From ec4dec93d254000cf0f3dd9dc53768bcff794ee0 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Tue, 16 Jun 2026 00:54:36 +1000
Subject: [PATCH 01/30] Comfy Aimdo 0.4.10 + Dynamic --reserve-vram +
 --vram-headroom (#14480)

* main: implement --vram-headroom

Implement --vram-headroom for dynamic vram as a hybrid debug/diagnostic
option that can be used for people who still report shared VRAM spills.
They can trial and error the setting to maintain a bit more headroom to
avoid shared VRAM spills.

* main: implement --reserve-vram

Implement --reserve-vram as extra headroom on the simple method which
is semantically as close as possible to the stated functionality and
formet behaviour of non-dynamic VRAM.
---
 comfy/cli_args.py | 1 +
 main.py           | 4 ++--
 requirements.txt  | 2 +-
 3 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/comfy/cli_args.py b/comfy/cli_args.py
index e7ee0d5eb..e3099a230 100644
--- a/comfy/cli_args.py
+++ b/comfy/cli_args.py
@@ -145,6 +145,7 @@ vram_group.add_argument("--novram", action="store_true", help="When lowvram isn'
 vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).")
 
 parser.add_argument("--reserve-vram", type=float, default=None, help="Set the amount of vram in GB you want to reserve for use by your OS/other software. By default some amount is reserved depending on your OS.")
+parser.add_argument("--vram-headroom", type=float, default=0, help="Set the amount of vram in GB for DynamicVRAM to maintain as extra headroom above default. ComfyUI will try and keep this much VRAM completely free and unused, even counting VRAM from other apps.")
 
 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.")
diff --git a/main.py b/main.py
index 0ad660376..2cdb9caad 100644
--- a/main.py
+++ b/main.py
@@ -55,7 +55,7 @@ if __name__ == "__main__" and args.debug_hang:
 import comfy_aimdo.control
 
 if enables_dynamic_vram():
-    comfy_aimdo.control.init()
+    comfy_aimdo.control.init(simple_vram_headroom=None if args.reserve_vram is None else int(args.reserve_vram * 1024 ** 3))
 
 if os.name == "nt":
     os.environ['MIMALLOC_PURGE_DELAY'] = '0'
@@ -231,7 +231,7 @@ import comfy.model_patcher
 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_devices(d.index for d in comfy.model_management.get_all_torch_devices()):
+    elif comfy_aimdo.control.init_devices((d.index, int(args.vram_headroom * 1024 ** 3)) for d in comfy.model_management.get_all_torch_devices()):
         if args.verbose == 'DEBUG':
             comfy_aimdo.control.set_log_debug()
         elif args.verbose == 'CRITICAL':
diff --git a/requirements.txt b/requirements.txt
index 993baf975..f7c7da654 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -23,7 +23,7 @@ SQLAlchemy>=2.0.0
 filelock
 av>=16.0.0
 comfy-kitchen==0.2.10
-comfy-aimdo==0.4.9
+comfy-aimdo==0.4.10
 requests
 simpleeval>=1.0.0
 blake3

From 83a3f03218cbc055e74ac0bd8e96b7a7f4c4f3e4 Mon Sep 17 00:00:00 2001
From: "Daxiong (Lin)" <contact@comfyui-wiki.com>
Date: Mon, 15 Jun 2026 23:06:15 +0800
Subject: [PATCH 02/30] chore: update workflow templates to v0.10.0 (#14482)

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

diff --git a/requirements.txt b/requirements.txt
index f7c7da654..392709e64 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.45.15
-comfyui-workflow-templates==0.9.98
+comfyui-workflow-templates==0.10.0
 comfyui-embedded-docs==0.5.4
 torch
 torchsde

From 2f4c4e983c63dc60ae781bcca01e0e17f4f404d6 Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Mon, 15 Jun 2026 19:20:01 +0300
Subject: [PATCH 03/30] [Partner Nodes] fix(SoniloTextToMusic): always require
 "duration" to be specified (#14484)

---
 comfy_api_nodes/nodes_sonilo.py | 14 ++++++--------
 1 file changed, 6 insertions(+), 8 deletions(-)

diff --git a/comfy_api_nodes/nodes_sonilo.py b/comfy_api_nodes/nodes_sonilo.py
index 24a9a0b06..d146f63ea 100644
--- a/comfy_api_nodes/nodes_sonilo.py
+++ b/comfy_api_nodes/nodes_sonilo.py
@@ -111,11 +111,10 @@ class SoniloTextToMusic(IO.ComfyNode):
                 ),
                 IO.Int.Input(
                     "duration",
-                    default=0,
-                    min=0,
+                    default=30,
+                    min=1,
                     max=360,
-                    tooltip="Target duration in seconds. Set to 0 to let the model "
-                    "infer the duration from the prompt. Maximum: 6 minutes.",
+                    tooltip="Target duration in seconds. Maximum: 6 minutes.",
                 ),
                 IO.Int.Input(
                     "seed",
@@ -150,14 +149,13 @@ class SoniloTextToMusic(IO.ComfyNode):
     async def execute(
         cls,
         prompt: str,
-        duration: int = 0,
+        duration: int = 1,
         seed: int = 0,
     ) -> IO.NodeOutput:
-        validate_string(prompt, strip_whitespace=True, min_length=1)
+        validate_string(prompt, strip_whitespace=True, min_length=1, max_length=1000)
         form = aiohttp.FormData()
         form.add_field("prompt", prompt)
-        if duration > 0:
-            form.add_field("duration", str(duration))
+        form.add_field("duration", str(duration))
         audio_bytes = await _stream_sonilo_music(
             cls,
             ApiEndpoint(path="/proxy/sonilo/t2m/generate", method="POST"),

From b13ca1ce7b10565eb162e38fcd433865499d5d38 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Tue, 16 Jun 2026 13:22:24 +1000
Subject: [PATCH 04/30] main: support fallback to aimdo 0.4.9 (#14489)

The aimdo 0.4.10 protocol causing startup failure to be too early and
before the aimdo version warning can happen. This causes user
confusion. Limp on with 0.4.9 as it will work and users will see the
version warning.
---
 main.py | 45 ++++++++++++++++++++++++++++-----------------
 1 file changed, 28 insertions(+), 17 deletions(-)

diff --git a/main.py b/main.py
index 2cdb9caad..82f2bab64 100644
--- a/main.py
+++ b/main.py
@@ -55,7 +55,11 @@ if __name__ == "__main__" and args.debug_hang:
 import comfy_aimdo.control
 
 if enables_dynamic_vram():
-    comfy_aimdo.control.init(simple_vram_headroom=None if args.reserve_vram is None else int(args.reserve_vram * 1024 ** 3))
+    try:
+        comfy_aimdo.control.init(simple_vram_headroom=None if args.reserve_vram is None else int(args.reserve_vram * 1024 ** 3))
+    except TypeError:
+        # comfy-aimdo 0.4.9 protocol.
+        comfy_aimdo.control.init()
 
 if os.name == "nt":
     os.environ['MIMALLOC_PURGE_DELAY'] = '0'
@@ -231,23 +235,30 @@ import comfy.model_patcher
 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_devices((d.index, int(args.vram_headroom * 1024 ** 3)) for d in comfy.model_management.get_all_torch_devices()):
-        if args.verbose == 'DEBUG':
-            comfy_aimdo.control.set_log_debug()
-        elif args.verbose == 'CRITICAL':
-            comfy_aimdo.control.set_log_critical()
-        elif args.verbose == 'ERROR':
-            comfy_aimdo.control.set_log_error()
-        elif args.verbose == 'WARNING':
-            comfy_aimdo.control.set_log_warning()
-        else: #INFO
-            comfy_aimdo.control.set_log_info()
-
-        comfy.model_patcher.CoreModelPatcher = comfy.model_patcher.ModelPatcherDynamic
-        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")
+        try:
+            aimdo_initialized = comfy_aimdo.control.init_devices((d.index, int(args.vram_headroom * 1024 ** 3)) for d in comfy.model_management.get_all_torch_devices())
+        except TypeError:
+            # comfy-aimdo 0.4.9 protocol.
+            aimdo_initialized = comfy_aimdo.control.init_devices(d.index for d in comfy.model_management.get_all_torch_devices())
+
+        if aimdo_initialized:
+            if args.verbose == 'DEBUG':
+                comfy_aimdo.control.set_log_debug()
+            elif args.verbose == 'CRITICAL':
+                comfy_aimdo.control.set_log_critical()
+            elif args.verbose == 'ERROR':
+                comfy_aimdo.control.set_log_error()
+            elif args.verbose == 'WARNING':
+                comfy_aimdo.control.set_log_warning()
+            else: #INFO
+                comfy_aimdo.control.set_log_info()
+
+            comfy.model_patcher.CoreModelPatcher = comfy.model_patcher.ModelPatcherDynamic
+            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")
 
 
 def cuda_malloc_warning():

From 5db51b76b402ba9064af68618aacf81f74c7ca26 Mon Sep 17 00:00:00 2001
From: John Pollock <pollockjj@users.noreply.github.com>
Date: Mon, 15 Jun 2026 22:23:09 -0500
Subject: [PATCH 05/30] Fix odd-height crash and edge bleed in unaligned-width
 image/video decode (#14491)

a1d95f3f padded the decode width to the next multiple of 32 with the pad filter to fix libswscale's float YUV->GBR edge corruption, but kept the pad target height equal to the source height. The pad filter requires the target height to be a multiple of the input's vertical chroma subsampling factor, so a chroma-subsampled input such as yuv420p (the format the gbrpf32le float branch decodes) with an odd height makes the filter round the target below the input height and fail to configure: 'Padded dimensions cannot be smaller than input dimensions' (Errno 22). This is reachable from LoadImage, which routes static images through VideoFromFile, on a lossy WebP whose width is not a multiple of 32 and whose height is odd.

The pad filter also fills the added border with black, and chroma upsampling bleeds that black into the cropped edge of every unaligned-width subsampled decode.

Pad both axes to the next multiple of 32 (32 is a multiple of every vertical subsampling factor, including yuv410p's 4 that a plain even rounding misses) and run fillborders mode=smear to replicate the real edge into the padding so it never bleeds into the cropped output, then crop both axes back to the source size. Aligned-width and uint8 paths run the identical to_ndarray call as before and are byte-identical to master; only unaligned-width subsampled inputs change, from a crash or edge artifact to a clean, deterministic decode.
---
 comfy_api/latest/_input_impl/video_types.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/comfy_api/latest/_input_impl/video_types.py b/comfy_api/latest/_input_impl/video_types.py
index 92a1298c0..6c69256ab 100644
--- a/comfy_api/latest/_input_impl/video_types.py
+++ b/comfy_api/latest/_input_impl/video_types.py
@@ -325,21 +325,25 @@ class VideoFromFile(VideoInput):
                             checked_alpha = True
 
                         # Fix non-deterministic video decode when the video width is not a multiple of 32
-                        # For non-yuvj pixel formats (all H.264/H.265 video)
+                        # For non-yuvj pixel formats: most H.264/H.265 video and static images (e.g. lossy WebP via LoadImage)
+                        # Pad both axes to a multiple of 32 and smear the border so the alignment padding never bleeds into the cropped edges
                         if image_format in ('gbrpf32le', 'gbrapf32le') and frame.width % 32 != 0:
                             if align_graph is None:
                                 pad_w = ((frame.width + 31) // 32) * 32
+                                pad_h = ((frame.height + 31) // 32) * 32
                                 g = av.filter.Graph()
                                 g_src = g.add_buffer(width=frame.width, height=frame.height,
                                                      format=frame.format.name, time_base=video_stream.time_base)
-                                g_pad = g.add('pad', f'{pad_w}:{frame.height}:0:0')
+                                g_pad = g.add('pad', f'{pad_w}:{pad_h}:0:0')
+                                g_fill = g.add('fillborders', f'left=0:right={pad_w - frame.width}:top=0:bottom={pad_h - frame.height}:mode=smear')
                                 g_sink = g.add('buffersink')
                                 g_src.link_to(g_pad)
-                                g_pad.link_to(g_sink)
+                                g_pad.link_to(g_fill)
+                                g_fill.link_to(g_sink)
                                 g.configure()
                                 align_graph = (g, g_src, g_sink)
                             align_graph[1].push(frame)
-                            img = np.ascontiguousarray(align_graph[2].pull().to_ndarray(format=image_format)[:, :frame.width])
+                            img = np.ascontiguousarray(align_graph[2].pull().to_ndarray(format=image_format)[:frame.height, :frame.width])
                         else:
                             img = frame.to_ndarray(format=image_format)
                         if frame.rotation != 0:

From a439dcae07d683a8d52b01b830a6da68953d2969 Mon Sep 17 00:00:00 2001
From: Alexis Rolland <alexisrolland@hotmail.com>
Date: Tue, 16 Jun 2026 11:42:00 +0800
Subject: [PATCH 06/30] Update nodes titles (#14417)

---
 comfy_extras/nodes_rtdetr.py | 2 +-
 comfy_extras/nodes_sam3.py   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfy_extras/nodes_rtdetr.py b/comfy_extras/nodes_rtdetr.py
index e5a9b3902..653f3af2f 100644
--- a/comfy_extras/nodes_rtdetr.py
+++ b/comfy_extras/nodes_rtdetr.py
@@ -14,7 +14,7 @@ class RTDETR_detect(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="RTDETR_detect",
-            display_name="RT-DETR Detect",
+            display_name="Run Real-Time Detection (RT-DETR)",
             category="image/detection",
             search_aliases=["bbox", "bounding box", "object detection", "coco"],
             inputs=[
diff --git a/comfy_extras/nodes_sam3.py b/comfy_extras/nodes_sam3.py
index daac52f9b..f88aec925 100644
--- a/comfy_extras/nodes_sam3.py
+++ b/comfy_extras/nodes_sam3.py
@@ -264,7 +264,7 @@ class SAM3_VideoTrack(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SAM3_VideoTrack",
-            display_name="SAM3 Video Track",
+            display_name="Run SAM3 Video Track",
             category="image/detection",
             search_aliases=["sam3", "video", "track", "propagate"],
             inputs=[

From 135abed8da169e33ab0b86550e05e3ae55d6df8c Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 15 Jun 2026 23:27:33 -0400
Subject: [PATCH 07/30] ComfyUI v0.25.0

---
 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 4e3c924e6..cee317f3d 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__ = "0.24.0"
+__version__ = "0.25.0"
diff --git a/pyproject.toml b/pyproject.toml
index 4107b4911..54f11d7fa 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.24.0"
+version = "0.25.0"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

From 86f987ca7c887d0a37daaf341a57c6a0473ddab0 Mon Sep 17 00:00:00 2001
From: Comfy Org PR Bot <snomiao+comfy-pr@gmail.com>
Date: Tue, 16 Jun 2026 13:24:41 +0900
Subject: [PATCH 08/30] chore(openapi): sync shared API contract from
 cloud@00ef9cc (#14423)

---
 openapi.yaml | 20 +++++++++++++-------
 1 file changed, 13 insertions(+), 7 deletions(-)

diff --git a/openapi.yaml b/openapi.yaml
index 6e203b1cd..82ff5b003 100644
--- a/openapi.yaml
+++ b/openapi.yaml
@@ -896,11 +896,6 @@ components:
                     additionalProperties: true
                     description: The workflow graph to execute
                     type: object
-                prompt_id:
-                    description: Optional client-supplied job id. Must be a UUID in canonical lowercase hyphenated form; it is echoed back in the response. Omitted or null means the server generates one.
-                    format: uuid
-                    nullable: true
-                    type: string
                 workflow_id:
                     description: UUID identifying the cloud workflow entity to associate with this job
                     type: string
@@ -1800,7 +1795,9 @@ paths:
                         application/json:
                             schema:
                                 $ref: '#/components/schemas/ErrorResponse'
-                    description: Invalid request (no fields provided)
+                    description: |
+                        Invalid request — no fields provided, or `preview_id` is the zero UUID
+                        (`INVALID_PREVIEW_ID`).
                 "401":
                     content:
                         application/json:
@@ -1812,7 +1809,10 @@ paths:
                         application/json:
                             schema:
                                 $ref: '#/components/schemas/ErrorResponse'
-                    description: Asset not found
+                    description: |
+                        Asset not found — returned both when the asset being updated does
+                        not exist and when `preview_id` does not reference an asset
+                        accessible to the caller.
                 "500":
                     content:
                         application/json:
@@ -3050,6 +3050,12 @@ paths:
                             schema:
                                 $ref: '#/components/schemas/PromptErrorResponse'
                     description: Payment required - Insufficient credits
+                "413":
+                    content:
+                        application/json:
+                            schema:
+                                $ref: '#/components/schemas/PromptErrorResponse'
+                    description: Workflow JSON too large
                 "429":
                     content:
                         application/json:

From b732aa192f83f926b4ce7e54e6a6224d8e312ce4 Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Tue, 16 Jun 2026 10:12:39 +0300
Subject: [PATCH 09/30] [Partner Nodes] chore(SoniloTextToMusic): reduce price
 by half (#14500)

Signed-off-by: bigcat88 <bigcat88@icloud.com>
---
 comfy_api_nodes/nodes_sonilo.py | 11 ++---------
 1 file changed, 2 insertions(+), 9 deletions(-)

diff --git a/comfy_api_nodes/nodes_sonilo.py b/comfy_api_nodes/nodes_sonilo.py
index d146f63ea..2ad35531a 100644
--- a/comfy_api_nodes/nodes_sonilo.py
+++ b/comfy_api_nodes/nodes_sonilo.py
@@ -100,8 +100,7 @@ class SoniloTextToMusic(IO.ComfyNode):
             node_id="SoniloTextToMusic",
             display_name="Sonilo Text to Music",
             category="partner/audio/Sonilo",
-            description="Generate music from a text prompt using Sonilo's AI model. "
-            "Leave duration at 0 to let the model infer it from the prompt.",
+            description="Generate music from a text prompt using Sonilo's AI model.",
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -135,13 +134,7 @@ class SoniloTextToMusic(IO.ComfyNode):
             is_api_node=True,
             price_badge=IO.PriceBadge(
                 depends_on=IO.PriceBadgeDepends(widgets=["duration"]),
-                expr="""
-                (
-                  widgets.duration > 0
-                    ? {"type":"usd","usd": 0.005 * widgets.duration}
-                    : {"type":"usd","usd": 0.005, "format":{"suffix":"/second"}}
-                )
-                """,
+                expr='{"type":"usd","usd": 0.0025 * widgets.duration}',
             ),
         )
 

From d38ea29d6244582404533a8e52cbb069c3e251f6 Mon Sep 17 00:00:00 2001
From: Maksim <rommaks91@yandex.ru>
Date: Tue, 16 Jun 2026 11:21:04 +0300
Subject: [PATCH 10/30] Add the checkbox to disable head drawing in node
 SDPoseDrawKeypoints (#14446)

---
 comfy_extras/nodes_sdpose.py | 41 ++++++++++++++++++++++++++----------
 1 file changed, 30 insertions(+), 11 deletions(-)

diff --git a/comfy_extras/nodes_sdpose.py b/comfy_extras/nodes_sdpose.py
index 20d459b00..d1cbff2a6 100644
--- a/comfy_extras/nodes_sdpose.py
+++ b/comfy_extras/nodes_sdpose.py
@@ -96,8 +96,12 @@ class KeypointDraw:
         # 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]
+            [10, 11], [2, 12], [12, 13], [13, 14]
+        ]
+
+        # Head connections (1-indexed, converted to 0-indexed)
+        self.head_edges = [
+            [2, 1], [1, 15], [15, 17], [1, 16], [16, 18]
         ]
 
         # Colors matching DWPose
@@ -215,7 +219,7 @@ class KeypointDraw:
         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_body=True, draw_head=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.
 
@@ -237,9 +241,17 @@ class KeypointDraw:
         """
         H, W, C = canvas.shape
 
-        # Draw body limbs
-        if draw_body and len(keypoints) >= 18:
-            for i, limb in enumerate(self.body_limbSeq):
+        # Draw body limbs & head connections
+        if (draw_body or draw_head) and len(keypoints) >= 18:
+            colorIndexOffset = 0
+            edges = []
+            if draw_body:
+                edges += self.body_limbSeq
+            else:
+                colorIndexOffset += len(self.body_limbSeq)
+            if draw_head:
+                edges += self.head_edges
+            for i, limb in enumerate(edges):
                 # Convert from 1-indexed to 0-indexed
                 idx1, idx2 = limb[0] - 1, limb[1] - 1
 
@@ -262,11 +274,17 @@ class KeypointDraw:
 
                 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)])
+                self.draw.fillConvexPoly(canvas, polygon, self.colors[(i + colorIndexOffset) % len(self.colors)])
 
-        # Draw body keypoints
-        if draw_body and len(keypoints) >= 18:
+        # Draw body & head keypoints
+        if (draw_body or draw_head) and len(keypoints) >= 18:
+            head_keypoints = {0, 14, 15, 16, 17} # nose, eyes, ears
+            neck_point = 1
             for i in range(18):
+                if not draw_head and i in head_keypoints:
+                    continue
+                if not draw_body and i not in head_keypoints and i != neck_point:
+                    continue
                 if scores is not None and scores[i] < threshold:
                     continue
                 x, y = int(keypoints[i][0]), int(keypoints[i][1])
@@ -365,6 +383,7 @@ class SDPoseDrawKeypoints(io.ComfyNode):
                 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),
+                io.Boolean.Input("draw_head", default=True),
             ],
             outputs=[
                 io.Image.Output(),
@@ -372,7 +391,7 @@ class SDPoseDrawKeypoints(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, keypoints, draw_body, draw_hands, draw_face, draw_feet, stick_width, face_point_size, score_threshold) -> io.NodeOutput:
+    def execute(cls, keypoints, draw_body, draw_hands, draw_face, draw_feet, stick_width, face_point_size, score_threshold, draw_head) -> io.NodeOutput:
         if not keypoints:
             return io.NodeOutput(torch.zeros((1, 64, 64, 3), dtype=torch.float32))
         height = keypoints[0]["canvas_height"]
@@ -405,7 +424,7 @@ class SDPoseDrawKeypoints(io.ComfyNode):
                 canvas = drawer.draw_wholebody_keypoints(
                     canvas, kp, sc,
                     threshold=score_threshold,
-                    draw_body=draw_body, draw_feet=draw_feet,
+                    draw_body=draw_body, draw_head=draw_head, draw_feet=draw_feet,
                     draw_face=draw_face, draw_hands=draw_hands,
                     stick_width=stick_width, face_point_size=face_point_size,
                 )

From 90eeeb21390b746da522bb2882af6090d869ba2a Mon Sep 17 00:00:00 2001
From: Octopus <liyuan851277048@icloud.com>
Date: Tue, 16 Jun 2026 19:21:36 +0800
Subject: [PATCH 11/30] fix: log base directory to startup messages when
 --base-directory is used (fixes #13363) (#13370)

---
 main.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/main.py b/main.py
index 82f2bab64..ad5c11e16 100644
--- a/main.py
+++ b/main.py
@@ -127,6 +127,10 @@ def apply_custom_paths():
         for config_path in itertools.chain(*args.extra_model_paths_config):
             utils.extra_config.load_extra_path_config(config_path)
 
+    # --base-directory
+    if args.base_directory:
+        logging.info(f"Setting base directory to: {folder_paths.base_path}")
+
     # --output-directory, --input-directory, --user-directory
     if args.output_directory:
         output_dir = os.path.abspath(args.output_directory)

From fc964047e7f6e837eca776e7c34706c04690ecfd Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?=
 <40791699+kijai@users.noreply.github.com>
Date: Wed, 17 Jun 2026 03:12:44 +0300
Subject: [PATCH 12/30] feat: Support text generation with Qwen3-VL (CORE-276)
 (#14298)

---
 comfy/sd.py                      |  15 +++
 comfy/text_encoders/ideogram4.py |  41 +++++++
 comfy/text_encoders/llama.py     |  35 +++++-
 comfy/text_encoders/qwen35.py    |  35 ++----
 comfy/text_encoders/qwen3vl.py   | 193 +++++++++++++++++++++++++++++++
 comfy/text_encoders/qwen_vl.py   |  26 +++++
 comfy_extras/nodes_textgen.py    |   2 +-
 7 files changed, 317 insertions(+), 30 deletions(-)
 create mode 100644 comfy/text_encoders/qwen3vl.py

diff --git a/comfy/sd.py b/comfy/sd.py
index a66ba1bfb..688e6db90 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -67,6 +67,7 @@ import comfy.text_encoders.anima
 import comfy.text_encoders.ace15
 import comfy.text_encoders.longcat_image
 import comfy.text_encoders.qwen35
+import comfy.text_encoders.qwen3vl
 import comfy.text_encoders.ernie
 import comfy.text_encoders.gemma4
 import comfy.text_encoders.cogvideo
@@ -1353,6 +1354,8 @@ class TEModel(Enum):
     GEMMA_4_31B = 31
     T5_GEMMA = 32
     GPT_OSS_20B = 33
+    QWEN3VL_4B = 34
+    QWEN3VL_8B = 35
 
 
 def detect_te_model(sd):
@@ -1414,6 +1417,8 @@ def detect_te_model(sd):
         if weight.shape[0] == 5120:
             return TEModel.QWEN35_27B
         return TEModel.QWEN35_2B
+    if "model.visual.deepstack_merger_list.0.norm.weight" in sd:  # DeepStack is unique to Qwen3-VL
+        return TEModel.QWEN3VL_4B if sd["model.visual.merger.linear_fc2.weight"].shape[0] == 2560 else TEModel.QWEN3VL_8B
     if "model.layers.0.post_attention_layernorm.weight" in sd:
         weight = sd['model.layers.0.post_attention_layernorm.weight']
         if 'model.layers.0.self_attn.q_norm.weight' in sd:
@@ -1612,6 +1617,16 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             qwen35_type = {TEModel.QWEN35_08B: "qwen35_08b", TEModel.QWEN35_2B: "qwen35_2b", TEModel.QWEN35_4B: "qwen35_4b", TEModel.QWEN35_9B: "qwen35_9b", TEModel.QWEN35_27B: "qwen35_27b"}[te_model]
             clip_target.clip = comfy.text_encoders.qwen35.te(**llama_detect(clip_data), model_type=qwen35_type)
             clip_target.tokenizer = comfy.text_encoders.qwen35.tokenizer(model_type=qwen35_type)
+        elif te_model in (TEModel.QWEN3VL_4B, TEModel.QWEN3VL_8B):
+            if clip_type == CLIPType.IDEOGRAM4 and te_model == TEModel.QWEN3VL_8B:  # Ideogram4 reuses the full Qwen3-VL-8B (13-layer tap for conditioning + multimodal generate).
+                clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
+                clip_target.clip = comfy.text_encoders.ideogram4.te_qwen3vl(**llama_detect(clip_data))
+                clip_target.tokenizer = comfy.text_encoders.ideogram4.Ideogram4Qwen3VLTokenizer
+            else:
+                clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
+                qwen3vl_type = {TEModel.QWEN3VL_4B: "qwen3vl_4b", TEModel.QWEN3VL_8B: "qwen3vl_8b"}[te_model]
+                clip_target.clip = comfy.text_encoders.qwen3vl.te(**llama_detect(clip_data), model_type=qwen3vl_type)
+                clip_target.tokenizer = comfy.text_encoders.qwen3vl.tokenizer(model_type=qwen3vl_type)
         elif te_model == TEModel.QWEN3_06B:
             clip_target.clip = comfy.text_encoders.anima.te(**llama_detect(clip_data))
             clip_target.tokenizer = comfy.text_encoders.anima.AnimaTokenizer
diff --git a/comfy/text_encoders/ideogram4.py b/comfy/text_encoders/ideogram4.py
index 84243772d..151b43c53 100644
--- a/comfy/text_encoders/ideogram4.py
+++ b/comfy/text_encoders/ideogram4.py
@@ -9,6 +9,7 @@ import os
 from transformers import Qwen2Tokenizer
 
 import comfy.text_encoders.llama
+import comfy.text_encoders.qwen3vl
 from comfy import sd1_clip
 
 # Reference taps outputs of layers (0,3,...,35); comfy captures layer inputs, offset by +1.
@@ -77,3 +78,43 @@ def te(dtype_llama=None, llama_quantization_metadata=None):
                 model_options["quantization_metadata"] = llama_quantization_metadata
             super().__init__(device=device, dtype=dtype, model_options=model_options)
     return Ideogram4TEModel_
+
+
+# Full Qwen3-VL-8B variant with vision
+
+class Ideogram4Qwen3VLClipModel(comfy.text_encoders.qwen3vl.Qwen3VLClipModel):
+    def __init__(self, device="cpu", dtype=None, attention_mask=True, model_options={}):
+        super().__init__(device=device, layer=IDEOGRAM4_TAP_LAYERS, layer_idx=None, dtype=dtype,
+                         attention_mask=attention_mask, model_options=model_options, model_type="qwen3vl_8b")
+
+
+class Ideogram4Qwen3VLTEModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, name="qwen3vl_8b", clip_model=Ideogram4Qwen3VLClipModel, model_options=model_options)
+
+    def encode_token_weights(self, token_weight_pairs):
+        out, pooled, extra = super().encode_token_weights(token_weight_pairs)
+        b, n, seq, h = out.shape  # (B, n_taps=13, seq, 4096), ascending layer order.
+        out = out.permute(0, 2, 3, 1).reshape(b, seq, h * n)  # (B, seq, 4096*13 = 53248).
+        return out, pooled, extra
+
+
+class Ideogram4Qwen3VLTokenizer(comfy.text_encoders.qwen3vl.Qwen3VLTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, model_type="qwen3vl_8b")
+
+    def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, thinking=True, **kwargs):
+        # Ideogram 4 conditions on the no-think template; default thinking=True drops the empty think block qwen3vl adds.
+        return super().tokenize_with_weights(text, return_word_ids=return_word_ids, llama_template=llama_template, images=images, prevent_empty_text=prevent_empty_text, thinking=thinking, **kwargs)
+
+
+def te_qwen3vl(dtype_llama=None, llama_quantization_metadata=None):
+    class Ideogram4Qwen3VLTEModel_(Ideogram4Qwen3VLTEModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            if llama_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["quantization_metadata"] = llama_quantization_metadata
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return Ideogram4Qwen3VLTEModel_
diff --git a/comfy/text_encoders/llama.py b/comfy/text_encoders/llama.py
index 5087228ca..e9f38a9a2 100644
--- a/comfy/text_encoders/llama.py
+++ b/comfy/text_encoders/llama.py
@@ -251,6 +251,19 @@ class Qwen3_8BConfig:
     lm_head: bool = True
     stop_tokens = [151643, 151645]
 
+@dataclass
+class Qwen3VL_8BConfig(Qwen3_8BConfig):
+    max_position_embeddings: int = 262144
+    rope_theta: float = 5000000.0
+    rope_dims = [24, 20, 20]
+    interleaved_mrope = True
+
+@dataclass
+class Qwen3VL_4BConfig(Qwen3VL_8BConfig):
+    hidden_size: int = 2560
+    intermediate_size: int = 9728
+    lm_head: bool = False  # 4B ties word embeddings
+
 @dataclass
 class Ovis25_2BConfig:
     vocab_size: int = 151936
@@ -703,7 +716,8 @@ class Llama2_(nn.Module):
                                     interleaved_mrope=getattr(self.config, "interleaved_mrope", False),
                                     device=device)
 
-    def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, position_ids=None, embeds_info=[], past_key_values=None, input_ids=None):
+    def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True,
+                dtype=None, position_ids=None, embeds_info=[], past_key_values=None, input_ids=None,deepstack_embeds=None, visual_pos_masks=None):
         if embeds is not None:
             x = embeds
         else:
@@ -767,6 +781,10 @@ class Llama2_(nn.Module):
             if current_kv is not None:
                 next_key_values.append(current_kv)
 
+            # DeepStack: add per-layer visual features into the first len() decoder layers at image positions (Qwen3-VL)
+            if deepstack_embeds is not None and i < len(deepstack_embeds):
+                x[visual_pos_masks] = x[visual_pos_masks] + deepstack_embeds[i].to(x)
+
             if i == intermediate_output:
                 intermediate = x.clone()
 
@@ -860,7 +878,7 @@ class BaseGenerate:
                                     torch.empty([batch, model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype), 0))
         return past_key_values
 
-    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, presence_penalty=0.0, initial_input_ids=None):
+    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, presence_penalty=0.0, initial_input_ids=None, position_ids=None, deepstack_embeds=None, visual_pos_masks=None):
         device = embeds.device
 
         if stop_tokens is None:
@@ -884,10 +902,18 @@ class BaseGenerate:
         generated_token_ids = []
         pbar = comfy.utils.ProgressBar(max_length)
 
+        # MRoPE: prefill uses explicit 3D position_ids, decode continues from the last position
+        next_pos = int(position_ids[:, -1].max()) + 1 if position_ids is not None else None
+
         # Generation loop
         current_input_ids = initial_input_ids
         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, input_ids=current_input_ids)
+            # DeepStack visual features are injected on the prefill only; gemma4's forward lacks these kwargs.
+            extra = {}
+            if step == 0 and deepstack_embeds is not None:
+                extra["deepstack_embeds"] = deepstack_embeds
+                extra["visual_pos_masks"] = visual_pos_masks
+            x, _, past_key_values = self.model.forward(None, embeds=embeds, attention_mask=None, past_key_values=past_key_values, input_ids=current_input_ids, position_ids=position_ids, **extra)
             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, presence_penalty=presence_penalty)
             token_id = next_token[0].item()
@@ -895,6 +921,9 @@ class BaseGenerate:
 
             embeds = self.model.embed_tokens(next_token).to(execution_dtype)
             current_input_ids = next_token if initial_input_ids is not None else None
+            if next_pos is not None:  # advance MRoPE position for the next (decode) step
+                position_ids = torch.tensor([[next_pos]], device=device)
+                next_pos += 1
             pbar.update(1)
 
             if token_id in stop_tokens:
diff --git a/comfy/text_encoders/qwen35.py b/comfy/text_encoders/qwen35.py
index 416ce9d18..71a17990f 100644
--- a/comfy/text_encoders/qwen35.py
+++ b/comfy/text_encoders/qwen35.py
@@ -3,7 +3,6 @@ import torch.nn as nn
 import torch.nn.functional as F
 from dataclasses import dataclass, field
 import os
-import math
 
 import comfy.model_management
 from comfy.ldm.modules.attention import optimized_attention_for_device
@@ -563,6 +562,8 @@ class Qwen35VisionModel(nn.Module):
             for _ in range(config["depth"])
         ])
         self.merger = Qwen35VisionPatchMerger(self.hidden_size, self.spatial_merge_size, config["out_hidden_size"], device=device, dtype=dtype, ops=ops)
+        self.deepstack_visual_indexes = [] # DeepStack, per-layer visual features (Qwen3-VL)
+        self.deepstack_merger_list = None
 
     def rot_pos_emb(self, grid_thw):
         merge_size = self.spatial_merge_size
@@ -664,9 +665,14 @@ class Qwen35VisionModel(nn.Module):
         ).cumsum(dim=0, dtype=torch.int32)
         cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
         optimized_attention = optimized_attention_for_device(x.device, mask=False, small_input=True)
-        for blk in self.blocks:
+        deepstack_features = []
+        for layer_num, blk in enumerate(self.blocks):
             x = blk(x, cu_seqlens=cu_seqlens, position_embeddings=position_embeddings, optimized_attention=optimized_attention)
+            if self.deepstack_merger_list is not None and layer_num in self.deepstack_visual_indexes:
+                deepstack_features.append(self.deepstack_merger_list[self.deepstack_visual_indexes.index(layer_num)](x))
         merged = self.merger(x)
+        if self.deepstack_merger_list is not None:
+            return merged, deepstack_features
         return merged
 
