Merge de97192962 into c168960a12

get_save_image_path now properly supports filenames without
trailing underscores.

This will be the saving behavior when using a mix of save image nodes using the old and the new format.

ComfyUI_00001_.png
ComfyUI_00002.png
ComfyUI_00003.png
ComfyUI_00004_.png

.github/workflows/openapi-lint.yml

@@ -0,0 +1,31 @@
+name: OpenAPI Lint
+
+on:
+  pull_request:
+    paths:
+      - 'openapi.yaml'
+      - '.spectral.yaml'
+      - '.github/workflows/openapi-lint.yml'
+
+permissions:
+  contents: read
+
+jobs:
+  spectral:
+    name: Run Spectral
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: '20'
+
+      - name: Install Spectral
+        run: npm install -g @stoplight/spectral-cli@6
+
+      - name: Lint openapi.yaml
+        run: spectral lint openapi.yaml --ruleset .spectral.yaml --fail-severity=error

.spectral.yaml

@@ -0,0 +1,91 @@
+extends:
+  - spectral:oas
+
+# Severity levels: error, warn, info, hint, off
+# Rules from the built-in "spectral:oas" ruleset are active by default.
+# Below we tune severity and add custom rules for our conventions.
+#
+# This ruleset mirrors Comfy-Org/cloud/.spectral.yaml so specs across the
+# organization are linted against a single consistent standard.
+
+rules:
+  # -----------------------------------------------------------------------
+  # Built-in rule severity overrides
+  # -----------------------------------------------------------------------
+  operation-operationId: error
+  operation-description: warn
+  operation-tag-defined: error
+  info-contact: off
+  info-description: warn
+  no-eval-in-markdown: error
+  no-$ref-siblings: error
+
+  # -----------------------------------------------------------------------
+  # Custom rules: naming conventions
+  # -----------------------------------------------------------------------
+
+  # Property names should be snake_case
+  property-name-snake-case:
+    description: Property names must be snake_case
+    severity: warn
+    given: "$.components.schemas.*.properties[*]~"
+    then:
+      function: pattern
+      functionOptions:
+        match: "^[a-z][a-z0-9]*(_[a-z0-9]+)*$"
+
+  # Operation IDs should be camelCase
+  operation-id-camel-case:
+    description: Operation IDs must be camelCase
+    severity: warn
+    given: "$.paths.*.*.operationId"
+    then:
+      function: pattern
+      functionOptions:
+        match: "^[a-z][a-zA-Z0-9]*$"
+
+  # -----------------------------------------------------------------------
+  # Custom rules: response conventions
+  # -----------------------------------------------------------------------
+
+  # Error responses (4xx, 5xx) should use a consistent shape
+  error-response-schema:
+    description: Error responses should reference a standard error schema
+    severity: hint
+    given: "$.paths.*.*.responses[?(@property >= '400' && @property < '600')].content['application/json'].schema"
+    then:
+      field: "$ref"
+      function: truthy
+
+  # All 2xx responses with JSON body should have a schema
+  response-schema-defined:
+    description: Success responses with JSON content should define a schema
+    severity: warn
+    given: "$.paths.*.*.responses[?(@property >= '200' && @property < '300')].content['application/json']"
+    then:
+      field: schema
+      function: truthy
+
+  # -----------------------------------------------------------------------
+  # Custom rules: best practices
+  # -----------------------------------------------------------------------
+
+  # Path parameters must have a description
+  path-param-description:
+    description: Path parameters should have a description
+    severity: warn
+    given:
+      - "$.paths.*.parameters[?(@.in == 'path')]"
+      - "$.paths.*.*.parameters[?(@.in == 'path')]"
+    then:
+      field: description
+      function: truthy
+
+  # Schemas should have a description
+  schema-description:
+    description: Component schemas should have a description
+    severity: hint
+    given: "$.components.schemas.*"
+    then:
+      field: description
+      function: truthy

comfy/context_windows.py

@@ -63,7 +63,11 @@ class IndexListContextWindow(ContextWindowABC):
             dim = self.dim
         if dim == 0 and full.shape[dim] == 1:
             return full
-        idx = tuple([slice(None)] * dim + [self.index_list])
+        indices = self.index_list
+        anchor_idx = getattr(self, 'causal_anchor_index', None)
+        if anchor_idx is not None and anchor_idx >= 0:
+            indices = [anchor_idx] + list(indices)
+        idx = tuple([slice(None)] * dim + [indices])
         window = full[idx]
         if retain_index_list:
             idx = tuple([slice(None)] * dim + [retain_index_list])
@@ -113,7 +117,14 @@ def slice_cond(cond_value, window: IndexListContextWindow, x_in: torch.Tensor, d
 
     # skip leading latent positions that have no corresponding conditioning (e.g. reference frames)
     if temporal_offset > 0:
-        indices = [i - temporal_offset for i in window.index_list[temporal_offset:]]
+        anchor_idx = getattr(window, 'causal_anchor_index', None)
+        if anchor_idx is not None and anchor_idx >= 0:
+            # anchor occupies one of the no-cond positions, so skip one fewer from window.index_list
+            skip_count = temporal_offset - 1
+        else:
+            skip_count = temporal_offset
+
+        indices = [i - temporal_offset for i in window.index_list[skip_count:]]
         indices = [i for i in indices if 0 <= i]
     else:
         indices = list(window.index_list)
@@ -150,7 +161,8 @@ class ContextFuseMethod:
 ContextResults = collections.namedtuple("ContextResults", ['window_idx', 'sub_conds_out', 'sub_conds', 'window'])
 class IndexListContextHandler(ContextHandlerABC):
     def __init__(self, context_schedule: ContextSchedule, fuse_method: ContextFuseMethod, context_length: int=1, context_overlap: int=0, context_stride: int=1,
-                 closed_loop: bool=False, dim:int=0, freenoise: bool=False, cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False):
+                 closed_loop: bool=False, dim:int=0, freenoise: bool=False, cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False,
+                 causal_window_fix: bool=True):
         self.context_schedule = context_schedule
         self.fuse_method = fuse_method
         self.context_length = context_length
@@ -162,6 +174,7 @@ class IndexListContextHandler(ContextHandlerABC):
         self.freenoise = freenoise
         self.cond_retain_index_list = [int(x.strip()) for x in cond_retain_index_list.split(",")] if cond_retain_index_list else []
         self.split_conds_to_windows = split_conds_to_windows
+        self.causal_window_fix = causal_window_fix
 
         self.callbacks = {}
 
@@ -318,6 +331,14 @@ class IndexListContextHandler(ContextHandlerABC):
             # allow processing to end between context window executions for faster Cancel
             comfy.model_management.throw_exception_if_processing_interrupted()
 
+            # causal_window_fix: prepend a pre-window frame that will be stripped post-forward
+            anchor_applied = False
+            if self.causal_window_fix:
+                anchor_idx = window.index_list[0] - 1
+                if 0 <= anchor_idx < x_in.size(self.dim):
+                    window.causal_anchor_index = anchor_idx
+                    anchor_applied = True
+
             for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EVALUATE_CONTEXT_WINDOWS, self.callbacks):
                 callback(self, model, x_in, conds, timestep, model_options, window_idx, window, model_options, device, first_device)
 
