path"], unique=True
+    )
+    op.create_index("ix_asset_references_asset_id", "asset_references", ["asset_id"])
+    op.create_index("ix_asset_references_owner_id", "asset_references", ["owner_id"])
+    op.create_index("ix_asset_references_name", "asset_references", ["name"])
+    op.create_index("ix_asset_references_is_missing", "asset_references", ["is_missing"])
+    op.create_index(
+        "ix_asset_references_enrichment_level", "asset_references", ["enrichment_level"]
+    )
+    op.create_index("ix_asset_references_created_at", "asset_references", ["created_at"])
+    op.create_index(
+        "ix_asset_references_last_access_time", "asset_references", ["last_access_time"]
+    )
+    op.create_index(
+        "ix_asset_references_owner_name", "asset_references", ["owner_id", "name"]
+    )
+    op.create_index("ix_asset_references_deleted_at", "asset_references", ["deleted_at"])
+
+    # Create asset_reference_tags table
+    op.create_table(
+        "asset_reference_tags",
+        sa.Column(
+            "asset_reference_id",
+            sa.String(length=36),
+            sa.ForeignKey("asset_references.id", ondelete="CASCADE"),
+            nullable=False,
+        ),
+        sa.Column(
+            "tag_name",
+            sa.String(length=512),
+            sa.ForeignKey("tags.name", ondelete="RESTRICT"),
+            nullable=False,
+        ),
+        sa.Column(
+            "origin", sa.String(length=32), nullable=False, server_default="manual"
+        ),
+        sa.Column("added_at", sa.DateTime(timezone=False), nullable=False),
+        sa.PrimaryKeyConstraint(
+            "asset_reference_id", "tag_name", name="pk_asset_reference_tags"
+        ),
+    )
+    op.create_index(
+        "ix_asset_reference_tags_tag_name", "asset_reference_tags", ["tag_name"]
+    )
+    op.create_index(
+        "ix_asset_reference_tags_asset_reference_id",
+        "asset_reference_tags",
+        ["asset_reference_id"],
+    )
+
+    # Create asset_reference_meta table
+    op.create_table(
+        "asset_reference_meta",
+        sa.Column(
+            "asset_reference_id",
+            sa.String(length=36),
+            sa.ForeignKey("asset_references.id", ondelete="CASCADE"),
+            nullable=False,
+        ),
+        sa.Column("key", sa.String(length=256), nullable=False),
+        sa.Column("ordinal", sa.Integer(), nullable=False, server_default="0"),
+        sa.Column("val_str", sa.String(length=2048), nullable=True),
+        sa.Column("val_num", sa.Numeric(38, 10), nullable=True),
+        sa.Column("val_bool", sa.Boolean(), nullable=True),
+        sa.Column("val_json", sa.JSON(), nullable=True),
+        sa.PrimaryKeyConstraint(
+            "asset_reference_id", "key", "ordinal", name="pk_asset_reference_meta"
+        ),
+    )
+    op.create_index("ix_asset_reference_meta_key", "asset_reference_meta", ["key"])
+    op.create_index(
+        "ix_asset_reference_meta_key_val_str", "asset_reference_meta", ["key", "val_str"]
+    )
+    op.create_index(
+        "ix_asset_reference_meta_key_val_num", "asset_reference_meta", ["key", "val_num"]
+    )
+    op.create_index(
+        "ix_asset_reference_meta_key_val_bool",
+        "asset_reference_meta",
+        ["key", "val_bool"],
+    )
+
+
+def downgrade() -> None:
+    """Reverse 0002_merge_to_asset_references: drop new tables, recreate old schema.
+
+    NOTE: Data is not recoverable. The upgrade discards all rows from the old
+    tables and truncates assets. After downgrade the old schema will be empty.
+    A filesystem rescan will repopulate data once the older code is running.
+    """
+    # Drop new tables (order matters due to FK constraints)
+    op.drop_index("ix_asset_reference_meta_key_val_bool", table_name="asset_reference_meta")
+    op.drop_index("ix_asset_reference_meta_key_val_num", table_name="asset_reference_meta")
+    op.drop_index("ix_asset_reference_meta_key_val_str", table_name="asset_reference_meta")
+    op.drop_index("ix_asset_reference_meta_key", table_name="asset_reference_meta")
+    op.drop_table("asset_reference_meta")
+
+    op.drop_index("ix_asset_reference_tags_asset_reference_id", table_name="asset_reference_tags")
+    op.drop_index("ix_asset_reference_tags_tag_name", table_name="asset_reference_tags")
+    op.drop_table("asset_reference_tags")
+
+    op.drop_index("ix_asset_references_deleted_at", table_name="asset_references")
+    op.drop_index("ix_asset_references_owner_name", table_name="asset_references")
+    op.drop_index("ix_asset_references_last_access_time", table_name="asset_references")
+    op.drop_index("ix_asset_references_created_at", table_name="asset_references")
+    op.drop_index("ix_asset_references_enrichment_level", table_name="asset_references")
+    op.drop_index("ix_asset_references_is_missing", table_name="asset_references")
+    op.drop_index("ix_asset_references_name", table_name="asset_references")
+    op.drop_index("ix_asset_references_owner_id", table_name="asset_references")
+    op.drop_index("ix_asset_references_asset_id", table_name="asset_references")
+    op.drop_index("uq_asset_references_file_path", table_name="asset_references")
+    op.drop_table("asset_references")
+
+    # Truncate assets (upgrade deleted all rows; downgrade starts fresh too)
+    op.execute("DELETE FROM assets")
+
+    # Recreate old tables from 0001_assets schema
+    op.create_table(
+        "assets_info",
+        sa.Column("id", sa.String(length=36), primary_key=True),
+        sa.Column("owner_id", sa.String(length=128), nullable=False, server_default=""),
+        sa.Column("name", sa.String(length=512), nullable=False),
+        sa.Column("asset_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="RESTRICT"), nullable=False),
+        sa.Column("preview_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="SET NULL"), nullable=True),
+        sa.Column("user_metadata", sa.JSON(), nullable=True),
+        sa.Column("created_at", sa.DateTime(timezone=False), nullable=False),
+        sa.Column("updated_at", sa.DateTime(timezone=False), nullable=False),
+        sa.Column("last_access_time", sa.DateTime(timezone=False), nullable=False),
+        sa.UniqueConstraint("asset_id", "owner_id", "name", name="uq_assets_info_asset_owner_name"),
+    )
+    op.create_index("ix_assets_info_owner_id", "assets_info", ["owner_id"])
+    op.create_index("ix_assets_info_asset_id", "assets_info", ["asset_id"])
+    op.create_index("ix_assets_info_name", "assets_info", ["name"])
+    op.create_index("ix_assets_info_created_at", "assets_info", ["created_at"])
+    op.create_index("ix_assets_info_last_access_time", "assets_info", ["last_access_time"])
+    op.create_index("ix_assets_info_owner_name", "assets_info", ["owner_id", "name"])
+
+    op.create_table(
+        "asset_cache_state",
+        sa.Column("id", sa.Integer(), primary_key=True, autoincrement=True),
+        sa.Column("asset_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="CASCADE"), nullable=False),
+        sa.Column("file_path", sa.Text(), nullable=False),
+        sa.Column("mtime_ns", sa.BigInteger(), nullable=True),
+        sa.Column("needs_verify", sa.Boolean(), nullable=False, server_default=sa.text("false")),
+        sa.CheckConstraint("(mtime_ns IS NULL) OR (mtime_ns >= 0)", name="ck_acs_mtime_nonneg"),
+        sa.UniqueConstraint("file_path", name="uq_asset_cache_state_file_path"),
+    )
+    op.create_index("ix_asset_cache_state_file_path", "asset_cache_state", ["file_path"])
+    op.create_index("ix_asset_cache_state_asset_id", "asset_cache_state", ["asset_id"])
+
+    op.create_table(
+        "asset_info_tags",
+        sa.Column("asset_info_id", sa.String(length=36), sa.ForeignKey("assets_info.id", ondelete="CASCADE"), nullable=False),
+        sa.Column("tag_name", sa.String(length=512), sa.ForeignKey("tags.name", ondelete="RESTRICT"), nullable=False),
+        sa.Column("origin", sa.String(length=32), nullable=False, server_default="manual"),
+        sa.Column("added_at", sa.DateTime(timezone=False), nullable=False),
+        sa.PrimaryKeyConstraint("asset_info_id", "tag_name", name="pk_asset_info_tags"),
+    )
+    op.create_index("ix_asset_info_tags_tag_name", "asset_info_tags", ["tag_name"])
+    op.create_index("ix_asset_info_tags_asset_info_id", "asset_info_tags", ["asset_info_id"])
+
+    op.create_table(
+        "asset_info_meta",
+        sa.Column("asset_info_id", sa.String(length=36), sa.ForeignKey("assets_info.id", ondelete="CASCADE"), nullable=False),
+        sa.Column("key", sa.String(length=256), nullable=False),
+        sa.Column("ordinal", sa.Integer(), nullable=False, server_default="0"),
+        sa.Column("val_str", sa.String(length=2048), nullable=True),
+        sa.Column("val_num", sa.Numeric(38, 10), nullable=True),
+        sa.Column("val_bool", sa.Boolean(), nullable=True),
+        sa.Column("val_json", sa.JSON(), nullable=True),
+        sa.PrimaryKeyConstraint("asset_info_id", "key", "ordinal", name="pk_asset_info_meta"),
+    )
+    op.create_index("ix_asset_info_meta_key", "asset_info_meta", ["key"])
+    op.create_index("ix_asset_info_meta_key_val_str", "asset_info_meta", ["key", "val_str"])
+    op.create_index("ix_asset_info_meta_key_val_num", "asset_info_meta", ["key", "val_num"])
+    op.create_index("ix_asset_info_meta_key_val_bool", "asset_info_meta", ["key", "val_bool"])
diff --git a/app/assets/api/routes.py b/app/assets/api/routes.py
index 7676e50b4..40dee9f46 100644
--- a/app/assets/api/routes.py
+++ b/app/assets/api/routes.py
@@ -1,56 +1,144 @@
+import asyncio
+import functools
+import json
 import logging
-import uuid
-import urllib.parse
 import os
-import contextlib
-from aiohttp import web
+import urllib.parse
+import uuid
+from typing import Any
 
+from aiohttp import web
 from pydantic import ValidationError
 
-import app.assets.manager as manager
-from app import user_manager
-from app.assets.api import schemas_in
-from app.assets.helpers import get_query_dict
-from app.assets.scanner import seed_assets
-
 import folder_paths
+from app import user_manager
+from app.assets.api import schemas_in, schemas_out
+from app.assets.api.schemas_in import (
+    AssetValidationError,
+    UploadError,
+)
+from app.assets.helpers import validate_blake3_hash
+from app.assets.api.upload import (
+    delete_temp_file_if_exists,
+    parse_multipart_upload,
+)
+from app.assets.seeder import ScanInProgressError, asset_seeder
+from app.assets.services import (
+    DependencyMissingError,
+    HashMismatchError,
+    apply_tags,
+    asset_exists,
+    create_from_hash,
+    delete_asset_reference,
+    get_asset_detail,
+    list_assets_page,
+    list_tags,
+    remove_tags,
+    resolve_asset_for_download,
+    update_asset_metadata,
+    upload_from_temp_path,
+)
 
 ROUTES = web.RouteTableDef()
 USER_MANAGER: user_manager.UserManager | None = None
+_ASSETS_ENABLED = False
+
+
+def _require_assets_feature_enabled(handler):
+    @functools.wraps(handler)
+    async def wrapper(request: web.Request) -> web.Response:
+        if not _ASSETS_ENABLED:
+            return _build_error_response(
+                503,
+                "SERVICE_DISABLED",
+                "Assets system is disabled. Start the server with --enable-assets to use this feature.",
+            )
+        return await handler(request)
+
+    return wrapper
+
 
 # UUID regex (canonical hyphenated form, case-insensitive)
 UUID_RE = r"[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}"
 
-# Note to any custom node developers reading this code:
-# The assets system is not yet fully implemented, do not rely on the code in /app/assets remaining the same.
 
-def register_assets_system(app: web.Application, user_manager_instance: user_manager.UserManager) -> None:
-    global USER_MANAGER
-    USER_MANAGER = user_manager_instance
+def get_query_dict(request: web.Request) -> dict[str, Any]:
+    """Gets a dictionary of query parameters from the request.
+
+    request.query is a MultiMapping[str], needs to be converted to a dict
+    to be validated by Pydantic.
+    """
+    query_dict = {
+        key: request.query.getall(key)
+        if len(request.query.getall(key)) > 1
+        else request.query.get(key)
+        for key in request.query.keys()
+    }
+    return query_dict
+
+
+# Note to any custom node developers reading this code:
+# The assets system is not yet fully implemented,
+# do not rely on the code in /app/assets remaining the same.
+
+
+def register_assets_routes(
+    app: web.Application,
+    user_manager_instance: user_manager.UserManager | None = None,
+) -> None:
+    global USER_MANAGER, _ASSETS_ENABLED
+    if user_manager_instance is not None:
+        USER_MANAGER = user_manager_instance
+        _ASSETS_ENABLED = True
     app.add_routes(ROUTES)
 
-def _error_response(status: int, code: str, message: str, details: dict | None = None) -> web.Response:
-    return web.json_response({"error": {"code": code, "message": message, "details": details or {}}}, status=status)
+
+def disable_assets_routes() -> None:
+    """Disable asset routes at runtime (e.g. after DB init failure)."""
+    global _ASSETS_ENABLED
+    _ASSETS_ENABLED = False
 
 
-def _validation_error_response(code: str, ve: ValidationError) -> web.Response:
-    return _error_response(400, code, "Validation failed.", {"errors": ve.json()})
+def _build_error_response(
+    status: int, code: str, message: str, details: dict | None = None
+) -> web.Response:
+    return web.json_response(
+        {"error": {"code": code, "message": message, "details": details or {}}},
+        status=status,
+    )
+
+
+def _build_validation_error_response(code: str, ve: ValidationError) -> web.Response:
+    errors = json.loads(ve.json())
+    return _build_error_response(400, code, "Validation failed.", {"errors": errors})
+
+
+def _validate_sort_field(requested: str | None) -> str:
+    if not requested:
+        return "created_at"
+    v = requested.lower()
+    if v in {"name", "created_at", "updated_at", "size", "last_access_time"}:
+        return v
+    return "created_at"
 
 
 @ROUTES.head("/api/assets/hash/{hash}")
+@_require_assets_feature_enabled
 async def head_asset_by_hash(request: web.Request) -> web.Response:
     hash_str = request.match_info.get("hash", "").strip().lower()
-    if not hash_str or ":" not in hash_str:
-        return _error_response(400, "INVALID_HASH", "hash must be like 'blake3:<hex>'")
-    algo, digest = hash_str.split(":", 1)
-    if algo != "blake3" or not digest or any(c for c in digest if c not in "0123456789abcdef"):
-        return _error_response(400, "INVALID_HASH", "hash must be like 'blake3:<hex>'")
-    exists = manager.asset_exists(asset_hash=hash_str)
+    try:
+        hash_str = validate_blake3_hash(hash_str)
+    except ValueError:
+        return _build_error_response(
+            400, "INVALID_HASH", "hash must be like 'blake3:<hex>'"
+        )
+    exists = asset_exists(hash_str)
     return web.Response(status=200 if exists else 404)
 
 
 @ROUTES.get("/api/assets")
-async def list_assets(request: web.Request) -> web.Response:
+@_require_assets_feature_enabled
+async def list_assets_route(request: web.Request) -> web.Response:
     """
     GET request to list assets.
     """
@@ -58,78 +146,140 @@ async def list_assets(request: web.Request) -> web.Response:
     try:
         q = schemas_in.ListAssetsQuery.model_validate(query_dict)
     except ValidationError as ve:
-        return _validation_error_response("INVALID_QUERY", ve)
+        return _build_validation_error_response("INVALID_QUERY", ve)
 
-    payload = manager.list_assets(
+    sort = _validate_sort_field(q.sort)
+    order_candidate = (q.order or "desc").lower()
+    order = order_candidate if order_candidate in {"asc", "desc"} else "desc"
+
+    result = list_assets_page(
+        owner_id=USER_MANAGER.get_request_user_id(request),
         include_tags=q.include_tags,
         exclude_tags=q.exclude_tags,
         name_contains=q.name_contains,
         metadata_filter=q.metadata_filter,
         limit=q.limit,
         offset=q.offset,
-        sort=q.sort,
-        order=q.order,
-        owner_id=USER_MANAGER.get_request_user_id(request),
+        sort=sort,
+        order=order,
+    )
+
+    summaries = [
+        schemas_out.AssetSummary(
+            id=item.ref.id,
+            name=item.ref.name,
+            asset_hash=item.asset.hash if item.asset else None,
+            size=int(item.asset.size_bytes) if item.asset else None,
+            mime_type=item.asset.mime_type if item.asset else None,
+            tags=item.tags,
+            created_at=item.ref.created_at,
+            updated_at=item.ref.updated_at,
+            last_access_time=item.ref.last_access_time,
+        )
+        for item in result.items
+    ]
+
+    payload = schemas_out.AssetsList(
+        assets=summaries,
+        total=result.total,
+        has_more=(q.offset + len(summaries)) < result.total,
     )
     return web.json_response(payload.model_dump(mode="json", exclude_none=True))
 
 
 @ROUTES.get(f"/api/assets/{{id:{UUID_RE}}}")
-async def get_asset(request: web.Request) -> web.Response:
+@_require_assets_feature_enabled
+async def get_asset_route(request: web.Request) -> web.Response:
     """
     GET request to get an asset's info as JSON.
     """
-    asset_info_id = str(uuid.UUID(request.match_info["id"]))
+    reference_id = str(uuid.UUID(request.match_info["id"]))
     try:
-        result = manager.get_asset(
-            asset_info_id=asset_info_id,
+        result = get_asset_detail(
+            reference_id=reference_id,
             owner_id=USER_MANAGER.get_request_user_id(request),
         )
+        if not result:
+            return _build_error_response(
+                404,
+                "ASSET_NOT_FOUND",
+                f"AssetReference {reference_id} not found",
+                {"id": reference_id},
+            )
+
+        payload = schemas_out.AssetDetail(
+            id=result.ref.id,
+            name=result.ref.name,
+            asset_hash=result.asset.hash if result.asset else None,
+            size=int(result.asset.size_bytes) if result.asset else None,
+            mime_type=result.asset.mime_type if result.asset else None,
+            tags=result.tags,
+            user_metadata=result.ref.user_metadata or {},
+            preview_id=result.ref.preview_id,
+            created_at=result.ref.created_at,
+            last_access_time=result.ref.last_access_time,
+        )
     except ValueError as e:
-        return _error_response(404, "ASSET_NOT_FOUND", str(e), {"id": asset_info_id})
+        return _build_error_response(
+            404, "ASSET_NOT_FOUND", str(e), {"id": reference_id}
+        )
     except Exception:
         logging.exception(
-            "get_asset failed for asset_info_id=%s, owner_id=%s",
-            asset_info_id,
+            "get_asset failed for reference_id=%s, owner_id=%s",
+            reference_id,
             USER_MANAGER.get_request_user_id(request),
         )
-        return _error_response(500, "INTERNAL", "Unexpected server error.")
-    return web.json_response(result.model_dump(mode="json"), status=200)
+        return _build_error_response(500, "INTERNAL", "Unexpected server error.")
+    return web.json_response(payload.model_dump(mode="json"), status=200)
 
 
 @ROUTES.get(f"/api/assets/{{id:{UUID_RE}}}/content")
+@_require_assets_feature_enabled
 async def download_asset_content(request: web.Request) -> web.Response:
-    # question: do we need disposition? could we just stick with one of these?
     disposition = request.query.get("disposition", "attachment").lower().strip()
     if disposition not in {"inline", "attachment"}:
         disposition = "attachment"
 
     try:
-        abs_path, content_type, filename = manager.resolve_asset_content_for_download(
-            asset_info_id=str(uuid.UUID(request.match_info["id"])),
+        result = resolve_asset_for_download(
+            reference_id=str(uuid.UUID(request.match_info["id"])),
             owner_id=USER_MANAGER.get_request_user_id(request),
         )
+        abs_path = result.abs_path
+        content_type = result.content_type
+        filename = result.download_name
     except ValueError as ve:
-        return _error_response(404, "ASSET_NOT_FOUND", str(ve))
+        return _build_error_response(404, "ASSET_NOT_FOUND", str(ve))
     except NotImplementedError as nie:
-        return _error_response(501, "BACKEND_UNSUPPORTED", str(nie))
+        return _build_error_response(501, "BACKEND_UNSUPPORTED", str(nie))
     except FileNotFoundError:
-        return _error_response(404, "FILE_NOT_FOUND", "Underlying file not found on disk.")
+        return _build_error_response(
+            404, "FILE_NOT_FOUND", "Underlying file not found on disk."
+        )
 
-    quoted = (filename or "").replace("\r", "").replace("\n", "").replace('"', "'")
-    cd = f'{disposition}; filename="{quoted}"; filename*=UTF-8\'\'{urllib.parse.quote(filename)}'
+    _DANGEROUS_MIME_TYPES = {
+        "text/html", "text/html-sandboxed", "application/xhtml+xml",
+        "text/javascript", "text/css",
+    }
+    if content_type in _DANGEROUS_MIME_TYPES:
+        content_type = "application/octet-stream"
+
+    safe_name = (filename or "").replace("\r", "").replace("\n", "")
+    encoded = urllib.parse.quote(safe_name)
+    cd = f"{disposition}; filename*=UTF-8''{encoded}"
 
     file_size = os.path.getsize(abs_path)
+    size_mb = file_size / (1024 * 1024)
     logging.info(
-        "download_asset_content: path=%s, size=%d bytes (%.2f MB), content_type=%s, filename=%s",
+        "download_asset_content: path=%s, size=%d bytes (%.2f MB), type=%s, name=%s",
         abs_path,
         file_size,
-        file_size / (1024 * 1024),
+        size_mb,
         content_type,
         filename,
     )
 
-    async def file_sender():
+    async def stream_file_chunks():
         chunk_size = 64 * 1024
         with open(abs_path, "rb") as f:
             while True:
@@ -139,26 +289,30 @@ async def download_asset_content(request: web.Request) -> web.Response:
                 yield chunk
 
     return web.Response(
-        body=file_sender(),
+        body=stream_file_chunks(),
         content_type=content_type,
         headers={
             "Content-Disposition": cd,
             "Content-Length": str(file_size),
+            "X-Content-Type-Options": "nosniff",
         },
     )
 
 
 @ROUTES.post("/api/assets/from-hash")
-async def create_asset_from_hash(request: web.Request) -> web.Response:
+@_require_assets_feature_enabled
+async def create_asset_from_hash_route(request: web.Request) -> web.Response:
     try:
         payload = await request.json()
         body = schemas_in.CreateFromHashBody.model_validate(payload)
     except ValidationError as ve:
-        return _validation_error_response("INVALID_BODY", ve)
+        return _build_validation_error_response("INVALID_BODY", ve)
     except Exception:
-        return _error_response(400, "INVALID_JSON", "Request body must be valid JSON.")
+        return _build_error_response(
+            400, "INVALID_JSON", "Request body must be valid JSON."
+        )
 
-    result = manager.create_asset_from_hash(
+    result = create_from_hash(
         hash_str=body.hash,
         name=body.name,
         tags=body.tags,
@@ -166,246 +320,209 @@ async def create_asset_from_hash(request: web.Request) -> web.Response:
         owner_id=USER_MANAGER.get_request_user_id(request),
     )
     if result is None:
-        return _error_response(404, "ASSET_NOT_FOUND", f"Asset content {body.hash} does not exist")
-    return web.json_response(result.model_dump(mode="json"), status=201)
+        return _build_error_response(
+            404, "ASSET_NOT_FOUND", f"Asset content {body.hash} does not exist"
+        )
+
+    payload_out = schemas_out.AssetCreated(
+        id=result.ref.id,
+        name=result.ref.name,
+        asset_hash=result.asset.hash,
+        size=int(result.asset.size_bytes),
+        mime_type=result.asset.mime_type,
+        tags=result.tags,
+        user_metadata=result.ref.user_metadata or {},
+        preview_id=result.ref.preview_id,
+        created_at=result.ref.created_at,
+        last_access_time=result.ref.last_access_time,
+        created_new=result.created_new,
+    )
+    return web.json_response(payload_out.model_dump(mode="json"), status=201)
 
 
 @ROUTES.post("/api/assets")
+@_require_assets_feature_enabled
 async def upload_asset(request: web.Request) -> web.Response:
     """Multipart/form-data endpoint for Asset uploads."""
-    if not (request.content_type or "").lower().startswith("multipart/"):
-        return _error_response(415, "UNSUPPORTED_MEDIA_TYPE", "Use multipart/form-data for uploads.")
-
-    reader = await request.multipart()
-
-    file_present = False
-    file_client_name: str | None = None
-    tags_raw: list[str] = []
-    provided_name: str | None = None
-    user_metadata_raw: str | None = None
-    provided_hash: str | None = None
-    provided_hash_exists: bool | None = None
-
-    file_written = 0
-    tmp_path: str | None = None
-    while True:
-        field = await reader.next()
-        if field is None:
-            break
-
-        fname = getattr(field, "name", "") or ""
-
-        if fname == "hash":
-            try:
-                s = ((await field.text()) or "").strip().lower()
-            except Exception:
-                return _error_response(400, "INVALID_HASH", "hash must be like 'blake3:<hex>'")
-
-            if s:
-                if ":" not in s:
-                    return _error_response(400, "INVALID_HASH", "hash must be like 'blake3:<hex>'")
-                algo, digest = s.split(":", 1)
-                if algo != "blake3" or not digest or any(c for c in digest if c not in "0123456789abcdef"):
-                    return _error_response(400, "INVALID_HASH", "hash must be like 'blake3:<hex>'")
-                provided_hash = f"{algo}:{digest}"
-                try:
-                    provided_hash_exists = manager.asset_exists(asset_hash=provided_hash)
-                except Exception:
-                    provided_hash_exists = None  # do not fail the whole request here
-
-        elif fname == "file":
-            file_present = True
-            file_client_name = (field.filename or "").strip()
-
-            if provided_hash and provided_hash_exists is True:
-                # If client supplied a hash that we know exists, drain but do not write to disk
-                try:
-                    while True:
-                        chunk = await field.read_chunk(8 * 1024 * 1024)
-                        if not chunk:
-                            break
-                        file_written += len(chunk)
-                except Exception:
-                    return _error_response(500, "UPLOAD_IO_ERROR", "Failed to receive uploaded file.")
-                continue  # Do not create temp file; we will create AssetInfo from the existing content
-
-            # Otherwise, store to temp for hashing/ingest
-            uploads_root = os.path.join(folder_paths.get_temp_directory(), "uploads")
-            unique_dir = os.path.join(uploads_root, uuid.uuid4().hex)
-            os.makedirs(unique_dir, exist_ok=True)
-            tmp_path = os.path.join(unique_dir, ".upload.part")
-
-            try:
-                with open(tmp_path, "wb") as f:
-                    while True:
-                        chunk = await field.read_chunk(8 * 1024 * 1024)
-                        if not chunk:
-                            break
-                        f.write(chunk)
-                        file_written += len(chunk)
-            except Exception:
-                try:
-                    if os.path.exists(tmp_path or ""):
-                        os.remove(tmp_path)
-                finally:
-                    return _error_response(500, "UPLOAD_IO_ERROR", "Failed to receive and store uploaded file.")
-        elif fname == "tags":
-            tags_raw.append((await field.text()) or "")
-        elif fname == "name":
-            provided_name = (await field.text()) or None
-        elif fname == "user_metadata":
-            user_metadata_raw = (await field.text()) or None
-
-    # If client did not send file, and we are not doing a from-hash fast path -> error
-    if not file_present and not (provided_hash and provided_hash_exists):
-        return _error_response(400, "MISSING_FILE", "Form must include a 'file' part or a known 'hash'.")
-
-    if file_present and file_written == 0 and not (provided_hash and provided_hash_exists):
-        # Empty upload is only acceptable if we are fast-pathing from existing hash
-        try:
-            if tmp_path and os.path.exists(tmp_path):
-                os.remove(tmp_path)
-        finally:
-            return _error_response(400, "EMPTY_UPLOAD", "Uploaded file is empty.")
-
     try:
-        spec = schemas_in.UploadAssetSpec.model_validate({
-            "tags": tags_raw,
-            "name": provided_name,
-            "user_metadata": user_metadata_raw,
-            "hash": provided_hash,
-        })
-    except ValidationError as ve:
-        try:
-            if tmp_path and os.path.exists(tmp_path):
-                os.remove(tmp_path)
-        finally:
-            return _validation_error_response("INVALID_BODY", ve)
-
-    # Validate models category against configured folders (consistent with previous behavior)
-    if spec.tags and spec.tags[0] == "models":
-        if len(spec.tags) < 2 or spec.tags[1] not in folder_paths.folder_names_and_paths:
-            if tmp_path and os.path.exists(tmp_path):
-                os.remove(tmp_path)
-            return _error_response(
-                400, "INVALID_BODY", f"unknown models category '{spec.tags[1] if len(spec.tags) >= 2 else ''}'"
-            )
+        parsed = await parse_multipart_upload(request, check_hash_exists=asset_exists)
+    except UploadError as e:
+        return _build_error_response(e.status, e.code, e.message)
 
     owner_id = USER_MANAGER.get_request_user_id(request)
 
-    # Fast path: if a valid provided hash exists, create AssetInfo without writing anything
-    if spec.hash and provided_hash_exists is True:
-        try:
-            result = manager.create_asset_from_hash(
+    try:
+        spec = schemas_in.UploadAssetSpec.model_validate(
+            {
+                "tags": parsed.tags_raw,
+                "name": parsed.provided_name,
+                "user_metadata": parsed.user_metadata_raw,
+                "hash": parsed.provided_hash,
+            }
+        )
+    except ValidationError as ve:
+        delete_temp_file_if_exists(parsed.tmp_path)
+        return _build_error_response(
+            400, "INVALID_BODY", f"Validation failed: {ve.json()}"
+        )
+
+    if spec.tags and spec.tags[0] == "models":
+        if (
+            len(spec.tags) < 2
+            or spec.tags[1] not in folder_paths.folder_names_and_paths
+        ):
+            delete_temp_file_if_exists(parsed.tmp_path)
+            category = spec.tags[1] if len(spec.tags) >= 2 else ""
+            return _build_error_response(
+                400, "INVALID_BODY", f"unknown models category '{category}'"
+            )
+
+    try:
+        # Fast path: hash exists, create AssetReference without writing anything
+        if spec.hash and parsed.provided_hash_exists is True:
+            result = create_from_hash(
                 hash_str=spec.hash,
                 name=spec.name or (spec.hash.split(":", 1)[1]),
                 tags=spec.tags,
                 user_metadata=spec.user_metadata or {},
                 owner_id=owner_id,
             )
-        except Exception:
-            logging.exception("create_asset_from_hash failed for hash=%s, owner_id=%s", spec.hash, owner_id)
-            return _error_response(500, "INTERNAL", "Unexpected server error.")
+            if result is None:
+                delete_temp_file_if_exists(parsed.tmp_path)
+                return _build_error_response(
+                    404, "ASSET_NOT_FOUND", f"Asset content {spec.hash} does not exist"
+                )
+            delete_temp_file_if_exists(parsed.tmp_path)
+        else:
+            # Otherwise, we must have a temp file path to ingest
+            if not parsed.tmp_path or not os.path.exists(parsed.tmp_path):
+                return _build_error_response(
+                    400,
+                    "MISSING_INPUT",
+                    "Provided hash not found and no file uploaded.",
+                )
 
-        if result is None:
-            return _error_response(404, "ASSET_NOT_FOUND", f"Asset content {spec.hash} does not exist")
-
-        # Drain temp if we accidentally saved (e.g., hash field came after file)
-        if tmp_path and os.path.exists(tmp_path):
-            with contextlib.suppress(Exception):
-                os.remove(tmp_path)
-
-        status = 200 if (not result.created_new) else 201
-        return web.json_response(result.model_dump(mode="json"), status=status)
-
-    # Otherwise, we must have a temp file path to ingest
-    if not tmp_path or not os.path.exists(tmp_path):
-        # The only case we reach here without a temp file is: client sent a hash that does not exist and no file
-        return _error_response(404, "ASSET_NOT_FOUND", "Provided hash not found and no file uploaded.")
-
-    try:
-        created = manager.upload_asset_from_temp_path(
-            spec,
-            temp_path=tmp_path,
-            client_filename=file_client_name,
-            owner_id=owner_id,
-            expected_asset_hash=spec.hash,
-        )
-        status = 201 if created.created_new else 200
-        return web.json_response(created.model_dump(mode="json"), status=status)
-    except ValueError as e:
-        if tmp_path and os.path.exists(tmp_path):
-            os.remove(tmp_path)
-        msg = str(e)
-        if "HASH_MISMATCH" in msg or msg.strip().upper() == "HASH_MISMATCH":
-            return _error_response(
-                400,
-                "HASH_MISMATCH",
-                "Uploaded file hash does not match provided hash.",
+            result = upload_from_temp_path(
+                temp_path=parsed.tmp_path,
+                name=spec.name,
+                tags=spec.tags,
+                user_metadata=spec.user_metadata or {},
+                client_filename=parsed.file_client_name,
+                owner_id=owner_id,
+                expected_hash=spec.hash,
             )
-        return _error_response(400, "BAD_REQUEST", "Invalid inputs.")
+    except AssetValidationError as e:
+        delete_temp_file_if_exists(parsed.tmp_path)
+        return _build_error_response(400, e.code, str(e))
+    except ValueError as e:
+        delete_temp_file_if_exists(parsed.tmp_path)
+        return _build_error_response(400, "BAD_REQUEST", str(e))
+    except HashMismatchError as e:
+        delete_temp_file_if_exists(parsed.tmp_path)
+        return _build_error_response(400, "HASH_MISMATCH", str(e))
+    except DependencyMissingError as e:
+        delete_temp_file_if_exists(parsed.tmp_path)
+        return _build_error_response(503, "DEPENDENCY_MISSING", e.message)
     except Exception:
-        if tmp_path and os.path.exists(tmp_path):
-            os.remove(tmp_path)
-        logging.exception("upload_asset_from_temp_path failed for tmp_path=%s, owner_id=%s", tmp_path, owner_id)
-        return _error_response(500, "INTERNAL", "Unexpected server error.")
+        delete_temp_file_if_exists(parsed.tmp_path)
+        logging.exception("upload_asset failed for owner_id=%s", owner_id)
+        return _build_error_response(500, "INTERNAL", "Unexpected server error.")
+
+    payload = schemas_out.AssetCreated(
+        id=result.ref.id,
+        name=result.ref.name,
+        asset_hash=result.asset.hash,
+        size=int(result.asset.size_bytes),
+        mime_type=result.asset.mime_type,
+        tags=result.tags,
+        user_metadata=result.ref.user_metadata or {},
+        preview_id=result.ref.preview_id,
+        created_at=result.ref.created_at,
+        last_access_time=result.ref.last_access_time,
+        created_new=result.created_new,
+    )
+    status = 201 if result.created_new else 200
+    return web.json_response(payload.model_dump(mode="json"), status=status)
 
 
 @ROUTES.put(f"/api/assets/{{id:{UUID_RE}}}")
-async def update_asset(request: web.Request) -> web.Response:
-    asset_info_id = str(uuid.UUID(request.match_info["id"]))
+@_require_assets_feature_enabled
+async def update_asset_route(request: web.Request) -> web.Response:
+    reference_id = str(uuid.UUID(request.match_info["id"]))
     try:
         body = schemas_in.UpdateAssetBody.model_validate(await request.json())
     except ValidationError as ve:
-        return _validation_error_response("INVALID_BODY", ve)
+        return _build_validation_error_response("INVALID_BODY", ve)
     except Exception:
-        return _error_response(400, "INVALID_JSON", "Request body must be valid JSON.")
+        return _build_error_response(
+            400, "INVALID_JSON", "Request body must be valid JSON."
+        )
 
     try:
-        result = manager.update_asset(
-            asset_info_id=asset_info_id,
+        result = update_asset_metadata(
+            reference_id=reference_id,
             name=body.name,
             user_metadata=body.user_metadata,
             owner_id=USER_MANAGER.get_request_user_id(request),
         )
-    except (ValueError, PermissionError) as ve:
-        return _error_response(404, "ASSET_NOT_FOUND", str(ve), {"id": asset_info_id})
+        payload = schemas_out.AssetUpdated(
+            id=result.ref.id,
+            name=result.ref.name,
+            asset_hash=result.asset.hash if result.asset else None,
+            tags=result.tags,
+            user_metadata=result.ref.user_metadata or {},
+            updated_at=result.ref.updated_at,
+        )
+    except PermissionError as pe:
+        return _build_error_response(403, "FORBIDDEN", str(pe), {"id": reference_id})
+    except ValueError as ve:
+        return _build_error_response(
+            404, "ASSET_NOT_FOUND", str(ve), {"id": reference_id}
+        )
     except Exception:
         logging.exception(
-            "update_asset failed for asset_info_id=%s, owner_id=%s",
-            asset_info_id,
+            "update_asset failed for reference_id=%s, owner_id=%s",
+            reference_id,
             USER_MANAGER.get_request_user_id(request),
         )
-        return _error_response(500, "INTERNAL", "Unexpected server error.")
-    return web.json_response(result.model_dump(mode="json"), status=200)
+        return _build_error_response(500, "INTERNAL", "Unexpected server error.")
+    return web.json_response(payload.model_dump(mode="json"), status=200)
 
 
 @ROUTES.delete(f"/api/assets/{{id:{UUID_RE}}}")
-async def delete_asset(request: web.Request) -> web.Response:
-    asset_info_id = str(uuid.UUID(request.match_info["id"]))
-    delete_content = request.query.get("delete_content")
-    delete_content = True if delete_content is None else delete_content.lower() not in {"0", "false", "no"}
+@_require_assets_feature_enabled
+async def delete_asset_route(request: web.Request) -> web.Response:
+    reference_id = str(uuid.UUID(request.match_info["id"]))
+    delete_content_param = request.query.get("delete_content")
+    delete_content = (
+        False
+        if delete_content_param is None
+        else delete_content_param.lower() not in {"0", "false", "no"}
+    )
 
     try:
-        deleted = manager.delete_asset_reference(
-            asset_info_id=asset_info_id,
+        deleted = delete_asset_reference(
+            reference_id=reference_id,
             owner_id=USER_MANAGER.get_request_user_id(request),
             delete_content_if_orphan=delete_content,
         )
     except Exception:
         logging.exception(
-            "delete_asset_reference failed for asset_info_id=%s, owner_id=%s",
-            asset_info_id,
+            "delete_asset_reference failed for reference_id=%s, owner_id=%s",
+            reference_id,
             USER_MANAGER.get_request_user_id(request),
         )
-        return _error_response(500, "INTERNAL", "Unexpected server error.")
+        return _build_error_response(500, "INTERNAL", "Unexpected server error.")
 
     if not deleted:
-        return _error_response(404, "ASSET_NOT_FOUND", f"AssetInfo {asset_info_id} not found.")
+        return _build_error_response(
+            404, "ASSET_NOT_FOUND", f"AssetReference {reference_id} not found."
+        )
     return web.Response(status=204)
 
 
 @ROUTES.get("/api/tags")
+@_require_assets_feature_enabled
 async def get_tags(request: web.Request) -> web.Response:
     """
     GET request to list all tags based on query parameters.
@@ -415,12 +532,14 @@ async def get_tags(request: web.Request) -> web.Response:
     try:
         query = schemas_in.TagsListQuery.model_validate(query_map)
     except ValidationError as e:
-        return web.json_response(
-            {"error": {"code": "INVALID_QUERY", "message": "Invalid query parameters", "details": e.errors()}},
-            status=400,
+        return _build_error_response(
+            400,
+            "INVALID_QUERY",
+            "Invalid query parameters",
+            {"errors": json.loads(e.json())},
         )
 
-    result = manager.list_tags(
+    rows, total = list_tags(
         prefix=query.prefix,
         limit=query.limit,
         offset=query.offset,
@@ -428,87 +547,212 @@ async def get_tags(request: web.Request) -> web.Response:
         include_zero=query.include_zero,
         owner_id=USER_MANAGER.get_request_user_id(request),
     )
-    return web.json_response(result.model_dump(mode="json"))
+
+    tags = [
+        schemas_out.TagUsage(name=name, count=count, type=tag_type)
+        for (name, tag_type, count) in rows
+    ]
+    payload = schemas_out.TagsList(
+        tags=tags, total=total, has_more=(query.offset + len(tags)) < total
+    )
+    return web.json_response(payload.model_dump(mode="json"))
 
 
 @ROUTES.post(f"/api/assets/{{id:{UUID_RE}}}/tags")
+@_require_assets_feature_enabled
 async def add_asset_tags(request: web.Request) -> web.Response:
-    asset_info_id = str(uuid.UUID(request.match_info["id"]))
+    reference_id = str(uuid.UUID(request.match_info["id"]))
     try:
-        payload = await request.json()
-        data = schemas_in.TagsAdd.model_validate(payload)
+        json_payload = await request.json()
+        data = schemas_in.TagsAdd.model_validate(json_payload)
     except ValidationError as ve:
-        return _error_response(400, "INVALID_BODY", "Invalid JSON body for tags add.", {"errors": ve.errors()})
+        return _build_error_response(
+            400,
+            "INVALID_BODY",
+            "Invalid JSON body for tags add.",
+            {"errors": ve.errors()},
+        )
     except Exception:
-        return _error_response(400, "INVALID_JSON", "Request body must be valid JSON.")
+        return _build_error_response(
+            400, "INVALID_JSON", "Request body must be valid JSON."
+        )
 
     try:
-        result = manager.add_tags_to_asset(
-            asset_info_id=asset_info_id,
+        result = apply_tags(
+            reference_id=reference_id,
             tags=data.tags,
             origin="manual",
             owner_id=USER_MANAGER.get_request_user_id(request),
         )
-    except (ValueError, PermissionError) as ve:
-        return _error_response(404, "ASSET_NOT_FOUND", str(ve), {"id": asset_info_id})
+        payload = schemas_out.TagsAdd(
+            added=result.added,
+            already_present=result.already_present,
+            total_tags=result.total_tags,
+        )
+    except PermissionError as pe:
+        return _build_error_response(403, "FORBIDDEN", str(pe), {"id": reference_id})
+    except ValueError as ve:
+        return _build_error_response(
+            404, "ASSET_NOT_FOUND", str(ve), {"id": reference_id}
+        )
     except Exception:
         logging.exception(
-            "add_tags_to_asset failed for asset_info_id=%s, owner_id=%s",
-            asset_info_id,
+            "add_tags_to_asset failed for reference_id=%s, owner_id=%s",
+            reference_id,
             USER_MANAGER.get_request_user_id(request),
         )
-        return _error_response(500, "INTERNAL", "Unexpected server error.")
+        return _build_error_response(500, "INTERNAL", "Unexpected server error.")
 
-    return web.json_response(result.model_dump(mode="json"), status=200)
+    return web.json_response(payload.model_dump(mode="json"), status=200)
 
 
 @ROUTES.delete(f"/api/assets/{{id:{UUID_RE}}}/tags")
+@_require_assets_feature_enabled
 async def delete_asset_tags(request: web.Request) -> web.Response:
-    asset_info_id = str(uuid.UUID(request.match_info["id"]))
+    reference_id = str(uuid.UUID(request.match_info["id"]))
     try:
-        payload = await request.json()
-        data = schemas_in.TagsRemove.model_validate(payload)
+        json_payload = await request.json()
+        data = schemas_in.TagsRemove.model_validate(json_payload)
     except ValidationError as ve:
-        return _error_response(400, "INVALID_BODY", "Invalid JSON body for tags remove.", {"errors": ve.errors()})
+        return _build_error_response(
+            400,
+            "INVALID_BODY",
+            "Invalid JSON body for tags remove.",
+            {"errors": ve.errors()},
+        )
     except Exception:
-        return _error_response(400, "INVALID_JSON", "Request body must be valid JSON.")
+        return _build_error_response(
+            400, "INVALID_JSON", "Request body must be valid JSON."
+        )
 
     try:
-        result = manager.remove_tags_from_asset(
-            asset_info_id=asset_info_id,
+        result = remove_tags(
+            reference_id=reference_id,
             tags=data.tags,
             owner_id=USER_MANAGER.get_request_user_id(request),
         )
+        payload = schemas_out.TagsRemove(
+            removed=result.removed,
+            not_present=result.not_present,
+            total_tags=result.total_tags,
+        )
+    except PermissionError as pe:
+        return _build_error_response(403, "FORBIDDEN", str(pe), {"id": reference_id})
     except ValueError as ve:
-        return _error_response(404, "ASSET_NOT_FOUND", str(ve), {"id": asset_info_id})
+        return _build_error_response(
+            404, "ASSET_NOT_FOUND", str(ve), {"id": reference_id}
+        )
     except Exception:
         logging.exception(
-            "remove_tags_from_asset failed for asset_info_id=%s, owner_id=%s",
-            asset_info_id,
+            "remove_tags_from_asset failed for reference_id=%s, owner_id=%s",
+            reference_id,
             USER_MANAGER.get_request_user_id(request),
         )
-        return _error_response(500, "INTERNAL", "Unexpected server error.")
+        return _build_error_response(500, "INTERNAL", "Unexpected server error.")
 
-    return web.json_response(result.model_dump(mode="json"), status=200)
+    return web.json_response(payload.model_dump(mode="json"), status=200)
 
 
 @ROUTES.post("/api/assets/seed")
-async def seed_assets_endpoint(request: web.Request) -> web.Response:
-    """Trigger asset seeding for specified roots (models, input, output)."""
+@_require_assets_feature_enabled
+async def seed_assets(request: web.Request) -> web.Response:
+    """Trigger asset seeding for specified roots (models, input, output).
+
+    Query params:
+        wait: If "true", block until scan completes (synchronous behavior for tests)
+
+    Returns:
+        202 Accepted if scan started
+        409 Conflict if scan already running
+        200 OK with final stats if wait=true
+    """
     try:
         payload = await request.json()
         roots = payload.get("roots", ["models", "input", "output"])
     except Exception:
         roots = ["models", "input", "output"]
 
-    valid_roots = [r for r in roots if r in ("models", "input", "output")]
+    valid_roots = tuple(r for r in roots if r in ("models", "input", "output"))
     if not valid_roots:
-        return _error_response(400, "INVALID_BODY", "No valid roots specified")
+        return _build_error_response(400, "INVALID_BODY", "No valid roots specified")
 
+    wait_param = request.query.get("wait", "").lower()
+    should_wait = wait_param in ("true", "1", "yes")
+
+    started = asset_seeder.start(roots=valid_roots)
+    if not started:
+        return web.json_response({"status": "already_running"}, status=409)
+
+    if should_wait:
+        await asyncio.to_thread(asset_seeder.wait)
+        status = asset_seeder.get_status()
+        return web.json_response(
+            {
+                "status": "completed",
+                "progress": {
+                    "scanned": status.progress.scanned if status.progress else 0,
+                    "total": status.progress.total if status.progress else 0,
+                    "created": status.progress.created if status.progress else 0,
+                    "skipped": status.progress.skipped if status.progress else 0,
+                },
+                "errors": status.errors,
+            },
+            status=200,
+        )
+
+    return web.json_response({"status": "started"}, status=202)
+
+
+@ROUTES.get("/api/assets/seed/status")
+@_require_assets_feature_enabled
+async def get_seed_status(request: web.Request) -> web.Response:
+    """Get current scan status and progress."""
+    status = asset_seeder.get_status()
+    return web.json_response(
+        {
+            "state": status.state.value,
+            "progress": {
+                "scanned": status.progress.scanned,
+                "total": status.progress.total,
+                "created": status.progress.created,
+                "skipped": status.progress.skipped,
+            }
+            if status.progress
+            else None,
+            "errors": status.errors,
+        },
+        status=200,
+    )
+
+
+@ROUTES.post("/api/assets/seed/cancel")
+@_require_assets_feature_enabled
+async def cancel_seed(request: web.Request) -> web.Response:
+    """Request cancellation of in-progress scan."""
+    cancelled = asset_seeder.cancel()
+    if cancelled:
+        return web.json_response({"status": "cancelling"}, status=200)
+    return web.json_response({"status": "idle"}, status=200)
+
+
+@ROUTES.post("/api/assets/prune")
+@_require_assets_feature_enabled
+async def mark_missing_assets(request: web.Request) -> web.Response:
+    """Mark assets as missing when outside all known root prefixes.
+
+    This is a non-destructive soft-delete operation. Assets and metadata
+    are preserved, but references are flagged as missing. They can be
+    restored if the file reappears in a future scan.
+
+    Returns:
+        200 OK with count of marked assets
+        409 Conflict if a scan is currently running
+    """
     try:
-        seed_assets(tuple(valid_roots))
-    except Exception:
-        logging.exception("seed_assets failed for roots=%s", valid_roots)
-        return _error_response(500, "INTERNAL", "Seed operation failed")
-
-    return web.json_response({"seeded": valid_roots}, status=200)
+        marked = asset_seeder.mark_missing_outside_prefixes()
+    except ScanInProgressError:
+        return web.json_response(
+            {"status": "scan_running", "marked": 0},
+            status=409,
+        )
+    return web.json_response({"status": "completed", "marked": marked}, status=200)
diff --git a/app/assets/api/schemas_in.py b/app/assets/api/schemas_in.py
index 6707ffb0c..d255c938e 100644
--- a/app/assets/api/schemas_in.py
+++ b/app/assets/api/schemas_in.py
@@ -1,6 +1,8 @@
 import json
+from dataclasses import dataclass
 from typing import Any, Literal
 
+from app.assets.helpers import validate_blake3_hash
 from pydantic import (
     BaseModel,
     ConfigDict,
@@ -10,6 +12,41 @@ from pydantic import (
     model_validator,
 )
 
+
+class UploadError(Exception):
+    """Error during upload parsing with HTTP status and code."""
+
+    def __init__(self, status: int, code: str, message: str):
+        super().__init__(message)
+        self.status = status
+        self.code = code
+        self.message = message
+
+
+class AssetValidationError(Exception):
+    """Validation error in asset processing (invalid tags, metadata, etc.)."""
+
+    def __init__(self, code: str, message: str):
+        super().__init__(message)
+        self.code = code
+        self.message = message
+
+
+@dataclass
+class ParsedUpload:
+    """Result of parsing a multipart upload request."""
+
+    file_present: bool
+    file_written: int
+    file_client_name: str | None
+    tmp_path: str | None
+    tags_raw: list[str]
+    provided_name: str | None
+    user_metadata_raw: str | None
+    provided_hash: str | None
+    provided_hash_exists: bool | None
+
+
 class ListAssetsQuery(BaseModel):
     include_tags: list[str] = Field(default_factory=list)
     exclude_tags: list[str] = Field(default_factory=list)
@@ -21,7 +58,9 @@ class ListAssetsQuery(BaseModel):
     limit: conint(ge=1, le=500) = 20
     offset: conint(ge=0) = 0
 
-    sort: Literal["name", "created_at", "updated_at", "size", "last_access_time"] = "created_at"
+    sort: Literal["name", "created_at", "updated_at", "size", "last_access_time"] = (
+        "created_at"
+    )
     order: Literal["asc", "desc"] = "desc"
 
     @field_validator("include_tags", "exclude_tags", mode="before")
@@ -61,7 +100,7 @@ class UpdateAssetBody(BaseModel):
     user_metadata: dict[str, Any] | None = None
 
     @model_validator(mode="after")
-    def _at_least_one(self):
+    def _validate_at_least_one_field(self):
         if self.name is None and self.user_metadata is None:
             raise ValueError("Provide at least one of: name, user_metadata.")
         return self
@@ -78,19 +117,11 @@ class CreateFromHashBody(BaseModel):
     @field_validator("hash")
     @classmethod
     def _require_blake3(cls, v):
-        s = (v or "").strip().lower()
-        if ":" not in s:
-            raise ValueError("hash must be 'blake3:<hex>'")
-        algo, digest = s.split(":", 1)
-        if algo != "blake3":
-            raise ValueError("only canonical 'blake3:<hex>' is accepted here")
-        if not digest or any(c for c in digest if c not in "0123456789abcdef"):
-            raise ValueError("hash digest must be lowercase hex")
-        return s
+        return validate_blake3_hash(v or "")
 
     @field_validator("tags", mode="before")
     @classmethod
-    def _tags_norm(cls, v):
+    def _normalize_tags_field(cls, v):
         if v is None:
             return []
         if isinstance(v, list):
@@ -154,15 +185,16 @@ class TagsRemove(TagsAdd):
 
 class UploadAssetSpec(BaseModel):
     """Upload Asset operation.
+
     - tags: ordered; first is root ('models'|'input'|'output');
-            if root == 'models', second must be a valid category from folder_paths.folder_names_and_paths
+            if root == 'models', second must be a valid category
     - name: display name
     - user_metadata: arbitrary JSON object (optional)
-    - hash: optional canonical 'blake3:<hex>' provided by the client for validation / fast-path
+    - hash: optional canonical 'blake3:<hex>' for validation / fast-path
 
-    Files created via this endpoint are stored on disk using the **content hash** as the filename stem
-    and the original extension is preserved when available.
+    Files are stored using the content hash as filename stem.
     """
+
     model_config = ConfigDict(extra="ignore", str_strip_whitespace=True)
 
     tags: list[str] = Field(..., min_length=1)
@@ -175,17 +207,10 @@ class UploadAssetSpec(BaseModel):
     def _parse_hash(cls, v):
         if v is None:
             return None
-        s = str(v).strip().lower()
+        s = str(v).strip()
         if not s:
             return None
-        if ":" not in s:
-            raise ValueError("hash must be 'blake3:<hex>'")
-        algo, digest = s.split(":", 1)
-        if algo != "blake3":
-            raise ValueError("only canonical 'blake3:<hex>' is accepted here")
-        if not digest or any(c for c in digest if c not in "0123456789abcdef"):
-            raise ValueError("hash digest must be lowercase hex")
-        return f"{algo}:{digest}"
+        return validate_blake3_hash(s)
 
     @field_validator("tags", mode="before")
     @classmethod
@@ -260,5 +285,7 @@ class UploadAssetSpec(BaseModel):
             raise ValueError("first tag must be one of: models, input, output")
         if root == "models":
             if len(self.tags) < 2:
-                raise ValueError("models uploads require a category tag as the second tag")
+                raise ValueError(
+                    "models uploads require a category tag as the second tag"
+                )
         return self
diff --git a/app/assets/api/schemas_out.py b/app/assets/api/schemas_out.py
index b6fb3da0c..f36447856 100644
--- a/app/assets/api/schemas_out.py
+++ b/app/assets/api/schemas_out.py
@@ -19,7 +19,7 @@ class AssetSummary(BaseModel):
     model_config = ConfigDict(from_attributes=True)
 
     @field_serializer("created_at", "updated_at", "last_access_time")
-    def _ser_dt(self, v: datetime | None, _info):
+    def _serialize_datetime(self, v: datetime | None, _info):
         return v.isoformat() if v else None
 
 
@@ -40,7 +40,7 @@ class AssetUpdated(BaseModel):
     model_config = ConfigDict(from_attributes=True)
 
     @field_serializer("updated_at")
-    def _ser_updated(self, v: datetime | None, _info):
+    def _serialize_updated_at(self, v: datetime | None, _info):
         return v.isoformat() if v else None
 
 
@@ -59,7 +59,7 @@ class AssetDetail(BaseModel):
     model_config = ConfigDict(from_attributes=True)
 
     @field_serializer("created_at", "last_access_time")
-    def _ser_dt(self, v: datetime | None, _info):
+    def _serialize_datetime(self, v: datetime | None, _info):
         return v.isoformat() if v else None
 
 
diff --git a/app/assets/api/upload.py b/app/assets/api/upload.py
new file mode 100644
index 000000000..721c12f4d
--- /dev/null
+++ b/app/assets/api/upload.py
@@ -0,0 +1,171 @@
+import logging
+import os
+import uuid
+from typing import Callable
+
+from aiohttp import web
+
+import folder_paths
+from app.assets.api.schemas_in import ParsedUpload, UploadError
+from app.assets.helpers import validate_blake3_hash
+
+
+def normalize_and_validate_hash(s: str) -> str:
+    """Validate and normalize a hash string.
+
+    Returns canonical 'blake3:<hex>' or raises UploadError.
+    """
+    try:
+        return validate_blake3_hash(s)
+    except ValueError:
+        raise UploadError(400, "INVALID_HASH", "hash must be like 'blake3:<hex>'")
+
+
+async def parse_multipart_upload(
+    request: web.Request,
+    check_hash_exists: Callable[[str], bool],
+) -> ParsedUpload:
+    """
+    Parse a multipart/form-data upload request.
+
+    Args:
+        request: The aiohttp request
+        check_hash_exists: Callable(hash_str) -> bool to check if a hash exists
+
+    Returns:
+        ParsedUpload with parsed fields and temp file path
+
+    Raises:
+        UploadError: On validation or I/O errors
+    """
+    if not (request.content_type or "").lower().startswith("multipart/"):
+        raise UploadError(
+            415, "UNSUPPORTED_MEDIA_TYPE", "Use multipart/form-data for uploads."
+        )
+
+    reader = await request.multipart()
+
+    file_present = False
+    file_client_name: str | None = None
+    tags_raw: list[str] = []
+    provided_name: str | None = None
+    user_metadata_raw: str | None = None
+    provided_hash: str | None = None
+    provided_hash_exists: bool | None = None
+
+    file_written = 0
+    tmp_path: str | None = None
+
+    while True:
+        field = await reader.next()
+        if field is None:
+            break
+
+        fname = getattr(field, "name", "") or ""
+
+        if fname == "hash":
+            try:
+                s = ((await field.text()) or "").strip().lower()
+            except Exception:
+                raise UploadError(
+                    400, "INVALID_HASH", "hash must be like 'blake3:<hex>'"
+                )
+
+            if s:
+                provided_hash = normalize_and_validate_hash(s)
+                try:
+                    provided_hash_exists = check_hash_exists(provided_hash)
+                except Exception as e:
+                    logging.exception(
+                        "check_hash_exists failed for hash=%s: %s", provided_hash, e
+                    )
+                    raise UploadError(
+                        500,
+                        "HASH_CHECK_FAILED",
+                        "Backend error while checking asset hash.",
+                    )
+
+        elif fname == "file":
+            file_present = True
+            file_client_name = (field.filename or "").strip()
+
+            if provided_hash and provided_hash_exists is True:
+                # Hash exists - drain file but don't write to disk
+                try:
+                    while True:
+                        chunk = await field.read_chunk(8 * 1024 * 1024)
+                        if not chunk:
+                            break
+                        file_written += len(chunk)
+                except Exception:
+                    raise UploadError(
+                        500, "UPLOAD_IO_ERROR", "Failed to receive uploaded file."
+                    )
+                continue
+
+            uploads_root = os.path.join(folder_paths.get_temp_directory(), "uploads")
+            unique_dir = os.path.join(uploads_root, uuid.uuid4().hex)
+            os.makedirs(unique_dir, exist_ok=True)
+            tmp_path = os.path.join(unique_dir, ".upload.part")
+
+            try:
+                with open(tmp_path, "wb") as f:
+                    while True:
+                        chunk = await field.read_chunk(8 * 1024 * 1024)
+                        if not chunk:
+                            break
+                        f.write(chunk)
+                        file_written += len(chunk)
+            except Exception:
+                delete_temp_file_if_exists(tmp_path)
+                raise UploadError(
+                    500, "UPLOAD_IO_ERROR", "Failed to receive and store uploaded file."
+                )
+
+        elif fname == "tags":
+            tags_raw.append((await field.text()) or "")
+        elif fname == "name":
+            provided_name = (await field.text()) or None
+        elif fname == "user_metadata":
+            user_metadata_raw = (await field.text()) or None
+
+    if not file_present and not (provided_hash and provided_hash_exists):
+        raise UploadError(
+            400, "MISSING_FILE", "Form must include a 'file' part or a known 'hash'."
+        )
+
+    if (
+        file_present
+        and file_written == 0
+        and not (provided_hash and provided_hash_exists)
+    ):
+        delete_temp_file_if_exists(tmp_path)
+        raise UploadError(400, "EMPTY_UPLOAD", "Uploaded file is empty.")
+
+    return ParsedUpload(
+        file_present=file_present,
+        file_written=file_written,
+        file_client_name=file_client_name,
+        tmp_path=tmp_path,
+        tags_raw=tags_raw,
+        provided_name=provided_name,
+        user_metadata_raw=user_metadata_raw,
+        provided_hash=provided_hash,
+        provided_hash_exists=provided_hash_exists,
+    )
+
+
+def delete_temp_file_if_exists(tmp_path: str | None) -> None:
+    """Safely remove a temp file and its parent directory if empty."""
+    if tmp_path:
+        try:
+            if os.path.exists(tmp_path):
+                os.remove(tmp_path)
+        except OSError as e:
+            logging.debug("Failed to delete temp file %s: %s", tmp_path, e)
+        try:
+            parent = os.path.dirname(tmp_path)
+            if parent and os.path.isdir(parent):
+                os.rmdir(parent)  # only succeeds if empty
+        except OSError:
+            pass
diff --git a/app/assets/database/bulk_ops.py b/app/assets/database/bulk_ops.py
deleted file mode 100644
index c7b75290a..000000000
--- a/app/assets/database/bulk_ops.py
+++ /dev/null
@@ -1,204 +0,0 @@
-import os
-import uuid
-import sqlalchemy
-from typing import Iterable
-from sqlalchemy.orm import Session
-from sqlalchemy.dialects import sqlite
-
-from app.assets.helpers import utcnow
-from app.assets.database.models import Asset, AssetCacheState, AssetInfo, AssetInfoTag, AssetInfoMeta
-
-MAX_BIND_PARAMS = 800
-
-def _chunk_rows(rows: list[dict], cols_per_row: int, max_bind_params: int) -> Iterable[list[dict]]:
-    if not rows:
-        return []
-    rows_per_stmt = max(1, max_bind_params // max(1, cols_per_row))
-    for i in range(0, len(rows), rows_per_stmt):
-        yield rows[i:i + rows_per_stmt]
-
-def _iter_chunks(seq, n: int):
-    for i in range(0, len(seq), n):
-        yield seq[i:i + n]
-
-def _rows_per_stmt(cols: int) -> int:
-    return max(1, MAX_BIND_PARAMS // max(1, cols))
-
-
-def seed_from_paths_batch(
-    session: Session,
-    *,
-    specs: list[dict],
-    owner_id: str = "",
-) -> dict:
-    """Each spec is a dict with keys:
-      - abs_path: str
-      - size_bytes: int
-      - mtime_ns: int
-      - info_name: str
-      - tags: list[str]
-      - fname: Optional[str]
-    """
-    if not specs:
-        return {"inserted_infos": 0, "won_states": 0, "lost_states": 0}
-
-    now = utcnow()
-    asset_rows: list[dict] = []
-    state_rows: list[dict] = []
-    path_to_asset: dict[str, str] = {}
-    asset_to_info: dict[str, dict] = {}  # asset_id -> prepared info row
-    path_list: list[str] = []
-
-    for sp in specs:
-        ap = os.path.abspath(sp["abs_path"])
-        aid = str(uuid.uuid4())
-        iid = str(uuid.uuid4())
-        path_list.append(ap)
-        path_to_asset[ap] = aid
-
-        asset_rows.append(
-            {
-                "id": aid,
-                "hash": None,
-                "size_bytes": sp["size_bytes"],
-                "mime_type": None,
-                "created_at": now,
-            }
-        )
-        state_rows.append(
-            {
-                "asset_id": aid,
-                "file_path": ap,
-                "mtime_ns": sp["mtime_ns"],
-            }
-        )
-        asset_to_info[aid] = {
-            "id": iid,
-            "owner_id": owner_id,
-            "name": sp["info_name"],
-            "asset_id": aid,
-            "preview_id": None,
-            "user_metadata": {"filename": sp["fname"]} if sp["fname"] else None,
-            "created_at": now,
-            "updated_at": now,
-            "last_access_time": now,
-            "_tags": sp["tags"],
-            "_filename": sp["fname"],
-        }
-
-    # insert all seed Assets (hash=NULL)
-    ins_asset = sqlite.insert(Asset)
-    for chunk in _iter_chunks(asset_rows, _rows_per_stmt(5)):
-        session.execute(ins_asset, chunk)
-
-    # try to claim AssetCacheState (file_path)
-    # Insert with ON CONFLICT DO NOTHING, then query to find which paths were actually inserted
-    ins_state = (
-        sqlite.insert(AssetCacheState)
-        .on_conflict_do_nothing(index_elements=[AssetCacheState.file_path])
-    )
-    for chunk in _iter_chunks(state_rows, _rows_per_stmt(3)):
-        session.execute(ins_state, chunk)
-
-    # Query to find which of our paths won (were actually inserted)
-    winners_by_path: set[str] = set()
-    for chunk in _iter_chunks(path_list, MAX_BIND_PARAMS):
-        result = session.execute(
-            sqlalchemy.select(AssetCacheState.file_path)
-            .where(AssetCacheState.file_path.in_(chunk))
-            .where(AssetCacheState.asset_id.in_([path_to_asset[p] for p in chunk]))
-        )
-        winners_by_path.update(result.scalars().all())
-
-    all_paths_set = set(path_list)
-    losers_by_path = all_paths_set - winners_by_path
-    lost_assets = [path_to_asset[p] for p in losers_by_path]
-    if lost_assets:  # losers get their Asset removed
-        for id_chunk in _iter_chunks(lost_assets, MAX_BIND_PARAMS):
-            session.execute(sqlalchemy.delete(Asset).where(Asset.id.in_(id_chunk)))
-
-    if not winners_by_path:
-        return {"inserted_infos": 0, "won_states": 0, "lost_states": len(losers_by_path)}
-
-    # insert AssetInfo only for winners
-    # Insert with ON CONFLICT DO NOTHING, then query to find which were actually inserted
-    winner_info_rows = [asset_to_info[path_to_asset[p]] for p in winners_by_path]
-    ins_info = (
-        sqlite.insert(AssetInfo)
-        .on_conflict_do_nothing(index_elements=[AssetInfo.asset_id, AssetInfo.owner_id, AssetInfo.name])
-    )
-    for chunk in _iter_chunks(winner_info_rows, _rows_per_stmt(9)):
-        session.execute(ins_info, chunk)
-
-    # Query to find which info rows were actually inserted (by matching our generated IDs)
-    all_info_ids = [row["id"] for row in winner_info_rows]
-    inserted_info_ids: set[str] = set()
-    for chunk in _iter_chunks(all_info_ids, MAX_BIND_PARAMS):
-        result = session.execute(
-            sqlalchemy.select(AssetInfo.id).where(AssetInfo.id.in_(chunk))
-        )
-        inserted_info_ids.update(result.scalars().all())
-
-    # build and insert tag + meta rows for the AssetInfo
-    tag_rows: list[dict] = []
-    meta_rows: list[dict] = []
-    if inserted_info_ids:
-        for row in winner_info_rows:
-            iid = row["id"]
-            if iid not in inserted_info_ids:
-                continue
-            for t in row["_tags"]:
-                tag_rows.append({
-                    "asset_info_id": iid,
-                    "tag_name": t,
-                    "origin": "automatic",
-                    "added_at": now,
-                })
-            if row["_filename"]:
-                meta_rows.append(
-                    {
-                        "asset_info_id": iid,
-                        "key": "filename",
-                        "ordinal": 0,
-                        "val_str": row["_filename"],
-                        "val_num": None,
-                        "val_bool": None,
-                        "val_json": None,
-                    }
-                )
-
-    bulk_insert_tags_and_meta(session, tag_rows=tag_rows, meta_rows=meta_rows, max_bind_params=MAX_BIND_PARAMS)
-    return {
-        "inserted_infos": len(inserted_info_ids),
-        "won_states": len(winners_by_path),
-        "lost_states": len(losers_by_path),
-    }
-
-
-def bulk_insert_tags_and_meta(
-    session: Session,
-    *,
-    tag_rows: list[dict],
-    meta_rows: list[dict],
-    max_bind_params: int,
-) -> None:
-    """Batch insert into asset_info_tags and asset_info_meta with ON CONFLICT DO NOTHING.
-    - tag_rows keys: asset_info_id, tag_name, origin, added_at
-    - meta_rows keys: asset_info_id, key, ordinal, val_str, val_num, val_bool, val_json
-    """
-    if tag_rows:
-        ins_links = (
-            sqlite.insert(AssetInfoTag)
-            .on_conflict_do_nothing(index_elements=[AssetInfoTag.asset_info_id, AssetInfoTag.tag_name])
-        )
-        for chunk in _chunk_rows(tag_rows, cols_per_row=4, max_bind_params=max_bind_params):
-            session.execute(ins_links, chunk)
-    if meta_rows:
-        ins_meta = (
-            sqlite.insert(AssetInfoMeta)
-            .on_conflict_do_nothing(
-                index_elements=[AssetInfoMeta.asset_info_id, AssetInfoMeta.key, AssetInfoMeta.ordinal]
-            )
-        )
-        for chunk in _chunk_rows(meta_rows, cols_per_row=7, max_bind_params=max_bind_params):
-            session.execute(ins_meta, chunk)
diff --git a/app/assets/database/models.py b/app/assets/database/models.py
index 3cd28f68b..03c1c1707 100644
--- a/app/assets/database/models.py
+++ b/app/assets/database/models.py
@@ -2,8 +2,8 @@ from __future__ import annotations
 
 import uuid
 from datetime import datetime
-
 from typing import Any
+
 from sqlalchemy import (
     JSON,
     BigInteger,
@@ -16,102 +16,102 @@ from sqlalchemy import (
     Numeric,
     String,
     Text,
-    UniqueConstraint,
 )
 from sqlalchemy.orm import Mapped, foreign, mapped_column, relationship
 
-from app.assets.helpers import utcnow
-from app.database.models import to_dict, Base
+from app.assets.helpers import get_utc_now
+from app.database.models import Base
 
 
 class Asset(Base):
     __tablename__ = "assets"
 
-    id: Mapped[str] = mapped_column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
+    id: Mapped[str] = mapped_column(
+        String(36), primary_key=True, default=lambda: str(uuid.uuid4())
+    )
     hash: Mapped[str | None] = mapped_column(String(256), nullable=True)
     size_bytes: Mapped[int] = mapped_column(BigInteger, nullable=False, default=0)
     mime_type: Mapped[str | None] = mapped_column(String(255))
     created_at: Mapped[datetime] = mapped_column(
-        DateTime(timezone=False), nullable=False, default=utcnow
+        DateTime(timezone=False), nullable=False, default=get_utc_now
     )
 
-    infos: Mapped[list[AssetInfo]] = relationship(
-        "AssetInfo",
+    references: Mapped[list[AssetReference]] = relationship(
+        "AssetReference",
         back_populates="asset",
-        primaryjoin=lambda: Asset.id == foreign(AssetInfo.asset_id),
-        foreign_keys=lambda: [AssetInfo.asset_id],
+        primaryjoin=lambda: Asset.id == foreign(AssetReference.asset_id),
+        foreign_keys=lambda: [AssetReference.asset_id],
         cascade="all,delete-orphan",
         passive_deletes=True,
     )
 
-    preview_of: Mapped[list[AssetInfo]] = relationship(
-        "AssetInfo",
+    preview_of: Mapped[list[AssetReference]] = relationship(
+        "AssetReference",
         back_populates="preview_asset",
-        primaryjoin=lambda: Asset.id == foreign(AssetInfo.preview_id),
-        foreign_keys=lambda: [AssetInfo.preview_id],
+        primaryjoin=lambda: Asset.id == foreign(AssetReference.preview_id),
+        foreign_keys=lambda: [AssetReference.preview_id],
         viewonly=True,
     )
 
-    cache_states: Mapped[list[AssetCacheState]] = relationship(
-        back_populates="asset",
-        cascade="all, delete-orphan",
-        passive_deletes=True,
-    )
-
     __table_args__ = (
         Index("uq_assets_hash", "hash", unique=True),
         Index("ix_assets_mime_type", "mime_type"),
         CheckConstraint("size_bytes >= 0", name="ck_assets_size_nonneg"),
     )
 
-    def to_dict(self, include_none: bool = False) -> dict[str, Any]:
-        return to_dict(self, include_none=include_none)
-
     def __repr__(self) -> str:
         return f"<Asset id={self.id} hash={(self.hash or '')[:12]}>"
 
 
-class AssetCacheState(Base):
-    __tablename__ = "asset_cache_state"
+class AssetReference(Base):
+    """Unified model combining file cache state and user-facing metadata.
 
-    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
-    asset_id: Mapped[str] = mapped_column(String(36), ForeignKey("assets.id", ondelete="CASCADE"), nullable=False)
-    file_path: Mapped[str] = mapped_column(Text, nullable=False)
-    mtime_ns: Mapped[int | None] = mapped_column(BigInteger, nullable=True)
-    needs_verify: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+    Each row represents either:
+    - A filesystem reference (file_path is set) with cache state
+    - An API-created reference (file_path is NULL) without cache state
+    """
 
-    asset: Mapped[Asset] = relationship(back_populates="cache_states")
+    __tablename__ = "asset_references"
 
-    __table_args__ = (
-        Index("ix_asset_cache_state_file_path", "file_path"),
-        Index("ix_asset_cache_state_asset_id", "asset_id"),
-        CheckConstraint("(mtime_ns IS NULL) OR (mtime_ns >= 0)", name="ck_acs_mtime_nonneg"),
-        UniqueConstraint("file_path", name="uq_asset_cache_state_file_path"),
+    id: Mapped[str] = mapped_column(
+        String(36), primary_key=True, default=lambda: str(uuid.uuid4())
+    )
+    asset_id: Mapped[str] = mapped_column(
+        String(36), ForeignKey("assets.id", ondelete="CASCADE"), nullable=False
     )
 
-    def to_dict(self, include_none: bool = False) -> dict[str, Any]:
-        return to_dict(self, include_none=include_none)
+    # Cache state fields (from former AssetCacheState)
+    file_path: Mapped[str | None] = mapped_column(Text, nullable=True)
+    mtime_ns: Mapped[int | None] = mapped_column(BigInteger, nullable=True)
+    needs_verify: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+    is_missing: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+    enrichment_level: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
 
-    def __repr__(self) -> str:
-        return f"<AssetCacheState id={self.id} asset_id={self.asset_id} path={self.file_path!r}>"
-
-
-class AssetInfo(Base):
-    __tablename__ = "assets_info"
-
-    id: Mapped[str] = mapped_column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
+    # Info fields (from former AssetInfo)
     owner_id: Mapped[str] = mapped_column(String(128), nullable=False, default="")
     name: Mapped[str] = mapped_column(String(512), nullable=False)
-    asset_id: Mapped[str] = mapped_column(String(36), ForeignKey("assets.id", ondelete="RESTRICT"), nullable=False)
-    preview_id: Mapped[str | None] = mapped_column(String(36), ForeignKey("assets.id", ondelete="SET NULL"))
-    user_metadata: Mapped[dict[str, Any] | None] = mapped_column(JSON(none_as_null=True))
-    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=False), nullable=False, default=utcnow)
-    updated_at: Mapped[datetime] = mapped_column(DateTime(timezone=False), nullable=False, default=utcnow)
-    last_access_time: Mapped[datetime] = mapped_column(DateTime(timezone=False), nullable=False, default=utcnow)
+    preview_id: Mapped[str | None] = mapped_column(
+        String(36), ForeignKey("assets.id", ondelete="SET NULL")
+    )
+    user_metadata: Mapped[dict[str, Any] | None] = mapped_column(
+        JSON(none_as_null=True)
+    )
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=False), nullable=False, default=get_utc_now
+    )
+    updated_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=False), nullable=False, default=get_utc_now
+    )
+    last_access_time: Mapped[datetime] = mapped_column(
+        DateTime(timezone=False), nullable=False, default=get_utc_now
+    )
+    deleted_at: Mapped[datetime | None] = mapped_column(
+        DateTime(timezone=False), nullable=True, default=None
+    )
 
     asset: Mapped[Asset] = relationship(
         "Asset",
-        back_populates="infos",
+        back_populates="references",
         foreign_keys=[asset_id],
         lazy="selectin",
     )
@@ -121,51 +121,59 @@ class AssetInfo(Base):
         foreign_keys=[preview_id],
     )
 
-    metadata_entries: Mapped[list[AssetInfoMeta]] = relationship(
-        back_populates="asset_info",
+    metadata_entries: Mapped[list[AssetReferenceMeta]] = relationship(
+        back_populates="asset_reference",
         cascade="all,delete-orphan",
         passive_deletes=True,
     )
 
-    tag_links: Mapped[list[AssetInfoTag]] = relationship(
-        back_populates="asset_info",
+    tag_links: Mapped[list[AssetReferenceTag]] = relationship(
+        back_populates="asset_reference",
         cascade="all,delete-orphan",
         passive_deletes=True,
-        overlaps="tags,asset_infos",
+        overlaps="tags,asset_references",
     )
 
     tags: Mapped[list[Tag]] = relationship(
-        secondary="asset_info_tags",
-        back_populates="asset_infos",
+        secondary="asset_reference_tags",
+        back_populates="asset_references",
         lazy="selectin",
         viewonly=True,
-        overlaps="tag_links,asset_info_links,asset_infos,tag",
+        overlaps="tag_links,asset_reference_links,asset_references,tag",
     )
 
     __table_args__ = (
-        UniqueConstraint("asset_id", "owner_id", "name", name="uq_assets_info_asset_owner_name"),
-        Index("ix_assets_info_owner_name", "owner_id", "name"),
-        Index("ix_assets_info_owner_id", "owner_id"),
-        Index("ix_assets_info_asset_id", "asset_id"),
-        Index("ix_assets_info_name", "name"),
-        Index("ix_assets_info_created_at", "created_at"),
-        Index("ix_assets_info_last_access_time", "last_access_time"),
+        Index("uq_asset_references_file_path", "file_path", unique=True),
+        Index("ix_asset_references_asset_id", "asset_id"),
+        Index("ix_asset_references_owner_id", "owner_id"),
+        Index("ix_asset_references_name", "name"),
+        Index("ix_asset_references_is_missing", "is_missing"),
+        Index("ix_asset_references_enrichment_level", "enrichment_level"),
+        Index("ix_asset_references_created_at", "created_at"),
+        Index("ix_asset_references_last_access_time", "last_access_time"),
+        Index("ix_asset_references_deleted_at", "deleted_at"),
+        Index("ix_asset_references_owner_name", "owner_id", "name"),
+        CheckConstraint(
+            "(mtime_ns IS NULL) OR (mtime_ns >= 0)", name="ck_ar_mtime_nonneg"
+        ),
+        CheckConstraint(
+            "enrichment_level >= 0 AND enrichment_level <= 2",
+            name="ck_ar_enrichment_level_range",
+        ),
     )
 
-    def to_dict(self, include_none: bool = False) -> dict[str, Any]:
-        data = to_dict(self, include_none=include_none)
-        data["tags"] = [t.name for t in self.tags]
-        return data
-
     def __repr__(self) -> str:
-        return f"<AssetInfo id={self.id} name={self.name!r} asset_id={self.asset_id}>"
+        path_part = f" path={self.file_path!r}" if self.file_path else ""
+        return f"<AssetReference id={self.id} name={self.name!r}{path_part}>"
 
 
-class AssetInfoMeta(Base):
-    __tablename__ = "asset_info_meta"
+class AssetReferenceMeta(Base):
+    __tablename__ = "asset_reference_meta"
 
-    asset_info_id: Mapped[str] = mapped_column(
-        String(36), ForeignKey("assets_info.id", ondelete="CASCADE"), primary_key=True
+    asset_reference_id: Mapped[str] = mapped_column(
+        String(36),
+        ForeignKey("asset_references.id", ondelete="CASCADE"),
+        primary_key=True,
     )
     key: Mapped[str] = mapped_column(String(256), primary_key=True)
     ordinal: Mapped[int] = mapped_column(Integer, primary_key=True, default=0)
@@ -175,36 +183,40 @@ class AssetInfoMeta(Base):
     val_bool: Mapped[bool | None] = mapped_column(Boolean, nullable=True)
     val_json: Mapped[Any | None] = mapped_column(JSON(none_as_null=True), nullable=True)
 
-    asset_info: Mapped[AssetInfo] = relationship(back_populates="metadata_entries")
+    asset_reference: Mapped[AssetReference] = relationship(
+        back_populates="metadata_entries"
+    )
 
     __table_args__ = (
-        Index("ix_asset_info_meta_key", "key"),
-        Index("ix_asset_info_meta_key_val_str", "key", "val_str"),
-        Index("ix_asset_info_meta_key_val_num", "key", "val_num"),
-        Index("ix_asset_info_meta_key_val_bool", "key", "val_bool"),
+        Index("ix_asset_reference_meta_key", "key"),
+        Index("ix_asset_reference_meta_key_val_str", "key", "val_str"),
+        Index("ix_asset_reference_meta_key_val_num", "key", "val_num"),
+        Index("ix_asset_reference_meta_key_val_bool", "key", "val_bool"),
     )
 
 
-class AssetInfoTag(Base):
-    __tablename__ = "asset_info_tags"
+class AssetReferenceTag(Base):
+    __tablename__ = "asset_reference_tags"
 
-    asset_info_id: Mapped[str] = mapped_column(
-        String(36), ForeignKey("assets_info.id", ondelete="CASCADE"), primary_key=True
+    asset_reference_id: Mapped[str] = mapped_column(
+        String(36),
+        ForeignKey("asset_references.id", ondelete="CASCADE"),
+        primary_key=True,
     )
     tag_name: Mapped[str] = mapped_column(
         String(512), ForeignKey("tags.name", ondelete="RESTRICT"), primary_key=True
     )
     origin: Mapped[str] = mapped_column(String(32), nullable=False, default="manual")
     added_at: Mapped[datetime] = mapped_column(
-        DateTime(timezone=False), nullable=False, default=utcnow
+        DateTime(timezone=False), nullable=False, default=get_utc_now
     )
 
-    asset_info: Mapped[AssetInfo] = relationship(back_populates="tag_links")
-    tag: Mapped[Tag] = relationship(back_populates="asset_info_links")
+    asset_reference: Mapped[AssetReference] = relationship(back_populates="tag_links")
+    tag: Mapped[Tag] = relationship(back_populates="asset_reference_links")
 
     __table_args__ = (
-        Index("ix_asset_info_tags_tag_name", "tag_name"),
-        Index("ix_asset_info_tags_asset_info_id", "asset_info_id"),
+        Index("ix_asset_reference_tags_tag_name", "tag_name"),
+        Index("ix_asset_reference_tags_asset_reference_id", "asset_reference_id"),
     )
 
 
@@ -214,20 +226,18 @@ class Tag(Base):
     name: Mapped[str] = mapped_column(String(512), primary_key=True)
     tag_type: Mapped[str] = mapped_column(String(32), nullable=False, default="user")
 
-    asset_info_links: Mapped[list[AssetInfoTag]] = relationship(
+    asset_reference_links: Mapped[list[AssetReferenceTag]] = relationship(
         back_populates="tag",
-        overlaps="asset_infos,tags",
+        overlaps="asset_references,tags",
     )
-    asset_infos: Mapped[list[AssetInfo]] = relationship(
-        secondary="asset_info_tags",
+    asset_references: Mapped[list[AssetReference]] = relationship(
+        secondary="asset_reference_tags",
         back_populates="tags",
         viewonly=True,
-        overlaps="asset_info_links,tag_links,tags,asset_info",
+        overlaps="asset_reference_links,tag_links,tags,asset_reference",
     )
 
-    __table_args__ = (
-        Index("ix_tags_tag_type", "tag_type"),
-    )
+    __table_args__ = (Index("ix_tags_tag_type", "tag_type"),)
 