 # Model Wrapper
@@ -690,30 +696,7 @@ class Qwen35(BaseLlama, BaseGenerate, torch.nn.Module):
         return None, None
 
     def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[], past_key_values=None):
-        grid = None
-        position_ids = None
-        offset = 0
-        for e in embeds_info:
-            if e.get("type") == "image":
-                grid = e.get("extra", None)
-                start = e.get("index")
-                if position_ids is None:
-                    position_ids = torch.zeros((3, embeds.shape[1]), device=embeds.device)
-                    position_ids[:, :start] = torch.arange(0, start, device=embeds.device)
-                end = e.get("size") + start
-                len_max = int(grid.max()) // 2
-                start_next = len_max + start
-                position_ids[:, end:] = torch.arange(start_next + offset, start_next + (embeds.shape[1] - end) + offset, device=embeds.device)
-                position_ids[0, start:end] = start + offset
-                max_d = int(grid[0][1]) // 2
-                position_ids[1, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(1).repeat(1, math.ceil((end - start) / max_d)).flatten(0)[:end - start]
-                max_d = int(grid[0][2]) // 2
-                position_ids[2, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(0).repeat(math.ceil((end - start) / max_d), 1).flatten(0)[:end - start]
-                offset += len_max - (end - start)
-
-        if grid is None:
-            position_ids = None
-
+        position_ids = comfy.text_encoders.qwen_vl.qwen2vl_mrope_position_ids(embeds_info, embeds.shape[1], embeds.device)
         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, past_key_values=past_key_values)
 
     def init_kv_cache(self, batch, max_cache_len, device, execution_dtype):
diff --git a/comfy/text_encoders/qwen3vl.py b/comfy/text_encoders/qwen3vl.py
new file mode 100644
index 000000000..59c9aae6d
--- /dev/null
+++ b/comfy/text_encoders/qwen3vl.py
@@ -0,0 +1,193 @@
+import os
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import Qwen2Tokenizer
+
+from comfy import sd1_clip
+import comfy.text_encoders.qwen_vl
+from .qwen35 import Qwen35VisionModel
+from .llama import BaseLlama, BaseQwen3, BaseGenerate, Llama2_, Qwen3VL_4BConfig, Qwen3VL_8BConfig
+
+
+QWEN3VL_VISION = {
+    "qwen3vl_4b": dict(hidden_size=1024, intermediate_size=4096, depth=24, deepstack_visual_indexes=[5, 11, 17]),
+    "qwen3vl_8b": dict(hidden_size=1152, intermediate_size=4304, depth=27, deepstack_visual_indexes=[8, 16, 24]),
+}
+QWEN3VL_VISION_COMMON = dict(num_heads=16, patch_size=16, temporal_patch_size=2, in_channels=3,
+                             spatial_merge_size=2, num_position_embeddings=2304)
+
+QWEN3VL_CONFIGS = {"qwen3vl_4b": Qwen3VL_4BConfig, "qwen3vl_8b": Qwen3VL_8BConfig}
+
+
+class Qwen3VLDeepstackMerger(nn.Module):
+    # DeepStack merger: postshuffle LayerNorm (applied after spatial merge), unlike the main merger.
+    def __init__(self, hidden_size, spatial_merge_size, out_hidden_size, device=None, dtype=None, ops=None):
+        super().__init__()
+        self.merge_dim = hidden_size * (spatial_merge_size ** 2)
+        self.norm = ops.LayerNorm(self.merge_dim, eps=1e-6, device=device, dtype=dtype)
+        self.linear_fc1 = ops.Linear(self.merge_dim, self.merge_dim, device=device, dtype=dtype)
+        self.linear_fc2 = ops.Linear(self.merge_dim, out_hidden_size, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.norm(x.view(-1, self.merge_dim))
+        return self.linear_fc2(F.gelu(self.linear_fc1(x)))
+
+
+class Qwen3VLVisionModel(Qwen35VisionModel):
+    # Qwen3.5 vision + DeepStack
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        super().__init__(config, device=device, dtype=dtype, ops=ops)
+        self.deepstack_visual_indexes = config["deepstack_visual_indexes"]
+        self.deepstack_merger_list = nn.ModuleList([
+            Qwen3VLDeepstackMerger(self.hidden_size, self.spatial_merge_size, config["out_hidden_size"], device=device, dtype=dtype, ops=ops)
+            for _ in self.deepstack_visual_indexes
+        ])
+
+
+class Qwen3VL(BaseLlama, BaseQwen3, BaseGenerate, torch.nn.Module):
+    model_type = "qwen3vl_8b"
+
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        config = QWEN3VL_CONFIGS[self.model_type](**config_dict)
+        self.num_layers = config.num_hidden_layers
+        self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
+        vision_config = {**QWEN3VL_VISION_COMMON, **QWEN3VL_VISION[self.model_type], "out_hidden_size": config.hidden_size}
+        self.visual = Qwen3VLVisionModel(vision_config, device=device, dtype=dtype, ops=operations)
+        self.dtype = dtype
+
+    def preprocess_embed(self, embed, device):
+        if embed["type"] == "image":
+            # Qwen3-VL normalizes to [-1, 1] (mean/std 0.5), unlike Qwen2.5-VL's CLIP normalization.
+            image, grid = comfy.text_encoders.qwen_vl.process_qwen2vl_images(embed["data"], patch_size=16, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5])
+            merged, deepstack = self.visual(image.to(device, dtype=torch.float32), grid)
+            return merged, {"grid": grid, "deepstack": deepstack}
+        return None, None
+
+    def build_image_inputs(self, embeds, embeds_info):
+        # Returns (position_ids, visual_pos_masks, deepstack) for the prompt
+        images = sorted([e for e in embeds_info if e.get("type") == "image"], key=lambda e: e["index"])
+        if len(images) == 0:
+            return None, None, None
+
+        device = embeds.device
+        seq = embeds.shape[1]
+        position_ids = comfy.text_encoders.qwen_vl.qwen2vl_mrope_position_ids(embeds_info, seq, device)
+
+        # DeepStack: mask of image positions + per-vision-layer features to inject there.
+        visual_pos_masks = torch.zeros((1, seq), dtype=torch.bool, device=device)
+        deepstack = None
+        for e in images:
+            start = e["index"]
+            end = e["size"] + start
+            visual_pos_masks[0, start:end] = True
+            ds = e["extra"]["deepstack"]
+            if deepstack is None:
+                deepstack = [d for d in ds]
+            else:
+                deepstack = [torch.cat([deepstack[i], ds[i]], dim=0) for i in range(len(ds))]
+        return position_ids, visual_pos_masks, deepstack
+
+
+def _make_qwen3vl_model(model_type):
+    class Qwen3VL_(Qwen3VL):
+        pass
+    Qwen3VL_.model_type = model_type
+    return Qwen3VL_
+
+
+class Qwen3VLClipModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="hidden", layer_idx=-1, dtype=None, attention_mask=True, model_options={}, model_type="qwen3vl_8b"):
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={},
+                         dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False,
+                         model_class=_make_qwen3vl_model(model_type), enable_attention_masks=attention_mask,
+                         return_attention_masks=attention_mask, model_options=model_options)
+
+    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, presence_penalty=0.0):
+        if isinstance(tokens, dict):
+            tokens = next(iter(tokens.values()))
+        tokens_only = [[t[0] for t in b] for b in tokens]
+        embeds, _, _, embeds_info = self.process_tokens(tokens_only, self.execution_device)
+        position_ids, visual_pos_masks, deepstack = self.transformer.build_image_inputs(embeds, embeds_info)
+        return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed,
+                                         presence_penalty=presence_penalty, position_ids=position_ids,
+                                         visual_pos_masks=visual_pos_masks, deepstack_embeds=deepstack)
+
+
+class Qwen3VLTEModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}, model_type="qwen3vl_8b"):
+        clip_model = lambda **kw: Qwen3VLClipModel(**kw, model_type=model_type)
+        super().__init__(device=device, dtype=dtype, name=model_type, clip_model=clip_model, model_options=model_options)
+
+
+class Qwen3VLSDTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}, embedding_size=4096, embedding_key="qwen3vl_8b"):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer")
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=embedding_size, embedding_key=embedding_key, tokenizer_class=Qwen2Tokenizer,
+                         has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data)
+
+
+class Qwen3VLTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}, model_type="qwen3vl_8b"):
+        embedding_size = 2560 if model_type == "qwen3vl_4b" else 4096
+        tokenizer = lambda *a, **kw: Qwen3VLSDTokenizer(*a, **kw, embedding_size=embedding_size, embedding_key=model_type)
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name=model_type, tokenizer=tokenizer)
+        self.llama_template = "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
+        self.llama_template_images = "<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n"
+
+    def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, thinking=False, **kwargs):
+        image = kwargs.get("image", None)
+        if image is not None and len(images) == 0:
+            images = [image[i:i + 1] for i in range(image.shape[0])]
+
+        skip_template = text.startswith('<|im_start|>')
+        if prevent_empty_text and text == '':
+            text = ' '
+
+        if skip_template:
+            llama_text = text
+        else:
+            if llama_template is not None:
+                template = llama_template
+            elif len(images) == 0:
+                template = self.llama_template
+            else:
+                template = self.llama_template_images
+                if len(images) > 1:
+                    vision_block = "<|vision_start|><|image_pad|><|vision_end|>"
+                    template = template.replace(vision_block, vision_block * len(images), 1)
+            llama_text = template.format(text)
+            if not thinking:  # Qwen3 convention: empty think block suppresses reasoning
+                llama_text += "<think>\n\n</think>\n\n"
+
+        tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)
+        key_name = next(iter(tokens))
+        embed_count = 0
+        for r in tokens[key_name]:
+            for i in range(len(r)):
+                if r[i][0] == 151655:  # <|image_pad|>
+                    if len(images) > embed_count:
+                        r[i] = ({"type": "image", "data": images[embed_count], "original_type": "image"},) + r[i][1:]
+                        embed_count += 1
+        return tokens
+
+
+def tokenizer(model_type="qwen3vl_8b"):
+    class Qwen3VLTokenizer_(Qwen3VLTokenizer):
+        def __init__(self, embedding_directory=None, tokenizer_data={}):
+            super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, model_type=model_type)
+    return Qwen3VLTokenizer_
+
+
+def te(dtype_llama=None, llama_quantization_metadata=None, model_type="qwen3vl_8b"):
+    class Qwen3VLTEModel_(Qwen3VLTEModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            if llama_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["quantization_metadata"] = llama_quantization_metadata
+            super().__init__(device=device, dtype=dtype, model_options=model_options, model_type=model_type)
+    return Qwen3VLTEModel_
diff --git a/comfy/text_encoders/qwen_vl.py b/comfy/text_encoders/qwen_vl.py
index 98c350a12..924eb6ad8 100644
--- a/comfy/text_encoders/qwen_vl.py
+++ b/comfy/text_encoders/qwen_vl.py
@@ -88,6 +88,32 @@ def process_qwen2vl_images(
     return flatten_patches, image_grid_thw
 
 
+def qwen2vl_mrope_position_ids(embeds_info, seq_len, device):
+    # (3, seq_len) T/H/W MRoPE position ids: text runs sequentially, each image span gets its grid positions.
+    # Returns None when there are no image embeds. `extra` is the image grid_thw, or a dict carrying it under "grid".
+    position_ids = None
+    offset = 0
+    for e in embeds_info:
+        if e.get("type") == "image":
+            extra = e.get("extra", None)
+            grid = extra["grid"] if isinstance(extra, dict) else extra
+            start = e.get("index")
+            if position_ids is None:
+                position_ids = torch.zeros((3, seq_len), device=device)
+                position_ids[:, :start] = torch.arange(0, start, device=device)
+            end = e.get("size") + start
+            len_max = int(grid.max()) // 2
+            start_next = len_max + start
+            position_ids[:, end:] = torch.arange(start_next + offset, start_next + (seq_len - end) + offset, device=device)
+            position_ids[0, start:end] = start + offset
+            max_d = int(grid[0][1]) // 2
+            position_ids[1, start:end] = torch.arange(start + offset, start + max_d + offset, device=device).unsqueeze(1).repeat(1, math.ceil((end - start) / max_d)).flatten(0)[:end - start]
+            max_d = int(grid[0][2]) // 2
+            position_ids[2, start:end] = torch.arange(start + offset, start + max_d + offset, device=device).unsqueeze(0).repeat(math.ceil((end - start) / max_d), 1).flatten(0)[:end - start]
+            offset += len_max - (end - start)
+    return position_ids
+
+
 class VisionPatchEmbed(nn.Module):
     def __init__(
         self,
diff --git a/comfy_extras/nodes_textgen.py b/comfy_extras/nodes_textgen.py
index d52faf815..5a947d5c5 100644
--- a/comfy_extras/nodes_textgen.py
+++ b/comfy_extras/nodes_textgen.py
@@ -35,7 +35,7 @@ class TextGenerate(io.ComfyNode):
                 io.Image.Input("image", optional=True),
                 io.Image.Input("video", optional=True, tooltip="Video frames as image batch. Assumed to be 24 FPS; subsampled to 1 FPS internally."),
                 io.Audio.Input("audio", optional=True),
-                io.Int.Input("max_length", default=256, min=1, max=2048),
+                io.Int.Input("max_length", default=512, min=1, max=32768),
                 io.DynamicCombo.Input("sampling_mode", options=sampling_options, display_name="Sampling Mode"),
                 io.Boolean.Input("thinking", optional=True, default=False, tooltip="Operate in thinking mode if the model supports it."),
                 io.Boolean.Input("use_default_template", optional=True, default=True, tooltip="Use the built in system prompt/template if the model has one.", advanced=True),

From ca1622ca24bbdbbc19721b0577ffab98cf64eb4d Mon Sep 17 00:00:00 2001
From: Alexis Rolland <alexisrolland@hotmail.com>
Date: Wed, 17 Jun 2026 08:33:09 +0800
Subject: [PATCH 13/30] chore: Update nodes categories (CORE-263) (#14460)

---
 comfy_extras/nodes_ace.py                     | 12 ++--
 comfy_extras/nodes_apg.py                     |  2 +-
 comfy_extras/nodes_ar_video.py                |  4 +-
 comfy_extras/nodes_audio.py                   | 10 ++--
 comfy_extras/nodes_bernini.py                 | 23 +++-----
 comfy_extras/nodes_camera_trajectory.py       |  2 +-
 comfy_extras/nodes_chroma_radiance.py         |  4 +-
 comfy_extras/nodes_clip_sdxl.py               |  6 +-
 comfy_extras/nodes_context_windows.py         |  1 +
 comfy_extras/nodes_controlnet.py              |  3 +
 comfy_extras/nodes_cosmos.py                  |  6 +-
 comfy_extras/nodes_custom_sampler.py          |  6 +-
 comfy_extras/nodes_easycache.py               |  4 +-
 comfy_extras/nodes_edit_model.py              |  3 +-
 comfy_extras/nodes_flux.py                    | 12 ++--
 comfy_extras/nodes_hidream.py                 |  8 ++-
 comfy_extras/nodes_hidream_o1.py              |  6 +-
 comfy_extras/nodes_hunyuan.py                 | 20 ++++---
 comfy_extras/nodes_hunyuan3d.py               |  8 +--
 comfy_extras/nodes_ideogram4.py               |  2 +-
 comfy_extras/nodes_kandinsky5.py              |  7 ++-
 comfy_extras/nodes_latent.py                  |  2 +
 comfy_extras/nodes_lt.py                      | 22 +++----
 comfy_extras/nodes_lt_audio.py                | 12 ++--
 comfy_extras/nodes_lt_upsampler.py            |  2 +-
 comfy_extras/nodes_lumina2.py                 |  6 +-
 comfy_extras/nodes_mask.py                    |  1 +
 comfy_extras/nodes_mochi.py                   |  2 +-
 comfy_extras/nodes_model_advanced.py          | 19 +++---
 comfy_extras/nodes_model_merging.py           | 22 +++----
 .../nodes_model_merging_model_specific.py     | 28 ++++-----
 comfy_extras/nodes_model_patch.py             | 14 ++++-
 comfy_extras/nodes_pid.py                     |  6 +-
 comfy_extras/nodes_pixart.py                  |  3 +-
 comfy_extras/nodes_post_processing.py         |  2 +-
 comfy_extras/nodes_qwen.py                    |  4 +-
 comfy_extras/nodes_scail.py                   | 16 +++--
 comfy_extras/nodes_sd3.py                     | 10 ++--
 comfy_extras/nodes_sdupscale.py               |  2 +-
 comfy_extras/nodes_stable3d.py                |  6 +-
 comfy_extras/nodes_stable_cascade.py          |  6 +-
 comfy_extras/nodes_train.py                   |  2 +-
 comfy_extras/nodes_video_model.py             |  7 ++-
 comfy_extras/nodes_void.py                    |  6 +-
 comfy_extras/nodes_wan.py                     | 33 ++++++-----
 comfy_extras/nodes_wandancer.py               |  4 +-
 comfy_extras/nodes_wanmove.py                 | 11 ++--
 comfy_extras/nodes_zimage.py                  |  2 +-
 nodes.py                                      | 58 ++++++++++---------
 49 files changed, 240 insertions(+), 217 deletions(-)

diff --git a/comfy_extras/nodes_ace.py b/comfy_extras/nodes_ace.py
index 044077b18..eaf234d5b 100644
--- a/comfy_extras/nodes_ace.py
+++ b/comfy_extras/nodes_ace.py
@@ -11,7 +11,7 @@ class TextEncodeAceStepAudio(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="TextEncodeAceStepAudio",
-            category="model/conditioning",
+            category="model/conditioning/ace",
             inputs=[
                 IO.Clip.Input("clip"),
                 IO.String.Input("tags", multiline=True, dynamic_prompts=True),
@@ -33,7 +33,7 @@ class TextEncodeAceStepAudio15(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="TextEncodeAceStepAudio1.5",
-            category="model/conditioning",
+            category="model/conditioning/ace",
             inputs=[
                 IO.Clip.Input("clip"),
                 IO.String.Input("tags", multiline=True, dynamic_prompts=True),
@@ -67,7 +67,7 @@ class EmptyAceStepLatentAudio(IO.ComfyNode):
         return IO.Schema(
             node_id="EmptyAceStepLatentAudio",
             display_name="Empty Ace Step 1.0 Latent Audio",
-            category="model/latent/audio",
+            category="model/latent/ace",
             inputs=[
                 IO.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.1),
                 IO.Int.Input(
@@ -90,7 +90,7 @@ class EmptyAceStep15LatentAudio(IO.ComfyNode):
         return IO.Schema(
             node_id="EmptyAceStep1.5LatentAudio",
             display_name="Empty Ace Step 1.5 Latent Audio",
-            category="model/latent/audio",
+            category="model/latent/ace",
             inputs=[
                 IO.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.01),
                 IO.Int.Input(
@@ -111,8 +111,8 @@ class ReferenceAudio(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ReferenceTimbreAudio",
-            display_name="Reference Audio",
-            category="advanced/conditioning/audio",
+            display_name="Set Reference Audio",
+            category="model/conditioning",
             is_experimental=True,
             description="This node sets the reference audio for ace step 1.5",
             inputs=[
diff --git a/comfy_extras/nodes_apg.py b/comfy_extras/nodes_apg.py
index 4a352038a..6e69b73f7 100644
--- a/comfy_extras/nodes_apg.py
+++ b/comfy_extras/nodes_apg.py
@@ -16,7 +16,7 @@ class APG(io.ComfyNode):
         return io.Schema(
             node_id="APG",
             display_name="Adaptive Projected Guidance",
-            category="model/sampling/custom_sampling",
+            category="model/sampling/custom",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input(
diff --git a/comfy_extras/nodes_ar_video.py b/comfy_extras/nodes_ar_video.py
index c22359eb2..9d8f64b20 100644
--- a/comfy_extras/nodes_ar_video.py
+++ b/comfy_extras/nodes_ar_video.py
@@ -19,7 +19,7 @@ class EmptyARVideoLatent(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="EmptyARVideoLatent",
-            category="model/latent/video",
+            category="model/latent/autoregressive",
             inputs=[
                 io.Int.Input("width", default=832, min=16, max=8192, step=16),
                 io.Int.Input("height", default=480, min=16, max=8192, step=16),
@@ -85,7 +85,7 @@ class ARVideoI2V(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ARVideoI2V",
-            category="model/conditioning/video_models",
+            category="model/conditioning/autoregressive",
             inputs=[
                 io.Model.Input("model"),
                 io.Vae.Input("vae"),
diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py
index 1dc97ecd7..77f124e28 100644
--- a/comfy_extras/nodes_audio.py
+++ b/comfy_extras/nodes_audio.py
@@ -16,7 +16,7 @@ class EmptyLatentAudio(IO.ComfyNode):
         return IO.Schema(
             node_id="EmptyLatentAudio",
             display_name="Empty Latent Audio",
-            category="model/latent/audio",
+            category="model/latent",
             essentials_category="Audio",
             inputs=[
                 IO.Float.Input("seconds", default=47.6, min=1.0, max=1000.0, step=0.1),
@@ -41,7 +41,7 @@ class ConditioningStableAudio(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ConditioningStableAudio",
-            category="model/conditioning",
+            category="model/conditioning/stable audio",
             inputs=[
                 IO.Conditioning.Input("positive"),
                 IO.Conditioning.Input("negative"),
@@ -70,7 +70,7 @@ class VAEEncodeAudio(IO.ComfyNode):
             node_id="VAEEncodeAudio",
             search_aliases=["audio to latent"],
             display_name="VAE Encode Audio",
-            category="model/latent/audio",
+            category="model/latent",
             inputs=[
                 IO.Audio.Input("audio"),
                 IO.Vae.Input("vae"),
@@ -115,7 +115,7 @@ class VAEDecodeAudio(IO.ComfyNode):
             node_id="VAEDecodeAudio",
             search_aliases=["latent to audio"],
             display_name="VAE Decode Audio",
-            category="model/latent/audio",
+            category="model/latent",
             inputs=[
                 IO.Latent.Input("samples"),
                 IO.Vae.Input("vae"),
@@ -137,7 +137,7 @@ class VAEDecodeAudioTiled(IO.ComfyNode):
             node_id="VAEDecodeAudioTiled",
             search_aliases=["latent to audio"],
             display_name="VAE Decode Audio (Tiled)",
-            category="model/latent/audio",
+            category="model/latent",
             inputs=[
                 IO.Latent.Input("samples"),
                 IO.Vae.Input("vae"),
diff --git a/comfy_extras/nodes_bernini.py b/comfy_extras/nodes_bernini.py
index 227fa5753..0537e0806 100644
--- a/comfy_extras/nodes_bernini.py
+++ b/comfy_extras/nodes_bernini.py
@@ -39,9 +39,9 @@ class BerniniConditioning(io.ComfyNode):
         return io.Schema(
             node_id="BerniniConditioning",
             display_name="Bernini Conditioning",
-            category="conditioning/video_models",
+            category="model/conditioning/bernini",
             description="Conditioning node for Bernini in-context video/image conditioning. It can be used for the following tasks: t2v (text-to-video), v2v (video-to-video), rv2v (reference-guided video editing), r2v (reference-to-video), ads2v (insert image/video into video)."
-                         "Reference images injected as in-context tokens (r2v, rv2v) are encoded independently at their own native aspect ratio (long edge capped at ref_max_size)",
+                "Reference images injected as in-context tokens (r2v, rv2v) are encoded independently at their own native aspect ratio (long edge capped at ref_max_size)",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -50,14 +50,11 @@ class BerniniConditioning(io.ComfyNode):
                 io.Int.Input("height", default=480, min=16, max=8192, step=16),
                 io.Int.Input("length", default=81, min=1, max=8192, step=4),
                 io.Int.Input("batch_size", default=1, min=1, max=4096),
-                io.Image.Input("source_video", optional=True, tooltip=(
-                    "Source video to edit or restyle (v2v, rv2v). Resized to width/height and trimmed to length.")),
-                io.Image.Input("reference_video", optional=True, tooltip=(
-                    "Video to insert into the source video (ads2v).")),
+                io.Image.Input("source_video", optional=True, tooltip=("Source video to edit or restyle (v2v, rv2v). Resized to width/height and trimmed to length.")),
+                io.Image.Input("reference_video", optional=True, tooltip=("Video to insert into the source video (ads2v).")),
                 io.Autogrow.Input("reference_images", optional=True,
                     template=io.Autogrow.TemplatePrefix(
-                        input=io.Image.Input("reference_image", tooltip=(
-                            "Reference image injected as an in-context token (r2v, rv2v).")),
+                        input=io.Image.Input("reference_image", tooltip=("Reference image injected as an in-context token (r2v, rv2v).")),
                         prefix="reference_image_", min=0, max=8)),
                 io.Int.Input("ref_max_size", default=848, min=16, max=8192, step=16, optional=True, tooltip=(
                     "Max size for the long edge of reference_video and reference_images. Resized with preserved aspect ratio and snapped to 16px.")),
@@ -70,10 +67,8 @@ class BerniniConditioning(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, positive, negative, vae, width, height, length, batch_size,
-                source_video=None, reference_video=None, reference_images=None, ref_max_size=848) -> io.NodeOutput:
-        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8],
-                             device=comfy.model_management.intermediate_device())
+    def execute(cls, positive, negative, vae, width, height, length, batch_size, source_video=None, reference_video=None, reference_images=None, ref_max_size=848) -> io.NodeOutput:
+        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
 
         # source_video (1), reference_video (2), reference_images (3, 4, ...).
         context = []
@@ -106,9 +101,7 @@ class BerniniConditioning(io.ComfyNode):
 class BerniniExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
-        return [
-            BerniniConditioning,
-        ]
+        return [BerniniConditioning,]
 
 
 async def comfy_entrypoint() -> BerniniExtension:
diff --git a/comfy_extras/nodes_camera_trajectory.py b/comfy_extras/nodes_camera_trajectory.py
index 13a1448f4..280d136af 100644
--- a/comfy_extras/nodes_camera_trajectory.py
+++ b/comfy_extras/nodes_camera_trajectory.py
@@ -153,7 +153,7 @@ class WanCameraEmbedding(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanCameraEmbedding",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/camera",
             inputs=[
                 io.Combo.Input(
                     "camera_pose",
diff --git a/comfy_extras/nodes_chroma_radiance.py b/comfy_extras/nodes_chroma_radiance.py
index a4f673001..059344f3c 100644
--- a/comfy_extras/nodes_chroma_radiance.py
+++ b/comfy_extras/nodes_chroma_radiance.py
@@ -13,7 +13,7 @@ class EmptyChromaRadianceLatentImage(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="EmptyChromaRadianceLatentImage",
-            category="model/latent/chroma_radiance",
+            category="model/latent/chroma radiance",
             inputs=[
                 io.Int.Input(id="width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input(id="height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
@@ -33,7 +33,7 @@ class ChromaRadianceOptions(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="ChromaRadianceOptions",
-            category="model/patch/chroma_radiance",
+            category="model/patch/chroma radiance",
             description="Allows setting advanced options for the Chroma Radiance model.",
             inputs=[
                 io.Model.Input(id="model"),
diff --git a/comfy_extras/nodes_clip_sdxl.py b/comfy_extras/nodes_clip_sdxl.py
index 7a001af6f..08fbbd827 100644
--- a/comfy_extras/nodes_clip_sdxl.py
+++ b/comfy_extras/nodes_clip_sdxl.py
@@ -9,7 +9,8 @@ class CLIPTextEncodeSDXLRefiner(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CLIPTextEncodeSDXLRefiner",
-            category="advanced/conditioning",
+            display_name="CLIP Text Encode (SDXL Refiner)",
+            category="model/conditioning/stable diffusion",
             inputs=[
                 io.Float.Input("ascore", default=6.0, min=0.0, max=1000.0, step=0.01),
                 io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
@@ -30,7 +31,8 @@ class CLIPTextEncodeSDXL(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CLIPTextEncodeSDXL",
-            category="advanced/conditioning",
+            display_name="CLIP Text Encode (SDXL)",
+            category="model/conditioning/stable diffusion",
             inputs=[
                 io.Clip.Input("clip"),
                 io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
diff --git a/comfy_extras/nodes_context_windows.py b/comfy_extras/nodes_context_windows.py
index d9e32b9d9..098c26f23 100644
--- a/comfy_extras/nodes_context_windows.py
+++ b/comfy_extras/nodes_context_windows.py
@@ -66,6 +66,7 @@ class WanContextWindowsManualNode(ContextWindowsManualNode):
         schema.node_id = "WanContextWindowsManual"
         schema.display_name = "WAN Context Windows (Manual)"
         schema.description = "Manually set context windows for WAN-like models (dim=2)."
+        schema.category="model/patch/wan"
         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.", advanced=True),
diff --git a/comfy_extras/nodes_controlnet.py b/comfy_extras/nodes_controlnet.py
index 17d965405..eb476f497 100644
--- a/comfy_extras/nodes_controlnet.py
+++ b/comfy_extras/nodes_controlnet.py
@@ -9,6 +9,8 @@ class SetUnionControlNetType(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SetUnionControlNetType",
+            search_aliases=["set controlnet type", "union controlnet type"],
+            display_name="Set Union ControlNet Type",
             category="model/conditioning/controlnet",
             inputs=[
                 io.ControlNet.Input("control_net"),
@@ -39,6 +41,7 @@ class ControlNetInpaintingAliMamaApply(io.ComfyNode):
         return io.Schema(
             node_id="ControlNetInpaintingAliMamaApply",
             search_aliases=["masked controlnet"],
+            display_name="Apply ControlNet Inpainting (AliMama)",
             category="model/conditioning/controlnet",
             inputs=[
                 io.Conditioning.Input("positive"),
diff --git a/comfy_extras/nodes_cosmos.py b/comfy_extras/nodes_cosmos.py
index d754ab442..93cc67a6c 100644
--- a/comfy_extras/nodes_cosmos.py
+++ b/comfy_extras/nodes_cosmos.py
@@ -13,7 +13,7 @@ class EmptyCosmosLatentVideo(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="EmptyCosmosLatentVideo",
-            category="model/latent/video",
+            category="model/latent/cosmos",
             inputs=[
                 io.Int.Input("width", default=1280, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("height", default=704, min=16, max=nodes.MAX_RESOLUTION, step=16),
@@ -45,7 +45,7 @@ class CosmosImageToVideoLatent(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="CosmosImageToVideoLatent",
-            category="model/conditioning/inpaint",
+            category="model/conditioning/cosmos",
             inputs=[
                 io.Vae.Input("vae"),
                 io.Int.Input("width", default=1280, min=16, max=nodes.MAX_RESOLUTION, step=16),
@@ -88,7 +88,7 @@ class CosmosPredict2ImageToVideoLatent(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="CosmosPredict2ImageToVideoLatent",
-            category="model/conditioning/inpaint",
+            category="model/conditioning/cosmos",
             inputs=[
                 io.Vae.Input("vae"),
                 io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py
index 3e97084a4..c9d7e06fc 100644
--- a/comfy_extras/nodes_custom_sampler.py
+++ b/comfy_extras/nodes_custom_sampler.py
@@ -729,7 +729,7 @@ class SamplerCustom(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SamplerCustom",
-            category="model/sampling/custom_sampling",
+            category="model/sampling/custom",
             inputs=[
                 io.Model.Input("model"),
                 io.Boolean.Input("add_noise", default=True, advanced=True),
@@ -1015,7 +1015,7 @@ class SamplerCustomAdvanced(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SamplerCustomAdvanced",
-            category="model/sampling/custom_sampling",
+            category="model/sampling/custom",
             inputs=[
                 io.Noise.Input("noise"),
                 io.Guider.Input("guider"),
@@ -1143,7 +1143,7 @@ class CFGOverride(io.ComfyNode):
             display_name="CFG Override",
             description="Override cfg to a fixed value over a [start, end] percent (sigma) range. "
                         "With multiple overrides, the one nearest the sampler wins on overlap.",
-            category="sampling/custom_sampling",
+            category="model/sampling/guiders",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("cfg", default=1.0, min=0.0, max=100.0, step=0.1, round=0.01),
diff --git a/comfy_extras/nodes_easycache.py b/comfy_extras/nodes_easycache.py
index 923c2bb05..9e907d371 100644
--- a/comfy_extras/nodes_easycache.py
+++ b/comfy_extras/nodes_easycache.py
@@ -363,7 +363,7 @@ class EasyCacheNode(io.ComfyNode):
             node_id="EasyCache",
             display_name="EasyCache",
             description="Native EasyCache implementation.",
-            category="advanced/debug/model",
+            category="advanced/debug",
             is_experimental=True,
             inputs=[
                 io.Model.Input("model", tooltip="The model to add EasyCache to."),
@@ -496,7 +496,7 @@ class LazyCacheNode(io.ComfyNode):
             node_id="LazyCache",
             display_name="LazyCache",
             description="A homebrew version of EasyCache - even 'easier' version of EasyCache to implement. Overall works worse than EasyCache, but better in some rare cases AND universal compatibility with everything in ComfyUI.",
-            category="advanced/debug/model",
+            category="advanced/debug",
             is_experimental=True,
             inputs=[
                 io.Model.Input("model", tooltip="The model to add LazyCache to."),
diff --git a/comfy_extras/nodes_edit_model.py b/comfy_extras/nodes_edit_model.py
index 36da66f34..d0d20ae7a 100644
--- a/comfy_extras/nodes_edit_model.py
+++ b/comfy_extras/nodes_edit_model.py
@@ -8,7 +8,8 @@ class ReferenceLatent(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ReferenceLatent",
-            category="advanced/conditioning/edit_models",
+            display_name="Set Reference Latent",
+            category="model/conditioning",
             description="This node sets the guiding latent for an edit model. If the model supports it you can chain multiple to set multiple reference images.",
             inputs=[
                 io.Conditioning.Input("conditioning"),
diff --git a/comfy_extras/nodes_flux.py b/comfy_extras/nodes_flux.py
index ef1757ae5..e9986c9e7 100644
--- a/comfy_extras/nodes_flux.py
+++ b/comfy_extras/nodes_flux.py
@@ -13,7 +13,7 @@ class CLIPTextEncodeFlux(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CLIPTextEncodeFlux",
-            category="advanced/conditioning/flux",
+            category="model/conditioning/flux",
             inputs=[
                 io.Clip.Input("clip"),
                 io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
@@ -40,7 +40,7 @@ class EmptyFlux2LatentImage(io.ComfyNode):
         return io.Schema(
             node_id="EmptyFlux2LatentImage",
             display_name="Empty Flux 2 Latent",
-            category="model/latent",
+            category="model/latent/flux",
             inputs=[
                 io.Int.Input("width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
@@ -61,7 +61,7 @@ class FluxGuidance(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="FluxGuidance",
-            category="advanced/conditioning/flux",
+            category="model/conditioning/flux",
             inputs=[
                 io.Conditioning.Input("conditioning"),
                 io.Float.Input("guidance", default=3.5, min=0.0, max=100.0, step=0.1),
@@ -84,7 +84,7 @@ class FluxDisableGuidance(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="FluxDisableGuidance",
-            category="advanced/conditioning/flux",
+            category="model/conditioning/flux",
             description="This node completely disables the guidance embed on Flux and Flux like models",
             inputs=[
                 io.Conditioning.Input("conditioning"),
@@ -128,7 +128,7 @@ class FluxKontextImageScale(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="FluxKontextImageScale",
-            category="advanced/conditioning/flux",
+            category="model/conditioning/flux",
             description="This node resizes the image to one that is more optimal for flux kontext.",
             inputs=[
                 io.Image.Input("image"),
@@ -156,7 +156,7 @@ class FluxKontextMultiReferenceLatentMethod(io.ComfyNode):
         return io.Schema(
             node_id="FluxKontextMultiReferenceLatentMethod",
             display_name="Edit Model Reference Method",
-            category="advanced/conditioning/flux",
+            category="model/conditioning/flux",
             inputs=[
                 io.Conditioning.Input("conditioning"),
                 io.Combo.Input(
diff --git a/comfy_extras/nodes_hidream.py b/comfy_extras/nodes_hidream.py
index e345fe51d..65248561b 100644
--- a/comfy_extras/nodes_hidream.py
+++ b/comfy_extras/nodes_hidream.py
@@ -11,8 +11,9 @@ class QuadrupleCLIPLoader(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="QuadrupleCLIPLoader",
-            category="advanced/loaders",
-            description="[Recipes]\n\nhidream: long clip-l, long clip-g, t5xxl, llama_8b_3.1_instruct",
+            display_name="Load CLIP (Quadruple)",
+            category="model/loaders",
+            description="Recipes:\nhidream: long clip-l, long clip-g, t5xxl, llama_8b_3.1_instruct",
             inputs=[
                 io.Combo.Input("clip_name1", options=folder_paths.get_filename_list("text_encoders")),
                 io.Combo.Input("clip_name2", options=folder_paths.get_filename_list("text_encoders")),
@@ -38,8 +39,9 @@ class CLIPTextEncodeHiDream(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CLIPTextEncodeHiDream",
+            display_name="CLIP Text Encode (HiDream)",
             search_aliases=["hidream prompt"],
-            category="advanced/conditioning",
+            category="model/conditioning/hidream",
             inputs=[
                 io.Clip.Input("clip"),
                 io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
diff --git a/comfy_extras/nodes_hidream_o1.py b/comfy_extras/nodes_hidream_o1.py
index 8648d2e26..85693fce6 100644
--- a/comfy_extras/nodes_hidream_o1.py
+++ b/comfy_extras/nodes_hidream_o1.py
@@ -14,7 +14,7 @@ class EmptyHiDreamO1LatentImage(io.ComfyNode):
         return io.Schema(
             node_id="EmptyHiDreamO1LatentImage",
             display_name="Empty HiDream-O1 Latent Image",
-            category="model/latent/image",
+            category="model/latent/hidream",
             description=(
                 "Empty pixel-space latent for HiDream-O1-Image. The model was "
                 "trained at ~4 megapixels; lower resolutions go off-distribution "
@@ -47,7 +47,7 @@ class HiDreamO1ReferenceImages(io.ComfyNode):
         return io.Schema(
             node_id="HiDreamO1ReferenceImages",
             display_name="HiDream-O1 Reference Images",
-            category="model/conditioning/image",
+            category="model/conditioning/hidream",
             description=(
                 "Attach 1-10 reference images to conditioning, one for edit instruction"
                 "or multiple for subject-driven personalization."
@@ -117,7 +117,7 @@ class HiDreamO1PatchSeamSmoothing(io.ComfyNode):
         return io.Schema(
             node_id="HiDreamO1PatchSeamSmoothing",
             display_name="HiDream-O1 Patch Seam Smoothing",
-            category="advanced/model",
+            category="model/patch/hidream",
             is_experimental=True,
             description=(
                 "Average the model output across multiple shifted patch-grid "
diff --git a/comfy_extras/nodes_hunyuan.py b/comfy_extras/nodes_hunyuan.py
index 16fff12af..8df2c8908 100644
--- a/comfy_extras/nodes_hunyuan.py
+++ b/comfy_extras/nodes_hunyuan.py
@@ -14,7 +14,8 @@ class CLIPTextEncodeHunyuanDiT(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CLIPTextEncodeHunyuanDiT",
-            category="advanced/conditioning",
+            display_name="CLIP Text Encode (Hunyuan Image)",
+            category="model/conditioning/hunyuan image",
             inputs=[
                 io.Clip.Input("clip"),
                 io.String.Input("bert", multiline=True, dynamic_prompts=True),
@@ -41,7 +42,7 @@ class EmptyHunyuanLatentVideo(io.ComfyNode):
         return io.Schema(
             node_id="EmptyHunyuanLatentVideo",
             display_name="Empty HunyuanVideo 1.0 Latent",
-            category="model/latent/video",
+            category="model/latent/hunyuan video",
             inputs=[
                 io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
@@ -67,6 +68,7 @@ class EmptyHunyuanVideo15Latent(EmptyHunyuanLatentVideo):
         schema = super().define_schema()
         schema.node_id = "EmptyHunyuanVideo15Latent"
         schema.display_name = "Empty HunyuanVideo 1.5 Latent"
+        schema.category = "model/latent/hunyuan video"
         return schema
 