@@ -332,6 +353,12 @@ class IndexListContextHandler(ContextHandlerABC):
             if device is not None:
                 for i in range(len(sub_conds_out)):
                     sub_conds_out[i] = sub_conds_out[i].to(x_in.device)
+
+            # strip causal_window_fix anchor if applied
+            if anchor_applied:
+                for i in range(len(sub_conds_out)):
+                    sub_conds_out[i] = sub_conds_out[i].narrow(self.dim, 1, sub_conds_out[i].shape[self.dim] - 1)
+
             results.append(ContextResults(window_idx, sub_conds_out, sub_conds, window))
         return results
 

comfy/latent_formats.py

@@ -9,6 +9,7 @@ class LatentFormat:
     latent_rgb_factors_reshape = None
     taesd_decoder_name = None
     spacial_downscale_ratio = 8
+    temporal_downscale_ratio = 1
 
     def process_in(self, latent):
         return latent * self.scale_factor
@@ -235,6 +236,7 @@ class Flux2(LatentFormat):
 class Mochi(LatentFormat):
     latent_channels = 12
     latent_dimensions = 3
+    temporal_downscale_ratio = 6
 
     def __init__(self):
         self.scale_factor = 1.0
@@ -278,6 +280,7 @@ class LTXV(LatentFormat):
     latent_channels = 128
     latent_dimensions = 3
     spacial_downscale_ratio = 32
+    temporal_downscale_ratio = 8
 
     def __init__(self):
         self.latent_rgb_factors = [
@@ -421,6 +424,7 @@ class LTXAV(LTXV):
 class HunyuanVideo(LatentFormat):
     latent_channels = 16
     latent_dimensions = 3
+    temporal_downscale_ratio = 4
     scale_factor = 0.476986
     latent_rgb_factors = [
         [-0.0395, -0.0331,  0.0445],
@@ -447,6 +451,7 @@ class HunyuanVideo(LatentFormat):
 class Cosmos1CV8x8x8(LatentFormat):
     latent_channels = 16
     latent_dimensions = 3
+    temporal_downscale_ratio = 8
 
     latent_rgb_factors = [
         [ 0.1817,  0.2284,  0.2423],
@@ -472,6 +477,7 @@ class Cosmos1CV8x8x8(LatentFormat):
 class Wan21(LatentFormat):
     latent_channels = 16
     latent_dimensions = 3
+    temporal_downscale_ratio = 4
 
     latent_rgb_factors = [
             [-0.1299, -0.1692,  0.2932],
@@ -734,6 +740,7 @@ class HunyuanVideo15(LatentFormat):
     latent_channels = 32
     latent_dimensions = 3
     spacial_downscale_ratio = 16
+    temporal_downscale_ratio = 4
     scale_factor = 1.03682
     taesd_decoder_name = "lighttaehy1_5"
 
@@ -788,6 +795,7 @@ class ZImagePixelSpace(ChromaRadiance):
 class CogVideoX(LatentFormat):
     latent_channels = 16
     latent_dimensions = 3
+    temporal_downscale_ratio = 4
 
     def __init__(self):
         self.scale_factor = 1.15258426

comfy_api/latest/_io.py

@@ -395,7 +395,6 @@ class Combo(ComfyTypeIO):
 @comfytype(io_type="COMBO")
 class MultiCombo(ComfyTypeI):
     '''Multiselect Combo input (dropdown for selecting potentially more than one value).'''
-    # TODO: something is wrong with the serialization, frontend does not recognize it as multiselect
     Type = list[str]
     class Input(Combo.Input):
         def __init__(self, id: str, options: list[str], display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
@@ -408,12 +407,14 @@ class MultiCombo(ComfyTypeI):
             self.default: list[str]
 
         def as_dict(self):
-            to_return = super().as_dict() | prune_dict({
-                "multi_select": self.multiselect,
-                "placeholder": self.placeholder,
-                "chip": self.chip,
+            # Frontend expects `multi_select` to be an object config (not a boolean).
+            # Keep top-level `multiselect` from Combo.Input for backwards compatibility.
+            return super().as_dict() | prune_dict({
+                "multi_select": prune_dict({
+                    "placeholder": self.placeholder,
+                    "chip": self.chip,
+                }),
             })
-            return to_return
 
 @comfytype(io_type="IMAGE")
 class Image(ComfyTypeIO):

comfy_api_nodes/apis/luma.py

@@ -1,15 +1,12 @@
 from __future__ import annotations
 
-
-import torch
-
 from enum import Enum
 from typing import Optional, Union
 
+import torch
 from pydantic import BaseModel, Field, confloat
 
 
-
 class LumaIO:
     LUMA_REF = "LUMA_REF"
     LUMA_CONCEPTS = "LUMA_CONCEPTS"
@@ -183,13 +180,13 @@ class LumaAssets(BaseModel):
 
 
 class LumaImageRef(BaseModel):
-    '''Used for image gen'''
+    """Used for image gen"""
     url: str = Field(..., description='The URL of the image reference')
     weight: confloat(ge=0.0, le=1.0) = Field(..., description='The weight of the image reference')
 
 
 class LumaImageReference(BaseModel):
-    '''Used for video gen'''
+    """Used for video gen"""
     type: Optional[str] = Field('image', description='Input type, defaults to image')
     url: str = Field(..., description='The URL of the image')
 
@@ -251,3 +248,32 @@ class LumaGeneration(BaseModel):
     assets: Optional[LumaAssets] = Field(None, description='The assets of the generation')
     model: str = Field(..., description='The model used for the generation')
     request: Union[LumaGenerationRequest, LumaImageGenerationRequest] = Field(..., description="The request used for the generation")
+
+
+class Luma2ImageRef(BaseModel):
+    url: str | None = None
+    data: str | None = None
+    media_type: str | None = None
+
+
+class Luma2GenerationRequest(BaseModel):
+    prompt: str = Field(..., min_length=1, max_length=6000)
+    model: str | None = None
+    type: str | None = None
+    aspect_ratio: str | None = None
+    style: str | None = None
+    output_format: str | None = None
+    web_search: bool | None = None
+    image_ref: list[Luma2ImageRef] | None = None
+    source: Luma2ImageRef | None = None
+
+
+class Luma2Generation(BaseModel):
+    id: str | None = None
+    type: str | None = None
+    state: str | None = None
+    model: str | None = None
+    created_at: str | None = None
+    output: list[LumaImageReference] | None = None
+    failure_reason: str | None = None
+    failure_code: str | None = None

comfy_api_nodes/apis/openai.py

@@ -56,14 +56,14 @@ class ModelResponseProperties(BaseModel):
     instructions: str | None = Field(None)
     max_output_tokens: int | None = Field(None)
     model: str | None = Field(None)
-    temperature: float | None = Field(1, description="Controls randomness in the response", ge=0.0, le=2.0)
+    temperature: float | None = Field(None, description="Controls randomness in the response", ge=0.0, le=2.0)
     top_p: float | None = Field(
-        1,
+        None,
         description="Controls diversity of the response via nucleus sampling",
         ge=0.0,
         le=1.0,
     )
-    truncation: str | None = Field("disabled", description="Allowed values: 'auto' or 'disabled'")
+    truncation: str | None = Field(None, description="Allowed values: 'auto' or 'disabled'")
 
 
 class ResponseProperties(BaseModel):

comfy_api_nodes/nodes_luma.py

@@ -1,10 +1,11 @@
-from typing import Optional
-
 import torch
 from typing_extensions import override
 