     def __repr__(self) -> str:
         return f"<Tag {self.name}>"
diff --git a/app/assets/database/queries.py b/app/assets/database/queries.py
deleted file mode 100644
index d6b33ec7b..000000000
--- a/app/assets/database/queries.py
+++ /dev/null
@@ -1,976 +0,0 @@
-import os
-import logging
-import sqlalchemy as sa
-from collections import defaultdict
-from datetime import datetime
-from typing import Iterable, Any
-from sqlalchemy import select, delete, exists, func
-from sqlalchemy.dialects import sqlite
-from sqlalchemy.exc import IntegrityError
-from sqlalchemy.orm import Session, contains_eager, noload
-from app.assets.database.models import Asset, AssetInfo, AssetCacheState, AssetInfoMeta, AssetInfoTag, Tag
-from app.assets.helpers import (
-    compute_relative_filename, escape_like_prefix, normalize_tags, project_kv, utcnow
-)
-from typing import Sequence
-
-
-def visible_owner_clause(owner_id: str) -> sa.sql.ClauseElement:
-    """Build owner visibility predicate for reads. Owner-less rows are visible to everyone."""
-    owner_id = (owner_id or "").strip()
-    if owner_id == "":
-        return AssetInfo.owner_id == ""
-    return AssetInfo.owner_id.in_(["", owner_id])
-
-
-def pick_best_live_path(states: Sequence[AssetCacheState]) -> str:
-    """
-    Return the best on-disk path among cache states:
-      1) Prefer a path that exists with needs_verify == False (already verified).
-      2) Otherwise, pick the first path that exists.
-      3) Otherwise return empty string.
-    """
-    alive = [s for s in states if getattr(s, "file_path", None) and os.path.isfile(s.file_path)]
-    if not alive:
-        return ""
-    for s in alive:
-        if not getattr(s, "needs_verify", False):
-            return s.file_path
-    return alive[0].file_path
-
-
-def apply_tag_filters(
-    stmt: sa.sql.Select,
-    include_tags: Sequence[str] | None = None,
-    exclude_tags: Sequence[str] | None = None,
-) -> sa.sql.Select:
-    """include_tags: every tag must be present; exclude_tags: none may be present."""
-    include_tags = normalize_tags(include_tags)
-    exclude_tags = normalize_tags(exclude_tags)
-
-    if include_tags:
-        for tag_name in include_tags:
-            stmt = stmt.where(
-                exists().where(
-                    (AssetInfoTag.asset_info_id == AssetInfo.id)
-                    & (AssetInfoTag.tag_name == tag_name)
-                )
-            )
-
-    if exclude_tags:
-        stmt = stmt.where(
-            ~exists().where(
-                (AssetInfoTag.asset_info_id == AssetInfo.id)
-                & (AssetInfoTag.tag_name.in_(exclude_tags))
-            )
-        )
-    return stmt
-
-
-def apply_metadata_filter(
-    stmt: sa.sql.Select,
-    metadata_filter: dict | None = None,
-) -> sa.sql.Select:
-    """Apply filters using asset_info_meta projection table."""
-    if not metadata_filter:
-        return stmt
-
-    def _exists_for_pred(key: str, *preds) -> sa.sql.ClauseElement:
-        return sa.exists().where(
-            AssetInfoMeta.asset_info_id == AssetInfo.id,
-            AssetInfoMeta.key == key,
-            *preds,
-        )
-
-    def _exists_clause_for_value(key: str, value) -> sa.sql.ClauseElement:
-        if value is None:
-            no_row_for_key = sa.not_(
-                sa.exists().where(
-                    AssetInfoMeta.asset_info_id == AssetInfo.id,
-                    AssetInfoMeta.key == key,
-                )
-            )
-            null_row = _exists_for_pred(
-                key,
-                AssetInfoMeta.val_json.is_(None),
-                AssetInfoMeta.val_str.is_(None),
-                AssetInfoMeta.val_num.is_(None),
-                AssetInfoMeta.val_bool.is_(None),
-            )
-            return sa.or_(no_row_for_key, null_row)
-
-        if isinstance(value, bool):
-            return _exists_for_pred(key, AssetInfoMeta.val_bool == bool(value))
-        if isinstance(value, (int, float)):
-            from decimal import Decimal
-            num = value if isinstance(value, Decimal) else Decimal(str(value))
-            return _exists_for_pred(key, AssetInfoMeta.val_num == num)
-        if isinstance(value, str):
-            return _exists_for_pred(key, AssetInfoMeta.val_str == value)
-        return _exists_for_pred(key, AssetInfoMeta.val_json == value)
-
-    for k, v in metadata_filter.items():
-        if isinstance(v, list):
-            ors = [_exists_clause_for_value(k, elem) for elem in v]
-            if ors:
-                stmt = stmt.where(sa.or_(*ors))
-        else:
-            stmt = stmt.where(_exists_clause_for_value(k, v))
-    return stmt
-
-
-def asset_exists_by_hash(
-    session: Session,
-    *,
-    asset_hash: str,
-) -> bool:
-    """
-    Check if an asset with a given hash exists in database.
-    """
-    row = (
-        session.execute(
-            select(sa.literal(True)).select_from(Asset).where(Asset.hash == asset_hash).limit(1)
-        )
-    ).first()
-    return row is not None
-
-
-def asset_info_exists_for_asset_id(
-    session: Session,
-    *,
-    asset_id: str,
-) -> bool:
-    q = (
-        select(sa.literal(True))
-        .select_from(AssetInfo)
-        .where(AssetInfo.asset_id == asset_id)
-        .limit(1)
-    )
-    return (session.execute(q)).first() is not None
-
-
-def get_asset_by_hash(
-    session: Session,
-    *,
-    asset_hash: str,
-) -> Asset | None:
-    return (
-        session.execute(select(Asset).where(Asset.hash == asset_hash).limit(1))
-    ).scalars().first()
-
-
-def get_asset_info_by_id(
-    session: Session,
-    *,
-    asset_info_id: str,
-) -> AssetInfo | None:
-    return session.get(AssetInfo, asset_info_id)
-
-
-def list_asset_infos_page(
-    session: Session,
-    owner_id: str = "",
-    include_tags: Sequence[str] | None = None,
-    exclude_tags: Sequence[str] | None = None,
-    name_contains: str | None = None,
-    metadata_filter: dict | None = None,
-    limit: int = 20,
-    offset: int = 0,
-    sort: str = "created_at",
-    order: str = "desc",
-) -> tuple[list[AssetInfo], dict[str, list[str]], int]:
-    base = (
-        select(AssetInfo)
-        .join(Asset, Asset.id == AssetInfo.asset_id)
-        .options(contains_eager(AssetInfo.asset), noload(AssetInfo.tags))
-        .where(visible_owner_clause(owner_id))
-    )
-
-    if name_contains:
-        escaped, esc = escape_like_prefix(name_contains)
-        base = base.where(AssetInfo.name.ilike(f"%{escaped}%", escape=esc))
-
-    base = apply_tag_filters(base, include_tags, exclude_tags)
-    base = apply_metadata_filter(base, metadata_filter)
-
-    sort = (sort or "created_at").lower()
-    order = (order or "desc").lower()
-    sort_map = {
-        "name": AssetInfo.name,
-        "created_at": AssetInfo.created_at,
-        "updated_at": AssetInfo.updated_at,
-        "last_access_time": AssetInfo.last_access_time,
-        "size": Asset.size_bytes,
-    }
-    sort_col = sort_map.get(sort, AssetInfo.created_at)
-    sort_exp = sort_col.desc() if order == "desc" else sort_col.asc()
-
-    base = base.order_by(sort_exp).limit(limit).offset(offset)
-
-    count_stmt = (
-        select(sa.func.count())
-        .select_from(AssetInfo)
-        .join(Asset, Asset.id == AssetInfo.asset_id)
-        .where(visible_owner_clause(owner_id))
-    )
-    if name_contains:
-        escaped, esc = escape_like_prefix(name_contains)
-        count_stmt = count_stmt.where(AssetInfo.name.ilike(f"%{escaped}%", escape=esc))
-    count_stmt = apply_tag_filters(count_stmt, include_tags, exclude_tags)
-    count_stmt = apply_metadata_filter(count_stmt, metadata_filter)
-
-    total = int((session.execute(count_stmt)).scalar_one() or 0)
-
-    infos = (session.execute(base)).unique().scalars().all()
-
-    id_list: list[str] = [i.id for i in infos]
-    tag_map: dict[str, list[str]] = defaultdict(list)
-    if id_list:
-        rows = session.execute(
-            select(AssetInfoTag.asset_info_id, Tag.name)
-            .join(Tag, Tag.name == AssetInfoTag.tag_name)
-            .where(AssetInfoTag.asset_info_id.in_(id_list))
-            .order_by(AssetInfoTag.added_at)
-        )
-        for aid, tag_name in rows.all():
-            tag_map[aid].append(tag_name)
-
-    return infos, tag_map, total
-
-
-def fetch_asset_info_asset_and_tags(
-    session: Session,
-    asset_info_id: str,
-    owner_id: str = "",
-) -> tuple[AssetInfo, Asset, list[str]] | None:
-    stmt = (
-        select(AssetInfo, Asset, Tag.name)
-        .join(Asset, Asset.id == AssetInfo.asset_id)
-        .join(AssetInfoTag, AssetInfoTag.asset_info_id == AssetInfo.id, isouter=True)
-        .join(Tag, Tag.name == AssetInfoTag.tag_name, isouter=True)
-        .where(
-            AssetInfo.id == asset_info_id,
-            visible_owner_clause(owner_id),
-        )
-        .options(noload(AssetInfo.tags))
-        .order_by(Tag.name.asc())
-    )
-
-    rows = (session.execute(stmt)).all()
-    if not rows:
-        return None
-
-    first_info, first_asset, _ = rows[0]
-    tags: list[str] = []
-    seen: set[str] = set()
-    for _info, _asset, tag_name in rows:
-        if tag_name and tag_name not in seen:
-            seen.add(tag_name)
-            tags.append(tag_name)
-    return first_info, first_asset, tags
-
-
-def fetch_asset_info_and_asset(
-    session: Session,
-    *,
-    asset_info_id: str,
-    owner_id: str = "",
-) -> tuple[AssetInfo, Asset] | None:
-    stmt = (
-        select(AssetInfo, Asset)
-        .join(Asset, Asset.id == AssetInfo.asset_id)
-        .where(
-            AssetInfo.id == asset_info_id,
-            visible_owner_clause(owner_id),
-        )
-        .limit(1)
-        .options(noload(AssetInfo.tags))
-    )
-    row = session.execute(stmt)
-    pair = row.first()
-    if not pair:
-        return None
-    return pair[0], pair[1]
-
-def list_cache_states_by_asset_id(
-    session: Session, *, asset_id: str
-) -> Sequence[AssetCacheState]:
-    return (
-        session.execute(
-            select(AssetCacheState)
-            .where(AssetCacheState.asset_id == asset_id)
-            .order_by(AssetCacheState.id.asc())
-        )
-    ).scalars().all()
-
-
-def touch_asset_info_by_id(
-    session: Session,
-    *,
-    asset_info_id: str,
-    ts: datetime | None = None,
-    only_if_newer: bool = True,
-) -> None:
-    ts = ts or utcnow()
-    stmt = sa.update(AssetInfo).where(AssetInfo.id == asset_info_id)
-    if only_if_newer:
-        stmt = stmt.where(
-            sa.or_(AssetInfo.last_access_time.is_(None), AssetInfo.last_access_time < ts)
-        )
-    session.execute(stmt.values(last_access_time=ts))
-
-
-def create_asset_info_for_existing_asset(
-    session: Session,
-    *,
-    asset_hash: str,
-    name: str,
-    user_metadata: dict | None = None,
-    tags: Sequence[str] | None = None,
-    tag_origin: str = "manual",
-    owner_id: str = "",
-) -> AssetInfo:
-    """Create or return an existing AssetInfo for an Asset identified by asset_hash."""
-    now = utcnow()
-    asset = get_asset_by_hash(session, asset_hash=asset_hash)
-    if not asset:
-        raise ValueError(f"Unknown asset hash {asset_hash}")
-
-    info = AssetInfo(
-        owner_id=owner_id,
-        name=name,
-        asset_id=asset.id,
-        preview_id=None,
-        created_at=now,
-        updated_at=now,
-        last_access_time=now,
-    )
-    try:
-        with session.begin_nested():
-            session.add(info)
-            session.flush()
-    except IntegrityError:
-        existing = (
-            session.execute(
-                select(AssetInfo)
-                .options(noload(AssetInfo.tags))
-                .where(
-                    AssetInfo.asset_id == asset.id,
-                    AssetInfo.name == name,
-                    AssetInfo.owner_id == owner_id,
-                )
-                .limit(1)
-            )
-        ).unique().scalars().first()
-        if not existing:
-            raise RuntimeError("AssetInfo upsert failed to find existing row after conflict.")
-        return existing
-
-    # metadata["filename"] hack
-    new_meta = dict(user_metadata or {})
-    computed_filename = None
-    try:
-        p = pick_best_live_path(list_cache_states_by_asset_id(session, asset_id=asset.id))
-        if p:
-            computed_filename = compute_relative_filename(p)
-    except Exception:
-        computed_filename = None
-    if computed_filename:
-        new_meta["filename"] = computed_filename
-    if new_meta:
-        replace_asset_info_metadata_projection(
-            session,
-            asset_info_id=info.id,
-            user_metadata=new_meta,
-        )
-
-    if tags is not None:
-        set_asset_info_tags(
-            session,
-            asset_info_id=info.id,
-            tags=tags,
-            origin=tag_origin,
-        )
-    return info
-
-
-def set_asset_info_tags(
-    session: Session,
-    *,
-    asset_info_id: str,
-    tags: Sequence[str],
-    origin: str = "manual",
-) -> dict:
-    desired = normalize_tags(tags)
-
-    current = set(
-        tag_name for (tag_name,) in (
-            session.execute(select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == asset_info_id))
-        ).all()
-    )
-
-    to_add = [t for t in desired if t not in current]
-    to_remove = [t for t in current if t not in desired]
-
-    if to_add:
-        ensure_tags_exist(session, to_add, tag_type="user")
-        session.add_all([
-            AssetInfoTag(asset_info_id=asset_info_id, tag_name=t, origin=origin, added_at=utcnow())
-            for t in to_add
-        ])
-        session.flush()
-
-    if to_remove:
-        session.execute(
-            delete(AssetInfoTag)
-            .where(AssetInfoTag.asset_info_id == asset_info_id, AssetInfoTag.tag_name.in_(to_remove))
-        )
-        session.flush()
-
-    return {"added": to_add, "removed": to_remove, "total": desired}
-
-
-def replace_asset_info_metadata_projection(
-    session: Session,
-    *,
-    asset_info_id: str,
-    user_metadata: dict | None = None,
-) -> None:
-    info = session.get(AssetInfo, asset_info_id)
-    if not info:
-        raise ValueError(f"AssetInfo {asset_info_id} not found")
-
-    info.user_metadata = user_metadata or {}
-    info.updated_at = utcnow()
-    session.flush()
-
-    session.execute(delete(AssetInfoMeta).where(AssetInfoMeta.asset_info_id == asset_info_id))
-    session.flush()
-
-    if not user_metadata:
-        return
-
-    rows: list[AssetInfoMeta] = []
-    for k, v in user_metadata.items():
-        for r in project_kv(k, v):
-            rows.append(
-                AssetInfoMeta(
-                    asset_info_id=asset_info_id,
-                    key=r["key"],
-                    ordinal=int(r["ordinal"]),
-                    val_str=r.get("val_str"),
-                    val_num=r.get("val_num"),
-                    val_bool=r.get("val_bool"),
-                    val_json=r.get("val_json"),
-                )
-            )
-    if rows:
-        session.add_all(rows)
-        session.flush()
-
-
-def ingest_fs_asset(
-    session: Session,
-    *,
-    asset_hash: str,
-    abs_path: str,
-    size_bytes: int,
-    mtime_ns: int,
-    mime_type: str | None = None,
-    info_name: str | None = None,
-    owner_id: str = "",
-    preview_id: str | None = None,
-    user_metadata: dict | None = None,
-    tags: Sequence[str] = (),
-    tag_origin: str = "manual",
-    require_existing_tags: bool = False,
-) -> dict:
-    """
-    Idempotently upsert:
-      - Asset by content hash (create if missing)
-      - AssetCacheState(file_path) pointing to asset_id
-      - Optionally AssetInfo + tag links and metadata projection
-    Returns flags and ids.
-    """
-    locator = os.path.abspath(abs_path)
-    now = utcnow()
-
-    if preview_id:
-        if not session.get(Asset, preview_id):
-            preview_id = None
-
-    out: dict[str, Any] = {
-        "asset_created": False,
-        "asset_updated": False,
-        "state_created": False,
-        "state_updated": False,
-        "asset_info_id": None,
-    }
-
-    # 1) Asset by hash
-    asset = (
-        session.execute(select(Asset).where(Asset.hash == asset_hash).limit(1))
-    ).scalars().first()
-    if not asset:
-        vals = {
-            "hash": asset_hash,
-            "size_bytes": int(size_bytes),
-            "mime_type": mime_type,
-            "created_at": now,
-        }
-        res = session.execute(
-            sqlite.insert(Asset)
-            .values(**vals)
-            .on_conflict_do_nothing(index_elements=[Asset.hash])
-        )
-        if int(res.rowcount or 0) > 0:
-            out["asset_created"] = True
-        asset = (
-            session.execute(
-                select(Asset).where(Asset.hash == asset_hash).limit(1)
-            )
-        ).scalars().first()
-        if not asset:
-            raise RuntimeError("Asset row not found after upsert.")
-    else:
-        changed = False
-        if asset.size_bytes != int(size_bytes) and int(size_bytes) > 0:
-            asset.size_bytes = int(size_bytes)
-            changed = True
-        if mime_type and asset.mime_type != mime_type:
-            asset.mime_type = mime_type
-            changed = True
-        if changed:
-            out["asset_updated"] = True
-
-    # 2) AssetCacheState upsert by file_path (unique)
-    vals = {
-        "asset_id": asset.id,
-        "file_path": locator,
-        "mtime_ns": int(mtime_ns),
-    }
-    ins = (
-        sqlite.insert(AssetCacheState)
-        .values(**vals)
-        .on_conflict_do_nothing(index_elements=[AssetCacheState.file_path])
-    )
-
-    res = session.execute(ins)
-    if int(res.rowcount or 0) > 0:
-        out["state_created"] = True
-    else:
-        upd = (
-            sa.update(AssetCacheState)
-            .where(AssetCacheState.file_path == locator)
-            .where(
-                sa.or_(
-                    AssetCacheState.asset_id != asset.id,
-                    AssetCacheState.mtime_ns.is_(None),
-                    AssetCacheState.mtime_ns != int(mtime_ns),
-                )
-            )
-            .values(asset_id=asset.id, mtime_ns=int(mtime_ns))
-        )
-        res2 = session.execute(upd)
-        if int(res2.rowcount or 0) > 0:
-            out["state_updated"] = True
-
-    # 3) Optional AssetInfo + tags + metadata
-    if info_name:
-        try:
-            with session.begin_nested():
-                info = AssetInfo(
-                    owner_id=owner_id,
-                    name=info_name,
-                    asset_id=asset.id,
-                    preview_id=preview_id,
-                    created_at=now,
-                    updated_at=now,
-                    last_access_time=now,
-                )
-                session.add(info)
-                session.flush()
-                out["asset_info_id"] = info.id
-        except IntegrityError:
-            pass
-
-        existing_info = (
-            session.execute(
-                select(AssetInfo)
-                .where(
-                    AssetInfo.asset_id == asset.id,
-                    AssetInfo.name == info_name,
-                    (AssetInfo.owner_id == owner_id),
-                )
-                .limit(1)
-            )
-        ).unique().scalar_one_or_none()
-        if not existing_info:
-            raise RuntimeError("Failed to update or insert AssetInfo.")
-
-        if preview_id and existing_info.preview_id != preview_id:
-            existing_info.preview_id = preview_id
-
-        existing_info.updated_at = now
-        if existing_info.last_access_time < now:
-            existing_info.last_access_time = now
-        session.flush()
-        out["asset_info_id"] = existing_info.id
-
-        norm = [t.strip().lower() for t in (tags or []) if (t or "").strip()]
-        if norm and out["asset_info_id"] is not None:
-            if not require_existing_tags:
-                ensure_tags_exist(session, norm, tag_type="user")
-
-            existing_tag_names = set(
-                name for (name,) in (session.execute(select(Tag.name).where(Tag.name.in_(norm)))).all()
-            )
-            missing = [t for t in norm if t not in existing_tag_names]
-            if missing and require_existing_tags:
-                raise ValueError(f"Unknown tags: {missing}")
-
-            existing_links = set(
-                tag_name
-                for (tag_name,) in (
-                    session.execute(
-                        select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == out["asset_info_id"])
-                    )
-                ).all()
-            )
-            to_add = [t for t in norm if t in existing_tag_names and t not in existing_links]
-            if to_add:
-                session.add_all(
-                    [
-                        AssetInfoTag(
-                            asset_info_id=out["asset_info_id"],
-                            tag_name=t,
-                            origin=tag_origin,
-                            added_at=now,
-                        )
-                        for t in to_add
-                    ]
-                )
-                session.flush()
-
-        # metadata["filename"] hack
-        if out["asset_info_id"] is not None:
-            primary_path = pick_best_live_path(list_cache_states_by_asset_id(session, asset_id=asset.id))
-            computed_filename = compute_relative_filename(primary_path) if primary_path else None
-
-            current_meta = existing_info.user_metadata or {}
-            new_meta = dict(current_meta)
-            if user_metadata is not None:
-                for k, v in user_metadata.items():
-                    new_meta[k] = v
-            if computed_filename:
-                new_meta["filename"] = computed_filename
-
-            if new_meta != current_meta:
-                replace_asset_info_metadata_projection(
-                    session,
-                    asset_info_id=out["asset_info_id"],
-                    user_metadata=new_meta,
-                )
-
-    try:
-        remove_missing_tag_for_asset_id(session, asset_id=asset.id)
-    except Exception:
-        logging.exception("Failed to clear 'missing' tag for asset %s", asset.id)
-    return out
-
-
-def update_asset_info_full(
-    session: Session,
-    *,
-    asset_info_id: str,
-    name: str | None = None,
-    tags: Sequence[str] | None = None,
-    user_metadata: dict | None = None,
-    tag_origin: str = "manual",
-    asset_info_row: Any = None,
-) -> AssetInfo:
-    if not asset_info_row:
-        info = session.get(AssetInfo, asset_info_id)
-        if not info:
-            raise ValueError(f"AssetInfo {asset_info_id} not found")
-    else:
-        info = asset_info_row
-
-    touched = False
-    if name is not None and name != info.name:
-        info.name = name
-        touched = True
-
-    computed_filename = None
-    try:
-        p = pick_best_live_path(list_cache_states_by_asset_id(session, asset_id=info.asset_id))
-        if p:
-            computed_filename = compute_relative_filename(p)
-    except Exception:
-        computed_filename = None
-
-    if user_metadata is not None:
-        new_meta = dict(user_metadata)
-        if computed_filename:
-            new_meta["filename"] = computed_filename
-        replace_asset_info_metadata_projection(
-            session, asset_info_id=asset_info_id, user_metadata=new_meta
-        )
-        touched = True
-    else:
-        if computed_filename:
-            current_meta = info.user_metadata or {}
-            if current_meta.get("filename") != computed_filename:
-                new_meta = dict(current_meta)
-                new_meta["filename"] = computed_filename
-                replace_asset_info_metadata_projection(
-                    session, asset_info_id=asset_info_id, user_metadata=new_meta
-                )
-                touched = True
-
-    if tags is not None:
-        set_asset_info_tags(
-            session,
-            asset_info_id=asset_info_id,
-            tags=tags,
-            origin=tag_origin,
-        )
-        touched = True
-
-    if touched and user_metadata is None:
-        info.updated_at = utcnow()
-        session.flush()
-
-    return info
-
-
-def delete_asset_info_by_id(
-    session: Session,
-    *,
-    asset_info_id: str,
-    owner_id: str,
-) -> bool:
-    stmt = sa.delete(AssetInfo).where(
-        AssetInfo.id == asset_info_id,
-        visible_owner_clause(owner_id),
-    )
-    return int((session.execute(stmt)).rowcount or 0) > 0
-
-
-def list_tags_with_usage(
-    session: Session,
-    prefix: str | None = None,
-    limit: int = 100,
-    offset: int = 0,
-    include_zero: bool = True,
-    order: str = "count_desc",
-    owner_id: str = "",
-) -> tuple[list[tuple[str, str, int]], int]:
-    counts_sq = (
-        select(
-            AssetInfoTag.tag_name.label("tag_name"),
-            func.count(AssetInfoTag.asset_info_id).label("cnt"),
-        )
-        .select_from(AssetInfoTag)
-        .join(AssetInfo, AssetInfo.id == AssetInfoTag.asset_info_id)
-        .where(visible_owner_clause(owner_id))
-        .group_by(AssetInfoTag.tag_name)
-        .subquery()
-    )
-
-    q = (
-        select(
-            Tag.name,
-            Tag.tag_type,
-            func.coalesce(counts_sq.c.cnt, 0).label("count"),
-        )
-        .select_from(Tag)
-        .join(counts_sq, counts_sq.c.tag_name == Tag.name, isouter=True)
-    )
-
-    if prefix:
-        escaped, esc = escape_like_prefix(prefix.strip().lower())
-        q = q.where(Tag.name.like(escaped + "%", escape=esc))
-
-    if not include_zero:
-        q = q.where(func.coalesce(counts_sq.c.cnt, 0) > 0)
-
-    if order == "name_asc":
-        q = q.order_by(Tag.name.asc())
-    else:
-        q = q.order_by(func.coalesce(counts_sq.c.cnt, 0).desc(), Tag.name.asc())
-
-    total_q = select(func.count()).select_from(Tag)
-    if prefix:
-        escaped, esc = escape_like_prefix(prefix.strip().lower())
-        total_q = total_q.where(Tag.name.like(escaped + "%", escape=esc))
-    if not include_zero:
-        total_q = total_q.where(
-            Tag.name.in_(select(AssetInfoTag.tag_name).group_by(AssetInfoTag.tag_name))
-        )
-
-    rows = (session.execute(q.limit(limit).offset(offset))).all()
-    total = (session.execute(total_q)).scalar_one()
-
-    rows_norm = [(name, ttype, int(count or 0)) for (name, ttype, count) in rows]
-    return rows_norm, int(total or 0)
-
-
-def ensure_tags_exist(session: Session, names: Iterable[str], tag_type: str = "user") -> None:
-    wanted = normalize_tags(list(names))
-    if not wanted:
-        return
-    rows = [{"name": n, "tag_type": tag_type} for n in list(dict.fromkeys(wanted))]
-    ins = (
-        sqlite.insert(Tag)
-        .values(rows)
-        .on_conflict_do_nothing(index_elements=[Tag.name])
-    )
-    session.execute(ins)
-
-
-def get_asset_tags(session: Session, *, asset_info_id: str) -> list[str]:
-    return [
-        tag_name for (tag_name,) in (
-            session.execute(
-                select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == asset_info_id)
-            )
-        ).all()
-    ]
-
-
-def add_tags_to_asset_info(
-    session: Session,
-    *,
-    asset_info_id: str,
-    tags: Sequence[str],
-    origin: str = "manual",
-    create_if_missing: bool = True,
-    asset_info_row: Any = None,
-) -> dict:
-    if not asset_info_row:
-        info = session.get(AssetInfo, asset_info_id)
-        if not info:
-            raise ValueError(f"AssetInfo {asset_info_id} not found")
-
-    norm = normalize_tags(tags)
-    if not norm:
-        total = get_asset_tags(session, asset_info_id=asset_info_id)
-        return {"added": [], "already_present": [], "total_tags": total}
-
-    if create_if_missing:
-        ensure_tags_exist(session, norm, tag_type="user")
-
-    current = {
-        tag_name
-        for (tag_name,) in (
-            session.execute(
-                sa.select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == asset_info_id)
-            )
-        ).all()
-    }
-
-    want = set(norm)
-    to_add = sorted(want - current)
-
-    if to_add:
-        with session.begin_nested() as nested:
-            try:
-                session.add_all(
-                    [
-                        AssetInfoTag(
-                            asset_info_id=asset_info_id,
-                            tag_name=t,
-                            origin=origin,
-                            added_at=utcnow(),
-                        )
-                        for t in to_add
-                    ]
-                )
-                session.flush()
-            except IntegrityError:
-                nested.rollback()
-
-    after = set(get_asset_tags(session, asset_info_id=asset_info_id))
-    return {
-        "added": sorted(((after - current) & want)),
-        "already_present": sorted(want & current),
-        "total_tags": sorted(after),
-    }
-
-
-def remove_tags_from_asset_info(
-    session: Session,
-    *,
-    asset_info_id: str,
-    tags: Sequence[str],
-) -> dict:
-    info = session.get(AssetInfo, asset_info_id)
-    if not info:
-        raise ValueError(f"AssetInfo {asset_info_id} not found")
-
-    norm = normalize_tags(tags)
-    if not norm:
-        total = get_asset_tags(session, asset_info_id=asset_info_id)
-        return {"removed": [], "not_present": [], "total_tags": total}
-
-    existing = {
-        tag_name
-        for (tag_name,) in (
-            session.execute(
-                sa.select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == asset_info_id)
-            )
-        ).all()
-    }
-
-    to_remove = sorted(set(t for t in norm if t in existing))
-    not_present = sorted(set(t for t in norm if t not in existing))
-
-    if to_remove:
-        session.execute(
-            delete(AssetInfoTag)
-            .where(
-                AssetInfoTag.asset_info_id == asset_info_id,
-                AssetInfoTag.tag_name.in_(to_remove),
-            )
-        )
-        session.flush()
-
-    total = get_asset_tags(session, asset_info_id=asset_info_id)
-    return {"removed": to_remove, "not_present": not_present, "total_tags": total}
-
-
-def remove_missing_tag_for_asset_id(
-    session: Session,
-    *,
-    asset_id: str,
-) -> None:
-    session.execute(
-        sa.delete(AssetInfoTag).where(
-            AssetInfoTag.asset_info_id.in_(sa.select(AssetInfo.id).where(AssetInfo.asset_id == asset_id)),
-            AssetInfoTag.tag_name == "missing",
-        )
-    )
-
-
-def set_asset_info_preview(
-    session: Session,
-    *,
-    asset_info_id: str,
-    preview_asset_id: str | None = None,
-) -> None:
-    """Set or clear preview_id and bump updated_at. Raises on unknown IDs."""
-    info = session.get(AssetInfo, asset_info_id)
-    if not info:
-        raise ValueError(f"AssetInfo {asset_info_id} not found")
-
-    if preview_asset_id is None:
-        info.preview_id = None
-    else:
-        # validate preview asset exists
-        if not session.get(Asset, preview_asset_id):
-            raise ValueError(f"Preview Asset {preview_asset_id} not found")
-        info.preview_id = preview_asset_id
-
-    info.updated_at = utcnow()
-    session.flush()
diff --git a/app/assets/database/queries/__init__.py b/app/assets/database/queries/__init__.py
new file mode 100644
index 000000000..7888d0645
--- /dev/null
+++ b/app/assets/database/queries/__init__.py
@@ -0,0 +1,121 @@
+from app.assets.database.queries.asset import (
+    asset_exists_by_hash,
+    bulk_insert_assets,
+    get_asset_by_hash,
+    get_existing_asset_ids,
+    reassign_asset_references,
+    update_asset_hash_and_mime,
+    upsert_asset,
+)
+from app.assets.database.queries.asset_reference import (
+    CacheStateRow,
+    UnenrichedReferenceRow,
+    bulk_insert_references_ignore_conflicts,
+    bulk_update_enrichment_level,
+    bulk_update_is_missing,
+    bulk_update_needs_verify,
+    convert_metadata_to_rows,
+    delete_assets_by_ids,
+    delete_orphaned_seed_asset,
+    delete_reference_by_id,
+    delete_references_by_ids,
+    fetch_reference_and_asset,
+    fetch_reference_asset_and_tags,
+    get_or_create_reference,
+    get_reference_by_file_path,
+    get_reference_by_id,
+    get_reference_with_owner_check,
+    get_reference_ids_by_ids,
+    get_references_by_paths_and_asset_ids,
+    get_references_for_prefixes,
+    get_unenriched_references,
+    get_unreferenced_unhashed_asset_ids,
+    insert_reference,
+    list_references_by_asset_id,
+    list_references_page,
+    mark_references_missing_outside_prefixes,
+    reference_exists_for_asset_id,
+    restore_references_by_paths,
+    set_reference_metadata,
+    set_reference_preview,
+    soft_delete_reference_by_id,
+    update_reference_access_time,
+    update_reference_name,
+    update_reference_timestamps,
+    update_reference_updated_at,
+    upsert_reference,
+)
+from app.assets.database.queries.tags import (
+    AddTagsResult,
+    RemoveTagsResult,
+    SetTagsResult,
+    add_missing_tag_for_asset_id,
+    add_tags_to_reference,
+    bulk_insert_tags_and_meta,
+    ensure_tags_exist,
+    get_reference_tags,
+    list_tags_with_usage,
+    remove_missing_tag_for_asset_id,
+    remove_tags_from_reference,
+    set_reference_tags,
+    validate_tags_exist,
+)
+
+__all__ = [
+    "AddTagsResult",
+    "CacheStateRow",
+    "RemoveTagsResult",
+    "SetTagsResult",
+    "UnenrichedReferenceRow",
+    "add_missing_tag_for_asset_id",
+    "add_tags_to_reference",
+    "asset_exists_by_hash",
+    "bulk_insert_assets",
+    "bulk_insert_references_ignore_conflicts",
+    "bulk_insert_tags_and_meta",
+    "bulk_update_enrichment_level",
+    "bulk_update_is_missing",
+    "bulk_update_needs_verify",
+    "convert_metadata_to_rows",
+    "delete_assets_by_ids",
+    "delete_orphaned_seed_asset",
+    "delete_reference_by_id",
+    "delete_references_by_ids",
+    "ensure_tags_exist",
+    "fetch_reference_and_asset",
+    "fetch_reference_asset_and_tags",
+    "get_asset_by_hash",
+    "get_existing_asset_ids",
+    "get_or_create_reference",
+    "get_reference_by_file_path",
+    "get_reference_by_id",
+    "get_reference_with_owner_check",
+    "get_reference_ids_by_ids",
+    "get_reference_tags",
+    "get_references_by_paths_and_asset_ids",
+    "get_references_for_prefixes",
+    "get_unenriched_references",
+    "get_unreferenced_unhashed_asset_ids",
+    "insert_reference",
+    "list_references_by_asset_id",
+    "list_references_page",
+    "list_tags_with_usage",
+    "mark_references_missing_outside_prefixes",
+    "reassign_asset_references",
+    "reference_exists_for_asset_id",
+    "remove_missing_tag_for_asset_id",
+    "remove_tags_from_reference",
+    "restore_references_by_paths",
+    "set_reference_metadata",
+    "set_reference_preview",
+    "soft_delete_reference_by_id",
+    "set_reference_tags",
+    "update_asset_hash_and_mime",
+    "update_reference_access_time",
+    "update_reference_name",
+    "update_reference_timestamps",
+    "update_reference_updated_at",
+    "upsert_asset",
+    "upsert_reference",
+    "validate_tags_exist",
+]
diff --git a/app/assets/database/queries/asset.py b/app/assets/database/queries/asset.py
new file mode 100644
index 000000000..a21f5b68f
--- /dev/null
+++ b/app/assets/database/queries/asset.py
@@ -0,0 +1,140 @@
+import sqlalchemy as sa
+from sqlalchemy import select
+from sqlalchemy.dialects import sqlite
+from sqlalchemy.orm import Session
+
+from app.assets.database.models import Asset, AssetReference
+from app.assets.database.queries.common import MAX_BIND_PARAMS, calculate_rows_per_statement, iter_chunks
+
+
+def asset_exists_by_hash(
+    session: Session,
+    asset_hash: str,
+) -> bool:
+    """
+    Check if an asset with a given hash exists in database.
+    """
+    row = (
+        session.execute(
+            select(sa.literal(True))
+            .select_from(Asset)
+            .where(Asset.hash == asset_hash)
+            .limit(1)
+        )
+    ).first()
+    return row is not None
+
+
+def get_asset_by_hash(
+    session: Session,
+    asset_hash: str,
+) -> Asset | None:
+    return (
+        (session.execute(select(Asset).where(Asset.hash == asset_hash).limit(1)))
+        .scalars()
+        .first()
+    )
+
+
+def upsert_asset(
+    session: Session,
+    asset_hash: str,
+    size_bytes: int,
+    mime_type: str | None = None,
+) -> tuple[Asset, bool, bool]:
+    """Upsert an Asset by hash. Returns (asset, created, updated)."""
+    vals = {"hash": asset_hash, "size_bytes": int(size_bytes)}
+    if mime_type:
+        vals["mime_type"] = mime_type
+
+    ins = (
+        sqlite.insert(Asset)
+        .values(**vals)
+        .on_conflict_do_nothing(index_elements=[Asset.hash])
+    )
+    res = session.execute(ins)
+    created = int(res.rowcount or 0) > 0
+
+    asset = (
+        session.execute(select(Asset).where(Asset.hash == asset_hash).limit(1))
+        .scalars()
+        .first()
+    )
+    if not asset:
+        raise RuntimeError("Asset row not found after upsert.")
+
+    updated = False
+    if not created:
+        changed = False
+        if asset.size_bytes != int(size_bytes) and int(size_bytes) > 0:
+            asset.size_bytes = int(size_bytes)
+            changed = True
+        if mime_type and asset.mime_type != mime_type:
+            asset.mime_type = mime_type
+            changed = True
+        if changed:
+            updated = True
+
+    return asset, created, updated
+
+
+def bulk_insert_assets(
+    session: Session,
+    rows: list[dict],
+) -> None:
+    """Bulk insert Asset rows with ON CONFLICT DO NOTHING on hash."""
+    if not rows:
+        return
+    ins = sqlite.insert(Asset).on_conflict_do_nothing(index_elements=[Asset.hash])
+    for chunk in iter_chunks(rows, calculate_rows_per_statement(5)):
+        session.execute(ins, chunk)
+
+
+def get_existing_asset_ids(
+    session: Session,
+    asset_ids: list[str],
+) -> set[str]:
+    """Return the subset of asset_ids that exist in the database."""
+    if not asset_ids:
+        return set()
+    found: set[str] = set()
+    for chunk in iter_chunks(asset_ids, MAX_BIND_PARAMS):
+        rows = session.execute(
+            select(Asset.id).where(Asset.id.in_(chunk))
+        ).fetchall()
+        found.update(row[0] for row in rows)
+    return found
+
+
+def update_asset_hash_and_mime(
+    session: Session,
+    asset_id: str,
+    asset_hash: str | None = None,
+    mime_type: str | None = None,
+) -> bool:
+    """Update asset hash and/or mime_type. Returns True if asset was found."""
+    asset = session.get(Asset, asset_id)
+    if not asset:
+        return False
+    if asset_hash is not None:
+        asset.hash = asset_hash
+    if mime_type is not None:
+        asset.mime_type = mime_type
+    return True
+
+
+def reassign_asset_references(
+    session: Session,
+    from_asset_id: str,
+    to_asset_id: str,
+    reference_id: str,
+) -> None:
+    """Reassign a reference from one asset to another.
+
+    Used when merging a stub asset into an existing asset with the same hash.
+    """
+    ref = session.get(AssetReference, reference_id)
+    if ref and ref.asset_id == from_asset_id:
+        ref.asset_id = to_asset_id
+
+    session.flush()
diff --git a/app/assets/database/queries/asset_reference.py b/app/assets/database/queries/asset_reference.py
new file mode 100644
index 000000000..6524791cc
--- /dev/null
+++ b/app/assets/database/queries/asset_reference.py
@@ -0,0 +1,1033 @@
+"""Query functions for the unified AssetReference table.
+
+This module replaces the separate asset_info.py and cache_state.py query modules,
+providing a unified interface for the merged asset_references table.
+"""
+
+from collections import defaultdict
+from datetime import datetime
+from decimal import Decimal
+from typing import NamedTuple, Sequence
+
+import sqlalchemy as sa
+from sqlalchemy import delete, exists, select
+from sqlalchemy.dialects import sqlite
+from sqlalchemy.exc import IntegrityError
+from sqlalchemy.orm import Session, noload
+
+from app.assets.database.models import (
+    Asset,
+    AssetReference,
+    AssetReferenceMeta,
+    AssetReferenceTag,
+    Tag,
+)
+from app.assets.database.queries.common import (
+    MAX_BIND_PARAMS,
+    build_prefix_like_conditions,
+    build_visible_owner_clause,
+    calculate_rows_per_statement,
+    iter_chunks,
+)
+from app.assets.helpers import escape_sql_like_string, get_utc_now, normalize_tags
+
+
+def _check_is_scalar(v):
+    if v is None:
+        return True
+    if isinstance(v, bool):
+        return True
+    if isinstance(v, (int, float, Decimal, str)):
+        return True
+    return False
+
+
+def _scalar_to_row(key: str, ordinal: int, value) -> dict:
+    """Convert a scalar value to a typed projection row."""
+    if value is None:
+        return {
+            "key": key,
+            "ordinal": ordinal,
+            "val_str": None,
+            "val_num": None,
+            "val_bool": None,
+            "val_json": None,
+        }
+    if isinstance(value, bool):
+        return {"key": key, "ordinal": ordinal, "val_bool": bool(value)}
+    if isinstance(value, (int, float, Decimal)):
+        num = value if isinstance(value, Decimal) else Decimal(str(value))
+        return {"key": key, "ordinal": ordinal, "val_num": num}
+    if isinstance(value, str):
+        return {"key": key, "ordinal": ordinal, "val_str": value}
+    return {"key": key, "ordinal": ordinal, "val_json": value}
+
+
+def convert_metadata_to_rows(key: str, value) -> list[dict]:
+    """Turn a metadata key/value into typed projection rows."""
+    if value is None:
+        return [_scalar_to_row(key, 0, None)]
+
+    if _check_is_scalar(value):
+        return [_scalar_to_row(key, 0, value)]
+
+    if isinstance(value, list):
+        if all(_check_is_scalar(x) for x in value):
+            return [_scalar_to_row(key, i, x) for i, x in enumerate(value)]
+        return [{"key": key, "ordinal": i, "val_json": x} for i, x in enumerate(value)]
+
+    return [{"key": key, "ordinal": 0, "val_json": value}]
+
+
+def _apply_tag_filters(
+    stmt: sa.sql.Select,
+    include_tags: Sequence[str] | None = None,
+    exclude_tags: Sequence[str] | None = None,
+) -> sa.sql.Select:
+    """include_tags: every tag must be present; exclude_tags: none may be present."""
+    include_tags = normalize_tags(include_tags)
+    exclude_tags = normalize_tags(exclude_tags)
+
+    if include_tags:
+        for tag_name in include_tags:
+            stmt = stmt.where(
+                exists().where(
+                    (AssetReferenceTag.asset_reference_id == AssetReference.id)
+                    & (AssetReferenceTag.tag_name == tag_name)
+                )
+            )
+
+    if exclude_tags:
+        stmt = stmt.where(
+            ~exists().where(
+                (AssetReferenceTag.asset_reference_id == AssetReference.id)
+                & (AssetReferenceTag.tag_name.in_(exclude_tags))
+            )
+        )
+    return stmt
+
+
+def _apply_metadata_filter(
+    stmt: sa.sql.Select,
+    metadata_filter: dict | None = None,
+) -> sa.sql.Select:
+    """Apply filters using asset_reference_meta projection table."""
+    if not metadata_filter:
+        return stmt
+
+    def _exists_for_pred(key: str, *preds) -> sa.sql.ClauseElement:
+        return sa.exists().where(
+            AssetReferenceMeta.asset_reference_id == AssetReference.id,
+            AssetReferenceMeta.key == key,
+            *preds,
+        )
+
+    def _exists_clause_for_value(key: str, value) -> sa.sql.ClauseElement:
+        if value is None:
+            no_row_for_key = sa.not_(
+                sa.exists().where(
+                    AssetReferenceMeta.asset_reference_id == AssetReference.id,
+                    AssetReferenceMeta.key == key,
+                )
+            )
+            null_row = _exists_for_pred(
+                key,
+                AssetReferenceMeta.val_json.is_(None),
+                AssetReferenceMeta.val_str.is_(None),
+                AssetReferenceMeta.val_num.is_(None),
+                AssetReferenceMeta.val_bool.is_(None),
+            )
+            return sa.or_(no_row_for_key, null_row)
+
+        if isinstance(value, bool):
+            return _exists_for_pred(key, AssetReferenceMeta.val_bool == bool(value))
+        if isinstance(value, (int, float, Decimal)):
+            num = value if isinstance(value, Decimal) else Decimal(str(value))
+            return _exists_for_pred(key, AssetReferenceMeta.val_num == num)
+        if isinstance(value, str):
+            return _exists_for_pred(key, AssetReferenceMeta.val_str == value)
+        return _exists_for_pred(key, AssetReferenceMeta.val_json == value)
+
+    for k, v in metadata_filter.items():
+        if isinstance(v, list):
+            ors = [_exists_clause_for_value(k, elem) for elem in v]
+            if ors:
+                stmt = stmt.where(sa.or_(*ors))
+        else:
+            stmt = stmt.where(_exists_clause_for_value(k, v))
+    return stmt
+
+
+def get_reference_by_id(
+    session: Session,
+    reference_id: str,
+) -> AssetReference | None:
+    return session.get(AssetReference, reference_id)
+
+
+def get_reference_with_owner_check(
+    session: Session,
+    reference_id: str,
+    owner_id: str,
+) -> AssetReference:
+    """Fetch a reference and verify ownership.
+
+    Raises:
+        ValueError: if reference not found or soft-deleted
+        PermissionError: if owner_id doesn't match
+    """
+    ref = get_reference_by_id(session, reference_id=reference_id)
+    if not ref or ref.deleted_at is not None:
+        raise ValueError(f"AssetReference {reference_id} not found")
+    if ref.owner_id and ref.owner_id != owner_id:
+        raise PermissionError("not owner")
+    return ref
+
+
+def get_reference_by_file_path(
+    session: Session,
+    file_path: str,
+) -> AssetReference | None:
+    """Get a reference by its file path."""
+    return (
+        session.execute(
+            select(AssetReference).where(AssetReference.file_path == file_path).limit(1)
+        )
+        .scalars()
+        .first()
+    )
+
+
+def reference_exists_for_asset_id(
+    session: Session,
+    asset_id: str,
+) -> bool:
+    q = (
+        select(sa.literal(True))
+        .select_from(AssetReference)
+        .where(AssetReference.asset_id == asset_id)
+        .where(AssetReference.deleted_at.is_(None))
+        .limit(1)
+    )
+    return session.execute(q).first() is not None
+
+
+def insert_reference(
+    session: Session,
+    asset_id: str,
+    name: str,
+    owner_id: str = "",
+    file_path: str | None = None,
+    mtime_ns: int | None = None,
+    preview_id: str | None = None,
+) -> AssetReference | None:
+    """Insert a new AssetReference. Returns None if unique constraint violated."""
+    now = get_utc_now()
+    try:
+        with session.begin_nested():
+            ref = AssetReference(
+                asset_id=asset_id,
+                name=name,
+                owner_id=owner_id,
+                file_path=file_path,
+                mtime_ns=mtime_ns,
+                preview_id=preview_id,
+                created_at=now,
+                updated_at=now,
+                last_access_time=now,
+            )
+            session.add(ref)
+            session.flush()
+            return ref
+    except IntegrityError:
+        return None
+
+
+def get_or_create_reference(
+    session: Session,
+    asset_id: str,
+    name: str,
+    owner_id: str = "",
+    file_path: str | None = None,
+    mtime_ns: int | None = None,
+    preview_id: str | None = None,
+) -> tuple[AssetReference, bool]:
+    """Get existing or create new AssetReference.
+
+    For filesystem references (file_path is set), uniqueness is by file_path.
+    For API references (file_path is None), we look for matching
+    asset_id + owner_id + name.
+
+    Returns (reference, created).
+    """
+    ref = insert_reference(
+        session,
+        asset_id=asset_id,
+        name=name,
+        owner_id=owner_id,
+        file_path=file_path,
+        mtime_ns=mtime_ns,
+        preview_id=preview_id,
+    )
+    if ref:
+        return ref, True
+
+    # Find existing - priority to file_path match, then name match
+    if file_path:
+        existing = get_reference_by_file_path(session, file_path)
+    else:
+        existing = (
+            session.execute(
+                select(AssetReference)
+                .where(
+                    AssetReference.asset_id == asset_id,
+                    AssetReference.name == name,
+                    AssetReference.owner_id == owner_id,
+                    AssetReference.file_path.is_(None),
+                )
+                .limit(1)
+            )
+            .unique()
+            .scalar_one_or_none()
+        )
+    if not existing:
+        raise RuntimeError("Failed to find AssetReference after insert conflict.")
+    return existing, False
+
+
+def update_reference_timestamps(
+    session: Session,
+    reference: AssetReference,
+    preview_id: str | None = None,
+) -> None:
+    """Update timestamps and optionally preview_id on existing AssetReference."""
+    now = get_utc_now()
+    if preview_id and reference.preview_id != preview_id:
+        reference.preview_id = preview_id
+    reference.updated_at = now
+
+
+def list_references_page(
+    session: Session,
+    owner_id: str = "",
+    limit: int = 100,
+    offset: int = 0,
+    name_contains: str | None = None,
+    include_tags: Sequence[str] | None = None,
+    exclude_tags: Sequence[str] | None = None,
+    metadata_filter: dict | None = None,
+    sort: str | None = None,
+    order: str | None = None,
+) -> tuple[list[AssetReference], dict[str, list[str]], int]:
+    """List references with pagination, filtering, and sorting.
+
+    Returns (references, tag_map, total_count).
+    """
+    base = (
+        select(AssetReference)
+        .join(Asset, Asset.id == AssetReference.asset_id)
+        .where(build_visible_owner_clause(owner_id))
+        .where(AssetReference.is_missing == False)  # noqa: E712
+        .where(AssetReference.deleted_at.is_(None))
+        .options(noload(AssetReference.tags))
+    )
+
+    if name_contains:
+        escaped, esc = escape_sql_like_string(name_contains)
+        base = base.where(AssetReference.name.ilike(f"%{escaped}%", escape=esc))
+
+    base = _apply_tag_filters(base, include_tags, exclude_tags)
+    base = _apply_metadata_filter(base, metadata_filter)
+
+    sort = (sort or "created_at").lower()
+    order = (order or "desc").lower()
+    sort_map = {
+        "name": AssetReference.name,
+        "created_at": AssetReference.created_at,
+        "updated_at": AssetReference.updated_at,
+        "last_access_time": AssetReference.last_access_time,
+        "size": Asset.size_bytes,
+    }
+    sort_col = sort_map.get(sort, AssetReference.created_at)
+    sort_exp = sort_col.desc() if order == "desc" else sort_col.asc()
+
+    base = base.order_by(sort_exp).limit(limit).offset(offset)
+
+    count_stmt = (
+        select(sa.func.count())
+        .select_from(AssetReference)
+        .join(Asset, Asset.id == AssetReference.asset_id)
+        .where(build_visible_owner_clause(owner_id))
+        .where(AssetReference.is_missing == False)  # noqa: E712
+        .where(AssetReference.deleted_at.is_(None))
+    )
+    if name_contains:
+        escaped, esc = escape_sql_like_string(name_contains)
+        count_stmt = count_stmt.where(
+            AssetReference.name.ilike(f"%{escaped}%", escape=esc)
+        )
+    count_stmt = _apply_tag_filters(count_stmt, include_tags, exclude_tags)
+    count_stmt = _apply_metadata_filter(count_stmt, metadata_filter)
+
+    total = int(session.execute(count_stmt).scalar_one() or 0)
+    refs = session.execute(base).unique().scalars().all()
+
+    id_list: list[str] = [r.id for r in refs]
+    tag_map: dict[str, list[str]] = defaultdict(list)
+    if id_list:
+        rows = session.execute(
+            select(AssetReferenceTag.asset_reference_id, Tag.name)
+            .join(Tag, Tag.name == AssetReferenceTag.tag_name)
+            .where(AssetReferenceTag.asset_reference_id.in_(id_list))
+            .order_by(AssetReferenceTag.added_at)
+        )
+        for ref_id, tag_name in rows.all():
+            tag_map[ref_id].append(tag_name)
+
+    return list(refs), tag_map, total
+
+
+def fetch_reference_asset_and_tags(
+    session: Session,
+    reference_id: str,
+    owner_id: str = "",
+) -> tuple[AssetReference, Asset, list[str]] | None:
+    stmt = (
+        select(AssetReference, Asset, Tag.name)
+        .join(Asset, Asset.id == AssetReference.asset_id)
+        .join(
+            AssetReferenceTag,
+            AssetReferenceTag.asset_reference_id == AssetReference.id,
+            isouter=True,
+        )
+        .join(Tag, Tag.name == AssetReferenceTag.tag_name, isouter=True)
+        .where(
+            AssetReference.id == reference_id,
+            AssetReference.deleted_at.is_(None),
+            build_visible_owner_clause(owner_id),
+        )
+        .options(noload(AssetReference.tags))
+        .order_by(Tag.name.asc())
+    )
+
+    rows = session.execute(stmt).all()
+    if not rows:
+        return None
+
+    first_ref, first_asset, _ = rows[0]
+    tags: list[str] = []
+    seen: set[str] = set()
+    for _ref, _asset, tag_name in rows:
+        if tag_name and tag_name not in seen:
+            seen.add(tag_name)
+            tags.append(tag_name)
+    return first_ref, first_asset, tags
+
+
+def fetch_reference_and_asset(
+    session: Session,
+    reference_id: str,
+    owner_id: str = "",
+) -> tuple[AssetReference, Asset] | None:
+    stmt = (
+        select(AssetReference, Asset)
+        .join(Asset, Asset.id == AssetReference.asset_id)
+        .where(
+            AssetReference.id == reference_id,
+            AssetReference.deleted_at.is_(None),
+            build_visible_owner_clause(owner_id),
+        )
+        .limit(1)
+        .options(noload(AssetReference.tags))
+    )
+    pair = session.execute(stmt).first()
+    if not pair:
+        return None
+    return pair[0], pair[1]
+
+
+def update_reference_access_time(
+    session: Session,
+    reference_id: str,
+    ts: datetime | None = None,
+    only_if_newer: bool = True,
+) -> None:
+    ts = ts or get_utc_now()
+    stmt = sa.update(AssetReference).where(AssetReference.id == reference_id)
+    if only_if_newer:
+        stmt = stmt.where(
+            sa.or_(
+                AssetReference.last_access_time.is_(None),
+                AssetReference.last_access_time < ts,
+            )
+        )
+    session.execute(stmt.values(last_access_time=ts))
+
+
+def update_reference_name(
+    session: Session,
+    reference_id: str,
+    name: str,
+) -> None:
+    """Update the name of an AssetReference."""
+    now = get_utc_now()
+    session.execute(
+        sa.update(AssetReference)
+        .where(AssetReference.id == reference_id)
+        .values(name=name, updated_at=now)
+    )
+
+
+def update_reference_updated_at(
+    session: Session,
+    reference_id: str,
+    ts: datetime | None = None,
+) -> None:
+    """Update the updated_at timestamp of an AssetReference."""
+    ts = ts or get_utc_now()
+    session.execute(
+        sa.update(AssetReference)
+        .where(AssetReference.id == reference_id)
+        .values(updated_at=ts)
+    )
+
+
+def set_reference_metadata(
+    session: Session,
+    reference_id: str,
+    user_metadata: dict | None = None,
+) -> None:
+    ref = session.get(AssetReference, reference_id)
+    if not ref:
+        raise ValueError(f"AssetReference {reference_id} not found")
+
+    ref.user_metadata = user_metadata or {}
+    ref.updated_at = get_utc_now()
+    session.flush()
+
+    session.execute(
+        delete(AssetReferenceMeta).where(
+            AssetReferenceMeta.asset_reference_id == reference_id
+        )
+    )
+    session.flush()
+
+    if not user_metadata:
+        return
+
+    rows: list[AssetReferenceMeta] = []
+    for k, v in user_metadata.items():
+        for r in convert_metadata_to_rows(k, v):
+            rows.append(
+                AssetReferenceMeta(
+                    asset_reference_id=reference_id,
+                    key=r["key"],
+                    ordinal=int(r["ordinal"]),
+                    val_str=r.get("val_str"),
+                    val_num=r.get("val_num"),
+                    val_bool=r.get("val_bool"),
+                    val_json=r.get("val_json"),
+                )
+            )
+    if rows:
+        session.add_all(rows)
+        session.flush()
+
+
+def delete_reference_by_id(
+    session: Session,
+    reference_id: str,
+    owner_id: str,
+) -> bool:
+    stmt = sa.delete(AssetReference).where(
+        AssetReference.id == reference_id,
+        build_visible_owner_clause(owner_id),
+    )
+    return int(session.execute(stmt).rowcount or 0) > 0
+
+
+def soft_delete_reference_by_id(
+    session: Session,
+    reference_id: str,
+    owner_id: str,
+) -> bool:
+    """Mark a reference as soft-deleted by setting deleted_at timestamp.
+
+    Returns True if the reference was found and marked deleted.
+    """
+    now = get_utc_now()
+    stmt = (
+        sa.update(AssetReference)
+        .where(
+            AssetReference.id == reference_id,
+            AssetReference.deleted_at.is_(None),
+            build_visible_owner_clause(owner_id),
+        )
+        .values(deleted_at=now)
+    )
+    return int(session.execute(stmt).rowcount or 0) > 0
+
+
+def set_reference_preview(
+    session: Session,
+    reference_id: str,
+    preview_asset_id: str | None = None,
+) -> None:
+    """Set or clear preview_id and bump updated_at. Raises on unknown IDs."""
+    ref = session.get(AssetReference, reference_id)
+    if not ref:
+        raise ValueError(f"AssetReference {reference_id} not found")
+
+    if preview_asset_id is None:
+        ref.preview_id = None
+    else:
+        if not session.get(Asset, preview_asset_id):
+            raise ValueError(f"Preview Asset {preview_asset_id} not found")
+        ref.preview_id = preview_asset_id
+
+    ref.updated_at = get_utc_now()
+    session.flush()
+
+
+class CacheStateRow(NamedTuple):
+    """Row from reference query with cache state data."""
+
+    reference_id: str
+    file_path: str
+    mtime_ns: int | None
+    needs_verify: bool
+    asset_id: str
+    asset_hash: str | None
+    size_bytes: int | None
+
+
+def list_references_by_asset_id(
+    session: Session,
+    asset_id: str,
+) -> Sequence[AssetReference]:
+    return (
+        session.execute(
+            select(AssetReference)
+            .where(AssetReference.asset_id == asset_id)
+            .order_by(AssetReference.id.asc())
+        )
+        .scalars()
+        .all()
+    )
+
+
+def upsert_reference(
+    session: Session,
+    asset_id: str,
+    file_path: str,
+    name: str,
+    mtime_ns: int,
+    owner_id: str = "",
+) -> tuple[bool, bool]:
+    """Upsert a reference by file_path. Returns (created, updated).
+
+    Also restores references that were previously marked as missing.
+    """
+    now = get_utc_now()
+    vals = {
+        "asset_id": asset_id,
+        "file_path": file_path,
+        "name": name,
+        "owner_id": owner_id,
+        "mtime_ns": int(mtime_ns),
+        "is_missing": False,
+        "created_at": now,
+        "updated_at": now,
+        "last_access_time": now,
+    }
+    ins = (
+        sqlite.insert(AssetReference)
+        .values(**vals)
+        .on_conflict_do_nothing(index_elements=[AssetReference.file_path])
+    )
+    res = session.execute(ins)
+    created = int(res.rowcount or 0) > 0
+
+    if created:
+        return True, False
+
+    upd = (
+        sa.update(AssetReference)
+        .where(AssetReference.file_path == file_path)
+        .where(
+            sa.or_(
+                AssetReference.asset_id != asset_id,
+                AssetReference.mtime_ns.is_(None),
+                AssetReference.mtime_ns != int(mtime_ns),
+                AssetReference.is_missing == True,  # noqa: E712
+                AssetReference.deleted_at.isnot(None),
+            )
+        )
+        .values(
+            asset_id=asset_id, mtime_ns=int(mtime_ns), is_missing=False,
+            deleted_at=None, updated_at=now,
+        )
+    )
+    res2 = session.execute(upd)
+    updated = int(res2.rowcount or 0) > 0
+    return False, updated
+
+
+def mark_references_missing_outside_prefixes(
+    session: Session,
+    valid_prefixes: list[str],
+) -> int:
+    """Mark references as missing when file_path doesn't match any valid prefix.
+
+    Returns number of references marked as missing.
+    """
+    if not valid_prefixes:
+        return 0
+
+    conds = build_prefix_like_conditions(valid_prefixes)
+    matches_valid_prefix = sa.or_(*conds)
+    result = session.execute(
+        sa.update(AssetReference)
+        .where(AssetReference.file_path.isnot(None))
+        .where(AssetReference.deleted_at.is_(None))
+        .where(~matches_valid_prefix)
+        .where(AssetReference.is_missing == False)  # noqa: E712
+        .values(is_missing=True)
+    )
+    return result.rowcount
+
+
+def restore_references_by_paths(session: Session, file_paths: list[str]) -> int:
+    """Restore references that were previously marked as missing.
+
+    Returns number of references restored.
+    """
+    if not file_paths:
+        return 0
+
+    total = 0
+    for chunk in iter_chunks(file_paths, MAX_BIND_PARAMS):
+        result = session.execute(
+            sa.update(AssetReference)
+            .where(AssetReference.file_path.in_(chunk))
+            .where(AssetReference.is_missing == True)  # noqa: E712
+            .where(AssetReference.deleted_at.is_(None))
+            .values(is_missing=False)
+        )
+        total += result.rowcount
+    return total
+
+
+def get_unreferenced_unhashed_asset_ids(session: Session) -> list[str]:
+    """Get IDs of unhashed assets (hash=None) with no active references.
+
+    An asset is considered unreferenced if it has no references,
+    or all its references are marked as missing.
+
+    Returns list of asset IDs that are unreferenced.
+    """
+    active_ref_exists = (
+        sa.select(sa.literal(1))
+        .where(AssetReference.asset_id == Asset.id)
+        .where(AssetReference.is_missing == False)  # noqa: E712
+        .where(AssetReference.deleted_at.is_(None))
+        .correlate(Asset)
+        .exists()
+    )
+    unreferenced_subq = sa.select(Asset.id).where(
+        Asset.hash.is_(None), ~active_ref_exists
+    )
+    return [row[0] for row in session.execute(unreferenced_subq).all()]
+
+
+def delete_assets_by_ids(session: Session, asset_ids: list[str]) -> int:
+    """Delete assets and their references by ID.
+
+    Returns number of assets deleted.
+    """
+    if not asset_ids:
+        return 0
+    total = 0
+    for chunk in iter_chunks(asset_ids, MAX_BIND_PARAMS):
+        session.execute(
+            sa.delete(AssetReference).where(AssetReference.asset_id.in_(chunk))
+        )
+        result = session.execute(sa.delete(Asset).where(Asset.id.in_(chunk)))
+        total += result.rowcount
+    return total
+
+
+def get_references_for_prefixes(
+    session: Session,
+    prefixes: list[str],
+    *,
+    include_missing: bool = False,
+) -> list[CacheStateRow]:
+    """Get all references with file paths matching any of the given prefixes.
+
+    Args:
+        session: Database session
+        prefixes: List of absolute directory prefixes to match
+        include_missing: If False (default), exclude references marked as missing
+
+    Returns:
+        List of cache state rows with joined asset data
+    """
+    if not prefixes:
+        return []
+
+    conds = build_prefix_like_conditions(prefixes)
+
+    query = (
+        sa.select(
+            AssetReference.id,
+            AssetReference.file_path,
+            AssetReference.mtime_ns,
+            AssetReference.needs_verify,
+            AssetReference.asset_id,
+            Asset.hash,
+            Asset.size_bytes,
+        )
+        .join(Asset, Asset.id == AssetReference.asset_id)
+        .where(AssetReference.file_path.isnot(None))
+        .where(AssetReference.deleted_at.is_(None))
+        .where(sa.or_(*conds))
+    )
+
+    if not include_missing:
+        query = query.where(AssetReference.is_missing == False)  # noqa: E712
+
+    rows = session.execute(
+        query.order_by(AssetReference.asset_id.asc(), AssetReference.id.asc())
+    ).all()
+
+    return [
+        CacheStateRow(
+            reference_id=row[0],
+            file_path=row[1],
+            mtime_ns=row[2],
+            needs_verify=row[3],
+            asset_id=row[4],
+            asset_hash=row[5],
+            size_bytes=int(row[6]) if row[6] is not None else None,
+        )
+        for row in rows
+    ]
+
+
+def bulk_update_needs_verify(
+    session: Session, reference_ids: list[str], value: bool
+) -> int:
+    """Set needs_verify flag for multiple references.
+
+    Returns: Number of rows updated
+    """
+    if not reference_ids:
+        return 0
+    total = 0
+    for chunk in iter_chunks(reference_ids, MAX_BIND_PARAMS):
+        result = session.execute(
+            sa.update(AssetReference)
+            .where(AssetReference.id.in_(chunk))
+            .values(needs_verify=value)
+        )
+        total += result.rowcount
+    return total
+
+
+def bulk_update_is_missing(
+    session: Session, reference_ids: list[str], value: bool
+) -> int:
+    """Set is_missing flag for multiple references.
+
+    Returns: Number of rows updated
+    """
+    if not reference_ids:
+        return 0
+    total = 0
+    for chunk in iter_chunks(reference_ids, MAX_BIND_PARAMS):
+        result = session.execute(
+            sa.update(AssetReference)
+            .where(AssetReference.id.in_(chunk))
+            .values(is_missing=value)
+        )
+        total += result.rowcount
+    return total
+
+
+def delete_references_by_ids(session: Session, reference_ids: list[str]) -> int:
+    """Delete references by their IDs.
+
+    Returns: Number of rows deleted
+    """
+    if not reference_ids:
+        return 0
+    total = 0
+    for chunk in iter_chunks(reference_ids, MAX_BIND_PARAMS):
+        result = session.execute(
+            sa.delete(AssetReference).where(AssetReference.id.in_(chunk))
+        )
+        total += result.rowcount
+    return total
+
+
+def delete_orphaned_seed_asset(session: Session, asset_id: str) -> bool:
+    """Delete a seed asset (hash is None) and its references.
+
+    Returns: True if asset was deleted, False if not found or has a hash
+    """
+    asset = session.get(Asset, asset_id)
+    if not asset:
+        return False
+    if asset.hash is not None:
+        return False
+    session.execute(
+        sa.delete(AssetReference).where(AssetReference.asset_id == asset_id)
+    )
+    session.delete(asset)
+    return True
+
+
+class UnenrichedReferenceRow(NamedTuple):
+    """Row for references needing enrichment."""
+
+    reference_id: str
+    asset_id: str
+    file_path: str
+    enrichment_level: int
+
+
+def get_unenriched_references(
+    session: Session,
+    prefixes: list[str],
+    max_level: int = 0,
+    limit: int = 1000,
+) -> list[UnenrichedReferenceRow]:
+    """Get references that need enrichment (enrichment_level <= max_level).
+
+    Args:
+        session: Database session
+        prefixes: List of absolute directory prefixes to scan
+        max_level: Maximum enrichment level to include (0=stubs, 1=metadata done)
+        limit: Maximum number of rows to return
+
+    Returns:
+        List of unenriched reference rows with file paths
+    """
+    if not prefixes:
+        return []
+
+    conds = build_prefix_like_conditions(prefixes)
+
+    query = (
+        sa.select(
+            AssetReference.id,
+            AssetReference.asset_id,
+            AssetReference.file_path,
+            AssetReference.enrichment_level,
+        )
+        .where(AssetReference.file_path.isnot(None))
+        .where(AssetReference.deleted_at.is_(None))
+        .where(sa.or_(*conds))
+        .where(AssetReference.is_missing == False)  # noqa: E712
+        .where(AssetReference.enrichment_level <= max_level)
+        .order_by(AssetReference.id.asc())
+        .limit(limit)
+    )
+
+    rows = session.execute(query).all()
+    return [
+        UnenrichedReferenceRow(
+            reference_id=row[0],
+            asset_id=row[1],
+            file_path=row[2],
+            enrichment_level=row[3],
+        )
+        for row in rows
+    ]
+
+
+def bulk_update_enrichment_level(
+    session: Session,
+    reference_ids: list[str],
+    level: int,
+) -> int:
+    """Update enrichment level for multiple references.
+
+    Returns: Number of rows updated
+    """
+    if not reference_ids:
+        return 0
+    result = session.execute(
+        sa.update(AssetReference)
+        .where(AssetReference.id.in_(reference_ids))
+        .values(enrichment_level=level)
+    )
+    return result.rowcount
+
+
+def bulk_insert_references_ignore_conflicts(
+    session: Session,
+    rows: list[dict],
+) -> None:
+    """Bulk insert reference rows with ON CONFLICT DO NOTHING on file_path.
+
+    Each dict should have: id, asset_id, file_path, name, owner_id, mtime_ns, etc.
+    The is_missing field is automatically set to False for new inserts.
+    """
+    if not rows:
+        return
+    enriched_rows = [{**row, "is_missing": False} for row in rows]
+    ins = sqlite.insert(AssetReference).on_conflict_do_nothing(
+        index_elements=[AssetReference.file_path]
+    )
+    for chunk in iter_chunks(enriched_rows, calculate_rows_per_statement(14)):
+        session.execute(ins, chunk)
+
+
+def get_references_by_paths_and_asset_ids(
+    session: Session,
+    path_to_asset: dict[str, str],
+) -> set[str]:
+    """Query references to find paths where our asset_id won the insert.
+
+    Args:
+        path_to_asset: Mapping of file_path -> asset_id we tried to insert
+
+    Returns:
+        Set of file_paths where our asset_id is present
+    """
+    if not path_to_asset:
+        return set()
+
+    pairs = list(path_to_asset.items())
+    winners: set[str] = set()
+
+    # Each pair uses 2 bind params, so chunk at MAX_BIND_PARAMS // 2
+    for chunk in iter_chunks(pairs, MAX_BIND_PARAMS // 2):
+        pairwise = sa.tuple_(AssetReference.file_path, AssetReference.asset_id).in_(
+            chunk
+        )
+        result = session.execute(
+            select(AssetReference.file_path).where(pairwise)
+        )
+        winners.update(result.scalars().all())
+
+    return winners
+
+
+def get_reference_ids_by_ids(
+    session: Session,
+    reference_ids: list[str],
+) -> set[str]:
+    """Query to find which reference IDs exist in the database."""
+    if not reference_ids:
+        return set()
+
+    found: set[str] = set()
+    for chunk in iter_chunks(reference_ids, MAX_BIND_PARAMS):
+        result = session.execute(
+            select(AssetReference.id).where(AssetReference.id.in_(chunk))
+        )
+        found.update(result.scalars().all())
+    return found
diff --git a/app/assets/database/queries/common.py b/app/assets/database/queries/common.py
new file mode 100644
index 000000000..194c39a1e
--- /dev/null
+++ b/app/assets/database/queries/common.py
@@ -0,0 +1,54 @@
+"""Shared utilities for database query modules."""
+
+import os
+from typing import Iterable
+
+import sqlalchemy as sa
+
+from app.assets.database.models import AssetReference
+from app.assets.helpers import escape_sql_like_string
+
+MAX_BIND_PARAMS = 800
+
+
+def calculate_rows_per_statement(cols: int) -> int:
+    """Calculate how many rows can fit in one statement given column count."""
+    return max(1, MAX_BIND_PARAMS // max(1, cols))
+
+
+def iter_chunks(seq, n: int):
+    """Yield successive n-sized chunks from seq."""
+    for i in range(0, len(seq), n):
+        yield seq[i : i + n]
+
+
+def iter_row_chunks(rows: list[dict], cols_per_row: int) -> Iterable[list[dict]]:
+    """Yield chunks of rows sized to fit within bind param limits."""
+    if not rows:
+        return
+    yield from iter_chunks(rows, calculate_rows_per_statement(cols_per_row))
+
+
+def build_visible_owner_clause(owner_id: str) -> sa.sql.ClauseElement:
+    """Build owner visibility predicate for reads.
+
+    Owner-less rows are visible to everyone.
+    """
+    owner_id = (owner_id or "").strip()
+    if owner_id == "":
+        return AssetReference.owner_id == ""
+    return AssetReference.owner_id.in_(["", owner_id])
+
+
+def build_prefix_like_conditions(
+    prefixes: list[str],
+) -> list[sa.sql.ColumnElement]:
+    """Build LIKE conditions for matching file paths under directory prefixes."""
+    conds = []
+    for p in prefixes:
+        base = os.path.abspath(p)
+        if not base.endswith(os.sep):
+            base += os.sep
+        escaped, esc = escape_sql_like_string(base)
+        conds.append(AssetReference.file_path.like(escaped + "%", escape=esc))
+    return conds
diff --git a/app/assets/database/queries/tags.py b/app/assets/database/queries/tags.py
new file mode 100644
index 000000000..8b25fee67
--- /dev/null
+++ b/app/assets/database/queries/tags.py
@@ -0,0 +1,356 @@
+from dataclasses import dataclass
+from typing import Iterable, Sequence
+
+import sqlalchemy as sa
+from sqlalchemy import delete, func, select
+from sqlalchemy.dialects import sqlite
+from sqlalchemy.exc import IntegrityError
+from sqlalchemy.orm import Session
+
+from app.assets.database.models import (
+    AssetReference,
+    AssetReferenceMeta,
+    AssetReferenceTag,
+    Tag,
+)
+from app.assets.database.queries.common import (
+    build_visible_owner_clause,
+    iter_row_chunks,
+)
+from app.assets.helpers import escape_sql_like_string, get_utc_now, normalize_tags
+
+
+@dataclass(frozen=True)
+class AddTagsResult:
+    added: list[str]
+    already_present: list[str]
+    total_tags: list[str]
+
+
+@dataclass(frozen=True)
+class RemoveTagsResult:
+    removed: list[str]
+    not_present: list[str]
+    total_tags: list[str]
+
+
+@dataclass(frozen=True)
+class SetTagsResult:
+    added: list[str]
+    removed: list[str]
+    total: list[str]
+
+
+def validate_tags_exist(session: Session, tags: list[str]) -> None:
+    """Raise ValueError if any of the given tag names do not exist."""
+    existing_tag_names = set(
+        name
+        for (name,) in session.execute(select(Tag.name).where(Tag.name.in_(tags))).all()
+    )
+    missing = [t for t in tags if t not in existing_tag_names]
+    if missing:
+        raise ValueError(f"Unknown tags: {missing}")
+
+
+def ensure_tags_exist(
+    session: Session, names: Iterable[str], tag_type: str = "user"
+) -> None:
+    wanted = normalize_tags(list(names))
+    if not wanted:
+        return
+    rows = [{"name": n, "tag_type": tag_type} for n in list(dict.fromkeys(wanted))]
+    ins = (
+        sqlite.insert(Tag)
+        .values(rows)
+        .on_conflict_do_nothing(index_elements=[Tag.name])
+    )
+    session.execute(ins)
+
+
+def get_reference_tags(session: Session, reference_id: str) -> list[str]:
+    return [
+        tag_name
+        for (tag_name,) in (
+            session.execute(
+                select(AssetReferenceTag.tag_name).where(
+                    AssetReferenceTag.asset_reference_id == reference_id
+                )
+            )
+        ).all()
+    ]
+
+
+def set_reference_tags(
+    session: Session,
+    reference_id: str,
+    tags: Sequence[str],
+    origin: str = "manual",
+) -> SetTagsResult:
+    desired = normalize_tags(tags)
+
+    current = set(get_reference_tags(session, reference_id))
+
+    to_add = [t for t in desired if t not in current]
+    to_remove = [t for t in current if t not in desired]
+
+    if to_add:
+        ensure_tags_exist(session, to_add, tag_type="user")
+        session.add_all(
+            [
+                AssetReferenceTag(
+                    asset_reference_id=reference_id,
+                    tag_name=t,
+                    origin=origin,
+                    added_at=get_utc_now(),
+                )
+                for t in to_add
+            ]
+        )
+        session.flush()
+
+    if to_remove:
+        session.execute(
+            delete(AssetReferenceTag).where(
+                AssetReferenceTag.asset_reference_id == reference_id,
+                AssetReferenceTag.tag_name.in_(to_remove),
+            )
+        )
+        session.flush()
+
+    return SetTagsResult(added=to_add, removed=to_remove, total=desired)
+
+
+def add_tags_to_reference(
+    session: Session,
+    reference_id: str,
+    tags: Sequence[str],
+    origin: str = "manual",
+    create_if_missing: bool = True,
+    reference_row: AssetReference | None = None,
+) -> AddTagsResult:
+    if not reference_row:
+        ref = session.get(AssetReference, reference_id)
+        if not ref:
+            raise ValueError(f"AssetReference {reference_id} not found")
+
+    norm = normalize_tags(tags)
+    if not norm:
+        total = get_reference_tags(session, reference_id=reference_id)
+        return AddTagsResult(added=[], already_present=[], total_tags=total)
+
+    if create_if_missing:
+        ensure_tags_exist(session, norm, tag_type="user")
+
+    current = set(get_reference_tags(session, reference_id))
+
+    want = set(norm)
+    to_add = sorted(want - current)
+
+    if to_add:
+        with session.begin_nested() as nested:
+            try:
+                session.add_all(
+                    [
+                        AssetReferenceTag(
+                            asset_reference_id=reference_id,
+                            tag_name=t,
+                            origin=origin,
+                            added_at=get_utc_now(),
+                        )
+                        for t in to_add
+                    ]
+                )
+                session.flush()
+            except IntegrityError:
+                nested.rollback()
+
+    after = set(get_reference_tags(session, reference_id=reference_id))
+    return AddTagsResult(
+        added=sorted(((after - current) & want)),
+        already_present=sorted(want & current),
+        total_tags=sorted(after),
+    )
+
+
+def remove_tags_from_reference(
+    session: Session,
+    reference_id: str,
+    tags: Sequence[str],
+) -> RemoveTagsResult:
+    ref = session.get(AssetReference, reference_id)
+    if not ref:
+        raise ValueError(f"AssetReference {reference_id} not found")
+
+    norm = normalize_tags(tags)
+    if not norm:
+        total = get_reference_tags(session, reference_id=reference_id)
+        return RemoveTagsResult(removed=[], not_present=[], total_tags=total)
+
+    existing = set(get_reference_tags(session, reference_id))
+
+    to_remove = sorted(set(t for t in norm if t in existing))
+    not_present = sorted(set(t for t in norm if t not in existing))
+
+    if to_remove:
+        session.execute(
+            delete(AssetReferenceTag).where(
+                AssetReferenceTag.asset_reference_id == reference_id,
+                AssetReferenceTag.tag_name.in_(to_remove),
+            )
+        )
+        session.flush()
+
+    total = get_reference_tags(session, reference_id=reference_id)
+    return RemoveTagsResult(removed=to_remove, not_present=not_present, total_tags=total)
+
+
+def add_missing_tag_for_asset_id(
+    session: Session,
+    asset_id: str,
+    origin: str = "automatic",
+) -> None:
+    select_rows = (
+        sa.select(
+            AssetReference.id.label("asset_reference_id"),
+            sa.literal("missing").label("tag_name"),
+            sa.literal(origin).label("origin"),
+            sa.literal(get_utc_now()).label("added_at"),
+        )
+        .where(AssetReference.asset_id == asset_id)
+        .where(
+            sa.not_(
+                sa.exists().where(
+                    (AssetReferenceTag.asset_reference_id == AssetReference.id)
+                    & (AssetReferenceTag.tag_name == "missing")
+                )
+            )
+        )
+    )
+    session.execute(
+        sqlite.insert(AssetReferenceTag)
+        .from_select(
+            ["asset_reference_id", "tag_name", "origin", "added_at"],
+            select_rows,
+        )
+        .on_conflict_do_nothing(
+            index_elements=[
+                AssetReferenceTag.asset_reference_id,
+                AssetReferenceTag.tag_name,
+            ]
+        )
+    )
+
+
+def remove_missing_tag_for_asset_id(
+    session: Session,
+    asset_id: str,
+) -> None:
+    session.execute(
+        sa.delete(AssetReferenceTag).where(
+            AssetReferenceTag.asset_reference_id.in_(
+                sa.select(AssetReference.id).where(AssetReference.asset_id == asset_id)
+            ),
+            AssetReferenceTag.tag_name == "missing",
+        )
+    )
+
+
+def list_tags_with_usage(
+    session: Session,
+    prefix: str | None = None,
+    limit: int = 100,
+    offset: int = 0,
+    include_zero: bool = True,
+    order: str = "count_desc",
+    owner_id: str = "",
+) -> tuple[list[tuple[str, str, int]], int]:
+    counts_sq = (
+        select(
+            AssetReferenceTag.tag_name.label("tag_name"),
+            func.count(AssetReferenceTag.asset_reference_id).label("cnt"),
+        )
+        .select_from(AssetReferenceTag)
+        .join(AssetReference, AssetReference.id == AssetReferenceTag.asset_reference_id)
+        .where(build_visible_owner_clause(owner_id))
+        .where(AssetReference.deleted_at.is_(None))
+        .group_by(AssetReferenceTag.tag_name)
+        .subquery()
+    )
+
+    q = (
+        select(
+            Tag.name,
+            Tag.tag_type,
+            func.coalesce(counts_sq.c.cnt, 0).label("count"),
+        )
+        .select_from(Tag)
+        .join(counts_sq, counts_sq.c.tag_name == Tag.name, isouter=True)
+    )
+
+    if prefix:
+        escaped, esc = escape_sql_like_string(prefix.strip().lower())
+        q = q.where(Tag.name.like(escaped + "%", escape=esc))
+
+    if not include_zero:
+        q = q.where(func.coalesce(counts_sq.c.cnt, 0) > 0)
+
+    if order == "name_asc":
+        q = q.order_by(Tag.name.asc())
+    else:
+        q = q.order_by(func.coalesce(counts_sq.c.cnt, 0).desc(), Tag.name.asc())
+
+    total_q = select(func.count()).select_from(Tag)
+    if prefix:
+        escaped, esc = escape_sql_like_string(prefix.strip().lower())
+        total_q = total_q.where(Tag.name.like(escaped + "%", escape=esc))
+    if not include_zero:
+        visible_tags_sq = (
+            select(AssetReferenceTag.tag_name)
+            .join(AssetReference, AssetReference.id == AssetReferenceTag.asset_reference_id)
+            .where(build_visible_owner_clause(owner_id))
+            .where(AssetReference.deleted_at.is_(None))
+            .group_by(AssetReferenceTag.tag_name)
+        )
+        total_q = total_q.where(Tag.name.in_(visible_tags_sq))
+
+    rows = (session.execute(q.limit(limit).offset(offset))).all()
+    total = (session.execute(total_q)).scalar_one()
+
+    rows_norm = [(name, ttype, int(count or 0)) for (name, ttype, count) in rows]
+    return rows_norm, int(total or 0)
+
+
+def bulk_insert_tags_and_meta(
+    session: Session,
+    tag_rows: list[dict],
+    meta_rows: list[dict],
+) -> None:
+    """Batch insert into asset_reference_tags and asset_reference_meta.
+
+    Uses ON CONFLICT DO NOTHING.
+
+    Args:
+        session: Database session
+        tag_rows: Dicts with: asset_reference_id, tag_name, origin, added_at
+        meta_rows: Dicts with: asset_reference_id, key, ordinal, val_*
+    """
+    if tag_rows:
+        ins_tags = sqlite.insert(AssetReferenceTag).on_conflict_do_nothing(
+            index_elements=[
+                AssetReferenceTag.asset_reference_id,
+                AssetReferenceTag.tag_name,
+            ]
+        )
+        for chunk in iter_row_chunks(tag_rows, cols_per_row=4):
+            session.execute(ins_tags, chunk)
+
+    if meta_rows:
+        ins_meta = sqlite.insert(AssetReferenceMeta).on_conflict_do_nothing(
+            index_elements=[
+                AssetReferenceMeta.asset_reference_id,
+                AssetReferenceMeta.key,
+                AssetReferenceMeta.ordinal,
+            ]
+        )
+        for chunk in iter_row_chunks(meta_rows, cols_per_row=7):
+            session.execute(ins_meta, chunk)
diff --git a/app/assets/database/tags.py b/app/assets/database/tags.py
deleted file mode 100644
index 3ab6497c2..000000000
--- a/app/assets/database/tags.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from typing import Iterable
-
-import sqlalchemy
-from sqlalchemy.orm import Session
-from sqlalchemy.dialects import sqlite
-
-from app.assets.helpers import normalize_tags, utcnow
-from app.assets.database.models import Tag, AssetInfoTag, AssetInfo
-
-
-def ensure_tags_exist(session: Session, names: Iterable[str], tag_type: str = "user") -> None:
-    wanted = normalize_tags(list(names))
-    if not wanted:
-        return
-    rows = [{"name": n, "tag_type": tag_type} for n in list(dict.fromkeys(wanted))]
-    ins = (
-            sqlite.insert(Tag)
-            .values(rows)
-            .on_conflict_do_nothing(index_elements=[Tag.name])
-        )
-    return session.execute(ins)
-
-def add_missing_tag_for_asset_id(
-    session: Session,
-    *,
-    asset_id: str,
-    origin: str = "automatic",
-) -> None:
-    select_rows = (
-        sqlalchemy.select(
-            AssetInfo.id.label("asset_info_id"),
-            sqlalchemy.literal("missing").label("tag_name"),
-            sqlalchemy.literal(origin).label("origin"),
-            sqlalchemy.literal(utcnow()).label("added_at"),
-        )
-        .where(AssetInfo.asset_id == asset_id)
-        .where(
-            sqlalchemy.not_(
-                sqlalchemy.exists().where((AssetInfoTag.asset_info_id == AssetInfo.id) & (AssetInfoTag.tag_name == "missing"))
-            )
-        )
-    )
-    session.execute(
-        sqlite.insert(AssetInfoTag)
-        .from_select(
-            ["asset_info_id", "tag_name", "origin", "added_at"],
-            select_rows,
-        )
-        .on_conflict_do_nothing(index_elements=[AssetInfoTag.asset_info_id, AssetInfoTag.tag_name])
-    )
-
-def remove_missing_tag_for_asset_id(
-    session: Session,
-    *,
-    asset_id: str,
-) -> None:
-    session.execute(
-        sqlalchemy.delete(AssetInfoTag).where(
-            AssetInfoTag.asset_info_id.in_(sqlalchemy.select(AssetInfo.id).where(AssetInfo.asset_id == asset_id)),
-            AssetInfoTag.tag_name == "missing",
-        )
-    )
diff --git a/app/assets/hashing.py b/app/assets/hashing.py
deleted file mode 100644
index 4b72084b9..000000000
--- a/app/assets/hashing.py
+++ /dev/null
@@ -1,75 +0,0 @@
-from blake3 import blake3
-from typing import IO
-import os
-import asyncio
-
-
-DEFAULT_CHUNK = 8 * 1024 *1024 # 8MB
-
-# NOTE: this allows hashing different representations of a file-like object
-def blake3_hash(
-    fp: str | IO[bytes],
-    chunk_size: int = DEFAULT_CHUNK,
-) -> str:
-    """
-    Returns a BLAKE3 hex digest for ``fp``, which may be:
-      - a filename (str/bytes) or PathLike
-      - an open binary file object
-    If ``fp`` is a file object, it must be opened in **binary** mode and support
-    ``read``, ``seek``, and ``tell``. The function will seek to the start before
-    reading and will attempt to restore the original position afterward.
-    """
-    # duck typing to check if input is a file-like object
-    if hasattr(fp, "read"):
-        return _hash_file_obj(fp, chunk_size)
-
-    with open(os.fspath(fp), "rb") as f:
-        return _hash_file_obj(f, chunk_size)
-
-
-async def blake3_hash_async(
-    fp: str | IO[bytes],
-    chunk_size: int = DEFAULT_CHUNK,
-) -> str:
-    """Async wrapper for ``blake3_hash_sync``.
-    Uses a worker thread so the event loop remains responsive.
-    """
-    # If it is a path, open inside the worker thread to keep I/O off the loop.
-    if hasattr(fp, "read"):
-        return await asyncio.to_thread(blake3_hash, fp, chunk_size)
-
-    def _worker() -> str:
-        with open(os.fspath(fp), "rb") as f:
-            return _hash_file_obj(f, chunk_size)
-
-    return await asyncio.to_thread(_worker)
-
-
-def _hash_file_obj(file_obj: IO, chunk_size: int = DEFAULT_CHUNK) -> str:
-    """
-    Hash an already-open binary file object by streaming in chunks.
-    - Seeks to the beginning before reading (if supported).
-    - Restores the original position afterward (if tell/seek are supported).
-    """
-    if chunk_size <= 0:
-        chunk_size = DEFAULT_CHUNK
-
-    # in case file object is already open and not at the beginning, track so can be restored after hashing
-    orig_pos = file_obj.tell()
-
-    try:
-        # seek to the beginning before reading
-        if orig_pos != 0:
-            file_obj.seek(0)
-
-        h = blake3()
-        while True:
-            chunk = file_obj.read(chunk_size)
-            if not chunk:
-                break
-            h.update(chunk)
-        return h.hexdigest()
-    finally:
-        # restore original position in file object, if needed
-        if orig_pos != 0:
-            file_obj.seek(orig_pos)
diff --git a/app/assets/helpers.py b/app/assets/helpers.py
index 5030b123a..3798f3933 100644
--- a/app/assets/helpers.py
+++ b/app/assets/helpers.py
@@ -1,226 +1,42 @@
-import contextlib
 import os
-from decimal import Decimal
-from aiohttp import web
 from datetime import datetime, timezone
-from pathlib import Path
-from typing import Literal, Any
-
-import folder_paths
+from typing import Sequence
 
 
-RootType = Literal["models", "input", "output"]
-ALLOWED_ROOTS: tuple[RootType, ...] = ("models", "input", "output")
-
-def get_query_dict(request: web.Request) -> dict[str, Any]:
+def select_best_live_path(states: Sequence) -> str:
     """
-    Gets a dictionary of query parameters from the request.
-
-    'request.query' is a MultiMapping[str], needs to be converted to a dictionary to be validated by Pydantic.
+    Return the best on-disk path among cache states:
+      1) Prefer a path that exists with needs_verify == False (already verified).
+      2) Otherwise, pick the first path that exists.
+      3) Otherwise return empty string.
     """
-    query_dict = {
-        key: request.query.getall(key) if len(request.query.getall(key)) > 1 else request.query.get(key)
-        for key in request.query.keys()
-    }
-    return query_dict
+    alive = [
+        s
+        for s in states
+        if getattr(s, "file_path", None) and os.path.isfile(s.file_path)
+    ]
+    if not alive:
+        return ""
+    for s in alive:
+        if not getattr(s, "needs_verify", False):
+            return s.file_path
+    return alive[0].file_path
 
-def list_tree(base_dir: str) -> list[str]:
-    out: list[str] = []
-    base_abs = os.path.abspath(base_dir)
-    if not os.path.isdir(base_abs):
-        return out
-    for dirpath, _subdirs, filenames in os.walk(base_abs, topdown=True, followlinks=False):
-        for name in filenames:
-            out.append(os.path.abspath(os.path.join(dirpath, name)))
-    return out
 
-def prefixes_for_root(root: RootType) -> list[str]:
-    if root == "models":
-        bases: list[str] = []
-        for _bucket, paths in get_comfy_models_folders():
-            bases.extend(paths)
-        return [os.path.abspath(p) for p in bases]
-    if root == "input":
-        return [os.path.abspath(folder_paths.get_input_directory())]
-    if root == "output":
-        return [os.path.abspath(folder_paths.get_output_directory())]
-    return []
+def escape_sql_like_string(s: str, escape: str = "!") -> tuple[str, str]:
+    """Escapes %, _ and the escape char in a LIKE prefix.
 
-def escape_like_prefix(s: str, escape: str = "!") -> tuple[str, str]:
-    """Escapes %, _ and the escape char itself in a LIKE prefix.
-    Returns (escaped_prefix, escape_char). Caller should append '%' and pass escape=escape_char to .like().
+    Returns (escaped_prefix, escape_char).
     """
     s = s.replace(escape, escape + escape)  # escape the escape char first
     s = s.replace("%", escape + "%").replace("_", escape + "_")  # escape LIKE wildcards
     return s, escape
 
-def fast_asset_file_check(
-    *,
-    mtime_db: int | None,
-    size_db: int | None,
-    stat_result: os.stat_result,
-) -> bool:
-    if mtime_db is None:
-        return False
-    actual_mtime_ns = getattr(stat_result, "st_mtime_ns", int(stat_result.st_mtime * 1_000_000_000))
-    if int(mtime_db) != int(actual_mtime_ns):
-        return False
-    sz = int(size_db or 0)
-    if sz > 0:
-        return int(stat_result.st_size) == sz
-    return True
 
-def utcnow() -> datetime:
+def get_utc_now() -> datetime:
     """Naive UTC timestamp (no tzinfo). We always treat DB datetimes as UTC."""
     return datetime.now(timezone.utc).replace(tzinfo=None)
 
-def get_comfy_models_folders() -> list[tuple[str, list[str]]]:
-    """Build a list of (folder_name, base_paths[]) categories that are configured for model locations.
-
-    We trust `folder_paths.folder_names_and_paths` and include a category if
-    *any* of its base paths lies under the Comfy `models_dir`.
-    """
-    targets: list[tuple[str, list[str]]] = []
-    models_root = os.path.abspath(folder_paths.models_dir)
-    for name, values in folder_paths.folder_names_and_paths.items():
-        paths, _exts = values[0], values[1]  # NOTE: this prevents nodepacks that hackily edit folder_... from breaking ComfyUI
-        if any(os.path.abspath(p).startswith(models_root + os.sep) for p in paths):
-            targets.append((name, paths))
-    return targets
-
-def resolve_destination_from_tags(tags: list[str]) -> tuple[str, list[str]]:
-    """Validates and maps tags -> (base_dir, subdirs_for_fs)"""
-    root = tags[0]
-    if root == "models":
-        if len(tags) < 2:
-            raise ValueError("at least two tags required for model asset")
-        try:
-            bases = folder_paths.folder_names_and_paths[tags[1]][0]
-        except KeyError:
-            raise ValueError(f"unknown model category '{tags[1]}'")
-        if not bases:
-            raise ValueError(f"no base path configured for category '{tags[1]}'")
-        base_dir = os.path.abspath(bases[0])
-        raw_subdirs = tags[2:]
-    else:
-        base_dir = os.path.abspath(
-            folder_paths.get_input_directory() if root == "input" else folder_paths.get_output_directory()
-        )
-        raw_subdirs = tags[1:]
-    for i in raw_subdirs:
-        if i in (".", ".."):
-            raise ValueError("invalid path component in tags")
-
-    return base_dir, raw_subdirs if raw_subdirs else []
-
-def ensure_within_base(candidate: str, base: str) -> None:
-    cand_abs = os.path.abspath(candidate)
-    base_abs = os.path.abspath(base)
-    try:
-        if os.path.commonpath([cand_abs, base_abs]) != base_abs:
-            raise ValueError("destination escapes base directory")
-    except Exception:
-        raise ValueError("invalid destination path")
-
-def compute_relative_filename(file_path: str) -> str | None:
-    """
-    Return the model's path relative to the last well-known folder (the model category),
-    using forward slashes, eg:
-      /.../models/checkpoints/flux/123/flux.safetensors -> "flux/123/flux.safetensors"
-      /.../models/text_encoders/clip_g.safetensors -> "clip_g.safetensors"
-
-    For non-model paths, returns None.
-    NOTE: this is a temporary helper, used only for initializing metadata["filename"] field.
-    """
-    try:
-        root_category, rel_path = get_relative_to_root_category_path_of_asset(file_path)
-    except ValueError:
-        return None
-
-    p = Path(rel_path)
-    parts = [seg for seg in p.parts if seg not in (".", "..", p.anchor)]
-    if not parts:
-        return None
-
-    if root_category == "models":
-        # parts[0] is the category ("checkpoints", "vae", etc) – drop it
-        inside = parts[1:] if len(parts) > 1 else [parts[0]]
-        return "/".join(inside)
-    return "/".join(parts)  # input/output: keep all parts
-
-def get_relative_to_root_category_path_of_asset(file_path: str) -> tuple[Literal["input", "output", "models"], str]:
-    """Given an absolute or relative file path, determine which root category the path belongs to:
-      - 'input' if the file resides under `folder_paths.get_input_directory()`
-      - 'output' if the file resides under `folder_paths.get_output_directory()`
-      - 'models' if the file resides under any base path of categories returned by `get_comfy_models_folders()`
-
-    Returns:
-        (root_category, relative_path_inside_that_root)
-        For 'models', the relative path is prefixed with the category name:
-            e.g. ('models', 'vae/test/sub/ae.safetensors')
-
-    Raises:
-        ValueError: if the path does not belong to input, output, or configured model bases.
-    """
-    fp_abs = os.path.abspath(file_path)
-
-    def _is_within(child: str, parent: str) -> bool:
-        try:
-            return os.path.commonpath([child, parent]) == parent
-        except Exception:
-            return False
-
-    def _rel(child: str, parent: str) -> str:
-        return os.path.relpath(os.path.join(os.sep, os.path.relpath(child, parent)), os.sep)
-
-    # 1) input
-    input_base = os.path.abspath(folder_paths.get_input_directory())
-    if _is_within(fp_abs, input_base):
-        return "input", _rel(fp_abs, input_base)
-
-    # 2) output
-    output_base = os.path.abspath(folder_paths.get_output_directory())
-    if _is_within(fp_abs, output_base):
-        return "output", _rel(fp_abs, output_base)
-
-    # 3) models (check deepest matching base to avoid ambiguity)
-    best: tuple[int, str, str] | None = None  # (base_len, bucket, rel_inside_bucket)
-    for bucket, bases in get_comfy_models_folders():
-        for b in bases:
-            base_abs = os.path.abspath(b)
-            if not _is_within(fp_abs, base_abs):
-                continue
-            cand = (len(base_abs), bucket, _rel(fp_abs, base_abs))
-            if best is None or cand[0] > best[0]:
-                best = cand
-
-    if best is not None:
-        _, bucket, rel_inside = best
-        combined = os.path.join(bucket, rel_inside)
-        return "models", os.path.relpath(os.path.join(os.sep, combined), os.sep)
-
-    raise ValueError(f"Path is not within input, output, or configured model bases: {file_path}")
-
-def get_name_and_tags_from_asset_path(file_path: str) -> tuple[str, list[str]]:
-    """Return a tuple (name, tags) derived from a filesystem path.
-
-    Semantics:
-      - Root category is determined by `get_relative_to_root_category_path_of_asset`.
-      - The returned `name` is the base filename with extension from the relative path.
-      - The returned `tags` are:
-            [root_category] + parent folders of the relative path (in order)
-        For 'models', this means:
-            file '/.../ModelsDir/vae/test_tag/ae.safetensors'
-            -> root_category='models', some_path='vae/test_tag/ae.safetensors'
-            -> name='ae.safetensors', tags=['models', 'vae', 'test_tag']
-
-    Raises:
-        ValueError: if the path does not belong to input, output, or configured model bases.
-    """
-    root_category, some_path = get_relative_to_root_category_path_of_asset(file_path)
-    p = Path(some_path)
-    parent_parts = [part for part in p.parent.parts if part not in (".", "..", p.anchor)]
-    return p.name, list(dict.fromkeys(normalize_tags([root_category, *parent_parts])))
 
 def normalize_tags(tags: list[str] | None) -> list[str]:
     """
@@ -228,85 +44,22 @@ def normalize_tags(tags: list[str] | None) -> list[str]:
       - Stripping whitespace and converting to lowercase.
       - Removing duplicates.
     """
-    return [t.strip().lower() for t in (tags or []) if (t or "").strip()]
+    return list(dict.fromkeys(t.strip().lower() for t in (tags or []) if (t or "").strip()))
 
-def collect_models_files() -> list[str]:
-    out: list[str] = []
-    for folder_name, bases in get_comfy_models_folders():
-        rel_files = folder_paths.get_filename_list(folder_name) or []
-        for rel_path in rel_files:
-            abs_path = folder_paths.get_full_path(folder_name, rel_path)
-            if not abs_path:
-                continue
-            abs_path = os.path.abspath(abs_path)
-            allowed = False
-            for b in bases:
-                base_abs = os.path.abspath(b)
-                with contextlib.suppress(Exception):
-                    if os.path.commonpath([abs_path, base_abs]) == base_abs:
-                        allowed = True
-                        break
-            if allowed:
-                out.append(abs_path)
-    return out
 
-def is_scalar(v):
-    if v is None:
-        return True
-    if isinstance(v, bool):
-        return True
-    if isinstance(v, (int, float, Decimal, str)):
-        return True
-    return False
+def validate_blake3_hash(s: str) -> str:
+    """Validate and normalize a blake3 hash string.
 
-def project_kv(key: str, value):
+    Returns canonical 'blake3:<hex>' or raises ValueError.
     """
-    Turn a metadata key/value into typed projection rows.
-    Returns list[dict] with keys:
-      key, ordinal, and one of val_str / val_num / val_bool / val_json (others None)
-    """
-    rows: list[dict] = []
-
-    def _null_row(ordinal: int) -> dict:
-        return {
-            "key": key, "ordinal": ordinal,
-            "val_str": None, "val_num": None, "val_bool": None, "val_json": None
-        }
-
-    if value is None:
-        rows.append(_null_row(0))
-        return rows
-
-    if is_scalar(value):
-        if isinstance(value, bool):
-            rows.append({"key": key, "ordinal": 0, "val_bool": bool(value)})
-        elif isinstance(value, (int, float, Decimal)):
-            num = value if isinstance(value, Decimal) else Decimal(str(value))
-            rows.append({"key": key, "ordinal": 0, "val_num": num})
-        elif isinstance(value, str):
-            rows.append({"key": key, "ordinal": 0, "val_str": value})
-        else:
-            rows.append({"key": key, "ordinal": 0, "val_json": value})
-        return rows
-
-    if isinstance(value, list):
-        if all(is_scalar(x) for x in value):
-            for i, x in enumerate(value):
-                if x is None:
-                    rows.append(_null_row(i))
-                elif isinstance(x, bool):
-                    rows.append({"key": key, "ordinal": i, "val_bool": bool(x)})
-                elif isinstance(x, (int, float, Decimal)):
-                    num = x if isinstance(x, Decimal) else Decimal(str(x))
-                    rows.append({"key": key, "ordinal": i, "val_num": num})
-                elif isinstance(x, str):
-                    rows.append({"key": key, "ordinal": i, "val_str": x})
-                else:
-                    rows.append({"key": key, "ordinal": i, "val_json": x})
-            return rows
-        for i, x in enumerate(value):
-            rows.append({"key": key, "ordinal": i, "val_json": x})
-        return rows
-
-    rows.append({"key": key, "ordinal": 0, "val_json": value})
-    return rows
+    s = s.strip().lower()
+    if not s or ":" not in s:
+        raise ValueError("hash must be 'blake3:<hex>'")
+    algo, digest = s.split(":", 1)
+    if (
+        algo != "blake3"
+        or len(digest) != 64
+        or any(c for c in digest if c not in "0123456789abcdef")
+    ):
+        raise ValueError("hash must be 'blake3:<hex>'")
+    return f"{algo}:{digest}"
diff --git a/app/assets/manager.py b/app/assets/manager.py
deleted file mode 100644
index a68c8c8ae..000000000
--- a/app/assets/manager.py
+++ /dev/null
@@ -1,516 +0,0 @@
-import os
-import mimetypes
-import contextlib
-from typing import Sequence
-
-from app.database.db import create_session
-from app.assets.api import schemas_out, schemas_in
-from app.assets.database.queries import (
-    asset_exists_by_hash,
-    asset_info_exists_for_asset_id,
-    get_asset_by_hash,
-    get_asset_info_by_id,
-    fetch_asset_info_asset_and_tags,
-    fetch_asset_info_and_asset,
-    create_asset_info_for_existing_asset,
-    touch_asset_info_by_id,
-    update_asset_info_full,
-    delete_asset_info_by_id,
-    list_cache_states_by_asset_id,
-    list_asset_infos_page,
-    list_tags_with_usage,
-    get_asset_tags,
-    add_tags_to_asset_info,
-    remove_tags_from_asset_info,
-    pick_best_live_path,
-    ingest_fs_asset,
-    set_asset_info_preview,
-)
-from app.assets.helpers import resolve_destination_from_tags, ensure_within_base
-from app.assets.database.models import Asset
-
-
-def _safe_sort_field(requested: str | None) -> str:
-    if not requested:
-        return "created_at"
-    v = requested.lower()
-    if v in {"name", "created_at", "updated_at", "size", "last_access_time"}:
-        return v
-    return "created_at"
-
-
-def _get_size_mtime_ns(path: str) -> tuple[int, int]:
-    st = os.stat(path, follow_symlinks=True)
-    return st.st_size, getattr(st, "st_mtime_ns", int(st.st_mtime * 1_000_000_000))
-
-
-def _safe_filename(name: str | None, fallback: str) -> str:
-    n = os.path.basename((name or "").strip() or fallback)
-    if n:
-        return n
-    return fallback
-
-
-def asset_exists(*, asset_hash: str) -> bool:
-    """
-    Check if an asset with a given hash exists in database.
-    """
-    with create_session() as session:
-        return asset_exists_by_hash(session, asset_hash=asset_hash)
-
-
-def list_assets(
-    *,
-    include_tags: Sequence[str] | None = None,
-    exclude_tags: Sequence[str] | None = None,
-    name_contains: str | None = None,
-    metadata_filter: dict | None = None,
-    limit: int = 20,
-    offset: int = 0,
-    sort: str = "created_at",
-    order: str = "desc",
-    owner_id: str = "",
-) -> schemas_out.AssetsList:
-    sort = _safe_sort_field(sort)
-    order = "desc" if (order or "desc").lower() not in {"asc", "desc"} else order.lower()
-
-    with create_session() as session:
-        infos, tag_map, total = list_asset_infos_page(
-            session,
-            owner_id=owner_id,
-            include_tags=include_tags,
-            exclude_tags=exclude_tags,
-            name_contains=name_contains,
-            metadata_filter=metadata_filter,
-            limit=limit,
-            offset=offset,
-            sort=sort,
-            order=order,
-        )
-
-    summaries: list[schemas_out.AssetSummary] = []
-    for info in infos:
-        asset = info.asset
-        tags = tag_map.get(info.id, [])
-        summaries.append(
-            schemas_out.AssetSummary(
-                id=info.id,
-                name=info.name,
-                asset_hash=asset.hash if asset else None,
-                size=int(asset.size_bytes) if asset else None,
-                mime_type=asset.mime_type if asset else None,
-                tags=tags,
-                created_at=info.created_at,
-                updated_at=info.updated_at,
-                last_access_time=info.last_access_time,
-            )
-        )
-
-    return schemas_out.AssetsList(
-        assets=summaries,
-        total=total,
-        has_more=(offset + len(summaries)) < total,
-    )
-
-
-def get_asset(
-    *,
-    asset_info_id: str,
-    owner_id: str = "",
-) -> schemas_out.AssetDetail:
-    with create_session() as session:
-        res = fetch_asset_info_asset_and_tags(session, asset_info_id=asset_info_id, owner_id=owner_id)
-        if not res:
-            raise ValueError(f"AssetInfo {asset_info_id} not found")
-        info, asset, tag_names = res
-        preview_id = info.preview_id
-
-    return schemas_out.AssetDetail(
-        id=info.id,
-        name=info.name,
-        asset_hash=asset.hash if asset else None,
-        size=int(asset.size_bytes) if asset and asset.size_bytes is not None else None,
-        mime_type=asset.mime_type if asset else None,
-        tags=tag_names,
-        user_metadata=info.user_metadata or {},
-        preview_id=preview_id,
-        created_at=info.created_at,
-        last_access_time=info.last_access_time,
-    )
-
-
-def resolve_asset_content_for_download(
-    *,
-    asset_info_id: str,
-    owner_id: str = "",
-) -> tuple[str, str, str]:
-    with create_session() as session:
-        pair = fetch_asset_info_and_asset(session, asset_info_id=asset_info_id, owner_id=owner_id)
-        if not pair:
-            raise ValueError(f"AssetInfo {asset_info_id} not found")
-
-        info, asset = pair
-        states = list_cache_states_by_asset_id(session, asset_id=asset.id)
-        abs_path = pick_best_live_path(states)
-        if not abs_path:
-            raise FileNotFoundError
-
-        touch_asset_info_by_id(session, asset_info_id=asset_info_id)
-        session.commit()
-
-        ctype = asset.mime_type or mimetypes.guess_type(info.name or abs_path)[0] or "application/octet-stream"
-        download_name = info.name or os.path.basename(abs_path)
-        return abs_path, ctype, download_name
-
-
-def upload_asset_from_temp_path(
-    spec: schemas_in.UploadAssetSpec,
-    *,
-    temp_path: str,
-    client_filename: str | None = None,
-    owner_id: str = "",
-    expected_asset_hash: str | None = None,
-) -> schemas_out.AssetCreated:
-    """
-    Create new asset or update existing asset from a temporary file path.
-    """
-    try:
-        # NOTE: blake3 is not required right now, so this will fail if blake3 is not installed in local environment
-        import app.assets.hashing as hashing
-        digest = hashing.blake3_hash(temp_path)
-    except Exception as e:
-        raise RuntimeError(f"failed to hash uploaded file: {e}")
-    asset_hash = "blake3:" + digest
-
-    if expected_asset_hash and asset_hash != expected_asset_hash.strip().lower():
-        raise ValueError("HASH_MISMATCH")
-
-    with create_session() as session:
-        existing = get_asset_by_hash(session, asset_hash=asset_hash)
-        if existing is not None:
-            with contextlib.suppress(Exception):
-                if temp_path and os.path.exists(temp_path):
-                    os.remove(temp_path)
-
-            display_name = _safe_filename(spec.name or (client_filename or ""), fallback=digest)
-            info = create_asset_info_for_existing_asset(
-                session,
-                asset_hash=asset_hash,
-                name=display_name,
-                user_metadata=spec.user_metadata or {},
-                tags=spec.tags or [],
-                tag_origin="manual",
-                owner_id=owner_id,
-            )
-            tag_names = get_asset_tags(session, asset_info_id=info.id)
-            session.commit()
-
-            return schemas_out.AssetCreated(
-                id=info.id,
-                name=info.name,
-                asset_hash=existing.hash,
-                size=int(existing.size_bytes) if existing.size_bytes is not None else None,
-                mime_type=existing.mime_type,
-                tags=tag_names,
-                user_metadata=info.user_metadata or {},
-                preview_id=info.preview_id,
-                created_at=info.created_at,
-                last_access_time=info.last_access_time,
-                created_new=False,
-            )
-
-    base_dir, subdirs = resolve_destination_from_tags(spec.tags)
-    dest_dir = os.path.join(base_dir, *subdirs) if subdirs else base_dir
-    os.makedirs(dest_dir, exist_ok=True)
-
-    src_for_ext = (client_filename or spec.name or "").strip()
-    _ext = os.path.splitext(os.path.basename(src_for_ext))[1] if src_for_ext else ""
-    ext = _ext if 0 < len(_ext) <= 16 else ""
-    hashed_basename = f"{digest}{ext}"
-    dest_abs = os.path.abspath(os.path.join(dest_dir, hashed_basename))
-    ensure_within_base(dest_abs, base_dir)
-
-    content_type = (
-        mimetypes.guess_type(os.path.basename(src_for_ext), strict=False)[0]
-        or mimetypes.guess_type(hashed_basename, strict=False)[0]
-        or "application/octet-stream"
-    )
-
-    try:
-        os.replace(temp_path, dest_abs)
-    except Exception as e:
-        raise RuntimeError(f"failed to move uploaded file into place: {e}")
-
-    try:
-        size_bytes, mtime_ns = _get_size_mtime_ns(dest_abs)
-    except OSError as e:
-        raise RuntimeError(f"failed to stat destination file: {e}")
-
-    with create_session() as session:
-        result = ingest_fs_asset(
-            session,
-            asset_hash=asset_hash,
-            abs_path=dest_abs,
-            size_bytes=size_bytes,
-            mtime_ns=mtime_ns,
-            mime_type=content_type,
-            info_name=_safe_filename(spec.name or (client_filename or ""), fallback=digest),
-            owner_id=owner_id,
-            preview_id=None,
-            user_metadata=spec.user_metadata or {},
-            tags=spec.tags,
-            tag_origin="manual",
-            require_existing_tags=False,
-        )
-        info_id = result["asset_info_id"]
-        if not info_id:
-            raise RuntimeError("failed to create asset metadata")
-
-        pair = fetch_asset_info_and_asset(session, asset_info_id=info_id, owner_id=owner_id)
-        if not pair:
-            raise RuntimeError("inconsistent DB state after ingest")
-        info, asset = pair
-        tag_names = get_asset_tags(session, asset_info_id=info.id)
-        created_result = schemas_out.AssetCreated(
-            id=info.id,
-            name=info.name,
-            asset_hash=asset.hash,
-            size=int(asset.size_bytes),
-            mime_type=asset.mime_type,
-            tags=tag_names,
-            user_metadata=info.user_metadata or {},
-            preview_id=info.preview_id,
-            created_at=info.created_at,
-            last_access_time=info.last_access_time,
-            created_new=result["asset_created"],
-        )
-        session.commit()
-
-    return created_result
-
-
-def update_asset(
-    *,
-    asset_info_id: str,
-    name: str | None = None,
-    tags: list[str] | None = None,
-    user_metadata: dict | None = None,
-    owner_id: str = "",
-) -> schemas_out.AssetUpdated:
-    with create_session() as session:
-        info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
-        if not info_row:
-            raise ValueError(f"AssetInfo {asset_info_id} not found")
-        if info_row.owner_id and info_row.owner_id != owner_id:
-            raise PermissionError("not owner")
-
-        info = update_asset_info_full(
-            session,
-            asset_info_id=asset_info_id,
-            name=name,
-            tags=tags,
-            user_metadata=user_metadata,
-            tag_origin="manual",
-            asset_info_row=info_row,
-        )
-
-        tag_names = get_asset_tags(session, asset_info_id=asset_info_id)
-        result = schemas_out.AssetUpdated(
-            id=info.id,
-            name=info.name,
-            asset_hash=info.asset.hash if info.asset else None,
-            tags=tag_names,
-            user_metadata=info.user_metadata or {},
-            updated_at=info.updated_at,
-        )
-        session.commit()
-
-    return result
-
-
-def set_asset_preview(
-    *,
-    asset_info_id: str,
-    preview_asset_id: str | None = None,
-    owner_id: str = "",
-) -> schemas_out.AssetDetail:
-    with create_session() as session:
-        info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
-        if not info_row:
-            raise ValueError(f"AssetInfo {asset_info_id} not found")
-        if info_row.owner_id and info_row.owner_id != owner_id:
-            raise PermissionError("not owner")
-
-        set_asset_info_preview(
-            session,
-            asset_info_id=asset_info_id,
-            preview_asset_id=preview_asset_id,
-        )
-
-        res = fetch_asset_info_asset_and_tags(session, asset_info_id=asset_info_id, owner_id=owner_id)
-        if not res:
-            raise RuntimeError("State changed during preview update")
-        info, asset, tags = res
-        result = schemas_out.AssetDetail(
-            id=info.id,
-            name=info.name,
-            asset_hash=asset.hash if asset else None,
-            size=int(asset.size_bytes) if asset and asset.size_bytes is not None else None,
-            mime_type=asset.mime_type if asset else None,
-            tags=tags,
-            user_metadata=info.user_metadata or {},
-            preview_id=info.preview_id,
-            created_at=info.created_at,
-            last_access_time=info.last_access_time,
-        )
-        session.commit()
-
-    return result
-
-
-def delete_asset_reference(*, asset_info_id: str, owner_id: str, delete_content_if_orphan: bool = True) -> bool:
-    with create_session() as session:
-        info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
-        asset_id = info_row.asset_id if info_row else None
-        deleted = delete_asset_info_by_id(session, asset_info_id=asset_info_id, owner_id=owner_id)
-        if not deleted:
-            session.commit()
-            return False
-
-        if not delete_content_if_orphan or not asset_id:
-            session.commit()
-            return True
-
-        still_exists = asset_info_exists_for_asset_id(session, asset_id=asset_id)
-        if still_exists:
-            session.commit()
-            return True
-
-        states = list_cache_states_by_asset_id(session, asset_id=asset_id)
-        file_paths = [s.file_path for s in (states or []) if getattr(s, "file_path", None)]
-
-        asset_row = session.get(Asset, asset_id)
-        if asset_row is not None:
-            session.delete(asset_row)
-
-        session.commit()
-        for p in file_paths:
-            with contextlib.suppress(Exception):
-                if p and os.path.isfile(p):
-                    os.remove(p)
-    return True
-
-
-def create_asset_from_hash(
-    *,
-    hash_str: str,
-    name: str,
-    tags: list[str] | None = None,
-    user_metadata: dict | None = None,
-    owner_id: str = "",
-) -> schemas_out.AssetCreated | None:
-    canonical = hash_str.strip().lower()
-    with create_session() as session:
-        asset = get_asset_by_hash(session, asset_hash=canonical)
-        if not asset:
-            return None
-
-        info = create_asset_info_for_existing_asset(
-            session,
-            asset_hash=canonical,
-            name=_safe_filename(name, fallback=canonical.split(":", 1)[1]),
-            user_metadata=user_metadata or {},
-            tags=tags or [],
-            tag_origin="manual",
-            owner_id=owner_id,
-        )
-        tag_names = get_asset_tags(session, asset_info_id=info.id)
-        result = schemas_out.AssetCreated(
-            id=info.id,
-            name=info.name,
-            asset_hash=asset.hash,
-            size=int(asset.size_bytes),
-            mime_type=asset.mime_type,
-            tags=tag_names,
-            user_metadata=info.user_metadata or {},
-            preview_id=info.preview_id,
-            created_at=info.created_at,
-            last_access_time=info.last_access_time,
-            created_new=False,
-        )
-        session.commit()
-
-    return result
-
-
-def add_tags_to_asset(
-    *,
-    asset_info_id: str,
-    tags: list[str],
-    origin: str = "manual",
-    owner_id: str = "",
-) -> schemas_out.TagsAdd:
-    with create_session() as session:
-        info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
-        if not info_row:
-            raise ValueError(f"AssetInfo {asset_info_id} not found")
-        if info_row.owner_id and info_row.owner_id != owner_id:
-            raise PermissionError("not owner")
-        data = add_tags_to_asset_info(
-            session,
-            asset_info_id=asset_info_id,
-            tags=tags,
-            origin=origin,
-            create_if_missing=True,
-            asset_info_row=info_row,
-        )
-        session.commit()
-    return schemas_out.TagsAdd(**data)
-
-
-def remove_tags_from_asset(
-    *,
-    asset_info_id: str,
-    tags: list[str],
-    owner_id: str = "",
-) -> schemas_out.TagsRemove:
-    with create_session() as session:
-        info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
-        if not info_row:
-            raise ValueError(f"AssetInfo {asset_info_id} not found")
-        if info_row.owner_id and info_row.owner_id != owner_id:
-            raise PermissionError("not owner")
-
-        data = remove_tags_from_asset_info(
-            session,
-            asset_info_id=asset_info_id,
-            tags=tags,
-        )
-        session.commit()
-    return schemas_out.TagsRemove(**data)
-
-
-def list_tags(
-    prefix: str | None = None,
-    limit: int = 100,
-    offset: int = 0,
-    order: str = "count_desc",
-    include_zero: bool = True,
-    owner_id: str = "",
-) -> schemas_out.TagsList:
-    limit = max(1, min(1000, limit))
-    offset = max(0, offset)
-
-    with create_session() as session:
-        rows, total = list_tags_with_usage(
-            session,
-            prefix=prefix,
-            limit=limit,
-            offset=offset,
-            include_zero=include_zero,
-            order=order,
-            owner_id=owner_id,
-        )
-
-    tags = [schemas_out.TagUsage(name=name, count=count, type=tag_type) for (name, tag_type, count) in rows]
-    return schemas_out.TagsList(tags=tags, total=total, has_more=(offset + len(tags)) < total)
diff --git a/app/assets/scanner.py b/app/assets/scanner.py
index 0172a5c2f..e27ea5123 100644
--- a/app/assets/scanner.py
+++ b/app/assets/scanner.py
@@ -1,263 +1,567 @@
-import contextlib
-import time
 import logging
 import os
-import sqlalchemy
+from pathlib import Path
+from typing import Callable, Literal, TypedDict
 
 import folder_paths
-from app.database.db import create_session, dependencies_available
-from app.assets.helpers import (
-    collect_models_files, compute_relative_filename, fast_asset_file_check, get_name_and_tags_from_asset_path,
-    list_tree,prefixes_for_root, escape_like_prefix,
-    RootType
+from app.assets.database.queries import (
+    add_missing_tag_for_asset_id,
+    bulk_update_enrichment_level,
+    bulk_update_is_missing,
+    bulk_update_needs_verify,
+    delete_orphaned_seed_asset,
+    delete_references_by_ids,
+    ensure_tags_exist,
+    get_asset_by_hash,
+    get_references_for_prefixes,
+    get_unenriched_references,
+    mark_references_missing_outside_prefixes,
+    reassign_asset_references,
+    remove_missing_tag_for_asset_id,
+    set_reference_metadata,
+    update_asset_hash_and_mime,
 )
-from app.assets.database.tags import add_missing_tag_for_asset_id, ensure_tags_exist, remove_missing_tag_for_asset_id
-from app.assets.database.bulk_ops import seed_from_paths_batch
-from app.assets.database.models import Asset, AssetCacheState, AssetInfo
+from app.assets.services.bulk_ingest import (
+    SeedAssetSpec,
+    batch_insert_seed_assets,
+)
+from app.assets.services.file_utils import (
+    get_mtime_ns,
+    is_visible,
+    list_files_recursively,
+    verify_file_unchanged,
+)
+from app.assets.services.hashing import HashCheckpoint, compute_blake3_hash
+from app.assets.services.metadata_extract import extract_file_metadata
+from app.assets.services.path_utils import (
+    compute_relative_filename,
+    get_comfy_models_folders,
+    get_name_and_tags_from_asset_path,
+)
+from app.database.db import create_session
 
 
-def seed_assets(roots: tuple[RootType, ...], enable_logging: bool = False) -> None:
-    """
-    Scan the given roots and seed the assets into the database.
-    """
-    if not dependencies_available():
-        if enable_logging:
-            logging.warning("Database dependencies not available, skipping assets scan")
-        return
-    t_start = time.perf_counter()
-    created = 0
-    skipped_existing = 0
-    orphans_pruned = 0
-    paths: list[str] = []
-    try:
-        existing_paths: set[str] = set()
-        for r in roots:
-            try:
-                survivors: set[str] = _fast_db_consistency_pass(r, collect_existing_paths=True, update_missing_tags=True)
-                if survivors:
-                    existing_paths.update(survivors)
-            except Exception as e:
-                logging.exception("fast DB scan failed for %s: %s", r, e)
+class _RefInfo(TypedDict):
+    ref_id: str
+    file_path: str
+    exists: bool
+    stat_unchanged: bool
+    needs_verify: bool
 
-        try:
-            orphans_pruned = _prune_orphaned_assets(roots)
-        except Exception as e:
-            logging.exception("orphan pruning failed: %s", e)
 
-        if "models" in roots:
-            paths.extend(collect_models_files())
-        if "input" in roots:
-            paths.extend(list_tree(folder_paths.get_input_directory()))
-        if "output" in roots:
-            paths.extend(list_tree(folder_paths.get_output_directory()))
+class _AssetAccumulator(TypedDict):
+    hash: str | None
+    size_db: int
+    refs: list[_RefInfo]
 
-        specs: list[dict] = []
-        tag_pool: set[str] = set()
-        for p in paths:
-            abs_p = os.path.abspath(p)
-            if abs_p in existing_paths:
-                skipped_existing += 1
+
+RootType = Literal["models", "input", "output"]
+
+
+def get_prefixes_for_root(root: RootType) -> list[str]:
+    if root == "models":
+        bases: list[str] = []
+        for _bucket, paths in get_comfy_models_folders():
+            bases.extend(paths)
+        return [os.path.abspath(p) for p in bases]
+    if root == "input":
+        return [os.path.abspath(folder_paths.get_input_directory())]
+    if root == "output":
+        return [os.path.abspath(folder_paths.get_output_directory())]
+    return []
+
+
+def get_all_known_prefixes() -> list[str]:
+    """Get all known asset prefixes across all root types."""
+    all_roots: tuple[RootType, ...] = ("models", "input", "output")
+    return [p for root in all_roots for p in get_prefixes_for_root(root)]
+
+
+def collect_models_files() -> list[str]:
+    out: list[str] = []
+    for folder_name, bases in get_comfy_models_folders():
+        rel_files = folder_paths.get_filename_list(folder_name) or []
+        for rel_path in rel_files:
+            if not all(is_visible(part) for part in Path(rel_path).parts):
                 continue
-            try:
-                stat_p = os.stat(abs_p, follow_symlinks=False)
-            except OSError:
+            abs_path = folder_paths.get_full_path(folder_name, rel_path)
+            if not abs_path:
                 continue
-            # skip empty files
-            if not stat_p.st_size:
-                continue
-            name, tags = get_name_and_tags_from_asset_path(abs_p)
-            specs.append(
-                {
-                    "abs_path": abs_p,
-                    "size_bytes": stat_p.st_size,
-                    "mtime_ns": getattr(stat_p, "st_mtime_ns", int(stat_p.st_mtime * 1_000_000_000)),
-                    "info_name": name,
-                    "tags": tags,
-                    "fname": compute_relative_filename(abs_p),
-                }
-            )
-            for t in tags:
-                tag_pool.add(t)
-        # if no file specs, nothing to do
-        if not specs:
-            return
-        with create_session() as sess:
-            if tag_pool:
-                ensure_tags_exist(sess, tag_pool, tag_type="user")
-
-            result = seed_from_paths_batch(sess, specs=specs, owner_id="")
-            created += result["inserted_infos"]
-            sess.commit()
-    finally:
-        if enable_logging:
-            logging.info(
-                "Assets scan(roots=%s) completed in %.3fs (created=%d, skipped_existing=%d, orphans_pruned=%d, total_seen=%d)",
-                roots,
-                time.perf_counter() - t_start,
-                created,
-                skipped_existing,
-                orphans_pruned,
-                len(paths),
-            )
+            abs_path = os.path.abspath(abs_path)
+            allowed = False
+            abs_p = Path(abs_path)
+            for b in bases:
+                if abs_p.is_relative_to(os.path.abspath(b)):
+                    allowed = True
+                    break
+            if allowed:
+                out.append(abs_path)
+    return out
 
 
-def _prune_orphaned_assets(roots: tuple[RootType, ...]) -> int:
-    """Prune cache states outside configured prefixes, then delete orphaned seed assets."""
-    all_prefixes = [os.path.abspath(p) for r in roots for p in prefixes_for_root(r)]
-    if not all_prefixes:
-        return 0
-
-    def make_prefix_condition(prefix: str):
-        base = prefix if prefix.endswith(os.sep) else prefix + os.sep
-        escaped, esc = escape_like_prefix(base)
-        return AssetCacheState.file_path.like(escaped + "%", escape=esc)
-
-    matches_valid_prefix = sqlalchemy.or_(*[make_prefix_condition(p) for p in all_prefixes])
-
-    orphan_subq = (
-        sqlalchemy.select(Asset.id)
-        .outerjoin(AssetCacheState, AssetCacheState.asset_id == Asset.id)
-        .where(Asset.hash.is_(None), AssetCacheState.id.is_(None))
-    ).scalar_subquery()
-
-    with create_session() as sess:
-        sess.execute(sqlalchemy.delete(AssetCacheState).where(~matches_valid_prefix))
-        sess.execute(sqlalchemy.delete(AssetInfo).where(AssetInfo.asset_id.in_(orphan_subq)))
-        result = sess.execute(sqlalchemy.delete(Asset).where(Asset.id.in_(orphan_subq)))
-        sess.commit()
-        return result.rowcount
-
-
-def _fast_db_consistency_pass(
+def sync_references_with_filesystem(
+    session,
     root: RootType,
-    *,
     collect_existing_paths: bool = False,
     update_missing_tags: bool = False,
 ) -> set[str] | None:
-    """Fast DB+FS pass for a root:
-      - Toggle needs_verify per state using fast check
-      - For hashed assets with at least one fast-ok state in this root: delete stale missing states
-      - For seed assets with all states missing: delete Asset and its AssetInfos
-      - Optionally add/remove 'missing' tags based on fast-ok in this root
-      - Optionally return surviving absolute paths
+    """Reconcile asset references with filesystem for a root.
+
+    - Toggle needs_verify per reference using mtime/size stat check
+    - For hashed assets with at least one stat-unchanged ref: delete stale missing refs
+    - For seed assets with all refs missing: delete Asset and its references
+    - Optionally add/remove 'missing' tags based on stat check in this root
+    - Optionally return surviving absolute paths
+
+    Args:
+        session: Database session
+        root: Root type to scan
+        collect_existing_paths: If True, return set of surviving file paths
+        update_missing_tags: If True, update 'missing' tags based on file status
+
+    Returns:
+        Set of surviving absolute paths if collect_existing_paths=True, else None
     """
-    prefixes = prefixes_for_root(root)
+    prefixes = get_prefixes_for_root(root)
     if not prefixes:
         return set() if collect_existing_paths else None
 
-    conds = []
-    for p in prefixes:
-        base = os.path.abspath(p)
-        if not base.endswith(os.sep):
-            base += os.sep
-        escaped, esc = escape_like_prefix(base)
-        conds.append(AssetCacheState.file_path.like(escaped + "%", escape=esc))
+    rows = get_references_for_prefixes(
+        session, prefixes, include_missing=update_missing_tags
+    )
+
+    by_asset: dict[str, _AssetAccumulator] = {}
+    for row in rows:
+        acc = by_asset.get(row.asset_id)
+        if acc is None:
+            acc = {"hash": row.asset_hash, "size_db": row.size_bytes, "refs": []}
+            by_asset[row.asset_id] = acc
+
+        stat_unchanged = False
+        try:
+            exists = True
+            stat_unchanged = verify_file_unchanged(
+                mtime_db=row.mtime_ns,
+                size_db=acc["size_db"],
+                stat_result=os.stat(row.file_path, follow_symlinks=True),
+            )
+        except FileNotFoundError:
+            exists = False
+        except PermissionError:
+            exists = True
+            logging.debug("Permission denied accessing %s", row.file_path)
+        except OSError as e:
+            exists = False
+            logging.debug("OSError checking %s: %s", row.file_path, e)
+
+        acc["refs"].append(
+            {
+                "ref_id": row.reference_id,
+                "file_path": row.file_path,
+                "exists": exists,
+                "stat_unchanged": stat_unchanged,
+                "needs_verify": row.needs_verify,
+            }
+        )
+
+    to_set_verify: list[str] = []
+    to_clear_verify: list[str] = []
+    stale_ref_ids: list[str] = []
+    to_mark_missing: list[str] = []
+    to_clear_missing: list[str] = []
+    survivors: set[str] = set()
+
+    for aid, acc in by_asset.items():
+        a_hash = acc["hash"]
+        refs = acc["refs"]
+        any_unchanged = any(r["stat_unchanged"] for r in refs)
+        all_missing = all(not r["exists"] for r in refs)
+
+        for r in refs:
+            if not r["exists"]:
+                to_mark_missing.append(r["ref_id"])
+                continue
+            if r["stat_unchanged"]:
+                to_clear_missing.append(r["ref_id"])
+                if r["needs_verify"]:
+                    to_clear_verify.append(r["ref_id"])
+            if not r["stat_unchanged"] and not r["needs_verify"]:
+                to_set_verify.append(r["ref_id"])
+
+        if a_hash is None:
+            if refs and all_missing:
+                delete_orphaned_seed_asset(session, aid)
+            else:
+                for r in refs:
+                    if r["exists"]:
+                        survivors.add(os.path.abspath(r["file_path"]))
+            continue
+
+        if any_unchanged:
+            for r in refs:
+                if not r["exists"]:
+                    stale_ref_ids.append(r["ref_id"])
+            if update_missing_tags:
+                try:
+                    remove_missing_tag_for_asset_id(session, asset_id=aid)
+                except Exception as e:
+                    logging.warning(
+                        "Failed to remove missing tag for asset %s: %s", aid, e
+                    )
+        elif update_missing_tags:
+            try:
+                add_missing_tag_for_asset_id(session, asset_id=aid, origin="automatic")
+            except Exception as e:
+                logging.warning("Failed to add missing tag for asset %s: %s", aid, e)
+
+        for r in refs:
+            if r["exists"]:
+                survivors.add(os.path.abspath(r["file_path"]))
+
+    delete_references_by_ids(session, stale_ref_ids)
+    stale_set = set(stale_ref_ids)
+    to_mark_missing = [ref_id for ref_id in to_mark_missing if ref_id not in stale_set]
+    bulk_update_is_missing(session, to_mark_missing, value=True)
+    bulk_update_is_missing(session, to_clear_missing, value=False)
+    bulk_update_needs_verify(session, to_set_verify, value=True)
+    bulk_update_needs_verify(session, to_clear_verify, value=False)
+
+    return survivors if collect_existing_paths else None
+
+
+def sync_root_safely(root: RootType) -> set[str]:
+    """Sync a single root's references with the filesystem.
+
+    Returns survivors (existing paths) or empty set on failure.
+    """
+    try:
+        with create_session() as sess:
+            survivors = sync_references_with_filesystem(
+                sess,
+                root,
+                collect_existing_paths=True,
+                update_missing_tags=True,
+            )
+            sess.commit()
+            return survivors or set()
+    except Exception as e:
+        logging.exception("fast DB scan failed for %s: %s", root, e)
+        return set()
+
+
+def mark_missing_outside_prefixes_safely(prefixes: list[str]) -> int:
+    """Mark references as missing when outside the given prefixes.
+
+    This is a non-destructive soft-delete. Returns count marked or 0 on failure.
+    """
+    try:
+        with create_session() as sess:
+            count = mark_references_missing_outside_prefixes(sess, prefixes)
+            sess.commit()
+            return count
+    except Exception as e:
+        logging.exception("marking missing assets failed: %s", e)
+        return 0
+
+
+def collect_paths_for_roots(roots: tuple[RootType, ...]) -> list[str]:
+    """Collect all file paths for the given roots."""
+    paths: list[str] = []
+    if "models" in roots:
+        paths.extend(collect_models_files())
+    if "input" in roots:
+        paths.extend(list_files_recursively(folder_paths.get_input_directory()))
+    if "output" in roots:
+        paths.extend(list_files_recursively(folder_paths.get_output_directory()))
+    return paths
+
+
+def build_asset_specs(
+    paths: list[str],
+    existing_paths: set[str],
+    enable_metadata_extraction: bool = True,
+    compute_hashes: bool = False,
+) -> tuple[list[SeedAssetSpec], set[str], int]:
+    """Build asset specs from paths, returning (specs, tag_pool, skipped_count).
+
+    Args:
+        paths: List of file paths to process
+        existing_paths: Set of paths that already exist in the database
+        enable_metadata_extraction: If True, extract tier 1 & 2 metadata
+        compute_hashes: If True, compute blake3 hashes (slow for large files)
+    """
+    specs: list[SeedAssetSpec] = []
+    tag_pool: set[str] = set()
+    skipped = 0
+
+    for p in paths:
+        abs_p = os.path.abspath(p)
+        if abs_p in existing_paths:
+            skipped += 1
+            continue
+        try:
+            stat_p = os.stat(abs_p, follow_symlinks=True)
+        except OSError:
+            continue
+        if not stat_p.st_size:
+            continue
+        name, tags = get_name_and_tags_from_asset_path(abs_p)
+        rel_fname = compute_relative_filename(abs_p)
+
+        # Extract metadata (tier 1: filesystem, tier 2: safetensors header)
+        metadata = None
+        if enable_metadata_extraction:
+            metadata = extract_file_metadata(
+                abs_p,
+                stat_result=stat_p,
+                relative_filename=rel_fname,
+            )
+
+        # Compute hash if requested
+        asset_hash: str | None = None
+        if compute_hashes:
+            try:
+                digest, _ = compute_blake3_hash(abs_p)
+                asset_hash = "blake3:" + digest
+            except Exception as e:
+                logging.warning("Failed to hash %s: %s", abs_p, e)
+
+        mime_type = metadata.content_type if metadata else None
+        specs.append(
+            {
+                "abs_path": abs_p,
+                "size_bytes": stat_p.st_size,
+                "mtime_ns": get_mtime_ns(stat_p),
+                "info_name": name,
+                "tags": tags,
+                "fname": rel_fname,
+                "metadata": metadata,
+                "hash": asset_hash,
+                "mime_type": mime_type,
+            }
+        )
+        tag_pool.update(tags)
+
+    return specs, tag_pool, skipped
+
+
+
+def insert_asset_specs(specs: list[SeedAssetSpec], tag_pool: set[str]) -> int:
+    """Insert asset specs into database, returning count of created refs."""
+    if not specs:
+        return 0
+    with create_session() as sess:
+        if tag_pool:
+            ensure_tags_exist(sess, tag_pool, tag_type="user")
+        result = batch_insert_seed_assets(sess, specs=specs, owner_id="")
+        sess.commit()
+        return result.inserted_refs
+
+
+# Enrichment level constants
+ENRICHMENT_STUB = 0  # Fast scan: path, size, mtime only
+ENRICHMENT_METADATA = 1  # Metadata extracted (safetensors header, mime type)
+ENRICHMENT_HASHED = 2  # Hash computed (blake3)
+
+
+def get_unenriched_assets_for_roots(
+    roots: tuple[RootType, ...],
+    max_level: int = ENRICHMENT_STUB,
+    limit: int = 1000,
+) -> list:
+    """Get assets that need enrichment for the given roots.
+
+    Args:
+        roots: Tuple of root types to scan
+        max_level: Maximum enrichment level to include
+        limit: Maximum number of rows to return
+
+    Returns:
+        List of UnenrichedReferenceRow
+    """
+    prefixes: list[str] = []
+    for root in roots:
+        prefixes.extend(get_prefixes_for_root(root))
+
+    if not prefixes:
+        return []
 
     with create_session() as sess:
-        rows = (
-            sess.execute(
-                sqlalchemy.select(
-                    AssetCacheState.id,
-                    AssetCacheState.file_path,
-                    AssetCacheState.mtime_ns,
-                    AssetCacheState.needs_verify,
-                    AssetCacheState.asset_id,
-                    Asset.hash,
-                    Asset.size_bytes,
-                )
-                .join(Asset, Asset.id == AssetCacheState.asset_id)
-                .where(sqlalchemy.or_(*conds))
-                .order_by(AssetCacheState.asset_id.asc(), AssetCacheState.id.asc())
+        return get_unenriched_references(
+            sess, prefixes, max_level=max_level, limit=limit
+        )
+
+
+def enrich_asset(
+    session,
+    file_path: str,
+    reference_id: str,
+    asset_id: str,
+    extract_metadata: bool = True,
+    compute_hash: bool = False,
+    interrupt_check: Callable[[], bool] | None = None,
+    hash_checkpoints: dict[str, HashCheckpoint] | None = None,
+) -> int:
+    """Enrich a single asset with metadata and/or hash.
+
+    Args:
+        session: Database session (caller manages lifecycle)
+        file_path: Absolute path to the file
+        reference_id: ID of the reference to update
+        asset_id: ID of the asset to update (for mime_type and hash)
+        extract_metadata: If True, extract safetensors header and mime type
+        compute_hash: If True, compute blake3 hash
+        interrupt_check: Optional non-blocking callable that returns True if
+            the operation should be interrupted (e.g. paused or cancelled)
+        hash_checkpoints: Optional dict for saving/restoring hash progress
+            across interruptions, keyed by file path
+
+    Returns:
+        New enrichment level achieved
+    """
+    new_level = ENRICHMENT_STUB
+
+    try:
+        stat_p = os.stat(file_path, follow_symlinks=True)
+    except OSError:
+        return new_level
+
+    rel_fname = compute_relative_filename(file_path)
+    mime_type: str | None = None
+    metadata = None
+
+    if extract_metadata:
+        metadata = extract_file_metadata(
+            file_path,
+            stat_result=stat_p,
+            relative_filename=rel_fname,
+        )
+        if metadata:
+            mime_type = metadata.content_type
+            new_level = ENRICHMENT_METADATA
+
+    full_hash: str | None = None
+    if compute_hash:
+        try:
+            mtime_before = get_mtime_ns(stat_p)
+            size_before = stat_p.st_size
+
+            # Restore checkpoint if available and file unchanged
+            checkpoint = None
+            if hash_checkpoints is not None:
+                checkpoint = hash_checkpoints.get(file_path)
+                if checkpoint is not None:
+                    cur_stat = os.stat(file_path, follow_symlinks=True)
+                    if (checkpoint.mtime_ns != get_mtime_ns(cur_stat)
+                            or checkpoint.file_size != cur_stat.st_size):
+                        checkpoint = None
+                        hash_checkpoints.pop(file_path, None)
+                    else:
+                        mtime_before = get_mtime_ns(cur_stat)
+
+            digest, new_checkpoint = compute_blake3_hash(
+                file_path,
+                interrupt_check=interrupt_check,
+                checkpoint=checkpoint,
             )
-        ).all()
 
-        by_asset: dict[str, dict] = {}
-        for sid, fp, mtime_db, needs_verify, aid, a_hash, a_size in rows:
-            acc = by_asset.get(aid)
-            if acc is None:
-                acc = {"hash": a_hash, "size_db": int(a_size or 0), "states": []}
-                by_asset[aid] = acc
+            if digest is None:
+                # Interrupted — save checkpoint for later resumption
+                if hash_checkpoints is not None and new_checkpoint is not None:
+                    new_checkpoint.mtime_ns = mtime_before
+                    new_checkpoint.file_size = size_before
+                    hash_checkpoints[file_path] = new_checkpoint
+                return new_level
+
+            # Completed — clear any saved checkpoint
+            if hash_checkpoints is not None:
+                hash_checkpoints.pop(file_path, None)
+
+            stat_after = os.stat(file_path, follow_symlinks=True)
+            mtime_after = get_mtime_ns(stat_after)
+            if mtime_before != mtime_after:
+                logging.warning("File modified during hashing, discarding hash: %s", file_path)
+            else:
+                full_hash = f"blake3:{digest}"
+                metadata_ok = not extract_metadata or metadata is not None
+                if metadata_ok:
+                    new_level = ENRICHMENT_HASHED
+        except Exception as e:
+            logging.warning("Failed to hash %s: %s", file_path, e)
+
+    if extract_metadata and metadata:
+        user_metadata = metadata.to_user_metadata()
+        set_reference_metadata(session, reference_id, user_metadata)
+
+    if full_hash:
+        existing = get_asset_by_hash(session, full_hash)
+        if existing and existing.id != asset_id:
+            reassign_asset_references(session, asset_id, existing.id, reference_id)
+            delete_orphaned_seed_asset(session, asset_id)
+            if mime_type:
+                update_asset_hash_and_mime(session, existing.id, mime_type=mime_type)
+        else:
+            update_asset_hash_and_mime(session, asset_id, full_hash, mime_type)
+    elif mime_type:
+        update_asset_hash_and_mime(session, asset_id, mime_type=mime_type)
+
+    bulk_update_enrichment_level(session, [reference_id], new_level)
+    session.commit()
+
+    return new_level
+
+
+def enrich_assets_batch(
+    rows: list,
+    extract_metadata: bool = True,
+    compute_hash: bool = False,
+    interrupt_check: Callable[[], bool] | None = None,
+    hash_checkpoints: dict[str, HashCheckpoint] | None = None,
+) -> tuple[int, list[str]]:
+    """Enrich a batch of assets.
+
+    Uses a single DB session for the entire batch, committing after each
+    individual asset to avoid long-held transactions while eliminating
+    per-asset session creation overhead.
+
+    Args:
+        rows: List of UnenrichedReferenceRow from get_unenriched_assets_for_roots
+        extract_metadata: If True, extract metadata for each asset
+        compute_hash: If True, compute hash for each asset
+        interrupt_check: Optional non-blocking callable that returns True if
+            the operation should be interrupted (e.g. paused or cancelled)
+        hash_checkpoints: Optional dict for saving/restoring hash progress
+            across interruptions, keyed by file path
+
+    Returns:
+        Tuple of (enriched_count, failed_reference_ids)
+    """
+    enriched = 0
+    failed_ids: list[str] = []
+
+    with create_session() as sess:
+        for row in rows:
+            if interrupt_check is not None and interrupt_check():
+                break
 
-            fast_ok = False
             try:
-                exists = True
-                fast_ok = fast_asset_file_check(
-                    mtime_db=mtime_db,
-                    size_db=acc["size_db"],
-                    stat_result=os.stat(fp, follow_symlinks=True),
+                new_level = enrich_asset(
+                    sess,
+                    file_path=row.file_path,
+                    reference_id=row.reference_id,
+                    asset_id=row.asset_id,
+                    extract_metadata=extract_metadata,
+                    compute_hash=compute_hash,
+                    interrupt_check=interrupt_check,
+                    hash_checkpoints=hash_checkpoints,
                 )
-            except FileNotFoundError:
-                exists = False
-            except OSError:
-                exists = False
-
-            acc["states"].append({
-                "sid": sid,
-                "fp": fp,
-                "exists": exists,
-                "fast_ok": fast_ok,
-                "needs_verify": bool(needs_verify),
-            })
-
-        to_set_verify: list[int] = []
-        to_clear_verify: list[int] = []
-        stale_state_ids: list[int] = []
-        survivors: set[str] = set()
-
-        for aid, acc in by_asset.items():
-            a_hash = acc["hash"]
-            states = acc["states"]
-            any_fast_ok = any(s["fast_ok"] for s in states)
-            all_missing = all(not s["exists"] for s in states)
-
-            for s in states:
-                if not s["exists"]:
-                    continue
-                if s["fast_ok"] and s["needs_verify"]:
-                    to_clear_verify.append(s["sid"])
-                if not s["fast_ok"] and not s["needs_verify"]:
-                    to_set_verify.append(s["sid"])
-
-            if a_hash is None:
-                if states and all_missing:  # remove seed Asset completely, if no valid AssetCache exists
-                    sess.execute(sqlalchemy.delete(AssetInfo).where(AssetInfo.asset_id == aid))
-                    asset = sess.get(Asset, aid)
-                    if asset:
-                        sess.delete(asset)
+                if new_level > row.enrichment_level:
+                    enriched += 1
                 else:
-                    for s in states:
-                        if s["exists"]:
-                            survivors.add(os.path.abspath(s["fp"]))
-                continue
+                    failed_ids.append(row.reference_id)
+            except Exception as e:
+                logging.warning("Failed to enrich %s: %s", row.file_path, e)
+                sess.rollback()
+                failed_ids.append(row.reference_id)
 