     @classmethod
@@ -81,7 +83,7 @@ class HunyuanVideo15ImageToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="HunyuanVideo15ImageToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/hunyuan video",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -132,7 +134,7 @@ class HunyuanVideo15SuperResolution(io.ComfyNode):
         return io.Schema(
             node_id="HunyuanVideo15SuperResolution",
             display_name="Hunyuan Video 1.5 Super Resolution",
-            category="model/conditioning/video_models",
+            category="model/conditioning/hunyuan video",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -227,7 +229,7 @@ class HunyuanVideo15LatentUpscaleWithModel(io.ComfyNode):
         return io.Schema(
             node_id="HunyuanVideo15LatentUpscaleWithModel",
             display_name="Hunyuan Video 15 Latent Upscale With Model",
-            category="model/latent",
+            category="model/latent/hunyhuan video",
             inputs=[
                 io.LatentUpscaleModel.Input("model"),
                 io.Latent.Input("samples"),
@@ -276,7 +278,7 @@ class TextEncodeHunyuanVideo_ImageToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="TextEncodeHunyuanVideo_ImageToVideo",
-            category="advanced/conditioning",
+            category="model/conditioning/hunyuan video",
             inputs=[
                 io.Clip.Input("clip"),
                 io.ClipVisionOutput.Input("clip_vision_output"),
@@ -308,7 +310,7 @@ class HunyuanImageToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="HunyuanImageToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/hunyuan video",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Vae.Input("vae"),
@@ -359,7 +361,7 @@ class EmptyHunyuanImageLatent(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="EmptyHunyuanImageLatent",
-            category="model/latent",
+            category="model/latent/hunyuan image",
             inputs=[
                 io.Int.Input("width", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32),
                 io.Int.Input("height", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32),
@@ -384,7 +386,7 @@ class HunyuanRefinerLatent(io.ComfyNode):
         return io.Schema(
             node_id="HunyuanRefinerLatent",
             display_name="Hunyuan Latent Refiner",
-            category="model/conditioning/video_models",
+            category="model/conditioning/hunyuan video",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
diff --git a/comfy_extras/nodes_hunyuan3d.py b/comfy_extras/nodes_hunyuan3d.py
index 60e530626..c5fa946cc 100644
--- a/comfy_extras/nodes_hunyuan3d.py
+++ b/comfy_extras/nodes_hunyuan3d.py
@@ -12,7 +12,7 @@ class EmptyLatentHunyuan3Dv2(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="EmptyLatentHunyuan3Dv2",
-            category="model/latent/3d",
+            category="model/latent/hunyuan 3d",
             inputs=[
                 IO.Int.Input("resolution", default=3072, min=1, max=8192),
                 IO.Int.Input("batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."),
@@ -35,7 +35,7 @@ class Hunyuan3Dv2Conditioning(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="Hunyuan3Dv2Conditioning",
-            category="model/conditioning/3d_models",
+            category="model/conditioning/hunyuan 3d",
             inputs=[
                 IO.ClipVisionOutput.Input("clip_vision_output"),
             ],
@@ -60,7 +60,7 @@ class Hunyuan3Dv2ConditioningMultiView(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="Hunyuan3Dv2ConditioningMultiView",
-            category="model/conditioning/3d_models",
+            category="model/conditioning/hunyuan 3d",
             inputs=[
                 IO.ClipVisionOutput.Input("front", optional=True),
                 IO.ClipVisionOutput.Input("left", optional=True),
@@ -97,7 +97,7 @@ class VAEDecodeHunyuan3D(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="VAEDecodeHunyuan3D",
-            category="model/latent/3d",
+            category="model/latent/hunyuan 3d",
             inputs=[
                 IO.Latent.Input("samples"),
                 IO.Vae.Input("vae"),
diff --git a/comfy_extras/nodes_ideogram4.py b/comfy_extras/nodes_ideogram4.py
index d5827db4f..4070db17c 100644
--- a/comfy_extras/nodes_ideogram4.py
+++ b/comfy_extras/nodes_ideogram4.py
@@ -38,7 +38,7 @@ class Ideogram4Scheduler(io.ComfyNode):
         return io.Schema(
             node_id="Ideogram4Scheduler",
             display_name="Ideogram 4 Scheduler",
-            category="sampling/custom_sampling/schedulers",
+            category="model/sampling/schedulers",
             inputs=[
                 io.Int.Input("steps", default=20, min=1, max=200),
                 io.Int.Input("width", default=1024, min=256, max=8192, step=16),
diff --git a/comfy_extras/nodes_kandinsky5.py b/comfy_extras/nodes_kandinsky5.py
index 015965498..96cca0386 100644
--- a/comfy_extras/nodes_kandinsky5.py
+++ b/comfy_extras/nodes_kandinsky5.py
@@ -13,7 +13,7 @@ class Kandinsky5ImageToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="Kandinsky5ImageToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/kandinsky",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -71,7 +71,7 @@ class NormalizeVideoLatentStart(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="NormalizeVideoLatentStart",
-            category="model/conditioning/video_models",
+            category="model/conditioning",
             description="Normalizes the initial frames of a video latent to match the mean and standard deviation of subsequent reference frames. Helps reduce differences between the starting frames and the rest of the video.",
             inputs=[
                 io.Latent.Input("latent"),
@@ -104,8 +104,9 @@ class CLIPTextEncodeKandinsky5(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CLIPTextEncodeKandinsky5",
+            display_name="CLIP Text Encode (Kandinsky 5)",
             search_aliases=["kandinsky prompt"],
-            category="advanced/conditioning/kandinsky5",
+            category="model/conditioning/kandinsky",
             inputs=[
                 io.Clip.Input("clip"),
                 io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
diff --git a/comfy_extras/nodes_latent.py b/comfy_extras/nodes_latent.py
index 32da9e8ac..1f93e34d6 100644
--- a/comfy_extras/nodes_latent.py
+++ b/comfy_extras/nodes_latent.py
@@ -262,6 +262,7 @@ class LatentBatch(io.ComfyNode):
         return io.Schema(
             node_id="LatentBatch",
             search_aliases=["combine latents", "merge latents", "join latents"],
+            display_name="Batch Latents (DEPRECATED)",
             category="model/latent/batch",
             is_deprecated=True,
             inputs=[
@@ -447,6 +448,7 @@ class ReplaceVideoLatentFrames(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ReplaceVideoLatentFrames",
+            display_name="Replace Video Latent Frames",
             category="model/latent/batch",
             inputs=[
                 io.Latent.Input("destination", tooltip="The destination latent where frames will be replaced."),
diff --git a/comfy_extras/nodes_lt.py b/comfy_extras/nodes_lt.py
index 6d6078abe..85d76ecef 100644
--- a/comfy_extras/nodes_lt.py
+++ b/comfy_extras/nodes_lt.py
@@ -25,7 +25,7 @@ class GetICLoRAParameters(io.ComfyNode):
             display_name="Get IC-LoRA Parameters",
             description="Extracts IC-LoRA parameters from the safetensors metadata of a LoRA-loaded "
                         "model and outputs them for LTXVAddGuide (eg. reference_downscale_factor).",
-            category="model/conditioning/video_models",
+            category="model/conditioning/ltxv",
             search_aliases=["ic-lora", "ic lora", "iclora", "downscale factor", "reference downscale"],
             inputs=[
                 io.Model.Input(
@@ -62,7 +62,7 @@ class EmptyLTXVLatentVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="EmptyLTXVLatentVideo",
-            category="model/latent/video/ltxv",
+            category="model/latent/ltxv",
             inputs=[
                 io.Int.Input("width", default=768, min=64, max=nodes.MAX_RESOLUTION, step=32),
                 io.Int.Input("height", default=512, min=64, max=nodes.MAX_RESOLUTION, step=32),
@@ -86,7 +86,7 @@ class LTXVImgToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LTXVImgToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/ltxv",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -131,7 +131,7 @@ class LTXVImgToVideoInplace(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LTXVImgToVideoInplace",
-            category="model/conditioning/video_models",
+            category="model/conditioning/ltxv",
             inputs=[
                 io.Vae.Input("vae"),
                 io.Image.Input("image"),
@@ -251,7 +251,7 @@ class LTXVAddGuide(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LTXVAddGuide",
-            category="model/conditioning/video_models",
+            category="model/conditioning/ltxv",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -498,7 +498,7 @@ class LTXVCropGuides(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LTXVCropGuides",
-            category="model/conditioning/video_models",
+            category="model/conditioning/ltxv",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -542,7 +542,7 @@ class LTXVConditioning(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LTXVConditioning",
-            category="model/conditioning/video_models",
+            category="model/conditioning/ltxv",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -566,7 +566,7 @@ class ModelSamplingLTXV(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ModelSamplingLTXV",
-            category="advanced/model",
+            category="model/patch/ltxv",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("max_shift", default=2.05, min=0.0, max=100.0, step=0.01),
@@ -746,7 +746,7 @@ class LTXVConcatAVLatent(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LTXVConcatAVLatent",
-            category="model/latent/video/ltxv",
+            category="model/latent/ltxv",
             inputs=[
                 io.Latent.Input("video_latent"),
                 io.Latent.Input("audio_latent"),
@@ -781,7 +781,7 @@ class LTXVSeparateAVLatent(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LTXVSeparateAVLatent",
-            category="model/latent/video/ltxv",
+            category="model/latent/ltxv",
             description="LTXV Separate AV Latent",
             inputs=[
                 io.Latent.Input("av_latent"),
@@ -814,7 +814,7 @@ class LTXVReferenceAudio(io.ComfyNode):
         return io.Schema(
             node_id="LTXVReferenceAudio",
             display_name="LTXV Reference Audio (ID-LoRA)",
-            category="model/conditioning/audio",
+            category="model/conditioning/ltxv",
             description="Set reference audio for ID-LoRA speaker identity transfer. Encodes a reference audio clip into the conditioning and optionally patches the model with identity guidance (extra forward pass without reference, amplifying the speaker identity effect).",
             inputs=[
                 io.Model.Input("model"),
diff --git a/comfy_extras/nodes_lt_audio.py b/comfy_extras/nodes_lt_audio.py
index 052186083..2d774a0a3 100644
--- a/comfy_extras/nodes_lt_audio.py
+++ b/comfy_extras/nodes_lt_audio.py
@@ -40,7 +40,7 @@ class LTXVAudioVAEEncode(VAEEncodeAudio):
         return io.Schema(
             node_id="LTXVAudioVAEEncode",
             display_name="LTXV Audio VAE Encode",
-            category="model/latent/audio",
+            category="model/latent/ltxv",
             inputs=[
                 io.Audio.Input("audio", tooltip="The audio to be encoded."),
                 io.Vae.Input(
@@ -63,7 +63,7 @@ class LTXVAudioVAEDecode(io.ComfyNode):
         return io.Schema(
             node_id="LTXVAudioVAEDecode",
             display_name="LTXV Audio VAE Decode",
-            category="model/latent/audio",
+            category="model/latent/ltxv",
             inputs=[
                 io.Latent.Input("samples", tooltip="The latent to be decoded."),
                 io.Vae.Input(
@@ -96,7 +96,7 @@ class LTXVEmptyLatentAudio(io.ComfyNode):
         return io.Schema(
             node_id="LTXVEmptyLatentAudio",
             display_name="LTXV Empty Latent Audio",
-            category="model/latent/audio",
+            category="model/latent/ltxv",
             inputs=[
                 io.Int.Input(
                     "frames_number",
@@ -168,9 +168,9 @@ class LTXAVTextEncoderLoader(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="LTXAVTextEncoderLoader",
-            display_name="LTXV Audio Text Encoder Loader",
-            category="advanced/loaders",
-            description="[Recipes]\n\nltxav: gemma 3 12B",
+            display_name="Load LTXV Audio Text Encoder",
+            category="model/loaders",
+            description="Recipes:\nltxav: gemma 3 12B",
             inputs=[
                 io.Combo.Input(
                     "text_encoder",
diff --git a/comfy_extras/nodes_lt_upsampler.py b/comfy_extras/nodes_lt_upsampler.py
index be9a36e69..ef36109d1 100644
--- a/comfy_extras/nodes_lt_upsampler.py
+++ b/comfy_extras/nodes_lt_upsampler.py
@@ -13,7 +13,7 @@ class LTXVLatentUpsampler(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="LTXVLatentUpsampler",
-            category="model/latent/video",
+            category="model/latent/ltxv",
             is_experimental=True,
             inputs=[
                 IO.Latent.Input("samples"),
diff --git a/comfy_extras/nodes_lumina2.py b/comfy_extras/nodes_lumina2.py
index c060a86a0..bc543c242 100644
--- a/comfy_extras/nodes_lumina2.py
+++ b/comfy_extras/nodes_lumina2.py
@@ -9,7 +9,7 @@ class RenormCFG(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="RenormCFG",
-            category="advanced/model",
+            category="model/patch",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("cfg_trunc", default=100, min=0.0, max=100.0, step=0.01, advanced=True),
@@ -80,8 +80,8 @@ class CLIPTextEncodeLumina2(io.ComfyNode):
         return io.Schema(
             node_id="CLIPTextEncodeLumina2",
             search_aliases=["lumina prompt"],
-            display_name="CLIP Text Encode for Lumina2",
-            category="model/conditioning",
+            display_name="CLIP Text Encode (Lumina 2)",
+            category="model/conditioning/lumina",
             description="Encodes a system prompt and a user prompt using a CLIP model into an embedding "
                         "that can be used to guide the diffusion model towards generating specific images.",
             inputs=[
diff --git a/comfy_extras/nodes_mask.py b/comfy_extras/nodes_mask.py
index 52484697a..76af338de 100644
--- a/comfy_extras/nodes_mask.py
+++ b/comfy_extras/nodes_mask.py
@@ -53,6 +53,7 @@ class LatentCompositeMasked(IO.ComfyNode):
         return IO.Schema(
             node_id="LatentCompositeMasked",
             search_aliases=["overlay latent", "layer latent", "paste latent", "inpaint latent"],
+            display_name="Latent Composite Masked",
             category="model/latent",
             inputs=[
                 IO.Latent.Input("destination"),
diff --git a/comfy_extras/nodes_mochi.py b/comfy_extras/nodes_mochi.py
index 3dcea6ab3..3aaf23e69 100644
--- a/comfy_extras/nodes_mochi.py
+++ b/comfy_extras/nodes_mochi.py
@@ -10,7 +10,7 @@ class EmptyMochiLatentVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="EmptyMochiLatentVideo",
-            category="model/latent/video",
+            category="model/latent/mochi",
             inputs=[
                 io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py
index b27ac1296..a336ba079 100644
--- a/comfy_extras/nodes_model_advanced.py
+++ b/comfy_extras/nodes_model_advanced.py
@@ -59,7 +59,7 @@ class ModelSamplingDiscrete:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "advanced/model"
+    CATEGORY = "model/patch"
 
     def patch(self, model, sampling, zsnr):
         m = model.clone()
@@ -97,7 +97,7 @@ class ModelSamplingStableCascade:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "advanced/model"
+    CATEGORY = "model/patch/stable cascade"
 
     def patch(self, model, shift):
         m = model.clone()
@@ -123,7 +123,7 @@ class ModelSamplingSD3:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "advanced/model"
+    CATEGORY = "model/patch/stable diffusion"
 
     def patch(self, model, shift, multiplier=1000):
         m = model.clone()
@@ -150,6 +150,7 @@ class ModelSamplingAuraFlow(ModelSamplingSD3):
                               }}
 
     FUNCTION = "patch_aura"
+    CATEGORY = "model/patch"
 
     def patch_aura(self, model, shift):
         return self.patch(model, shift, multiplier=1.0)
@@ -167,7 +168,7 @@ class ModelSamplingFlux:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "advanced/model"
+    CATEGORY = "model/patch/flux"
 
     def patch(self, model, max_shift, base_shift, width, height):
         m = model.clone()
@@ -202,7 +203,7 @@ class ModelSamplingContinuousEDM:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "advanced/model"
+    CATEGORY = "model/patch"
 
     def patch(self, model, sampling, sigma_max, sigma_min):
         m = model.clone()
@@ -247,7 +248,7 @@ class ModelSamplingContinuousV:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "advanced/model"
+    CATEGORY = "model/patch"
 
     def patch(self, model, sampling, sigma_max, sigma_min):
         m = model.clone()
@@ -273,7 +274,7 @@ class RescaleCFG:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "advanced/model"
+    CATEGORY = "model/patch"
 
     def patch(self, model, multiplier):
         def rescale_cfg(args):
@@ -314,7 +315,7 @@ class ModelNoiseScale:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "advanced/model"
+    CATEGORY = "model/patch"
 
     def patch(self, model, noise_scale):
         m = model.clone()
@@ -337,7 +338,7 @@ class ModelComputeDtype:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "advanced/debug/model"
+    CATEGORY = "advanced/debug"
 
     def patch(self, model, dtype):
         m = model.clone()
diff --git a/comfy_extras/nodes_model_merging.py b/comfy_extras/nodes_model_merging.py
index b6b29e34a..962d2a0bb 100644
--- a/comfy_extras/nodes_model_merging.py
+++ b/comfy_extras/nodes_model_merging.py
@@ -21,7 +21,7 @@ class ModelMergeSimple:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "merge"
 
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     def merge(self, model1, model2, ratio):
         m = model1.clone()
@@ -40,7 +40,7 @@ class ModelSubtract:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "merge"
 
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     def merge(self, model1, model2, multiplier):
         m = model1.clone()
@@ -58,7 +58,7 @@ class ModelAdd:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "merge"
 
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     def merge(self, model1, model2):
         m = model1.clone()
@@ -78,7 +78,7 @@ class CLIPMergeSimple:
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "merge"
 
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     def merge(self, clip1, clip2, ratio):
         m = clip1.clone()
@@ -101,7 +101,7 @@ class CLIPSubtract:
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "merge"
 
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     def merge(self, clip1, clip2, multiplier):
         m = clip1.clone()
@@ -123,7 +123,7 @@ class CLIPAdd:
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "merge"
 
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     def merge(self, clip1, clip2):
         m = clip1.clone()
@@ -147,7 +147,7 @@ class ModelMergeBlocks:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "merge"
 
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     def merge(self, model1, model2, **kwargs):
         m = model1.clone()
@@ -242,7 +242,7 @@ class CheckpointSave:
     FUNCTION = "save"
     OUTPUT_NODE = True
 
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None):
         save_checkpoint(model, clip=clip, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo)
@@ -261,7 +261,7 @@ class CLIPSave:
     FUNCTION = "save"
     OUTPUT_NODE = True
 
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     def save(self, clip, filename_prefix, prompt=None, extra_pnginfo=None):
         prompt_info = ""
@@ -318,7 +318,7 @@ class VAESave:
     FUNCTION = "save"
     OUTPUT_NODE = True
 
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     def save(self, vae, filename_prefix, prompt=None, extra_pnginfo=None):
         full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
@@ -353,7 +353,7 @@ class ModelSave:
     FUNCTION = "save"
     OUTPUT_NODE = True
 
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     def save(self, model, filename_prefix, prompt=None, extra_pnginfo=None):
         save_checkpoint(model, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo)
diff --git a/comfy_extras/nodes_model_merging_model_specific.py b/comfy_extras/nodes_model_merging_model_specific.py
index 55eb3ccfe..2fa684b3a 100644
--- a/comfy_extras/nodes_model_merging_model_specific.py
+++ b/comfy_extras/nodes_model_merging_model_specific.py
@@ -1,7 +1,7 @@
 import comfy_extras.nodes_model_merging
 
 class ModelMergeSD1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
     @classmethod
     def INPUT_TYPES(s):
         arg_dict = { "model1": ("MODEL",),
@@ -27,7 +27,7 @@ class ModelMergeSD1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
 
 
 class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -53,7 +53,7 @@ class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks):
         return {"required": arg_dict}
 
 class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -77,7 +77,7 @@ class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
 
 
 class ModelMergeAuraflow(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -104,7 +104,7 @@ class ModelMergeAuraflow(comfy_extras.nodes_model_merging.ModelMergeBlocks):
         return {"required": arg_dict}
 
 class ModelMergeFlux1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -130,7 +130,7 @@ class ModelMergeFlux1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
         return {"required": arg_dict}
 
 class ModelMergeSD35_Large(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -153,7 +153,7 @@ class ModelMergeSD35_Large(comfy_extras.nodes_model_merging.ModelMergeBlocks):
         return {"required": arg_dict}
 
 class ModelMergeMochiPreview(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -175,7 +175,7 @@ class ModelMergeMochiPreview(comfy_extras.nodes_model_merging.ModelMergeBlocks):
         return {"required": arg_dict}
 
 class ModelMergeLTXV(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -197,7 +197,7 @@ class ModelMergeLTXV(comfy_extras.nodes_model_merging.ModelMergeBlocks):
         return {"required": arg_dict}
 
 class ModelMergeCosmos7B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -221,7 +221,7 @@ class ModelMergeCosmos7B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
         return {"required": arg_dict}
 
 class ModelMergeCosmos14B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -245,7 +245,7 @@ class ModelMergeCosmos14B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
         return {"required": arg_dict}
 
 class ModelMergeWAN2_1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
     DESCRIPTION = "1.3B model has 30 blocks, 14B model has 40 blocks. Image to video model has the extra img_emb."
 
     @classmethod
@@ -269,7 +269,7 @@ class ModelMergeWAN2_1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
         return {"required": arg_dict}
 
 class ModelMergeCosmosPredict2_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -292,7 +292,7 @@ class ModelMergeCosmosPredict2_2B(comfy_extras.nodes_model_merging.ModelMergeBlo
         return {"required": arg_dict}
 
 class ModelMergeCosmosPredict2_14B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -315,7 +315,7 @@ class ModelMergeCosmosPredict2_14B(comfy_extras.nodes_model_merging.ModelMergeBl
         return {"required": arg_dict}
 
 class ModelMergeQwenImage(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):
diff --git a/comfy_extras/nodes_model_patch.py b/comfy_extras/nodes_model_patch.py
index bdccbf8c4..3f785c8b5 100644
--- a/comfy_extras/nodes_model_patch.py
+++ b/comfy_extras/nodes_model_patch.py
@@ -232,7 +232,7 @@ class ModelPatchLoader:
     FUNCTION = "load_model_patch"
     EXPERIMENTAL = True
 
-    CATEGORY = "advanced/loaders"
+    CATEGORY = "model/loaders"
 
     def load_model_patch(self, name):
         model_patch_path = folder_paths.get_full_path_or_raise("model_patches", name)
@@ -479,7 +479,7 @@ class QwenImageDiffsynthControlnet:
     FUNCTION = "diffsynth_controlnet"
     EXPERIMENTAL = True
 
-    CATEGORY = "advanced/loaders/qwen"
+    CATEGORY = "model/patch/qwen"
 
     def diffsynth_controlnet(self, model, model_patch, vae, image=None, strength=1.0, inpaint_image=None, mask=None):
         model_patched = model.clone()
@@ -512,7 +512,7 @@ class ZImageFunControlnet(QwenImageDiffsynthControlnet):
                               },
                 "optional": {"image": ("IMAGE",), "inpaint_image": ("IMAGE",), "mask": ("MASK",)}}
 