-from comfy_api.latest import IO, ComfyExtension
+from comfy_api.latest import IO, ComfyExtension, Input
 from comfy_api_nodes.apis.luma import (
+    Luma2Generation,
+    Luma2GenerationRequest,
+    Luma2ImageRef,
     LumaAspectRatio,
     LumaCharacterRef,
     LumaConceptChain,
@@ -30,6 +31,7 @@ from comfy_api_nodes.util import (
     download_url_to_video_output,
     poll_op,
     sync_op,
+    upload_image_to_comfyapi,
     upload_images_to_comfyapi,
     validate_string,
 )
@@ -212,9 +214,9 @@ class LumaImageGenerationNode(IO.ComfyNode):
         aspect_ratio: str,
         seed,
         style_image_weight: float,
-        image_luma_ref: Optional[LumaReferenceChain] = None,
-        style_image: Optional[torch.Tensor] = None,
-        character_image: Optional[torch.Tensor] = None,
+        image_luma_ref: LumaReferenceChain | None = None,
+        style_image: torch.Tensor | None = None,
+        character_image: torch.Tensor | None = None,
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=True, min_length=3)
         # handle image_luma_ref
@@ -434,7 +436,7 @@ class LumaTextToVideoGenerationNode(IO.ComfyNode):
         duration: str,
         loop: bool,
         seed,
-        luma_concepts: Optional[LumaConceptChain] = None,
+        luma_concepts: LumaConceptChain | None = None,
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=False, min_length=3)
         duration = duration if model != LumaVideoModel.ray_1_6 else None
@@ -533,7 +535,6 @@ class LumaImageToVideoGenerationNode(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=PRICE_BADGE_VIDEO,
-
         )
 
     @classmethod
@@ -644,6 +645,293 @@ PRICE_BADGE_VIDEO = IO.PriceBadge(
 )
 
 
+def _luma2_uni1_common_inputs(max_image_refs: int) -> list:
+    return [
+        IO.Combo.Input(
+            "style",
+            options=["auto", "manga"],
+            default="auto",
+            tooltip="Style preset. 'auto' picks based on the prompt; "
+            "'manga' applies a manga/anime aesthetic and requires a portrait "
+            "aspect ratio (2:3, 9:16, 1:2, 1:3).",
+        ),
+        IO.Boolean.Input(
+            "web_search",
+            default=False,
+            tooltip="Search the web for visual references before generating.",
+        ),
+        IO.Autogrow.Input(
+            "image_ref",
+            template=IO.Autogrow.TemplateNames(
+                IO.Image.Input("image"),
+                names=[f"image_{i}" for i in range(1, max_image_refs + 1)],
+                min=0,
+            ),
+            optional=True,
+            tooltip=f"Up to {max_image_refs} reference images for style/content guidance.",
+        ),
+    ]
+
+
+async def _luma2_upload_image_refs(
+    cls: type[IO.ComfyNode],
+    refs: dict | None,
+    max_count: int,
+) -> list[Luma2ImageRef] | None:
+    if not refs:
+        return None
+    out: list[Luma2ImageRef] = []
+    for key in refs:
+        url = await upload_image_to_comfyapi(cls, refs[key])
+        out.append(Luma2ImageRef(url=url))
+    if len(out) > max_count:
+        raise ValueError(f"Maximum {max_count} reference images are allowed.")
+    return out or None
+
+
+async def _luma2_submit_and_poll(
+    cls: type[IO.ComfyNode],
+    request: Luma2GenerationRequest,
+) -> Input.Image:
+    initial = await sync_op(
+        cls,
+        ApiEndpoint(path="/proxy/luma_2/generations", method="POST"),
+        response_model=Luma2Generation,
+        data=request,
+    )
+    if not initial.id:
+        raise RuntimeError("Luma 2 API did not return a generation id.")
+    final = await poll_op(
+        cls,
+        ApiEndpoint(path=f"/proxy/luma_2/generations/{initial.id}", method="GET"),
+        response_model=Luma2Generation,
+        status_extractor=lambda r: r.state,
+        progress_extractor=lambda r: None,
+    )
+    if not final.output:
+        msg = final.failure_reason or "no output returned"
+        raise RuntimeError(f"Luma 2 generation failed: {msg}")
+    url = final.output[0].url
+    if not url:
+        raise RuntimeError("Luma 2 generation completed without an output URL.")
+    return await download_url_to_image_tensor(url)
+
+
+class LumaImageNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaImageNode2",
+            display_name="Luma UNI-1 Image",
+            category="api node/image/Luma",
+            description="Generate images from text using the Luma UNI-1 model.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text description of the desired image. 1–6000 characters.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "uni-1",
+                            [
+                                IO.Combo.Input(
+                                    "aspect_ratio",
+                                    options=[
+                                        "auto",
+                                        "3:1",
+                                        "2:1",
+                                        "16:9",
+                                        "3:2",
+                                        "1:1",
+                                        "2:3",
+                                        "9:16",
+                                        "1:2",
+                                        "1:3",
+                                    ],
+                                    default="auto",
+                                    tooltip="Output image aspect ratio. 'auto' lets "
+                                    "the model pick based on the prompt.",
+                                ),
+                                *_luma2_uni1_common_inputs(max_image_refs=9),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "uni-1-max",
+                            [
+                                IO.Combo.Input(
+                                    "aspect_ratio",
+                                    options=[
+                                        "auto",
+                                        "3:1",
+                                        "2:1",
+                                        "16:9",
+                                        "3:2",
+                                        "1:1",
+                                        "2:3",
+                                        "9:16",
+                                        "1:2",
+                                        "1:3",
+                                    ],
+                                    default="auto",
+                                    tooltip="Output image aspect ratio. 'auto' lets "
+                                    "the model pick based on the prompt.",
+                                ),
+                                *_luma2_uni1_common_inputs(max_image_refs=9),
+                            ],
+                        ),
+                    ],
+                    tooltip="Model to use for generation.",
+                ),
+                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"], input_groups=["model.image_ref"]),
+                expr="""
+                (
+                  $m := widgets.model;
+                  $refs := $lookup(inputGroups, "model.image_ref");
+                  $base := $m = "uni-1-max" ? 0.1 : 0.0404;
+                  {"type":"usd","usd": $round($base + 0.003 * $refs, 4)}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=6000)
+        aspect_ratio = model["aspect_ratio"]
+        style = model["style"]
+        allowed_manga_ratios = {"2:3", "9:16", "1:2", "1:3"}
+        if style == "manga" and aspect_ratio != "auto" and aspect_ratio not in allowed_manga_ratios:
+            raise ValueError(
+                f"'manga' style requires a portrait aspect ratio "
+                f"({', '.join(sorted(allowed_manga_ratios))}) or 'auto'; got '{aspect_ratio}'."
+            )
+        request = Luma2GenerationRequest(
+            prompt=prompt,
+            model=model["model"],
+            type="image",
+            aspect_ratio=aspect_ratio if aspect_ratio != "auto" else None,
+            style=style if style != "auto" else None,
+            output_format="png",
+            web_search=model["web_search"],
+            image_ref=await _luma2_upload_image_refs(cls, model.get("image_ref"), max_count=9),
+        )
+        return IO.NodeOutput(await _luma2_submit_and_poll(cls, request))
+
+
+class LumaImageEditNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaImageEditNode2",
+            display_name="Luma UNI-1 Image Edit",
+            category="api node/image/Luma",
+            description="Edit an existing image with a text prompt using the Luma UNI-1 model.",
+            inputs=[
+                IO.Image.Input(
+                    "source",
+                    tooltip="Source image to edit.",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Description of the desired edit. 1–6000 characters.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "uni-1",
+                            _luma2_uni1_common_inputs(max_image_refs=8),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "uni-1-max",
+                            _luma2_uni1_common_inputs(max_image_refs=8),
+                        ),
+                    ],
+                    tooltip="Model to use for editing.",
+                ),
+                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"], input_groups=["model.image_ref"]),
+                expr="""
+                (
+                  $m := widgets.model;
+                  $refs := $lookup(inputGroups, "model.image_ref");
+                  $base := $m = "uni-1-max" ? 0.103 : 0.0434;
+                  {"type":"usd","usd": $round($base + 0.003 * $refs, 4)}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        source: Input.Image,
+        prompt: str,
+        model: dict,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=6000)
+        request = Luma2GenerationRequest(
+            prompt=prompt,
+            model=model["model"],
+            type="image_edit",
+            source=Luma2ImageRef(url=await upload_image_to_comfyapi(cls, source)),
+            style=model["style"] if model["style"] != "auto" else None,
+            output_format="png",
+            web_search=model["web_search"],
+            image_ref=await _luma2_upload_image_refs(cls, model.get("image_ref"), max_count=8),
+        )
+        return IO.NodeOutput(await _luma2_submit_and_poll(cls, request))
+
+
 class LumaExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -654,6 +942,8 @@ class LumaExtension(ComfyExtension):
             LumaImageToVideoGenerationNode,
             LumaReferenceNode,
             LumaConceptsNode,
+            LumaImageNode,
+            LumaImageEditNode,
         ]
 