-            if any_fast_ok:  # if Asset has at least one valid AssetCache record, remove any invalid AssetCache records
-                for s in states:
-                    if not s["exists"]:
-                        stale_state_ids.append(s["sid"])
-                if update_missing_tags:
-                    with contextlib.suppress(Exception):
-                        remove_missing_tag_for_asset_id(sess, asset_id=aid)
-            elif update_missing_tags:
-                with contextlib.suppress(Exception):
-                    add_missing_tag_for_asset_id(sess, asset_id=aid, origin="automatic")
-
-            for s in states:
-                if s["exists"]:
-                    survivors.add(os.path.abspath(s["fp"]))
-
-        if stale_state_ids:
-            sess.execute(sqlalchemy.delete(AssetCacheState).where(AssetCacheState.id.in_(stale_state_ids)))
-        if to_set_verify:
-            sess.execute(
-                sqlalchemy.update(AssetCacheState)
-                .where(AssetCacheState.id.in_(to_set_verify))
-                .values(needs_verify=True)
-            )
-        if to_clear_verify:
-            sess.execute(
-                sqlalchemy.update(AssetCacheState)
-                .where(AssetCacheState.id.in_(to_clear_verify))
-                .values(needs_verify=False)
-            )
-        sess.commit()
-        return survivors if collect_existing_paths else None
+    return enriched, failed_ids
diff --git a/app/assets/seeder.py b/app/assets/seeder.py
new file mode 100644
index 000000000..029448464
--- /dev/null
+++ b/app/assets/seeder.py
@@ -0,0 +1,794 @@
+"""Background asset seeder with thread management and cancellation support."""
+
+import logging
+import os
+import threading
+import time
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Callable
+
+from app.assets.scanner import (
+    ENRICHMENT_METADATA,
+    ENRICHMENT_STUB,
+    RootType,
+    build_asset_specs,
+    collect_paths_for_roots,
+    enrich_assets_batch,
+    get_all_known_prefixes,
+    get_prefixes_for_root,
+    get_unenriched_assets_for_roots,
+    insert_asset_specs,
+    mark_missing_outside_prefixes_safely,
+    sync_root_safely,
+)
+from app.database.db import dependencies_available
+
+
+class ScanInProgressError(Exception):
+    """Raised when an operation cannot proceed because a scan is running."""
+
+
+class State(Enum):
+    """Seeder state machine states."""
+
+    IDLE = "IDLE"
+    RUNNING = "RUNNING"
+    PAUSED = "PAUSED"
+    CANCELLING = "CANCELLING"
+
+
+class ScanPhase(Enum):
+    """Scan phase options."""
+
+    FAST = "fast"  # Phase 1: filesystem only (stubs)
+    ENRICH = "enrich"  # Phase 2: metadata + hash
+    FULL = "full"  # Both phases sequentially
+
+
+@dataclass
+class Progress:
+    """Progress information for a scan operation."""
+
+    scanned: int = 0
+    total: int = 0
+    created: int = 0
+    skipped: int = 0
+
+
+@dataclass
+class ScanStatus:
+    """Current status of the asset seeder."""
+
+    state: State
+    progress: Progress | None
+    errors: list[str] = field(default_factory=list)
+
+
+ProgressCallback = Callable[[Progress], None]
+
+
+class _AssetSeeder:
+    """Background asset scanning manager.
+
+    Spawns ephemeral daemon threads for scanning.
+    Each scan creates a new thread that exits when complete.
+    Use the module-level ``asset_seeder`` instance.
+    """
+
+    def __init__(self) -> None:
+        self._lock = threading.Lock()
+        self._state = State.IDLE
+        self._progress: Progress | None = None
+        self._last_progress: Progress | None = None
+        self._errors: list[str] = []
+        self._thread: threading.Thread | None = None
+        self._cancel_event = threading.Event()
+        self._run_gate = threading.Event()
+        self._run_gate.set()  # Start unpaused (set = running, clear = paused)
+        self._roots: tuple[RootType, ...] = ()
+        self._phase: ScanPhase = ScanPhase.FULL
+        self._compute_hashes: bool = False
+        self._prune_first: bool = False
+        self._progress_callback: ProgressCallback | None = None
+        self._disabled: bool = False
+
+    def disable(self) -> None:
+        """Disable the asset seeder, preventing any scans from starting."""
+        self._disabled = True
+        logging.info("Asset seeder disabled")
+
+    def is_disabled(self) -> bool:
+        """Check if the asset seeder is disabled."""
+        return self._disabled
+
+    def start(
+        self,
+        roots: tuple[RootType, ...] = ("models", "input", "output"),
+        phase: ScanPhase = ScanPhase.FULL,
+        progress_callback: ProgressCallback | None = None,
+        prune_first: bool = False,
+        compute_hashes: bool = False,
+    ) -> bool:
+        """Start a background scan for the given roots.
+
+        Args:
+            roots: Tuple of root types to scan (models, input, output)
+            phase: Scan phase to run (FAST, ENRICH, or FULL for both)
+            progress_callback: Optional callback called with progress updates
+            prune_first: If True, prune orphaned assets before scanning
+            compute_hashes: If True, compute blake3 hashes (slow)
+
+        Returns:
+            True if scan was started, False if already running
+        """
+        if self._disabled:
+            logging.debug("Asset seeder is disabled, skipping start")
+            return False
+        logging.info("Seeder start (roots=%s, phase=%s)", roots, phase.value)
+        with self._lock:
+            if self._state != State.IDLE:
+                logging.info("Asset seeder already running, skipping start")
+                return False
+            self._state = State.RUNNING
+            self._progress = Progress()
+            self._errors = []
+            self._roots = roots
+            self._phase = phase
+            self._prune_first = prune_first
+            self._compute_hashes = compute_hashes
+            self._progress_callback = progress_callback
+            self._cancel_event.clear()
+            self._run_gate.set()  # Ensure unpaused when starting
+            self._thread = threading.Thread(
+                target=self._run_scan,
+                name="_AssetSeeder",
+                daemon=True,
+            )
+            self._thread.start()
+            return True
+
+    def start_fast(
+        self,
+        roots: tuple[RootType, ...] = ("models", "input", "output"),
+        progress_callback: ProgressCallback | None = None,
+        prune_first: bool = False,
+    ) -> bool:
+        """Start a fast scan (phase 1 only) - creates stub records.
+
+        Args:
+            roots: Tuple of root types to scan
+            progress_callback: Optional callback for progress updates
+            prune_first: If True, prune orphaned assets before scanning
+
+        Returns:
+            True if scan was started, False if already running
+        """
+        return self.start(
+            roots=roots,
+            phase=ScanPhase.FAST,
+            progress_callback=progress_callback,
+            prune_first=prune_first,
+            compute_hashes=False,
+        )
+
+    def start_enrich(
+        self,
+        roots: tuple[RootType, ...] = ("models", "input", "output"),
+        progress_callback: ProgressCallback | None = None,
+        compute_hashes: bool = False,
+    ) -> bool:
+        """Start an enrichment scan (phase 2 only) - extracts metadata and hashes.
+
+        Args:
+            roots: Tuple of root types to scan
+            progress_callback: Optional callback for progress updates
+            compute_hashes: If True, compute blake3 hashes
+
+        Returns:
+            True if scan was started, False if already running
+        """
+        return self.start(
+            roots=roots,
+            phase=ScanPhase.ENRICH,
+            progress_callback=progress_callback,
+            prune_first=False,
+            compute_hashes=compute_hashes,
+        )
+
+    def cancel(self) -> bool:
+        """Request cancellation of the current scan.
+
+        Returns:
+            True if cancellation was requested, False if not running or paused
+        """
+        with self._lock:
+            if self._state not in (State.RUNNING, State.PAUSED):
+                return False
+            logging.info("Asset seeder cancelling (was %s)", self._state.value)
+            self._state = State.CANCELLING
+            self._cancel_event.set()
+            self._run_gate.set()  # Unblock if paused so thread can exit
+            return True
+
+    def stop(self) -> bool:
+        """Stop the current scan (alias for cancel).
+
+        Returns:
+            True if stop was requested, False if not running
+        """
+        return self.cancel()
+
+    def pause(self) -> bool:
+        """Pause the current scan.
+
+        The scan will complete its current batch before pausing.
+
+        Returns:
+            True if pause was requested, False if not running
+        """
+        with self._lock:
+            if self._state != State.RUNNING:
+                return False
+            logging.info("Asset seeder pausing")
+            self._state = State.PAUSED
+            self._run_gate.clear()
+            return True
+
+    def resume(self) -> bool:
+        """Resume a paused scan.
+
+        This is a noop if the scan is not in the PAUSED state
+
+        Returns:
+            True if resumed, False if not paused
+        """
+        with self._lock:
+            if self._state != State.PAUSED:
+                return False
+            logging.info("Asset seeder resuming")
+            self._state = State.RUNNING
+            self._run_gate.set()
+        self._emit_event("assets.seed.resumed", {})
+        return True
+
+    def restart(
+        self,
+        roots: tuple[RootType, ...] | None = None,
+        phase: ScanPhase | None = None,
+        progress_callback: ProgressCallback | None = None,
+        prune_first: bool | None = None,
+        compute_hashes: bool | None = None,
+        timeout: float = 5.0,
+    ) -> bool:
+        """Cancel any running scan and start a new one.
+
+        Args:
+            roots: Roots to scan (defaults to previous roots)
+            phase: Scan phase (defaults to previous phase)
+            progress_callback: Progress callback (defaults to previous)
+            prune_first: Prune before scan (defaults to previous)
+            compute_hashes: Compute hashes (defaults to previous)
+            timeout: Max seconds to wait for current scan to stop
+
+        Returns:
+            True if new scan was started, False if failed to stop previous
+        """
+        logging.info("Asset seeder restart requested")
+        with self._lock:
+            prev_roots = self._roots
+            prev_phase = self._phase
+            prev_callback = self._progress_callback
+            prev_prune = self._prune_first
+            prev_hashes = self._compute_hashes
+
+        self.cancel()
+        if not self.wait(timeout=timeout):
+            return False
+
+        cb = progress_callback if progress_callback is not None else prev_callback
+        return self.start(
+            roots=roots if roots is not None else prev_roots,
+            phase=phase if phase is not None else prev_phase,
+            progress_callback=cb,
+            prune_first=prune_first if prune_first is not None else prev_prune,
+            compute_hashes=(
+                compute_hashes if compute_hashes is not None else prev_hashes
+            ),
+        )
+
+    def wait(self, timeout: float | None = None) -> bool:
+        """Wait for the current scan to complete.
+
+        Args:
+            timeout: Maximum seconds to wait, or None for no timeout
+
+        Returns:
+            True if scan completed, False if timeout expired or no scan running
+        """
+        with self._lock:
+            thread = self._thread
+        if thread is None:
+            return True
+        thread.join(timeout=timeout)
+        return not thread.is_alive()
+
+    def get_status(self) -> ScanStatus:
+        """Get the current status and progress of the seeder."""
+        with self._lock:
+            src = self._progress or self._last_progress
+            return ScanStatus(
+                state=self._state,
+                progress=Progress(
+                    scanned=src.scanned,
+                    total=src.total,
+                    created=src.created,
+                    skipped=src.skipped,
+                )
+                if src
+                else None,
+                errors=list(self._errors),
+            )
+
+    def shutdown(self, timeout: float = 5.0) -> None:
+        """Gracefully shutdown: cancel any running scan and wait for thread.
+
+        Args:
+            timeout: Maximum seconds to wait for thread to exit
+        """
+        self.cancel()
+        self.wait(timeout=timeout)
+        with self._lock:
+            self._thread = None
+
+    def mark_missing_outside_prefixes(self) -> int:
+        """Mark references as missing when outside all known root prefixes.
+
+        This is a non-destructive soft-delete operation. Assets and their
+        metadata are preserved, but references are flagged as missing.
+        They can be restored if the file reappears in a future scan.
+
+        This operation is decoupled from scanning to prevent partial scans
+        from accidentally marking assets belonging to other roots.
+
+        Should be called explicitly when cleanup is desired, typically after
+        a full scan of all roots or during maintenance.
+
+        Returns:
+            Number of references marked as missing
+
+        Raises:
+            ScanInProgressError: If a scan is currently running
+        """
+        with self._lock:
+            if self._state != State.IDLE:
+                raise ScanInProgressError(
+                    "Cannot mark missing assets while scan is running"
+                )
+            self._state = State.RUNNING
+
+        try:
+            if not dependencies_available():
+                logging.warning(
+                    "Database dependencies not available, skipping mark missing"
+                )
+                return 0
+
+            all_prefixes = get_all_known_prefixes()
+            marked = mark_missing_outside_prefixes_safely(all_prefixes)
+            if marked > 0:
+                logging.info("Marked %d references as missing", marked)
+            return marked
+        finally:
+            with self._lock:
+                self._last_progress = self._progress
+                self._state = State.IDLE
+                self._progress = None
+
+    def _is_cancelled(self) -> bool:
+        """Check if cancellation has been requested."""
+        return self._cancel_event.is_set()
+
+    def _is_paused_or_cancelled(self) -> bool:
+        """Non-blocking check: True if paused or cancelled.
+
+        Use as interrupt_check for I/O-bound work (e.g. hashing) so that
+        file handles are released immediately on pause rather than held
+        open while blocked. The caller is responsible for blocking on
+        _check_pause_and_cancel() afterward.
+        """
+        return not self._run_gate.is_set() or self._cancel_event.is_set()
+
+    def _check_pause_and_cancel(self) -> bool:
+        """Block while paused, then check if cancelled.
+
+        Call this at checkpoint locations in scan loops. It will:
+        1. Block indefinitely while paused (until resume or cancel)
+        2. Return True if cancelled, False to continue
+
+        Returns:
+            True if scan should stop, False to continue
+        """
+        if not self._run_gate.is_set():
+            self._emit_event("assets.seed.paused", {})
+        self._run_gate.wait()  # Blocks if paused
+        return self._is_cancelled()
+
+    def _emit_event(self, event_type: str, data: dict) -> None:
+        """Emit a WebSocket event if server is available."""
+        try:
+            from server import PromptServer
+
+            if hasattr(PromptServer, "instance") and PromptServer.instance:
+                PromptServer.instance.send_sync(event_type, data)
+        except Exception:
+            pass
+
+    def _update_progress(
+        self,
+        scanned: int | None = None,
+        total: int | None = None,
+        created: int | None = None,
+        skipped: int | None = None,
+    ) -> None:
+        """Update progress counters (thread-safe)."""
+        callback: ProgressCallback | None = None
+        progress: Progress | None = None
+
+        with self._lock:
+            if self._progress is None:
+                return
+            if scanned is not None:
+                self._progress.scanned = scanned
+            if total is not None:
+                self._progress.total = total
+            if created is not None:
+                self._progress.created = created
+            if skipped is not None:
+                self._progress.skipped = skipped
+            if self._progress_callback:
+                callback = self._progress_callback
+                progress = Progress(
+                    scanned=self._progress.scanned,
+                    total=self._progress.total,
+                    created=self._progress.created,
+                    skipped=self._progress.skipped,
+                )
+
+        if callback and progress:
+            try:
+                callback(progress)
+            except Exception:
+                pass
+
+    _MAX_ERRORS = 200
+
+    def _add_error(self, message: str) -> None:
+        """Add an error message (thread-safe), capped at _MAX_ERRORS."""
+        with self._lock:
+            if len(self._errors) < self._MAX_ERRORS:
+                self._errors.append(message)
+
+    def _log_scan_config(self, roots: tuple[RootType, ...]) -> None:
+        """Log the directories that will be scanned."""
+        import folder_paths
+
+        for root in roots:
+            if root == "models":
+                logging.info(
+                    "Asset scan [models] directory: %s",
+                    os.path.abspath(folder_paths.models_dir),
+                )
+            else:
+                prefixes = get_prefixes_for_root(root)
+                if prefixes:
+                    logging.info("Asset scan [%s] directories: %s", root, prefixes)
+
+    def _run_scan(self) -> None:
+        """Main scan loop running in background thread."""
+        t_start = time.perf_counter()
+        roots = self._roots
+        phase = self._phase
+        cancelled = False
+        total_created = 0
+        total_enriched = 0
+        skipped_existing = 0
+        total_paths = 0
+
+        try:
+            if not dependencies_available():
+                self._add_error("Database dependencies not available")
+                self._emit_event(
+                    "assets.seed.error",
+                    {"message": "Database dependencies not available"},
+                )
+                return
+
+            if self._prune_first:
+                all_prefixes = get_all_known_prefixes()
+                marked = mark_missing_outside_prefixes_safely(all_prefixes)
+                if marked > 0:
+                    logging.info("Marked %d refs as missing before scan", marked)
+
+            if self._check_pause_and_cancel():
+                logging.info("Asset scan cancelled after pruning phase")
+                cancelled = True
+                return
+
+            self._log_scan_config(roots)
+
+            # Phase 1: Fast scan (stub records)
+            if phase in (ScanPhase.FAST, ScanPhase.FULL):
+                created, skipped, paths = self._run_fast_phase(roots)
+                total_created, skipped_existing, total_paths = created, skipped, paths
+
+                if self._check_pause_and_cancel():
+                    cancelled = True
+                    return
+
+                self._emit_event(
+                    "assets.seed.fast_complete",
+                    {
+                        "roots": list(roots),
+                        "created": total_created,
+                        "skipped": skipped_existing,
+                        "total": total_paths,
+                    },
+                )
+
+            # Phase 2: Enrichment scan (metadata + hashes)
+            if phase in (ScanPhase.ENRICH, ScanPhase.FULL):
+                if self._check_pause_and_cancel():
+                    cancelled = True
+                    return
+
+                enrich_cancelled, total_enriched = self._run_enrich_phase(roots)
+
+                if enrich_cancelled:
+                    cancelled = True
+                    return
+
+                self._emit_event(
+                    "assets.seed.enrich_complete",
+                    {
+                        "roots": list(roots),
+                        "enriched": total_enriched,
+                    },
+                )
+
+            elapsed = time.perf_counter() - t_start
+            logging.info(
+                "Scan(%s, %s) done %.3fs: created=%d enriched=%d skipped=%d",
+                roots,
+                phase.value,
+                elapsed,
+                total_created,
+                total_enriched,
+                skipped_existing,
+            )
+
+            self._emit_event(
+                "assets.seed.completed",
+                {
+                    "phase": phase.value,
+                    "total": total_paths,
+                    "created": total_created,
+                    "enriched": total_enriched,
+                    "skipped": skipped_existing,
+                    "elapsed": round(elapsed, 3),
+                },
+            )
+
+        except Exception as e:
+            self._add_error(f"Scan failed: {e}")
+            logging.exception("Asset scan failed")
+            self._emit_event("assets.seed.error", {"message": str(e)})
+        finally:
+            if cancelled:
+                self._emit_event(
+                    "assets.seed.cancelled",
+                    {
+                        "scanned": self._progress.scanned if self._progress else 0,
+                        "total": total_paths,
+                        "created": total_created,
+                    },
+                )
+            with self._lock:
+                self._last_progress = self._progress
+                self._state = State.IDLE
+                self._progress = None
+
+    def _run_fast_phase(self, roots: tuple[RootType, ...]) -> tuple[int, int, int]:
+        """Run phase 1: fast scan to create stub records.
+
+        Returns:
+            Tuple of (total_created, skipped_existing, total_paths)
+        """
+        t_fast_start = time.perf_counter()
+        total_created = 0
+        skipped_existing = 0
+
+        existing_paths: set[str] = set()
+        t_sync = time.perf_counter()
+        for r in roots:
+            if self._check_pause_and_cancel():
+                return total_created, skipped_existing, 0
+            existing_paths.update(sync_root_safely(r))
+        logging.debug(
+            "Fast scan: sync_root phase took %.3fs (%d existing paths)",
+            time.perf_counter() - t_sync,
+            len(existing_paths),
+        )
+
+        if self._check_pause_and_cancel():
+            return total_created, skipped_existing, 0
+
+        t_collect = time.perf_counter()
+        paths = collect_paths_for_roots(roots)
+        logging.debug(
+            "Fast scan: collect_paths took %.3fs (%d paths found)",
+            time.perf_counter() - t_collect,
+            len(paths),
+        )
+        total_paths = len(paths)
+        self._update_progress(total=total_paths)
+
+        self._emit_event(
+            "assets.seed.started",
+            {"roots": list(roots), "total": total_paths, "phase": "fast"},
+        )
+
+        # Use stub specs (no metadata extraction, no hashing)
+        t_specs = time.perf_counter()
+        specs, tag_pool, skipped_existing = build_asset_specs(
+            paths,
+            existing_paths,
+            enable_metadata_extraction=False,
+            compute_hashes=False,
+        )
+        logging.debug(
+            "Fast scan: build_asset_specs took %.3fs (%d specs, %d skipped)",
+            time.perf_counter() - t_specs,
+            len(specs),
+            skipped_existing,
+        )
+        self._update_progress(skipped=skipped_existing)
+
+        if self._check_pause_and_cancel():
+            return total_created, skipped_existing, total_paths
+
+        batch_size = 500
+        last_progress_time = time.perf_counter()
+        progress_interval = 1.0
+
+        for i in range(0, len(specs), batch_size):
+            if self._check_pause_and_cancel():
+                logging.info(
+                    "Fast scan cancelled after %d/%d files (created=%d)",
+                    i,
+                    len(specs),
+                    total_created,
+                )
+                return total_created, skipped_existing, total_paths
+
+            batch = specs[i : i + batch_size]
+            batch_tags = {t for spec in batch for t in spec["tags"]}
+            try:
+                created = insert_asset_specs(batch, batch_tags)
+                total_created += created
+            except Exception as e:
+                self._add_error(f"Batch insert failed at offset {i}: {e}")
+                logging.exception("Batch insert failed at offset %d", i)
+
+            scanned = i + len(batch)
+            now = time.perf_counter()
+            self._update_progress(scanned=scanned, created=total_created)
+
+            if now - last_progress_time >= progress_interval:
+                self._emit_event(
+                    "assets.seed.progress",
+                    {
+                        "phase": "fast",
+                        "scanned": scanned,
+                        "total": len(specs),
+                        "created": total_created,
+                    },
+                )
+                last_progress_time = now
+
+        self._update_progress(scanned=len(specs), created=total_created)
+        logging.info(
+            "Fast scan complete: %.3fs total (created=%d, skipped=%d, total_paths=%d)",
+            time.perf_counter() - t_fast_start,
+            total_created,
+            skipped_existing,
+            total_paths,
+        )
+        return total_created, skipped_existing, total_paths
+
+    def _run_enrich_phase(self, roots: tuple[RootType, ...]) -> tuple[bool, int]:
+        """Run phase 2: enrich existing records with metadata and hashes.
+
+        Returns:
+            Tuple of (cancelled, total_enriched)
+        """
+        total_enriched = 0
+        batch_size = 100
+        last_progress_time = time.perf_counter()
+        progress_interval = 1.0
+
+        # Get the target enrichment level based on compute_hashes
+        if not self._compute_hashes:
+            target_max_level = ENRICHMENT_STUB
+        else:
+            target_max_level = ENRICHMENT_METADATA
+
+        self._emit_event(
+            "assets.seed.started",
+            {"roots": list(roots), "phase": "enrich"},
+        )
+
+        skip_ids: set[str] = set()
+        consecutive_empty = 0
+        max_consecutive_empty = 3
+
+        # Hash checkpoints survive across batches so interrupted hashes
+        # can be resumed without re-reading the entire file.
+        hash_checkpoints: dict[str, object] = {}
+
+        while True:
+            if self._check_pause_and_cancel():
+                logging.info("Enrich scan cancelled after %d assets", total_enriched)
+                return True, total_enriched
+
+            # Fetch next batch of unenriched assets
+            unenriched = get_unenriched_assets_for_roots(
+                roots,
+                max_level=target_max_level,
+                limit=batch_size,
+            )
+
+            # Filter out previously failed references
+            if skip_ids:
+                unenriched = [r for r in unenriched if r.reference_id not in skip_ids]
+
+            if not unenriched:
+                break
+
+            enriched, failed_ids = enrich_assets_batch(
+                unenriched,
+                extract_metadata=True,
+                compute_hash=self._compute_hashes,
+                interrupt_check=self._is_paused_or_cancelled,
+                hash_checkpoints=hash_checkpoints,
+            )
+            total_enriched += enriched
+            skip_ids.update(failed_ids)
+
+            if enriched == 0:
+                consecutive_empty += 1
+                if consecutive_empty >= max_consecutive_empty:
+                    logging.warning(
+                        "Enrich phase stopping: %d consecutive batches with no progress (%d skipped)",
+                        consecutive_empty,
+                        len(skip_ids),
+                    )
+                    break
+            else:
+                consecutive_empty = 0
+
+            now = time.perf_counter()
+            if now - last_progress_time >= progress_interval:
+                self._emit_event(
+                    "assets.seed.progress",
+                    {
+                        "phase": "enrich",
+                        "enriched": total_enriched,
+                    },
+                )
+                last_progress_time = now
+
+        return False, total_enriched
+
+
+asset_seeder = _AssetSeeder()
diff --git a/app/assets/services/__init__.py b/app/assets/services/__init__.py
new file mode 100644
index 000000000..11fcb4122
--- /dev/null
+++ b/app/assets/services/__init__.py
@@ -0,0 +1,87 @@
+from app.assets.services.asset_management import (
+    asset_exists,
+    delete_asset_reference,
+    get_asset_by_hash,
+    get_asset_detail,
+    list_assets_page,
+    resolve_asset_for_download,
+    set_asset_preview,
+    update_asset_metadata,
+)
+from app.assets.services.bulk_ingest import (
+    BulkInsertResult,
+    batch_insert_seed_assets,
+    cleanup_unreferenced_assets,
+)
+from app.assets.services.file_utils import (
+    get_mtime_ns,
+    get_size_and_mtime_ns,
+    list_files_recursively,
+    verify_file_unchanged,
+)
+from app.assets.services.ingest import (
+    DependencyMissingError,
+    HashMismatchError,
+    create_from_hash,
+    upload_from_temp_path,
+)
+from app.assets.database.queries import (
+    AddTagsResult,
+    RemoveTagsResult,
+)
+from app.assets.services.schemas import (
+    AssetData,
+    AssetDetailResult,
+    AssetSummaryData,
+    DownloadResolutionResult,
+    IngestResult,
+    ListAssetsResult,
+    ReferenceData,
+    RegisterAssetResult,
+    TagUsage,
+    UploadResult,
+    UserMetadata,
+)
+from app.assets.services.tagging import (
+    apply_tags,
+    list_tags,
+    remove_tags,
+)
+
+__all__ = [
+    "AddTagsResult",
+    "AssetData",
+    "AssetDetailResult",
+    "AssetSummaryData",
+    "ReferenceData",
+    "BulkInsertResult",
+    "DependencyMissingError",
+    "DownloadResolutionResult",
+    "HashMismatchError",
+    "IngestResult",
+    "ListAssetsResult",
+    "RegisterAssetResult",
+    "RemoveTagsResult",
+    "TagUsage",
+    "UploadResult",
+    "UserMetadata",
+    "apply_tags",
+    "asset_exists",
+    "batch_insert_seed_assets",
+    "create_from_hash",
+    "delete_asset_reference",
+    "get_asset_by_hash",
+    "get_asset_detail",
+    "get_mtime_ns",
+    "get_size_and_mtime_ns",
+    "list_assets_page",
+    "list_files_recursively",
+    "list_tags",
+    "cleanup_unreferenced_assets",
+    "remove_tags",
+    "resolve_asset_for_download",
+    "set_asset_preview",
+    "update_asset_metadata",
+    "upload_from_temp_path",
+    "verify_file_unchanged",
+]
diff --git a/app/assets/services/asset_management.py b/app/assets/services/asset_management.py
new file mode 100644
index 000000000..3fe7115c8
--- /dev/null
+++ b/app/assets/services/asset_management.py
@@ -0,0 +1,309 @@
+import contextlib
+import mimetypes
+import os
+from typing import Sequence
+
+
+from app.assets.database.models import Asset
+from app.assets.database.queries import (
+    asset_exists_by_hash,
+    reference_exists_for_asset_id,
+    delete_reference_by_id,
+    fetch_reference_and_asset,
+    soft_delete_reference_by_id,
+    fetch_reference_asset_and_tags,
+    get_asset_by_hash as queries_get_asset_by_hash,
+    get_reference_by_id,
+    get_reference_with_owner_check,
+    list_references_page,
+    list_references_by_asset_id,
+    set_reference_metadata,
+    set_reference_preview,
+    set_reference_tags,
+    update_reference_access_time,
+    update_reference_name,
+    update_reference_updated_at,
+)
+from app.assets.helpers import select_best_live_path
+from app.assets.services.path_utils import compute_relative_filename
+from app.assets.services.schemas import (
+    AssetData,
+    AssetDetailResult,
+    AssetSummaryData,
+    DownloadResolutionResult,
+    ListAssetsResult,
+    UserMetadata,
+    extract_asset_data,
+    extract_reference_data,
+)
+from app.database.db import create_session
+
+
+def get_asset_detail(
+    reference_id: str,
+    owner_id: str = "",
+) -> AssetDetailResult | None:
+    with create_session() as session:
+        result = fetch_reference_asset_and_tags(
+            session,
+            reference_id=reference_id,
+            owner_id=owner_id,
+        )
+        if not result:
+            return None
+
+        ref, asset, tags = result
+        return AssetDetailResult(
+            ref=extract_reference_data(ref),
+            asset=extract_asset_data(asset),
+            tags=tags,
+        )
+
+
+def update_asset_metadata(
+    reference_id: str,
+    name: str | None = None,
+    tags: Sequence[str] | None = None,
+    user_metadata: UserMetadata = None,
+    tag_origin: str = "manual",
+    owner_id: str = "",
+) -> AssetDetailResult:
+    with create_session() as session:
+        ref = get_reference_with_owner_check(session, reference_id, owner_id)
+
+        touched = False
+        if name is not None and name != ref.name:
+            update_reference_name(session, reference_id=reference_id, name=name)
+            touched = True
+
+        computed_filename = compute_relative_filename(ref.file_path) if ref.file_path else None
+
+        new_meta: dict | None = None
+        if user_metadata is not None:
+            new_meta = dict(user_metadata)
+        elif computed_filename:
+            current_meta = ref.user_metadata or {}
+            if current_meta.get("filename") != computed_filename:
+                new_meta = dict(current_meta)
+
+        if new_meta is not None:
+            if computed_filename:
+                new_meta["filename"] = computed_filename
+            set_reference_metadata(
+                session, reference_id=reference_id, user_metadata=new_meta
+            )
+            touched = True
+
+        if tags is not None:
+            set_reference_tags(
+                session,
+                reference_id=reference_id,
+                tags=tags,
+                origin=tag_origin,
+            )
+            touched = True
+
+        if touched and user_metadata is None:
+            update_reference_updated_at(session, reference_id=reference_id)
+
+        result = fetch_reference_asset_and_tags(
+            session,
+            reference_id=reference_id,
+            owner_id=owner_id,
+        )
+        if not result:
+            raise RuntimeError("State changed during update")
+
+        ref, asset, tag_list = result
+        detail = AssetDetailResult(
+            ref=extract_reference_data(ref),
+            asset=extract_asset_data(asset),
+            tags=tag_list,
+        )
+        session.commit()
+
+        return detail
+
+
+def delete_asset_reference(
+    reference_id: str,
+    owner_id: str,
+    delete_content_if_orphan: bool = True,
+) -> bool:
+    with create_session() as session:
+        if not delete_content_if_orphan:
+            # Soft delete: mark the reference as deleted but keep everything
+            deleted = soft_delete_reference_by_id(
+                session, reference_id=reference_id, owner_id=owner_id
+            )
+            session.commit()
+            return deleted
+
+        ref_row = get_reference_by_id(session, reference_id=reference_id)
+        asset_id = ref_row.asset_id if ref_row else None
+        file_path = ref_row.file_path if ref_row else None
+
+        deleted = delete_reference_by_id(
+            session, reference_id=reference_id, owner_id=owner_id
+        )
+        if not deleted:
+            session.commit()
+            return False
+
+        if not asset_id:
+            session.commit()
+            return True
+
+        still_exists = reference_exists_for_asset_id(session, asset_id=asset_id)
+        if still_exists:
+            session.commit()
+            return True
+
+        # Orphaned asset - delete it and its files
+        refs = list_references_by_asset_id(session, asset_id=asset_id)
+        file_paths = [
+            r.file_path for r in (refs or []) if getattr(r, "file_path", None)
+        ]
+        # Also include the just-deleted file path
+        if file_path:
+            file_paths.append(file_path)
+
+        asset_row = session.get(Asset, asset_id)
+        if asset_row is not None:
+            session.delete(asset_row)
+
+        session.commit()
+
+        # Delete files after commit
+        for p in file_paths:
+            with contextlib.suppress(Exception):
+                if p and os.path.isfile(p):
+                    os.remove(p)
+
+    return True
+
+
+def set_asset_preview(
+    reference_id: str,
+    preview_asset_id: str | None = None,
+    owner_id: str = "",
+) -> AssetDetailResult:
+    with create_session() as session:
+        get_reference_with_owner_check(session, reference_id, owner_id)
+
+        set_reference_preview(
+            session,
+            reference_id=reference_id,
+            preview_asset_id=preview_asset_id,
+        )
+
+        result = fetch_reference_asset_and_tags(
+            session, reference_id=reference_id, owner_id=owner_id
+        )
+        if not result:
+            raise RuntimeError("State changed during preview update")
+
+        ref, asset, tags = result
+        detail = AssetDetailResult(
+            ref=extract_reference_data(ref),
+            asset=extract_asset_data(asset),
+            tags=tags,
+        )
+        session.commit()
+
+        return detail
+
+
+def asset_exists(asset_hash: str) -> bool:
+    with create_session() as session:
+        return asset_exists_by_hash(session, asset_hash=asset_hash)
+
+
+def get_asset_by_hash(asset_hash: str) -> AssetData | None:
+    with create_session() as session:
+        asset = queries_get_asset_by_hash(session, asset_hash=asset_hash)
+        return extract_asset_data(asset)
+
+
+def list_assets_page(
+    owner_id: str = "",
+    include_tags: Sequence[str] | None = None,
+    exclude_tags: Sequence[str] | None = None,
+    name_contains: str | None = None,
+    metadata_filter: dict | None = None,
+    limit: int = 20,
+    offset: int = 0,
+    sort: str = "created_at",
+    order: str = "desc",
+) -> ListAssetsResult:
+    with create_session() as session:
+        refs, tag_map, total = list_references_page(
+            session,
+            owner_id=owner_id,
+            include_tags=include_tags,
+            exclude_tags=exclude_tags,
+            name_contains=name_contains,
+            metadata_filter=metadata_filter,
+            limit=limit,
+            offset=offset,
+            sort=sort,
+            order=order,
+        )
+
+        items: list[AssetSummaryData] = []
+        for ref in refs:
+            items.append(
+                AssetSummaryData(
+                    ref=extract_reference_data(ref),
+                    asset=extract_asset_data(ref.asset),
+                    tags=tag_map.get(ref.id, []),
+                )
+            )
+
+        return ListAssetsResult(items=items, total=total)
+
+
+def resolve_asset_for_download(
+    reference_id: str,
+    owner_id: str = "",
+) -> DownloadResolutionResult:
+    with create_session() as session:
+        pair = fetch_reference_and_asset(
+            session, reference_id=reference_id, owner_id=owner_id
+        )
+        if not pair:
+            raise ValueError(f"AssetReference {reference_id} not found")
+
+        ref, asset = pair
+
+        # For references with file_path, use that directly
+        if ref.file_path and os.path.isfile(ref.file_path):
+            abs_path = ref.file_path
+        else:
+            # For API-created refs without file_path, find a path from other refs
+            refs = list_references_by_asset_id(session, asset_id=asset.id)
+            abs_path = select_best_live_path(refs)
+            if not abs_path:
+                raise FileNotFoundError(
+                    f"No live path for AssetReference {reference_id} "
+                    f"(asset id={asset.id}, name={ref.name})"
+                )
+
+        # Capture ORM attributes before commit (commit expires loaded objects)
+        ref_name = ref.name
+        asset_mime = asset.mime_type
+
+        update_reference_access_time(session, reference_id=reference_id)
+        session.commit()
+
+        ctype = (
+            asset_mime
+            or mimetypes.guess_type(ref_name or abs_path)[0]
+            or "application/octet-stream"
+        )
+        download_name = ref_name or os.path.basename(abs_path)
+        return DownloadResolutionResult(
+            abs_path=abs_path,
+            content_type=ctype,
+            download_name=download_name,
+        )
diff --git a/app/assets/services/bulk_ingest.py b/app/assets/services/bulk_ingest.py
new file mode 100644
index 000000000..54e72730c
--- /dev/null
+++ b/app/assets/services/bulk_ingest.py
@@ -0,0 +1,280 @@
+from __future__ import annotations
+
+import os
+import uuid
+from dataclasses import dataclass
+from datetime import datetime
+from typing import TYPE_CHECKING, Any, TypedDict
+
+from sqlalchemy.orm import Session
+
+from app.assets.database.queries import (
+    bulk_insert_assets,
+    bulk_insert_references_ignore_conflicts,
+    bulk_insert_tags_and_meta,
+    delete_assets_by_ids,
+    get_existing_asset_ids,
+    get_reference_ids_by_ids,
+    get_references_by_paths_and_asset_ids,
+    get_unreferenced_unhashed_asset_ids,
+    restore_references_by_paths,
+)
+from app.assets.helpers import get_utc_now
+
+if TYPE_CHECKING:
+    from app.assets.services.metadata_extract import ExtractedMetadata
+
+
+class SeedAssetSpec(TypedDict):
+    """Spec for seeding an asset from filesystem."""
+
+    abs_path: str
+    size_bytes: int
+    mtime_ns: int
+    info_name: str
+    tags: list[str]
+    fname: str
+    metadata: ExtractedMetadata | None
+    hash: str | None
+    mime_type: str | None
+
+
+class AssetRow(TypedDict):
+    """Row data for inserting an Asset."""
+
+    id: str
+    hash: str | None
+    size_bytes: int
+    mime_type: str | None
+    created_at: datetime
+
+
+class ReferenceRow(TypedDict):
+    """Row data for inserting an AssetReference."""
+
+    id: str
+    asset_id: str
+    file_path: str
+    mtime_ns: int
+    owner_id: str
+    name: str
+    preview_id: str | None
+    user_metadata: dict[str, Any] | None
+    created_at: datetime
+    updated_at: datetime
+    last_access_time: datetime
+
+
+class TagRow(TypedDict):
+    """Row data for inserting a Tag."""
+
+    asset_reference_id: str
+    tag_name: str
+    origin: str
+    added_at: datetime
+
+
+class MetadataRow(TypedDict):
+    """Row data for inserting asset metadata."""
+
+    asset_reference_id: str
+    key: str
+    ordinal: int
+    val_str: str | None
+    val_num: float | None
+    val_bool: bool | None
+    val_json: dict[str, Any] | None
+
+
+@dataclass
+class BulkInsertResult:
+    """Result of bulk asset insertion."""
+
+    inserted_refs: int
+    won_paths: int
+    lost_paths: int
+
+
+def batch_insert_seed_assets(
+    session: Session,
+    specs: list[SeedAssetSpec],
+    owner_id: str = "",
+) -> BulkInsertResult:
+    """Seed assets from filesystem specs in batch.
+
+    Each spec is a dict with keys:
+      - abs_path: str
+      - size_bytes: int
+      - mtime_ns: int
+      - info_name: str
+      - tags: list[str]
+      - fname: Optional[str]
+
+    This function orchestrates:
+    1. Insert seed Assets (hash=NULL)
+    2. Claim references with ON CONFLICT DO NOTHING on file_path
+    3. Query to find winners (paths where our asset_id was inserted)
+    4. Delete Assets for losers (path already claimed by another asset)
+    5. Insert tags and metadata for successfully inserted references
+
+    Returns:
+        BulkInsertResult with inserted_refs, won_paths, lost_paths
+    """
+    if not specs:
+        return BulkInsertResult(inserted_refs=0, won_paths=0, lost_paths=0)
+
+    current_time = get_utc_now()
+    asset_rows: list[AssetRow] = []
+    reference_rows: list[ReferenceRow] = []
+    path_to_asset_id: dict[str, str] = {}
+    asset_id_to_ref_data: dict[str, dict] = {}
+    absolute_path_list: list[str] = []
+
+    for spec in specs:
+        absolute_path = os.path.abspath(spec["abs_path"])
+        asset_id = str(uuid.uuid4())
+        reference_id = str(uuid.uuid4())
+        absolute_path_list.append(absolute_path)
+        path_to_asset_id[absolute_path] = asset_id
+
+        mime_type = spec.get("mime_type")
+        asset_rows.append(
+            {
+                "id": asset_id,
+                "hash": spec.get("hash"),
+                "size_bytes": spec["size_bytes"],
+                "mime_type": mime_type,
+                "created_at": current_time,
+            }
+        )
+
+        # Build user_metadata from extracted metadata or fallback to filename
+        extracted_metadata = spec.get("metadata")
+        if extracted_metadata:
+            user_metadata: dict[str, Any] | None = extracted_metadata.to_user_metadata()
+        elif spec["fname"]:
+            user_metadata = {"filename": spec["fname"]}
+        else:
+            user_metadata = None
+
+        reference_rows.append(
+            {
+                "id": reference_id,
+                "asset_id": asset_id,
+                "file_path": absolute_path,
+                "mtime_ns": spec["mtime_ns"],
+                "owner_id": owner_id,
+                "name": spec["info_name"],
+                "preview_id": None,
+                "user_metadata": user_metadata,
+                "created_at": current_time,
+                "updated_at": current_time,
+                "last_access_time": current_time,
+            }
+        )
+
+        asset_id_to_ref_data[asset_id] = {
+            "reference_id": reference_id,
+            "tags": spec["tags"],
+            "filename": spec["fname"],
+            "extracted_metadata": extracted_metadata,
+        }
+
+    bulk_insert_assets(session, asset_rows)
+
+    # Filter reference rows to only those whose assets were actually inserted
+    # (assets with duplicate hashes are silently dropped by ON CONFLICT DO NOTHING)
+    inserted_asset_ids = get_existing_asset_ids(
+        session, [r["asset_id"] for r in reference_rows]
+    )
+    reference_rows = [r for r in reference_rows if r["asset_id"] in inserted_asset_ids]
+
+    bulk_insert_references_ignore_conflicts(session, reference_rows)
+    restore_references_by_paths(session, absolute_path_list)
+    winning_paths = get_references_by_paths_and_asset_ids(session, path_to_asset_id)
+
+    inserted_paths = {
+        path
+        for path in absolute_path_list
+        if path_to_asset_id[path] in inserted_asset_ids
+    }
+    losing_paths = inserted_paths - winning_paths
+    lost_asset_ids = [path_to_asset_id[path] for path in losing_paths]
+
+    if lost_asset_ids:
+        delete_assets_by_ids(session, lost_asset_ids)
+
+    if not winning_paths:
+        return BulkInsertResult(
+            inserted_refs=0,
+            won_paths=0,
+            lost_paths=len(losing_paths),
+        )
+
+    # Get reference IDs for winners
+    winning_ref_ids = [
+        asset_id_to_ref_data[path_to_asset_id[path]]["reference_id"]
+        for path in winning_paths
+    ]
+    inserted_ref_ids = get_reference_ids_by_ids(session, winning_ref_ids)
+
+    tag_rows: list[TagRow] = []
+    metadata_rows: list[MetadataRow] = []
+
+    if inserted_ref_ids:
+        for path in winning_paths:
+            asset_id = path_to_asset_id[path]
+            ref_data = asset_id_to_ref_data[asset_id]
+            ref_id = ref_data["reference_id"]
+
+            if ref_id not in inserted_ref_ids:
+                continue
+
+            for tag in ref_data["tags"]:
+                tag_rows.append(
+                    {
+                        "asset_reference_id": ref_id,
+                        "tag_name": tag,
+                        "origin": "automatic",
+                        "added_at": current_time,
+                    }
+                )
+
+            # Use extracted metadata for meta rows if available
+            extracted_metadata = ref_data.get("extracted_metadata")
+            if extracted_metadata:
+                metadata_rows.extend(extracted_metadata.to_meta_rows(ref_id))
+            elif ref_data["filename"]:
+                # Fallback: just store filename
+                metadata_rows.append(
+                    {
+                        "asset_reference_id": ref_id,
+                        "key": "filename",
+                        "ordinal": 0,
+                        "val_str": ref_data["filename"],
+                        "val_num": None,
+                        "val_bool": None,
+                        "val_json": None,
+                    }
+                )
+
+    bulk_insert_tags_and_meta(session, tag_rows=tag_rows, meta_rows=metadata_rows)
+
+    return BulkInsertResult(
+        inserted_refs=len(inserted_ref_ids),
+        won_paths=len(winning_paths),
+        lost_paths=len(losing_paths),
+    )
+
+
+def cleanup_unreferenced_assets(session: Session) -> int:
+    """Hard-delete unhashed assets with no active references.
+
+    This is a destructive operation intended for explicit cleanup.
+    Only deletes assets where hash=None and all references are missing.
+
+    Returns:
+        Number of assets deleted
+    """
+    unreferenced_ids = get_unreferenced_unhashed_asset_ids(session)
+    return delete_assets_by_ids(session, unreferenced_ids)
diff --git a/app/assets/services/file_utils.py b/app/assets/services/file_utils.py
new file mode 100644
index 000000000..c47ebe460
--- /dev/null
+++ b/app/assets/services/file_utils.py
@@ -0,0 +1,70 @@
+import os
+
+
+def get_mtime_ns(stat_result: os.stat_result) -> int:
+    """Extract mtime in nanoseconds from a stat result."""
+    return getattr(
+        stat_result, "st_mtime_ns", int(stat_result.st_mtime * 1_000_000_000)
+    )
+
+
+def get_size_and_mtime_ns(path: str, follow_symlinks: bool = True) -> tuple[int, int]:
+    """Get file size in bytes and mtime in nanoseconds."""
+    st = os.stat(path, follow_symlinks=follow_symlinks)
+    return st.st_size, get_mtime_ns(st)
+
+
+def verify_file_unchanged(
+    mtime_db: int | None,
+    size_db: int | None,
+    stat_result: os.stat_result,
+) -> bool:
+    """Check if a file is unchanged based on mtime and size.
+
+    Returns True if the file's mtime and size match the database values.
+    Returns False if mtime_db is None or values don't match.
+
+    size_db=None means don't check size; 0 is a valid recorded size.
+    """
+    if mtime_db is None:
+        return False
+    actual_mtime_ns = get_mtime_ns(stat_result)
+    if int(mtime_db) != int(actual_mtime_ns):
+        return False
+    if size_db is not None:
+        return int(stat_result.st_size) == int(size_db)
+    return True
+
+
+def is_visible(name: str) -> bool:
+    """Return True if a file or directory name is visible (not hidden)."""
+    return not name.startswith(".")
+
+
+def list_files_recursively(base_dir: str) -> list[str]:
+    """Recursively list all files in a directory, following symlinks."""
+    out: list[str] = []
+    base_abs = os.path.abspath(base_dir)
+    if not os.path.isdir(base_abs):
+        return out
+    # Track seen real directory identities to prevent circular symlink loops
+    seen_dirs: set[tuple[int, int]] = set()
+    for dirpath, subdirs, filenames in os.walk(
+        base_abs, topdown=True, followlinks=True
+    ):
+        try:
+            st = os.stat(dirpath)
+            dir_id = (st.st_dev, st.st_ino)
+        except OSError:
+            subdirs.clear()
+            continue
+        if dir_id in seen_dirs:
+            subdirs.clear()
+            continue
+        seen_dirs.add(dir_id)
+        subdirs[:] = [d for d in subdirs if is_visible(d)]
+        for name in filenames:
+            if not is_visible(name):
+                continue
+            out.append(os.path.abspath(os.path.join(dirpath, name)))
+    return out
diff --git a/app/assets/services/hashing.py b/app/assets/services/hashing.py
new file mode 100644
index 000000000..92aee6402
--- /dev/null
+++ b/app/assets/services/hashing.py
@@ -0,0 +1,95 @@
+import io
+import os
+from contextlib import contextmanager
+from dataclasses import dataclass
+from typing import IO, Any, Callable, Iterator
+
+from blake3 import blake3
+
+DEFAULT_CHUNK = 8 * 1024 * 1024
+
+InterruptCheck = Callable[[], bool]
+
+
+@dataclass
+class HashCheckpoint:
+    """Saved state for resuming an interrupted hash computation."""
+
+    bytes_processed: int
+    hasher: Any  # blake3 hasher instance
+    mtime_ns: int = 0
+    file_size: int = 0
+
+
+@contextmanager
+def _open_for_hashing(fp: str | IO[bytes]) -> Iterator[tuple[IO[bytes], bool]]:
+    """Yield (file_object, is_path) with appropriate setup/teardown."""
+    if hasattr(fp, "read"):
+        seekable = getattr(fp, "seekable", lambda: False)()
+        orig_pos = None
+        if seekable:
+            try:
+                orig_pos = fp.tell()
+                if orig_pos != 0:
+                    fp.seek(0)
+            except io.UnsupportedOperation:
+                orig_pos = None
+        try:
+            yield fp, False
+        finally:
+            if orig_pos is not None:
+                fp.seek(orig_pos)
+    else:
+        with open(os.fspath(fp), "rb") as f:
+            yield f, True
+
+
+def compute_blake3_hash(
+    fp: str | IO[bytes],
+    chunk_size: int = DEFAULT_CHUNK,
+    interrupt_check: InterruptCheck | None = None,
+    checkpoint: HashCheckpoint | None = None,
+) -> tuple[str | None, HashCheckpoint | None]:
+    """Compute BLAKE3 hash of a file, with optional checkpoint support.
+
+    Args:
+        fp: File path or file-like object
+        chunk_size: Size of chunks to read at a time
+        interrupt_check: Optional callable that returns True if the operation
+            should be interrupted (e.g. paused or cancelled). Must be
+            non-blocking so file handles are released immediately. Checked
+            between chunk reads.
+        checkpoint: Optional checkpoint to resume from (file paths only)
+
+    Returns:
+        Tuple of (hex_digest, None) on completion, or
+        (None, checkpoint) on interruption (file paths only), or
+        (None, None) on interruption of a file object
+    """
+    if chunk_size <= 0:
+        chunk_size = DEFAULT_CHUNK
+
+    with _open_for_hashing(fp) as (f, is_path):
+        if checkpoint is not None and is_path:
+            f.seek(checkpoint.bytes_processed)
+            h = checkpoint.hasher
+            bytes_processed = checkpoint.bytes_processed
+        else:
+            h = blake3()
+            bytes_processed = 0
+
+        while True:
+            if interrupt_check is not None and interrupt_check():
+                if is_path:
+                    return None, HashCheckpoint(
+                        bytes_processed=bytes_processed,
+                        hasher=h,
+                    )
+                return None, None
+            chunk = f.read(chunk_size)
+            if not chunk:
+                break
+            h.update(chunk)
+            bytes_processed += len(chunk)
+
+        return h.hexdigest(), None
diff --git a/app/assets/services/ingest.py b/app/assets/services/ingest.py
new file mode 100644
index 000000000..44d7aef36
--- /dev/null
+++ b/app/assets/services/ingest.py
@@ -0,0 +1,375 @@
+import contextlib
+import logging
+import mimetypes
+import os
+from typing import Any, Sequence
+
+from sqlalchemy.orm import Session
+
+import app.assets.services.hashing as hashing
+from app.assets.database.queries import (
+    add_tags_to_reference,
+    fetch_reference_and_asset,
+    get_asset_by_hash,
+    get_existing_asset_ids,
+    get_reference_by_file_path,
+    get_reference_tags,
+    get_or_create_reference,
+    remove_missing_tag_for_asset_id,
+    set_reference_metadata,
+    set_reference_tags,
+    upsert_asset,
+    upsert_reference,
+    validate_tags_exist,
+)
+from app.assets.helpers import normalize_tags
+from app.assets.services.file_utils import get_size_and_mtime_ns
+from app.assets.services.path_utils import (
+    compute_relative_filename,
+    resolve_destination_from_tags,
+    validate_path_within_base,
+)
+from app.assets.services.schemas import (
+    IngestResult,
+    RegisterAssetResult,
+    UploadResult,
+    UserMetadata,
+    extract_asset_data,
+    extract_reference_data,
+)
+from app.database.db import create_session
+
+
+def _ingest_file_from_path(
+    abs_path: str,
+    asset_hash: str,
+    size_bytes: int,
+    mtime_ns: int,
+    mime_type: str | None = None,
+    info_name: str | None = None,
+    owner_id: str = "",
+    preview_id: str | None = None,
+    user_metadata: UserMetadata = None,
+    tags: Sequence[str] = (),
+    tag_origin: str = "manual",
+    require_existing_tags: bool = False,
+) -> IngestResult:
+    locator = os.path.abspath(abs_path)
+    user_metadata = user_metadata or {}
+
+    asset_created = False
+    asset_updated = False
+    ref_created = False
+    ref_updated = False
+    reference_id: str | None = None
+
+    with create_session() as session:
+        if preview_id:
+            if preview_id not in get_existing_asset_ids(session, [preview_id]):
+                preview_id = None
+
+        asset, asset_created, asset_updated = upsert_asset(
+            session,
+            asset_hash=asset_hash,
+            size_bytes=size_bytes,
+            mime_type=mime_type,
+        )
+
+        ref_created, ref_updated = upsert_reference(
+            session,
+            asset_id=asset.id,
+            file_path=locator,
+            name=info_name or os.path.basename(locator),
+            mtime_ns=mtime_ns,
+            owner_id=owner_id,
+        )
+
+        # Get the reference we just created/updated
+        ref = get_reference_by_file_path(session, locator)
+        if ref:
+            reference_id = ref.id
+
+            if preview_id and ref.preview_id != preview_id:
+                ref.preview_id = preview_id
+
+            norm = normalize_tags(list(tags))
+            if norm:
+                if require_existing_tags:
+                    validate_tags_exist(session, norm)
+                add_tags_to_reference(
+                    session,
+                    reference_id=reference_id,
+                    tags=norm,
+                    origin=tag_origin,
+                    create_if_missing=not require_existing_tags,
+                )
+
+            _update_metadata_with_filename(
+                session,
+                reference_id=reference_id,
+                file_path=ref.file_path,
+                current_metadata=ref.user_metadata,
+                user_metadata=user_metadata,
+            )
+
+        try:
+            remove_missing_tag_for_asset_id(session, asset_id=asset.id)
+        except Exception:
+            logging.exception("Failed to clear 'missing' tag for asset %s", asset.id)
+
+        session.commit()
+
+    return IngestResult(
+        asset_created=asset_created,
+        asset_updated=asset_updated,
+        ref_created=ref_created,
+        ref_updated=ref_updated,
+        reference_id=reference_id,
+    )
+
+
+def _register_existing_asset(
+    asset_hash: str,
+    name: str,
+    user_metadata: UserMetadata = None,
+    tags: list[str] | None = None,
+    tag_origin: str = "manual",
+    owner_id: str = "",
+) -> RegisterAssetResult:
+    user_metadata = user_metadata or {}
+
+    with create_session() as session:
+        asset = get_asset_by_hash(session, asset_hash=asset_hash)
+        if not asset:
+            raise ValueError(f"No asset with hash {asset_hash}")
+
+        ref, ref_created = get_or_create_reference(
+            session,
+            asset_id=asset.id,
+            owner_id=owner_id,
+            name=name,
+        )
+
+        if not ref_created:
+            tag_names = get_reference_tags(session, reference_id=ref.id)
+            result = RegisterAssetResult(
+                ref=extract_reference_data(ref),
+                asset=extract_asset_data(asset),
+                tags=tag_names,
+                created=False,
+            )
+            session.commit()
+            return result
+
+        new_meta = dict(user_metadata)
+        computed_filename = compute_relative_filename(ref.file_path) if ref.file_path else None
+        if computed_filename:
+            new_meta["filename"] = computed_filename
+
+        if new_meta:
+            set_reference_metadata(
+                session,
+                reference_id=ref.id,
+                user_metadata=new_meta,
+            )
+
+        if tags is not None:
+            set_reference_tags(
+                session,
+                reference_id=ref.id,
+                tags=tags,
+                origin=tag_origin,
+            )
+
+        tag_names = get_reference_tags(session, reference_id=ref.id)
+        session.refresh(ref)
+        result = RegisterAssetResult(
+            ref=extract_reference_data(ref),
+            asset=extract_asset_data(asset),
+            tags=tag_names,
+            created=True,
+        )
+        session.commit()
+
+        return result
+
+
+
+def _update_metadata_with_filename(
+    session: Session,
+    reference_id: str,
+    file_path: str | None,
+    current_metadata: dict | None,
+    user_metadata: dict[str, Any],
+) -> None:
+    computed_filename = compute_relative_filename(file_path) if file_path else None
+
+    current_meta = current_metadata or {}
+    new_meta = dict(current_meta)
+    for k, v in user_metadata.items():
+        new_meta[k] = v
+    if computed_filename:
+        new_meta["filename"] = computed_filename
+
+    if new_meta != current_meta:
+        set_reference_metadata(
+            session,
+            reference_id=reference_id,
+            user_metadata=new_meta,
+        )
+
+
+def _sanitize_filename(name: str | None, fallback: str) -> str:
+    n = os.path.basename((name or "").strip() or fallback)
+    return n if n else fallback
+
+
+class HashMismatchError(Exception):
+    pass
+
+
+class DependencyMissingError(Exception):
+    def __init__(self, message: str):
+        self.message = message
+        super().__init__(message)
+
+
+def upload_from_temp_path(
+    temp_path: str,
+    name: str | None = None,
+    tags: list[str] | None = None,
+    user_metadata: dict | None = None,
+    client_filename: str | None = None,
+    owner_id: str = "",
+    expected_hash: str | None = None,
+) -> UploadResult:
+    try:
+        digest, _ = hashing.compute_blake3_hash(temp_path)
+    except ImportError as e:
+        raise DependencyMissingError(str(e))
+    except Exception as e:
+        raise RuntimeError(f"failed to hash uploaded file: {e}")
+    asset_hash = "blake3:" + digest
+
+    if expected_hash and asset_hash != expected_hash.strip().lower():
+        raise HashMismatchError("Uploaded file hash does not match provided hash.")
+
+    with create_session() as session:
+        existing = get_asset_by_hash(session, asset_hash=asset_hash)
+
+    if existing is not None:
+        with contextlib.suppress(Exception):
+            if temp_path and os.path.exists(temp_path):
+                os.remove(temp_path)
+
+        display_name = _sanitize_filename(name or client_filename, fallback=digest)
+        result = _register_existing_asset(
+            asset_hash=asset_hash,
+            name=display_name,
+            user_metadata=user_metadata or {},
+            tags=tags or [],
+            tag_origin="manual",
+            owner_id=owner_id,
+        )
+        return UploadResult(
+            ref=result.ref,
+            asset=result.asset,
+            tags=result.tags,
+            created_new=False,
+        )
+
+    if not tags:
+        raise ValueError("tags are required for new asset uploads")
+    base_dir, subdirs = resolve_destination_from_tags(tags)
+    dest_dir = os.path.join(base_dir, *subdirs) if subdirs else base_dir
+    os.makedirs(dest_dir, exist_ok=True)
+
+    src_for_ext = (client_filename or name or "").strip()
+    _ext = os.path.splitext(os.path.basename(src_for_ext))[1] if src_for_ext else ""
+    ext = _ext if 0 < len(_ext) <= 16 else ""
+    hashed_basename = f"{digest}{ext}"
+    dest_abs = os.path.abspath(os.path.join(dest_dir, hashed_basename))
+    validate_path_within_base(dest_abs, base_dir)
+
+    content_type = (
+        mimetypes.guess_type(os.path.basename(src_for_ext), strict=False)[0]
+        or mimetypes.guess_type(hashed_basename, strict=False)[0]
+        or "application/octet-stream"
+    )
+
+    try:
+        os.replace(temp_path, dest_abs)
+    except Exception as e:
+        raise RuntimeError(f"failed to move uploaded file into place: {e}")
+
+    try:
+        size_bytes, mtime_ns = get_size_and_mtime_ns(dest_abs)
+    except OSError as e:
+        raise RuntimeError(f"failed to stat destination file: {e}")
+
+    ingest_result = _ingest_file_from_path(
+        asset_hash=asset_hash,
+        abs_path=dest_abs,
+        size_bytes=size_bytes,
+        mtime_ns=mtime_ns,
+        mime_type=content_type,
+        info_name=_sanitize_filename(name or client_filename, fallback=digest),
+        owner_id=owner_id,
+        preview_id=None,
+        user_metadata=user_metadata or {},
+        tags=tags,
+        tag_origin="manual",
+        require_existing_tags=False,
+    )
+    reference_id = ingest_result.reference_id
+    if not reference_id:
+        raise RuntimeError("failed to create asset reference")
+
+    with create_session() as session:
+        pair = fetch_reference_and_asset(
+            session, reference_id=reference_id, owner_id=owner_id
+        )
+        if not pair:
+            raise RuntimeError("inconsistent DB state after ingest")
+        ref, asset = pair
+        tag_names = get_reference_tags(session, reference_id=ref.id)
+
+    return UploadResult(
+        ref=extract_reference_data(ref),
+        asset=extract_asset_data(asset),
+        tags=tag_names,
+        created_new=ingest_result.asset_created,
+    )
+
+
+def create_from_hash(
+    hash_str: str,
+    name: str,
+    tags: list[str] | None = None,
+    user_metadata: dict | None = None,
+    owner_id: str = "",
+) -> UploadResult | None:
+    canonical = hash_str.strip().lower()
+
+    with create_session() as session:
+        asset = get_asset_by_hash(session, asset_hash=canonical)
+        if not asset:
+            return None
+
+    result = _register_existing_asset(
+        asset_hash=canonical,
+        name=_sanitize_filename(
+            name, fallback=canonical.split(":", 1)[1] if ":" in canonical else canonical
+        ),
+        user_metadata=user_metadata or {},
+        tags=tags or [],
+        tag_origin="manual",
+        owner_id=owner_id,
+    )
+
+    return UploadResult(
+        ref=result.ref,
+        asset=result.asset,
+        tags=result.tags,
+        created_new=False,
+    )
diff --git a/app/assets/services/metadata_extract.py b/app/assets/services/metadata_extract.py
new file mode 100644
index 000000000..a004929bc
--- /dev/null
+++ b/app/assets/services/metadata_extract.py
@@ -0,0 +1,327 @@
+"""Metadata extraction for asset scanning.
+
+Tier 1: Filesystem metadata (zero parsing)
+Tier 2: Safetensors header metadata (fast JSON read only)
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import mimetypes
+import os
+import struct
+from dataclasses import dataclass
+from typing import Any
+
+from utils.mime_types import init_mime_types
+
+init_mime_types()
+
+# Supported safetensors extensions
+SAFETENSORS_EXTENSIONS = frozenset({".safetensors", ".sft"})
+
+# Maximum safetensors header size to read (8MB)
+MAX_SAFETENSORS_HEADER_SIZE = 8 * 1024 * 1024
+
+
+@dataclass
+class ExtractedMetadata:
+    """Metadata extracted from a file during scanning."""
+
+    # Tier 1: Filesystem (always available)
+    filename: str = ""
+    file_path: str = ""  # Full absolute path to the file
+    content_length: int = 0
+    content_type: str | None = None
+    format: str = ""  # file extension without dot
+
+    # Tier 2: Safetensors header (if available)
+    base_model: str | None = None
+    trained_words: list[str] | None = None
+    air: str | None = None  # CivitAI AIR identifier
+    has_preview_images: bool = False
+
+    # Source provenance (populated if embedded in safetensors)
+    source_url: str | None = None
+    source_arn: str | None = None
+    repo_url: str | None = None
+    preview_url: str | None = None
+    source_hash: str | None = None
+
+    # HuggingFace specific
+    repo_id: str | None = None
+    revision: str | None = None
+    filepath: str | None = None
+    resolve_url: str | None = None
+
+    def to_user_metadata(self) -> dict[str, Any]:
+        """Convert to user_metadata dict for AssetReference.user_metadata JSON field."""
+        data: dict[str, Any] = {
+            "filename": self.filename,
+            "content_length": self.content_length,
+            "format": self.format,
+        }
+        if self.file_path:
+            data["file_path"] = self.file_path
+        if self.content_type:
+            data["content_type"] = self.content_type
+
+        # Tier 2 fields
+        if self.base_model:
+            data["base_model"] = self.base_model
+        if self.trained_words:
+            data["trained_words"] = self.trained_words
+        if self.air:
+            data["air"] = self.air
+        if self.has_preview_images:
+            data["has_preview_images"] = True
+
+        # Source provenance
+        if self.source_url:
+            data["source_url"] = self.source_url
+        if self.source_arn:
+            data["source_arn"] = self.source_arn
+        if self.repo_url:
+            data["repo_url"] = self.repo_url
+        if self.preview_url:
+            data["preview_url"] = self.preview_url
+        if self.source_hash:
+            data["source_hash"] = self.source_hash
+
+        # HuggingFace
+        if self.repo_id:
+            data["repo_id"] = self.repo_id
+        if self.revision:
+            data["revision"] = self.revision
+        if self.filepath:
+            data["filepath"] = self.filepath
+        if self.resolve_url:
+            data["resolve_url"] = self.resolve_url
+
+        return data
+
+    def to_meta_rows(self, reference_id: str) -> list[dict]:
+        """Convert to asset_reference_meta rows for typed/indexed querying."""
+        rows: list[dict] = []
+
+        def add_str(key: str, val: str | None, ordinal: int = 0) -> None:
+            if val:
+                rows.append({
+                    "asset_reference_id": reference_id,
+                    "key": key,
+                    "ordinal": ordinal,
+                    "val_str": val[:2048] if len(val) > 2048 else val,
+                    "val_num": None,
+                    "val_bool": None,
+                    "val_json": None,
+                })
+
+        def add_num(key: str, val: int | float | None) -> None:
+            if val is not None:
+                rows.append({
+                    "asset_reference_id": reference_id,
+                    "key": key,
+                    "ordinal": 0,
+                    "val_str": None,
+                    "val_num": val,
+                    "val_bool": None,
+                    "val_json": None,
+                })
+
+        def add_bool(key: str, val: bool | None) -> None:
+            if val is not None:
+                rows.append({
+                    "asset_reference_id": reference_id,
+                    "key": key,
+                    "ordinal": 0,
+                    "val_str": None,
+                    "val_num": None,
+                    "val_bool": val,
+                    "val_json": None,
+                })
+
+        # Tier 1
+        add_str("filename", self.filename)
+        add_num("content_length", self.content_length)
+        add_str("content_type", self.content_type)
+        add_str("format", self.format)
+
+        # Tier 2
+        add_str("base_model", self.base_model)
+        add_str("air", self.air)
+        has_previews = self.has_preview_images if self.has_preview_images else None
+        add_bool("has_preview_images", has_previews)
+
+        # trained_words as multiple rows with ordinals
+        if self.trained_words:
+            for i, word in enumerate(self.trained_words[:100]):  # limit to 100 words
+                add_str("trained_words", word, ordinal=i)
+
+        # Source provenance
+        add_str("source_url", self.source_url)
+        add_str("source_arn", self.source_arn)
+        add_str("repo_url", self.repo_url)
+        add_str("preview_url", self.preview_url)
+        add_str("source_hash", self.source_hash)
+
+        # HuggingFace
+        add_str("repo_id", self.repo_id)
+        add_str("revision", self.revision)
+        add_str("filepath", self.filepath)
+        add_str("resolve_url", self.resolve_url)
+
+        return rows
+
+
+def _read_safetensors_header(
+    path: str, max_size: int = MAX_SAFETENSORS_HEADER_SIZE
+) -> dict[str, Any] | None:
+    """Read only the JSON header from a safetensors file.
+
+    This is very fast - reads 8 bytes for header length, then the JSON header.
+    No tensor data is loaded.
+
+    Args:
+        path: Absolute path to safetensors file
+        max_size: Maximum header size to read (default 8MB)
+
+    Returns:
+        Parsed header dict or None if failed
+    """
+    try:
+        with open(path, "rb") as f:
+            header_bytes = f.read(8)
+            if len(header_bytes) < 8:
+                return None
+            length_of_header = struct.unpack("<Q", header_bytes)[0]
+            if length_of_header > max_size:
+                return None
+            header_data = f.read(length_of_header)
+            if len(header_data) < length_of_header:
+                return None
+            return json.loads(header_data.decode("utf-8"))
+    except (OSError, json.JSONDecodeError, UnicodeDecodeError, struct.error):
+        return None
+
+
+def _extract_safetensors_metadata(
+    header: dict[str, Any], meta: ExtractedMetadata
+) -> None:
+    """Extract metadata from safetensors header __metadata__ section.
+
+    Modifies meta in-place.
+    """
+    st_meta = header.get("__metadata__", {})
+    if not isinstance(st_meta, dict):
+        return
+
+    # Common model metadata
+    meta.base_model = (
+        st_meta.get("ss_base_model_version")
+        or st_meta.get("modelspec.base_model")
+        or st_meta.get("base_model")
+    )
+
+    # Trained words / trigger words
+    trained_words = st_meta.get("ss_tag_frequency")
+    if trained_words and isinstance(trained_words, str):
+        try:
+            tag_freq = json.loads(trained_words)
+            # Extract unique tags from all datasets
+            all_tags: set[str] = set()
+            for dataset_tags in tag_freq.values():
+                if isinstance(dataset_tags, dict):
+                    all_tags.update(dataset_tags.keys())
+            if all_tags:
+                meta.trained_words = sorted(all_tags)[:100]
+        except json.JSONDecodeError:
+            pass
+
+    # Direct trained_words field (some formats)
+    if not meta.trained_words:
+        tw = st_meta.get("trained_words")
+        if isinstance(tw, str):
+            try:
+                parsed = json.loads(tw)
+                if isinstance(parsed, list):
+                    meta.trained_words = [str(x) for x in parsed]
+                else:
+                    meta.trained_words = [w.strip() for w in tw.split(",") if w.strip()]
+            except json.JSONDecodeError:
+                meta.trained_words = [w.strip() for w in tw.split(",") if w.strip()]
+        elif isinstance(tw, list):
+            meta.trained_words = [str(x) for x in tw]
+
+    # CivitAI AIR
+    meta.air = st_meta.get("air") or st_meta.get("modelspec.air")
+
+    # Preview images (ssmd_cover_images)
+    cover_images = st_meta.get("ssmd_cover_images")
+    if cover_images:
+        meta.has_preview_images = True
+
+    # Source provenance fields
+    meta.source_url = st_meta.get("source_url")
+    meta.source_arn = st_meta.get("source_arn")
+    meta.repo_url = st_meta.get("repo_url")
+    meta.preview_url = st_meta.get("preview_url")
+    meta.source_hash = st_meta.get("source_hash") or st_meta.get("sshs_model_hash")
+
+    # HuggingFace fields
+    meta.repo_id = st_meta.get("repo_id") or st_meta.get("hf_repo_id")
+    meta.revision = st_meta.get("revision") or st_meta.get("hf_revision")
+    meta.filepath = st_meta.get("filepath") or st_meta.get("hf_filepath")
+    meta.resolve_url = st_meta.get("resolve_url") or st_meta.get("hf_url")
+
+
+def extract_file_metadata(
+    abs_path: str,
+    stat_result: os.stat_result | None = None,
+    relative_filename: str | None = None,
+) -> ExtractedMetadata:
+    """Extract metadata from a file using tier 1 and tier 2 methods.
+
+    Tier 1: Filesystem metadata from path and stat
+    Tier 2: Safetensors header parsing if applicable
+
+    Args:
+        abs_path: Absolute path to the file
+        stat_result: Optional pre-fetched stat result (saves a syscall)
+        relative_filename: Optional relative filename to use instead of basename
+            (e.g., "flux/123/model.safetensors" for model paths)
+
+    Returns:
+        ExtractedMetadata with all available fields populated
+    """
+    meta = ExtractedMetadata()
+
+    # Tier 1: Filesystem metadata
+    meta.filename = relative_filename or os.path.basename(abs_path)
+    meta.file_path = abs_path
+    _, ext = os.path.splitext(abs_path)
+    meta.format = ext.lstrip(".").lower() if ext else ""
+
+    mime_type, _ = mimetypes.guess_type(abs_path)
+    meta.content_type = mime_type
+
+    # Size from stat
+    if stat_result is None:
+        try:
+            stat_result = os.stat(abs_path, follow_symlinks=True)
+        except OSError:
+            pass
+
+    if stat_result:
+        meta.content_length = stat_result.st_size
+
+    # Tier 2: Safetensors header (if applicable and enabled)
+    if ext.lower() in SAFETENSORS_EXTENSIONS:
+        header = _read_safetensors_header(abs_path)
+        if header:
+            try:
+                _extract_safetensors_metadata(header, meta)
+            except Exception as e:
+                logging.debug("Safetensors meta extract failed %s: %s", abs_path, e)
+
+    return meta
diff --git a/app/assets/services/path_utils.py b/app/assets/services/path_utils.py
new file mode 100644
index 000000000..f5dd7f7fd
--- /dev/null
+++ b/app/assets/services/path_utils.py
@@ -0,0 +1,167 @@
+import os
+from pathlib import Path
+from typing import Literal
+
+import folder_paths
+from app.assets.helpers import normalize_tags
+
+
+_NON_MODEL_FOLDER_NAMES = frozenset({"custom_nodes"})
+
+
+def get_comfy_models_folders() -> list[tuple[str, list[str]]]:
+    """Build list of (folder_name, base_paths[]) for all model locations.
+
+    Includes every category registered in folder_names_and_paths,
+    regardless of whether its paths are under the main models_dir,
+    but excludes non-model entries like custom_nodes.
+    """
+    targets: list[tuple[str, list[str]]] = []
+    for name, values in folder_paths.folder_names_and_paths.items():
+        if name in _NON_MODEL_FOLDER_NAMES:
+            continue
+        paths, _exts = values[0], values[1]
+        if paths:
+            targets.append((name, paths))
+    return targets
+
+
+def resolve_destination_from_tags(tags: list[str]) -> tuple[str, list[str]]:
+    """Validates and maps tags -> (base_dir, subdirs_for_fs)"""
+    if not tags:
+        raise ValueError("tags must not be empty")
+    root = tags[0].lower()
+    if root == "models":
+        if len(tags) < 2:
+            raise ValueError("at least two tags required for model asset")
+        try:
+            bases = folder_paths.folder_names_and_paths[tags[1]][0]
+        except KeyError:
+            raise ValueError(f"unknown model category '{tags[1]}'")
+        if not bases:
+            raise ValueError(f"no base path configured for category '{tags[1]}'")
+        base_dir = os.path.abspath(bases[0])
+        raw_subdirs = tags[2:]
+    elif root == "input":
+        base_dir = os.path.abspath(folder_paths.get_input_directory())
+        raw_subdirs = tags[1:]
+    elif root == "output":
+        base_dir = os.path.abspath(folder_paths.get_output_directory())
+        raw_subdirs = tags[1:]
+    else:
+        raise ValueError(f"unknown root tag '{tags[0]}'; expected 'models', 'input', or 'output'")
+    _sep_chars = frozenset(("/", "\\", os.sep))
+    for i in raw_subdirs:
+        if i in (".", "..") or _sep_chars & set(i):
+            raise ValueError("invalid path component in tags")
+
+    return base_dir, raw_subdirs if raw_subdirs else []
+
+
+def validate_path_within_base(candidate: str, base: str) -> None:
+    cand_abs = Path(os.path.abspath(candidate))
+    base_abs = Path(os.path.abspath(base))
+    if not cand_abs.is_relative_to(base_abs):
+        raise ValueError("destination escapes base directory")
+
+
+def compute_relative_filename(file_path: str) -> str | None:
+    """
+    Return the model's path relative to the last well-known folder (the model category),
+    using forward slashes, eg:
+      /.../models/checkpoints/flux/123/flux.safetensors -> "flux/123/flux.safetensors"
+      /.../models/text_encoders/clip_g.safetensors -> "clip_g.safetensors"
+
+    For non-model paths, returns None.
+    """
+    try:
+        root_category, rel_path = get_asset_category_and_relative_path(file_path)
+    except ValueError:
+        return None
+
+    p = Path(rel_path)
+    parts = [seg for seg in p.parts if seg not in (".", "..", p.anchor)]
+    if not parts:
+        return None
+
+    if root_category == "models":
+        # parts[0] is the category ("checkpoints", "vae", etc) – drop it
+        inside = parts[1:] if len(parts) > 1 else [parts[0]]
+        return "/".join(inside)
+    return "/".join(parts)  # input/output: keep all parts
+
+
+def get_asset_category_and_relative_path(
+    file_path: str,
+) -> tuple[Literal["input", "output", "models"], str]:
+    """Determine which root category a file path belongs to.
+
+    Categories:
+      - 'input': under folder_paths.get_input_directory()
+      - 'output': under folder_paths.get_output_directory()
+      - 'models': under any base path from get_comfy_models_folders()
+
+    Returns:
+        (root_category, relative_path_inside_that_root)
+
+    Raises:
+        ValueError: path does not belong to any known root.
+    """
+    fp_abs = os.path.abspath(file_path)
+
+    def _check_is_within(child: str, parent: str) -> bool:
+        return Path(child).is_relative_to(parent)
+
+    def _compute_relative(child: str, parent: str) -> str:
+        # Normalize relative path, stripping any leading ".." components
+        # by anchoring to root (os.sep) then computing relpath back from it.
+        return os.path.relpath(
+            os.path.join(os.sep, os.path.relpath(child, parent)), os.sep
+        )
+
+    # 1) input
+    input_base = os.path.abspath(folder_paths.get_input_directory())
+    if _check_is_within(fp_abs, input_base):
+        return "input", _compute_relative(fp_abs, input_base)
+
+    # 2) output
+    output_base = os.path.abspath(folder_paths.get_output_directory())
+    if _check_is_within(fp_abs, output_base):
+        return "output", _compute_relative(fp_abs, output_base)
+
+    # 3) models (check deepest matching base to avoid ambiguity)
+    best: tuple[int, str, str] | None = None  # (base_len, bucket, rel_inside_bucket)
+    for bucket, bases in get_comfy_models_folders():
+        for b in bases:
+            base_abs = os.path.abspath(b)
+            if not _check_is_within(fp_abs, base_abs):
+                continue
+            cand = (len(base_abs), bucket, _compute_relative(fp_abs, base_abs))
+            if best is None or cand[0] > best[0]:
+                best = cand
+
+    if best is not None:
+        _, bucket, rel_inside = best
+        combined = os.path.join(bucket, rel_inside)
+        return "models", os.path.relpath(os.path.join(os.sep, combined), os.sep)
+
+    raise ValueError(
+        f"Path is not within input, output, or configured model bases: {file_path}"
+    )
+
+
+def get_name_and_tags_from_asset_path(file_path: str) -> tuple[str, list[str]]:
+    """Return (name, tags) derived from a filesystem path.
+
+    - name: base filename with extension
+    - tags: [root_category] + parent folder names in order
+
+    Raises:
+        ValueError: path does not belong to any known root.
+    """
+    root_category, some_path = get_asset_category_and_relative_path(file_path)
+    p = Path(some_path)
+    parent_parts = [
+        part for part in p.parent.parts if part not in (".", "..", p.anchor)
+    ]
+    return p.name, list(dict.fromkeys(normalize_tags([root_category, *parent_parts])))
diff --git a/app/assets/services/schemas.py b/app/assets/services/schemas.py
new file mode 100644
index 000000000..8b1f1f4dc
--- /dev/null
+++ b/app/assets/services/schemas.py
@@ -0,0 +1,109 @@
+from dataclasses import dataclass
+from datetime import datetime
+from typing import Any, NamedTuple
+
+from app.assets.database.models import Asset, AssetReference
+
+UserMetadata = dict[str, Any] | None
+
+
+@dataclass(frozen=True)
+class AssetData:
+    hash: str | None
+    size_bytes: int | None
+    mime_type: str | None
+
+
+@dataclass(frozen=True)
+class ReferenceData:
+    """Data transfer object for AssetReference."""
+
+    id: str
+    name: str
+    file_path: str | None
+    user_metadata: UserMetadata
+    preview_id: str | None
+    created_at: datetime
+    updated_at: datetime
+    last_access_time: datetime | None
+
+
+@dataclass(frozen=True)
+class AssetDetailResult:
+    ref: ReferenceData
+    asset: AssetData | None
+    tags: list[str]
+
+
+@dataclass(frozen=True)
+class RegisterAssetResult:
+    ref: ReferenceData
+    asset: AssetData
+    tags: list[str]
+    created: bool
+
+
+@dataclass(frozen=True)
+class IngestResult:
+    asset_created: bool
+    asset_updated: bool
+    ref_created: bool
+    ref_updated: bool
+    reference_id: str | None
+
+
+class TagUsage(NamedTuple):
+    name: str
+    tag_type: str
+    count: int
+
+
+@dataclass(frozen=True)
+class AssetSummaryData:
+    ref: ReferenceData
+    asset: AssetData | None
+    tags: list[str]
+
+
+@dataclass(frozen=True)
+class ListAssetsResult:
+    items: list[AssetSummaryData]
+    total: int
+
+
+@dataclass(frozen=True)
+class DownloadResolutionResult:
+    abs_path: str
+    content_type: str
+    download_name: str
+
+
+@dataclass(frozen=True)
+class UploadResult:
+    ref: ReferenceData
+    asset: AssetData
+    tags: list[str]
+    created_new: bool
+
+
+def extract_reference_data(ref: AssetReference) -> ReferenceData:
+    return ReferenceData(
+        id=ref.id,
+        name=ref.name,
+        file_path=ref.file_path,
+        user_metadata=ref.user_metadata,
+        preview_id=ref.preview_id,
+        created_at=ref.created_at,
+        updated_at=ref.updated_at,
+        last_access_time=ref.last_access_time,
+    )
+
+
+def extract_asset_data(asset: Asset | None) -> AssetData | None:
+    if asset is None:
+        return None
+    return AssetData(
+        hash=asset.hash,
+        size_bytes=asset.size_bytes,
+        mime_type=asset.mime_type,
+    )
diff --git a/app/assets/services/tagging.py b/app/assets/services/tagging.py
new file mode 100644
index 000000000..28900464d
--- /dev/null
+++ b/app/assets/services/tagging.py
@@ -0,0 +1,75 @@
+from app.assets.database.queries import (
+    AddTagsResult,
+    RemoveTagsResult,
+    add_tags_to_reference,
+    get_reference_with_owner_check,
+    list_tags_with_usage,
+    remove_tags_from_reference,
+)
+from app.assets.services.schemas import TagUsage
+from app.database.db import create_session
+
+
+def apply_tags(
+    reference_id: str,
+    tags: list[str],
+    origin: str = "manual",
+    owner_id: str = "",
+) -> AddTagsResult:
+    with create_session() as session:
+        ref_row = get_reference_with_owner_check(session, reference_id, owner_id)
+
+        result = add_tags_to_reference(
+            session,
+            reference_id=reference_id,
+            tags=tags,
+            origin=origin,
+            create_if_missing=True,
+            reference_row=ref_row,
+        )
+        session.commit()
+
+    return result
+
+
+def remove_tags(
+    reference_id: str,
+    tags: list[str],
+    owner_id: str = "",
+) -> RemoveTagsResult:
+    with create_session() as session:
+        get_reference_with_owner_check(session, reference_id, owner_id)
+
+        result = remove_tags_from_reference(
+            session,
+            reference_id=reference_id,
+            tags=tags,
+        )
+        session.commit()
+
+    return result
+
+
+def list_tags(
+    prefix: str | None = None,
+    limit: int = 100,
+    offset: int = 0,
+    order: str = "count_desc",
+    include_zero: bool = True,
+    owner_id: str = "",
+) -> tuple[list[TagUsage], int]:
+    limit = max(1, min(1000, limit))
+    offset = max(0, offset)
+
+    with create_session() as session:
+        rows, total = list_tags_with_usage(
+            session,
+            prefix=prefix,
+            limit=limit,
+            offset=offset,
+            include_zero=include_zero,
+            order=order,
+            owner_id=owner_id,
+        )
+
+    return [TagUsage(name, tag_type, count) for name, tag_type, count in rows], total
diff --git a/app/database/db.py b/app/database/db.py
index 1de8b80ed..0aab09a49 100644
--- a/app/database/db.py
+++ b/app/database/db.py
@@ -3,6 +3,7 @@ import os
 import shutil
 from app.logger import log_startup_warning
 from utils.install_util import get_missing_requirements_message
+from filelock import FileLock, Timeout
 from comfy.cli_args import args
 
 _DB_AVAILABLE = False
@@ -14,8 +15,12 @@ try:
     from alembic.config import Config
     from alembic.runtime.migration import MigrationContext
     from alembic.script import ScriptDirectory
-    from sqlalchemy import create_engine
+    from sqlalchemy import create_engine, event
     from sqlalchemy.orm import sessionmaker
+    from sqlalchemy.pool import StaticPool
+
+    from app.database.models import Base
+    import app.assets.database.models  # noqa: F401 — register models with Base.metadata
 
     _DB_AVAILABLE = True
 except ImportError as e:
@@ -65,9 +70,69 @@ def get_db_path():
         raise ValueError(f"Unsupported database URL '{url}'.")
 
 
+_db_lock = None
+
+def _acquire_file_lock(db_path):
+    """Acquire an OS-level file lock to prevent multi-process access.
+
+    Uses filelock for cross-platform support (macOS, Linux, Windows).
+    The OS automatically releases the lock when the process exits, even on crashes.
+    """
+    global _db_lock
+    lock_path = db_path + ".lock"
+    _db_lock = FileLock(lock_path)
+    try:
+        _db_lock.acquire(timeout=0)
+    except Timeout:
+        raise RuntimeError(
+            f"Could not acquire lock on database '{db_path}'. "
+            "Another ComfyUI process may already be using it. "
+            "Use --database-url to specify a separate database file."
+        )
+
+
+def _is_memory_db(db_url):
+    """Check if the database URL refers to an in-memory SQLite database."""
+    return db_url in ("sqlite:///:memory:", "sqlite://")
+
+
 def init_db():
     db_url = args.database_url
     logging.debug(f"Database URL: {db_url}")
+
+    if _is_memory_db(db_url):
+        _init_memory_db(db_url)
+    else:
+        _init_file_db(db_url)
+
+
+def _init_memory_db(db_url):
+    """Initialize an in-memory SQLite database using metadata.create_all.
+
+    Alembic migrations don't work with in-memory SQLite because each
+    connection gets its own separate database — tables created by Alembic's
+    internal connection are lost immediately.
+    """
+    engine = create_engine(
+        db_url,
+        poolclass=StaticPool,
+        connect_args={"check_same_thread": False},
+    )
+
+    @event.listens_for(engine, "connect")
+    def set_sqlite_pragma(dbapi_connection, connection_record):
+        cursor = dbapi_connection.cursor()
+        cursor.execute("PRAGMA foreign_keys=ON")
+        cursor.close()
+
+    Base.metadata.create_all(engine)
+
+    global Session
+    Session = sessionmaker(bind=engine)
+
+
+def _init_file_db(db_url):
+    """Initialize a file-backed SQLite database using Alembic migrations."""
     db_path = get_db_path()
     db_exists = os.path.exists(db_path)
 
@@ -75,6 +140,14 @@ def init_db():
 
     # Check if we need to upgrade
     engine = create_engine(db_url)
+
+    # Enable foreign key enforcement for SQLite
+    @event.listens_for(engine, "connect")
+    def set_sqlite_pragma(dbapi_connection, connection_record):
+        cursor = dbapi_connection.cursor()
+        cursor.execute("PRAGMA foreign_keys=ON")
+        cursor.close()
+
     conn = engine.connect()
 
     context = MigrationContext.configure(conn)
@@ -104,6 +177,12 @@ def init_db():
             logging.exception("Error upgrading database: ")
             raise e
 
+    # Acquire an OS-level file lock after migrations are complete.
+    # Alembic uses its own connection, so we must wait until it's done
+    # before locking — otherwise our own lock blocks the migration.
+    conn.close()
+    _acquire_file_lock(db_path)
+
     global Session
     Session = sessionmaker(bind=engine)
 
diff --git a/comfy/cli_args.py b/comfy/cli_args.py
index 13079c7bc..e9832acaf 100644
--- a/comfy/cli_args.py
+++ b/comfy/cli_args.py
@@ -232,7 +232,7 @@ database_default_path = os.path.abspath(
     os.path.join(os.path.dirname(__file__), "..", "user", "comfyui.db")
 )
 parser.add_argument("--database-url", type=str, default=f"sqlite:///{database_default_path}", help="Specify the database URL, e.g. for an in-memory database you can use 'sqlite:///:memory:'.")
-parser.add_argument("--disable-assets-autoscan", action="store_true", help="Disable asset scanning on startup for database synchronization.")
+parser.add_argument("--enable-assets", action="store_true", help="Enable the assets system (API routes, database synchronization, and background scanning).")
 
 if comfy.options.args_parsing:
     args = parser.parse_args()
diff --git a/comfy_api/feature_flags.py b/comfy_api/feature_flags.py
index a90a5ca40..9f6918315 100644
--- a/comfy_api/feature_flags.py
+++ b/comfy_api/feature_flags.py
@@ -15,6 +15,7 @@ SERVER_FEATURE_FLAGS: dict[str, Any] = {
     "max_upload_size": args.max_upload_size * 1024 * 1024, # Convert MB to bytes
     "extension": {"manager": {"supports_v4": True}},
     "node_replacements": True,
+    "assets": args.enable_assets,
 }
 
 
diff --git a/main.py b/main.py
index 0f58d57b8..a8fc1a28d 100644
--- a/main.py
+++ b/main.py
@@ -7,14 +7,16 @@ import folder_paths
 import time
 from comfy.cli_args import args, enables_dynamic_vram
 from app.logger import setup_logger
-from app.assets.scanner import seed_assets
+from app.assets.seeder import asset_seeder
 import itertools
 import utils.extra_config
+from utils.mime_types import init_mime_types
 import logging
 import sys
 from comfy_execution.progress import get_progress_state
 from comfy_execution.utils import get_executing_context
 from comfy_api import feature_flags
+from app.database.db import init_db, dependencies_available
 
 if __name__ == "__main__":
     #NOTE: These do not do anything on core ComfyUI, they are for custom nodes.
@@ -161,6 +163,7 @@ def execute_prestartup_script():
         logging.info("")
 
 apply_custom_paths()
+init_mime_types()
 
 if args.enable_manager:
     comfyui_manager.prestartup()
@@ -258,6 +261,7 @@ def prompt_worker(q, server_instance):
             for k in sensitive:
                 extra_data[k] = sensitive[k]
 
+            asset_seeder.pause()
             e.execute(item[2], prompt_id, extra_data, item[4])
             need_gc = True
 
@@ -302,6 +306,7 @@ def prompt_worker(q, server_instance):
                 last_gc_collect = current_time
                 need_gc = False
                 hook_breaker_ac10a0.restore_functions()
+                asset_seeder.resume()
 
 
 async def run(server_instance, address='', port=8188, verbose=True, call_on_start=None):
@@ -352,12 +357,29 @@ def cleanup_temp():
 
 def setup_database():
     try:
-        from app.database.db import init_db, dependencies_available
         if dependencies_available():
             init_db()
-            if not args.disable_assets_autoscan:
-                seed_assets(["models"], enable_logging=True)
+            if args.enable_assets:
+                if asset_seeder.start(roots=("models", "input", "output"), prune_first=True, compute_hashes=True):
+                    logging.info("Background asset scan initiated for models, input, output")
     except Exception as e:
+        if "database is locked" in str(e):
+            logging.error(
+                "Database is locked. Another ComfyUI process is already using this database.\n"
+                "To resolve this, specify a separate database file for this instance:\n"
+                "  --database-url sqlite:///path/to/another.db"
+            )
+            sys.exit(1)
+        if args.enable_assets:
+            logging.error(
+                f"Failed to initialize database: {e}\n"
+                "The --enable-assets flag requires a working database connection.\n"
+                "To resolve this, try one of the following:\n"
+                "  1. Install the latest requirements: pip install -r requirements.txt\n"
+                "  2. Specify an alternative database URL: --database-url sqlite:///path/to/your.db\n"
+                "  3. Use an in-memory database: --database-url sqlite:///:memory:"
+            )
+            sys.exit(1)
         logging.error(f"Failed to initialize database. Please ensure you have installed the latest requirements. If the error persists, please report this as in future the database will be required: {e}")
 
 
@@ -440,5 +462,6 @@ if __name__ == "__main__":
         event_loop.run_until_complete(x)
     except KeyboardInterrupt:
         logging.info("\nStopped server")
-
-    cleanup_temp()
+    finally:
+        asset_seeder.shutdown()
+        cleanup_temp()
diff --git a/requirements.txt b/requirements.txt
index dc9a9ded0..9527135ec 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -20,11 +20,13 @@ tqdm
 psutil
 alembic
 SQLAlchemy
+filelock
 av>=14.2.0
 comfy-kitchen>=0.2.7
 comfy-aimdo>=0.2.7
 requests
 simpleeval>=1.0.0
+blake3
 
 #non essential dependencies:
 kornia>=0.7.1
diff --git a/server.py b/server.py
index 275bce5a7..76904ebc9 100644
--- a/server.py
+++ b/server.py
@@ -33,8 +33,8 @@ import node_helpers
 from comfyui_version import __version__
 from app.frontend_management import FrontendManager, parse_version
 from comfy_api.internal import _ComfyNodeInternal
-from app.assets.scanner import seed_assets
-from app.assets.api.routes import register_assets_system
+from app.assets.seeder import asset_seeder
+from app.assets.api.routes import register_assets_routes
 
 from app.user_manager import UserManager
 from app.model_manager import ModelFileManager
@@ -197,10 +197,6 @@ class PromptServer():
     def __init__(self, loop):
         PromptServer.instance = self
 
-        mimetypes.init()
-        mimetypes.add_type('application/javascript; charset=utf-8', '.js')
-        mimetypes.add_type('image/webp', '.webp')
-
         self.user_manager = UserManager()
         self.model_file_manager = ModelFileManager()
         self.custom_node_manager = CustomNodeManager()
@@ -239,7 +235,11 @@ class PromptServer():
             else args.front_end_root
         )
         logging.info(f"[Prompt Server] web root: {self.web_root}")
-        register_assets_system(self.app, self.user_manager)
+        if args.enable_assets:
+            register_assets_routes(self.app, self.user_manager)
+        else:
+            register_assets_routes(self.app)
+            asset_seeder.disable()
         routes = web.RouteTableDef()
         self.routes = routes
         self.last_node_id = None
@@ -697,10 +697,7 @@ class PromptServer():
 
         @routes.get("/object_info")
         async def get_object_info(request):
-            try:
-                seed_assets(["models"])
-            except Exception as e:
-                logging.error(f"Failed to seed assets: {e}")
+            asset_seeder.start(roots=("models", "input", "output"))
             with folder_paths.cache_helper:
                 out = {}
                 for x in nodes.NODE_CLASS_MAPPINGS:
diff --git a/tests-unit/assets_test/conftest.py b/tests-unit/assets_test/conftest.py
index 0a57dd7b5..6c5c56113 100644
--- a/tests-unit/assets_test/conftest.py
+++ b/tests-unit/assets_test/conftest.py
@@ -108,7 +108,7 @@ def comfy_url_and_proc(comfy_tmp_base_dir: Path, request: pytest.FixtureRequest)
             "main.py",
             f"--base-directory={str(comfy_tmp_base_dir)}",
             f"--database-url={db_url}",
-            "--disable-assets-autoscan",
+            "--enable-assets",
             "--listen",
             "127.0.0.1",
             "--port",
@@ -212,7 +212,7 @@ def asset_factory(http: requests.Session, api_base: str):
 
     for aid in created:
         with contextlib.suppress(Exception):
-            http.delete(f"{api_base}/api/assets/{aid}", timeout=30)
+            http.delete(f"{api_base}/api/assets/{aid}?delete_content=true", timeout=30)
 