-    CATEGORY = "advanced/loaders/zimage"
+    CATEGORY = "model/patch/z-image"
 
 class UsoStyleProjectorPatch:
     def __init__(self, model_patch, encoded_image):
@@ -675,3 +675,11 @@ NODE_CLASS_MAPPINGS = {
     "USOStyleReference": USOStyleReference,
     "SUPIRApply": SUPIRApply,
 }
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ModelPatchLoader": "Load Model Patch",
+    "QwenImageDiffsynthControlnet": "Apply Qwen Image DiffSynth ControlNet",
+    "ZImageFunControlnet": "Apply Z-Image Fun ControlNet",
+    "USOStyleReference": "Apply USO Style Reference",
+    "SUPIRApply": "Apply SUPIR Patch",
+}
diff --git a/comfy_extras/nodes_pid.py b/comfy_extras/nodes_pid.py
index 71855254e..a3ffd9671 100644
--- a/comfy_extras/nodes_pid.py
+++ b/comfy_extras/nodes_pid.py
@@ -14,10 +14,8 @@ class PiDConditioning(io.ComfyNode):
         return io.Schema(
             node_id="PiDConditioning",
             display_name="PiD Conditioning",
-            category="advanced/conditioning",
-            description=(
-                "Attaches a latent and a degrade_sigma scalar to a CONDITIONING for PiD decoding/upscaling"
-            ),
+            category="model/conditioning",
+            description=("Attaches a latent and a degrade_sigma scalar to a CONDITIONING for PiD decoding/upscaling"),
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Latent.Input("latent", tooltip="latent (from VAEEncode or a KSampler)."),
diff --git a/comfy_extras/nodes_pixart.py b/comfy_extras/nodes_pixart.py
index 2f1b73e60..f878a33b5 100644
--- a/comfy_extras/nodes_pixart.py
+++ b/comfy_extras/nodes_pixart.py
@@ -7,8 +7,9 @@ class CLIPTextEncodePixArtAlpha(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CLIPTextEncodePixArtAlpha",
+            display_name="CLIP Text Encode (PixArt Alpha)",
             search_aliases=["pixart prompt"],
-            category="advanced/conditioning",
+            category="model/conditioning/pixart",
             description="Encodes text and sets the resolution conditioning for PixArt Alpha. Does not apply to PixArt Sigma.",
             inputs=[
                 io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py
index 3e440433e..763b8a52f 100644
--- a/comfy_extras/nodes_post_processing.py
+++ b/comfy_extras/nodes_post_processing.py
@@ -616,7 +616,7 @@ class BatchLatentsNode(io.ComfyNode):
             node_id="BatchLatentsNode",
             search_aliases=["combine latents", "stack latents", "merge latents"],
             display_name="Batch Latents",
-            category="model/latent",
+            category="model/latent/batch",
             inputs=[
                 io.Autogrow.Input("latents", template=autogrow_template)
             ],
diff --git a/comfy_extras/nodes_qwen.py b/comfy_extras/nodes_qwen.py
index 5b92814a4..4960774db 100644
--- a/comfy_extras/nodes_qwen.py
+++ b/comfy_extras/nodes_qwen.py
@@ -12,7 +12,7 @@ class TextEncodeQwenImageEdit(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="TextEncodeQwenImageEdit",
-            category="advanced/conditioning",
+            category="model/conditioning/qwen image",
             inputs=[
                 io.Clip.Input("clip"),
                 io.String.Input("prompt", multiline=True, dynamic_prompts=True),
@@ -55,7 +55,7 @@ class TextEncodeQwenImageEditPlus(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="TextEncodeQwenImageEditPlus",
-            category="advanced/conditioning",
+            category="model/conditioning/qwen image",
             inputs=[
                 io.Clip.Input("clip"),
                 io.String.Input("prompt", multiline=True, dynamic_prompts=True),
diff --git a/comfy_extras/nodes_scail.py b/comfy_extras/nodes_scail.py
index bba0942d7..007733efc 100644
--- a/comfy_extras/nodes_scail.py
+++ b/comfy_extras/nodes_scail.py
@@ -123,7 +123,7 @@ class WanSCAILToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanSCAILToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/scail",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -257,18 +257,16 @@ class SCAIL2ColoredMask(io.ComfyNode):
         return io.Schema(
             node_id="SCAIL2ColoredMask",
             display_name="Create SCAIL-2 Colored Mask",
-            category="conditioning/video_models/scail",
+            category="model/conditioning/wan/scail",
             inputs=[
                 SAM3TrackData.Input("driving_track_data", tooltip="SAM3 track of the driving pose video. Will be rendered into the pose_video_mask output."),
-                SAM3TrackData.Input("ref_track_data", optional=True,
-                                    tooltip="SAM3 track of the reference image."),
-                io.String.Input("object_indices", default="",
-                                tooltip="Comma-separated list of person indices to include (e.g. '0,2,3'). Applied to both reference and pose video masks. Empty = all."),
+                SAM3TrackData.Input("ref_track_data", optional=True, tooltip="SAM3 track of the reference image."),
+                io.String.Input("object_indices", default="", tooltip="Comma-separated list of person indices to include (e.g. '0,2,3'). Applied to both reference and pose video masks. Empty = all."),
                 io.Combo.Input("sort_by", options=["none", "left_to_right", "area"], default="left_to_right",
-                               tooltip="Order in which palette colors are assigned to the tracked objects (applied to both reference and pose video so each identity keeps the same color). left_to_right = leftmost object (by first-frame centroid) gets the first color; area = biggest object (by first-frame mask area) gets the first color; none = keep SAM3's order."),
+                    tooltip="Order in which palette colors are assigned to the tracked objects (applied to both reference and pose video so each identity keeps the same color). left_to_right = leftmost object (by first-frame centroid) gets the first color; area = biggest object (by first-frame mask area) gets the first color; none = keep SAM3's order."),
                 io.Boolean.Input("replacement_mode", default=False,
-                                 tooltip="False = Animation Mode (pose_video_mask has black background, reference_image_mask has white background). "
-                                         "True = Replacement Mode (pose_video_mask has white background, reference_image_mask has black background)."),
+                    tooltip="False = Animation Mode (pose_video_mask has black background, reference_image_mask has white background). "
+                    "True = Replacement Mode (pose_video_mask has white background, reference_image_mask has black background)."),
             ],
             outputs=[
                 io.Image.Output("pose_video_mask"),
diff --git a/comfy_extras/nodes_sd3.py b/comfy_extras/nodes_sd3.py
index 38cbf117b..40e84656b 100644
--- a/comfy_extras/nodes_sd3.py
+++ b/comfy_extras/nodes_sd3.py
@@ -13,8 +13,9 @@ class TripleCLIPLoader(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="TripleCLIPLoader",
-            category="advanced/loaders",
-            description="[Recipes]\n\nsd3: clip-l, clip-g, t5",
+            display_name="Load CLIP (Triple)",
+            category="model/loaders",
+            description="Recipes:\nsd3: clip-l, clip-g, t5",
             inputs=[
                 io.Combo.Input("clip_name1", options=folder_paths.get_filename_list("text_encoders")),
                 io.Combo.Input("clip_name2", options=folder_paths.get_filename_list("text_encoders")),
@@ -41,7 +42,7 @@ class EmptySD3LatentImage(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="EmptySD3LatentImage",
-            category="model/latent/sd3",
+            category="model/latent/stable diffusion",
             inputs=[
                 io.Int.Input("width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
@@ -66,7 +67,8 @@ class CLIPTextEncodeSD3(io.ComfyNode):
         return io.Schema(
             node_id="CLIPTextEncodeSD3",
             search_aliases=["sd3 prompt"],
-            category="advanced/conditioning",
+            display_name="CLIP Text Encode (SD3)",
+            category="model/conditioning/stable diffusion",
             inputs=[
                 io.Clip.Input("clip"),
                 io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
diff --git a/comfy_extras/nodes_sdupscale.py b/comfy_extras/nodes_sdupscale.py
index ea283e971..5c247fb49 100644
--- a/comfy_extras/nodes_sdupscale.py
+++ b/comfy_extras/nodes_sdupscale.py
@@ -9,7 +9,7 @@ class SD_4XUpscale_Conditioning(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SD_4XUpscale_Conditioning",
-            category="model/conditioning/upscale_diffusion",
+            category="model/conditioning/stable diffusion upscaler",
             inputs=[
                 io.Image.Input("images"),
                 io.Conditioning.Input("positive"),
diff --git a/comfy_extras/nodes_stable3d.py b/comfy_extras/nodes_stable3d.py
index 8a6e5b726..b0eba819b 100644
--- a/comfy_extras/nodes_stable3d.py
+++ b/comfy_extras/nodes_stable3d.py
@@ -27,7 +27,7 @@ class StableZero123_Conditioning(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StableZero123_Conditioning",
-            category="model/conditioning/3d_models",
+            category="model/conditioning/stable zero123",
             inputs=[
                 io.ClipVision.Input("clip_vision"),
                 io.Image.Input("init_image"),
@@ -65,7 +65,7 @@ class StableZero123_Conditioning_Batched(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StableZero123_Conditioning_Batched",
-            category="model/conditioning/3d_models",
+            category="model/conditioning/stable zero123",
             inputs=[
                 io.ClipVision.Input("clip_vision"),
                 io.Image.Input("init_image"),
@@ -112,7 +112,7 @@ class SV3D_Conditioning(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SV3D_Conditioning",
-            category="model/conditioning/3d_models",
+            category="model/conditioning/stable video 3d",
             inputs=[
                 io.ClipVision.Input("clip_vision"),
                 io.Image.Input("init_image"),
diff --git a/comfy_extras/nodes_stable_cascade.py b/comfy_extras/nodes_stable_cascade.py
index e55f248ae..6a78ffb47 100644
--- a/comfy_extras/nodes_stable_cascade.py
+++ b/comfy_extras/nodes_stable_cascade.py
@@ -29,7 +29,7 @@ class StableCascade_EmptyLatentImage(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StableCascade_EmptyLatentImage",
-            category="model/latent/stable_cascade",
+            category="model/latent/stable cascade",
             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),
@@ -58,7 +58,7 @@ class StableCascade_StageC_VAEEncode(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StableCascade_StageC_VAEEncode",
-            category="model/latent/stable_cascade",
+            category="model/latent/stable cascade",
             inputs=[
                 io.Image.Input("image"),
                 io.Vae.Input("vae"),
@@ -93,7 +93,7 @@ class StableCascade_StageB_Conditioning(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StableCascade_StageB_Conditioning",
-            category="model/conditioning/stable_cascade",
+            category="model/conditioning/stable cascade",
             inputs=[
                 io.Conditioning.Input("conditioning"),
                 io.Latent.Input("stage_c"),
diff --git a/comfy_extras/nodes_train.py b/comfy_extras/nodes_train.py
index bb68da6fa..a27217b80 100644
--- a/comfy_extras/nodes_train.py
+++ b/comfy_extras/nodes_train.py
@@ -1367,7 +1367,7 @@ class SaveLoRA(io.ComfyNode):
             node_id="SaveLoRA",
             search_aliases=["export lora"],
             display_name="Save LoRA Weights",
-            category="advanced/model_merging",
+            category="model/merging",
             is_experimental=True,
             is_output_node=True,
             inputs=[
diff --git a/comfy_extras/nodes_video_model.py b/comfy_extras/nodes_video_model.py
index 0d6cae6a8..01d48d4d4 100644
--- a/comfy_extras/nodes_video_model.py
+++ b/comfy_extras/nodes_video_model.py
@@ -41,7 +41,7 @@ class SVD_img2vid_Conditioning:
 
     FUNCTION = "encode"
 
-    CATEGORY = "model/conditioning/video_models"
+    CATEGORY = "model/conditioning/stable video"
 
     def encode(self, clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level):
         output = clip_vision.encode_image(init_image)
@@ -108,7 +108,7 @@ class VideoTriangleCFGGuidance:
         return (m, )
 
 class ImageOnlyCheckpointSave(comfy_extras.nodes_model_merging.CheckpointSave):
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -138,7 +138,7 @@ class ConditioningSetAreaPercentageVideo:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "append"
 
-    CATEGORY = "model/conditioning"
+    CATEGORY = "model/conditioning/transform"
 
     def append(self, conditioning, width, height, temporal, x, y, z, strength):
         c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", temporal, height, width, z, y, x),
@@ -160,4 +160,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ImageOnlyCheckpointLoader": "Load Checkpoint Image Only (img2vid model)",
     "VideoLinearCFGGuidance": "Video Linear CFG Guidance",
     "VideoTriangleCFGGuidance": "Video Triangle CFG Guidance",
+    "ConditioningSetAreaPercentageVideo": "Conditioning (Set Area with Percentage for Video)",
 }
diff --git a/comfy_extras/nodes_void.py b/comfy_extras/nodes_void.py
index b43154b8d..7527baf43 100644
--- a/comfy_extras/nodes_void.py
+++ b/comfy_extras/nodes_void.py
@@ -175,7 +175,7 @@ class VOIDInpaintConditioning(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="VOIDInpaintConditioning",
-            category="model/conditioning/video_models",
+            category="model/conditioning/void",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -288,7 +288,7 @@ class VOIDWarpedNoise(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="VOIDWarpedNoise",
-            category="model/latent/video",
+            category="model/latent/void",
             inputs=[
                 OpticalFlow.Input(
                     "optical_flow",
@@ -393,7 +393,7 @@ class VOIDWarpedNoiseSource(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="VOIDWarpedNoiseSource",
-            category="model/sampling/noise",
+            category="model/latent/void",
             inputs=[
                 io.Latent.Input("warped_noise",
                     tooltip="Warped noise latent from VOIDWarpedNoise"),
diff --git a/comfy_extras/nodes_wan.py b/comfy_extras/nodes_wan.py
index d73be8e00..0e47a58df 100644
--- a/comfy_extras/nodes_wan.py
+++ b/comfy_extras/nodes_wan.py
@@ -18,7 +18,7 @@ class WanImageToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanImageToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -66,7 +66,7 @@ class WanFunControlToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanFunControlToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/fun control",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -119,7 +119,7 @@ class Wan22FunControlToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="Wan22FunControlToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/fun control",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -184,7 +184,7 @@ class WanFirstLastFrameToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanFirstLastFrameToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -256,7 +256,7 @@ class WanFunInpaintToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanFunInpaintToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/fun inpaint",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -288,7 +288,7 @@ class WanVaceToVideo(io.ComfyNode):
         return io.Schema(
             node_id="WanVaceToVideo",
             search_aliases=["video conditioning", "video control"],
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/vace",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -375,7 +375,8 @@ class TrimVideoLatent(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="TrimVideoLatent",
-            category="model/latent/video",
+            display_name="Trim Video Latent",
+            category="model/latent",
             inputs=[
                 io.Latent.Input("samples"),
                 io.Int.Input("trim_amount", default=0, min=0, max=99999),
@@ -398,7 +399,7 @@ class WanCameraImageToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanCameraImageToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/camera",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -452,7 +453,7 @@ class WanPhantomSubjectToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanPhantomSubjectToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/phantom subject",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -707,7 +708,7 @@ class WanTrackToVideo(io.ComfyNode):
         return io.Schema(
             node_id="WanTrackToVideo",
             search_aliases=["motion tracking", "trajectory video", "point tracking", "keypoint animation"],
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/move",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -951,7 +952,7 @@ class WanSoundImageToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanSoundImageToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/sound",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -984,7 +985,7 @@ class WanSoundImageToVideoExtend(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanSoundImageToVideoExtend",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/sound",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -1046,7 +1047,7 @@ class WanHuMoImageToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanHuMoImageToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/humo",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -1112,7 +1113,7 @@ class WanAnimateToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanAnimateToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/animate",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -1252,7 +1253,7 @@ class Wan22ImageToVideoLatent(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="Wan22ImageToVideoLatent",
-            category="model/conditioning/inpaint",
+            category="model/conditioning/wan",
             inputs=[
                 io.Vae.Input("vae"),
                 io.Int.Input("width", default=1280, min=32, max=nodes.MAX_RESOLUTION, step=32),
@@ -1302,7 +1303,7 @@ class WanInfiniteTalkToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanInfiniteTalkToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/infinite talk",
             inputs=[
                 io.DynamicCombo.Input("mode", options=[
                 io.DynamicCombo.Option("single_speaker", []),
diff --git a/comfy_extras/nodes_wandancer.py b/comfy_extras/nodes_wandancer.py
index a96885745..fdb2b5e57 100644
--- a/comfy_extras/nodes_wandancer.py
+++ b/comfy_extras/nodes_wandancer.py
@@ -713,7 +713,7 @@ class WanDancerEncodeAudio(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanDancerEncodeAudio",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/dancer",
             inputs=[
                 io.Audio.Input("audio"),
                 io.Int.Input("video_frames", default=149, min=1, max=nodes.MAX_RESOLUTION, step=4),
@@ -787,7 +787,7 @@ class WanDancerVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanDancerVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/dancer",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
diff --git a/comfy_extras/nodes_wanmove.py b/comfy_extras/nodes_wanmove.py
index 2db064922..d1f924a40 100644
--- a/comfy_extras/nodes_wanmove.py
+++ b/comfy_extras/nodes_wanmove.py
@@ -247,7 +247,7 @@ class WanMoveVisualizeTracks(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanMoveVisualizeTracks",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/move",
             inputs=[
                 io.Image.Input("images"),
                 io.Tracks.Input("tracks", optional=True),
@@ -283,7 +283,7 @@ class WanMoveTracksFromCoords(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanMoveTracksFromCoords",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/move",
             inputs=[
                 io.String.Input("track_coords", force_input=True, default="[]", optional=True),
                 io.Mask.Input("track_mask", optional=True),
@@ -325,7 +325,8 @@ class GenerateTracks(io.ComfyNode):
         return io.Schema(
             node_id="GenerateTracks",
             search_aliases=["motion paths", "camera movement", "trajectory"],
-            category="model/conditioning/video_models",
+            display_name="Generate Video Tracks",
+            category="model/conditioning/wan/move",
             inputs=[
                 io.Int.Input("width", default=832, min=16, max=4096, step=16),
                 io.Int.Input("height", default=480, min=16, max=4096, step=16),
@@ -434,7 +435,7 @@ class WanMoveConcatTrack(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanMoveConcatTrack",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/move",
             inputs=[
                 io.Tracks.Input("tracks_1"),
                 io.Tracks.Input("tracks_2", optional=True),
@@ -463,7 +464,7 @@ class WanMoveTrackToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanMoveTrackToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/move",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
diff --git a/comfy_extras/nodes_zimage.py b/comfy_extras/nodes_zimage.py
index 70ddc4afa..ce946b377 100644
--- a/comfy_extras/nodes_zimage.py
+++ b/comfy_extras/nodes_zimage.py
@@ -10,7 +10,7 @@ class TextEncodeZImageOmni(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="TextEncodeZImageOmni",
-            category="advanced/conditioning",
+            category="model/conditioning/z-image",
             is_experimental=True,
             inputs=[
                 io.Clip.Input("clip"),
diff --git a/nodes.py b/nodes.py
index 0d422d418..bb4649478 100644
--- a/nodes.py
+++ b/nodes.py
@@ -87,7 +87,7 @@ class ConditioningCombine:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "combine"
 
-    CATEGORY = "model/conditioning"
+    CATEGORY = "model/conditioning/transform"
     SEARCH_ALIASES = ["combine", "merge conditioning", "combine prompts", "merge prompts", "mix prompts", "add prompt"]
 
     def combine(self, conditioning_1, conditioning_2):
@@ -104,7 +104,7 @@ class ConditioningAverage :
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "addWeighted"
 
-    CATEGORY = "model/conditioning"
+    CATEGORY = "model/conditioning/transform"
 
     def addWeighted(self, conditioning_to, conditioning_from, conditioning_to_strength):
         out = []
@@ -143,7 +143,7 @@ class ConditioningConcat:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "concat"
 
-    CATEGORY = "model/conditioning"
+    CATEGORY = "model/conditioning/transform"
 
     def concat(self, conditioning_to, conditioning_from):
         out = []
@@ -176,7 +176,7 @@ class ConditioningSetArea:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "append"
 
-    CATEGORY = "model/conditioning"
+    CATEGORY = "model/conditioning/transform"
 
     def append(self, conditioning, width, height, x, y, strength):
         c = node_helpers.conditioning_set_values(conditioning, {"area": (height // 8, width // 8, y // 8, x // 8),
@@ -197,7 +197,7 @@ class ConditioningSetAreaPercentage:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "append"
 
-    CATEGORY = "model/conditioning"
+    CATEGORY = "model/conditioning/transform"
 
     def append(self, conditioning, width, height, x, y, strength):
         c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", height, width, y, x),
@@ -214,7 +214,7 @@ class ConditioningSetAreaStrength:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "append"
 
-    CATEGORY = "model/conditioning"
+    CATEGORY = "model/conditioning/transform"
 
     def append(self, conditioning, strength):
         c = node_helpers.conditioning_set_values(conditioning, {"strength": strength})
@@ -234,7 +234,7 @@ class ConditioningSetMask:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "append"
 
-    CATEGORY = "model/conditioning"
+    CATEGORY = "model/conditioning/transform"
 
     def append(self, conditioning, mask, set_cond_area, strength):
         set_area_to_bounds = False
@@ -257,7 +257,7 @@ class ConditioningZeroOut:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "zero_out"
 
-    CATEGORY = "advanced/conditioning"
+    CATEGORY = "model/conditioning/transform"
 
     def zero_out(self, conditioning):
         c = []
@@ -283,11 +283,10 @@ class ConditioningSetTimestepRange:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "set_range"
 
-    CATEGORY = "advanced/conditioning"
+    CATEGORY = "model/conditioning/transform"
 
     def set_range(self, conditioning, start, end):
-        c = node_helpers.conditioning_set_values(conditioning, {"start_percent": start,
-                                                                "end_percent": end})
+        c = node_helpers.conditioning_set_values(conditioning, {"start_percent": start, "end_percent": end})
         return (c, )
 
 class VAEDecode:
@@ -389,7 +388,7 @@ class VAEEncodeForInpaint:
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "encode"
 
-    CATEGORY = "model/latent/inpaint"
+    CATEGORY = "model/latent"
 
     def encode(self, vae, pixels, mask, grow_mask_by=6):
         downscale_ratio = vae.spacial_compression_encode()
@@ -438,7 +437,7 @@ class InpaintModelConditioning:
     RETURN_NAMES = ("positive", "negative", "latent")
     FUNCTION = "encode"
 
-    CATEGORY = "model/conditioning/inpaint"
+    CATEGORY = "model/conditioning"
 
     def encode(self, positive, negative, pixels, vae, mask, noise_mask=True):
         x = (pixels.shape[1] // 8) * 8
@@ -576,7 +575,7 @@ class CheckpointLoader:
     RETURN_TYPES = ("MODEL", "CLIP", "VAE")
     FUNCTION = "load_checkpoint"
 
-    CATEGORY = "advanced/loaders"
+    CATEGORY = "model/loaders"
     DEPRECATED = True
 
     def load_checkpoint(self, config_name, ckpt_name):
@@ -622,8 +621,9 @@ class DiffusersLoader:
         return {"required": {"model_path": (paths,), }}
     RETURN_TYPES = ("MODEL", "CLIP", "VAE")
     FUNCTION = "load_checkpoint"
+    DEPRECATED = True
 
-    CATEGORY = "advanced/loaders/deprecated"
+    CATEGORY = "model/loaders"
 
     def load_checkpoint(self, model_path, output_vae=True, output_clip=True):
         for search_path in folder_paths.get_folder_paths("diffusers"):
@@ -949,7 +949,7 @@ class UNETLoader:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "load_unet"
 
-    CATEGORY = "advanced/loaders"
+    CATEGORY = "model/loaders"
 
     def load_unet(self, unet_name, weight_dtype):
         model_options = {}
@@ -977,9 +977,9 @@ class CLIPLoader:
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
 
-    CATEGORY = "advanced/loaders"
+    CATEGORY = "model/loaders"
 
-    DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 xxl/ clip-g / clip-l\nstable_audio: t5 base\nmochi: t5 xxl\ncogvideox: t5 xxl (226-token padding)\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\n hidream: llama-3.1 (Recommend) or t5\nomnigen2: qwen vl 2.5 3B\nlens: gpt-oss-20b\n pixeldit: gemma 2 2B elm"
+    DESCRIPTION = "Recipes:\nsd: clip-l\nstable cascade: clip-g\nsd3: t5 xxl / clip-g / clip-l\nstable audio: t5 base\nmochi: t5 xxl\ncogvideox: t5 xxl (226-token padding)\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\nhidream: llama-3.1 (Recommend) or t5\nomnigen2: qwen vl 2.5 3B\nlens: gpt-oss-20b\npixeldit: gemma 2 2B elm"
 
     def load_clip(self, clip_name, type="stable_diffusion", device="default"):
         clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
@@ -1005,9 +1005,9 @@ class DualCLIPLoader:
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
 
-    CATEGORY = "advanced/loaders"
+    CATEGORY = "model/loaders"
 
-    DESCRIPTION = "[Recipes]\n\nsdxl: clip-l, clip-g\nsd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\nflux: clip-l, t5\nhidream: at least one of t5 or llama, recommended t5 and llama\nhunyuan_image: qwen2.5vl 7b and byt5 small\nnewbie: gemma-3-4b-it, jina clip v2"
+    DESCRIPTION = "Recipes:\nsdxl: clip-l, clip-g\nsd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\nflux: clip-l, t5\nhidream: at least one of t5 or llama, recommended t5 and llama\nhunyuan_image: qwen2.5vl 7b and byt5 small\nnewbie: gemma-3-4b-it, jina clip v2"
 
     def load_clip(self, clip_name1, clip_name2, type, device="default"):
         clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
@@ -1088,7 +1088,7 @@ class StyleModelApply:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "apply_stylemodel"
 
-    CATEGORY = "model/conditioning/style_model"
+    CATEGORY = "model/conditioning"
 
     def apply_stylemodel(self, conditioning, style_model, clip_vision_output, strength, strength_type):
         cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0)
@@ -1518,13 +1518,11 @@ class LatentCrop:
 class SetLatentNoiseMask:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "samples": ("LATENT",),
-                              "mask": ("MASK",),
-                              }}
+        return {"required": { "samples": ("LATENT",), "mask": ("MASK",), }}
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "set_mask"
 
-    CATEGORY = "model/latent/inpaint"
+    CATEGORY = "model/latent"
 
     def set_mask(self, samples, mask):
         s = samples.copy()
@@ -2045,7 +2043,7 @@ NODE_CLASS_MAPPINGS = {
     "ImageBatch": ImageBatch,
     "ImagePadForOutpaint": ImagePadForOutpaint,
     "EmptyImage": EmptyImage,
-    "ConditioningAverage": ConditioningAverage ,
+    "ConditioningAverage": ConditioningAverage,
     "ConditioningCombine": ConditioningCombine,
     "ConditioningConcat": ConditioningConcat,
     "ConditioningSetArea": ConditioningSetArea,
@@ -2101,6 +2099,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "LoraLoader": "Load LoRA (Model and CLIP)",
     "LoraLoaderModelOnly": "Load LoRA",
     "CLIPLoader": "Load CLIP",
+    "DualCLIPLoader": "Load CLIP (Dual)",
     "ControlNetLoader": "Load ControlNet Model",
     "DiffControlNetLoader": "Load ControlNet Model (diff)",
     "StyleModelLoader": "Load Style Model",
@@ -2108,6 +2107,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "UNETLoader": "Load Diffusion Model",
     "unCLIPCheckpointLoader": "Load unCLIP Checkpoint",
     "GLIGENLoader": "Load GLIGEN Model",
+    "DiffusersLoader": "Load Diffusers Model (DEPRECATED)",
     # Conditioning
     "CLIPVisionEncode": "CLIP Vision Encode",
     "StyleModelApply": "Apply Style Model",
@@ -2115,12 +2115,16 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "CLIPSetLastLayer": "CLIP Set Last Layer",
     "ConditioningCombine": "Conditioning (Combine)",
     "ConditioningAverage ": "Conditioning (Average)",
+    "ConditioningAverage": "Conditioning (Average)",
     "ConditioningConcat": "Conditioning (Concat)",
     "ConditioningSetArea": "Conditioning (Set Area)",
     "ConditioningSetAreaPercentage": "Conditioning (Set Area with Percentage)",
+    "ConditioningSetAreaStrength": "Conditioning (Set Area Strength)",
     "ConditioningSetMask": "Conditioning (Set Mask)",
     "ControlNetApply": "Apply ControlNet (DEPRECATED)",
     "ControlNetApplyAdvanced": "Apply ControlNet",
+    "GLIGENTextBoxApply": "Apply GLIGEN Text Box",
+    "ConditioningZeroOut": "Conditioning Zero Out",
     # Latent
     "VAEEncodeForInpaint": "VAE Encode (for Inpainting)",
     "SetLatentNoiseMask": "Set Latent Noise Mask",
@@ -2134,7 +2138,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "LatentUpscaleBy": "Upscale Latent By",
     "LatentComposite": "Latent Composite",
     "LatentBlend": "Latent Blend",
-    "LatentFromBatch" : "Latent From Batch",
+    "LatentFromBatch" : "Get Latent From Batch",
     "RepeatLatentBatch": "Repeat Latent Batch",
     # Image
     "EmptyImage": "Empty Image",

From c44d261fc20a0a1032fccfe1d6472915a514d577 Mon Sep 17 00:00:00 2001
From: "Daxiong (Lin)" <contact@comfyui-wiki.com>
Date: Wed, 17 Jun 2026 08:52:55 +0800
Subject: [PATCH 14/30] Add new model blueprints (#14506)

---
 .../Character Replacement (SCAIL-2 Base).json | 4191 ++++++++++++++++
 ...haracter Replacement (SCAIL-2 Extend).json | 4461 +++++++++++++++++
 ...e Depth Estimation (Depth Anything 3).json |  569 +++
 blueprints/Image Edit (Bernini-R).json        | 3549 +++++++++++++
 .../Image to Gaussian Splat (TripoSplat).json | 1983 ++++++++
 .../Text to Image (Anima Base 1.0).json       | 1088 ++++
 blueprints/Text to Image (Anima).json         |    7 +-
 blueprints/Text to Image (Ideogram v4).json   | 2473 +++++++++
 ...o Depth Estimation (Depth Anything 3).json |  825 +++
 blueprints/Video Edit (Bernini-R).json        | 3732 ++++++++++++++
 10 files changed, 22876 insertions(+), 2 deletions(-)
 create mode 100644 blueprints/Character Replacement (SCAIL-2 Base).json
 create mode 100644 blueprints/Character Replacement (SCAIL-2 Extend).json
 create mode 100644 blueprints/Image Depth Estimation (Depth Anything 3).json
 create mode 100644 blueprints/Image Edit (Bernini-R).json
 create mode 100644 blueprints/Image to Gaussian Splat (TripoSplat).json
 create mode 100644 blueprints/Text to Image (Anima Base 1.0).json
 create mode 100644 blueprints/Text to Image (Ideogram v4).json
 create mode 100644 blueprints/Video Depth Estimation (Depth Anything 3).json
 create mode 100644 blueprints/Video Edit (Bernini-R).json