 

comfy_api_nodes/nodes_openai.py

@@ -39,16 +39,18 @@ STARTING_POINT_ID_PATTERN = r"<starting_point_id:(.*)>"
 
 
 class SupportedOpenAIModel(str, Enum):
-    o4_mini = "o4-mini"
-    o1 = "o1"
-    o3 = "o3"
-    o1_pro = "o1-pro"
-    gpt_4_1 = "gpt-4.1"
-    gpt_4_1_mini = "gpt-4.1-mini"
-    gpt_4_1_nano = "gpt-4.1-nano"
+    gpt_5_5_pro = "gpt-5.5-pro"
+    gpt_5_5 = "gpt-5.5"
     gpt_5 = "gpt-5"
     gpt_5_mini = "gpt-5-mini"
     gpt_5_nano = "gpt-5-nano"
+    gpt_4_1 = "gpt-4.1"
+    gpt_4_1_mini = "gpt-4.1-mini"
+    gpt_4_1_nano = "gpt-4.1-nano"
+    o4_mini = "o4-mini"
+    o3 = "o3"
+    o1_pro = "o1-pro"
+    o1 = "o1"
 
 
 async def validate_and_cast_response(response, timeout: int = None) -> torch.Tensor:
@@ -739,6 +741,16 @@ class OpenAIChatNode(IO.ComfyNode):
                     "usd": [0.002, 0.008],
                     "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
                   }
+                  : $contains($m, "gpt-5.5-pro") ? {
+                    "type": "list_usd",
+                    "usd": [0.03, 0.18],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "gpt-5.5") ? {
+                    "type": "list_usd",
+                    "usd": [0.005, 0.03],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
                   : $contains($m, "gpt-5-nano") ? {
                     "type": "list_usd",
                     "usd": [0.00005, 0.0004],

comfy_extras/nodes_context_windows.py

@@ -29,6 +29,7 @@ class ContextWindowsManualNode(io.ComfyNode):
                 io.Boolean.Input("freenoise", default=False, tooltip="Whether to apply FreeNoise noise shuffling, improves window blending."),
                 io.String.Input("cond_retain_index_list", default="", tooltip="List of latent indices to retain in the conditioning tensors for each window, for example setting this to '0' will use the initial start image for each window."),
                 io.Boolean.Input("split_conds_to_windows", default=False, tooltip="Whether to split multiple conditionings (created by ConditionCombine) to each window based on region index."),
+                io.Boolean.Input("causal_window_fix", default=True, tooltip="Whether to add a causal fix frame to non-0-indexed context windows."),
             ],
             outputs=[
                 io.Model.Output(tooltip="The model with context windows applied during sampling."),
@@ -38,7 +39,7 @@ class ContextWindowsManualNode(io.ComfyNode):
 
     @classmethod
     def execute(cls, model: io.Model.Type, context_length: int, context_overlap: int, context_schedule: str, context_stride: int, closed_loop: bool, fuse_method: str, dim: int, freenoise: bool,
-                cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False) -> io.Model:
+                cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False, causal_window_fix: bool=True) -> io.Model:
         model = model.clone()
         model.model_options["context_handler"] = comfy.context_windows.IndexListContextHandler(
             context_schedule=comfy.context_windows.get_matching_context_schedule(context_schedule),
@@ -50,7 +51,8 @@ class ContextWindowsManualNode(io.ComfyNode):
             dim=dim,
             freenoise=freenoise,
             cond_retain_index_list=cond_retain_index_list,
-            split_conds_to_windows=split_conds_to_windows
+            split_conds_to_windows=split_conds_to_windows,
+            causal_window_fix=causal_window_fix,
         )
         # make memory usage calculation only take into account the context window latents
         comfy.context_windows.create_prepare_sampling_wrapper(model)

comfy_extras/nodes_custom_sampler.py

@@ -11,6 +11,142 @@ from typing_extensions import override
 from comfy_api.latest import ComfyExtension, io
 import re
 