 
 @pytest.fixture
@@ -258,14 +258,4 @@ def autoclean_unit_test_assets(http: requests.Session, api_base: str):
             break
         for aid in ids:
             with contextlib.suppress(Exception):
-                http.delete(f"{api_base}/api/assets/{aid}", timeout=30)
-
-
-def trigger_sync_seed_assets(session: requests.Session, base_url: str) -> None:
-    """Force a fast sync/seed pass by calling the seed endpoint."""
-    session.post(base_url + "/api/assets/seed", json={"roots": ["models", "input", "output"]}, timeout=30)
-    time.sleep(0.2)
-
-
-def get_asset_filename(asset_hash: str, extension: str) -> str:
-    return asset_hash.removeprefix("blake3:") + extension
+                http.delete(f"{api_base}/api/assets/{aid}?delete_content=true", timeout=30)
diff --git a/tests-unit/assets_test/helpers.py b/tests-unit/assets_test/helpers.py
new file mode 100644
index 000000000..770e011f4
--- /dev/null
+++ b/tests-unit/assets_test/helpers.py
@@ -0,0 +1,28 @@
+"""Helper functions for assets integration tests."""
+import time
+
+import requests
+
+
+def trigger_sync_seed_assets(session: requests.Session, base_url: str) -> None:
+    """Force a synchronous sync/seed pass by calling the seed endpoint with wait=true.
+
+    Retries on 409 (already running) until the previous scan finishes.
+    """
+    deadline = time.monotonic() + 60
+    while True:
+        r = session.post(
+            base_url + "/api/assets/seed?wait=true",
+            json={"roots": ["models", "input", "output"]},
+            timeout=60,
+        )
+        if r.status_code != 409:
+            assert r.status_code == 200, f"seed endpoint returned {r.status_code}: {r.text}"
+            return
+        if time.monotonic() > deadline:
+            raise TimeoutError("seed endpoint stuck in 409 (already running)")
+        time.sleep(0.25)
+
+
+def get_asset_filename(asset_hash: str, extension: str) -> str:
+    return asset_hash.removeprefix("blake3:") + extension
diff --git a/tests-unit/assets_test/queries/conftest.py b/tests-unit/assets_test/queries/conftest.py
new file mode 100644
index 000000000..4ca0e86a9
--- /dev/null
+++ b/tests-unit/assets_test/queries/conftest.py
@@ -0,0 +1,20 @@
+import pytest
+from sqlalchemy import create_engine
+from sqlalchemy.orm import Session
+
+from app.assets.database.models import Base
+
+
+@pytest.fixture
+def session():
+    """In-memory SQLite session for fast unit tests."""
+    engine = create_engine("sqlite:///:memory:")
+    Base.metadata.create_all(engine)
+    with Session(engine) as sess:
+        yield sess
+
+
+@pytest.fixture(autouse=True)
+def autoclean_unit_test_assets():
+    """Override parent autouse fixture - query tests don't need server cleanup."""
+    yield
diff --git a/tests-unit/assets_test/queries/test_asset.py b/tests-unit/assets_test/queries/test_asset.py
new file mode 100644
index 000000000..08f84cd11
--- /dev/null
+++ b/tests-unit/assets_test/queries/test_asset.py
@@ -0,0 +1,144 @@
+import uuid
+
+import pytest
+from sqlalchemy.orm import Session
+
+from app.assets.helpers import get_utc_now
+from app.assets.database.models import Asset
+from app.assets.database.queries import (
+    asset_exists_by_hash,
+    get_asset_by_hash,
+    upsert_asset,
+    bulk_insert_assets,
+)
+
+
+class TestAssetExistsByHash:
+    @pytest.mark.parametrize(
+        "setup_hash,query_hash,expected",
+        [
+            (None, "nonexistent", False),  # No asset exists
+            ("blake3:abc123", "blake3:abc123", True),  # Asset exists with matching hash
+            (None, "", False),  # Null hash in DB doesn't match empty string
+        ],
+        ids=["nonexistent", "existing", "null_hash_no_match"],
+    )
+    def test_exists_by_hash(self, session: Session, setup_hash, query_hash, expected):
+        if setup_hash is not None or query_hash == "":
+            asset = Asset(hash=setup_hash, size_bytes=100)
+            session.add(asset)
+            session.commit()
+
+        assert asset_exists_by_hash(session, asset_hash=query_hash) is expected
+
+
+class TestGetAssetByHash:
+    @pytest.mark.parametrize(
+        "setup_hash,query_hash,should_find",
+        [
+            (None, "nonexistent", False),
+            ("blake3:def456", "blake3:def456", True),
+        ],
+        ids=["nonexistent", "existing"],
+    )
+    def test_get_by_hash(self, session: Session, setup_hash, query_hash, should_find):
+        if setup_hash is not None:
+            asset = Asset(hash=setup_hash, size_bytes=200, mime_type="image/png")
+            session.add(asset)
+            session.commit()
+
+        result = get_asset_by_hash(session, asset_hash=query_hash)
+        if should_find:
+            assert result is not None
+            assert result.size_bytes == 200
+            assert result.mime_type == "image/png"
+        else:
+            assert result is None
+
+
+class TestUpsertAsset:
+    @pytest.mark.parametrize(
+        "first_size,first_mime,second_size,second_mime,expect_created,expect_updated,final_size,final_mime",
+        [
+            # New asset creation
+            (None, None, 1024, "application/octet-stream", True, False, 1024, "application/octet-stream"),
+            # Existing asset, same values - no update
+            (500, "text/plain", 500, "text/plain", False, False, 500, "text/plain"),
+            # Existing asset with size 0, update with new values
+            (0, None, 2048, "image/png", False, True, 2048, "image/png"),
+            # Existing asset, second call with size 0 - no update
+            (1000, None, 0, None, False, False, 1000, None),
+        ],
+        ids=["new_asset", "existing_no_change", "update_from_zero", "zero_size_no_update"],
+    )
+    def test_upsert_scenarios(
+        self,
+        session: Session,
+        first_size,
+        first_mime,
+        second_size,
+        second_mime,
+        expect_created,
+        expect_updated,
+        final_size,
+        final_mime,
+    ):
+        asset_hash = f"blake3:test_{first_size}_{second_size}"
+
+        # First upsert (if first_size is not None, we're testing the second call)
+        if first_size is not None:
+            upsert_asset(
+                session,
+                asset_hash=asset_hash,
+                size_bytes=first_size,
+                mime_type=first_mime,
+            )
+            session.commit()
+
+        # The upsert call we're testing
+        asset, created, updated = upsert_asset(
+            session,
+            asset_hash=asset_hash,
+            size_bytes=second_size,
+            mime_type=second_mime,
+        )
+        session.commit()
+
+        assert created is expect_created
+        assert updated is expect_updated
+        assert asset.size_bytes == final_size
+        assert asset.mime_type == final_mime
+
+
+class TestBulkInsertAssets:
+    def test_inserts_multiple_assets(self, session: Session):
+        now = get_utc_now()
+        rows = [
+            {"id": str(uuid.uuid4()), "hash": "blake3:bulk1", "size_bytes": 100, "mime_type": "text/plain", "created_at": now},
+            {"id": str(uuid.uuid4()), "hash": "blake3:bulk2", "size_bytes": 200, "mime_type": "image/png", "created_at": now},
+            {"id": str(uuid.uuid4()), "hash": "blake3:bulk3", "size_bytes": 300, "mime_type": None, "created_at": now},
+        ]
+        bulk_insert_assets(session, rows)
+        session.commit()
+
+        assets = session.query(Asset).all()
+        assert len(assets) == 3
+        hashes = {a.hash for a in assets}
+        assert hashes == {"blake3:bulk1", "blake3:bulk2", "blake3:bulk3"}
+
+    def test_empty_list_is_noop(self, session: Session):
+        bulk_insert_assets(session, [])
+        session.commit()
+        assert session.query(Asset).count() == 0
+
+    def test_handles_large_batch(self, session: Session):
+        """Test chunking logic with more rows than MAX_BIND_PARAMS allows."""
+        now = get_utc_now()
+        rows = [
+            {"id": str(uuid.uuid4()), "hash": f"blake3:large{i}", "size_bytes": i, "mime_type": None, "created_at": now}
+            for i in range(200)
+        ]
+        bulk_insert_assets(session, rows)
+        session.commit()
+
+        assert session.query(Asset).count() == 200
diff --git a/tests-unit/assets_test/queries/test_asset_info.py b/tests-unit/assets_test/queries/test_asset_info.py
new file mode 100644
index 000000000..8f6c7fcdb
--- /dev/null
+++ b/tests-unit/assets_test/queries/test_asset_info.py
@@ -0,0 +1,517 @@
+import time
+import uuid
+import pytest
+from sqlalchemy.orm import Session
+
+from app.assets.database.models import Asset, AssetReference, AssetReferenceMeta
+from app.assets.database.queries import (
+    reference_exists_for_asset_id,
+    get_reference_by_id,
+    insert_reference,
+    get_or_create_reference,
+    update_reference_timestamps,
+    list_references_page,
+    fetch_reference_asset_and_tags,
+    fetch_reference_and_asset,
+    update_reference_access_time,
+    set_reference_metadata,
+    delete_reference_by_id,
+    set_reference_preview,
+    bulk_insert_references_ignore_conflicts,
+    get_reference_ids_by_ids,
+    ensure_tags_exist,
+    add_tags_to_reference,
+)
+from app.assets.helpers import get_utc_now
+
+
+def _make_asset(session: Session, hash_val: str | None = None, size: int = 1024) -> Asset:
+    asset = Asset(hash=hash_val, size_bytes=size, mime_type="application/octet-stream")
+    session.add(asset)
+    session.flush()
+    return asset
+
+
+def _make_reference(
+    session: Session,
+    asset: Asset,
+    name: str = "test",
+    owner_id: str = "",
+) -> AssetReference:
+    now = get_utc_now()
+    ref = AssetReference(
+        owner_id=owner_id,
+        name=name,
+        asset_id=asset.id,
+        created_at=now,
+        updated_at=now,
+        last_access_time=now,
+    )
+    session.add(ref)
+    session.flush()
+    return ref
+
+
+class TestReferenceExistsForAssetId:
+    def test_returns_false_when_no_reference(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        assert reference_exists_for_asset_id(session, asset_id=asset.id) is False
+
+    def test_returns_true_when_reference_exists(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        _make_reference(session, asset)
+        assert reference_exists_for_asset_id(session, asset_id=asset.id) is True
+
+
+class TestGetReferenceById:
+    def test_returns_none_for_nonexistent(self, session: Session):
+        assert get_reference_by_id(session, reference_id="nonexistent") is None
+
+    def test_returns_reference(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset, name="myfile.txt")
+
+        result = get_reference_by_id(session, reference_id=ref.id)
+        assert result is not None
+        assert result.name == "myfile.txt"
+
+
+class TestListReferencesPage:
+    def test_empty_db(self, session: Session):
+        refs, tag_map, total = list_references_page(session)
+        assert refs == []
+        assert tag_map == {}
+        assert total == 0
+
+    def test_returns_references_with_tags(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset, name="test.bin")
+        ensure_tags_exist(session, ["alpha", "beta"])
+        add_tags_to_reference(session, reference_id=ref.id, tags=["alpha", "beta"])
+        session.commit()
+
+        refs, tag_map, total = list_references_page(session)
+        assert len(refs) == 1
+        assert refs[0].id == ref.id
+        assert set(tag_map[ref.id]) == {"alpha", "beta"}
+        assert total == 1
+
+    def test_name_contains_filter(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        _make_reference(session, asset, name="model_v1.safetensors")
+        _make_reference(session, asset, name="config.json")
+        session.commit()
+
+        refs, _, total = list_references_page(session, name_contains="model")
+        assert total == 1
+        assert refs[0].name == "model_v1.safetensors"
+
+    def test_owner_visibility(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        _make_reference(session, asset, name="public", owner_id="")
+        _make_reference(session, asset, name="private", owner_id="user1")
+        session.commit()
+
+        # Empty owner sees only public
+        refs, _, total = list_references_page(session, owner_id="")
+        assert total == 1
+        assert refs[0].name == "public"
+
+        # Owner sees both
+        refs, _, total = list_references_page(session, owner_id="user1")
+        assert total == 2
+
+    def test_include_tags_filter(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref1 = _make_reference(session, asset, name="tagged")
+        _make_reference(session, asset, name="untagged")
+        ensure_tags_exist(session, ["wanted"])
+        add_tags_to_reference(session, reference_id=ref1.id, tags=["wanted"])
+        session.commit()
+
+        refs, _, total = list_references_page(session, include_tags=["wanted"])
+        assert total == 1
+        assert refs[0].name == "tagged"
+
+    def test_exclude_tags_filter(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        _make_reference(session, asset, name="keep")
+        ref_exclude = _make_reference(session, asset, name="exclude")
+        ensure_tags_exist(session, ["bad"])
+        add_tags_to_reference(session, reference_id=ref_exclude.id, tags=["bad"])
+        session.commit()
+
+        refs, _, total = list_references_page(session, exclude_tags=["bad"])
+        assert total == 1
+        assert refs[0].name == "keep"
+
+    def test_sorting(self, session: Session):
+        asset = _make_asset(session, "hash1", size=100)
+        asset2 = _make_asset(session, "hash2", size=500)
+        _make_reference(session, asset, name="small")
+        _make_reference(session, asset2, name="large")
+        session.commit()
+
+        refs, _, _ = list_references_page(session, sort="size", order="desc")
+        assert refs[0].name == "large"
+
+        refs, _, _ = list_references_page(session, sort="name", order="asc")
+        assert refs[0].name == "large"
+
+
+class TestFetchReferenceAssetAndTags:
+    def test_returns_none_for_nonexistent(self, session: Session):
+        result = fetch_reference_asset_and_tags(session, "nonexistent")
+        assert result is None
+
+    def test_returns_tuple(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset, name="test.bin")
+        ensure_tags_exist(session, ["tag1"])
+        add_tags_to_reference(session, reference_id=ref.id, tags=["tag1"])
+        session.commit()
+
+        result = fetch_reference_asset_and_tags(session, ref.id)
+        assert result is not None
+        ret_ref, ret_asset, ret_tags = result
+        assert ret_ref.id == ref.id
+        assert ret_asset.id == asset.id
+        assert ret_tags == ["tag1"]
+
+
+class TestFetchReferenceAndAsset:
+    def test_returns_none_for_nonexistent(self, session: Session):
+        result = fetch_reference_and_asset(session, reference_id="nonexistent")
+        assert result is None
+
+    def test_returns_tuple(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+        session.commit()
+
+        result = fetch_reference_and_asset(session, reference_id=ref.id)
+        assert result is not None
+        ret_ref, ret_asset = result
+        assert ret_ref.id == ref.id
+        assert ret_asset.id == asset.id
+
+
+class TestUpdateReferenceAccessTime:
+    def test_updates_last_access_time(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+        original_time = ref.last_access_time
+        session.commit()
+
+        import time
+        time.sleep(0.01)
+
+        update_reference_access_time(session, reference_id=ref.id)
+        session.commit()
+
+        session.refresh(ref)
+        assert ref.last_access_time > original_time
+
+
+class TestDeleteReferenceById:
+    def test_deletes_existing(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+        session.commit()
+
+        result = delete_reference_by_id(session, reference_id=ref.id, owner_id="")
+        assert result is True
+        assert get_reference_by_id(session, reference_id=ref.id) is None
+
+    def test_returns_false_for_nonexistent(self, session: Session):
+        result = delete_reference_by_id(session, reference_id="nonexistent", owner_id="")
+        assert result is False
+
+    def test_respects_owner_visibility(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset, owner_id="user1")
+        session.commit()
+
+        result = delete_reference_by_id(session, reference_id=ref.id, owner_id="user2")
+        assert result is False
+        assert get_reference_by_id(session, reference_id=ref.id) is not None
+
+
+class TestSetReferencePreview:
+    def test_sets_preview(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        preview_asset = _make_asset(session, "preview_hash")
+        ref = _make_reference(session, asset)
+        session.commit()
+
+        set_reference_preview(session, reference_id=ref.id, preview_asset_id=preview_asset.id)
+        session.commit()
+
+        session.refresh(ref)
+        assert ref.preview_id == preview_asset.id
+
+    def test_clears_preview(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        preview_asset = _make_asset(session, "preview_hash")
+        ref = _make_reference(session, asset)
+        ref.preview_id = preview_asset.id
+        session.commit()
+
+        set_reference_preview(session, reference_id=ref.id, preview_asset_id=None)
+        session.commit()
+
+        session.refresh(ref)
+        assert ref.preview_id is None
+
+    def test_raises_for_nonexistent_reference(self, session: Session):
+        with pytest.raises(ValueError, match="not found"):
+            set_reference_preview(session, reference_id="nonexistent", preview_asset_id=None)
+
+    def test_raises_for_nonexistent_preview(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+        session.commit()
+
+        with pytest.raises(ValueError, match="Preview Asset"):
+            set_reference_preview(session, reference_id=ref.id, preview_asset_id="nonexistent")
+
+
+class TestInsertReference:
+    def test_creates_new_reference(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = insert_reference(
+            session, asset_id=asset.id, owner_id="user1", name="test.bin"
+        )
+        session.commit()
+
+        assert ref is not None
+        assert ref.name == "test.bin"
+        assert ref.owner_id == "user1"
+
+    def test_allows_duplicate_names(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref1 = insert_reference(session, asset_id=asset.id, owner_id="user1", name="dup.bin")
+        session.commit()
+
+        # Duplicate names are now allowed
+        ref2 = insert_reference(
+            session, asset_id=asset.id, owner_id="user1", name="dup.bin"
+        )
+        session.commit()
+
+        assert ref1 is not None
+        assert ref2 is not None
+        assert ref1.id != ref2.id
+
+
+class TestGetOrCreateReference:
+    def test_creates_new_reference(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref, created = get_or_create_reference(
+            session, asset_id=asset.id, owner_id="user1", name="new.bin"
+        )
+        session.commit()
+
+        assert created is True
+        assert ref.name == "new.bin"
+
+    def test_always_creates_new_reference(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref1, created1 = get_or_create_reference(
+            session, asset_id=asset.id, owner_id="user1", name="existing.bin"
+        )
+        session.commit()
+
+        # Duplicate names are allowed, so always creates new
+        ref2, created2 = get_or_create_reference(
+            session, asset_id=asset.id, owner_id="user1", name="existing.bin"
+        )
+        session.commit()
+
+        assert created1 is True
+        assert created2 is True
+        assert ref1.id != ref2.id
+
+
+class TestUpdateReferenceTimestamps:
+    def test_updates_timestamps(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+        original_updated_at = ref.updated_at
+        session.commit()
+
+        time.sleep(0.01)
+        update_reference_timestamps(session, ref)
+        session.commit()
+
+        session.refresh(ref)
+        assert ref.updated_at > original_updated_at
+
+    def test_updates_preview_id(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        preview_asset = _make_asset(session, "preview_hash")
+        ref = _make_reference(session, asset)
+        session.commit()
+
+        update_reference_timestamps(session, ref, preview_id=preview_asset.id)
+        session.commit()
+
+        session.refresh(ref)
+        assert ref.preview_id == preview_asset.id
+
+
+class TestSetReferenceMetadata:
+    def test_sets_metadata(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+        session.commit()
+
+        set_reference_metadata(
+            session, reference_id=ref.id, user_metadata={"key": "value"}
+        )
+        session.commit()
+
+        session.refresh(ref)
+        assert ref.user_metadata == {"key": "value"}
+        # Check metadata table
+        meta = session.query(AssetReferenceMeta).filter_by(asset_reference_id=ref.id).all()
+        assert len(meta) == 1
+        assert meta[0].key == "key"
+        assert meta[0].val_str == "value"
+
+    def test_replaces_existing_metadata(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+        session.commit()
+
+        set_reference_metadata(
+            session, reference_id=ref.id, user_metadata={"old": "data"}
+        )
+        session.commit()
+
+        set_reference_metadata(
+            session, reference_id=ref.id, user_metadata={"new": "data"}
+        )
+        session.commit()
+
+        meta = session.query(AssetReferenceMeta).filter_by(asset_reference_id=ref.id).all()
+        assert len(meta) == 1
+        assert meta[0].key == "new"
+
+    def test_clears_metadata_with_empty_dict(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+        session.commit()
+
+        set_reference_metadata(
+            session, reference_id=ref.id, user_metadata={"key": "value"}
+        )
+        session.commit()
+
+        set_reference_metadata(
+            session, reference_id=ref.id, user_metadata={}
+        )
+        session.commit()
+
+        session.refresh(ref)
+        assert ref.user_metadata == {}
+        meta = session.query(AssetReferenceMeta).filter_by(asset_reference_id=ref.id).all()
+        assert len(meta) == 0
+
+    def test_raises_for_nonexistent(self, session: Session):
+        with pytest.raises(ValueError, match="not found"):
+            set_reference_metadata(
+                session, reference_id="nonexistent", user_metadata={"key": "value"}
+            )
+
+
+class TestBulkInsertReferencesIgnoreConflicts:
+    def test_inserts_multiple_references(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        now = get_utc_now()
+        rows = [
+            {
+                "id": str(uuid.uuid4()),
+                "owner_id": "",
+                "name": "bulk1.bin",
+                "asset_id": asset.id,
+                "preview_id": None,
+                "user_metadata": {},
+                "created_at": now,
+                "updated_at": now,
+                "last_access_time": now,
+            },
+            {
+                "id": str(uuid.uuid4()),
+                "owner_id": "",
+                "name": "bulk2.bin",
+                "asset_id": asset.id,
+                "preview_id": None,
+                "user_metadata": {},
+                "created_at": now,
+                "updated_at": now,
+                "last_access_time": now,
+            },
+        ]
+        bulk_insert_references_ignore_conflicts(session, rows)
+        session.commit()
+
+        refs = session.query(AssetReference).all()
+        assert len(refs) == 2
+
+    def test_allows_duplicate_names(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        _make_reference(session, asset, name="existing.bin", owner_id="")
+        session.commit()
+
+        now = get_utc_now()
+        rows = [
+            {
+                "id": str(uuid.uuid4()),
+                "owner_id": "",
+                "name": "existing.bin",
+                "asset_id": asset.id,
+                "preview_id": None,
+                "user_metadata": {},
+                "created_at": now,
+                "updated_at": now,
+                "last_access_time": now,
+            },
+            {
+                "id": str(uuid.uuid4()),
+                "owner_id": "",
+                "name": "new.bin",
+                "asset_id": asset.id,
+                "preview_id": None,
+                "user_metadata": {},
+                "created_at": now,
+                "updated_at": now,
+                "last_access_time": now,
+            },
+        ]
+        bulk_insert_references_ignore_conflicts(session, rows)
+        session.commit()
+
+        # Duplicate names allowed, so all 3 rows exist
+        refs = session.query(AssetReference).all()
+        assert len(refs) == 3
+
+    def test_empty_list_is_noop(self, session: Session):
+        bulk_insert_references_ignore_conflicts(session, [])
+        assert session.query(AssetReference).count() == 0
+
+
+class TestGetReferenceIdsByIds:
+    def test_returns_existing_ids(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref1 = _make_reference(session, asset, name="a.bin")
+        ref2 = _make_reference(session, asset, name="b.bin")
+        session.commit()
+
+        found = get_reference_ids_by_ids(session, [ref1.id, ref2.id, "nonexistent"])
+
+        assert found == {ref1.id, ref2.id}
+
+    def test_empty_list_returns_empty(self, session: Session):
+        found = get_reference_ids_by_ids(session, [])
+        assert found == set()
diff --git a/tests-unit/assets_test/queries/test_cache_state.py b/tests-unit/assets_test/queries/test_cache_state.py
new file mode 100644
index 000000000..ead60e570
--- /dev/null
+++ b/tests-unit/assets_test/queries/test_cache_state.py
@@ -0,0 +1,499 @@
+"""Tests for cache_state (AssetReference file path) query functions."""
+import pytest
+from sqlalchemy.orm import Session
+
+from app.assets.database.models import Asset, AssetReference
+from app.assets.database.queries import (
+    list_references_by_asset_id,
+    upsert_reference,
+    get_unreferenced_unhashed_asset_ids,
+    delete_assets_by_ids,
+    get_references_for_prefixes,
+    bulk_update_needs_verify,
+    delete_references_by_ids,
+    delete_orphaned_seed_asset,
+    bulk_insert_references_ignore_conflicts,
+    get_references_by_paths_and_asset_ids,
+    mark_references_missing_outside_prefixes,
+    restore_references_by_paths,
+)
+from app.assets.helpers import select_best_live_path, get_utc_now
+
+
+def _make_asset(session: Session, hash_val: str | None = None, size: int = 1024) -> Asset:
+    asset = Asset(hash=hash_val, size_bytes=size)
+    session.add(asset)
+    session.flush()
+    return asset
+
+
+def _make_reference(
+    session: Session,
+    asset: Asset,
+    file_path: str,
+    name: str = "test",
+    mtime_ns: int | None = None,
+    needs_verify: bool = False,
+) -> AssetReference:
+    now = get_utc_now()
+    ref = AssetReference(
+        asset_id=asset.id,
+        file_path=file_path,
+        name=name,
+        mtime_ns=mtime_ns,
+        needs_verify=needs_verify,
+        created_at=now,
+        updated_at=now,
+        last_access_time=now,
+    )
+    session.add(ref)
+    session.flush()
+    return ref
+
+
+class TestListReferencesByAssetId:
+    def test_returns_empty_for_no_references(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        refs = list_references_by_asset_id(session, asset_id=asset.id)
+        assert list(refs) == []
+
+    def test_returns_references_for_asset(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        _make_reference(session, asset, "/path/a.bin", name="a")
+        _make_reference(session, asset, "/path/b.bin", name="b")
+        session.commit()
+
+        refs = list_references_by_asset_id(session, asset_id=asset.id)
+        paths = [r.file_path for r in refs]
+        assert set(paths) == {"/path/a.bin", "/path/b.bin"}
+
+    def test_does_not_return_other_assets_references(self, session: Session):
+        asset1 = _make_asset(session, "hash1")
+        asset2 = _make_asset(session, "hash2")
+        _make_reference(session, asset1, "/path/asset1.bin", name="a1")
+        _make_reference(session, asset2, "/path/asset2.bin", name="a2")
+        session.commit()
+
+        refs = list_references_by_asset_id(session, asset_id=asset1.id)
+        paths = [r.file_path for r in refs]
+        assert paths == ["/path/asset1.bin"]
+
+
+class TestSelectBestLivePath:
+    def test_returns_empty_for_empty_list(self):
+        result = select_best_live_path([])
+        assert result == ""
+
+    def test_returns_empty_when_no_files_exist(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset, "/nonexistent/path.bin")
+        session.commit()
+
+        result = select_best_live_path([ref])
+        assert result == ""
+
+    def test_prefers_verified_path(self, session: Session, tmp_path):
+        """needs_verify=False should be preferred."""
+        asset = _make_asset(session, "hash1")
+
+        verified_file = tmp_path / "verified.bin"
+        verified_file.write_bytes(b"data")
+
+        unverified_file = tmp_path / "unverified.bin"
+        unverified_file.write_bytes(b"data")
+
+        ref_verified = _make_reference(
+            session, asset, str(verified_file), name="verified", needs_verify=False
+        )
+        ref_unverified = _make_reference(
+            session, asset, str(unverified_file), name="unverified", needs_verify=True
+        )
+        session.commit()
+
+        refs = [ref_unverified, ref_verified]
+        result = select_best_live_path(refs)
+        assert result == str(verified_file)
+
+    def test_falls_back_to_existing_unverified(self, session: Session, tmp_path):
+        """If all references need verification, return first existing path."""
+        asset = _make_asset(session, "hash1")
+
+        existing_file = tmp_path / "exists.bin"
+        existing_file.write_bytes(b"data")
+
+        ref = _make_reference(session, asset, str(existing_file), needs_verify=True)
+        session.commit()
+
+        result = select_best_live_path([ref])
+        assert result == str(existing_file)
+
+
+class TestSelectBestLivePathWithMocking:
+    def test_handles_missing_file_path_attr(self):
+        """Gracefully handle references with None file_path."""
+
+        class MockRef:
+            file_path = None
+            needs_verify = False
+
+        result = select_best_live_path([MockRef()])
+        assert result == ""
+
+
+class TestUpsertReference:
+    @pytest.mark.parametrize(
+        "initial_mtime,second_mtime,expect_created,expect_updated,final_mtime",
+        [
+            # New reference creation
+            (None, 12345, True, False, 12345),
+            # Existing reference, same mtime - no update
+            (100, 100, False, False, 100),
+            # Existing reference, different mtime - update
+            (100, 200, False, True, 200),
+        ],
+        ids=["new_reference", "existing_no_change", "existing_update_mtime"],
+    )
+    def test_upsert_scenarios(
+        self, session: Session, initial_mtime, second_mtime, expect_created, expect_updated, final_mtime
+    ):
+        asset = _make_asset(session, "hash1")
+        file_path = f"/path_{initial_mtime}_{second_mtime}.bin"
+        name = f"file_{initial_mtime}_{second_mtime}"
+
+        # Create initial reference if needed
+        if initial_mtime is not None:
+            upsert_reference(session, asset_id=asset.id, file_path=file_path, name=name, mtime_ns=initial_mtime)
+            session.commit()
+
+        # The upsert call we're testing
+        created, updated = upsert_reference(
+            session, asset_id=asset.id, file_path=file_path, name=name, mtime_ns=second_mtime
+        )
+        session.commit()
+
+        assert created is expect_created
+        assert updated is expect_updated
+        ref = session.query(AssetReference).filter_by(file_path=file_path).one()
+        assert ref.mtime_ns == final_mtime
+
+    def test_upsert_restores_missing_reference(self, session: Session):
+        """Upserting a reference that was marked missing should restore it."""
+        asset = _make_asset(session, "hash1")
+        file_path = "/restored/file.bin"
+
+        ref = _make_reference(session, asset, file_path, mtime_ns=100)
+        ref.is_missing = True
+        session.commit()
+
+        created, updated = upsert_reference(
+            session, asset_id=asset.id, file_path=file_path, name="restored", mtime_ns=100
+        )
+        session.commit()
+
+        assert created is False
+        assert updated is True
+        restored_ref = session.query(AssetReference).filter_by(file_path=file_path).one()
+        assert restored_ref.is_missing is False
+
+
+class TestRestoreReferencesByPaths:
+    def test_restores_missing_references(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        missing_path = "/missing/file.bin"
+        active_path = "/active/file.bin"
+
+        missing_ref = _make_reference(session, asset, missing_path, name="missing")
+        missing_ref.is_missing = True
+        _make_reference(session, asset, active_path, name="active")
+        session.commit()
+
+        restored = restore_references_by_paths(session, [missing_path])
+        session.commit()
+
+        assert restored == 1
+        ref = session.query(AssetReference).filter_by(file_path=missing_path).one()
+        assert ref.is_missing is False
+
+    def test_empty_list_restores_nothing(self, session: Session):
+        restored = restore_references_by_paths(session, [])
+        assert restored == 0
+
+
+class TestMarkReferencesMissingOutsidePrefixes:
+    def test_marks_references_missing_outside_prefixes(self, session: Session, tmp_path):
+        asset = _make_asset(session, "hash1")
+        valid_dir = tmp_path / "valid"
+        valid_dir.mkdir()
+        invalid_dir = tmp_path / "invalid"
+        invalid_dir.mkdir()
+
+        valid_path = str(valid_dir / "file.bin")
+        invalid_path = str(invalid_dir / "file.bin")
+
+        _make_reference(session, asset, valid_path, name="valid")
+        _make_reference(session, asset, invalid_path, name="invalid")
+        session.commit()
+
+        marked = mark_references_missing_outside_prefixes(session, [str(valid_dir)])
+        session.commit()
+
+        assert marked == 1
+        all_refs = session.query(AssetReference).all()
+        assert len(all_refs) == 2
+
+        valid_ref = next(r for r in all_refs if r.file_path == valid_path)
+        invalid_ref = next(r for r in all_refs if r.file_path == invalid_path)
+        assert valid_ref.is_missing is False
+        assert invalid_ref.is_missing is True
+
+    def test_empty_prefixes_marks_nothing(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        _make_reference(session, asset, "/some/path.bin")
+        session.commit()
+
+        marked = mark_references_missing_outside_prefixes(session, [])
+
+        assert marked == 0
+
+
+class TestGetUnreferencedUnhashedAssetIds:
+    def test_returns_unreferenced_unhashed_assets(self, session: Session):
+        # Unhashed asset (hash=None) with no references (no file_path)
+        no_refs = _make_asset(session, hash_val=None)
+        # Unhashed asset with active reference (not unreferenced)
+        with_active_ref = _make_asset(session, hash_val=None)
+        _make_reference(session, with_active_ref, "/has/ref.bin", name="has_ref")
+        # Unhashed asset with only missing reference (should be unreferenced)
+        with_missing_ref = _make_asset(session, hash_val=None)
+        missing_ref = _make_reference(session, with_missing_ref, "/missing/ref.bin", name="missing_ref")
+        missing_ref.is_missing = True
+        # Regular asset (hash not None) - should not be returned
+        _make_asset(session, hash_val="blake3:regular")
+        session.commit()
+
+        unreferenced = get_unreferenced_unhashed_asset_ids(session)
+
+        assert no_refs.id in unreferenced
+        assert with_missing_ref.id in unreferenced
+        assert with_active_ref.id not in unreferenced
+
+
+class TestDeleteAssetsByIds:
+    def test_deletes_assets_and_references(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        _make_reference(session, asset, "/test/path.bin", name="test")
+        session.commit()
+
+        deleted = delete_assets_by_ids(session, [asset.id])
+        session.commit()
+
+        assert deleted == 1
+        assert session.query(Asset).count() == 0
+        assert session.query(AssetReference).count() == 0
+
+    def test_empty_list_deletes_nothing(self, session: Session):
+        _make_asset(session, "hash1")
+        session.commit()
+
+        deleted = delete_assets_by_ids(session, [])
+
+        assert deleted == 0
+        assert session.query(Asset).count() == 1
+
+
+class TestGetReferencesForPrefixes:
+    def test_returns_references_matching_prefix(self, session: Session, tmp_path):
+        asset = _make_asset(session, "hash1")
+        dir1 = tmp_path / "dir1"
+        dir1.mkdir()
+        dir2 = tmp_path / "dir2"
+        dir2.mkdir()
+
+        path1 = str(dir1 / "file.bin")
+        path2 = str(dir2 / "file.bin")
+
+        _make_reference(session, asset, path1, name="file1", mtime_ns=100)
+        _make_reference(session, asset, path2, name="file2", mtime_ns=200)
+        session.commit()
+
+        rows = get_references_for_prefixes(session, [str(dir1)])
+
+        assert len(rows) == 1
+        assert rows[0].file_path == path1
+
+    def test_empty_prefixes_returns_empty(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        _make_reference(session, asset, "/some/path.bin")
+        session.commit()
+
+        rows = get_references_for_prefixes(session, [])
+
+        assert rows == []
+
+
+class TestBulkSetNeedsVerify:
+    def test_sets_needs_verify_flag(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref1 = _make_reference(session, asset, "/path1.bin", needs_verify=False)
+        ref2 = _make_reference(session, asset, "/path2.bin", needs_verify=False)
+        session.commit()
+
+        updated = bulk_update_needs_verify(session, [ref1.id, ref2.id], True)
+        session.commit()
+
+        assert updated == 2
+        session.refresh(ref1)
+        session.refresh(ref2)
+        assert ref1.needs_verify is True
+        assert ref2.needs_verify is True
+
+    def test_empty_list_updates_nothing(self, session: Session):
+        updated = bulk_update_needs_verify(session, [], True)
+        assert updated == 0
+
+
+class TestDeleteReferencesByIds:
+    def test_deletes_references_by_id(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref1 = _make_reference(session, asset, "/path1.bin")
+        _make_reference(session, asset, "/path2.bin")
+        session.commit()
+
+        deleted = delete_references_by_ids(session, [ref1.id])
+        session.commit()
+
+        assert deleted == 1
+        assert session.query(AssetReference).count() == 1
+
+    def test_empty_list_deletes_nothing(self, session: Session):
+        deleted = delete_references_by_ids(session, [])
+        assert deleted == 0
+
+
+class TestDeleteOrphanedSeedAsset:
+    @pytest.mark.parametrize(
+        "create_asset,expected_deleted,expected_count",
+        [
+            (True, True, 0),   # Existing asset gets deleted
+            (False, False, 0),  # Nonexistent returns False
+        ],
+        ids=["deletes_existing", "nonexistent_returns_false"],
+    )
+    def test_delete_orphaned_seed_asset(
+        self, session: Session, create_asset, expected_deleted, expected_count
+    ):
+        asset_id = "nonexistent-id"
+        if create_asset:
+            asset = _make_asset(session, hash_val=None)
+            asset_id = asset.id
+            _make_reference(session, asset, "/test/path.bin", name="test")
+            session.commit()
+
+        deleted = delete_orphaned_seed_asset(session, asset_id)
+        if create_asset:
+            session.commit()
+
+        assert deleted is expected_deleted
+        assert session.query(Asset).count() == expected_count
+
+
+class TestBulkInsertReferencesIgnoreConflicts:
+    def test_inserts_multiple_references(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        now = get_utc_now()
+        rows = [
+            {
+                "asset_id": asset.id,
+                "file_path": "/bulk1.bin",
+                "name": "bulk1",
+                "mtime_ns": 100,
+                "created_at": now,
+                "updated_at": now,
+                "last_access_time": now,
+            },
+            {
+                "asset_id": asset.id,
+                "file_path": "/bulk2.bin",
+                "name": "bulk2",
+                "mtime_ns": 200,
+                "created_at": now,
+                "updated_at": now,
+                "last_access_time": now,
+            },
+        ]
+        bulk_insert_references_ignore_conflicts(session, rows)
+        session.commit()
+
+        assert session.query(AssetReference).count() == 2
+
+    def test_ignores_conflicts(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        _make_reference(session, asset, "/existing.bin", mtime_ns=100)
+        session.commit()
+
+        now = get_utc_now()
+        rows = [
+            {
+                "asset_id": asset.id,
+                "file_path": "/existing.bin",
+                "name": "existing",
+                "mtime_ns": 999,
+                "created_at": now,
+                "updated_at": now,
+                "last_access_time": now,
+            },
+            {
+                "asset_id": asset.id,
+                "file_path": "/new.bin",
+                "name": "new",
+                "mtime_ns": 200,
+                "created_at": now,
+                "updated_at": now,
+                "last_access_time": now,
+            },
+        ]
+        bulk_insert_references_ignore_conflicts(session, rows)
+        session.commit()
+
+        assert session.query(AssetReference).count() == 2
+        existing = session.query(AssetReference).filter_by(file_path="/existing.bin").one()
+        assert existing.mtime_ns == 100  # Original value preserved
+
+    def test_empty_list_is_noop(self, session: Session):
+        bulk_insert_references_ignore_conflicts(session, [])
+        assert session.query(AssetReference).count() == 0
+
+
+class TestGetReferencesByPathsAndAssetIds:
+    def test_returns_matching_paths(self, session: Session):
+        asset1 = _make_asset(session, "hash1")
+        asset2 = _make_asset(session, "hash2")
+
+        _make_reference(session, asset1, "/path1.bin")
+        _make_reference(session, asset2, "/path2.bin")
+        session.commit()
+
+        path_to_asset = {
+            "/path1.bin": asset1.id,
+            "/path2.bin": asset2.id,
+        }
+        winners = get_references_by_paths_and_asset_ids(session, path_to_asset)
+
+        assert winners == {"/path1.bin", "/path2.bin"}
+
+    def test_excludes_non_matching_asset_ids(self, session: Session):
+        asset1 = _make_asset(session, "hash1")
+        asset2 = _make_asset(session, "hash2")
+
+        _make_reference(session, asset1, "/path1.bin")
+        session.commit()
+
+        # Path exists but with different asset_id
+        path_to_asset = {"/path1.bin": asset2.id}
+        winners = get_references_by_paths_and_asset_ids(session, path_to_asset)
+
+        assert winners == set()
+
+    def test_empty_dict_returns_empty(self, session: Session):
+        winners = get_references_by_paths_and_asset_ids(session, {})
+        assert winners == set()
diff --git a/tests-unit/assets_test/queries/test_metadata.py b/tests-unit/assets_test/queries/test_metadata.py
new file mode 100644
index 000000000..6a545e819
--- /dev/null
+++ b/tests-unit/assets_test/queries/test_metadata.py
@@ -0,0 +1,184 @@
+"""Tests for metadata filtering logic in asset_reference queries."""
+import pytest
+from sqlalchemy.orm import Session
+
+from app.assets.database.models import Asset, AssetReference, AssetReferenceMeta
+from app.assets.database.queries import list_references_page
+from app.assets.database.queries.asset_reference import convert_metadata_to_rows
+from app.assets.helpers import get_utc_now
+
+
+def _make_asset(session: Session, hash_val: str) -> Asset:
+    asset = Asset(hash=hash_val, size_bytes=1024)
+    session.add(asset)
+    session.flush()
+    return asset
+
+
+def _make_reference(
+    session: Session,
+    asset: Asset,
+    name: str,
+    metadata: dict | None = None,
+) -> AssetReference:
+    now = get_utc_now()
+    ref = AssetReference(
+        owner_id="",
+        name=name,
+        asset_id=asset.id,
+        user_metadata=metadata,
+        created_at=now,
+        updated_at=now,
+        last_access_time=now,
+    )
+    session.add(ref)
+    session.flush()
+
+    if metadata:
+        for key, val in metadata.items():
+            for row in convert_metadata_to_rows(key, val):
+                meta_row = AssetReferenceMeta(
+                    asset_reference_id=ref.id,
+                    key=row["key"],
+                    ordinal=row.get("ordinal", 0),
+                    val_str=row.get("val_str"),
+                    val_num=row.get("val_num"),
+                    val_bool=row.get("val_bool"),
+                    val_json=row.get("val_json"),
+                )
+                session.add(meta_row)
+        session.flush()
+
+    return ref
+
+
+class TestMetadataFilterByType:
+    """Table-driven tests for metadata filtering by different value types."""
+
+    @pytest.mark.parametrize(
+        "match_meta,nomatch_meta,filter_key,filter_val",
+        [
+            # String matching
+            ({"category": "models"}, {"category": "images"}, "category", "models"),
+            # Integer matching
+            ({"epoch": 5}, {"epoch": 10}, "epoch", 5),
+            # Float matching
+            ({"score": 0.95}, {"score": 0.5}, "score", 0.95),
+            # Boolean True matching
+            ({"enabled": True}, {"enabled": False}, "enabled", True),
+            # Boolean False matching
+            ({"enabled": False}, {"enabled": True}, "enabled", False),
+        ],
+        ids=["string", "int", "float", "bool_true", "bool_false"],
+    )
+    def test_filter_matches_correct_value(
+        self, session: Session, match_meta, nomatch_meta, filter_key, filter_val
+    ):
+        asset = _make_asset(session, "hash1")
+        _make_reference(session, asset, "match", match_meta)
+        _make_reference(session, asset, "nomatch", nomatch_meta)
+        session.commit()
+
+        refs, _, total = list_references_page(
+            session, metadata_filter={filter_key: filter_val}
+        )
+        assert total == 1
+        assert refs[0].name == "match"
+
+    @pytest.mark.parametrize(
+        "stored_meta,filter_key,filter_val",
+        [
+            # String no match
+            ({"category": "models"}, "category", "other"),
+            # Int no match
+            ({"epoch": 5}, "epoch", 99),
+            # Float no match
+            ({"score": 0.5}, "score", 0.99),
+        ],
+        ids=["string_no_match", "int_no_match", "float_no_match"],
+    )
+    def test_filter_returns_empty_when_no_match(
+        self, session: Session, stored_meta, filter_key, filter_val
+    ):
+        asset = _make_asset(session, "hash1")
+        _make_reference(session, asset, "item", stored_meta)
+        session.commit()
+
+        refs, _, total = list_references_page(
+            session, metadata_filter={filter_key: filter_val}
+        )
+        assert total == 0
+
+
+class TestMetadataFilterNull:
+    """Tests for null/missing key filtering."""
+
+    @pytest.mark.parametrize(
+        "match_name,match_meta,nomatch_name,nomatch_meta,filter_key",
+        [
+            # Null matches missing key
+            ("missing_key", {}, "has_key", {"optional": "value"}, "optional"),
+            # Null matches explicit null
+            ("explicit_null", {"nullable": None}, "has_value", {"nullable": "present"}, "nullable"),
+        ],
+        ids=["missing_key", "explicit_null"],
+    )
+    def test_null_filter_matches(
+        self, session: Session, match_name, match_meta, nomatch_name, nomatch_meta, filter_key
+    ):
+        asset = _make_asset(session, "hash1")
+        _make_reference(session, asset, match_name, match_meta)
+        _make_reference(session, asset, nomatch_name, nomatch_meta)
+        session.commit()
+
+        refs, _, total = list_references_page(session, metadata_filter={filter_key: None})
+        assert total == 1
+        assert refs[0].name == match_name
+
+
+class TestMetadataFilterList:
+    """Tests for list-based (OR) filtering."""
+
+    def test_filter_by_list_matches_any(self, session: Session):
+        """List values should match ANY of the values (OR)."""
+        asset = _make_asset(session, "hash1")
+        _make_reference(session, asset, "cat_a", {"category": "a"})
+        _make_reference(session, asset, "cat_b", {"category": "b"})
+        _make_reference(session, asset, "cat_c", {"category": "c"})
+        session.commit()
+
+        refs, _, total = list_references_page(session, metadata_filter={"category": ["a", "b"]})
+        assert total == 2
+        names = {r.name for r in refs}
+        assert names == {"cat_a", "cat_b"}
+
+
+class TestMetadataFilterMultipleKeys:
+    """Tests for multiple filter keys (AND semantics)."""
+
+    def test_multiple_keys_must_all_match(self, session: Session):
+        """Multiple keys should ALL match (AND)."""
+        asset = _make_asset(session, "hash1")
+        _make_reference(session, asset, "match", {"type": "model", "version": 2})
+        _make_reference(session, asset, "wrong_type", {"type": "config", "version": 2})
+        _make_reference(session, asset, "wrong_version", {"type": "model", "version": 1})
+        session.commit()
+
+        refs, _, total = list_references_page(
+            session, metadata_filter={"type": "model", "version": 2}
+        )
+        assert total == 1
+        assert refs[0].name == "match"
+
+
+class TestMetadataFilterEmptyDict:
+    """Tests for empty filter behavior."""
+
+    def test_empty_filter_returns_all(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        _make_reference(session, asset, "a", {"key": "val"})
+        _make_reference(session, asset, "b", {})
+        session.commit()
+
+        refs, _, total = list_references_page(session, metadata_filter={})
+        assert total == 2
diff --git a/tests-unit/assets_test/queries/test_tags.py b/tests-unit/assets_test/queries/test_tags.py
new file mode 100644
index 000000000..4ed99aa37
--- /dev/null
+++ b/tests-unit/assets_test/queries/test_tags.py
@@ -0,0 +1,366 @@
+import pytest
+from sqlalchemy.orm import Session
+
+from app.assets.database.models import Asset, AssetReference, AssetReferenceTag, AssetReferenceMeta, Tag
+from app.assets.database.queries import (
+    ensure_tags_exist,
+    get_reference_tags,
+    set_reference_tags,
+    add_tags_to_reference,
+    remove_tags_from_reference,
+    add_missing_tag_for_asset_id,
+    remove_missing_tag_for_asset_id,
+    list_tags_with_usage,
+    bulk_insert_tags_and_meta,
+)
+from app.assets.helpers import get_utc_now
+
+
+def _make_asset(session: Session, hash_val: str | None = None) -> Asset:
+    asset = Asset(hash=hash_val, size_bytes=1024)
+    session.add(asset)
+    session.flush()
+    return asset
+
+
+def _make_reference(session: Session, asset: Asset, name: str = "test", owner_id: str = "") -> AssetReference:
+    now = get_utc_now()
+    ref = AssetReference(
+        owner_id=owner_id,
+        name=name,
+        asset_id=asset.id,
+        created_at=now,
+        updated_at=now,
+        last_access_time=now,
+    )
+    session.add(ref)
+    session.flush()
+    return ref
+
+
+class TestEnsureTagsExist:
+    def test_creates_new_tags(self, session: Session):
+        ensure_tags_exist(session, ["alpha", "beta"], tag_type="user")
+        session.commit()
+
+        tags = session.query(Tag).all()
+        assert {t.name for t in tags} == {"alpha", "beta"}
+
+    def test_is_idempotent(self, session: Session):
+        ensure_tags_exist(session, ["alpha"], tag_type="user")
+        ensure_tags_exist(session, ["alpha"], tag_type="user")
+        session.commit()
+
+        assert session.query(Tag).count() == 1
+
+    def test_normalizes_tags(self, session: Session):
+        ensure_tags_exist(session, ["  ALPHA  ", "Beta", "alpha"])
+        session.commit()
+
+        tags = session.query(Tag).all()
+        assert {t.name for t in tags} == {"alpha", "beta"}
+
+    def test_empty_list_is_noop(self, session: Session):
+        ensure_tags_exist(session, [])
+        session.commit()
+        assert session.query(Tag).count() == 0
+
+    def test_tag_type_is_set(self, session: Session):
+        ensure_tags_exist(session, ["system-tag"], tag_type="system")
+        session.commit()
+
+        tag = session.query(Tag).filter_by(name="system-tag").one()
+        assert tag.tag_type == "system"
+
+
+class TestGetReferenceTags:
+    def test_returns_empty_for_no_tags(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+
+        tags = get_reference_tags(session, reference_id=ref.id)
+        assert tags == []
+
+    def test_returns_tags_for_reference(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+
+        ensure_tags_exist(session, ["tag1", "tag2"])
+        session.add_all([
+            AssetReferenceTag(asset_reference_id=ref.id, tag_name="tag1", origin="manual", added_at=get_utc_now()),
+            AssetReferenceTag(asset_reference_id=ref.id, tag_name="tag2", origin="manual", added_at=get_utc_now()),
+        ])
+        session.flush()
+
+        tags = get_reference_tags(session, reference_id=ref.id)
+        assert set(tags) == {"tag1", "tag2"}
+
+
+class TestSetReferenceTags:
+    def test_adds_new_tags(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+
+        result = set_reference_tags(session, reference_id=ref.id, tags=["a", "b"])
+        session.commit()
+
+        assert set(result.added) == {"a", "b"}
+        assert result.removed == []
+        assert set(result.total) == {"a", "b"}
+
+    def test_removes_old_tags(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+
+        set_reference_tags(session, reference_id=ref.id, tags=["a", "b", "c"])
+        result = set_reference_tags(session, reference_id=ref.id, tags=["a"])
+        session.commit()
+
+        assert result.added == []
+        assert set(result.removed) == {"b", "c"}
+        assert result.total == ["a"]
+
+    def test_replaces_tags(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+
+        set_reference_tags(session, reference_id=ref.id, tags=["a", "b"])
+        result = set_reference_tags(session, reference_id=ref.id, tags=["b", "c"])
+        session.commit()
+
+        assert result.added == ["c"]
+        assert result.removed == ["a"]
+        assert set(result.total) == {"b", "c"}
+
+
+class TestAddTagsToReference:
+    def test_adds_tags(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+
+        result = add_tags_to_reference(session, reference_id=ref.id, tags=["x", "y"])
+        session.commit()
+
+        assert set(result.added) == {"x", "y"}
+        assert result.already_present == []
+
+    def test_reports_already_present(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+
+        add_tags_to_reference(session, reference_id=ref.id, tags=["x"])
+        result = add_tags_to_reference(session, reference_id=ref.id, tags=["x", "y"])
+        session.commit()
+
+        assert result.added == ["y"]
+        assert result.already_present == ["x"]
+
+    def test_raises_for_missing_reference(self, session: Session):
+        with pytest.raises(ValueError, match="not found"):
+            add_tags_to_reference(session, reference_id="nonexistent", tags=["x"])
+
+
+class TestRemoveTagsFromReference:
+    def test_removes_tags(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+
+        add_tags_to_reference(session, reference_id=ref.id, tags=["a", "b", "c"])
+        result = remove_tags_from_reference(session, reference_id=ref.id, tags=["a", "b"])
+        session.commit()
+
+        assert set(result.removed) == {"a", "b"}
+        assert result.not_present == []
+        assert result.total_tags == ["c"]
+
+    def test_reports_not_present(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+
+        add_tags_to_reference(session, reference_id=ref.id, tags=["a"])
+        result = remove_tags_from_reference(session, reference_id=ref.id, tags=["a", "x"])
+        session.commit()
+
+        assert result.removed == ["a"]
+        assert result.not_present == ["x"]
+
+    def test_raises_for_missing_reference(self, session: Session):
+        with pytest.raises(ValueError, match="not found"):
+            remove_tags_from_reference(session, reference_id="nonexistent", tags=["x"])
+
+
+class TestMissingTagFunctions:
+    def test_add_missing_tag_for_asset_id(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+        ensure_tags_exist(session, ["missing"], tag_type="system")
+
+        add_missing_tag_for_asset_id(session, asset_id=asset.id)
+        session.commit()
+
+        tags = get_reference_tags(session, reference_id=ref.id)
+        assert "missing" in tags
+
+    def test_add_missing_tag_is_idempotent(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+        ensure_tags_exist(session, ["missing"], tag_type="system")
+
+        add_missing_tag_for_asset_id(session, asset_id=asset.id)
+        add_missing_tag_for_asset_id(session, asset_id=asset.id)
+        session.commit()
+
+        links = session.query(AssetReferenceTag).filter_by(asset_reference_id=ref.id, tag_name="missing").all()
+        assert len(links) == 1
+
+    def test_remove_missing_tag_for_asset_id(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+        ensure_tags_exist(session, ["missing"], tag_type="system")
+        add_missing_tag_for_asset_id(session, asset_id=asset.id)
+
+        remove_missing_tag_for_asset_id(session, asset_id=asset.id)
+        session.commit()
+
+        tags = get_reference_tags(session, reference_id=ref.id)
+        assert "missing" not in tags
+
+
+class TestListTagsWithUsage:
+    def test_returns_tags_with_counts(self, session: Session):
+        ensure_tags_exist(session, ["used", "unused"])
+
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+        add_tags_to_reference(session, reference_id=ref.id, tags=["used"])
+        session.commit()
+
+        rows, total = list_tags_with_usage(session)
+
+        tag_dict = {name: count for name, _, count in rows}
+        assert tag_dict["used"] == 1
+        assert tag_dict["unused"] == 0
+        assert total == 2
+
+    def test_exclude_zero_counts(self, session: Session):
+        ensure_tags_exist(session, ["used", "unused"])
+
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+        add_tags_to_reference(session, reference_id=ref.id, tags=["used"])
+        session.commit()
+
+        rows, total = list_tags_with_usage(session, include_zero=False)
+
+        tag_names = {name for name, _, _ in rows}
+        assert "used" in tag_names
+        assert "unused" not in tag_names
+
+    def test_prefix_filter(self, session: Session):
+        ensure_tags_exist(session, ["alpha", "beta", "alphabet"])
+        session.commit()
+
+        rows, total = list_tags_with_usage(session, prefix="alph")
+
+        tag_names = {name for name, _, _ in rows}
+        assert tag_names == {"alpha", "alphabet"}
+
+    def test_order_by_name(self, session: Session):
+        ensure_tags_exist(session, ["zebra", "alpha", "middle"])
+        session.commit()
+
+        rows, _ = list_tags_with_usage(session, order="name_asc")
+
+        names = [name for name, _, _ in rows]
+        assert names == ["alpha", "middle", "zebra"]
+
+    def test_owner_visibility(self, session: Session):
+        ensure_tags_exist(session, ["shared-tag", "owner-tag"])
+
+        asset = _make_asset(session, "hash1")
+        shared_ref = _make_reference(session, asset, name="shared", owner_id="")
+        owner_ref = _make_reference(session, asset, name="owned", owner_id="user1")
+
+        add_tags_to_reference(session, reference_id=shared_ref.id, tags=["shared-tag"])
+        add_tags_to_reference(session, reference_id=owner_ref.id, tags=["owner-tag"])
+        session.commit()
+
+        # Empty owner sees only shared
+        rows, _ = list_tags_with_usage(session, owner_id="", include_zero=False)
+        tag_dict = {name: count for name, _, count in rows}
+        assert tag_dict.get("shared-tag", 0) == 1
+        assert tag_dict.get("owner-tag", 0) == 0
+
+        # User1 sees both
+        rows, _ = list_tags_with_usage(session, owner_id="user1", include_zero=False)
+        tag_dict = {name: count for name, _, count in rows}
+        assert tag_dict.get("shared-tag", 0) == 1
+        assert tag_dict.get("owner-tag", 0) == 1
+
+
+class TestBulkInsertTagsAndMeta:
+    def test_inserts_tags(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+        ensure_tags_exist(session, ["bulk-tag1", "bulk-tag2"])
+        session.commit()
+
+        now = get_utc_now()
+        tag_rows = [
+            {"asset_reference_id": ref.id, "tag_name": "bulk-tag1", "origin": "manual", "added_at": now},
+            {"asset_reference_id": ref.id, "tag_name": "bulk-tag2", "origin": "manual", "added_at": now},
+        ]
+        bulk_insert_tags_and_meta(session, tag_rows=tag_rows, meta_rows=[])
+        session.commit()
+
+        tags = get_reference_tags(session, reference_id=ref.id)
+        assert set(tags) == {"bulk-tag1", "bulk-tag2"}
+
+    def test_inserts_meta(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+        session.commit()
+
+        meta_rows = [
+            {
+                "asset_reference_id": ref.id,
+                "key": "meta-key",
+                "ordinal": 0,
+                "val_str": "meta-value",
+                "val_num": None,
+                "val_bool": None,
+                "val_json": None,
+            },
+        ]
+        bulk_insert_tags_and_meta(session, tag_rows=[], meta_rows=meta_rows)
+        session.commit()
+
+        meta = session.query(AssetReferenceMeta).filter_by(asset_reference_id=ref.id).all()
+        assert len(meta) == 1
+        assert meta[0].key == "meta-key"
+        assert meta[0].val_str == "meta-value"
+
+    def test_ignores_conflicts(self, session: Session):
+        asset = _make_asset(session, "hash1")
+        ref = _make_reference(session, asset)
+        ensure_tags_exist(session, ["existing-tag"])
+        add_tags_to_reference(session, reference_id=ref.id, tags=["existing-tag"])
+        session.commit()
+
+        now = get_utc_now()
+        tag_rows = [
+            {"asset_reference_id": ref.id, "tag_name": "existing-tag", "origin": "duplicate", "added_at": now},
+        ]
+        bulk_insert_tags_and_meta(session, tag_rows=tag_rows, meta_rows=[])
+        session.commit()
+
+        # Should still have only one tag link
+        links = session.query(AssetReferenceTag).filter_by(asset_reference_id=ref.id, tag_name="existing-tag").all()
+        assert len(links) == 1
+        # Origin should be original, not overwritten
+        assert links[0].origin == "manual"
+
+    def test_empty_lists_is_noop(self, session: Session):
+        bulk_insert_tags_and_meta(session, tag_rows=[], meta_rows=[])
+        assert session.query(AssetReferenceTag).count() == 0
+        assert session.query(AssetReferenceMeta).count() == 0
diff --git a/tests-unit/assets_test/services/__init__.py b/tests-unit/assets_test/services/__init__.py
new file mode 100644
index 000000000..d0213422e
--- /dev/null
+++ b/tests-unit/assets_test/services/__init__.py
@@ -0,0 +1 @@
+# Service layer tests
diff --git a/tests-unit/assets_test/services/conftest.py b/tests-unit/assets_test/services/conftest.py
new file mode 100644
index 000000000..31c763d48
--- /dev/null
+++ b/tests-unit/assets_test/services/conftest.py
@@ -0,0 +1,54 @@
+import tempfile
+from pathlib import Path
+from unittest.mock import patch
+
+import pytest
+from sqlalchemy import create_engine
+from sqlalchemy.orm import Session
+
+from app.assets.database.models import Base
+
+
+@pytest.fixture(autouse=True)
+def autoclean_unit_test_assets():
+    """Override parent autouse fixture - service unit tests don't need server cleanup."""
+    yield
+
+
+@pytest.fixture
+def db_engine():
+    """In-memory SQLite engine for fast unit tests."""
+    engine = create_engine("sqlite:///:memory:")
+    Base.metadata.create_all(engine)
+    return engine
+
+
+@pytest.fixture
+def session(db_engine):
+    """Session fixture for tests that need direct DB access."""
+    with Session(db_engine) as sess:
+        yield sess
+
+
+@pytest.fixture
+def mock_create_session(db_engine):
+    """Patch create_session to use our in-memory database."""
+    from contextlib import contextmanager
+    from sqlalchemy.orm import Session as SASession
+
+    @contextmanager
+    def _create_session():
+        with SASession(db_engine) as sess:
+            yield sess
+
+    with patch("app.assets.services.ingest.create_session", _create_session), \
+         patch("app.assets.services.asset_management.create_session", _create_session), \
+         patch("app.assets.services.tagging.create_session", _create_session):
+        yield _create_session
+
+
+@pytest.fixture
+def temp_dir():
+    """Temporary directory for file operations."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        yield Path(tmpdir)
diff --git a/tests-unit/assets_test/services/test_asset_management.py b/tests-unit/assets_test/services/test_asset_management.py
new file mode 100644
index 000000000..101ef7292
--- /dev/null
+++ b/tests-unit/assets_test/services/test_asset_management.py
@@ -0,0 +1,268 @@
+"""Tests for asset_management services."""
+import pytest
+from sqlalchemy.orm import Session
+
+from app.assets.database.models import Asset, AssetReference
+from app.assets.database.queries import ensure_tags_exist, add_tags_to_reference
+from app.assets.helpers import get_utc_now
+from app.assets.services import (
+    get_asset_detail,
+    update_asset_metadata,
+    delete_asset_reference,
+    set_asset_preview,
+)
+
+
+def _make_asset(session: Session, hash_val: str = "blake3:test", size: int = 1024) -> Asset:
+    asset = Asset(hash=hash_val, size_bytes=size, mime_type="application/octet-stream")
+    session.add(asset)
+    session.flush()
+    return asset
+
+
+def _make_reference(
+    session: Session,
+    asset: Asset,
+    name: str = "test",
+    owner_id: str = "",
+) -> AssetReference:
+    now = get_utc_now()
+    ref = AssetReference(
+        owner_id=owner_id,
+        name=name,
+        asset_id=asset.id,
+        created_at=now,
+        updated_at=now,
+        last_access_time=now,
+    )
+    session.add(ref)
+    session.flush()
+    return ref
+
+
+class TestGetAssetDetail:
+    def test_returns_none_for_nonexistent(self, mock_create_session):
+        result = get_asset_detail(reference_id="nonexistent")
+        assert result is None
+
+    def test_returns_asset_with_tags(self, mock_create_session, session: Session):
+        asset = _make_asset(session)
+        ref = _make_reference(session, asset, name="test.bin")
+        ensure_tags_exist(session, ["alpha", "beta"])
+        add_tags_to_reference(session, reference_id=ref.id, tags=["alpha", "beta"])
+        session.commit()
+
+        result = get_asset_detail(reference_id=ref.id)
+
+        assert result is not None
+        assert result.ref.id == ref.id
+        assert result.asset.hash == asset.hash
+        assert set(result.tags) == {"alpha", "beta"}
+
+    def test_respects_owner_visibility(self, mock_create_session, session: Session):
+        asset = _make_asset(session)
+        ref = _make_reference(session, asset, owner_id="user1")
+        session.commit()
+
+        # Wrong owner cannot see
+        result = get_asset_detail(reference_id=ref.id, owner_id="user2")
+        assert result is None
+
+        # Correct owner can see
+        result = get_asset_detail(reference_id=ref.id, owner_id="user1")
+        assert result is not None
+
+
+class TestUpdateAssetMetadata:
+    def test_updates_name(self, mock_create_session, session: Session):
+        asset = _make_asset(session)
+        ref = _make_reference(session, asset, name="old_name.bin")
+        ref_id = ref.id
+        session.commit()
+
+        update_asset_metadata(
+            reference_id=ref_id,
+            name="new_name.bin",
+        )
+
+        # Verify by re-fetching from DB
+        session.expire_all()
+        updated_ref = session.get(AssetReference, ref_id)
+        assert updated_ref.name == "new_name.bin"
+
+    def test_updates_tags(self, mock_create_session, session: Session):
+        asset = _make_asset(session)
+        ref = _make_reference(session, asset)
+        ensure_tags_exist(session, ["old"])
+        add_tags_to_reference(session, reference_id=ref.id, tags=["old"])
+        session.commit()
+
+        result = update_asset_metadata(
+            reference_id=ref.id,
+            tags=["new1", "new2"],
+        )
+
+        assert set(result.tags) == {"new1", "new2"}
+        assert "old" not in result.tags
+
+    def test_updates_user_metadata(self, mock_create_session, session: Session):
+        asset = _make_asset(session)
+        ref = _make_reference(session, asset)
+        ref_id = ref.id
+        session.commit()
+
+        update_asset_metadata(
+            reference_id=ref_id,
+            user_metadata={"key": "value", "num": 42},
+        )
+
+        # Verify by re-fetching from DB
+        session.expire_all()
+        updated_ref = session.get(AssetReference, ref_id)
+        assert updated_ref.user_metadata["key"] == "value"
+        assert updated_ref.user_metadata["num"] == 42
+
+    def test_raises_for_nonexistent(self, mock_create_session):
+        with pytest.raises(ValueError, match="not found"):
+            update_asset_metadata(reference_id="nonexistent", name="fail")
+
+    def test_raises_for_wrong_owner(self, mock_create_session, session: Session):
+        asset = _make_asset(session)
+        ref = _make_reference(session, asset, owner_id="user1")
+        session.commit()
+
+        with pytest.raises(PermissionError, match="not owner"):
+            update_asset_metadata(
+                reference_id=ref.id,
+                name="new",
+                owner_id="user2",
+            )
+
+
+class TestDeleteAssetReference:
+    def test_soft_deletes_reference(self, mock_create_session, session: Session):
+        asset = _make_asset(session)
+        ref = _make_reference(session, asset)
+        ref_id = ref.id
+        session.commit()
+
+        result = delete_asset_reference(
+            reference_id=ref_id,
+            owner_id="",
+            delete_content_if_orphan=False,
+        )
+
+        assert result is True
+        # Row still exists but is marked as soft-deleted
+        session.expire_all()
+        row = session.get(AssetReference, ref_id)
+        assert row is not None
+        assert row.deleted_at is not None
+
+    def test_returns_false_for_nonexistent(self, mock_create_session):
+        result = delete_asset_reference(
+            reference_id="nonexistent",
+            owner_id="",
+        )
+        assert result is False
+
+    def test_returns_false_for_wrong_owner(self, mock_create_session, session: Session):
+        asset = _make_asset(session)
+        ref = _make_reference(session, asset, owner_id="user1")
+        ref_id = ref.id
+        session.commit()
+
+        result = delete_asset_reference(
+            reference_id=ref_id,
+            owner_id="user2",
+        )
+
+        assert result is False
+        assert session.get(AssetReference, ref_id) is not None
+
+    def test_keeps_asset_if_other_references_exist(self, mock_create_session, session: Session):
+        asset = _make_asset(session)
+        ref1 = _make_reference(session, asset, name="ref1")
+        _make_reference(session, asset, name="ref2")  # Second ref keeps asset alive
+        asset_id = asset.id
+        session.commit()
+
+        delete_asset_reference(
+            reference_id=ref1.id,
+            owner_id="",
+            delete_content_if_orphan=True,
+        )
+
+        # Asset should still exist
+        assert session.get(Asset, asset_id) is not None
+
+    def test_deletes_orphaned_asset(self, mock_create_session, session: Session):
+        asset = _make_asset(session)
+        ref = _make_reference(session, asset)
+        asset_id = asset.id
+        ref_id = ref.id
+        session.commit()
+
+        delete_asset_reference(
+            reference_id=ref_id,
+            owner_id="",
+            delete_content_if_orphan=True,
+        )
+
+        # Both ref and asset should be gone
+        assert session.get(AssetReference, ref_id) is None
+        assert session.get(Asset, asset_id) is None
+
+
+class TestSetAssetPreview:
+    def test_sets_preview(self, mock_create_session, session: Session):
+        asset = _make_asset(session, hash_val="blake3:main")
+        preview_asset = _make_asset(session, hash_val="blake3:preview")
+        ref = _make_reference(session, asset)
+        ref_id = ref.id
+        preview_id = preview_asset.id
+        session.commit()
+
+        set_asset_preview(
+            reference_id=ref_id,
+            preview_asset_id=preview_id,
+        )
+
+        # Verify by re-fetching from DB
+        session.expire_all()
+        updated_ref = session.get(AssetReference, ref_id)
+        assert updated_ref.preview_id == preview_id
+
+    def test_clears_preview(self, mock_create_session, session: Session):
+        asset = _make_asset(session)
+        preview_asset = _make_asset(session, hash_val="blake3:preview")
+        ref = _make_reference(session, asset)
+        ref.preview_id = preview_asset.id
+        ref_id = ref.id
+        session.commit()
+
+        set_asset_preview(
+            reference_id=ref_id,
+            preview_asset_id=None,
+        )
+
+        # Verify by re-fetching from DB
+        session.expire_all()
+        updated_ref = session.get(AssetReference, ref_id)
+        assert updated_ref.preview_id is None
+
+    def test_raises_for_nonexistent_ref(self, mock_create_session):
+        with pytest.raises(ValueError, match="not found"):
+            set_asset_preview(reference_id="nonexistent")
+
+    def test_raises_for_wrong_owner(self, mock_create_session, session: Session):
+        asset = _make_asset(session)
+        ref = _make_reference(session, asset, owner_id="user1")
+        session.commit()
+
+        with pytest.raises(PermissionError, match="not owner"):
+            set_asset_preview(
+                reference_id=ref.id,
+                preview_asset_id=None,
+                owner_id="user2",
+            )
diff --git a/tests-unit/assets_test/services/test_bulk_ingest.py b/tests-unit/assets_test/services/test_bulk_ingest.py
new file mode 100644
index 000000000..26e22a01d
--- /dev/null
+++ b/tests-unit/assets_test/services/test_bulk_ingest.py
@@ -0,0 +1,137 @@
+"""Tests for bulk ingest services."""
+
+from pathlib import Path
+
+from sqlalchemy.orm import Session
+
+from app.assets.database.models import Asset, AssetReference
+from app.assets.services.bulk_ingest import SeedAssetSpec, batch_insert_seed_assets
+
+
+class TestBatchInsertSeedAssets:
+    def test_populates_mime_type_for_model_files(self, session: Session, temp_dir: Path):
+        """Verify mime_type is stored in the Asset table for model files."""
+        file_path = temp_dir / "model.safetensors"
+        file_path.write_bytes(b"fake safetensors content")
+
+        specs: list[SeedAssetSpec] = [
+            {
+                "abs_path": str(file_path),
+                "size_bytes": 24,
+                "mtime_ns": 1234567890000000000,
+                "info_name": "Test Model",
+                "tags": ["models"],
+                "fname": "model.safetensors",
+                "metadata": None,
+                "hash": None,
+                "mime_type": "application/safetensors",
+            }
+        ]
+
+        result = batch_insert_seed_assets(session, specs=specs, owner_id="")
+
+        assert result.inserted_refs == 1
+
+        # Verify Asset has mime_type populated
+        assets = session.query(Asset).all()
+        assert len(assets) == 1
+        assert assets[0].mime_type == "application/safetensors"
+
+    def test_mime_type_none_when_not_provided(self, session: Session, temp_dir: Path):
+        """Verify mime_type is None when not provided in spec."""
+        file_path = temp_dir / "unknown.bin"
+        file_path.write_bytes(b"binary data")
+
+        specs: list[SeedAssetSpec] = [
+            {
+                "abs_path": str(file_path),
+                "size_bytes": 11,
+                "mtime_ns": 1234567890000000000,
+                "info_name": "Unknown File",
+                "tags": [],
+                "fname": "unknown.bin",
+                "metadata": None,
+                "hash": None,
+                "mime_type": None,
+            }
+        ]
+
+        result = batch_insert_seed_assets(session, specs=specs, owner_id="")
+
+        assert result.inserted_refs == 1
+
+        assets = session.query(Asset).all()
+        assert len(assets) == 1
+        assert assets[0].mime_type is None
+
+    def test_various_model_mime_types(self, session: Session, temp_dir: Path):
+        """Verify various model file types get correct mime_type."""
+        test_cases = [
+            ("model.safetensors", "application/safetensors"),
+            ("model.pt", "application/pytorch"),
+            ("model.ckpt", "application/pickle"),
+            ("model.gguf", "application/gguf"),
+        ]
+
+        specs: list[SeedAssetSpec] = []
+        for filename, mime_type in test_cases:
+            file_path = temp_dir / filename
+            file_path.write_bytes(b"content")
+            specs.append(
+                {
+                    "abs_path": str(file_path),
+                    "size_bytes": 7,
+                    "mtime_ns": 1234567890000000000,
+                    "info_name": filename,
+                    "tags": [],
+                    "fname": filename,
+                    "metadata": None,
+                    "hash": None,
+                    "mime_type": mime_type,
+                }
+            )
+
+        result = batch_insert_seed_assets(session, specs=specs, owner_id="")
+
+        assert result.inserted_refs == len(test_cases)
+
+        for filename, expected_mime in test_cases:
+            ref = session.query(AssetReference).filter_by(name=filename).first()
+            assert ref is not None
+            asset = session.query(Asset).filter_by(id=ref.asset_id).first()
+            assert asset.mime_type == expected_mime, f"Expected {expected_mime} for {filename}, got {asset.mime_type}"
+
+
+class TestMetadataExtraction:
+    def test_extracts_mime_type_for_model_files(self, temp_dir: Path):
+        """Verify metadata extraction returns correct mime_type for model files."""
+        from app.assets.services.metadata_extract import extract_file_metadata
+
+        file_path = temp_dir / "model.safetensors"
+        file_path.write_bytes(b"fake safetensors content")
+
+        meta = extract_file_metadata(str(file_path))
+
+        assert meta.content_type == "application/safetensors"
+
+    def test_mime_type_for_various_model_formats(self, temp_dir: Path):
+        """Verify various model file types get correct mime_type from metadata."""
+        from app.assets.services.metadata_extract import extract_file_metadata
+
+        test_cases = [
+            ("model.safetensors", "application/safetensors"),
+            ("model.sft", "application/safetensors"),
+            ("model.pt", "application/pytorch"),
+            ("model.pth", "application/pytorch"),
+            ("model.ckpt", "application/pickle"),
+            ("model.pkl", "application/pickle"),
+            ("model.gguf", "application/gguf"),
+        ]
+
+        for filename, expected_mime in test_cases:
+            file_path = temp_dir / filename
+            file_path.write_bytes(b"content")
+
+            meta = extract_file_metadata(str(file_path))
+
+            assert meta.content_type == expected_mime, f"Expected {expected_mime} for {filename}, got {meta.content_type}"
diff --git a/tests-unit/assets_test/services/test_enrich.py b/tests-unit/assets_test/services/test_enrich.py
new file mode 100644
index 000000000..2bd79a01a
--- /dev/null
+++ b/tests-unit/assets_test/services/test_enrich.py
@@ -0,0 +1,207 @@
+"""Tests for asset enrichment (mime_type and hash population)."""
+from pathlib import Path
+
+from sqlalchemy.orm import Session
+
+from app.assets.database.models import Asset, AssetReference
+from app.assets.scanner import (
+    ENRICHMENT_HASHED,
+    ENRICHMENT_METADATA,
+    ENRICHMENT_STUB,
+    enrich_asset,
+)
+
+
+def _create_stub_asset(
+    session: Session,
+    file_path: str,
+    asset_id: str = "test-asset-id",
+    reference_id: str = "test-ref-id",
+    name: str | None = None,
+) -> tuple[Asset, AssetReference]:
+    """Create a stub asset with reference for testing enrichment."""
+    asset = Asset(
+        id=asset_id,
+        hash=None,
+        size_bytes=100,
+        mime_type=None,
+    )
+    session.add(asset)
+    session.flush()
+
+    ref = AssetReference(
+        id=reference_id,
+        asset_id=asset_id,
+        name=name or f"test-asset-{asset_id}",
+        owner_id="system",
+        file_path=file_path,
+        mtime_ns=1234567890000000000,
+        enrichment_level=ENRICHMENT_STUB,
+    )
+    session.add(ref)
+    session.flush()
+
+    return asset, ref
+
+
+class TestEnrichAsset:
+    def test_extracts_mime_type_and_updates_asset(
+        self, db_engine, temp_dir: Path, session: Session
+    ):
+        """Verify mime_type is written to the Asset table during enrichment."""
+        file_path = temp_dir / "model.safetensors"
+        file_path.write_bytes(b"\x00" * 100)
+
+        asset, ref = _create_stub_asset(
+            session, str(file_path), "asset-1", "ref-1"
+        )
+        session.commit()
+
+        new_level = enrich_asset(
+            session,
+            file_path=str(file_path),
+            reference_id=ref.id,
+            asset_id=asset.id,
+            extract_metadata=True,
+            compute_hash=False,
+        )
+
+        assert new_level == ENRICHMENT_METADATA
+
+        session.expire_all()
+        updated_asset = session.get(Asset, "asset-1")
+        assert updated_asset is not None
+        assert updated_asset.mime_type == "application/safetensors"
+
+    def test_computes_hash_and_updates_asset(
+        self, db_engine, temp_dir: Path, session: Session
+    ):
+        """Verify hash is written to the Asset table during enrichment."""
+        file_path = temp_dir / "data.bin"
+        file_path.write_bytes(b"test content for hashing")
+
+        asset, ref = _create_stub_asset(
+            session, str(file_path), "asset-2", "ref-2"
+        )
+        session.commit()
+
+        new_level = enrich_asset(
+            session,
+            file_path=str(file_path),
+            reference_id=ref.id,
+            asset_id=asset.id,
+            extract_metadata=True,
+            compute_hash=True,
+        )
+
+        assert new_level == ENRICHMENT_HASHED
+
+        session.expire_all()
+        updated_asset = session.get(Asset, "asset-2")
+        assert updated_asset is not None
+        assert updated_asset.hash is not None
+        assert updated_asset.hash.startswith("blake3:")
+
+    def test_enrichment_updates_both_mime_and_hash(
+        self, db_engine, temp_dir: Path, session: Session
+    ):
+        """Verify both mime_type and hash are set when full enrichment runs."""
+        file_path = temp_dir / "model.safetensors"
+        file_path.write_bytes(b"\x00" * 50)
+
+        asset, ref = _create_stub_asset(
+            session, str(file_path), "asset-3", "ref-3"
+        )
+        session.commit()
+
+        enrich_asset(
+            session,
+            file_path=str(file_path),
+            reference_id=ref.id,
+            asset_id=asset.id,
+            extract_metadata=True,
+            compute_hash=True,
+        )
+
+        session.expire_all()
+        updated_asset = session.get(Asset, "asset-3")
+        assert updated_asset is not None
+        assert updated_asset.mime_type == "application/safetensors"
+        assert updated_asset.hash is not None
+        assert updated_asset.hash.startswith("blake3:")
+
+    def test_missing_file_returns_stub_level(
+        self, db_engine, temp_dir: Path, session: Session
+    ):
+        """Verify missing files don't cause errors and return STUB level."""
+        file_path = temp_dir / "nonexistent.bin"
+
+        asset, ref = _create_stub_asset(
+            session, str(file_path), "asset-4", "ref-4"
+        )
+        session.commit()
+
+        new_level = enrich_asset(
+            session,
+            file_path=str(file_path),
+            reference_id=ref.id,
+            asset_id=asset.id,
+            extract_metadata=True,
+            compute_hash=True,
+        )
+
+        assert new_level == ENRICHMENT_STUB
+
+        session.expire_all()
+        updated_asset = session.get(Asset, "asset-4")
+        assert updated_asset.mime_type is None
+        assert updated_asset.hash is None
+
+    def test_duplicate_hash_merges_into_existing_asset(
+        self, db_engine, temp_dir: Path, session: Session
+    ):
+        """Verify duplicate files merge into existing asset instead of failing."""
+        file_path_1 = temp_dir / "file1.bin"
+        file_path_2 = temp_dir / "file2.bin"
+        content = b"identical content"
+        file_path_1.write_bytes(content)
+        file_path_2.write_bytes(content)
+
+        asset1, ref1 = _create_stub_asset(
+            session, str(file_path_1), "asset-dup-1", "ref-dup-1"
+        )
+        asset2, ref2 = _create_stub_asset(
+            session, str(file_path_2), "asset-dup-2", "ref-dup-2"
+        )
+        session.commit()
+
+        enrich_asset(
+            session,
+            file_path=str(file_path_1),
+            reference_id=ref1.id,
+            asset_id=asset1.id,
+            extract_metadata=True,
+            compute_hash=True,
+        )
+
+        enrich_asset(
+            session,
+            file_path=str(file_path_2),
+            reference_id=ref2.id,
+            asset_id=asset2.id,
+            extract_metadata=True,
+            compute_hash=True,
+        )
+
+        session.expire_all()
+
+        updated_asset1 = session.get(Asset, "asset-dup-1")
+        assert updated_asset1 is not None
+        assert updated_asset1.hash is not None
+
+        updated_asset2 = session.get(Asset, "asset-dup-2")
+        assert updated_asset2 is None
+
+        updated_ref2 = session.get(AssetReference, "ref-dup-2")
+        assert updated_ref2 is not None
+        assert updated_ref2.asset_id == "asset-dup-1"
diff --git a/tests-unit/assets_test/services/test_ingest.py b/tests-unit/assets_test/services/test_ingest.py
new file mode 100644
index 000000000..367bc7721
--- /dev/null
+++ b/tests-unit/assets_test/services/test_ingest.py
@@ -0,0 +1,229 @@
+"""Tests for ingest services."""
+from pathlib import Path
+
+import pytest
+from sqlalchemy.orm import Session
+
+from app.assets.database.models import Asset, AssetReference, Tag
+from app.assets.database.queries import get_reference_tags
+from app.assets.services.ingest import _ingest_file_from_path, _register_existing_asset
+
+
+class TestIngestFileFromPath:
+    def test_creates_asset_and_reference(self, mock_create_session, temp_dir: Path, session: Session):
+        file_path = temp_dir / "test_file.bin"
+        file_path.write_bytes(b"test content")
+
+        result = _ingest_file_from_path(
+            abs_path=str(file_path),
+            asset_hash="blake3:abc123",
+            size_bytes=12,
+            mtime_ns=1234567890000000000,
+            mime_type="application/octet-stream",
+        )
+
+        assert result.asset_created is True
+        assert result.ref_created is True
+        assert result.reference_id is not None
+
+        # Verify DB state
+        assets = session.query(Asset).all()
+        assert len(assets) == 1
+        assert assets[0].hash == "blake3:abc123"
+
+        refs = session.query(AssetReference).all()
+        assert len(refs) == 1
+        assert refs[0].file_path == str(file_path)
+
+    def test_creates_reference_when_name_provided(self, mock_create_session, temp_dir: Path, session: Session):
+        file_path = temp_dir / "model.safetensors"
+        file_path.write_bytes(b"model data")
+
+        result = _ingest_file_from_path(
+            abs_path=str(file_path),
+            asset_hash="blake3:def456",
+            size_bytes=10,
+            mtime_ns=1234567890000000000,
+            mime_type="application/octet-stream",
+            info_name="My Model",
+            owner_id="user1",
+        )
+
+        assert result.asset_created is True
+        assert result.reference_id is not None
+
+        ref = session.query(AssetReference).first()
+        assert ref is not None
+        assert ref.name == "My Model"
+        assert ref.owner_id == "user1"
+
+    def test_creates_tags_when_provided(self, mock_create_session, temp_dir: Path, session: Session):
+        file_path = temp_dir / "tagged.bin"
+        file_path.write_bytes(b"data")
+
+        result = _ingest_file_from_path(
+            abs_path=str(file_path),
+            asset_hash="blake3:ghi789",
+            size_bytes=4,
+            mtime_ns=1234567890000000000,
+            info_name="Tagged Asset",
+            tags=["models", "checkpoints"],
+        )
+
+        assert result.reference_id is not None
+
+        # Verify tags were created and linked
+        tags = session.query(Tag).all()
+        tag_names = {t.name for t in tags}
+        assert "models" in tag_names
+        assert "checkpoints" in tag_names
+
+        ref_tags = get_reference_tags(session, reference_id=result.reference_id)
+        assert set(ref_tags) == {"models", "checkpoints"}
+
+    def test_idempotent_upsert(self, mock_create_session, temp_dir: Path, session: Session):
+        file_path = temp_dir / "dup.bin"
+        file_path.write_bytes(b"content")
+
+        # First ingest
+        r1 = _ingest_file_from_path(
+            abs_path=str(file_path),
+            asset_hash="blake3:repeat",
+            size_bytes=7,
+            mtime_ns=1234567890000000000,
+        )
+        assert r1.asset_created is True
+
+        # Second ingest with same hash - should update, not create
+        r2 = _ingest_file_from_path(
+            abs_path=str(file_path),
+            asset_hash="blake3:repeat",
+            size_bytes=7,
+            mtime_ns=1234567890000000001,  # different mtime
+        )
+        assert r2.asset_created is False
+        assert r2.ref_created is False
+        assert r2.ref_updated is True
+
+        # Still only one asset
+        assets = session.query(Asset).all()
+        assert len(assets) == 1
+
+    def test_validates_preview_id(self, mock_create_session, temp_dir: Path, session: Session):
+        file_path = temp_dir / "with_preview.bin"
+        file_path.write_bytes(b"data")
+
+        # Create a preview asset first
+        preview_asset = Asset(hash="blake3:preview", size_bytes=100)
+        session.add(preview_asset)
+        session.commit()
+        preview_id = preview_asset.id
+
+        result = _ingest_file_from_path(
+            abs_path=str(file_path),
+            asset_hash="blake3:main",
+            size_bytes=4,
+            mtime_ns=1234567890000000000,
+            info_name="With Preview",
+            preview_id=preview_id,
+        )
+
+        assert result.reference_id is not None
+        ref = session.query(AssetReference).filter_by(id=result.reference_id).first()
+        assert ref.preview_id == preview_id
+
+    def test_invalid_preview_id_is_cleared(self, mock_create_session, temp_dir: Path, session: Session):
+        file_path = temp_dir / "bad_preview.bin"
+        file_path.write_bytes(b"data")
+
+        result = _ingest_file_from_path(
+            abs_path=str(file_path),
+            asset_hash="blake3:badpreview",
+            size_bytes=4,
+            mtime_ns=1234567890000000000,
+            info_name="Bad Preview",
+            preview_id="nonexistent-uuid",
+        )
+
+        assert result.reference_id is not None
+        ref = session.query(AssetReference).filter_by(id=result.reference_id).first()
+        assert ref.preview_id is None
+
+
+class TestRegisterExistingAsset:
+    def test_creates_reference_for_existing_asset(self, mock_create_session, session: Session):
+        # Create existing asset
+        asset = Asset(hash="blake3:existing", size_bytes=1024, mime_type="image/png")
+        session.add(asset)
+        session.commit()
+
+        result = _register_existing_asset(
+            asset_hash="blake3:existing",
+            name="Registered Asset",
+            user_metadata={"key": "value"},
+            tags=["models"],
+        )
+
+        assert result.created is True
+        assert "models" in result.tags
+
+        # Verify by re-fetching from DB
+        session.expire_all()
+        refs = session.query(AssetReference).filter_by(name="Registered Asset").all()
+        assert len(refs) == 1
+
+    def test_creates_new_reference_even_with_same_name(self, mock_create_session, session: Session):
+        # Create asset and reference
+        asset = Asset(hash="blake3:withref", size_bytes=512)
+        session.add(asset)
+        session.flush()
+
+        from app.assets.helpers import get_utc_now
+        ref = AssetReference(
+            owner_id="",
+            name="Existing Ref",
+            asset_id=asset.id,
+            created_at=get_utc_now(),
+            updated_at=get_utc_now(),
+            last_access_time=get_utc_now(),
+        )
+        session.add(ref)
+        session.flush()
+        ref_id = ref.id
+        session.commit()
+
+        result = _register_existing_asset(
+            asset_hash="blake3:withref",
+            name="Existing Ref",
+            owner_id="",
+        )
+
+        # Multiple files with same name are allowed
+        assert result.created is True
+
+        # Verify two AssetReferences exist for this name
+        session.expire_all()
+        refs = session.query(AssetReference).filter_by(name="Existing Ref").all()
+        assert len(refs) == 2
+        assert ref_id in [r.id for r in refs]
+
+    def test_raises_for_nonexistent_hash(self, mock_create_session):
+        with pytest.raises(ValueError, match="No asset with hash"):
+            _register_existing_asset(
+                asset_hash="blake3:doesnotexist",
+                name="Fail",
+            )
+
+    def test_applies_tags_to_new_reference(self, mock_create_session, session: Session):
+        asset = Asset(hash="blake3:tagged", size_bytes=256)
+        session.add(asset)
+        session.commit()
+
+        result = _register_existing_asset(
+            asset_hash="blake3:tagged",
+            name="Tagged Ref",
+            tags=["alpha", "beta"],
+        )
+
+        assert result.created is True
+        assert set(result.tags) == {"alpha", "beta"}
diff --git a/tests-unit/assets_test/services/test_tagging.py b/tests-unit/assets_test/services/test_tagging.py
new file mode 100644
index 000000000..ab69e5dc1
--- /dev/null
+++ b/tests-unit/assets_test/services/test_tagging.py
@@ -0,0 +1,197 @@
+"""Tests for tagging services."""
+import pytest
+from sqlalchemy.orm import Session
+
+from app.assets.database.models import Asset, AssetReference
+from app.assets.database.queries import ensure_tags_exist, add_tags_to_reference
+from app.assets.helpers import get_utc_now
+from app.assets.services import apply_tags, remove_tags, list_tags
+
+
+def _make_asset(session: Session, hash_val: str = "blake3:test") -> Asset:
+    asset = Asset(hash=hash_val, size_bytes=1024)
+    session.add(asset)
+    session.flush()
+    return asset
+
+
+def _make_reference(
+    session: Session,
+    asset: Asset,
+    name: str = "test",
+    owner_id: str = "",
+) -> AssetReference:
+    now = get_utc_now()
+    ref = AssetReference(
+        owner_id=owner_id,
+        name=name,
+        asset_id=asset.id,
+        created_at=now,
+        updated_at=now,
+        last_access_time=now,
+    )
+    session.add(ref)
+    session.flush()
+    return ref
+
+
+class TestApplyTags:
+    def test_adds_new_tags(self, mock_create_session, session: Session):
+        asset = _make_asset(session)
+        ref = _make_reference(session, asset)
+        session.commit()
+
+        result = apply_tags(
+            reference_id=ref.id,
+            tags=["alpha", "beta"],
+        )
+
+        assert set(result.added) == {"alpha", "beta"}
+        assert result.already_present == []
+        assert set(result.total_tags) == {"alpha", "beta"}
+
+    def test_reports_already_present(self, mock_create_session, session: Session):
+        asset = _make_asset(session)
+        ref = _make_reference(session, asset)
+        ensure_tags_exist(session, ["existing"])
+        add_tags_to_reference(session, reference_id=ref.id, tags=["existing"])
+        session.commit()
+
+        result = apply_tags(
+            reference_id=ref.id,
+            tags=["existing", "new"],
+        )
+
+        assert result.added == ["new"]
+        assert result.already_present == ["existing"]
+
+    def test_raises_for_nonexistent_ref(self, mock_create_session):
+        with pytest.raises(ValueError, match="not found"):
+            apply_tags(reference_id="nonexistent", tags=["x"])
+
+    def test_raises_for_wrong_owner(self, mock_create_session, session: Session):
+        asset = _make_asset(session)
+        ref = _make_reference(session, asset, owner_id="user1")
+        session.commit()
+
+        with pytest.raises(PermissionError, match="not owner"):
+            apply_tags(
+                reference_id=ref.id,
+                tags=["new"],
+                owner_id="user2",
+            )
+
+
+class TestRemoveTags:
+    def test_removes_tags(self, mock_create_session, session: Session):
+        asset = _make_asset(session)
+        ref = _make_reference(session, asset)
+        ensure_tags_exist(session, ["a", "b", "c"])
+        add_tags_to_reference(session, reference_id=ref.id, tags=["a", "b", "c"])
+        session.commit()
+
+        result = remove_tags(
+            reference_id=ref.id,
+            tags=["a", "b"],
+        )
+
+        assert set(result.removed) == {"a", "b"}
+        assert result.not_present == []
+        assert result.total_tags == ["c"]
+
+    def test_reports_not_present(self, mock_create_session, session: Session):
+        asset = _make_asset(session)
+        ref = _make_reference(session, asset)
+        ensure_tags_exist(session, ["present"])
+        add_tags_to_reference(session, reference_id=ref.id, tags=["present"])
+        session.commit()
+
+        result = remove_tags(
+            reference_id=ref.id,
+            tags=["present", "absent"],
+        )
+
+        assert result.removed == ["present"]
+        assert result.not_present == ["absent"]
+
+    def test_raises_for_nonexistent_ref(self, mock_create_session):
+        with pytest.raises(ValueError, match="not found"):
+            remove_tags(reference_id="nonexistent", tags=["x"])
+
+    def test_raises_for_wrong_owner(self, mock_create_session, session: Session):
+        asset = _make_asset(session)
+        ref = _make_reference(session, asset, owner_id="user1")
+        session.commit()
+
+        with pytest.raises(PermissionError, match="not owner"):
+            remove_tags(
+                reference_id=ref.id,
+                tags=["x"],
+                owner_id="user2",
+            )
+
+
+class TestListTags:
+    def test_returns_tags_with_counts(self, mock_create_session, session: Session):
+        ensure_tags_exist(session, ["used", "unused"])
+        asset = _make_asset(session)
+        ref = _make_reference(session, asset)
+        add_tags_to_reference(session, reference_id=ref.id, tags=["used"])
+        session.commit()
+
+        rows, total = list_tags()
+
+        tag_dict = {name: count for name, _, count in rows}
+        assert tag_dict["used"] == 1
+        assert tag_dict["unused"] == 0
+        assert total == 2
+
+    def test_excludes_zero_counts(self, mock_create_session, session: Session):
+        ensure_tags_exist(session, ["used", "unused"])
+        asset = _make_asset(session)
+        ref = _make_reference(session, asset)
+        add_tags_to_reference(session, reference_id=ref.id, tags=["used"])
+        session.commit()
+
+        rows, total = list_tags(include_zero=False)
+
+        tag_names = {name for name, _, _ in rows}
+        assert "used" in tag_names
+        assert "unused" not in tag_names
+
+    def test_prefix_filter(self, mock_create_session, session: Session):
+        ensure_tags_exist(session, ["alpha", "beta", "alphabet"])
+        session.commit()
+
+        rows, _ = list_tags(prefix="alph")
+
+        tag_names = {name for name, _, _ in rows}
+        assert tag_names == {"alpha", "alphabet"}
+
+    def test_order_by_name(self, mock_create_session, session: Session):
+        ensure_tags_exist(session, ["zebra", "alpha", "middle"])
+        session.commit()
+
+        rows, _ = list_tags(order="name_asc")
+
+        names = [name for name, _, _ in rows]
+        assert names == ["alpha", "middle", "zebra"]
+
+    def test_pagination(self, mock_create_session, session: Session):
+        ensure_tags_exist(session, ["a", "b", "c", "d", "e"])
+        session.commit()
+
+        rows, total = list_tags(limit=2, offset=1, order="name_asc")
+
+        assert total == 5
+        assert len(rows) == 2
+        names = [name for name, _, _ in rows]
+        assert names == ["b", "c"]
+
+    def test_clamps_limit(self, mock_create_session, session: Session):
+        ensure_tags_exist(session, ["a"])
+        session.commit()
+
+        # Service should clamp limit to max 1000
+        rows, _ = list_tags(limit=2000)
+        assert len(rows) <= 1000
diff --git a/tests-unit/assets_test/test_assets_missing_sync.py b/tests-unit/assets_test/test_assets_missing_sync.py
index 78fa7b404..47dc130cb 100644
--- a/tests-unit/assets_test/test_assets_missing_sync.py
+++ b/tests-unit/assets_test/test_assets_missing_sync.py
@@ -4,7 +4,7 @@ from pathlib import Path
 
 import pytest
 import requests
-from conftest import get_asset_filename, trigger_sync_seed_assets
+from helpers import get_asset_filename, trigger_sync_seed_assets
 
 
 
diff --git a/tests-unit/assets_test/test_crud.py b/tests-unit/assets_test/test_crud.py
index d2b69f475..07310223e 100644
--- a/tests-unit/assets_test/test_crud.py
+++ b/tests-unit/assets_test/test_crud.py
@@ -4,7 +4,7 @@ from pathlib import Path
 
 import pytest
 import requests
-from conftest import get_asset_filename, trigger_sync_seed_assets
+from helpers import get_asset_filename, trigger_sync_seed_assets
 
 
 def test_create_from_hash_success(
@@ -24,11 +24,11 @@ def test_create_from_hash_success(
     assert b1["created_new"] is False
     aid = b1["id"]
 
-    # Calling again with the same name should return the same AssetInfo id
+    # Calling again with the same name creates a new AssetReference (duplicates allowed)
     r2 = http.post(f"{api_base}/api/assets/from-hash", json=payload, timeout=120)
     b2 = r2.json()
     assert r2.status_code == 201, b2
-    assert b2["id"] == aid
+    assert b2["id"] != aid  # new reference, not the same one
 
 
 def test_get_and_delete_asset(http: requests.Session, api_base: str, seeded_asset: dict):
@@ -42,8 +42,8 @@ def test_get_and_delete_asset(http: requests.Session, api_base: str, seeded_asse
     assert "user_metadata" in detail
     assert "filename" in detail["user_metadata"]
 
-    # DELETE
-    rd = http.delete(f"{api_base}/api/assets/{aid}", timeout=120)
+    # DELETE (hard delete to also remove underlying asset and file)
+    rd = http.delete(f"{api_base}/api/assets/{aid}?delete_content=true", timeout=120)
     assert rd.status_code == 204
 
     # GET again -> 404
@@ -53,6 +53,35 @@ def test_get_and_delete_asset(http: requests.Session, api_base: str, seeded_asse
     assert body["error"]["code"] == "ASSET_NOT_FOUND"
 
 
+def test_soft_delete_hides_from_get(http: requests.Session, api_base: str, seeded_asset: dict):
+    aid = seeded_asset["id"]
+    asset_hash = seeded_asset["asset_hash"]
+
+    # Soft-delete (default, no delete_content param)
+    rd = http.delete(f"{api_base}/api/assets/{aid}", timeout=120)
+    assert rd.status_code == 204
+
+    # GET by reference ID -> 404 (soft-deleted references are hidden)
+    rg = http.get(f"{api_base}/api/assets/{aid}", timeout=120)
+    assert rg.status_code == 404
+
+    # Asset identity is preserved (underlying content still exists)
+    rh = http.head(f"{api_base}/api/assets/hash/{asset_hash}", timeout=120)
+    assert rh.status_code == 200
+
+    # Soft-deleted reference should not appear in listings
+    rl = http.get(
+        f"{api_base}/api/assets",
+        params={"include_tags": "unit-tests", "limit": "500"},
+        timeout=120,
+    )
+    ids = [a["id"] for a in rl.json().get("assets", [])]
+    assert aid not in ids
+
+    # Clean up: hard-delete the soft-deleted reference and orphaned asset
+    http.delete(f"{api_base}/api/assets/{aid}?delete_content=true", timeout=120)
+
+
 def test_delete_upon_reference_count(
     http: requests.Session, api_base: str, seeded_asset: dict
 ):
@@ -70,21 +99,32 @@ def test_delete_upon_reference_count(
     assert copy["asset_hash"] == src_hash
     assert copy["created_new"] is False
 
-    # Delete original reference -> asset identity must remain
+    # Soft-delete original reference (default) -> asset identity must remain
     aid1 = seeded_asset["id"]
     rd1 = http.delete(f"{api_base}/api/assets/{aid1}", timeout=120)
     assert rd1.status_code == 204
 
     rh1 = http.head(f"{api_base}/api/assets/hash/{src_hash}", timeout=120)
-    assert rh1.status_code == 200  # identity still present
+    assert rh1.status_code == 200  # identity still present (second ref exists)
 
-    # Delete the last reference with default semantics -> identity and cached files removed
+    # Soft-delete the last reference -> asset identity preserved (no hard delete)
     aid2 = copy["id"]
     rd2 = http.delete(f"{api_base}/api/assets/{aid2}", timeout=120)
     assert rd2.status_code == 204
 
     rh2 = http.head(f"{api_base}/api/assets/hash/{src_hash}", timeout=120)
-    assert rh2.status_code == 404  # orphan content removed
+    assert rh2.status_code == 200  # asset identity preserved (soft delete)
+
+    # Re-associate via from-hash, then hard-delete -> orphan content removed
+    r3 = http.post(f"{api_base}/api/assets/from-hash", json=payload, timeout=120)
+    assert r3.status_code == 201, r3.json()
+    aid3 = r3.json()["id"]
+
+    rd3 = http.delete(f"{api_base}/api/assets/{aid3}?delete_content=true", timeout=120)
+    assert rd3.status_code == 204
+
+    rh3 = http.head(f"{api_base}/api/assets/hash/{src_hash}", timeout=120)
+    assert rh3.status_code == 404  # orphan content removed
 
 
 def test_update_asset_fields(http: requests.Session, api_base: str, seeded_asset: dict):
@@ -126,42 +166,52 @@ def test_head_asset_bad_hash_returns_400_and_no_body(http: requests.Session, api
     assert body == b""
 
 
-def test_delete_nonexistent_returns_404(http: requests.Session, api_base: str):
-    bogus = str(uuid.uuid4())
-    r = http.delete(f"{api_base}/api/assets/{bogus}", timeout=120)
+@pytest.mark.parametrize(
+    "method,endpoint_template,payload,expected_status,error_code",
+    [
+        # Delete nonexistent asset
+        ("delete", "/api/assets/{uuid}", None, 404, "ASSET_NOT_FOUND"),
+        # Bad hash algorithm in from-hash
+        (
+            "post",
+            "/api/assets/from-hash",
+            {"hash": "sha256:" + "0" * 64, "name": "x.bin", "tags": ["models", "checkpoints", "unit-tests"]},
+            400,
+            "INVALID_BODY",
+        ),
+        # Get with bad UUID format
+        ("get", "/api/assets/not-a-uuid", None, 404, None),
+        # Get content with bad UUID format
+        ("get", "/api/assets/not-a-uuid/content", None, 404, None),
+    ],
+    ids=["delete_nonexistent", "bad_hash_algorithm", "get_bad_uuid", "content_bad_uuid"],
+)
+def test_error_responses(
+    http: requests.Session, api_base: str, method, endpoint_template, payload, expected_status, error_code
+):
+    # Replace {uuid} placeholder with a random UUID for delete test
+    endpoint = endpoint_template.replace("{uuid}", str(uuid.uuid4()))
+    url = f"{api_base}{endpoint}"
+
+    if method == "get":
+        r = http.get(url, timeout=120)
+    elif method == "post":
+        r = http.post(url, json=payload, timeout=120)
+    elif method == "delete":
+        r = http.delete(url, timeout=120)
+
+    assert r.status_code == expected_status
+    if error_code:
+        body = r.json()
+        assert body["error"]["code"] == error_code
+
+
+def test_create_from_hash_invalid_json(http: requests.Session, api_base: str):
+    """Invalid JSON body requires special handling (data= instead of json=)."""
+    r = http.post(f"{api_base}/api/assets/from-hash", data=b"{not json}", timeout=120)
     body = r.json()
-    assert r.status_code == 404
-    assert body["error"]["code"] == "ASSET_NOT_FOUND"
-
-
-def test_create_from_hash_invalids(http: requests.Session, api_base: str):
-    # Bad hash algorithm
-    bad = {
-        "hash": "sha256:" + "0" * 64,
-        "name": "x.bin",
-        "tags": ["models", "checkpoints", "unit-tests"],
-    }
-    r1 = http.post(f"{api_base}/api/assets/from-hash", json=bad, timeout=120)
-    b1 = r1.json()
-    assert r1.status_code == 400
-    assert b1["error"]["code"] == "INVALID_BODY"
-
-    # Invalid JSON body
-    r2 = http.post(f"{api_base}/api/assets/from-hash", data=b"{not json}", timeout=120)
-    b2 = r2.json()
-    assert r2.status_code == 400
-    assert b2["error"]["code"] == "INVALID_JSON"
-
-
-def test_get_update_download_bad_ids(http: requests.Session, api_base: str):
-    # All endpoints should be not found, as we UUID regex directly in the route definition.
-    bad_id = "not-a-uuid"
-
-    r1 = http.get(f"{api_base}/api/assets/{bad_id}", timeout=120)
-    assert r1.status_code == 404
-
-    r3 = http.get(f"{api_base}/api/assets/{bad_id}/content", timeout=120)
-    assert r3.status_code == 404
+    assert r.status_code == 400
+    assert body["error"]["code"] == "INVALID_JSON"
 
 
 def test_update_requires_at_least_one_field(http: requests.Session, api_base: str, seeded_asset: dict):
diff --git a/tests-unit/assets_test/test_downloads.py b/tests-unit/assets_test/test_downloads.py
index cdebf9082..672ba9728 100644
--- a/tests-unit/assets_test/test_downloads.py
+++ b/tests-unit/assets_test/test_downloads.py
@@ -6,7 +6,7 @@ from typing import Optional
 
 import pytest
 import requests
-from conftest import get_asset_filename, trigger_sync_seed_assets
+from helpers import get_asset_filename, trigger_sync_seed_assets
 
 
 def test_download_attachment_and_inline(http: requests.Session, api_base: str, seeded_asset: dict):
@@ -117,7 +117,7 @@ def test_download_missing_file_returns_404(
         assert body["error"]["code"] == "FILE_NOT_FOUND"
     finally:
         # We created asset without the "unit-tests" tag(see `autoclean_unit_test_assets`), we need to clear it manually.
-        dr = http.delete(f"{api_base}/api/assets/{aid}", timeout=120)
+        dr = http.delete(f"{api_base}/api/assets/{aid}?delete_content=true", timeout=120)
         dr.content
 
 
diff --git a/tests-unit/assets_test/test_file_utils.py b/tests-unit/assets_test/test_file_utils.py
new file mode 100644
index 000000000..e3591d49b
--- /dev/null
+++ b/tests-unit/assets_test/test_file_utils.py
@@ -0,0 +1,121 @@
+import os
+import sys
+
+import pytest
+
+from app.assets.services.file_utils import is_visible, list_files_recursively
+
+
+class TestIsVisible:
+    def test_visible_file(self):
+        assert is_visible("file.txt") is True
+
+    def test_hidden_file(self):
+        assert is_visible(".hidden") is False
+
+    def test_hidden_directory(self):
+        assert is_visible(".git") is False
+
+    def test_visible_directory(self):
+        assert is_visible("src") is True
+
+    def test_dotdot_is_hidden(self):
+        assert is_visible("..") is False
+
+    def test_dot_is_hidden(self):
+        assert is_visible(".") is False
+
+
+class TestListFilesRecursively:
+    def test_skips_hidden_files(self, tmp_path):
+        (tmp_path / "visible.txt").write_text("a")
+        (tmp_path / ".hidden").write_text("b")
+
+        result = list_files_recursively(str(tmp_path))
+
+        assert len(result) == 1
+        assert result[0].endswith("visible.txt")
+
+    def test_skips_hidden_directories(self, tmp_path):
+        hidden_dir = tmp_path / ".hidden_dir"
+        hidden_dir.mkdir()
+        (hidden_dir / "file.txt").write_text("a")
+
+        visible_dir = tmp_path / "visible_dir"
+        visible_dir.mkdir()
+        (visible_dir / "file.txt").write_text("b")
+
+        result = list_files_recursively(str(tmp_path))
+
+        assert len(result) == 1
+        assert "visible_dir" in result[0]
+        assert ".hidden_dir" not in result[0]
+
+    def test_empty_directory(self, tmp_path):
+        result = list_files_recursively(str(tmp_path))
+        assert result == []
+
+    def test_nonexistent_directory(self, tmp_path):
+        result = list_files_recursively(str(tmp_path / "nonexistent"))
+        assert result == []
+
+    @pytest.mark.skipif(sys.platform == "win32", reason="symlinks need privileges on Windows")
+    def test_follows_symlinked_directories(self, tmp_path):
+        target = tmp_path / "real_dir"
+        target.mkdir()
+        (target / "model.safetensors").write_text("data")
+
+        root = tmp_path / "root"
+        root.mkdir()
+        (root / "link").symlink_to(target)
+
+        result = list_files_recursively(str(root))
+
+        assert len(result) == 1
+        assert result[0].endswith("model.safetensors")
+        assert "link" in result[0]
+
+    @pytest.mark.skipif(sys.platform == "win32", reason="symlinks need privileges on Windows")
+    def test_follows_symlinked_files(self, tmp_path):
+        real_file = tmp_path / "real.txt"
+        real_file.write_text("content")
+
+        root = tmp_path / "root"
+        root.mkdir()
+        (root / "link.txt").symlink_to(real_file)
+
+        result = list_files_recursively(str(root))
+
+        assert len(result) == 1
+        assert result[0].endswith("link.txt")
+
+    @pytest.mark.skipif(sys.platform == "win32", reason="symlinks need privileges on Windows")
+    def test_circular_symlinks_do_not_loop(self, tmp_path):
+        dir_a = tmp_path / "a"
+        dir_a.mkdir()
+        (dir_a / "file.txt").write_text("a")
+        # a/b -> a  (circular)
+        (dir_a / "b").symlink_to(dir_a)
+
+        result = list_files_recursively(str(dir_a))
+
+        assert len(result) == 1
+        assert result[0].endswith("file.txt")
+
+    @pytest.mark.skipif(sys.platform == "win32", reason="symlinks need privileges on Windows")
+    def test_mutual_circular_symlinks(self, tmp_path):
+        dir_a = tmp_path / "a"
+        dir_b = tmp_path / "b"
+        dir_a.mkdir()
+        dir_b.mkdir()
+        (dir_a / "file_a.txt").write_text("a")
+        (dir_b / "file_b.txt").write_text("b")
+        # a/link_b -> b and b/link_a -> a
+        (dir_a / "link_b").symlink_to(dir_b)
+        (dir_b / "link_a").symlink_to(dir_a)
+
+        result = list_files_recursively(str(dir_a))
+        basenames = sorted(os.path.basename(p) for p in result)
+
+        assert "file_a.txt" in basenames
+        assert "file_b.txt" in basenames
diff --git a/tests-unit/assets_test/test_list_filter.py b/tests-unit/assets_test/test_list_filter.py
index 82e109832..dcb7a73ca 100644
--- a/tests-unit/assets_test/test_list_filter.py
+++ b/tests-unit/assets_test/test_list_filter.py
@@ -1,6 +1,7 @@
 import time
 import uuid
 
+import pytest
 import requests
 
 
@@ -283,30 +284,21 @@ def test_list_assets_offset_beyond_total_and_limit_boundary(http, api_base, asse
     assert b2["has_more"] is False
 
 
-def test_list_assets_offset_negative_and_limit_nonint_rejected(http, api_base):
-    r1 = http.get(api_base + "/api/assets", params={"offset": "-1"}, timeout=120)
-    b1 = r1.json()
-    assert r1.status_code == 400
-    assert b1["error"]["code"] == "INVALID_QUERY"
-
-    r2 = http.get(api_base + "/api/assets", params={"limit": "abc"}, timeout=120)
-    b2 = r2.json()
-    assert r2.status_code == 400
-    assert b2["error"]["code"] == "INVALID_QUERY"
-
-
-def test_list_assets_invalid_query_rejected(http: requests.Session, api_base: str):
-    # limit too small
-    r1 = http.get(api_base + "/api/assets", params={"limit": "0"}, timeout=120)
-    b1 = r1.json()
-    assert r1.status_code == 400
-    assert b1["error"]["code"] == "INVALID_QUERY"
-
-    # bad metadata JSON
-    r2 = http.get(api_base + "/api/assets", params={"metadata_filter": "{not json"}, timeout=120)
-    b2 = r2.json()
-    assert r2.status_code == 400
-    assert b2["error"]["code"] == "INVALID_QUERY"
+@pytest.mark.parametrize(
+    "params,error_code",
+    [
+        ({"offset": "-1"}, "INVALID_QUERY"),
+        ({"limit": "abc"}, "INVALID_QUERY"),
+        ({"limit": "0"}, "INVALID_QUERY"),
+        ({"metadata_filter": "{not json"}, "INVALID_QUERY"),
+    ],
+    ids=["negative_offset", "non_int_limit", "zero_limit", "invalid_metadata_json"],
+)
+def test_list_assets_invalid_query_rejected(http: requests.Session, api_base: str, params, error_code):
+    r = http.get(api_base + "/api/assets", params=params, timeout=120)
+    body = r.json()
+    assert r.status_code == 400
+    assert body["error"]["code"] == error_code
 
 
 def test_list_assets_name_contains_literal_underscore(
diff --git a/tests-unit/assets_test/test_prune_orphaned_assets.py b/tests-unit/assets_test/test_prune_orphaned_assets.py
index f602e5a77..1fbd4d4e2 100644
--- a/tests-unit/assets_test/test_prune_orphaned_assets.py
+++ b/tests-unit/assets_test/test_prune_orphaned_assets.py
@@ -3,7 +3,7 @@ from pathlib import Path
 
 import pytest
 import requests
-from conftest import get_asset_filename, trigger_sync_seed_assets
+from helpers import get_asset_filename, trigger_sync_seed_assets
 
 
 @pytest.fixture
diff --git a/tests-unit/assets_test/test_sync_references.py b/tests-unit/assets_test/test_sync_references.py
new file mode 100644
index 000000000..94cc255bc
--- /dev/null
+++ b/tests-unit/assets_test/test_sync_references.py
@@ -0,0 +1,482 @@
+"""Tests for sync_references_with_filesystem in scanner.py."""
+
+import os
+import tempfile
+from datetime import datetime
+from pathlib import Path
+from unittest.mock import patch
+
+import pytest
+from sqlalchemy import create_engine
+from sqlalchemy.orm import Session
+
+from app.assets.database.models import (
+    Asset,
+    AssetReference,
+    AssetReferenceTag,
+    Base,
+    Tag,
+)
+from app.assets.database.queries.asset_reference import (
+    bulk_insert_references_ignore_conflicts,
+    get_references_for_prefixes,
+    get_unenriched_references,
+    restore_references_by_paths,
+)
+from app.assets.scanner import sync_references_with_filesystem
+from app.assets.services.file_utils import get_mtime_ns
+
+
+@pytest.fixture
+def db_engine():
+    engine = create_engine("sqlite:///:memory:")
+    Base.metadata.create_all(engine)
+    return engine
+
+
+@pytest.fixture
+def session(db_engine):
+    with Session(db_engine) as sess:
+        yield sess
+
+
+@pytest.fixture
+def temp_dir():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        yield Path(tmpdir)
+
+
+def _create_file(temp_dir: Path, name: str, content: bytes = b"\x00" * 100) -> str:
+    """Create a file and return its absolute path (no symlink resolution)."""
+    p = temp_dir / name
+    p.parent.mkdir(parents=True, exist_ok=True)
+    p.write_bytes(content)
+    return os.path.abspath(str(p))
+
+
+def _stat_mtime_ns(path: str) -> int:
+    return get_mtime_ns(os.stat(path, follow_symlinks=True))
+
+
+def _make_asset(
+    session: Session,
+    asset_id: str,
+    file_path: str,
+    ref_id: str,
+    *,
+    asset_hash: str | None = None,
+    size_bytes: int = 100,
+    mtime_ns: int | None = None,
+    needs_verify: bool = False,
+    is_missing: bool = False,
+) -> tuple[Asset, AssetReference]:
+    """Insert an Asset + AssetReference and flush."""
+    asset = session.get(Asset, asset_id)
+    if asset is None:
+        asset = Asset(id=asset_id, hash=asset_hash, size_bytes=size_bytes)
+        session.add(asset)
+        session.flush()
+
+    ref = AssetReference(
+        id=ref_id,
+        asset_id=asset_id,
+        name=f"test-{ref_id}",
+        owner_id="system",
+        file_path=file_path,
+        mtime_ns=mtime_ns,
+        needs_verify=needs_verify,
+        is_missing=is_missing,
+    )
+    session.add(ref)
+    session.flush()
+    return asset, ref
+
+
+def _ensure_missing_tag(session: Session):
+    """Ensure the 'missing' tag exists."""
+    if not session.get(Tag, "missing"):
+        session.add(Tag(name="missing", tag_type="system"))
+        session.flush()
+
+
+class _VerifyCase:
+    def __init__(self, id, stat_unchanged, needs_verify_before, expect_needs_verify):
+        self.id = id
+        self.stat_unchanged = stat_unchanged
+        self.needs_verify_before = needs_verify_before
+        self.expect_needs_verify = expect_needs_verify
+
+
+VERIFY_CASES = [
+    _VerifyCase(
+        id="unchanged_clears_verify",
+        stat_unchanged=True,
+        needs_verify_before=True,
+        expect_needs_verify=False,
+    ),
+    _VerifyCase(
+        id="unchanged_keeps_clear",
+        stat_unchanged=True,
+        needs_verify_before=False,
+        expect_needs_verify=False,
+    ),
+    _VerifyCase(
+        id="changed_sets_verify",
+        stat_unchanged=False,
+        needs_verify_before=False,
+        expect_needs_verify=True,
+    ),
+    _VerifyCase(
+        id="changed_keeps_verify",
+        stat_unchanged=False,
+        needs_verify_before=True,
+        expect_needs_verify=True,
+    ),
+]
+
+
+@pytest.mark.parametrize("case", VERIFY_CASES, ids=lambda c: c.id)
+def test_needs_verify_toggling(session, temp_dir, case):
+    """needs_verify is set/cleared based on mtime+size match."""
+    fp = _create_file(temp_dir, "model.bin")
+    real_mtime = _stat_mtime_ns(fp)
+
+    mtime_for_db = real_mtime if case.stat_unchanged else real_mtime + 1
+    _make_asset(
+        session, "a1", fp, "r1",
+        asset_hash="blake3:abc",
+        mtime_ns=mtime_for_db,
+        needs_verify=case.needs_verify_before,
+    )
+    session.commit()
+
+    with patch("app.assets.scanner.get_prefixes_for_root", return_value=[str(temp_dir)]):
+        sync_references_with_filesystem(session, "models")
+        session.commit()
+
+    session.expire_all()
+    ref = session.get(AssetReference, "r1")
+    assert ref.needs_verify is case.expect_needs_verify
+
+
+class _MissingCase:
+    def __init__(self, id, file_exists, expect_is_missing):
+        self.id = id
+        self.file_exists = file_exists
+        self.expect_is_missing = expect_is_missing
+
+
+MISSING_CASES = [
+    _MissingCase(id="existing_file_not_missing", file_exists=True, expect_is_missing=False),
+    _MissingCase(id="missing_file_marked_missing", file_exists=False, expect_is_missing=True),
+]
+
+
+@pytest.mark.parametrize("case", MISSING_CASES, ids=lambda c: c.id)
+def test_is_missing_flag(session, temp_dir, case):
+    """is_missing reflects whether the file exists on disk."""
+    if case.file_exists:
+        fp = _create_file(temp_dir, "model.bin")
+        mtime = _stat_mtime_ns(fp)
+    else:
+        fp = str(temp_dir / "gone.bin")
+        mtime = 999
+
+    _make_asset(session, "a1", fp, "r1", asset_hash="blake3:abc", mtime_ns=mtime)
+    session.commit()
+
+    with patch("app.assets.scanner.get_prefixes_for_root", return_value=[str(temp_dir)]):
+        sync_references_with_filesystem(session, "models")
+        session.commit()
+
+    session.expire_all()
+    ref = session.get(AssetReference, "r1")
+    assert ref.is_missing is case.expect_is_missing
+
+
+def test_seed_asset_all_missing_deletes_asset(session, temp_dir):
+    """Seed asset with all refs missing gets deleted entirely."""
+    fp = str(temp_dir / "gone.bin")
+    _make_asset(session, "seed1", fp, "r1", asset_hash=None, mtime_ns=999)
+    session.commit()
+
+    with patch("app.assets.scanner.get_prefixes_for_root", return_value=[str(temp_dir)]):
+        sync_references_with_filesystem(session, "models")
+        session.commit()
+
+    assert session.get(Asset, "seed1") is None
+    assert session.get(AssetReference, "r1") is None
+
+
+def test_seed_asset_some_exist_returns_survivors(session, temp_dir):
+    """Seed asset with at least one existing ref survives and is returned."""
+    fp = _create_file(temp_dir, "model.bin")
+    mtime = _stat_mtime_ns(fp)
+    _make_asset(session, "seed1", fp, "r1", asset_hash=None, mtime_ns=mtime)
+    session.commit()
+
+    with patch("app.assets.scanner.get_prefixes_for_root", return_value=[str(temp_dir)]):
+        survivors = sync_references_with_filesystem(
+            session, "models", collect_existing_paths=True,
+        )
+        session.commit()
+
+    assert session.get(Asset, "seed1") is not None
+    assert os.path.abspath(fp) in survivors
+
+
+def test_hashed_asset_prunes_missing_refs_when_one_is_ok(session, temp_dir):
+    """Hashed asset with one stat-unchanged ref deletes missing refs."""
+    fp_ok = _create_file(temp_dir, "good.bin")
+    fp_gone = str(temp_dir / "gone.bin")
+    mtime = _stat_mtime_ns(fp_ok)
+
+    _make_asset(session, "h1", fp_ok, "r_ok", asset_hash="blake3:aaa", mtime_ns=mtime)
+    # Second ref on same asset, file missing
+    ref_gone = AssetReference(
+        id="r_gone", asset_id="h1", name="gone",
+        owner_id="system", file_path=fp_gone, mtime_ns=999,
+    )
+    session.add(ref_gone)
+    session.commit()
+
+    with patch("app.assets.scanner.get_prefixes_for_root", return_value=[str(temp_dir)]):
+        sync_references_with_filesystem(session, "models")
+        session.commit()
+
+    session.expire_all()
+    assert session.get(AssetReference, "r_ok") is not None
+    assert session.get(AssetReference, "r_gone") is None
+
+
+def test_hashed_asset_all_missing_keeps_refs(session, temp_dir):
+    """Hashed asset with all refs missing keeps refs (no pruning)."""
+    fp = str(temp_dir / "gone.bin")
+    _make_asset(session, "h1", fp, "r1", asset_hash="blake3:aaa", mtime_ns=999)
+    session.commit()
+
+    with patch("app.assets.scanner.get_prefixes_for_root", return_value=[str(temp_dir)]):
+        sync_references_with_filesystem(session, "models")
+        session.commit()
+
+    session.expire_all()
+    assert session.get(AssetReference, "r1") is not None
+    ref = session.get(AssetReference, "r1")
+    assert ref.is_missing is True
+
+
+def test_missing_tag_added_when_all_refs_gone(session, temp_dir):
+    """Missing tag is added to hashed asset when all refs are missing."""
+    _ensure_missing_tag(session)
+    fp = str(temp_dir / "gone.bin")
+    _make_asset(session, "h1", fp, "r1", asset_hash="blake3:aaa", mtime_ns=999)
+    session.commit()
+
+    with patch("app.assets.scanner.get_prefixes_for_root", return_value=[str(temp_dir)]):
+        sync_references_with_filesystem(
+            session, "models", update_missing_tags=True,
+        )
+        session.commit()
+
+    session.expire_all()
+    tag_link = session.get(AssetReferenceTag, ("r1", "missing"))
+    assert tag_link is not None
+
+
+def test_missing_tag_removed_when_ref_ok(session, temp_dir):
+    """Missing tag is removed from hashed asset when a ref is stat-unchanged."""
+    _ensure_missing_tag(session)
+    fp = _create_file(temp_dir, "model.bin")
+    mtime = _stat_mtime_ns(fp)
+    _make_asset(session, "h1", fp, "r1", asset_hash="blake3:aaa", mtime_ns=mtime)
+    # Pre-add a stale missing tag
+    session.add(AssetReferenceTag(
+        asset_reference_id="r1", tag_name="missing", origin="automatic",
+    ))
+    session.commit()
+
+    with patch("app.assets.scanner.get_prefixes_for_root", return_value=[str(temp_dir)]):
+        sync_references_with_filesystem(
+            session, "models", update_missing_tags=True,
+        )
+        session.commit()
+
+    session.expire_all()
+    tag_link = session.get(AssetReferenceTag, ("r1", "missing"))
+    assert tag_link is None
+
+
+def test_missing_tags_not_touched_when_flag_false(session, temp_dir):
+    """Missing tags are not modified when update_missing_tags=False."""
+    _ensure_missing_tag(session)
+    fp = str(temp_dir / "gone.bin")
+    _make_asset(session, "h1", fp, "r1", asset_hash="blake3:aaa", mtime_ns=999)
+    session.commit()
+
+    with patch("app.assets.scanner.get_prefixes_for_root", return_value=[str(temp_dir)]):
+        sync_references_with_filesystem(
+            session, "models", update_missing_tags=False,
+        )
+        session.commit()
+
+    tag_link = session.get(AssetReferenceTag, ("r1", "missing"))
+    assert tag_link is None  # tag was never added
+
+
+def test_returns_none_when_collect_false(session, temp_dir):
+    fp = _create_file(temp_dir, "model.bin")
+    mtime = _stat_mtime_ns(fp)
+    _make_asset(session, "a1", fp, "r1", asset_hash="blake3:abc", mtime_ns=mtime)
+    session.commit()
+
+    with patch("app.assets.scanner.get_prefixes_for_root", return_value=[str(temp_dir)]):
+        result = sync_references_with_filesystem(
+            session, "models", collect_existing_paths=False,
+        )
+
+    assert result is None
+
+
+def test_returns_empty_set_for_no_prefixes(session):
+    with patch("app.assets.scanner.get_prefixes_for_root", return_value=[]):
+        result = sync_references_with_filesystem(
+            session, "models", collect_existing_paths=True,
+        )
+
+    assert result == set()
+
+
+def test_no_references_is_noop(session, temp_dir):
+    """No crash and no side effects when there are no references."""
+    with patch("app.assets.scanner.get_prefixes_for_root", return_value=[str(temp_dir)]):
+        survivors = sync_references_with_filesystem(
+            session, "models", collect_existing_paths=True,
+        )
+        session.commit()
+
+    assert survivors == set()
+
+
+# ---------------------------------------------------------------------------
+# Soft-delete persistence across scanner operations
+# ---------------------------------------------------------------------------
+
+def _soft_delete_ref(session: Session, ref_id: str) -> None:
+    """Mark a reference as soft-deleted (mimics the API DELETE behaviour)."""
+    ref = session.get(AssetReference, ref_id)
+    ref.deleted_at = datetime(2025, 1, 1)
+    session.flush()
+
+
+def test_soft_deleted_ref_excluded_from_get_references_for_prefixes(session, temp_dir):
+    """get_references_for_prefixes skips soft-deleted references."""
+    fp = _create_file(temp_dir, "model.bin")
+    mtime = _stat_mtime_ns(fp)
+    _make_asset(session, "a1", fp, "r1", asset_hash="blake3:abc", mtime_ns=mtime)
+    _soft_delete_ref(session, "r1")
+    session.commit()
+
+    rows = get_references_for_prefixes(session, [str(temp_dir)], include_missing=True)
+    assert len(rows) == 0
+
+
+def test_sync_does_not_resurrect_soft_deleted_ref(session, temp_dir):
+    """Scanner sync leaves soft-deleted refs untouched even when file exists on disk."""
+    fp = _create_file(temp_dir, "model.bin")
+    mtime = _stat_mtime_ns(fp)
+    _make_asset(session, "a1", fp, "r1", asset_hash="blake3:abc", mtime_ns=mtime)
+    _soft_delete_ref(session, "r1")
+    session.commit()
+
+    with patch("app.assets.scanner.get_prefixes_for_root", return_value=[str(temp_dir)]):
+        sync_references_with_filesystem(session, "models")
+        session.commit()
+
+    session.expire_all()
+    ref = session.get(AssetReference, "r1")
+    assert ref.deleted_at is not None, "soft-deleted ref must stay deleted after sync"
+
+
+def test_bulk_insert_does_not_overwrite_soft_deleted_ref(session, temp_dir):
+    """bulk_insert_references_ignore_conflicts cannot replace a soft-deleted row."""
+    fp = _create_file(temp_dir, "model.bin")
+    mtime = _stat_mtime_ns(fp)
+    _make_asset(session, "a1", fp, "r1", asset_hash="blake3:abc", mtime_ns=mtime)
+    _soft_delete_ref(session, "r1")
+    session.commit()
+
+    now = datetime.now(tz=None)
+    bulk_insert_references_ignore_conflicts(session, [
+        {
+            "id": "r_new",
+            "asset_id": "a1",
+            "file_path": fp,
+            "name": "model.bin",
+            "owner_id": "",
+            "mtime_ns": mtime,
+            "preview_id": None,
+            "user_metadata": None,
+            "created_at": now,
+            "updated_at": now,
+            "last_access_time": now,
+        }
+    ])
+    session.commit()
+
+    session.expire_all()
+    # Original row is still the soft-deleted one
+    ref = session.get(AssetReference, "r1")
+    assert ref is not None
+    assert ref.deleted_at is not None
+    # The new row was not inserted (conflict on file_path)
+    assert session.get(AssetReference, "r_new") is None
+
+
+def test_restore_references_by_paths_skips_soft_deleted(session, temp_dir):
+    """restore_references_by_paths does not clear is_missing on soft-deleted refs."""
+    fp = _create_file(temp_dir, "model.bin")
+    mtime = _stat_mtime_ns(fp)
+    _make_asset(
+        session, "a1", fp, "r1",
+        asset_hash="blake3:abc", mtime_ns=mtime, is_missing=True,
+    )
+    _soft_delete_ref(session, "r1")
+    session.commit()
+
+    restored = restore_references_by_paths(session, [fp])
+    session.commit()
+
+    assert restored == 0
+    session.expire_all()
+    ref = session.get(AssetReference, "r1")
+    assert ref.is_missing is True, "is_missing must not be cleared on soft-deleted ref"
+    assert ref.deleted_at is not None
+
+
+def test_get_unenriched_references_excludes_soft_deleted(session, temp_dir):
+    """Enrichment queries do not pick up soft-deleted references."""
+    fp = _create_file(temp_dir, "model.bin")
+    mtime = _stat_mtime_ns(fp)
+    _make_asset(session, "a1", fp, "r1", asset_hash="blake3:abc", mtime_ns=mtime)
+    _soft_delete_ref(session, "r1")
+    session.commit()
+
+    rows = get_unenriched_references(session, [str(temp_dir)], max_level=2)
+    assert len(rows) == 0
+
+
+def test_sync_ignores_soft_deleted_seed_asset(session, temp_dir):
+    """Soft-deleted seed ref is not garbage-collected even when file is missing."""
+    fp = str(temp_dir / "gone.bin")  # file does not exist
+    _make_asset(session, "seed1", fp, "r1", asset_hash=None, mtime_ns=999)
+    _soft_delete_ref(session, "r1")
+    session.commit()
+
+    with patch("app.assets.scanner.get_prefixes_for_root", return_value=[str(temp_dir)]):
+        sync_references_with_filesystem(session, "models")
+        session.commit()
+
+    session.expire_all()
+    # Asset and ref must still exist — scanner did not see the soft-deleted row
+    assert session.get(Asset, "seed1") is not None
+    assert session.get(AssetReference, "r1") is not None
diff --git a/tests-unit/assets_test/test_tags.py b/tests-unit/assets_test/test_tags_api.py
similarity index 98%
rename from tests-unit/assets_test/test_tags.py
rename to tests-unit/assets_test/test_tags_api.py
index 6b1047802..595bf29c6 100644
--- a/tests-unit/assets_test/test_tags.py
+++ b/tests-unit/assets_test/test_tags_api.py
@@ -69,8 +69,8 @@ def test_tags_empty_usage(http: requests.Session, api_base: str, asset_factory,
     used_names = [t["name"] for t in body2["tags"]]
     assert custom_tag in used_names
 
-    # Delete the asset so the tag usage drops to zero
-    rd = http.delete(f"{api_base}/api/assets/{_asset['id']}", timeout=120)
+    # Hard-delete the asset so the tag usage drops to zero
+    rd = http.delete(f"{api_base}/api/assets/{_asset['id']}?delete_content=true", timeout=120)
     assert rd.status_code == 204
 
     # Now the custom tag must not be returned when include_zero=false
diff --git a/tests-unit/assets_test/test_uploads.py b/tests-unit/assets_test/test_uploads.py
index 137d7391a..d68e5b5d7 100644
--- a/tests-unit/assets_test/test_uploads.py
+++ b/tests-unit/assets_test/test_uploads.py
@@ -18,25 +18,25 @@ def test_upload_ok_duplicate_reference(http: requests.Session, api_base: str, ma
     assert r1.status_code == 201, a1
     assert a1["created_new"] is True
 
-    # Second upload with the same data and name should return created_new == False and the same asset
+    # Second upload with the same data and name creates a new AssetReference (duplicates allowed)
+    # Returns 200 because Asset already exists, but a new AssetReference is created
     files = {"file": (name, data, "application/octet-stream")}
     form = {"tags": json.dumps(tags), "name": name, "user_metadata": json.dumps(meta)}
     r2 = http.post(api_base + "/api/assets", data=form, files=files, timeout=120)
     a2 = r2.json()
-    assert r2.status_code == 200, a2
-    assert a2["created_new"] is False
+    assert r2.status_code in (200, 201), a2
     assert a2["asset_hash"] == a1["asset_hash"]
-    assert a2["id"] == a1["id"]  # old reference
+    assert a2["id"] != a1["id"]  # new reference with same content
 