diff --git a/blueprints/Character Replacement (SCAIL-2 Base).json b/blueprints/Character Replacement (SCAIL-2 Base).json
new file mode 100644
index 000000000..61803df65
--- /dev/null
+++ b/blueprints/Character Replacement (SCAIL-2 Base).json	
@@ -0,0 +1,4191 @@
+{
+  "revision": 0,
+  "last_node_id": 410,
+  "last_link_id": 0,
+  "nodes": [
+    {
+      "id": 410,
+      "type": "35331397-69fb-40ad-b99a-7f17b1a53017",
+      "pos": [
+        2450,
+        5670
+      ],
+      "size": [
+        490,
+        1120
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "label": "pose_video",
+          "localized_name": "video",
+          "name": "video",
+          "type": "VIDEO",
+          "link": null
+        },
+        {
+          "label": "reference_image",
+          "localized_name": "images",
+          "name": "images",
+          "type": "IMAGE",
+          "link": null
+        },
+        {
+          "label": "prompt",
+          "name": "text",
+          "type": "STRING",
+          "widget": {
+            "name": "text"
+          },
+          "link": null
+        },
+        {
+          "label": "segment_index",
+          "name": "value",
+          "type": "INT",
+          "widget": {
+            "name": "value"
+          },
+          "link": null
+        },
+        {
+          "label": "replace_mode",
+          "name": "value_2",
+          "type": "BOOLEAN",
+          "widget": {
+            "name": "value_2"
+          },
+          "link": null
+        },
+        {
+          "label": "width",
+          "name": "value_3",
+          "type": "INT",
+          "widget": {
+            "name": "value_3"
+          },
+          "link": null
+        },
+        {
+          "label": "height",
+          "name": "value_4",
+          "type": "INT",
+          "widget": {
+            "name": "value_4"
+          },
+          "link": null
+        },
+        {
+          "label": "frame_count",
+          "name": "length",
+          "type": "INT",
+          "widget": {
+            "name": "length"
+          },
+          "link": null
+        },
+        {
+          "name": "previous_frame_count",
+          "type": "INT",
+          "widget": {
+            "name": "previous_frame_count"
+          },
+          "link": null
+        },
+        {
+          "name": "pose_strength",
+          "type": "FLOAT",
+          "widget": {
+            "name": "pose_strength"
+          },
+          "link": null
+        },
+        {
+          "name": "pose_start",
+          "type": "FLOAT",
+          "widget": {
+            "name": "pose_start"
+          },
+          "link": null
+        },
+        {
+          "name": "pose_end",
+          "type": "FLOAT",
+          "widget": {
+            "name": "pose_end"
+          },
+          "link": null
+        },
+        {
+          "label": "turbo_mode",
+          "name": "value_5",
+          "type": "BOOLEAN",
+          "widget": {
+            "name": "value_5"
+          },
+          "link": null
+        },
+        {
+          "name": "unet_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "unet_name"
+          },
+          "link": null
+        },
+        {
+          "label": "distill_lora",
+          "name": "lora_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "lora_name"
+          },
+          "link": null
+        },
+        {
+          "label": "dpo_lora",
+          "name": "lora_name_1",
+          "type": "COMBO",
+          "widget": {
+            "name": "lora_name_1"
+          },
+          "link": null
+        },
+        {
+          "name": "clip_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "clip_name"
+          },
+          "link": null
+        },
+        {
+          "name": "vae_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "vae_name"
+          },
+          "link": null
+        },
+        {
+          "label": "clip_vision",
+          "name": "clip_name_1",
+          "type": "COMBO",
+          "widget": {
+            "name": "clip_name_1"
+          },
+          "link": null
+        },
+        {
+          "label": "sam3_video_object",
+          "name": "text_1",
+          "type": "STRING",
+          "widget": {
+            "name": "text_1"
+          },
+          "link": null
+        },
+        {
+          "label": "sam3_image_object",
+          "name": "text_2",
+          "type": "STRING",
+          "widget": {
+            "name": "text_2"
+          },
+          "link": null
+        },
+        {
+          "label": "sam3_model",
+          "name": "ckpt_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "ckpt_name"
+          },
+          "link": null
+        },
+        {
+          "name": "noise_seed",
+          "type": "INT",
+          "widget": {
+            "name": "noise_seed"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "output",
+          "name": "output",
+          "type": "IMAGE",
+          "links": []
+        }
+      ],
+      "properties": {
+        "proxyWidgets": [
+          [
+            "405",
+            "text"
+          ],
+          [
+            "391",
+            "value"
+          ],
+          [
+            "398",
+            "value"
+          ],
+          [
+            "387",
+            "value"
+          ],
+          [
+            "388",
+            "value"
+          ],
+          [
+            "386",
+            "length"
+          ],
+          [
+            "406",
+            "previous_frame_count"
+          ],
+          [
+            "406",
+            "pose_strength"
+          ],
+          [
+            "406",
+            "pose_start"
+          ],
+          [
+            "406",
+            "pose_end"
+          ],
+          [
+            "402",
+            "value"
+          ],
+          [
+            "374",
+            "unet_name"
+          ],
+          [
+            "367",
+            "lora_name"
+          ],
+          [
+            "408",
+            "lora_name"
+          ],
+          [
+            "372",
+            "clip_name"
+          ],
+          [
+            "368",
+            "vae_name"
+          ],
+          [
+            "373",
+            "clip_name"
+          ],
+          [
+            "393",
+            "text"
+          ],
+          [
+            "407",
+            "text"
+          ],
+          [
+            "394",
+            "ckpt_name"
+          ],
+          [
+            "378",
+            "noise_seed"
+          ],
+          [
+            "399",
+            "$$canvas-image-preview"
+          ],
+          [
+            "400",
+            "$$canvas-image-preview"
+          ]
+        ],
+        "cnr_id": "comfy-core",
+        "ver": "0.24.0"
+      },
+      "widgets_values": [],
+      "title": "Character Replacement (SCAIL-2 Base)"
+    }
+  ],
+  "links": [],
+  "version": 0.4,
+  "definitions": {
+    "subgraphs": [
+      {
+        "id": "35331397-69fb-40ad-b99a-7f17b1a53017",
+        "version": 1,
+        "state": {
+          "lastGroupId": 17,
+          "lastNodeId": 410,
+          "lastLinkId": 570,
+          "lastRerouteId": 0
+        },
+        "revision": 0,
+        "config": {},
+        "name": "Character Replacement (SCAIL-2 Base)",
+        "inputNode": {
+          "id": -10,
+          "bounding": [
+            -440,
+            4190,
+            173.015625,
+            508
+          ]
+        },
+        "outputNode": {
+          "id": -20,
+          "bounding": [
+            3980,
+            3870,
+            128,
+            68
+          ]
+        },
+        "inputs": [
+          {
+            "id": "e9aedfaf-1991-4a75-ad9b-8c3a279b0488",
+            "name": "video",
+            "type": "VIDEO",
+            "linkIds": [
+              409
+            ],
+            "localized_name": "video",
+            "label": "pose_video",
+            "pos": [
+              -290.984375,
+              4214
+            ]
+          },
+          {
+            "id": "0badb26e-9abd-4e9c-b221-aab1237b8773",
+            "name": "images",
+            "type": "IMAGE",
+            "linkIds": [
+              469,
+              408,
+              473
+            ],
+            "localized_name": "images",
+            "label": "reference_image",
+            "pos": [
+              -290.984375,
+              4234
+            ]
+          },
+          {
+            "id": "098f15aa-a066-422e-b491-eaf140bafb2c",
+            "name": "text",
+            "type": "STRING",
+            "linkIds": [
+              499
+            ],
+            "label": "prompt",
+            "pos": [
+              -290.984375,
+              4254
+            ]
+          },
+          {
+            "id": "861ea850-8329-491b-9413-2588cdd77bbd",
+            "name": "value",
+            "type": "INT",
+            "linkIds": [
+              502
+            ],
+            "label": "segment_index",
+            "pos": [
+              -290.984375,
+              4274
+            ]
+          },
+          {
+            "id": "dcf3ebf8-1263-458a-966b-9e77a5ad704a",
+            "name": "value_2",
+            "type": "BOOLEAN",
+            "linkIds": [
+              504
+            ],
+            "label": "replace_mode",
+            "pos": [
+              -290.984375,
+              4294
+            ]
+          },
+          {
+            "id": "dbee10ce-bb98-4733-88a0-d4cd5b13c691",
+            "name": "value_3",
+            "type": "INT",
+            "linkIds": [
+              505
+            ],
+            "label": "width",
+            "pos": [
+              -290.984375,
+              4314
+            ]
+          },
+          {
+            "id": "363e3ac7-0096-4480-b875-1d6ff1b8a2d9",
+            "name": "value_4",
+            "type": "INT",
+            "linkIds": [
+              506
+            ],
+            "label": "height",
+            "pos": [
+              -290.984375,
+              4334
+            ]
+          },
+          {
+            "id": "4017e1ed-96cd-4c1c-9949-dc1a8c30386a",
+            "name": "length",
+            "type": "INT",
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+        "description": "Replaces a character in a video with a reference image using the SCAIL-2 model for end-to-end controlled animation without intermediate pose maps. Key inputs include a source video, a reference character image, and optional text prompts for style or context. Suitable for animated or live-action footage, multi-character scenes, and creative video editing where direct pose-free animation is needed; works best with moderate-length videos."
+      }
+    ]
+  },
+  "extra": {
+    "BlueprintDescription": "Replaces a character in a video with a reference image using the SCAIL-2 model for end-to-end controlled animation without intermediate pose maps. Key inputs include a source video, a reference character image, and optional text prompts for style or context. Suitable for animated or live-action footage, multi-character scenes, and creative video editing where direct pose-free animation is needed; works best with moderate-length videos.",
+    "BlueprintSearchAliases": [
+      "character replacement",
+      "SCAIL-2",
+      "pose-free animation",
+      "video editing",
+      "cross-identity replacement"
+    ]
+  }
+}
\ No newline at end of file
diff --git a/blueprints/Character Replacement (SCAIL-2 Extend).json b/blueprints/Character Replacement (SCAIL-2 Extend).json
new file mode 100644
index 000000000..bf2f67dfd
--- /dev/null
+++ b/blueprints/Character Replacement (SCAIL-2 Extend).json	
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+  "extra": {
+    "BlueprintDescription": "Replaces a character in a video with a reference image using the SCAIL-2 model for end-to-end controlled animation without intermediate pose maps. Key inputs include a source video, a reference character image, and optional text prompts for style or context. Suitable for animated or live-action footage, multi-character scenes, and creative video editing where direct pose-free animation is needed; works best with moderate-length videos.",
+    "BlueprintSearchAliases": [
+      "character replacement",
+      "SCAIL-2 extend",
+      "video character swap"
+    ]
+  }
+}
\ No newline at end of file
diff --git a/blueprints/Image Depth Estimation (Depth Anything 3).json b/blueprints/Image Depth Estimation (Depth Anything 3).json
new file mode 100644
index 000000000..3c5f24845
--- /dev/null
+++ b/blueprints/Image Depth Estimation (Depth Anything 3).json	
@@ -0,0 +1,569 @@
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+  }
+}
\ No newline at end of file
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new file mode 100644
index 000000000..9ad7867a4
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+}
\ No newline at end of file
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index 000000000..13da12be3
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diff --git a/blueprints/Text to Image (Anima Base 1.0).json b/blueprints/Text to Image (Anima Base 1.0).json
new file mode 100644
index 000000000..379e7c605
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+            "target_slot": 0,
+            "type": "COMBO"
+          }
+        ],
+        "extra": {},
+        "category": "Image generation and editing/Text to image",
+        "description": "This subgraph generates non-photorealistic illustrations from text prompts using a 2-billion-parameter model optimized for anime concepts, characters, and styles. It is ideal for creating artistic images, concept art, or stylized illustrations where photorealism is not required. The model excels with anime and artistic content but performs poorly on realistic subjects."
+      }
+    ]
+  },
+  "extra": {
+    "BlueprintDescription": "This subgraph generates non-photorealistic illustrations from text prompts using a 2-billion-parameter model optimized for anime concepts, characters, and styles. It is ideal for creating artistic images, concept art, or stylized illustrations where photorealism is not required. The model excels with anime and artistic content but performs poorly on realistic subjects."
+  }
+}
\ No newline at end of file
diff --git a/blueprints/Text to Image (Anima).json b/blueprints/Text to Image (Anima).json
index 787908ca9..dcf6e5973 100644
--- a/blueprints/Text to Image (Anima).json	
+++ b/blueprints/Text to Image (Anima).json	
@@ -1077,9 +1077,12 @@
           }
         ],
         "extra": {},
-        "category": "Image generation and editing/Text to image"
+        "category": "Image generation and editing/Text to image",
+        "description": "This subgraph converts text prompts into non-photorealistic illustrations using a 2-billion-parameter model optimized for anime and artistic styles. It is ideal for generating concept art, character designs, or stylized illustrations where photorealism is not required. The model excels with anime and artistic content but performs poorly on realistic subjects."
       }
     ]
   },
-  "extra": {}
+  "extra": {
+    "BlueprintDescription": "This subgraph converts text prompts into non-photorealistic illustrations using a 2-billion-parameter model optimized for anime and artistic styles. It is ideal for generating concept art, character designs, or stylized illustrations where photorealism is not required. The model excels with anime and artistic content but performs poorly on realistic subjects."
+  }
 }
\ No newline at end of file
diff --git a/blueprints/Text to Image (Ideogram v4).json b/blueprints/Text to Image (Ideogram v4).json
new file mode 100644
index 000000000..0d5c1d7c7
--- /dev/null
+++ b/blueprints/Text to Image (Ideogram v4).json	
@@ -0,0 +1,2473 @@
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+                "name": "INT",
+                "type": "INT",
+                "links": [
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+                "type": "STRING",
+                "widget": {
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+              }
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+              "secondTabText": "Send Back",
+              "secondTabOffset": 80,
+              "secondTabWidth": 65
+            },
+            "widgets_values": [
+              "max(((a + 15) // 16) * 16, 256)"
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+          },
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+              "secondTabText": "Send Back",
+              "secondTabOffset": 80,
+              "secondTabWidth": 65
+            },
+            "widgets_values": [
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+              "secondTabWidth": 65
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+                "type": "STRING",
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+                "type": "STRING",
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+            "widgets_values": [
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+                "widget": {
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+              },
+              {
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+                "type": "STRING",
+                "widget": {
+                  "name": "replace"
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+                "link": null
+              }
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+            "outputs": [
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+                "name": "STRING",
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+                  201
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+              "secondTabWidth": 65
+            },
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+              "secondTabWidth": 65
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+                "localized_name": "value",
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+            ],
+            "outputs": [
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+                "name": "FLOAT",
+                "type": "FLOAT",
+                "links": []
+              },
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+                "widget": {
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+                "localized_name": "weight_dtype",
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+                "type": "COMBO",
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+                  "name": "weight_dtype"
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+                "link": null
+              }
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+                "name": "MODEL",
+                "type": "MODEL",
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+                "widget": {
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+              }
+            ],
+            "outputs": [
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+                "localized_name": "GUIDER",
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+                "type": "GUIDER",
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+            "mode": 0,
+            "inputs": [
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+}
\ No newline at end of file
diff --git a/blueprints/Video Depth Estimation (Depth Anything 3).json b/blueprints/Video Depth Estimation (Depth Anything 3).json
new file mode 100644
index 000000000..88084a393
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+        "extra": {},
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+  }
+}
\ No newline at end of file
diff --git a/blueprints/Video Edit (Bernini-R).json b/blueprints/Video Edit (Bernini-R).json
new file mode 100644
index 000000000..2f09db5fa
--- /dev/null
+++ b/blueprints/Video Edit (Bernini-R).json	
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+            "target_slot": 9,
+            "type": "INT"
+          },
+          {
+            "id": 482,
+            "origin_id": -10,
+            "origin_slot": 9,
+            "target_id": 352,
+            "target_slot": 11,
+            "type": "INT"
+          },
+          {
+            "id": 488,
+            "origin_id": -10,
+            "origin_slot": 10,
+            "target_id": 368,
+            "target_slot": 0,
+            "type": "BOOLEAN"
+          },
+          {
+            "id": 489,
+            "origin_id": -10,
+            "origin_slot": 11,
+            "target_id": 344,
+            "target_slot": 0,
+            "type": "COMBO"
+          },
+          {
+            "id": 490,
+            "origin_id": -10,
+            "origin_slot": 12,
+            "target_id": 346,
+            "target_slot": 0,
+            "type": "COMBO"
+          },
+          {
+            "id": 491,
+            "origin_id": -10,
+            "origin_slot": 13,
+            "target_id": 345,
+            "target_slot": 1,
+            "type": "COMBO"
+          },
+          {
+            "id": 492,
+            "origin_id": -10,
+            "origin_slot": 13,
+            "target_id": 340,
+            "target_slot": 1,
+            "type": "COMBO"
+          },
+          {
+            "id": 493,
+            "origin_id": -10,
+            "origin_slot": 14,
+            "target_id": 338,
+            "target_slot": 0,
+            "type": "COMBO"
+          },
+          {
+            "id": 494,
+            "origin_id": -10,
+            "origin_slot": 15,
+            "target_id": 339,
+            "target_slot": 0,
+            "type": "COMBO"
+          }
+        ],
+        "extra": {},
+        "category": "Video generation and editing/Video Edit",
+        "description": "This subgraph uses Depth Anything 3 to predict spatially consistent geometry from any number of images or video frames, with or without known camera poses. It outputs depth maps, camera poses, and optionally 3D Gaussian parameters for novel view synthesis."
+      },
+      {
+        "id": "a98d3dcb-12b1-467c-94b8-723a89533c30",
+        "version": 1,
+        "state": {
+          "lastGroupId": 9,
+          "lastNodeId": 376,
+          "lastLinkId": 496,
+          "lastRerouteId": 0
+        },
+        "revision": 0,
+        "config": {},
+        "name": "Select Per-Line Text by Index",
+        "description": "Selects one line from multiline text by zero-based index for batch or list-driven prompt workflows.",
+        "inputNode": {
+          "id": -10,
+          "bounding": [
+            -990,
+            8595,
+            128,
+            88
+          ]
+        },
+        "outputNode": {
+          "id": -20,
+          "bounding": [
+            710,
+            8585,
+            128,
+            68
+          ]
+        },
+        "inputs": [
+          {
+            "id": "75417d82-a934-4ac9-b667-d8dcd5a3bfb3",
+            "name": "text_per_line",
+            "type": "STRING",
+            "linkIds": [
+              13
+            ],
+            "localized_name": "text_per_line",
+            "pos": [
+              -886,
+              8619
+            ]
+          },
+          {
+            "id": "46e69a73-1804-4ca6-9175-31445bf0be96",
+            "name": "index",
+            "type": "INT",
+            "linkIds": [
+              14
+            ],
+            "localized_name": "index",
+            "pos": [
+              -886,
+              8639
+            ]
+          }
+        ],
+        "outputs": [
+          {
+            "id": "e34e8ad1-84d2-4bd2-a460-eb7de6067c10",
+            "name": "selected_line",
+            "type": "STRING",
+            "linkIds": [
+              10
+            ],
+            "localized_name": "selected_line",
+            "pos": [
+              734,
+              8609
+            ]
+          }
+        ],
+        "widgets": [],
+        "nodes": [
+          {
+            "id": 372,
+            "type": "PreviewAny",
+            "pos": [
+              -500,
+              8400
+            ],
+            "size": [
+              230,
+              180
+            ],
+            "flags": {},
+            "order": 0,
+            "mode": 0,
+            "inputs": [
+              {
+                "localized_name": "source",
+                "name": "source",
+                "type": "*",
+                "link": 1
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "STRING",
+                "name": "STRING",
+                "type": "STRING",
+                "links": [
+                  6
+                ]
+              }
+            ],
+            "properties": {
+              "Node name for S&R": "PreviewAny",
+              "cnr_id": "comfy-core",
+              "ver": "0.19.0",
+              "ue_properties": {
+                "widget_ue_connectable": {},
+                "input_ue_unconnectable": {}
+              }
+            },
+            "widgets_values": [
+              null,
+              null,
+              null
+            ]
+          },
+          {
+            "id": 373,
+            "type": "RegexExtract",
+            "pos": [
+              -240,
+              8740
+            ],
+            "size": [
+              470,
+              460
+            ],
+            "flags": {},
+            "order": 1,
+            "mode": 0,
+            "showAdvanced": false,
+            "inputs": [
+              {
+                "localized_name": "string",
+                "name": "string",
+                "type": "STRING",
+                "widget": {
+                  "name": "string"
+                },
+                "link": 13
+              },
+              {
+                "localized_name": "regex_pattern",
+                "name": "regex_pattern",
+                "type": "STRING",
+                "widget": {
+                  "name": "regex_pattern"
+                },
+                "link": 9
+              },
+              {
+                "localized_name": "mode",
+                "name": "mode",
+                "type": "COMBO",
+                "widget": {
+                  "name": "mode"
+                },
+                "link": null
+              },
+              {
+                "localized_name": "case_insensitive",
+                "name": "case_insensitive",
+                "type": "BOOLEAN",
+                "widget": {
+                  "name": "case_insensitive"
+                },
+                "link": null
+              },
+              {
+                "localized_name": "multiline",
+                "name": "multiline",
+                "type": "BOOLEAN",
+                "widget": {
+                  "name": "multiline"
+                },
+                "link": null
+              },
+              {
+                "localized_name": "dotall",
+                "name": "dotall",
+                "type": "BOOLEAN",
+                "widget": {
+                  "name": "dotall"
+                },
+                "link": null
+              },
+              {
+                "localized_name": "group_index",
+                "name": "group_index",
+                "type": "INT",
+                "widget": {
+                  "name": "group_index"
+                },
+                "link": null
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "STRING",
+                "name": "STRING",
+                "type": "STRING",
+                "links": [
+                  10
+                ]
+              }
+            ],
+            "properties": {
+              "Node name for S&R": "RegexExtract",
+              "cnr_id": "comfy-core",
+              "ver": "0.19.0",
+              "ue_properties": {
+                "widget_ue_connectable": {},
+                "input_ue_unconnectable": {}
+              }
+            },
+            "widgets_values": [
+              "You are a helpful assistant.\nYou are a helpful assistant specialized in text-to-image generation.\nYou are a helpful assistant specialized in text-to-video generation.\nYou are a helpful assistant specialized in image editing.\nYou are a helpful assistant specialized in subject-to-image generation.\nYou are a helpful assistant specialized in image-to-video generation.\nYou are a helpful assistant specialized in video editing.\nYou are a helpful assistant specialized in video editing on content propagation.\nYou are a helpful assistant specialized in video editing with reference.\nYou are a helpful assistant specialized in ads insertion.\nYou are a helpful assistant for editing. You may need to adjust the subject's action or position.\nYou are a helpful assistant for editing. You might need to adjust the video's style, lighting, colors, textures, and the subject's pose or action.",
+              "",
+              "First Group",
+              false,
+              false,
+              false,
+              1
+            ]
+          },
+          {
+            "id": 374,
+            "type": "PrimitiveInt",
+            "pos": [
+              -810,
+              8400
+            ],
+            "size": [
+              270,
+              110
+            ],
+            "flags": {},
+            "order": 2,
+            "mode": 0,
+            "inputs": [
+              {
+                "localized_name": "value",
+                "name": "value",
+                "type": "INT",
+                "widget": {
+                  "name": "value"
+                },
+                "link": 14
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "INT",
+                "name": "INT",
+                "type": "INT",
+                "links": [
+                  1
+                ]
+              }
+            ],
+            "title": "Int (line index)",
+            "properties": {
+              "Node name for S&R": "Int (line index)",
+              "cnr_id": "comfy-core",
+              "ver": "0.19.0",
+              "ue_properties": {
+                "widget_ue_connectable": {},
+                "input_ue_unconnectable": {}
+              }
+            },
+            "widgets_values": [
+              0,
+              "fixed"
+            ]
+          },
+          {
+            "id": 375,
+            "type": "StringReplace",
+            "pos": [
+              -240,
+              8400
+            ],
+            "size": [
+              400,
+              280
+            ],
+            "flags": {},
+            "order": 3,
+            "mode": 0,
+            "inputs": [
+              {
+                "localized_name": "string",
+                "name": "string",
+                "type": "STRING",
+                "widget": {
+                  "name": "string"
+                },
+                "link": null
+              },
+              {
+                "localized_name": "find",
+                "name": "find",
+                "type": "STRING",
+                "widget": {
+                  "name": "find"
+                },
+                "link": null
+              },
+              {
+                "localized_name": "replace",
+                "name": "replace",
+                "type": "STRING",
+                "widget": {
+                  "name": "replace"
+                },
+                "link": 6
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "STRING",
+                "name": "STRING",
+                "type": "STRING",
+                "links": [
+                  9
+                ]
+              }
+            ],
+            "properties": {
+              "Node name for S&R": "StringReplace",
+              "cnr_id": "comfy-core",
+              "ver": "0.19.0",
+              "ue_properties": {
+                "widget_ue_connectable": {},
+                "input_ue_unconnectable": {}
+              }
+            },
+            "widgets_values": [
+              "^(?:[^\\n]*\\n){index}([^\\n]*)(?:\\n|$)",
+              "index",
+              ""
+            ]
+          }
+        ],
+        "groups": [],
+        "links": [
+          {
+            "id": 1,
+            "origin_id": 374,
+            "origin_slot": 0,
+            "target_id": 372,
+            "target_slot": 0,
+            "type": "INT"
+          },
+          {
+            "id": 9,
+            "origin_id": 375,
+            "origin_slot": 0,
+            "target_id": 373,
+            "target_slot": 1,
+            "type": "STRING"
+          },
+          {
+            "id": 6,
+            "origin_id": 372,
+            "origin_slot": 0,
+            "target_id": 375,
+            "target_slot": 2,
+            "type": "STRING"
+          },
+          {
+            "id": 10,
+            "origin_id": 373,
+            "origin_slot": 0,
+            "target_id": -20,
+            "target_slot": 0,
+            "type": "STRING"
+          },
+          {
+            "id": 13,
+            "origin_id": -10,
+            "origin_slot": 0,
+            "target_id": 373,
+            "target_slot": 0,
+            "type": "STRING"
+          },
+          {
+            "id": 14,
+            "origin_id": -10,
+            "origin_slot": 1,
+            "target_id": 374,
+            "target_slot": 0,
+            "type": "INT"
+          }
+        ],
+        "extra": {
+          "ue_links": [],
+          "links_added_by_ue": []
+        }
+      }
+    ]
+  },
+  "extra": {
+    "BlueprintDescription": "This subgraph uses Depth Anything 3 to predict spatially consistent geometry from any number of images or video frames, with or without known camera poses. It outputs depth maps, camera poses, and optionally 3D Gaussian parameters for novel view synthesis."
+  }
+}
\ No newline at end of file

From c7b246edc4401307172b22eb257896c1b5eb6a24 Mon Sep 17 00:00:00 2001
From: EXA4V <xavier@exa4virtual.com>
Date: Wed, 17 Jun 2026 04:01:05 +0200
Subject: [PATCH 15/30] docs: add M3 and M4 to Apple Silicon supported chips
 list (#14449)

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 61036bd81..06f0d7050 100644
--- a/README.md
+++ b/README.md
@@ -309,7 +309,7 @@ After this you should have everything installed and can proceed to running Comfy
 
 #### Apple Mac silicon
 
-You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS version.
+You can install ComfyUI in Apple Mac silicon (M1, M2, M3 or M4) with any recent macOS version.
 
 1. Install pytorch nightly. For instructions, read the [Accelerated PyTorch training on Mac](https://developer.apple.com/metal/pytorch/) Apple Developer guide (make sure to install the latest pytorch nightly).
 1. Follow the [ComfyUI manual installation](#manual-install-windows-linux) instructions for Windows and Linux.

From f026b01ba576d98442839861a0eb0046bc2250d3 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Tue, 16 Jun 2026 20:02:53 -0700
Subject: [PATCH 16/30] Update links to new comfyui desktop repo. (#14516)

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 06f0d7050..c75353d36 100644
--- a/README.md
+++ b/README.md
@@ -140,7 +140,7 @@ ComfyUI follows a weekly release cycle targeting Monday but this regularly chang
    - Commits outside of the stable release tags may be very unstable and break many custom nodes.
    - Serves as the foundation for the desktop release
 
-2. **[ComfyUI Desktop](https://github.com/Comfy-Org/desktop)**
+2. **[ComfyUI Desktop](https://github.com/Comfy-Org/Comfy-Desktop)**
    - Builds a new release using the latest stable core version
 
 3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)**

From d202707ff23e677dbed0272047ae9ff938652592 Mon Sep 17 00:00:00 2001
From: Alexis Rolland <alexisrolland@hotmail.com>
Date: Wed, 17 Jun 2026 21:02:45 +0800
Subject: [PATCH 17/30] Update TripoSplat categories (#14512)

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

diff --git a/comfy_extras/nodes_triposplat.py b/comfy_extras/nodes_triposplat.py
index 1848ad31a..7bf4703fe 100644
--- a/comfy_extras/nodes_triposplat.py
+++ b/comfy_extras/nodes_triposplat.py
@@ -65,7 +65,7 @@ class TripoSplatPreprocessImage(IO.ComfyNode):
         return IO.Schema(
             node_id="TripoSplatPreprocessImage",
             display_name="TripoSplat Preprocess Image",
-            category="3d/conditioning",
+            category="model/conditioning/triposplat",
             description="Crop center each image to a square canvas on a black background and add padding.",
             inputs=[
                 IO.Image.Input("image"),
@@ -95,7 +95,7 @@ class TripoSplatConditioning(IO.ComfyNode):
         return IO.Schema(
             node_id="TripoSplatConditioning",
             display_name="TripoSplat Conditioning",
-            category="3d/conditioning",
+            category="model/conditioning/triposplat",
             description="Encode the image with DINOv3 and the Flux2 VAE into TripoSplat positive/negative "
                         "conditioning, and create the fixed size noise target (latent + camera) for the KSampler",
             inputs=[

From a590d60bb1d7d47c1cdb49fc8116b0e919fc4bd1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?=
 <40791699+kijai@users.noreply.github.com>
Date: Wed, 17 Jun 2026 16:21:23 +0300
Subject: [PATCH 18/30] feat: SCAIL-2 multireference (CORE-310) (#14509)

* SCAIl-2: support multiref
---
 comfy/ldm/wan/model.py      |  19 +++----
 comfy/model_base.py         |  20 ++++---
 comfy_extras/nodes_scail.py | 110 ++++++++++++++++++++++--------------
 3 files changed, 91 insertions(+), 58 deletions(-)

diff --git a/comfy/ldm/wan/model.py b/comfy/ldm/wan/model.py
index 282408891..1c9782a38 100644
--- a/comfy/ldm/wan/model.py
+++ b/comfy/ldm/wan/model.py
@@ -1665,7 +1665,7 @@ class SCAILWanModel(WanModel):
 
         # embeddings
         x = self.patch_embedding(x.float()).to(x.dtype)
-        if ref_mask_latents is not None:  # SCAIL-2 additive mask stream
+        if ref_mask_latents is not None:  # SCAIL-2 additive mask stream (one identity mask frame per reference, then video)
             x = x + self.patch_embedding_mask(ref_mask_latents.float()).to(x.dtype)
         grid_sizes = x.shape[2:]
         transformer_options["grid_sizes"] = grid_sizes
@@ -1728,22 +1728,25 @@ class SCAILWanModel(WanModel):
 
     # ref_mask_flag is a scalar bool (CONDConstant, SCAIL-2 only). False => replacement mode,
     # which places ref/pose via H/W rope shifts instead of the animation-mode temporal offset.
+    # reference_latent may stack several frames: the last is the primary reference adjacent to the video, the earlier frames are additional references.
     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, ref_mask_flag=None, transformer_options={}):
+        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]
+
         if ref_mask_flag is not None and not bool(ref_mask_flag):
             REF_ROPE_H = 120.0
             POSE_ROPE_W = 120.0
 
-            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]
             main_t_patches = t - ref_t_patches
+            video_t_start = max(ref_t_patches - 1, 0)
 
             parts = []
             if ref_t_patches > 0:
                 ref_tf = {"rope_options": {"shift_y": REF_ROPE_H, "shift_x": 0.0, "scale_y": 1.0, "scale_x": 1.0}}
                 parts.append(super().rope_encode(ref_t_patches, h, w, t_start=0, device=device, dtype=dtype, transformer_options=ref_tf))
             if main_t_patches > 0:
-                parts.append(super().rope_encode(main_t_patches, h, w, t_start=0, device=device, dtype=dtype, transformer_options=transformer_options))
+                parts.append(super().rope_encode(main_t_patches, h, w, t_start=video_t_start, device=device, dtype=dtype, transformer_options=transformer_options))
 
             if pose_latents is not None:
                 F_pose, H_pose, W_pose = pose_latents.shape[-3], pose_latents.shape[-2], pose_latents.shape[-1]
@@ -1752,7 +1755,7 @@ class SCAILWanModel(WanModel):
                 h_shift = (h_scale - 1) / 2
                 w_shift = (w_scale - 1) / 2
                 pose_tf = {"rope_options": {"shift_y": h_shift, "shift_x": POSE_ROPE_W + w_shift, "scale_y": h_scale, "scale_x": w_scale}}
-                parts.append(super().rope_encode(F_pose, H_pose, W_pose, t_start=0, device=device, dtype=dtype, transformer_options=pose_tf))
+                parts.append(super().rope_encode(F_pose, H_pose, W_pose, t_start=video_t_start, device=device, dtype=dtype, transformer_options=pose_tf))
 
             return torch.cat(parts, dim=1)
 
@@ -1761,10 +1764,6 @@ class SCAILWanModel(WanModel):
         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
diff --git a/comfy/model_base.py b/comfy/model_base.py
index ab4a11022..d143dc06f 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -1747,10 +1747,14 @@ class WAN21_SCAIL(WAN21):
 
         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)
+            # SCAIL-2 multi-reference: reference_latents[0] is the primary ref, [1:] are additional
+            # references. Stack as [additional..., primary] so the primary stays adjacent to the video.
+            ordered = list(reference_latents[1:]) + list(reference_latents[:1])
+            stacked = []
+            for lat in ordered:
+                lat = self.process_latent_in(lat)
+                stacked.append(torch.cat([lat, torch.ones_like(lat[:, :4])], dim=1))
+            out['reference_latent'] = comfy.conds.CONDRegular(torch.cat(stacked, dim=2))
 
         pose_latents = kwargs.get("pose_video_latent", None)
         if pose_latents is not None:
@@ -1792,6 +1796,7 @@ class WAN21_SCAIL2(WAN21_SCAIL):
         if driving_mask_28ch is not None:
             out['sam_latents'] = comfy.conds.CONDRegular(driving_mask_28ch.movedim(1, 2).contiguous())
 
+        # ref_mask_28ch holds one identity mask per stacked reference frame (additional refs first, then the primary ref), followed by zeros over the video frames.
         ref_mask_28ch = kwargs.get("ref_mask_28ch", None)
         if ref_mask_28ch is not None:
             out['ref_mask_latents'] = comfy.conds.CONDRegular(ref_mask_28ch.movedim(1, 2).contiguous())
@@ -1819,10 +1824,11 @@ class WAN21_SCAIL2(WAN21_SCAIL):
             # Return sliced view omitting retain_index_list
             return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=2, temporal_offset=0)
         if cond_key == "ref_mask_latents" and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
-            # The ref mask is just a single frame padded with frames of zeros, so just grab the first frames for all windows
+            # The ref mask is N leading ref frames padded with frames of zeros, so just grab the first frames for all windows
             full_ref_mask = cond_value.cond
             video_frame_count = x_in.shape[2]
-            if full_ref_mask.shape[2] != video_frame_count + 1:
+            ref_frame_count = full_ref_mask.shape[2] - video_frame_count
+            if ref_frame_count < 1:
                 return None
             window_length = len(window.index_list)
 
@@ -1831,7 +1837,7 @@ class WAN21_SCAIL2(WAN21_SCAIL):
             if anchor_index is not None and anchor_index >= 0:
                 window_length += 1
 
-            window_ref_mask = full_ref_mask[:, :, :window_length + 1].to(device)
+            window_ref_mask = full_ref_mask[:, :, :window_length + ref_frame_count].to(device)
             return cond_value._copy_with(window_ref_mask)
 
         return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list)
diff --git a/comfy_extras/nodes_scail.py b/comfy_extras/nodes_scail.py
index 007733efc..55c9897e3 100644
--- a/comfy_extras/nodes_scail.py
+++ b/comfy_extras/nodes_scail.py
@@ -34,14 +34,20 @@ def _unpack(track_data):
     return unpack_masks(packed)
 
 
-def _first_frame_cx_area(masks_bool):
-    first = masks_bool[0].float()
-    H, W = first.shape[-2], first.shape[-1]
-    n_pixels = H * W
-    grid_x = torch.arange(W, device=first.device, dtype=first.dtype).view(1, W)
-    area = first.sum(dim=(-1, -2)).clamp_(min=1)
-    cx = (first * grid_x).sum(dim=(-1, -2)) / area
-    return (cx / W).tolist(), (area / n_pixels).tolist()
+def _first_appearance_cx_area(masks_bool):
+    """Per object: first frame it appears in, plus centroid-x and area in that frame."""
+    m = masks_bool.float()
+    T, H, W = m.shape[0], m.shape[-2], m.shape[-1]
+    grid_x = torch.arange(W, device=m.device, dtype=m.dtype).view(1, 1, 1, W)
+    area_t = m.sum(dim=(-1, -2))
+    cx_t = (m * grid_x).sum(dim=(-1, -2)) / area_t.clamp(min=1)
+    present = area_t > 0
+    frame_idx = torch.arange(T, device=m.device).unsqueeze(1)
+    first_t = torch.where(present, frame_idx, T).amin(dim=0)
+    sel = first_t.clamp(max=T - 1).unsqueeze(0)
+    cx = cx_t.gather(0, sel).squeeze(0)
+    area = area_t.gather(0, sel).squeeze(0)
+    return first_t.tolist(), (cx / W).tolist(), (area / (H * W)).tolist()
 
 
 def _subset_track_data(track_data, obj_indices):
@@ -81,12 +87,26 @@ def _render_colored_masks(track_data, background="black"):
         masks_full.view(T * N_obj, 1, Hm, Wm), size=(H, W), mode="nearest"
     ).view(T, N_obj, H, W) > 0.5
     any_mask = masks_full.any(dim=1)
-    obj_idx_map = masks_full.to(torch.uint8).argmax(dim=1)
-    color_overlay = colors[obj_idx_map]
+    color_overlay = colors[masks_full.to(torch.uint8).argmax(dim=1)]
     bg_tensor = torch.tensor(bg_rgb, device=device, dtype=color_overlay.dtype).view(1, 1, 1, 3)
     return torch.where(any_mask.unsqueeze(-1), color_overlay, bg_tensor.expand_as(color_overlay))
 
 
+def _render_mask_as_identity(mask, background="black"):
+    """Plain comfy MASK (B,H,W) or (H,W) -> (B,H,W,3) rendered as a single identity (palette[0])
+    on the given background. A batch is treated as multiple views of that one subject."""
+    device = comfy.model_management.intermediate_device()
+    dtype = comfy.model_management.intermediate_dtype()
+    if mask.ndim == 2:
+        mask = mask.unsqueeze(0)
+    mask = mask.to(device=device, dtype=dtype)
+    B, H, W = mask.shape
+    bg_rgb = (1.0, 1.0, 1.0) if background.startswith("white") else (0.0, 0.0, 0.0)
+    color = torch.tensor(DEFAULT_PALETTE[0], device=device, dtype=dtype).view(1, 1, 1, 3)
+    bg = torch.tensor(bg_rgb, device=device, dtype=dtype).view(1, 1, 1, 3)
+    return torch.where((mask > 0.5).unsqueeze(-1), color.expand(B, H, W, 3), bg.expand(B, H, W, 3))
+
+
 def _extract_mask_to_28ch(rgb_video):
     """Colored RGB mask (T, H, W, 3) in [0, 1] -> SCAIL-2 28-channel binary latent
     (1, T_lat, 28, H_lat, W_lat). 7 per-color binary channels (white/r/g/b/y/m/c)
@@ -138,8 +158,8 @@ class WanSCAILToVideo(io.ComfyNode):
                 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 of the pose conditioning."),
                 io.Float.Input("pose_end", default=1.0, min=0.0, max=1.0, step=0.01, tooltip="End step of the pose conditioning."),
-                io.Image.Input("reference_image", optional=True, tooltip="Reference image, for multiple references composite all on single image."),
-                io.Image.Input("reference_image_mask", optional=True, tooltip="SCAIL-2 only. Colored reference mask at the same resolution as reference_image."),
+                io.Image.Input("reference_image", optional=True, tooltip="Reference image. The first image is the primary reference (composite all identities onto it). SCAIL-2: extra batch images are used as additional views (back view, close-up, occluded background), each needing a matching reference_image_mask in that identity's color."),
+                io.Image.Input("reference_image_mask", optional=True, tooltip="SCAIL-2 only. Colored reference mask, batch matching reference_image (first = primary reference mask, rest = identity masks for the additional reference_image)."),
                 io.ClipVisionOutput.Input("clip_vision_output", optional=True, tooltip="CLIP vision features for conditioning. Model is trained with stretch resize to aspect ratio."),
                 io.Int.Input("video_frame_offset", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1, tooltip="Cumulative output frame this chunk begins at. Wire from the previous chunk's video_frame_offset output."),
                 io.Int.Input("previous_frame_count", default=5, min=1, max=nodes.MAX_RESOLUTION, step=4, tooltip="Tail frames of previous_frames to anchor. SCAIL-2 trained at 5 (81-frame chunks, 76-frame step)."),
@@ -171,19 +191,21 @@ class WanSCAILToVideo(io.ComfyNode):
             video_frame_offset -= prev_trimmed.shape[0]
             video_frame_offset = max(0, video_frame_offset)
 
-        ref_latent = None
         if reference_image is not None:
-            reference_image = comfy.utils.common_upscale(reference_image[:1].movedim(-1, 1), width, height, "bicubic", "center").movedim(1, -1)
-            # Replacement Mode: composite ref on black bg using reference_image_mask as alpha matte
-            if replacement_mode and reference_image_mask is not None:
-                rm = comfy.utils.common_upscale(reference_image_mask[:1].movedim(-1, 1), width, height, "nearest-exact", "center").movedim(1, -1)
-                is_char = (rm[..., :3].max(dim=-1, keepdim=True).values > 0.1).to(reference_image.dtype)
-                reference_image = reference_image * is_char
-            ref_latent = vae.encode(reference_image[:, :, :, :3])
+            ref_imgs = comfy.utils.common_upscale(reference_image.movedim(-1, 1), width, height, "bicubic", "center").movedim(1, -1)
+            n_ref = ref_imgs.shape[0]
+            # SCAIL-2 multi-reference: the first image is the primary ref, the rest are additional references.
 