+def video_latent_composite(destination, source, x, y, mask=None, multiplier=8, resize_source=False):
+    # destination/source shape: [B, C, F, H, W]
+    source = source.to(destination.device)
+
+    if resize_source:
+        target_size = (source.shape[2], destination.shape[3], destination.shape[4])
+        source = torch.nn.functional.interpolate(
+            source, 
+            size=target_size, 
+            mode="trilinear", 
+            align_corners=False
+        )
+
+    x_latent = x // multiplier
+    y_latent = y // multiplier
+
+    if mask is None:
+        mask = torch.ones_like(source)
+    else:
+        mask = mask.to(destination.device, copy=True)
+        mask = mask.unsqueeze(0).unsqueeze(0)
+        mask_target_size = (mask.shape[2], source.shape[3], source.shape[4])
+        mask = torch.nn.functional.interpolate(
+            mask, 
+            size=mask_target_size, 
+            mode="trilinear", 
+            align_corners=False
+        )
+
+    dst_h, dst_w = destination.shape[3], destination.shape[4]
+    src_h, src_w = source.shape[3], source.shape[4]
+
+    visible_h = max(0, min(y_latent + src_h, dst_h) - max(0, y_latent))
+    visible_w = max(0, min(x_latent + src_w, dst_w) - max(0, x_latent))
+
+    if visible_h <= 0 or visible_w <= 0:
+        return destination
+
+    src_top = max(0, -y_latent)
+    src_left = max(0, -x_latent)
+    dst_top = max(0, y_latent)
+    dst_left = max(0, x_latent)
+
+    m = mask[:, :, :, src_top:src_top+visible_h, src_left:src_left+visible_w]
+    s = source[:, :, :, src_top:src_top+visible_h, src_left:src_left+visible_w]
+    d = destination[:, :, :, dst_top:dst_top+visible_h, dst_left:dst_left+visible_w]
+
+    destination[:, :, :, dst_top:dst_top+visible_h, dst_left:dst_left+visible_w] = (m * s) + ((1.0 - m) * d)
+    
+    return destination
+
+def time_to_move_sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, latent_mask, denoise=1.0, start_step=None, time_to_move_last_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
+
+    sampler = comfy.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
+    model_sampling = model.get_model_object("model_sampling")
+    process_latent_out = model.get_model_object("process_latent_out")
+    process_latent_in = model.get_model_object("process_latent_in")
+    
+    reference_latent_image = latent_image.clone()
+    
+    reference_sigmas = sampler.sigmas
+    reference_noise = noise.clone()
+    
+    if last_step == None or last_step > steps:
+        last_step = steps
+
+    if time_to_move_last_step == None or time_to_move_last_step > last_step:
+        time_to_move_last_step = last_step
+
+    if start_step == None:
+        start_step = 0
+
+    total_iterations = min(last_step, steps) - start_step
+    if total_iterations <= 0:
+        return latent_image.to(
+            device=comfy.model_management.intermediate_device(),
+            dtype=comfy.model_management.intermediate_dtype(),
+        )
+
+    for i in range(total_iterations):
+        if i > 0:
+            #don't add new noise to samples after first step taken
+            noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
+
+        temp_start = start_step + i
+
+        if temp_start < last_step - 1:
+            temp_force_full_denoise = False
+        else:
+            temp_force_full_denoise = force_full_denoise
+
+        samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=temp_start, last_step=temp_start + 1, force_full_denoise=temp_force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
+
+        if temp_start < time_to_move_last_step:
+            scale = reference_sigmas[temp_start + 1].to(noise.device)
+            
+            if torch.count_nonzero(reference_latent_image) > 0: #Don't shift the empty latent image.
+                noisy = model_sampling.noise_scaling(scale, reference_noise, process_latent_in(reference_latent_image))
+                noisy = model_sampling.inverse_noise_scaling(scale, noisy)
+                noisy = process_latent_out(noisy)
+            else:
+                noisy = reference_latent_image
+            
+            noisy.to(samples.device)            
+            
+            samples = video_latent_composite(samples, noisy, 0, 0, latent_mask, multiplier=1, resize_source=True)
+
+        latent_image = samples
+        
+    samples = samples.to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
+    return samples
+
+
+def time_to_move_common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, latent_mask, denoise=1.0, disable_noise=False, start_step=None, time_to_move_last_step = None, last_step=None, force_full_denoise=False):
+    latent_image = latent["samples"]
+    latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image, latent.get("downscale_ratio_spacial", None))
+
+    if disable_noise:
+        noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
+    else:
+        batch_inds = latent["batch_index"] if "batch_index" in latent else None
+        noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)
+
+    noise_mask = None
+    if "noise_mask" in latent:
+        noise_mask = latent["noise_mask"]
+
+    callback = latent_preview.prepare_callback(model, steps)
+    disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
+    samples = time_to_move_sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, latent_mask,
+                                  denoise=denoise, start_step=start_step, time_to_move_last_step = time_to_move_last_step, last_step=last_step,
+                                  force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
+    out = latent.copy()
+    out.pop("downscale_ratio_spacial", None)
+    out["samples"] = samples
+    return (out, )
 
 class BasicScheduler(io.ComfyNode):
     @classmethod
@@ -978,6 +1114,46 @@ class SamplerCustomAdvanced(io.ComfyNode):
         return io.NodeOutput(out, out_denoised)
 
     sample = execute
+    
+class TimeToMoveKSamplerAdvanced(io.ComfyNode):
+    @classmethod
+
+    def define_schema(cls):
+        return io.Schema(
+            node_id="TimeToMoveKSamplerAdvanced",
+            category="sampling/time_to_move",
+            inputs=[
+                io.Model.Input("model"),
+                io.Combo.Input("add_noise", options=["enable", "disable"], advanced=True),
+                io.Int.Input("noise_seed", default=0, min=0, max=0xffffffffffffffff, control_after_generate=True),
+                io.Int.Input("steps", default=20, min=1, max=10000),
+                io.Float.Input("cfg", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01),
+                io.Combo.Input("sampler_name", options = comfy.samplers.KSampler.SAMPLERS),
+                io.Combo.Input("scheduler", options = comfy.samplers.KSampler.SCHEDULERS),
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Latent.Input("latent_image"),
+                io.Mask.Input("latent_mask", tooltip = "Make sure mask is the same length as the latents rather than the original video."),
+                io.Int.Input("start_at_step", default = 0, min = 0, max = 10000, advanced = True, tooltip = "Generally should set at a step greater than 0."),
+                io.Int.Input("time_to_move_end_at_step", default = 0, min = 0, max = 10000, advanced = True, tooltip = "Generally should set at a step greater than 0 and less than total number of steps."),
+                io.Int.Input("end_at_step", default = 10000, min = 0, max = 10000, advanced = True, tooltip = "Use just like typical end_at_step with normal KSamplerAdvanced"),
+                io.Combo.Input("return_with_leftover_noise",  options=["disable", "enable"], advanced = True),
+            ],
+            outputs=[
+                io.Latent.Output(display_name="latent"),
+            ]
+        )
+
+    @classmethod
+    def execute(cls, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, latent_mask, start_at_step, time_to_move_end_at_step, end_at_step, return_with_leftover_noise, denoise=1.0) -> io.NodeOutput:
+        force_full_denoise = True
+        if return_with_leftover_noise == "enable":
+            force_full_denoise = False
+        disable_noise = False
+        if add_noise == "disable":
+            disable_noise = True          
+
+        return time_to_move_common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, latent_mask, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, time_to_move_last_step = time_to_move_end_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise)
 
 class AddNoise(io.ComfyNode):
     @classmethod
@@ -1087,6 +1263,7 @@ class CustomSamplersExtension(ComfyExtension):
             DisableNoise,
             AddNoise,
             SamplerCustomAdvanced,
+            TimeToMoveKSamplerAdvanced,
             ManualSigmas,
         ]
 

comfy_extras/nodes_lt_audio.py

@@ -147,7 +147,6 @@ class LTXVEmptyLatentAudio(io.ComfyNode):
 
         z_channels = audio_vae.latent_channels
         audio_freq = audio_vae.first_stage_model.latent_frequency_bins
-        sampling_rate = int(audio_vae.first_stage_model.sample_rate)
 
         num_audio_latents = audio_vae.first_stage_model.num_of_latents_from_frames(frames_number, frame_rate)
 
@@ -159,7 +158,6 @@ class LTXVEmptyLatentAudio(io.ComfyNode):
         return io.NodeOutput(
             {
                 "samples": audio_latents,
-                "sample_rate": sampling_rate,
                 "type": "audio",
             }
         )

comfy_extras/nodes_mask.py

@@ -46,6 +46,42 @@ def composite(destination, source, x, y, mask = None, multiplier = 8, resize_sou
     destination[..., top:bottom, left:right] = source_portion + destination_portion
     return destination
 