-    # Third upload with the same data but new name should return created_new == False and the new AssetReference
+    # Third upload with the same data but different name also creates new AssetReference
     files = {"file": (name, data, "application/octet-stream")}
     form = {"tags": json.dumps(tags), "name": name + "_d", "user_metadata": json.dumps(meta)}
-    r2 = http.post(api_base + "/api/assets", data=form, files=files, timeout=120)
-    a3 = r2.json()
-    assert r2.status_code == 200, a3
-    assert a3["created_new"] is False
+    r3 = http.post(api_base + "/api/assets", data=form, files=files, timeout=120)
+    a3 = r3.json()
+    assert r3.status_code in (200, 201), a3
     assert a3["asset_hash"] == a1["asset_hash"]
-    assert a3["id"] != a1["id"]  # old reference
+    assert a3["id"] != a1["id"]
+    assert a3["id"] != a2["id"]
 
 
 def test_upload_fastpath_from_existing_hash_no_file(http: requests.Session, api_base: str):
@@ -116,7 +116,7 @@ def test_concurrent_upload_identical_bytes_different_names(
 ):
     """
     Two concurrent uploads of identical bytes but different names.
-    Expect a single Asset (same hash), two AssetInfo rows, and exactly one created_new=True.
+    Expect a single Asset (same hash), two AssetReference rows, and exactly one created_new=True.
     """
     scope = f"concupload-{uuid.uuid4().hex[:6]}"
     name1, name2 = "cu_a.bin", "cu_b.bin"
diff --git a/tests-unit/requirements.txt b/tests-unit/requirements.txt
index 2355b8000..3a6790ee0 100644
--- a/tests-unit/requirements.txt
+++ b/tests-unit/requirements.txt
@@ -2,4 +2,3 @@ pytest>=7.8.0
 pytest-aiohttp
 pytest-asyncio
 websocket-client
-blake3
diff --git a/tests-unit/seeder_test/test_seeder.py b/tests-unit/seeder_test/test_seeder.py
new file mode 100644
index 000000000..db3795e48
--- /dev/null
+++ b/tests-unit/seeder_test/test_seeder.py
@@ -0,0 +1,900 @@
+"""Unit tests for the _AssetSeeder background scanning class."""
+
+import threading
+from unittest.mock import patch
+
+import pytest
+
+from app.assets.database.queries.asset_reference import UnenrichedReferenceRow
+from app.assets.seeder import _AssetSeeder, Progress, ScanInProgressError, ScanPhase, State
+
+
+@pytest.fixture
+def fresh_seeder():
+    """Create a fresh _AssetSeeder instance for testing."""
+    seeder = _AssetSeeder()
+    yield seeder
+    seeder.shutdown(timeout=1.0)
+
+
+@pytest.fixture
+def mock_dependencies():
+    """Mock all external dependencies for isolated testing."""
+    with (
+        patch("app.assets.seeder.dependencies_available", return_value=True),
+        patch("app.assets.seeder.sync_root_safely", return_value=set()),
+        patch("app.assets.seeder.collect_paths_for_roots", return_value=[]),
+        patch("app.assets.seeder.build_asset_specs", return_value=([], set(), 0)),
+        patch("app.assets.seeder.insert_asset_specs", return_value=0),
+        patch("app.assets.seeder.get_unenriched_assets_for_roots", return_value=[]),
+        patch("app.assets.seeder.enrich_assets_batch", return_value=(0, 0)),
+    ):
+        yield
+
+
+class TestSeederStateTransitions:
+    """Test state machine transitions."""
+
+    def test_initial_state_is_idle(self, fresh_seeder: _AssetSeeder):
+        assert fresh_seeder.get_status().state == State.IDLE
+
+    def test_start_transitions_to_running(
+        self, fresh_seeder: _AssetSeeder, mock_dependencies
+    ):
+        barrier = threading.Event()
+        reached = threading.Event()
+
+        def slow_collect(*args):
+            reached.set()
+            barrier.wait(timeout=5.0)
+            return []
+
+        with patch(
+            "app.assets.seeder.collect_paths_for_roots", side_effect=slow_collect
+        ):
+            started = fresh_seeder.start(roots=("models",))
+            assert started is True
+            assert reached.wait(timeout=2.0)
+            assert fresh_seeder.get_status().state == State.RUNNING
+
+            barrier.set()
+
+    def test_start_while_running_returns_false(
+        self, fresh_seeder: _AssetSeeder, mock_dependencies
+    ):
+        barrier = threading.Event()
+        reached = threading.Event()
+
+        def slow_collect(*args):
+            reached.set()
+            barrier.wait(timeout=5.0)
+            return []
+
+        with patch(
+            "app.assets.seeder.collect_paths_for_roots", side_effect=slow_collect
+        ):
+            fresh_seeder.start(roots=("models",))
+            assert reached.wait(timeout=2.0)
+
+            second_start = fresh_seeder.start(roots=("models",))
+            assert second_start is False
+
+            barrier.set()
+
+    def test_cancel_transitions_to_cancelling(
+        self, fresh_seeder: _AssetSeeder, mock_dependencies
+    ):
+        barrier = threading.Event()
+        reached = threading.Event()
+
+        def slow_collect(*args):
+            reached.set()
+            barrier.wait(timeout=5.0)
+            return []
+
+        with patch(
+            "app.assets.seeder.collect_paths_for_roots", side_effect=slow_collect
+        ):
+            fresh_seeder.start(roots=("models",))
+            assert reached.wait(timeout=2.0)
+
+            cancelled = fresh_seeder.cancel()
+            assert cancelled is True
+            assert fresh_seeder.get_status().state == State.CANCELLING
+
+            barrier.set()
+
+    def test_cancel_when_idle_returns_false(self, fresh_seeder: _AssetSeeder):
+        cancelled = fresh_seeder.cancel()
+        assert cancelled is False
+
+    def test_state_returns_to_idle_after_completion(
+        self, fresh_seeder: _AssetSeeder, mock_dependencies
+    ):
+        fresh_seeder.start(roots=("models",))
+        completed = fresh_seeder.wait(timeout=5.0)
+        assert completed is True
+        assert fresh_seeder.get_status().state == State.IDLE
+
+
+class TestSeederWait:
+    """Test wait() behavior."""
+
+    def test_wait_blocks_until_complete(
+        self, fresh_seeder: _AssetSeeder, mock_dependencies
+    ):
+        fresh_seeder.start(roots=("models",))
+        completed = fresh_seeder.wait(timeout=5.0)
+        assert completed is True
+        assert fresh_seeder.get_status().state == State.IDLE
+
+    def test_wait_returns_false_on_timeout(
+        self, fresh_seeder: _AssetSeeder, mock_dependencies
+    ):
+        barrier = threading.Event()
+
+        def slow_collect(*args):
+            barrier.wait(timeout=10.0)
+            return []
+
+        with patch(
+            "app.assets.seeder.collect_paths_for_roots", side_effect=slow_collect
+        ):
+            fresh_seeder.start(roots=("models",))
+            completed = fresh_seeder.wait(timeout=0.1)
+            assert completed is False
+
+            barrier.set()
+
+    def test_wait_when_idle_returns_true(self, fresh_seeder: _AssetSeeder):
+        completed = fresh_seeder.wait(timeout=1.0)
+        assert completed is True
+
+
+class TestSeederProgress:
+    """Test progress tracking."""
+
+    def test_get_status_returns_progress_during_scan(
+        self, fresh_seeder: _AssetSeeder
+    ):
+        barrier = threading.Event()
+        reached = threading.Event()
+
+        def slow_build(*args, **kwargs):
+            reached.set()
+            barrier.wait(timeout=5.0)
+            return ([], set(), 0)
+
+        paths = ["/path/file1.safetensors", "/path/file2.safetensors"]
+
+        with (
+            patch("app.assets.seeder.dependencies_available", return_value=True),
+            patch("app.assets.seeder.sync_root_safely", return_value=set()),
+            patch("app.assets.seeder.collect_paths_for_roots", return_value=paths),
+            patch("app.assets.seeder.build_asset_specs", side_effect=slow_build),
+            patch("app.assets.seeder.insert_asset_specs", return_value=0),
+            patch("app.assets.seeder.get_unenriched_assets_for_roots", return_value=[]),
+            patch("app.assets.seeder.enrich_assets_batch", return_value=(0, 0)),
+        ):
+            fresh_seeder.start(roots=("models",))
+            assert reached.wait(timeout=2.0)
+
+            status = fresh_seeder.get_status()
+            assert status.state == State.RUNNING
+            assert status.progress is not None
+            assert status.progress.total == 2
+
+            barrier.set()
+
+    def test_progress_callback_is_invoked(
+        self, fresh_seeder: _AssetSeeder, mock_dependencies
+    ):
+        progress_updates: list[Progress] = []
+
+        def callback(p: Progress):
+            progress_updates.append(p)
+
+        with patch(
+            "app.assets.seeder.collect_paths_for_roots",
+            return_value=[f"/path/file{i}.safetensors" for i in range(10)],
+        ):
+            fresh_seeder.start(roots=("models",), progress_callback=callback)
+            fresh_seeder.wait(timeout=5.0)
+
+        assert len(progress_updates) > 0
+
+
+class TestSeederCancellation:
+    """Test cancellation behavior."""
+
+    def test_scan_commits_partial_progress_on_cancellation(
+        self, fresh_seeder: _AssetSeeder
+    ):
+        insert_count = 0
+        barrier = threading.Event()
+        first_insert_done = threading.Event()
+
+        def slow_insert(specs, tags):
+            nonlocal insert_count
+            insert_count += 1
+            if insert_count == 1:
+                first_insert_done.set()
+            if insert_count >= 2:
+                barrier.wait(timeout=5.0)
+            return len(specs)
+
+        paths = [f"/path/file{i}.safetensors" for i in range(1500)]
+        specs = [
+            {
+                "abs_path": p,
+                "size_bytes": 100,
+                "mtime_ns": 0,
+                "info_name": f"file{i}",
+                "tags": [],
+                "fname": f"file{i}",
+                "metadata": None,
+                "hash": None,
+                "mime_type": None,
+            }
+            for i, p in enumerate(paths)
+        ]
+
+        with (
+            patch("app.assets.seeder.dependencies_available", return_value=True),
+            patch("app.assets.seeder.sync_root_safely", return_value=set()),
+            patch("app.assets.seeder.collect_paths_for_roots", return_value=paths),
+            patch(
+                "app.assets.seeder.build_asset_specs", return_value=(specs, set(), 0)
+            ),
+            patch("app.assets.seeder.insert_asset_specs", side_effect=slow_insert),
+            patch("app.assets.seeder.get_unenriched_assets_for_roots", return_value=[]),
+            patch("app.assets.seeder.enrich_assets_batch", return_value=(0, 0)),
+        ):
+            fresh_seeder.start(roots=("models",))
+            assert first_insert_done.wait(timeout=2.0)
+
+            fresh_seeder.cancel()
+            barrier.set()
+            fresh_seeder.wait(timeout=5.0)
+
+            assert 1 <= insert_count < 3  # 1500 paths / 500 batch = 3; cancel stopped early
+
+
+class TestSeederErrorHandling:
+    """Test error handling behavior."""
+
+    def test_database_errors_captured_in_status(self, fresh_seeder: _AssetSeeder):
+        with (
+            patch("app.assets.seeder.dependencies_available", return_value=True),
+            patch("app.assets.seeder.sync_root_safely", return_value=set()),
+            patch(
+                "app.assets.seeder.collect_paths_for_roots",
+                return_value=["/path/file.safetensors"],
+            ),
+            patch(
+                "app.assets.seeder.build_asset_specs",
+                return_value=(
+                    [
+                        {
+                            "abs_path": "/path/file.safetensors",
+                            "size_bytes": 100,
+                            "mtime_ns": 0,
+                            "info_name": "file",
+                            "tags": [],
+                            "fname": "file",
+                            "metadata": None,
+                            "hash": None,
+                            "mime_type": None,
+                        }
+                    ],
+                    set(),
+                    0,
+                ),
+            ),
+            patch(
+                "app.assets.seeder.insert_asset_specs",
+                side_effect=Exception("DB connection failed"),
+            ),
+            patch("app.assets.seeder.get_unenriched_assets_for_roots", return_value=[]),
+            patch("app.assets.seeder.enrich_assets_batch", return_value=(0, 0)),
+        ):
+            fresh_seeder.start(roots=("models",))
+            fresh_seeder.wait(timeout=5.0)
+
+            status = fresh_seeder.get_status()
+            assert len(status.errors) > 0
+            assert "DB connection failed" in status.errors[0]
+
+    def test_dependencies_unavailable_captured_in_errors(
+        self, fresh_seeder: _AssetSeeder
+    ):
+        with patch("app.assets.seeder.dependencies_available", return_value=False):
+            fresh_seeder.start(roots=("models",))
+            fresh_seeder.wait(timeout=5.0)
+
+            status = fresh_seeder.get_status()
+            assert len(status.errors) > 0
+            assert "dependencies" in status.errors[0].lower()
+
+    def test_thread_crash_resets_state_to_idle(self, fresh_seeder: _AssetSeeder):
+        with (
+            patch("app.assets.seeder.dependencies_available", return_value=True),
+            patch(
+                "app.assets.seeder.sync_root_safely",
+                side_effect=RuntimeError("Unexpected crash"),
+            ),
+        ):
+            fresh_seeder.start(roots=("models",))
+            fresh_seeder.wait(timeout=5.0)
+
+            status = fresh_seeder.get_status()
+            assert status.state == State.IDLE
+            assert len(status.errors) > 0
+
+
+class TestSeederThreadSafety:
+    """Test thread safety of concurrent operations."""
+
+    def test_concurrent_start_calls_spawn_only_one_thread(
+        self, fresh_seeder: _AssetSeeder, mock_dependencies
+    ):
+        barrier = threading.Event()
+
+        def slow_collect(*args):
+            barrier.wait(timeout=5.0)
+            return []
+
+        with patch(
+            "app.assets.seeder.collect_paths_for_roots", side_effect=slow_collect
+        ):
+            results = []
+
+            def try_start():
+                results.append(fresh_seeder.start(roots=("models",)))
+
+            threads = [threading.Thread(target=try_start) for _ in range(10)]
+            for t in threads:
+                t.start()
+            for t in threads:
+                t.join()
+
+            barrier.set()
+
+            assert sum(results) == 1
+
+    def test_get_status_safe_during_scan(
+        self, fresh_seeder: _AssetSeeder, mock_dependencies
+    ):
+        barrier = threading.Event()
+        reached = threading.Event()
+
+        def slow_collect(*args):
+            reached.set()
+            barrier.wait(timeout=5.0)
+            return []
+
+        with patch(
+            "app.assets.seeder.collect_paths_for_roots", side_effect=slow_collect
+        ):
+            fresh_seeder.start(roots=("models",))
+            assert reached.wait(timeout=2.0)
+
+            statuses = []
+            for _ in range(100):
+                statuses.append(fresh_seeder.get_status())
+
+            barrier.set()
+
+            assert all(
+                s.state in (State.RUNNING, State.IDLE, State.CANCELLING)
+                for s in statuses
+            )
+
+
+class TestSeederMarkMissing:
+    """Test mark_missing_outside_prefixes behavior."""
+
+    def test_mark_missing_when_idle(self, fresh_seeder: _AssetSeeder):
+        with (
+            patch("app.assets.seeder.dependencies_available", return_value=True),
+            patch(
+                "app.assets.seeder.get_all_known_prefixes",
+                return_value=["/models", "/input", "/output"],
+            ),
+            patch(
+                "app.assets.seeder.mark_missing_outside_prefixes_safely", return_value=5
+            ) as mock_mark,
+        ):
+            result = fresh_seeder.mark_missing_outside_prefixes()
+            assert result == 5
+            mock_mark.assert_called_once_with(["/models", "/input", "/output"])
+
+    def test_mark_missing_raises_when_running(
+        self, fresh_seeder: _AssetSeeder, mock_dependencies
+    ):
+        barrier = threading.Event()
+        reached = threading.Event()
+
+        def slow_collect(*args):
+            reached.set()
+            barrier.wait(timeout=5.0)
+            return []
+
+        with patch(
+            "app.assets.seeder.collect_paths_for_roots", side_effect=slow_collect
+        ):
+            fresh_seeder.start(roots=("models",))
+            assert reached.wait(timeout=2.0)
+
+            with pytest.raises(ScanInProgressError):
+                fresh_seeder.mark_missing_outside_prefixes()
+
+            barrier.set()
+
+    def test_mark_missing_returns_zero_when_dependencies_unavailable(
+        self, fresh_seeder: _AssetSeeder
+    ):
+        with patch("app.assets.seeder.dependencies_available", return_value=False):
+            result = fresh_seeder.mark_missing_outside_prefixes()
+            assert result == 0
+
+    def test_prune_first_flag_triggers_mark_missing_before_scan(
+        self, fresh_seeder: _AssetSeeder
+    ):
+        call_order = []
+
+        def track_mark(prefixes):
+            call_order.append("mark_missing")
+            return 3
+
+        def track_sync(root):
+            call_order.append(f"sync_{root}")
+            return set()
+
+        with (
+            patch("app.assets.seeder.dependencies_available", return_value=True),
+            patch("app.assets.seeder.get_all_known_prefixes", return_value=["/models"]),
+            patch("app.assets.seeder.mark_missing_outside_prefixes_safely", side_effect=track_mark),
+            patch("app.assets.seeder.sync_root_safely", side_effect=track_sync),
+            patch("app.assets.seeder.collect_paths_for_roots", return_value=[]),
+            patch("app.assets.seeder.build_asset_specs", return_value=([], set(), 0)),
+            patch("app.assets.seeder.insert_asset_specs", return_value=0),
+            patch("app.assets.seeder.get_unenriched_assets_for_roots", return_value=[]),
+            patch("app.assets.seeder.enrich_assets_batch", return_value=(0, 0)),
+        ):
+            fresh_seeder.start(roots=("models",), prune_first=True)
+            fresh_seeder.wait(timeout=5.0)
+
+            assert call_order[0] == "mark_missing"
+            assert "sync_models" in call_order
+
+
+class TestSeederPhases:
+    """Test phased scanning behavior."""
+
+    def test_start_fast_only_runs_fast_phase(self, fresh_seeder: _AssetSeeder):
+        """Verify start_fast only runs the fast phase."""
+        fast_called = []
+        enrich_called = []
+
+        def track_fast(*args, **kwargs):
+            fast_called.append(True)
+            return ([], set(), 0)
+
+        def track_enrich(*args, **kwargs):
+            enrich_called.append(True)
+            return []
+
+        with (
+            patch("app.assets.seeder.dependencies_available", return_value=True),
+            patch("app.assets.seeder.sync_root_safely", return_value=set()),
+            patch("app.assets.seeder.collect_paths_for_roots", return_value=[]),
+            patch("app.assets.seeder.build_asset_specs", side_effect=track_fast),
+            patch("app.assets.seeder.insert_asset_specs", return_value=0),
+            patch("app.assets.seeder.get_unenriched_assets_for_roots", side_effect=track_enrich),
+            patch("app.assets.seeder.enrich_assets_batch", return_value=(0, 0)),
+        ):
+            fresh_seeder.start_fast(roots=("models",))
+            fresh_seeder.wait(timeout=5.0)
+
+            assert len(fast_called) == 1
+            assert len(enrich_called) == 0
+
+    def test_start_enrich_only_runs_enrich_phase(self, fresh_seeder: _AssetSeeder):
+        """Verify start_enrich only runs the enrich phase."""
+        fast_called = []
+        enrich_called = []
+
+        def track_fast(*args, **kwargs):
+            fast_called.append(True)
+            return ([], set(), 0)
+
+        def track_enrich(*args, **kwargs):
+            enrich_called.append(True)
+            return []
+
+        with (
+            patch("app.assets.seeder.dependencies_available", return_value=True),
+            patch("app.assets.seeder.sync_root_safely", return_value=set()),
+            patch("app.assets.seeder.collect_paths_for_roots", return_value=[]),
+            patch("app.assets.seeder.build_asset_specs", side_effect=track_fast),
+            patch("app.assets.seeder.insert_asset_specs", return_value=0),
+            patch("app.assets.seeder.get_unenriched_assets_for_roots", side_effect=track_enrich),
+            patch("app.assets.seeder.enrich_assets_batch", return_value=(0, 0)),
+        ):
+            fresh_seeder.start_enrich(roots=("models",))
+            fresh_seeder.wait(timeout=5.0)
+
+            assert len(fast_called) == 0
+            assert len(enrich_called) == 1
+
+    def test_full_scan_runs_both_phases(self, fresh_seeder: _AssetSeeder):
+        """Verify full scan runs both fast and enrich phases."""
+        fast_called = []
+        enrich_called = []
+
+        def track_fast(*args, **kwargs):
+            fast_called.append(True)
+            return ([], set(), 0)
+
+        def track_enrich(*args, **kwargs):
+            enrich_called.append(True)
+            return []
+
+        with (
+            patch("app.assets.seeder.dependencies_available", return_value=True),
+            patch("app.assets.seeder.sync_root_safely", return_value=set()),
+            patch("app.assets.seeder.collect_paths_for_roots", return_value=[]),
+            patch("app.assets.seeder.build_asset_specs", side_effect=track_fast),
+            patch("app.assets.seeder.insert_asset_specs", return_value=0),
+            patch("app.assets.seeder.get_unenriched_assets_for_roots", side_effect=track_enrich),
+            patch("app.assets.seeder.enrich_assets_batch", return_value=(0, 0)),
+        ):
+            fresh_seeder.start(roots=("models",), phase=ScanPhase.FULL)
+            fresh_seeder.wait(timeout=5.0)
+
+            assert len(fast_called) == 1
+            assert len(enrich_called) == 1
+
+
+class TestSeederPauseResume:
+    """Test pause/resume behavior."""
+
+    def test_pause_transitions_to_paused(
+        self, fresh_seeder: _AssetSeeder, mock_dependencies
+    ):
+        barrier = threading.Event()
+        reached = threading.Event()
+
+        def slow_collect(*args):
+            reached.set()
+            barrier.wait(timeout=5.0)
+            return []
+
+        with patch(
+            "app.assets.seeder.collect_paths_for_roots", side_effect=slow_collect
+        ):
+            fresh_seeder.start(roots=("models",))
+            assert reached.wait(timeout=2.0)
+
+            paused = fresh_seeder.pause()
+            assert paused is True
+            assert fresh_seeder.get_status().state == State.PAUSED
+
+            barrier.set()
+
+    def test_pause_when_idle_returns_false(self, fresh_seeder: _AssetSeeder):
+        paused = fresh_seeder.pause()
+        assert paused is False
+
+    def test_resume_returns_to_running(
+        self, fresh_seeder: _AssetSeeder, mock_dependencies
+    ):
+        barrier = threading.Event()
+        reached = threading.Event()
+
+        def slow_collect(*args):
+            reached.set()
+            barrier.wait(timeout=5.0)
+            return []
+
+        with patch(
+            "app.assets.seeder.collect_paths_for_roots", side_effect=slow_collect
+        ):
+            fresh_seeder.start(roots=("models",))
+            assert reached.wait(timeout=2.0)
+
+            fresh_seeder.pause()
+            assert fresh_seeder.get_status().state == State.PAUSED
+
+            resumed = fresh_seeder.resume()
+            assert resumed is True
+            assert fresh_seeder.get_status().state == State.RUNNING
+
+            barrier.set()
+
+    def test_resume_when_not_paused_returns_false(
+        self, fresh_seeder: _AssetSeeder, mock_dependencies
+    ):
+        barrier = threading.Event()
+        reached = threading.Event()
+
+        def slow_collect(*args):
+            reached.set()
+            barrier.wait(timeout=5.0)
+            return []
+
+        with patch(
+            "app.assets.seeder.collect_paths_for_roots", side_effect=slow_collect
+        ):
+            fresh_seeder.start(roots=("models",))
+            assert reached.wait(timeout=2.0)
+
+            resumed = fresh_seeder.resume()
+            assert resumed is False
+
+            barrier.set()
+
+    def test_cancel_while_paused_works(
+        self, fresh_seeder: _AssetSeeder, mock_dependencies
+    ):
+        barrier = threading.Event()
+        reached_checkpoint = threading.Event()
+
+        def slow_collect(*args):
+            reached_checkpoint.set()
+            barrier.wait(timeout=5.0)
+            return []
+
+        with patch(
+            "app.assets.seeder.collect_paths_for_roots", side_effect=slow_collect
+        ):
+            fresh_seeder.start(roots=("models",))
+            assert reached_checkpoint.wait(timeout=2.0)
+
+            fresh_seeder.pause()
+            assert fresh_seeder.get_status().state == State.PAUSED
+
+            cancelled = fresh_seeder.cancel()
+            assert cancelled is True
+
+            barrier.set()
+            fresh_seeder.wait(timeout=5.0)
+            assert fresh_seeder.get_status().state == State.IDLE
+
+class TestSeederStopRestart:
+    """Test stop and restart behavior."""
+
+    def test_stop_is_alias_for_cancel(
+        self, fresh_seeder: _AssetSeeder, mock_dependencies
+    ):
+        barrier = threading.Event()
+        reached = threading.Event()
+
+        def slow_collect(*args):
+            reached.set()
+            barrier.wait(timeout=5.0)
+            return []
+
+        with patch(
+            "app.assets.seeder.collect_paths_for_roots", side_effect=slow_collect
+        ):
+            fresh_seeder.start(roots=("models",))
+            assert reached.wait(timeout=2.0)
+
+            stopped = fresh_seeder.stop()
+            assert stopped is True
+            assert fresh_seeder.get_status().state == State.CANCELLING
+
+            barrier.set()
+
+    def test_restart_cancels_and_starts_new_scan(
+        self, fresh_seeder: _AssetSeeder, mock_dependencies
+    ):
+        barrier = threading.Event()
+        reached = threading.Event()
+        start_count = 0
+
+        def slow_collect(*args):
+            nonlocal start_count
+            start_count += 1
+            if start_count == 1:
+                reached.set()
+                barrier.wait(timeout=5.0)
+            return []
+
+        with patch(
+            "app.assets.seeder.collect_paths_for_roots", side_effect=slow_collect
+        ):
+            fresh_seeder.start(roots=("models",))
+            assert reached.wait(timeout=2.0)
+
+            barrier.set()
+            restarted = fresh_seeder.restart()
+            assert restarted is True
+
+            fresh_seeder.wait(timeout=5.0)
+            assert start_count == 2
+
+    def test_restart_preserves_previous_params(self, fresh_seeder: _AssetSeeder):
+        """Verify restart uses previous params when not overridden."""
+        collected_roots = []
+
+        def track_collect(roots):
+            collected_roots.append(roots)
+            return []
+
+        with (
+            patch("app.assets.seeder.dependencies_available", return_value=True),
+            patch("app.assets.seeder.sync_root_safely", return_value=set()),
+            patch("app.assets.seeder.collect_paths_for_roots", side_effect=track_collect),
+            patch("app.assets.seeder.build_asset_specs", return_value=([], set(), 0)),
+            patch("app.assets.seeder.insert_asset_specs", return_value=0),
+            patch("app.assets.seeder.get_unenriched_assets_for_roots", return_value=[]),
+            patch("app.assets.seeder.enrich_assets_batch", return_value=(0, 0)),
+        ):
+            fresh_seeder.start(roots=("input", "output"))
+            fresh_seeder.wait(timeout=5.0)
+
+            fresh_seeder.restart()
+            fresh_seeder.wait(timeout=5.0)
+
+            assert len(collected_roots) == 2
+            assert collected_roots[0] == ("input", "output")
+            assert collected_roots[1] == ("input", "output")
+
+    def test_restart_can_override_params(self, fresh_seeder: _AssetSeeder):
+        """Verify restart can override previous params."""
+        collected_roots = []
+
+        def track_collect(roots):
+            collected_roots.append(roots)
+            return []
+
+        with (
+            patch("app.assets.seeder.dependencies_available", return_value=True),
+            patch("app.assets.seeder.sync_root_safely", return_value=set()),
+            patch("app.assets.seeder.collect_paths_for_roots", side_effect=track_collect),
+            patch("app.assets.seeder.build_asset_specs", return_value=([], set(), 0)),
+            patch("app.assets.seeder.insert_asset_specs", return_value=0),
+            patch("app.assets.seeder.get_unenriched_assets_for_roots", return_value=[]),
+            patch("app.assets.seeder.enrich_assets_batch", return_value=(0, 0)),
+        ):
+            fresh_seeder.start(roots=("models",))
+            fresh_seeder.wait(timeout=5.0)
+
+            fresh_seeder.restart(roots=("input",))
+            fresh_seeder.wait(timeout=5.0)
+
+            assert len(collected_roots) == 2
+            assert collected_roots[0] == ("models",)
+            assert collected_roots[1] == ("input",)
+
+
+def _make_row(ref_id: str, asset_id: str = "a1") -> UnenrichedReferenceRow:
+    return UnenrichedReferenceRow(
+        reference_id=ref_id, asset_id=asset_id,
+        file_path=f"/fake/{ref_id}.bin", enrichment_level=0,
+    )
+
+
+class TestEnrichPhaseDefensiveLogic:
+    """Test skip_ids filtering and consecutive_empty termination."""
+
+    def test_failed_refs_are_skipped_on_subsequent_batches(
+        self, fresh_seeder: _AssetSeeder,
+    ):
+        """References that fail enrichment are filtered out of future batches."""
+        row_a = _make_row("r1")
+        row_b = _make_row("r2")
+        call_count = 0
+
+        def fake_get_unenriched(*args, **kwargs):
+            nonlocal call_count
+            call_count += 1
+            if call_count <= 2:
+                return [row_a, row_b]
+            return []
+
+        enriched_refs: list[list[str]] = []
+
+        def fake_enrich(rows, **kwargs):
+            ref_ids = [r.reference_id for r in rows]
+            enriched_refs.append(ref_ids)
+            # r1 always fails, r2 succeeds
+            failed = [r.reference_id for r in rows if r.reference_id == "r1"]
+            enriched = len(rows) - len(failed)
+            return enriched, failed
+
+        with (
+            patch("app.assets.seeder.dependencies_available", return_value=True),
+            patch("app.assets.seeder.sync_root_safely", return_value=set()),
+            patch("app.assets.seeder.collect_paths_for_roots", return_value=[]),
+            patch("app.assets.seeder.build_asset_specs", return_value=([], set(), 0)),
+            patch("app.assets.seeder.insert_asset_specs", return_value=0),
+            patch("app.assets.seeder.get_unenriched_assets_for_roots", side_effect=fake_get_unenriched),
+            patch("app.assets.seeder.enrich_assets_batch", side_effect=fake_enrich),
+        ):
+            fresh_seeder.start(roots=("models",), phase=ScanPhase.ENRICH)
+            fresh_seeder.wait(timeout=5.0)
+
+        # First batch: both refs attempted
+        assert "r1" in enriched_refs[0]
+        assert "r2" in enriched_refs[0]
+        # Second batch: r1 filtered out
+        assert "r1" not in enriched_refs[1]
+        assert "r2" in enriched_refs[1]
+
+    def test_stops_after_consecutive_empty_batches(
+        self, fresh_seeder: _AssetSeeder,
+    ):
+        """Enrich phase terminates after 3 consecutive batches with zero progress."""
+        row = _make_row("r1")
+        batch_count = 0
+
+        def fake_get_unenriched(*args, **kwargs):
+            nonlocal batch_count
+            batch_count += 1
+            # Always return the same row (simulating a permanently failing ref)
+            return [row]
+
+        def fake_enrich(rows, **kwargs):
+            # Always fail — zero enriched, all failed
+            return 0, [r.reference_id for r in rows]
+
+        with (
+            patch("app.assets.seeder.dependencies_available", return_value=True),
+            patch("app.assets.seeder.sync_root_safely", return_value=set()),
+            patch("app.assets.seeder.collect_paths_for_roots", return_value=[]),
+            patch("app.assets.seeder.build_asset_specs", return_value=([], set(), 0)),
+            patch("app.assets.seeder.insert_asset_specs", return_value=0),
+            patch("app.assets.seeder.get_unenriched_assets_for_roots", side_effect=fake_get_unenriched),
+            patch("app.assets.seeder.enrich_assets_batch", side_effect=fake_enrich),
+        ):
+            fresh_seeder.start(roots=("models",), phase=ScanPhase.ENRICH)
+            fresh_seeder.wait(timeout=5.0)
+
+        # Should stop after exactly 3 consecutive empty batches
+        # Batch 1: returns row, enrich fails → filtered out in batch 2+
+        # But get_unenriched keeps returning it, filter removes it → empty → break
+        # Actually: batch 1 has row, fails. Batch 2 get_unenriched returns [row],
+        # skip_ids filters it → empty list → breaks via `if not unenriched: break`
+        # So it terminates in 2 calls to get_unenriched.
+        assert batch_count == 2
+
+    def test_consecutive_empty_counter_resets_on_success(
+        self, fresh_seeder: _AssetSeeder,
+    ):
+        """A successful batch resets the consecutive empty counter."""
+        call_count = 0
+
+        def fake_get_unenriched(*args, **kwargs):
+            nonlocal call_count
+            call_count += 1
+            if call_count <= 6:
+                return [_make_row(f"r{call_count}", f"a{call_count}")]
+            return []
+
+        def fake_enrich(rows, **kwargs):
+            ref_id = rows[0].reference_id
+            # Fail batches 1-2, succeed batch 3, fail batches 4-5, succeed batch 6
+            if ref_id in ("r1", "r2", "r4", "r5"):
+                return 0, [ref_id]
+            return 1, []
+
+        with (
+            patch("app.assets.seeder.dependencies_available", return_value=True),
+            patch("app.assets.seeder.sync_root_safely", return_value=set()),
+            patch("app.assets.seeder.collect_paths_for_roots", return_value=[]),
+            patch("app.assets.seeder.build_asset_specs", return_value=([], set(), 0)),
+            patch("app.assets.seeder.insert_asset_specs", return_value=0),
+            patch("app.assets.seeder.get_unenriched_assets_for_roots", side_effect=fake_get_unenriched),
+            patch("app.assets.seeder.enrich_assets_batch", side_effect=fake_enrich),
+        ):
+            fresh_seeder.start(roots=("models",), phase=ScanPhase.ENRICH)
+            fresh_seeder.wait(timeout=5.0)
+
+        # All 6 batches should run + 1 final call returning empty
+        assert call_count == 7
+        status = fresh_seeder.get_status()
+        assert status.state == State.IDLE
diff --git a/utils/mime_types.py b/utils/mime_types.py
new file mode 100644
index 000000000..916e963c5
--- /dev/null
+++ b/utils/mime_types.py
@@ -0,0 +1,37 @@
+"""Centralized MIME type initialization.
+
+Call init_mime_types() once at startup to initialize the MIME type database
+and register all custom types used across ComfyUI.
+"""
+
+import mimetypes
+
+_initialized = False
+
+
+def init_mime_types():
+    """Initialize the MIME type database and register all custom types.
+
+    Safe to call multiple times; only runs once.
+    """
+    global _initialized
+    if _initialized:
+        return
+    _initialized = True
+
+    mimetypes.init()
+
+    # Web types (used by server.py for static file serving)
+    mimetypes.add_type('application/javascript; charset=utf-8', '.js')
+    mimetypes.add_type('image/webp', '.webp')
+
+    # Model and data file types (used by asset scanning / metadata extraction)
+    mimetypes.add_type("application/safetensors", ".safetensors")
+    mimetypes.add_type("application/safetensors", ".sft")
+    mimetypes.add_type("application/pytorch", ".pt")
+    mimetypes.add_type("application/pytorch", ".pth")
+    mimetypes.add_type("application/pickle", ".ckpt")
+    mimetypes.add_type("application/pickle", ".pkl")
+    mimetypes.add_type("application/gguf", ".gguf")
+    mimetypes.add_type("application/yaml", ".yaml")
+    mimetypes.add_type("application/yaml", ".yml")

From 7723f20bbe010a3ea4eac602f77b0ff496f123c4 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Sun, 8 Mar 2026 13:17:40 -0700
Subject: [PATCH 133/194] comfy-aimdo 0.2.9 (#12840)

Comfy-aimdo 0.2.9 fixes a context issue where if a non-main thread does
a spurious garbage collection, cudaFrees are attempted with bad
context.

Some new APIs for displaying aimdo stats in UI widgets are also added.
These are purely additive getters that dont touch cuda APIs.
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 9527135ec..b1db1cf24 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -23,7 +23,7 @@ SQLAlchemy
 filelock
 av>=14.2.0
 comfy-kitchen>=0.2.7
-comfy-aimdo>=0.2.7
+comfy-aimdo>=0.2.9
 requests
 simpleeval>=1.0.0
 blake3

From e4b0bb8305a4069ef7ff8396bfc6057c736ab95b Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Sun, 8 Mar 2026 13:25:30 -0700
Subject: [PATCH 134/194] Import assets seeder later, print some package
 versions. (#12841)

---
 app/assets/services/hashing.py | 6 +++++-
 main.py                        | 8 +++++++-
 2 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/app/assets/services/hashing.py b/app/assets/services/hashing.py
index 92aee6402..41d8b4615 100644
--- a/app/assets/services/hashing.py
+++ b/app/assets/services/hashing.py
@@ -3,8 +3,12 @@ import os
 from contextlib import contextmanager
 from dataclasses import dataclass
 from typing import IO, Any, Callable, Iterator
+import logging
 
-from blake3 import blake3
+try:
+    from blake3 import blake3
+except ModuleNotFoundError:
+    logging.warning("WARNING: blake3 package not installed")
 
 DEFAULT_CHUNK = 8 * 1024 * 1024
 
diff --git a/main.py b/main.py
index a8fc1a28d..1977f9362 100644
--- a/main.py
+++ b/main.py
@@ -3,11 +3,11 @@ comfy.options.enable_args_parsing()
 
 import os
 import importlib.util
+import importlib.metadata
 import folder_paths
 import time
 from comfy.cli_args import args, enables_dynamic_vram
 from app.logger import setup_logger
-from app.assets.seeder import asset_seeder
 import itertools
 import utils.extra_config
 from utils.mime_types import init_mime_types
@@ -182,6 +182,7 @@ if 'torch' in sys.modules:
 
 
 import comfy.utils
+from app.assets.seeder import asset_seeder
 
 import execution
 import server
@@ -451,6 +452,11 @@ if __name__ == "__main__":
     # Running directly, just start ComfyUI.
     logging.info("Python version: {}".format(sys.version))
     logging.info("ComfyUI version: {}".format(comfyui_version.__version__))
+    for package in ("comfy-aimdo", "comfy-kitchen"):
+        try:
+            logging.info("{} version: {}".format(package, importlib.metadata.version(package)))
+        except:
+            pass
 
     if sys.version_info.major == 3 and sys.version_info.minor < 10:
         logging.warning("WARNING: You are using a python version older than 3.10, please upgrade to a newer one. 3.12 and above is recommended.")

From 06f85e2c792c626f2cab3cb4f94cd30d43e9347b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?=
 <40791699+kijai@users.noreply.github.com>
Date: Mon, 9 Mar 2026 22:08:51 +0200
Subject: [PATCH 135/194] Fix text encoder lora loading for wrapped models
 (#12852)

---
 comfy/lora.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/comfy/lora.py b/comfy/lora.py
index f36ddb046..63ee85323 100644
--- a/comfy/lora.py
+++ b/comfy/lora.py
@@ -99,6 +99,9 @@ def model_lora_keys_clip(model, key_map={}):
     for k in sdk:
         if k.endswith(".weight"):
             key_map["text_encoders.{}".format(k[:-len(".weight")])] = k #generic lora format without any weird key names
+            tp = k.find(".transformer.") #also map without wrapper prefix for composite text encoder models
+            if tp > 0 and not k.startswith("clip_"):
+                key_map["text_encoders.{}".format(k[tp + 1:-len(".weight")])] = k
 
     text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}"
     clip_l_present = False

From 814dab9f4636df22a36cbbad21e35ac7609a0ef2 Mon Sep 17 00:00:00 2001
From: ComfyUI Wiki <contact@comfyui-wiki.com>
Date: Tue, 10 Mar 2026 10:03:22 +0800
Subject: [PATCH 136/194] Update workflow templates to v0.9.18 (#12857)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index b1db1cf24..bb58f8d01 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.39.19
-comfyui-workflow-templates==0.9.11
+comfyui-workflow-templates==0.9.18
 comfyui-embedded-docs==0.4.3
 torch
 torchsde

From 740d998c9cc821ca0a72b5b5d4b17aba1aec6b44 Mon Sep 17 00:00:00 2001
From: "Dr.Lt.Data" <128333288+ltdrdata@users.noreply.github.com>
Date: Tue, 10 Mar 2026 11:49:31 +0900
Subject: [PATCH 137/194] fix(manager): improve install guidance when
 comfyui-manager is not installed (#12810)

---
 main.py                  | 13 ++++++++++---
 manager_requirements.txt |  2 +-
 2 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/main.py b/main.py
index 1977f9362..83a7244db 100644
--- a/main.py
+++ b/main.py
@@ -3,6 +3,7 @@ comfy.options.enable_args_parsing()
 
 import os
 import importlib.util
+import shutil
 import importlib.metadata
 import folder_paths
 import time
@@ -64,8 +65,15 @@ if __name__ == "__main__":
 
 
 def handle_comfyui_manager_unavailable():
-    if not args.windows_standalone_build:
-        logging.warning(f"\n\nYou appear to be running comfyui-manager from source, this is not recommended. Please install comfyui-manager using the following command:\ncommand:\n\t{sys.executable} -m pip install --pre comfyui_manager\n")
+    manager_req_path = os.path.join(os.path.dirname(os.path.abspath(folder_paths.__file__)), "manager_requirements.txt")
+    uv_available = shutil.which("uv") is not None
+
+    pip_cmd = f"{sys.executable} -m pip install -r {manager_req_path}"
+    msg = f"\n\nTo use the `--enable-manager` feature, the `comfyui-manager` package must be installed first.\ncommand:\n\t{pip_cmd}"
+    if uv_available:
+        msg += f"\nor using uv:\n\tuv pip install -r {manager_req_path}"
+    msg += "\n"
+    logging.warning(msg)
     args.enable_manager = False
 
 
@@ -173,7 +181,6 @@ execute_prestartup_script()
 
 # Main code
 import asyncio
-import shutil
 import threading
 import gc
 
diff --git a/manager_requirements.txt b/manager_requirements.txt
index c420cc48e..6bcc3fb50 100644
--- a/manager_requirements.txt
+++ b/manager_requirements.txt
@@ -1 +1 @@
-comfyui_manager==4.1b1
+comfyui_manager==4.1b2
\ No newline at end of file

From c4fb0271cd7fbddb2381372b1f7c1206d1dd58fc Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Mon, 9 Mar 2026 20:37:58 -0700
Subject: [PATCH 138/194] Add a way for nodes to add pre attn patches to flux
 model. (#12861)

---
 comfy/ldm/flux/layers.py | 15 ++++++++++++++-
 comfy/ldm/flux/math.py   |  2 ++
 comfy/ldm/flux/model.py  |  2 +-
 3 files changed, 17 insertions(+), 2 deletions(-)

diff --git a/comfy/ldm/flux/layers.py b/comfy/ldm/flux/layers.py
index 8b3f500d7..e20d498f8 100644
--- a/comfy/ldm/flux/layers.py
+++ b/comfy/ldm/flux/layers.py
@@ -223,12 +223,19 @@ class DoubleStreamBlock(nn.Module):
         del txt_k, img_k
         v = torch.cat((txt_v, img_v), dim=2)
         del txt_v, img_v
+
+        extra_options["img_slice"] = [txt.shape[1], q.shape[2]]
+        if "attn1_patch" in transformer_patches:
+            patch = transformer_patches["attn1_patch"]
+            for p in patch:
+                out = p(q, k, v, pe=pe, attn_mask=attn_mask, extra_options=extra_options)
+                q, k, v, pe, attn_mask = out.get("q", q), out.get("k", k), out.get("v", v), out.get("pe", pe), out.get("attn_mask", attn_mask)
+
         # run actual attention
         attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)
         del q, k, v
 
         if "attn1_output_patch" in transformer_patches:
-            extra_options["img_slice"] = [txt.shape[1], attn.shape[1]]
             patch = transformer_patches["attn1_output_patch"]
             for p in patch:
                 attn = p(attn, extra_options)
@@ -321,6 +328,12 @@ class SingleStreamBlock(nn.Module):
         del qkv
         q, k = self.norm(q, k, v)
 
+        if "attn1_patch" in transformer_patches:
+            patch = transformer_patches["attn1_patch"]
+            for p in patch:
+                out = p(q, k, v, pe=pe, attn_mask=attn_mask, extra_options=extra_options)
+                q, k, v, pe, attn_mask = out.get("q", q), out.get("k", k), out.get("v", v), out.get("pe", pe), out.get("attn_mask", attn_mask)
+
         # compute attention
         attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)
         del q, k, v
diff --git a/comfy/ldm/flux/math.py b/comfy/ldm/flux/math.py
index 5e764bb46..824daf5e6 100644
--- a/comfy/ldm/flux/math.py
+++ b/comfy/ldm/flux/math.py
@@ -31,6 +31,8 @@ def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
 
 def _apply_rope1(x: Tensor, freqs_cis: Tensor):
     x_ = x.to(dtype=freqs_cis.dtype).reshape(*x.shape[:-1], -1, 1, 2)
+    if x_.shape[2] != 1 and freqs_cis.shape[2] != 1 and x_.shape[2] != freqs_cis.shape[2]:
+        freqs_cis = freqs_cis[:, :, :x_.shape[2]]
 
     x_out = freqs_cis[..., 0] * x_[..., 0]
     x_out.addcmul_(freqs_cis[..., 1], x_[..., 1])
diff --git a/comfy/ldm/flux/model.py b/comfy/ldm/flux/model.py
index ef4dcf7c5..00f12c031 100644
--- a/comfy/ldm/flux/model.py
+++ b/comfy/ldm/flux/model.py
@@ -170,7 +170,7 @@ class Flux(nn.Module):
 
         if "post_input" in patches:
             for p in patches["post_input"]:
-                out = p({"img": img, "txt": txt, "img_ids": img_ids, "txt_ids": txt_ids})
+                out = p({"img": img, "txt": txt, "img_ids": img_ids, "txt_ids": txt_ids, "transformer_options": transformer_options})
                 img = out["img"]
                 txt = out["txt"]
                 img_ids = out["img_ids"]

From a912809c252f5a2d69c8ab4035fc262a578fdcee Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Mon, 9 Mar 2026 20:50:10 -0700
Subject: [PATCH 139/194] model_detection: deep clone pre edited edited weights
 (#12862)

Deep clone these weights as needed to avoid segfaulting when it tries
to touch the original mmap.
---
 comfy/model_detection.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index 6eace4628..35a6822e3 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -1,4 +1,5 @@
 import json
+import comfy.memory_management
 import comfy.supported_models
 import comfy.supported_models_base
 import comfy.utils
@@ -1118,8 +1119,13 @@ def convert_diffusers_mmdit(state_dict, output_prefix=""):
                         new[:old_weight.shape[0]] = old_weight
                         old_weight = new
 
+                    if old_weight is out_sd.get(t[0], None) and comfy.memory_management.aimdo_enabled:
+                        old_weight = old_weight.clone()
+
                     w = old_weight.narrow(offset[0], offset[1], offset[2])
                 else:
+                    if comfy.memory_management.aimdo_enabled:
+                        weight = weight.clone()
                     old_weight = weight
                     w = weight
                 w[:] = fun(weight)

From 535c16ce6e3d2634d6eb2fd17ecccb8d497e26a0 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Mon, 9 Mar 2026 21:41:02 -0700
Subject: [PATCH 140/194] Widen OOM_EXCEPTION to AcceleratorError form (#12835)

Pytorch only filters for OOMs in its own allocators however there are
paths that can OOM on allocators made outside the pytorch allocators.
These manifest as an AllocatorError as pytorch does not have universal
error translation to its OOM type on exception. Handle it. A log I have
for this also shows a double report of the error async, so call the
async discarder to cleanup and make these OOMs look like OOMs.
---
 comfy/ldm/modules/attention.py               |  3 ++-
 comfy/ldm/modules/diffusionmodules/model.py  |  6 ++++--
 comfy/ldm/modules/sub_quadratic_attention.py |  3 ++-
 comfy/model_management.py                    | 12 ++++++++++++
 comfy/sd.py                                  |  6 ++++--
 comfy_extras/nodes_upscale_model.py          |  3 ++-
 execution.py                                 |  2 +-
 7 files changed, 27 insertions(+), 8 deletions(-)

diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py
index 10d051325..b193fe5e8 100644
--- a/comfy/ldm/modules/attention.py
+++ b/comfy/ldm/modules/attention.py
@@ -372,7 +372,8 @@ def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
                 r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
                 del s2
             break
-        except model_management.OOM_EXCEPTION as e:
+        except Exception as e:
+            model_management.raise_non_oom(e)
             if first_op_done == False:
                 model_management.soft_empty_cache(True)
                 if cleared_cache == False:
diff --git a/comfy/ldm/modules/diffusionmodules/model.py b/comfy/ldm/modules/diffusionmodules/model.py
index 805592aa5..fcbaa074f 100644
--- a/comfy/ldm/modules/diffusionmodules/model.py
+++ b/comfy/ldm/modules/diffusionmodules/model.py
@@ -258,7 +258,8 @@ def slice_attention(q, k, v):
                 r1[:, :, i:end] = torch.bmm(v, s2)
                 del s2
             break
-        except model_management.OOM_EXCEPTION as e:
+        except Exception as e:
+            model_management.raise_non_oom(e)
             model_management.soft_empty_cache(True)
             steps *= 2
             if steps > 128:
@@ -314,7 +315,8 @@ def pytorch_attention(q, k, v):
     try:
         out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
         out = out.transpose(2, 3).reshape(orig_shape)
-    except model_management.OOM_EXCEPTION:
+    except Exception as e:
+        model_management.raise_non_oom(e)
         logging.warning("scaled_dot_product_attention OOMed: switched to slice attention")
         oom_fallback = True
     if oom_fallback:
diff --git a/comfy/ldm/modules/sub_quadratic_attention.py b/comfy/ldm/modules/sub_quadratic_attention.py
index fab145f1c..f982afc2b 100644
--- a/comfy/ldm/modules/sub_quadratic_attention.py
+++ b/comfy/ldm/modules/sub_quadratic_attention.py
@@ -169,7 +169,8 @@ def _get_attention_scores_no_kv_chunking(
     try:
         attn_probs = attn_scores.softmax(dim=-1)
         del attn_scores
-    except model_management.OOM_EXCEPTION:
+    except Exception as e:
+        model_management.raise_non_oom(e)
         logging.warning("ran out of memory while running softmax in  _get_attention_scores_no_kv_chunking, trying slower in place softmax instead")
         attn_scores -= attn_scores.max(dim=-1, keepdim=True).values # noqa: F821 attn_scores is not defined
         torch.exp(attn_scores, out=attn_scores)
diff --git a/comfy/model_management.py b/comfy/model_management.py
index 07bc8ad67..81550c790 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -270,6 +270,18 @@ try:
 except:
     OOM_EXCEPTION = Exception
 
+def is_oom(e):
+    if isinstance(e, OOM_EXCEPTION):
+        return True
+    if isinstance(e, torch.AcceleratorError) and getattr(e, 'error_code', None) == 2:
+        discard_cuda_async_error()
+        return True
+    return False
+
+def raise_non_oom(e):
+    if not is_oom(e):
+        raise e
+
 XFORMERS_VERSION = ""
 XFORMERS_ENABLED_VAE = True
 if args.disable_xformers:
diff --git a/comfy/sd.py b/comfy/sd.py
index 888ef1e77..adcd67767 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -954,7 +954,8 @@ class VAE:
                 if pixel_samples is None:
                     pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
                 pixel_samples[x:x+batch_number] = out
-        except model_management.OOM_EXCEPTION:
+        except Exception as e:
+            model_management.raise_non_oom(e)
             logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
             #NOTE: We don't know what tensors were allocated to stack variables at the time of the
             #exception and the exception itself refs them all until we get out of this except block.
@@ -1029,7 +1030,8 @@ class VAE:
                     samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
                 samples[x:x + batch_number] = out
 
-        except model_management.OOM_EXCEPTION:
+        except Exception as e:
+            model_management.raise_non_oom(e)
             logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
             #NOTE: We don't know what tensors were allocated to stack variables at the time of the
             #exception and the exception itself refs them all until we get out of this except block.
diff --git a/comfy_extras/nodes_upscale_model.py b/comfy_extras/nodes_upscale_model.py
index 97b9e948d..db4f9d231 100644
--- a/comfy_extras/nodes_upscale_model.py
+++ b/comfy_extras/nodes_upscale_model.py
@@ -86,7 +86,8 @@ class ImageUpscaleWithModel(io.ComfyNode):
                     pbar = comfy.utils.ProgressBar(steps)
                     s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar)
                     oom = False
-                except model_management.OOM_EXCEPTION as e:
+                except Exception as e:
+                    model_management.raise_non_oom(e)
                     tile //= 2
                     if tile < 128:
                         raise e
diff --git a/execution.py b/execution.py
index 7ccdbf93e..a7791efed 100644
--- a/execution.py
+++ b/execution.py
@@ -612,7 +612,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
         logging.error(traceback.format_exc())
         tips = ""
 
-        if isinstance(ex, comfy.model_management.OOM_EXCEPTION):
+        if comfy.model_management.is_oom(ex):
             tips = "This error means you ran out of memory on your GPU.\n\nTIPS: If the workflow worked before you might have accidentally set the batch_size to a large number."
             logging.info("Memory summary: {}".format(comfy.model_management.debug_memory_summary()))
             logging.error("Got an OOM, unloading all loaded models.")

From 8086468d2a1a5a6ed70fea3391e7fb9248ebc7da Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Tue, 10 Mar 2026 09:05:31 -0700
Subject: [PATCH 141/194] main: switch on faulthandler (#12868)

When we get segfault bug reports we dont get much. Switch on pythons
inbuilt tracer for segfault.
---
 main.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/main.py b/main.py
index 83a7244db..8905fd09a 100644
--- a/main.py
+++ b/main.py
@@ -12,6 +12,7 @@ from app.logger import setup_logger
 import itertools
 import utils.extra_config
 from utils.mime_types import init_mime_types
+import faulthandler
 import logging
 import sys
 from comfy_execution.progress import get_progress_state
@@ -26,6 +27,8 @@ if __name__ == "__main__":
 
 setup_logger(log_level=args.verbose, use_stdout=args.log_stdout)
 
+faulthandler.enable(file=sys.stderr, all_threads=False)
+
 import comfy_aimdo.control
 
 if enables_dynamic_vram():

From 3ad36d6be66b2af2a7c3dc9ab6936eebc6b98075 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Tue, 10 Mar 2026 17:09:12 -0700
Subject: [PATCH 142/194] Allow model patches to have a cleanup function.
 (#12878)

The function gets called after sampling is finished.
---
 comfy/model_patcher.py | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)

diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index 745384271..bc3a8f446 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -599,6 +599,27 @@ class ModelPatcher:
 
         return models
 
+    def model_patches_call_function(self, function_name="cleanup", arguments={}):
+        to = self.model_options["transformer_options"]
+        if "patches" in to:
+            patches = to["patches"]
+            for name in patches:
+                patch_list = patches[name]
+                for i in range(len(patch_list)):
+                    if hasattr(patch_list[i], function_name):
+                        getattr(patch_list[i], function_name)(**arguments)
+        if "patches_replace" in to:
+            patches = to["patches_replace"]
+            for name in patches:
+                patch_list = patches[name]
+                for k in patch_list:
+                    if hasattr(patch_list[k], function_name):
+                        getattr(patch_list[k], function_name)(**arguments)
+        if "model_function_wrapper" in self.model_options:
+            wrap_func = self.model_options["model_function_wrapper"]
+            if hasattr(wrap_func, function_name):
+                getattr(wrap_func, function_name)(**arguments)
+
     def model_dtype(self):
         if hasattr(self.model, "get_dtype"):
             return self.model.get_dtype()
@@ -1062,6 +1083,7 @@ class ModelPatcher:
         return comfy.lora.calculate_weight(patches, weight, key, intermediate_dtype=intermediate_dtype)
 
     def cleanup(self):
+        self.model_patches_call_function(function_name="cleanup")
         self.clean_hooks()
         if hasattr(self.model, "current_patcher"):
             self.model.current_patcher = None

From 9642e4407b60b291744cc1d34501783cff6702e5 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Tue, 10 Mar 2026 21:09:35 -0700
Subject: [PATCH 143/194] Add pre attention and post input patches to qwen
 image model. (#12879)

---
 comfy/ldm/qwen_image/model.py | 35 +++++++++++++++++++++++++++++------
 1 file changed, 29 insertions(+), 6 deletions(-)

diff --git a/comfy/ldm/qwen_image/model.py b/comfy/ldm/qwen_image/model.py
index 6eb744286..0862f72f7 100644
--- a/comfy/ldm/qwen_image/model.py
+++ b/comfy/ldm/qwen_image/model.py
@@ -149,6 +149,9 @@ class Attention(nn.Module):
         seq_img = hidden_states.shape[1]
         seq_txt = encoder_hidden_states.shape[1]
 
+        transformer_patches = transformer_options.get("patches", {})
+        extra_options = transformer_options.copy()
+
         # Project and reshape to BHND format (batch, heads, seq, dim)
         img_query = self.to_q(hidden_states).view(batch_size, seq_img, self.heads, -1).transpose(1, 2).contiguous()
         img_key = self.to_k(hidden_states).view(batch_size, seq_img, self.heads, -1).transpose(1, 2).contiguous()
@@ -167,15 +170,22 @@ class Attention(nn.Module):
         joint_key = torch.cat([txt_key, img_key], dim=2)
         joint_value = torch.cat([txt_value, img_value], dim=2)
 
-        joint_query = apply_rope1(joint_query, image_rotary_emb)
-        joint_key = apply_rope1(joint_key, image_rotary_emb)
-
         if encoder_hidden_states_mask is not None:
             attn_mask = torch.zeros((batch_size, 1, seq_txt + seq_img), dtype=hidden_states.dtype, device=hidden_states.device)
             attn_mask[:, 0, :seq_txt] = encoder_hidden_states_mask
         else:
             attn_mask = None
 
+        extra_options["img_slice"] = [txt_query.shape[2], joint_query.shape[2]]
+        if "attn1_patch" in transformer_patches:
+            patch = transformer_patches["attn1_patch"]
+            for p in patch:
+                out = p(joint_query, joint_key, joint_value, pe=image_rotary_emb, attn_mask=encoder_hidden_states_mask, extra_options=extra_options)
+                joint_query, joint_key, joint_value, image_rotary_emb, encoder_hidden_states_mask = out.get("q", joint_query), out.get("k", joint_key), out.get("v", joint_value), out.get("pe", image_rotary_emb), out.get("attn_mask", encoder_hidden_states_mask)
+
+        joint_query = apply_rope1(joint_query, image_rotary_emb)
+        joint_key = apply_rope1(joint_key, image_rotary_emb)
+
         joint_hidden_states = optimized_attention_masked(joint_query, joint_key, joint_value, self.heads,
                                                          attn_mask, transformer_options=transformer_options,
                                                          skip_reshape=True)
@@ -444,6 +454,7 @@ class QwenImageTransformer2DModel(nn.Module):
 
         timestep_zero_index = None
         if ref_latents is not None:
+            ref_num_tokens = []
             h = 0
             w = 0
             index = 0
@@ -474,16 +485,16 @@ class QwenImageTransformer2DModel(nn.Module):
                 kontext, kontext_ids, _ = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset)
                 hidden_states = torch.cat([hidden_states, kontext], dim=1)
                 img_ids = torch.cat([img_ids, kontext_ids], dim=1)
+                ref_num_tokens.append(kontext.shape[1])
             if timestep_zero:
                 if index > 0:
                     timestep = torch.cat([timestep, timestep * 0], dim=0)
                     timestep_zero_index = num_embeds
+            transformer_options = transformer_options.copy()
+            transformer_options["reference_image_num_tokens"] = ref_num_tokens
 
         txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size) // 2, ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size) // 2))
         txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3)
-        ids = torch.cat((txt_ids, img_ids), dim=1)
-        image_rotary_emb = self.pe_embedder(ids).to(x.dtype).contiguous()
-        del ids, txt_ids, img_ids
 
         hidden_states = self.img_in(hidden_states)
         encoder_hidden_states = self.txt_norm(encoder_hidden_states)
@@ -495,6 +506,18 @@ class QwenImageTransformer2DModel(nn.Module):
         patches = transformer_options.get("patches", {})
         blocks_replace = patches_replace.get("dit", {})
 
+        if "post_input" in patches:
+            for p in patches["post_input"]:
+                out = p({"img": hidden_states, "txt": encoder_hidden_states, "img_ids": img_ids, "txt_ids": txt_ids, "transformer_options": transformer_options})
+                hidden_states = out["img"]
+                encoder_hidden_states = out["txt"]
+                img_ids = out["img_ids"]
+                txt_ids = out["txt_ids"]
+
+        ids = torch.cat((txt_ids, img_ids), dim=1)
+        image_rotary_emb = self.pe_embedder(ids).to(x.dtype).contiguous()
+        del ids, txt_ids, img_ids
+
         transformer_options["total_blocks"] = len(self.transformer_blocks)
         transformer_options["block_type"] = "double"
         for i, block in enumerate(self.transformer_blocks):

From 980621da83267beffcb84839a27101b7092256e7 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Wed, 11 Mar 2026 08:49:38 -0700
Subject: [PATCH 144/194] comfy-aimdo 0.2.10 (#12890)

Comfy Aimdo 0.2.10 fixes the aimdo allocator hook for legacy cudaMalloc
consumers. Some consumers of cudaMalloc assume implicit synchronization
built in closed source logic inside cuda. This is preserved by passing
through to cuda as-is and accouting after the fact as opposed to
integrating these hooks with Aimdos VMA based allocator.
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index bb58f8d01..89cd994e9 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -23,7 +23,7 @@ SQLAlchemy
 filelock
 av>=14.2.0
 comfy-kitchen>=0.2.7
-comfy-aimdo>=0.2.9
+comfy-aimdo>=0.2.10
 requests
 simpleeval>=1.0.0
 blake3

From 3365008dfe5a7a46cbe76d8ad0d7efb054617733 Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Wed, 11 Mar 2026 18:53:55 +0200
Subject: [PATCH 145/194] feat(api-nodes): add Reve Image nodes (#12848)

---
 comfy_api_nodes/apis/reve.py   |  68 ++++++
 comfy_api_nodes/nodes_reve.py  | 395 +++++++++++++++++++++++++++++++++
 comfy_api_nodes/util/client.py |  12 +-
 3 files changed, 474 insertions(+), 1 deletion(-)
 create mode 100644 comfy_api_nodes/apis/reve.py
 create mode 100644 comfy_api_nodes/nodes_reve.py

diff --git a/comfy_api_nodes/apis/reve.py b/comfy_api_nodes/apis/reve.py
new file mode 100644
index 000000000..c6b5a69d8
--- /dev/null
+++ b/comfy_api_nodes/apis/reve.py
@@ -0,0 +1,68 @@
+from pydantic import BaseModel, Field
+
+
+class RevePostprocessingOperation(BaseModel):
+    process: str = Field(..., description="The postprocessing operation: upscale or remove_background.")
+    upscale_factor: int | None = Field(
+        None,
+        description="Upscale factor (2, 3, or 4). Only used when process is upscale.",
+        ge=2,
+        le=4,
+    )
+
+
+class ReveImageCreateRequest(BaseModel):
+    prompt: str = Field(...)
+    aspect_ratio: str | None = Field(...)
+    version: str = Field(...)
+    test_time_scaling: int = Field(
+        ...,
+        description="If included, the model will spend more effort making better images. Values between 1 and 15.",
+        ge=1,
+        le=15,
+    )
+    postprocessing: list[RevePostprocessingOperation] | None = Field(
+        None, description="Optional postprocessing operations to apply after generation."
+    )
+
+
+class ReveImageEditRequest(BaseModel):
+    edit_instruction: str = Field(...)
+    reference_image: str = Field(..., description="A base64 encoded image to use as reference for the edit.")
+    aspect_ratio: str | None = Field(...)
+    version: str = Field(...)
+    test_time_scaling: int | None = Field(
+        ...,
+        description="If included, the model will spend more effort making better images. Values between 1 and 15.",
+        ge=1,
+        le=15,
+    )
+    postprocessing: list[RevePostprocessingOperation] | None = Field(
+        None, description="Optional postprocessing operations to apply after generation."
+    )
+
+
+class ReveImageRemixRequest(BaseModel):
+    prompt: str = Field(...)
+    reference_images: list[str] = Field(..., description="A list of 1-6 base64 encoded reference images.")
+    aspect_ratio: str | None = Field(...)
+    version: str = Field(...)
+    test_time_scaling: int | None = Field(
+        ...,
+        description="If included, the model will spend more effort making better images. Values between 1 and 15.",
+        ge=1,
+        le=15,
+    )
+    postprocessing: list[RevePostprocessingOperation] | None = Field(
+        None, description="Optional postprocessing operations to apply after generation."
+    )
+
+
+class ReveImageResponse(BaseModel):
+    image: str | None = Field(None, description="The base64 encoded image data.")
+    request_id: str | None = Field(None, description="A unique id for the request.")
+    credits_used: float | None = Field(None, description="The number of credits used for this request.")
+    version: str | None = Field(None, description="The specific model version used.")
+    content_violation: bool | None = Field(
+        None, description="Indicates whether the generated image violates the content policy."
+    )
diff --git a/comfy_api_nodes/nodes_reve.py b/comfy_api_nodes/nodes_reve.py
new file mode 100644
index 000000000..608d9f058
--- /dev/null
+++ b/comfy_api_nodes/nodes_reve.py
@@ -0,0 +1,395 @@
+from io import BytesIO
+
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.reve import (
+    ReveImageCreateRequest,
+    ReveImageEditRequest,
+    ReveImageRemixRequest,
+    RevePostprocessingOperation,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    bytesio_to_image_tensor,
+    sync_op_raw,
+    tensor_to_base64_string,
+    validate_string,
+)
+
+
+def _build_postprocessing(upscale: dict, remove_background: bool) -> list[RevePostprocessingOperation] | None:
+    ops = []
+    if upscale["upscale"] == "enabled":
+        ops.append(
+            RevePostprocessingOperation(
+                process="upscale",
+                upscale_factor=upscale["upscale_factor"],
+            )
+        )
+    if remove_background:
+        ops.append(RevePostprocessingOperation(process="remove_background"))
+    return ops or None
+
+
+def _postprocessing_inputs():
+    return [
+        IO.DynamicCombo.Input(
+            "upscale",
+            options=[
+                IO.DynamicCombo.Option("disabled", []),
+                IO.DynamicCombo.Option(
+                    "enabled",
+                    [
+                        IO.Int.Input(
+                            "upscale_factor",
+                            default=2,
+                            min=2,
+                            max=4,
+                            step=1,
+                            tooltip="Upscale factor (2x, 3x, or 4x).",
+                        ),
+                    ],
+                ),
+            ],
+            tooltip="Upscale the generated image. May add additional cost.",
+        ),
+        IO.Boolean.Input(
+            "remove_background",
+            default=False,
+            tooltip="Remove the background from the generated image. May add additional cost.",
+        ),
+    ]
+
+
+def _reve_price_extractor(headers: dict) -> float | None:
+    credits_used = headers.get("x-reve-credits-used")
+    if credits_used is not None:
+        return float(credits_used) / 524.48
+    return None
+
+
+def _reve_response_header_validator(headers: dict) -> None:
+    error_code = headers.get("x-reve-error-code")
+    if error_code:
+        raise ValueError(f"Reve API error: {error_code}")
+    if headers.get("x-reve-content-violation", "").lower() == "true":
+        raise ValueError("The generated image was flagged for content policy violation.")
+
+
+def _model_inputs(versions: list[str], aspect_ratios: list[str]):
+    return [
+        IO.DynamicCombo.Option(
+            version,
+            [
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=aspect_ratios,
+                    tooltip="Aspect ratio of the output image.",
+                ),
+                IO.Int.Input(
+                    "test_time_scaling",
+                    default=1,
+                    min=1,
+                    max=5,
+                    step=1,
+                    tooltip="Higher values produce better images but cost more credits.",
+                    advanced=True,
+                ),
+            ],
+        )
+        for version in versions
+    ]
+
+
+class ReveImageCreateNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ReveImageCreateNode",
+            display_name="Reve Image Create",
+            category="api node/image/Reve",
+            description="Generate images from text descriptions using Reve.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text description of the desired image. Maximum 2560 characters.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=_model_inputs(
+                        ["reve-create@20250915"],
+                        aspect_ratios=["3:2", "16:9", "9:16", "2:3", "4:3", "3:4", "1:1"],
+                    ),
+                    tooltip="Model version to use for generation.",
+                ),
+                *_postprocessing_inputs(),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.03432,"format":{"approximate":true,"note":"(base)"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        upscale: dict,
+        remove_background: bool,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=2560)
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(
+                path="/proxy/reve/v1/image/create",
+                method="POST",
+                headers={"Accept": "image/webp"},
+            ),
+            as_binary=True,
+            price_extractor=_reve_price_extractor,
+            response_header_validator=_reve_response_header_validator,
+            data=ReveImageCreateRequest(
+                prompt=prompt,
+                aspect_ratio=model["aspect_ratio"],
+                version=model["model"],
+                test_time_scaling=model["test_time_scaling"],
+                postprocessing=_build_postprocessing(upscale, remove_background),
+            ),
+        )
+        return IO.NodeOutput(bytesio_to_image_tensor(BytesIO(response)))
+
+
+class ReveImageEditNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ReveImageEditNode",
+            display_name="Reve Image Edit",
+            category="api node/image/Reve",
+            description="Edit images using natural language instructions with Reve.",
+            inputs=[
+                IO.Image.Input("image", tooltip="The image to edit."),
+                IO.String.Input(
+                    "edit_instruction",
+                    multiline=True,
+                    default="",
+                    tooltip="Text description of how to edit the image. Maximum 2560 characters.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=_model_inputs(
+                        ["reve-edit@20250915", "reve-edit-fast@20251030"],
+                        aspect_ratios=["auto", "16:9", "9:16", "3:2", "2:3", "4:3", "3:4", "1:1"],
+                    ),
+                    tooltip="Model version to use for editing.",
+                ),
+                *_postprocessing_inputs(),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["model"],
+                ),
+                expr="""
+                (
+                    $isFast := $contains(widgets.model, "fast");
+                    $base := $isFast ? 0.01001 : 0.0572;
+                    {"type": "usd", "usd": $base, "format": {"approximate": true, "note": "(base)"}}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        edit_instruction: str,
+        model: dict,
+        upscale: dict,
+        remove_background: bool,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(edit_instruction, min_length=1, max_length=2560)
+        tts = model["test_time_scaling"]
+        ar = model["aspect_ratio"]
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(
+                path="/proxy/reve/v1/image/edit",
+                method="POST",
+                headers={"Accept": "image/webp"},
+            ),
+            as_binary=True,
+            price_extractor=_reve_price_extractor,
+            response_header_validator=_reve_response_header_validator,
+            data=ReveImageEditRequest(
+                edit_instruction=edit_instruction,
+                reference_image=tensor_to_base64_string(image),
+                aspect_ratio=ar if ar != "auto" else None,
+                version=model["model"],
+                test_time_scaling=tts if tts and tts > 1 else None,
+                postprocessing=_build_postprocessing(upscale, remove_background),
+            ),
+        )
+        return IO.NodeOutput(bytesio_to_image_tensor(BytesIO(response)))
+
+
+class ReveImageRemixNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ReveImageRemixNode",
+            display_name="Reve Image Remix",
+            category="api node/image/Reve",
+            description="Combine reference images with text prompts to create new images using Reve.",
+            inputs=[
+                IO.Autogrow.Input(
+                    "reference_images",
+                    template=IO.Autogrow.TemplatePrefix(
+                        IO.Image.Input("image"),
+                        prefix="image_",
+                        min=1,
+                        max=6,
+                    ),
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text description of the desired image. "
+                    "May include XML img tags to reference specific images by index, "
+                    "e.g. <img>0</img>, <img>1</img>, etc.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=_model_inputs(
+                        ["reve-remix@20250915", "reve-remix-fast@20251030"],
+                        aspect_ratios=["auto", "16:9", "9:16", "3:2", "2:3", "4:3", "3:4", "1:1"],
+                    ),
+                    tooltip="Model version to use for remixing.",
+                ),
+                *_postprocessing_inputs(),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["model"],
+                ),
+                expr="""
+                (
+                    $isFast := $contains(widgets.model, "fast");
+                    $base := $isFast ? 0.01001 : 0.0572;
+                    {"type": "usd", "usd": $base, "format": {"approximate": true, "note": "(base)"}}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        reference_images: IO.Autogrow.Type,
+        prompt: str,
+        model: dict,
+        upscale: dict,
+        remove_background: bool,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=2560)
+        if not reference_images:
+            raise ValueError("At least one reference image is required.")
+        ref_base64_list = []
+        for key in reference_images:
+            ref_base64_list.append(tensor_to_base64_string(reference_images[key]))
+        if len(ref_base64_list) > 6:
+            raise ValueError("Maximum 6 reference images are allowed.")
+        tts = model["test_time_scaling"]
+        ar = model["aspect_ratio"]
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(
+                path="/proxy/reve/v1/image/remix",
+                method="POST",
+                headers={"Accept": "image/webp"},
+            ),
+            as_binary=True,
+            price_extractor=_reve_price_extractor,
+            response_header_validator=_reve_response_header_validator,
+            data=ReveImageRemixRequest(
+                prompt=prompt,
+                reference_images=ref_base64_list,
+                aspect_ratio=ar if ar != "auto" else None,
+                version=model["model"],
+                test_time_scaling=tts if tts and tts > 1 else None,
+                postprocessing=_build_postprocessing(upscale, remove_background),
+            ),
+        )
+        return IO.NodeOutput(bytesio_to_image_tensor(BytesIO(response)))
+
+
+class ReveExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            ReveImageCreateNode,
+            ReveImageEditNode,
+            ReveImageRemixNode,
+        ]
+
+
+async def comfy_entrypoint() -> ReveExtension:
+    return ReveExtension()
diff --git a/comfy_api_nodes/util/client.py b/comfy_api_nodes/util/client.py
index 79ffb77c1..9d730b81a 100644
--- a/comfy_api_nodes/util/client.py
+++ b/comfy_api_nodes/util/client.py
@@ -67,6 +67,7 @@ class _RequestConfig:
     progress_origin_ts: float | None = None
     price_extractor: Callable[[dict[str, Any]], float | None] | None = None
     is_rate_limited: Callable[[int, Any], bool] | None = None
+    response_header_validator: Callable[[dict[str, str]], None] | None = None
 
 
 @dataclass
@@ -202,11 +203,13 @@ async def sync_op_raw(
     monitor_progress: bool = True,
     max_retries_on_rate_limit: int = 16,
     is_rate_limited: Callable[[int, Any], bool] | None = None,
+    response_header_validator: Callable[[dict[str, str]], None] | None = None,
 ) -> dict[str, Any] | bytes:
     """
     Make a single network request.
       - If as_binary=False (default): returns JSON dict (or {'_raw': '<text>'} if non-JSON).
       - If as_binary=True: returns bytes.
+      - response_header_validator: optional callback receiving response headers dict
     """
     if isinstance(data, BaseModel):
         data = data.model_dump(exclude_none=True)
@@ -232,6 +235,7 @@ async def sync_op_raw(
         price_extractor=price_extractor,
         max_retries_on_rate_limit=max_retries_on_rate_limit,
         is_rate_limited=is_rate_limited,
+        response_header_validator=response_header_validator,
     )
     return await _request_base(cfg, expect_binary=as_binary)
 
@@ -769,6 +773,12 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool):
                                     cfg.node_cls, cfg.wait_label, int(now - start_time), cfg.estimated_total
                                 )
                     bytes_payload = bytes(buff)
+                    resp_headers = {k.lower(): v for k, v in resp.headers.items()}
+                    if cfg.price_extractor:
+                        with contextlib.suppress(Exception):
+                            extracted_price = cfg.price_extractor(resp_headers)
+                    if cfg.response_header_validator:
+                        cfg.response_header_validator(resp_headers)
                     operation_succeeded = True
                     final_elapsed_seconds = int(time.monotonic() - start_time)
                     request_logger.log_request_response(
@@ -776,7 +786,7 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool):
                         request_method=method,
                         request_url=url,
                         response_status_code=resp.status,
-                        response_headers=dict(resp.headers),
+                        response_headers=resp_headers,
                         response_content=bytes_payload,
                     )
                     return bytes_payload

From 4f4f8659c205069f74da8ac47378a5b1c0e142ca Mon Sep 17 00:00:00 2001
From: Adi Borochov <58855640+adiborochov@users.noreply.github.com>
Date: Wed, 11 Mar 2026 19:04:13 +0200
Subject: [PATCH 146/194] fix: guard torch.AcceleratorError for compatibility
 with torch < 2.8.0 (#12874)

* fix: guard torch.AcceleratorError for compatibility with torch < 2.8.0

torch.AcceleratorError was introduced in PyTorch 2.8.0. Accessing it
directly raises AttributeError on older versions. Use a try/except
fallback at module load time, consistent with the existing pattern used
for OOM_EXCEPTION.


* fix: address review feedback for AcceleratorError compat

- Fall back to RuntimeError instead of type(None) for ACCELERATOR_ERROR,
  consistent with OOM_EXCEPTION fallback pattern and valid for except clauses
- Add "out of memory" message introspection for RuntimeError fallback case
- Use RuntimeError directly in discard_cuda_async_error except clause
---------
---
 comfy/model_management.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 81550c790..81c89b180 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -270,10 +270,15 @@ try:
 except:
     OOM_EXCEPTION = Exception
 
+try:
+    ACCELERATOR_ERROR = torch.AcceleratorError
+except AttributeError:
+    ACCELERATOR_ERROR = RuntimeError
+
 def is_oom(e):
     if isinstance(e, OOM_EXCEPTION):
         return True
-    if isinstance(e, torch.AcceleratorError) and getattr(e, 'error_code', None) == 2:
+    if isinstance(e, ACCELERATOR_ERROR) and (getattr(e, 'error_code', None) == 2 or "out of memory" in str(e).lower()):
         discard_cuda_async_error()
         return True
     return False
@@ -1275,7 +1280,7 @@ def discard_cuda_async_error():
         b = torch.tensor([1], dtype=torch.uint8, device=get_torch_device())
         _ = a + b
         synchronize()
-    except torch.AcceleratorError:
+    except RuntimeError:
         #Dump it! We already know about it from the synchronous return
         pass
 

From f6274c06b4e7bce8adbc1c60ae5a4c168825a614 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Wed, 11 Mar 2026 13:37:31 -0700
Subject: [PATCH 147/194] Fix issue with batch_size > 1 on some models.
 (#12892)

---
 comfy/ldm/flux/layers.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfy/ldm/flux/layers.py b/comfy/ldm/flux/layers.py
index e20d498f8..e28d704b4 100644
--- a/comfy/ldm/flux/layers.py
+++ b/comfy/ldm/flux/layers.py
@@ -144,9 +144,9 @@ def apply_mod(tensor, m_mult, m_add=None, modulation_dims=None):
             return tensor * m_mult
     else:
         for d in modulation_dims:
-            tensor[:, d[0]:d[1]] *= m_mult[:, d[2]]
+            tensor[:, d[0]:d[1]] *= m_mult[:, d[2]:d[2] + 1]
             if m_add is not None:
-                tensor[:, d[0]:d[1]] += m_add[:, d[2]]
+                tensor[:, d[0]:d[1]] += m_add[:, d[2]:d[2] + 1]
         return tensor
 
 

From abc87d36693b007bdbdab5ee753ccea6326acb34 Mon Sep 17 00:00:00 2001
From: Comfy Org PR Bot <snomiao+comfy-pr@gmail.com>
Date: Thu, 12 Mar 2026 06:04:51 +0900
Subject: [PATCH 148/194] Bump comfyui-frontend-package to 1.41.15 (#12891)

---------

Co-authored-by: Alexander Brown <DrJKL0424@gmail.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 89cd994e9..ffa5fa376 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-comfyui-frontend-package==1.39.19
+comfyui-frontend-package==1.41.15
 comfyui-workflow-templates==0.9.18
 comfyui-embedded-docs==0.4.3
 torch

From 9ce4c3dd87c9c77dfe0371045fa920ce55e08973 Mon Sep 17 00:00:00 2001
From: Comfy Org PR Bot <snomiao+comfy-pr@gmail.com>
Date: Thu, 12 Mar 2026 10:16:30 +0900
Subject: [PATCH 149/194] Bump comfyui-frontend-package to 1.41.16 (#12894)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index ffa5fa376..2272d121a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-comfyui-frontend-package==1.41.15
+comfyui-frontend-package==1.41.16
 comfyui-workflow-templates==0.9.18
 comfyui-embedded-docs==0.4.3
 torch

From 8f9ea495713d4565dfe564e0c06f362bd627f902 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Wed, 11 Mar 2026 21:17:31 -0700
Subject: [PATCH 150/194] Bump comfy-kitchen version to 0.2.8 (#12895)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 2272d121a..96cd0254f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -22,7 +22,7 @@ alembic
 SQLAlchemy
 filelock
 av>=14.2.0
-comfy-kitchen>=0.2.7
+comfy-kitchen>=0.2.8
 comfy-aimdo>=0.2.10
 requests
 simpleeval>=1.0.0

From 44f1246c899ed188759f799dbd00c31def289114 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Thu, 12 Mar 2026 08:30:50 -0700
Subject: [PATCH 151/194] Support flux 2 klein kv cache model: Use the
 FluxKVCache node. (#12905)

---
 comfy/ldm/flux/model.py    | 76 ++++++++++++++++++++++++++++++++------
 comfy_extras/nodes_flux.py | 64 ++++++++++++++++++++++++++++++++
 2 files changed, 129 insertions(+), 11 deletions(-)

diff --git a/comfy/ldm/flux/model.py b/comfy/ldm/flux/model.py
index 00f12c031..8e7912e6d 100644
--- a/comfy/ldm/flux/model.py
+++ b/comfy/ldm/flux/model.py
@@ -44,6 +44,22 @@ class FluxParams:
     txt_norm: bool = False
 
 
+def invert_slices(slices, length):
+    sorted_slices = sorted(slices)
+    result = []
+    current = 0
+
+    for start, end in sorted_slices:
+        if current < start:
+            result.append((current, start))
+        current = max(current, end)
+
+    if current < length:
+        result.append((current, length))
+
+    return result
+
+
 class Flux(nn.Module):
     """
     Transformer model for flow matching on sequences.
@@ -138,6 +154,7 @@ class Flux(nn.Module):
         y: Tensor,
         guidance: Tensor = None,
         control = None,
+        timestep_zero_index=None,
         transformer_options={},
         attn_mask: Tensor = None,
     ) -> Tensor:
@@ -164,10 +181,6 @@ class Flux(nn.Module):
             txt = self.txt_norm(txt)
         txt = self.txt_in(txt)
 
-        vec_orig = vec
-        if self.params.global_modulation:
-            vec = (self.double_stream_modulation_img(vec_orig), self.double_stream_modulation_txt(vec_orig))
-
         if "post_input" in patches:
             for p in patches["post_input"]:
                 out = p({"img": img, "txt": txt, "img_ids": img_ids, "txt_ids": txt_ids, "transformer_options": transformer_options})
@@ -182,6 +195,24 @@ class Flux(nn.Module):
         else:
             pe = None
 
+        vec_orig = vec
+        txt_vec = vec
+        extra_kwargs = {}
+        if timestep_zero_index is not None:
+            modulation_dims = []
+            batch = vec.shape[0] // 2
+            vec_orig = vec_orig.reshape(2, batch, vec.shape[1]).movedim(0, 1)
+            invert = invert_slices(timestep_zero_index, img.shape[1])
+            for s in invert:
+                modulation_dims.append((s[0], s[1], 0))
+            for s in timestep_zero_index:
+                modulation_dims.append((s[0], s[1], 1))
+            extra_kwargs["modulation_dims_img"] = modulation_dims
+            txt_vec = vec[:batch]
+
+        if self.params.global_modulation:
+            vec = (self.double_stream_modulation_img(vec_orig), self.double_stream_modulation_txt(txt_vec))
+
         blocks_replace = patches_replace.get("dit", {})
         transformer_options["total_blocks"] = len(self.double_blocks)
         transformer_options["block_type"] = "double"
@@ -195,7 +226,8 @@ class Flux(nn.Module):
                                                    vec=args["vec"],
                                                    pe=args["pe"],
                                                    attn_mask=args.get("attn_mask"),
-                                                   transformer_options=args.get("transformer_options"))
+                                                   transformer_options=args.get("transformer_options"),
+                                                   **extra_kwargs)
                     return out
 
                 out = blocks_replace[("double_block", i)]({"img": img,
@@ -213,7 +245,8 @@ class Flux(nn.Module):
                                  vec=vec,
                                  pe=pe,
                                  attn_mask=attn_mask,
-                                 transformer_options=transformer_options)
+                                 transformer_options=transformer_options,
+                                 **extra_kwargs)
 
             if control is not None: # Controlnet
                 control_i = control.get("input")
@@ -230,6 +263,12 @@ class Flux(nn.Module):
         if self.params.global_modulation:
             vec, _ = self.single_stream_modulation(vec_orig)
 
+        extra_kwargs = {}
+        if timestep_zero_index is not None:
+            lambda a: 0 if a == 0 else a + txt.shape[1]
+            modulation_dims_combined = list(map(lambda x: (0 if x[0] == 0 else x[0] + txt.shape[1], x[1] + txt.shape[1], x[2]), modulation_dims))
+            extra_kwargs["modulation_dims"] = modulation_dims_combined
+
         transformer_options["total_blocks"] = len(self.single_blocks)
         transformer_options["block_type"] = "single"
         transformer_options["img_slice"] = [txt.shape[1], img.shape[1]]
@@ -242,7 +281,8 @@ class Flux(nn.Module):
                                        vec=args["vec"],
                                        pe=args["pe"],
                                        attn_mask=args.get("attn_mask"),
-                                       transformer_options=args.get("transformer_options"))
+                                       transformer_options=args.get("transformer_options"),
+                                       **extra_kwargs)
                     return out
 
                 out = blocks_replace[("single_block", i)]({"img": img,
@@ -253,7 +293,7 @@ class Flux(nn.Module):
                                                           {"original_block": block_wrap})
                 img = out["img"]
             else:
-                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options)
+                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options, **extra_kwargs)
 
             if control is not None: # Controlnet
                 control_o = control.get("output")
@@ -264,7 +304,11 @@ class Flux(nn.Module):
 
         img = img[:, txt.shape[1] :, ...]
 
-        img = self.final_layer(img, vec_orig)  # (N, T, patch_size ** 2 * out_channels)
+        extra_kwargs = {}
+        if timestep_zero_index is not None:
+            extra_kwargs["modulation_dims"] = modulation_dims
+
+        img = self.final_layer(img, vec_orig, **extra_kwargs)  # (N, T, patch_size ** 2 * out_channels)
         return img
 
     def process_img(self, x, index=0, h_offset=0, w_offset=0, transformer_options={}):
@@ -312,13 +356,16 @@ class Flux(nn.Module):
         w_len = ((w_orig + (patch_size // 2)) // patch_size)
         img, img_ids = self.process_img(x, transformer_options=transformer_options)
         img_tokens = img.shape[1]
+        timestep_zero_index = None
         if ref_latents is not None:
+            ref_num_tokens = []
             h = 0
             w = 0
             index = 0
             ref_latents_method = kwargs.get("ref_latents_method", self.params.default_ref_method)
+            timestep_zero = ref_latents_method == "index_timestep_zero"
             for ref in ref_latents:
-                if ref_latents_method == "index":
+                if ref_latents_method in ("index", "index_timestep_zero"):
                     index += self.params.ref_index_scale
                     h_offset = 0
                     w_offset = 0
@@ -342,6 +389,13 @@ class Flux(nn.Module):
                 kontext, kontext_ids = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset)
                 img = torch.cat([img, kontext], dim=1)
                 img_ids = torch.cat([img_ids, kontext_ids], dim=1)
+                ref_num_tokens.append(kontext.shape[1])
+            if timestep_zero:
+                if index > 0:
+                    timestep = torch.cat([timestep, timestep * 0], dim=0)
+                    timestep_zero_index = [[img_tokens, img_ids.shape[1]]]
+            transformer_options = transformer_options.copy()
+            transformer_options["reference_image_num_tokens"] = ref_num_tokens
 
         txt_ids = torch.zeros((bs, context.shape[1], len(self.params.axes_dim)), device=x.device, dtype=torch.float32)
 
@@ -349,6 +403,6 @@ class Flux(nn.Module):
             for i in self.params.txt_ids_dims:
                 txt_ids[:, :, i] = torch.linspace(0, context.shape[1] - 1, steps=context.shape[1], device=x.device, dtype=torch.float32)
 
-        out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None))
+        out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, timestep_zero_index=timestep_zero_index, transformer_options=transformer_options, attn_mask=kwargs.get("attention_mask", None))
         out = out[:, :img_tokens]
         return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=self.patch_size, pw=self.patch_size)[:,:,:h_orig,:w_orig]
diff --git a/comfy_extras/nodes_flux.py b/comfy_extras/nodes_flux.py
index fe9552022..c366d0d5b 100644
--- a/comfy_extras/nodes_flux.py
+++ b/comfy_extras/nodes_flux.py
@@ -6,6 +6,7 @@ import comfy.model_management
 import torch
 import math
 import nodes
+import comfy.ldm.flux.math
 
 class CLIPTextEncodeFlux(io.ComfyNode):
     @classmethod
@@ -231,6 +232,68 @@ class Flux2Scheduler(io.ComfyNode):
         sigmas = get_schedule(steps, round(seq_len))
         return io.NodeOutput(sigmas)
 
+class KV_Attn_Input:
+    def __init__(self):
+        self.cache = {}
+
+    def __call__(self, q, k, v, extra_options, **kwargs):
+        reference_image_num_tokens = extra_options.get("reference_image_num_tokens", [])
+        if len(reference_image_num_tokens) == 0:
+            return {}
+
+        ref_toks = sum(reference_image_num_tokens)
+        cache_key = "{}_{}".format(extra_options["block_type"], extra_options["block_index"])
+        if cache_key in self.cache:
+            kk, vv = self.cache[cache_key]
+            self.set_cache = False
+            return {"q": q, "k": torch.cat((k, kk), dim=2), "v": torch.cat((v, vv), dim=2)}
+
+        self.cache[cache_key] = (k[:, :, -ref_toks:], v[:, :, -ref_toks:])
+        self.set_cache = True
+        return {"q": q, "k": k, "v": v}
+
+    def cleanup(self):
+        self.cache = {}
+
+
+class FluxKVCache(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="FluxKVCache",
+            display_name="Flux KV Cache",
+            description="Enables KV Cache optimization for reference images on Flux family models.",
+            category="",
+            is_experimental=True,
+            inputs=[
+                io.Model.Input("model", tooltip="The model to use KV Cache on."),
+            ],
+            outputs=[
+                io.Model.Output(tooltip="The patched model with KV Cache enabled."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model: io.Model.Type) -> io.NodeOutput:
+        m = model.clone()
+        input_patch_obj = KV_Attn_Input()
+
+        def model_input_patch(inputs):
+            if len(input_patch_obj.cache) > 0:
+                ref_image_tokens = sum(inputs["transformer_options"].get("reference_image_num_tokens", []))
+                if ref_image_tokens > 0:
+                    img = inputs["img"]
+                    inputs["img"] = img[:, :-ref_image_tokens]
+            return inputs
+
+        m.set_model_attn1_patch(input_patch_obj)
+        m.set_model_post_input_patch(model_input_patch)
+        if hasattr(model.model.diffusion_model, "params"):
+            m.add_object_patch("diffusion_model.params.default_ref_method", "index_timestep_zero")
+        else:
+            m.add_object_patch("diffusion_model.default_ref_method", "index_timestep_zero")
+
+        return io.NodeOutput(m)
 
 class FluxExtension(ComfyExtension):
     @override
@@ -243,6 +306,7 @@ class FluxExtension(ComfyExtension):
             FluxKontextMultiReferenceLatentMethod,
             EmptyFlux2LatentImage,
             Flux2Scheduler,
+            FluxKVCache,
         ]
 
 

From 73d9599495e45c22ef3672176f34945deeea5444 Mon Sep 17 00:00:00 2001
From: Terry Jia <terryjia88@gmail.com>
Date: Thu, 12 Mar 2026 09:55:29 -0700
Subject: [PATCH 152/194] add painter node (#12294)

* add painter node

* use io.Color

* code improve

---------

Co-authored-by: guill <jacob.e.segal@gmail.com>
---
 comfy_extras/nodes_painter.py | 127 ++++++++++++++++++++++++++++++++++
 nodes.py                      |   1 +
 2 files changed, 128 insertions(+)
 create mode 100644 comfy_extras/nodes_painter.py

diff --git a/comfy_extras/nodes_painter.py b/comfy_extras/nodes_painter.py
new file mode 100644
index 000000000..b9ecdf5ea
--- /dev/null
+++ b/comfy_extras/nodes_painter.py
@@ -0,0 +1,127 @@
+from __future__ import annotations
+
+import hashlib
+import os
+
+import numpy as np
+import torch
+from PIL import Image
+
+import folder_paths
+import node_helpers
+from comfy_api.latest import ComfyExtension, io, UI
+from typing_extensions import override
+
+
+def hex_to_rgb(hex_color: str) -> tuple[float, float, float]:
+    hex_color = hex_color.lstrip("#")
+    if len(hex_color) != 6:
+        return (0.0, 0.0, 0.0)
+    r = int(hex_color[0:2], 16) / 255.0
+    g = int(hex_color[2:4], 16) / 255.0
+    b = int(hex_color[4:6], 16) / 255.0
+    return (r, g, b)
+
+
+class PainterNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="Painter",
+            display_name="Painter",
+            category="image",
+            inputs=[
+                io.Image.Input(
+                    "image",
+                    optional=True,
+                    tooltip="Optional base image to paint over",
+                ),
+                io.String.Input(
+                    "mask",
+                    default="",
+                    socketless=True,
+                    extra_dict={"widgetType": "PAINTER", "image_upload": True},
+                ),
+                io.Int.Input(
+                    "width",
+                    default=512,
+                    min=64,
+                    max=4096,
+                    step=64,
+                    socketless=True,
+                    extra_dict={"hidden": True},
+                ),
+                io.Int.Input(
+                    "height",
+                    default=512,
+                    min=64,
+                    max=4096,
+                    step=64,
+                    socketless=True,
+                    extra_dict={"hidden": True},
+                ),
+                io.Color.Input("bg_color", default="#000000"),
+            ],
+            outputs=[
+                io.Image.Output("IMAGE"),
+                io.Mask.Output("MASK"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, mask, width, height, bg_color="#000000", image=None) -> io.NodeOutput:
+        if image is not None:
+            base_image = image[:1]
+            h, w = base_image.shape[1], base_image.shape[2]
+        else:
+            h, w = height, width
+            r, g, b = hex_to_rgb(bg_color)
+            base_image = torch.zeros((1, h, w, 3), dtype=torch.float32)
+            base_image[0, :, :, 0] = r
+            base_image[0, :, :, 1] = g
+            base_image[0, :, :, 2] = b
+
+        if mask and mask.strip():
+            mask_path = folder_paths.get_annotated_filepath(mask)
+            painter_img = node_helpers.pillow(Image.open, mask_path)
+            painter_img = painter_img.convert("RGBA")
+
+            if painter_img.size != (w, h):
+                painter_img = painter_img.resize((w, h), Image.LANCZOS)
+
+            painter_np = np.array(painter_img).astype(np.float32) / 255.0
+            painter_rgb = painter_np[:, :, :3]
+            painter_alpha = painter_np[:, :, 3:4]
+
+            mask_tensor = torch.from_numpy(painter_np[:, :, 3]).unsqueeze(0)
+
+            base_np = base_image[0].cpu().numpy()
+            composited = painter_rgb * painter_alpha + base_np * (1.0 - painter_alpha)
+            out_image = torch.from_numpy(composited).unsqueeze(0)
+        else:
+            mask_tensor = torch.zeros((1, h, w), dtype=torch.float32)
+            out_image = base_image
+
+        return io.NodeOutput(out_image, mask_tensor, ui=UI.PreviewImage(out_image))
+
+    @classmethod
+    def fingerprint_inputs(cls, mask, width, height, bg_color="#000000", image=None):
+        if mask and mask.strip():
+            mask_path = folder_paths.get_annotated_filepath(mask)
+            if os.path.exists(mask_path):
+                m = hashlib.sha256()
+                with open(mask_path, "rb") as f:
+                    m.update(f.read())
+                return m.digest().hex()
+        return ""
+
+
+
+class PainterExtension(ComfyExtension):
+    @override
+    async def get_node_list(self):
+        return [PainterNode]
+
+
+async def comfy_entrypoint():
+    return PainterExtension()
diff --git a/nodes.py b/nodes.py
index 0ef23b640..eb63f9d44 100644
--- a/nodes.py
+++ b/nodes.py
@@ -2450,6 +2450,7 @@ async def init_builtin_extra_nodes():
         "nodes_nag.py",
         "nodes_sdpose.py",
         "nodes_math.py",
+        "nodes_painter.py",
     ]
 
     import_failed = []

From 3fa8c5686dc86fe4e63ad3ca84d71524792a17b1 Mon Sep 17 00:00:00 2001
From: Terry Jia <terryjia88@gmail.com>
Date: Thu, 12 Mar 2026 10:14:28 -0700
Subject: [PATCH 153/194] fix: use frontend-compatible format for Float
 gradient_stops (#12789)

Co-authored-by: guill <jacob.e.segal@gmail.com>
Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
---
 comfy/comfy_types/node_typing.py | 4 ++--
 comfy_api/latest/_io.py          | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/comfy/comfy_types/node_typing.py b/comfy/comfy_types/node_typing.py
index 92b1acbd5..57126fa4a 100644
--- a/comfy/comfy_types/node_typing.py
+++ b/comfy/comfy_types/node_typing.py
@@ -176,8 +176,8 @@ class InputTypeOptions(TypedDict):
     """COMBO type only. Specifies the configuration for a multi-select widget.
     Available after ComfyUI frontend v1.13.4
     https://github.com/Comfy-Org/ComfyUI_frontend/pull/2987"""
-    gradient_stops: NotRequired[list[list[float]]]
-    """Gradient color stops for gradientslider display mode. Each stop is [offset, r, g, b] (``FLOAT``)."""
+    gradient_stops: NotRequired[list[dict]]
+    """Gradient color stops for gradientslider display mode. Each stop is {"offset": float, "color": [r, g, b]}."""
 
 
 class HiddenInputTypeDict(TypedDict):
diff --git a/comfy_api/latest/_io.py b/comfy_api/latest/_io.py
index 050031dc0..7ca8f4e0c 100644
--- a/comfy_api/latest/_io.py
+++ b/comfy_api/latest/_io.py
@@ -297,7 +297,7 @@ class Float(ComfyTypeIO):
         '''Float input.'''
         def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                     default: float=None, min: float=None, max: float=None, step: float=None, round: float=None,
-                    display_mode: NumberDisplay=None, gradient_stops: list[list[float]]=None,
+                    display_mode: NumberDisplay=None, gradient_stops: list[dict]=None,
                     socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
             super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link, advanced)
             self.min = min

From 712411d53919350ae5050cbdf7ed60fcc2b52cda Mon Sep 17 00:00:00 2001
From: ComfyUI Wiki <contact@comfyui-wiki.com>
Date: Fri, 13 Mar 2026 03:16:54 +0800
Subject: [PATCH 154/194] chore: update workflow templates to v0.9.21 (#12908)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 96cd0254f..a2e53671e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.41.16
-comfyui-workflow-templates==0.9.18
+comfyui-workflow-templates==0.9.21
 comfyui-embedded-docs==0.4.3
 torch
 torchsde

From 47e1e316c580ce6bf264cb069bffc10a50d3f167 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Thu, 12 Mar 2026 13:54:38 -0700
Subject: [PATCH 155/194] Lower kv cache memory usage. (#12909)

---
 comfy_extras/nodes_flux.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy_extras/nodes_flux.py b/comfy_extras/nodes_flux.py
index c366d0d5b..3a23c7d04 100644
--- a/comfy_extras/nodes_flux.py
+++ b/comfy_extras/nodes_flux.py
@@ -248,7 +248,7 @@ class KV_Attn_Input:
             self.set_cache = False
             return {"q": q, "k": torch.cat((k, kk), dim=2), "v": torch.cat((v, vv), dim=2)}
 
-        self.cache[cache_key] = (k[:, :, -ref_toks:], v[:, :, -ref_toks:])
+        self.cache[cache_key] = (k[:, :, -ref_toks:].clone(), v[:, :, -ref_toks:].clone())
         self.set_cache = True
         return {"q": q, "k": k, "v": v}
 

From 8d9faaa181b9089cf8e4e00284443ef5c3405a12 Mon Sep 17 00:00:00 2001
From: Christian Byrne <cbyrne@comfy.org>
Date: Thu, 12 Mar 2026 15:14:59 -0700
Subject: [PATCH 156/194] Update requirements.txt (#12910)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index a2e53671e..511c62fee 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-comfyui-frontend-package==1.41.16
+comfyui-frontend-package==1.41.18
 comfyui-workflow-templates==0.9.21
 comfyui-embedded-docs==0.4.3
 torch

From af7b4a921d7abab7c852d7b5febb654be6e57eba Mon Sep 17 00:00:00 2001
From: Deep Mehta <42841935+deepme987@users.noreply.github.com>
Date: Thu, 12 Mar 2026 16:09:07 -0700
Subject: [PATCH 157/194] feat: Add CacheProvider API for external distributed
 caching (#12056)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* feat: Add CacheProvider API for external distributed caching

Introduces a public API for external cache providers, enabling distributed
caching across multiple ComfyUI instances (e.g., Kubernetes pods).

New files:
- comfy_execution/cache_provider.py: CacheProvider ABC, CacheContext/CacheValue
  dataclasses, thread-safe provider registry, serialization utilities

Modified files:
- comfy_execution/caching.py: Add provider hooks to BasicCache (_notify_providers_store,
  _check_providers_lookup), subcache exclusion, prompt ID propagation
- execution.py: Add prompt lifecycle hooks (on_prompt_start/on_prompt_end) to
  PromptExecutor, set _current_prompt_id on caches

Key features:
- Local-first caching (check local before external for performance)
- NaN detection to prevent incorrect external cache hits
- Subcache exclusion (ephemeral subgraph results not cached externally)
- Thread-safe provider snapshot caching
- Graceful error handling (provider errors logged, never break execution)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* fix: use deterministic hash for cache keys instead of pickle

Pickle serialization is NOT deterministic across Python sessions due
to hash randomization affecting frozenset iteration order. This causes
distributed caching to fail because different pods compute different
hashes for identical cache keys.

Fix: Use _canonicalize() + JSON serialization which ensures deterministic
ordering regardless of Python's hash randomization.

This is critical for cross-pod cache key consistency in Kubernetes
deployments.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* test: add unit tests for CacheProvider API

- Add comprehensive tests for _canonicalize deterministic ordering
- Add tests for serialize_cache_key hash consistency
- Add tests for contains_nan utility
- Add tests for estimate_value_size
- Add tests for provider registry (register, unregister, clear)
- Move json import to top-level (fix inline import)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* style: remove unused imports in test_cache_provider.py

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* fix: move _torch_available before usage and use importlib.util.find_spec

Fixes ruff F821 (undefined name) and F401 (unused import) errors.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* fix: use hashable types in frozenset test and add dict test

Frozensets can only contain hashable types, so use nested frozensets
instead of dicts. Added separate test for dict handling via serialize_cache_key.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* refactor: expose CacheProvider API via comfy_api.latest.Caching

- Add Caching class to comfy_api/latest/__init__.py that re-exports
  from comfy_execution.cache_provider (source of truth)
- Fix docstring: "Skip large values" instead of "Skip small values"
  (small compute-heavy values are good cache targets)
- Maintain backward compatibility: comfy_execution.cache_provider
  imports still work

Usage:
    from comfy_api.latest import Caching

    class MyProvider(Caching.CacheProvider):
        def on_lookup(self, context): ...
        def on_store(self, context, value): ...

    Caching.register_provider(MyProvider())

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* docs: clarify should_cache filtering criteria

Change docstring from "Skip large values" to "Skip if download time > compute time"
which better captures the cost/benefit tradeoff for external caching.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* docs: make should_cache docstring implementation-agnostic

Remove prescriptive filtering suggestions - let implementations
decide their own caching logic based on their use case.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* feat: add optional ui field to CacheValue

- Add ui field to CacheValue dataclass (default None)
- Pass ui when creating CacheValue for external providers
- Use result.ui (or default {}) when returning from external cache lookup

This allows external cache implementations to store/retrieve UI data
if desired, while remaining optional for implementations that skip it.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* refactor: rename _is_cacheable_value to _is_external_cacheable_value

Clearer name since objects are also cached locally - this specifically
checks for external caching eligibility.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* refactor: async CacheProvider API + reduce public surface

- Make on_lookup/on_store async on CacheProvider ABC
- Simplify CacheContext: replace cache_key + cache_key_bytes with
  cache_key_hash (str hex digest)
- Make registry/utility functions internal (_prefix)
- Trim comfy_api.latest.Caching exports to core API only
- Make cache get/set async throughout caching.py hierarchy
- Use asyncio.create_task for fire-and-forget on_store
- Add NaN gating before provider calls in Core
- Add await to 5 cache call sites in execution.py

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: remove unused imports (ruff) and update tests for internal API

- Remove unused CacheContext and _serialize_cache_key imports from
  caching.py (now handled by _build_context helper)
- Update test_cache_provider.py to use _-prefixed internal names
- Update tests for new CacheContext.cache_key_hash field (str)
- Make MockCacheProvider methods async to match ABC

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: address coderabbit review feedback

- Add try/except to _build_context, return None when hash fails
- Return None from _serialize_cache_key on total failure (no id()-based fallback)
- Replace hex-like test literal with non-secret placeholder

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: use _-prefixed imports in _notify_prompt_lifecycle

The lifecycle notification method was importing the old non-prefixed
names (has_cache_providers, get_cache_providers, logger) which no
longer exist after the API cleanup.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: add sync get_local/set_local for graph traversal

ExecutionList in graph.py calls output_cache.get() and .set() from
sync methods (is_cached, cache_link, get_cache). These cannot await
the now-async get/set. Add get_local/set_local that bypass external
providers and only access the local dict — which is all graph
traversal needs.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* chore: remove cloud-specific language from cache provider API

Make all docstrings and comments generic for the OSS codebase.
Remove references to Kubernetes, Redis, GCS, pods, and other
infrastructure-specific terminology.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* style: align documentation with codebase conventions

Strip verbose docstrings and section banners to match existing minimal
documentation style used throughout the codebase.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: add usage example to Caching class, remove pickle fallback

- Add docstring with usage example to Caching class matching the
  convention used by sibling APIs (Execution.set_progress, ComfyExtension)
- Remove non-deterministic pickle fallback from _serialize_cache_key;
  return None on JSON failure instead of producing unretrievable hashes
- Move cache_provider imports to top of execution.py (no circular dep)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* refactor: move public types to comfy_api, eager provider snapshot

Address review feedback:
- Move CacheProvider/CacheContext/CacheValue definitions to
  comfy_api/latest/_caching.py (source of truth for public API)
- comfy_execution/cache_provider.py re-exports types from there
- Build _providers_snapshot eagerly on register/unregister instead
  of lazy memoization in _get_cache_providers

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: generalize self-inequality check, fail-closed canonicalization

Address review feedback from guill:
- Rename _contains_nan to _contains_self_unequal, use not (x == x)
  instead of math.isnan to catch any self-unequal value
- Remove Unhashable and repr() fallbacks from _canonicalize; raise
  ValueError for unknown types so _serialize_cache_key returns None
  and external caching is skipped (fail-closed)
- Update tests for renamed function and new fail-closed behavior

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: suppress ruff F401 for re-exported CacheContext

CacheContext is imported from _caching and re-exported for use by
caching.py. Add noqa comment to satisfy the linter.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: enable external caching for subcache (expanded) nodes

Subcache nodes (from node expansion) now participate in external
provider store/lookup. Previously skipped to avoid duplicates, but
the cost of missing partial-expansion cache hits outweighs redundant
stores — especially with looping behavior on the horizon.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: wrap register/unregister as explicit static methods

Define register_provider and unregister_provider as wrapper functions
in the Caching class instead of re-importing. This locks the public
API signature in comfy_api/ so internal changes can't accidentally
break it.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: use debug-level logging for provider registration

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: follow ProxiedSingleton pattern for Caching class

Add Caching as a nested class inside ComfyAPI_latest inheriting from
ProxiedSingleton with async instance methods, matching the Execution
and NodeReplacement patterns. Retains standalone Caching class for
direct import convenience.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: inline registration logic in Caching class

Follow the Execution/NodeReplacement pattern — the public API methods
contain the actual logic operating on cache_provider module state,
not wrapper functions delegating to free functions.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: single Caching definition inside ComfyAPI_latest

Remove duplicate standalone Caching class. Define it once as a nested
class in ComfyAPI_latest (matching Execution/NodeReplacement pattern),
with a module-level alias for import convenience.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: remove prompt_id from CacheContext, type-safe canonicalization

Remove prompt_id from CacheContext — it's not relevant for cache
matching and added unnecessary plumbing (_current_prompt_id on every
cache). Lifecycle hooks still receive prompt_id directly.