-        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": [ref_latent]}, append=True)
+            # Replacement Mode: composite each ref on black bg using its mask as alpha matte
+            if replacement_mode and reference_image_mask is not None:
+                rm = comfy.utils.common_upscale(reference_image_mask.movedim(-1, 1), width, height, "nearest-exact", "center").movedim(1, -1)
+                rm = rm[[min(i, rm.shape[0] - 1) for i in range(n_ref)]]
+                is_char = (rm[..., :3].max(dim=-1, keepdim=True).values > 0.1).to(ref_imgs.dtype)
+                ref_imgs = ref_imgs * is_char
+            # encode each ref individually so each stays a single latent frame (a batched encode would be treated as a video)
+            ref_latents = [vae.encode(ref_imgs[i:i + 1, :, :, :3]) for i in range(n_ref)]
+            positive = node_helpers.conditioning_set_values(positive, {"reference_latents": ref_latents}, append=True)
+            negative = node_helpers.conditioning_set_values(negative, {"reference_latents": ref_latents}, append=True)
 
         if clip_vision_output is not None:
             positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
@@ -221,11 +243,16 @@ class WanSCAILToVideo(io.ComfyNode):
             positive = node_helpers.conditioning_set_values(positive, {"driving_mask_28ch": driving_mask_28ch})
             negative = node_helpers.conditioning_set_values(negative, {"driving_mask_28ch": driving_mask_28ch})
 
-        if reference_image_mask is not None:
-            ref_mask_hw = comfy.utils.common_upscale(reference_image_mask[:1].movedim(-1, 1), width, height, "bicubic", "center").movedim(1, -1)
-            ref_mask_1f = _extract_mask_to_28ch(ref_mask_hw)
+        # The ref mask binds reference frames to identities, so it only applies when there's a reference image.
+        if reference_image_mask is not None and reference_image is not None:
+            ref_mask_hw = comfy.utils.common_upscale(reference_image_mask.movedim(-1, 1), width, height, "nearest-exact", "center").movedim(1, -1)
+            n_masks = ref_mask_hw.shape[0]
+            n_ref = reference_image.shape[0]
+
+            add_masks = [_extract_mask_to_28ch(ref_mask_hw[min(i, n_masks - 1)][None]) for i in range(1, n_ref)]
+            ref_mask_1f = _extract_mask_to_28ch(ref_mask_hw[:1])
             zeros = torch.zeros((1, latent.shape[2], 28, ref_mask_1f.shape[-2], ref_mask_1f.shape[-1]), device=ref_mask_1f.device, dtype=ref_mask_1f.dtype)
-            ref_mask_28ch = torch.cat([ref_mask_1f, zeros], dim=1)
+            ref_mask_28ch = torch.cat(add_masks + [ref_mask_1f, zeros], dim=1)
             positive = node_helpers.conditioning_set_values(positive, {"ref_mask_28ch": ref_mask_28ch})
             negative = node_helpers.conditioning_set_values(negative, {"ref_mask_28ch": ref_mask_28ch})
 
@@ -244,12 +271,9 @@ class WanSCAILToVideo(io.ComfyNode):
 
 
 class SCAIL2ColoredMask(io.ComfyNode):
-    """Render SAM3 tracks for the driving pose video and (optionally) the reference
-    image into the two colored masks WanSCAILToVideo consumes. Shared `sort_by`
-    across both outputs guarantees identity K maps to the same color on both
-    sides, for multi-person workflow consistency.
-    reference_image_mask is always rendered black-bg (model convention)
-    pose_video_mask bg follows replacement_mode: black = Animation Mode, white = Replacement Mode
+    """Render SAM3 tracks for the driving pose video and reference image(s) into the
+    colored masks WanSCAILToVideo consumes. Shared `sort_by` keeps each identity on the
+    same color across both outputs.
     """
 
     @classmethod
@@ -260,10 +284,12 @@ class SCAIL2ColoredMask(io.ComfyNode):
             category="model/conditioning/wan/scail",
             inputs=[
                 SAM3TrackData.Input("driving_track_data", tooltip="SAM3 track of the driving pose video. Will be rendered into the pose_video_mask output."),
-                SAM3TrackData.Input("ref_track_data", optional=True, tooltip="SAM3 track of the reference image."),
-                io.String.Input("object_indices", default="", tooltip="Comma-separated list of person indices to include (e.g. '0,2,3'). Applied to both reference and pose video masks. Empty = all."),
+                io.MultiType.Input("ref_track_data", [SAM3TrackData, io.Mask], optional=True, display_name="reference_masks",
+                                   tooltip="SAM3 track of the reference image(s) (one identity per object, colored in batch order), or a plain MASK of the reference subject (rendered as a single identity)."),
+                io.String.Input("object_indices", default="",
+                                tooltip="Comma-separated list of person indices to include (e.g. '0,2,3'). Applied to both reference and pose video masks. Empty = all."),
                 io.Combo.Input("sort_by", options=["none", "left_to_right", "area"], default="left_to_right",
-                    tooltip="Order in which palette colors are assigned to the tracked objects (applied to both reference and pose video so each identity keeps the same color). left_to_right = leftmost object (by first-frame centroid) gets the first color; area = biggest object (by first-frame mask area) gets the first color; none = keep SAM3's order."),
+                               tooltip="Order in which palette colors are assigned to the tracked objects (applied to both reference and pose video so each identity keeps the same color). Objects that appear in earlier frames always come first; within a frame, left_to_right = leftmost object (by centroid at first appearance) gets the first color, area = biggest object (by mask area at first appearance) gets the first color; none = keep SAM3's order."),
                 io.Boolean.Input("replacement_mode", default=False,
                     tooltip="False = Animation Mode (pose_video_mask has black background, reference_image_mask has white background). "
                     "True = Replacement Mode (pose_video_mask has white background, reference_image_mask has black background)."),
@@ -280,11 +306,11 @@ class SCAIL2ColoredMask(io.ComfyNode):
         def _prep(td):
             masks_bool = _unpack(td)
             if sort_by != "none" and masks_bool is not None:
-                cx, area = _first_frame_cx_area(masks_bool)
+                first_t, cx, area = _first_appearance_cx_area(masks_bool)
                 if sort_by == "left_to_right":
-                    order = sorted(range(len(cx)), key=lambda i: cx[i])
+                    order = sorted(range(len(cx)), key=lambda i: (first_t[i], cx[i]))
                 else:  # "area"
-                    order = sorted(range(len(area)), key=lambda i: -area[i])
+                    order = sorted(range(len(area)), key=lambda i: (first_t[i], -area[i]))
                 td = _subset_track_data(td, order)
             if object_indices.strip():
                 indices = [int(i.strip()) for i in object_indices.split(",") if i.strip().isdigit()]
@@ -300,8 +326,10 @@ class SCAIL2ColoredMask(io.ComfyNode):
         ref_bg = "black" if replacement_mode else "white"
 
         if ref_track_data is not None:
-            ref = _prep(ref_track_data)
-            reference_image_mask = _render_colored_masks(ref, ref_bg)
+            if isinstance(ref_track_data, torch.Tensor):  # plain comfy MASK
+                reference_image_mask = _render_mask_as_identity(ref_track_data, ref_bg)
+            else:
+                reference_image_mask = _render_colored_masks(_prep(ref_track_data), ref_bg)
         else:
             H, W = drv["orig_size"]
             fill_value = 1.0 if ref_bg == "white" else 0.0

From ca3dbe206c2fea84f2af4371ca13e9f2bfeb82e5 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?=
 <40791699+kijai@users.noreply.github.com>
Date: Wed, 17 Jun 2026 18:45:06 +0300
Subject: [PATCH 19/30] Allow using Qwen3-VL as flux2 klein text encoder
 (again) (#14526)

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

diff --git a/comfy/sd.py b/comfy/sd.py
index 688e6db90..348fe4958 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -1622,6 +1622,10 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
                 clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
                 clip_target.clip = comfy.text_encoders.ideogram4.te_qwen3vl(**llama_detect(clip_data))
                 clip_target.tokenizer = comfy.text_encoders.ideogram4.Ideogram4Qwen3VLTokenizer
+            elif clip_type in (CLIPType.FLUX, CLIPType.FLUX2):  # Flux2 Klein reuses the Qwen3-VL LM (3-layer tap -> 12288); visual unused.
+                klein_model_type = "qwen3_8b" if te_model == TEModel.QWEN3VL_8B else "qwen3_4b"
+                clip_target.clip = comfy.text_encoders.flux.klein_te(**llama_detect(clip_data), model_type=klein_model_type)
+                clip_target.tokenizer = comfy.text_encoders.flux.KleinTokenizer8B if te_model == TEModel.QWEN3VL_8B else comfy.text_encoders.flux.KleinTokenizer
             else:
                 clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
                 qwen3vl_type = {TEModel.QWEN3VL_4B: "qwen3vl_4b", TEModel.QWEN3VL_8B: "qwen3vl_8b"}[te_model]

From e25c391888618967f7ef199f466ff29da2ed14b3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?=
 <40791699+kijai@users.noreply.github.com>
Date: Thu, 18 Jun 2026 00:22:36 +0300
Subject: [PATCH 20/30] feat: Support Boogu-Image (CORE-308) (#14523)

---
 comfy/ldm/boogu/model.py      | 321 ++++++++++++++++++++++++++++++++++
 comfy/ldm/omnigen/omnigen2.py |   2 +-
 comfy/model_base.py           |   6 +
 comfy/model_detection.py      |  10 ++
 comfy/sd.py                   |   6 +
 comfy/supported_models.py     |  23 +++
 comfy/text_encoders/boogu.py  |  58 ++++++
 comfy_extras/nodes_boogu.py   |  96 ++++++++++
 nodes.py                      |   3 +-
 9 files changed, 523 insertions(+), 2 deletions(-)
 create mode 100644 comfy/ldm/boogu/model.py
 create mode 100644 comfy/text_encoders/boogu.py
 create mode 100644 comfy_extras/nodes_boogu.py

diff --git a/comfy/ldm/boogu/model.py b/comfy/ldm/boogu/model.py
new file mode 100644
index 000000000..966f3c583
--- /dev/null
+++ b/comfy/ldm/boogu/model.py
@@ -0,0 +1,321 @@
+# Boogu-Image-0.1 transformer
+# Architecture is an OmniGen2 derivative (see comfy/ldm/omnigen/omnigen2.py) with an
+# added dual-stream ("double_stream") stage before the single-stream layers, conditioned
+# by a Qwen3-VL multimodal LLM. Reuses the OmniGen2/Lumina building blocks and the Flux
+# RoPE core, the only new component is the double-stream block + the hybrid forward order.
+
+from typing import Optional, Tuple
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+
+import comfy.ldm.common_dit
+import comfy.ldm.omnigen.omnigen2
+from comfy.ldm.modules.attention import optimized_attention_masked
+from comfy.ldm.omnigen.omnigen2 import (
+    OmniGen2RotaryPosEmbed,
+    Lumina2CombinedTimestepCaptionEmbedding,
+    LuminaRMSNormZero,
+    LuminaLayerNormContinuous,
+    LuminaFeedForward,
+    Attention,
+    OmniGen2TransformerBlock,
+    apply_rotary_emb,
+)
+
+class BooguDoubleStreamProcessor(nn.Module):
+    # Joint attention over [instruct ; img] with separate per-stream q/k/v and output projections.
+    def __init__(self, dim, head_dim, heads, kv_heads, dtype=None, device=None, operations=None):
+        super().__init__()
+        query_dim = head_dim * heads
+        kv_dim = head_dim * kv_heads
+
+        self.img_to_q = operations.Linear(query_dim, query_dim, bias=False, dtype=dtype, device=device)
+        self.img_to_k = operations.Linear(query_dim, kv_dim, bias=False, dtype=dtype, device=device)
+        self.img_to_v = operations.Linear(query_dim, kv_dim, bias=False, dtype=dtype, device=device)
+
+        self.instruct_to_q = operations.Linear(query_dim, query_dim, bias=False, dtype=dtype, device=device)
+        self.instruct_to_k = operations.Linear(query_dim, kv_dim, bias=False, dtype=dtype, device=device)
+        self.instruct_to_v = operations.Linear(query_dim, kv_dim, bias=False, dtype=dtype, device=device)
+
+        self.instruct_out = operations.Linear(query_dim, query_dim, bias=False, dtype=dtype, device=device)
+        self.img_out = operations.Linear(query_dim, query_dim, bias=False, dtype=dtype, device=device)
+
+    def forward(self, attn, img_hidden_states, instruct_hidden_states, rotary_emb, attention_mask=None, transformer_options={}):
+        batch_size = img_hidden_states.shape[0]
+        L_instruct = instruct_hidden_states.shape[1]
+
+        img_q = self.img_to_q(img_hidden_states)
+        img_k = self.img_to_k(img_hidden_states)
+        img_v = self.img_to_v(img_hidden_states)
+
+        instruct_q = self.instruct_to_q(instruct_hidden_states)
+        instruct_k = self.instruct_to_k(instruct_hidden_states)
+        instruct_v = self.instruct_to_v(instruct_hidden_states)
+
+        # Concatenate instruction first, then image (matches reference processor order).
+        query = torch.cat([instruct_q, img_q], dim=1)
+        key = torch.cat([instruct_k, img_k], dim=1)
+        value = torch.cat([instruct_v, img_v], dim=1)
+
+        query = query.view(batch_size, -1, attn.heads, attn.dim_head)
+        key = key.view(batch_size, -1, attn.kv_heads, attn.dim_head)
+        value = value.view(batch_size, -1, attn.kv_heads, attn.dim_head)
+
+        query = attn.norm_q(query)
+        key = attn.norm_k(key)
+
+        if rotary_emb is not None:
+            query = apply_rotary_emb(query, rotary_emb)
+            key = apply_rotary_emb(key, rotary_emb)
+
+        query = query.transpose(1, 2)
+        key = key.transpose(1, 2)
+        value = value.transpose(1, 2)
+
+        if attn.kv_heads < attn.heads:
+            key = key.repeat_interleave(attn.heads // attn.kv_heads, dim=1)
+            value = value.repeat_interleave(attn.heads // attn.kv_heads, dim=1)
+
+        hidden_states = optimized_attention_masked(query, key, value, attn.heads, attention_mask, skip_reshape=True, transformer_options=transformer_options)
+
+        # Split back to instruction/image, apply per-stream output projections, recombine.
+        instruct_hidden_states = self.instruct_out(hidden_states[:, :L_instruct])
+        img_hidden_states = self.img_out(hidden_states[:, L_instruct:])
+        hidden_states = torch.cat([instruct_hidden_states, img_hidden_states], dim=1)
+
+        hidden_states = attn.to_out[0](hidden_states)
+        return hidden_states
+
+
+class BooguJointAttention(nn.Module):
+    # Holds the shared q/k RMSNorm + final output projection
+    def __init__(self, dim, head_dim, heads, kv_heads, eps=1e-5, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.heads = heads
+        self.kv_heads = kv_heads
+        self.dim_head = head_dim
+        self.scale = head_dim ** -0.5
+
+        self.norm_q = operations.RMSNorm(head_dim, eps=eps, dtype=dtype, device=device)
+        self.norm_k = operations.RMSNorm(head_dim, eps=eps, dtype=dtype, device=device)
+        self.to_out = nn.Sequential(
+            operations.Linear(heads * head_dim, dim, bias=False, dtype=dtype, device=device),
+            nn.Dropout(0.0),
+        )
+        self.processor = BooguDoubleStreamProcessor(dim, head_dim, heads, kv_heads, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, img_hidden_states, instruct_hidden_states, rotary_emb, attention_mask=None, transformer_options={}):
+        return self.processor(self, img_hidden_states, instruct_hidden_states, rotary_emb, attention_mask, transformer_options=transformer_options)
+
+
+class BooguDoubleStreamBlock(nn.Module):
+    # Dual-stream block: joint attention over [instruct ; img] + image self-attention, each stream with its own modulation/MLP.
+    def __init__(self, dim, num_attention_heads, num_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, dtype=None, device=None, operations=None):
+        super().__init__()
+        head_dim = dim // num_attention_heads
+
+        self.img_instruct_attn = BooguJointAttention(dim, head_dim, num_attention_heads, num_kv_heads, eps=1e-5, dtype=dtype, device=device, operations=operations)
+        self.img_self_attn = Attention(
+            query_dim=dim, dim_head=head_dim, heads=num_attention_heads, kv_heads=num_kv_heads,
+            eps=1e-5, bias=False, dtype=dtype, device=device, operations=operations,
+        )
+
+        self.img_feed_forward = LuminaFeedForward(dim=dim, inner_dim=4 * dim, multiple_of=multiple_of, dtype=dtype, device=device, operations=operations)
+        self.instruct_feed_forward = LuminaFeedForward(dim=dim, inner_dim=4 * dim, multiple_of=multiple_of, dtype=dtype, device=device, operations=operations)
+
+        self.img_norm1 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations)
+        self.img_norm2 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations)
+        self.img_norm3 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations)
+        self.instruct_norm1 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations)
+        self.instruct_norm2 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations)
+
+        self.img_attn_norm = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
+        self.img_self_attn_norm = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
+        self.img_ffn_norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
+        self.img_ffn_norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
+
+        self.instruct_attn_norm = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
+        self.instruct_ffn_norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
+        self.instruct_ffn_norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
+
+    def forward(self, img_hidden_states, instruct_hidden_states, joint_rotary_emb, img_rotary_emb, temb, joint_attention_mask=None, img_attention_mask=None, transformer_options={}):
+        L_instruct = instruct_hidden_states.shape[1]
+
+        img_norm1_out, img_gate_msa, img_scale_mlp, img_gate_mlp = self.img_norm1(img_hidden_states, temb)
+        img_norm2_out, img_shift_mlp, _, _ = self.img_norm2(img_hidden_states, temb)
+        img_norm3_out, img_gate_self, _, _ = self.img_norm3(img_hidden_states, temb)
+
+        instruct_norm1_out, instruct_gate_msa, instruct_scale_mlp, instruct_gate_mlp = self.instruct_norm1(instruct_hidden_states, temb)
+        instruct_norm2_out, instruct_shift_mlp, _, _ = self.instruct_norm2(instruct_hidden_states, temb)
+
+        joint_attn_out = self.img_instruct_attn(img_norm1_out, instruct_norm1_out, joint_rotary_emb, joint_attention_mask, transformer_options=transformer_options)
+        instruct_attn_out = joint_attn_out[:, :L_instruct]
+        img_attn_out = joint_attn_out[:, L_instruct:]
+
+        img_self_attn_out = self.img_self_attn(img_norm3_out, img_norm3_out, img_attention_mask, img_rotary_emb, transformer_options=transformer_options)
+
+        img_hidden_states = img_hidden_states + img_gate_msa.unsqueeze(1).tanh() * self.img_attn_norm(img_attn_out)
+        img_hidden_states = img_hidden_states + img_gate_self.unsqueeze(1).tanh() * self.img_self_attn_norm(img_self_attn_out)
+        img_mlp_input = (1 + img_scale_mlp.unsqueeze(1)) * img_norm2_out + img_shift_mlp.unsqueeze(1)
+        img_mlp_out = self.img_feed_forward(self.img_ffn_norm1(img_mlp_input))
+        img_hidden_states = img_hidden_states + img_gate_mlp.unsqueeze(1).tanh() * self.img_ffn_norm2(img_mlp_out)
+
+        instruct_hidden_states = instruct_hidden_states + instruct_gate_msa.unsqueeze(1).tanh() * self.instruct_attn_norm(instruct_attn_out)
+        instruct_mlp_input = (1 + instruct_scale_mlp.unsqueeze(1)) * instruct_norm2_out + instruct_shift_mlp.unsqueeze(1)
+        instruct_mlp_out = self.instruct_feed_forward(self.instruct_ffn_norm1(instruct_mlp_input))
+        instruct_hidden_states = instruct_hidden_states + instruct_gate_mlp.unsqueeze(1).tanh() * self.instruct_ffn_norm2(instruct_mlp_out)
+
+        return img_hidden_states, instruct_hidden_states
+
+
+class BooguTransformer2DModel(nn.Module):
+    def __init__(
+        self,
+        patch_size: int = 2,
+        in_channels: int = 16,
+        out_channels: Optional[int] = None,
+        hidden_size: int = 3360,
+        num_layers: int = 32,
+        num_double_stream_layers: int = 8,
+        num_refiner_layers: int = 2,
+        num_attention_heads: int = 28,
+        num_kv_heads: int = 7,
+        multiple_of: int = 256,
+        ffn_dim_multiplier: Optional[float] = None,
+        norm_eps: float = 1e-5,
+        axes_dim_rope: Tuple[int, int, int] = (40, 40, 40),
+        axes_lens: Tuple[int, int, int] = (2048, 1664, 1664),
+        instruction_feat_dim: int = 4096,
+        timestep_scale: float = 1000.0,
+        image_model=None,
+        device=None, dtype=None, operations=None,
+    ):
+        super().__init__()
+
+        self.patch_size = patch_size
+        self.out_channels = out_channels or in_channels
+        self.hidden_size = hidden_size
+        self.dtype = dtype
+
+        self.rope_embedder = OmniGen2RotaryPosEmbed(
+            theta=10000,
+            axes_dim=axes_dim_rope,
+            axes_lens=axes_lens,
+            patch_size=patch_size,
+        )
+
+        self.x_embedder = operations.Linear(patch_size * patch_size * in_channels, hidden_size, dtype=dtype, device=device)
+        self.ref_image_patch_embedder = operations.Linear(patch_size * patch_size * in_channels, hidden_size, dtype=dtype, device=device)
+
+        self.time_caption_embed = Lumina2CombinedTimestepCaptionEmbedding(
+            hidden_size=hidden_size,
+            text_feat_dim=instruction_feat_dim,
+            norm_eps=norm_eps,
+            timestep_scale=timestep_scale, dtype=dtype, device=device, operations=operations
+        )
+
+        self.noise_refiner = nn.ModuleList([
+            OmniGen2TransformerBlock(hidden_size, num_attention_heads, num_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations)
+            for _ in range(num_refiner_layers)
+        ])
+
+        self.ref_image_refiner = nn.ModuleList([
+            OmniGen2TransformerBlock(hidden_size, num_attention_heads, num_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations)
+            for _ in range(num_refiner_layers)
+        ])
+
+        self.context_refiner = nn.ModuleList([
+            OmniGen2TransformerBlock(hidden_size, num_attention_heads, num_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, modulation=False, dtype=dtype, device=device, operations=operations)
+            for _ in range(num_refiner_layers)
+        ])
+
+        self.double_stream_layers = nn.ModuleList([
+            BooguDoubleStreamBlock(hidden_size, num_attention_heads, num_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, dtype=dtype, device=device, operations=operations)
+            for _ in range(num_double_stream_layers)
+        ])
+
+        self.single_stream_layers = nn.ModuleList([
+            OmniGen2TransformerBlock(hidden_size, num_attention_heads, num_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations)
+            for _ in range(num_layers)
+        ])
+
+        self.norm_out = LuminaLayerNormContinuous(
+            embedding_dim=hidden_size,
+            conditioning_embedding_dim=min(hidden_size, 1024),
+            elementwise_affine=False,
+            eps=1e-6,
+            out_dim=patch_size * patch_size * self.out_channels, dtype=dtype, device=device, operations=operations
+        )
+
+        self.image_index_embedding = nn.Parameter(torch.empty(5, hidden_size, device=device, dtype=dtype))
+
+    # Patchify/refine helpers are identical to OmniGen2; reuse via bound methods.
+    flat_and_pad_to_seq = comfy.ldm.omnigen.omnigen2.OmniGen2Transformer2DModel.flat_and_pad_to_seq
+    img_patch_embed_and_refine = comfy.ldm.omnigen.omnigen2.OmniGen2Transformer2DModel.img_patch_embed_and_refine
+
+    def forward(self, x, timesteps, context, num_tokens, ref_latents=None, attention_mask=None, transformer_options={}, **kwargs):
+        B, C, H, W = x.shape
+        hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
+        _, _, H_padded, W_padded = hidden_states.shape
+        timestep = 1.0 - timesteps
+        text_hidden_states = context
+        text_attention_mask = attention_mask
+        ref_image_hidden_states = ref_latents
+        device = hidden_states.device
+
+        temb, text_hidden_states = self.time_caption_embed(timestep, text_hidden_states, hidden_states[0].dtype)
+
+        (
+            hidden_states, ref_image_hidden_states,
+            img_mask, ref_img_mask,
+            l_effective_ref_img_len, l_effective_img_len,
+            ref_img_sizes, img_sizes,
+        ) = self.flat_and_pad_to_seq(hidden_states, ref_image_hidden_states)
+
+        (
+            context_rotary_emb, ref_img_rotary_emb, noise_rotary_emb,
+            rotary_emb, encoder_seq_lengths, seq_lengths,
+        ) = self.rope_embedder(
+            hidden_states.shape[0], text_hidden_states.shape[1], [num_tokens] * text_hidden_states.shape[0],
+            l_effective_ref_img_len, l_effective_img_len,
+            ref_img_sizes, img_sizes, device,
+        )
+
+        for layer in self.context_refiner:
+            text_hidden_states = layer(text_hidden_states, text_attention_mask, context_rotary_emb, transformer_options=transformer_options)
+
+        img_len = hidden_states.shape[1]
+        combined_img_hidden_states = self.img_patch_embed_and_refine(
+            hidden_states, ref_image_hidden_states,
+            img_mask, ref_img_mask,
+            noise_rotary_emb, ref_img_rotary_emb,
+            l_effective_ref_img_len, l_effective_img_len,
+            temb,
+            transformer_options=transformer_options,
+        )
+
+        # Double-stream stage: the image self-attention only sees the [ref ; noise] tokens,
+        # which sit after the instruction tokens in the joint rope.
+        L_instruct = text_hidden_states.shape[1]
+        combined_img_rotary_emb = rotary_emb[:, L_instruct:]
+        for layer in self.double_stream_layers:
+            combined_img_hidden_states, text_hidden_states = layer(
+                combined_img_hidden_states, text_hidden_states,
+                rotary_emb, combined_img_rotary_emb, temb,
+                joint_attention_mask=None, img_attention_mask=None,
+                transformer_options=transformer_options,
+            )
+
+        hidden_states = torch.cat([text_hidden_states, combined_img_hidden_states], dim=1)
+
+        for layer in self.single_stream_layers:
+            hidden_states = layer(hidden_states, None, rotary_emb, temb, transformer_options=transformer_options)
+
+        hidden_states = self.norm_out(hidden_states, temb)
+
+        p = self.patch_size
+        output = rearrange(hidden_states[:, -img_len:], 'b (h w) (p1 p2 c) -> b c (h p1) (w p2)', h=H_padded // p, w=W_padded // p, p1=p, p2=p)[:, :, :H, :W]
+
+        return -output
diff --git a/comfy/ldm/omnigen/omnigen2.py b/comfy/ldm/omnigen/omnigen2.py
index e9ca5229d..b8da4cf39 100644
--- a/comfy/ldm/omnigen/omnigen2.py
+++ b/comfy/ldm/omnigen/omnigen2.py
@@ -22,7 +22,7 @@ def apply_rotary_emb(x, freqs_cis):
 
 
 def swiglu(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
-    return F.silu(x) * y
+    return F.silu(x, inplace=True).mul_(y)
 
 
 class TimestepEmbedding(nn.Module):
diff --git a/comfy/model_base.py b/comfy/model_base.py
index d143dc06f..f49da50ae 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -54,6 +54,7 @@ import comfy.ldm.pixeldit.model
 import comfy.ldm.pixeldit.pid
 import comfy.ldm.ace.model
 import comfy.ldm.omnigen.omnigen2
+import comfy.ldm.boogu.model
 import comfy.ldm.qwen_image.model
 import comfy.ldm.ideogram4.model
 import comfy.ldm.kandinsky5.model
@@ -2103,6 +2104,11 @@ class Omnigen2(BaseModel):
             out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16])
         return out
 
+class Boogu(Omnigen2):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super(Omnigen2, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.boogu.model.BooguTransformer2DModel)
+        self.memory_usage_factor_conds = ("ref_latents",)
+
 class QwenImage(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.qwen_image.model.QwenImageTransformer2DModel)
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index 7d0cab308..b773f0393 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -761,6 +761,16 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
 
         return dit_config
 
+    if '{}double_stream_layers.0.img_instruct_attn.processor.img_to_q.weight'.format(key_prefix) in state_dict_keys:  # Boogu-Image (OmniGen2 derivative + dual-stream stage)
+        dit_config = {}
+        dit_config["image_model"] = "boogu"
+        dit_config["hidden_size"] = state_dict['{}x_embedder.weight'.format(key_prefix)].shape[0]
+        dit_config["num_layers"] = count_blocks(state_dict_keys, '{}single_stream_layers.'.format(key_prefix) + '{}.')
+        dit_config["num_double_stream_layers"] = count_blocks(state_dict_keys, '{}double_stream_layers.'.format(key_prefix) + '{}.')
+        dit_config["num_refiner_layers"] = count_blocks(state_dict_keys, '{}noise_refiner.'.format(key_prefix) + '{}.')
+        dit_config["instruction_feat_dim"] = state_dict['{}time_caption_embed.caption_embedder.0.weight'.format(key_prefix)].shape[0]
+        return dit_config
+
     if '{}time_caption_embed.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys:  # Omnigen2
         dit_config = {}
         dit_config["image_model"] = "omnigen2"
diff --git a/comfy/sd.py b/comfy/sd.py
index 348fe4958..d9b1c0553 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -68,6 +68,7 @@ import comfy.text_encoders.ace15
 import comfy.text_encoders.longcat_image
 import comfy.text_encoders.qwen35
 import comfy.text_encoders.qwen3vl
+import comfy.text_encoders.boogu
 import comfy.text_encoders.ernie
 import comfy.text_encoders.gemma4
 import comfy.text_encoders.cogvideo
@@ -1301,6 +1302,7 @@ class CLIPType(Enum):
     LENS = 28
     PIXELDIT = 29
     IDEOGRAM4 = 30
+    BOOGU = 31
 
 
 
@@ -1622,6 +1624,10 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
                 clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
                 clip_target.clip = comfy.text_encoders.ideogram4.te_qwen3vl(**llama_detect(clip_data))
                 clip_target.tokenizer = comfy.text_encoders.ideogram4.Ideogram4Qwen3VLTokenizer
+            elif clip_type == CLIPType.BOOGU and te_model == TEModel.QWEN3VL_8B:  # Boogu-Image: full Qwen3-VL-8B, last hidden state, no-think template.
+                clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
+                clip_target.clip = comfy.text_encoders.boogu.te(**llama_detect(clip_data))
+                clip_target.tokenizer = comfy.text_encoders.boogu.BooguTokenizer
             elif clip_type in (CLIPType.FLUX, CLIPType.FLUX2):  # Flux2 Klein reuses the Qwen3-VL LM (3-layer tap -> 12288); visual unused.
                 klein_model_type = "qwen3_8b" if te_model == TEModel.QWEN3VL_8B else "qwen3_4b"
                 clip_target.clip = comfy.text_encoders.flux.klein_te(**llama_detect(clip_data), model_type=klein_model_type)
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index 3be935577..d78b6ae87 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -25,6 +25,7 @@ import comfy.text_encoders.hunyuan_image
 import comfy.text_encoders.kandinsky5
 import comfy.text_encoders.z_image
 import comfy.text_encoders.ideogram4
+import comfy.text_encoders.boogu
 import comfy.text_encoders.anima
 import comfy.text_encoders.ace15
 import comfy.text_encoders.longcat_image
@@ -1758,6 +1759,27 @@ class Omnigen2(supported_models_base.BASE):
         hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_3b.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.omnigen2.Omnigen2Tokenizer, comfy.text_encoders.omnigen2.te(**hunyuan_detect))
 