+def convert_rgb_mask_to_latent_mask(
+    mask: torch.Tensor, 
+    k: int, 
+    spatial_downsample_h: int,
+    spatial_downsample_w: int
+) -> torch.Tensor: 
+    """ 
+    Converts [T, H, W] mask to [T_latent, H_latent, W_latent].
+    Handles non-square spatial downsampling.
+    """ 
+    # 1. Temporal Sampling
+    # Select first frame and every k-th frame thereafter
+    mask0 = mask[0:1]           
+    mask1 = mask[1::k]          
+    sampled = torch.cat([mask0, mask1], dim=0)  # [T_latent, H, W] 
+
+    # 2. Prepare for Spatial Interpolation
+    # Shape: [Batch=1, Channels=1, Depth=T_latent, Height=H, Width=W]
+    sampled = sampled.unsqueeze(0).unsqueeze(0)
+
+    # 3. Calculate New Spatial Dimensions
+    h_latent = sampled.shape[-2] // spatial_downsample_h
+    w_latent = sampled.shape[-1] // spatial_downsample_w
+    
+    # 4. Interpolate
+    # We maintain the temporal count (sampled.shape[2]) 
+    # but resize H and W independently
+    pooled = torch.nn.functional.interpolate(
+        sampled, 
+        size=(sampled.shape[2], h_latent, w_latent), 
+        mode="nearest"
+    ) 
+
+    # 5. Return to [T_latent, H_latent, W_latent]
+    return pooled.squeeze(0).squeeze(0)
+
 class LatentCompositeMasked(IO.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -73,8 +109,7 @@ class LatentCompositeMasked(IO.ComfyNode):
         return IO.NodeOutput(output)
 
     composite = execute  # TODO: remove
-
-
+    
 class ImageCompositeMasked(IO.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -403,6 +438,30 @@ class ThresholdMask(IO.ComfyNode):
 
     image_to_mask = execute  # TODO: remove
 
+class RGBMaskToLatentMask(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RGBMasktoLatentMask",
+            search_aliases=["rgb mask to latent mask", "rgb mask", "latent mask"],
+            description="Converts an RGB mask to a latent-space mask for use with causal Video VAEs (e.g., Wan).",
+            category="latent",
+            inputs=[
+                IO.Mask.Input("mask", optional=False),
+                IO.Vae.Input("vae", optional=False),
+            ],
+            outputs=[IO.Mask.Output()],
+        )
+
+    @classmethod
+    def execute(cls, mask, vae) -> IO.NodeOutput:
+        # Ensure we work on a copy of the mask to remain non-destructive
+        mask_copy = mask.clone()
+        downscale_ratio = vae.downscale_ratio
+        if not isinstance(downscale_ratio, tuple) or len(downscale_ratio) < 3:
+            raise ValueError("RGBMaskToLatentMask requires a causal Video VAE (e.g., Wan). The provided VAE does not have a compatible downscale_ratio.")
+        k = (mask.shape[0] - 1) // (downscale_ratio[0](mask.shape[0]) - 1) if (downscale_ratio[0](mask.shape[0]) - 1) > 1 else 1
+        return IO.NodeOutput(convert_rgb_mask_to_latent_mask(mask_copy, k, spatial_downsample_h = downscale_ratio[1], spatial_downsample_w = downscale_ratio[2]))
 
 # Mask Preview - original implement from
 # https://github.com/cubiq/ComfyUI_essentials/blob/9d9f4bedfc9f0321c19faf71855e228c93bd0dc9/mask.py#L81
@@ -444,6 +503,7 @@ class MaskExtension(ComfyExtension):
             FeatherMask,
             GrowMask,
             ThresholdMask,
+            RGBMaskToLatentMask,
             MaskPreview,
         ]
 

comfy_extras/nodes_primitive.py

@@ -9,7 +9,8 @@ class String(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="PrimitiveString",
-            display_name="String",
+            search_aliases=["text", "string", "text box", "prompt"],
+            display_name="Text String",
             category="utils/primitive",
             inputs=[
                 io.String.Input("value"),
@@ -27,7 +28,8 @@ class StringMultiline(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="PrimitiveStringMultiline",
-            display_name="String (Multiline)",
+            search_aliases=["text", "string", "text multiline", "string multiline", "text box", "prompt"],
+            display_name="Text String (Multiline)",
             category="utils/primitive",
             essentials_category="Basics",
             inputs=[

comfy_extras/nodes_string.py

@@ -10,9 +10,9 @@ class StringConcatenate(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringConcatenate",
-            display_name="Text Concatenate",
-            category="utils/string",
-            search_aliases=["Concatenate", "text concat", "join text", "merge text", "combine strings", "concat", "concatenate", "append text", "combine text", "string"],
+            search_aliases=["concatenate", "text concat", "join text", "merge text", "combine strings", "string concat", "append text", "combine text"],
+            display_name="Concatenate Text",
+            category="text",
             inputs=[
                 io.String.Input("string_a", multiline=True),
                 io.String.Input("string_b", multiline=True),
@@ -33,9 +33,9 @@ class StringSubstring(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringSubstring",
-            search_aliases=["Substring", "extract text", "text portion"],
-            display_name="Text Substring",
-            category="utils/string",
+            search_aliases=["substring", "extract text", "text portion"],
+            display_name="Substring",
+            category="text",
             inputs=[
                 io.String.Input("string", multiline=True),
                 io.Int.Input("start"),
@@ -58,7 +58,7 @@ class StringLength(io.ComfyNode):
             node_id="StringLength",
             search_aliases=["character count", "text size", "string length"],
             display_name="Text Length",
-            category="utils/string",
+            category="text",
             inputs=[
                 io.String.Input("string", multiline=True),
             ],
@@ -77,9 +77,9 @@ class CaseConverter(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CaseConverter",
-            search_aliases=["Case Converter", "text case", "uppercase", "lowercase", "capitalize"],
-            display_name="Text Case Converter",
-            category="utils/string",
+            search_aliases=["case converter", "text case", "uppercase", "lowercase", "capitalize"],
+            display_name="Convert Text Case",
+            category="text",
             inputs=[
                 io.String.Input("string", multiline=True),
                 io.Combo.Input("mode", options=["UPPERCASE", "lowercase", "Capitalize", "Title Case"]),
@@ -110,9 +110,9 @@ class StringTrim(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringTrim",
-            search_aliases=["Trim", "clean whitespace", "remove whitespace", "strip"],
-            display_name="Text Trim",
-            category="utils/string",
+            search_aliases=["trim", "clean whitespace", "remove whitespace", "remove spaces","strip"],
+            display_name="Trim Text",
+            category="text",
             inputs=[
                 io.String.Input("string", multiline=True),
                 io.Combo.Input("mode", options=["Both", "Left", "Right"]),
@@ -141,9 +141,9 @@ class StringReplace(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringReplace",
-            search_aliases=["Replace", "find and replace", "substitute", "swap text"],
-            display_name="Text Replace",
-            category="utils/string",
+            search_aliases=["replace", "find and replace", "substitute", "swap text"],
+            display_name="Replace Text",
+            category="text",
             inputs=[
                 io.String.Input("string", multiline=True),
                 io.String.Input("find", multiline=True),
@@ -164,9 +164,9 @@ class StringContains(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringContains",
-            search_aliases=["Contains", "text includes", "string includes"],
-            display_name="Text Contains",
-            category="utils/string",
+            search_aliases=["contains", "text includes", "string includes"],
+            display_name="Contains Text",
+            category="text",
             inputs=[
                 io.String.Input("string", multiline=True),
                 io.String.Input("substring", multiline=True),
@@ -192,9 +192,9 @@ class StringCompare(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringCompare",
-            search_aliases=["Compare", "text match", "string equals", "starts with", "ends with"],
-            display_name="Text Compare",
-            category="utils/string",
+            search_aliases=["compare", "text match", "string equals", "starts with", "ends with"],
+            display_name="Compare Text",
+            category="text",
             inputs=[
                 io.String.Input("string_a", multiline=True),
                 io.String.Input("string_b", multiline=True),
@@ -228,9 +228,9 @@ class RegexMatch(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="RegexMatch",
-            search_aliases=["Regex Match", "regex", "pattern match", "text contains", "string match"],
-            display_name="Text Match",
-            category="utils/string",
+            search_aliases=["regex match", "regex", "pattern match", "text contains", "string match"],
+            display_name="Match Text",
+            category="text",
             inputs=[
                 io.String.Input("string", multiline=True),
                 io.String.Input("regex_pattern", multiline=True),
@@ -269,9 +269,9 @@ class RegexExtract(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="RegexExtract",
-            search_aliases=["Regex Extract", "regex", "pattern extract", "text parser", "parse text"],
-            display_name="Text Extract Substring",
-            category="utils/string",
+            search_aliases=["regex extract", "regex", "pattern extract", "text parser", "parse text"],
+            display_name="Extract Text",
+            category="text",
             inputs=[
                 io.String.Input("string", multiline=True),
                 io.String.Input("regex_pattern", multiline=True),
@@ -344,9 +344,9 @@ class RegexReplace(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="RegexReplace",
-            search_aliases=["Regex Replace", "regex", "pattern replace", "regex replace", "substitution"],
-            display_name="Text Replace (Regex)",
-            category="utils/string",
+            search_aliases=["regex replace", "regex", "pattern replace", "substitution"],
+            display_name="Replace Text (Regex)",
+            category="text",
             description="Find and replace text using regex patterns.",
             inputs=[
                 io.String.Input("string", multiline=True),
@@ -381,8 +381,8 @@ class JsonExtractString(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="JsonExtractString",
-            display_name="Extract String from JSON",
-            category="utils/string",
+            display_name="Extract Text from JSON",
+            category="text",
             search_aliases=["json", "extract json", "parse json", "json value", "read json"],
             inputs=[
                 io.String.Input("json_string", multiline=True),