Include type name in canonicalized primitives so that int 7 and
str "7" produce distinct hashes. Also canonicalize dict keys properly
instead of str() coercion.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: address review feedback on cache provider API

- Hold references to pending store tasks to prevent "Task was destroyed
  but it is still pending" warnings (bigcat88)
- Parallel cache lookups with asyncio.gather instead of sequential
  awaits for better performance (bigcat88)
- Delegate Caching.register/unregister_provider to existing functions
  in cache_provider.py instead of reimplementing (bigcat88)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

---------

Co-authored-by: Claude <noreply@anthropic.com>
---
 comfy_api/latest/__init__.py                  |  35 ++
 comfy_api/latest/_caching.py                  |  42 ++
 comfy_execution/cache_provider.py             | 138 ++++++
 comfy_execution/caching.py                    | 177 +++++++-
 comfy_execution/graph.py                      |   6 +-
 execution.py                                  | 141 +++---
 .../execution_test/test_cache_provider.py     | 403 ++++++++++++++++++
 7 files changed, 859 insertions(+), 83 deletions(-)
 create mode 100644 comfy_api/latest/_caching.py
 create mode 100644 comfy_execution/cache_provider.py
 create mode 100644 tests-unit/execution_test/test_cache_provider.py

diff --git a/comfy_api/latest/__init__.py b/comfy_api/latest/__init__.py
index f2399422b..04973fea0 100644
--- a/comfy_api/latest/__init__.py
+++ b/comfy_api/latest/__init__.py
@@ -25,6 +25,7 @@ class ComfyAPI_latest(ComfyAPIBase):
         super().__init__()
         self.node_replacement = self.NodeReplacement()
         self.execution = self.Execution()
+        self.caching = self.Caching()
 
     class NodeReplacement(ProxiedSingleton):
         async def register(self, node_replace: io.NodeReplace) -> None:
@@ -84,6 +85,36 @@ class ComfyAPI_latest(ComfyAPIBase):
                 image=to_display,
             )
 
+    class Caching(ProxiedSingleton):
+        """
+        External cache provider API for sharing cached node outputs
+        across ComfyUI instances.
+
+        Example::
+
+            from comfy_api.latest import Caching
+
+            class MyCacheProvider(Caching.CacheProvider):
+                async def on_lookup(self, context):
+                    ...  # check external storage
+
+                async def on_store(self, context, value):
+                    ...  # store to external storage
+
+            Caching.register_provider(MyCacheProvider())
+        """
+        from ._caching import CacheProvider, CacheContext, CacheValue
+
+        async def register_provider(self, provider: "ComfyAPI_latest.Caching.CacheProvider") -> None:
+            """Register an external cache provider. Providers are called in registration order."""
+            from comfy_execution.cache_provider import register_cache_provider
+            register_cache_provider(provider)
+
+        async def unregister_provider(self, provider: "ComfyAPI_latest.Caching.CacheProvider") -> None:
+            """Unregister a previously registered cache provider."""
+            from comfy_execution.cache_provider import unregister_cache_provider
+            unregister_cache_provider(provider)
+
 class ComfyExtension(ABC):
     async def on_load(self) -> None:
         """
@@ -116,6 +147,9 @@ class Types:
     VOXEL = VOXEL
     File3D = File3D
 
+
+Caching = ComfyAPI_latest.Caching
+
 ComfyAPI = ComfyAPI_latest
 
 # Create a synchronous version of the API
@@ -135,6 +169,7 @@ __all__ = [
     "Input",
     "InputImpl",
     "Types",
+    "Caching",
     "ComfyExtension",
     "io",
     "IO",
diff --git a/comfy_api/latest/_caching.py b/comfy_api/latest/_caching.py
new file mode 100644
index 000000000..30c8848cd
--- /dev/null
+++ b/comfy_api/latest/_caching.py
@@ -0,0 +1,42 @@
+from abc import ABC, abstractmethod
+from typing import Optional
+from dataclasses import dataclass
+
+
+@dataclass
+class CacheContext:
+    node_id: str
+    class_type: str
+    cache_key_hash: str  # SHA256 hex digest
+
+
+@dataclass
+class CacheValue:
+    outputs: list
+    ui: dict = None
+
+
+class CacheProvider(ABC):
+    """Abstract base class for external cache providers.
+    Exceptions from provider methods are caught by the caller and never break execution.
+    """
+
+    @abstractmethod
+    async def on_lookup(self, context: CacheContext) -> Optional[CacheValue]:
+        """Called on local cache miss. Return CacheValue if found, None otherwise."""
+        pass
+
+    @abstractmethod
+    async def on_store(self, context: CacheContext, value: CacheValue) -> None:
+        """Called after local store. Dispatched via asyncio.create_task."""
+        pass
+
+    def should_cache(self, context: CacheContext, value: Optional[CacheValue] = None) -> bool:
+        """Return False to skip external caching for this node. Default: True."""
+        return True
+
+    def on_prompt_start(self, prompt_id: str) -> None:
+        pass
+
+    def on_prompt_end(self, prompt_id: str) -> None:
+        pass
diff --git a/comfy_execution/cache_provider.py b/comfy_execution/cache_provider.py
new file mode 100644
index 000000000..d455d08e8
--- /dev/null
+++ b/comfy_execution/cache_provider.py
@@ -0,0 +1,138 @@
+from typing import Any, Optional, Tuple, List
+import hashlib
+import json
+import logging
+import threading
+
+# Public types — source of truth is comfy_api.latest._caching
+from comfy_api.latest._caching import CacheProvider, CacheContext, CacheValue  # noqa: F401 (re-exported)
+
+_logger = logging.getLogger(__name__)
+
+
+_providers: List[CacheProvider] = []
+_providers_lock = threading.Lock()
+_providers_snapshot: Tuple[CacheProvider, ...] = ()
+
+
+def register_cache_provider(provider: CacheProvider) -> None:
+    """Register an external cache provider. Providers are called in registration order."""
+    global _providers_snapshot
+    with _providers_lock:
+        if provider in _providers:
+            _logger.warning(f"Provider {provider.__class__.__name__} already registered")
+            return
+        _providers.append(provider)
+        _providers_snapshot = tuple(_providers)
+        _logger.debug(f"Registered cache provider: {provider.__class__.__name__}")
+
+
+def unregister_cache_provider(provider: CacheProvider) -> None:
+    global _providers_snapshot
+    with _providers_lock:
+        try:
+            _providers.remove(provider)
+            _providers_snapshot = tuple(_providers)
+            _logger.debug(f"Unregistered cache provider: {provider.__class__.__name__}")
+        except ValueError:
+            _logger.warning(f"Provider {provider.__class__.__name__} was not registered")
+
+
+def _get_cache_providers() -> Tuple[CacheProvider, ...]:
+    return _providers_snapshot
+
+
+def _has_cache_providers() -> bool:
+    return bool(_providers_snapshot)
+
+
+def _clear_cache_providers() -> None:
+    global _providers_snapshot
+    with _providers_lock:
+        _providers.clear()
+        _providers_snapshot = ()
+
+
+def _canonicalize(obj: Any) -> Any:
+    # Convert to canonical JSON-serializable form with deterministic ordering.
+    # Frozensets have non-deterministic iteration order between Python sessions.
+    # Raises ValueError for non-cacheable types (Unhashable, unknown) so that
+    # _serialize_cache_key returns None and external caching is skipped.
+    if isinstance(obj, frozenset):
+        return ("__frozenset__", sorted(
+            [_canonicalize(item) for item in obj],
+            key=lambda x: json.dumps(x, sort_keys=True)
+        ))
+    elif isinstance(obj, set):
+        return ("__set__", sorted(
+            [_canonicalize(item) for item in obj],
+            key=lambda x: json.dumps(x, sort_keys=True)
+        ))
+    elif isinstance(obj, tuple):
+        return ("__tuple__", [_canonicalize(item) for item in obj])
+    elif isinstance(obj, list):
+        return [_canonicalize(item) for item in obj]
+    elif isinstance(obj, dict):
+        return {"__dict__": sorted(
+            [[_canonicalize(k), _canonicalize(v)] for k, v in obj.items()],
+            key=lambda x: json.dumps(x, sort_keys=True)
+        )}
+    elif isinstance(obj, (int, float, str, bool, type(None))):
+        return (type(obj).__name__, obj)
+    elif isinstance(obj, bytes):
+        return ("__bytes__", obj.hex())
+    else:
+        raise ValueError(f"Cannot canonicalize type: {type(obj).__name__}")
+
+
+def _serialize_cache_key(cache_key: Any) -> Optional[str]:
+    # Returns deterministic SHA256 hex digest, or None on failure.
+    # Uses JSON (not pickle) because pickle is non-deterministic across sessions.
+    try:
+        canonical = _canonicalize(cache_key)
+        json_str = json.dumps(canonical, sort_keys=True, separators=(',', ':'))
+        return hashlib.sha256(json_str.encode('utf-8')).hexdigest()
+    except Exception as e:
+        _logger.warning(f"Failed to serialize cache key: {e}")
+        return None
+
+
+def _contains_self_unequal(obj: Any) -> bool:
+    # Local cache matches by ==. Values where not (x == x) (NaN, etc.) will
+    # never hit locally, but serialized form would match externally. Skip these.
+    try:
+        if not (obj == obj):
+            return True
+    except Exception:
+        return True
+    if isinstance(obj, (frozenset, tuple, list, set)):
+        return any(_contains_self_unequal(item) for item in obj)
+    if isinstance(obj, dict):
+        return any(_contains_self_unequal(k) or _contains_self_unequal(v) for k, v in obj.items())
+    if hasattr(obj, 'value'):
+        return _contains_self_unequal(obj.value)
+    return False
+
+
+def _estimate_value_size(value: CacheValue) -> int:
+    try:
+        import torch
+    except ImportError:
+        return 0
+
+    total = 0
+
+    def estimate(obj):
+        nonlocal total
+        if isinstance(obj, torch.Tensor):
+            total += obj.numel() * obj.element_size()
+        elif isinstance(obj, dict):
+            for v in obj.values():
+                estimate(v)
+        elif isinstance(obj, (list, tuple)):
+            for item in obj:
+                estimate(item)
+
+    for output in value.outputs:
+        estimate(output)
+    return total
diff --git a/comfy_execution/caching.py b/comfy_execution/caching.py
index 326a279fc..750bddf2e 100644
--- a/comfy_execution/caching.py
+++ b/comfy_execution/caching.py
@@ -1,3 +1,4 @@
+import asyncio
 import bisect
 import gc
 import itertools
@@ -154,6 +155,7 @@ class BasicCache:
         self.cache_key_set: CacheKeySet
         self.cache = {}
         self.subcaches = {}
+        self._pending_store_tasks: set = set()
 
     async def set_prompt(self, dynprompt, node_ids, is_changed_cache):
         self.dynprompt = dynprompt
@@ -196,18 +198,134 @@ class BasicCache:
     def poll(self, **kwargs):
         pass
 
-    def _set_immediate(self, node_id, value):
-        assert self.initialized
-        cache_key = self.cache_key_set.get_data_key(node_id)
-        self.cache[cache_key] = value
-
-    def _get_immediate(self, node_id):
+    def get_local(self, node_id):
         if not self.initialized:
             return None
         cache_key = self.cache_key_set.get_data_key(node_id)
         if cache_key in self.cache:
             return self.cache[cache_key]
-        else:
+        return None
+
+    def set_local(self, node_id, value):
+        assert self.initialized
+        cache_key = self.cache_key_set.get_data_key(node_id)
+        self.cache[cache_key] = value
+
+    async def _set_immediate(self, node_id, value):
+        assert self.initialized
+        cache_key = self.cache_key_set.get_data_key(node_id)
+        self.cache[cache_key] = value
+
+        await self._notify_providers_store(node_id, cache_key, value)
+
+    async def _get_immediate(self, node_id):
+        if not self.initialized:
+            return None
+        cache_key = self.cache_key_set.get_data_key(node_id)
+
+        if cache_key in self.cache:
+            return self.cache[cache_key]
+
+        external_result = await self._check_providers_lookup(node_id, cache_key)
+        if external_result is not None:
+            self.cache[cache_key] = external_result
+            return external_result
+
+        return None
+
+    async def _notify_providers_store(self, node_id, cache_key, value):
+        from comfy_execution.cache_provider import (
+            _has_cache_providers, _get_cache_providers,
+            CacheValue, _contains_self_unequal, _logger
+        )
+
+        if not _has_cache_providers():
+            return
+        if not self._is_external_cacheable_value(value):
+            return
+        if _contains_self_unequal(cache_key):
+            return
+
+        context = self._build_context(node_id, cache_key)
+        if context is None:
+            return
+        cache_value = CacheValue(outputs=value.outputs, ui=value.ui)
+
+        for provider in _get_cache_providers():
+            try:
+                if provider.should_cache(context, cache_value):
+                    task = asyncio.create_task(self._safe_provider_store(provider, context, cache_value))
+                    self._pending_store_tasks.add(task)
+                    task.add_done_callback(self._pending_store_tasks.discard)
+            except Exception as e:
+                _logger.warning(f"Cache provider {provider.__class__.__name__} error on store: {e}")
+
+    @staticmethod
+    async def _safe_provider_store(provider, context, cache_value):
+        from comfy_execution.cache_provider import _logger
+        try:
+            await provider.on_store(context, cache_value)
+        except Exception as e:
+            _logger.warning(f"Cache provider {provider.__class__.__name__} async store error: {e}")
+
+    async def _check_providers_lookup(self, node_id, cache_key):
+        from comfy_execution.cache_provider import (
+            _has_cache_providers, _get_cache_providers,
+            CacheValue, _contains_self_unequal, _logger
+        )
+
+        if not _has_cache_providers():
+            return None
+        if _contains_self_unequal(cache_key):
+            return None
+
+        context = self._build_context(node_id, cache_key)
+        if context is None:
+            return None
+
+        for provider in _get_cache_providers():
+            try:
+                if not provider.should_cache(context):
+                    continue
+                result = await provider.on_lookup(context)
+                if result is not None:
+                    if not isinstance(result, CacheValue):
+                        _logger.warning(f"Provider {provider.__class__.__name__} returned invalid type")
+                        continue
+                    if not isinstance(result.outputs, (list, tuple)):
+                        _logger.warning(f"Provider {provider.__class__.__name__} returned invalid outputs")
+                        continue
+                    from execution import CacheEntry
+                    return CacheEntry(ui=result.ui or {}, outputs=list(result.outputs))
+            except Exception as e:
+                _logger.warning(f"Cache provider {provider.__class__.__name__} error on lookup: {e}")
+
+        return None
+
+    def _is_external_cacheable_value(self, value):
+        return hasattr(value, 'outputs') and hasattr(value, 'ui')
+
+    def _get_class_type(self, node_id):
+        if not self.initialized or not self.dynprompt:
+            return ''
+        try:
+            return self.dynprompt.get_node(node_id).get('class_type', '')
+        except Exception:
+            return ''
+
+    def _build_context(self, node_id, cache_key):
+        from comfy_execution.cache_provider import CacheContext, _serialize_cache_key, _logger
+        try:
+            cache_key_hash = _serialize_cache_key(cache_key)
+            if cache_key_hash is None:
+                return None
+            return CacheContext(
+                node_id=node_id,
+                class_type=self._get_class_type(node_id),
+                cache_key_hash=cache_key_hash,
+            )
+        except Exception as e:
+            _logger.warning(f"Failed to build cache context for node {node_id}: {e}")
             return None
 
     async def _ensure_subcache(self, node_id, children_ids):
@@ -257,16 +375,27 @@ class HierarchicalCache(BasicCache):
                 return None
         return cache
 
-    def get(self, node_id):
+    async def get(self, node_id):
         cache = self._get_cache_for(node_id)
         if cache is None:
             return None
-        return cache._get_immediate(node_id)
+        return await cache._get_immediate(node_id)
 
-    def set(self, node_id, value):
+    def get_local(self, node_id):
+        cache = self._get_cache_for(node_id)
+        if cache is None:
+            return None
+        return BasicCache.get_local(cache, node_id)
+
+    async def set(self, node_id, value):
         cache = self._get_cache_for(node_id)
         assert cache is not None
-        cache._set_immediate(node_id, value)
+        await cache._set_immediate(node_id, value)
+
+    def set_local(self, node_id, value):
+        cache = self._get_cache_for(node_id)
+        assert cache is not None
+        BasicCache.set_local(cache, node_id, value)
 
     async def ensure_subcache_for(self, node_id, children_ids):
         cache = self._get_cache_for(node_id)
@@ -287,10 +416,16 @@ class NullCache:
     def poll(self, **kwargs):
         pass
 
-    def get(self, node_id):
+    async def get(self, node_id):
         return None
 
-    def set(self, node_id, value):
+    def get_local(self, node_id):
+        return None
+
+    async def set(self, node_id, value):
+        pass
+
+    def set_local(self, node_id, value):
         pass
 
     async def ensure_subcache_for(self, node_id, children_ids):
@@ -322,18 +457,18 @@ class LRUCache(BasicCache):
                     del self.children[key]
         self._clean_subcaches()
 
-    def get(self, node_id):
+    async def get(self, node_id):
         self._mark_used(node_id)
-        return self._get_immediate(node_id)
+        return await self._get_immediate(node_id)
 
     def _mark_used(self, node_id):
         cache_key = self.cache_key_set.get_data_key(node_id)
         if cache_key is not None:
             self.used_generation[cache_key] = self.generation
 
-    def set(self, node_id, value):
+    async def set(self, node_id, value):
         self._mark_used(node_id)
-        return self._set_immediate(node_id, value)
+        return await self._set_immediate(node_id, value)
 
     async def ensure_subcache_for(self, node_id, children_ids):
         # Just uses subcaches for tracking 'live' nodes
@@ -373,13 +508,13 @@ class RAMPressureCache(LRUCache):
     def clean_unused(self):
         self._clean_subcaches()
 
-    def set(self, node_id, value):
+    async def set(self, node_id, value):
         self.timestamps[self.cache_key_set.get_data_key(node_id)] = time.time()
-        super().set(node_id, value)
+        await super().set(node_id, value)
 
-    def get(self, node_id):
+    async def get(self, node_id):
         self.timestamps[self.cache_key_set.get_data_key(node_id)] = time.time()
-        return super().get(node_id)
+        return await super().get(node_id)
 
     def poll(self, ram_headroom):
         def _ram_gb():
diff --git a/comfy_execution/graph.py b/comfy_execution/graph.py
index 9d170b16e..c47f3c79b 100644
--- a/comfy_execution/graph.py
+++ b/comfy_execution/graph.py
@@ -204,12 +204,12 @@ class ExecutionList(TopologicalSort):
         self.execution_cache_listeners = {}
 
     def is_cached(self, node_id):
-        return self.output_cache.get(node_id) is not None
+        return self.output_cache.get_local(node_id) is not None
 
     def cache_link(self, from_node_id, to_node_id):
         if to_node_id not in self.execution_cache:
             self.execution_cache[to_node_id] = {}
-        self.execution_cache[to_node_id][from_node_id] = self.output_cache.get(from_node_id)
+        self.execution_cache[to_node_id][from_node_id] = self.output_cache.get_local(from_node_id)
         if from_node_id not in self.execution_cache_listeners:
             self.execution_cache_listeners[from_node_id] = set()
         self.execution_cache_listeners[from_node_id].add(to_node_id)
@@ -221,7 +221,7 @@ class ExecutionList(TopologicalSort):
         if value is None:
             return None
         #Write back to the main cache on touch.
-        self.output_cache.set(from_node_id, value)
+        self.output_cache.set_local(from_node_id, value)
         return value
 
     def cache_update(self, node_id, value):
diff --git a/execution.py b/execution.py
index a7791efed..a8e8fc59f 100644
--- a/execution.py
+++ b/execution.py
@@ -40,6 +40,7 @@ from comfy_execution.progress import get_progress_state, reset_progress_state, a
 from comfy_execution.utils import CurrentNodeContext
 from comfy_api.internal import _ComfyNodeInternal, _NodeOutputInternal, first_real_override, is_class, make_locked_method_func
 from comfy_api.latest import io, _io
+from comfy_execution.cache_provider import _has_cache_providers, _get_cache_providers, _logger as _cache_logger
 
 
 class ExecutionResult(Enum):
@@ -418,7 +419,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
     inputs = dynprompt.get_node(unique_id)['inputs']
     class_type = dynprompt.get_node(unique_id)['class_type']
     class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
-    cached = caches.outputs.get(unique_id)
+    cached = await caches.outputs.get(unique_id)
     if cached is not None:
         if server.client_id is not None:
             cached_ui = cached.ui or {}
@@ -474,10 +475,10 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
                 server.last_node_id = display_node_id
                 server.send_sync("executing", { "node": unique_id, "display_node": display_node_id, "prompt_id": prompt_id }, server.client_id)
 
-            obj = caches.objects.get(unique_id)
+            obj = await caches.objects.get(unique_id)
             if obj is None:
                 obj = class_def()
-                caches.objects.set(unique_id, obj)
+                await caches.objects.set(unique_id, obj)
 
             if issubclass(class_def, _ComfyNodeInternal):
                 lazy_status_present = first_real_override(class_def, "check_lazy_status") is not None
@@ -588,7 +589,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
 
         cache_entry = CacheEntry(ui=ui_outputs.get(unique_id), outputs=output_data)
         execution_list.cache_update(unique_id, cache_entry)
-        caches.outputs.set(unique_id, cache_entry)
+        await caches.outputs.set(unique_id, cache_entry)
 
     except comfy.model_management.InterruptProcessingException as iex:
         logging.info("Processing interrupted")
@@ -684,6 +685,19 @@ class PromptExecutor:
             }
             self.add_message("execution_error", mes, broadcast=False)
 
+    def _notify_prompt_lifecycle(self, event: str, prompt_id: str):
+        if not _has_cache_providers():
+            return
+
+        for provider in _get_cache_providers():
+            try:
+                if event == "start":
+                    provider.on_prompt_start(prompt_id)
+                elif event == "end":
+                    provider.on_prompt_end(prompt_id)
+            except Exception as e:
+                _cache_logger.warning(f"Cache provider {provider.__class__.__name__} error on {event}: {e}")
+
     def execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]):
         asyncio.run(self.execute_async(prompt, prompt_id, extra_data, execute_outputs))
 
@@ -700,66 +714,75 @@ class PromptExecutor:
         self.status_messages = []
         self.add_message("execution_start", { "prompt_id": prompt_id}, broadcast=False)
 
-        with torch.inference_mode():
-            dynamic_prompt = DynamicPrompt(prompt)
-            reset_progress_state(prompt_id, dynamic_prompt)
-            add_progress_handler(WebUIProgressHandler(self.server))
-            is_changed_cache = IsChangedCache(prompt_id, dynamic_prompt, self.caches.outputs)
-            for cache in self.caches.all:
-                await cache.set_prompt(dynamic_prompt, prompt.keys(), is_changed_cache)
-                cache.clean_unused()
+        self._notify_prompt_lifecycle("start", prompt_id)
 
-            cached_nodes = []
-            for node_id in prompt:
-                if self.caches.outputs.get(node_id) is not None:
-                    cached_nodes.append(node_id)
+        try:
+            with torch.inference_mode():
+                dynamic_prompt = DynamicPrompt(prompt)
+                reset_progress_state(prompt_id, dynamic_prompt)
+                add_progress_handler(WebUIProgressHandler(self.server))
+                is_changed_cache = IsChangedCache(prompt_id, dynamic_prompt, self.caches.outputs)
+                for cache in self.caches.all:
+                    await cache.set_prompt(dynamic_prompt, prompt.keys(), is_changed_cache)
+                    cache.clean_unused()
 
-            comfy.model_management.cleanup_models_gc()
-            self.add_message("execution_cached",
-                          { "nodes": cached_nodes, "prompt_id": prompt_id},
-                          broadcast=False)
-            pending_subgraph_results = {}
-            pending_async_nodes = {} # TODO - Unify this with pending_subgraph_results
-            ui_node_outputs = {}
-            executed = set()
-            execution_list = ExecutionList(dynamic_prompt, self.caches.outputs)
-            current_outputs = self.caches.outputs.all_node_ids()
-            for node_id in list(execute_outputs):
-                execution_list.add_node(node_id)
+                node_ids = list(prompt.keys())
+                cache_results = await asyncio.gather(
+                    *(self.caches.outputs.get(node_id) for node_id in node_ids)
+                )
+                cached_nodes = [
+                    node_id for node_id, result in zip(node_ids, cache_results)
+                    if result is not None
+                ]
 
-            while not execution_list.is_empty():
-                node_id, error, ex = await execution_list.stage_node_execution()
-                if error is not None:
-                    self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
-                    break
+                comfy.model_management.cleanup_models_gc()
+                self.add_message("execution_cached",
+                              { "nodes": cached_nodes, "prompt_id": prompt_id},
+                              broadcast=False)
+                pending_subgraph_results = {}
+                pending_async_nodes = {} # TODO - Unify this with pending_subgraph_results
+                ui_node_outputs = {}
+                executed = set()
+                execution_list = ExecutionList(dynamic_prompt, self.caches.outputs)
+                current_outputs = self.caches.outputs.all_node_ids()
+                for node_id in list(execute_outputs):
+                    execution_list.add_node(node_id)
 
-                assert node_id is not None, "Node ID should not be None at this point"
-                result, error, ex = await execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes, ui_node_outputs)
-                self.success = result != ExecutionResult.FAILURE
-                if result == ExecutionResult.FAILURE:
-                    self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
-                    break
-                elif result == ExecutionResult.PENDING:
-                    execution_list.unstage_node_execution()
-                else: # result == ExecutionResult.SUCCESS:
-                    execution_list.complete_node_execution()
-                self.caches.outputs.poll(ram_headroom=self.cache_args["ram"])
-            else:
-                # Only execute when the while-loop ends without break
-                self.add_message("execution_success", { "prompt_id": prompt_id }, broadcast=False)
+                while not execution_list.is_empty():
+                    node_id, error, ex = await execution_list.stage_node_execution()
+                    if error is not None:
+                        self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
+                        break
 
-            ui_outputs = {}
-            meta_outputs = {}
-            for node_id, ui_info in ui_node_outputs.items():
-                ui_outputs[node_id] = ui_info["output"]
-                meta_outputs[node_id] = ui_info["meta"]
-            self.history_result = {
-                "outputs": ui_outputs,
-                "meta": meta_outputs,
-            }
-            self.server.last_node_id = None
-            if comfy.model_management.DISABLE_SMART_MEMORY:
-                comfy.model_management.unload_all_models()
+                    assert node_id is not None, "Node ID should not be None at this point"
+                    result, error, ex = await execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes, ui_node_outputs)
+                    self.success = result != ExecutionResult.FAILURE
+                    if result == ExecutionResult.FAILURE:
+                        self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
+                        break
+                    elif result == ExecutionResult.PENDING:
+                        execution_list.unstage_node_execution()
+                    else: # result == ExecutionResult.SUCCESS:
+                        execution_list.complete_node_execution()
+                    self.caches.outputs.poll(ram_headroom=self.cache_args["ram"])
+                else:
+                    # Only execute when the while-loop ends without break
+                    self.add_message("execution_success", { "prompt_id": prompt_id }, broadcast=False)
+
+                ui_outputs = {}
+                meta_outputs = {}
+                for node_id, ui_info in ui_node_outputs.items():
+                    ui_outputs[node_id] = ui_info["output"]
+                    meta_outputs[node_id] = ui_info["meta"]
+                self.history_result = {
+                    "outputs": ui_outputs,
+                    "meta": meta_outputs,
+                }
+                self.server.last_node_id = None
+                if comfy.model_management.DISABLE_SMART_MEMORY:
+                    comfy.model_management.unload_all_models()
+        finally:
+            self._notify_prompt_lifecycle("end", prompt_id)
 
 
 async def validate_inputs(prompt_id, prompt, item, validated):
diff --git a/tests-unit/execution_test/test_cache_provider.py b/tests-unit/execution_test/test_cache_provider.py
new file mode 100644
index 000000000..ac3814746
--- /dev/null
+++ b/tests-unit/execution_test/test_cache_provider.py
@@ -0,0 +1,403 @@
+"""Tests for external cache provider API."""
+
+import importlib.util
+import pytest
+from typing import Optional
+
+
+def _torch_available() -> bool:
+    """Check if PyTorch is available."""
+    return importlib.util.find_spec("torch") is not None
+
+
+from comfy_execution.cache_provider import (
+    CacheProvider,
+    CacheContext,
+    CacheValue,
+    register_cache_provider,
+    unregister_cache_provider,
+    _get_cache_providers,
+    _has_cache_providers,
+    _clear_cache_providers,
+    _serialize_cache_key,
+    _contains_self_unequal,
+    _estimate_value_size,
+    _canonicalize,
+)
+
+
+class TestCanonicalize:
+    """Test _canonicalize function for deterministic ordering."""
+
+    def test_frozenset_ordering_is_deterministic(self):
+        """Frozensets should produce consistent canonical form regardless of iteration order."""
+        # Create two frozensets with same content
+        fs1 = frozenset([("a", 1), ("b", 2), ("c", 3)])
+        fs2 = frozenset([("c", 3), ("a", 1), ("b", 2)])
+
+        result1 = _canonicalize(fs1)
+        result2 = _canonicalize(fs2)
+
+        assert result1 == result2
+
+    def test_nested_frozenset_ordering(self):
+        """Nested frozensets should also be deterministically ordered."""
+        inner1 = frozenset([1, 2, 3])
+        inner2 = frozenset([3, 2, 1])
+
+        fs1 = frozenset([("key", inner1)])
+        fs2 = frozenset([("key", inner2)])
+
+        result1 = _canonicalize(fs1)
+        result2 = _canonicalize(fs2)
+
+        assert result1 == result2
+
+    def test_dict_ordering(self):
+        """Dicts should be sorted by key."""
+        d1 = {"z": 1, "a": 2, "m": 3}
+        d2 = {"a": 2, "m": 3, "z": 1}
+
+        result1 = _canonicalize(d1)
+        result2 = _canonicalize(d2)
+
+        assert result1 == result2
+
+    def test_tuple_preserved(self):
+        """Tuples should be marked and preserved."""
+        t = (1, 2, 3)
+        result = _canonicalize(t)
+
+        assert result[0] == "__tuple__"
+
+    def test_list_preserved(self):
+        """Lists should be recursively canonicalized."""
+        lst = [{"b": 2, "a": 1}, frozenset([3, 2, 1])]
+        result = _canonicalize(lst)
+
+        # First element should be canonicalized dict
+        assert "__dict__" in result[0]
+        # Second element should be canonicalized frozenset
+        assert result[1][0] == "__frozenset__"
+
+    def test_primitives_include_type(self):
+        """Primitive types should include type name for disambiguation."""
+        assert _canonicalize(42) == ("int", 42)
+        assert _canonicalize(3.14) == ("float", 3.14)
+        assert _canonicalize("hello") == ("str", "hello")
+        assert _canonicalize(True) == ("bool", True)
+        assert _canonicalize(None) == ("NoneType", None)
+
+    def test_int_and_str_distinguished(self):
+        """int 7 and str '7' must produce different canonical forms."""
+        assert _canonicalize(7) != _canonicalize("7")
+
+    def test_bytes_converted(self):
+        """Bytes should be converted to hex string."""
+        b = b"\x00\xff"
+        result = _canonicalize(b)
+
+        assert result[0] == "__bytes__"
+        assert result[1] == "00ff"
+
+    def test_set_ordering(self):
+        """Sets should be sorted like frozensets."""
+        s1 = {3, 1, 2}
+        s2 = {1, 2, 3}
+
+        result1 = _canonicalize(s1)
+        result2 = _canonicalize(s2)
+
+        assert result1 == result2
+        assert result1[0] == "__set__"
+
+    def test_unknown_type_raises(self):
+        """Unknown types should raise ValueError (fail-closed)."""
+        class CustomObj:
+            pass
+        with pytest.raises(ValueError):
+            _canonicalize(CustomObj())
+
+    def test_object_with_value_attr_raises(self):
+        """Objects with .value attribute (Unhashable-like) should raise ValueError."""
+        class FakeUnhashable:
+            def __init__(self):
+                self.value = float('nan')
+        with pytest.raises(ValueError):
+            _canonicalize(FakeUnhashable())
+
+
+class TestSerializeCacheKey:
+    """Test _serialize_cache_key for deterministic hashing."""
+
+    def test_same_content_same_hash(self):
+        """Same content should produce same hash."""
+        key1 = frozenset([("node_1", frozenset([("input", "value")]))])
+        key2 = frozenset([("node_1", frozenset([("input", "value")]))])
+
+        hash1 = _serialize_cache_key(key1)
+        hash2 = _serialize_cache_key(key2)
+
+        assert hash1 == hash2
+
+    def test_different_content_different_hash(self):
+        """Different content should produce different hash."""
+        key1 = frozenset([("node_1", "value_a")])
+        key2 = frozenset([("node_1", "value_b")])
+
+        hash1 = _serialize_cache_key(key1)
+        hash2 = _serialize_cache_key(key2)
+
+        assert hash1 != hash2
+
+    def test_returns_hex_string(self):
+        """Should return hex string (SHA256 hex digest)."""
+        key = frozenset([("test", 123)])
+        result = _serialize_cache_key(key)
+
+        assert isinstance(result, str)
+        assert len(result) == 64  # SHA256 hex digest is 64 chars
+
+    def test_complex_nested_structure(self):
+        """Complex nested structures should hash deterministically."""
+        # Note: frozensets can only contain hashable types, so we use
+        # nested frozensets of tuples to represent dict-like structures
+        key = frozenset([
+            ("node_1", frozenset([
+                ("input_a", ("tuple", "value")),
+                ("input_b", frozenset([("nested", "dict")])),
+            ])),
+            ("node_2", frozenset([
+                ("param", 42),
+            ])),
+        ])
+
+        # Hash twice to verify determinism
+        hash1 = _serialize_cache_key(key)
+        hash2 = _serialize_cache_key(key)
+
+        assert hash1 == hash2
+
+    def test_dict_in_cache_key(self):
+        """Dicts passed directly to _serialize_cache_key should work."""
+        key = {"node_1": {"input": "value"}, "node_2": 42}
+
+        hash1 = _serialize_cache_key(key)
+        hash2 = _serialize_cache_key(key)
+
+        assert hash1 == hash2
+        assert isinstance(hash1, str)
+        assert len(hash1) == 64
+
+    def test_unknown_type_returns_none(self):
+        """Non-cacheable types should return None (fail-closed)."""
+        class CustomObj:
+            pass
+        assert _serialize_cache_key(CustomObj()) is None
+
+
+class TestContainsSelfUnequal:
+    """Test _contains_self_unequal utility function."""
+
+    def test_nan_float_detected(self):
+        """NaN floats should be detected (not equal to itself)."""
+        assert _contains_self_unequal(float('nan')) is True
+
+    def test_regular_float_not_detected(self):
+        """Regular floats are equal to themselves."""
+        assert _contains_self_unequal(3.14) is False
+        assert _contains_self_unequal(0.0) is False
+        assert _contains_self_unequal(-1.5) is False
+
+    def test_infinity_not_detected(self):
+        """Infinity is equal to itself."""
+        assert _contains_self_unequal(float('inf')) is False
+        assert _contains_self_unequal(float('-inf')) is False
+
+    def test_nan_in_list(self):
+        """NaN in list should be detected."""
+        assert _contains_self_unequal([1, 2, float('nan'), 4]) is True
+        assert _contains_self_unequal([1, 2, 3, 4]) is False
+
+    def test_nan_in_tuple(self):
+        """NaN in tuple should be detected."""
+        assert _contains_self_unequal((1, float('nan'))) is True
+        assert _contains_self_unequal((1, 2, 3)) is False
+
+    def test_nan_in_frozenset(self):
+        """NaN in frozenset should be detected."""
+        assert _contains_self_unequal(frozenset([1, float('nan')])) is True
+        assert _contains_self_unequal(frozenset([1, 2, 3])) is False
+
+    def test_nan_in_dict_value(self):
+        """NaN in dict value should be detected."""
+        assert _contains_self_unequal({"key": float('nan')}) is True
+        assert _contains_self_unequal({"key": 42}) is False
+
+    def test_nan_in_nested_structure(self):
+        """NaN in deeply nested structure should be detected."""
+        nested = {"level1": [{"level2": (1, 2, float('nan'))}]}
+        assert _contains_self_unequal(nested) is True
+
+    def test_non_numeric_types(self):
+        """Non-numeric types should not be self-unequal."""
+        assert _contains_self_unequal("string") is False
+        assert _contains_self_unequal(None) is False
+        assert _contains_self_unequal(True) is False
+
+    def test_object_with_nan_value_attribute(self):
+        """Objects wrapping NaN in .value should be detected."""
+        class NanWrapper:
+            def __init__(self):
+                self.value = float('nan')
+        assert _contains_self_unequal(NanWrapper()) is True
+
+    def test_custom_self_unequal_object(self):
+        """Custom objects where not (x == x) should be detected."""
+        class NeverEqual:
+            def __eq__(self, other):
+                return False
+        assert _contains_self_unequal(NeverEqual()) is True
+
+
+class TestEstimateValueSize:
+    """Test _estimate_value_size utility function."""
+
+    def test_empty_outputs(self):
+        """Empty outputs should have zero size."""
+        value = CacheValue(outputs=[])
+        assert _estimate_value_size(value) == 0
+
+    @pytest.mark.skipif(
+        not _torch_available(),
+        reason="PyTorch not available"
+    )
+    def test_tensor_size_estimation(self):
+        """Tensor size should be estimated correctly."""
+        import torch
+
+        # 1000 float32 elements = 4000 bytes
+        tensor = torch.zeros(1000, dtype=torch.float32)
+        value = CacheValue(outputs=[[tensor]])
+
+        size = _estimate_value_size(value)
+        assert size == 4000
+
+    @pytest.mark.skipif(
+        not _torch_available(),
+        reason="PyTorch not available"
+    )
+    def test_nested_tensor_in_dict(self):
+        """Tensors nested in dicts should be counted."""
+        import torch
+
+        tensor = torch.zeros(100, dtype=torch.float32)  # 400 bytes
+        value = CacheValue(outputs=[[{"samples": tensor}]])
+
+        size = _estimate_value_size(value)
+        assert size == 400
+
+
+class TestProviderRegistry:
+    """Test cache provider registration and retrieval."""
+
+    def setup_method(self):
+        """Clear providers before each test."""
+        _clear_cache_providers()
+
+    def teardown_method(self):
+        """Clear providers after each test."""
+        _clear_cache_providers()
+
+    def test_register_provider(self):
+        """Provider should be registered successfully."""
+        provider = MockCacheProvider()
+        register_cache_provider(provider)
+
+        assert _has_cache_providers() is True
+        providers = _get_cache_providers()
+        assert len(providers) == 1
+        assert providers[0] is provider
+
+    def test_unregister_provider(self):
+        """Provider should be unregistered successfully."""
+        provider = MockCacheProvider()
+        register_cache_provider(provider)
+        unregister_cache_provider(provider)
+
+        assert _has_cache_providers() is False
+
+    def test_multiple_providers(self):
+        """Multiple providers can be registered."""
+        provider1 = MockCacheProvider()
+        provider2 = MockCacheProvider()
+
+        register_cache_provider(provider1)
+        register_cache_provider(provider2)
+
+        providers = _get_cache_providers()
+        assert len(providers) == 2
+
+    def test_duplicate_registration_ignored(self):
+        """Registering same provider twice should be ignored."""
+        provider = MockCacheProvider()
+
+        register_cache_provider(provider)
+        register_cache_provider(provider)  # Should be ignored
+
+        providers = _get_cache_providers()
+        assert len(providers) == 1
+
+    def test_clear_providers(self):
+        """_clear_cache_providers should remove all providers."""
+        provider1 = MockCacheProvider()
+        provider2 = MockCacheProvider()
+
+        register_cache_provider(provider1)
+        register_cache_provider(provider2)
+        _clear_cache_providers()
+
+        assert _has_cache_providers() is False
+        assert len(_get_cache_providers()) == 0
+
+
+class TestCacheContext:
+    """Test CacheContext dataclass."""
+
+    def test_context_creation(self):
+        """CacheContext should be created with all fields."""
+        context = CacheContext(
+            node_id="node-456",
+            class_type="KSampler",
+            cache_key_hash="a" * 64,
+        )
+
+        assert context.node_id == "node-456"
+        assert context.class_type == "KSampler"
+        assert context.cache_key_hash == "a" * 64
+
+
+class TestCacheValue:
+    """Test CacheValue dataclass."""
+
+    def test_value_creation(self):
+        """CacheValue should be created with outputs."""
+        outputs = [[{"samples": "tensor_data"}]]
+        value = CacheValue(outputs=outputs)
+
+        assert value.outputs == outputs
+
+
+class MockCacheProvider(CacheProvider):
+    """Mock cache provider for testing."""
+
+    def __init__(self):
+        self.lookups = []
+        self.stores = []
+
+    async def on_lookup(self, context: CacheContext) -> Optional[CacheValue]:
+        self.lookups.append(context)
+        return None
+
+    async def on_store(self, context: CacheContext, value: CacheValue) -> None:
+        self.stores.append((context, value))

From d1d53c14be8442fca19aae978e944edad1935d46 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Thu, 12 Mar 2026 17:21:23 -0700
Subject: [PATCH 158/194] Revert "feat: Add CacheProvider API for external
 distributed caching (#12056)" (#12912)

This reverts commit af7b4a921d7abab7c852d7b5febb654be6e57eba.
---
 comfy_api/latest/__init__.py                  |  35 --
 comfy_api/latest/_caching.py                  |  42 --
 comfy_execution/cache_provider.py             | 138 ------
 comfy_execution/caching.py                    | 177 +-------
 comfy_execution/graph.py                      |   6 +-
 execution.py                                  | 141 +++---
 .../execution_test/test_cache_provider.py     | 403 ------------------
 7 files changed, 83 insertions(+), 859 deletions(-)
 delete mode 100644 comfy_api/latest/_caching.py
 delete mode 100644 comfy_execution/cache_provider.py
 delete mode 100644 tests-unit/execution_test/test_cache_provider.py

diff --git a/comfy_api/latest/__init__.py b/comfy_api/latest/__init__.py
index 04973fea0..f2399422b 100644
--- a/comfy_api/latest/__init__.py
+++ b/comfy_api/latest/__init__.py
@@ -25,7 +25,6 @@ class ComfyAPI_latest(ComfyAPIBase):
         super().__init__()
         self.node_replacement = self.NodeReplacement()
         self.execution = self.Execution()
-        self.caching = self.Caching()
 
     class NodeReplacement(ProxiedSingleton):
         async def register(self, node_replace: io.NodeReplace) -> None:
@@ -85,36 +84,6 @@ class ComfyAPI_latest(ComfyAPIBase):
                 image=to_display,
             )
 
-    class Caching(ProxiedSingleton):
-        """
-        External cache provider API for sharing cached node outputs
-        across ComfyUI instances.
-
-        Example::
-
-            from comfy_api.latest import Caching
-
-            class MyCacheProvider(Caching.CacheProvider):
-                async def on_lookup(self, context):
-                    ...  # check external storage
-
-                async def on_store(self, context, value):
-                    ...  # store to external storage
-
-            Caching.register_provider(MyCacheProvider())
-        """
-        from ._caching import CacheProvider, CacheContext, CacheValue
-
-        async def register_provider(self, provider: "ComfyAPI_latest.Caching.CacheProvider") -> None:
-            """Register an external cache provider. Providers are called in registration order."""
-            from comfy_execution.cache_provider import register_cache_provider
-            register_cache_provider(provider)
-
-        async def unregister_provider(self, provider: "ComfyAPI_latest.Caching.CacheProvider") -> None:
-            """Unregister a previously registered cache provider."""
-            from comfy_execution.cache_provider import unregister_cache_provider
-            unregister_cache_provider(provider)
-
 class ComfyExtension(ABC):
     async def on_load(self) -> None:
         """
@@ -147,9 +116,6 @@ class Types:
     VOXEL = VOXEL
     File3D = File3D
 
-
-Caching = ComfyAPI_latest.Caching
-
 ComfyAPI = ComfyAPI_latest
 
 # Create a synchronous version of the API
@@ -169,7 +135,6 @@ __all__ = [
     "Input",
     "InputImpl",
     "Types",
-    "Caching",
     "ComfyExtension",
     "io",
     "IO",
diff --git a/comfy_api/latest/_caching.py b/comfy_api/latest/_caching.py
deleted file mode 100644
index 30c8848cd..000000000
--- a/comfy_api/latest/_caching.py
+++ /dev/null
@@ -1,42 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Optional
-from dataclasses import dataclass
-
-
-@dataclass
-class CacheContext:
-    node_id: str
-    class_type: str
-    cache_key_hash: str  # SHA256 hex digest
-
-
-@dataclass
-class CacheValue:
-    outputs: list
-    ui: dict = None
-
-
-class CacheProvider(ABC):
-    """Abstract base class for external cache providers.
-    Exceptions from provider methods are caught by the caller and never break execution.
-    """
-
-    @abstractmethod
-    async def on_lookup(self, context: CacheContext) -> Optional[CacheValue]:
-        """Called on local cache miss. Return CacheValue if found, None otherwise."""
-        pass
-
-    @abstractmethod
-    async def on_store(self, context: CacheContext, value: CacheValue) -> None:
-        """Called after local store. Dispatched via asyncio.create_task."""
-        pass
-
-    def should_cache(self, context: CacheContext, value: Optional[CacheValue] = None) -> bool:
-        """Return False to skip external caching for this node. Default: True."""
-        return True
-
-    def on_prompt_start(self, prompt_id: str) -> None:
-        pass
-
-    def on_prompt_end(self, prompt_id: str) -> None:
-        pass
diff --git a/comfy_execution/cache_provider.py b/comfy_execution/cache_provider.py
deleted file mode 100644
index d455d08e8..000000000
--- a/comfy_execution/cache_provider.py
+++ /dev/null
@@ -1,138 +0,0 @@
-from typing import Any, Optional, Tuple, List
-import hashlib
-import json
-import logging
-import threading
-
-# Public types — source of truth is comfy_api.latest._caching
-from comfy_api.latest._caching import CacheProvider, CacheContext, CacheValue  # noqa: F401 (re-exported)
-
-_logger = logging.getLogger(__name__)
-
-
-_providers: List[CacheProvider] = []
-_providers_lock = threading.Lock()
-_providers_snapshot: Tuple[CacheProvider, ...] = ()
-
-
-def register_cache_provider(provider: CacheProvider) -> None:
-    """Register an external cache provider. Providers are called in registration order."""
-    global _providers_snapshot
-    with _providers_lock:
-        if provider in _providers:
-            _logger.warning(f"Provider {provider.__class__.__name__} already registered")
-            return
-        _providers.append(provider)
-        _providers_snapshot = tuple(_providers)
-        _logger.debug(f"Registered cache provider: {provider.__class__.__name__}")
-
-
-def unregister_cache_provider(provider: CacheProvider) -> None:
-    global _providers_snapshot
-    with _providers_lock:
-        try:
-            _providers.remove(provider)
-            _providers_snapshot = tuple(_providers)
-            _logger.debug(f"Unregistered cache provider: {provider.__class__.__name__}")
-        except ValueError:
-            _logger.warning(f"Provider {provider.__class__.__name__} was not registered")
-
-
-def _get_cache_providers() -> Tuple[CacheProvider, ...]:
-    return _providers_snapshot
-
-
-def _has_cache_providers() -> bool:
-    return bool(_providers_snapshot)
-
-
-def _clear_cache_providers() -> None:
-    global _providers_snapshot
-    with _providers_lock:
-        _providers.clear()
-        _providers_snapshot = ()
-
-
-def _canonicalize(obj: Any) -> Any:
-    # Convert to canonical JSON-serializable form with deterministic ordering.
-    # Frozensets have non-deterministic iteration order between Python sessions.
-    # Raises ValueError for non-cacheable types (Unhashable, unknown) so that
-    # _serialize_cache_key returns None and external caching is skipped.
-    if isinstance(obj, frozenset):
-        return ("__frozenset__", sorted(
-            [_canonicalize(item) for item in obj],
-            key=lambda x: json.dumps(x, sort_keys=True)
-        ))
-    elif isinstance(obj, set):
-        return ("__set__", sorted(
-            [_canonicalize(item) for item in obj],
-            key=lambda x: json.dumps(x, sort_keys=True)
-        ))
-    elif isinstance(obj, tuple):
-        return ("__tuple__", [_canonicalize(item) for item in obj])
-    elif isinstance(obj, list):
-        return [_canonicalize(item) for item in obj]
-    elif isinstance(obj, dict):
-        return {"__dict__": sorted(
-            [[_canonicalize(k), _canonicalize(v)] for k, v in obj.items()],
-            key=lambda x: json.dumps(x, sort_keys=True)
-        )}
-    elif isinstance(obj, (int, float, str, bool, type(None))):
-        return (type(obj).__name__, obj)
-    elif isinstance(obj, bytes):
-        return ("__bytes__", obj.hex())
-    else:
-        raise ValueError(f"Cannot canonicalize type: {type(obj).__name__}")
-
-
-def _serialize_cache_key(cache_key: Any) -> Optional[str]:
-    # Returns deterministic SHA256 hex digest, or None on failure.
-    # Uses JSON (not pickle) because pickle is non-deterministic across sessions.
-    try:
-        canonical = _canonicalize(cache_key)
-        json_str = json.dumps(canonical, sort_keys=True, separators=(',', ':'))
-        return hashlib.sha256(json_str.encode('utf-8')).hexdigest()
-    except Exception as e:
-        _logger.warning(f"Failed to serialize cache key: {e}")
-        return None
-
-
-def _contains_self_unequal(obj: Any) -> bool:
-    # Local cache matches by ==. Values where not (x == x) (NaN, etc.) will
-    # never hit locally, but serialized form would match externally. Skip these.
-    try:
-        if not (obj == obj):
-            return True
-    except Exception:
-        return True
-    if isinstance(obj, (frozenset, tuple, list, set)):
-        return any(_contains_self_unequal(item) for item in obj)
-    if isinstance(obj, dict):
-        return any(_contains_self_unequal(k) or _contains_self_unequal(v) for k, v in obj.items())
-    if hasattr(obj, 'value'):
-        return _contains_self_unequal(obj.value)
-    return False
-
-
-def _estimate_value_size(value: CacheValue) -> int:
-    try:
-        import torch
-    except ImportError:
-        return 0
-
-    total = 0
-
-    def estimate(obj):
-        nonlocal total
-        if isinstance(obj, torch.Tensor):
-            total += obj.numel() * obj.element_size()
-        elif isinstance(obj, dict):
-            for v in obj.values():
-                estimate(v)
-        elif isinstance(obj, (list, tuple)):
-            for item in obj:
-                estimate(item)
-
-    for output in value.outputs:
-        estimate(output)
-    return total
diff --git a/comfy_execution/caching.py b/comfy_execution/caching.py
index 750bddf2e..326a279fc 100644
--- a/comfy_execution/caching.py
+++ b/comfy_execution/caching.py
@@ -1,4 +1,3 @@
-import asyncio
 import bisect
 import gc
 import itertools
@@ -155,7 +154,6 @@ class BasicCache:
         self.cache_key_set: CacheKeySet
         self.cache = {}
         self.subcaches = {}
-        self._pending_store_tasks: set = set()
 
     async def set_prompt(self, dynprompt, node_ids, is_changed_cache):
         self.dynprompt = dynprompt
@@ -198,134 +196,18 @@ class BasicCache:
     def poll(self, **kwargs):
         pass
 
-    def get_local(self, node_id):
+    def _set_immediate(self, node_id, value):
+        assert self.initialized
+        cache_key = self.cache_key_set.get_data_key(node_id)
+        self.cache[cache_key] = value
+
+    def _get_immediate(self, node_id):
         if not self.initialized:
             return None
         cache_key = self.cache_key_set.get_data_key(node_id)
         if cache_key in self.cache:
             return self.cache[cache_key]
-        return None
-
-    def set_local(self, node_id, value):
-        assert self.initialized
-        cache_key = self.cache_key_set.get_data_key(node_id)
-        self.cache[cache_key] = value
-
-    async def _set_immediate(self, node_id, value):
-        assert self.initialized
-        cache_key = self.cache_key_set.get_data_key(node_id)
-        self.cache[cache_key] = value
-
-        await self._notify_providers_store(node_id, cache_key, value)
-
-    async def _get_immediate(self, node_id):
-        if not self.initialized:
-            return None
-        cache_key = self.cache_key_set.get_data_key(node_id)
-
-        if cache_key in self.cache:
-            return self.cache[cache_key]
-
-        external_result = await self._check_providers_lookup(node_id, cache_key)
-        if external_result is not None:
-            self.cache[cache_key] = external_result
-            return external_result
-
-        return None
-
-    async def _notify_providers_store(self, node_id, cache_key, value):
-        from comfy_execution.cache_provider import (
-            _has_cache_providers, _get_cache_providers,
-            CacheValue, _contains_self_unequal, _logger
-        )
-
-        if not _has_cache_providers():
-            return
-        if not self._is_external_cacheable_value(value):
-            return
-        if _contains_self_unequal(cache_key):
-            return
-
-        context = self._build_context(node_id, cache_key)
-        if context is None:
-            return
-        cache_value = CacheValue(outputs=value.outputs, ui=value.ui)
-
-        for provider in _get_cache_providers():
-            try:
-                if provider.should_cache(context, cache_value):
-                    task = asyncio.create_task(self._safe_provider_store(provider, context, cache_value))
-                    self._pending_store_tasks.add(task)
-                    task.add_done_callback(self._pending_store_tasks.discard)
-            except Exception as e:
-                _logger.warning(f"Cache provider {provider.__class__.__name__} error on store: {e}")
-
-    @staticmethod
-    async def _safe_provider_store(provider, context, cache_value):
-        from comfy_execution.cache_provider import _logger
-        try:
-            await provider.on_store(context, cache_value)
-        except Exception as e:
-            _logger.warning(f"Cache provider {provider.__class__.__name__} async store error: {e}")
-
-    async def _check_providers_lookup(self, node_id, cache_key):
-        from comfy_execution.cache_provider import (
-            _has_cache_providers, _get_cache_providers,
-            CacheValue, _contains_self_unequal, _logger
-        )
-
-        if not _has_cache_providers():
-            return None
-        if _contains_self_unequal(cache_key):
-            return None
-
-        context = self._build_context(node_id, cache_key)
-        if context is None:
-            return None
-
-        for provider in _get_cache_providers():
-            try:
-                if not provider.should_cache(context):
-                    continue
-                result = await provider.on_lookup(context)
-                if result is not None:
-                    if not isinstance(result, CacheValue):
-                        _logger.warning(f"Provider {provider.__class__.__name__} returned invalid type")
-                        continue
-                    if not isinstance(result.outputs, (list, tuple)):
-                        _logger.warning(f"Provider {provider.__class__.__name__} returned invalid outputs")
-                        continue
-                    from execution import CacheEntry
-                    return CacheEntry(ui=result.ui or {}, outputs=list(result.outputs))
-            except Exception as e:
-                _logger.warning(f"Cache provider {provider.__class__.__name__} error on lookup: {e}")
-
-        return None
-
-    def _is_external_cacheable_value(self, value):
-        return hasattr(value, 'outputs') and hasattr(value, 'ui')
-
-    def _get_class_type(self, node_id):
-        if not self.initialized or not self.dynprompt:
-            return ''
-        try:
-            return self.dynprompt.get_node(node_id).get('class_type', '')
-        except Exception:
-            return ''
-
-    def _build_context(self, node_id, cache_key):
-        from comfy_execution.cache_provider import CacheContext, _serialize_cache_key, _logger
-        try:
-            cache_key_hash = _serialize_cache_key(cache_key)
-            if cache_key_hash is None:
-                return None
-            return CacheContext(
-                node_id=node_id,
-                class_type=self._get_class_type(node_id),
-                cache_key_hash=cache_key_hash,
-            )
-        except Exception as e:
-            _logger.warning(f"Failed to build cache context for node {node_id}: {e}")
+        else:
             return None
 
     async def _ensure_subcache(self, node_id, children_ids):
@@ -375,27 +257,16 @@ class HierarchicalCache(BasicCache):
                 return None
         return cache
 
-    async def get(self, node_id):
+    def get(self, node_id):
         cache = self._get_cache_for(node_id)
         if cache is None:
             return None
-        return await cache._get_immediate(node_id)
+        return cache._get_immediate(node_id)
 
-    def get_local(self, node_id):
-        cache = self._get_cache_for(node_id)
-        if cache is None:
-            return None
-        return BasicCache.get_local(cache, node_id)
-
-    async def set(self, node_id, value):
+    def set(self, node_id, value):
         cache = self._get_cache_for(node_id)
         assert cache is not None
-        await cache._set_immediate(node_id, value)
-
-    def set_local(self, node_id, value):
-        cache = self._get_cache_for(node_id)
-        assert cache is not None
-        BasicCache.set_local(cache, node_id, value)
+        cache._set_immediate(node_id, value)
 
     async def ensure_subcache_for(self, node_id, children_ids):
         cache = self._get_cache_for(node_id)
@@ -416,16 +287,10 @@ class NullCache:
     def poll(self, **kwargs):
         pass
 
-    async def get(self, node_id):
+    def get(self, node_id):
         return None
 
-    def get_local(self, node_id):
-        return None
-
-    async def set(self, node_id, value):
-        pass
-
-    def set_local(self, node_id, value):
+    def set(self, node_id, value):
         pass
 
     async def ensure_subcache_for(self, node_id, children_ids):
@@ -457,18 +322,18 @@ class LRUCache(BasicCache):
                     del self.children[key]
         self._clean_subcaches()
 
-    async def get(self, node_id):
+    def get(self, node_id):
         self._mark_used(node_id)
-        return await self._get_immediate(node_id)
+        return self._get_immediate(node_id)
 
     def _mark_used(self, node_id):
         cache_key = self.cache_key_set.get_data_key(node_id)
         if cache_key is not None:
             self.used_generation[cache_key] = self.generation
 
-    async def set(self, node_id, value):
+    def set(self, node_id, value):
         self._mark_used(node_id)
-        return await self._set_immediate(node_id, value)
+        return self._set_immediate(node_id, value)
 
     async def ensure_subcache_for(self, node_id, children_ids):
         # Just uses subcaches for tracking 'live' nodes
@@ -508,13 +373,13 @@ class RAMPressureCache(LRUCache):
     def clean_unused(self):
         self._clean_subcaches()
 
-    async def set(self, node_id, value):
+    def set(self, node_id, value):
         self.timestamps[self.cache_key_set.get_data_key(node_id)] = time.time()
-        await super().set(node_id, value)
+        super().set(node_id, value)
 
-    async def get(self, node_id):
+    def get(self, node_id):
         self.timestamps[self.cache_key_set.get_data_key(node_id)] = time.time()
-        return await super().get(node_id)
+        return super().get(node_id)
 
     def poll(self, ram_headroom):
         def _ram_gb():
diff --git a/comfy_execution/graph.py b/comfy_execution/graph.py
index c47f3c79b..9d170b16e 100644
--- a/comfy_execution/graph.py
+++ b/comfy_execution/graph.py
@@ -204,12 +204,12 @@ class ExecutionList(TopologicalSort):
         self.execution_cache_listeners = {}
 
     def is_cached(self, node_id):
-        return self.output_cache.get_local(node_id) is not None
+        return self.output_cache.get(node_id) is not None
 
     def cache_link(self, from_node_id, to_node_id):
         if to_node_id not in self.execution_cache:
             self.execution_cache[to_node_id] = {}
-        self.execution_cache[to_node_id][from_node_id] = self.output_cache.get_local(from_node_id)
+        self.execution_cache[to_node_id][from_node_id] = self.output_cache.get(from_node_id)
         if from_node_id not in self.execution_cache_listeners:
             self.execution_cache_listeners[from_node_id] = set()
         self.execution_cache_listeners[from_node_id].add(to_node_id)
@@ -221,7 +221,7 @@ class ExecutionList(TopologicalSort):
         if value is None:
             return None
         #Write back to the main cache on touch.
-        self.output_cache.set_local(from_node_id, value)
+        self.output_cache.set(from_node_id, value)
         return value
 
     def cache_update(self, node_id, value):
diff --git a/execution.py b/execution.py
index a8e8fc59f..a7791efed 100644
--- a/execution.py
+++ b/execution.py
@@ -40,7 +40,6 @@ from comfy_execution.progress import get_progress_state, reset_progress_state, a
 from comfy_execution.utils import CurrentNodeContext
 from comfy_api.internal import _ComfyNodeInternal, _NodeOutputInternal, first_real_override, is_class, make_locked_method_func
 from comfy_api.latest import io, _io
-from comfy_execution.cache_provider import _has_cache_providers, _get_cache_providers, _logger as _cache_logger
 
 
 class ExecutionResult(Enum):
@@ -419,7 +418,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
     inputs = dynprompt.get_node(unique_id)['inputs']
     class_type = dynprompt.get_node(unique_id)['class_type']
     class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
-    cached = await caches.outputs.get(unique_id)
+    cached = caches.outputs.get(unique_id)
     if cached is not None:
         if server.client_id is not None:
             cached_ui = cached.ui or {}
@@ -475,10 +474,10 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
                 server.last_node_id = display_node_id
                 server.send_sync("executing", { "node": unique_id, "display_node": display_node_id, "prompt_id": prompt_id }, server.client_id)
 
-            obj = await caches.objects.get(unique_id)
+            obj = caches.objects.get(unique_id)
             if obj is None:
                 obj = class_def()
-                await caches.objects.set(unique_id, obj)
+                caches.objects.set(unique_id, obj)
 
             if issubclass(class_def, _ComfyNodeInternal):
                 lazy_status_present = first_real_override(class_def, "check_lazy_status") is not None
@@ -589,7 +588,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
 
         cache_entry = CacheEntry(ui=ui_outputs.get(unique_id), outputs=output_data)
         execution_list.cache_update(unique_id, cache_entry)
-        await caches.outputs.set(unique_id, cache_entry)
+        caches.outputs.set(unique_id, cache_entry)
 
     except comfy.model_management.InterruptProcessingException as iex:
         logging.info("Processing interrupted")
@@ -685,19 +684,6 @@ class PromptExecutor:
             }
             self.add_message("execution_error", mes, broadcast=False)
 
-    def _notify_prompt_lifecycle(self, event: str, prompt_id: str):
-        if not _has_cache_providers():
-            return
-
-        for provider in _get_cache_providers():
-            try:
-                if event == "start":
-                    provider.on_prompt_start(prompt_id)
-                elif event == "end":
-                    provider.on_prompt_end(prompt_id)
-            except Exception as e:
-                _cache_logger.warning(f"Cache provider {provider.__class__.__name__} error on {event}: {e}")
-
     def execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]):
         asyncio.run(self.execute_async(prompt, prompt_id, extra_data, execute_outputs))
 
@@ -714,75 +700,66 @@ class PromptExecutor:
         self.status_messages = []
         self.add_message("execution_start", { "prompt_id": prompt_id}, broadcast=False)
 
-        self._notify_prompt_lifecycle("start", prompt_id)
+        with torch.inference_mode():
+            dynamic_prompt = DynamicPrompt(prompt)
+            reset_progress_state(prompt_id, dynamic_prompt)
+            add_progress_handler(WebUIProgressHandler(self.server))
+            is_changed_cache = IsChangedCache(prompt_id, dynamic_prompt, self.caches.outputs)
+            for cache in self.caches.all:
+                await cache.set_prompt(dynamic_prompt, prompt.keys(), is_changed_cache)
+                cache.clean_unused()
 
-        try:
-            with torch.inference_mode():
-                dynamic_prompt = DynamicPrompt(prompt)
-                reset_progress_state(prompt_id, dynamic_prompt)
-                add_progress_handler(WebUIProgressHandler(self.server))
-                is_changed_cache = IsChangedCache(prompt_id, dynamic_prompt, self.caches.outputs)
-                for cache in self.caches.all:
-                    await cache.set_prompt(dynamic_prompt, prompt.keys(), is_changed_cache)
-                    cache.clean_unused()
+            cached_nodes = []
+            for node_id in prompt:
+                if self.caches.outputs.get(node_id) is not None:
+                    cached_nodes.append(node_id)
 
-                node_ids = list(prompt.keys())
-                cache_results = await asyncio.gather(
-                    *(self.caches.outputs.get(node_id) for node_id in node_ids)
-                )
-                cached_nodes = [
-                    node_id for node_id, result in zip(node_ids, cache_results)
-                    if result is not None
-                ]
+            comfy.model_management.cleanup_models_gc()
+            self.add_message("execution_cached",
+                          { "nodes": cached_nodes, "prompt_id": prompt_id},
+                          broadcast=False)
+            pending_subgraph_results = {}
+            pending_async_nodes = {} # TODO - Unify this with pending_subgraph_results
+            ui_node_outputs = {}
+            executed = set()
+            execution_list = ExecutionList(dynamic_prompt, self.caches.outputs)
+            current_outputs = self.caches.outputs.all_node_ids()
+            for node_id in list(execute_outputs):
+                execution_list.add_node(node_id)
 
-                comfy.model_management.cleanup_models_gc()
-                self.add_message("execution_cached",
-                              { "nodes": cached_nodes, "prompt_id": prompt_id},
-                              broadcast=False)
-                pending_subgraph_results = {}
-                pending_async_nodes = {} # TODO - Unify this with pending_subgraph_results
-                ui_node_outputs = {}
-                executed = set()
-                execution_list = ExecutionList(dynamic_prompt, self.caches.outputs)
-                current_outputs = self.caches.outputs.all_node_ids()
-                for node_id in list(execute_outputs):
-                    execution_list.add_node(node_id)
+            while not execution_list.is_empty():
+                node_id, error, ex = await execution_list.stage_node_execution()
+                if error is not None:
+                    self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
+                    break
 
-                while not execution_list.is_empty():
-                    node_id, error, ex = await execution_list.stage_node_execution()
-                    if error is not None:
-                        self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
-                        break
+                assert node_id is not None, "Node ID should not be None at this point"
+                result, error, ex = await execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes, ui_node_outputs)
+                self.success = result != ExecutionResult.FAILURE
+                if result == ExecutionResult.FAILURE:
+                    self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
+                    break
+                elif result == ExecutionResult.PENDING:
+                    execution_list.unstage_node_execution()
+                else: # result == ExecutionResult.SUCCESS:
+                    execution_list.complete_node_execution()
+                self.caches.outputs.poll(ram_headroom=self.cache_args["ram"])
+            else:
+                # Only execute when the while-loop ends without break
+                self.add_message("execution_success", { "prompt_id": prompt_id }, broadcast=False)
 
-                    assert node_id is not None, "Node ID should not be None at this point"
-                    result, error, ex = await execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes, ui_node_outputs)
-                    self.success = result != ExecutionResult.FAILURE
-                    if result == ExecutionResult.FAILURE:
-                        self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
-                        break
-                    elif result == ExecutionResult.PENDING:
-                        execution_list.unstage_node_execution()
-                    else: # result == ExecutionResult.SUCCESS:
-                        execution_list.complete_node_execution()
-                    self.caches.outputs.poll(ram_headroom=self.cache_args["ram"])
-                else:
-                    # Only execute when the while-loop ends without break
-                    self.add_message("execution_success", { "prompt_id": prompt_id }, broadcast=False)
-
-                ui_outputs = {}
-                meta_outputs = {}
-                for node_id, ui_info in ui_node_outputs.items():
-                    ui_outputs[node_id] = ui_info["output"]
-                    meta_outputs[node_id] = ui_info["meta"]
-                self.history_result = {
-                    "outputs": ui_outputs,
-                    "meta": meta_outputs,
-                }
-                self.server.last_node_id = None
-                if comfy.model_management.DISABLE_SMART_MEMORY:
-                    comfy.model_management.unload_all_models()
-        finally:
-            self._notify_prompt_lifecycle("end", prompt_id)
+            ui_outputs = {}
+            meta_outputs = {}
+            for node_id, ui_info in ui_node_outputs.items():
+                ui_outputs[node_id] = ui_info["output"]
+                meta_outputs[node_id] = ui_info["meta"]
+            self.history_result = {
+                "outputs": ui_outputs,
+                "meta": meta_outputs,
+            }
+            self.server.last_node_id = None
+            if comfy.model_management.DISABLE_SMART_MEMORY:
+                comfy.model_management.unload_all_models()
 
 
 async def validate_inputs(prompt_id, prompt, item, validated):
diff --git a/tests-unit/execution_test/test_cache_provider.py b/tests-unit/execution_test/test_cache_provider.py
deleted file mode 100644
index ac3814746..000000000
--- a/tests-unit/execution_test/test_cache_provider.py
+++ /dev/null
@@ -1,403 +0,0 @@
-"""Tests for external cache provider API."""
-
-import importlib.util
-import pytest
-from typing import Optional
-
-
-def _torch_available() -> bool:
-    """Check if PyTorch is available."""
-    return importlib.util.find_spec("torch") is not None
-
-
-from comfy_execution.cache_provider import (
-    CacheProvider,
-    CacheContext,
-    CacheValue,
-    register_cache_provider,
-    unregister_cache_provider,
-    _get_cache_providers,
-    _has_cache_providers,
-    _clear_cache_providers,
-    _serialize_cache_key,
-    _contains_self_unequal,
-    _estimate_value_size,
-    _canonicalize,
-)
-
-
-class TestCanonicalize:
-    """Test _canonicalize function for deterministic ordering."""
-
-    def test_frozenset_ordering_is_deterministic(self):
-        """Frozensets should produce consistent canonical form regardless of iteration order."""
-        # Create two frozensets with same content
-        fs1 = frozenset([("a", 1), ("b", 2), ("c", 3)])
-        fs2 = frozenset([("c", 3), ("a", 1), ("b", 2)])
-
-        result1 = _canonicalize(fs1)
-        result2 = _canonicalize(fs2)
-
-        assert result1 == result2
-
-    def test_nested_frozenset_ordering(self):
-        """Nested frozensets should also be deterministically ordered."""
-        inner1 = frozenset([1, 2, 3])
-        inner2 = frozenset([3, 2, 1])
-
-        fs1 = frozenset([("key", inner1)])
-        fs2 = frozenset([("key", inner2)])
-
-        result1 = _canonicalize(fs1)
-        result2 = _canonicalize(fs2)
-
-        assert result1 == result2
-
-    def test_dict_ordering(self):
-        """Dicts should be sorted by key."""
-        d1 = {"z": 1, "a": 2, "m": 3}
-        d2 = {"a": 2, "m": 3, "z": 1}
-
-        result1 = _canonicalize(d1)
-        result2 = _canonicalize(d2)
-
-        assert result1 == result2
-
-    def test_tuple_preserved(self):
-        """Tuples should be marked and preserved."""
-        t = (1, 2, 3)
-        result = _canonicalize(t)
-
-        assert result[0] == "__tuple__"
-
-    def test_list_preserved(self):
-        """Lists should be recursively canonicalized."""
-        lst = [{"b": 2, "a": 1}, frozenset([3, 2, 1])]
-        result = _canonicalize(lst)
-
-        # First element should be canonicalized dict
-        assert "__dict__" in result[0]
-        # Second element should be canonicalized frozenset
-        assert result[1][0] == "__frozenset__"
-
-    def test_primitives_include_type(self):
-        """Primitive types should include type name for disambiguation."""
-        assert _canonicalize(42) == ("int", 42)
-        assert _canonicalize(3.14) == ("float", 3.14)
-        assert _canonicalize("hello") == ("str", "hello")
-        assert _canonicalize(True) == ("bool", True)
-        assert _canonicalize(None) == ("NoneType", None)
-
-    def test_int_and_str_distinguished(self):
-        """int 7 and str '7' must produce different canonical forms."""
-        assert _canonicalize(7) != _canonicalize("7")
-
-    def test_bytes_converted(self):
-        """Bytes should be converted to hex string."""
-        b = b"\x00\xff"
-        result = _canonicalize(b)
-
-        assert result[0] == "__bytes__"
-        assert result[1] == "00ff"
-
-    def test_set_ordering(self):
-        """Sets should be sorted like frozensets."""
-        s1 = {3, 1, 2}
-        s2 = {1, 2, 3}
-
-        result1 = _canonicalize(s1)
-        result2 = _canonicalize(s2)
-
-        assert result1 == result2
-        assert result1[0] == "__set__"
-
-    def test_unknown_type_raises(self):
-        """Unknown types should raise ValueError (fail-closed)."""
-        class CustomObj:
-            pass
-        with pytest.raises(ValueError):
-            _canonicalize(CustomObj())
-
-    def test_object_with_value_attr_raises(self):
-        """Objects with .value attribute (Unhashable-like) should raise ValueError."""
-        class FakeUnhashable:
-            def __init__(self):
-                self.value = float('nan')
-        with pytest.raises(ValueError):
-            _canonicalize(FakeUnhashable())
-
-
-class TestSerializeCacheKey:
-    """Test _serialize_cache_key for deterministic hashing."""
-
-    def test_same_content_same_hash(self):
-        """Same content should produce same hash."""
-        key1 = frozenset([("node_1", frozenset([("input", "value")]))])
-        key2 = frozenset([("node_1", frozenset([("input", "value")]))])
-
-        hash1 = _serialize_cache_key(key1)
-        hash2 = _serialize_cache_key(key2)
-
-        assert hash1 == hash2
-
-    def test_different_content_different_hash(self):
-        """Different content should produce different hash."""
-        key1 = frozenset([("node_1", "value_a")])
-        key2 = frozenset([("node_1", "value_b")])
-
-        hash1 = _serialize_cache_key(key1)
-        hash2 = _serialize_cache_key(key2)
-
-        assert hash1 != hash2
-
-    def test_returns_hex_string(self):
-        """Should return hex string (SHA256 hex digest)."""
-        key = frozenset([("test", 123)])
-        result = _serialize_cache_key(key)
-
-        assert isinstance(result, str)
-        assert len(result) == 64  # SHA256 hex digest is 64 chars
-
-    def test_complex_nested_structure(self):
-        """Complex nested structures should hash deterministically."""
-        # Note: frozensets can only contain hashable types, so we use
-        # nested frozensets of tuples to represent dict-like structures
-        key = frozenset([
-            ("node_1", frozenset([
-                ("input_a", ("tuple", "value")),
-                ("input_b", frozenset([("nested", "dict")])),
-            ])),
-            ("node_2", frozenset([
-                ("param", 42),
-            ])),
-        ])
-
-        # Hash twice to verify determinism
-        hash1 = _serialize_cache_key(key)
-        hash2 = _serialize_cache_key(key)
-
-        assert hash1 == hash2
-
-    def test_dict_in_cache_key(self):
-        """Dicts passed directly to _serialize_cache_key should work."""
-        key = {"node_1": {"input": "value"}, "node_2": 42}
-
-        hash1 = _serialize_cache_key(key)
-        hash2 = _serialize_cache_key(key)
-
-        assert hash1 == hash2
-        assert isinstance(hash1, str)
-        assert len(hash1) == 64
-
-    def test_unknown_type_returns_none(self):
-        """Non-cacheable types should return None (fail-closed)."""
-        class CustomObj:
-            pass
-        assert _serialize_cache_key(CustomObj()) is None
-
-
-class TestContainsSelfUnequal:
-    """Test _contains_self_unequal utility function."""
-
-    def test_nan_float_detected(self):
-        """NaN floats should be detected (not equal to itself)."""
-        assert _contains_self_unequal(float('nan')) is True
-
-    def test_regular_float_not_detected(self):
-        """Regular floats are equal to themselves."""
-        assert _contains_self_unequal(3.14) is False
-        assert _contains_self_unequal(0.0) is False
-        assert _contains_self_unequal(-1.5) is False
-
-    def test_infinity_not_detected(self):
-        """Infinity is equal to itself."""
-        assert _contains_self_unequal(float('inf')) is False
-        assert _contains_self_unequal(float('-inf')) is False
-
-    def test_nan_in_list(self):
-        """NaN in list should be detected."""
-        assert _contains_self_unequal([1, 2, float('nan'), 4]) is True
-        assert _contains_self_unequal([1, 2, 3, 4]) is False
-
-    def test_nan_in_tuple(self):
-        """NaN in tuple should be detected."""
-        assert _contains_self_unequal((1, float('nan'))) is True
-        assert _contains_self_unequal((1, 2, 3)) is False
-
-    def test_nan_in_frozenset(self):
-        """NaN in frozenset should be detected."""
-        assert _contains_self_unequal(frozenset([1, float('nan')])) is True
-        assert _contains_self_unequal(frozenset([1, 2, 3])) is False
-
-    def test_nan_in_dict_value(self):
-        """NaN in dict value should be detected."""
-        assert _contains_self_unequal({"key": float('nan')}) is True
-        assert _contains_self_unequal({"key": 42}) is False
-
-    def test_nan_in_nested_structure(self):
-        """NaN in deeply nested structure should be detected."""
-        nested = {"level1": [{"level2": (1, 2, float('nan'))}]}
-        assert _contains_self_unequal(nested) is True
-
-    def test_non_numeric_types(self):
-        """Non-numeric types should not be self-unequal."""
-        assert _contains_self_unequal("string") is False
-        assert _contains_self_unequal(None) is False
-        assert _contains_self_unequal(True) is False
-
-    def test_object_with_nan_value_attribute(self):
-        """Objects wrapping NaN in .value should be detected."""
-        class NanWrapper:
-            def __init__(self):
-                self.value = float('nan')
-        assert _contains_self_unequal(NanWrapper()) is True
-
-    def test_custom_self_unequal_object(self):
-        """Custom objects where not (x == x) should be detected."""
-        class NeverEqual:
-            def __eq__(self, other):
-                return False
-        assert _contains_self_unequal(NeverEqual()) is True
-
-
-class TestEstimateValueSize:
-    """Test _estimate_value_size utility function."""
-
-    def test_empty_outputs(self):
-        """Empty outputs should have zero size."""
-        value = CacheValue(outputs=[])
-        assert _estimate_value_size(value) == 0
-
-    @pytest.mark.skipif(
-        not _torch_available(),
-        reason="PyTorch not available"
-    )
-    def test_tensor_size_estimation(self):
-        """Tensor size should be estimated correctly."""
-        import torch
-
-        # 1000 float32 elements = 4000 bytes
-        tensor = torch.zeros(1000, dtype=torch.float32)
-        value = CacheValue(outputs=[[tensor]])
-
-        size = _estimate_value_size(value)
-        assert size == 4000
-
-    @pytest.mark.skipif(
-        not _torch_available(),
-        reason="PyTorch not available"
-    )
-    def test_nested_tensor_in_dict(self):
-        """Tensors nested in dicts should be counted."""
-        import torch
-
-        tensor = torch.zeros(100, dtype=torch.float32)  # 400 bytes
-        value = CacheValue(outputs=[[{"samples": tensor}]])
-
-        size = _estimate_value_size(value)
-        assert size == 400
-
-
-class TestProviderRegistry:
-    """Test cache provider registration and retrieval."""
-
-    def setup_method(self):
-        """Clear providers before each test."""
-        _clear_cache_providers()
-
-    def teardown_method(self):
-        """Clear providers after each test."""
-        _clear_cache_providers()
-
-    def test_register_provider(self):
-        """Provider should be registered successfully."""
-        provider = MockCacheProvider()
-        register_cache_provider(provider)
-
-        assert _has_cache_providers() is True
-        providers = _get_cache_providers()
-        assert len(providers) == 1
-        assert providers[0] is provider
-
-    def test_unregister_provider(self):
-        """Provider should be unregistered successfully."""
-        provider = MockCacheProvider()
-        register_cache_provider(provider)
-        unregister_cache_provider(provider)
-
-        assert _has_cache_providers() is False
-
-    def test_multiple_providers(self):
-        """Multiple providers can be registered."""
-        provider1 = MockCacheProvider()
-        provider2 = MockCacheProvider()
-
-        register_cache_provider(provider1)
-        register_cache_provider(provider2)
-
-        providers = _get_cache_providers()
-        assert len(providers) == 2
-
-    def test_duplicate_registration_ignored(self):
-        """Registering same provider twice should be ignored."""
-        provider = MockCacheProvider()
-
-        register_cache_provider(provider)
-        register_cache_provider(provider)  # Should be ignored
-
-        providers = _get_cache_providers()
-        assert len(providers) == 1
-
-    def test_clear_providers(self):
-        """_clear_cache_providers should remove all providers."""
-        provider1 = MockCacheProvider()
-        provider2 = MockCacheProvider()
-
-        register_cache_provider(provider1)
-        register_cache_provider(provider2)
-        _clear_cache_providers()
-
-        assert _has_cache_providers() is False
-        assert len(_get_cache_providers()) == 0
-
-
-class TestCacheContext:
-    """Test CacheContext dataclass."""
-
-    def test_context_creation(self):
-        """CacheContext should be created with all fields."""
-        context = CacheContext(
-            node_id="node-456",
-            class_type="KSampler",
-            cache_key_hash="a" * 64,
-        )
-
-        assert context.node_id == "node-456"
-        assert context.class_type == "KSampler"
-        assert context.cache_key_hash == "a" * 64
-
-
-class TestCacheValue:
-    """Test CacheValue dataclass."""
-
-    def test_value_creation(self):
-        """CacheValue should be created with outputs."""
-        outputs = [[{"samples": "tensor_data"}]]
-        value = CacheValue(outputs=outputs)
-
-        assert value.outputs == outputs
-
-
-class MockCacheProvider(CacheProvider):
-    """Mock cache provider for testing."""
-
-    def __init__(self):
-        self.lookups = []
-        self.stores = []
-
-    async def on_lookup(self, context: CacheContext) -> Optional[CacheValue]:
-        self.lookups.append(context)
-        return None
-
-    async def on_store(self, context: CacheContext, value: CacheValue) -> None:
-        self.stores.append((context, value))

From 5df1427124f6ceb70166326ee257d52076adea37 Mon Sep 17 00:00:00 2001
From: PxTicks <PxTicks@gmail.com>
Date: Fri, 13 Mar 2026 00:44:15 +0000
Subject: [PATCH 159/194] Fix audio extraction and truncation bugs (#12652)

Bug report in #12651

- to_skip fix: Prevents negative array slicing when the start offset is negative.
- __duration check: Prevents the extraction loop from breaking after a single audio chunk when the requested duration is 0 (which is a sentinel for unlimited).
---
 comfy_api/latest/_input_impl/video_types.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfy_api/latest/_input_impl/video_types.py b/comfy_api/latest/_input_impl/video_types.py
index 58a37c9e8..1b4993aa7 100644
--- a/comfy_api/latest/_input_impl/video_types.py
+++ b/comfy_api/latest/_input_impl/video_types.py
@@ -272,7 +272,7 @@ class VideoFromFile(VideoInput):
             has_first_frame = False
             for frame in frames:
                 offset_seconds = start_time - frame.pts * audio_stream.time_base
-                to_skip = int(offset_seconds * audio_stream.sample_rate)
+                to_skip = max(0, int(offset_seconds * audio_stream.sample_rate))
                 if to_skip < frame.samples:
                     has_first_frame = True
                     break
@@ -280,7 +280,7 @@ class VideoFromFile(VideoInput):
                 audio_frames.append(frame.to_ndarray()[..., to_skip:])
 
             for frame in frames:
-                if frame.time > start_time + self.__duration:
+                if self.__duration and frame.time > start_time + self.__duration:
                     break
                 audio_frames.append(frame.to_ndarray())  # shape: (channels, samples)
             if len(audio_frames) > 0:

From 63d1bbdb407c69370d407ce5ced6ca3f917528a8 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 12 Mar 2026 20:41:48 -0400
Subject: [PATCH 160/194] ComfyUI v0.17.0

---
 comfyui_version.py | 2 +-
 pyproject.toml     | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfyui_version.py b/comfyui_version.py
index 2723d02e7..701f4d66a 100644
--- a/comfyui_version.py
+++ b/comfyui_version.py
@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.16.4"
+__version__ = "0.17.0"
diff --git a/pyproject.toml b/pyproject.toml
index 753b219b3..e2ca79be7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.16.4"
+version = "0.17.0"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

From 4a8cf359fe596fc4c25a0d335d303e42c3f8605d Mon Sep 17 00:00:00 2001
From: Deep Mehta <42841935+deepme987@users.noreply.github.com>
Date: Thu, 12 Mar 2026 21:17:50 -0700
Subject: [PATCH 161/194] Revert "Revert "feat: Add CacheProvider API for
 external distributed caching"" (#12915)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Revert "Revert "feat: Add CacheProvider API for external distributed caching …"

This reverts commit d1d53c14be8442fca19aae978e944edad1935d46.

* fix: gate provider lookups to outputs cache and fix UI coercion

- Add `enable_providers` flag to BasicCache so only the outputs cache
  triggers external provider lookups/stores. The objects cache stores
  node class instances, not CacheEntry values, so provider calls were
  wasted round-trips that always missed.
- Remove `or {}` coercion on `result.ui` — an empty dict passes the
  `is not None` gate in execution.py and causes KeyError when the
  history builder indexes `["output"]` and `["meta"]`. Preserving
  `None` correctly skips the ui_node_outputs addition.
---
 comfy_api/latest/__init__.py                  |  35 ++
 comfy_api/latest/_caching.py                  |  42 ++
 comfy_execution/cache_provider.py             | 138 ++++++
 comfy_execution/caching.py                    | 196 +++++++--
 comfy_execution/graph.py                      |   6 +-
 execution.py                                  | 147 ++++---
 .../execution_test/test_cache_provider.py     | 403 ++++++++++++++++++
 7 files changed, 874 insertions(+), 93 deletions(-)
 create mode 100644 comfy_api/latest/_caching.py
 create mode 100644 comfy_execution/cache_provider.py
 create mode 100644 tests-unit/execution_test/test_cache_provider.py

diff --git a/comfy_api/latest/__init__.py b/comfy_api/latest/__init__.py
index f2399422b..04973fea0 100644
--- a/comfy_api/latest/__init__.py
+++ b/comfy_api/latest/__init__.py
@@ -25,6 +25,7 @@ class ComfyAPI_latest(ComfyAPIBase):
         super().__init__()
         self.node_replacement = self.NodeReplacement()
         self.execution = self.Execution()
+        self.caching = self.Caching()
 
     class NodeReplacement(ProxiedSingleton):
         async def register(self, node_replace: io.NodeReplace) -> None:
@@ -84,6 +85,36 @@ class ComfyAPI_latest(ComfyAPIBase):
                 image=to_display,
             )
 
+    class Caching(ProxiedSingleton):
+        """
+        External cache provider API for sharing cached node outputs
+        across ComfyUI instances.
+
+        Example::
+
+            from comfy_api.latest import Caching
+
+            class MyCacheProvider(Caching.CacheProvider):
+                async def on_lookup(self, context):
+                    ...  # check external storage
+
+                async def on_store(self, context, value):
+                    ...  # store to external storage
+
+            Caching.register_provider(MyCacheProvider())
+        """
+        from ._caching import CacheProvider, CacheContext, CacheValue
+
+        async def register_provider(self, provider: "ComfyAPI_latest.Caching.CacheProvider") -> None:
+            """Register an external cache provider. Providers are called in registration order."""
+            from comfy_execution.cache_provider import register_cache_provider
+            register_cache_provider(provider)
+
+        async def unregister_provider(self, provider: "ComfyAPI_latest.Caching.CacheProvider") -> None:
+            """Unregister a previously registered cache provider."""
+            from comfy_execution.cache_provider import unregister_cache_provider
+            unregister_cache_provider(provider)
+
 class ComfyExtension(ABC):
     async def on_load(self) -> None:
         """
@@ -116,6 +147,9 @@ class Types:
     VOXEL = VOXEL
     File3D = File3D
 
+
+Caching = ComfyAPI_latest.Caching
+
 ComfyAPI = ComfyAPI_latest
 
 # Create a synchronous version of the API
@@ -135,6 +169,7 @@ __all__ = [
     "Input",
     "InputImpl",
     "Types",
+    "Caching",
     "ComfyExtension",
     "io",
     "IO",
diff --git a/comfy_api/latest/_caching.py b/comfy_api/latest/_caching.py
new file mode 100644
index 000000000..30c8848cd
--- /dev/null
+++ b/comfy_api/latest/_caching.py
@@ -0,0 +1,42 @@
+from abc import ABC, abstractmethod
+from typing import Optional
+from dataclasses import dataclass
+
+
+@dataclass
+class CacheContext:
+    node_id: str
+    class_type: str
+    cache_key_hash: str  # SHA256 hex digest
+
+
+@dataclass
+class CacheValue:
+    outputs: list
+    ui: dict = None
+
+
+class CacheProvider(ABC):
+    """Abstract base class for external cache providers.
+    Exceptions from provider methods are caught by the caller and never break execution.
+    """
+
+    @abstractmethod
+    async def on_lookup(self, context: CacheContext) -> Optional[CacheValue]:
+        """Called on local cache miss. Return CacheValue if found, None otherwise."""
+        pass
+
+    @abstractmethod
+    async def on_store(self, context: CacheContext, value: CacheValue) -> None:
+        """Called after local store. Dispatched via asyncio.create_task."""
+        pass
+
+    def should_cache(self, context: CacheContext, value: Optional[CacheValue] = None) -> bool:
+        """Return False to skip external caching for this node. Default: True."""
+        return True
+
+    def on_prompt_start(self, prompt_id: str) -> None:
+        pass
+
+    def on_prompt_end(self, prompt_id: str) -> None:
+        pass
diff --git a/comfy_execution/cache_provider.py b/comfy_execution/cache_provider.py
new file mode 100644
index 000000000..d455d08e8
--- /dev/null
+++ b/comfy_execution/cache_provider.py
@@ -0,0 +1,138 @@
+from typing import Any, Optional, Tuple, List
+import hashlib
+import json
+import logging
+import threading
+
+# Public types — source of truth is comfy_api.latest._caching
+from comfy_api.latest._caching import CacheProvider, CacheContext, CacheValue  # noqa: F401 (re-exported)
+
+_logger = logging.getLogger(__name__)
+
+
+_providers: List[CacheProvider] = []
+_providers_lock = threading.Lock()
+_providers_snapshot: Tuple[CacheProvider, ...] = ()
+
+
+def register_cache_provider(provider: CacheProvider) -> None:
+    """Register an external cache provider. Providers are called in registration order."""
+    global _providers_snapshot
+    with _providers_lock:
+        if provider in _providers:
+            _logger.warning(f"Provider {provider.__class__.__name__} already registered")
+            return
+        _providers.append(provider)
+        _providers_snapshot = tuple(_providers)
+        _logger.debug(f"Registered cache provider: {provider.__class__.__name__}")
+
+
+def unregister_cache_provider(provider: CacheProvider) -> None:
+    global _providers_snapshot
+    with _providers_lock:
+        try:
+            _providers.remove(provider)
+            _providers_snapshot = tuple(_providers)
+            _logger.debug(f"Unregistered cache provider: {provider.__class__.__name__}")
+        except ValueError:
+            _logger.warning(f"Provider {provider.__class__.__name__} was not registered")
+
+
+def _get_cache_providers() -> Tuple[CacheProvider, ...]:
+    return _providers_snapshot
+
+
+def _has_cache_providers() -> bool:
+    return bool(_providers_snapshot)
+
+
+def _clear_cache_providers() -> None:
+    global _providers_snapshot
+    with _providers_lock:
+        _providers.clear()
+        _providers_snapshot = ()
+
+
+def _canonicalize(obj: Any) -> Any:
+    # Convert to canonical JSON-serializable form with deterministic ordering.
+    # Frozensets have non-deterministic iteration order between Python sessions.
+    # Raises ValueError for non-cacheable types (Unhashable, unknown) so that
+    # _serialize_cache_key returns None and external caching is skipped.
+    if isinstance(obj, frozenset):
+        return ("__frozenset__", sorted(
+            [_canonicalize(item) for item in obj],
+            key=lambda x: json.dumps(x, sort_keys=True)
+        ))
+    elif isinstance(obj, set):
+        return ("__set__", sorted(
+            [_canonicalize(item) for item in obj],
+            key=lambda x: json.dumps(x, sort_keys=True)
+        ))
+    elif isinstance(obj, tuple):
+        return ("__tuple__", [_canonicalize(item) for item in obj])
+    elif isinstance(obj, list):
+        return [_canonicalize(item) for item in obj]
+    elif isinstance(obj, dict):
+        return {"__dict__": sorted(
+            [[_canonicalize(k), _canonicalize(v)] for k, v in obj.items()],
+            key=lambda x: json.dumps(x, sort_keys=True)
+        )}
+    elif isinstance(obj, (int, float, str, bool, type(None))):
+        return (type(obj).__name__, obj)
+    elif isinstance(obj, bytes):
+        return ("__bytes__", obj.hex())
+    else:
+        raise ValueError(f"Cannot canonicalize type: {type(obj).__name__}")
+
+
+def _serialize_cache_key(cache_key: Any) -> Optional[str]:
+    # Returns deterministic SHA256 hex digest, or None on failure.
+    # Uses JSON (not pickle) because pickle is non-deterministic across sessions.
+    try:
+        canonical = _canonicalize(cache_key)
+        json_str = json.dumps(canonical, sort_keys=True, separators=(',', ':'))
+        return hashlib.sha256(json_str.encode('utf-8')).hexdigest()
+    except Exception as e:
+        _logger.warning(f"Failed to serialize cache key: {e}")
+        return None
+
+
+def _contains_self_unequal(obj: Any) -> bool:
+    # Local cache matches by ==. Values where not (x == x) (NaN, etc.) will
+    # never hit locally, but serialized form would match externally. Skip these.
+    try:
+        if not (obj == obj):
+            return True
+    except Exception:
+        return True
+    if isinstance(obj, (frozenset, tuple, list, set)):
+        return any(_contains_self_unequal(item) for item in obj)
+    if isinstance(obj, dict):
+        return any(_contains_self_unequal(k) or _contains_self_unequal(v) for k, v in obj.items())
+    if hasattr(obj, 'value'):
+        return _contains_self_unequal(obj.value)
+    return False
+
+
+def _estimate_value_size(value: CacheValue) -> int:
+    try:
+        import torch
+    except ImportError:
+        return 0
+
+    total = 0
+
+    def estimate(obj):
+        nonlocal total
+        if isinstance(obj, torch.Tensor):
+            total += obj.numel() * obj.element_size()
+        elif isinstance(obj, dict):
+            for v in obj.values():
+                estimate(v)
+        elif isinstance(obj, (list, tuple)):
+            for item in obj:
+                estimate(item)
+
+    for output in value.outputs:
+        estimate(output)
+    return total
diff --git a/comfy_execution/caching.py b/comfy_execution/caching.py
index 326a279fc..78212bde3 100644
--- a/comfy_execution/caching.py
+++ b/comfy_execution/caching.py
@@ -1,3 +1,4 @@
+import asyncio
 import bisect
 import gc
 import itertools
@@ -147,13 +148,15 @@ class CacheKeySetInputSignature(CacheKeySet):
                     self.get_ordered_ancestry_internal(dynprompt, ancestor_id, ancestors, order_mapping)
 
 class BasicCache:
-    def __init__(self, key_class):
+    def __init__(self, key_class, enable_providers=False):
         self.key_class = key_class
         self.initialized = False
+        self.enable_providers = enable_providers
         self.dynprompt: DynamicPrompt
         self.cache_key_set: CacheKeySet
         self.cache = {}
         self.subcaches = {}
+        self._pending_store_tasks: set = set()
 
     async def set_prompt(self, dynprompt, node_ids, is_changed_cache):
         self.dynprompt = dynprompt
@@ -196,18 +199,138 @@ class BasicCache:
     def poll(self, **kwargs):
         pass
 
-    def _set_immediate(self, node_id, value):
-        assert self.initialized
-        cache_key = self.cache_key_set.get_data_key(node_id)
-        self.cache[cache_key] = value
-
-    def _get_immediate(self, node_id):
+    def get_local(self, node_id):
         if not self.initialized:
             return None
         cache_key = self.cache_key_set.get_data_key(node_id)
         if cache_key in self.cache:
             return self.cache[cache_key]
-        else:
+        return None
+
+    def set_local(self, node_id, value):
+        assert self.initialized
+        cache_key = self.cache_key_set.get_data_key(node_id)
+        self.cache[cache_key] = value
+
+    async def _set_immediate(self, node_id, value):
+        assert self.initialized
+        cache_key = self.cache_key_set.get_data_key(node_id)
+        self.cache[cache_key] = value
+
+        await self._notify_providers_store(node_id, cache_key, value)
+
+    async def _get_immediate(self, node_id):
+        if not self.initialized:
+            return None
+        cache_key = self.cache_key_set.get_data_key(node_id)
+
+        if cache_key in self.cache:
+            return self.cache[cache_key]
+
+        external_result = await self._check_providers_lookup(node_id, cache_key)
+        if external_result is not None:
+            self.cache[cache_key] = external_result
+            return external_result
+
+        return None
+
+    async def _notify_providers_store(self, node_id, cache_key, value):
+        from comfy_execution.cache_provider import (
+            _has_cache_providers, _get_cache_providers,
+            CacheValue, _contains_self_unequal, _logger
+        )
+
+        if not self.enable_providers:
+            return
+        if not _has_cache_providers():
+            return
+        if not self._is_external_cacheable_value(value):
+            return
+        if _contains_self_unequal(cache_key):
+            return
+
+        context = self._build_context(node_id, cache_key)
+        if context is None:
+            return
+        cache_value = CacheValue(outputs=value.outputs, ui=value.ui)
+
+        for provider in _get_cache_providers():
+            try:
+                if provider.should_cache(context, cache_value):
+                    task = asyncio.create_task(self._safe_provider_store(provider, context, cache_value))
+                    self._pending_store_tasks.add(task)
+                    task.add_done_callback(self._pending_store_tasks.discard)
+            except Exception as e:
+                _logger.warning(f"Cache provider {provider.__class__.__name__} error on store: {e}")
+
+    @staticmethod
+    async def _safe_provider_store(provider, context, cache_value):
+        from comfy_execution.cache_provider import _logger
+        try:
+            await provider.on_store(context, cache_value)
+        except Exception as e:
+            _logger.warning(f"Cache provider {provider.__class__.__name__} async store error: {e}")
+
+    async def _check_providers_lookup(self, node_id, cache_key):
+        from comfy_execution.cache_provider import (
+            _has_cache_providers, _get_cache_providers,
+            CacheValue, _contains_self_unequal, _logger
+        )
+
+        if not self.enable_providers:
+            return None
+        if not _has_cache_providers():
+            return None
+        if _contains_self_unequal(cache_key):
+            return None
+
+        context = self._build_context(node_id, cache_key)
+        if context is None:
+            return None
+
+        for provider in _get_cache_providers():
+            try:
+                if not provider.should_cache(context):
+                    continue
+                result = await provider.on_lookup(context)
+                if result is not None:
+                    if not isinstance(result, CacheValue):
+                        _logger.warning(f"Provider {provider.__class__.__name__} returned invalid type")
+                        continue
+                    if not isinstance(result.outputs, (list, tuple)):
+                        _logger.warning(f"Provider {provider.__class__.__name__} returned invalid outputs")
+                        continue
+                    from execution import CacheEntry
+                    return CacheEntry(ui=result.ui, outputs=list(result.outputs))
+            except Exception as e:
+                _logger.warning(f"Cache provider {provider.__class__.__name__} error on lookup: {e}")
+
+        return None
+
+    def _is_external_cacheable_value(self, value):
+        return hasattr(value, 'outputs') and hasattr(value, 'ui')
+
+    def _get_class_type(self, node_id):
+        if not self.initialized or not self.dynprompt:
+            return ''
+        try:
+            return self.dynprompt.get_node(node_id).get('class_type', '')
+        except Exception:
+            return ''
+
+    def _build_context(self, node_id, cache_key):
+        from comfy_execution.cache_provider import CacheContext, _serialize_cache_key, _logger
+        try:
+            cache_key_hash = _serialize_cache_key(cache_key)
+            if cache_key_hash is None:
+                return None
+            return CacheContext(
+                node_id=node_id,
+                class_type=self._get_class_type(node_id),
+                cache_key_hash=cache_key_hash,
+            )
+        except Exception as e:
+            _logger.warning(f"Failed to build cache context for node {node_id}: {e}")
             return None
 
     async def _ensure_subcache(self, node_id, children_ids):
@@ -236,8 +359,8 @@ class BasicCache:
         return result
 
 class HierarchicalCache(BasicCache):
-    def __init__(self, key_class):
-        super().__init__(key_class)
+    def __init__(self, key_class, enable_providers=False):
+        super().__init__(key_class, enable_providers=enable_providers)
 
     def _get_cache_for(self, node_id):
         assert self.dynprompt is not None
@@ -257,16 +380,27 @@ class HierarchicalCache(BasicCache):
                 return None
         return cache
 
-    def get(self, node_id):
+    async def get(self, node_id):
         cache = self._get_cache_for(node_id)
         if cache is None:
             return None
-        return cache._get_immediate(node_id)
+        return await cache._get_immediate(node_id)
 
-    def set(self, node_id, value):
+    def get_local(self, node_id):
+        cache = self._get_cache_for(node_id)
+        if cache is None:
+            return None
+        return BasicCache.get_local(cache, node_id)
+
+    async def set(self, node_id, value):
         cache = self._get_cache_for(node_id)
         assert cache is not None
-        cache._set_immediate(node_id, value)
+        await cache._set_immediate(node_id, value)
+
+    def set_local(self, node_id, value):
+        cache = self._get_cache_for(node_id)
+        assert cache is not None
+        BasicCache.set_local(cache, node_id, value)
 
     async def ensure_subcache_for(self, node_id, children_ids):
         cache = self._get_cache_for(node_id)
@@ -287,18 +421,24 @@ class NullCache:
     def poll(self, **kwargs):
         pass
 
-    def get(self, node_id):
+    async def get(self, node_id):
         return None
 
-    def set(self, node_id, value):
+    def get_local(self, node_id):
+        return None
+
+    async def set(self, node_id, value):
+        pass
+
+    def set_local(self, node_id, value):
         pass
 
     async def ensure_subcache_for(self, node_id, children_ids):
         return self
 
 class LRUCache(BasicCache):
-    def __init__(self, key_class, max_size=100):
-        super().__init__(key_class)
+    def __init__(self, key_class, max_size=100, enable_providers=False):
+        super().__init__(key_class, enable_providers=enable_providers)
         self.max_size = max_size
         self.min_generation = 0
         self.generation = 0
@@ -322,18 +462,18 @@ class LRUCache(BasicCache):
                     del self.children[key]
         self._clean_subcaches()
 
-    def get(self, node_id):
+    async def get(self, node_id):
         self._mark_used(node_id)
-        return self._get_immediate(node_id)
+        return await self._get_immediate(node_id)
 
     def _mark_used(self, node_id):
         cache_key = self.cache_key_set.get_data_key(node_id)
         if cache_key is not None:
             self.used_generation[cache_key] = self.generation
 
-    def set(self, node_id, value):
+    async def set(self, node_id, value):
         self._mark_used(node_id)
-        return self._set_immediate(node_id, value)
+        return await self._set_immediate(node_id, value)
 
     async def ensure_subcache_for(self, node_id, children_ids):
         # Just uses subcaches for tracking 'live' nodes
@@ -366,20 +506,20 @@ RAM_CACHE_OLD_WORKFLOW_OOM_MULTIPLIER = 1.3
 
 class RAMPressureCache(LRUCache):
 
-    def __init__(self, key_class):
-        super().__init__(key_class, 0)
+    def __init__(self, key_class, enable_providers=False):
+        super().__init__(key_class, 0, enable_providers=enable_providers)
         self.timestamps = {}
 
     def clean_unused(self):
         self._clean_subcaches()
 
-    def set(self, node_id, value):
+    async def set(self, node_id, value):
         self.timestamps[self.cache_key_set.get_data_key(node_id)] = time.time()
-        super().set(node_id, value)
+        await super().set(node_id, value)
 
-    def get(self, node_id):
+    async def get(self, node_id):
         self.timestamps[self.cache_key_set.get_data_key(node_id)] = time.time()
-        return super().get(node_id)
+        return await super().get(node_id)
 
     def poll(self, ram_headroom):
         def _ram_gb():
diff --git a/comfy_execution/graph.py b/comfy_execution/graph.py
index 9d170b16e..c47f3c79b 100644
--- a/comfy_execution/graph.py
+++ b/comfy_execution/graph.py
@@ -204,12 +204,12 @@ class ExecutionList(TopologicalSort):
         self.execution_cache_listeners = {}
 
     def is_cached(self, node_id):
-        return self.output_cache.get(node_id) is not None
+        return self.output_cache.get_local(node_id) is not None
 
     def cache_link(self, from_node_id, to_node_id):
         if to_node_id not in self.execution_cache:
             self.execution_cache[to_node_id] = {}
-        self.execution_cache[to_node_id][from_node_id] = self.output_cache.get(from_node_id)
+        self.execution_cache[to_node_id][from_node_id] = self.output_cache.get_local(from_node_id)
         if from_node_id not in self.execution_cache_listeners:
             self.execution_cache_listeners[from_node_id] = set()
         self.execution_cache_listeners[from_node_id].add(to_node_id)
@@ -221,7 +221,7 @@ class ExecutionList(TopologicalSort):
         if value is None:
             return None
         #Write back to the main cache on touch.
-        self.output_cache.set(from_node_id, value)
+        self.output_cache.set_local(from_node_id, value)
         return value
 
     def cache_update(self, node_id, value):
diff --git a/execution.py b/execution.py
index a7791efed..1a6c3429c 100644
--- a/execution.py
+++ b/execution.py
@@ -40,6 +40,7 @@ from comfy_execution.progress import get_progress_state, reset_progress_state, a
 from comfy_execution.utils import CurrentNodeContext
 from comfy_api.internal import _ComfyNodeInternal, _NodeOutputInternal, first_real_override, is_class, make_locked_method_func
 from comfy_api.latest import io, _io
+from comfy_execution.cache_provider import _has_cache_providers, _get_cache_providers, _logger as _cache_logger
 
 
 class ExecutionResult(Enum):
@@ -126,15 +127,15 @@ class CacheSet:
 
     # Performs like the old cache -- dump data ASAP
     def init_classic_cache(self):
-        self.outputs = HierarchicalCache(CacheKeySetInputSignature)
+        self.outputs = HierarchicalCache(CacheKeySetInputSignature, enable_providers=True)
         self.objects = HierarchicalCache(CacheKeySetID)
 
     def init_lru_cache(self, cache_size):
-        self.outputs = LRUCache(CacheKeySetInputSignature, max_size=cache_size)
+        self.outputs = LRUCache(CacheKeySetInputSignature, max_size=cache_size, enable_providers=True)
         self.objects = HierarchicalCache(CacheKeySetID)
 
     def init_ram_cache(self, min_headroom):
-        self.outputs = RAMPressureCache(CacheKeySetInputSignature)
+        self.outputs = RAMPressureCache(CacheKeySetInputSignature, enable_providers=True)
         self.objects = HierarchicalCache(CacheKeySetID)
 
     def init_null_cache(self):
@@ -418,7 +419,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
     inputs = dynprompt.get_node(unique_id)['inputs']
     class_type = dynprompt.get_node(unique_id)['class_type']
     class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
-    cached = caches.outputs.get(unique_id)
+    cached = await caches.outputs.get(unique_id)
     if cached is not None:
         if server.client_id is not None:
             cached_ui = cached.ui or {}
@@ -474,10 +475,10 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
                 server.last_node_id = display_node_id
                 server.send_sync("executing", { "node": unique_id, "display_node": display_node_id, "prompt_id": prompt_id }, server.client_id)
 
-            obj = caches.objects.get(unique_id)
+            obj = await caches.objects.get(unique_id)
             if obj is None:
                 obj = class_def()
-                caches.objects.set(unique_id, obj)
+                await caches.objects.set(unique_id, obj)
 
             if issubclass(class_def, _ComfyNodeInternal):
                 lazy_status_present = first_real_override(class_def, "check_lazy_status") is not None
@@ -588,7 +589,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
 
         cache_entry = CacheEntry(ui=ui_outputs.get(unique_id), outputs=output_data)
         execution_list.cache_update(unique_id, cache_entry)
-        caches.outputs.set(unique_id, cache_entry)
+        await caches.outputs.set(unique_id, cache_entry)
 
     except comfy.model_management.InterruptProcessingException as iex:
         logging.info("Processing interrupted")
@@ -684,6 +685,19 @@ class PromptExecutor:
             }
             self.add_message("execution_error", mes, broadcast=False)
 
+    def _notify_prompt_lifecycle(self, event: str, prompt_id: str):
+        if not _has_cache_providers():
+            return
+
+        for provider in _get_cache_providers():
+            try:
+                if event == "start":
+                    provider.on_prompt_start(prompt_id)
+                elif event == "end":
+                    provider.on_prompt_end(prompt_id)
+            except Exception as e:
+                _cache_logger.warning(f"Cache provider {provider.__class__.__name__} error on {event}: {e}")
+
     def execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]):
         asyncio.run(self.execute_async(prompt, prompt_id, extra_data, execute_outputs))
 
@@ -700,66 +714,75 @@ class PromptExecutor:
         self.status_messages = []
         self.add_message("execution_start", { "prompt_id": prompt_id}, broadcast=False)
 
-        with torch.inference_mode():
-            dynamic_prompt = DynamicPrompt(prompt)
-            reset_progress_state(prompt_id, dynamic_prompt)
-            add_progress_handler(WebUIProgressHandler(self.server))
-            is_changed_cache = IsChangedCache(prompt_id, dynamic_prompt, self.caches.outputs)
-            for cache in self.caches.all:
-                await cache.set_prompt(dynamic_prompt, prompt.keys(), is_changed_cache)
-                cache.clean_unused()
+        self._notify_prompt_lifecycle("start", prompt_id)
 
-            cached_nodes = []
-            for node_id in prompt:
-                if self.caches.outputs.get(node_id) is not None:
-                    cached_nodes.append(node_id)
+        try:
+            with torch.inference_mode():
+                dynamic_prompt = DynamicPrompt(prompt)
+                reset_progress_state(prompt_id, dynamic_prompt)
+                add_progress_handler(WebUIProgressHandler(self.server))
+                is_changed_cache = IsChangedCache(prompt_id, dynamic_prompt, self.caches.outputs)
+                for cache in self.caches.all:
+                    await cache.set_prompt(dynamic_prompt, prompt.keys(), is_changed_cache)
+                    cache.clean_unused()
 
-            comfy.model_management.cleanup_models_gc()
-            self.add_message("execution_cached",
-                          { "nodes": cached_nodes, "prompt_id": prompt_id},
-                          broadcast=False)
-            pending_subgraph_results = {}
-            pending_async_nodes = {} # TODO - Unify this with pending_subgraph_results
-            ui_node_outputs = {}
-            executed = set()
-            execution_list = ExecutionList(dynamic_prompt, self.caches.outputs)
-            current_outputs = self.caches.outputs.all_node_ids()
-            for node_id in list(execute_outputs):
-                execution_list.add_node(node_id)
+                node_ids = list(prompt.keys())
+                cache_results = await asyncio.gather(
+                    *(self.caches.outputs.get(node_id) for node_id in node_ids)
+                )
+                cached_nodes = [
+                    node_id for node_id, result in zip(node_ids, cache_results)
+                    if result is not None
+                ]
 
-            while not execution_list.is_empty():
-                node_id, error, ex = await execution_list.stage_node_execution()
-                if error is not None:
-                    self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
-                    break
+                comfy.model_management.cleanup_models_gc()
+                self.add_message("execution_cached",
+                              { "nodes": cached_nodes, "prompt_id": prompt_id},
+                              broadcast=False)
+                pending_subgraph_results = {}
+                pending_async_nodes = {} # TODO - Unify this with pending_subgraph_results
+                ui_node_outputs = {}
+                executed = set()
+                execution_list = ExecutionList(dynamic_prompt, self.caches.outputs)
+                current_outputs = self.caches.outputs.all_node_ids()
+                for node_id in list(execute_outputs):
+                    execution_list.add_node(node_id)
 
-                assert node_id is not None, "Node ID should not be None at this point"
-                result, error, ex = await execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes, ui_node_outputs)
-                self.success = result != ExecutionResult.FAILURE
-                if result == ExecutionResult.FAILURE:
-                    self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
-                    break
-                elif result == ExecutionResult.PENDING:
-                    execution_list.unstage_node_execution()
-                else: # result == ExecutionResult.SUCCESS:
-                    execution_list.complete_node_execution()
-                self.caches.outputs.poll(ram_headroom=self.cache_args["ram"])
-            else:
-                # Only execute when the while-loop ends without break
-                self.add_message("execution_success", { "prompt_id": prompt_id }, broadcast=False)
+                while not execution_list.is_empty():
+                    node_id, error, ex = await execution_list.stage_node_execution()
+                    if error is not None:
+                        self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
+                        break
 
-            ui_outputs = {}
-            meta_outputs = {}
-            for node_id, ui_info in ui_node_outputs.items():
-                ui_outputs[node_id] = ui_info["output"]
-                meta_outputs[node_id] = ui_info["meta"]
-            self.history_result = {
-                "outputs": ui_outputs,
-                "meta": meta_outputs,
-            }
-            self.server.last_node_id = None
-            if comfy.model_management.DISABLE_SMART_MEMORY:
-                comfy.model_management.unload_all_models()
+                    assert node_id is not None, "Node ID should not be None at this point"
+                    result, error, ex = await execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes, ui_node_outputs)
+                    self.success = result != ExecutionResult.FAILURE
+                    if result == ExecutionResult.FAILURE:
+                        self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
+                        break
+                    elif result == ExecutionResult.PENDING:
+                        execution_list.unstage_node_execution()
+                    else: # result == ExecutionResult.SUCCESS:
+                        execution_list.complete_node_execution()
+                    self.caches.outputs.poll(ram_headroom=self.cache_args["ram"])
+                else:
+                    # Only execute when the while-loop ends without break
+                    self.add_message("execution_success", { "prompt_id": prompt_id }, broadcast=False)
+
+                ui_outputs = {}
+                meta_outputs = {}
+                for node_id, ui_info in ui_node_outputs.items():
+                    ui_outputs[node_id] = ui_info["output"]
+                    meta_outputs[node_id] = ui_info["meta"]
+                self.history_result = {
+                    "outputs": ui_outputs,
+                    "meta": meta_outputs,
+                }
+                self.server.last_node_id = None
+                if comfy.model_management.DISABLE_SMART_MEMORY:
+                    comfy.model_management.unload_all_models()
+        finally:
+            self._notify_prompt_lifecycle("end", prompt_id)
 
 
 async def validate_inputs(prompt_id, prompt, item, validated):
diff --git a/tests-unit/execution_test/test_cache_provider.py b/tests-unit/execution_test/test_cache_provider.py
new file mode 100644
index 000000000..ac3814746
--- /dev/null
+++ b/tests-unit/execution_test/test_cache_provider.py
@@ -0,0 +1,403 @@
+"""Tests for external cache provider API."""
+
+import importlib.util
+import pytest
+from typing import Optional
+
+
+def _torch_available() -> bool:
+    """Check if PyTorch is available."""
+    return importlib.util.find_spec("torch") is not None
+
+
+from comfy_execution.cache_provider import (
+    CacheProvider,
+    CacheContext,
+    CacheValue,
+    register_cache_provider,
+    unregister_cache_provider,
+    _get_cache_providers,
+    _has_cache_providers,
+    _clear_cache_providers,
+    _serialize_cache_key,
+    _contains_self_unequal,
+    _estimate_value_size,
+    _canonicalize,
+)
+
+
+class TestCanonicalize:
+    """Test _canonicalize function for deterministic ordering."""
+
+    def test_frozenset_ordering_is_deterministic(self):
+        """Frozensets should produce consistent canonical form regardless of iteration order."""
+        # Create two frozensets with same content
+        fs1 = frozenset([("a", 1), ("b", 2), ("c", 3)])
+        fs2 = frozenset([("c", 3), ("a", 1), ("b", 2)])
+
+        result1 = _canonicalize(fs1)
+        result2 = _canonicalize(fs2)
+
+        assert result1 == result2
+
+    def test_nested_frozenset_ordering(self):
+        """Nested frozensets should also be deterministically ordered."""
+        inner1 = frozenset([1, 2, 3])
+        inner2 = frozenset([3, 2, 1])
+
+        fs1 = frozenset([("key", inner1)])
+        fs2 = frozenset([("key", inner2)])
+
+        result1 = _canonicalize(fs1)
+        result2 = _canonicalize(fs2)
+
+        assert result1 == result2
+
+    def test_dict_ordering(self):
+        """Dicts should be sorted by key."""
+        d1 = {"z": 1, "a": 2, "m": 3}
+        d2 = {"a": 2, "m": 3, "z": 1}
+
+        result1 = _canonicalize(d1)
+        result2 = _canonicalize(d2)
+
+        assert result1 == result2
+
+    def test_tuple_preserved(self):
+        """Tuples should be marked and preserved."""
+        t = (1, 2, 3)
+        result = _canonicalize(t)
+
+        assert result[0] == "__tuple__"
+
+    def test_list_preserved(self):
+        """Lists should be recursively canonicalized."""
+        lst = [{"b": 2, "a": 1}, frozenset([3, 2, 1])]
+        result = _canonicalize(lst)
+
+        # First element should be canonicalized dict
+        assert "__dict__" in result[0]
+        # Second element should be canonicalized frozenset
+        assert result[1][0] == "__frozenset__"
+
+    def test_primitives_include_type(self):
+        """Primitive types should include type name for disambiguation."""
+        assert _canonicalize(42) == ("int", 42)
+        assert _canonicalize(3.14) == ("float", 3.14)
+        assert _canonicalize("hello") == ("str", "hello")
+        assert _canonicalize(True) == ("bool", True)
+        assert _canonicalize(None) == ("NoneType", None)
+
+    def test_int_and_str_distinguished(self):
+        """int 7 and str '7' must produce different canonical forms."""
+        assert _canonicalize(7) != _canonicalize("7")
+
+    def test_bytes_converted(self):
+        """Bytes should be converted to hex string."""
+        b = b"\x00\xff"
+        result = _canonicalize(b)
+
+        assert result[0] == "__bytes__"
+        assert result[1] == "00ff"
+
+    def test_set_ordering(self):
+        """Sets should be sorted like frozensets."""
+        s1 = {3, 1, 2}
+        s2 = {1, 2, 3}
+
+        result1 = _canonicalize(s1)
+        result2 = _canonicalize(s2)
+
+        assert result1 == result2
+        assert result1[0] == "__set__"
+
+    def test_unknown_type_raises(self):
+        """Unknown types should raise ValueError (fail-closed)."""
+        class CustomObj:
+            pass
+        with pytest.raises(ValueError):
+            _canonicalize(CustomObj())
+
+    def test_object_with_value_attr_raises(self):
+        """Objects with .value attribute (Unhashable-like) should raise ValueError."""
+        class FakeUnhashable:
+            def __init__(self):
+                self.value = float('nan')
+        with pytest.raises(ValueError):
+            _canonicalize(FakeUnhashable())
+
+
+class TestSerializeCacheKey:
+    """Test _serialize_cache_key for deterministic hashing."""
+
+    def test_same_content_same_hash(self):
+        """Same content should produce same hash."""
+        key1 = frozenset([("node_1", frozenset([("input", "value")]))])
+        key2 = frozenset([("node_1", frozenset([("input", "value")]))])
+
+        hash1 = _serialize_cache_key(key1)
+        hash2 = _serialize_cache_key(key2)
+
+        assert hash1 == hash2
+
+    def test_different_content_different_hash(self):
+        """Different content should produce different hash."""
+        key1 = frozenset([("node_1", "value_a")])
+        key2 = frozenset([("node_1", "value_b")])
+
+        hash1 = _serialize_cache_key(key1)
+        hash2 = _serialize_cache_key(key2)
+
+        assert hash1 != hash2
+
+    def test_returns_hex_string(self):
+        """Should return hex string (SHA256 hex digest)."""
+        key = frozenset([("test", 123)])
+        result = _serialize_cache_key(key)
+
+        assert isinstance(result, str)
+        assert len(result) == 64  # SHA256 hex digest is 64 chars
+
+    def test_complex_nested_structure(self):
+        """Complex nested structures should hash deterministically."""
+        # Note: frozensets can only contain hashable types, so we use
+        # nested frozensets of tuples to represent dict-like structures
+        key = frozenset([
+            ("node_1", frozenset([
+                ("input_a", ("tuple", "value")),
+                ("input_b", frozenset([("nested", "dict")])),
+            ])),
+            ("node_2", frozenset([
+                ("param", 42),
+            ])),
+        ])
+
+        # Hash twice to verify determinism
+        hash1 = _serialize_cache_key(key)
+        hash2 = _serialize_cache_key(key)
+
+        assert hash1 == hash2
+
+    def test_dict_in_cache_key(self):
+        """Dicts passed directly to _serialize_cache_key should work."""
+        key = {"node_1": {"input": "value"}, "node_2": 42}
+
+        hash1 = _serialize_cache_key(key)
+        hash2 = _serialize_cache_key(key)
+
+        assert hash1 == hash2
+        assert isinstance(hash1, str)
+        assert len(hash1) == 64
+
+    def test_unknown_type_returns_none(self):
+        """Non-cacheable types should return None (fail-closed)."""
+        class CustomObj:
+            pass
+        assert _serialize_cache_key(CustomObj()) is None
+
+
+class TestContainsSelfUnequal:
+    """Test _contains_self_unequal utility function."""
+
+    def test_nan_float_detected(self):
+        """NaN floats should be detected (not equal to itself)."""
+        assert _contains_self_unequal(float('nan')) is True
+
+    def test_regular_float_not_detected(self):
+        """Regular floats are equal to themselves."""
+        assert _contains_self_unequal(3.14) is False
+        assert _contains_self_unequal(0.0) is False
+        assert _contains_self_unequal(-1.5) is False
+
+    def test_infinity_not_detected(self):
+        """Infinity is equal to itself."""
+        assert _contains_self_unequal(float('inf')) is False
+        assert _contains_self_unequal(float('-inf')) is False
+
+    def test_nan_in_list(self):
+        """NaN in list should be detected."""
+        assert _contains_self_unequal([1, 2, float('nan'), 4]) is True
+        assert _contains_self_unequal([1, 2, 3, 4]) is False
+
+    def test_nan_in_tuple(self):
+        """NaN in tuple should be detected."""
+        assert _contains_self_unequal((1, float('nan'))) is True
+        assert _contains_self_unequal((1, 2, 3)) is False
+
+    def test_nan_in_frozenset(self):
+        """NaN in frozenset should be detected."""
+        assert _contains_self_unequal(frozenset([1, float('nan')])) is True
+        assert _contains_self_unequal(frozenset([1, 2, 3])) is False
+
+    def test_nan_in_dict_value(self):
+        """NaN in dict value should be detected."""
+        assert _contains_self_unequal({"key": float('nan')}) is True
+        assert _contains_self_unequal({"key": 42}) is False
+
+    def test_nan_in_nested_structure(self):
+        """NaN in deeply nested structure should be detected."""
+        nested = {"level1": [{"level2": (1, 2, float('nan'))}]}
+        assert _contains_self_unequal(nested) is True
+
+    def test_non_numeric_types(self):
+        """Non-numeric types should not be self-unequal."""
+        assert _contains_self_unequal("string") is False
+        assert _contains_self_unequal(None) is False
+        assert _contains_self_unequal(True) is False
+
+    def test_object_with_nan_value_attribute(self):
+        """Objects wrapping NaN in .value should be detected."""
+        class NanWrapper:
+            def __init__(self):
+                self.value = float('nan')
+        assert _contains_self_unequal(NanWrapper()) is True
+
+    def test_custom_self_unequal_object(self):
+        """Custom objects where not (x == x) should be detected."""
+        class NeverEqual:
+            def __eq__(self, other):
+                return False
+        assert _contains_self_unequal(NeverEqual()) is True
+
+
+class TestEstimateValueSize:
+    """Test _estimate_value_size utility function."""
+
+    def test_empty_outputs(self):
+        """Empty outputs should have zero size."""
+        value = CacheValue(outputs=[])
+        assert _estimate_value_size(value) == 0
+
+    @pytest.mark.skipif(
+        not _torch_available(),
+        reason="PyTorch not available"
+    )
+    def test_tensor_size_estimation(self):
+        """Tensor size should be estimated correctly."""
+        import torch
+
+        # 1000 float32 elements = 4000 bytes
+        tensor = torch.zeros(1000, dtype=torch.float32)
+        value = CacheValue(outputs=[[tensor]])
+
+        size = _estimate_value_size(value)
+        assert size == 4000
+
+    @pytest.mark.skipif(
+        not _torch_available(),
+        reason="PyTorch not available"
+    )
+    def test_nested_tensor_in_dict(self):
+        """Tensors nested in dicts should be counted."""
+        import torch
+
+        tensor = torch.zeros(100, dtype=torch.float32)  # 400 bytes
+        value = CacheValue(outputs=[[{"samples": tensor}]])
+
+        size = _estimate_value_size(value)
+        assert size == 400
+
+
+class TestProviderRegistry:
+    """Test cache provider registration and retrieval."""
+
+    def setup_method(self):
+        """Clear providers before each test."""
+        _clear_cache_providers()
+
+    def teardown_method(self):
+        """Clear providers after each test."""
+        _clear_cache_providers()
+
+    def test_register_provider(self):
+        """Provider should be registered successfully."""
+        provider = MockCacheProvider()
+        register_cache_provider(provider)
+
+        assert _has_cache_providers() is True
+        providers = _get_cache_providers()
+        assert len(providers) == 1
+        assert providers[0] is provider
+
+    def test_unregister_provider(self):
+        """Provider should be unregistered successfully."""
+        provider = MockCacheProvider()
+        register_cache_provider(provider)
+        unregister_cache_provider(provider)
+
+        assert _has_cache_providers() is False
+
+    def test_multiple_providers(self):
+        """Multiple providers can be registered."""
+        provider1 = MockCacheProvider()
+        provider2 = MockCacheProvider()
+
+        register_cache_provider(provider1)
+        register_cache_provider(provider2)
+
+        providers = _get_cache_providers()
+        assert len(providers) == 2
+
+    def test_duplicate_registration_ignored(self):
+        """Registering same provider twice should be ignored."""
+        provider = MockCacheProvider()
+
+        register_cache_provider(provider)
+        register_cache_provider(provider)  # Should be ignored
+
+        providers = _get_cache_providers()
+        assert len(providers) == 1
+
+    def test_clear_providers(self):
+        """_clear_cache_providers should remove all providers."""
+        provider1 = MockCacheProvider()
+        provider2 = MockCacheProvider()
+
+        register_cache_provider(provider1)
+        register_cache_provider(provider2)
+        _clear_cache_providers()
+
+        assert _has_cache_providers() is False
+        assert len(_get_cache_providers()) == 0
+
+
+class TestCacheContext:
+    """Test CacheContext dataclass."""
+
+    def test_context_creation(self):
+        """CacheContext should be created with all fields."""
+        context = CacheContext(
+            node_id="node-456",
+            class_type="KSampler",
+            cache_key_hash="a" * 64,
+        )
+
+        assert context.node_id == "node-456"
+        assert context.class_type == "KSampler"
+        assert context.cache_key_hash == "a" * 64
+
+
+class TestCacheValue:
+    """Test CacheValue dataclass."""
+
+    def test_value_creation(self):
+        """CacheValue should be created with outputs."""
+        outputs = [[{"samples": "tensor_data"}]]
+        value = CacheValue(outputs=outputs)
+
+        assert value.outputs == outputs
+
+
+class MockCacheProvider(CacheProvider):
+    """Mock cache provider for testing."""
+
+    def __init__(self):
+        self.lookups = []
+        self.stores = []
+
+    async def on_lookup(self, context: CacheContext) -> Optional[CacheValue]:
+        self.lookups.append(context)
+        return None
+
+    async def on_store(self, context: CacheContext, value: CacheValue) -> None:
+        self.stores.append((context, value))

From f9ceed9eefe20f6b54b801096cb80f874316f5b2 Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Fri, 13 Mar 2026 19:10:40 +0200
Subject: [PATCH 162/194] fix(api-nodes): Tencent TextToModel and ImageToModel
 nodes (#12680)

* fix(api-nodes): added "texture_image" output to TencentTextToModel and TencentImageToModel nodes. Fixed `OBJ` output when it is zipped

* support additional solid texture outputs

* fixed and enabled Tencent3DTextureEdit node
---
 comfy_api_nodes/nodes_hunyuan3d.py | 97 +++++++++++++++++++++++++++---
 1 file changed, 88 insertions(+), 9 deletions(-)

diff --git a/comfy_api_nodes/nodes_hunyuan3d.py b/comfy_api_nodes/nodes_hunyuan3d.py
index bd8bde997..753c09b6e 100644
--- a/comfy_api_nodes/nodes_hunyuan3d.py
+++ b/comfy_api_nodes/nodes_hunyuan3d.py
@@ -1,3 +1,7 @@
+import zipfile
+from io import BytesIO
+
+import torch
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension, Input, Types
@@ -17,7 +21,10 @@ from comfy_api_nodes.apis.hunyuan3d import (
 )
 from comfy_api_nodes.util import (
     ApiEndpoint,
+    bytesio_to_image_tensor,
+    download_url_to_bytesio,
     download_url_to_file_3d,
+    download_url_to_image_tensor,
     downscale_image_tensor_by_max_side,
     poll_op,
     sync_op,
@@ -36,6 +43,68 @@ def _is_tencent_rate_limited(status: int, body: object) -> bool:
     )
 
 
+class ObjZipResult:
+    __slots__ = ("obj", "texture", "metallic", "normal", "roughness")
+
+    def __init__(
+        self,
+        obj: Types.File3D,
+        texture: Input.Image | None = None,
+        metallic: Input.Image | None = None,
+        normal: Input.Image | None = None,
+        roughness: Input.Image | None = None,
+    ):
+        self.obj = obj
+        self.texture = texture
+        self.metallic = metallic
+        self.normal = normal
+        self.roughness = roughness
+
+
+async def download_and_extract_obj_zip(url: str) -> ObjZipResult:
+    """The Tencent API returns OBJ results as ZIP archives containing the .obj mesh, and texture images.
+
+    When PBR is enabled, the ZIP may contain additional metallic, normal, and roughness maps
+    identified by their filename suffixes.
+    """
+    data = BytesIO()
+    await download_url_to_bytesio(url, data)
+    data.seek(0)
+    if not zipfile.is_zipfile(data):
+        data.seek(0)
+        return ObjZipResult(obj=Types.File3D(source=data, file_format="obj"))
+    data.seek(0)
+    obj_bytes = None
+    textures: dict[str, Input.Image] = {}
+    with zipfile.ZipFile(data) as zf:
+        for name in zf.namelist():
+            lower = name.lower()
+            if lower.endswith(".obj"):
+                obj_bytes = zf.read(name)
+            elif any(lower.endswith(ext) for ext in (".png", ".jpg", ".jpeg", ".bmp", ".tiff", ".webp")):
+                stem = lower.rsplit(".", 1)[0]
+                tensor = bytesio_to_image_tensor(BytesIO(zf.read(name)), mode="RGB")
+                matched_key = "texture"
+                for suffix, key in {
+                    "_metallic": "metallic",
+                    "_normal": "normal",
+                    "_roughness": "roughness",
+                }.items():
+                    if stem.endswith(suffix):
+                        matched_key = key
+                        break
+                textures[matched_key] = tensor
+    if obj_bytes is None:
+        raise ValueError("ZIP archive does not contain an OBJ file.")
+    return ObjZipResult(
+        obj=Types.File3D(source=BytesIO(obj_bytes), file_format="obj"),
+        texture=textures.get("texture"),
+        metallic=textures.get("metallic"),
+        normal=textures.get("normal"),
+        roughness=textures.get("roughness"),
+    )
+
+
 def get_file_from_response(
     response_objs: list[ResultFile3D], file_type: str, raise_if_not_found: bool = True
 ) -> ResultFile3D | None:
@@ -93,6 +162,7 @@ class TencentTextToModelNode(IO.ComfyNode):
                 IO.String.Output(display_name="model_file"),  # for backward compatibility only
                 IO.File3DGLB.Output(display_name="GLB"),
                 IO.File3DOBJ.Output(display_name="OBJ"),
+                IO.Image.Output(display_name="texture_image"),
             ],
             hidden=[
                 IO.Hidden.auth_token_comfy_org,
@@ -151,14 +221,14 @@ class TencentTextToModelNode(IO.ComfyNode):
             response_model=To3DProTaskResultResponse,
             status_extractor=lambda r: r.Status,
         )
+        obj_result = await download_and_extract_obj_zip(get_file_from_response(result.ResultFile3Ds, "obj").Url)
         return IO.NodeOutput(
             f"{task_id}.glb",
             await download_url_to_file_3d(
                 get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb", task_id=task_id
             ),
-            await download_url_to_file_3d(
-                get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj", task_id=task_id
-            ),
+            obj_result.obj,
+            obj_result.texture,
         )
 
 
@@ -211,6 +281,10 @@ class TencentImageToModelNode(IO.ComfyNode):
                 IO.String.Output(display_name="model_file"),  # for backward compatibility only
                 IO.File3DGLB.Output(display_name="GLB"),
                 IO.File3DOBJ.Output(display_name="OBJ"),
+                IO.Image.Output(display_name="texture_image"),
+                IO.Image.Output(display_name="optional_metallic"),
+                IO.Image.Output(display_name="optional_normal"),
+                IO.Image.Output(display_name="optional_roughness"),
             ],
             hidden=[
                 IO.Hidden.auth_token_comfy_org,
@@ -304,14 +378,17 @@ class TencentImageToModelNode(IO.ComfyNode):
             response_model=To3DProTaskResultResponse,
             status_extractor=lambda r: r.Status,
         )
+        obj_result = await download_and_extract_obj_zip(get_file_from_response(result.ResultFile3Ds, "obj").Url)
         return IO.NodeOutput(
             f"{task_id}.glb",
             await download_url_to_file_3d(
                 get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb", task_id=task_id
             ),
-            await download_url_to_file_3d(
-                get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj", task_id=task_id
-            ),
+            obj_result.obj,
+            obj_result.texture,
+            obj_result.metallic if obj_result.metallic is not None else torch.zeros(1, 1, 1, 3),
+            obj_result.normal if obj_result.normal is not None else torch.zeros(1, 1, 1, 3),
+            obj_result.roughness if obj_result.roughness is not None else torch.zeros(1, 1, 1, 3),
         )
 
 
@@ -431,7 +508,8 @@ class Tencent3DTextureEditNode(IO.ComfyNode):
             ],
             outputs=[
                 IO.File3DGLB.Output(display_name="GLB"),
-                IO.File3DFBX.Output(display_name="FBX"),
+                IO.File3DOBJ.Output(display_name="OBJ"),
+                IO.Image.Output(display_name="texture_image"),
             ],
             hidden=[
                 IO.Hidden.auth_token_comfy_org,
@@ -480,7 +558,8 @@ class Tencent3DTextureEditNode(IO.ComfyNode):
         )
         return IO.NodeOutput(
             await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb"),
-            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "fbx").Url, "fbx"),
+            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj"),
+            await download_url_to_image_tensor(get_file_from_response(result.ResultFile3Ds, "texture_image").Url),
         )
 
 
@@ -654,7 +733,7 @@ class TencentHunyuan3DExtension(ComfyExtension):
             TencentTextToModelNode,
             TencentImageToModelNode,
             TencentModelTo3DUVNode,
-            # Tencent3DTextureEditNode,
+            Tencent3DTextureEditNode,
             Tencent3DPartNode,
             TencentSmartTopologyNode,
         ]

From 6cd35a0c5fd7d22df858be175f6a6e6ee0212e55 Mon Sep 17 00:00:00 2001
From: Comfy Org PR Bot <snomiao+comfy-pr@gmail.com>
Date: Sat, 14 Mar 2026 03:31:25 +0900
Subject: [PATCH 163/194] Bump comfyui-frontend-package to 1.41.19 (#12923)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 511c62fee..6efb77f29 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-comfyui-frontend-package==1.41.18
+comfyui-frontend-package==1.41.19
 comfyui-workflow-templates==0.9.21
 comfyui-embedded-docs==0.4.3
 torch

From e1f10ca0932faf289757e7ec27a54894e271fdde Mon Sep 17 00:00:00 2001
From: "Dr.Lt.Data" <128333288+ltdrdata@users.noreply.github.com>
Date: Sat, 14 Mar 2026 09:14:27 +0900
Subject: [PATCH 164/194] bump manager version to 4.1b4 (#12930)

---
 manager_requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/manager_requirements.txt b/manager_requirements.txt
index 6bcc3fb50..37a33bd4f 100644
--- a/manager_requirements.txt
+++ b/manager_requirements.txt
@@ -1 +1 @@
-comfyui_manager==4.1b2
\ No newline at end of file
+comfyui_manager==4.1b4
\ No newline at end of file

From 7810f49702eac6e617eb7f2c30b00a8939ef1404 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Fri, 13 Mar 2026 19:18:08 -0700
Subject: [PATCH 165/194] comfy aimdo 0.2.11 + Improved RAM Pressure release
 strategies - Windows speedups (#12925)

* Implement seek and read for pins

Source pins from an mmap is pad because its its a CPU->CPU copy that
attempts to fully buffer the same data twice. Instead, use seek and
read which avoids the mmap buffering while usually being a faster
read in the first place (avoiding mmap faulting etc).