+class Boogu(Omnigen2):
+    unet_config = {
+        "image_model": "boogu",
+    }
+
+    sampling_settings = {
+        "multiplier": 1.0,
+        "shift": 3.16,
+    }
+
+    memory_usage_factor = 1.95 #TODO
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.Boogu(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, "{}qwen3vl_8b.transformer.".format(pref))
+        return supported_models_base.ClipTarget(comfy.text_encoders.boogu.BooguTokenizer, comfy.text_encoders.boogu.te(**hunyuan_detect))
+
 class Ideogram4(supported_models_base.BASE):
     unet_config = {
         "image_model": "ideogram4",
@@ -2300,6 +2322,7 @@ models = [
     ACEStep,
     ACEStep15,
     Omnigen2,
+    Boogu,
     QwenImage,
     Ideogram4,
     Flux2,
diff --git a/comfy/text_encoders/boogu.py b/comfy/text_encoders/boogu.py
new file mode 100644
index 000000000..d9de92f10
--- /dev/null
+++ b/comfy/text_encoders/boogu.py
@@ -0,0 +1,58 @@
+"""Boogu-Image text encoder: full Qwen3-VL-8B, last hidden state (4096-dim).
+
+Boogu uses the final hidden state of Qwen3-VL as the per-token instruction feature
+(num_instruction_feature_layers=1, reduce_type=mean -> just the last layer).
+The model itself is the standard Qwen3-VL TE, only the chat template differs
+(a fixed system prompt and no <think> block).
+"""
+
+import comfy.text_encoders.qwen3vl
+from comfy import sd1_clip
+
+
+# System prompts from the reference pipeline (pipeline_boogu.py).
+# T2I (non-empty instruction, no image) uses the helpful-assistant prompt
+# everything else (the CFG negative / "drop" condition, and any image case) uses the TI2I "describe" prompt.
+BOOGU_T2I_SYSTEM = "You are a helpful assistant that generates high-quality images based on user instructions. The instructions are as follows."
+BOOGU_DROP_SYSTEM = "Describe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate."
+
+
+class BooguTokenizer(comfy.text_encoders.qwen3vl.Qwen3VLTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, model_type="qwen3vl_8b")
+        # apply_chat_template without add_generation_prompt
+        self.llama_template = "<|im_start|>system\n" + BOOGU_T2I_SYSTEM + "<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n"
+        self.llama_template_images = "<|im_start|>system\n" + BOOGU_DROP_SYSTEM + "<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n"
+        # Reference SYSTEM_PROMPT_DROP: used for the empty negative/uncond instruction.
+        self.llama_template_drop = "<|im_start|>system\n" + BOOGU_DROP_SYSTEM + "<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n"
+
+    def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, thinking=True, **kwargs):
+        if llama_template is None and len(images) == 0 and text.strip() == "":
+            llama_template = self.llama_template_drop
+        # Boogu conditions on the no-think template; thinking=True drops the empty <think> block qwen3vl adds by default.
+        return super().tokenize_with_weights(text, return_word_ids=return_word_ids, llama_template=llama_template, images=images, prevent_empty_text=prevent_empty_text, thinking=thinking, **kwargs)
+
+
+class BooguQwen3VLClipModel(comfy.text_encoders.qwen3vl.Qwen3VLClipModel):
+    def __init__(self, device="cpu", dtype=None, attention_mask=True, model_options={}, model_type="qwen3vl_8b"):
+        super().__init__(device=device, dtype=dtype, attention_mask=attention_mask, model_options=model_options, model_type=model_type)
+        # apply the final RMSNorm to the tapped last layer
+        self.layer_norm_hidden_state = True
+
+
+class BooguTEModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        clip_model = lambda **kw: BooguQwen3VLClipModel(**kw, model_type="qwen3vl_8b")
+        super().__init__(device=device, dtype=dtype, name="qwen3vl_8b", clip_model=clip_model, model_options=model_options)
+
+
+def te(dtype_llama=None, llama_quantization_metadata=None):
+    class BooguTEModel_(BooguTEModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            if llama_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["quantization_metadata"] = llama_quantization_metadata
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return BooguTEModel_
diff --git a/comfy_extras/nodes_boogu.py b/comfy_extras/nodes_boogu.py
new file mode 100644
index 000000000..590623e12
--- /dev/null
+++ b/comfy_extras/nodes_boogu.py
@@ -0,0 +1,96 @@
+import math
+
+import node_helpers
+import comfy.utils
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
+
+class TextEncodeBooguEdit(io.ComfyNode):
+    """Boogu-Image Edit conditioning.
+
+    The edit image is used twice, matching the reference pipeline:
+      - Qwen3-VL vision tokens (instruction understanding) -> positive only
+      - VAE reference latent (image identity)              -> positive and negative
+    The ref latent is in both conds so it cancels under CFG (identity preserved);
+    the vision tokens are only in the positive so CFG amplifies the instruction.
+    The tokenizer selects the right system prompt automatically (image -> TI2I,
+    empty negative -> DROP), so no template plumbing is needed here.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="TextEncodeBooguEdit",
+            category="model/conditioning/boogu",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.String.Input("prompt", multiline=True, dynamic_prompts=True),
+                io.Vae.Input("vae"),
+                io.Autogrow.Input(
+                    "images",
+                    template=io.Autogrow.TemplateNames(
+                        io.Image.Input("image"),
+                        names=[f"image_{i}" for i in range(1, 17)],
+                        min=1,
+                    ),
+                    tooltip="Reference image(s) to edit. Boogu focuses on one reference per sample; more are allowed.",
+                ),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, clip, prompt, vae=None, images: io.Autogrow.Type = None) -> io.NodeOutput:
+        ref_latents = []
+        images_vl = []
+
+        images = images or {}
+        for name in sorted(images, key=lambda n: int(n.rsplit("_", 1)[-1])):
+            image = images[name]
+            if image is None:
+                continue
+            samples = image.movedim(-1, 1)
+
+            # Vision tower input: the reference caps the VLM image at 384x384
+            # (max_vlm_input_pil_pixels in pipeline_boogu.py).
+            total = int(384 * 384)
+            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")
+            images_vl.append(s.movedim(1, -1)[:, :, :, :3])
+
+            # Reference latent: align to 16 px (VAE /8 * patch_size 2).
+            if vae is not None:
+                total = int(1024 * 1024)
+                scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
+                width = round(samples.shape[3] * scale_by / 16.0) * 16
+                height = round(samples.shape[2] * scale_by / 16.0) * 16
+                s = comfy.utils.common_upscale(samples, width, height, "area", "disabled")
+                ref_latents.append(vae.encode(s.movedim(1, -1)[:, :, :, :3]))
+
+        # positive: instruction + vision tokens; negative: empty (no vision). Ref latent on both.
+        positive = clip.encode_from_tokens_scheduled(clip.tokenize(prompt, images=images_vl))
+        negative = clip.encode_from_tokens_scheduled(clip.tokenize(""))
+
+        if len(ref_latents) > 0:
+            positive = node_helpers.conditioning_set_values(positive, {"reference_latents": ref_latents}, append=True)
+            negative = node_helpers.conditioning_set_values(negative, {"reference_latents": ref_latents}, append=True)
+
+        return io.NodeOutput(positive, negative)
+
+
+class BooguExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            TextEncodeBooguEdit,
+        ]
+
+
+async def comfy_entrypoint() -> BooguExtension:
+    return BooguExtension()
diff --git a/nodes.py b/nodes.py
index bb4649478..0b3fdab63 100644
--- a/nodes.py
+++ b/nodes.py
@@ -969,7 +969,7 @@ 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", "longcat_image", "cogvideox", "lens", "pixeldit", "ideogram4"], ),
+                              "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", "cogvideox", "lens", "pixeldit", "ideogram4", "boogu"], ),
                               },
                 "optional": {
                               "device": (["default", "cpu"], {"advanced": True}),
@@ -2425,6 +2425,7 @@ async def init_builtin_extra_nodes():
         "nodes_tcfg.py",
         "nodes_context_windows.py",
         "nodes_qwen.py",
+        "nodes_boogu.py",
         "nodes_chroma_radiance.py",
         "nodes_pid.py",
         "nodes_model_patch.py",

From 52257bb435162ff345fcc8cfde99c52c8923589d Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Wed, 17 Jun 2026 15:42:29 -0700
Subject: [PATCH 21/30] Add negative prompt to boogu edit node and set min
 images to 0. (#14529)

---
 comfy/supported_models.py   | 2 +-
 comfy_extras/nodes_boogu.py | 7 ++++---
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index d78b6ae87..cc05908ee 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -1769,7 +1769,7 @@ class Boogu(Omnigen2):
         "shift": 3.16,
     }
 
-    memory_usage_factor = 1.95 #TODO
+    memory_usage_factor = 2.15
 
     def get_model(self, state_dict, prefix="", device=None):
         out = model_base.Boogu(self, device=device)
diff --git a/comfy_extras/nodes_boogu.py b/comfy_extras/nodes_boogu.py
index 590623e12..f3951c290 100644
--- a/comfy_extras/nodes_boogu.py
+++ b/comfy_extras/nodes_boogu.py
@@ -26,13 +26,14 @@ class TextEncodeBooguEdit(io.ComfyNode):
             inputs=[
                 io.Clip.Input("clip"),
                 io.String.Input("prompt", multiline=True, dynamic_prompts=True),
+                io.String.Input("negative_prompt", multiline=True, dynamic_prompts=True, advanced=True),
                 io.Vae.Input("vae"),
                 io.Autogrow.Input(
                     "images",
                     template=io.Autogrow.TemplateNames(
                         io.Image.Input("image"),
                         names=[f"image_{i}" for i in range(1, 17)],
-                        min=1,
+                        min=0,
                     ),
                     tooltip="Reference image(s) to edit. Boogu focuses on one reference per sample; more are allowed.",
                 ),
@@ -44,7 +45,7 @@ class TextEncodeBooguEdit(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, clip, prompt, vae=None, images: io.Autogrow.Type = None) -> io.NodeOutput:
+    def execute(cls, clip, prompt, negative_prompt, vae=None, images: io.Autogrow.Type = None) -> io.NodeOutput:
         ref_latents = []
         images_vl = []
 
@@ -75,7 +76,7 @@ class TextEncodeBooguEdit(io.ComfyNode):
 
         # positive: instruction + vision tokens; negative: empty (no vision). Ref latent on both.
         positive = clip.encode_from_tokens_scheduled(clip.tokenize(prompt, images=images_vl))
-        negative = clip.encode_from_tokens_scheduled(clip.tokenize(""))
+        negative = clip.encode_from_tokens_scheduled(clip.tokenize(negative_prompt))
 
         if len(ref_latents) > 0:
             positive = node_helpers.conditioning_set_values(positive, {"reference_latents": ref_latents}, append=True)

From 191a75a2cdc021236fb7cbd4631511246b79a8c6 Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Thu, 18 Jun 2026 02:54:53 +0300
Subject: [PATCH 22/30] [Partner Nodes] feat(Kling): add support for Kling
 V3-Turbo model (#14528)

---
 comfy_api_nodes/apis/kling.py  |  56 ++++++++++++++++
 comfy_api_nodes/nodes_kling.py | 116 ++++++++++++++++++++++++++++++---
 2 files changed, 163 insertions(+), 9 deletions(-)

diff --git a/comfy_api_nodes/apis/kling.py b/comfy_api_nodes/apis/kling.py
index fe0f97cb3..2c98c23b7 100644
--- a/comfy_api_nodes/apis/kling.py
+++ b/comfy_api_nodes/apis/kling.py
@@ -149,3 +149,59 @@ class MotionControlRequest(BaseModel):
     character_orientation: str = Field(...)
     mode: str = Field(..., description="'pro' or 'std'")
     model_name: str = Field(...)
+
+
+class Kling3TurboSettings(BaseModel):
+    resolution: str = Field("720p", description="'720p' or '1080p'")
+    aspect_ratio: str | None = Field(None, description="'16:9'/'9:16'/'1:1'; text-to-video only")
+    duration: int = Field(5, description="3-15 second")
+
+
+class Kling3TurboText2VideoRequest(BaseModel):
+    prompt: str = Field(..., description="<=3072 chars; may use multi-shot 'shot n, m, words; ...'")
+    settings: Kling3TurboSettings | None = Field(None)
+
+
+class Kling3TurboContent(BaseModel):
+    type: str = Field(..., description="'prompt' or 'first_frame'")
+    text: str | None = Field(None, description="for type=prompt; <=2500 chars")
+    url: str | None = Field(None, description="for type=first_frame")
+
+
+class Kling3TurboImage2VideoRequest(BaseModel):
+    contents: list[Kling3TurboContent] = Field(..., description="prompt + first_frame materials")
+    settings: Kling3TurboSettings | None = Field(None)
+
+
+class Kling3TurboCreateData(BaseModel):
+    id: str | None = Field(None, description="Task ID")
+    status: str | None = Field(None)
+    message: str | None = Field(None)
+
+
+class Kling3TurboCreateResponse(BaseModel):
+    code: int | None = Field(None)
+    message: str | None = Field(None)
+    request_id: str | None = Field(None)
+    data: Kling3TurboCreateData | None = Field(None)
+
+
+class Kling3TurboOutput(BaseModel):
+    type: str | None = Field(None, description="'video', 'image', 'audio', ...")
+    id: str | None = Field(None)
+    url: str | None = Field(None)
+    duration: str | None = Field(None)
+
+
+class Kling3TurboTaskData(BaseModel):
+    id: str | None = Field(None)
+    status: str | None = Field(None, description="submitted | processing | succeeded | failed")
+    message: str | None = Field(None)
+    outputs: list[Kling3TurboOutput] | None = Field(None)
+
+
+class Kling3TurboQueryResponse(BaseModel):
+    code: int | None = Field(None)
+    message: str | None = Field(None)
+    request_id: str | None = Field(None)
+    data: list[Kling3TurboTaskData] | None = Field(None)
diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py
index c81d3503d..b27de2549 100644
--- a/comfy_api_nodes/nodes_kling.py
+++ b/comfy_api_nodes/nodes_kling.py
@@ -60,6 +60,12 @@ from comfy_api_nodes.apis.kling import (
     OmniProImageRequest,
     OmniProReferences2VideoRequest,
     OmniProText2VideoRequest,
+    Kling3TurboSettings,
+    Kling3TurboText2VideoRequest,
+    Kling3TurboContent,
+    Kling3TurboImage2VideoRequest,
+    Kling3TurboCreateResponse,
+    Kling3TurboQueryResponse,
     TaskStatusResponse,
     TextToVideoWithAudioRequest,
 )
@@ -2847,6 +2853,67 @@ class MotionControl(IO.ComfyNode):
         return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
 
 
+def build_turbo_shot_prompt(multi_prompt: list[MultiPromptEntry]) -> str:
+    """Render storyboard entries into the Turbo multi-shot prompt 'shot n, m, words; ...'."""
+    return "; ".join(f"shot {i}, {int(e.duration)}, {e.prompt}" for i, e in enumerate(multi_prompt, 1)) + ";"
+
+
+def _turbo_video_url(response: Kling3TurboQueryResponse) -> str:
+    """Extract the result video URL from a /tasks response (data[].outputs[] where type == 'video')."""
+    task = response.data[0] if response.data else None
+    if task and task.outputs:
+        for output in task.outputs:
+            if output.type == "video" and output.url:
+                return output.url
+    raise RuntimeError(f"Kling 3.0 Turbo task finished without a video output: {response.model_dump()}")
+
+
+async def execute_kling_turbo(
+    cls: type[IO.ComfyNode],
+    *,
+    prompt: str,
+    resolution: str,
+    aspect_ratio: str,
+    duration: int,
+    start_frame: torch.Tensor | None,
+) -> IO.NodeOutput:
+    """Create + poll a Kling 3.0 Turbo task. Image-to-video when start_frame is given, else text-to-video."""
+    if start_frame is not None:
+        validate_image_dimensions(start_frame, min_width=300, min_height=300)
+        validate_image_aspect_ratio(start_frame, (1, 2.5), (2.5, 1))
+        contents = [Kling3TurboContent(type="first_frame", url=tensor_to_base64_string(start_frame))]
+        if prompt:
+            contents.insert(0, Kling3TurboContent(type="prompt", text=prompt))
+        create = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/image-to-video/kling-3.0-turbo", method="POST"),
+            response_model=Kling3TurboCreateResponse,
+            data=Kling3TurboImage2VideoRequest(
+                contents=contents,
+                settings=Kling3TurboSettings(resolution=resolution, duration=duration),  # i2v: no aspect_ratio
+            ),
+        )
+    else:
+        create = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/text-to-video/kling-3.0-turbo", method="POST"),
+            response_model=Kling3TurboCreateResponse,
+            data=Kling3TurboText2VideoRequest(
+                prompt=prompt,
+                settings=Kling3TurboSettings(resolution=resolution, aspect_ratio=aspect_ratio, duration=duration),
+            ),
+        )
+    if not (create.data and create.data.id):
+        raise RuntimeError(f"Kling 3.0 Turbo create failed. Code: {create.code}, Message: {create.message}")
+    final_response = await poll_op(
+        cls,
+        ApiEndpoint(path="/proxy/kling/tasks", query_params={"task_ids": create.data.id}),
+        response_model=Kling3TurboQueryResponse,
+        status_extractor=lambda r: (r.data[0].status if r.data else None),
+    )
+    return IO.NodeOutput(await download_url_to_video_output(_turbo_video_url(final_response)))
+
+
 class KlingVideoNode(IO.ComfyNode):
 
     @classmethod
@@ -2884,7 +2951,11 @@ class KlingVideoNode(IO.ComfyNode):
                     ],
                     tooltip="Generate a series of video segments with individual prompts and durations.",
                 ),
-                IO.Boolean.Input("generate_audio", default=True),
+                IO.Boolean.Input(
+                    "generate_audio",
+                    default=True,
+                    tooltip="'kling-3.0-turbo' always generates native audio, so the audio toggle is ignored.",
+                ),
                 IO.DynamicCombo.Input(
                     "model",
                     options=[
@@ -2899,6 +2970,17 @@ class KlingVideoNode(IO.ComfyNode):
                                 ),
                             ],
                         ),
+                        IO.DynamicCombo.Option(
+                            "kling-3.0-turbo",
+                            [
+                                IO.Combo.Input("resolution", options=["1080p", "720p"], default="720p"),
+                                IO.Combo.Input(
+                                    "aspect_ratio",
+                                    options=["16:9", "9:16", "1:1"],
+                                    tooltip="Ignored in image-to-video mode.",
+                                ),
+                            ],
+                        ),
                     ],
                     tooltip="Model and generation settings.",
                 ),
@@ -2930,6 +3012,7 @@ class KlingVideoNode(IO.ComfyNode):
             price_badge=IO.PriceBadge(
                 depends_on=IO.PriceBadgeDepends(
                     widgets=[
+                        "model",
                         "model.resolution",
                         "generate_audio",
                         "multi_shot",
@@ -2944,14 +3027,7 @@ class KlingVideoNode(IO.ComfyNode):
                 ),
                 expr="""
                 (
-                  $rates := {
-                    "4k": {"off": 0.42, "on": 0.42},
-                    "1080p": {"off": 0.112, "on": 0.168},
-                    "720p": {"off": 0.084, "on": 0.126}
-                  };
                   $res := $lookup(widgets, "model.resolution");
-                  $audio := widgets.generate_audio ? "on" : "off";
-                  $rate := $lookup($lookup($rates, $res), $audio);
                   $ms := widgets.multi_shot;
                   $isSb := $ms != "disabled";
                   $n := $isSb ? $number($substring($ms, 0, 1)) : 0;
@@ -2962,7 +3038,18 @@ class KlingVideoNode(IO.ComfyNode):
                   $d5 := $n >= 5 ? $lookup(widgets, "multi_shot.storyboard_5_duration") : 0;
                   $d6 := $n >= 6 ? $lookup(widgets, "multi_shot.storyboard_6_duration") : 0;
                   $dur := $isSb ? $d1 + $d2 + $d3 + $d4 + $d5 + $d6 : $lookup(widgets, "multi_shot.duration");
-                  {"type":"usd","usd": $rate * $dur}
+                  widgets.model = "kling-3.0-turbo"
+                    ? {"type":"usd","usd": ($res = "1080p" ? 0.14 : 0.112) * $dur}
+                    : (
+                        $rates := {
+                          "4k": {"off": 0.42, "on": 0.42},
+                          "1080p": {"off": 0.112, "on": 0.168},
+                          "720p": {"off": 0.084, "on": 0.126}
+                        };
+                        $audio := widgets.generate_audio ? "on" : "off";
+                        $rate := $lookup($lookup($rates, $res), $audio);
+                        {"type":"usd","usd": $rate * $dur}
+                      )
                 )
                 """,
             ),
@@ -3015,6 +3102,17 @@ class KlingVideoNode(IO.ComfyNode):
             duration = multi_shot["duration"]
             validate_string(multi_shot["prompt"], min_length=1, max_length=2500)
 
+        if model["model"] == "kling-3.0-turbo":
+            turbo_prompt = build_turbo_shot_prompt(multi_prompt_list) if custom_multi_shot else multi_shot["prompt"]
+            return await execute_kling_turbo(
+                cls,
+                prompt=turbo_prompt,
+                resolution=model["resolution"],
+                aspect_ratio=model["aspect_ratio"],
+                duration=duration,
+                start_frame=start_frame,
+            )
+
         if start_frame is not None:
             validate_image_dimensions(start_frame, min_width=300, min_height=300)
             validate_image_aspect_ratio(start_frame, (1, 2.5), (2.5, 1))

From f2270f070a372898ca748299a51dcba5bba0ba53 Mon Sep 17 00:00:00 2001
From: Jedrzej Kosinski <kosinkadink1@gmail.com>
Date: Wed, 17 Jun 2026 19:35:05 -0700
Subject: [PATCH 23/30] feat: add enable_telemetry CLI feature flag (#14530)

---
 comfy_api/feature_flags.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/comfy_api/feature_flags.py b/comfy_api/feature_flags.py
index adb5a3144..0f30608a9 100644
--- a/comfy_api/feature_flags.py
+++ b/comfy_api/feature_flags.py
@@ -25,6 +25,11 @@ CLI_FEATURE_FLAG_REGISTRY: dict[str, FeatureFlagInfo] = {
         "default": False,
         "description": "Show the sign-in button in the frontend even when not signed in",
     },
+    "enable_telemetry": {
+        "type": "bool",
+        "default": False,
+        "description": "Signal the frontend that telemetry collection is enabled",
+    },
 }
 
 

From 8483c215dc4b3958a75a4b094755446e4de63e23 Mon Sep 17 00:00:00 2001
From: "Daxiong (Lin)" <contact@comfyui-wiki.com>
Date: Thu, 18 Jun 2026 17:24:05 +0800
Subject: [PATCH 24/30] Update ComfyUI Desktop to Comfy Desktop for consistent
 product naming (#14533)

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c75353d36..bcec86377 100644
--- a/README.md
+++ b/README.md
@@ -140,7 +140,7 @@ ComfyUI follows a weekly release cycle targeting Monday but this regularly chang
    - Commits outside of the stable release tags may be very unstable and break many custom nodes.
    - Serves as the foundation for the desktop release
 
-2. **[ComfyUI Desktop](https://github.com/Comfy-Org/Comfy-Desktop)**
+2. **[Comfy Desktop](https://github.com/Comfy-Org/Comfy-Desktop)**
    - Builds a new release using the latest stable core version
 
 3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)**

From 16514da2e75fe73e592675ef1740eb3c07e4f80c Mon Sep 17 00:00:00 2001
From: Comfy Org PR Bot <snomiao+comfy-pr@gmail.com>
Date: Thu, 18 Jun 2026 18:27:53 +0900
Subject: [PATCH 25/30] chore(openapi): sync shared API contract from
 cloud@d10ff72 (#14518)

---
 openapi.yaml | 195 ++++++++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 187 insertions(+), 8 deletions(-)

diff --git a/openapi.yaml b/openapi.yaml
index 82ff5b003..2446e64e4 100644
--- a/openapi.yaml
+++ b/openapi.yaml
@@ -673,6 +673,35 @@ components:
                 - created_at
                 - updated_at
             type: object
+        JobsCancelRequest:
+            additionalProperties: false
+            description: Request to cancel multiple jobs by ID.
+            properties:
+                job_ids:
+                    description: Job identifiers (UUIDs) to cancel.
+                    items:
+                        format: uuid
+                        type: string
+                    maxItems: 100
+                    minItems: 1
+                    type: array
+            required:
+                - job_ids
+            type: object
+        JobsCancelResponse:
+            description: Response for POST /api/jobs/cancel.
+            properties:
+                cancelled:
+                    description: |
+                        Job IDs for which a cancel event was successfully dispatched by this
+                        call. Jobs already in a terminal or cancelling state are idempotently
+                        skipped and will not appear here.
+                    items:
+                        type: string
+                    type: array
+            required:
+                - cancelled
+            type: object
         JobsListResponse:
             description: Paginated list of jobs for the authenticated user.
             properties:
@@ -1006,7 +1035,7 @@ components:
                     description: If true, clear all pending jobs from the queue
                     type: boolean
                 delete:
-                    description: Array of PENDING job IDs to cancel
+                    description: Array of job IDs to cancel; pending and running jobs transition to cancelled
                     items:
                         type: string
                     type: array
@@ -1822,6 +1851,83 @@ paths:
             summary: Update asset metadata
             tags:
                 - file
+    /api/assets/{id}/content:
+        get:
+            description: |
+                Returns the binary content of an asset by ID.
+
+                The contract is the same across runtimes — "GET this path and you
+                receive the asset's bytes" — but the mechanism differs:
+                - **Local ComfyUI** streams the bytes directly (`200`,
+                  `application/octet-stream`).
+                - **Cloud** does not proxy large files; it responds `302` with a
+                  `Location` redirect to a short-lived signed storage URL. Clients that
+                  follow redirects (browsers, `fetch`/XHR, `<img>`/`<video>`) receive
+                  the bytes transparently.
+
+                Prefer this over the filename-addressed `/api/view` when you have an
+                asset ID.
+            operationId: getAssetContent
+            parameters:
+                - description: Asset ID
+                  in: path
+                  name: id
+                  required: true
+                  schema:
+                    type: string
+                - description: |
+                    Content-Disposition for the response: `attachment` (download) or
+                    `inline` (render in browser). Defaults to `attachment`.
+                  in: query
+                  name: disposition
+                  schema:
+                    default: attachment
+                    enum:
+                        - inline
+                        - attachment
+                    type: string
+            responses:
+                "200":
+                    content:
+                        application/octet-stream:
+                            schema:
+                                format: binary
+                                type: string
+                    description: Asset content stream (local runtime streams the bytes directly)
+                "302":
+                    description: Redirect to a signed storage URL (cloud runtime)
+                    headers:
+                        Cache-Control:
+                            description: Private caching directive scoped to the signed URL lifetime
+                            schema:
+                                type: string
+                        Location:
+                            description: Short-lived signed URL to the asset content in storage
+                            schema:
+                                type: string
+                        Vary:
+                            description: Partitions any cached redirect by auth credentials so a private redirect is not reused across users
+                            schema:
+                                type: string
+                "404":
+                    content:
+                        application/json:
+                            schema:
+                                $ref: '#/components/schemas/ErrorResponse'
+                    description: Asset not found
+                "500":
+                    content:
+                        application/json:
+                            schema:
+                                $ref: '#/components/schemas/ErrorResponse'
+                    description: Internal server error
+            security:
+                - ApiKeyAuth: []
+                - BearerAuth: []
+                - CookieAuth: []
+            summary: Get asset content
+            tags:
+                - file
     /api/assets/{id}/tags:
         delete:
             description: Removes one or more tags from an existing asset
@@ -2675,14 +2781,20 @@ paths:
             summary: Get internationalisation translation strings
     /api/interrupt:
         post:
+            deprecated: true
             description: |
-                Cancel all currently RUNNING jobs for the authenticated user.
-                This will interrupt any job that is currently in 'in_progress' status.
-                Note: This endpoint only affects running jobs. To cancel pending jobs, use /api/queue.
+                Deprecated. Prefer the jobs-namespace cancel endpoints:
+                POST /api/jobs/{job_id}/cancel for a single job, or
+                POST /api/jobs/cancel to cancel jobs by ID.
+
+                Cancels the first active job for the authenticated user (the currently
+                running job if there is one, otherwise the next pending job). Takes no
+                body and cannot target a specific job — use the jobs-namespace endpoints
+                for that.
             operationId: interruptJob
             responses:
                 "200":
-                    description: Success - Job interrupted or no running job found
+                    description: Success - first active job cancelled, or no active job found
                 "401":
                     content:
                         application/json:
@@ -2695,7 +2807,7 @@ paths:
                             schema:
                                 $ref: '#/components/schemas/ErrorResponse'
                     description: Internal server error
-            summary: Interrupt currently running jobs
+            summary: Interrupt the first active job
             tags:
                 - queue
     /api/job/{job_id}/status:
@@ -2954,6 +3066,64 @@ paths:
             summary: Cancel a job
             tags:
                 - workflow
+    /api/jobs/cancel:
+        post:
+            description: |
+                Cancel one or more jobs for the authenticated user in a single request.
+
+                State-agnostic: cancels both pending and running jobs (both transition to
+                the cancelled state via the same mechanism as the single-job endpoint).
+
+                Idempotent per job: a job already in a terminal or cancelling state is a
+                no-op and simply will not appear in the returned `cancelled` list.
+
+                Fail-fast on unknown IDs: if any provided job ID does not exist for this
+                user, the request returns 404 and no jobs are cancelled. This surfaces
+                bad IDs to the caller rather than silently dropping them.
+
+                This is the canonical batch-cancel endpoint. The delete operation on
+                POST /api/queue is deprecated in favour of this.
+            operationId: cancelJobs
+            requestBody:
+                content:
+                    application/json:
+                        schema:
+                            $ref: '#/components/schemas/JobsCancelRequest'
+                required: true
+            responses:
+                "200":
+                    content:
+                        application/json:
+                            schema:
+                                $ref: '#/components/schemas/JobsCancelResponse'
+                    description: Success - cancel requests dispatched (or jobs were already terminal)
+                "400":
+                    content:
+                        application/json:
+                            schema:
+                                $ref: '#/components/schemas/ErrorResponse'
+                    description: Bad Request - job_ids is missing, empty, exceeds the maximum count, or contains an invalid UUID
+                "401":
+                    content:
+                        application/json:
+                            schema:
+                                $ref: '#/components/schemas/ErrorResponse'
+                    description: Unauthorized - Authentication required
+                "404":
+                    content:
+                        application/json:
+                            schema:
+                                $ref: '#/components/schemas/ErrorResponse'
+                    description: One or more job IDs not found for this user (no jobs cancelled)
+                "500":
+                    content:
+                        application/json:
+                            schema:
+                                $ref: '#/components/schemas/ErrorResponse'
+                    description: Internal server error - cancellation failed
+            summary: Cancel multiple jobs
+            tags:
+                - workflow
     /api/node_replacements:
         get:
             description: |
@@ -3104,9 +3274,18 @@ paths:
             tags:
                 - queue
         post:
+            deprecated: true
             description: |
-                Cancel specific PENDING jobs by ID or clear all pending jobs in the queue.
-                Note: This endpoint only affects pending jobs. To cancel running jobs, use /api/interrupt.
+                Deprecated. Prefer the jobs-namespace cancel endpoints:
+                POST /api/jobs/cancel for cancelling jobs by ID, and
+                POST /api/jobs/{job_id}/cancel for a single job.
+
+                Cancel specific jobs by ID (the `delete` field) or clear all pending
+                jobs in the queue (the `clear` field). Despite the `delete` naming, this
+                does not delete anything — listed jobs transition to the cancelled state,
+                and `delete` cancels both pending and running jobs (not pending-only as
+                previously documented). Job-by-ID cancellation is superseded by
+                POST /api/jobs/cancel; `clear` has no jobs-namespace replacement yet.
             operationId: manageQueue
             requestBody:
                 content:

From 94ee49b1612824366a8631ea069b2a1fa5c73720 Mon Sep 17 00:00:00 2001
From: Matt Miller <mattmiller@comfy.org>
Date: Thu, 18 Jun 2026 12:30:57 -0700
Subject: [PATCH 26/30] harden: load training-dataset shards with
 weights_only=True (#14543)

LoadTrainingDataset was the only torch.load call in the codebase without
weights_only=True; comfy/utils.py and comfy/sd1_clip.py already pass it.
Recent PyTorch defaults to weights_only=True, so this is defense-in-depth
for installs pinned to older PyTorch. Verified a typical shard (latents +
standard conditioning) round-trips cleanly under weights_only=True.
---
 comfy_extras/nodes_dataset.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy_extras/nodes_dataset.py b/comfy_extras/nodes_dataset.py
index 0253b4b4f..73fe75b7f 100644
--- a/comfy_extras/nodes_dataset.py
+++ b/comfy_extras/nodes_dataset.py
@@ -1583,7 +1583,7 @@ class LoadTrainingDataset(io.ComfyNode):
             shard_path = os.path.join(dataset_dir, shard_file)
 
             with open(shard_path, "rb") as f:
-                shard_data = torch.load(f)
+                shard_data = torch.load(f, weights_only=True)
 
             all_latents.extend(shard_data["latents"])
             all_conditioning.extend(shard_data["conditioning"])

From 5ef0092af943cf17c0cd3ba3ea5507137cdc37d0 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Thu, 18 Jun 2026 19:32:55 -0700
Subject: [PATCH 27/30] Move comfy sys path insert to custom node loading.
 (#14459)

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

diff --git a/nodes.py b/nodes.py
index 0b3fdab63..b1a663f4c 100644
--- a/nodes.py
+++ b/nodes.py
@@ -20,8 +20,6 @@ from PIL.PngImagePlugin import PngInfo
 import numpy as np
 import safetensors.torch
 
-sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
-
 import comfy.diffusers_load
 import comfy.samplers
 import comfy.sample
@@ -2299,6 +2297,9 @@ async def init_external_custom_nodes():
     Returns:
         None
     """
+    # TODO: remove at some point when custom nodes don't break.
+    sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
+
     base_node_names = set(NODE_CLASS_MAPPINGS.keys())
     node_paths = folder_paths.get_folder_paths("custom_nodes")
     node_import_times = []

From 5955ddff52a2eda2ba0cf7f3fb0927c93fb2fbb8 Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Fri, 19 Jun 2026 08:46:07 +0300
Subject: [PATCH 28/30] [Partner Nodes] feat(Luma): add support for Luma Rays
 3.2 (#14540)

Signed-off-by: bigcat88 <bigcat88@icloud.com>
---
 comfy_api_nodes/apis/luma.py  | 142 ++++++---
 comfy_api_nodes/nodes_luma.py | 564 +++++++++++++++++++++++++++++++++-
 2 files changed, 652 insertions(+), 54 deletions(-)

diff --git a/comfy_api_nodes/apis/luma.py b/comfy_api_nodes/apis/luma.py
index 8c6db2022..2465c3b37 100644
--- a/comfy_api_nodes/apis/luma.py
+++ b/comfy_api_nodes/apis/luma.py
@@ -10,6 +10,7 @@ from pydantic import BaseModel, Field, confloat
 class LumaIO:
     LUMA_REF = "LUMA_REF"
     LUMA_CONCEPTS = "LUMA_CONCEPTS"
+    LUMA_RAY32_KEYFRAME = "LUMA_RAY32_KEYFRAME"
 
 
 class LumaReference:
@@ -20,13 +21,14 @@ class LumaReference:
     def create_api_model(self, download_url: str):
         return LumaImageRef(url=download_url, weight=self.weight)
 
+
 class LumaReferenceChain:
-    def __init__(self, first_ref: LumaReference=None):
+    def __init__(self, first_ref: LumaReference = None):
         self.refs: list[LumaReference] = []
         if first_ref:
             self.refs.append(first_ref)
 
-    def add(self, luma_ref: LumaReference=None):
+    def add(self, luma_ref: LumaReference = None):
         self.refs.append(luma_ref)
 
     def create_api_model(self, download_urls: list[str], max_refs=4):
@@ -124,7 +126,7 @@ def get_luma_concepts(include_none=False):
         "pull_out",
         "aerial",
         "crane_up",
-        "eye_level"
+        "eye_level",
     ]
 
 
@@ -162,8 +164,8 @@ class LumaVideoModelOutputDuration(str, Enum):
 
 
 class LumaGenerationType(str, Enum):
-    video = 'video'
-    image = 'image'
+    video = "video"
+    image = "image"
 
 
 class LumaState(str, Enum):
@@ -174,86 +176,109 @@ class LumaState(str, Enum):
 
 
 class LumaAssets(BaseModel):
-    video: Optional[str] = Field(None, description='The URL of the video')
-    image: Optional[str] = Field(None, description='The URL of the image')
-    progress_video: Optional[str] = Field(None, description='The URL of the progress video')
+    video: Optional[str] = Field(None, description="The URL of the video")
+    image: Optional[str] = Field(None, description="The URL of the image")
+    progress_video: Optional[str] = Field(None, description="The URL of the progress video")
 
 
 class LumaImageRef(BaseModel):
     """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')
+
+    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"""
-    type: Optional[str] = Field('image', description='Input type, defaults to image')
-    url: str = Field(..., description='The URL of the image')
+
+    type: Optional[str] = Field("image", description="Input type, defaults to image")
+    url: str = Field(..., description="The URL of the image")
 
 
 class LumaModifyImageRef(BaseModel):
-    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')
+    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 LumaCharacterRef(BaseModel):
-    identity0: LumaImageIdentity = Field(..., description='The image identity object')
+    identity0: LumaImageIdentity = Field(..., description="The image identity object")
 
 
 class LumaImageIdentity(BaseModel):
-    images: list[str] = Field(..., description='The URLs of the image identity')
+    images: list[str] = Field(..., description="The URLs of the image identity")
 
 
 class LumaGenerationReference(BaseModel):
-    type: str = Field('generation', description='Input type, defaults to generation')
-    id: str = Field(..., description='The ID of the generation')
+    type: str = Field("generation", description="Input type, defaults to generation")
+    id: str = Field(..., description="The ID of the generation")
 
 
 class LumaKeyframes(BaseModel):
-    frame0: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description='')
-    frame1: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description='')
+    frame0: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description="")
+    frame1: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description="")
 
 
 class LumaConceptObject(BaseModel):
-    key: str = Field(..., description='Camera Concept name')
+    key: str = Field(..., description="Camera Concept name")
 
 
 class LumaImageGenerationRequest(BaseModel):
-    prompt: str = Field(..., description='The prompt of the generation')
-    model: LumaImageModel = Field(LumaImageModel.photon_1, description='The image model used for the generation')
-    aspect_ratio: Optional[LumaAspectRatio] = Field(LumaAspectRatio.ratio_16_9, description='The aspect ratio of the generation')
-    image_ref: Optional[list[LumaImageRef]] = Field(None, description='List of image reference objects')
-    style_ref: Optional[list[LumaImageRef]] = Field(None, description='List of style reference objects')
-    character_ref: Optional[LumaCharacterRef] = Field(None, description='The image identity object')
-    modify_image_ref: Optional[LumaModifyImageRef] = Field(None, description='The modify image reference object')
+    prompt: str = Field(..., description="The prompt of the generation")
+    model: LumaImageModel = Field(LumaImageModel.photon_1, description="The image model used for the generation")
+    aspect_ratio: Optional[LumaAspectRatio] = Field(LumaAspectRatio.ratio_16_9)
+    image_ref: Optional[list[LumaImageRef]] = Field(None, description="List of image reference objects")
+    style_ref: Optional[list[LumaImageRef]] = Field(None, description="List of style reference objects")
+    character_ref: Optional[LumaCharacterRef] = Field(None, description="The image identity object")
+    modify_image_ref: Optional[LumaModifyImageRef] = Field(None, description="The modify image reference object")
 
 
 class LumaGenerationRequest(BaseModel):
-    prompt: str = Field(..., description='The prompt of the generation')
-    model: LumaVideoModel = Field(LumaVideoModel.ray_2, description='The video model used for the generation')
-    duration: Optional[LumaVideoModelOutputDuration] = Field(None, description='The duration of the generation')
-    aspect_ratio: Optional[LumaAspectRatio] = Field(None, description='The aspect ratio of the generation')
-    resolution: Optional[LumaVideoOutputResolution] = Field(None, description='The resolution of the generation')
-    loop: Optional[bool] = Field(None, description='Whether to loop the video')
-    keyframes: Optional[LumaKeyframes] = Field(None, description='The keyframes of the generation')
-    concepts: Optional[list[LumaConceptObject]] = Field(None, description='Camera Concepts to apply to generation')
+    prompt: str = Field(..., description="The prompt of the generation")
+    model: LumaVideoModel = Field(LumaVideoModel.ray_2, description="The video model used for the generation")
+    duration: Optional[LumaVideoModelOutputDuration] = Field(None, description="The duration of the generation")
+    aspect_ratio: Optional[LumaAspectRatio] = Field(None, description="The aspect ratio of the generation")
+    resolution: Optional[LumaVideoOutputResolution] = Field(None, description="The resolution of the generation")
+    loop: Optional[bool] = Field(None, description="Whether to loop the video")
+    keyframes: Optional[LumaKeyframes] = Field(None, description="The keyframes of the generation")
+    concepts: Optional[list[LumaConceptObject]] = Field(None, description="Camera Concepts to apply to generation")
 
 
 class LumaGeneration(BaseModel):
-    id: str = Field(..., description='The ID of the generation')
-    generation_type: LumaGenerationType = Field(..., description='Generation type, image or video')
-    state: LumaState = Field(..., description='The state of the generation')
-    failure_reason: Optional[str] = Field(None, description='The reason for the state of the generation')
-    created_at: str = Field(..., description='The date and time when the generation was created')
-    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")
+    id: str = Field(..., description="The ID of the generation")
+    generation_type: LumaGenerationType = Field(..., description="Generation type, image or video")
+    state: LumaState = Field(..., description="The state of the generation")
+    failure_reason: Optional[str] = Field(None, description="The reason for the state of the generation")
+    created_at: str = Field(..., description="The date and time when the generation was created")
+    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(...)
 
 
 class Luma2ImageRef(BaseModel):
     url: str | None = None
     data: str | None = None
     media_type: str | None = None
+    generation_id: str | None = Field(None, description="reference a prior generation (extend / source reuse)")
+
+
+class Luma2VideoEdit(BaseModel):
+    """Edit controls for Ray 3.2 ``video_edit`` generations."""
+
+    auto_controls: bool | None = Field(None, description="derive a conditioning schedule from the source (recommended)")
+    strength: str | None = Field(None, description="'adhere_1' .. 'reimagine_3'; constrained by IO.Combo")
+
+
+class Luma2VideoOptions(BaseModel):
+    """Ray 3.2 ``video`` output settings (text / image / keyframe / edit / extend)."""
+
+    resolution: str | None = Field(None, description="360p | 540p | 720p | 1080p")
+    duration: str | None = Field(None, description="5s | 10s")
+    loop: bool | None = Field(None)
+    start_frame: Luma2ImageRef | None = Field(None)
+    end_frame: Luma2ImageRef | None = Field(None)
+    keyframes: list[Luma2ImageRef] | None = Field(None)
+    keyframe_indexes: list[int] | None = Field(None)
+    edit: Luma2VideoEdit | None = Field(None)
 
 
 class Luma2GenerationRequest(BaseModel):
@@ -266,6 +291,7 @@ class Luma2GenerationRequest(BaseModel):
     web_search: bool | None = None
     image_ref: list[Luma2ImageRef] | None = None
     source: Luma2ImageRef | None = None
+    video: Luma2VideoOptions | None = Field(None)
 
 
 class Luma2Generation(BaseModel):
@@ -277,3 +303,31 @@ class Luma2Generation(BaseModel):
     output: list[LumaImageReference] | None = None
     failure_reason: str | None = None
     failure_code: str | None = None
+
+
+# --- Ray 3.2 multi-keyframe chain ---
+
+LUMA_KEYFRAME_MODE_FRACTION = "fraction"  # value in [0.0, 1.0] of the output video duration
+LUMA_KEYFRAME_MODE_SECONDS = "seconds"  # absolute time, in seconds, from the start of the output
+
+
+class LumaRay32KeyframeItem:
+    """One guide image anchored at a position on the Ray 3.2 output timeline."""
+
+    def __init__(self, image: torch.Tensor, mode: str, value: float):
+        self.image = image
+        self.mode = mode  # LUMA_KEYFRAME_MODE_FRACTION | LUMA_KEYFRAME_MODE_SECONDS
+        self.value = value
+
+
+class LumaRay32KeyframeChain:
+    def __init__(self):
+        self.items: list[LumaRay32KeyframeItem] = []
+
+    def add(self, item: LumaRay32KeyframeItem) -> None:
+        self.items.append(item)
+
+    def clone(self) -> "LumaRay32KeyframeChain":
+        c = LumaRay32KeyframeChain()
+        c.items = list(self.items)
+        return c
diff --git a/comfy_api_nodes/nodes_luma.py b/comfy_api_nodes/nodes_luma.py
index 0d31ac77e..cdfa32d8b 100644
--- a/comfy_api_nodes/nodes_luma.py
+++ b/comfy_api_nodes/nodes_luma.py
@@ -3,9 +3,13 @@ from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension, Input
 from comfy_api_nodes.apis.luma import (
+    LUMA_KEYFRAME_MODE_FRACTION,
+    LUMA_KEYFRAME_MODE_SECONDS,
     Luma2Generation,
     Luma2GenerationRequest,
     Luma2ImageRef,
+    Luma2VideoEdit,
+    Luma2VideoOptions,
     LumaAspectRatio,
     LumaCharacterRef,
     LumaConceptChain,
@@ -18,6 +22,8 @@ from comfy_api_nodes.apis.luma import (
     LumaIO,
     LumaKeyframes,
     LumaModifyImageRef,
+    LumaRay32KeyframeChain,
+    LumaRay32KeyframeItem,
     LumaReference,
     LumaReferenceChain,
     LumaVideoModel,
@@ -33,6 +39,7 @@ from comfy_api_nodes.util import (
     sync_op,
     upload_image_to_comfyapi,
     upload_images_to_comfyapi,
+    upload_video_to_comfyapi,
     validate_string,
 )
 
@@ -692,7 +699,10 @@ async def _luma2_upload_image_refs(
 async def _luma2_submit_and_poll(
     cls: type[IO.ComfyNode],
     request: Luma2GenerationRequest,
-) -> Input.Image:
+    *,
+    estimated_duration: int | None = None,
+) -> Luma2Generation:
+    """Submit a Luma Agents generation and poll until done; returns the completed generation."""
     initial = await sync_op(
         cls,
         ApiEndpoint(path="/proxy/luma_2/generations", method="POST"),
@@ -700,21 +710,21 @@ async def _luma2_submit_and_poll(
         data=request,
     )
     if not initial.id:
-        raise RuntimeError("Luma 2 API did not return a generation id.")
+        raise RuntimeError("Luma 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,
+        estimated_duration=estimated_duration,
     )
-    if not final.output:
+    if not final.output or not final.output[0].url:
         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)
+        if final.failure_code:
+            msg = f"{msg} [{final.failure_code}]"
+        raise RuntimeError(f"Luma generation failed: {msg}")
+    return final
 
 
 class LumaImageNode(IO.ComfyNode):
@@ -843,7 +853,8 @@ class LumaImageNode(IO.ComfyNode):
             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))
+        final = await _luma2_submit_and_poll(cls, request)
+        return IO.NodeOutput(await download_url_to_image_tensor(final.output[0].url))
 
 
 class LumaImageEditNode(IO.ComfyNode):
@@ -929,7 +940,533 @@ class LumaImageEditNode(IO.ComfyNode):
             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))
+        final = await _luma2_submit_and_poll(cls, request)
+        return IO.NodeOutput(await download_url_to_image_tensor(final.output[0].url))
+
+
+_BADGE_RAY32_VIDEO = IO.PriceBadge(
+    depends_on=IO.PriceBadgeDepends(widgets=["resolution", "duration"]),
+    expr="""
+    (
+      $p := {
+        "360p": {"5s": 0.06, "10s": 0.18},
+        "540p": {"5s": 0.15, "10s": 0.45},
+        "720p": {"5s": 0.3, "10s": 0.9},
+        "1080p": {"5s": 1.2, "10s": 3.6}
+      };
+      {"type": "usd", "usd": $lookup($lookup($p, widgets.resolution), widgets.duration)}
+    )
+    """,
+)
+
+_BADGE_RAY32_VIDEO_5S = IO.PriceBadge(
+    depends_on=IO.PriceBadgeDepends(widgets=["resolution"]),
+    expr="""
+    (
+      $p := {"360p": 0.06, "540p": 0.15, "720p": 0.3, "1080p": 1.2};
+      {"type": "usd", "usd": $lookup($p, widgets.resolution)}
+    )
+    """,
+)
+
+_BADGE_RAY32_EDIT = IO.PriceBadge(
+    depends_on=IO.PriceBadgeDepends(widgets=["resolution"]),
+    expr="""
+    (
+      $p := {
+        "360p": {"min": 0.54, "max": 1.08},
+        "540p": {"min": 0.72, "max": 1.44},
+        "720p": {"min": 1.08, "max": 2.16},
+        "1080p": {"min": 2.16, "max": 4.32}
+      };
+      $r := $lookup($p, widgets.resolution);
+      {"type": "range_usd", "min_usd": $r.min, "max_usd": $r.max, "format": {"note": "(by source length)"}}
+    )
+    """,
+)
+
+_BADGE_RAY32_REFRAME = IO.PriceBadge(
+    depends_on=IO.PriceBadgeDepends(widgets=["resolution"]),
+    expr="""
+    (
+      $p := {"360p": 0.03, "540p": 0.06, "720p": 0.12, "1080p": 0.36};
+      {"type": "usd", "usd": $lookup($p, widgets.resolution), "format": {"suffix": "/second"}}
+    )
+    """,
+)
+
+
+def _ray32_seed_input() -> IO.Input:
+    return IO.Int.Input(
+        "seed",
+        default=0,
+        min=0,
+        max=0xFFFFFFFFFFFFFFFF,
+        control_after_generate=True,
+        tooltip="Seed to determine if node should re-run; results are nondeterministic regardless of seed.",
+    )
+
+
+async def _ray32_generate(cls: type[IO.ComfyNode], request: Luma2GenerationRequest) -> IO.NodeOutput:
+    """Run a ray-3.2 generation and return (video, generation_id)."""
+    final = await _luma2_submit_and_poll(cls, request, estimated_duration=120)
+    video = await download_url_to_video_output(final.output[0].url)
+    return IO.NodeOutput(video, final.id or "")
+
+
+class LumaRay32TextToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaRay32TextToVideoNode",
+            display_name="Luma Ray 3.2 Text to Video",
+            category="partner/video/Luma",
+            description="Generate a video from a text prompt using Luma's Ray 3.2 model.",
+            inputs=[
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Text prompt for the video generation."),
+                IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1", "4:3", "3:4", "21:9"]),
+                IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"),
+                IO.Combo.Input("duration", options=["5s", "10s"]),
+                IO.Boolean.Input(
+                    "loop",
+                    default=False,
+                    tooltip="Make the video loop seamlessly. Only available with 5s duration.",
+                ),
+                _ray32_seed_input(),
+            ],
+            outputs=[IO.Video.Output(), IO.String.Output(display_name="generation_id")],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=_BADGE_RAY32_VIDEO,
+        )
+
+    @classmethod
+    async def execute(
+        cls, prompt: str, aspect_ratio: str, resolution: str, duration: str, loop: bool, seed: int
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1, max_length=6000)
+        if loop and duration == "10s":
+            raise ValueError("Looping is only available with 5s duration on Ray 3.2.")
+        request = Luma2GenerationRequest(
+            prompt=prompt,
+            model="ray-3.2",
+            type="video",
+            aspect_ratio=aspect_ratio,
+            video=Luma2VideoOptions(resolution=resolution, duration=duration, loop=loop or None),
+        )
+        return await _ray32_generate(cls, request)
+
+
+class LumaRay32ImageToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaRay32ImageToVideoNode",
+            display_name="Luma Ray 3.2 Image to Video",
+            category="partner/video/Luma",
+            description="Generate a video from a start and/or end frame using Luma's Ray 3.2 model. "
+            "Image-anchored generations are always 5 seconds.",
+            inputs=[
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Text prompt for the video generation."),
+                IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"),
+                IO.Boolean.Input(
+                    "loop",
+                    default=False,
+                    tooltip="Make the video loop seamlessly. Not available when an end_frame is set.",
+                ),
+                _ray32_seed_input(),
+                IO.Image.Input("start_frame", optional=True, tooltip="First frame of the generated video."),
+                IO.Image.Input("end_frame", optional=True, tooltip="Last frame of the generated video."),
+            ],
+            outputs=[IO.Video.Output(), IO.String.Output(display_name="generation_id")],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=_BADGE_RAY32_VIDEO_5S,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        resolution: str,
+        loop: bool,
+        seed: int,
+        start_frame: torch.Tensor | None = None,
+        end_frame: torch.Tensor | None = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1, max_length=6000)
+        if start_frame is None and end_frame is None:
+            raise ValueError("Provide at least one of start_frame / end_frame.")
+        if loop and end_frame is not None:
+            raise ValueError("Looping is not available when an end_frame is set.")
+        video = Luma2VideoOptions(resolution=resolution, duration="5s", loop=loop or None)
+        if start_frame is not None:
+            url = await upload_image_to_comfyapi(cls, start_frame, mime_type="image/png")
+            video.start_frame = Luma2ImageRef(url=url)
+        if end_frame is not None:
+            url = await upload_image_to_comfyapi(cls, end_frame, mime_type="image/png")
+            video.end_frame = Luma2ImageRef(url=url)
+        request = Luma2GenerationRequest(prompt=prompt, model="ray-3.2", type="video", video=video)
+        return await _ray32_generate(cls, request)
+
+
+class LumaRay32KeyframeNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaRay32KeyframeNode",
+            display_name="Luma Ray 3.2 Keyframe",
+            category="partner/video/Luma",
+            description="Anchor a guide image to a position on the Ray 3.2 output video timeline. Connect this to "
+            "the 'keyframes' input of the Luma Ray 3.2 Keyframes to Video node; chain several together via the "
+            "optional 'keyframes' input below.",
+            inputs=[
+                IO.Image.Input("image", tooltip="Guide image to place at the chosen moment of the output video."),
+                IO.DynamicCombo.Input(
+                    "position",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "Fraction of duration (0.0-1.0)",
+                            [
+                                IO.Float.Input(
+                                    "fraction",
+                                    default=0.0,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Where in the output video this image applies " "(0.0 = start, 1.0 = end).",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "Absolute time (seconds)",
+                            [
+                                IO.Float.Input(
+                                    "seconds",
+                                    default=0.0,
+                                    min=0.0,
+                                    max=10.0,
+                                    step=0.1,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Time in seconds from the start of the output video where this "
+                                    "image applies.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="How to place this image on the output video's timeline.",
+                ),
+                IO.Custom(LumaIO.LUMA_RAY32_KEYFRAME).Input(
+                    "keyframes",
+                    optional=True,
+                    tooltip="Optional earlier keyframes to chain with this one.",
+                ),
+            ],
+            outputs=[IO.Custom(LumaIO.LUMA_RAY32_KEYFRAME).Output(display_name="keyframes")],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        image: torch.Tensor,
+        position: dict,
+        keyframes: LumaRay32KeyframeChain | None = None,
+    ) -> IO.NodeOutput:
+        chain = keyframes.clone() if keyframes is not None else LumaRay32KeyframeChain()
+        if position["position"] == "Absolute time (seconds)":
+            mode, value = LUMA_KEYFRAME_MODE_SECONDS, float(position["seconds"])
+        else:
+            mode, value = LUMA_KEYFRAME_MODE_FRACTION, float(position["fraction"])
+        chain.add(LumaRay32KeyframeItem(image=image, mode=mode, value=value))
+        return IO.NodeOutput(chain)
+
+
+class LumaRay32KeyframesToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaRay32KeyframesToVideoNode",
+            display_name="Luma Ray 3.2 Keyframes to Video",
+            category="partner/video/Luma",
+            description="Generate a video that interpolates through a sequence of guide images, each anchored to a "
+            "position on the timeline, using Luma Ray 3.2. Build the sequence with Luma Ray 3.2 Keyframe nodes "
+            "(at least 2).",
+            inputs=[
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Text prompt for the video generation."),
+                IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"),
+                IO.Combo.Input("duration", options=["5s", "10s"]),
+                _ray32_seed_input(),
+                IO.Custom(LumaIO.LUMA_RAY32_KEYFRAME).Input(
+                    "keyframes",
+                    tooltip="Keyframe sequence from Luma Ray 3.2 Keyframe nodes (at least 2).",
+                ),
+            ],
+            outputs=[IO.Video.Output(), IO.String.Output(display_name="generation_id")],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=_BADGE_RAY32_VIDEO,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        resolution: str,
+        duration: str,
+        seed: int,
+        keyframes: LumaRay32KeyframeChain | None = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1, max_length=6000)
+        items = keyframes.items if keyframes is not None else []
+        if len(items) < 2:
+            raise ValueError(
+                "Connect at least 2 Luma Ray 3.2 Keyframe nodes "
+                "(use Luma Ray 3.2 Image to Video for a single start/end frame)."
+            )
+        if len(items) > 64:
+            raise ValueError(f"Ray 3.2 supports at most 64 keyframes; got {len(items)}.")
+        maxframe = 120 if duration == "5s" else 240
+        duration_seconds = maxframe / 24  # 5.0 or 10.0
+        # Resolve each keyframe to an output-frame index, then order by position
+        # (so the user can chain keyframes in any order — the position is what places them)
+        placed: list[tuple[int, torch.Tensor]] = []
+        for item in items:
+            if item.mode == LUMA_KEYFRAME_MODE_SECONDS:
+                if item.value > duration_seconds:
+                    raise ValueError(
+                        f"Keyframe position {item.value:g}s is past the end of the {duration} video; "
+                        f"use 0-{duration_seconds:g}s (or switch the keyframe to fraction mode)."
+                    )
+                idx = round(item.value * 24)
+            else:
+                idx = round(item.value * maxframe)
+            placed.append((max(0, min(maxframe, idx)), item.image))
+        placed.sort(key=lambda p: p[0])
+        indexes = [idx for idx, _ in placed]
+        for a, b in zip(indexes, indexes[1:]):
+            if a == b:
+                raise ValueError(
+                    f"Two keyframes resolve to the same output frame ({a}) for a {duration} video "
+                    f"(valid range 0-{maxframe}); give each keyframe a distinct position."
+                )
+        refs: list[Luma2ImageRef] = []
+        for _, image in placed:
+            url = await upload_image_to_comfyapi(cls, image, mime_type="image/png")
+            refs.append(Luma2ImageRef(url=url))
+        request = Luma2GenerationRequest(
+            prompt=prompt,
+            model="ray-3.2",
+            type="video",
+            video=Luma2VideoOptions(resolution=resolution, duration=duration, keyframes=refs, keyframe_indexes=indexes),
+        )
+        return await _ray32_generate(cls, request)
+
+
+class LumaRay32VideoEditNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaRay32VideoEditNode",
+            display_name="Luma Ray 3.2 Video Edit",
+            category="partner/video/Luma",
+            description="Re-render an existing video under a new prompt using Luma Ray 3.2 (restyle, relight, add "
+            "or remove elements) while keeping the original motion. Source video up to 18 seconds; the edited "
+            "video keeps the source's length.",
+            inputs=[
+                IO.Video.Input("video", tooltip="Source video to edit. Up to 18 seconds."),
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Describes the desired edit."),
+                IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"),
+                IO.Combo.Input(
+                    "strength",
+                    options=[
+                        "auto",
+                        "adhere_1",
+                        "adhere_2",
+                        "adhere_3",
+                        "flex_1",
+                        "flex_2",
+                        "flex_3",
+                        "reimagine_1",
+                        "reimagine_2",
+                        "reimagine_3",
+                    ],
+                    default="auto",
+                    tooltip="How strongly to preserve vs. reimagine the source. 'auto' lets Ray 3.2 choose; "
+                    "adhere_* preserves the most, flex_* is balanced, reimagine_* changes the most.",
+                ),
+                _ray32_seed_input(),
+            ],
+            outputs=[
+                IO.Video.Output(),
+                IO.String.Output(display_name="generation_id"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=_BADGE_RAY32_EDIT,
+        )
+
+    @classmethod
+    async def execute(
+        cls, video: Input.Video, prompt: str, resolution: str, strength: str, seed: int
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1, max_length=6000)
+        try:
+            duration = "5s" if video.get_duration() <= 5.0 else "10s"
+        except Exception:
+            duration = "10s"
+        source_url = await upload_video_to_comfyapi(cls, video, max_duration=18)
+        edit = Luma2VideoEdit(auto_controls=True) if strength == "auto" else Luma2VideoEdit(strength=strength)
+        request = Luma2GenerationRequest(
+            prompt=prompt,
+            model="ray-3.2",
+            type="video_edit",
+            source=Luma2ImageRef(url=source_url, media_type="video/mp4"),
+            video=Luma2VideoOptions(resolution=resolution, duration=duration, edit=edit),
+        )
+        return await _ray32_generate(cls, request)
+
+
+class LumaRay32VideoReframeNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaRay32VideoReframeNode",
+            display_name="Luma Ray 3.2 Video Reframe",
+            category="partner/video/Luma",
+            description="Change the aspect ratio of an existing video, using Luma Ray 3.2 to fill the newly "
+            "exposed canvas areas. Source video up to 30 seconds. Billed per second of output.",
+            inputs=[
+                IO.Video.Input("video", tooltip="Source video to reframe. Up to 30 seconds."),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Describes how the newly exposed canvas areas should be filled.",
+                ),
+                IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1", "4:3", "3:4", "21:9"]),
+                IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"),
+                _ray32_seed_input(),
+            ],
+            outputs=[
+                IO.Video.Output(),
+                IO.String.Output(display_name="generation_id"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=_BADGE_RAY32_REFRAME,
+        )
+
+    @classmethod
+    async def execute(
+        cls, video: Input.Video, prompt: str, aspect_ratio: str, resolution: str, seed: int
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False, min_length=1, max_length=6000)
+        if resolution == "1080p" and aspect_ratio in {"9:16", "3:4"}:
+            raise ValueError("1080p is not available for vertical aspect ratios (9:16, 3:4) when reframing.")
+        source_url = await upload_video_to_comfyapi(cls, video, max_duration=30)
+        request = Luma2GenerationRequest(
+            prompt=prompt,
+            model="ray-3.2",
+            type="video_reframe",
+            aspect_ratio=aspect_ratio,
+            source=Luma2ImageRef(url=source_url, media_type="video/mp4"),
+            video=Luma2VideoOptions(resolution=resolution),
+        )
+        return await _ray32_generate(cls, request)
+
+
+class LumaRay32ExtendVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaRay32ExtendVideoNode",
+            display_name="Luma Ray 3.2 Extend Video",
+            category="partner/video/Luma",
+            description="Extend a previous Ray 3.2 generation forward (continue after it) or backward (lead-in "
+            "before it). Connect the generation_id output of a prior Luma Ray 3.2 node."
+            " Extensions are always 5 seconds.",
+            inputs=[
+                IO.String.Input(
+                    "source_generation_id",
+                    default="",
+                    tooltip="generation_id of the prior Ray 3.2 video to extend."
+                    " Connect the generation_id output of another Luma Ray 3.2 node.",
+                ),
+                IO.DynamicCombo.Input(
+                    "direction",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "Forward (continue after)",
+                            [
+                                IO.Boolean.Input(
+                                    "loop",
+                                    default=False,
+                                    tooltip="Loop the extended video seamlessly (forward extend only).",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option("Backward (lead-in before)", []),
+                    ],
+                    tooltip="Forward continues after the prior clip; backward is prepended before it.",
+                ),
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Text prompt for the new content."),
+                IO.Combo.Input("resolution", options=["540p", "720p", "1080p"], default="720p"),
+                _ray32_seed_input(),
+            ],
+            outputs=[
+                IO.Video.Output(),
+                IO.String.Output(display_name="generation_id"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=_BADGE_RAY32_VIDEO_5S,
+        )
+
+    @classmethod
+    async def execute(
+        cls, source_generation_id: str, direction: dict, prompt: str, resolution: str, seed: int
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False, min_length=1, max_length=6000)
+        gen_id = (source_generation_id or "").strip()
+        if not gen_id:
+            raise ValueError(
+                "source_generation_id is required (connect the generation_id output of a prior Luma Ray 3.2 node)."
+            )
+        video = Luma2VideoOptions(resolution=resolution, duration="5s")
+        ref = Luma2ImageRef(generation_id=gen_id)
+        if direction["direction"] == "Forward (continue after)":
+            video.start_frame = ref
+            if direction.get("loop"):
+                video.loop = True
+        else:
+            video.end_frame = ref
+        request = Luma2GenerationRequest(prompt=prompt, model="ray-3.2", type="video", video=video)
+        return await _ray32_generate(cls, request)
 
 
 class LumaExtension(ComfyExtension):
@@ -944,6 +1481,13 @@ class LumaExtension(ComfyExtension):
             LumaConceptsNode,
             LumaImageNode,
             LumaImageEditNode,
+            LumaRay32TextToVideoNode,
+            LumaRay32ImageToVideoNode,
+            LumaRay32KeyframeNode,
+            LumaRay32KeyframesToVideoNode,
+            LumaRay32VideoEditNode,
+            LumaRay32VideoReframeNode,
+            LumaRay32ExtendVideoNode,
         ]
 
 

From bd39bbf0678ebd31c972fd365733a8c729f2cd74 Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Fri, 19 Jun 2026 11:32:56 +0300
Subject: [PATCH 29/30] [Partner Nodes] fix: respect Retry-After header
 (#14234)

Signed-off-by: bigcat88 <bigcat88@icloud.com>
---
 comfy_api_nodes/util/_helpers.py | 28 ++++++++++++++++++++++++++++
 comfy_api_nodes/util/client.py   |  3 +++
 2 files changed, 31 insertions(+)

diff --git a/comfy_api_nodes/util/_helpers.py b/comfy_api_nodes/util/_helpers.py
index 83cf7b001..6b8121cab 100644
--- a/comfy_api_nodes/util/_helpers.py
+++ b/comfy_api_nodes/util/_helpers.py
@@ -4,6 +4,8 @@ import os
 import re
 import time
 from collections.abc import Callable
+from datetime import datetime, timezone
+from email.utils import parsedate_to_datetime
 from io import BytesIO
 
 from yarl import URL
@@ -91,6 +93,32 @@ async def sleep_with_interrupt(
         await asyncio.sleep(min(1.0, end - now))
 
 
+def _retry_after_wait(value: str | None, fallback: float, max_wait: float) -> float:
+    """Delay before the next retry, honoring a server ``Retry-After`` header."""
+
+    seconds: float | None = None
+    if value is not None:
+        value = value.strip()
+        if value.isascii() and value.isdigit():
+            # delay-seconds form. The ASCII-digit guard keeps exotic Unicode "digit" characters away from float()
+            # an all-digit string always converts (huge values become inf, never raising).
+            seconds = float(value)
+        elif value:
+            # HTTP-date form. parsedate_to_datetime raises OverflowError (not a ValueError) on absurd years/offsets
+            try:
+                parsed = parsedate_to_datetime(value)
+            except (TypeError, ValueError, OverflowError):
+                parsed = None
+            if parsed is not None:
+                if parsed.tzinfo is None:  # naive datetime: HTTP-date is UTC
+                    parsed = parsed.replace(tzinfo=timezone.utc)
+                delta = (parsed - datetime.now(timezone.utc)).total_seconds()
+                seconds = delta if delta > 0 else 0.0
+    if seconds is None:
+        return fallback
+    return min(seconds, max_wait)
+
+
 def mimetype_to_extension(mime_type: str) -> str:
     """Converts a MIME type to a file extension."""
     return mime_type.split("/")[-1].lower()
diff --git a/comfy_api_nodes/util/client.py b/comfy_api_nodes/util/client.py
index adcde7bcb..66aab17f8 100644
--- a/comfy_api_nodes/util/client.py
+++ b/comfy_api_nodes/util/client.py
@@ -21,6 +21,7 @@ from server import PromptServer
 
 from . import request_logger
 from ._helpers import (
+    _retry_after_wait,
     default_base_url,
     get_comfy_api_headers,
     get_node_id,
@@ -82,6 +83,7 @@ class _PollUIState:
 
 
 _RETRY_STATUS = {408, 500, 502, 503, 504}  # status 429 is handled separately
+_MAX_RETRY_AFTER_WAIT = 150.0  # Cap a server Retry-After at this many seconds so a large hint can't block execution
 COMPLETED_STATUSES = ["succeeded", "succeed", "success", "completed", "finished", "done", "complete"]
 FAILED_STATUSES = ["cancelled", "canceled", "canceling", "fail", "failed", "error"]
 QUEUED_STATUSES = ["created", "queued", "queueing", "submitted", "initializing", "wait", "in_queue"]
@@ -747,6 +749,7 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool):
                         should_retry = True
 
                     if should_retry:
+                        wait_time = _retry_after_wait(resp.headers.get("Retry-After"), wait_time, _MAX_RETRY_AFTER_WAIT)
                         logging.warning(
                             "HTTP %s %s -> %s. Waiting %.2fs (%s).",
                             method,

From bc11e8a65a57dc6bd1768edfca14ddb1523f7882 Mon Sep 17 00:00:00 2001
From: Comfy Org PR Bot <snomiao+comfy-pr@gmail.com>
Date: Sat, 20 Jun 2026 08:01:34 +0900
Subject: [PATCH 30/30] Bump comfyui-frontend-package to 1.45.19 (#14559)

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

diff --git a/requirements.txt b/requirements.txt
index 392709e64..ad8b1c2ee 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-comfyui-frontend-package==1.45.15
+comfyui-frontend-package==1.45.19
 comfyui-workflow-templates==0.10.0
 comfyui-embedded-docs==0.5.4
 torch