execution.py

@@ -1019,7 +1019,12 @@ async def validate_inputs(prompt_id, prompt, item, validated, visiting=None):
                         combo_options = extra_info.get("options", [])
                     else:
                         combo_options = input_type
-                    if val not in combo_options:
+                    is_multiselect = extra_info.get("multiselect", False)
+                    if is_multiselect and isinstance(val, list):
+                        invalid_vals = [v for v in val if v not in combo_options]
+                    else:
+                        invalid_vals = [val] if val not in combo_options else []
+                    if invalid_vals:
                         input_config = info
                         list_info = ""
 
@@ -1034,7 +1039,7 @@ async def validate_inputs(prompt_id, prompt, item, validated, visiting=None):
                         error = {
                             "type": "value_not_in_list",
                             "message": "Value not in list",
-                            "details": f"{x}: '{val}' not in {list_info}",
+                            "details": f"{x}: {', '.join(repr(v) for v in invalid_vals)} not in {list_info}",
                             "extra_info": {
                                 "input_name": x,
                                 "input_config": input_config,

folder_paths.py

@@ -432,7 +432,9 @@ def get_save_image_path(filename_prefix: str, output_dir: str, image_width=0, im
         prefix_len = len(os.path.basename(filename_prefix))
         prefix = filename[:prefix_len + 1]
         try:
-            digits = int(filename[prefix_len + 1:].split('_')[0])
+            remainder = filename[prefix_len + 1:]
+            base_remainder = remainder.split('.')[0]
+            digits = int(base_remainder.split('_')[0])
         except:
             digits = 0
         return digits, prefix

nodes.py

@@ -2262,7 +2262,7 @@ async def load_custom_node(module_path: str, ignore=set(), module_parent="custom
                 logging.warning(f"Error while calling comfy_entrypoint in {module_path}: {e}")
                 return False
         else:
-            logging.warning(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS or NODES_LIST (need one).")
+            logging.warning(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS or comfy_entrypoint (need one).")
             return False
     except Exception as e:
         logging.warning(traceback.format_exc())

openapi.yaml

@@ -631,7 +631,7 @@ paths:
       operationId: getFeatures
       tags: [system]
       summary: Get enabled feature flags
-      description: Returns a dictionary of feature flag names to their enabled state.
+      description: Returns a dictionary of feature flag names to their enabled state. Cloud deployments may include additional typed fields alongside the boolean flags.
       responses:
         "200":
           description: Feature flags
@@ -641,6 +641,43 @@ paths:
                 type: object
                 additionalProperties:
                   type: boolean
+                properties:
+                  max_upload_size:
+                    type: integer
+                    format: int64
+                    minimum: 0
+                    description: "Maximum file upload size in bytes."
+                  free_tier_credits:
+                    type: integer
+                    format: int32
+                    minimum: 0
+                    nullable: true
+                    x-runtime: [cloud]
+                    description: "[cloud-only] Credits available to free-tier users. Local ComfyUI returns null."
+                  posthog_api_host:
+                    type: string
+                    format: uri
+                    nullable: true
+                    x-runtime: [cloud]
+                    description: "[cloud-only] PostHog analytics proxy URL for frontend telemetry. Local ComfyUI returns null."
+                  max_concurrent_jobs:
+                    type: integer
+                    format: int32
+                    minimum: 0
+                    nullable: true
+                    x-runtime: [cloud]
+                    description: "[cloud-only] Maximum concurrent jobs the authenticated user can run. Local ComfyUI returns null."
+                  workflow_templates_version:
+                    type: string
+                    nullable: true
+                    x-runtime: [cloud]
+                    description: "[cloud-only] Version identifier for the workflow templates bundle. Local ComfyUI returns null."
+                  workflow_templates_source:
+                    type: string
+                    nullable: true
+                    enum: [dynamic_config_override, workflow_templates_version_json]
+                    x-runtime: [cloud]
+                    description: "[cloud-only] How the templates version was resolved. Local ComfyUI returns null."
 