* pinned_memory: Use Aimdo pinner

The aimdo pinner bypasses pytorches CPU allocator which can leak
windows commit charge.

* ops: bypass init() of weight for embedding layer

This similarly consumes large commit charge especially for TEs. It can
cause a permanement leaked commit charge which can destabilize on
systems close to the commit ceiling and generally confuses the RAM
stats.

* model_patcher: implement pinned memory counter

Implement a pinned memory counter for better accounting of what volume
of memory pins have.

* implement touch accounting

Implement accounting of touching mmapped tensors.

* mm+mp: add residency mmap getter

* utils: use the aimdo mmap to load sft files

* model_management: Implement tigher RAM pressure semantics

Implement a pressure release on entire MMAPs as windows does perform
faster when mmaps are unloaded and model loads free ramp into fully
unallocated RAM.

Make the concept of freeing for pins a completely separate concept.
Now that pins are loadable directly from original file and don' touch
the mmap, tighten the freeing budget to just the current loaded model
- what you have left over. This still over-frees pins, but its a lot
better than before.

So after the pins are freed with that algorithm, bounce entire MMAPs
to free RAM based on what the model needs, deducting off any known
resident-in-mmap tensors to the free quota to keep it as tight as
possible.

* comfy-aimdo 0.2.11

Comfy aimdo 0.2.11

* mm: Implement file_slice path for QT

* ruff

* ops: put meta-tensors in place to allow custom nodes to check geo
---
 comfy/memory_management.py |  59 +++++++++++++++++++++
 comfy/model_management.py  |  74 ++++++++++++++++++++++-----
 comfy/model_patcher.py     |  17 +++++++
 comfy/ops.py               | 102 ++++++++++++++++++++++++++++---------
 comfy/pinned_memory.py     |  26 +++++++---
 comfy/utils.py             |  28 +++++++---
 requirements.txt           |   2 +-
 7 files changed, 258 insertions(+), 50 deletions(-)

diff --git a/comfy/memory_management.py b/comfy/memory_management.py
index 0b7da2852..563224098 100644
--- a/comfy/memory_management.py
+++ b/comfy/memory_management.py
@@ -1,9 +1,68 @@
 import math
+import ctypes
+import threading
+import dataclasses
 import torch
 from typing import NamedTuple
 
 from comfy.quant_ops import QuantizedTensor
 
+
+class TensorFileSlice(NamedTuple):
+    file_ref: object
+    thread_id: int
+    offset: int
+    size: int
+
+
+def read_tensor_file_slice_into(tensor, destination):
+
+    if isinstance(tensor, QuantizedTensor):
+        if not isinstance(destination, QuantizedTensor):
+            return False
+        if tensor._layout_cls != destination._layout_cls:
+            return False
+
+        if not read_tensor_file_slice_into(tensor._qdata, destination._qdata):
+            return False
+
+        dst_orig_dtype = destination._params.orig_dtype
+        destination._params.copy_from(tensor._params, non_blocking=False)
+        destination._params = dataclasses.replace(destination._params, orig_dtype=dst_orig_dtype)
+        return True
+
+    info = getattr(tensor.untyped_storage(), "_comfy_tensor_file_slice", None)
+    if info is None:
+        return False
+
+    file_obj = info.file_ref
+    if (destination.device.type != "cpu"
+            or file_obj is None
+            or threading.get_ident() != info.thread_id
+            or destination.numel() * destination.element_size() < info.size):
+        return False
+
+    if info.size == 0:
+        return True
+
+    buf_type = ctypes.c_ubyte * info.size
+    view = memoryview(buf_type.from_address(destination.data_ptr()))
+
+    try:
+        file_obj.seek(info.offset)
+        done = 0
+        while done < info.size:
+            try:
+                n = file_obj.readinto(view[done:])
+            except OSError:
+                return False
+            if n <= 0:
+                return False
+            done += n
+        return True
+    finally:
+        view.release()
+
 class TensorGeometry(NamedTuple):
     shape: any
     dtype: torch.dtype
diff --git a/comfy/model_management.py b/comfy/model_management.py
index 81c89b180..4d5851bc0 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -505,6 +505,28 @@ def module_size(module):
         module_mem += t.nbytes
     return module_mem
 
+def module_mmap_residency(module, free=False):
+    mmap_touched_mem = 0
+    module_mem = 0
+    bounced_mmaps = set()
+    sd = module.state_dict()
+    for k in sd:
+        t = sd[k]
+        module_mem += t.nbytes
+        storage = t._qdata.untyped_storage() if isinstance(t, comfy.quant_ops.QuantizedTensor) else t.untyped_storage()
+        if not getattr(storage, "_comfy_tensor_mmap_touched", False):
+            continue
+        mmap_touched_mem += t.nbytes
+        if not free:
+            continue
+        storage._comfy_tensor_mmap_touched = False
+        mmap_obj = storage._comfy_tensor_mmap_refs[0]
+        if mmap_obj in bounced_mmaps:
+            continue
+        mmap_obj.bounce()
+        bounced_mmaps.add(mmap_obj)
+    return mmap_touched_mem, module_mem
+
 class LoadedModel:
     def __init__(self, model):
         self._set_model(model)
@@ -532,6 +554,9 @@ class LoadedModel:
     def model_memory(self):
         return self.model.model_size()
 
+    def model_mmap_residency(self, free=False):
+        return self.model.model_mmap_residency(free=free)
+
     def model_loaded_memory(self):
         return self.model.loaded_size()
 
@@ -633,7 +658,7 @@ def extra_reserved_memory():
 def minimum_inference_memory():
     return (1024 * 1024 * 1024) * 0.8 + extra_reserved_memory()
 
-def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, ram_required=0):
+def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, pins_required=0, ram_required=0):
     cleanup_models_gc()
     unloaded_model = []
     can_unload = []
@@ -646,13 +671,14 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, ram_
                 can_unload.append((-shift_model.model_offloaded_memory(), sys.getrefcount(shift_model.model), shift_model.model_memory(), i))
                 shift_model.currently_used = False
 
-    for x in sorted(can_unload):
+    can_unload_sorted = sorted(can_unload)
+    for x in can_unload_sorted:
         i = x[-1]
         memory_to_free = 1e32
-        ram_to_free = 1e32
+        pins_to_free = 1e32
         if not DISABLE_SMART_MEMORY:
             memory_to_free = memory_required - get_free_memory(device)
-            ram_to_free = ram_required - get_free_ram()
+            pins_to_free = pins_required - get_free_ram()
             if current_loaded_models[i].model.is_dynamic() and for_dynamic:
                 #don't actually unload dynamic models for the sake of other dynamic models
                 #as that works on-demand.
@@ -661,9 +687,18 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, ram_
         if memory_to_free > 0 and current_loaded_models[i].model_unload(memory_to_free):
             logging.debug(f"Unloading {current_loaded_models[i].model.model.__class__.__name__}")
             unloaded_model.append(i)
-        if ram_to_free > 0:
+        if pins_to_free > 0:
+            logging.debug(f"PIN Unloading {current_loaded_models[i].model.model.__class__.__name__}")
+            current_loaded_models[i].model.partially_unload_ram(pins_to_free)
+
+    for x in can_unload_sorted:
+        i = x[-1]
+        ram_to_free = ram_required - psutil.virtual_memory().available
+        if ram_to_free <= 0 and i not in unloaded_model:
+            continue
+        resident_memory, _ = current_loaded_models[i].model_mmap_residency(free=True)
+        if resident_memory > 0:
             logging.debug(f"RAM Unloading {current_loaded_models[i].model.model.__class__.__name__}")
-            current_loaded_models[i].model.partially_unload_ram(ram_to_free)
 
     for i in sorted(unloaded_model, reverse=True):
         unloaded_models.append(current_loaded_models.pop(i))
@@ -729,17 +764,27 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
 
 
     total_memory_required = {}
+    total_pins_required = {}
     total_ram_required = {}
     for loaded_model in models_to_load:
-        total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device)
-        #x2, one to make sure the OS can fit the model for loading in disk cache, and for us to do any pinning we
-        #want to do.
-        #FIXME: This should subtract off the to_load current pin consumption.
-        total_ram_required[loaded_model.device] = total_ram_required.get(loaded_model.device, 0) + loaded_model.model_memory() * 2
+        device = loaded_model.device
+        total_memory_required[device] = total_memory_required.get(device, 0) + loaded_model.model_memory_required(device)
+        resident_memory, model_memory = loaded_model.model_mmap_residency()
+        pinned_memory = loaded_model.model.pinned_memory_size()
+        #FIXME: This can over-free the pins as it budgets to pin the entire model. We should
+        #make this JIT to keep as much pinned as possible.
+        pins_required = model_memory - pinned_memory
+        ram_required = model_memory - resident_memory
+        total_pins_required[device] = total_pins_required.get(device, 0) + pins_required
+        total_ram_required[device] = total_ram_required.get(device, 0) + ram_required
 
     for device in total_memory_required:
         if device != torch.device("cpu"):
-            free_memory(total_memory_required[device] * 1.1 + extra_mem, device, for_dynamic=free_for_dynamic, ram_required=total_ram_required[device])
+            free_memory(total_memory_required[device] * 1.1 + extra_mem,
+                        device,
+                        for_dynamic=free_for_dynamic,
+                        pins_required=total_pins_required[device],
+                        ram_required=total_ram_required[device])
 
     for device in total_memory_required:
         if device != torch.device("cpu"):
@@ -1225,6 +1270,11 @@ def cast_to_gathered(tensors, r, non_blocking=False, stream=None):
             dest_view = dest_views.pop(0)
             if tensor is None:
                 continue
+            if comfy.memory_management.read_tensor_file_slice_into(tensor, dest_view):
+                continue
+            storage = tensor._qdata.untyped_storage() if isinstance(tensor, comfy.quant_ops.QuantizedTensor) else tensor.untyped_storage()
+            if hasattr(storage, "_comfy_tensor_mmap_touched"):
+                storage._comfy_tensor_mmap_touched = True
             dest_view.copy_(tensor, non_blocking=non_blocking)
 
 
diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index bc3a8f446..c26d37db2 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -297,6 +297,9 @@ class ModelPatcher:
         self.size = comfy.model_management.module_size(self.model)
         return self.size
 
+    def model_mmap_residency(self, free=False):
+        return comfy.model_management.module_mmap_residency(self.model, free=free)
+
     def get_ram_usage(self):
         return self.model_size()
 
@@ -1063,6 +1066,10 @@ class ModelPatcher:
 
             return self.model.model_loaded_weight_memory - current_used
 
+    def pinned_memory_size(self):
+        # Pinned memory pressure tracking is only implemented for DynamicVram loading
+        return 0
+
     def partially_unload_ram(self, ram_to_unload):
         pass
 
@@ -1653,6 +1660,16 @@ class ModelPatcherDynamic(ModelPatcher):
 
         return freed
 
+    def pinned_memory_size(self):
+        total = 0
+        loading = self._load_list(for_dynamic=True)
+        for x in loading:
+            _, _, _, _, m, _ = x
+            pin = comfy.pinned_memory.get_pin(m)
+            if pin is not None:
+                total += pin.numel() * pin.element_size()
+        return total
+
     def partially_unload_ram(self, ram_to_unload):
         loading = self._load_list(for_dynamic=True, default_device=self.offload_device)
         for x in loading:
diff --git a/comfy/ops.py b/comfy/ops.py
index 87b36b5c5..3f2da4e63 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -306,6 +306,33 @@ class CastWeightBiasOp:
     bias_function = []
 
 class disable_weight_init:
+    @staticmethod
+    def _lazy_load_from_state_dict(module, state_dict, prefix, local_metadata,
+                                   missing_keys, unexpected_keys, weight_shape,
+                                   bias_shape=None):
+        assign_to_params_buffers = local_metadata.get("assign_to_params_buffers", False)
+        prefix_len = len(prefix)
+        for k, v in state_dict.items():
+            key = k[prefix_len:]
+            if key == "weight":
+                if not assign_to_params_buffers:
+                    v = v.clone()
+                module.weight = torch.nn.Parameter(v, requires_grad=False)
+            elif bias_shape is not None and key == "bias" and v is not None:
+                if not assign_to_params_buffers:
+                    v = v.clone()
+                module.bias = torch.nn.Parameter(v, requires_grad=False)
+            else:
+                unexpected_keys.append(k)
+
+        if module.weight is None:
+            module.weight = torch.nn.Parameter(torch.zeros(weight_shape), requires_grad=False)
+            missing_keys.append(prefix + "weight")
+
+        if bias_shape is not None and module.bias is None and getattr(module, "comfy_need_lazy_init_bias", False):
+            module.bias = torch.nn.Parameter(torch.zeros(bias_shape), requires_grad=False)
+            missing_keys.append(prefix + "bias")
+
     class Linear(torch.nn.Linear, CastWeightBiasOp):
 
         def __init__(self, in_features, out_features, bias=True, device=None, dtype=None):
@@ -333,29 +360,16 @@ class disable_weight_init:
             if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled:
                 return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict,
                                                      missing_keys, unexpected_keys, error_msgs)
-            assign_to_params_buffers = local_metadata.get("assign_to_params_buffers", False)
-            prefix_len = len(prefix)
-            for k,v in state_dict.items():
-                if k[prefix_len:] == "weight":
-                    if not assign_to_params_buffers:
-                        v = v.clone()
-                    self.weight = torch.nn.Parameter(v, requires_grad=False)
-                elif k[prefix_len:] == "bias" and v is not None:
-                    if not assign_to_params_buffers:
-                        v = v.clone()
-                    self.bias = torch.nn.Parameter(v, requires_grad=False)
-                else:
-                    unexpected_keys.append(k)
-
-            #Reconcile default construction of the weight if its missing.
-            if self.weight is None:
-                v = torch.zeros(self.in_features, self.out_features)
-                self.weight = torch.nn.Parameter(v, requires_grad=False)
-                missing_keys.append(prefix+"weight")
-            if self.bias is None and self.comfy_need_lazy_init_bias:
-                v = torch.zeros(self.out_features,)
-                self.bias = torch.nn.Parameter(v, requires_grad=False)
-                missing_keys.append(prefix+"bias")
+            disable_weight_init._lazy_load_from_state_dict(
+                self,
+                state_dict,
+                prefix,
+                local_metadata,
+                missing_keys,
+                unexpected_keys,
+                weight_shape=(self.in_features, self.out_features),
+                bias_shape=(self.out_features,),
+            )
 
 
         def reset_parameters(self):
@@ -547,6 +561,48 @@ class disable_weight_init:
                 return super().forward(*args, **kwargs)
 
     class Embedding(torch.nn.Embedding, CastWeightBiasOp):
+        def __init__(self, num_embeddings, embedding_dim, padding_idx=None, max_norm=None,
+                     norm_type=2.0, scale_grad_by_freq=False, sparse=False, _weight=None,
+                     _freeze=False, device=None, dtype=None):
+            if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled:
+                super().__init__(num_embeddings, embedding_dim, padding_idx, max_norm,
+                                 norm_type, scale_grad_by_freq, sparse, _weight,
+                                 _freeze, device, dtype)
+                return
+
+            torch.nn.Module.__init__(self)
+            self.num_embeddings = num_embeddings
+            self.embedding_dim = embedding_dim
+            self.padding_idx = padding_idx
+            self.max_norm = max_norm
+            self.norm_type = norm_type
+            self.scale_grad_by_freq = scale_grad_by_freq
+            self.sparse = sparse
+            # Keep shape/dtype visible for module introspection without reserving storage.
+            embedding_dtype = dtype if dtype is not None else torch.get_default_dtype()
+            self.weight = torch.nn.Parameter(
+                torch.empty((num_embeddings, embedding_dim), device="meta", dtype=embedding_dtype),
+                requires_grad=False,
+            )
+            self.bias = None
+            self.weight_comfy_model_dtype = dtype
+
+        def _load_from_state_dict(self, state_dict, prefix, local_metadata,
+                                strict, missing_keys, unexpected_keys, error_msgs):
+
+            if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled:
+                return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict,
+                                                     missing_keys, unexpected_keys, error_msgs)
+            disable_weight_init._lazy_load_from_state_dict(
+                self,
+                state_dict,
+                prefix,
+                local_metadata,
+                missing_keys,
+                unexpected_keys,
+                weight_shape=(self.num_embeddings, self.embedding_dim),
+            )
+
         def reset_parameters(self):
             self.bias = None
             return None
diff --git a/comfy/pinned_memory.py b/comfy/pinned_memory.py
index 8acc327a7..f6fb806c4 100644
--- a/comfy/pinned_memory.py
+++ b/comfy/pinned_memory.py
@@ -1,6 +1,7 @@
-import torch
 import comfy.model_management
 import comfy.memory_management
+import comfy_aimdo.host_buffer
+import comfy_aimdo.torch
 
 from comfy.cli_args import args
 
@@ -12,18 +13,31 @@ def pin_memory(module):
         return
     #FIXME: This is a RAM cache trigger event
     size = comfy.memory_management.vram_aligned_size([ module.weight, module.bias ])
-    pin = torch.empty((size,), dtype=torch.uint8)
-    if comfy.model_management.pin_memory(pin):
-        module._pin = pin
-    else:
+
+    if comfy.model_management.MAX_PINNED_MEMORY <= 0 or (comfy.model_management.TOTAL_PINNED_MEMORY + size) > comfy.model_management.MAX_PINNED_MEMORY:
         module.pin_failed = True
         return False
+
+    try:
+        hostbuf = comfy_aimdo.host_buffer.HostBuffer(size)
+    except RuntimeError:
+        module.pin_failed = True
+        return False
+
+    module._pin = comfy_aimdo.torch.hostbuf_to_tensor(hostbuf)
+    module._pin_hostbuf = hostbuf
+    comfy.model_management.TOTAL_PINNED_MEMORY += size
     return True
 
 def unpin_memory(module):
     if get_pin(module) is None:
         return 0
     size = module._pin.numel() * module._pin.element_size()
-    comfy.model_management.unpin_memory(module._pin)
+
+    comfy.model_management.TOTAL_PINNED_MEMORY -= size
+    if comfy.model_management.TOTAL_PINNED_MEMORY < 0:
+        comfy.model_management.TOTAL_PINNED_MEMORY = 0
+
     del module._pin
+    del module._pin_hostbuf
     return size
diff --git a/comfy/utils.py b/comfy/utils.py
index 6e1d14419..9931fe3b4 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -20,6 +20,8 @@
 import torch
 import math
 import struct
+import ctypes
+import os
 import comfy.memory_management
 import safetensors.torch
 import numpy as np
@@ -32,7 +34,7 @@ from einops import rearrange
 from comfy.cli_args import args
 import json
 import time
-import mmap
+import threading
 import warnings
 
 MMAP_TORCH_FILES = args.mmap_torch_files
@@ -81,14 +83,17 @@ _TYPES = {
 }
 
 def load_safetensors(ckpt):
-    f = open(ckpt, "rb")
-    mapping = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)
-    mv = memoryview(mapping)
+    import comfy_aimdo.model_mmap
 
-    header_size = struct.unpack("<Q", mapping[:8])[0]
-    header = json.loads(mapping[8:8+header_size].decode("utf-8"))
+    f = open(ckpt, "rb", buffering=0)
+    model_mmap = comfy_aimdo.model_mmap.ModelMMAP(ckpt)
+    file_size = os.path.getsize(ckpt)
+    mv = memoryview((ctypes.c_uint8 * file_size).from_address(model_mmap.get()))
 
-    mv = mv[8 + header_size:]
+    header_size = struct.unpack("<Q", mv[:8])[0]
+    header = json.loads(mv[8:8 + header_size].tobytes().decode("utf-8"))
+
+    mv = mv[(data_base_offset := 8 + header_size):]
 
     sd = {}
     for name, info in header.items():
@@ -102,7 +107,14 @@ def load_safetensors(ckpt):
             with warnings.catch_warnings():
                 #We are working with read-only RAM by design
                 warnings.filterwarnings("ignore", message="The given buffer is not writable")
-                sd[name] = torch.frombuffer(mv[start:end], dtype=_TYPES[info["dtype"]]).view(info["shape"])
+                tensor = torch.frombuffer(mv[start:end], dtype=_TYPES[info["dtype"]]).view(info["shape"])
+                storage = tensor.untyped_storage()
+                setattr(storage,
+                        "_comfy_tensor_file_slice",
+                        comfy.memory_management.TensorFileSlice(f, threading.get_ident(), data_base_offset + start, end - start))
+                setattr(storage, "_comfy_tensor_mmap_refs", (model_mmap, mv))
+                setattr(storage, "_comfy_tensor_mmap_touched", False)
+                sd[name] = tensor
 
     return sd, header.get("__metadata__", {}),
 
diff --git a/requirements.txt b/requirements.txt
index 6efb77f29..52bc0fd12 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -23,7 +23,7 @@ SQLAlchemy
 filelock
 av>=14.2.0
 comfy-kitchen>=0.2.8
-comfy-aimdo>=0.2.10
+comfy-aimdo>=0.2.11
 requests
 simpleeval>=1.0.0
 blake3

From 16cd8d8a8f5f16ce7e5f929fdba9f783990254ea Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Fri, 13 Mar 2026 19:33:28 -0700
Subject: [PATCH 166/194] Update README. (#12931)

---
 README.md | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 56b7966cf..62c4f528c 100644
--- a/README.md
+++ b/README.md
@@ -38,6 +38,8 @@ ComfyUI lets you design and execute advanced stable diffusion pipelines using a
 
 ## Get Started
 
+### Local
+
 #### [Desktop Application](https://www.comfy.org/download)
 - The easiest way to get started.
 - Available on Windows & macOS.
@@ -49,8 +51,13 @@ ComfyUI lets you design and execute advanced stable diffusion pipelines using a
 #### [Manual Install](#manual-install-windows-linux)
 Supports all operating systems and GPU types (NVIDIA, AMD, Intel, Apple Silicon, Ascend).
 
-## [Examples](https://comfyanonymous.github.io/ComfyUI_examples/)
-See what ComfyUI can do with the [example workflows](https://comfyanonymous.github.io/ComfyUI_examples/).
+### Cloud
+
+#### [Comfy Cloud](https://www.comfy.org/cloud)
+- Our official paid cloud version for those who can't afford local hardware.
+
+## Examples
+See what ComfyUI can do with the [newer template workflows](https://comfy.org/workflows) or old [example workflows](https://comfyanonymous.github.io/ComfyUI_examples/).
 
 ## Features
 - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.

From 4c4be1bba5ae714c6f455a49757bd7fc2e32c577 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Sat, 14 Mar 2026 07:53:00 -0700
Subject: [PATCH 167/194] comfy-aimdo 0.2.12 (#12941)

comfy-aimdo 0.2.12 fixes support for non-ASCII filepaths in the new
mmap helper.
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 52bc0fd12..c32a765a0 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -23,7 +23,7 @@ SQLAlchemy
 filelock
 av>=14.2.0
 comfy-kitchen>=0.2.8
-comfy-aimdo>=0.2.11
+comfy-aimdo>=0.2.12
 requests
 simpleeval>=1.0.0
 blake3

From e0982a7174a9cacb0c3cd3fb6bd1f8e06d9aaf51 Mon Sep 17 00:00:00 2001
From: Christian Byrne <cbyrne@comfy.org>
Date: Sat, 14 Mar 2026 15:25:09 -0700
Subject: [PATCH 168/194] fix: use no-store cache headers to prevent stale
 frontend chunks (#12911)

After a frontend update (e.g. nightly build), browsers could load
outdated cached index.html and JS/CSS chunks, causing dynamically
imported modules to fail with MIME type errors and vite:preloadError.

Hard refresh (Ctrl+Shift+R) was insufficient to fix the issue because
Cache-Control: no-cache still allows the browser to cache and
revalidate via ETags. aiohttp's FileResponse auto-generates ETags
based on file mtime+size, which may not change after pip reinstall,
so the browser gets 304 Not Modified and serves stale content.

Clearing ALL site data in DevTools did fix it, confirming the HTTP
cache was the root cause.

The fix changes:
- index.html: no-cache -> no-store, must-revalidate
- JS/CSS/JSON entry points: no-cache -> no-store

no-store instructs browsers to never cache these responses, ensuring
every page load fetches the current index.html with correct chunk
references. This is a small tradeoff (~5KB re-download per page load)
for guaranteed correctness after updates.
---
 middleware/cache_middleware.py               |  2 +-
 server.py                                    |  2 +-
 tests-unit/server_test/test_cache_control.py | 16 ++++++++--------
 3 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/middleware/cache_middleware.py b/middleware/cache_middleware.py
index f02135369..7a18821b0 100644
--- a/middleware/cache_middleware.py
+++ b/middleware/cache_middleware.py
@@ -32,7 +32,7 @@ async def cache_control(
     )
 
     if request.path.endswith(".js") or request.path.endswith(".css") or is_entry_point:
-        response.headers.setdefault("Cache-Control", "no-cache")
+        response.headers.setdefault("Cache-Control", "no-store")
         return response
 
     # Early return for non-image files - no cache headers needed
diff --git a/server.py b/server.py
index 76904ebc9..85a8964be 100644
--- a/server.py
+++ b/server.py
@@ -310,7 +310,7 @@ class PromptServer():
         @routes.get("/")
         async def get_root(request):
             response = web.FileResponse(os.path.join(self.web_root, "index.html"))
-            response.headers['Cache-Control'] = 'no-cache'
+            response.headers['Cache-Control'] = 'no-store, must-revalidate'
             response.headers["Pragma"] = "no-cache"
             response.headers["Expires"] = "0"
             return response
diff --git a/tests-unit/server_test/test_cache_control.py b/tests-unit/server_test/test_cache_control.py
index fa68d9408..1d0366387 100644
--- a/tests-unit/server_test/test_cache_control.py
+++ b/tests-unit/server_test/test_cache_control.py
@@ -28,31 +28,31 @@ CACHE_SCENARIOS = [
     },
     # JavaScript/CSS scenarios
     {
-        "name": "js_no_cache",
+        "name": "js_no_store",
         "path": "/script.js",
         "status": 200,
-        "expected_cache": "no-cache",
+        "expected_cache": "no-store",
         "should_have_header": True,
     },
     {
-        "name": "css_no_cache",
+        "name": "css_no_store",
         "path": "/styles.css",
         "status": 200,
-        "expected_cache": "no-cache",
+        "expected_cache": "no-store",
         "should_have_header": True,
     },
     {
-        "name": "index_json_no_cache",
+        "name": "index_json_no_store",
         "path": "/api/index.json",
         "status": 200,
-        "expected_cache": "no-cache",
+        "expected_cache": "no-store",
         "should_have_header": True,
     },
     {
-        "name": "localized_index_json_no_cache",
+        "name": "localized_index_json_no_store",
         "path": "/templates/index.zh.json",
         "status": 200,
-        "expected_cache": "no-cache",
+        "expected_cache": "no-store",
         "should_have_header": True,
     },
     # Non-matching files

From 1c5db7397d59eace38acef078b618c2f04e4e7fe Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?=
 <40791699+kijai@users.noreply.github.com>
Date: Sun, 15 Mar 2026 00:36:29 +0200
Subject: [PATCH 169/194] feat: Support mxfp8 (#12907)

---
 comfy/float.py            | 36 ++++++++++++++++++++++++++++++
 comfy/model_management.py | 13 +++++++++++
 comfy/ops.py              | 19 ++++++++++++++++
 comfy/quant_ops.py        | 47 +++++++++++++++++++++++++++++++++++++++
 4 files changed, 115 insertions(+)

diff --git a/comfy/float.py b/comfy/float.py
index 88c47cd80..184b3d6d0 100644
--- a/comfy/float.py
+++ b/comfy/float.py
@@ -209,3 +209,39 @@ def stochastic_round_quantize_nvfp4_by_block(x, per_tensor_scale, pad_16x, seed=
         output_block[i:i + slice_size].copy_(block)
 
     return output_fp4, to_blocked(output_block, flatten=False)
+
+
+def stochastic_round_quantize_mxfp8_by_block(x, pad_32x, seed=0):
+    def roundup(x_val, multiple):
+        return ((x_val + multiple - 1) // multiple) * multiple
+
+    if pad_32x:
+        rows, cols = x.shape
+        padded_rows = roundup(rows, 32)
+        padded_cols = roundup(cols, 32)
+        if padded_rows != rows or padded_cols != cols:
+            x = torch.nn.functional.pad(x, (0, padded_cols - cols, 0, padded_rows - rows))
+
+    F8_E4M3_MAX = 448.0
+    E8M0_BIAS = 127
+    BLOCK_SIZE = 32
+
+    rows, cols = x.shape
+    x_blocked = x.reshape(rows, -1, BLOCK_SIZE)
+    max_abs = torch.amax(torch.abs(x_blocked), dim=-1)
+
+    # E8M0 block scales (power-of-2 exponents)
+    scale_needed = torch.clamp(max_abs.float() / F8_E4M3_MAX, min=2**(-127))
+    exp_biased = torch.clamp(torch.ceil(torch.log2(scale_needed)).to(torch.int32) + E8M0_BIAS, 0, 254)
+    block_scales_e8m0 = exp_biased.to(torch.uint8)
+
+    zero_mask = (max_abs == 0)
+    block_scales_f32 = (block_scales_e8m0.to(torch.int32) << 23).view(torch.float32)
+    block_scales_f32 = torch.where(zero_mask, torch.ones_like(block_scales_f32), block_scales_f32)
+
+    # Scale per-block then stochastic round
+    data_scaled = (x_blocked.float() / block_scales_f32.unsqueeze(-1)).reshape(rows, cols)
+    output_fp8 = stochastic_rounding(data_scaled, torch.float8_e4m3fn, seed=seed)
+
+    block_scales_e8m0 = torch.where(zero_mask, torch.zeros_like(block_scales_e8m0), block_scales_e8m0)
+    return output_fp8, to_blocked(block_scales_e8m0, flatten=False).view(torch.float8_e8m0fnu)
diff --git a/comfy/model_management.py b/comfy/model_management.py
index 4d5851bc0..bb77cff47 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -1712,6 +1712,19 @@ def supports_nvfp4_compute(device=None):
 
     return True
 
+def supports_mxfp8_compute(device=None):
+    if not is_nvidia():
+        return False
+
+    if torch_version_numeric < (2, 10):
+        return False
+
+    props = torch.cuda.get_device_properties(device)
+    if props.major < 10:
+        return False
+
+    return True
+
 def extended_fp16_support():
     # TODO: check why some models work with fp16 on newer torch versions but not on older
     if torch_version_numeric < (2, 7):
diff --git a/comfy/ops.py b/comfy/ops.py
index 3f2da4e63..59c0df87d 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -857,6 +857,22 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                             orig_shape=(self.out_features, self.in_features),
                         )
 
+                    elif self.quant_format == "mxfp8":
+                        # MXFP8: E8M0 block scales stored as uint8 in safetensors
+                        block_scale = self._load_scale_param(state_dict, prefix, "weight_scale", device, manually_loaded_keys,
+                                                             dtype=torch.uint8)
+
+                        if block_scale is None:
+                            raise ValueError(f"Missing MXFP8 block scales for layer {layer_name}")
+
+                        block_scale = block_scale.view(torch.float8_e8m0fnu)
+
+                        params = layout_cls.Params(
+                            scale=block_scale,
+                            orig_dtype=MixedPrecisionOps._compute_dtype,
+                            orig_shape=(self.out_features, self.in_features),
+                        )
+
                     elif self.quant_format == "nvfp4":
                         # NVFP4: tensor_scale (weight_scale_2) + block_scale (weight_scale)
                         tensor_scale = self._load_scale_param(state_dict, prefix, "weight_scale_2", device, manually_loaded_keys)
@@ -1006,12 +1022,15 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
 def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, model_config=None):
     fp8_compute = comfy.model_management.supports_fp8_compute(load_device) # TODO: if we support more ops this needs to be more granular
     nvfp4_compute = comfy.model_management.supports_nvfp4_compute(load_device)
+    mxfp8_compute = comfy.model_management.supports_mxfp8_compute(load_device)
 
     if model_config and hasattr(model_config, 'quant_config') and model_config.quant_config:
         logging.info("Using mixed precision operations")
         disabled = set()
         if not nvfp4_compute:
             disabled.add("nvfp4")
+        if not mxfp8_compute:
+            disabled.add("mxfp8")
         if not fp8_compute:
             disabled.add("float8_e4m3fn")
             disabled.add("float8_e5m2")
diff --git a/comfy/quant_ops.py b/comfy/quant_ops.py
index 15a4f457b..42ee08fb2 100644
--- a/comfy/quant_ops.py
+++ b/comfy/quant_ops.py
@@ -43,6 +43,18 @@ except ImportError as e:
     def get_layout_class(name):
         return None
 
+_CK_MXFP8_AVAILABLE = False
+if _CK_AVAILABLE:
+    try:
+        from comfy_kitchen.tensor import TensorCoreMXFP8Layout as _CKMxfp8Layout
+        _CK_MXFP8_AVAILABLE = True
+    except ImportError:
+        logging.warning("comfy_kitchen does not support MXFP8, please update comfy_kitchen.")
+
+if not _CK_MXFP8_AVAILABLE:
+    class _CKMxfp8Layout:
+        pass
+
 import comfy.float
 
 # ==============================================================================
@@ -84,6 +96,31 @@ class _TensorCoreFP8LayoutBase(_CKFp8Layout):
         return qdata, params
 
 
+class TensorCoreMXFP8Layout(_CKMxfp8Layout):
+    @classmethod
+    def quantize(cls, tensor, scale=None, stochastic_rounding=0, inplace_ops=False):
+        if tensor.dim() != 2:
+            raise ValueError(f"MXFP8 requires 2D tensor, got {tensor.dim()}D")
+
+        orig_dtype = tensor.dtype
+        orig_shape = tuple(tensor.shape)
+
+        padded_shape = cls.get_padded_shape(orig_shape)
+        needs_padding = padded_shape != orig_shape
+
+        if stochastic_rounding > 0:
+            qdata, block_scale = comfy.float.stochastic_round_quantize_mxfp8_by_block(tensor, pad_32x=needs_padding, seed=stochastic_rounding)
+        else:
+            qdata, block_scale = ck.quantize_mxfp8(tensor, pad_32x=needs_padding)
+
+        params = cls.Params(
+            scale=block_scale,
+            orig_dtype=orig_dtype,
+            orig_shape=orig_shape,
+        )
+        return qdata, params
+
+
 class TensorCoreNVFP4Layout(_CKNvfp4Layout):
     @classmethod
     def quantize(cls, tensor, scale=None, stochastic_rounding=0, inplace_ops=False):
@@ -137,6 +174,8 @@ register_layout_class("TensorCoreFP8Layout", TensorCoreFP8Layout)
 register_layout_class("TensorCoreFP8E4M3Layout", TensorCoreFP8E4M3Layout)
 register_layout_class("TensorCoreFP8E5M2Layout", TensorCoreFP8E5M2Layout)
 register_layout_class("TensorCoreNVFP4Layout", TensorCoreNVFP4Layout)
+if _CK_MXFP8_AVAILABLE:
+    register_layout_class("TensorCoreMXFP8Layout", TensorCoreMXFP8Layout)
 
 QUANT_ALGOS = {
     "float8_e4m3fn": {
@@ -157,6 +196,14 @@ QUANT_ALGOS = {
     },
 }
 
+if _CK_MXFP8_AVAILABLE:
+    QUANT_ALGOS["mxfp8"] = {
+        "storage_t": torch.float8_e4m3fn,
+        "parameters": {"weight_scale", "input_scale"},
+        "comfy_tensor_layout": "TensorCoreMXFP8Layout",
+        "group_size": 32,
+    }
+
 
 # ==============================================================================
 # Re-exports for backward compatibility

From c711b8f437923d9e732fa1d22ed101f81575683c Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Sat, 14 Mar 2026 16:18:19 -0700
Subject: [PATCH 170/194] Add --fp16-intermediates to use fp16 for intermediate
 values between nodes (#12953)

This is an experimental WIP option that might not work in your workflow but
should lower memory usage if it does.

Currently only the VAE and the load image node will output in fp16 when
this option is turned on.
---
 comfy/cli_args.py         |  2 ++
 comfy/model_management.py |  6 ++++++
 comfy/sd.py               | 27 +++++++++++++++------------
 nodes.py                  |  6 ++++--
 4 files changed, 27 insertions(+), 14 deletions(-)

diff --git a/comfy/cli_args.py b/comfy/cli_args.py
index e9832acaf..0a0bf2f30 100644
--- a/comfy/cli_args.py
+++ b/comfy/cli_args.py
@@ -83,6 +83,8 @@ fpte_group.add_argument("--fp16-text-enc", action="store_true", help="Store text
 fpte_group.add_argument("--fp32-text-enc", action="store_true", help="Store text encoder weights in fp32.")
 fpte_group.add_argument("--bf16-text-enc", action="store_true", help="Store text encoder weights in bf16.")
 
+parser.add_argument("--fp16-intermediates", action="store_true", help="Experimental: Use fp16 for intermediate tensors between nodes instead of fp32.")
+
 parser.add_argument("--force-channels-last", action="store_true", help="Force channels last format when inferencing the models.")
 
 parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
diff --git a/comfy/model_management.py b/comfy/model_management.py
index bb77cff47..442d5a40a 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -1050,6 +1050,12 @@ def intermediate_device():
     else:
         return torch.device("cpu")
 
+def intermediate_dtype():
+    if args.fp16_intermediates:
+        return torch.float16
+    else:
+        return torch.float32
+
 def vae_device():
     if args.cpu_vae:
         return torch.device("cpu")
diff --git a/comfy/sd.py b/comfy/sd.py
index adcd67767..4d427bb9a 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -871,13 +871,16 @@ class VAE:
                 pixels = torch.nn.functional.pad(pixels, (0, self.output_channels - pixels.shape[-1]), mode=mode, value=value)
         return pixels
 
+    def vae_output_dtype(self):
+        return model_management.intermediate_dtype()
+
     def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):
         steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap)
         steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap)
         steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap)
         pbar = comfy.utils.ProgressBar(steps)
 
-        decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
+        decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
         output = self.process_output(
             (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) +
             comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) +
@@ -887,16 +890,16 @@ class VAE:
 
     def decode_tiled_1d(self, samples, tile_x=256, overlap=32):
         if samples.ndim == 3:
-            decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
+            decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
         else:
             og_shape = samples.shape
             samples = samples.reshape((og_shape[0], og_shape[1] * og_shape[2], -1))
-            decode_fn = lambda a: self.first_stage_model.decode(a.reshape((-1, og_shape[1], og_shape[2], a.shape[-1])).to(self.vae_dtype).to(self.device)).float()
+            decode_fn = lambda a: self.first_stage_model.decode(a.reshape((-1, og_shape[1], og_shape[2], a.shape[-1])).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
 
         return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device))
 
     def decode_tiled_3d(self, samples, tile_t=999, tile_x=32, tile_y=32, overlap=(1, 8, 8)):
-        decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
+        decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
         return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, index_formulas=self.upscale_index_formula, output_device=self.output_device))
 
     def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
@@ -905,7 +908,7 @@ class VAE:
         steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
         pbar = comfy.utils.ProgressBar(steps)
 
-        encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
+        encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
         samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
         samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
         samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
@@ -914,7 +917,7 @@ class VAE:
 
     def encode_tiled_1d(self, samples, tile_x=256 * 2048, overlap=64 * 2048):
         if self.latent_dim == 1:
-            encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
+            encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
             out_channels = self.latent_channels
             upscale_amount = 1 / self.downscale_ratio
         else:
@@ -923,7 +926,7 @@ class VAE:
             tile_x = tile_x // extra_channel_size
             overlap = overlap // extra_channel_size
             upscale_amount = 1 / self.downscale_ratio
-            encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).reshape(1, out_channels, -1).float()
+            encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).reshape(1, out_channels, -1).to(dtype=self.vae_output_dtype())
 
         out = comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=upscale_amount, out_channels=out_channels, output_device=self.output_device)
         if self.latent_dim == 1:
@@ -932,7 +935,7 @@ class VAE:
             return out.reshape(samples.shape[0], self.latent_channels, extra_channel_size, -1)
 
     def encode_tiled_3d(self, samples, tile_t=9999, tile_x=512, tile_y=512, overlap=(1, 64, 64)):
-        encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
+        encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
         return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.downscale_ratio, out_channels=self.latent_channels, downscale=True, index_formulas=self.downscale_index_formula, output_device=self.output_device)
 
     def decode(self, samples_in, vae_options={}):
@@ -950,9 +953,9 @@ class VAE:
 
             for x in range(0, samples_in.shape[0], batch_number):
                 samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
-                out = self.process_output(self.first_stage_model.decode(samples, **vae_options).to(self.output_device).float())
+                out = self.process_output(self.first_stage_model.decode(samples, **vae_options).to(self.output_device).to(dtype=self.vae_output_dtype()))
                 if pixel_samples is None:
-                    pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
+                    pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype())
                 pixel_samples[x:x+batch_number] = out
         except Exception as e:
             model_management.raise_non_oom(e)
@@ -1025,9 +1028,9 @@ class VAE:
             samples = None
             for x in range(0, pixel_samples.shape[0], batch_number):
                 pixels_in = self.process_input(pixel_samples[x:x + batch_number]).to(self.vae_dtype).to(self.device)
-                out = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
+                out = self.first_stage_model.encode(pixels_in).to(self.output_device).to(dtype=self.vae_output_dtype())
                 if samples is None:
-                    samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
+                    samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype())
                 samples[x:x + batch_number] = out
 
         except Exception as e:
diff --git a/nodes.py b/nodes.py
index eb63f9d44..1e19a8223 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1724,6 +1724,8 @@ class LoadImage:
         output_masks = []
         w, h = None, None
 
+        dtype = comfy.model_management.intermediate_dtype()
+
         for i in ImageSequence.Iterator(img):
             i = node_helpers.pillow(ImageOps.exif_transpose, i)
 
@@ -1748,8 +1750,8 @@ class LoadImage:
                 mask = 1. - torch.from_numpy(mask)
             else:
                 mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
-            output_images.append(image)
-            output_masks.append(mask.unsqueeze(0))
+            output_images.append(image.to(dtype=dtype))
+            output_masks.append(mask.unsqueeze(0).to(dtype=dtype))
 
             if img.format == "MPO":
                 break  # ignore all frames except the first one for MPO format

From 4941cd046eb1cd3021708ab7fe4e81e90a7b5dbe Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Sat, 14 Mar 2026 16:53:31 -0700
Subject: [PATCH 171/194] Update comfyui-frontend-package to version 1.41.20
 (#12954)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index c32a765a0..7e59ef206 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-comfyui-frontend-package==1.41.19
+comfyui-frontend-package==1.41.20
 comfyui-workflow-templates==0.9.21
 comfyui-embedded-docs==0.4.3
 torch

From 0904cc3fe5a551e3716851f12a568e481badd301 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?=
 <40791699+kijai@users.noreply.github.com>
Date: Sun, 15 Mar 2026 03:09:09 +0200
Subject: [PATCH 172/194] LTXV: Accumulate VAE decode results on
 intermediate_device (#12955)

---
 comfy/ldm/lightricks/vae/causal_video_autoencoder.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py
index 5b57dfc5e..9f14f64a5 100644
--- a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py
+++ b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py
@@ -11,6 +11,7 @@ from .causal_conv3d import CausalConv3d
 from .pixel_norm import PixelNorm
 from ..model import PixArtAlphaCombinedTimestepSizeEmbeddings
 import comfy.ops
+import comfy.model_management
 from comfy.ldm.modules.diffusionmodules.model import torch_cat_if_needed
 
 ops = comfy.ops.disable_weight_init
@@ -536,7 +537,7 @@ class Decoder(nn.Module):
                     mark_conv3d_ended(self.conv_out)
                 sample = self.conv_out(sample, causal=self.causal)
                 if sample is not None and sample.shape[2] > 0:
-                    output.append(sample)
+                    output.append(sample.to(comfy.model_management.intermediate_device()))
                 return
 
             up_block = self.up_blocks[idx]

From 192cb8eeb9f644cda8e52ae24171491228ac8bb1 Mon Sep 17 00:00:00 2001
From: "Dr.Lt.Data" <128333288+ltdrdata@users.noreply.github.com>
Date: Mon, 16 Mar 2026 03:48:56 +0900
Subject: [PATCH 173/194] bump manager version to 4.1b5 (#12957)

---
 manager_requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/manager_requirements.txt b/manager_requirements.txt
index 37a33bd4f..1c5e8f071 100644
--- a/manager_requirements.txt
+++ b/manager_requirements.txt
@@ -1 +1 @@
-comfyui_manager==4.1b4
\ No newline at end of file
+comfyui_manager==4.1b5
\ No newline at end of file

From e84a200a3c68044c2b5d6621ea80d27d1585703f Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Sun, 15 Mar 2026 11:49:49 -0700
Subject: [PATCH 174/194] ops: opt out of deferred weight init if subclassed
 (#12967)

If a subclass BYO _load_from_state_dict and doesnt call the super() the
needed default init of these weights is missed and can lead to problems
for uninitialized weights.
---
 comfy/ops.py | 18 ++++++++++++++----
 1 file changed, 14 insertions(+), 4 deletions(-)

diff --git a/comfy/ops.py b/comfy/ops.py
index 59c0df87d..f47d4137a 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -336,7 +336,10 @@ class disable_weight_init:
     class Linear(torch.nn.Linear, CastWeightBiasOp):
 
         def __init__(self, in_features, out_features, bias=True, device=None, dtype=None):
-            if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled:
+            # don't trust subclasses that BYO state dict loader to call us.
+            if (not comfy.model_management.WINDOWS
+                or not comfy.memory_management.aimdo_enabled
+                or type(self)._load_from_state_dict is not disable_weight_init.Linear._load_from_state_dict):
                 super().__init__(in_features, out_features, bias, device, dtype)
                 return
 
@@ -357,7 +360,9 @@ class disable_weight_init:
         def _load_from_state_dict(self, state_dict, prefix, local_metadata,
                                 strict, missing_keys, unexpected_keys, error_msgs):
 
-            if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled:
+            if (not comfy.model_management.WINDOWS
+                or not comfy.memory_management.aimdo_enabled
+                or type(self)._load_from_state_dict is not disable_weight_init.Linear._load_from_state_dict):
                 return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict,
                                                      missing_keys, unexpected_keys, error_msgs)
             disable_weight_init._lazy_load_from_state_dict(
@@ -564,7 +569,10 @@ class disable_weight_init:
         def __init__(self, num_embeddings, embedding_dim, padding_idx=None, max_norm=None,
                      norm_type=2.0, scale_grad_by_freq=False, sparse=False, _weight=None,
                      _freeze=False, device=None, dtype=None):
-            if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled:
+            # don't trust subclasses that BYO state dict loader to call us.
+            if (not comfy.model_management.WINDOWS
+                or not comfy.memory_management.aimdo_enabled
+                or type(self)._load_from_state_dict is not disable_weight_init.Embedding._load_from_state_dict):
                 super().__init__(num_embeddings, embedding_dim, padding_idx, max_norm,
                                  norm_type, scale_grad_by_freq, sparse, _weight,
                                  _freeze, device, dtype)
@@ -590,7 +598,9 @@ class disable_weight_init:
         def _load_from_state_dict(self, state_dict, prefix, local_metadata,
                                 strict, missing_keys, unexpected_keys, error_msgs):
 
-            if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled:
+            if (not comfy.model_management.WINDOWS
+                or not comfy.memory_management.aimdo_enabled
+                or type(self)._load_from_state_dict is not disable_weight_init.Embedding._load_from_state_dict):
                 return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict,
                                                      missing_keys, unexpected_keys, error_msgs)
             disable_weight_init._lazy_load_from_state_dict(

From d062becb336da8430052381111e952d6ab51d39c Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Sun, 15 Mar 2026 12:37:27 -0700
Subject: [PATCH 175/194] Make EmptyLatentImage follow intermediate dtype.
 (#12974)

---
 nodes.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/nodes.py b/nodes.py
index 1e19a8223..dd9298b18 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1211,9 +1211,6 @@ class GLIGENTextBoxApply:
         return (c, )
 
 class EmptyLatentImage:
-    def __init__(self):
-        self.device = comfy.model_management.intermediate_device()
-
     @classmethod
     def INPUT_TYPES(s):
         return {
@@ -1232,7 +1229,7 @@ class EmptyLatentImage:
     SEARCH_ALIASES = ["empty", "empty latent", "new latent", "create latent", "blank latent", "blank"]
 
     def generate(self, width, height, batch_size=1):
-        latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device)
+        latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
         return ({"samples": latent, "downscale_ratio_spacial": 8}, )
 
 

From 3814bf4454ef3302fd7f91750d7a194dcf979630 Mon Sep 17 00:00:00 2001
From: lostdisc <194321775+lostdisc@users.noreply.github.com>
Date: Sun, 15 Mar 2026 15:45:30 -0400
Subject: [PATCH 176/194] Enable Pytorch Attention for gfx1150 (#12973)

---
 comfy/model_management.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 442d5a40a..a4af5ddb2 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -400,7 +400,7 @@ try:
         if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
             if aotriton_supported(arch):  # AMD efficient attention implementation depends on aotriton.
                 if torch_version_numeric >= (2, 7):  # works on 2.6 but doesn't actually seem to improve much
-                    if any((a in arch) for a in ["gfx90a", "gfx942", "gfx950", "gfx1100", "gfx1101", "gfx1151"]):  # TODO: more arches, TODO: gfx950
+                    if any((a in arch) for a in ["gfx90a", "gfx942", "gfx950", "gfx1100", "gfx1101", "gfx1150", "gfx1151"]):  # TODO: more arches, TODO: gfx950
                         ENABLE_PYTORCH_ATTENTION = True
                 if rocm_version >= (7, 0):
                    if any((a in arch) for a in ["gfx1200", "gfx1201"]):

From 593be209a45a8a306c26de550e240a363de405a7 Mon Sep 17 00:00:00 2001
From: Christian Byrne <cbyrne@comfy.org>
Date: Sun, 15 Mar 2026 16:18:04 -0700
Subject: [PATCH 177/194] feat: add essentials_category to nodes and blueprints
 for Essentials tab (#12573)

* feat: add essentials_category to nodes and blueprints for Essentials tab

Add ESSENTIALS_CATEGORY or essentials_category to 12 node classes and all
36 blueprint JSONs. Update SubgraphEntry TypedDict and subgraph_manager to
extract and pass through the field.

Fixes COM-15221

Amp-Thread-ID: https://ampcode.com/threads/T-019c83de-f7ab-7779-a451-0ba5940b56a9

* fix: import NotRequired from typing_extensions for Python 3.10 compat

* refactor: keep only node class ESSENTIALS_CATEGORY, remove blueprint/subgraph changes

Frontend will own blueprint categorization separately.

* fix: remove essentials_category from CreateVideo (not in spec)

---------

Co-authored-by: guill <jacob.e.segal@gmail.com>
---
 comfy_api_nodes/nodes_kling.py        | 1 +
 comfy_api_nodes/nodes_recraft.py      | 1 +
 comfy_extras/nodes_audio.py           | 2 ++
 comfy_extras/nodes_image_compare.py   | 1 +
 comfy_extras/nodes_images.py          | 1 +
 comfy_extras/nodes_post_processing.py | 1 +
 nodes.py                              | 3 +++
 7 files changed, 10 insertions(+)

diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py
index 8963c335d..9a37ccc53 100644
--- a/comfy_api_nodes/nodes_kling.py
+++ b/comfy_api_nodes/nodes_kling.py
@@ -1459,6 +1459,7 @@ class OmniProEditVideoNode(IO.ComfyNode):
             node_id="KlingOmniProEditVideoNode",
             display_name="Kling 3.0 Omni Edit Video",
             category="api node/video/Kling",
+            essentials_category="Video Generation",
             description="Edit an existing video with the latest model from Kling.",
             inputs=[
                 IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-video-o1"]),
diff --git a/comfy_api_nodes/nodes_recraft.py b/comfy_api_nodes/nodes_recraft.py
index 4d1d508fa..c60cfbc4a 100644
--- a/comfy_api_nodes/nodes_recraft.py
+++ b/comfy_api_nodes/nodes_recraft.py
@@ -833,6 +833,7 @@ class RecraftVectorizeImageNode(IO.ComfyNode):
             node_id="RecraftVectorizeImageNode",
             display_name="Recraft Vectorize Image",
             category="api node/image/Recraft",
+            essentials_category="Image Tools",
             description="Generates SVG synchronously from an input image.",
             inputs=[
                 IO.Image.Input("image"),
diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py
index 5d8d9bf6f..a395392d8 100644
--- a/comfy_extras/nodes_audio.py
+++ b/comfy_extras/nodes_audio.py
@@ -19,6 +19,7 @@ class EmptyLatentAudio(IO.ComfyNode):
             node_id="EmptyLatentAudio",
             display_name="Empty Latent Audio",
             category="latent/audio",
+            essentials_category="Audio",
             inputs=[
                 IO.Float.Input("seconds", default=47.6, min=1.0, max=1000.0, step=0.1),
                 IO.Int.Input(
@@ -185,6 +186,7 @@ class SaveAudioMP3(IO.ComfyNode):
             search_aliases=["export mp3"],
             display_name="Save Audio (MP3)",
             category="audio",
+            essentials_category="Audio",
             inputs=[
                 IO.Audio.Input("audio"),
                 IO.String.Input("filename_prefix", default="audio/ComfyUI"),
diff --git a/comfy_extras/nodes_image_compare.py b/comfy_extras/nodes_image_compare.py
index 8e9f809e6..3d943be67 100644
--- a/comfy_extras/nodes_image_compare.py
+++ b/comfy_extras/nodes_image_compare.py
@@ -14,6 +14,7 @@ class ImageCompare(IO.ComfyNode):
             display_name="Image Compare",
             description="Compares two images side by side with a slider.",
             category="image",
+            essentials_category="Image Tools",
             is_experimental=True,
             is_output_node=True,
             inputs=[
diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py
index 4c57bb5cb..a8223cf8b 100644
--- a/comfy_extras/nodes_images.py
+++ b/comfy_extras/nodes_images.py
@@ -58,6 +58,7 @@ class ImageCropV2(IO.ComfyNode):
             search_aliases=["trim"],
             display_name="Image Crop",
             category="image/transform",
+            essentials_category="Image Tools",
             inputs=[
                 IO.Image.Input("image"),
                 IO.BoundingBox.Input("crop_region", component="ImageCrop"),
diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py
index 4a0f7141a..06626f9dd 100644
--- a/comfy_extras/nodes_post_processing.py
+++ b/comfy_extras/nodes_post_processing.py
@@ -21,6 +21,7 @@ class Blend(io.ComfyNode):
             node_id="ImageBlend",
             display_name="Image Blend",
             category="image/postprocessing",
+            essentials_category="Image Tools",
             inputs=[
                 io.Image.Input("image1"),
                 io.Image.Input("image2"),
diff --git a/nodes.py b/nodes.py
index dd9298b18..03dcc9d4a 100644
--- a/nodes.py
+++ b/nodes.py
@@ -81,6 +81,7 @@ class CLIPTextEncode(ComfyNodeABC):
 
 
 class ConditioningCombine:
+    ESSENTIALS_CATEGORY = "Image Generation"
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", )}}
@@ -1778,6 +1779,7 @@ class LoadImage:
         return True
 
 class LoadImageMask:
+    ESSENTIALS_CATEGORY = "Image Tools"
     SEARCH_ALIASES = ["import mask", "alpha mask", "channel mask"]
 
     _color_channels = ["alpha", "red", "green", "blue"]
@@ -1886,6 +1888,7 @@ class ImageScale:
         return (s,)
 
 class ImageScaleBy:
+    ESSENTIALS_CATEGORY = "Image Tools"
     upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
 
     @classmethod

From 2bd4d82b4f19c30dc979a3a16ddae97068e1bdc8 Mon Sep 17 00:00:00 2001
From: Luke Mino-Altherr <lminoaltherr@gmail.com>
Date: Mon, 16 Mar 2026 15:34:04 -0400
Subject: [PATCH 178/194] feat(assets): align local API with cloud spec
 (#12863)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* feat(assets): align local API with cloud spec

Unify response models, add missing fields, and align input schemas with
the cloud OpenAPI spec at cloud.comfy.org/openapi.

- Replace AssetSummary/AssetDetail/AssetUpdated with single Asset model
- Add is_immutable, metadata (system_metadata), prompt_id fields
- Support mime_type and preview_id in update endpoint
- Make CreateFromHashBody.name optional, add mime_type, require >=1 tag
- Add id/mime_type/preview_id to upload, relax tags to optional
- Rename total_tags → tags in tag add/remove responses
- Add GET /api/assets/tags/refine histogram endpoint
- Add DB migration for system_metadata and prompt_id columns

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* Fix review issues: tags validation, size nullability, type annotation, hash mismatch check, and add tag histogram tests

- Remove contradictory min_length=1 from CreateFromHashBody.tags default
- Restore size field to int|None=None for proper null semantics
- Add Union type annotation to _build_asset_response result param
- Add hash mismatch validation on idempotent upload path (409 HASH_MISMATCH)
- Add unit tests for list_tag_histogram service function

Amp-Thread-ID: https://ampcode.com/threads/T-019cd993-f43c-704e-b3d7-6cfc3d4d4a80
Co-authored-by: Amp <amp@ampcode.com>

* Add preview_url to /assets API response using /api/view endpoint

For input and output assets, generate a preview_url pointing to the
existing /api/view endpoint using the asset's filename and tag-derived
type (input/output). Handles subdirectories via subfolder param and
URL-encodes filenames with spaces, unicode, and special characters.

This aligns the OSS backend response with the frontend AssetCard
expectation for thumbnail rendering.

Amp-Thread-ID: https://ampcode.com/threads/T-019cda3f-5c2c-751a-a906-ac6c9153ac5c
Co-authored-by: Amp <amp@ampcode.com>

* chore: remove unused imports from asset_reference queries

Amp-Thread-ID: https://ampcode.com/threads/T-019cda7d-cb21-77b4-a51b-b965af60208c
Co-authored-by: Amp <amp@ampcode.com>

* feat: resolve blake3 hashes in /view endpoint via asset database

Amp-Thread-ID: https://ampcode.com/threads/T-019cda7d-cb21-77b4-a51b-b965af60208c
Co-authored-by: Amp <amp@ampcode.com>

* Register uploaded images in asset database when --enable-assets is set

Add register_file_in_place() service function to ingest module for
registering already-saved files without moving them. Call it from the
/upload/image endpoint to return asset metadata in the response.

Amp-Thread-ID: https://ampcode.com/threads/T-019ce023-3384-7560-bacf-de40b0de0dd2
Co-authored-by: Amp <amp@ampcode.com>

* Exclude None fields from asset API JSON responses

Add exclude_none=True to model_dump() calls across asset routes to
keep response payloads clean by omitting unset optional fields.

Amp-Thread-ID: https://ampcode.com/threads/T-019ce023-3384-7560-bacf-de40b0de0dd2
Co-authored-by: Amp <amp@ampcode.com>

* Add comment explaining why /view resolves blake3 hashes

Amp-Thread-ID: https://ampcode.com/threads/T-019ce023-3384-7560-bacf-de40b0de0dd2
Co-authored-by: Amp <amp@ampcode.com>

* Move blake3 hash resolution to asset_management service

Extract resolve_hash_to_path() into asset_management.py and remove
_resolve_blake3_to_path from server.py. Also revert loopback origin
check to original logic.

Amp-Thread-ID: https://ampcode.com/threads/T-019ce023-3384-7560-bacf-de40b0de0dd2
Co-authored-by: Amp <amp@ampcode.com>

* Require at least one tag in UploadAssetSpec

Enforce non-empty tags at the Pydantic validation layer so uploads
with no tags are rejected with a 400 before reaching ingest. Adds
test_upload_empty_tags_rejected to cover this case.

Amp-Thread-ID: https://ampcode.com/threads/T-019ce377-8bde-7048-bc28-a9df063409f9
Co-authored-by: Amp <amp@ampcode.com>

* Add owner_id check to resolve_hash_to_path

Filter asset references by owner visibility so the /view endpoint
only resolves hashes for assets the requesting user can access.
Adds table-driven tests for owner visibility cases.

Amp-Thread-ID: https://ampcode.com/threads/T-019ce377-8bde-7048-bc28-a9df063409f9
Co-authored-by: Amp <amp@ampcode.com>

* Make ReferenceData.created_at and updated_at required

Remove None defaults and type: ignore comments. Move fields before
optional fields to satisfy dataclass ordering.

Amp-Thread-ID: https://ampcode.com/threads/T-019ce377-8bde-7048-bc28-a9df063409f9
Co-authored-by: Amp <amp@ampcode.com>

* Fix double commit in create_from_hash

Move mime_type update into _register_existing_asset so it shares a
single transaction with reference creation. Log a warning when the
hash is not found instead of silently returning None.

Amp-Thread-ID: https://ampcode.com/threads/T-019ce377-8bde-7048-bc28-a9df063409f9
Co-authored-by: Amp <amp@ampcode.com>

* Add exclude_none=True to create/upload responses

Align with get/update/list endpoints for consistent JSON output.

Amp-Thread-ID: https://ampcode.com/threads/T-019ce377-8bde-7048-bc28-a9df063409f9
Co-authored-by: Amp <amp@ampcode.com>

* Change preview_id to reference asset by reference ID, not content ID

Clients receive preview_id in API responses but could not dereference it
through public routes (which use reference IDs). Now preview_id is a
self-referential FK to asset_references.id so the value is directly
usable in the public API.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* Filter soft-deleted and missing refs from visibility queries

list_references_by_asset_id and list_tags_with_usage were not filtering
out deleted_at/is_missing refs, allowing /view?filename=blake3:... to
serve files through hidden references and inflating tag usage counts.
Add list_all_file_paths_by_asset_id for orphan cleanup which
intentionally needs unfiltered access.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* Pass preview_id and mime_type through all asset creation fast paths

The duplicate-content upload path and hash-based creation paths were
silently dropping preview_id and mime_type. This wires both fields
through _register_existing_asset, create_from_hash, and all route
call sites so behavior is consistent regardless of whether the asset
content already exists.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* Remove unimplemented client-provided ID from upload API

The `id` field on UploadAssetSpec was advertised for idempotent creation
but never actually honored when creating new references. Remove it
rather than implementing the feature.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* Make asset mime_type immutable after first ingest

Prevents cross-tenant metadata mutation when multiple references share
the same content-addressed Asset row. mime_type can now only be set when
NULL (first ingest); subsequent attempts to change it are silently ignored.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* Use resolved content_type from asset lookup in /view endpoint

The /view endpoint was discarding the content_type computed by
resolve_hash_to_path() and re-guessing from the filename, which
produced wrong results for extensionless files or mismatched extensions.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* Merge system+user metadata into filter projection

Extract rebuild_metadata_projection() to build AssetReferenceMeta rows
from {**system_metadata, **user_metadata}, so system-generated metadata
is queryable via metadata_filter and user keys override system keys.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* Standardize tag ordering to alphabetical across all endpoints

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* Derive subfolder tags from path in register_file_in_place

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* Reject client-provided id, fix preview URLs, rename tags→total_tags

- Reject 'id' field in multipart upload with 400 UNSUPPORTED_FIELD
  instead of silently ignoring it
- Build preview URL from the preview asset's own metadata rather than
  the parent asset's
- Rename 'tags' to 'total_tags' in TagsAdd/TagsRemove response schemas
  for clarity

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: SQLite migration 0003 FK drop fails on file-backed DBs (MB-2)

Add naming_convention to Base.metadata so Alembic batch-mode reflection
can match unnamed FK constraints created by migration 0002. Pass
naming_convention and render_as_batch=True through env.py online config.

Add migration roundtrip tests (upgrade/downgrade/cycle from baseline).

Amp-Thread-ID: https://ampcode.com/threads/T-019ce466-1683-7471-b6e1-bb078223cda0
Co-authored-by: Amp <amp@ampcode.com>

* Fix missing tag count for is_missing references and update test for total_tags field

- Allow is_missing=True references to be counted in list_tags_with_usage
  when the tag is 'missing', so the missing tag count reflects all
  references that have been tagged as missing
- Add update_is_missing_by_asset_id query helper for bulk updates by asset
- Update test_add_and_remove_tags to use 'total_tags' matching the API schema

Amp-Thread-ID: https://ampcode.com/threads/T-019ce482-05e7-7324-a1b0-a56a929cc7ef
Co-authored-by: Amp <amp@ampcode.com>

* Remove unused imports in scanner.py

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* Rename prompt_id to job_id on asset_references

Rename the column in the DB model, migration, and service schemas.
The API response emits both job_id and prompt_id (deprecated alias)
for backward compatibility with the cloud API.

Amp-Thread-ID: https://ampcode.com/threads/T-019cef41-60b0-752a-aa3c-ed7f20fda2f7
Co-authored-by: Amp <amp@ampcode.com>

* Add index on asset_references.preview_id for FK cascade performance

Amp-Thread-ID: https://ampcode.com/threads/T-019cef45-a4d2-7548-86d2-d46bcd3db419
Co-authored-by: Amp <amp@ampcode.com>

* Add clarifying comments for Asset/AssetReference naming and preview_id

Amp-Thread-ID: https://ampcode.com/threads/T-019cef49-f94e-7348-bf23-9a19ebf65e0d
Co-authored-by: Amp <amp@ampcode.com>

* Disallow all-null meta rows: add CHECK constraint, skip null values on write

- convert_metadata_to_rows returns [] for None values instead of an all-null row
- Remove dead None branch from _scalar_to_row
- Simplify null filter in common.py to just check for row absence
- Add CHECK constraint ck_asset_reference_meta_has_value to model and migration 0003

Amp-Thread-ID: https://ampcode.com/threads/T-019cef4e-5240-7749-bb25-1f17fcf9c09c
Co-authored-by: Amp <amp@ampcode.com>

* Remove dead None guards on result.asset in upload handler

register_file_in_place guarantees a non-None asset, so the
'if result.asset else None' checks were unreachable.

Amp-Thread-ID: https://ampcode.com/threads/T-019cef5b-4cf8-723c-8a98-8fb8f333c133
Co-authored-by: Amp <amp@ampcode.com>

* Remove mime_type from asset update API

Clients can no longer modify mime_type after asset creation via the
PUT /api/assets/{id} endpoint. This reduces the risk of mime_type
spoofing. The internal update_asset_hash_and_mime function remains
available for server-side use (e.g., enrichment).

Amp-Thread-ID: https://ampcode.com/threads/T-019cef5d-8d61-75cc-a1c6-2841ac395648
Co-authored-by: Amp <amp@ampcode.com>

* Fix migration constraint naming double-prefix and NULL in mixed metadata lists

- Use fully-rendered constraint names in migration 0003 to avoid the
  naming convention doubling the ck_ prefix on batch operations.
- Add table_args to downgrade so SQLite batch mode can find the CHECK
  constraint (not exposed by SQLite reflection).
- Fix model CheckConstraint name to use bare 'has_value' (convention
  auto-prefixes).
- Skip None items when converting metadata lists to rows, preventing
  all-NULL rows that violate the has_value check constraint.

Amp-Thread-ID: https://ampcode.com/threads/T-019cef87-94f9-7172-a6af-c6282290ce4f
Co-authored-by: Amp <amp@ampcode.com>

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Amp <amp@ampcode.com>
---
 alembic_db/env.py                             |   7 +-
 .../versions/0003_add_metadata_job_id.py      |  98 +++++++
 app/assets/api/routes.py                      | 172 +++++++-----
 app/assets/api/schemas_in.py                  |  64 ++++-
 app/assets/api/schemas_out.py                 |  63 ++---
 app/assets/api/upload.py                      |  14 +
 app/assets/database/models.py                 |  25 +-
 app/assets/database/queries/__init__.py       |  12 +
 app/assets/database/queries/asset.py          |   4 +-
 .../database/queries/asset_reference.py       | 247 +++++++++---------
 app/assets/database/queries/common.py         |  79 +++++-
 app/assets/database/queries/tags.py           |  70 ++++-
 app/assets/scanner.py                         |   6 +-
 app/assets/services/asset_management.py       |  72 ++++-
 app/assets/services/ingest.py                 | 126 +++++++--
 app/assets/services/schemas.py                |   6 +-
 app/assets/services/tagging.py                |  23 ++
 app/database/models.py                        |  11 +-
 server.py                                     |  79 ++++--
 tests-unit/app_test/test_migrations.py        |  57 ++++
 tests-unit/assets_test/queries/test_asset.py  |  43 +++
 .../assets_test/queries/test_asset_info.py    |  21 +-
 .../assets_test/queries/test_metadata.py      |  51 +++-
 .../services/test_asset_management.py         |  54 +++-
 .../assets_test/services/test_ingest.py       |  12 +-
 .../services/test_tag_histogram.py            | 123 +++++++++
 tests-unit/assets_test/test_uploads.py        |   9 +
 27 files changed, 1218 insertions(+), 330 deletions(-)
 create mode 100644 alembic_db/versions/0003_add_metadata_job_id.py
 create mode 100644 tests-unit/app_test/test_migrations.py
 create mode 100644 tests-unit/assets_test/services/test_tag_histogram.py

diff --git a/alembic_db/env.py b/alembic_db/env.py
index 4d7770679..4ce37c012 100644
--- a/alembic_db/env.py
+++ b/alembic_db/env.py
@@ -8,7 +8,7 @@ from alembic import context
 config = context.config
 
 
-from app.database.models import Base
+from app.database.models import Base, NAMING_CONVENTION
 target_metadata = Base.metadata
 
 # other values from the config, defined by the needs of env.py,
@@ -51,7 +51,10 @@ def run_migrations_online() -> None:
 
     with connectable.connect() as connection:
         context.configure(
-            connection=connection, target_metadata=target_metadata
+            connection=connection,
+            target_metadata=target_metadata,
+            render_as_batch=True,
+            naming_convention=NAMING_CONVENTION,
         )
 
         with context.begin_transaction():
diff --git a/alembic_db/versions/0003_add_metadata_job_id.py b/alembic_db/versions/0003_add_metadata_job_id.py
new file mode 100644
index 000000000..2a14ee924
--- /dev/null
+++ b/alembic_db/versions/0003_add_metadata_job_id.py
@@ -0,0 +1,98 @@
+"""
+Add system_metadata and job_id columns to asset_references.
+Change preview_id FK from assets.id to asset_references.id.
+
+Revision ID: 0003_add_metadata_job_id
+Revises: 0002_merge_to_asset_references
+Create Date: 2026-03-09
+"""
+
+from alembic import op
+import sqlalchemy as sa
+
+from app.database.models import NAMING_CONVENTION
+
+revision = "0003_add_metadata_job_id"
+down_revision = "0002_merge_to_asset_references"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    with op.batch_alter_table("asset_references") as batch_op:
+        batch_op.add_column(
+            sa.Column("system_metadata", sa.JSON(), nullable=True)
+        )
+        batch_op.add_column(
+            sa.Column("job_id", sa.String(length=36), nullable=True)
+        )
+
+    # Change preview_id FK from assets.id to asset_references.id (self-ref).
+    # Existing values are asset-content IDs that won't match reference IDs,
+    # so null them out first.
+    op.execute("UPDATE asset_references SET preview_id = NULL WHERE preview_id IS NOT NULL")
+    with op.batch_alter_table(
+        "asset_references", naming_convention=NAMING_CONVENTION
+    ) as batch_op:
+        batch_op.drop_constraint(
+            "fk_asset_references_preview_id_assets", type_="foreignkey"
+        )
+        batch_op.create_foreign_key(
+            "fk_asset_references_preview_id_asset_references",
+            "asset_references",
+            ["preview_id"],
+            ["id"],
+            ondelete="SET NULL",
+        )
+        batch_op.create_index(
+            "ix_asset_references_preview_id", ["preview_id"]
+        )
+
+    # Purge any all-null meta rows before adding the constraint
+    op.execute(
+        "DELETE FROM asset_reference_meta"
+        " WHERE val_str IS NULL AND val_num IS NULL AND val_bool IS NULL AND val_json IS NULL"
+    )
+    with op.batch_alter_table("asset_reference_meta") as batch_op:
+        batch_op.create_check_constraint(
+            "ck_asset_reference_meta_has_value",
+            "val_str IS NOT NULL OR val_num IS NOT NULL OR val_bool IS NOT NULL OR val_json IS NOT NULL",
+        )
+
+
+def downgrade() -> None:
+    # SQLite doesn't reflect CHECK constraints, so we must declare it
+    # explicitly via table_args for the batch recreate to find it.
+    # Use the fully-rendered constraint name to avoid the naming convention
+    # doubling the prefix.
+    with op.batch_alter_table(
+        "asset_reference_meta",
+        table_args=[
+            sa.CheckConstraint(
+                "val_str IS NOT NULL OR val_num IS NOT NULL OR val_bool IS NOT NULL OR val_json IS NOT NULL",
+                name="ck_asset_reference_meta_has_value",
+            ),
+        ],
+    ) as batch_op:
+        batch_op.drop_constraint(
+            "ck_asset_reference_meta_has_value", type_="check"
+        )
+
+    with op.batch_alter_table(
+        "asset_references", naming_convention=NAMING_CONVENTION
+    ) as batch_op:
+        batch_op.drop_index("ix_asset_references_preview_id")
+        batch_op.drop_constraint(
+            "fk_asset_references_preview_id_asset_references", type_="foreignkey"
+        )
+        batch_op.create_foreign_key(
+            "fk_asset_references_preview_id_assets",
+            "assets",
+            ["preview_id"],
+            ["id"],
+            ondelete="SET NULL",
+        )
+
+    with op.batch_alter_table("asset_references") as batch_op:
+        batch_op.drop_column("job_id")
+        batch_op.drop_column("system_metadata")
diff --git a/app/assets/api/routes.py b/app/assets/api/routes.py
index 40dee9f46..68126b6a5 100644
--- a/app/assets/api/routes.py
+++ b/app/assets/api/routes.py
@@ -13,6 +13,7 @@ from pydantic import ValidationError
 import folder_paths
 from app import user_manager
 from app.assets.api import schemas_in, schemas_out
+from app.assets.services import schemas
 from app.assets.api.schemas_in import (
     AssetValidationError,
     UploadError,
@@ -38,6 +39,7 @@ from app.assets.services import (
     update_asset_metadata,
     upload_from_temp_path,
 )
+from app.assets.services.tagging import list_tag_histogram
 
 ROUTES = web.RouteTableDef()
 USER_MANAGER: user_manager.UserManager | None = None
@@ -122,6 +124,61 @@ def _validate_sort_field(requested: str | None) -> str:
     return "created_at"
 
 
+def _build_preview_url_from_view(tags: list[str], user_metadata: dict[str, Any] | None) -> str | None:
+    """Build a /api/view preview URL from asset tags and user_metadata filename."""
+    if not user_metadata:
+        return None
+    filename = user_metadata.get("filename")
+    if not filename:
+        return None
+
+    if "input" in tags:
+        view_type = "input"
+    elif "output" in tags:
+        view_type = "output"
+    else:
+        return None
+
+    subfolder = ""
+    if "/" in filename:
+        subfolder, filename = filename.rsplit("/", 1)
+
+    encoded_filename = urllib.parse.quote(filename, safe="")
+    url = f"/api/view?type={view_type}&filename={encoded_filename}"
+    if subfolder:
+        url += f"&subfolder={urllib.parse.quote(subfolder, safe='')}"
+    return url
+
+
+def _build_asset_response(result: schemas.AssetDetailResult | schemas.UploadResult) -> schemas_out.Asset:
+    """Build an Asset response from a service result."""
+    if result.ref.preview_id:
+        preview_detail = get_asset_detail(result.ref.preview_id)
+        if preview_detail:
+            preview_url = _build_preview_url_from_view(preview_detail.tags, preview_detail.ref.user_metadata)
+        else:
+            preview_url = None
+    else:
+        preview_url = _build_preview_url_from_view(result.tags, result.ref.user_metadata)
+    return schemas_out.Asset(
+        id=result.ref.id,
+        name=result.ref.name,
+        asset_hash=result.asset.hash if result.asset else None,
+        size=int(result.asset.size_bytes) if result.asset else None,
+        mime_type=result.asset.mime_type if result.asset else None,
+        tags=result.tags,
+        preview_url=preview_url,
+        preview_id=result.ref.preview_id,
+        user_metadata=result.ref.user_metadata or {},
+        metadata=result.ref.system_metadata,
+        job_id=result.ref.job_id,
+        prompt_id=result.ref.job_id,  # deprecated: mirrors job_id for cloud compat
+        created_at=result.ref.created_at,
+        updated_at=result.ref.updated_at,
+        last_access_time=result.ref.last_access_time,
+    )
+
+
 @ROUTES.head("/api/assets/hash/{hash}")
 @_require_assets_feature_enabled
 async def head_asset_by_hash(request: web.Request) -> web.Response:
@@ -164,20 +221,7 @@ async def list_assets_route(request: web.Request) -> web.Response:
         order=order,
     )
 
-    summaries = [
-        schemas_out.AssetSummary(
-            id=item.ref.id,
-            name=item.ref.name,
-            asset_hash=item.asset.hash if item.asset else None,
-            size=int(item.asset.size_bytes) if item.asset else None,
-            mime_type=item.asset.mime_type if item.asset else None,
-            tags=item.tags,
-            created_at=item.ref.created_at,
-            updated_at=item.ref.updated_at,
-            last_access_time=item.ref.last_access_time,
-        )
-        for item in result.items
-    ]
+    summaries = [_build_asset_response(item) for item in result.items]
 
     payload = schemas_out.AssetsList(
         assets=summaries,
@@ -207,18 +251,7 @@ async def get_asset_route(request: web.Request) -> web.Response:
                 {"id": reference_id},
             )
 
-        payload = schemas_out.AssetDetail(
-            id=result.ref.id,
-            name=result.ref.name,
-            asset_hash=result.asset.hash if result.asset else None,
-            size=int(result.asset.size_bytes) if result.asset else None,
-            mime_type=result.asset.mime_type if result.asset else None,
-            tags=result.tags,
-            user_metadata=result.ref.user_metadata or {},
-            preview_id=result.ref.preview_id,
-            created_at=result.ref.created_at,
-            last_access_time=result.ref.last_access_time,
-        )
+        payload = _build_asset_response(result)
     except ValueError as e:
         return _build_error_response(
             404, "ASSET_NOT_FOUND", str(e), {"id": reference_id}
@@ -230,7 +263,7 @@ async def get_asset_route(request: web.Request) -> web.Response:
             USER_MANAGER.get_request_user_id(request),
         )
         return _build_error_response(500, "INTERNAL", "Unexpected server error.")
-    return web.json_response(payload.model_dump(mode="json"), status=200)
+    return web.json_response(payload.model_dump(mode="json", exclude_none=True), status=200)
 
 
 @ROUTES.get(f"/api/assets/{{id:{UUID_RE}}}/content")
@@ -312,32 +345,31 @@ async def create_asset_from_hash_route(request: web.Request) -> web.Response:
             400, "INVALID_JSON", "Request body must be valid JSON."
         )
 
+    # Derive name from hash if not provided
+    name = body.name
+    if name is None:
+        name = body.hash.split(":", 1)[1] if ":" in body.hash else body.hash
+
     result = create_from_hash(
         hash_str=body.hash,
-        name=body.name,
+        name=name,
         tags=body.tags,
         user_metadata=body.user_metadata,
         owner_id=USER_MANAGER.get_request_user_id(request),
+        mime_type=body.mime_type,
+        preview_id=body.preview_id,
     )
     if result is None:
         return _build_error_response(
             404, "ASSET_NOT_FOUND", f"Asset content {body.hash} does not exist"
         )
 
+    asset = _build_asset_response(result)
     payload_out = schemas_out.AssetCreated(
-        id=result.ref.id,
-        name=result.ref.name,
-        asset_hash=result.asset.hash,
-        size=int(result.asset.size_bytes),
-        mime_type=result.asset.mime_type,
-        tags=result.tags,
-        user_metadata=result.ref.user_metadata or {},
-        preview_id=result.ref.preview_id,
-        created_at=result.ref.created_at,
-        last_access_time=result.ref.last_access_time,
+        **asset.model_dump(),
         created_new=result.created_new,
     )
-    return web.json_response(payload_out.model_dump(mode="json"), status=201)
+    return web.json_response(payload_out.model_dump(mode="json", exclude_none=True), status=201)
 
 
 @ROUTES.post("/api/assets")
@@ -358,6 +390,8 @@ async def upload_asset(request: web.Request) -> web.Response:
                 "name": parsed.provided_name,
                 "user_metadata": parsed.user_metadata_raw,
                 "hash": parsed.provided_hash,
+                "mime_type": parsed.provided_mime_type,
+                "preview_id": parsed.provided_preview_id,
             }
         )
     except ValidationError as ve:
@@ -386,6 +420,8 @@ async def upload_asset(request: web.Request) -> web.Response:
                 tags=spec.tags,
                 user_metadata=spec.user_metadata or {},
                 owner_id=owner_id,
+                mime_type=spec.mime_type,
+                preview_id=spec.preview_id,
             )
             if result is None:
                 delete_temp_file_if_exists(parsed.tmp_path)
@@ -410,6 +446,8 @@ async def upload_asset(request: web.Request) -> web.Response:
                 client_filename=parsed.file_client_name,
                 owner_id=owner_id,
                 expected_hash=spec.hash,
+                mime_type=spec.mime_type,
+                preview_id=spec.preview_id,
             )
     except AssetValidationError as e:
         delete_temp_file_if_exists(parsed.tmp_path)
@@ -428,21 +466,13 @@ async def upload_asset(request: web.Request) -> web.Response:
         logging.exception("upload_asset failed for owner_id=%s", owner_id)
         return _build_error_response(500, "INTERNAL", "Unexpected server error.")
 
-    payload = schemas_out.AssetCreated(
-        id=result.ref.id,
-        name=result.ref.name,
-        asset_hash=result.asset.hash,
-        size=int(result.asset.size_bytes),
-        mime_type=result.asset.mime_type,
-        tags=result.tags,
-        user_metadata=result.ref.user_metadata or {},
-        preview_id=result.ref.preview_id,
-        created_at=result.ref.created_at,
-        last_access_time=result.ref.last_access_time,
+    asset = _build_asset_response(result)
+    payload_out = schemas_out.AssetCreated(
+        **asset.model_dump(),
         created_new=result.created_new,
     )
     status = 201 if result.created_new else 200
-    return web.json_response(payload.model_dump(mode="json"), status=status)
+    return web.json_response(payload_out.model_dump(mode="json", exclude_none=True), status=status)
 
 
 @ROUTES.put(f"/api/assets/{{id:{UUID_RE}}}")
@@ -464,15 +494,9 @@ async def update_asset_route(request: web.Request) -> web.Response:
             name=body.name,
             user_metadata=body.user_metadata,
             owner_id=USER_MANAGER.get_request_user_id(request),
+            preview_id=body.preview_id,
         )
-        payload = schemas_out.AssetUpdated(
-            id=result.ref.id,
-            name=result.ref.name,
-            asset_hash=result.asset.hash if result.asset else None,
-            tags=result.tags,
-            user_metadata=result.ref.user_metadata or {},
-            updated_at=result.ref.updated_at,
-        )
+        payload = _build_asset_response(result)
     except PermissionError as pe:
         return _build_error_response(403, "FORBIDDEN", str(pe), {"id": reference_id})
     except ValueError as ve:
@@ -486,7 +510,7 @@ async def update_asset_route(request: web.Request) -> web.Response:
             USER_MANAGER.get_request_user_id(request),
         )
         return _build_error_response(500, "INTERNAL", "Unexpected server error.")
-    return web.json_response(payload.model_dump(mode="json"), status=200)
+    return web.json_response(payload.model_dump(mode="json", exclude_none=True), status=200)
 
 
 @ROUTES.delete(f"/api/assets/{{id:{UUID_RE}}}")
@@ -555,7 +579,7 @@ async def get_tags(request: web.Request) -> web.Response:
     payload = schemas_out.TagsList(
         tags=tags, total=total, has_more=(query.offset + len(tags)) < total
     )
-    return web.json_response(payload.model_dump(mode="json"))
+    return web.json_response(payload.model_dump(mode="json", exclude_none=True))
 
 
 @ROUTES.post(f"/api/assets/{{id:{UUID_RE}}}/tags")
@@ -603,7 +627,7 @@ async def add_asset_tags(request: web.Request) -> web.Response:
         )
         return _build_error_response(500, "INTERNAL", "Unexpected server error.")
 
-    return web.json_response(payload.model_dump(mode="json"), status=200)
+    return web.json_response(payload.model_dump(mode="json", exclude_none=True), status=200)
 
 
 @ROUTES.delete(f"/api/assets/{{id:{UUID_RE}}}/tags")
@@ -650,7 +674,29 @@ async def delete_asset_tags(request: web.Request) -> web.Response:
         )
         return _build_error_response(500, "INTERNAL", "Unexpected server error.")
 
-    return web.json_response(payload.model_dump(mode="json"), status=200)
+    return web.json_response(payload.model_dump(mode="json", exclude_none=True), status=200)
+
+
+@ROUTES.get("/api/assets/tags/refine")
+@_require_assets_feature_enabled
+async def get_tags_refine(request: web.Request) -> web.Response:
+    """GET request to get tag histogram for filtered assets."""
+    query_dict = get_query_dict(request)
+    try:
+        q = schemas_in.TagsRefineQuery.model_validate(query_dict)
+    except ValidationError as ve:
+        return _build_validation_error_response("INVALID_QUERY", ve)
+
+    tag_counts = list_tag_histogram(
+        owner_id=USER_MANAGER.get_request_user_id(request),
+        include_tags=q.include_tags,
+        exclude_tags=q.exclude_tags,
+        name_contains=q.name_contains,
+        metadata_filter=q.metadata_filter,
+        limit=q.limit,
+    )
+    payload = schemas_out.TagHistogram(tag_counts=tag_counts)
+    return web.json_response(payload.model_dump(mode="json", exclude_none=True), status=200)
 
 
 @ROUTES.post("/api/assets/seed")
diff --git a/app/assets/api/schemas_in.py b/app/assets/api/schemas_in.py
index d255c938e..186a6ae1e 100644
--- a/app/assets/api/schemas_in.py
+++ b/app/assets/api/schemas_in.py
@@ -45,6 +45,8 @@ class ParsedUpload:
     user_metadata_raw: str | None
     provided_hash: str | None
     provided_hash_exists: bool | None
+    provided_mime_type: str | None = None
+    provided_preview_id: str | None = None
 
 
 class ListAssetsQuery(BaseModel):
@@ -98,11 +100,17 @@ class ListAssetsQuery(BaseModel):
 class UpdateAssetBody(BaseModel):
     name: str | None = None
     user_metadata: dict[str, Any] | None = None
+    preview_id: str | None = None  # references an asset_reference id, not an asset id
 
     @model_validator(mode="after")
     def _validate_at_least_one_field(self):
-        if self.name is None and self.user_metadata is None:
-            raise ValueError("Provide at least one of: name, user_metadata.")
+        if all(
+            v is None
+            for v in (self.name, self.user_metadata, self.preview_id)
+        ):
+            raise ValueError(
+                "Provide at least one of: name, user_metadata, preview_id."
+            )
         return self
 
 
@@ -110,9 +118,11 @@ class CreateFromHashBody(BaseModel):
     model_config = ConfigDict(extra="ignore", str_strip_whitespace=True)
 
     hash: str
-    name: str
+    name: str | None = None
     tags: list[str] = Field(default_factory=list)
     user_metadata: dict[str, Any] = Field(default_factory=dict)
+    mime_type: str | None = None
+    preview_id: str | None = None  # references an asset_reference id, not an asset id
 
     @field_validator("hash")
     @classmethod
@@ -138,6 +148,44 @@ class CreateFromHashBody(BaseModel):
         return []
 
 
+class TagsRefineQuery(BaseModel):
+    include_tags: list[str] = Field(default_factory=list)
+    exclude_tags: list[str] = Field(default_factory=list)
+    name_contains: str | None = None
+    metadata_filter: dict[str, Any] | None = None
+    limit: conint(ge=1, le=1000) = 100
+
+    @field_validator("include_tags", "exclude_tags", mode="before")
+    @classmethod
+    def _split_csv_tags(cls, v):
+        if v is None:
+            return []
+        if isinstance(v, str):
+            return [t.strip() for t in v.split(",") if t.strip()]
+        if isinstance(v, list):
+            out: list[str] = []
+            for item in v:
+                if isinstance(item, str):
+                    out.extend([t.strip() for t in item.split(",") if t.strip()])
+            return out
+        return v
+
+    @field_validator("metadata_filter", mode="before")
+    @classmethod
+    def _parse_metadata_json(cls, v):
+        if v is None or isinstance(v, dict):
+            return v
+        if isinstance(v, str) and v.strip():
+            try:
+                parsed = json.loads(v)
+            except Exception as e:
+                raise ValueError(f"metadata_filter must be JSON: {e}") from e
+            if not isinstance(parsed, dict):
+                raise ValueError("metadata_filter must be a JSON object")
+            return parsed
+        return None
+
+
 class TagsListQuery(BaseModel):
     model_config = ConfigDict(extra="ignore", str_strip_whitespace=True)
 
@@ -186,21 +234,25 @@ class TagsRemove(TagsAdd):
 class UploadAssetSpec(BaseModel):
     """Upload Asset operation.
 