   # ---------------------------------------------------------------------------
   # Node / Object Info
@@ -1497,6 +1534,24 @@ paths:
             type: string
             enum: [asc, desc]
           description: Sort direction
+        - name: job_ids
+          in: query
+          schema:
+            type: string
+          x-runtime: [cloud]
+          description: "[cloud-only] Comma-separated UUIDs to filter assets by associated job."
+        - name: include_public
+          in: query
+          schema:
+            type: boolean
+          x-runtime: [cloud]
+          description: "[cloud-only] Include workspace-public assets in addition to the caller's own."
+        - name: asset_hash
+          in: query
+          schema:
+            type: string
+          x-runtime: [cloud]
+          description: "[cloud-only] Filter by exact content hash."
       responses:
         "200":
           description: Asset list
@@ -1542,6 +1597,49 @@ paths:
                   type: string
                   format: uuid
                   description: ID of an existing asset to use as the preview image
+                id:
+                  type: string
+                  format: uuid
+                  nullable: true
+                  x-runtime: [cloud]
+                  description: "[cloud-only] Client-supplied asset ID for idempotent creation. If an asset with this ID already exists, the existing asset is returned."
+          application/json:
+            schema:
+              type: object
+              x-runtime: [cloud]
+              description: "[cloud-only] URL-based asset upload. Caller supplies a URL instead of a file body; the server fetches the content."
+              required:
+                - url
+              properties:
+                url:
+                  type: string
+                  format: uri
+                  description: "[cloud-only] URL of the file to import as an asset"
+                name:
+                  type: string
+                  description: Display name for the asset
+                tags:
+                  type: string
+                  description: Comma-separated tags
+                user_metadata:
+                  type: string
+                  description: JSON-encoded user metadata
+                hash:
+                  type: string
+                  description: "Blake3 hash of the file content (e.g. blake3:abc123...)"
+                mime_type:
+                  type: string
+                  description: MIME type of the file (overrides auto-detected type)
+                preview_id:
+                  type: string
+                  format: uuid
+                  description: ID of an existing asset to use as the preview image
+                id:
+                  type: string
+                  format: uuid
+                  nullable: true
+                  x-runtime: [cloud]
+                  description: "[cloud-only] Client-supplied asset ID for idempotent creation. If an asset with this ID already exists, the existing asset is returned."
       responses:
         "201":
           description: Asset created
@@ -1580,6 +1678,11 @@ paths:
                 user_metadata:
                   type: object
                   additionalProperties: true
+                mime_type:
+                  type: string
+                  nullable: true
+                  x-runtime: [cloud]
+                  description: "[cloud-only] MIME type of the content, so the type is preserved without re-inspecting content. Ignored by local ComfyUI."
       responses:
         "201":
           description: Asset created from hash
@@ -1644,6 +1747,11 @@ paths:
                   type: string
                   format: uuid
                   description: ID of the asset to use as the preview
+                mime_type:
+                  type: string
+                  nullable: true
+                  x-runtime: [cloud]
+                  description: "[cloud-only] MIME type override when auto-detection was wrong. Ignored by local ComfyUI."
       responses:
         "200":
           description: Asset updated
@@ -2004,21 +2112,13 @@ components:
           format: uuid
           nullable: true
           x-runtime: [cloud]
-          description: |
-            UUID identifying a hosted-cloud workflow entity to associate with this
-            job. Local ComfyUI doesn't track workflow entities and returns `null`
-            (or omits the field). The `x-runtime: [cloud]` extension marks this
-            as populated only by the hosted-cloud runtime; absence of the tag
-            means a field is populated by all runtimes.
+          description: "[cloud-only] Cloud workflow entity ID for tracking and gallery association. Ignored by local ComfyUI."
         workflow_version_id:
           type: string
           format: uuid
           nullable: true
           x-runtime: [cloud]
-          description: |
-            UUID identifying a hosted-cloud workflow version to associate with
-            this job. Local ComfyUI returns `null` (or omits the field). See
-            `workflow_id` above for `x-runtime` semantics.
+          description: "[cloud-only] Cloud workflow version ID for pinning execution to a specific version. Ignored by local ComfyUI."
 
     PromptResponse:
       type: object

requirements.txt

@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.42.15
-comfyui-workflow-templates==0.9.68
+comfyui-workflow-templates==0.9.69
 comfyui-embedded-docs==0.4.4
 torch
 torchsde

server.py

@@ -560,7 +560,7 @@ class PromptServer():
                             buffer.seek(0)
 
                             return web.Response(body=buffer.read(), content_type=f'image/{image_format}',
-                                                headers={"Content-Disposition": f"filename=\"{filename}\""})
+                                                headers={"Content-Disposition": f"attachment; filename=\"{filename}\""})
 
                     if 'channel' not in request.rel_url.query:
                         channel = 'rgba'
@@ -580,7 +580,7 @@ class PromptServer():
                             buffer.seek(0)
 
                             return web.Response(body=buffer.read(), content_type='image/png',
-                                                headers={"Content-Disposition": f"filename=\"{filename}\""})
+                                                headers={"Content-Disposition": f"attachment; filename=\"{filename}\""})
 
                     elif channel == 'a':
                         with Image.open(file) as img:
@@ -597,7 +597,7 @@ class PromptServer():
                             alpha_buffer.seek(0)
 
                             return web.Response(body=alpha_buffer.read(), content_type='image/png',
-                                                headers={"Content-Disposition": f"filename=\"{filename}\""})
+                                                headers={"Content-Disposition": f"attachment; filename=\"{filename}\""})
                     else:
                         # Use the content type from asset resolution if available,
                         # otherwise guess from the filename.
@@ -614,7 +614,7 @@ class PromptServer():
                         return web.FileResponse(
                             file,
                             headers={
-                                "Content-Disposition": f"filename=\"{filename}\"",
+                                "Content-Disposition": f"attachment; filename=\"{filename}\"",
                                 "Content-Type": content_type
                             }
                         )

tests-unit/comfy_api_test/multicombo_serialization_test.py

@@ -0,0 +1,78 @@
+from comfy_api.latest._io import Combo, MultiCombo
+
+
+def test_multicombo_serializes_multi_select_as_object():
+    multi_combo = MultiCombo.Input(
+        id="providers",
+        options=["a", "b", "c"],
+        default=["a"],
+    )
+
+    serialized = multi_combo.as_dict()
+
+    assert serialized["multiselect"] is True
+    assert "multi_select" in serialized
+    assert serialized["multi_select"] == {}
+
+
+def test_multicombo_serializes_multi_select_with_placeholder_and_chip():
+    multi_combo = MultiCombo.Input(
+        id="providers",
+        options=["a", "b", "c"],
+        default=["a"],
+        placeholder="Select providers",
+        chip=True,
+    )
+
+    serialized = multi_combo.as_dict()
+
+    assert serialized["multiselect"] is True
+    assert serialized["multi_select"] == {
+        "placeholder": "Select providers",
+        "chip": True,
+    }
+
+
+def test_combo_does_not_serialize_multiselect():
+    """Regular Combo should not have multiselect in its serialized output."""
+    combo = Combo.Input(
+        id="choice",
+        options=["a", "b", "c"],
+    )
+
+    serialized = combo.as_dict()
+
+    # Combo sets multiselect=False, but prune_dict keeps False (not None),
+    # so it should be present but False
+    assert serialized.get("multiselect") is False
+    assert "multi_select" not in serialized
+
+
+def _validate_combo_values(val, combo_options, is_multiselect):
+    """Reproduce the validation logic from execution.py for testing."""
+    if is_multiselect and isinstance(val, list):
+        return [v for v in val if v not in combo_options]
+    else:
+        return [val] if val not in combo_options else []
+
+
+def test_multicombo_validation_accepts_valid_list():
+    options = ["a", "b", "c"]
+    assert _validate_combo_values(["a", "b"], options, True) == []
+
+
+def test_multicombo_validation_rejects_invalid_values():
+    options = ["a", "b", "c"]
+    assert _validate_combo_values(["a", "x"], options, True) == ["x"]
+
+
+def test_multicombo_validation_accepts_empty_list():
+    options = ["a", "b", "c"]
+    assert _validate_combo_values([], options, True) == []
+
+
+def test_combo_validation_rejects_list_even_with_valid_items():
+    """A regular Combo should not accept a list value."""
+    options = ["a", "b", "c"]
+    invalid = _validate_combo_values(["a", "b"], options, False)
+    assert len(invalid) > 0