-    - tags: ordered; first is root ('models'|'input'|'output');
+    - tags: optional list; if provided, first is root ('models'|'input'|'output');
             if root == 'models', second must be a valid category
     - name: display name
     - user_metadata: arbitrary JSON object (optional)
     - hash: optional canonical 'blake3:<hex>' for validation / fast-path
+    - mime_type: optional MIME type override
+    - preview_id: optional asset_reference ID for preview
 
     Files are stored using the content hash as filename stem.
     """
 
     model_config = ConfigDict(extra="ignore", str_strip_whitespace=True)
 
-    tags: list[str] = Field(..., min_length=1)
+    tags: list[str] = Field(default_factory=list)
     name: str | None = Field(default=None, max_length=512, description="Display Name")
     user_metadata: dict[str, Any] = Field(default_factory=dict)
     hash: str | None = Field(default=None)
+    mime_type: str | None = Field(default=None)
+    preview_id: str | None = Field(default=None)  # references an asset_reference id
 
     @field_validator("hash", mode="before")
     @classmethod
@@ -279,7 +331,7 @@ class UploadAssetSpec(BaseModel):
     @model_validator(mode="after")
     def _validate_order(self):
         if not self.tags:
-            raise ValueError("tags must be provided and non-empty")
+            raise ValueError("at least one tag is required for uploads")
         root = self.tags[0]
         if root not in {"models", "input", "output"}:
             raise ValueError("first tag must be one of: models, input, output")
diff --git a/app/assets/api/schemas_out.py b/app/assets/api/schemas_out.py
index f36447856..d99b1098d 100644
--- a/app/assets/api/schemas_out.py
+++ b/app/assets/api/schemas_out.py
@@ -4,7 +4,10 @@ from typing import Any
 from pydantic import BaseModel, ConfigDict, Field, field_serializer
 
 
-class AssetSummary(BaseModel):
+class Asset(BaseModel):
+    """API view of an asset. Maps to DB ``AssetReference`` joined with its ``Asset`` blob;
+    ``id`` here is the AssetReference id, not the content-addressed Asset id."""
+
     id: str
     name: str
     asset_hash: str | None = None
@@ -12,8 +15,14 @@ class AssetSummary(BaseModel):
     mime_type: str | None = None
     tags: list[str] = Field(default_factory=list)
     preview_url: str | None = None
-    created_at: datetime | None = None
-    updated_at: datetime | None = None
+    preview_id: str | None = None  # references an asset_reference id, not an asset id
+    user_metadata: dict[str, Any] = Field(default_factory=dict)
+    is_immutable: bool = False
+    metadata: dict[str, Any] | None = None
+    job_id: str | None = None
+    prompt_id: str | None = None  # deprecated: use job_id
+    created_at: datetime
+    updated_at: datetime
     last_access_time: datetime | None = None
 
     model_config = ConfigDict(from_attributes=True)
@@ -23,50 +32,16 @@ class AssetSummary(BaseModel):
         return v.isoformat() if v else None
 
 
+class AssetCreated(Asset):
+    created_new: bool
+
+
 class AssetsList(BaseModel):
-    assets: list[AssetSummary]
+    assets: list[Asset]
     total: int
     has_more: bool
 
 
-class AssetUpdated(BaseModel):
-    id: str
-    name: str
-    asset_hash: str | None = None
-    tags: list[str] = Field(default_factory=list)
-    user_metadata: dict[str, Any] = Field(default_factory=dict)
-    updated_at: datetime | None = None
-
-    model_config = ConfigDict(from_attributes=True)
-
-    @field_serializer("updated_at")
-    def _serialize_updated_at(self, v: datetime | None, _info):
-        return v.isoformat() if v else None
-
-
-class AssetDetail(BaseModel):
-    id: str
-    name: str
-    asset_hash: str | None = None
-    size: int | None = None
-    mime_type: str | None = None
-    tags: list[str] = Field(default_factory=list)
-    user_metadata: dict[str, Any] = Field(default_factory=dict)
-    preview_id: str | None = None
-    created_at: datetime | None = None
-    last_access_time: datetime | None = None
-
-    model_config = ConfigDict(from_attributes=True)
-
-    @field_serializer("created_at", "last_access_time")
-    def _serialize_datetime(self, v: datetime | None, _info):
-        return v.isoformat() if v else None
-
-
-class AssetCreated(AssetDetail):
-    created_new: bool
-
-
 class TagUsage(BaseModel):
     name: str
     count: int
@@ -91,3 +66,7 @@ class TagsRemove(BaseModel):
     removed: list[str] = Field(default_factory=list)
     not_present: list[str] = Field(default_factory=list)
     total_tags: list[str] = Field(default_factory=list)
+
+
+class TagHistogram(BaseModel):
+    tag_counts: dict[str, int]
diff --git a/app/assets/api/upload.py b/app/assets/api/upload.py
index 721c12f4d..13d3d372c 100644
--- a/app/assets/api/upload.py
+++ b/app/assets/api/upload.py
@@ -52,6 +52,8 @@ async def parse_multipart_upload(
     user_metadata_raw: str | None = None
     provided_hash: str | None = None
     provided_hash_exists: bool | None = None
+    provided_mime_type: str | None = None
+    provided_preview_id: str | None = None
 
     file_written = 0
     tmp_path: str | None = None
@@ -128,6 +130,16 @@ async def parse_multipart_upload(
             provided_name = (await field.text()) or None
         elif fname == "user_metadata":
             user_metadata_raw = (await field.text()) or None
+        elif fname == "id":
+            raise UploadError(
+                400,
+                "UNSUPPORTED_FIELD",
+                "Client-provided 'id' is not supported. Asset IDs are assigned by the server.",
+            )
+        elif fname == "mime_type":
+            provided_mime_type = ((await field.text()) or "").strip() or None
+        elif fname == "preview_id":
+            provided_preview_id = ((await field.text()) or "").strip() or None
 
     if not file_present and not (provided_hash and provided_hash_exists):
         raise UploadError(
@@ -152,6 +164,8 @@ async def parse_multipart_upload(
         user_metadata_raw=user_metadata_raw,
         provided_hash=provided_hash,
         provided_hash_exists=provided_hash_exists,
+        provided_mime_type=provided_mime_type,
+        provided_preview_id=provided_preview_id,
     )
 
 
diff --git a/app/assets/database/models.py b/app/assets/database/models.py
index 03c1c1707..a3af8a192 100644
--- a/app/assets/database/models.py
+++ b/app/assets/database/models.py
@@ -45,13 +45,7 @@ class Asset(Base):
         passive_deletes=True,
     )
 
-    preview_of: Mapped[list[AssetReference]] = relationship(
-        "AssetReference",
-        back_populates="preview_asset",
-        primaryjoin=lambda: Asset.id == foreign(AssetReference.preview_id),
-        foreign_keys=lambda: [AssetReference.preview_id],
-        viewonly=True,
-    )
+    # preview_id on AssetReference is a self-referential FK to asset_references.id
 
     __table_args__ = (
         Index("uq_assets_hash", "hash", unique=True),
@@ -91,11 +85,15 @@ class AssetReference(Base):
     owner_id: Mapped[str] = mapped_column(String(128), nullable=False, default="")
     name: Mapped[str] = mapped_column(String(512), nullable=False)
     preview_id: Mapped[str | None] = mapped_column(
-        String(36), ForeignKey("assets.id", ondelete="SET NULL")
+        String(36), ForeignKey("asset_references.id", ondelete="SET NULL")
     )
     user_metadata: Mapped[dict[str, Any] | None] = mapped_column(
         JSON(none_as_null=True)
     )
+    system_metadata: Mapped[dict[str, Any] | None] = mapped_column(
+        JSON(none_as_null=True), nullable=True, default=None
+    )
+    job_id: Mapped[str | None] = mapped_column(String(36), nullable=True, default=None)
     created_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=False), nullable=False, default=get_utc_now
     )
@@ -115,10 +113,10 @@ class AssetReference(Base):
         foreign_keys=[asset_id],
         lazy="selectin",
     )
-    preview_asset: Mapped[Asset | None] = relationship(
-        "Asset",
-        back_populates="preview_of",
+    preview_ref: Mapped[AssetReference | None] = relationship(
+        "AssetReference",
         foreign_keys=[preview_id],
+        remote_side=lambda: [AssetReference.id],
     )
 
     metadata_entries: Mapped[list[AssetReferenceMeta]] = relationship(
@@ -152,6 +150,7 @@ class AssetReference(Base):
         Index("ix_asset_references_created_at", "created_at"),
         Index("ix_asset_references_last_access_time", "last_access_time"),
         Index("ix_asset_references_deleted_at", "deleted_at"),
+        Index("ix_asset_references_preview_id", "preview_id"),
         Index("ix_asset_references_owner_name", "owner_id", "name"),
         CheckConstraint(
             "(mtime_ns IS NULL) OR (mtime_ns >= 0)", name="ck_ar_mtime_nonneg"
@@ -192,6 +191,10 @@ class AssetReferenceMeta(Base):
         Index("ix_asset_reference_meta_key_val_str", "key", "val_str"),
         Index("ix_asset_reference_meta_key_val_num", "key", "val_num"),
         Index("ix_asset_reference_meta_key_val_bool", "key", "val_bool"),
+        CheckConstraint(
+            "val_str IS NOT NULL OR val_num IS NOT NULL OR val_bool IS NOT NULL OR val_json IS NOT NULL",
+            name="has_value",
+        ),
     )
 
 
diff --git a/app/assets/database/queries/__init__.py b/app/assets/database/queries/__init__.py
index 7888d0645..1632937b2 100644
--- a/app/assets/database/queries/__init__.py
+++ b/app/assets/database/queries/__init__.py
@@ -31,16 +31,21 @@ from app.assets.database.queries.asset_reference import (
     get_unenriched_references,
     get_unreferenced_unhashed_asset_ids,
     insert_reference,
+    list_all_file_paths_by_asset_id,
     list_references_by_asset_id,
     list_references_page,
     mark_references_missing_outside_prefixes,
+    rebuild_metadata_projection,
+    reference_exists,
     reference_exists_for_asset_id,
     restore_references_by_paths,
     set_reference_metadata,
     set_reference_preview,
+    set_reference_system_metadata,
     soft_delete_reference_by_id,
     update_reference_access_time,
     update_reference_name,
+    update_is_missing_by_asset_id,
     update_reference_timestamps,
     update_reference_updated_at,
     upsert_reference,
@@ -54,6 +59,7 @@ from app.assets.database.queries.tags import (
     bulk_insert_tags_and_meta,
     ensure_tags_exist,
     get_reference_tags,
+    list_tag_counts_for_filtered_assets,
     list_tags_with_usage,
     remove_missing_tag_for_asset_id,
     remove_tags_from_reference,
@@ -97,20 +103,26 @@ __all__ = [
     "get_unenriched_references",
     "get_unreferenced_unhashed_asset_ids",
     "insert_reference",
+    "list_all_file_paths_by_asset_id",
     "list_references_by_asset_id",
     "list_references_page",
+    "list_tag_counts_for_filtered_assets",
     "list_tags_with_usage",
     "mark_references_missing_outside_prefixes",
     "reassign_asset_references",
+    "rebuild_metadata_projection",
+    "reference_exists",
     "reference_exists_for_asset_id",
     "remove_missing_tag_for_asset_id",
     "remove_tags_from_reference",
     "restore_references_by_paths",
     "set_reference_metadata",
     "set_reference_preview",
+    "set_reference_system_metadata",
     "soft_delete_reference_by_id",
     "set_reference_tags",
     "update_asset_hash_and_mime",
+    "update_is_missing_by_asset_id",
     "update_reference_access_time",
     "update_reference_name",
     "update_reference_timestamps",
diff --git a/app/assets/database/queries/asset.py b/app/assets/database/queries/asset.py
index a21f5b68f..594d1f1b2 100644
--- a/app/assets/database/queries/asset.py
+++ b/app/assets/database/queries/asset.py
@@ -69,7 +69,7 @@ def upsert_asset(
         if asset.size_bytes != int(size_bytes) and int(size_bytes) > 0:
             asset.size_bytes = int(size_bytes)
             changed = True
-        if mime_type and asset.mime_type != mime_type:
+        if mime_type and not asset.mime_type:
             asset.mime_type = mime_type
             changed = True
         if changed:
@@ -118,7 +118,7 @@ def update_asset_hash_and_mime(
         return False
     if asset_hash is not None:
         asset.hash = asset_hash
-    if mime_type is not None:
+    if mime_type is not None and not asset.mime_type:
         asset.mime_type = mime_type
     return True
 
diff --git a/app/assets/database/queries/asset_reference.py b/app/assets/database/queries/asset_reference.py
index 6524791cc..084a32512 100644
--- a/app/assets/database/queries/asset_reference.py
+++ b/app/assets/database/queries/asset_reference.py
@@ -10,7 +10,7 @@ from decimal import Decimal
 from typing import NamedTuple, Sequence
 
 import sqlalchemy as sa
-from sqlalchemy import delete, exists, select
+from sqlalchemy import delete, select
 from sqlalchemy.dialects import sqlite
 from sqlalchemy.exc import IntegrityError
 from sqlalchemy.orm import Session, noload
@@ -24,12 +24,14 @@ from app.assets.database.models import (
 )
 from app.assets.database.queries.common import (
     MAX_BIND_PARAMS,
+    apply_metadata_filter,
+    apply_tag_filters,
     build_prefix_like_conditions,
     build_visible_owner_clause,
     calculate_rows_per_statement,
     iter_chunks,
 )
-from app.assets.helpers import escape_sql_like_string, get_utc_now, normalize_tags
+from app.assets.helpers import escape_sql_like_string, get_utc_now
 
 
 def _check_is_scalar(v):
@@ -44,15 +46,6 @@ def _check_is_scalar(v):
 
 def _scalar_to_row(key: str, ordinal: int, value) -> dict:
     """Convert a scalar value to a typed projection row."""
-    if value is None:
-        return {
-            "key": key,
-            "ordinal": ordinal,
-            "val_str": None,
-            "val_num": None,
-            "val_bool": None,
-            "val_json": None,
-        }
     if isinstance(value, bool):
         return {"key": key, "ordinal": ordinal, "val_bool": bool(value)}
     if isinstance(value, (int, float, Decimal)):
@@ -66,96 +59,19 @@ def _scalar_to_row(key: str, ordinal: int, value) -> dict:
 def convert_metadata_to_rows(key: str, value) -> list[dict]:
     """Turn a metadata key/value into typed projection rows."""
     if value is None:
-        return [_scalar_to_row(key, 0, None)]
+        return []
 
     if _check_is_scalar(value):
         return [_scalar_to_row(key, 0, value)]
 
     if isinstance(value, list):
         if all(_check_is_scalar(x) for x in value):
-            return [_scalar_to_row(key, i, x) for i, x in enumerate(value)]
-        return [{"key": key, "ordinal": i, "val_json": x} for i, x in enumerate(value)]
+            return [_scalar_to_row(key, i, x) for i, x in enumerate(value) if x is not None]
+        return [{"key": key, "ordinal": i, "val_json": x} for i, x in enumerate(value) if x is not None]
 
     return [{"key": key, "ordinal": 0, "val_json": value}]
 
 
-def _apply_tag_filters(
-    stmt: sa.sql.Select,
-    include_tags: Sequence[str] | None = None,
-    exclude_tags: Sequence[str] | None = None,
-) -> sa.sql.Select:
-    """include_tags: every tag must be present; exclude_tags: none may be present."""
-    include_tags = normalize_tags(include_tags)
-    exclude_tags = normalize_tags(exclude_tags)
-
-    if include_tags:
-        for tag_name in include_tags:
-            stmt = stmt.where(
-                exists().where(
-                    (AssetReferenceTag.asset_reference_id == AssetReference.id)
-                    & (AssetReferenceTag.tag_name == tag_name)
-                )
-            )
-
-    if exclude_tags:
-        stmt = stmt.where(
-            ~exists().where(
-                (AssetReferenceTag.asset_reference_id == AssetReference.id)
-                & (AssetReferenceTag.tag_name.in_(exclude_tags))
-            )
-        )
-    return stmt
-
-
-def _apply_metadata_filter(
-    stmt: sa.sql.Select,
-    metadata_filter: dict | None = None,
-) -> sa.sql.Select:
-    """Apply filters using asset_reference_meta projection table."""
-    if not metadata_filter:
-        return stmt
-
-    def _exists_for_pred(key: str, *preds) -> sa.sql.ClauseElement:
-        return sa.exists().where(
-            AssetReferenceMeta.asset_reference_id == AssetReference.id,
-            AssetReferenceMeta.key == key,
-            *preds,
-        )
-
-    def _exists_clause_for_value(key: str, value) -> sa.sql.ClauseElement:
-        if value is None:
-            no_row_for_key = sa.not_(
-                sa.exists().where(
-                    AssetReferenceMeta.asset_reference_id == AssetReference.id,
-                    AssetReferenceMeta.key == key,
-                )
-            )
-            null_row = _exists_for_pred(
-                key,
-                AssetReferenceMeta.val_json.is_(None),
-                AssetReferenceMeta.val_str.is_(None),
-                AssetReferenceMeta.val_num.is_(None),
-                AssetReferenceMeta.val_bool.is_(None),
-            )
-            return sa.or_(no_row_for_key, null_row)
-
-        if isinstance(value, bool):
-            return _exists_for_pred(key, AssetReferenceMeta.val_bool == bool(value))
-        if isinstance(value, (int, float, Decimal)):
-            num = value if isinstance(value, Decimal) else Decimal(str(value))
-            return _exists_for_pred(key, AssetReferenceMeta.val_num == num)
-        if isinstance(value, str):
-            return _exists_for_pred(key, AssetReferenceMeta.val_str == value)
-        return _exists_for_pred(key, AssetReferenceMeta.val_json == value)
-
-    for k, v in metadata_filter.items():
-        if isinstance(v, list):
-            ors = [_exists_clause_for_value(k, elem) for elem in v]
-            if ors:
-                stmt = stmt.where(sa.or_(*ors))
-        else:
-            stmt = stmt.where(_exists_clause_for_value(k, v))
-    return stmt
 
 
 def get_reference_by_id(
@@ -212,6 +128,21 @@ def reference_exists_for_asset_id(
     return session.execute(q).first() is not None
 
 
+def reference_exists(
+    session: Session,
+    reference_id: str,
+) -> bool:
+    """Return True if a reference with the given ID exists (not soft-deleted)."""
+    q = (
+        select(sa.literal(True))
+        .select_from(AssetReference)
+        .where(AssetReference.id == reference_id)
+        .where(AssetReference.deleted_at.is_(None))
+        .limit(1)
+    )
+    return session.execute(q).first() is not None
+
+
 def insert_reference(
     session: Session,
     asset_id: str,
@@ -336,8 +267,8 @@ def list_references_page(
         escaped, esc = escape_sql_like_string(name_contains)
         base = base.where(AssetReference.name.ilike(f"%{escaped}%", escape=esc))
 
-    base = _apply_tag_filters(base, include_tags, exclude_tags)
-    base = _apply_metadata_filter(base, metadata_filter)
+    base = apply_tag_filters(base, include_tags, exclude_tags)
+    base = apply_metadata_filter(base, metadata_filter)
 
     sort = (sort or "created_at").lower()
     order = (order or "desc").lower()
@@ -366,8 +297,8 @@ def list_references_page(
         count_stmt = count_stmt.where(
             AssetReference.name.ilike(f"%{escaped}%", escape=esc)
         )
-    count_stmt = _apply_tag_filters(count_stmt, include_tags, exclude_tags)
-    count_stmt = _apply_metadata_filter(count_stmt, metadata_filter)
+    count_stmt = apply_tag_filters(count_stmt, include_tags, exclude_tags)
+    count_stmt = apply_metadata_filter(count_stmt, metadata_filter)
 
     total = int(session.execute(count_stmt).scalar_one() or 0)
     refs = session.execute(base).unique().scalars().all()
@@ -379,7 +310,7 @@ def list_references_page(
             select(AssetReferenceTag.asset_reference_id, Tag.name)
             .join(Tag, Tag.name == AssetReferenceTag.tag_name)
             .where(AssetReferenceTag.asset_reference_id.in_(id_list))
-            .order_by(AssetReferenceTag.added_at)
+            .order_by(AssetReferenceTag.tag_name.asc())
         )
         for ref_id, tag_name in rows.all():
             tag_map[ref_id].append(tag_name)
@@ -492,6 +423,42 @@ def update_reference_updated_at(
     )
 
 
+def rebuild_metadata_projection(session: Session, ref: AssetReference) -> None:
+    """Delete and rebuild AssetReferenceMeta rows from merged system+user metadata.
+
+    The merged dict is ``{**system_metadata, **user_metadata}`` so user keys
+    override system keys of the same name.
+    """
+    session.execute(
+        delete(AssetReferenceMeta).where(
+            AssetReferenceMeta.asset_reference_id == ref.id
+        )
+    )
+    session.flush()
+
+    merged = {**(ref.system_metadata or {}), **(ref.user_metadata or {})}
+    if not merged:
+        return
+
+    rows: list[AssetReferenceMeta] = []
+    for k, v in merged.items():
+        for r in convert_metadata_to_rows(k, v):
+            rows.append(
+                AssetReferenceMeta(
+                    asset_reference_id=ref.id,
+                    key=r["key"],
+                    ordinal=int(r["ordinal"]),
+                    val_str=r.get("val_str"),
+                    val_num=r.get("val_num"),
+                    val_bool=r.get("val_bool"),
+                    val_json=r.get("val_json"),
+                )
+            )
+    if rows:
+        session.add_all(rows)
+        session.flush()
+
+
 def set_reference_metadata(
     session: Session,
     reference_id: str,
@@ -505,33 +472,24 @@ def set_reference_metadata(
     ref.updated_at = get_utc_now()
     session.flush()
 
-    session.execute(
-        delete(AssetReferenceMeta).where(
-            AssetReferenceMeta.asset_reference_id == reference_id
-        )
-    )
+    rebuild_metadata_projection(session, ref)
+
+
+def set_reference_system_metadata(
+    session: Session,
+    reference_id: str,
+    system_metadata: dict | None = None,
+) -> None:
+    """Set system_metadata on a reference and rebuild the merged projection."""
+    ref = session.get(AssetReference, reference_id)
+    if not ref:
+        raise ValueError(f"AssetReference {reference_id} not found")
+
+    ref.system_metadata = system_metadata or {}
+    ref.updated_at = get_utc_now()
     session.flush()
 
-    if not user_metadata:
-        return
-
-    rows: list[AssetReferenceMeta] = []
-    for k, v in user_metadata.items():
-        for r in convert_metadata_to_rows(k, v):
-            rows.append(
-                AssetReferenceMeta(
-                    asset_reference_id=reference_id,
-                    key=r["key"],
-                    ordinal=int(r["ordinal"]),
-                    val_str=r.get("val_str"),
-                    val_num=r.get("val_num"),
-                    val_bool=r.get("val_bool"),
-                    val_json=r.get("val_json"),
-                )
-            )
-    if rows:
-        session.add_all(rows)
-        session.flush()
+    rebuild_metadata_projection(session, ref)
 
 
 def delete_reference_by_id(
@@ -571,19 +529,19 @@ def soft_delete_reference_by_id(
 def set_reference_preview(
     session: Session,
     reference_id: str,
-    preview_asset_id: str | None = None,
+    preview_reference_id: str | None = None,
 ) -> None:
     """Set or clear preview_id and bump updated_at. Raises on unknown IDs."""
     ref = session.get(AssetReference, reference_id)
     if not ref:
         raise ValueError(f"AssetReference {reference_id} not found")
 
-    if preview_asset_id is None:
+    if preview_reference_id is None:
         ref.preview_id = None
     else:
-        if not session.get(Asset, preview_asset_id):
-            raise ValueError(f"Preview Asset {preview_asset_id} not found")
-        ref.preview_id = preview_asset_id
+        if not session.get(AssetReference, preview_reference_id):
+            raise ValueError(f"Preview AssetReference {preview_reference_id} not found")
+        ref.preview_id = preview_reference_id
 
     ref.updated_at = get_utc_now()
     session.flush()
@@ -609,6 +567,8 @@ def list_references_by_asset_id(
         session.execute(
             select(AssetReference)
             .where(AssetReference.asset_id == asset_id)
+            .where(AssetReference.is_missing == False)  # noqa: E712
+            .where(AssetReference.deleted_at.is_(None))
             .order_by(AssetReference.id.asc())
         )
         .scalars()
@@ -616,6 +576,25 @@ def list_references_by_asset_id(
     )
 
 
+def list_all_file_paths_by_asset_id(
+    session: Session,
+    asset_id: str,
+) -> list[str]:
+    """Return every file_path for an asset, including soft-deleted/missing refs.
+
+    Used for orphan cleanup where all on-disk files must be removed.
+    """
+    return list(
+        session.execute(
+            select(AssetReference.file_path)
+            .where(AssetReference.asset_id == asset_id)
+            .where(AssetReference.file_path.isnot(None))
+        )
+        .scalars()
+        .all()
+    )
+
+
 def upsert_reference(
     session: Session,
     asset_id: str,
@@ -855,6 +834,22 @@ def bulk_update_is_missing(
     return total
 
 
+def update_is_missing_by_asset_id(
+    session: Session, asset_id: str, value: bool
+) -> int:
+    """Set is_missing flag for ALL references belonging to an asset.
+
+    Returns: Number of rows updated
+    """
+    result = session.execute(
+        sa.update(AssetReference)
+        .where(AssetReference.asset_id == asset_id)
+        .where(AssetReference.deleted_at.is_(None))
+        .values(is_missing=value)
+    )
+    return result.rowcount
+
+
 def delete_references_by_ids(session: Session, reference_ids: list[str]) -> int:
     """Delete references by their IDs.
 
diff --git a/app/assets/database/queries/common.py b/app/assets/database/queries/common.py
index 194c39a1e..89bb49327 100644
--- a/app/assets/database/queries/common.py
+++ b/app/assets/database/queries/common.py
@@ -1,12 +1,14 @@
 """Shared utilities for database query modules."""
 
 import os
-from typing import Iterable
+from decimal import Decimal
+from typing import Iterable, Sequence
 
 import sqlalchemy as sa
+from sqlalchemy import exists
 
-from app.assets.database.models import AssetReference
-from app.assets.helpers import escape_sql_like_string
+from app.assets.database.models import AssetReference, AssetReferenceMeta, AssetReferenceTag
+from app.assets.helpers import escape_sql_like_string, normalize_tags
 
 MAX_BIND_PARAMS = 800
 
@@ -52,3 +54,74 @@ def build_prefix_like_conditions(
         escaped, esc = escape_sql_like_string(base)
         conds.append(AssetReference.file_path.like(escaped + "%", escape=esc))
     return conds
+
+
+def apply_tag_filters(
+    stmt: sa.sql.Select,
+    include_tags: Sequence[str] | None = None,
+    exclude_tags: Sequence[str] | None = None,
+) -> sa.sql.Select:
+    """include_tags: every tag must be present; exclude_tags: none may be present."""
+    include_tags = normalize_tags(include_tags)
+    exclude_tags = normalize_tags(exclude_tags)
+
+    if include_tags:
+        for tag_name in include_tags:
+            stmt = stmt.where(
+                exists().where(
+                    (AssetReferenceTag.asset_reference_id == AssetReference.id)
+                    & (AssetReferenceTag.tag_name == tag_name)
+                )
+            )
+
+    if exclude_tags:
+        stmt = stmt.where(
+            ~exists().where(
+                (AssetReferenceTag.asset_reference_id == AssetReference.id)
+                & (AssetReferenceTag.tag_name.in_(exclude_tags))
+            )
+        )
+    return stmt
+
+
+def apply_metadata_filter(
+    stmt: sa.sql.Select,
+    metadata_filter: dict | None = None,
+) -> sa.sql.Select:
+    """Apply filters using asset_reference_meta projection table."""
+    if not metadata_filter:
+        return stmt
+
+    def _exists_for_pred(key: str, *preds) -> sa.sql.ClauseElement:
+        return sa.exists().where(
+            AssetReferenceMeta.asset_reference_id == AssetReference.id,
+            AssetReferenceMeta.key == key,
+            *preds,
+        )
+
+    def _exists_clause_for_value(key: str, value) -> sa.sql.ClauseElement:
+        if value is None:
+            return sa.not_(
+                sa.exists().where(
+                    AssetReferenceMeta.asset_reference_id == AssetReference.id,
+                    AssetReferenceMeta.key == key,
+                )
+            )
+
+        if isinstance(value, bool):
+            return _exists_for_pred(key, AssetReferenceMeta.val_bool == bool(value))
+        if isinstance(value, (int, float, Decimal)):
+            num = value if isinstance(value, Decimal) else Decimal(str(value))
+            return _exists_for_pred(key, AssetReferenceMeta.val_num == num)
+        if isinstance(value, str):
+            return _exists_for_pred(key, AssetReferenceMeta.val_str == value)
+        return _exists_for_pred(key, AssetReferenceMeta.val_json == value)
+
+    for k, v in metadata_filter.items():
+        if isinstance(v, list):
+            ors = [_exists_clause_for_value(k, elem) for elem in v]
+            if ors:
+                stmt = stmt.where(sa.or_(*ors))
+        else:
+            stmt = stmt.where(_exists_clause_for_value(k, v))
+    return stmt
diff --git a/app/assets/database/queries/tags.py b/app/assets/database/queries/tags.py
index 8b25fee67..f4126dba8 100644
--- a/app/assets/database/queries/tags.py
+++ b/app/assets/database/queries/tags.py
@@ -8,12 +8,15 @@ from sqlalchemy.exc import IntegrityError
 from sqlalchemy.orm import Session
 
 from app.assets.database.models import (
+    Asset,
     AssetReference,
     AssetReferenceMeta,
     AssetReferenceTag,
     Tag,
 )
 from app.assets.database.queries.common import (
+    apply_metadata_filter,
+    apply_tag_filters,
     build_visible_owner_clause,
     iter_row_chunks,
 )
@@ -72,9 +75,9 @@ def get_reference_tags(session: Session, reference_id: str) -> list[str]:
         tag_name
         for (tag_name,) in (
             session.execute(
-                select(AssetReferenceTag.tag_name).where(
-                    AssetReferenceTag.asset_reference_id == reference_id
-                )
+                select(AssetReferenceTag.tag_name)
+                .where(AssetReferenceTag.asset_reference_id == reference_id)
+                .order_by(AssetReferenceTag.tag_name.asc())
             )
         ).all()
     ]
@@ -117,7 +120,7 @@ def set_reference_tags(
         )
         session.flush()
 
-    return SetTagsResult(added=to_add, removed=to_remove, total=desired)
+    return SetTagsResult(added=sorted(to_add), removed=sorted(to_remove), total=sorted(desired))
 
 
 def add_tags_to_reference(
@@ -272,6 +275,12 @@ def list_tags_with_usage(
         .select_from(AssetReferenceTag)
         .join(AssetReference, AssetReference.id == AssetReferenceTag.asset_reference_id)
         .where(build_visible_owner_clause(owner_id))
+        .where(
+            sa.or_(
+                AssetReference.is_missing == False,  # noqa: E712
+                AssetReferenceTag.tag_name == "missing",
+            )
+        )
         .where(AssetReference.deleted_at.is_(None))
         .group_by(AssetReferenceTag.tag_name)
         .subquery()
@@ -308,6 +317,12 @@ def list_tags_with_usage(
             select(AssetReferenceTag.tag_name)
             .join(AssetReference, AssetReference.id == AssetReferenceTag.asset_reference_id)
             .where(build_visible_owner_clause(owner_id))
+            .where(
+                sa.or_(
+                    AssetReference.is_missing == False,  # noqa: E712
+                    AssetReferenceTag.tag_name == "missing",
+                )
+            )
             .where(AssetReference.deleted_at.is_(None))
             .group_by(AssetReferenceTag.tag_name)
         )
@@ -320,6 +335,53 @@ def list_tags_with_usage(
     return rows_norm, int(total or 0)
 
 
+def list_tag_counts_for_filtered_assets(
+    session: Session,
+    owner_id: str = "",
+    include_tags: Sequence[str] | None = None,
+    exclude_tags: Sequence[str] | None = None,
+    name_contains: str | None = None,
+    metadata_filter: dict | None = None,
+    limit: int = 100,
+) -> dict[str, int]:
+    """Return tag counts for assets matching the given filters.
+
+    Uses the same filtering logic as list_references_page but returns
+    {tag_name: count} instead of paginated references.
+    """
+    # Build a subquery of matching reference IDs
+    ref_sq = (
+        select(AssetReference.id)
+        .join(Asset, Asset.id == AssetReference.asset_id)
+        .where(build_visible_owner_clause(owner_id))
+        .where(AssetReference.is_missing == False)  # noqa: E712
+        .where(AssetReference.deleted_at.is_(None))
+    )
+
+    if name_contains:
+        escaped, esc = escape_sql_like_string(name_contains)
+        ref_sq = ref_sq.where(AssetReference.name.ilike(f"%{escaped}%", escape=esc))
+
+    ref_sq = apply_tag_filters(ref_sq, include_tags, exclude_tags)
+    ref_sq = apply_metadata_filter(ref_sq, metadata_filter)
+    ref_sq = ref_sq.subquery()
+
+    # Count tags across those references
+    q = (
+        select(
+            AssetReferenceTag.tag_name,
+            func.count(AssetReferenceTag.asset_reference_id).label("cnt"),
+        )
+        .where(AssetReferenceTag.asset_reference_id.in_(select(ref_sq.c.id)))
+        .group_by(AssetReferenceTag.tag_name)
+        .order_by(func.count(AssetReferenceTag.asset_reference_id).desc(), AssetReferenceTag.tag_name.asc())
+        .limit(limit)
+    )
+
+    rows = session.execute(q).all()
+    return {tag_name: int(cnt) for tag_name, cnt in rows}
+
+
 def bulk_insert_tags_and_meta(
     session: Session,
     tag_rows: list[dict],
diff --git a/app/assets/scanner.py b/app/assets/scanner.py
index e27ea5123..4e05a97b5 100644
--- a/app/assets/scanner.py
+++ b/app/assets/scanner.py
@@ -18,7 +18,7 @@ from app.assets.database.queries import (
     mark_references_missing_outside_prefixes,
     reassign_asset_references,
     remove_missing_tag_for_asset_id,
-    set_reference_metadata,
+    set_reference_system_metadata,
     update_asset_hash_and_mime,
 )
 from app.assets.services.bulk_ingest import (
@@ -490,8 +490,8 @@ def enrich_asset(
             logging.warning("Failed to hash %s: %s", file_path, e)
 
     if extract_metadata and metadata:
-        user_metadata = metadata.to_user_metadata()
-        set_reference_metadata(session, reference_id, user_metadata)
+        system_metadata = metadata.to_user_metadata()
+        set_reference_system_metadata(session, reference_id, system_metadata)
 
     if full_hash:
         existing = get_asset_by_hash(session, full_hash)
diff --git a/app/assets/services/asset_management.py b/app/assets/services/asset_management.py
index 3fe7115c8..5aefd9956 100644
--- a/app/assets/services/asset_management.py
+++ b/app/assets/services/asset_management.py
@@ -16,10 +16,12 @@ from app.assets.database.queries import (
     get_reference_by_id,
     get_reference_with_owner_check,
     list_references_page,
+    list_all_file_paths_by_asset_id,
     list_references_by_asset_id,
     set_reference_metadata,
     set_reference_preview,
     set_reference_tags,
+    update_asset_hash_and_mime,
     update_reference_access_time,
     update_reference_name,
     update_reference_updated_at,
@@ -67,6 +69,8 @@ def update_asset_metadata(
     user_metadata: UserMetadata = None,
     tag_origin: str = "manual",
     owner_id: str = "",
+    mime_type: str | None = None,
+    preview_id: str | None = None,
 ) -> AssetDetailResult:
     with create_session() as session:
         ref = get_reference_with_owner_check(session, reference_id, owner_id)
@@ -103,6 +107,21 @@ def update_asset_metadata(
             )
             touched = True
 
+        if mime_type is not None:
+            updated = update_asset_hash_and_mime(
+                session, asset_id=ref.asset_id, mime_type=mime_type
+            )
+            if updated:
+                touched = True
+
+        if preview_id is not None:
+            set_reference_preview(
+                session,
+                reference_id=reference_id,
+                preview_reference_id=preview_id,
+            )
+            touched = True
+
         if touched and user_metadata is None:
             update_reference_updated_at(session, reference_id=reference_id)
 
@@ -159,11 +178,9 @@ def delete_asset_reference(
             session.commit()
             return True
 
-        # Orphaned asset - delete it and its files
-        refs = list_references_by_asset_id(session, asset_id=asset_id)
-        file_paths = [
-            r.file_path for r in (refs or []) if getattr(r, "file_path", None)
-        ]
+        # Orphaned asset - gather ALL file paths (including
+        # soft-deleted / missing refs) so their on-disk files get cleaned up.
+        file_paths = list_all_file_paths_by_asset_id(session, asset_id=asset_id)
         # Also include the just-deleted file path
         if file_path:
             file_paths.append(file_path)
@@ -185,7 +202,7 @@ def delete_asset_reference(
 
 def set_asset_preview(
     reference_id: str,
-    preview_asset_id: str | None = None,
+    preview_reference_id: str | None = None,
     owner_id: str = "",
 ) -> AssetDetailResult:
     with create_session() as session:
@@ -194,7 +211,7 @@ def set_asset_preview(
         set_reference_preview(
             session,
             reference_id=reference_id,
-            preview_asset_id=preview_asset_id,
+            preview_reference_id=preview_reference_id,
         )
 
         result = fetch_reference_asset_and_tags(
@@ -263,6 +280,47 @@ def list_assets_page(
         return ListAssetsResult(items=items, total=total)
 
 
+def resolve_hash_to_path(
+    asset_hash: str,
+    owner_id: str = "",
+) -> DownloadResolutionResult | None:
+    """Resolve a blake3 hash to an on-disk file path.
+
+    Only references visible to *owner_id* are considered (owner-less
+    references are always visible).
+
+    Returns a DownloadResolutionResult with abs_path, content_type, and
+    download_name, or None if no asset or live path is found.
+    """
+    with create_session() as session:
+        asset = queries_get_asset_by_hash(session, asset_hash)
+        if not asset:
+            return None
+        refs = list_references_by_asset_id(session, asset_id=asset.id)
+        visible = [
+            r for r in refs
+            if r.owner_id == "" or r.owner_id == owner_id
+        ]
+        abs_path = select_best_live_path(visible)
+        if not abs_path:
+            return None
+        display_name = os.path.basename(abs_path)
+        for ref in visible:
+            if ref.file_path == abs_path and ref.name:
+                display_name = ref.name
+                break
+        ctype = (
+            asset.mime_type
+            or mimetypes.guess_type(display_name)[0]
+            or "application/octet-stream"
+        )
+    return DownloadResolutionResult(
+        abs_path=abs_path,
+        content_type=ctype,
+        download_name=display_name,
+    )
+
+
 def resolve_asset_for_download(
     reference_id: str,
     owner_id: str = "",
diff --git a/app/assets/services/ingest.py b/app/assets/services/ingest.py
index 44d7aef36..90c51994f 100644
--- a/app/assets/services/ingest.py
+++ b/app/assets/services/ingest.py
@@ -11,13 +11,14 @@ from app.assets.database.queries import (
     add_tags_to_reference,
     fetch_reference_and_asset,
     get_asset_by_hash,
-    get_existing_asset_ids,
     get_reference_by_file_path,
     get_reference_tags,
     get_or_create_reference,
+    reference_exists,
     remove_missing_tag_for_asset_id,
     set_reference_metadata,
     set_reference_tags,
+    update_asset_hash_and_mime,
     upsert_asset,
     upsert_reference,
     validate_tags_exist,
@@ -26,6 +27,7 @@ from app.assets.helpers import normalize_tags
 from app.assets.services.file_utils import get_size_and_mtime_ns
 from app.assets.services.path_utils import (
     compute_relative_filename,
+    get_name_and_tags_from_asset_path,
     resolve_destination_from_tags,
     validate_path_within_base,
 )
@@ -65,7 +67,7 @@ def _ingest_file_from_path(
 
     with create_session() as session:
         if preview_id:
-            if preview_id not in get_existing_asset_ids(session, [preview_id]):
+            if not reference_exists(session, preview_id):
                 preview_id = None
 
         asset, asset_created, asset_updated = upsert_asset(
@@ -135,6 +137,8 @@ def _register_existing_asset(
     tags: list[str] | None = None,
     tag_origin: str = "manual",
     owner_id: str = "",
+    mime_type: str | None = None,
+    preview_id: str | None = None,
 ) -> RegisterAssetResult:
     user_metadata = user_metadata or {}
 
@@ -143,14 +147,25 @@ def _register_existing_asset(
         if not asset:
             raise ValueError(f"No asset with hash {asset_hash}")
 
+        if mime_type and not asset.mime_type:
+            update_asset_hash_and_mime(session, asset_id=asset.id, mime_type=mime_type)
+
+        if preview_id:
+            if not reference_exists(session, preview_id):
+                preview_id = None
+
         ref, ref_created = get_or_create_reference(
             session,
             asset_id=asset.id,
             owner_id=owner_id,
             name=name,
+            preview_id=preview_id,
         )
 
         if not ref_created:
+            if preview_id and ref.preview_id != preview_id:
+                ref.preview_id = preview_id
+
             tag_names = get_reference_tags(session, reference_id=ref.id)
             result = RegisterAssetResult(
                 ref=extract_reference_data(ref),
@@ -242,6 +257,8 @@ def upload_from_temp_path(
     client_filename: str | None = None,
     owner_id: str = "",
     expected_hash: str | None = None,
+    mime_type: str | None = None,
+    preview_id: str | None = None,
 ) -> UploadResult:
     try:
         digest, _ = hashing.compute_blake3_hash(temp_path)
@@ -270,6 +287,8 @@ def upload_from_temp_path(
             tags=tags or [],
             tag_origin="manual",
             owner_id=owner_id,
+            mime_type=mime_type,
+            preview_id=preview_id,
         )
         return UploadResult(
             ref=result.ref,
@@ -291,7 +310,7 @@ def upload_from_temp_path(
     dest_abs = os.path.abspath(os.path.join(dest_dir, hashed_basename))
     validate_path_within_base(dest_abs, base_dir)
 
-    content_type = (
+    content_type = mime_type or (
         mimetypes.guess_type(os.path.basename(src_for_ext), strict=False)[0]
         or mimetypes.guess_type(hashed_basename, strict=False)[0]
         or "application/octet-stream"
@@ -315,7 +334,7 @@ def upload_from_temp_path(
         mime_type=content_type,
         info_name=_sanitize_filename(name or client_filename, fallback=digest),
         owner_id=owner_id,
-        preview_id=None,
+        preview_id=preview_id,
         user_metadata=user_metadata or {},
         tags=tags,
         tag_origin="manual",
@@ -342,30 +361,99 @@ def upload_from_temp_path(
     )
 
 
+def register_file_in_place(
+    abs_path: str,
+    name: str,
+    tags: list[str],
+    owner_id: str = "",
+    mime_type: str | None = None,
+) -> UploadResult:
+    """Register an already-saved file in the asset database without moving it.
+
+    Tags are derived from the filesystem path (root category + subfolder names),
+    merged with any caller-provided tags, matching the behavior of the scanner.
+    If the path is not under a known root, only the caller-provided tags are used.
+    """
+    try:
+        _, path_tags = get_name_and_tags_from_asset_path(abs_path)
+    except ValueError:
+        path_tags = []
+    merged_tags = normalize_tags([*path_tags, *tags])
+
+    try:
+        digest, _ = hashing.compute_blake3_hash(abs_path)
+    except ImportError as e:
+        raise DependencyMissingError(str(e))
+    except Exception as e:
+        raise RuntimeError(f"failed to hash file: {e}")
+    asset_hash = "blake3:" + digest
+
+    size_bytes, mtime_ns = get_size_and_mtime_ns(abs_path)
+    content_type = mime_type or (
+        mimetypes.guess_type(abs_path, strict=False)[0]
+        or "application/octet-stream"
+    )
+
+    ingest_result = _ingest_file_from_path(
+        abs_path=abs_path,
+        asset_hash=asset_hash,
+        size_bytes=size_bytes,
+        mtime_ns=mtime_ns,
+        mime_type=content_type,
+        info_name=_sanitize_filename(name, fallback=digest),
+        owner_id=owner_id,
+        tags=merged_tags,
+        tag_origin="upload",
+        require_existing_tags=False,
+    )
+    reference_id = ingest_result.reference_id
+    if not reference_id:
+        raise RuntimeError("failed to create asset reference")
+
+    with create_session() as session:
+        pair = fetch_reference_and_asset(
+            session, reference_id=reference_id, owner_id=owner_id
+        )
+        if not pair:
+            raise RuntimeError("inconsistent DB state after ingest")
+        ref, asset = pair
+        tag_names = get_reference_tags(session, reference_id=ref.id)
+
+    return UploadResult(
+        ref=extract_reference_data(ref),
+        asset=extract_asset_data(asset),
+        tags=tag_names,
+        created_new=ingest_result.asset_created,
+    )
+
+
 def create_from_hash(
     hash_str: str,
     name: str,
     tags: list[str] | None = None,
     user_metadata: dict | None = None,
     owner_id: str = "",
+    mime_type: str | None = None,
+    preview_id: str | None = None,
 ) -> UploadResult | None:
     canonical = hash_str.strip().lower()
 
-    with create_session() as session:
-        asset = get_asset_by_hash(session, asset_hash=canonical)
-        if not asset:
-            return None
-
-    result = _register_existing_asset(
-        asset_hash=canonical,
-        name=_sanitize_filename(
-            name, fallback=canonical.split(":", 1)[1] if ":" in canonical else canonical
-        ),
-        user_metadata=user_metadata or {},
-        tags=tags or [],
-        tag_origin="manual",
-        owner_id=owner_id,
-    )
+    try:
+        result = _register_existing_asset(
+            asset_hash=canonical,
+            name=_sanitize_filename(
+                name, fallback=canonical.split(":", 1)[1] if ":" in canonical else canonical
+            ),
+            user_metadata=user_metadata or {},
+            tags=tags or [],
+            tag_origin="manual",
+            owner_id=owner_id,
+            mime_type=mime_type,
+            preview_id=preview_id,
+        )
+    except ValueError:
+        logging.warning("create_from_hash: no asset found for hash %s", canonical)
+        return None
 
     return UploadResult(
         ref=result.ref,
diff --git a/app/assets/services/schemas.py b/app/assets/services/schemas.py
index 8b1f1f4dc..0eb128f58 100644
--- a/app/assets/services/schemas.py
+++ b/app/assets/services/schemas.py
@@ -25,7 +25,9 @@ class ReferenceData:
     preview_id: str | None
     created_at: datetime
     updated_at: datetime
-    last_access_time: datetime | None
+    system_metadata: dict[str, Any] | None = None
+    job_id: str | None = None
+    last_access_time: datetime | None = None
 
 
 @dataclass(frozen=True)
@@ -93,6 +95,8 @@ def extract_reference_data(ref: AssetReference) -> ReferenceData:
         file_path=ref.file_path,
         user_metadata=ref.user_metadata,
         preview_id=ref.preview_id,
+        system_metadata=ref.system_metadata,
+        job_id=ref.job_id,
         created_at=ref.created_at,
         updated_at=ref.updated_at,
         last_access_time=ref.last_access_time,
diff --git a/app/assets/services/tagging.py b/app/assets/services/tagging.py
index 28900464d..37b612753 100644
--- a/app/assets/services/tagging.py
+++ b/app/assets/services/tagging.py
@@ -1,3 +1,5 @@
+from typing import Sequence
+
 from app.assets.database.queries import (
     AddTagsResult,
     RemoveTagsResult,
@@ -6,6 +8,7 @@ from app.assets.database.queries import (
     list_tags_with_usage,
     remove_tags_from_reference,
 )
+from app.assets.database.queries.tags import list_tag_counts_for_filtered_assets
 from app.assets.services.schemas import TagUsage
 from app.database.db import create_session
 
@@ -73,3 +76,23 @@ def list_tags(
         )
 
     return [TagUsage(name, tag_type, count) for name, tag_type, count in rows], total
+
+
+def list_tag_histogram(
+    owner_id: str = "",
+    include_tags: Sequence[str] | None = None,
+    exclude_tags: Sequence[str] | None = None,
+    name_contains: str | None = None,
+    metadata_filter: dict | None = None,
+    limit: int = 100,
+) -> dict[str, int]:
+    with create_session() as session:
+        return list_tag_counts_for_filtered_assets(
+            session,
+            owner_id=owner_id,
+            include_tags=include_tags,
+            exclude_tags=exclude_tags,
+            name_contains=name_contains,
+            metadata_filter=metadata_filter,
+            limit=limit,
+        )
diff --git a/app/database/models.py b/app/database/models.py
index e7572677a..b02856f6e 100644
--- a/app/database/models.py
+++ b/app/database/models.py
@@ -1,9 +1,18 @@
 from typing import Any
 from datetime import datetime
+from sqlalchemy import MetaData
 from sqlalchemy.orm import DeclarativeBase
 
+NAMING_CONVENTION = {
+    "ix": "ix_%(table_name)s_%(column_0_N_name)s",
+    "uq": "uq_%(table_name)s_%(column_0_N_name)s",
+    "ck": "ck_%(table_name)s_%(constraint_name)s",
+    "fk": "fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s",
+    "pk": "pk_%(table_name)s",
+}
+
 class Base(DeclarativeBase):
-    pass
+    metadata = MetaData(naming_convention=NAMING_CONVENTION)
 
 def to_dict(obj: Any, include_none: bool = False) -> dict[str, Any]:
     fields = obj.__table__.columns.keys()
diff --git a/server.py b/server.py
index 85a8964be..173a28376 100644
--- a/server.py
+++ b/server.py
@@ -35,6 +35,8 @@ from app.frontend_management import FrontendManager, parse_version
 from comfy_api.internal import _ComfyNodeInternal
 from app.assets.seeder import asset_seeder
 from app.assets.api.routes import register_assets_routes
+from app.assets.services.ingest import register_file_in_place
+from app.assets.services.asset_management import resolve_hash_to_path
 
 from app.user_manager import UserManager
 from app.model_manager import ModelFileManager
@@ -419,7 +421,24 @@ class PromptServer():
                         with open(filepath, "wb") as f:
                             f.write(image.file.read())
 
-                return web.json_response({"name" : filename, "subfolder": subfolder, "type": image_upload_type})
+                resp = {"name" : filename, "subfolder": subfolder, "type": image_upload_type}
+
+                if args.enable_assets:
+                    try:
+                        tag = image_upload_type if image_upload_type in ("input", "output") else "input"
+                        result = register_file_in_place(abs_path=filepath, name=filename, tags=[tag])
+                        resp["asset"] = {
+                            "id": result.ref.id,
+                            "name": result.ref.name,
+                            "asset_hash": result.asset.hash,
+                            "size": result.asset.size_bytes,
+                            "mime_type": result.asset.mime_type,
+                            "tags": result.tags,
+                        }
+                    except Exception:
+                        logging.warning("Failed to register uploaded image as asset", exc_info=True)
+
+                return web.json_response(resp)
             else:
                 return web.Response(status=400)
 
@@ -479,30 +498,43 @@ class PromptServer():
         async def view_image(request):
             if "filename" in request.rel_url.query:
                 filename = request.rel_url.query["filename"]
-                filename, output_dir = folder_paths.annotated_filepath(filename)
 
-                if not filename:
-                    return web.Response(status=400)
+                # The frontend's LoadImage combo widget uses asset_hash values
+                # (e.g. "blake3:...") as widget values. When litegraph renders the
+                # node preview, it constructs /view?filename=<asset_hash>, so this
+                # endpoint must resolve blake3 hashes to their on-disk file paths.
+                if filename.startswith("blake3:"):
+                    owner_id = self.user_manager.get_request_user_id(request)
+                    result = resolve_hash_to_path(filename, owner_id=owner_id)
+                    if result is None:
+                        return web.Response(status=404)
+                    file, filename, resolved_content_type = result.abs_path, result.download_name, result.content_type
+                else:
+                    resolved_content_type = None
+                    filename, output_dir = folder_paths.annotated_filepath(filename)
 
-                # validation for security: prevent accessing arbitrary path
-                if filename[0] == '/' or '..' in filename:
-                    return web.Response(status=400)
+                    if not filename:
+                        return web.Response(status=400)
 
-                if output_dir is None:
-                    type = request.rel_url.query.get("type", "output")
-                    output_dir = folder_paths.get_directory_by_type(type)
+                    # validation for security: prevent accessing arbitrary path
+                    if filename[0] == '/' or '..' in filename:
+                        return web.Response(status=400)
 
-                if output_dir is None:
-                    return web.Response(status=400)
+                    if output_dir is None:
+                        type = request.rel_url.query.get("type", "output")
+                        output_dir = folder_paths.get_directory_by_type(type)
 
-                if "subfolder" in request.rel_url.query:
-                    full_output_dir = os.path.join(output_dir, request.rel_url.query["subfolder"])
-                    if os.path.commonpath((os.path.abspath(full_output_dir), output_dir)) != output_dir:
-                        return web.Response(status=403)
-                    output_dir = full_output_dir
+                    if output_dir is None:
+                        return web.Response(status=400)
 
-                filename = os.path.basename(filename)
-                file = os.path.join(output_dir, filename)
+                    if "subfolder" in request.rel_url.query:
+                        full_output_dir = os.path.join(output_dir, request.rel_url.query["subfolder"])
+                        if os.path.commonpath((os.path.abspath(full_output_dir), output_dir)) != output_dir:
+                            return web.Response(status=403)
+                        output_dir = full_output_dir
+
+                    filename = os.path.basename(filename)
+                    file = os.path.join(output_dir, filename)
 
                 if os.path.isfile(file):
                     if 'preview' in request.rel_url.query:
@@ -562,8 +594,13 @@ class PromptServer():
                             return web.Response(body=alpha_buffer.read(), content_type='image/png',
                                                 headers={"Content-Disposition": f"filename=\"{filename}\""})
                     else:
-                        # Get content type from mimetype, defaulting to 'application/octet-stream'
-                        content_type = mimetypes.guess_type(filename)[0] or 'application/octet-stream'
+                        # Use the content type from asset resolution if available,
+                        # otherwise guess from the filename.
+                        content_type = (
+                            resolved_content_type
+                            or mimetypes.guess_type(filename)[0]
+                            or 'application/octet-stream'
+                        )
 
                         # For security, force certain mimetypes to download instead of display
                         if content_type in {'text/html', 'text/html-sandboxed', 'application/xhtml+xml', 'text/javascript', 'text/css'}:
diff --git a/tests-unit/app_test/test_migrations.py b/tests-unit/app_test/test_migrations.py
new file mode 100644
index 000000000..fa10c1727
--- /dev/null
+++ b/tests-unit/app_test/test_migrations.py
@@ -0,0 +1,57 @@
+"""Test that Alembic migrations run cleanly on a file-backed SQLite DB.
+
+This catches problems like unnamed FK constraints that prevent batch-mode
+drop_constraint from working on real SQLite files (see MB-2).
+
+Migrations 0001 and 0002 are already shipped, so we only exercise
+upgrade/downgrade for 0003+.
+"""
+
+import os
+
+import pytest
+from alembic import command
+from alembic.config import Config
+
+
+# Oldest shipped revision — we upgrade to here as a baseline and never
+# downgrade past it.
+_BASELINE = "0002_merge_to_asset_references"
+
+
+def _make_config(db_path: str) -> Config:
+    root = os.path.join(os.path.dirname(__file__), "../..")
+    config_path = os.path.abspath(os.path.join(root, "alembic.ini"))
+    scripts_path = os.path.abspath(os.path.join(root, "alembic_db"))
+
+    cfg = Config(config_path)
+    cfg.set_main_option("script_location", scripts_path)
+    cfg.set_main_option("sqlalchemy.url", f"sqlite:///{db_path}")
+    return cfg
+
+
+@pytest.fixture
+def migration_db(tmp_path):
+    """Yield an alembic Config pre-upgraded to the baseline revision."""
+    db_path = str(tmp_path / "test_migration.db")
+    cfg = _make_config(db_path)
+    command.upgrade(cfg, _BASELINE)
+    yield cfg
+
+
+def test_upgrade_to_head(migration_db):
+    """Upgrade from baseline to head must succeed on a file-backed DB."""
+    command.upgrade(migration_db, "head")
+
+
+def test_downgrade_to_baseline(migration_db):
+    """Upgrade to head then downgrade back to baseline."""
+    command.upgrade(migration_db, "head")
+    command.downgrade(migration_db, _BASELINE)
+
+
+def test_upgrade_downgrade_cycle(migration_db):
+    """Full cycle: upgrade → downgrade → upgrade again."""
+    command.upgrade(migration_db, "head")
+    command.downgrade(migration_db, _BASELINE)
+    command.upgrade(migration_db, "head")
diff --git a/tests-unit/assets_test/queries/test_asset.py b/tests-unit/assets_test/queries/test_asset.py
index 08f84cd11..9b7eb4bac 100644
--- a/tests-unit/assets_test/queries/test_asset.py
+++ b/tests-unit/assets_test/queries/test_asset.py
@@ -10,6 +10,7 @@ from app.assets.database.queries import (
     get_asset_by_hash,
     upsert_asset,
     bulk_insert_assets,
+    update_asset_hash_and_mime,
 )
 
 
@@ -142,3 +143,45 @@ class TestBulkInsertAssets:
         session.commit()
 
         assert session.query(Asset).count() == 200
+
+
+class TestMimeTypeImmutability:
+    """mime_type on Asset is write-once: set on first ingest, never overwritten."""
+
+    @pytest.mark.parametrize(
+        "initial_mime,second_mime,expected_mime",
+        [
+            ("image/png", "image/jpeg", "image/png"),
+            (None, "image/png", "image/png"),
+        ],
+        ids=["preserves_existing", "fills_null"],
+    )
+    def test_upsert_mime_immutability(self, session: Session, initial_mime, second_mime, expected_mime):
+        h = f"blake3:upsert_{initial_mime}_{second_mime}"
+        upsert_asset(session, asset_hash=h, size_bytes=100, mime_type=initial_mime)
+        session.commit()
+
+        asset, created, _ = upsert_asset(session, asset_hash=h, size_bytes=100, mime_type=second_mime)
+        assert created is False
+        assert asset.mime_type == expected_mime
+
+    @pytest.mark.parametrize(
+        "initial_mime,update_mime,update_hash,expected_mime,expected_hash",
+        [
+            (None, "image/png", None, "image/png", "blake3:upd0"),
+            ("image/png", "image/jpeg", None, "image/png", "blake3:upd1"),
+            ("image/png", "image/jpeg", "blake3:upd2_new", "image/png", "blake3:upd2_new"),
+        ],
+        ids=["fills_null", "preserves_existing", "hash_updates_mime_locked"],
+    )
+    def test_update_asset_hash_and_mime_immutability(
+        self, session: Session, initial_mime, update_mime, update_hash, expected_mime, expected_hash,
+    ):
+        h = expected_hash.removesuffix("_new")
+        asset = Asset(hash=h, size_bytes=100, mime_type=initial_mime)
+        session.add(asset)
+        session.flush()
+
+        update_asset_hash_and_mime(session, asset_id=asset.id, mime_type=update_mime, asset_hash=update_hash)
+        assert asset.mime_type == expected_mime
+        assert asset.hash == expected_hash
diff --git a/tests-unit/assets_test/queries/test_asset_info.py b/tests-unit/assets_test/queries/test_asset_info.py
index 8f6c7fcdb..fe510e342 100644
--- a/tests-unit/assets_test/queries/test_asset_info.py
+++ b/tests-unit/assets_test/queries/test_asset_info.py
@@ -242,22 +242,24 @@ class TestSetReferencePreview:
         asset = _make_asset(session, "hash1")
         preview_asset = _make_asset(session, "preview_hash")
         ref = _make_reference(session, asset)
+        preview_ref = _make_reference(session, preview_asset, name="preview.png")
         session.commit()
 
-        set_reference_preview(session, reference_id=ref.id, preview_asset_id=preview_asset.id)
+        set_reference_preview(session, reference_id=ref.id, preview_reference_id=preview_ref.id)
         session.commit()
 
         session.refresh(ref)
-        assert ref.preview_id == preview_asset.id
+        assert ref.preview_id == preview_ref.id
 
     def test_clears_preview(self, session: Session):
         asset = _make_asset(session, "hash1")
         preview_asset = _make_asset(session, "preview_hash")
         ref = _make_reference(session, asset)
-        ref.preview_id = preview_asset.id
+        preview_ref = _make_reference(session, preview_asset, name="preview.png")
+        ref.preview_id = preview_ref.id
         session.commit()
 
-        set_reference_preview(session, reference_id=ref.id, preview_asset_id=None)
+        set_reference_preview(session, reference_id=ref.id, preview_reference_id=None)
         session.commit()
 
         session.refresh(ref)
@@ -265,15 +267,15 @@ class TestSetReferencePreview:
 
     def test_raises_for_nonexistent_reference(self, session: Session):
         with pytest.raises(ValueError, match="not found"):
-            set_reference_preview(session, reference_id="nonexistent", preview_asset_id=None)
+            set_reference_preview(session, reference_id="nonexistent", preview_reference_id=None)
 
     def test_raises_for_nonexistent_preview(self, session: Session):
         asset = _make_asset(session, "hash1")
         ref = _make_reference(session, asset)
         session.commit()
 
-        with pytest.raises(ValueError, match="Preview Asset"):
-            set_reference_preview(session, reference_id=ref.id, preview_asset_id="nonexistent")
+        with pytest.raises(ValueError, match="Preview AssetReference"):
+            set_reference_preview(session, reference_id=ref.id, preview_reference_id="nonexistent")
 
 
 class TestInsertReference:
@@ -351,13 +353,14 @@ class TestUpdateReferenceTimestamps:
         asset = _make_asset(session, "hash1")
         preview_asset = _make_asset(session, "preview_hash")
         ref = _make_reference(session, asset)
+        preview_ref = _make_reference(session, preview_asset, name="preview.png")
         session.commit()
 
-        update_reference_timestamps(session, ref, preview_id=preview_asset.id)
+        update_reference_timestamps(session, ref, preview_id=preview_ref.id)
         session.commit()
 
         session.refresh(ref)
-        assert ref.preview_id == preview_asset.id
+        assert ref.preview_id == preview_ref.id
 
 
 class TestSetReferenceMetadata:
diff --git a/tests-unit/assets_test/queries/test_metadata.py b/tests-unit/assets_test/queries/test_metadata.py
index 6a545e819..d7a747789 100644
--- a/tests-unit/assets_test/queries/test_metadata.py
+++ b/tests-unit/assets_test/queries/test_metadata.py
@@ -20,6 +20,7 @@ def _make_reference(
     asset: Asset,
     name: str,
     metadata: dict | None = None,
+    system_metadata: dict | None = None,
 ) -> AssetReference:
     now = get_utc_now()
     ref = AssetReference(
@@ -27,6 +28,7 @@ def _make_reference(
         name=name,
         asset_id=asset.id,
         user_metadata=metadata,
+        system_metadata=system_metadata,
         created_at=now,
         updated_at=now,
         last_access_time=now,
@@ -34,8 +36,10 @@ def _make_reference(
     session.add(ref)
     session.flush()
 
-    if metadata:
-        for key, val in metadata.items():
+    # Build merged projection: {**system_metadata, **user_metadata}
+    merged = {**(system_metadata or {}), **(metadata or {})}
+    if merged:
+        for key, val in merged.items():
             for row in convert_metadata_to_rows(key, val):
                 meta_row = AssetReferenceMeta(
                     asset_reference_id=ref.id,
@@ -182,3 +186,46 @@ class TestMetadataFilterEmptyDict:
 
         refs, _, total = list_references_page(session, metadata_filter={})
         assert total == 2
+
+
+class TestSystemMetadataProjection:
+    """Tests for system_metadata merging into the filter projection."""
+
+    def test_system_metadata_keys_are_filterable(self, session: Session):
+        """system_metadata keys should appear in the merged projection."""
+        asset = _make_asset(session, "hash1")
+        _make_reference(
+            session, asset, "with_sys",
+            system_metadata={"source": "scanner"},
+        )
+        _make_reference(session, asset, "without_sys")
+        session.commit()
+
+        refs, _, total = list_references_page(
+            session, metadata_filter={"source": "scanner"}
+        )
+        assert total == 1
+        assert refs[0].name == "with_sys"
+
+    def test_user_metadata_overrides_system_metadata(self, session: Session):
+        """user_metadata should win when both have the same key."""
+        asset = _make_asset(session, "hash1")
+        _make_reference(
+            session, asset, "overridden",
+            metadata={"origin": "user_upload"},
+            system_metadata={"origin": "auto_scan"},
+        )
+        session.commit()
+
+        # Should match the user value, not the system value
+        refs, _, total = list_references_page(
+            session, metadata_filter={"origin": "user_upload"}
+        )
+        assert total == 1
+        assert refs[0].name == "overridden"
+
+        # Should NOT match the system value (it was overridden)
+        refs, _, total = list_references_page(
+            session, metadata_filter={"origin": "auto_scan"}
+        )
+        assert total == 0
diff --git a/tests-unit/assets_test/services/test_asset_management.py b/tests-unit/assets_test/services/test_asset_management.py
index 101ef7292..e8ff989e9 100644
--- a/tests-unit/assets_test/services/test_asset_management.py
+++ b/tests-unit/assets_test/services/test_asset_management.py
@@ -11,6 +11,7 @@ from app.assets.services import (
     delete_asset_reference,
     set_asset_preview,
 )
+from app.assets.services.asset_management import resolve_hash_to_path
 
 
 def _make_asset(session: Session, hash_val: str = "blake3:test", size: int = 1024) -> Asset:
@@ -219,31 +220,33 @@ class TestSetAssetPreview:
         asset = _make_asset(session, hash_val="blake3:main")
         preview_asset = _make_asset(session, hash_val="blake3:preview")
         ref = _make_reference(session, asset)
+        preview_ref = _make_reference(session, preview_asset, name="preview.png")
         ref_id = ref.id
-        preview_id = preview_asset.id
+        preview_ref_id = preview_ref.id
         session.commit()
 
         set_asset_preview(
             reference_id=ref_id,
-            preview_asset_id=preview_id,
+            preview_reference_id=preview_ref_id,
         )
 
         # Verify by re-fetching from DB
         session.expire_all()
         updated_ref = session.get(AssetReference, ref_id)
-        assert updated_ref.preview_id == preview_id
+        assert updated_ref.preview_id == preview_ref_id
 
     def test_clears_preview(self, mock_create_session, session: Session):
         asset = _make_asset(session)
         preview_asset = _make_asset(session, hash_val="blake3:preview")
         ref = _make_reference(session, asset)
-        ref.preview_id = preview_asset.id
+        preview_ref = _make_reference(session, preview_asset, name="preview.png")
+        ref.preview_id = preview_ref.id
         ref_id = ref.id
         session.commit()
 
         set_asset_preview(
             reference_id=ref_id,
-            preview_asset_id=None,
+            preview_reference_id=None,
         )
 
         # Verify by re-fetching from DB
@@ -263,6 +266,45 @@ class TestSetAssetPreview:
         with pytest.raises(PermissionError, match="not owner"):
             set_asset_preview(
                 reference_id=ref.id,
-                preview_asset_id=None,
+                preview_reference_id=None,
                 owner_id="user2",
             )
+
+
+class TestResolveHashToPath:
+    def test_returns_none_for_unknown_hash(self, mock_create_session):
+        result = resolve_hash_to_path("blake3:" + "a" * 64)
+        assert result is None
+
+    @pytest.mark.parametrize(
+        "ref_owner, query_owner, expect_found",
+        [
+            ("user1", "user1", True),
+            ("user1", "user2", False),
+            ("", "anyone", True),
+            ("", "", True),
+        ],
+        ids=[
+            "owner_sees_own_ref",
+            "other_owner_blocked",
+            "ownerless_visible_to_anyone",
+            "ownerless_visible_to_empty",
+        ],
+    )
+    def test_owner_visibility(
+        self, ref_owner, query_owner, expect_found,
+        mock_create_session, session: Session, temp_dir,
+    ):
+        f = temp_dir / "file.bin"
+        f.write_bytes(b"data")
+        asset = _make_asset(session, hash_val="blake3:" + "b" * 64)
+        ref = _make_reference(session, asset, name="file.bin", owner_id=ref_owner)
+        ref.file_path = str(f)
+        session.commit()
+
+        result = resolve_hash_to_path(asset.hash, owner_id=query_owner)
+        if expect_found:
+            assert result is not None
+            assert result.abs_path == str(f)
+        else:
+            assert result is None
diff --git a/tests-unit/assets_test/services/test_ingest.py b/tests-unit/assets_test/services/test_ingest.py
index 367bc7721..dbb8441c2 100644
--- a/tests-unit/assets_test/services/test_ingest.py
+++ b/tests-unit/assets_test/services/test_ingest.py
@@ -113,11 +113,19 @@ class TestIngestFileFromPath:
         file_path = temp_dir / "with_preview.bin"
         file_path.write_bytes(b"data")
 
-        # Create a preview asset first
+        # Create a preview asset and reference
         preview_asset = Asset(hash="blake3:preview", size_bytes=100)
         session.add(preview_asset)
+        session.flush()
+        from app.assets.helpers import get_utc_now
+        now = get_utc_now()
+        preview_ref = AssetReference(
+            asset_id=preview_asset.id, name="preview.png", owner_id="",
+            created_at=now, updated_at=now, last_access_time=now,
+        )
+        session.add(preview_ref)
         session.commit()
-        preview_id = preview_asset.id
+        preview_id = preview_ref.id
 
         result = _ingest_file_from_path(
             abs_path=str(file_path),
diff --git a/tests-unit/assets_test/services/test_tag_histogram.py b/tests-unit/assets_test/services/test_tag_histogram.py
new file mode 100644
index 000000000..7bcd518ec
--- /dev/null
+++ b/tests-unit/assets_test/services/test_tag_histogram.py
@@ -0,0 +1,123 @@
+"""Tests for list_tag_histogram service function."""
+from sqlalchemy.orm import Session
+
+from app.assets.database.models import Asset, AssetReference
+from app.assets.database.queries import ensure_tags_exist, add_tags_to_reference
+from app.assets.helpers import get_utc_now
+from app.assets.services.tagging import list_tag_histogram
+
+
+def _make_asset(session: Session, hash_val: str = "blake3:test") -> Asset:
+    asset = Asset(hash=hash_val, size_bytes=1024)
+    session.add(asset)
+    session.flush()
+    return asset
+
+
+def _make_reference(
+    session: Session,
+    asset: Asset,
+    name: str = "test",
+    owner_id: str = "",
+) -> AssetReference:
+    now = get_utc_now()
+    ref = AssetReference(
+        owner_id=owner_id,
+        name=name,
+        asset_id=asset.id,
+        created_at=now,
+        updated_at=now,
+        last_access_time=now,
+    )
+    session.add(ref)
+    session.flush()
+    return ref
+
+
+class TestListTagHistogram:
+    def test_returns_counts_for_all_tags(self, mock_create_session, session: Session):
+        ensure_tags_exist(session, ["alpha", "beta"])
+        a1 = _make_asset(session, "blake3:aaa")
+        r1 = _make_reference(session, a1, name="r1")
+        add_tags_to_reference(session, reference_id=r1.id, tags=["alpha", "beta"])
+
+        a2 = _make_asset(session, "blake3:bbb")
+        r2 = _make_reference(session, a2, name="r2")
+        add_tags_to_reference(session, reference_id=r2.id, tags=["alpha"])
+        session.commit()
+
+        result = list_tag_histogram()
+
+        assert result["alpha"] == 2
+        assert result["beta"] == 1
+
+    def test_empty_when_no_assets(self, mock_create_session, session: Session):
+        ensure_tags_exist(session, ["unused"])
+        session.commit()
+
+        result = list_tag_histogram()
+
+        assert result == {}
+
+    def test_include_tags_filter(self, mock_create_session, session: Session):
+        ensure_tags_exist(session, ["models", "loras", "input"])
+        a1 = _make_asset(session, "blake3:aaa")
+        r1 = _make_reference(session, a1, name="r1")
+        add_tags_to_reference(session, reference_id=r1.id, tags=["models", "loras"])
+
+        a2 = _make_asset(session, "blake3:bbb")
+        r2 = _make_reference(session, a2, name="r2")
+        add_tags_to_reference(session, reference_id=r2.id, tags=["input"])
+        session.commit()
+
+        result = list_tag_histogram(include_tags=["models"])
+
+        # Only r1 has "models", so only its tags appear
+        assert "models" in result
+        assert "loras" in result
+        assert "input" not in result
+
+    def test_exclude_tags_filter(self, mock_create_session, session: Session):
+        ensure_tags_exist(session, ["models", "loras", "input"])
+        a1 = _make_asset(session, "blake3:aaa")
+        r1 = _make_reference(session, a1, name="r1")
+        add_tags_to_reference(session, reference_id=r1.id, tags=["models", "loras"])
+
+        a2 = _make_asset(session, "blake3:bbb")
+        r2 = _make_reference(session, a2, name="r2")
+        add_tags_to_reference(session, reference_id=r2.id, tags=["input"])
+        session.commit()
+
+        result = list_tag_histogram(exclude_tags=["models"])
+
+        # r1 excluded, only r2's tags remain
+        assert "input" in result
+        assert "loras" not in result
+
+    def test_name_contains_filter(self, mock_create_session, session: Session):
+        ensure_tags_exist(session, ["alpha", "beta"])
+        a1 = _make_asset(session, "blake3:aaa")
+        r1 = _make_reference(session, a1, name="my_model.safetensors")
+        add_tags_to_reference(session, reference_id=r1.id, tags=["alpha"])
+
+        a2 = _make_asset(session, "blake3:bbb")
+        r2 = _make_reference(session, a2, name="picture.png")
+        add_tags_to_reference(session, reference_id=r2.id, tags=["beta"])
+        session.commit()
+
+        result = list_tag_histogram(name_contains="model")
+
+        assert "alpha" in result
+        assert "beta" not in result
+
+    def test_limit_caps_results(self, mock_create_session, session: Session):
+        tags = [f"tag{i}" for i in range(10)]
+        ensure_tags_exist(session, tags)
+        a = _make_asset(session, "blake3:aaa")
+        r = _make_reference(session, a, name="r1")
+        add_tags_to_reference(session, reference_id=r.id, tags=tags)
+        session.commit()
+
+        result = list_tag_histogram(limit=3)
+
+        assert len(result) == 3
diff --git a/tests-unit/assets_test/test_uploads.py b/tests-unit/assets_test/test_uploads.py
index d68e5b5d7..0f2b124a3 100644
--- a/tests-unit/assets_test/test_uploads.py
+++ b/tests-unit/assets_test/test_uploads.py
@@ -243,6 +243,15 @@ def test_upload_tags_traversal_guard(http: requests.Session, api_base: str):
     assert body["error"]["code"] in ("BAD_REQUEST", "INVALID_BODY")
 
 
+def test_upload_empty_tags_rejected(http: requests.Session, api_base: str):
+    files = {"file": ("notags.bin", b"A" * 64, "application/octet-stream")}
+    form = {"tags": json.dumps([]), "name": "notags.bin", "user_metadata": json.dumps({})}
+    r = http.post(api_base + "/api/assets", data=form, files=files, timeout=120)
+    body = r.json()
+    assert r.status_code == 400
+    assert body["error"]["code"] == "INVALID_BODY"
+
+
 @pytest.mark.parametrize("root", ["input", "output"])
 def test_duplicate_upload_same_display_name_does_not_clobber(
     root: str,

From 7d5f5252c3dfdc8a6227e6f6ffb7aab5b3ec827c Mon Sep 17 00:00:00 2001
From: Christian Byrne <cbyrne@comfy.org>
Date: Mon, 16 Mar 2026 12:53:13 -0700
Subject: [PATCH 179/194] ci: add check to block AI agent Co-authored-by
 trailers in PRs (#12799)

Add a GitHub Actions workflow and shell script that scan all commits
in a pull request for Co-authored-by trailers from known AI coding
agents (Claude, Cursor, Copilot, Codex, Aider, Devin, Gemini, Jules,
Windsurf, Cline, Amazon Q, Continue, OpenCode, etc.).

The check fails with clear instructions on how to remove the trailers
via interactive rebase.
---
 .github/scripts/check-ai-co-authors.sh    | 103 ++++++++++++++++++++++
 .github/workflows/check-ai-co-authors.yml |  19 ++++
 2 files changed, 122 insertions(+)
 create mode 100755 .github/scripts/check-ai-co-authors.sh
 create mode 100644 .github/workflows/check-ai-co-authors.yml

diff --git a/.github/scripts/check-ai-co-authors.sh b/.github/scripts/check-ai-co-authors.sh
new file mode 100755
index 000000000..842b1f2d8
--- /dev/null
+++ b/.github/scripts/check-ai-co-authors.sh
@@ -0,0 +1,103 @@
+#!/usr/bin/env bash
+# Checks pull request commits for AI agent Co-authored-by trailers.
+# Exits non-zero when any are found and prints fix instructions.
+set -euo pipefail
+
+base_sha="${1:?usage: check-ai-co-authors.sh <base_sha> <head_sha>}"
+head_sha="${2:?usage: check-ai-co-authors.sh <base_sha> <head_sha>}"
+
+# Known AI coding-agent trailer patterns (case-insensitive).
+# Each entry is an extended-regex fragment matched against Co-authored-by lines.
+AGENT_PATTERNS=(
+    # Anthropic — Claude Code / Amp
+    'noreply@anthropic\.com'
+    # Cursor
+    'cursoragent@cursor\.com'
+    # GitHub Copilot
+    'copilot-swe-agent\[bot\]'
+    'copilot@github\.com'
+    # OpenAI Codex
+    'noreply@openai\.com'
+    'codex@openai\.com'
+    # Aider
+    'aider@aider\.chat'
+    # Google — Gemini / Jules
+    'gemini@google\.com'
+    'jules@google\.com'
+    # Windsurf / Codeium
+    '@codeium\.com'
+    # Devin
+    'devin-ai-integration\[bot\]'
+    'devin@cognition\.ai'
+    'devin@cognition-labs\.com'
+    # Amazon Q Developer
+    'amazon-q-developer'
+    '@amazon\.com.*[Qq].[Dd]eveloper'
+    # Cline
+    'cline-bot'
+    'cline@cline\.ai'
+    # Continue
+    'continue-agent'
+    'continue@continue\.dev'
+    # Sourcegraph
+    'noreply@sourcegraph\.com'
+    # Generic catch-alls for common agent name patterns
+    'Co-authored-by:.*\b[Cc]laude\b'
+    'Co-authored-by:.*\b[Cc]opilot\b'
+    'Co-authored-by:.*\b[Cc]ursor\b'
+    'Co-authored-by:.*\b[Cc]odex\b'
+    'Co-authored-by:.*\b[Gg]emini\b'
+    'Co-authored-by:.*\b[Aa]ider\b'
+    'Co-authored-by:.*\b[Dd]evin\b'
+    'Co-authored-by:.*\b[Ww]indsurf\b'
+    'Co-authored-by:.*\b[Cc]line\b'
+    'Co-authored-by:.*\b[Aa]mazon Q\b'
+    'Co-authored-by:.*\b[Jj]ules\b'
+    'Co-authored-by:.*\bOpenCode\b'
+)
+
+# Build a single alternation regex from all patterns.
+regex=""
+for pattern in "${AGENT_PATTERNS[@]}"; do
+    if [[ -n "$regex" ]]; then
+        regex="${regex}|${pattern}"
+    else
+        regex="$pattern"
+    fi
+done
+
+# Collect Co-authored-by lines from every commit in the PR range.
+violations=""
+while IFS= read -r sha; do
+    message="$(git log -1 --format='%B' "$sha")"
+    matched_lines="$(echo "$message" | grep -iE "^Co-authored-by:" || true)"
+    if [[ -z "$matched_lines" ]]; then
+        continue
+    fi
+
+    while IFS= read -r line; do
+        if echo "$line" | grep -iqE "$regex"; then
+            short="$(git log -1 --format='%h' "$sha")"
+            violations="${violations}  ${short}: ${line}"$'\n'
+        fi
+    done <<< "$matched_lines"
+done < <(git rev-list "${base_sha}..${head_sha}")
+
+if [[ -n "$violations" ]]; then
+    echo "::error::AI agent Co-authored-by trailers detected in PR commits."
+    echo ""
+    echo "The following commits contain Co-authored-by trailers from AI coding agents:"
+    echo ""
+    echo "$violations"
+    echo "These trailers should be removed before merging."
+    echo ""
+    echo "To fix, rewrite the commit messages with:"
+    echo "  git rebase -i ${base_sha}"
+    echo ""
+    echo "and remove the Co-authored-by lines, then force-push your branch."
+    echo ""
+    echo "If you believe this is a false positive, please open an issue."
+    exit 1
+fi
+
+echo "No AI agent Co-authored-by trailers found."
diff --git a/.github/workflows/check-ai-co-authors.yml b/.github/workflows/check-ai-co-authors.yml
new file mode 100644
index 000000000..2ad9ac972
--- /dev/null
+++ b/.github/workflows/check-ai-co-authors.yml
@@ -0,0 +1,19 @@
+name: Check AI Co-Authors
+
+on:
+  pull_request:
+    branches: ['*']
+
+jobs:
+  check-ai-co-authors:
+    name: Check for AI agent co-author trailers
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Check commits for AI co-author trailers
+        run: bash .github/scripts/check-ai-co-authors.sh "${{ github.event.pull_request.base.sha }}" "${{ github.event.pull_request.head.sha }}"

From b202f842af10824b62a3158f0887ee371e16beb6 Mon Sep 17 00:00:00 2001
From: blepping <157360029+blepping@users.noreply.github.com>
Date: Mon, 16 Mar 2026 14:00:42 -0600
Subject: [PATCH 180/194] Skip running model finalizers at exit (#12994)

---
 comfy/model_management.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index a4af5ddb2..2c250dacc 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -541,6 +541,7 @@ class LoadedModel:
         if model.parent is not None:
             self._parent_model = weakref.ref(model.parent)
             self._patcher_finalizer = weakref.finalize(model, self._switch_parent)
+            self._patcher_finalizer.atexit = False
 
     def _switch_parent(self):
         model = self._parent_model()
@@ -587,6 +588,7 @@ class LoadedModel:
 
         self.real_model = weakref.ref(real_model)
         self.model_finalizer = weakref.finalize(real_model, cleanup_models)
+        self.model_finalizer.atexit = False
         return real_model
 
     def should_reload_model(self, force_patch_weights=False):

From 7a16e8aa4e4672733280887a38758be530ba13ea Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Mon, 16 Mar 2026 13:50:13 -0700
Subject: [PATCH 181/194] Add --enable-dynamic-vram options to force enable it.
 (#13002)

---
 comfy/cli_args.py | 3 +++
 main.py           | 4 ++--
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/comfy/cli_args.py b/comfy/cli_args.py
index 0a0bf2f30..13612175e 100644
--- a/comfy/cli_args.py
+++ b/comfy/cli_args.py
@@ -149,6 +149,7 @@ parser.add_argument("--reserve-vram", type=float, default=None, help="Set the am
 parser.add_argument("--async-offload", nargs='?', const=2, type=int, default=None, metavar="NUM_STREAMS", help="Use async weight offloading. An optional argument controls the amount of offload streams. Default is 2. Enabled by default on Nvidia.")
 parser.add_argument("--disable-async-offload", action="store_true", help="Disable async weight offloading.")
 parser.add_argument("--disable-dynamic-vram", action="store_true", help="Disable dynamic VRAM and use estimate based model loading.")
+parser.add_argument("--enable-dynamic-vram", action="store_true", help="Enable dynamic VRAM on systems where it's not enabled by default.")
 
 parser.add_argument("--force-non-blocking", action="store_true", help="Force ComfyUI to use non-blocking operations for all applicable tensors. This may improve performance on some non-Nvidia systems but can cause issues with some workflows.")
 
@@ -262,4 +263,6 @@ else:
     args.fast = set(args.fast)
 
 def enables_dynamic_vram():
+    if args.enable_dynamic_vram:
+        return True
     return not args.disable_dynamic_vram and not args.highvram and not args.gpu_only and not args.novram and not args.cpu
diff --git a/main.py b/main.py
index 8905fd09a..f99aee38e 100644
--- a/main.py
+++ b/main.py
@@ -206,8 +206,8 @@ import hook_breaker_ac10a0
 import comfy.memory_management
 import comfy.model_patcher
 
-if enables_dynamic_vram() and comfy.model_management.is_nvidia() and not comfy.model_management.is_wsl():
-    if comfy.model_management.torch_version_numeric < (2, 8):
+if args.enable_dynamic_vram or (enables_dynamic_vram() and comfy.model_management.is_nvidia() and not comfy.model_management.is_wsl()):
+    if (not args.enable_dynamic_vram) and (comfy.model_management.torch_version_numeric < (2, 8)):
         logging.warning("Unsupported Pytorch detected. DynamicVRAM support requires Pytorch version 2.8 or later. Falling back to legacy ModelPatcher. VRAM estimates may be unreliable especially on Windows")
     elif comfy_aimdo.control.init_device(comfy.model_management.get_torch_device().index):
         if args.verbose == 'DEBUG':

From 20561aa91926508c6ad6db185193c9604cfdf3c9 Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Tue, 17 Mar 2026 09:31:50 +0800
Subject: [PATCH 182/194] [Trainer] FP4, 8, 16 training by native dtype support
 and quant linear autograd function (#12681)

---
 comfy/ops.py                | 101 ++++++++++++++++++++++++++++++++++--
 comfy/utils.py              |   4 ++
 comfy_extras/nodes_train.py |  68 +++++++++++++++++-------
 3 files changed, 150 insertions(+), 23 deletions(-)

diff --git a/comfy/ops.py b/comfy/ops.py
index f47d4137a..1518ec9de 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -776,6 +776,71 @@ from .quant_ops import (
 )
 
 
+class QuantLinearFunc(torch.autograd.Function):
+    """Custom autograd function for quantized linear: quantized forward, compute_dtype backward.
+    Handles any input rank by flattening to 2D for matmul and restoring shape after.
+    """
+
+    @staticmethod
+    def forward(ctx, input_float, weight, bias, layout_type, input_scale, compute_dtype):
+        input_shape = input_float.shape
+        inp = input_float.detach().flatten(0, -2)  # zero-cost view to 2D
+
+        # Quantize input (same as inference path)
+        if layout_type is not None:
+            q_input = QuantizedTensor.from_float(inp, layout_type, scale=input_scale)
+        else:
+            q_input = inp
+
+        w = weight.detach() if weight.requires_grad else weight
+        b = bias.detach() if bias is not None and bias.requires_grad else bias
+
+        output = torch.nn.functional.linear(q_input, w, b)
+
+        # Restore original input shape
+        if len(input_shape) > 2:
+            output = output.unflatten(0, input_shape[:-1])
+
+        ctx.save_for_backward(input_float, weight)
+        ctx.input_shape = input_shape
+        ctx.has_bias = bias is not None
+        ctx.compute_dtype = compute_dtype
+        ctx.weight_requires_grad = weight.requires_grad
+
+        return output
+
+    @staticmethod
+    @torch.autograd.function.once_differentiable
+    def backward(ctx, grad_output):
+        input_float, weight = ctx.saved_tensors
+        compute_dtype = ctx.compute_dtype
+        grad_2d = grad_output.flatten(0, -2).to(compute_dtype)
+
+        # Dequantize weight to compute dtype for backward matmul
+        if isinstance(weight, QuantizedTensor):
+            weight_f = weight.dequantize().to(compute_dtype)
+        else:
+            weight_f = weight.to(compute_dtype)
+
+        # grad_input = grad_output @ weight
+        grad_input = torch.mm(grad_2d, weight_f)
+        if len(ctx.input_shape) > 2:
+            grad_input = grad_input.unflatten(0, ctx.input_shape[:-1])
+
+        # grad_weight (only if weight requires grad, typically frozen for quantized training)
+        grad_weight = None
+        if ctx.weight_requires_grad:
+            input_f = input_float.flatten(0, -2).to(compute_dtype)
+            grad_weight = torch.mm(grad_2d.t(), input_f)
+
+        # grad_bias
+        grad_bias = None
+        if ctx.has_bias:
+            grad_bias = grad_2d.sum(dim=0)
+
+        return grad_input, grad_weight, grad_bias, None, None, None
+
+
 def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_precision_mm=False, disabled=[]):
     class MixedPrecisionOps(manual_cast):
         _quant_config = quant_config
@@ -970,10 +1035,37 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 #If cast needs to apply lora, it should be done in the compute dtype
                 compute_dtype = input.dtype
 
-                if (getattr(self, 'layout_type', None) is not None and
+                _use_quantized = (
+                    getattr(self, 'layout_type', None) is not None and
                     not isinstance(input, QuantizedTensor) and not self._full_precision_mm and
                     not getattr(self, 'comfy_force_cast_weights', False) and
-                    len(self.weight_function) == 0 and len(self.bias_function) == 0):
+                    len(self.weight_function) == 0 and len(self.bias_function) == 0
+                )
+
+                # Training path: quantized forward with compute_dtype backward via autograd function
+                if (input.requires_grad and _use_quantized):
+
+                    weight, bias, offload_stream = cast_bias_weight(
+                        self,
+                        input,
+                        offloadable=True,
+                        compute_dtype=compute_dtype,
+                        want_requant=True
+                    )
+
+                    scale = getattr(self, 'input_scale', None)
+                    if scale is not None:
+                        scale = comfy.model_management.cast_to_device(scale, input.device, None)
+
+                    output = QuantLinearFunc.apply(
+                        input, weight, bias, self.layout_type, scale, compute_dtype
+                    )
+
+                    uncast_bias_weight(self, weight, bias, offload_stream)
+                    return output
+
+                # Inference path (unchanged)
+                if _use_quantized:
 
                     # Reshape 3D tensors to 2D for quantization (needed for NVFP4 and others)
                     input_reshaped = input.reshape(-1, input_shape[2]) if input.ndim == 3 else input
@@ -1021,7 +1113,10 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 for key, param in self._parameters.items():
                     if param is None:
                         continue
-                    self.register_parameter(key, torch.nn.Parameter(fn(param), requires_grad=False))
+                    p = fn(param)
+                    if p.is_inference():
+                        p = p.clone()
+                    self.register_parameter(key, torch.nn.Parameter(p, requires_grad=False))
                 for key, buf in self._buffers.items():
                     if buf is not None:
                         self._buffers[key] = fn(buf)
diff --git a/comfy/utils.py b/comfy/utils.py
index 9931fe3b4..e331b618b 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -897,6 +897,10 @@ def set_attr(obj, attr, value):
     return prev
 
 def set_attr_param(obj, attr, value):
+    # Clone inference tensors (created under torch.inference_mode) since
+    # their version counter is frozen and nn.Parameter() cannot wrap them.
+    if value.is_inference():
+        value = value.clone()
     return set_attr(obj, attr, torch.nn.Parameter(value, requires_grad=False))
 
 def set_attr_buffer(obj, attr, value):
diff --git a/comfy_extras/nodes_train.py b/comfy_extras/nodes_train.py
index aa2d88673..0ad0acee6 100644
--- a/comfy_extras/nodes_train.py
+++ b/comfy_extras/nodes_train.py
@@ -15,6 +15,7 @@ import comfy.sampler_helpers
 import comfy.sd
 import comfy.utils
 import comfy.model_management
+from comfy.cli_args import args, PerformanceFeature
 import comfy_extras.nodes_custom_sampler
 import folder_paths
 import node_helpers
@@ -138,6 +139,7 @@ class TrainSampler(comfy.samplers.Sampler):
         training_dtype=torch.bfloat16,
         real_dataset=None,
         bucket_latents=None,
+        use_grad_scaler=False,
     ):
         self.loss_fn = loss_fn
         self.optimizer = optimizer
@@ -152,6 +154,8 @@ class TrainSampler(comfy.samplers.Sampler):
         self.bucket_latents: list[torch.Tensor] | None = (
             bucket_latents  # list of (Bi, C, Hi, Wi)
         )
+        # GradScaler for fp16 training
+        self.grad_scaler = torch.amp.GradScaler() if use_grad_scaler else None
         # Precompute bucket offsets and weights for sampling
         if bucket_latents is not None:
             self._init_bucket_data(bucket_latents)
@@ -204,10 +208,13 @@ class TrainSampler(comfy.samplers.Sampler):
                 batch_sigmas.requires_grad_(True),
                 **batch_extra_args,
             )
-            loss = self.loss_fn(x0_pred, x0)
+            loss = self.loss_fn(x0_pred.float(), x0.float())
         if bwd:
             bwd_loss = loss / self.grad_acc
-            bwd_loss.backward()
+            if self.grad_scaler is not None:
+                self.grad_scaler.scale(bwd_loss).backward()
+            else:
+                bwd_loss.backward()
         return loss
 
     def _generate_batch_sigmas(self, model_wrap, batch_size, device):
@@ -307,7 +314,10 @@ class TrainSampler(comfy.samplers.Sampler):
             )
             total_loss += loss
         total_loss = total_loss / self.grad_acc / len(indicies)
-        total_loss.backward()
+        if self.grad_scaler is not None:
+            self.grad_scaler.scale(total_loss).backward()
+        else:
+            total_loss.backward()
         if self.loss_callback:
             self.loss_callback(total_loss.item())
         pbar.set_postfix({"loss": f"{total_loss.item():.4f}"})
@@ -348,12 +358,18 @@ class TrainSampler(comfy.samplers.Sampler):
                 self._train_step_multires_mode(model_wrap, cond, extra_args, noisegen, latent_image, dataset_size, pbar)
 
             if (i + 1) % self.grad_acc == 0:
+                if self.grad_scaler is not None:
+                    self.grad_scaler.unscale_(self.optimizer)
                 for param_groups in self.optimizer.param_groups:
                     for param in param_groups["params"]:
                         if param.grad is None:
                             continue
                         param.grad.data = param.grad.data.to(param.data.dtype)
-                self.optimizer.step()
+                if self.grad_scaler is not None:
+                    self.grad_scaler.step(self.optimizer)
+                    self.grad_scaler.update()
+                else:
+                    self.optimizer.step()
                 self.optimizer.zero_grad()
             ui_pbar.update(1)
         torch.cuda.empty_cache()
@@ -1004,9 +1020,9 @@ class TrainLoraNode(io.ComfyNode):
                 ),
                 io.Combo.Input(
                     "training_dtype",
-                    options=["bf16", "fp32"],
+                    options=["bf16", "fp32", "none"],
                     default="bf16",
-                    tooltip="The dtype to use for training.",
+                    tooltip="The dtype to use for training. 'none' preserves the model's native compute dtype instead of overriding it. For fp16 models, GradScaler is automatically enabled.",
                 ),
                 io.Combo.Input(
                     "lora_dtype",
@@ -1035,7 +1051,7 @@ class TrainLoraNode(io.ComfyNode):
                 io.Boolean.Input(
                     "offloading",
                     default=False,
-                    tooltip="Offload the Model to RAM. Requires Bypass Mode.",
+                    tooltip="Offload model weights to CPU during training to save GPU memory.",
                 ),
                 io.Combo.Input(
                     "existing_lora",
@@ -1120,22 +1136,32 @@ class TrainLoraNode(io.ComfyNode):
 
         # Setup model and dtype
         mp = model.clone()
-        dtype = node_helpers.string_to_torch_dtype(training_dtype)
+        use_grad_scaler = False
+        if training_dtype != "none":
+            dtype = node_helpers.string_to_torch_dtype(training_dtype)
+            mp.set_model_compute_dtype(dtype)
+        else:
+            # Detect model's native dtype for autocast
+            model_dtype = mp.model.get_dtype()
+            if model_dtype == torch.float16:
+                dtype = torch.float16
+                use_grad_scaler = True
+                # Warn about fp16 accumulation instability during training
+                if PerformanceFeature.Fp16Accumulation in args.fast:
+                    logging.warning(
+                        "WARNING: FP16 model detected with fp16_accumulation enabled. "
+                        "This combination can be numerically unstable during training and may cause NaN values. "
+                        "Suggested fixes: 1) Set training_dtype to 'bf16', or 2) Disable fp16_accumulation (remove from --fast flags)."
+                    )
+            else:
+                # For fp8, bf16, or other dtypes, use bf16 autocast
+                dtype = torch.bfloat16
         lora_dtype = node_helpers.string_to_torch_dtype(lora_dtype)
-        mp.set_model_compute_dtype(dtype)
-
-        if mp.is_dynamic():
-            if not bypass_mode:
-                logging.info("Training MP is Dynamic - forcing bypass mode. Start comfy with --highvram to force weight diff mode")
-                bypass_mode = True
-            offloading = True
-        elif offloading:
-            if not bypass_mode:
-                logging.info("Training Offload selected - forcing bypass mode. Set bypass = True to remove this message")
 
         # Prepare latents and compute counts
+        latents_dtype = dtype if dtype not in (None,) else torch.bfloat16
         latents, num_images, multi_res = _prepare_latents_and_count(
-            latents, dtype, bucket_mode
+            latents, latents_dtype, bucket_mode
         )
 
         # Validate and expand conditioning
@@ -1201,6 +1227,7 @@ class TrainLoraNode(io.ComfyNode):
                     seed=seed,
                     training_dtype=dtype,
                     bucket_latents=latents,
+                    use_grad_scaler=use_grad_scaler,
                 )
             else:
                 train_sampler = TrainSampler(
@@ -1213,6 +1240,7 @@ class TrainLoraNode(io.ComfyNode):
                     seed=seed,
                     training_dtype=dtype,
                     real_dataset=latents if multi_res else None,
+                    use_grad_scaler=use_grad_scaler,
                 )
 
             # Setup guider
@@ -1337,7 +1365,7 @@ class SaveLoRA(io.ComfyNode):
                 io.Int.Input(
                     "steps",
                     optional=True,
-                    tooltip="Optional: The number of steps to LoRA has been trained for, used to name the saved file.",
+                    tooltip="Optional: The number of steps the LoRA has been trained for, used to name the saved file.",
                 ),
             ],
             outputs=[],

From ca17fc835593593f04b0aec04e266afc32a2ccfb Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Mon, 16 Mar 2026 18:38:40 -0700
Subject: [PATCH 183/194] Fix potential issue. (#13009)

---
 comfy/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/utils.py b/comfy/utils.py
index e331b618b..13b7ca6c8 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -899,7 +899,7 @@ def set_attr(obj, attr, value):
 def set_attr_param(obj, attr, value):
     # Clone inference tensors (created under torch.inference_mode) since
     # their version counter is frozen and nn.Parameter() cannot wrap them.
-    if value.is_inference():
+    if (not torch.is_inference_mode_enabled()) and value.is_inference():
         value = value.clone()
     return set_attr(obj, attr, torch.nn.Parameter(value, requires_grad=False))
 

From 9a870b5102fa831d805f53b255123623d063f660 Mon Sep 17 00:00:00 2001
From: Christian Byrne <cbyrne@comfy.org>
Date: Mon, 16 Mar 2026 18:56:35 -0700
Subject: [PATCH 184/194] fix: atomic writes for userdata to prevent data loss
 on crash (#12987)

Write to a temp file in the same directory then os.replace() onto the
target path.  If the process crashes mid-write, the original file is
left intact instead of being truncated to zero bytes.

Fixes #11298
---
 app/user_manager.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/app/user_manager.py b/app/user_manager.py
index e2c00dab2..e18afb71b 100644
--- a/app/user_manager.py
+++ b/app/user_manager.py
@@ -6,6 +6,7 @@ import uuid
 import glob
 import shutil
 import logging
+import tempfile
 from aiohttp import web
 from urllib import parse
 from comfy.cli_args import args
@@ -377,8 +378,15 @@ class UserManager():
             try:
                 body = await request.read()
 
-                with open(path, "wb") as f:
-                    f.write(body)
+                dir_name = os.path.dirname(path)
+                fd, tmp_path = tempfile.mkstemp(dir=dir_name)
+                try:
+                    with os.fdopen(fd, "wb") as f:
+                        f.write(body)
+                    os.replace(tmp_path, path)
+                except:
+                    os.unlink(tmp_path)
+                    raise
             except OSError as e:
                 logging.warning(f"Error saving file '{path}': {e}")
                 return web.Response(

From 8cc746a86411bd7a08d42829dc805f39f8bced65 Mon Sep 17 00:00:00 2001
From: Paulo Muggler Moreira <pmugglerm@gmail.com>
Date: Tue, 17 Mar 2026 03:27:27 +0100
Subject: [PATCH 185/194] fix: disable SageAttention for Hunyuan3D v2.1 DiT
 (#12772)

---
 comfy/ldm/hunyuan3dv2_1/hunyuandit.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/comfy/ldm/hunyuan3dv2_1/hunyuandit.py b/comfy/ldm/hunyuan3dv2_1/hunyuandit.py
index d48d9d642..f67ba84e9 100644
--- a/comfy/ldm/hunyuan3dv2_1/hunyuandit.py
+++ b/comfy/ldm/hunyuan3dv2_1/hunyuandit.py
@@ -343,6 +343,7 @@ class CrossAttention(nn.Module):
             k.reshape(b, s2, self.num_heads * self.head_dim),
             v,
             heads=self.num_heads,
+            low_precision_attention=False,
         )
 
         out = self.out_proj(x)
@@ -412,6 +413,7 @@ class Attention(nn.Module):
             key.reshape(B, N, self.num_heads * self.head_dim),
             value,
             heads=self.num_heads,
+            low_precision_attention=False,
         )
 
         x = self.out_proj(x)

From 379fbd1a827cd2ce97984a7e8ea8b7159780cd1c Mon Sep 17 00:00:00 2001
From: ComfyUI Wiki <contact@comfyui-wiki.com>
Date: Tue, 17 Mar 2026 12:53:18 +0800
Subject: [PATCH 186/194] chore: update workflow templates to v0.9.26 (#13012)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 7e59ef206..0ce163f71 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.41.20
-comfyui-workflow-templates==0.9.21
+comfyui-workflow-templates==0.9.26
 comfyui-embedded-docs==0.4.3
 torch
 torchsde

From ed7c2c65790c36871b90fff2bdd3de25a17a5431 Mon Sep 17 00:00:00 2001
From: Christian Byrne <cbyrne@comfy.org>
Date: Tue, 17 Mar 2026 07:24:00 -0700
Subject: [PATCH 187/194] Mark weight_dtype as advanced input in Load Diffusion
 Model node (#12769)

Mark the weight_dtype parameter in UNETLoader (Load Diffusion Model) as
an advanced input to reduce UI complexity for new users. The parameter
is now hidden behind an expandable Advanced section, matching the
pattern used for other advanced inputs like device, tile_size, and
overlap.

Amp-Thread-ID: https://ampcode.com/threads/T-019cbaf1-d3c0-718e-a325-318baba86dec
---
 nodes.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nodes.py b/nodes.py
index 03dcc9d4a..e93fa9767 100644
--- a/nodes.py
+++ b/nodes.py
@@ -952,7 +952,7 @@ class UNETLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "unet_name": (folder_paths.get_filename_list("diffusion_models"), ),
-                              "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],)
+                              "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"], {"advanced": True})
                              }}
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "load_unet"

From 1a157e1f97d32c27b3b8bd842bfc5e448c240fe7 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Tue, 17 Mar 2026 14:32:43 -0700
Subject: [PATCH 188/194] Reduce LTX VAE VRAM usage and save use cases from
 OOMs/Tiler (#13013)

* ltx: vae: scale the chunk size with the users VRAM

Scale this linearly down for users with low VRAM.

* ltx: vae: free non-chunking recursive intermediates

* ltx: vae: cleanup some intermediates

The conv layer can be the VRAM peak and it does a torch.cat. So cleanup
the pieces of the cat. Also clear our the cache ASAP as each layer detect
its end as this VAE surges in VRAM at the end due to the ended padding
increasing the size of the final frame convolutions off-the-books to
the chunker. So if all the earlier layers free up their cache it can
offset that surge.

Its a fragmentation nightmare, and the chance of it having to recache the
pyt allocator is very high, but you wont OOM.
---
 comfy/ldm/lightricks/vae/causal_conv3d.py     |  4 ++
 .../vae/causal_video_autoencoder.py           | 41 +++++++++++++++----
 2 files changed, 38 insertions(+), 7 deletions(-)

diff --git a/comfy/ldm/lightricks/vae/causal_conv3d.py b/comfy/ldm/lightricks/vae/causal_conv3d.py
index b8341edbc..356394239 100644
--- a/comfy/ldm/lightricks/vae/causal_conv3d.py
+++ b/comfy/ldm/lightricks/vae/causal_conv3d.py
@@ -65,9 +65,13 @@ class CausalConv3d(nn.Module):
             self.temporal_cache_state[tid] = (x[:, :, -(self.time_kernel_size - 1):, :, :], False)
 
         x = torch.cat(pieces, dim=2)
+        del pieces
+        del cached
 
         if needs_caching:
             self.temporal_cache_state[tid] = (x[:, :, -(self.time_kernel_size - 1):, :, :], False)
+        elif is_end:
+            self.temporal_cache_state[tid] = (None, True)
 
         return self.conv(x) if x.shape[2] >= self.time_kernel_size else x[:, :, :0, :, :]
 
diff --git a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py
index 9f14f64a5..0504140ef 100644
--- a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py
+++ b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py
@@ -297,7 +297,23 @@ class Encoder(nn.Module):
                     module.temporal_cache_state.pop(tid, None)
 
 
-MAX_CHUNK_SIZE=(128 * 1024 ** 2)
+MIN_VRAM_FOR_CHUNK_SCALING = 6 * 1024 ** 3
+MAX_VRAM_FOR_CHUNK_SCALING = 24 * 1024 ** 3
+MIN_CHUNK_SIZE = 32 * 1024 ** 2
+MAX_CHUNK_SIZE = 128 * 1024 ** 2
+
+def get_max_chunk_size(device: torch.device) -> int:
+    total_memory = comfy.model_management.get_total_memory(dev=device)
+
+    if total_memory <= MIN_VRAM_FOR_CHUNK_SCALING:
+        return MIN_CHUNK_SIZE
+    if total_memory >= MAX_VRAM_FOR_CHUNK_SCALING:
+        return MAX_CHUNK_SIZE
+
+    interp = (total_memory - MIN_VRAM_FOR_CHUNK_SCALING) / (
+        MAX_VRAM_FOR_CHUNK_SCALING - MIN_VRAM_FOR_CHUNK_SCALING
+    )
+    return int(MIN_CHUNK_SIZE + interp * (MAX_CHUNK_SIZE - MIN_CHUNK_SIZE))
 
 class Decoder(nn.Module):
     r"""
@@ -525,8 +541,11 @@ class Decoder(nn.Module):
             timestep_shift_scale = ada_values.unbind(dim=1)
 
         output = []
+        max_chunk_size = get_max_chunk_size(sample.device)
 
-        def run_up(idx, sample, ended):
+        def run_up(idx, sample_ref, ended):
+            sample = sample_ref[0]
+            sample_ref[0] = None
             if idx >= len(self.up_blocks):
                 sample = self.conv_norm_out(sample)
                 if timestep_shift_scale is not None:
@@ -554,13 +573,21 @@ class Decoder(nn.Module):
                 return
 
             total_bytes = sample.numel() * sample.element_size()
-            num_chunks = (total_bytes + MAX_CHUNK_SIZE - 1) // MAX_CHUNK_SIZE
-            samples = torch.chunk(sample, chunks=num_chunks, dim=2)
+            num_chunks = (total_bytes + max_chunk_size - 1) // max_chunk_size
 
-            for chunk_idx, sample1 in enumerate(samples):
-                run_up(idx + 1, sample1, ended and chunk_idx == len(samples) - 1)
+            if num_chunks == 1:
+                # when we are not chunking, detach our x so the callee can free it as soon as they are done
+                next_sample_ref = [sample]
+                del sample
+                run_up(idx + 1, next_sample_ref, ended)
+                return
+            else:
+                samples = torch.chunk(sample, chunks=num_chunks, dim=2)
 
-        run_up(0, sample, True)
+                for chunk_idx, sample1 in enumerate(samples):
+                    run_up(idx + 1, [sample1], ended and chunk_idx == len(samples) - 1)
+
+        run_up(0, [sample], True)
         sample = torch.cat(output, dim=2)
 
         sample = unpatchify(sample, patch_size_hw=self.patch_size, patch_size_t=1)

From 035414ede49c1b043ea6de054ca512bcbf0f6b35 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Tue, 17 Mar 2026 14:34:39 -0700
Subject: [PATCH 189/194] Reduce WAN VAE VRAM, Save use cases for OOM/Tiler
 (#13014)

* wan: vae: encoder: Add feature cache layer that corks singles

If a downsample only gives you a single frame, save it to the feature
cache and return nothing to the top level. This increases the
efficiency of cacheability, but also prepares support for going two
by two rather than four by four on the frames.

* wan: remove all concatentation with the feature cache

The loopers are now responsible for ensuring that non-final frames are
processes at least two-by-two, elimiating the need for this cat case.

* wan: vae: recurse and chunk for 2+2 frames on decode

Avoid having to clone off slices of 4 frame chunks and reduce the size
of the big 6 frame convolutions down to 4. Save the VRAMs.

* wan: encode frames 2x2.

Reduce VRAM usage greatly by encoding frames 2 at a time rather than
4.

* wan: vae: remove cloning

The loopers now control the chunking such there is noever more than 2
frames, so just cache these slices directly and avoid the clone
allocations completely.

* wan: vae: free consumer caller tensors on recursion

* wan: vae: restyle a little to match LTX
---
 comfy/ldm/wan/vae.py | 180 +++++++++++++++++++------------------------
 1 file changed, 81 insertions(+), 99 deletions(-)

diff --git a/comfy/ldm/wan/vae.py b/comfy/ldm/wan/vae.py
index 71f73c64e..a96b83c6c 100644
--- a/comfy/ldm/wan/vae.py
+++ b/comfy/ldm/wan/vae.py
@@ -99,7 +99,7 @@ class Resample(nn.Module):
         else:
             self.resample = nn.Identity()
 
-    def forward(self, x, feat_cache=None, feat_idx=[0]):
+    def forward(self, x, feat_cache=None, feat_idx=[0], final=False):
         b, c, t, h, w = x.size()
         if self.mode == 'upsample3d':
             if feat_cache is not None:
@@ -109,22 +109,7 @@ class Resample(nn.Module):
                     feat_idx[0] += 1
                 else:
 
-                    cache_x = x[:, :, -CACHE_T:, :, :].clone()
-                    if cache_x.shape[2] < 2 and feat_cache[
-                            idx] is not None and feat_cache[idx] != 'Rep':
-                        # cache last frame of last two chunk
-                        cache_x = torch.cat([
-                            feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
-                                cache_x.device), cache_x
-                        ],
-                                            dim=2)
-                    if cache_x.shape[2] < 2 and feat_cache[
-                            idx] is not None and feat_cache[idx] == 'Rep':
-                        cache_x = torch.cat([
-                            torch.zeros_like(cache_x).to(cache_x.device),
-                            cache_x
-                        ],
-                                            dim=2)
+                    cache_x = x[:, :, -CACHE_T:, :, :]
                     if feat_cache[idx] == 'Rep':
                         x = self.time_conv(x)
                     else:
@@ -145,19 +130,24 @@ class Resample(nn.Module):
             if feat_cache is not None:
                 idx = feat_idx[0]
                 if feat_cache[idx] is None:
-                    feat_cache[idx] = x.clone()
-                    feat_idx[0] += 1
+                    feat_cache[idx] = x
                 else:
 
-                    cache_x = x[:, :, -1:, :, :].clone()
-                    # if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx]!='Rep':
-                    #     # cache last frame of last two chunk
-                    #     cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
-
+                    cache_x = x[:, :, -1:, :, :]
                     x = self.time_conv(
                         torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2))
                     feat_cache[idx] = cache_x
-                    feat_idx[0] += 1
+
+                    deferred_x = feat_cache[idx + 1]
+                    if deferred_x is not None:
+                        x = torch.cat([deferred_x, x], 2)
+                        feat_cache[idx + 1] = None
+
+                    if x.shape[2] == 1 and not final:
+                        feat_cache[idx + 1] = x
+                        x = None
+
+                feat_idx[0] += 2
         return x
 
 
@@ -177,19 +167,12 @@ class ResidualBlock(nn.Module):
         self.shortcut = CausalConv3d(in_dim, out_dim, 1) \
             if in_dim != out_dim else nn.Identity()
 
-    def forward(self, x, feat_cache=None, feat_idx=[0]):
+    def forward(self, x, feat_cache=None, feat_idx=[0], final=False):
         old_x = x
         for layer in self.residual:
             if isinstance(layer, CausalConv3d) and feat_cache is not None:
                 idx = feat_idx[0]
-                cache_x = x[:, :, -CACHE_T:, :, :].clone()
-                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
-                    # cache last frame of last two chunk
-                    cache_x = torch.cat([
-                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
-                            cache_x.device), cache_x
-                    ],
-                                        dim=2)
+                cache_x = x[:, :, -CACHE_T:, :, :]
                 x = layer(x, cache_list=feat_cache, cache_idx=idx)
                 feat_cache[idx] = cache_x
                 feat_idx[0] += 1
@@ -213,7 +196,7 @@ class AttentionBlock(nn.Module):
         self.proj = ops.Conv2d(dim, dim, 1)
         self.optimized_attention = vae_attention()
 
-    def forward(self, x):
+    def forward(self, x, feat_cache=None, feat_idx=[0], final=False):
         identity = x
         b, c, t, h, w = x.size()
         x = rearrange(x, 'b c t h w -> (b t) c h w')
@@ -283,17 +266,10 @@ class Encoder3d(nn.Module):
             RMS_norm(out_dim, images=False), nn.SiLU(),
             CausalConv3d(out_dim, z_dim, 3, padding=1))
 
-    def forward(self, x, feat_cache=None, feat_idx=[0]):
+    def forward(self, x, feat_cache=None, feat_idx=[0], final=False):
         if feat_cache is not None:
             idx = feat_idx[0]
-            cache_x = x[:, :, -CACHE_T:, :, :].clone()
-            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
-                # cache last frame of last two chunk
-                cache_x = torch.cat([
-                    feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
-                        cache_x.device), cache_x
-                ],
-                                    dim=2)
+            cache_x = x[:, :, -CACHE_T:, :, :]
             x = self.conv1(x, feat_cache[idx])
             feat_cache[idx] = cache_x
             feat_idx[0] += 1
@@ -303,14 +279,16 @@ class Encoder3d(nn.Module):
         ## downsamples
         for layer in self.downsamples:
             if feat_cache is not None:
-                x = layer(x, feat_cache, feat_idx)
+                x = layer(x, feat_cache, feat_idx, final=final)
+                if x is None:
+                    return None
             else:
                 x = layer(x)
 
         ## middle
         for layer in self.middle:
-            if isinstance(layer, ResidualBlock) and feat_cache is not None:
-                x = layer(x, feat_cache, feat_idx)
+            if feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx, final=final)
             else:
                 x = layer(x)
 
@@ -318,14 +296,7 @@ class Encoder3d(nn.Module):
         for layer in self.head:
             if isinstance(layer, CausalConv3d) and feat_cache is not None:
                 idx = feat_idx[0]
-                cache_x = x[:, :, -CACHE_T:, :, :].clone()
-                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
-                    # cache last frame of last two chunk
-                    cache_x = torch.cat([
-                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
-                            cache_x.device), cache_x
-                    ],
-                                        dim=2)
+                cache_x = x[:, :, -CACHE_T:, :, :]
                 x = layer(x, feat_cache[idx])
                 feat_cache[idx] = cache_x
                 feat_idx[0] += 1
@@ -393,14 +364,7 @@ class Decoder3d(nn.Module):
         ## conv1
         if feat_cache is not None:
             idx = feat_idx[0]
-            cache_x = x[:, :, -CACHE_T:, :, :].clone()
-            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
-                # cache last frame of last two chunk
-                cache_x = torch.cat([
-                    feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
-                        cache_x.device), cache_x
-                ],
-                                    dim=2)
+            cache_x = x[:, :, -CACHE_T:, :, :]
             x = self.conv1(x, feat_cache[idx])
             feat_cache[idx] = cache_x
             feat_idx[0] += 1
@@ -409,42 +373,56 @@ class Decoder3d(nn.Module):
 
         ## middle
         for layer in self.middle:
-            if isinstance(layer, ResidualBlock) and feat_cache is not None:
-                x = layer(x, feat_cache, feat_idx)
-            else:
-                x = layer(x)
-
-        ## upsamples
-        for layer in self.upsamples:
             if feat_cache is not None:
                 x = layer(x, feat_cache, feat_idx)
             else:
                 x = layer(x)
 
-        ## head
-        for layer in self.head:
-            if isinstance(layer, CausalConv3d) and feat_cache is not None:
-                idx = feat_idx[0]
-                cache_x = x[:, :, -CACHE_T:, :, :].clone()
-                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
-                    # cache last frame of last two chunk
-                    cache_x = torch.cat([
-                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
-                            cache_x.device), cache_x
-                    ],
-                                        dim=2)
-                x = layer(x, feat_cache[idx])
-                feat_cache[idx] = cache_x
-                feat_idx[0] += 1
+        out_chunks = []
+
+        def run_up(layer_idx, x_ref, feat_idx):
+            x = x_ref[0]
+            x_ref[0] = None
+            if layer_idx >= len(self.upsamples):
+                for layer in self.head:
+                    if isinstance(layer, CausalConv3d) and feat_cache is not None:
+                        cache_x = x[:, :, -CACHE_T:, :, :]
+                        x = layer(x, feat_cache[feat_idx[0]])
+                        feat_cache[feat_idx[0]] = cache_x
+                        feat_idx[0] += 1
+                    else:
+                        x = layer(x)
+                out_chunks.append(x)
+                return
+
+            layer = self.upsamples[layer_idx]
+            if isinstance(layer, Resample) and layer.mode == 'upsample3d' and x.shape[2] > 1:
+                for frame_idx in range(x.shape[2]):
+                    run_up(
+                        layer_idx,
+                        [x[:, :, frame_idx:frame_idx + 1, :, :]],
+                        feat_idx.copy(),
+                    )
+                del x
+                return
+
+            if feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
             else:
                 x = layer(x)
-        return x
+
+            next_x_ref = [x]
+            del x
+            run_up(layer_idx + 1, next_x_ref, feat_idx)
+
+        run_up(0, [x], feat_idx)
+        return out_chunks
 
 
-def count_conv3d(model):
+def count_cache_layers(model):
     count = 0
     for m in model.modules():
-        if isinstance(m, CausalConv3d):
+        if isinstance(m, CausalConv3d) or (isinstance(m, Resample) and m.mode == 'downsample3d'):
             count += 1
     return count
 
@@ -482,11 +460,12 @@ class WanVAE(nn.Module):
         conv_idx = [0]
         ## cache
         t = x.shape[2]
-        iter_ = 1 + (t - 1) // 4
+        t = 1 + ((t - 1) // 4) * 4
+        iter_ = 1 + (t - 1) // 2
         feat_map = None
         if iter_ > 1:
-            feat_map = [None] * count_conv3d(self.encoder)
-        ## 对encode输入的x，按时间拆分为1、4、4、4....
+            feat_map = [None] * count_cache_layers(self.encoder)
+        ## 对encode输入的x，按时间拆分为1、2、2、2....(总帧数先按4N+1向下取整)
         for i in range(iter_):
             conv_idx = [0]
             if i == 0:
@@ -496,20 +475,23 @@ class WanVAE(nn.Module):
                     feat_idx=conv_idx)
             else:
                 out_ = self.encoder(
-                    x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
+                    x[:, :, 1 + 2 * (i - 1):1 + 2 * i, :, :],
                     feat_cache=feat_map,
-                    feat_idx=conv_idx)
+                    feat_idx=conv_idx,
+                    final=(i == (iter_ - 1)))
+                if out_ is None:
+                    continue
                 out = torch.cat([out, out_], 2)
+
         mu, log_var = self.conv1(out).chunk(2, dim=1)
         return mu
 
     def decode(self, z):
-        conv_idx = [0]
         # z: [b,c,t,h,w]
-        iter_ = z.shape[2]
+        iter_ = 1 + z.shape[2] // 2
         feat_map = None
         if iter_ > 1:
-            feat_map = [None] * count_conv3d(self.decoder)
+            feat_map = [None] * count_cache_layers(self.decoder)
         x = self.conv2(z)
         for i in range(iter_):
             conv_idx = [0]
@@ -520,8 +502,8 @@ class WanVAE(nn.Module):
                     feat_idx=conv_idx)
             else:
                 out_ = self.decoder(
-                    x[:, :, i:i + 1, :, :],
+                    x[:, :, 1 + 2 * (i - 1):1 + 2 * i, :, :],
                     feat_cache=feat_map,
                     feat_idx=conv_idx)
-                out = torch.cat([out, out_], 2)
-        return out
+                out += out_
+        return torch.cat(out, 2)

From 8b9d039f26f5230ab3d3d6d9dd5d55590681b970 Mon Sep 17 00:00:00 2001
From: "Dr.Lt.Data" <128333288+ltdrdata@users.noreply.github.com>
Date: Wed, 18 Mar 2026 07:17:03 +0900
Subject: [PATCH 190/194] bump manager version to 4.1b6 (#13022)

---
 manager_requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/manager_requirements.txt b/manager_requirements.txt
index 1c5e8f071..5b06b56f6 100644
--- a/manager_requirements.txt
+++ b/manager_requirements.txt
@@ -1 +1 @@
-comfyui_manager==4.1b5
\ No newline at end of file
+comfyui_manager==4.1b6
\ No newline at end of file

From 735a0465e5daf1f77909b553b02a9d16d1671be9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?=
 <40791699+kijai@users.noreply.github.com>
Date: Wed, 18 Mar 2026 02:20:49 +0200
Subject: [PATCH 191/194] Inplace VAE output processing to reduce peak RAM
 consumption. (#13028)

---
 comfy/sd.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index 4d427bb9a..652e76d3e 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -455,7 +455,7 @@ class VAE:
         self.output_channels = 3
         self.pad_channel_value = None
         self.process_input = lambda image: image * 2.0 - 1.0
-        self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
+        self.process_output = lambda image: image.add_(1.0).div_(2.0).clamp_(0.0, 1.0)
         self.working_dtypes = [torch.bfloat16, torch.float32]
         self.disable_offload = False
         self.not_video = False

From 68d542cc0602132d3d2fe624ee7077e44b0fb0ab Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Tue, 17 Mar 2026 17:46:22 -0700
Subject: [PATCH 192/194] Fix case where pixel space VAE could cause issues.
 (#13030)

---
 comfy/sd.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index 652e76d3e..df0c4d1d1 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -952,8 +952,8 @@ class VAE:
             batch_number = max(1, batch_number)
 
             for x in range(0, samples_in.shape[0], batch_number):
-                samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
-                out = self.process_output(self.first_stage_model.decode(samples, **vae_options).to(self.output_device).to(dtype=self.vae_output_dtype()))
+                samples = samples_in[x:x + batch_number].to(device=self.device, dtype=self.vae_dtype)
+                out = self.process_output(self.first_stage_model.decode(samples, **vae_options).to(device=self.output_device, dtype=self.vae_output_dtype(), copy=True))
                 if pixel_samples is None:
                     pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype())
                 pixel_samples[x:x+batch_number] = out

From cad24ce26278a72095d33a2b4391572573201542 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Tue, 17 Mar 2026 17:59:10 -0700
Subject: [PATCH 193/194] cascade: remove dead weight init code (#13026)

This weight init process is fully shadowed be the weight load and
doesnt work in dynamic_vram were the weight allocation is deferred.
---
 comfy/ldm/cascade/stage_a.py | 11 +----------
 1 file changed, 1 insertion(+), 10 deletions(-)

diff --git a/comfy/ldm/cascade/stage_a.py b/comfy/ldm/cascade/stage_a.py
index 145e6e69a..e4e30cacd 100644
--- a/comfy/ldm/cascade/stage_a.py
+++ b/comfy/ldm/cascade/stage_a.py
@@ -136,16 +136,7 @@ class ResBlock(nn.Module):
             ops.Linear(c_hidden, c),
         )
 
-        self.gammas = nn.Parameter(torch.zeros(6), requires_grad=True)
-
-        # Init weights
-        def _basic_init(module):
-            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
-                torch.nn.init.xavier_uniform_(module.weight)
-                if module.bias is not None:
-                    nn.init.constant_(module.bias, 0)
-
-        self.apply(_basic_init)
+        self.gammas = nn.Parameter(torch.zeros(6), requires_grad=False)
 
     def _norm(self, x, norm):
         return norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)

From b941913f1d2d11dc69c098a375309b13c13bca23 Mon Sep 17 00:00:00 2001
From: Anton Bukov <k06aaa@gmail.com>
Date: Wed, 18 Mar 2026 05:21:32 +0400
Subject: [PATCH 194/194] fix: run text encoders on MPS GPU instead of CPU for
 Apple Silicon (#12809)

On Apple Silicon, `vram_state` is set to `VRAMState.SHARED` because
CPU and GPU share unified memory. However, `text_encoder_device()`
only checked for `HIGH_VRAM` and `NORMAL_VRAM`, causing all text
encoders to fall back to CPU on MPS devices.

Adding `VRAMState.SHARED` to the condition allows non-quantized text
encoders (e.g. bf16 Gemma 3 12B) to run on the MPS GPU, providing
significant speedup for text encoding and prompt generation.

Note: quantized models (fp4/fp8) that use float8_e4m3fn internally
will still fall back to CPU via the `supports_cast()` check in
`CLIP.__init__()`, since MPS does not support fp8 dtypes.
---
 comfy/model_management.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 2c250dacc..5f2e6ef67 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -1003,7 +1003,7 @@ def text_encoder_offload_device():
 def text_encoder_device():
     if args.gpu_only:
         return get_torch_device()
-    elif vram_state in (VRAMState.HIGH_VRAM, VRAMState.NORMAL_VRAM) or comfy.memory_management.aimdo_enabled:
+    elif vram_state in (VRAMState.HIGH_VRAM, VRAMState.NORMAL_VRAM, VRAMState.SHARED) or comfy.memory_management.aimdo_enabled:
         if should_use_fp16(prioritize_performance=False):
             return get_torch_device()
         else: