ComfyUI v0.19.4

* feat(api nodes): added 4K resolution for Veo models; added Veo 3 Lite model

Signed-off-by: bigcat88 <bigcat88@icloud.com>

* increase poll_interval from 5 to 9

---------

Signed-off-by: bigcat88 <bigcat88@icloud.com>
Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>

.ci/windows_amd_base_files/run_amd_gpu_disable_smart_memory.bat

@@ -1,2 +1,2 @@
-.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --enable-dynamic-vram
+.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --disable-smart-memory
 pause

.github/workflows/openapi-lint.yml

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

.github/workflows/stable-release.yml

@@ -145,8 +145,6 @@ jobs:
           cp -r ComfyUI/.ci/windows_${{ inputs.rel_name }}_base_files/* ./
           cp ../update_comfyui_and_python_dependencies.bat ./update/
 
-          echo 'local-portable' > ComfyUI/.comfy_environment
-
           cd ..
 
           "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=768m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable

.github/workflows/tag-dispatch-cloud.yml

@@ -1,45 +0,0 @@
-name: Tag Dispatch to Cloud
-
-on:
-  push:
-    tags:
-      - 'v*'
-
-jobs:
-  dispatch-cloud:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Send repository dispatch to cloud
-        env:
-          DISPATCH_TOKEN: ${{ secrets.CLOUD_REPO_DISPATCH_TOKEN }}
-          RELEASE_TAG: ${{ github.ref_name }}
-        run: |
-          set -euo pipefail
-
-          if [ -z "${DISPATCH_TOKEN:-}" ]; then
-            echo "::error::CLOUD_REPO_DISPATCH_TOKEN is required but not set."
-            exit 1
-          fi
-
-          RELEASE_URL="https://github.com/${{ github.repository }}/releases/tag/${RELEASE_TAG}"
-
-          PAYLOAD="$(jq -n \
-            --arg release_tag "$RELEASE_TAG" \
-            --arg release_url "$RELEASE_URL" \
-            '{
-              event_type: "comfyui_tag_pushed",
-              client_payload: {
-                release_tag: $release_tag,
-                release_url: $release_url
-              }
-            }')"
-
-          curl -fsSL \
-            -X POST \
-            -H "Accept: application/vnd.github+json" \
-            -H "Content-Type: application/json" \
-            -H "Authorization: Bearer ${DISPATCH_TOKEN}" \
-            https://api.github.com/repos/Comfy-Org/cloud/dispatches \
-            -d "$PAYLOAD"
-
-          echo "✅ Dispatched ComfyUI tag ${RELEASE_TAG} to Comfy-Org/cloud"

.gitignore

@@ -21,6 +21,6 @@ venv*/
 *.log
 web_custom_versions/
 .DS_Store
+openapi.yaml
 filtered-openapi.yaml
 uv.lock
-.comfy_environment

.spectral.yaml

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

CODEOWNERS

@@ -1,2 +1,2 @@
 # Admins
-* @comfyanonymous @kosinkadink @guill @alexisrolland @rattus128 @kijai
+* @comfyanonymous @kosinkadink @guill

README.md

@@ -1,7 +1,7 @@
 <div align="center">
 
 # ComfyUI
-**The most powerful and modular AI engine for content creation.**
+**The most powerful and modular visual AI engine and application.**
 
 
 [![Website][website-shield]][website-url]
@@ -31,16 +31,10 @@
 [github-downloads-latest-shield]: https://img.shields.io/github/downloads/comfyanonymous/ComfyUI/latest/total?style=flat&label=downloads%40latest
 [github-downloads-link]: https://github.com/comfyanonymous/ComfyUI/releases
 
-<img width="1590" height="795" alt="ComfyUI Screenshot" src="https://github.com/user-attachments/assets/36e065e0-bfae-4456-8c7f-8369d5ea48a2" />
-<br>
+![ComfyUI Screenshot](https://github.com/user-attachments/assets/7ccaf2c1-9b72-41ae-9a89-5688c94b7abe)
 </div>
 
-ComfyUI is the AI creation engine for visual professionals who demand control over every model, every parameter, and every output. Its powerful and modular node graph interface empowers creatives to generate images, videos, 3D models, audio, and more...
-- ComfyUI natively supports the latest open-source state of the art models.
-- API nodes provide access to the best closed source models such as Nano Banana, Seedance, Hunyuan3D, etc.
-- It is available on Windows, Linux, and macOS, locally with our desktop application or on our cloud.
-- The most sophisticated workflows can be exposed through a simple UI thanks to App Mode.
-- It integrates seamlessly into production pipelines with our API endpoints.
+ComfyUI lets you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. Available on Windows, Linux, and macOS.
 
 ## Get Started
 
@@ -83,7 +77,6 @@ See what ComfyUI can do with the [newer template workflows](https://comfy.org/wo
    - [Hunyuan Image 2.1](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_image/)
    - [Flux 2](https://comfyanonymous.github.io/ComfyUI_examples/flux2/)
    - [Z Image](https://comfyanonymous.github.io/ComfyUI_examples/z_image/)
-   - Ernie Image
 - Image Editing Models
    - [Omnigen 2](https://comfyanonymous.github.io/ComfyUI_examples/omnigen/)
    - [Flux Kontext](https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-kontext-image-editing-model)
@@ -133,7 +126,7 @@ Workflow examples can be found on the [Examples page](https://comfyanonymous.git
 ComfyUI follows a weekly release cycle targeting Monday but this regularly changes because of model releases or large changes to the codebase. There are three interconnected repositories:
 
 1. **[ComfyUI Core](https://github.com/comfyanonymous/ComfyUI)**
-   - Releases a new major stable version (e.g., v0.7.0) roughly every 2 weeks.
+   - Releases a new stable version (e.g., v0.7.0) roughly every week.
    - Starting from v0.4.0 patch versions will be used for fixes backported onto the current stable release.
    - Minor versions will be used for releases off the master branch.
    - Patch versions may still be used for releases on the master branch in cases where a backport would not make sense.
@@ -200,15 +193,11 @@ If you have trouble extracting it, right click the file -> properties -> unblock
 
 The portable above currently comes with python 3.13 and pytorch cuda 13.0. Update your Nvidia drivers if it doesn't start.
 
-#### All Official Portable Downloads:
+#### Alternative Downloads:
 
-[Portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z)
+[Experimental portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z)
 
-[Portable for Intel GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_intel.7z)
-
-[Portable for Nvidia GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia.7z) (supports 20 series and above).
-
-[Portable for Nvidia GPUs with pytorch cuda 12.6 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu126.7z) (Supports Nvidia 10 series and older GPUs).
+[Portable with pytorch cuda 12.6 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu126.7z) (Supports Nvidia 10 series and older GPUs).
 
 #### How do I share models between another UI and ComfyUI?
 

api_server/routes/internal/internal_routes.py

@@ -67,7 +67,7 @@ class InternalRoutes:
                 (entry for entry in os.scandir(directory) if is_visible_file(entry)),
                 key=lambda entry: -entry.stat().st_mtime
             )
-            return web.json_response([f"{entry.name} [{directory_type}]" for entry in sorted_files], status=200)
+            return web.json_response([entry.name for entry in sorted_files], status=200)
 
 
     def get_app(self):

app/frontend_management.py

@@ -27,7 +27,7 @@ def frontend_install_warning_message():
     return f"""
 {get_missing_requirements_message()}
 
-The ComfyUI frontend is shipped in a pip package so it needs to be updated separately from the ComfyUI code.
+This error is happening because the ComfyUI frontend is no longer shipped as part of the main repo but as a pip package instead.
 """.strip()
 
 def parse_version(version: str) -> tuple[int, int, int]:

app/node_replace_manager.py

@@ -1,7 +1,5 @@
 from __future__ import annotations
 
-import logging
-
 from aiohttp import web
 
 from typing import TYPE_CHECKING, TypedDict
@@ -33,22 +31,8 @@ class NodeReplaceManager:
         self._replacements: dict[str, list[NodeReplace]] = {}
 
     def register(self, node_replace: NodeReplace):
-        """Register a node replacement mapping.
-
-        Idempotent: if a replacement with the same (old_node_id, new_node_id)
-        is already registered, the duplicate is ignored. This prevents stale
-        entries from accumulating when custom nodes are reloaded in the same
-        process (e.g. via ComfyUI-Manager).
-        """
-        existing = self._replacements.setdefault(node_replace.old_node_id, [])
-        for entry in existing:
-            if entry.new_node_id == node_replace.new_node_id:
-                logging.debug(
-                    "Node replacement %s -> %s already registered, ignoring duplicate.",
-                    node_replace.old_node_id, node_replace.new_node_id,
-                )
-                return
-        existing.append(node_replace)
+        """Register a node replacement mapping."""
+        self._replacements.setdefault(node_replace.old_node_id, []).append(node_replace)
 
     def get_replacement(self, old_node_id: str) -> list[NodeReplace] | None:
         """Get replacements for an old node ID."""

app/user_manager.py

@@ -28,8 +28,8 @@ def get_file_info(path: str, relative_to: str) -> FileInfo:
     return {
         "path": os.path.relpath(path, relative_to).replace(os.sep, '/'),
         "size": os.path.getsize(path),
-        "modified": int(os.path.getmtime(path) * 1000),
-        "created": int(os.path.getctime(path) * 1000),
+        "modified": os.path.getmtime(path),
+        "created": os.path.getctime(path)
     }
 
 

blueprints/.glsl/Glow_30.frag

@@ -2,6 +2,7 @@
 precision mediump float;
 
 uniform sampler2D u_image0;
+uniform vec2 u_resolution;
 uniform int u_int0;      // Blend mode
 uniform int u_int1;      // Color tint
 uniform float u_float0;  // Intensity
@@ -74,7 +75,7 @@ void main() {
     float t0 = threshold - 0.15;
     float t1 = threshold + 0.15;
     
-    vec2 texelSize = 1.0 / vec2(textureSize(u_image0, 0));
+    vec2 texelSize = 1.0 / u_resolution;
     float radius2 = radius * radius;
     
     float sampleScale = clamp(radius * 0.75, 0.35, 1.0);

blueprints/.glsl/Image_Blur_1.frag

@@ -12,6 +12,7 @@ const int RADIAL_SAMPLES = 12;
 const float RADIAL_STRENGTH = 0.0003;
 
 uniform sampler2D u_image0;
+uniform vec2 u_resolution;
 uniform int u_int0;      // Blur type (BLUR_GAUSSIAN, BLUR_BOX, BLUR_RADIAL)
 uniform float u_float0;  // Blur radius/amount
 uniform int u_pass;      // Pass index (0 = horizontal, 1 = vertical)
@@ -24,7 +25,7 @@ float gaussian(float x, float sigma) {
 }
 
 void main() {
-    vec2 texelSize = 1.0 / vec2(textureSize(u_image0, 0));
+    vec2 texelSize = 1.0 / u_resolution;
     float radius = max(u_float0, 0.0);
 
     // Radial (angular) blur - single pass, doesn't use separable

blueprints/.glsl/Sharpen_23.frag

@@ -2,13 +2,14 @@
 precision highp float;
 
 uniform sampler2D u_image0;
+uniform vec2 u_resolution;
 uniform float u_float0;  // strength [0.0 – 2.0] typical: 0.3–1.0
 
 in vec2 v_texCoord;
 layout(location = 0) out vec4 fragColor0;
 
 void main() {
-    vec2 texel = 1.0 / vec2(textureSize(u_image0, 0));
+    vec2 texel = 1.0 / u_resolution;
     
     // Sample center and neighbors
     vec4 center = texture(u_image0, v_texCoord);

blueprints/.glsl/Unsharp_Mask_26.frag

@@ -2,6 +2,7 @@
 precision highp float;
 
 uniform sampler2D u_image0;
+uniform vec2 u_resolution;
 uniform float u_float0;  // amount    [0.0 - 3.0]  typical: 0.5-1.5
 uniform float u_float1;  // radius    [0.5 - 10.0] blur radius in pixels
 uniform float u_float2;  // threshold [0.0 - 0.1]  min difference to sharpen
@@ -18,7 +19,7 @@ float getLuminance(vec3 color) {
 }
 
 void main() {
-    vec2 texel = 1.0 / vec2(textureSize(u_image0, 0));
+    vec2 texel = 1.0 / u_resolution;
     float radius = max(u_float1, 0.5);
     float amount = u_float0;
     float threshold = u_float2;

blueprints/Brightness and Contrast.json

@@ -431,10 +431,9 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust",
-        "description": "Adjusts image brightness and contrast using a real-time GPU fragment shader."
+        "category": "Image Tools/Color adjust"
       }
     ]
   },
   "extra": {}
-}
+}

blueprints/Canny to Image (Z-Image-Turbo).json

@@ -162,7 +162,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "Canny to Image (Z-Image-Turbo)",
+        "name": "local-Canny to Image (Z-Image-Turbo)",
         "inputNode": {
           "id": -10,
           "bounding": [
@@ -1553,8 +1553,7 @@
           "VHS_MetadataImage": true,
           "VHS_KeepIntermediate": true
         },
-        "category": "Image generation and editing/Canny to image",
-        "description": "Generates an image from a Canny edge map using Z-Image-Turbo, with text conditioning."
+        "category": "Image generation and editing/Canny to image"
       }
     ]
   },
@@ -1575,4 +1574,4 @@
     }
   },
   "version": 0.4
-}
+}

blueprints/Canny to Video (LTX 2.0).json

@@ -192,7 +192,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "Canny to Video (LTX 2.0)",
+        "name": "local-Canny to Video (LTX 2.0)",
         "inputNode": {
           "id": -10,
           "bounding": [
@@ -3600,8 +3600,7 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Video generation and editing/Canny to video",
-        "description": "Generates video from Canny edge maps using LTX-2, with optional synchronized audio."
+        "category": "Video generation and editing/Canny to video"
       }
     ]
   },
@@ -3617,4 +3616,4 @@
     }
   },
   "version": 0.4
-}
+}

blueprints/Chromatic Aberration.json

@@ -377,9 +377,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust",
-        "description": "Adds lens-style chromatic aberration (color fringing) using a real-time GPU fragment shader."
+        "category": "Image Tools/Color adjust"
       }
     ]
   }
-}
+}

blueprints/Color Adjustment.json

@@ -596,8 +596,7 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust",
-        "description": "Adjusts saturation, temperature, tint, and vibrance using a real-time GPU fragment shader."
+        "category": "Image Tools/Color adjust"
       }
     ]
   }

blueprints/Color Balance.json

@@ -1129,8 +1129,7 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust",
-        "description": "Balances colors across shadows, midtones, and highlights using a real-time GPU fragment shader."
+        "category": "Image Tools/Color adjust"
       }
     ]
   }

blueprints/Color Curves.json

@@ -608,8 +608,7 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust",
-        "description": "Fine-tunes tone and color with per-channel curve adjustments using a real-time GPU fragment shader."
+        "category": "Image Tools/Color adjust"
       }
     ]
   }

blueprints/Depth to Image (Z-Image-Turbo).json

@@ -160,7 +160,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "Depth to Image (Z-Image-Turbo)",
+        "name": "local-Depth to Image (Z-Image-Turbo)",
         "inputNode": {
           "id": -10,
           "bounding": [
@@ -1579,8 +1579,7 @@
           "VHS_MetadataImage": true,
           "VHS_KeepIntermediate": true
         },
-        "category": "Image generation and editing/Depth to image",
-        "description": "Generates an image from a depth map using Z-Image-Turbo with text conditioning."
+        "category": "Image generation and editing/Depth to image"
       },
       {
         "id": "458bdf3c-4b58-421c-af50-c9c663a4d74c",
@@ -2462,8 +2461,7 @@
             ]
           },
           "workflowRendererVersion": "LG"
-        },
-        "description": "Estimates a monocular depth map from an input image using the Lotus depth estimation model."
+        }
       }
     ]
   },
@@ -2484,4 +2482,4 @@
     "VHS_KeepIntermediate": true
   },
   "version": 0.4
-}
+}

blueprints/Depth to Video (ltx 2.0).json

@@ -261,7 +261,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "Depth to Video (LTX 2.0)",
+        "name": "local-Depth to Video (LTX 2.0)",
         "inputNode": {
           "id": -10,
           "bounding": [
@@ -4233,8 +4233,7 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Video generation and editing/Depth to video",
-        "description": "Generates depth-controlled video with LTX-2: motion and structure follow a depth-reference video alongside text prompting, optional first-frame image conditioning, with optional synchronized audio."
+        "category": "Video generation and editing/Depth to video"
       },
       {
         "id": "38b60539-50a7-42f9-a5fe-bdeca26272e2",
@@ -5193,8 +5192,7 @@
         ],
         "extra": {
           "workflowRendererVersion": "LG"
-        },
-        "description": "Estimates a monocular depth map from an input image using the Lotus depth estimation model."
+        }
       }
     ]
   },
@@ -5210,4 +5208,4 @@
     "workflowRendererVersion": "LG"
   },
   "version": 0.4
-}
+}

blueprints/Edge-Preserving Blur.json

@@ -450,10 +450,9 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Blur",
-        "description": "Applies bilateral (edge-preserving) blur to soften images while retaining detail."
+        "category": "Image Tools/Blur"
       }
     ]
   },
   "extra": {}
-}
+}

blueprints/Film Grain.json

@@ -580,9 +580,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust",
-        "description": "Adds procedural film grain texture for a cinematic look via GPU fragment shader."
+        "category": "Image Tools/Color adjust"
       }
     ]
   }
-}
+}

blueprints/Frame Interpolation.json

@@ -1,858 +0,0 @@
-{
-  "revision": 0,
-  "last_node_id": 16,
-  "last_link_id": 0,
-  "nodes": [
-    {
-      "id": 16,
-      "type": "022693be-2baa-4009-870a-28921508a7ef",
-      "pos": [
-        -2990,
-        -3240
-      ],
-      "size": [
-        410,
-        200
-      ],
-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "inputs": [
-        {
-          "localized_name": "video",
-          "name": "video",
-          "type": "VIDEO",
-          "link": null
-        },
-        {
-          "label": "multiplier",
-          "name": "value",
-          "type": "INT",
-          "widget": {
-            "name": "value"
-          },
-          "link": null
-        },
-        {
-          "label": "enable_fps_multiplier",
-          "name": "value_1",
-          "type": "BOOLEAN",
-          "widget": {
-            "name": "value_1"
-          },
-          "link": null
-        },
-        {
-          "name": "model_name",
-          "type": "COMBO",
-          "widget": {
-            "name": "model_name"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
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blueprints/Get Any Video Frame.json

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-            "id": "1ab0684d-6a44-45b6-8aa4-a0b971a1d41e",
-            "name": "IMAGE",
-            "type": "IMAGE",
-            "linkIds": [
-              5
-            ],
-            "pos": [
-              1480,
-              -37
-            ]
-          }
-        ],
-        "widgets": [],
-        "nodes": [
-          {
-            "id": 1,
-            "type": "GetVideoComponents",
-            "pos": [
-              560,
-              -150
-            ],
-            "size": [
-              230,
-              120
-            ],
-            "flags": {},
-            "order": 0,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "video",
-                "name": "video",
-                "type": "VIDEO",
-                "link": 4
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "images",
-                "name": "images",
-                "type": "IMAGE",
-                "links": [
-                  1,
-                  2
-                ]
-              },
-              {
-                "localized_name": "audio",
-                "name": "audio",
-                "type": "AUDIO",
-                "links": null
-              },
-              {
-                "localized_name": "fps",
-                "name": "fps",
-                "type": "FLOAT",
-                "links": null
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "GetVideoComponents"
-            }
-          },
-          {
-            "id": 2,
-            "type": "GetImageSize",
-            "pos": [
-              560,
-              50
-            ],
-            "size": [
-              230,
-              120
-            ],
-            "flags": {},
-            "order": 1,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "image",
-                "name": "image",
-                "type": "IMAGE",
-                "link": 1
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "width",
-                "name": "width",
-                "type": "INT",
-                "links": null
-              },
-              {
-                "localized_name": "height",
-                "name": "height",
-                "type": "INT",
-                "links": null
-              },
-              {
-                "localized_name": "batch_size",
-                "name": "batch_size",
-                "type": "INT",
-                "links": [
-                  285
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "GetImageSize"
-            }
-          },
-          {
-            "id": 3,
-            "type": "ImageFromBatch",
-            "pos": [
-              1130,
-              -150
-            ],
-            "size": [
-              270,
-              140
-            ],
-            "flags": {},
-            "order": 2,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "image",
-                "name": "image",
-                "type": "IMAGE",
-                "link": 2
-              },
-              {
-                "localized_name": "batch_index",
-                "name": "batch_index",
-                "type": "INT",
-                "widget": {
-                  "name": "batch_index"
-                },
-                "link": 286
-              },
-              {
-                "localized_name": "length",
-                "name": "length",
-                "type": "INT",
-                "widget": {
-                  "name": "length"
-                },
-                "link": null
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "IMAGE",
-                "name": "IMAGE",
-                "type": "IMAGE",
-                "links": [
-                  5
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "ImageFromBatch"
-            },
-            "widgets_values": [
-              0,
-              1
-            ]
-          },
-          {
-            "id": 99,
-            "type": "ComfyMathExpression",
-            "pos": [
-              910,
-              100
-            ],
-            "size": [
-              400,
-              200
-            ],
-            "flags": {},
-            "order": 3,
-            "mode": 0,
-            "inputs": [
-              {
-                "label": "a",
-                "localized_name": "values.a",
-                "name": "values.a",
-                "type": "FLOAT,INT",
-                "link": 284
-              },
-              {
-                "label": "b",
-                "localized_name": "values.b",
-                "name": "values.b",
-                "shape": 7,
-                "type": "FLOAT,INT",
-                "link": 285
-              },
-              {
-                "label": "c",
-                "localized_name": "values.c",
-                "name": "values.c",
-                "shape": 7,
-                "type": "FLOAT,INT",
-                "link": null
-              },
-              {
-                "localized_name": "expression",
-                "name": "expression",
-                "type": "STRING",
-                "widget": {
-                  "name": "expression"
-                },
-                "link": null
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "FLOAT",
-                "name": "FLOAT",
-                "type": "FLOAT",
-                "links": null
-              },
-              {
-                "localized_name": "INT",
-                "name": "INT",
-                "type": "INT",
-                "links": [
-                  286
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "ComfyMathExpression"
-            },
-            "widgets_values": [
-              "min(max(int(a if a >= 0 else b + a), 0), b - 1)"
-            ]
-          },
-          {
-            "id": 100,
-            "type": "PrimitiveInt",
-            "pos": [
-              560,
-              250
-            ],
-            "size": [
-              270,
-              110
-            ],
-            "flags": {},
-            "order": 4,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "value",
-                "name": "value",
-                "type": "INT",
-                "widget": {
-                  "name": "value"
-                },
-                "link": 283
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "INT",
-                "name": "INT",
-                "type": "INT",
-                "links": [
-                  284
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "PrimitiveInt"
-            },
-            "widgets_values": [
-              0,
-              "fixed"
-            ]
-          }
-        ],
-        "groups": [],
-        "links": [
-          {
-            "id": 1,
-            "origin_id": 1,
-            "origin_slot": 0,
-            "target_id": 2,
-            "target_slot": 0,
-            "type": "IMAGE"
-          },
-          {
-            "id": 2,
-            "origin_id": 1,
-            "origin_slot": 0,
-            "target_id": 3,
-            "target_slot": 0,
-            "type": "IMAGE"
-          },
-          {
-            "id": 4,
-            "origin_id": -10,
-            "origin_slot": 0,
-            "target_id": 1,
-            "target_slot": 0,
-            "type": "VIDEO"
-          },
-          {
-            "id": 5,
-            "origin_id": 3,
-            "origin_slot": 0,
-            "target_id": -20,
-            "target_slot": 0,
-            "type": "IMAGE"
-          },
-          {
-            "id": 283,
-            "origin_id": -10,
-            "origin_slot": 1,
-            "target_id": 100,
-            "target_slot": 0,
-            "type": "INT"
-          },
-          {
-            "id": 284,
-            "origin_id": 100,
-            "origin_slot": 0,
-            "target_id": 99,
-            "target_slot": 0,
-            "type": "INT"
-          },
-          {
-            "id": 285,
-            "origin_id": 2,
-            "origin_slot": 2,
-            "target_id": 99,
-            "target_slot": 1,
-            "type": "INT"
-          },
-          {
-            "id": 286,
-            "origin_id": 99,
-            "origin_slot": 1,
-            "target_id": 3,
-            "target_slot": 1,
-            "type": "INT"
-          }
-        ],
-        "extra": {},
-        "category": "Video Tools",
-        "description": "Extracts one image frame from a video at a chosen index, with optional trim and FPS control."
-      }
-    ]
-  },
-  "extra": {
-    "ds": {
-      "scale": 1.197015527856339,
-      "offset": [
-        -168.76833554248222,
-        540.6638955283997
-      ]
-    },
-    "frontendVersion": "1.42.8"
-  }
-}

blueprints/Glow.json

@@ -268,7 +268,7 @@
               "Node name for S&R": "GLSLShader"
             },
             "widgets_values": [
-              "#version 300 es\nprecision mediump float;\n\nuniform sampler2D u_image0;\nuniform int u_int0;      // Blend mode\nuniform int u_int1;      // Color tint\nuniform float u_float0;  // Intensity\nuniform float u_float1;  // Radius\nuniform float u_float2;  // Threshold\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst int BLEND_ADD      = 0;\nconst int BLEND_SCREEN   = 1;\nconst int BLEND_SOFT     = 2;\nconst int BLEND_OVERLAY  = 3;\nconst int BLEND_LIGHTEN  = 4;\n\nconst float GOLDEN_ANGLE = 2.39996323;\nconst int MAX_SAMPLES = 48;\nconst vec3 LUMA = vec3(0.299, 0.587, 0.114);\n\nfloat hash(vec2 p) {\n    p = fract(p * vec2(123.34, 456.21));\n    p += dot(p, p + 45.32);\n    return fract(p.x * p.y);\n}\n\nvec3 hexToRgb(int h) {\n    return vec3(\n        float((h >> 16) & 255),\n        float((h >> 8) & 255),\n        float(h & 255)\n    ) * (1.0 / 255.0);\n}\n\nvec3 blend(vec3 base, vec3 glow, int mode) {\n    if (mode == BLEND_SCREEN) {\n        return 1.0 - (1.0 - base) * (1.0 - glow);\n    }\n    if (mode == BLEND_SOFT) {\n        return mix(\n            base - (1.0 - 2.0 * glow) * base * (1.0 - base),\n            base + (2.0 * glow - 1.0) * (sqrt(base) - base),\n            step(0.5, glow)\n        );\n    }\n    if (mode == BLEND_OVERLAY) {\n        return mix(\n            2.0 * base * glow,\n            1.0 - 2.0 * (1.0 - base) * (1.0 - glow),\n            step(0.5, base)\n        );\n    }\n    if (mode == BLEND_LIGHTEN) {\n        return max(base, glow);\n    }\n    return base + glow;\n}\n\nvoid main() {\n    vec4 original = texture(u_image0, v_texCoord);\n    \n    float intensity = u_float0 * 0.05;\n    float radius = u_float1 * u_float1 * 0.012;\n    \n    if (intensity < 0.001 || radius < 0.1) {\n        fragColor = original;\n        return;\n    }\n    \n    float threshold = 1.0 - u_float2 * 0.01;\n    float t0 = threshold - 0.15;\n    float t1 = threshold + 0.15;\n    \n    vec2 texelSize = 1.0 / vec2(textureSize(u_image0, 0));\n    float radius2 = radius * radius;\n    \n    float sampleScale = clamp(radius * 0.75, 0.35, 1.0);\n    int samples = int(float(MAX_SAMPLES) * sampleScale);\n    \n    float noise = hash(gl_FragCoord.xy);\n    float angleOffset = noise * GOLDEN_ANGLE;\n    float radiusJitter = 0.85 + noise * 0.3;\n    \n    float ca = cos(GOLDEN_ANGLE);\n    float sa = sin(GOLDEN_ANGLE);\n    vec2 dir = vec2(cos(angleOffset), sin(angleOffset));\n    \n    vec3 glow = vec3(0.0);\n    float totalWeight = 0.0;\n    \n    // Center tap\n    float centerMask = smoothstep(t0, t1, dot(original.rgb, LUMA));\n    glow += original.rgb * centerMask * 2.0;\n    totalWeight += 2.0;\n    \n    for (int i = 1; i < MAX_SAMPLES; i++) {\n        if (i >= samples) break;\n        \n        float fi = float(i);\n        float dist = sqrt(fi / float(samples)) * radius * radiusJitter;\n        \n        vec2 offset = dir * dist * texelSize;\n        vec3 c = texture(u_image0, v_texCoord + offset).rgb;\n        float mask = smoothstep(t0, t1, dot(c, LUMA));\n        \n        float w = 1.0 - (dist * dist) / (radius2 * 1.5);\n        w = max(w, 0.0);\n        w *= w;\n        \n        glow += c * mask * w;\n        totalWeight += w;\n        \n        dir = vec2(\n            dir.x * ca - dir.y * sa,\n            dir.x * sa + dir.y * ca\n        );\n    }\n    \n    glow *= intensity / max(totalWeight, 0.001);\n    \n    if (u_int1 > 0) {\n        glow *= hexToRgb(u_int1);\n    }\n    \n    vec3 result = blend(original.rgb, glow, u_int0);\n    result += (noise - 0.5) * (1.0 / 255.0);\n    \n    fragColor = vec4(clamp(result, 0.0, 1.0), original.a);\n}",
+              "#version 300 es\nprecision mediump float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform int u_int0;      // Blend mode\nuniform int u_int1;      // Color tint\nuniform float u_float0;  // Intensity\nuniform float u_float1;  // Radius\nuniform float u_float2;  // Threshold\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst int BLEND_ADD      = 0;\nconst int BLEND_SCREEN   = 1;\nconst int BLEND_SOFT     = 2;\nconst int BLEND_OVERLAY  = 3;\nconst int BLEND_LIGHTEN  = 4;\n\nconst float GOLDEN_ANGLE = 2.39996323;\nconst int MAX_SAMPLES = 48;\nconst vec3 LUMA = vec3(0.299, 0.587, 0.114);\n\nfloat hash(vec2 p) {\n    p = fract(p * vec2(123.34, 456.21));\n    p += dot(p, p + 45.32);\n    return fract(p.x * p.y);\n}\n\nvec3 hexToRgb(int h) {\n    return vec3(\n        float((h >> 16) & 255),\n        float((h >> 8) & 255),\n        float(h & 255)\n    ) * (1.0 / 255.0);\n}\n\nvec3 blend(vec3 base, vec3 glow, int mode) {\n    if (mode == BLEND_SCREEN) {\n        return 1.0 - (1.0 - base) * (1.0 - glow);\n    }\n    if (mode == BLEND_SOFT) {\n        return mix(\n            base - (1.0 - 2.0 * glow) * base * (1.0 - base),\n            base + (2.0 * glow - 1.0) * (sqrt(base) - base),\n            step(0.5, glow)\n        );\n    }\n    if (mode == BLEND_OVERLAY) {\n        return mix(\n            2.0 * base * glow,\n            1.0 - 2.0 * (1.0 - base) * (1.0 - glow),\n            step(0.5, base)\n        );\n    }\n    if (mode == BLEND_LIGHTEN) {\n        return max(base, glow);\n    }\n    return base + glow;\n}\n\nvoid main() {\n    vec4 original = texture(u_image0, v_texCoord);\n    \n    float intensity = u_float0 * 0.05;\n    float radius = u_float1 * u_float1 * 0.012;\n    \n    if (intensity < 0.001 || radius < 0.1) {\n        fragColor = original;\n        return;\n    }\n    \n    float threshold = 1.0 - u_float2 * 0.01;\n    float t0 = threshold - 0.15;\n    float t1 = threshold + 0.15;\n    \n    vec2 texelSize = 1.0 / u_resolution;\n    float radius2 = radius * radius;\n    \n    float sampleScale = clamp(radius * 0.75, 0.35, 1.0);\n    int samples = int(float(MAX_SAMPLES) * sampleScale);\n    \n    float noise = hash(gl_FragCoord.xy);\n    float angleOffset = noise * GOLDEN_ANGLE;\n    float radiusJitter = 0.85 + noise * 0.3;\n    \n    float ca = cos(GOLDEN_ANGLE);\n    float sa = sin(GOLDEN_ANGLE);\n    vec2 dir = vec2(cos(angleOffset), sin(angleOffset));\n    \n    vec3 glow = vec3(0.0);\n    float totalWeight = 0.0;\n    \n    // Center tap\n    float centerMask = smoothstep(t0, t1, dot(original.rgb, LUMA));\n    glow += original.rgb * centerMask * 2.0;\n    totalWeight += 2.0;\n    \n    for (int i = 1; i < MAX_SAMPLES; i++) {\n        if (i >= samples) break;\n        \n        float fi = float(i);\n        float dist = sqrt(fi / float(samples)) * radius * radiusJitter;\n        \n        vec2 offset = dir * dist * texelSize;\n        vec3 c = texture(u_image0, v_texCoord + offset).rgb;\n        float mask = smoothstep(t0, t1, dot(c, LUMA));\n        \n        float w = 1.0 - (dist * dist) / (radius2 * 1.5);\n        w = max(w, 0.0);\n        w *= w;\n        \n        glow += c * mask * w;\n        totalWeight += w;\n        \n        dir = vec2(\n            dir.x * ca - dir.y * sa,\n            dir.x * sa + dir.y * ca\n        );\n    }\n    \n    glow *= intensity / max(totalWeight, 0.001);\n    \n    if (u_int1 > 0) {\n        glow *= hexToRgb(u_int1);\n    }\n    \n    vec3 result = blend(original.rgb, glow, u_int0);\n    result += (noise - 0.5) * (1.0 / 255.0);\n    \n    fragColor = vec4(clamp(result, 0.0, 1.0), original.a);\n}",
               "from_input"
             ]
           },
@@ -575,9 +575,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust",
-        "description": "Adds a glow/bloom effect around bright image areas via GPU fragment shader."
+        "category": "Image Tools/Color adjust"
       }
     ]
   }
-}
+}

blueprints/Hue and Saturation.json

@@ -752,9 +752,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust",
-        "description": "Adjusts hue, saturation, and lightness of an image using a real-time GPU fragment shader."
+        "category": "Image Tools/Color adjust"
       }
     ]
   }
-}
+}

blueprints/Image Blur.json

@@ -331,7 +331,7 @@
               "Node name for S&R": "GLSLShader"
             },
             "widgets_values": [
-              "#version 300 es\n#pragma passes 2\nprecision highp float;\n\n// Blur type constants\nconst int BLUR_GAUSSIAN = 0;\nconst int BLUR_BOX = 1;\nconst int BLUR_RADIAL = 2;\n\n// Radial blur config\nconst int RADIAL_SAMPLES = 12;\nconst float RADIAL_STRENGTH = 0.0003;\n\nuniform sampler2D u_image0;\nuniform int u_int0;      // Blur type (BLUR_GAUSSIAN, BLUR_BOX, BLUR_RADIAL)\nuniform float u_float0;  // Blur radius/amount\nuniform int u_pass;      // Pass index (0 = horizontal, 1 = vertical)\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nfloat gaussian(float x, float sigma) {\n    return exp(-(x * x) / (2.0 * sigma * sigma));\n}\n\nvoid main() {\n    vec2 texelSize = 1.0 / vec2(textureSize(u_image0, 0));\n    float radius = max(u_float0, 0.0);\n\n    // Radial (angular) blur - single pass, doesn't use separable\n    if (u_int0 == BLUR_RADIAL) {\n        // Only execute on first pass\n        if (u_pass > 0) {\n            fragColor0 = texture(u_image0, v_texCoord);\n            return;\n        }\n\n        vec2 center = vec2(0.5);\n        vec2 dir = v_texCoord - center;\n        float dist = length(dir);\n\n        if (dist < 1e-4) {\n            fragColor0 = texture(u_image0, v_texCoord);\n            return;\n        }\n\n        vec4 sum = vec4(0.0);\n        float totalWeight = 0.0;\n        float angleStep = radius * RADIAL_STRENGTH;\n\n        dir /= dist;\n\n        float cosStep = cos(angleStep);\n        float sinStep = sin(angleStep);\n\n        float negAngle = -float(RADIAL_SAMPLES) * angleStep;\n        vec2 rotDir = vec2(\n            dir.x * cos(negAngle) - dir.y * sin(negAngle),\n            dir.x * sin(negAngle) + dir.y * cos(negAngle)\n        );\n\n        for (int i = -RADIAL_SAMPLES; i <= RADIAL_SAMPLES; i++) {\n            vec2 uv = center + rotDir * dist;\n            float w = 1.0 - abs(float(i)) / float(RADIAL_SAMPLES);\n            sum += texture(u_image0, uv) * w;\n            totalWeight += w;\n\n            rotDir = vec2(\n                rotDir.x * cosStep - rotDir.y * sinStep,\n                rotDir.x * sinStep + rotDir.y * cosStep\n            );\n        }\n\n        fragColor0 = sum / max(totalWeight, 0.001);\n        return;\n    }\n\n    // Separable Gaussian / Box blur\n    int samples = int(ceil(radius));\n\n    if (samples == 0) {\n        fragColor0 = texture(u_image0, v_texCoord);\n        return;\n    }\n\n    // Direction: pass 0 = horizontal, pass 1 = vertical\n    vec2 dir = (u_pass == 0) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);\n\n    vec4 color = vec4(0.0);\n    float totalWeight = 0.0;\n    float sigma = radius / 2.0;\n\n    for (int i = -samples; i <= samples; i++) {\n        vec2 offset = dir * float(i) * texelSize;\n        vec4 sample_color = texture(u_image0, v_texCoord + offset);\n\n        float weight;\n        if (u_int0 == BLUR_GAUSSIAN) {\n            weight = gaussian(float(i), sigma);\n        } else {\n            // BLUR_BOX\n            weight = 1.0;\n        }\n\n        color += sample_color * weight;\n        totalWeight += weight;\n    }\n\n    fragColor0 = color / totalWeight;\n}\n",
+              "#version 300 es\n#pragma passes 2\nprecision highp float;\n\n// Blur type constants\nconst int BLUR_GAUSSIAN = 0;\nconst int BLUR_BOX = 1;\nconst int BLUR_RADIAL = 2;\n\n// Radial blur config\nconst int RADIAL_SAMPLES = 12;\nconst float RADIAL_STRENGTH = 0.0003;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform int u_int0;      // Blur type (BLUR_GAUSSIAN, BLUR_BOX, BLUR_RADIAL)\nuniform float u_float0;  // Blur radius/amount\nuniform int u_pass;      // Pass index (0 = horizontal, 1 = vertical)\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nfloat gaussian(float x, float sigma) {\n    return exp(-(x * x) / (2.0 * sigma * sigma));\n}\n\nvoid main() {\n    vec2 texelSize = 1.0 / u_resolution;\n    float radius = max(u_float0, 0.0);\n\n    // Radial (angular) blur - single pass, doesn't use separable\n    if (u_int0 == BLUR_RADIAL) {\n        // Only execute on first pass\n        if (u_pass > 0) {\n            fragColor0 = texture(u_image0, v_texCoord);\n            return;\n        }\n\n        vec2 center = vec2(0.5);\n        vec2 dir = v_texCoord - center;\n        float dist = length(dir);\n\n        if (dist < 1e-4) {\n            fragColor0 = texture(u_image0, v_texCoord);\n            return;\n        }\n\n        vec4 sum = vec4(0.0);\n        float totalWeight = 0.0;\n        float angleStep = radius * RADIAL_STRENGTH;\n\n        dir /= dist;\n\n        float cosStep = cos(angleStep);\n        float sinStep = sin(angleStep);\n\n        float negAngle = -float(RADIAL_SAMPLES) * angleStep;\n        vec2 rotDir = vec2(\n            dir.x * cos(negAngle) - dir.y * sin(negAngle),\n            dir.x * sin(negAngle) + dir.y * cos(negAngle)\n        );\n\n        for (int i = -RADIAL_SAMPLES; i <= RADIAL_SAMPLES; i++) {\n            vec2 uv = center + rotDir * dist;\n            float w = 1.0 - abs(float(i)) / float(RADIAL_SAMPLES);\n            sum += texture(u_image0, uv) * w;\n            totalWeight += w;\n\n            rotDir = vec2(\n                rotDir.x * cosStep - rotDir.y * sinStep,\n                rotDir.x * sinStep + rotDir.y * cosStep\n            );\n        }\n\n        fragColor0 = sum / max(totalWeight, 0.001);\n        return;\n    }\n\n    // Separable Gaussian / Box blur\n    int samples = int(ceil(radius));\n\n    if (samples == 0) {\n        fragColor0 = texture(u_image0, v_texCoord);\n        return;\n    }\n\n    // Direction: pass 0 = horizontal, pass 1 = vertical\n    vec2 dir = (u_pass == 0) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);\n\n    vec4 color = vec4(0.0);\n    float totalWeight = 0.0;\n    float sigma = radius / 2.0;\n\n    for (int i = -samples; i <= samples; i++) {\n        vec2 offset = dir * float(i) * texelSize;\n        vec4 sample_color = texture(u_image0, v_texCoord + offset);\n\n        float weight;\n        if (u_int0 == BLUR_GAUSSIAN) {\n            weight = gaussian(float(i), sigma);\n        } else {\n            // BLUR_BOX\n            weight = 1.0;\n        }\n\n        color += sample_color * weight;\n        totalWeight += weight;\n    }\n\n    fragColor0 = color / totalWeight;\n}\n",
               "from_input"
             ]
           }
@@ -374,8 +374,7 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Blur",
-        "description": "Applies Gaussian, Box, or Radial blur to soften images and create stylized depth or motion effects."
+        "category": "Image Tools/Blur"
       }
     ]
   }

blueprints/Image Captioning (gemini).json

@@ -310,8 +310,7 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Text generation/Image Captioning",
-        "description": "Generates descriptive captions for images using Google's Gemini multimodal LLM."
+        "category": "Text generation/Image Captioning"
       }
     ]
   }

blueprints/Image Channels.json

@@ -315,9 +315,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust",
-        "description": "Manipulates individual RGBA channels for masking, compositing, and channel effects."
+        "category": "Image Tools/Color adjust"
       }
     ]
   }
-}
+}

blueprints/Image Edit (Flux.2 Klein 4B).json

@@ -128,7 +128,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "Image Edit (Flux.2 Klein 4B)",
+        "name": "local-Image Edit (Flux.2 Klein 4B)",
         "inputNode": {
           "id": -10,
           "bounding": [
@@ -1472,8 +1472,7 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Edit image",
-        "description": "Edits an input image via text instructions using FLUX.2 [klein] 4B."
+        "category": "Image generation and editing/Edit image"
       },
       {
         "id": "6007e698-2ebd-4917-84d8-299b35d7b7ab",
@@ -1822,8 +1821,7 @@
         ],
         "extra": {
           "workflowRendererVersion": "LG"
-        },
-        "description": "Applies reference image conditioning for style/identity transfer (Flux.2 Klein 4B)."
+        }
       }
     ]
   },

blueprints/Image Edit (Qwen 2511).json

@@ -132,7 +132,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "Image Edit (Qwen 2511)",
+        "name": "local-Image Edit (Qwen 2511)",
         "inputNode": {
           "id": -10,
           "bounding": [
@@ -1468,8 +1468,7 @@
           "VHS_MetadataImage": true,
           "VHS_KeepIntermediate": true
         },
-        "category": "Image generation and editing/Edit image",
-        "description": "Edits images via text instructions using Qwen-Image-Edit-2511 with improved character consistency and integrated LoRA."
+        "category": "Image generation and editing/Edit image"
       }
     ]
   },
@@ -1490,4 +1489,4 @@
     }
   },
   "version": 0.4
-}
+}

blueprints/Image Inpainting (Qwen-image).json

@@ -124,7 +124,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "Image Inpainting (Qwen-image)",
+        "name": "local-Image Inpainting (Qwen-image)",
         "inputNode": {
           "id": -10,
           "bounding": [
@@ -1548,8 +1548,7 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Inpaint image",
-        "description": "Inpaints masked regions using Qwen-Image, extending its multilingual text rendering to inpainting tasks."
+        "category": "Image generation and editing/Inpaint image"
       },
       {
         "id": "56a1f603-fbd2-40ed-94ef-c9ecbd96aca8",
@@ -1908,8 +1907,7 @@
         ],
         "extra": {
           "workflowRendererVersion": "LG"
-        },
-        "description": "Expands and softens mask edges to reduce visible seams after image processing."
+        }
       }
     ]
   },
@@ -1925,4 +1923,4 @@
     "workflowRendererVersion": "LG"
   },
   "version": 0.4
-}
+}

blueprints/Image Levels.json

@@ -742,10 +742,9 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust",
-        "description": "Adjusts black point, white point, and gamma for tonal range control via GPU shader."
+        "category": "Image Tools/Color adjust"
       }
     ]
   },
   "extra": {}
-}
+}

blueprints/Image Outpainting (Qwen-Image).json

@@ -204,7 +204,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "Image Outpainting (Qwen-Image)",
+        "name": "local-Image Outpainting (Qwen-Image)",
         "inputNode": {
           "id": -10,
           "bounding": [
@@ -1919,8 +1919,7 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Outpaint image",
-        "description": "Outpaints beyond image boundaries using Qwen-Image's outpainting capabilities."
+        "category": "Image generation and editing/Outpaint image"
       },
       {
         "id": "f93c215e-c393-460e-9534-ed2c3d8a652e",
@@ -2279,8 +2278,7 @@
         ],
         "extra": {
           "workflowRendererVersion": "LG"
-        },
-        "description": "Expands and softens mask edges to reduce visible seams after image processing."
+        }
       },
       {
         "id": "2a4b2cc0-db37-4302-a067-da392f38f06b",
@@ -2735,8 +2733,7 @@
         ],
         "extra": {
           "workflowRendererVersion": "LG"
-        },
-        "description": "Scales both image and mask together while preserving alignment for editing workflows."
+        }
       }
     ]
   },
@@ -2752,4 +2749,4 @@
     }
   },
   "version": 0.4
-}
+}

blueprints/Image Segmentation (SAM3).json

@@ -1,714 +0,0 @@
-{
-  "revision": 0,
-  "last_node_id": 99,
-  "last_link_id": 0,
-  "nodes": [
-    {
-      "id": 99,
-      "type": "6e7ab3ea-96aa-470f-9b94-3d9d0e01f481",
-      "pos": [
-        -1630,
-        -3270
-      ],
-      "size": [
-        290,
-        370
-      ],
-      "flags": {},
-      "order": 3,
-      "mode": 0,
-      "inputs": [
-        {
-          "label": "image",
-          "localized_name": "image",
-          "name": "image",
-          "type": "IMAGE",
-          "link": null
-        },
-        {
-          "label": "object",
-          "name": "text",
-          "type": "STRING",
-          "widget": {
-            "name": "text"
-          },
-          "link": null
-        },
-        {
-          "name": "bboxes",
-          "type": "BOUNDING_BOX",
-          "link": null
-        },
-        {
-          "name": "positive_coords",
-          "type": "STRING",
-          "link": null
-        },
-        {
-          "name": "negative_coords",
-          "type": "STRING",
-          "link": null
-        },
-        {
-          "name": "threshold",
-          "type": "FLOAT",
-          "widget": {
-            "name": "threshold"
-          },
-          "link": null
-        },
-        {
-          "name": "refine_iterations",
-          "type": "INT",
-          "widget": {
-            "name": "refine_iterations"
-          },
-          "link": null
-        },
-        {
-          "name": "individual_masks",
-          "type": "BOOLEAN",
-          "widget": {
-            "name": "individual_masks"
-          },
-          "link": null
-        },
-        {
-          "name": "ckpt_name",
-          "type": "COMBO",
-          "widget": {
-            "name": "ckpt_name"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "localized_name": "masks",
-          "name": "masks",
-          "type": "MASK",
-          "links": []
-        },
-        {
-          "localized_name": "bboxes",
-          "name": "bboxes",
-          "type": "BOUNDING_BOX",
-          "links": []
-        }
-      ],
-      "properties": {
-        "proxyWidgets": [
-          [
-            "78",
-            "text"
-          ],
-          [
-            "75",
-            "threshold"
-          ],
-          [
-            "75",
-            "refine_iterations"
-          ],
-          [
-            "75",
-            "individual_masks"
-          ],
-          [
-            "77",
-            "ckpt_name"
-          ]
-        ],
-        "ue_properties": {
-          "widget_ue_connectable": {
-            "text": true
-          },
-          "version": "7.7",
-          "input_ue_unconnectable": {}
-        },
-        "cnr_id": "comfy-core",
-        "ver": "0.19.3",
-        "enableTabs": false,
-        "tabWidth": 65,
-        "tabXOffset": 10,
-        "hasSecondTab": false,
-        "secondTabText": "Send Back",
-        "secondTabOffset": 80,
-        "secondTabWidth": 65
-      },
-      "widgets_values": [],
-      "title": "Image Segmentation (SAM3)"
-    }
-  ],
-  "links": [],
-  "version": 0.4,
-  "definitions": {
-    "subgraphs": [
-      {
-        "id": "6e7ab3ea-96aa-470f-9b94-3d9d0e01f481",
-        "version": 1,
-        "state": {
-          "lastGroupId": 0,
-          "lastNodeId": 113,
-          "lastLinkId": 283,
-          "lastRerouteId": 0
-        },
-        "revision": 0,
-        "config": {},
-        "name": "Image Segmentation (SAM3)",
-        "inputNode": {
-          "id": -10,
-          "bounding": [
-            -2260,
-            -3450,
-            136.369140625,
-            220
-          ]
-        },
-        "outputNode": {
-          "id": -20,
-          "bounding": [
-            -1130,
-            -3305,
-            120,
-            80
-          ]
-        },
-        "inputs": [
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-            "name": "image",
-            "type": "IMAGE",
-            "linkIds": [
-              264
-            ],
-            "localized_name": "image",
-            "label": "image",
-            "pos": [
-              -2143.630859375,
-              -3430
-            ]
-          },
-          {
-            "id": "3cefd304-7631-4ff6-a5a0-5a0ffb120745",
-            "name": "text",
-            "type": "STRING",
-            "linkIds": [
-              265
-            ],
-            "label": "object",
-            "pos": [
-              -2143.630859375,
-              -3410
-            ]
-          },
-          {
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-            "name": "bboxes",
-            "type": "BOUNDING_BOX",
-            "linkIds": [
-              266
-            ],
-            "pos": [
-              -2143.630859375,
-              -3390
-            ]
-          },
-          {
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-            "name": "positive_coords",
-            "type": "STRING",
-            "linkIds": [
-              267
-            ],
-            "pos": [
-              -2143.630859375,
-              -3370
-            ]
-          },
-          {
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-            "name": "negative_coords",
-            "type": "STRING",
-            "linkIds": [
-              268
-            ],
-            "pos": [
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-              -3350
-            ]
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-          {
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-            "name": "threshold",
-            "type": "FLOAT",
-            "linkIds": [
-              269
-            ],
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-              -3330
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-            "type": "INT",
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-              270
-            ],
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-              -3310
-            ]
-          },
-          {
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-            "name": "individual_masks",
-            "type": "BOOLEAN",
-            "linkIds": [
-              271
-            ],
-            "pos": [
-              -2143.630859375,
-              -3290
-            ]
-          },
-          {
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-            "name": "ckpt_name",
-            "type": "COMBO",
-            "linkIds": [
-              272
-            ],
-            "pos": [
-              -2143.630859375,
-              -3270
-            ]
-          }
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-        "outputs": [
-          {
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-            "name": "masks",
-            "type": "MASK",
-            "linkIds": [
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-            ],
-            "localized_name": "masks",
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-              -1110,
-              -3285
-            ]
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-            "localized_name": "bboxes",
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-              -3265
-            ]
-          }
-        ],
-        "widgets": [],
-        "nodes": [
-          {
-            "id": 75,
-            "type": "SAM3_Detect",
-            "pos": [
-              -1470,
-              -3460
-            ],
-            "size": [
-              270,
-              260
-            ],
-            "flags": {},
-            "order": 0,
-            "mode": 0,
-            "inputs": [
-              {
-                "label": "model",
-                "localized_name": "model",
-                "name": "model",
-                "type": "MODEL",
-                "link": 237
-              },
-              {
-                "label": "image",
-                "localized_name": "image",
-                "name": "image",
-                "type": "IMAGE",
-                "link": 264
-              },
-              {
-                "label": "conditioning",
-                "localized_name": "conditioning",
-                "name": "conditioning",
-                "shape": 7,
-                "type": "CONDITIONING",
-                "link": 200
-              },
-              {
-                "label": "bboxes",
-                "localized_name": "bboxes",
-                "name": "bboxes",
-                "shape": 7,
-                "type": "BOUNDING_BOX",
-                "link": 266
-              },
-              {
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-                "localized_name": "positive_coords",
-                "name": "positive_coords",
-                "shape": 7,
-                "type": "STRING",
-                "link": 267
-              },
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-                "type": "STRING",
-                "link": 268
-              },
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-                "widget": {
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-                "widget": {
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-                "link": 270
-              },
-              {
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-                "name": "individual_masks",
-                "type": "BOOLEAN",
-                "widget": {
-                  "name": "individual_masks"
-                },
-                "link": 271
-              }
-            ],
-            "outputs": [
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-                "name": "masks",
-                "type": "MASK",
-                "links": [
-                  231
-                ]
-              },
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-                "name": "bboxes",
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-              "cnr_id": "comfy-core",
-              "ver": "0.19.3",
-              "Node name for S&R": "SAM3_Detect",
-              "enableTabs": false,
-              "tabWidth": 65,
-              "tabXOffset": 10,
-              "hasSecondTab": false,
-              "secondTabText": "Send Back",
-              "secondTabOffset": 80,
-              "secondTabWidth": 65
-            },
-            "widgets_values": [
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@@ -141,7 +141,7 @@
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blueprints/Image to Depth Map (Lotus).json

@@ -99,7 +99,7 @@
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@@ -375,14 +283,9 @@
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@@ -442,14 +345,9 @@
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@@ -577,14 +470,9 @@
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@@ -592,18 +480,19 @@
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@@ -903,7 +769,8 @@
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             "id": 6,
@@ -913,11 +780,11 @@
               180
             ],
             "size": [
-              430,
-              170
+              422.84503173828125,
+              164.31304931640625
             ],
             "flags": {},
-            "order": 1,
+            "order": 6,
             "mode": 0,
             "inputs": [
               {
@@ -949,14 +816,9 @@
             ],
             "title": "CLIP Text Encode (Positive Prompt)",
             "properties": {
+              "Node name for S&R": "CLIPTextEncode",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
-              "ue_properties": {
-                "widget_ue_connectable": {},
-                "input_ue_unconnectable": {},
-                "version": "7.7"
-              },
-              "Node name for S&R": "CLIPTextEncode",
               "enableTabs": false,
               "tabWidth": 65,
               "tabXOffset": 10,
@@ -976,14 +838,14 @@
             "type": "KSampler",
             "pos": [
               530,
-              340
+              280
             ],
             "size": [
               270,
               400
             ],
             "flags": {},
-            "order": 0,
+            "order": 5,
             "mode": 0,
             "inputs": [
               {
@@ -1017,7 +879,7 @@
                 "widget": {
                   "name": "seed"
                 },
-                "link": 377
+                "link": null
               },
               {
                 "localized_name": "steps",
@@ -1077,14 +939,9 @@
               }
             ],
             "properties": {
+              "Node name for S&R": "KSampler",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
-              "ue_properties": {
-                "widget_ue_connectable": {},
-                "input_ue_unconnectable": {},
-                "version": "7.7"
-              },
-              "Node name for S&R": "KSampler",
               "enableTabs": false,
               "tabWidth": 65,
               "tabXOffset": 10,
@@ -1107,12 +964,12 @@
             "id": 78,
             "type": "GetImageSize",
             "pos": [
-              -280,
-              930
+              80,
+              790
             ],
             "size": [
-              230,
-              140
+              210,
+              136
             ],
             "flags": {},
             "order": 12,
@@ -1150,14 +1007,9 @@
               }
             ],
             "properties": {
+              "Node name for S&R": "GetImageSize",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
-              "ue_properties": {
-                "widget_ue_connectable": {},
-                "input_ue_unconnectable": {},
-                "version": "7.7"
-              },
-              "Node name for S&R": "GetImageSize",
               "enableTabs": false,
               "tabWidth": 65,
               "tabXOffset": 10,
@@ -1165,23 +1017,23 @@
               "secondTabText": "Send Back",
               "secondTabOffset": 80,
               "secondTabWidth": 65
-            }
+            },
+            "widgets_values": []
           },
           {
             "id": 83,
             "type": "EmptyQwenImageLayeredLatentImage",
             "pos": [
-              -280,
-              1120
+              320,
+              790
             ],
             "size": [
-              340,
-              200
+              330.9341796875,
+              130
             ],
             "flags": {},
             "order": 13,
             "mode": 0,
-            "showAdvanced": true,
             "inputs": [
               {
                 "localized_name": "width",
@@ -1231,14 +1083,9 @@
               }
             ],
             "properties": {
+              "Node name for S&R": "EmptyQwenImageLayeredLatentImage",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
-              "ue_properties": {
-                "widget_ue_connectable": {},
-                "input_ue_unconnectable": {},
-                "version": "7.7"
-              },
-              "Node name for S&R": "EmptyQwenImageLayeredLatentImage",
               "enableTabs": false,
               "tabWidth": 65,
               "tabXOffset": 10,
@@ -1262,11 +1109,11 @@
               180
             ],
             "size": [
-              350,
-              110
+              346.7470703125,
+              82
             ],
             "flags": {},
-            "order": 4,
+            "order": 2,
             "mode": 0,
             "inputs": [
               {
@@ -1276,7 +1123,7 @@
                 "widget": {
                   "name": "unet_name"
                 },
-                "link": 378
+                "link": null
               },
               {
                 "localized_name": "weight_dtype",
@@ -1300,14 +1147,9 @@
               }
             ],
             "properties": {
+              "Node name for S&R": "UNETLoader",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
-              "ue_properties": {
-                "widget_ue_connectable": {},
-                "input_ue_unconnectable": {},
-                "version": "7.7"
-              },
-              "Node name for S&R": "UNETLoader",
               "models": [
                 {
                   "name": "qwen_image_layered_bf16.safetensors",
@@ -1349,8 +1191,8 @@
             "bounding": [
               -330,
               110,
-              370,
-              610
+              366.7470703125,
+              421.6
             ],
             "color": "#3f789e",
             "font_size": 24,
@@ -1549,48 +1391,16 @@
             "target_id": 83,
             "target_slot": 2,
             "type": "INT"
-          },
-          {
-            "id": 377,
-            "origin_id": -10,
-            "origin_slot": 5,
-            "target_id": 3,
-            "target_slot": 4,
-            "type": "INT"
-          },
-          {
-            "id": 378,
-            "origin_id": -10,
-            "origin_slot": 6,
-            "target_id": 37,
-            "target_slot": 0,
-            "type": "COMBO"
-          },
-          {
-            "id": 379,
-            "origin_id": -10,
-            "origin_slot": 7,
-            "target_id": 38,
-            "target_slot": 0,
-            "type": "COMBO"
-          },
-          {
-            "id": 380,
-            "origin_id": -10,
-            "origin_slot": 8,
-            "target_id": 39,
-            "target_slot": 0,
-            "type": "COMBO"
           }
         ],
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Image to layers",
-        "description": "Decomposes an image into variable-resolution RGBA layers for independent editing using Qwen-Image-Layered."
+        "category": "Image generation and editing/Image to layers"
       }
     ]
   },
+  "config": {},
   "extra": {
     "ds": {
       "scale": 1.14,
@@ -1599,6 +1409,7 @@
         6.855893974423647
       ]
     },
-    "ue_links": []
-  }
-}
+    "workflowRendererVersion": "LG"
+  },
+  "version": 0.4
+}

blueprints/Image to Model (Hunyuan3d 2.1).json

@@ -72,7 +72,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "Image to 3D Model (Hunyuan3d 2.1)",
+        "name": "local-Image to Model (Hunyuan3d 2.1)",
         "inputNode": {
           "id": -10,
           "bounding": [
@@ -765,8 +765,7 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "3D/Image to 3D Model",
-        "description": "Generates 3D mesh models from a single input image using Hunyuan3D 2.0/2.1."
+        "category": "3D/Image to 3D Model"
       }
     ]
   },

blueprints/Image to Video (Wan 2.2).json

@@ -206,7 +206,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "Image to Video (Wan 2.2)",
+        "name": "local-Image to Video (Wan 2.2)",
         "inputNode": {
           "id": -10,
           "bounding": [
@@ -2027,8 +2027,7 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Video generation and editing/Image to video",
-        "description": "Image-to-video with Wan 2.2 using a start image plus text prompt to extend motion from the still frame."
+        "category": "Video generation and editing/Image to video"
       }
     ]
   },

blueprints/Pose to Image (Z-Image-Turbo).json

@@ -134,7 +134,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "Pose to Image (Z-Image-Turbo)",
+        "name": "local-Pose to Image (Z-Image-Turbo)",
         "inputNode": {
           "id": -10,
           "bounding": [
@@ -1298,8 +1298,7 @@
           "VHS_MetadataImage": true,
           "VHS_KeepIntermediate": true
         },
-        "category": "Image generation and editing/Pose to image",
-        "description": "Generates an image from pose keypoints using Z-Image-Turbo with text conditioning."
+        "category": "Image generation and editing/Pose to image"
       }
     ]
   },
@@ -1320,4 +1319,4 @@
     }
   },
   "version": 0.4
-}
+}

blueprints/Prompt Enhance.json

@@ -270,10 +270,9 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Text generation/Prompt enhance",
-        "description": "Expands short text prompts into detailed descriptions using a text generation model for better generation quality."
+        "category": "Text generation/Prompt enhance"
       }
     ]
   },
   "extra": {}
-}
+}

blueprints/Remove Background (BiRefNet).json

@@ -1,397 +0,0 @@
-{
-  "revision": 0,
-  "last_node_id": 19,
-  "last_link_id": 0,
-  "nodes": [
-    {
-      "id": 19,
-      "type": "5b40ca21-ba1a-41d5-b403-4d2d7acdc195",
-      "pos": [
-        -6411.330578108367,
-        1940.2638932730042
-      ],
-      "size": [
-        349.609375,
-        145.9375
-      ],
-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "inputs": [
-        {
-          "localized_name": "image",
-          "name": "image",
-          "type": "IMAGE",
-          "link": null
-        },
-        {
-          "name": "bg_removal_name",
-          "type": "COMBO",
-          "widget": {
-            "name": "bg_removal_name"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "localized_name": "IMAGE",
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": []
-        },
-        {
-          "name": "mask",
-          "type": "MASK",
-          "links": []
-        }
-      ],
-      "properties": {
-        "proxyWidgets": [
-          [
-            "14",
-            "bg_removal_name"
-          ]
-        ]
-      },
-      "widgets_values": [],
-      "title": "Remove Background (BiRefNet)"
-    }
-  ],
-  "links": [],
-  "version": 0.4,
-  "definitions": {
-    "subgraphs": [
-      {
-        "id": "5b40ca21-ba1a-41d5-b403-4d2d7acdc195",
-        "version": 1,
-        "state": {
-          "lastGroupId": 0,
-          "lastNodeId": 21,
-          "lastLinkId": 16,
-          "lastRerouteId": 0
-        },
-        "revision": 0,
-        "config": {},
-        "name": "Remove Background (BiRefNet)",
-        "description": "Removes or replaces image backgrounds using BiRefNet segmentation and alpha compositing.",
-        "inputNode": {
-          "id": -10,
-          "bounding": [
-            -6728.534070722246,
-            1475.2619799128663,
-            150.9140625,
-            88
-          ]
-        },
-        "outputNode": {
-          "id": -20,
-          "bounding": [
-            -6169.049695722246,
-            1475.2619799128663,
-            128,
-            88
-          ]
-        },
-        "inputs": [
-          {
-            "id": "7bc321cd-df31-4c39-aaf7-7f0d01326189",
-            "name": "image",
-            "type": "IMAGE",
-            "linkIds": [
-              5,
-              7
-            ],
-            "localized_name": "image",
-            "pos": [
-              -6601.620008222246,
-              1499.2619799128663
-            ]
-          },
-          {
-            "id": "e89d2cd8-daa3-4e29-8a69-851db85072cb",
-            "name": "bg_removal_name",
-            "type": "COMBO",
-            "linkIds": [
-              12
-            ],
-            "pos": [
-              -6601.620008222246,
-              1519.2619799128663
-            ]
-          }
-        ],
-        "outputs": [
-          {
-            "id": "16e7863c-4c38-46c2-aa74-e82991fbfe8d",
-            "name": "IMAGE",
-            "type": "IMAGE",
-            "linkIds": [
-              8
-            ],
-            "localized_name": "IMAGE",
-            "pos": [
-              -6145.049695722246,
-              1499.2619799128663
-            ]
-          },
-          {
-            "id": "f7240c19-5b80-406e-a8e2-9b12440ee2d6",
-            "name": "mask",
-            "type": "MASK",
-            "linkIds": [
-              11
-            ],
-            "pos": [
-              -6145.049695722246,
-              1519.2619799128663
-            ]
-          }
-        ],
-        "widgets": [],
-        "nodes": [
-          {
-            "id": 13,
-            "type": "RemoveBackground",
-            "pos": [
-              -6536.764823982709,
-              1444.9963409012412
-            ],
-            "size": [
-              302.25,
-              72
-            ],
-            "flags": {},
-            "order": 0,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "image",
-                "name": "image",
-                "type": "IMAGE",
-                "link": 5
-              },
-              {
-                "localized_name": "bg_removal_model",
-                "name": "bg_removal_model",
-                "type": "BACKGROUND_REMOVAL",
-                "link": 3
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "mask",
-                "name": "mask",
-                "type": "MASK",
-                "links": [
-                  4,
-                  11
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "RemoveBackground"
-            }
-          },
-          {
-            "id": 14,
-            "type": "LoadBackgroundRemovalModel",
-            "pos": [
-              -6540.534070722246,
-              1302.223464635445
-            ],
-            "size": [
-              311.484375,
-              85.515625
-            ],
-            "flags": {},
-            "order": 1,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "bg_removal_name",
-                "name": "bg_removal_name",
-                "type": "COMBO",
-                "widget": {
-                  "name": "bg_removal_name"
-                },
-                "link": 12
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "bg_model",
-                "name": "bg_model",
-                "type": "BACKGROUND_REMOVAL",
-                "links": [
-                  3
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "LoadBackgroundRemovalModel",
-              "models": [
-                {
-                  "name": "birefnet.safetensors",
-                  "url": "https://huggingface.co/Comfy-Org/BiRefNet/resolve/main/background_removal/birefnet.safetensors",
-                  "directory": "background_removal"
-                }
-              ]
-            },
-            "widgets_values": [
-              "birefnet.safetensors"
-            ]
-          },
-          {
-            "id": 15,
-            "type": "InvertMask",
-            "pos": [
-              -6532.446160529669,
-              1571.1111286839914
-            ],
-            "size": [
-              285.984375,
-              48
-            ],
-            "flags": {},
-            "order": 2,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "mask",
-                "name": "mask",
-                "type": "MASK",
-                "link": 4
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "MASK",
-                "name": "MASK",
-                "type": "MASK",
-                "links": [
-                  6
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "InvertMask"
-            }
-          },
-          {
-            "id": 16,
-            "type": "JoinImageWithAlpha",
-            "pos": [
-              -6527.4370171636665,
-              1674.3004951902876
-            ],
-            "size": [
-              284.96875,
-              72
-            ],
-            "flags": {},
-            "order": 3,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "image",
-                "name": "image",
-                "type": "IMAGE",
-                "link": 7
-              },
-              {
-                "localized_name": "alpha",
-                "name": "alpha",
-                "type": "MASK",
-                "link": 6
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "IMAGE",
-                "name": "IMAGE",
-                "type": "IMAGE",
-                "links": [
-                  8
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "JoinImageWithAlpha"
-            }
-          }
-        ],
-        "groups": [],
-        "links": [
-          {
-            "id": 3,
-            "origin_id": 14,
-            "origin_slot": 0,
-            "target_id": 13,
-            "target_slot": 1,
-            "type": "BACKGROUND_REMOVAL"
-          },
-          {
-            "id": 4,
-            "origin_id": 13,
-            "origin_slot": 0,
-            "target_id": 15,
-            "target_slot": 0,
-            "type": "MASK"
-          },
-          {
-            "id": 6,
-            "origin_id": 15,
-            "origin_slot": 0,
-            "target_id": 16,
-            "target_slot": 1,
-            "type": "MASK"
-          },
-          {
-            "id": 5,
-            "origin_id": -10,
-            "origin_slot": 0,
-            "target_id": 13,
-            "target_slot": 0,
-            "type": "IMAGE"
-          },
-          {
-            "id": 7,
-            "origin_id": -10,
-            "origin_slot": 0,
-            "target_id": 16,
-            "target_slot": 0,
-            "type": "IMAGE"
-          },
-          {
-            "id": 8,
-            "origin_id": 16,
-            "origin_slot": 0,
-            "target_id": -20,
-            "target_slot": 0,
-            "type": "IMAGE"
-          },
-          {
-            "id": 11,
-            "origin_id": 13,
-            "origin_slot": 0,
-            "target_id": -20,
-            "target_slot": 1,
-            "type": "MASK"
-          },
-          {
-            "id": 12,
-            "origin_id": -10,
-            "origin_slot": 1,
-            "target_id": 14,
-            "target_slot": 0,
-            "type": "COMBO"
-          }
-        ],
-        "extra": {},
-        "category": "Image generation and editing/Background Removal"
-      }
-    ]
-  },
-  "extra": {}
-}

blueprints/Sharpen.json

@@ -267,7 +267,7 @@
               "Node name for S&R": "GLSLShader"
             },
             "widgets_values": [
-              "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform float u_float0;  // strength [0.0 – 2.0] typical: 0.3–1.0\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nvoid main() {\n    vec2 texel = 1.0 / vec2(textureSize(u_image0, 0));\n    \n    // Sample center and neighbors\n    vec4 center = texture(u_image0, v_texCoord);\n    vec4 top    = texture(u_image0, v_texCoord + vec2( 0.0, -texel.y));\n    vec4 bottom = texture(u_image0, v_texCoord + vec2( 0.0,  texel.y));\n    vec4 left   = texture(u_image0, v_texCoord + vec2(-texel.x,  0.0));\n    vec4 right  = texture(u_image0, v_texCoord + vec2( texel.x,  0.0));\n    \n    // Edge enhancement (Laplacian)\n    vec4 edges = center * 4.0 - top - bottom - left - right;\n    \n    // Add edges back scaled by strength\n    vec4 sharpened = center + edges * u_float0;\n    \n    fragColor0 = vec4(clamp(sharpened.rgb, 0.0, 1.0), center.a);\n}",
+              "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform float u_float0;  // strength [0.0 – 2.0] typical: 0.3–1.0\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nvoid main() {\n    vec2 texel = 1.0 / u_resolution;\n    \n    // Sample center and neighbors\n    vec4 center = texture(u_image0, v_texCoord);\n    vec4 top    = texture(u_image0, v_texCoord + vec2( 0.0, -texel.y));\n    vec4 bottom = texture(u_image0, v_texCoord + vec2( 0.0,  texel.y));\n    vec4 left   = texture(u_image0, v_texCoord + vec2(-texel.x,  0.0));\n    vec4 right  = texture(u_image0, v_texCoord + vec2( texel.x,  0.0));\n    \n    // Edge enhancement (Laplacian)\n    vec4 edges = center * 4.0 - top - bottom - left - right;\n    \n    // Add edges back scaled by strength\n    vec4 sharpened = center + edges * u_float0;\n    \n    fragColor0 = vec4(clamp(sharpened.rgb, 0.0, 1.0), center.a);\n}",
               "from_input"
             ]
           }
@@ -302,9 +302,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Sharpen",
-        "description": "Sharpens image details using a GPU fragment shader for enhanced clarity."
+        "category": "Image Tools/Sharpen"
       }
     ]
   }
-}
+}

blueprints/Text to Audio (ACE-Step 1.5).json

@@ -222,7 +222,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "Text to Audio (ACE-Step 1.5)",
+        "name": "local-Text to Audio (ACE-Step 1.5)",
         "inputNode": {
           "id": -10,
           "bounding": [
@@ -1502,8 +1502,7 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Audio/Music generation",
-        "description": "Generates audio/music from text prompts using ACE-Step 1.5, a diffusion-based audio generation model."
+        "category": "Audio/Music generation"
       }
     ]
   },
@@ -1519,4 +1518,4 @@
     }
   },
   "version": 0.4
-}
+}

blueprints/Text to Image (Z-Image-Turbo).json

@@ -1,21 +1,22 @@
 {
+  "id": "1c3eaa76-5cfa-4dc7-8571-97a570324e01",
   "revision": 0,
-  "last_node_id": 57,
-  "last_link_id": 0,
+  "last_node_id": 34,
+  "last_link_id": 40,
   "nodes": [
     {
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@@ -95,15 +80,15 @@
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@@ -112,23 +97,19 @@
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+            "control_after_generate"
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@@ -141,40 +122,48 @@
         "secondTabOffset": 80,
         "secondTabWidth": 65
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-      "title": "Text to Image (Z-Image-Turbo)"
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+        1024,
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+        "z_image_turbo_bf16.safetensors",
+        "qwen_3_4b.safetensors",
+        "ae.safetensors"
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         "config": {},
-        "name": "Text to Image (Z-Image-Turbo)",
+        "name": "local-Text to Image (Z-Image-Turbo)",
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@@ -189,8 +178,8 @@
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@@ -201,8 +190,8 @@
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@@ -288,8 +253,8 @@
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@@ -350,9 +315,9 @@
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               "cnr_id": "comfy-core",
               "ver": "0.3.73",
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@@ -378,15 +343,15 @@
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@@ -396,7 +361,7 @@
                 "widget": {
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@@ -410,9 +375,9 @@
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             "properties": {
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               "cnr_id": "comfy-core",
               "ver": "0.3.73",
+              "Node name for S&R": "VAELoader",
               "models": [
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@@ -436,12 +401,12 @@
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@@ -465,9 +430,9 @@
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             "properties": {
-              "Node name for S&R": "ConditioningZeroOut",
               "cnr_id": "comfy-core",
               "ver": "0.3.73",
+              "Node name for S&R": "ConditioningZeroOut",
               "enableTabs": false,
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               "tabXOffset": 10,
@@ -475,21 +440,22 @@
               "secondTabText": "Send Back",
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               "secondTabWidth": 65
-            }
+            },
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             "properties": {
-              "Node name for S&R": "VAEDecode",
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               "ver": "0.3.64",
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               "tabWidth": 65,
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@@ -527,21 +493,22 @@
               "secondTabText": "Send Back",
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               "secondTabWidth": 65
-            }
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@@ -551,7 +518,7 @@
                 "widget": {
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@@ -574,9 +541,9 @@
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             ],
             "properties": {
-              "Node name for S&R": "UNETLoader",
               "cnr_id": "comfy-core",
               "ver": "0.3.73",
+              "Node name for S&R": "UNETLoader",
               "models": [
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@@ -601,15 +568,15 @@
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@@ -640,9 +607,9 @@
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             "properties": {
-              "Node name for S&R": "CLIPTextEncode",
               "cnr_id": "comfy-core",
               "ver": "0.3.73",
+              "Node name for S&R": "CLIPTextEncode",
               "enableTabs": false,
               "tabWidth": 65,
               "tabXOffset": 10,
@@ -659,15 +626,15 @@
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             "type": "EmptySD3LatentImage",
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@@ -710,9 +677,9 @@
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             "properties": {
-              "Node name for S&R": "EmptySD3LatentImage",
               "cnr_id": "comfy-core",
               "ver": "0.3.64",
+              "Node name for S&R": "EmptySD3LatentImage",
               "enableTabs": false,
               "tabWidth": 65,
               "tabXOffset": 10,
@@ -727,77 +694,19 @@
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-                "name": "MODEL",
-                "type": "MODEL",
-                "slot_index": 0,
-                "links": [
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-            "properties": {
-              "Node name for S&R": "ModelSamplingAuraFlow",
-              "cnr_id": "comfy-core",
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-              "enableTabs": false,
-              "tabWidth": 65,
-              "tabXOffset": 10,
-              "hasSecondTab": false,
-              "secondTabText": "Send Back",
-              "secondTabOffset": 80,
-              "secondTabWidth": 65
-            },
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           {
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             "type": "KSampler",
             "pos": [
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             "inputs": [
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@@ -831,7 +740,7 @@
                 "widget": {
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                 },
-                "link": 71
+                "link": null
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               {
                 "localized_name": "steps",
@@ -840,7 +749,7 @@
                 "widget": {
                   "name": "steps"
                 },
-                "link": 72
+                "link": null
               },
               {
                 "localized_name": "cfg",
@@ -891,9 +800,9 @@
               }
             ],
             "properties": {
-              "Node name for S&R": "KSampler",
               "cnr_id": "comfy-core",
               "ver": "0.3.64",
+              "Node name for S&R": "KSampler",
               "enableTabs": false,
               "tabWidth": 65,
               "tabXOffset": 10,
@@ -905,23 +814,81 @@
             "widgets_values": [
               0,
               "randomize",
-              8,
+              4,
               1,
               "res_multistep",
               "simple",
               1
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+            "type": "ModelSamplingAuraFlow",
+            "pos": [
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+            "mode": 0,
+            "inputs": [
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+                "name": "model",
+                "type": "MODEL",
+                "link": 26
+              },
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+                "name": "shift",
+                "type": "FLOAT",
+                "widget": {
+                  "name": "shift"
+                },
+                "link": null
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "MODEL",
+                "name": "MODEL",
+                "type": "MODEL",
+                "slot_index": 0,
+                "links": [
+                  13
+                ]
+              }
+            ],
+            "properties": {
+              "cnr_id": "comfy-core",
+              "ver": "0.3.64",
+              "Node name for S&R": "ModelSamplingAuraFlow",
+              "enableTabs": false,
+              "tabWidth": 65,
+              "tabXOffset": 10,
+              "hasSecondTab": false,
+              "secondTabText": "Send Back",
+              "secondTabOffset": 80,
+              "secondTabWidth": 65
+            },
+            "widgets_values": [
+              3
+            ]
           }
         ],
         "groups": [
           {
             "id": 2,
-            "title": "Step2 - Image size",
+            "title": "Image size",
             "bounding": [
-              10,
-              820,
-              320,
-              280
+              100,
+              560,
+              290,
+              200
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             "color": "#3f789e",
             "font_size": 24,
@@ -929,12 +896,12 @@
           },
           {
             "id": 3,
-            "title": "Step3 - Prompt",
+            "title": "Prompt",
             "bounding": [
-              360,
+              410,
               130,
-              530,
-              970
+              450,
+              540
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             "color": "#3f789e",
             "font_size": 24,
@@ -942,12 +909,12 @@
           },
           {
             "id": 4,
-            "title": "Step1 - Load models",
+            "title": "Models",
             "bounding": [
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+              100,
               130,
-              330,
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+              290,
+              413.6
             ],
             "color": "#3f789e",
             "font_size": 24,
@@ -1060,41 +1027,25 @@
             "type": "INT"
           },
           {
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             "origin_id": -10,
             "origin_slot": 3,
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-            "type": "INT"
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-            "origin_id": -10,
-            "origin_slot": 5,
             "target_id": 28,
             "target_slot": 0,
             "type": "COMBO"
           },
           {
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+            "id": 39,
             "origin_id": -10,
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             "target_id": 30,
             "target_slot": 0,
             "type": "COMBO"
           },
           {
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+            "id": 40,
             "origin_id": -10,
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+            "origin_slot": 5,
             "target_id": 29,
             "target_slot": 0,
             "type": "COMBO"
@@ -1103,10 +1054,25 @@
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           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Text to image",
-        "description": "Generates images from text prompts using Z-Image-Turbo, Alibaba's distilled 6B DiT model."
+        "category": "Image generation and editing/Text to image"
       }
     ]
   },
-  "extra": {}
-}
+  "config": {},
+  "extra": {
+    "frontendVersion": "1.37.10",
+    "workflowRendererVersion": "LG",
+    "VHS_latentpreview": false,
+    "VHS_latentpreviewrate": 0,
+    "VHS_MetadataImage": true,
+    "VHS_KeepIntermediate": true,
+    "ds": {
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+      "offset": [
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+        -830.7121087564725
+      ]
+    }
+  },
+  "version": 0.4
+}

blueprints/Text to Video (Wan 2.2).json

@@ -1572,8 +1572,7 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Video generation and editing/Text to video",
-        "description": "Generates video from text prompts using Wan2.2, Alibaba's diffusion video model."
+        "category": "Video generation and editing/Text to video"
       }
     ]
   },
@@ -1587,4 +1586,4 @@
     "VHS_KeepIntermediate": true
   },
   "version": 0.4
-}
+}

blueprints/Unsharp Mask.json

@@ -383,7 +383,7 @@
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             },
             "widgets_values": [
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+              "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform float u_float0;  // amount    [0.0 - 3.0]  typical: 0.5-1.5\nuniform float u_float1;  // radius    [0.5 - 10.0] blur radius in pixels\nuniform float u_float2;  // threshold [0.0 - 0.1]  min difference to sharpen\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nfloat gaussian(float x, float sigma) {\n    return exp(-(x * x) / (2.0 * sigma * sigma));\n}\n\nfloat getLuminance(vec3 color) {\n    return dot(color, vec3(0.2126, 0.7152, 0.0722));\n}\n\nvoid main() {\n    vec2 texel = 1.0 / u_resolution;\n    float radius = max(u_float1, 0.5);\n    float amount = u_float0;\n    float threshold = u_float2;\n\n    vec4 original = texture(u_image0, v_texCoord);\n\n    // Gaussian blur for the \"unsharp\" mask\n    int samples = int(ceil(radius));\n    float sigma = radius / 2.0;\n\n    vec4 blurred = vec4(0.0);\n    float totalWeight = 0.0;\n\n    for (int x = -samples; x <= samples; x++) {\n        for (int y = -samples; y <= samples; y++) {\n            vec2 offset = vec2(float(x), float(y)) * texel;\n            vec4 sample_color = texture(u_image0, v_texCoord + offset);\n\n            float dist = length(vec2(float(x), float(y)));\n            float weight = gaussian(dist, sigma);\n            blurred += sample_color * weight;\n            totalWeight += weight;\n        }\n    }\n    blurred /= totalWeight;\n\n    // Unsharp mask = original - blurred\n    vec3 mask = original.rgb - blurred.rgb;\n\n    // Luminance-based threshold with smooth falloff\n    float lumaDelta = abs(getLuminance(original.rgb) - getLuminance(blurred.rgb));\n    float thresholdScale = smoothstep(0.0, threshold, lumaDelta);\n    mask *= thresholdScale;\n\n    // Sharpen: original + mask * amount\n    vec3 sharpened = original.rgb + mask * amount;\n\n    fragColor0 = vec4(clamp(sharpened, 0.0, 1.0), original.a);\n}\n",
               "from_input"
             ]
           }
@@ -434,9 +434,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Sharpen",
-        "description": "Enhances edge contrast via unsharp masking for a sharper image appearance."
+        "category": "Image Tools/Sharpen"
       }
     ]
   }
-}
+}

blueprints/Video Captioning (Gemini).json

@@ -307,8 +307,7 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Text generation/Video Captioning",
-        "description": "Generates descriptive captions for video input using Google's Gemini multimodal LLM."
+        "category": "Text generation/Video Captioning"
       }
     ]
   }

blueprints/Video Inpaint(Wan2.1 VACE).json

@@ -165,7 +165,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "Video Inpaint (Wan 2.1 VACE)",
+        "name": "local-Video Inpaint(Wan2.1 VACE)",
         "inputNode": {
           "id": -10,
           "bounding": [
@@ -2368,8 +2368,7 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Video generation and editing/Inpaint video",
-        "description": "Inpaints masked regions in video frames using Wan 2.1 VACE."
+        "category": "Video generation and editing/Inpaint video"
       }
     ]
   },

blueprints/Video Segmentation (SAM3).json

@@ -1,827 +0,0 @@
-{
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-  "last_link_id": 0,
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-      "title": "Video Segmentation (SAM3)"
-    }
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-          "lastLinkId": 299,
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-                  200
-                ]
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-              "secondTabOffset": 80,
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-            "outputs": [],
-            "title": "Note: Prompt format",
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-            "widgets_values": [
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-            "target_id": 125,
-            "target_slot": 0,
-            "type": "CLIP"
-          },
-          {
-            "id": 231,
-            "origin_id": 126,
-            "origin_slot": 0,
-            "target_id": -20,
-            "target_slot": 0,
-            "type": "MASK"
-          },
-          {
-            "id": 232,
-            "origin_id": 126,
-            "origin_slot": 1,
-            "target_id": -20,
-            "target_slot": 1,
-            "type": "BOUNDING_BOX"
-          },
-          {
-            "id": 252,
-            "origin_id": -10,
-            "origin_slot": 0,
-            "target_id": 128,
-            "target_slot": 0,
-            "type": "VIDEO"
-          },
-          {
-            "id": 253,
-            "origin_id": 128,
-            "origin_slot": 0,
-            "target_id": 126,
-            "target_slot": 1,
-            "type": "IMAGE"
-          },
-          {
-            "id": 254,
-            "origin_id": -10,
-            "origin_slot": 1,
-            "target_id": 125,
-            "target_slot": 1,
-            "type": "STRING"
-          },
-          {
-            "id": 255,
-            "origin_id": -10,
-            "origin_slot": 2,
-            "target_id": 126,
-            "target_slot": 3,
-            "type": "BOUNDING_BOX"
-          },
-          {
-            "id": 256,
-            "origin_id": -10,
-            "origin_slot": 3,
-            "target_id": 126,
-            "target_slot": 4,
-            "type": "STRING"
-          },
-          {
-            "id": 257,
-            "origin_id": -10,
-            "origin_slot": 4,
-            "target_id": 126,
-            "target_slot": 5,
-            "type": "STRING"
-          },
-          {
-            "id": 259,
-            "origin_id": 128,
-            "origin_slot": 1,
-            "target_id": -20,
-            "target_slot": 2,
-            "type": "AUDIO"
-          },
-          {
-            "id": 260,
-            "origin_id": 128,
-            "origin_slot": 2,
-            "target_id": -20,
-            "target_slot": 3,
-            "type": "FLOAT"
-          },
-          {
-            "id": 261,
-            "origin_id": -10,
-            "origin_slot": 5,
-            "target_id": 126,
-            "target_slot": 6,
-            "type": "FLOAT"
-          },
-          {
-            "id": 262,
-            "origin_id": -10,
-            "origin_slot": 6,
-            "target_id": 126,
-            "target_slot": 7,
-            "type": "INT"
-          },
-          {
-            "id": 263,
-            "origin_id": -10,
-            "origin_slot": 7,
-            "target_id": 126,
-            "target_slot": 8,
-            "type": "BOOLEAN"
-          },
-          {
-            "id": 273,
-            "origin_id": -10,
-            "origin_slot": 8,
-            "target_id": 127,
-            "target_slot": 0,
-            "type": "COMBO"
-          }
-        ],
-        "extra": {},
-        "category": "Video Tools",
-        "description": "Segments video into temporally consistent masks using Meta SAM3 from text or interactive prompts."
-      }
-    ]
-  },
-  "extra": {}
-}

blueprints/Video Stitch.json

@@ -1,21 +1,21 @@
 {
   "revision": 0,
-  "last_node_id": 85,
+  "last_node_id": 84,
   "last_link_id": 0,
   "nodes": [
     {
-      "id": 85,
-      "type": "637913e7-0206-46ba-8ded-70ae3a7c2e19",
+      "id": 84,
+      "type": "8e8aa94a-647e-436d-8440-8ee4691864de",
       "pos": [
-        -880,
-        -2260
+        -6100,
+        2620
       ],
       "size": [
         290,
         160
       ],
       "flags": {},
-      "order": 2,
+      "order": 0,
       "mode": 0,
       "inputs": [
         {
@@ -76,26 +76,31 @@
       "properties": {
         "proxyWidgets": [
           [
-            "79",
+            "-1",
             "direction"
           ],
           [
-            "79",
+            "-1",
             "match_image_size"
           ],
           [
-            "79",
+            "-1",
             "spacing_width"
           ],
           [
-            "79",
+            "-1",
             "spacing_color"
           ]
         ],
         "cnr_id": "comfy-core",
         "ver": "0.13.0"
       },
-      "widgets_values": [],
+      "widgets_values": [
+        "right",
+        true,
+        0,
+        "white"
+      ],
       "title": "Video Stitch"
     }
   ],
@@ -104,12 +109,12 @@
   "definitions": {
     "subgraphs": [
       {
-        "id": "637913e7-0206-46ba-8ded-70ae3a7c2e19",
+        "id": "8e8aa94a-647e-436d-8440-8ee4691864de",
         "version": 1,
         "state": {
           "lastGroupId": 1,
-          "lastNodeId": 97,
-          "lastLinkId": 282,
+          "lastNodeId": 84,
+          "lastLinkId": 262,
           "lastRerouteId": 0
         },
         "revision": 0,
@@ -118,8 +123,8 @@
         "inputNode": {
           "id": -10,
           "bounding": [
-            -6810,
-            2580,
+            -6580,
+            2649,
             143.55859375,
             160
           ]
@@ -127,8 +132,8 @@
         "outputNode": {
           "id": -20,
           "bounding": [
-            -4770,
-            2600,
+            -5720,
+            2659,
             120,
             60
           ]
@@ -144,8 +149,8 @@
             "localized_name": "video",
             "label": "Before Video",
             "pos": [
-              -6686.44140625,
-              2600
+              -6456.44140625,
+              2669
             ]
           },
           {
@@ -158,8 +163,8 @@
             "localized_name": "video_1",
             "label": "After Video",
             "pos": [
-              -6686.44140625,
-              2620
+              -6456.44140625,
+              2689
             ]
           },
           {
@@ -170,8 +175,8 @@
               259
             ],
             "pos": [
-              -6686.44140625,
-              2640
+              -6456.44140625,
+              2709
             ]
           },
           {
@@ -182,8 +187,8 @@
               260
             ],
             "pos": [
-              -6686.44140625,
-              2660
+              -6456.44140625,
+              2729
             ]
           },
           {
@@ -194,8 +199,8 @@
               261
             ],
             "pos": [
-              -6686.44140625,
-              2680
+              -6456.44140625,
+              2749
             ]
           },
           {
@@ -206,8 +211,8 @@
               262
             ],
             "pos": [
-              -6686.44140625,
-              2700
+              -6456.44140625,
+              2769
             ]
           }
         ],
@@ -221,8 +226,8 @@
             ],
             "localized_name": "VIDEO",
             "pos": [
-              -4750,
-              2620
+              -5700,
+              2679
             ]
           }
         ],
@@ -233,11 +238,11 @@
             "type": "GetVideoComponents",
             "pos": [
               -6390,
-              2600
+              2560
             ],
             "size": [
-              230,
-              120
+              193.530859375,
+              66
             ],
             "flags": {},
             "order": 1,
@@ -273,9 +278,9 @@
               }
             ],
             "properties": {
-              "Node name for S&R": "GetVideoComponents",
               "cnr_id": "comfy-core",
-              "ver": "0.13.0"
+              "ver": "0.13.0",
+              "Node name for S&R": "GetVideoComponents"
             }
           },
           {
@@ -286,8 +291,8 @@
               2420
             ],
             "size": [
-              230,
-              120
+              193.530859375,
+              66
             ],
             "flags": {},
             "order": 0,
@@ -327,254 +332,21 @@
               }
             ],
             "properties": {
-              "Node name for S&R": "GetVideoComponents",
               "cnr_id": "comfy-core",
-              "ver": "0.13.0"
+              "ver": "0.13.0",
+              "Node name for S&R": "GetVideoComponents"
             }
           },
-          {
-            "id": 90,
-            "type": "GetImageSize",
-            "pos": [
-              -6390,
-              3030
-            ],
-            "size": [
-              230,
-              120
-            ],
-            "flags": {},
-            "order": 4,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "image",
-                "name": "image",
-                "type": "IMAGE",
-                "link": 266
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "width",
-                "name": "width",
-                "type": "INT",
-                "links": [
-                  274
-                ]
-              },
-              {
-                "localized_name": "height",
-                "name": "height",
-                "type": "INT",
-                "links": [
-                  276
-                ]
-              },
-              {
-                "localized_name": "batch_size",
-                "name": "batch_size",
-                "type": "INT",
-                "links": null
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "GetImageSize"
-            }
-          },
-          {
-            "id": 80,
-            "type": "CreateVideo",
-            "pos": [
-              -5190,
-              2420
-            ],
-            "size": [
-              270,
-              130
-            ],
-            "flags": {},
-            "order": 3,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "images",
-                "name": "images",
-                "type": "IMAGE",
-                "link": 282
-              },
-              {
-                "localized_name": "audio",
-                "name": "audio",
-                "shape": 7,
-                "type": "AUDIO",
-                "link": 251
-              },
-              {
-                "localized_name": "fps",
-                "name": "fps",
-                "type": "FLOAT",
-                "widget": {
-                  "name": "fps"
-                },
-                "link": 252
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "VIDEO",
-                "name": "VIDEO",
-                "type": "VIDEO",
-                "links": [
-                  255
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "CreateVideo",
-              "cnr_id": "comfy-core",
-              "ver": "0.13.0"
-            },
-            "widgets_values": [
-              30
-            ]
-          },
-          {
-            "id": 95,
-            "type": "ComfyMathExpression",
-            "pos": [
-              -6040,
-              3020
-            ],
-            "size": [
-              400,
-              200
-            ],
-            "flags": {},
-            "order": 5,
-            "mode": 0,
-            "inputs": [
-              {
-                "label": "a",
-                "localized_name": "values.a",
-                "name": "values.a",
-                "type": "FLOAT,INT",
-                "link": 274
-              },
-              {
-                "label": "b",
-                "localized_name": "values.b",
-                "name": "values.b",
-                "shape": 7,
-                "type": "FLOAT,INT",
-                "link": null
-              },
-              {
-                "localized_name": "expression",
-                "name": "expression",
-                "type": "STRING",
-                "widget": {
-                  "name": "expression"
-                },
-                "link": null
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "FLOAT",
-                "name": "FLOAT",
-                "type": "FLOAT",
-                "links": null
-              },
-              {
-                "localized_name": "INT",
-                "name": "INT",
-                "type": "INT",
-                "links": [
-                  279
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "ComfyMathExpression"
-            },
-            "widgets_values": [
-              "a & ~1"
-            ]
-          },
-          {
-            "id": 96,
-            "type": "ComfyMathExpression",
-            "pos": [
-              -6040,
-              3290
-            ],
-            "size": [
-              400,
-              200
-            ],
-            "flags": {},
-            "order": 6,
-            "mode": 0,
-            "inputs": [
-              {
-                "label": "a",
-                "localized_name": "values.a",
-                "name": "values.a",
-                "type": "FLOAT,INT",
-                "link": 276
-              },
-              {
-                "label": "b",
-                "localized_name": "values.b",
-                "name": "values.b",
-                "shape": 7,
-                "type": "FLOAT,INT",
-                "link": null
-              },
-              {
-                "localized_name": "expression",
-                "name": "expression",
-                "type": "STRING",
-                "widget": {
-                  "name": "expression"
-                },
-                "link": null
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "FLOAT",
-                "name": "FLOAT",
-                "type": "FLOAT",
-                "links": null
-              },
-              {
-                "localized_name": "INT",
-                "name": "INT",
-                "type": "INT",
-                "links": [
-                  280
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "ComfyMathExpression"
-            },
-            "widgets_values": [
-              "a & ~1"
-            ]
-          },
           {
             "id": 79,
             "type": "ImageStitch",
             "pos": [
               -6390,
-              2780
+              2700
             ],
             "size": [
               270,
-              160
+              150
             ],
             "flags": {},
             "order": 2,
@@ -636,15 +408,14 @@
                 "name": "IMAGE",
                 "type": "IMAGE",
                 "links": [
-                  266,
-                  281
+                  250
                 ]
               }
             ],
             "properties": {
-              "Node name for S&R": "ImageStitch",
               "cnr_id": "comfy-core",
-              "ver": "0.13.0"
+              "ver": "0.13.0",
+              "Node name for S&R": "ImageStitch"
             },
             "widgets_values": [
               "right",
@@ -654,91 +425,60 @@
             ]
           },
           {
-            "id": 97,
-            "type": "ResizeImageMaskNode",
+            "id": 80,
+            "type": "CreateVideo",
             "pos": [
-              -5560,
-              2790
+              -6040,
+              2610
             ],
             "size": [
               270,
-              160
+              78
             ],
             "flags": {},
-            "order": 7,
+            "order": 3,
             "mode": 0,
             "inputs": [
               {
-                "localized_name": "input",
-                "name": "input",
-                "type": "IMAGE,MASK",
-                "link": 281
+                "localized_name": "images",
+                "name": "images",
+                "type": "IMAGE",
+                "link": 250
               },
               {
-                "localized_name": "resize_type",
-                "name": "resize_type",
-                "type": "COMFY_DYNAMICCOMBO_V3",
-                "widget": {
-                  "name": "resize_type"
-                },
-                "link": null
+                "localized_name": "audio",
+                "name": "audio",
+                "shape": 7,
+                "type": "AUDIO",
+                "link": 251
               },
               {
-                "localized_name": "width",
-                "name": "resize_type.width",
-                "type": "INT",
+                "localized_name": "fps",
+                "name": "fps",
+                "type": "FLOAT",
                 "widget": {
-                  "name": "resize_type.width"
+                  "name": "fps"
                 },
-                "link": 279
-              },
-              {
-                "localized_name": "height",
-                "name": "resize_type.height",
-                "type": "INT",
-                "widget": {
-                  "name": "resize_type.height"
-                },
-                "link": 280
-              },
-              {
-                "localized_name": "crop",
-                "name": "resize_type.crop",
-                "type": "COMBO",
-                "widget": {
-                  "name": "resize_type.crop"
-                },
-                "link": null
-              },
-              {
-                "localized_name": "scale_method",
-                "name": "scale_method",
-                "type": "COMBO",
-                "widget": {
-                  "name": "scale_method"
-                },
-                "link": null
+                "link": 252
               }
             ],
             "outputs": [
               {
-                "localized_name": "resized",
-                "name": "resized",
-                "type": "*",
+                "localized_name": "VIDEO",
+                "name": "VIDEO",
+                "type": "VIDEO",
                 "links": [
-                  282
+                  255
                 ]
               }
             ],
             "properties": {
-              "Node name for S&R": "ResizeImageMaskNode"
+              "cnr_id": "comfy-core",
+              "ver": "0.13.0",
+              "Node name for S&R": "CreateVideo"
             },
             "widgets_values": [
-              "scale dimensions",
-              512,
-              512,
-              "center",
-              "area"
+              30
             ]
           }
         ],
@@ -760,6 +500,14 @@
             "target_slot": 1,
             "type": "IMAGE"
           },
+          {
+            "id": 250,
+            "origin_id": 79,
+            "origin_slot": 0,
+            "target_id": 80,
+            "target_slot": 0,
+            "type": "IMAGE"
+          },
           {
             "id": 251,
             "origin_id": 77,
@@ -831,71 +579,13 @@
             "target_id": 79,
             "target_slot": 5,
             "type": "COMBO"
-          },
-          {
-            "id": 266,
-            "origin_id": 79,
-            "origin_slot": 0,
-            "target_id": 90,
-            "target_slot": 0,
-            "type": "IMAGE"
-          },
-          {
-            "id": 274,
-            "origin_id": 90,
-            "origin_slot": 0,
-            "target_id": 95,
-            "target_slot": 0,
-            "type": "INT"
-          },
-          {
-            "id": 276,
-            "origin_id": 90,
-            "origin_slot": 1,
-            "target_id": 96,
-            "target_slot": 0,
-            "type": "INT"
-          },
-          {
-            "id": 279,
-            "origin_id": 95,
-            "origin_slot": 1,
-            "target_id": 97,
-            "target_slot": 2,
-            "type": "INT"
-          },
-          {
-            "id": 280,
-            "origin_id": 96,
-            "origin_slot": 1,
-            "target_id": 97,
-            "target_slot": 3,
-            "type": "INT"
-          },
-          {
-            "id": 281,
-            "origin_id": 79,
-            "origin_slot": 0,
-            "target_id": 97,
-            "target_slot": 0,
-            "type": "IMAGE"
-          },
-          {
-            "id": 282,
-            "origin_id": 97,
-            "origin_slot": 0,
-            "target_id": 80,
-            "target_slot": 0,
-            "type": "IMAGE"
           }
         ],
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Video Tools/Stitch videos",
-        "description": "Stitches multiple video clips into a single sequential video file."
+        "category": "Video Tools/Stitch videos"
       }
     ]
-  },
-  "extra": {}
-}
+  }
+}

blueprints/Video Upscale(GAN x4).json

@@ -412,10 +412,9 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Video generation and editing/Enhance video",
-        "description": "Upscales video to 4× resolution using a GAN-based upscaling model."
+        "category": "Video generation and editing/Enhance video"
       }
     ]
   },
   "extra": {}
-}
+}

comfy/background_removal/birefnet.json

@@ -1,7 +0,0 @@
-{
-    "model_type": "birefnet",
-    "image_std": [1.0, 1.0, 1.0],
-    "image_mean": [0.0, 0.0, 0.0],
-    "image_size": 1024,
-    "resize_to_original": true
-}

comfy/background_removal/birefnet.py

@@ -1,689 +0,0 @@
-import torch
-import comfy.ops
-import numpy as np
-import torch.nn as nn
-from functools import partial
-import torch.nn.functional as F
-from torchvision.ops import deform_conv2d
-from comfy.ldm.modules.attention import optimized_attention_for_device
-
-CXT = [3072, 1536, 768, 384][1:][::-1][-3:]
-
-class Attention(nn.Module):
-    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, device=None, dtype=None, operations=None):
-        super().__init__()
-
-        self.dim = dim
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim ** -0.5
-
-        self.q = operations.Linear(dim, dim, bias=qkv_bias, device=device, dtype=dtype)
-        self.kv = operations.Linear(dim, dim * 2, bias=qkv_bias, device=device, dtype=dtype)
-        self.proj = operations.Linear(dim, dim, device=device, dtype=dtype)
-
-    def forward(self, x):
-        B, N, C = x.shape
-        optimized_attention = optimized_attention_for_device(x.device, mask=False, small_input=True)
-        q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
-        kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        k, v = kv[0], kv[1]
-
-        x = optimized_attention(
-            q, k, v, heads=self.num_heads, skip_output_reshape=True, skip_reshape=True
-        ).transpose(1, 2).reshape(B, N, C)
-        x = self.proj(x)
-
-        return x
-
-class Mlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, device=None, dtype=None, operations=None):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = operations.Linear(in_features, hidden_features, device=device, dtype=dtype)
-        self.act = nn.GELU()
-        self.fc2 = operations.Linear(hidden_features, out_features, device=device, dtype=dtype)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.fc2(x)
-        return x
-
-
-def window_partition(x, window_size):
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, device=None, dtype=None, operations=None):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim ** -0.5
-
-        self.relative_position_bias_table = nn.Parameter(
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads, device=device, dtype=dtype))
-
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w], indexing='ij'))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, device=device, dtype=dtype)
-        self.proj = operations.Linear(dim, dim, device=device, dtype=dtype)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        B_, N, C = x.shape
-        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]
-
-        q = q * self.scale
-        attn = (q @ k.transpose(-2, -1))
-
-        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.long().view(-1)].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        return x
-
-
-class SwinTransformerBlock(nn.Module):
-    def __init__(self, dim, num_heads, window_size=7, shift_size=0,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None,
-                 norm_layer=nn.LayerNorm, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.dim = dim
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-
-        self.norm1 = norm_layer(dim, device=device, dtype=dtype)
-        self.attn = WindowAttention(
-            dim, window_size=(self.window_size, self.window_size), num_heads=num_heads,
-            qkv_bias=qkv_bias, qk_scale=qk_scale, device=device, dtype=dtype, operations=operations)
-
-        self.norm2 = norm_layer(dim, device=device, dtype=dtype)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, device=device, dtype=dtype, operations=operations)
-
-        self.H = None
-        self.W = None
-
-    def forward(self, x, mask_matrix):
-        B, L, C = x.shape
-        H, W = self.H, self.W
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(B, H, W, C)
-
-        pad_l = pad_t = 0
-        pad_r = (self.window_size - W % self.window_size) % self.window_size
-        pad_b = (self.window_size - H % self.window_size) % self.window_size
-        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
-        _, Hp, Wp, _ = x.shape
-
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-            attn_mask = mask_matrix
-        else:
-            shifted_x = x
-            attn_mask = None
-
-        x_windows = window_partition(shifted_x, self.window_size)
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)
-
-        attn_windows = self.attn(x_windows, mask=attn_mask)
-
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C
-
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-
-        if pad_r > 0 or pad_b > 0:
-            x = x[:, :H, :W, :].contiguous()
-
-        x = x.view(B, H * W, C)
-
-        x = shortcut + x
-        x = x + self.mlp(self.norm2(x))
-
-        return x
-
-
-class PatchMerging(nn.Module):
-    def __init__(self, dim, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.dim = dim
-        self.reduction = operations.Linear(4 * dim, 2 * dim, bias=False, device=device, dtype=dtype)
-        self.norm = operations.LayerNorm(4 * dim, device=device, dtype=dtype)
-
-    def forward(self, x, H, W):
-        B, L, C = x.shape
-        x = x.view(B, H, W, C)
-
-        # padding
-        pad_input = (H % 2 == 1) or (W % 2 == 1)
-        if pad_input:
-            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-
-class BasicLayer(nn.Module):
-    def __init__(self,
-                 dim,
-                 depth,
-                 num_heads,
-                 window_size=7,
-                 mlp_ratio=4.,
-                 qkv_bias=True,
-                 qk_scale=None,
-                 norm_layer=nn.LayerNorm,
-                 downsample=None,
-                 device=None, dtype=None, operations=None):
-        super().__init__()
-        self.window_size = window_size
-        self.shift_size = window_size // 2
-        self.depth = depth
-
-        # build blocks
-        self.blocks = nn.ModuleList([
-            SwinTransformerBlock(
-                dim=dim,
-                num_heads=num_heads,
-                window_size=window_size,
-                shift_size=0 if (i % 2 == 0) else window_size // 2,
-                mlp_ratio=mlp_ratio,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                norm_layer=norm_layer,
-                device=device, dtype=dtype, operations=operations)
-            for i in range(depth)])
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(dim=dim, device=device, dtype=dtype, operations=operations)
-        else:
-            self.downsample = None
-
-    def forward(self, x, H, W):
-        Hp = int(np.ceil(H / self.window_size)) * self.window_size
-        Wp = int(np.ceil(W / self.window_size)) * self.window_size
-        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
-        h_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        w_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(img_mask, self.window_size)
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
-        for blk in self.blocks:
-            blk.H, blk.W = H, W
-            x = blk(x, attn_mask)
-        if self.downsample is not None:
-            x_down = self.downsample(x, H, W)
-            Wh, Ww = (H + 1) // 2, (W + 1) // 2
-            return x, H, W, x_down, Wh, Ww
-        else:
-            return x, H, W, x, H, W
-
-
-class PatchEmbed(nn.Module):
-    def __init__(self, patch_size=4, in_channels=3, embed_dim=96, norm_layer=None, device=None, dtype=None, operations=None):
-        super().__init__()
-        patch_size = (patch_size, patch_size)
-        self.patch_size = patch_size
-
-        self.in_channels = in_channels
-        self.embed_dim = embed_dim
-
-        self.proj = operations.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size, device=device, dtype=dtype)
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim, device=device, dtype=dtype)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        _, _, H, W = x.size()
-        if W % self.patch_size[1] != 0:
-            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
-        if H % self.patch_size[0] != 0:
-            x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
-
-        x = self.proj(x)  # B C Wh Ww
-        if self.norm is not None:
-            Wh, Ww = x.size(2), x.size(3)
-            x = x.flatten(2).transpose(1, 2)
-            x = self.norm(x)
-            x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
-
-        return x
-
-
-class SwinTransformer(nn.Module):
-    def __init__(self,
-                 pretrain_img_size=224,
-                 patch_size=4,
-                 in_channels=3,
-                 embed_dim=96,
-                 depths=[2, 2, 6, 2],
-                 num_heads=[3, 6, 12, 24],
-                 window_size=7,
-                 mlp_ratio=4.,
-                 qkv_bias=True,
-                 qk_scale=None,
-                 patch_norm=True,
-                 out_indices=(0, 1, 2, 3),
-                 frozen_stages=-1,
-                 device=None, dtype=None, operations=None):
-        super().__init__()
-
-        norm_layer = partial(operations.LayerNorm, device=device, dtype=dtype)
-        self.pretrain_img_size = pretrain_img_size
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.patch_norm = patch_norm
-        self.out_indices = out_indices
-        self.frozen_stages = frozen_stages
-
-        self.patch_embed = PatchEmbed(
-            patch_size=patch_size, in_channels=in_channels, embed_dim=embed_dim,
-            device=device, dtype=dtype, operations=operations,
-            norm_layer=norm_layer if self.patch_norm else None)
-
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = BasicLayer(
-                dim=int(embed_dim * 2 ** i_layer),
-                depth=depths[i_layer],
-                num_heads=num_heads[i_layer],
-                window_size=window_size,
-                mlp_ratio=mlp_ratio,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                norm_layer=norm_layer,
-                downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
-                device=device, dtype=dtype, operations=operations)
-            self.layers.append(layer)
-
-        num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
-        self.num_features = num_features
-
-        for i_layer in out_indices:
-            layer = norm_layer(num_features[i_layer])
-            layer_name = f'norm{i_layer}'
-            self.add_module(layer_name, layer)
-
-
-    def forward(self, x):
-        x = self.patch_embed(x)
-
-        Wh, Ww = x.size(2), x.size(3)
-
-        outs = []
-        x = x.flatten(2).transpose(1, 2)
-        for i in range(self.num_layers):
-            layer = self.layers[i]
-            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
-
-            if i in self.out_indices:
-                norm_layer = getattr(self, f'norm{i}')
-                x_out = norm_layer(x_out)
-
-                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
-                outs.append(out)
-
-        return tuple(outs)
-
-class DeformableConv2d(nn.Module):
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size=3,
-                 stride=1,
-                 padding=1,
-                 bias=False, device=None, dtype=None, operations=None):
-
-        super(DeformableConv2d, self).__init__()
-
-        kernel_size = kernel_size if type(kernel_size) is tuple else (kernel_size, kernel_size)
-        self.stride = stride if type(stride) is tuple else (stride, stride)
-        self.padding = padding
-
-        self.offset_conv = operations.Conv2d(in_channels,
-                                     2 * kernel_size[0] * kernel_size[1],
-                                     kernel_size=kernel_size,
-                                     stride=stride,
-                                     padding=self.padding,
-                                     bias=True, device=device, dtype=dtype)
-
-        self.modulator_conv = operations.Conv2d(in_channels,
-                                     1 * kernel_size[0] * kernel_size[1],
-                                     kernel_size=kernel_size,
-                                     stride=stride,
-                                     padding=self.padding,
-                                     bias=True, device=device, dtype=dtype)
-
-        self.regular_conv = operations.Conv2d(in_channels,
-                                      out_channels=out_channels,
-                                      kernel_size=kernel_size,
-                                      stride=stride,
-                                      padding=self.padding,
-                                      bias=bias, device=device, dtype=dtype)
-
-    def forward(self, x):
-        offset = self.offset_conv(x)
-        modulator = 2. * torch.sigmoid(self.modulator_conv(x))
-        weight, bias, offload_info = comfy.ops.cast_bias_weight(self.regular_conv, x, offloadable=True)
-
-        x = deform_conv2d(
-            input=x,
-            offset=offset,
-            weight=weight,
-            bias=None,
-            padding=self.padding,
-            mask=modulator,
-            stride=self.stride,
-        )
-        comfy.ops.uncast_bias_weight(self.regular_conv, weight, bias, offload_info)
-        return x
-
-class BasicDecBlk(nn.Module):
-    def __init__(self, in_channels=64, out_channels=64, inter_channels=64, device=None, dtype=None, operations=None):
-        super(BasicDecBlk, self).__init__()
-        inter_channels = 64
-        self.conv_in = operations.Conv2d(in_channels, inter_channels, 3, 1, padding=1, device=device, dtype=dtype)
-        self.relu_in = nn.ReLU(inplace=True)
-        self.dec_att = ASPPDeformable(in_channels=inter_channels, device=device, dtype=dtype, operations=operations)
-        self.conv_out = operations.Conv2d(inter_channels, out_channels, 3, 1, padding=1, device=device, dtype=dtype)
-        self.bn_in = operations.BatchNorm2d(inter_channels, device=device, dtype=dtype)
-        self.bn_out = operations.BatchNorm2d(out_channels, device=device, dtype=dtype)
-
-    def forward(self, x):
-        x = self.conv_in(x)
-        x = self.bn_in(x)
-        x = self.relu_in(x)
-        x = self.dec_att(x)
-        x = self.conv_out(x)
-        x = self.bn_out(x)
-        return x
-
-
-class BasicLatBlk(nn.Module):
-    def __init__(self, in_channels=64, out_channels=64, device=None, dtype=None, operations=None):
-        super(BasicLatBlk, self).__init__()
-        self.conv = operations.Conv2d(in_channels, out_channels, 1, 1, 0, device=device, dtype=dtype)
-
-    def forward(self, x):
-        x = self.conv(x)
-        return x
-
-
-class _ASPPModuleDeformable(nn.Module):
-    def __init__(self, in_channels, planes, kernel_size, padding, device, dtype, operations):
-        super(_ASPPModuleDeformable, self).__init__()
-        self.atrous_conv = DeformableConv2d(in_channels, planes, kernel_size=kernel_size,
-                                            stride=1, padding=padding, bias=False, device=device, dtype=dtype, operations=operations)
-        self.bn = operations.BatchNorm2d(planes, device=device, dtype=dtype)
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        x = self.atrous_conv(x)
-        x = self.bn(x)
-
-        return self.relu(x)
-
-
-class ASPPDeformable(nn.Module):
-    def __init__(self, in_channels, out_channels=None, parallel_block_sizes=[1, 3, 7], device=None, dtype=None, operations=None):
-        super(ASPPDeformable, self).__init__()
-        self.down_scale = 1
-        if out_channels is None:
-            out_channels = in_channels
-        self.in_channelster = 256 // self.down_scale
-
-        self.aspp1 = _ASPPModuleDeformable(in_channels, self.in_channelster, 1, padding=0, device=device, dtype=dtype, operations=operations)
-        self.aspp_deforms = nn.ModuleList([
-            _ASPPModuleDeformable(in_channels, self.in_channelster, conv_size, padding=int(conv_size//2), device=device, dtype=dtype, operations=operations)
-              for conv_size in parallel_block_sizes
-        ])
-
-        self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
-                                             operations.Conv2d(in_channels, self.in_channelster, 1, stride=1, bias=False, device=device, dtype=dtype),
-                                             operations.BatchNorm2d(self.in_channelster, device=device, dtype=dtype),
-                                             nn.ReLU(inplace=True))
-        self.conv1 = operations.Conv2d(self.in_channelster * (2 + len(self.aspp_deforms)), out_channels, 1, bias=False, device=device, dtype=dtype)
-        self.bn1 = operations.BatchNorm2d(out_channels, device=device, dtype=dtype)
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        x1 = self.aspp1(x)
-        x_aspp_deforms = [aspp_deform(x) for aspp_deform in self.aspp_deforms]
-        x5 = self.global_avg_pool(x)
-        x5 = F.interpolate(x5, size=x1.size()[2:], mode='bilinear', align_corners=True)
-        x = torch.cat((x1, *x_aspp_deforms, x5), dim=1)
-
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.relu(x)
-
-        return x
-
-class BiRefNet(nn.Module):
-    def __init__(self, config=None, dtype=None, device=None, operations=None):
-        super(BiRefNet, self).__init__()
-        self.bb = SwinTransformer(embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12, device=device, dtype=dtype, operations=operations)
-
-        channels = [1536, 768, 384, 192]
-        channels = [c * 2 for c in channels]
-        self.cxt = channels[1:][::-1][-3:]
-        self.squeeze_module = nn.Sequential(*[
-            BasicDecBlk(channels[0]+sum(self.cxt), channels[0], device=device, dtype=dtype, operations=operations)
-            for _ in range(1)
-        ])
-
-        self.decoder = Decoder(channels, device=device, dtype=dtype, operations=operations)
-
-    def forward_enc(self, x):
-        x1, x2, x3, x4 = self.bb(x)
-        B, C, H, W = x.shape
-        x1_, x2_, x3_, x4_ = self.bb(F.interpolate(x, size=(H//2, W//2), mode='bilinear', align_corners=True))
-        x1 = torch.cat([x1, F.interpolate(x1_, size=x1.shape[2:], mode='bilinear', align_corners=True)], dim=1)
-        x2 = torch.cat([x2, F.interpolate(x2_, size=x2.shape[2:], mode='bilinear', align_corners=True)], dim=1)
-        x3 = torch.cat([x3, F.interpolate(x3_, size=x3.shape[2:], mode='bilinear', align_corners=True)], dim=1)
-        x4 = torch.cat([x4, F.interpolate(x4_, size=x4.shape[2:], mode='bilinear', align_corners=True)], dim=1)
-        x4 = torch.cat(
-            (
-                *[
-                    F.interpolate(x1, size=x4.shape[2:], mode='bilinear', align_corners=True),
-                    F.interpolate(x2, size=x4.shape[2:], mode='bilinear', align_corners=True),
-                    F.interpolate(x3, size=x4.shape[2:], mode='bilinear', align_corners=True),
-                ][-len(CXT):],
-                x4
-            ),
-            dim=1
-        )
-        return (x1, x2, x3, x4)
-
-    def forward_ori(self, x):
-        (x1, x2, x3, x4) = self.forward_enc(x)
-        x4 = self.squeeze_module(x4)
-        features = [x, x1, x2, x3, x4]
-        scaled_preds = self.decoder(features)
-        return scaled_preds
-
-    def forward(self, pixel_values, intermediate_output=None):
-        scaled_preds = self.forward_ori(pixel_values)
-        return scaled_preds
-
-
-class Decoder(nn.Module):
-    def __init__(self, channels, device, dtype, operations):
-        super(Decoder, self).__init__()
-        # factory kwargs
-        fk = {"device":device, "dtype":dtype, "operations":operations}
-        DecoderBlock = partial(BasicDecBlk, **fk)
-        LateralBlock = partial(BasicLatBlk, **fk)
-        DBlock = partial(SimpleConvs, **fk)
-
-        self.split = True
-        N_dec_ipt = 64
-        ic = 64
-        ipt_cha_opt = 1
-        self.ipt_blk5 = DBlock(2**10*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic)
-        self.ipt_blk4 = DBlock(2**8*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic)
-        self.ipt_blk3 = DBlock(2**6*3 if self.split else 3, [N_dec_ipt, channels[1]//8][ipt_cha_opt], inter_channels=ic)
-        self.ipt_blk2 = DBlock(2**4*3 if self.split else 3, [N_dec_ipt, channels[2]//8][ipt_cha_opt], inter_channels=ic)
-        self.ipt_blk1 = DBlock(2**0*3 if self.split else 3, [N_dec_ipt, channels[3]//8][ipt_cha_opt], inter_channels=ic)
-
-        self.decoder_block4 = DecoderBlock(channels[0]+([N_dec_ipt, channels[0]//8][ipt_cha_opt]), channels[1])
-        self.decoder_block3 = DecoderBlock(channels[1]+([N_dec_ipt, channels[0]//8][ipt_cha_opt]), channels[2])
-        self.decoder_block2 = DecoderBlock(channels[2]+([N_dec_ipt, channels[1]//8][ipt_cha_opt]), channels[3])
-        self.decoder_block1 = DecoderBlock(channels[3]+([N_dec_ipt, channels[2]//8][ipt_cha_opt]), channels[3]//2)
-
-        fk = {"device":device, "dtype":dtype}
-
-        self.conv_out1 = nn.Sequential(operations.Conv2d(channels[3]//2+([N_dec_ipt, channels[3]//8][ipt_cha_opt]), 1, 1, 1, 0, **fk))
-
-        self.lateral_block4 = LateralBlock(channels[1], channels[1])
-        self.lateral_block3 = LateralBlock(channels[2], channels[2])
-        self.lateral_block2 = LateralBlock(channels[3], channels[3])
-
-        self.conv_ms_spvn_4 = operations.Conv2d(channels[1], 1, 1, 1, 0, **fk)
-        self.conv_ms_spvn_3 = operations.Conv2d(channels[2], 1, 1, 1, 0, **fk)
-        self.conv_ms_spvn_2 = operations.Conv2d(channels[3], 1, 1, 1, 0, **fk)
-
-        _N = 16
-
-        self.gdt_convs_4 = nn.Sequential(operations.Conv2d(channels[0] // 2, _N, 3, 1, 1, **fk), operations.BatchNorm2d(_N, **fk), nn.ReLU(inplace=True))
-        self.gdt_convs_3 = nn.Sequential(operations.Conv2d(channels[1] // 2, _N, 3, 1, 1, **fk), operations.BatchNorm2d(_N, **fk), nn.ReLU(inplace=True))
-        self.gdt_convs_2 = nn.Sequential(operations.Conv2d(channels[2] // 2, _N, 3, 1, 1, **fk), operations.BatchNorm2d(_N, **fk), nn.ReLU(inplace=True))
-
-        [setattr(self, f"gdt_convs_pred_{i}", nn.Sequential(operations.Conv2d(_N, 1, 1, 1, 0, **fk))) for i in range(2, 5)]
-        [setattr(self, f"gdt_convs_attn_{i}", nn.Sequential(operations.Conv2d(_N, 1, 1, 1, 0, **fk))) for i in range(2, 5)]
-
-    def get_patches_batch(self, x, p):
-        _size_h, _size_w = p.shape[2:]
-        patches_batch = []
-        for idx in range(x.shape[0]):
-            columns_x = torch.split(x[idx], split_size_or_sections=_size_w, dim=-1)
-            patches_x = []
-            for column_x in columns_x:
-                patches_x += [p.unsqueeze(0) for p in torch.split(column_x, split_size_or_sections=_size_h, dim=-2)]
-            patch_sample = torch.cat(patches_x, dim=1)
-            patches_batch.append(patch_sample)
-        return torch.cat(patches_batch, dim=0)
-
-    def forward(self, features):
-        x, x1, x2, x3, x4 = features
-
-        patches_batch = self.get_patches_batch(x, x4) if self.split else x
-        x4 = torch.cat((x4, self.ipt_blk5(F.interpolate(patches_batch, size=x4.shape[2:], mode='bilinear', align_corners=True))), 1)
-        p4 = self.decoder_block4(x4)
-        p4_gdt = self.gdt_convs_4(p4)
-        gdt_attn_4 = self.gdt_convs_attn_4(p4_gdt).sigmoid()
-        p4 = p4 * gdt_attn_4
-        _p4 = F.interpolate(p4, size=x3.shape[2:], mode='bilinear', align_corners=True)
-        _p3 = _p4 + self.lateral_block4(x3)
-
-        patches_batch = self.get_patches_batch(x, _p3) if self.split else x
-        _p3 = torch.cat((_p3, self.ipt_blk4(F.interpolate(patches_batch, size=x3.shape[2:], mode='bilinear', align_corners=True))), 1)
-        p3 = self.decoder_block3(_p3)
-
-        p3_gdt = self.gdt_convs_3(p3)
-        gdt_attn_3 = self.gdt_convs_attn_3(p3_gdt).sigmoid()
-        p3 = p3 * gdt_attn_3
-        _p3 = F.interpolate(p3, size=x2.shape[2:], mode='bilinear', align_corners=True)
-        _p2 = _p3 + self.lateral_block3(x2)
-
-        patches_batch = self.get_patches_batch(x, _p2) if self.split else x
-        _p2 = torch.cat((_p2, self.ipt_blk3(F.interpolate(patches_batch, size=x2.shape[2:], mode='bilinear', align_corners=True))), 1)
-        p2 = self.decoder_block2(_p2)
-
-        p2_gdt = self.gdt_convs_2(p2)
-        gdt_attn_2 = self.gdt_convs_attn_2(p2_gdt).sigmoid()
-        p2 = p2 * gdt_attn_2
-
-        _p2 = F.interpolate(p2, size=x1.shape[2:], mode='bilinear', align_corners=True)
-        _p1 = _p2 + self.lateral_block2(x1)
-
-        patches_batch = self.get_patches_batch(x, _p1) if self.split else x
-        _p1 = torch.cat((_p1, self.ipt_blk2(F.interpolate(patches_batch, size=x1.shape[2:], mode='bilinear', align_corners=True))), 1)
-        _p1 = self.decoder_block1(_p1)
-        _p1 = F.interpolate(_p1, size=x.shape[2:], mode='bilinear', align_corners=True)
-
-        patches_batch = self.get_patches_batch(x, _p1) if self.split else x
-        _p1 = torch.cat((_p1, self.ipt_blk1(F.interpolate(patches_batch, size=x.shape[2:], mode='bilinear', align_corners=True))), 1)
-        p1_out = self.conv_out1(_p1)
-        return p1_out
-
-
-class SimpleConvs(nn.Module):
-    def __init__(
-        self, in_channels: int, out_channels: int, inter_channels=64, device=None, dtype=None, operations=None
-    ) -> None:
-        super().__init__()
-        self.conv1 = operations.Conv2d(in_channels, inter_channels, 3, 1, 1, device=device, dtype=dtype)
-        self.conv_out = operations.Conv2d(inter_channels, out_channels, 3, 1, 1, device=device, dtype=dtype)
-
-    def forward(self, x):
-        return self.conv_out(self.conv1(x))

comfy/bg_removal_model.py

@@ -1,78 +0,0 @@
-from .utils import load_torch_file
-import os
-import json
-import torch
-import logging
-
-import comfy.ops
-import comfy.model_patcher
-import comfy.model_management
-import comfy.clip_model
-import comfy.background_removal.birefnet
-
-BG_REMOVAL_MODELS = {
-    "birefnet": comfy.background_removal.birefnet.BiRefNet
-}
-
-class BackgroundRemovalModel():
-    def __init__(self, json_config):
-        with open(json_config) as f:
-            config = json.load(f)
-
-        self.image_size = config.get("image_size", 1024)
-        self.image_mean = config.get("image_mean", [0.0, 0.0, 0.0])
-        self.image_std = config.get("image_std", [1.0, 1.0, 1.0])
-        self.model_type = config.get("model_type", "birefnet")
-        self.config = config.copy()
-        model_class = BG_REMOVAL_MODELS.get(self.model_type)
-
-        self.load_device = comfy.model_management.text_encoder_device()
-        offload_device = comfy.model_management.text_encoder_offload_device()
-        self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)
-        self.model = model_class(config, self.dtype, offload_device, comfy.ops.manual_cast)
-        self.model.eval()
-
-        self.patcher = comfy.model_patcher.CoreModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
-
-    def load_sd(self, sd):
-        return self.model.load_state_dict(sd, strict=False, assign=self.patcher.is_dynamic())
-
-    def get_sd(self):
-        return self.model.state_dict()
-
-    def encode_image(self, image):
-        comfy.model_management.load_model_gpu(self.patcher)
-        H, W = image.shape[1], image.shape[2]
-        pixel_values = comfy.clip_model.clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=False)
-        out = self.model(pixel_values=pixel_values)
-        out = torch.nn.functional.interpolate(out, size=(H, W), mode="bicubic", antialias=False)
-
-        mask = out.sigmoid().to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
-        if mask.ndim == 3:
-            mask = mask.unsqueeze(0)
-        if mask.shape[1] != 1:
-            mask = mask.movedim(-1, 1)
-
-        return mask
-
-
-def load_background_removal_model(sd):
-    if "bb.layers.1.blocks.0.attn.relative_position_index" in sd:
-        json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "background_removal"), "birefnet.json")
-    else:
-        return None
-
-    bg_model = BackgroundRemovalModel(json_config)
-    m, u = bg_model.load_sd(sd)
-    if len(m) > 0:
-        logging.warning("missing background removal: {}".format(m))
-    u = set(u)
-    keys = list(sd.keys())
-    for k in keys:
-        if k not in u:
-            sd.pop(k)
-    return bg_model
-
-def load(ckpt_path):
-    sd = load_torch_file(ckpt_path)
-    return load_background_removal_model(sd)

comfy/cli_args.py

@@ -90,8 +90,8 @@ parser.add_argument("--force-channels-last", action="store_true", help="Force ch
 parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
 
 parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.")
+parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize default when loading models with Intel's Extension for Pytorch.")
 parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.")
-parser.add_argument("--enable-triton-backend", action="store_true", help="ComfyUI will enable the use of Triton backend in comfy-kitchen. Is disabled at launch by default.")
 
 class LatentPreviewMethod(enum.Enum):
     NoPreviews = "none"
@@ -238,8 +238,6 @@ database_default_path = os.path.abspath(
 )
 parser.add_argument("--database-url", type=str, default=f"sqlite:///{database_default_path}", help="Specify the database URL, e.g. for an in-memory database you can use 'sqlite:///:memory:'.")
 parser.add_argument("--enable-assets", action="store_true", help="Enable the assets system (API routes, database synchronization, and background scanning).")
-parser.add_argument("--feature-flag", type=str, action='append', default=[], metavar="KEY[=VALUE]", help="Set a server feature flag. Use KEY=VALUE to set an explicit value, or bare KEY to set it to true. Can be specified multiple times. Boolean values (true/false) and numbers are auto-converted. Examples: --feature-flag show_signin_button=true  or  --feature-flag show_signin_button")
-parser.add_argument("--list-feature-flags", action="store_true", help="Print the registry of known CLI-settable feature flags as JSON and exit.")
 
 if comfy.options.args_parsing:
     args = parser.parse_args()

comfy/context_windows.py

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

comfy/deploy_environment.py

@@ -1,34 +0,0 @@
-import functools
-import logging
-import os
-
-logger = logging.getLogger(__name__)
-
-_DEFAULT_DEPLOY_ENV = "local-git"
-_ENV_FILENAME = ".comfy_environment"
-
-# Resolve the ComfyUI install directory (the parent of this `comfy/` package).
-# We deliberately avoid `folder_paths.base_path` here because that is overridden
-# by the `--base-directory` CLI arg to a user-supplied path, whereas the
-# `.comfy_environment` marker is written by launchers/installers next to the
-# ComfyUI install itself.
-_COMFY_INSTALL_DIR = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
-
-
-@functools.cache
-def get_deploy_environment() -> str:
-    env_file = os.path.join(_COMFY_INSTALL_DIR, _ENV_FILENAME)
-    try:
-        with open(env_file, encoding="utf-8") as f:
-            # Cap the read so a malformed or maliciously crafted file (e.g.
-            # a single huge line with no newline) can't blow up memory.
-            first_line = f.readline(128).strip()
-            value = "".join(c for c in first_line if 32 <= ord(c) < 127)
-            if value:
-                return value
-    except FileNotFoundError:
-        pass
-    except Exception as e:
-        logger.error("Failed to read %s: %s", env_file, e)
-
-    return _DEFAULT_DEPLOY_ENV

comfy/hooks.py

@@ -93,7 +93,7 @@ class Hook:
         self.hook_scope = hook_scope
         '''Scope of where this hook should apply in terms of the conds used in sampling run.'''
         self.custom_should_register = default_should_register
-        '''Can be overridden with a compatible function to decide if this hook should be registered without the need to override .should_register'''
+        '''Can be overriden with a compatible function to decide if this hook should be registered without the need to override .should_register'''
 
     @property
     def strength(self):

comfy/k_diffusion/sampling.py

@@ -1810,119 +1810,3 @@ def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=F
 def sample_sa_solver_pece(model, x, sigmas, extra_args=None, callback=None, disable=False, tau_func=None, s_noise=1.0, noise_sampler=None, predictor_order=3, corrector_order=4, simple_order_2=False):
     """Stochastic Adams Solver with PECE (Predict–Evaluate–Correct–Evaluate) mode (NeurIPS 2023)."""
     return sample_sa_solver(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, tau_func=tau_func, s_noise=s_noise, noise_sampler=noise_sampler, predictor_order=predictor_order, corrector_order=corrector_order, use_pece=True, simple_order_2=simple_order_2)
-
-
-@torch.no_grad()
-def sample_ar_video(model, x, sigmas, extra_args=None, callback=None, disable=None,
-                    num_frame_per_block=1):
-    """
-    Autoregressive video sampler: block-by-block denoising with KV cache
-    and flow-match re-noising for Causal Forcing / Self-Forcing models.
-
-    Requires a Causal-WAN compatible model (diffusion_model must expose
-    init_kv_caches / init_crossattn_caches) and 5-D latents [B,C,T,H,W].
-
-    All AR-loop parameters are passed via the SamplerARVideo node, not read
-    from the checkpoint or transformer_options.
-    """
-    extra_args = {} if extra_args is None else extra_args
-    model_options = extra_args.get("model_options", {})
-    transformer_options = model_options.get("transformer_options", {})
-
-    if x.ndim != 5:
-        raise ValueError(
-            f"ar_video sampler requires 5-D video latents [B,C,T,H,W], got {x.ndim}-D tensor with shape {x.shape}. "
-            "This sampler is only compatible with autoregressive video models (e.g. Causal-WAN)."
-        )
-
-    inner_model = model.inner_model.inner_model
-    causal_model = inner_model.diffusion_model
-
-    if not (hasattr(causal_model, "init_kv_caches") and hasattr(causal_model, "init_crossattn_caches")):
-        raise TypeError(
-            "ar_video sampler requires a Causal-WAN compatible model whose diffusion_model "
-            "exposes init_kv_caches() and init_crossattn_caches(). The loaded checkpoint "
-            "does not support this interface — choose a different sampler."
-        )
-
-    seed = extra_args.get("seed", 0)
-
-    bs, c, lat_t, lat_h, lat_w = x.shape
-    frame_seq_len = -(-lat_h // 2) * -(-lat_w // 2) # ceiling division
-    num_blocks = -(-lat_t // num_frame_per_block)   # ceiling division
-    device = x.device
-    model_dtype = inner_model.get_dtype()
-
-    kv_caches = causal_model.init_kv_caches(bs, lat_t * frame_seq_len, device, model_dtype)
-    crossattn_caches = causal_model.init_crossattn_caches(bs, device, model_dtype)
-
-    output = torch.zeros_like(x)
-    s_in = x.new_ones([x.shape[0]])
-    current_start_frame = 0
-
-    # I2V: seed KV cache with the initial image latent before the denoising loop
-    initial_latent = transformer_options.get("ar_config", {}).get("initial_latent", None)
-    if initial_latent is not None:
-        initial_latent = inner_model.process_latent_in(initial_latent).to(device=device, dtype=model_dtype)
-        n_init = initial_latent.shape[2]
-        output[:, :, :n_init] = initial_latent
-
-        ar_state = {"start_frame": 0, "kv_caches": kv_caches, "crossattn_caches": crossattn_caches}
-        transformer_options["ar_state"] = ar_state
-        zero_sigma = sigmas.new_zeros([1])
-        _ = model(initial_latent, zero_sigma * s_in, **extra_args)
-
-        current_start_frame = n_init
-        remaining = lat_t - n_init
-        num_blocks = -(-remaining // num_frame_per_block)
-
-    num_sigma_steps = len(sigmas) - 1
-    total_real_steps = num_blocks * num_sigma_steps
-    step_count = 0
-
-    try:
-        for block_idx in trange(num_blocks, disable=disable):
-            bf = min(num_frame_per_block, lat_t - current_start_frame)
-            fs, fe = current_start_frame, current_start_frame + bf
-            noisy_input = x[:, :, fs:fe]
-
-            ar_state = {
-                "start_frame": current_start_frame,
-                "kv_caches": kv_caches,
-                "crossattn_caches": crossattn_caches,
-            }
-            transformer_options["ar_state"] = ar_state
-
-            for i in range(num_sigma_steps):
-                denoised = model(noisy_input, sigmas[i] * s_in, **extra_args)
-
-                if callback is not None:
-                    scaled_i = step_count * num_sigma_steps // total_real_steps
-                    callback({"x": noisy_input, "i": scaled_i, "sigma": sigmas[i],
-                              "sigma_hat": sigmas[i], "denoised": denoised})
-
-                if sigmas[i + 1] == 0:
-                    noisy_input = denoised
-                else:
-                    sigma_next = sigmas[i + 1]
-                    torch.manual_seed(seed + block_idx * 1000 + i)
-                    fresh_noise = torch.randn_like(denoised)
-                    noisy_input = (1.0 - sigma_next) * denoised + sigma_next * fresh_noise
-
-                    for cache in kv_caches:
-                        cache["end"] -= bf * frame_seq_len
-
-                step_count += 1
-
-            output[:, :, fs:fe] = noisy_input
-
-            for cache in kv_caches:
-                cache["end"] -= bf * frame_seq_len
-            zero_sigma = sigmas.new_zeros([1])
-            _ = model(noisy_input, zero_sigma * s_in, **extra_args)
-
-            current_start_frame += bf
-    finally:
-        transformer_options.pop("ar_state", None)
-
-    return output

comfy/latent_formats.py

@@ -9,7 +9,6 @@ class LatentFormat:
     latent_rgb_factors_reshape = None
     taesd_decoder_name = None
     spacial_downscale_ratio = 8
-    temporal_downscale_ratio = 1
 
     def process_in(self, latent):
         return latent * self.scale_factor
@@ -225,7 +224,6 @@ class Flux2(LatentFormat):
 
         self.latent_rgb_factors_bias = [-0.0329, -0.0718, -0.0851]
         self.latent_rgb_factors_reshape = lambda t: t.reshape(t.shape[0], 32, 2, 2, t.shape[-2], t.shape[-1]).permute(0, 1, 4, 2, 5, 3).reshape(t.shape[0], 32, t.shape[-2] * 2, t.shape[-1] * 2)
-        self.taesd_decoder_name = "taef2_decoder"
 
     def process_in(self, latent):
         return latent
@@ -236,7 +234,6 @@ class Flux2(LatentFormat):
 class Mochi(LatentFormat):
     latent_channels = 12
     latent_dimensions = 3
-    temporal_downscale_ratio = 6
 
     def __init__(self):
         self.scale_factor = 1.0
@@ -280,7 +277,6 @@ class LTXV(LatentFormat):
     latent_channels = 128
     latent_dimensions = 3
     spacial_downscale_ratio = 32
-    temporal_downscale_ratio = 8
 
     def __init__(self):
         self.latent_rgb_factors = [
@@ -424,7 +420,6 @@ class LTXAV(LTXV):
 class HunyuanVideo(LatentFormat):
     latent_channels = 16
     latent_dimensions = 3
-    temporal_downscale_ratio = 4
     scale_factor = 0.476986
     latent_rgb_factors = [
         [-0.0395, -0.0331,  0.0445],
@@ -451,7 +446,6 @@ class HunyuanVideo(LatentFormat):
 class Cosmos1CV8x8x8(LatentFormat):
     latent_channels = 16
     latent_dimensions = 3
-    temporal_downscale_ratio = 8
 
     latent_rgb_factors = [
         [ 0.1817,  0.2284,  0.2423],
@@ -477,7 +471,6 @@ class Cosmos1CV8x8x8(LatentFormat):
 class Wan21(LatentFormat):
     latent_channels = 16
     latent_dimensions = 3
-    temporal_downscale_ratio = 4
 
     latent_rgb_factors = [
             [-0.1299, -0.1692,  0.2932],
@@ -740,7 +733,6 @@ class HunyuanVideo15(LatentFormat):
     latent_channels = 32
     latent_dimensions = 3
     spacial_downscale_ratio = 16
-    temporal_downscale_ratio = 4
     scale_factor = 1.03682
     taesd_decoder_name = "lighttaehy1_5"
 
@@ -791,29 +783,3 @@ class ZImagePixelSpace(ChromaRadiance):
     No VAE encoding/decoding — the model operates directly on RGB pixels.
     """
     pass
-
-class CogVideoX(LatentFormat):
-    """Latent format for CogVideoX-2b (THUDM/CogVideoX-2b).
-
-    scale_factor matches the vae/config.json scaling_factor for the 2b variant.
-    The 5b-class checkpoints (CogVideoX-5b, CogVideoX-1.5-5B, CogVideoX-Fun-V1.5-*)
-    use a different value; see CogVideoX1_5 below.
-    """
-    latent_channels = 16
-    latent_dimensions = 3
-    temporal_downscale_ratio = 4
-
-    def __init__(self):
-        self.scale_factor = 1.15258426
-
-
-class CogVideoX1_5(CogVideoX):
-    """Latent format for 5b-class CogVideoX checkpoints.
-
-    Covers THUDM/CogVideoX-5b, THUDM/CogVideoX-1.5-5B, and the CogVideoX-Fun
-    V1.5-5b family (including VOID inpainting). All of these have
-    scaling_factor=0.7 in their vae/config.json. Auto-selected in
-    supported_models.CogVideoX_T2V based on transformer hidden dim.
-    """
-    def __init__(self):
-        self.scale_factor = 0.7

comfy/ldm/cogvideo/model.py

@@ -1,573 +0,0 @@
-# CogVideoX 3D Transformer - ported to ComfyUI native ops
-# Architecture reference: diffusers CogVideoXTransformer3DModel
-# Style reference: comfy/ldm/wan/model.py
-
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from comfy.ldm.modules.attention import optimized_attention
-import comfy.patcher_extension
-import comfy.ldm.common_dit
-
-
-def _get_1d_rotary_pos_embed(dim, pos, theta=10000.0):
-    """Returns (cos, sin) each with shape [seq_len, dim].
-
-    Frequencies are computed at dim//2 resolution then repeat_interleaved
-    to full dim, matching CogVideoX's interleaved (real, imag) pair format.
-    """
-    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float32, device=pos.device) / dim))
-    angles = torch.outer(pos.float(), freqs.float())
-    cos = angles.cos().repeat_interleave(2, dim=-1).float()
-    sin = angles.sin().repeat_interleave(2, dim=-1).float()
-    return (cos, sin)
-
-
-def apply_rotary_emb(x, freqs_cos_sin):
-    """Apply CogVideoX rotary embedding to query or key tensor.
-
-    x: [B, heads, seq_len, head_dim]
-    freqs_cos_sin: (cos, sin) each [seq_len, head_dim//2]
-
-    Uses interleaved pair rotation (same as diffusers CogVideoX/Flux).
-    head_dim is reshaped to (-1, 2) pairs, rotated, then flattened back.
-    """
-    cos, sin = freqs_cos_sin
-    cos = cos[None, None, :, :].to(x.device)
-    sin = sin[None, None, :, :].to(x.device)
-
-    # Interleaved pairs: [B, H, S, D] -> [B, H, S, D//2, 2] -> (real, imag)
-    x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1)
-    x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)
-
-    return (x.float() * cos + x_rotated.float() * sin).to(x.dtype)
-
-
-def get_timestep_embedding(timesteps, dim, flip_sin_to_cos=True, downscale_freq_shift=0, scale=1, max_period=10000):
-    half = dim // 2
-    freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device) / half)
-    args = timesteps[:, None].float() * freqs[None] * scale
-    embedding = torch.cat([torch.sin(args), torch.cos(args)], dim=-1)
-    if flip_sin_to_cos:
-        embedding = torch.cat([embedding[:, half:], embedding[:, :half]], dim=-1)
-    if dim % 2:
-        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-    return embedding
-
-
-def get_3d_sincos_pos_embed(embed_dim, spatial_size, temporal_size, spatial_interpolation_scale=1.0, temporal_interpolation_scale=1.0, device=None):
-    if isinstance(spatial_size, int):
-        spatial_size = (spatial_size, spatial_size)
-
-    grid_w = torch.arange(spatial_size[0], dtype=torch.float32, device=device) / spatial_interpolation_scale
-    grid_h = torch.arange(spatial_size[1], dtype=torch.float32, device=device) / spatial_interpolation_scale
-    grid_t = torch.arange(temporal_size, dtype=torch.float32, device=device) / temporal_interpolation_scale
-
-    grid_t, grid_h, grid_w = torch.meshgrid(grid_t, grid_h, grid_w, indexing="ij")
-
-    embed_dim_spatial = 2 * (embed_dim // 3)
-    embed_dim_temporal = embed_dim // 3
-
-    pos_embed_spatial = _get_2d_sincos_pos_embed(embed_dim_spatial, grid_h, grid_w, device=device)
-    pos_embed_temporal = _get_1d_sincos_pos_embed(embed_dim_temporal, grid_t[:, 0, 0], device=device)
-
-    T, H, W = grid_t.shape
-    pos_embed_temporal = pos_embed_temporal.unsqueeze(1).unsqueeze(1).expand(-1, H, W, -1)
-    pos_embed = torch.cat([pos_embed_temporal, pos_embed_spatial], dim=-1)
-
-    return pos_embed
-
-
-def _get_2d_sincos_pos_embed(embed_dim, grid_h, grid_w, device=None):
-    T, H, W = grid_h.shape
-    half_dim = embed_dim // 2
-    pos_h = _get_1d_sincos_pos_embed(half_dim, grid_h.reshape(-1), device=device).reshape(T, H, W, half_dim)
-    pos_w = _get_1d_sincos_pos_embed(half_dim, grid_w.reshape(-1), device=device).reshape(T, H, W, half_dim)
-    return torch.cat([pos_h, pos_w], dim=-1)
-
-
-def _get_1d_sincos_pos_embed(embed_dim, pos, device=None):
-    half = embed_dim // 2
-    freqs = torch.exp(-math.log(10000.0) * torch.arange(start=0, end=half, dtype=torch.float32, device=device) / half)
-    args = pos.float().reshape(-1)[:, None] * freqs[None]
-    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-    if embed_dim % 2:
-        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-    return embedding
-
-
-
-class CogVideoXPatchEmbed(nn.Module):
-    def __init__(self, patch_size=2, patch_size_t=None, in_channels=16, dim=1920,
-                 text_dim=4096, bias=True, sample_width=90, sample_height=60,
-                 sample_frames=49, temporal_compression_ratio=4,
-                 max_text_seq_length=226, spatial_interpolation_scale=1.875,
-                 temporal_interpolation_scale=1.0, use_positional_embeddings=True,
-                 use_learned_positional_embeddings=True,
-                 device=None, dtype=None, operations=None):
-        super().__init__()
-        self.patch_size = patch_size
-        self.patch_size_t = patch_size_t
-        self.dim = dim
-        self.sample_height = sample_height
-        self.sample_width = sample_width
-        self.sample_frames = sample_frames
-        self.temporal_compression_ratio = temporal_compression_ratio
-        self.max_text_seq_length = max_text_seq_length
-        self.spatial_interpolation_scale = spatial_interpolation_scale
-        self.temporal_interpolation_scale = temporal_interpolation_scale
-        self.use_positional_embeddings = use_positional_embeddings
-        self.use_learned_positional_embeddings = use_learned_positional_embeddings
-
-        if patch_size_t is None:
-            self.proj = operations.Conv2d(in_channels, dim, kernel_size=patch_size, stride=patch_size, bias=bias, device=device, dtype=dtype)
-        else:
-            self.proj = operations.Linear(in_channels * patch_size * patch_size * patch_size_t, dim, device=device, dtype=dtype)
-
-        self.text_proj = operations.Linear(text_dim, dim, device=device, dtype=dtype)
-
-        if use_positional_embeddings or use_learned_positional_embeddings:
-            persistent = use_learned_positional_embeddings
-            pos_embedding = self._get_positional_embeddings(sample_height, sample_width, sample_frames)
-            self.register_buffer("pos_embedding", pos_embedding, persistent=persistent)
-
-    def _get_positional_embeddings(self, sample_height, sample_width, sample_frames, device=None):
-        post_patch_height = sample_height // self.patch_size
-        post_patch_width = sample_width // self.patch_size
-        post_time_compression_frames = (sample_frames - 1) // self.temporal_compression_ratio + 1
-        if self.patch_size_t is not None:
-            post_time_compression_frames = post_time_compression_frames // self.patch_size_t
-        num_patches = post_patch_height * post_patch_width * post_time_compression_frames
-
-        pos_embedding = get_3d_sincos_pos_embed(
-            self.dim,
-            (post_patch_width, post_patch_height),
-            post_time_compression_frames,
-            self.spatial_interpolation_scale,
-            self.temporal_interpolation_scale,
-            device=device,
-        )
-        pos_embedding = pos_embedding.reshape(-1, self.dim)
-        joint_pos_embedding = pos_embedding.new_zeros(
-            1, self.max_text_seq_length + num_patches, self.dim, requires_grad=False
-        )
-        joint_pos_embedding.data[:, self.max_text_seq_length:].copy_(pos_embedding)
-        return joint_pos_embedding
-
-    def forward(self, text_embeds, image_embeds):
-        input_dtype = text_embeds.dtype
-        text_embeds = self.text_proj(text_embeds.to(self.text_proj.weight.dtype)).to(input_dtype)
-        batch_size, num_frames, channels, height, width = image_embeds.shape
-
-        proj_dtype = self.proj.weight.dtype
-        if self.patch_size_t is None:
-            image_embeds = image_embeds.reshape(-1, channels, height, width)
-            image_embeds = self.proj(image_embeds.to(proj_dtype)).to(input_dtype)
-            image_embeds = image_embeds.view(batch_size, num_frames, *image_embeds.shape[1:])
-            image_embeds = image_embeds.flatten(3).transpose(2, 3)
-            image_embeds = image_embeds.flatten(1, 2)
-        else:
-            p = self.patch_size
-            p_t = self.patch_size_t
-            image_embeds = image_embeds.permute(0, 1, 3, 4, 2)
-            image_embeds = image_embeds.reshape(
-                batch_size, num_frames // p_t, p_t, height // p, p, width // p, p, channels
-            )
-            image_embeds = image_embeds.permute(0, 1, 3, 5, 7, 2, 4, 6).flatten(4, 7).flatten(1, 3)
-            image_embeds = self.proj(image_embeds.to(proj_dtype)).to(input_dtype)
-
-        embeds = torch.cat([text_embeds, image_embeds], dim=1).contiguous()
-
-        if self.use_positional_embeddings or self.use_learned_positional_embeddings:
-            text_seq_length = text_embeds.shape[1]
-            num_image_patches = image_embeds.shape[1]
-
-            if self.use_learned_positional_embeddings:
-                image_pos = self.pos_embedding[
-                    :, self.max_text_seq_length:self.max_text_seq_length + num_image_patches
-                ].to(device=embeds.device, dtype=embeds.dtype)
-            else:
-                image_pos = get_3d_sincos_pos_embed(
-                    self.dim,
-                    (width // self.patch_size, height // self.patch_size),
-                    num_image_patches // ((height // self.patch_size) * (width // self.patch_size)),
-                    self.spatial_interpolation_scale,
-                    self.temporal_interpolation_scale,
-                    device=embeds.device,
-                ).reshape(1, num_image_patches, self.dim).to(dtype=embeds.dtype)
-
-            # Build joint: zeros for text + sincos for image
-            joint_pos = torch.zeros(1, text_seq_length + num_image_patches, self.dim, device=embeds.device, dtype=embeds.dtype)
-            joint_pos[:, text_seq_length:] = image_pos
-            embeds = embeds + joint_pos
-
-        return embeds
-
-
-class CogVideoXLayerNormZero(nn.Module):
-    def __init__(self, time_dim, dim, elementwise_affine=True, eps=1e-5, bias=True,
-                 device=None, dtype=None, operations=None):
-        super().__init__()
-        self.silu = nn.SiLU()
-        self.linear = operations.Linear(time_dim, 6 * dim, bias=bias, device=device, dtype=dtype)
-        self.norm = operations.LayerNorm(dim, eps=eps, elementwise_affine=elementwise_affine, device=device, dtype=dtype)
-
-    def forward(self, hidden_states, encoder_hidden_states, temb):
-        shift, scale, gate, enc_shift, enc_scale, enc_gate = self.linear(self.silu(temb)).chunk(6, dim=1)
-        hidden_states = self.norm(hidden_states) * (1 + scale)[:, None, :] + shift[:, None, :]
-        encoder_hidden_states = self.norm(encoder_hidden_states) * (1 + enc_scale)[:, None, :] + enc_shift[:, None, :]
-        return hidden_states, encoder_hidden_states, gate[:, None, :], enc_gate[:, None, :]
-
-
-class CogVideoXAdaLayerNorm(nn.Module):
-    def __init__(self, time_dim, dim, elementwise_affine=True, eps=1e-5,
-                 device=None, dtype=None, operations=None):
-        super().__init__()
-        self.silu = nn.SiLU()
-        self.linear = operations.Linear(time_dim, 2 * dim, device=device, dtype=dtype)
-        self.norm = operations.LayerNorm(dim, eps=eps, elementwise_affine=elementwise_affine, device=device, dtype=dtype)
-
-    def forward(self, x, temb):
-        temb = self.linear(self.silu(temb))
-        shift, scale = temb.chunk(2, dim=1)
-        x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
-        return x
-
-
-class CogVideoXBlock(nn.Module):
-    def __init__(self, dim, num_heads, head_dim, time_dim,
-                 eps=1e-5, ff_inner_dim=None, ff_bias=True,
-                 device=None, dtype=None, operations=None):
-        super().__init__()
-        self.dim = dim
-        self.num_heads = num_heads
-        self.head_dim = head_dim
-
-        self.norm1 = CogVideoXLayerNormZero(time_dim, dim, eps=eps, device=device, dtype=dtype, operations=operations)
-
-        # Self-attention (joint text + latent)
-        self.q = operations.Linear(dim, dim, bias=True, device=device, dtype=dtype)
-        self.k = operations.Linear(dim, dim, bias=True, device=device, dtype=dtype)
-        self.v = operations.Linear(dim, dim, bias=True, device=device, dtype=dtype)
-        self.norm_q = operations.LayerNorm(head_dim, eps=1e-6, elementwise_affine=True, device=device, dtype=dtype)
-        self.norm_k = operations.LayerNorm(head_dim, eps=1e-6, elementwise_affine=True, device=device, dtype=dtype)
-        self.attn_out = operations.Linear(dim, dim, bias=True, device=device, dtype=dtype)
-
-        self.norm2 = CogVideoXLayerNormZero(time_dim, dim, eps=eps, device=device, dtype=dtype, operations=operations)
-
-        # Feed-forward (GELU approximate)
-        inner_dim = ff_inner_dim or dim * 4
-        self.ff_proj = operations.Linear(dim, inner_dim, bias=ff_bias, device=device, dtype=dtype)
-        self.ff_out = operations.Linear(inner_dim, dim, bias=ff_bias, device=device, dtype=dtype)
-
-    def forward(self, hidden_states, encoder_hidden_states, temb, image_rotary_emb=None, transformer_options=None):
-        if transformer_options is None:
-            transformer_options = {}
-        text_seq_length = encoder_hidden_states.size(1)
-
-        # Norm & modulate
-        norm_hidden, norm_encoder, gate_msa, enc_gate_msa = self.norm1(hidden_states, encoder_hidden_states, temb)
-
-        # Joint self-attention
-        qkv_input = torch.cat([norm_encoder, norm_hidden], dim=1)
-        b, s, _ = qkv_input.shape
-        n, d = self.num_heads, self.head_dim
-
-        q = self.q(qkv_input).view(b, s, n, d)
-        k = self.k(qkv_input).view(b, s, n, d)
-        v = self.v(qkv_input)
-
-        q = self.norm_q(q).view(b, s, n, d)
-        k = self.norm_k(k).view(b, s, n, d)
-
-        # Apply rotary embeddings to image tokens only (diffusers format: [B, heads, seq, head_dim])
-        if image_rotary_emb is not None:
-            q_img = q[:, text_seq_length:].transpose(1, 2)  # [B, heads, img_seq, head_dim]
-            k_img = k[:, text_seq_length:].transpose(1, 2)
-            q_img = apply_rotary_emb(q_img, image_rotary_emb)
-            k_img = apply_rotary_emb(k_img, image_rotary_emb)
-            q = torch.cat([q[:, :text_seq_length], q_img.transpose(1, 2)], dim=1)
-            k = torch.cat([k[:, :text_seq_length], k_img.transpose(1, 2)], dim=1)
-
-        attn_out = optimized_attention(
-            q.reshape(b, s, n * d),
-            k.reshape(b, s, n * d),
-            v,
-            heads=self.num_heads,
-            transformer_options=transformer_options,
-        )
-
-        attn_out = self.attn_out(attn_out)
-
-        attn_encoder, attn_hidden = attn_out.split([text_seq_length, s - text_seq_length], dim=1)
-
-        hidden_states = hidden_states + gate_msa * attn_hidden
-        encoder_hidden_states = encoder_hidden_states + enc_gate_msa * attn_encoder
-
-        # Norm & modulate for FF
-        norm_hidden, norm_encoder, gate_ff, enc_gate_ff = self.norm2(hidden_states, encoder_hidden_states, temb)
-
-        # Feed-forward (GELU on concatenated text + latent)
-        ff_input = torch.cat([norm_encoder, norm_hidden], dim=1)
-        ff_output = self.ff_out(F.gelu(self.ff_proj(ff_input), approximate="tanh"))
-
-        hidden_states = hidden_states + gate_ff * ff_output[:, text_seq_length:]
-        encoder_hidden_states = encoder_hidden_states + enc_gate_ff * ff_output[:, :text_seq_length]
-
-        return hidden_states, encoder_hidden_states
-
-
-class CogVideoXTransformer3DModel(nn.Module):
-    def __init__(self,
-                 num_attention_heads=30,
-                 attention_head_dim=64,
-                 in_channels=16,
-                 out_channels=16,
-                 flip_sin_to_cos=True,
-                 freq_shift=0,
-                 time_embed_dim=512,
-                 ofs_embed_dim=None,
-                 text_embed_dim=4096,
-                 num_layers=30,
-                 dropout=0.0,
-                 attention_bias=True,
-                 sample_width=90,
-                 sample_height=60,
-                 sample_frames=49,
-                 patch_size=2,
-                 patch_size_t=None,
-                 temporal_compression_ratio=4,
-                 max_text_seq_length=226,
-                 spatial_interpolation_scale=1.875,
-                 temporal_interpolation_scale=1.0,
-                 use_rotary_positional_embeddings=False,
-                 use_learned_positional_embeddings=False,
-                 patch_bias=True,
-                 image_model=None,
-                 device=None,
-                 dtype=None,
-                 operations=None,
-                 ):
-        super().__init__()
-        self.dtype = dtype
-        dim = num_attention_heads * attention_head_dim
-        self.dim = dim
-        self.num_attention_heads = num_attention_heads
-        self.attention_head_dim = attention_head_dim
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.patch_size = patch_size
-        self.patch_size_t = patch_size_t
-        self.max_text_seq_length = max_text_seq_length
-        self.use_rotary_positional_embeddings = use_rotary_positional_embeddings
-
-        # 1. Patch embedding
-        self.patch_embed = CogVideoXPatchEmbed(
-            patch_size=patch_size,
-            patch_size_t=patch_size_t,
-            in_channels=in_channels,
-            dim=dim,
-            text_dim=text_embed_dim,
-            bias=patch_bias,
-            sample_width=sample_width,
-            sample_height=sample_height,
-            sample_frames=sample_frames,
-            temporal_compression_ratio=temporal_compression_ratio,
-            max_text_seq_length=max_text_seq_length,
-            spatial_interpolation_scale=spatial_interpolation_scale,
-            temporal_interpolation_scale=temporal_interpolation_scale,
-            use_positional_embeddings=not use_rotary_positional_embeddings,
-            use_learned_positional_embeddings=use_learned_positional_embeddings,
-            device=device, dtype=torch.float32, operations=operations,
-        )
-
-        # 2. Time embedding
-        self.time_proj_dim = dim
-        self.time_proj_flip = flip_sin_to_cos
-        self.time_proj_shift = freq_shift
-        self.time_embedding_linear_1 = operations.Linear(dim, time_embed_dim, device=device, dtype=dtype)
-        self.time_embedding_act = nn.SiLU()
-        self.time_embedding_linear_2 = operations.Linear(time_embed_dim, time_embed_dim, device=device, dtype=dtype)
-
-        # Optional OFS embedding (CogVideoX 1.5 I2V)
-        self.ofs_proj_dim = ofs_embed_dim
-        if ofs_embed_dim:
-            self.ofs_embedding_linear_1 = operations.Linear(ofs_embed_dim, ofs_embed_dim, device=device, dtype=dtype)
-            self.ofs_embedding_act = nn.SiLU()
-            self.ofs_embedding_linear_2 = operations.Linear(ofs_embed_dim, ofs_embed_dim, device=device, dtype=dtype)
-        else:
-            self.ofs_embedding_linear_1 = None
-
-        # 3. Transformer blocks
-        self.blocks = nn.ModuleList([
-            CogVideoXBlock(
-                dim=dim,
-                num_heads=num_attention_heads,
-                head_dim=attention_head_dim,
-                time_dim=time_embed_dim,
-                eps=1e-5,
-                device=device, dtype=dtype, operations=operations,
-            )
-            for _ in range(num_layers)
-        ])
-
-        self.norm_final = operations.LayerNorm(dim, eps=1e-5, elementwise_affine=True, device=device, dtype=dtype)
-
-        # 4. Output
-        self.norm_out = CogVideoXAdaLayerNorm(
-            time_dim=time_embed_dim, dim=dim, eps=1e-5,
-            device=device, dtype=dtype, operations=operations,
-        )
-
-        if patch_size_t is None:
-            output_dim = patch_size * patch_size * out_channels
-        else:
-            output_dim = patch_size * patch_size * patch_size_t * out_channels
-
-        self.proj_out = operations.Linear(dim, output_dim, device=device, dtype=dtype)
-
-        self.spatial_interpolation_scale = spatial_interpolation_scale
-        self.temporal_interpolation_scale = temporal_interpolation_scale
-        self.temporal_compression_ratio = temporal_compression_ratio
-
-    def forward(self, x, timestep, context, ofs=None, transformer_options=None, **kwargs):
-        if transformer_options is None:
-            transformer_options = {}
-        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
-            self._forward,
-            self,
-            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
-        ).execute(x, timestep, context, ofs, transformer_options, **kwargs)
-
-    def _forward(self, x, timestep, context, ofs=None, transformer_options=None, **kwargs):
-        if transformer_options is None:
-            transformer_options = {}
-        # ComfyUI passes [B, C, T, H, W]
-        batch_size, channels, t, h, w = x.shape
-
-        # Pad to patch size (temporal + spatial), same pattern as WAN
-        p_t = self.patch_size_t if self.patch_size_t is not None else 1
-        x = comfy.ldm.common_dit.pad_to_patch_size(x, (p_t, self.patch_size, self.patch_size))
-
-        # CogVideoX expects [B, T, C, H, W]
-        x = x.permute(0, 2, 1, 3, 4)
-        batch_size, num_frames, channels, height, width = x.shape
-
-        # Time embedding
-        t_emb = get_timestep_embedding(timestep, self.time_proj_dim, self.time_proj_flip, self.time_proj_shift)
-        t_emb = t_emb.to(dtype=x.dtype)
-        emb = self.time_embedding_linear_2(self.time_embedding_act(self.time_embedding_linear_1(t_emb)))
-
-        if self.ofs_embedding_linear_1 is not None and ofs is not None:
-            ofs_emb = get_timestep_embedding(ofs, self.ofs_proj_dim, self.time_proj_flip, self.time_proj_shift)
-            ofs_emb = ofs_emb.to(dtype=x.dtype)
-            ofs_emb = self.ofs_embedding_linear_2(self.ofs_embedding_act(self.ofs_embedding_linear_1(ofs_emb)))
-            emb = emb + ofs_emb
-
-        # Patch embedding
-        hidden_states = self.patch_embed(context, x)
-
-        text_seq_length = context.shape[1]
-        encoder_hidden_states = hidden_states[:, :text_seq_length]
-        hidden_states = hidden_states[:, text_seq_length:]
-
-        # Rotary embeddings (if used)
-        image_rotary_emb = None
-        if self.use_rotary_positional_embeddings:
-            post_patch_height = height // self.patch_size
-            post_patch_width = width // self.patch_size
-            if self.patch_size_t is None:
-                post_time = num_frames
-            else:
-                post_time = num_frames // self.patch_size_t
-            image_rotary_emb = self._get_rotary_emb(post_patch_height, post_patch_width, post_time, device=x.device)
-
-        # Transformer blocks
-        for i, block in enumerate(self.blocks):
-            hidden_states, encoder_hidden_states = block(
-                hidden_states=hidden_states,
-                encoder_hidden_states=encoder_hidden_states,
-                temb=emb,
-                image_rotary_emb=image_rotary_emb,
-                transformer_options=transformer_options,
-            )
-
-        hidden_states = self.norm_final(hidden_states)
-
-        # Output projection
-        hidden_states = self.norm_out(hidden_states, temb=emb)
-        hidden_states = self.proj_out(hidden_states)
-
-        # Unpatchify
-        p = self.patch_size
-        p_t = self.patch_size_t
-
-        if p_t is None:
-            output = hidden_states.reshape(batch_size, num_frames, height // p, width // p, -1, p, p)
-            output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)
-        else:
-            output = hidden_states.reshape(
-                batch_size, (num_frames + p_t - 1) // p_t, height // p, width // p, -1, p_t, p, p
-            )
-            output = output.permute(0, 1, 5, 4, 2, 6, 3, 7).flatten(6, 7).flatten(4, 5).flatten(1, 2)
-
-        # Back to ComfyUI format [B, C, T, H, W] and crop padding
-        output = output.permute(0, 2, 1, 3, 4)[:, :, :t, :h, :w]
-        return output
-
-    def _get_rotary_emb(self, h, w, t, device):
-        """Compute CogVideoX 3D rotary positional embeddings.
-
-        For CogVideoX 1.5 (patch_size_t != None): uses "slice" mode — grid positions
-        are integer arange computed at max_size, then sliced to actual size.
-        For CogVideoX 1.0 (patch_size_t == None): uses "linspace" mode with crop coords
-        scaled by spatial_interpolation_scale.
-        """
-        d = self.attention_head_dim
-        dim_t = d // 4
-        dim_h = d // 8 * 3
-        dim_w = d // 8 * 3
-
-        if self.patch_size_t is not None:
-            # CogVideoX 1.5: "slice" mode — positions are simple integer indices
-            # Compute at max(sample_size, actual_size) then slice to actual
-            base_h = self.patch_embed.sample_height // self.patch_size
-            base_w = self.patch_embed.sample_width // self.patch_size
-            max_h = max(base_h, h)
-            max_w = max(base_w, w)
-
-            grid_h = torch.arange(max_h, device=device, dtype=torch.float32)
-            grid_w = torch.arange(max_w, device=device, dtype=torch.float32)
-            grid_t = torch.arange(t, device=device, dtype=torch.float32)
-        else:
-            # CogVideoX 1.0: "linspace" mode with interpolation scale
-            grid_h = torch.linspace(0, h - 1, h, device=device, dtype=torch.float32) * self.spatial_interpolation_scale
-            grid_w = torch.linspace(0, w - 1, w, device=device, dtype=torch.float32) * self.spatial_interpolation_scale
-            grid_t = torch.arange(t, device=device, dtype=torch.float32)
-
-        freqs_t = _get_1d_rotary_pos_embed(dim_t, grid_t)
-        freqs_h = _get_1d_rotary_pos_embed(dim_h, grid_h)
-        freqs_w = _get_1d_rotary_pos_embed(dim_w, grid_w)
-
-        t_cos, t_sin = freqs_t
-        h_cos, h_sin = freqs_h
-        w_cos, w_sin = freqs_w
-
-        # Slice to actual size (for "slice" mode where grids may be larger)
-        t_cos, t_sin = t_cos[:t], t_sin[:t]
-        h_cos, h_sin = h_cos[:h], h_sin[:h]
-        w_cos, w_sin = w_cos[:w], w_sin[:w]
-
-        # Broadcast and concatenate into [T*H*W, head_dim]
-        t_cos = t_cos[:, None, None, :].expand(-1, h, w, -1)
-        t_sin = t_sin[:, None, None, :].expand(-1, h, w, -1)
-        h_cos = h_cos[None, :, None, :].expand(t, -1, w, -1)
-        h_sin = h_sin[None, :, None, :].expand(t, -1, w, -1)
-        w_cos = w_cos[None, None, :, :].expand(t, h, -1, -1)
-        w_sin = w_sin[None, None, :, :].expand(t, h, -1, -1)
-
-        cos = torch.cat([t_cos, h_cos, w_cos], dim=-1).reshape(t * h * w, -1)
-        sin = torch.cat([t_sin, h_sin, w_sin], dim=-1).reshape(t * h * w, -1)
-        return (cos, sin)

comfy/ldm/cogvideo/vae.py

@@ -1,566 +0,0 @@
-# CogVideoX VAE - ported to ComfyUI native ops
-# Architecture reference: diffusers AutoencoderKLCogVideoX
-# Style reference: comfy/ldm/wan/vae.py
-
-import numpy as np
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-import comfy.ops
-ops = comfy.ops.disable_weight_init
-
-
-class CausalConv3d(nn.Module):
-    """Causal 3D convolution with temporal padding.
-
-    Uses comfy.ops.Conv3d with autopad='causal_zero' fast path: when input has
-    a single temporal frame and no cache, the 3D conv weight is sliced to act
-    as a 2D conv, avoiding computation on zero-padded temporal dimensions.
-    """
-    def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, pad_mode="constant"):
-        super().__init__()
-        if isinstance(kernel_size, int):
-            kernel_size = (kernel_size,) * 3
-
-        time_kernel, height_kernel, width_kernel = kernel_size
-        self.time_kernel_size = time_kernel
-        self.pad_mode = pad_mode
-
-        height_pad = (height_kernel - 1) // 2
-        width_pad = (width_kernel - 1) // 2
-        self.time_causal_padding = (width_pad, width_pad, height_pad, height_pad, time_kernel - 1, 0)
-
-        stride = stride if isinstance(stride, tuple) else (stride, 1, 1)
-        dilation = (dilation, 1, 1)
-        self.conv = ops.Conv3d(
-            in_channels, out_channels, kernel_size,
-            stride=stride, dilation=dilation,
-            padding=(0, height_pad, width_pad),
-        )
-
-    def forward(self, x, conv_cache=None):
-        if self.pad_mode == "replicate":
-            x = F.pad(x, self.time_causal_padding, mode="replicate")
-            conv_cache = None
-        else:
-            kernel_t = self.time_kernel_size
-            if kernel_t > 1:
-                if conv_cache is None and x.shape[2] == 1:
-                    # Fast path: single frame, no cache. All temporal padding
-                    # frames are copies of the input (replicate-style), so the
-                    # 3D conv reduces to a 2D conv with summed temporal kernel.
-                    w = comfy.ops.cast_to_input(self.conv.weight, x)
-                    b = comfy.ops.cast_to_input(self.conv.bias, x) if self.conv.bias is not None else None
-                    w2d = w.sum(dim=2, keepdim=True)
-                    out = F.conv3d(x, w2d, b,
-                                   self.conv.stride, self.conv.padding,
-                                   self.conv.dilation, self.conv.groups)
-                    return out, None
-                cached = [conv_cache] if conv_cache is not None else [x[:, :, :1]] * (kernel_t - 1)
-                x = torch.cat(cached + [x], dim=2)
-            conv_cache = x[:, :, -self.time_kernel_size + 1:].clone() if self.time_kernel_size > 1 else None
-
-        out = self.conv(x)
-        return out, conv_cache
-
-
-def _interpolate_zq(zq, target_size):
-    """Interpolate latent z to target (T, H, W), matching CogVideoX's first-frame-special handling."""
-    t = target_size[0]
-    if t > 1 and t % 2 == 1:
-        z_first = F.interpolate(zq[:, :, :1], size=(1, target_size[1], target_size[2]))
-        z_rest = F.interpolate(zq[:, :, 1:], size=(t - 1, target_size[1], target_size[2]))
-        return torch.cat([z_first, z_rest], dim=2)
-    return F.interpolate(zq, size=target_size)
-
-
-class SpatialNorm3D(nn.Module):
-    """Spatially conditioned normalization."""
-    def __init__(self, f_channels, zq_channels, groups=32):
-        super().__init__()
-        self.norm_layer = ops.GroupNorm(num_channels=f_channels, num_groups=groups, eps=1e-6, affine=True)
-        self.conv_y = CausalConv3d(zq_channels, f_channels, kernel_size=1, stride=1)
-        self.conv_b = CausalConv3d(zq_channels, f_channels, kernel_size=1, stride=1)
-
-    def forward(self, f, zq, conv_cache=None):
-        new_cache = {}
-        conv_cache = conv_cache or {}
-
-        if zq.shape[-3:] != f.shape[-3:]:
-            zq = _interpolate_zq(zq, f.shape[-3:])
-
-        conv_y, new_cache["conv_y"] = self.conv_y(zq, conv_cache=conv_cache.get("conv_y"))
-        conv_b, new_cache["conv_b"] = self.conv_b(zq, conv_cache=conv_cache.get("conv_b"))
-
-        return self.norm_layer(f) * conv_y + conv_b, new_cache
-
-
-class ResnetBlock3D(nn.Module):
-    """3D ResNet block with optional spatial norm."""
-    def __init__(self, in_channels, out_channels=None, temb_channels=512, groups=32,
-                 eps=1e-6, act_fn="silu", spatial_norm_dim=None, pad_mode="first"):
-        super().__init__()
-        out_channels = out_channels or in_channels
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.spatial_norm_dim = spatial_norm_dim
-
-        if act_fn == "silu":
-            self.nonlinearity = nn.SiLU()
-        elif act_fn == "swish":
-            self.nonlinearity = nn.SiLU()
-        else:
-            self.nonlinearity = nn.SiLU()
-
-        if spatial_norm_dim is None:
-            self.norm1 = ops.GroupNorm(num_channels=in_channels, num_groups=groups, eps=eps)
-            self.norm2 = ops.GroupNorm(num_channels=out_channels, num_groups=groups, eps=eps)
-        else:
-            self.norm1 = SpatialNorm3D(in_channels, spatial_norm_dim, groups=groups)
-            self.norm2 = SpatialNorm3D(out_channels, spatial_norm_dim, groups=groups)
-
-        self.conv1 = CausalConv3d(in_channels, out_channels, kernel_size=3, pad_mode=pad_mode)
-
-        if temb_channels > 0:
-            self.temb_proj = ops.Linear(temb_channels, out_channels)
-
-        self.conv2 = CausalConv3d(out_channels, out_channels, kernel_size=3, pad_mode=pad_mode)
-
-        if in_channels != out_channels:
-            self.conv_shortcut = ops.Conv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
-        else:
-            self.conv_shortcut = None
-
-    def forward(self, x, temb=None, zq=None, conv_cache=None):
-        new_cache = {}
-        conv_cache = conv_cache or {}
-        residual = x
-
-        if zq is not None:
-            x, new_cache["norm1"] = self.norm1(x, zq, conv_cache=conv_cache.get("norm1"))
-        else:
-            x = self.norm1(x)
-
-        x = self.nonlinearity(x)
-        x, new_cache["conv1"] = self.conv1(x, conv_cache=conv_cache.get("conv1"))
-
-        if temb is not None and hasattr(self, "temb_proj"):
-            x = x + self.temb_proj(self.nonlinearity(temb))[:, :, None, None, None]
-
-        if zq is not None:
-            x, new_cache["norm2"] = self.norm2(x, zq, conv_cache=conv_cache.get("norm2"))
-        else:
-            x = self.norm2(x)
-
-        x = self.nonlinearity(x)
-        x, new_cache["conv2"] = self.conv2(x, conv_cache=conv_cache.get("conv2"))
-
-        if self.conv_shortcut is not None:
-            residual = self.conv_shortcut(residual)
-
-        return x + residual, new_cache
-
-
-class Downsample3D(nn.Module):
-    """3D downsampling with optional temporal compression."""
-    def __init__(self, in_channels, out_channels, kernel_size=3, stride=2, padding=0, compress_time=False):
-        super().__init__()
-        self.conv = ops.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
-        self.compress_time = compress_time
-
-    def forward(self, x):
-        if self.compress_time:
-            b, c, t, h, w = x.shape
-            x = x.permute(0, 3, 4, 1, 2).reshape(b * h * w, c, t)
-            if t % 2 == 1:
-                x_first, x_rest = x[..., 0], x[..., 1:]
-                if x_rest.shape[-1] > 0:
-                    x_rest = F.avg_pool1d(x_rest, kernel_size=2, stride=2)
-                x = torch.cat([x_first[..., None], x_rest], dim=-1)
-                x = x.reshape(b, h, w, c, x.shape[-1]).permute(0, 3, 4, 1, 2)
-            else:
-                x = F.avg_pool1d(x, kernel_size=2, stride=2)
-                x = x.reshape(b, h, w, c, x.shape[-1]).permute(0, 3, 4, 1, 2)
-
-        pad = (0, 1, 0, 1)
-        x = F.pad(x, pad, mode="constant", value=0)
-        b, c, t, h, w = x.shape
-        x = x.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
-        x = self.conv(x)
-        x = x.reshape(b, t, x.shape[1], x.shape[2], x.shape[3]).permute(0, 2, 1, 3, 4)
-        return x
-
-
-class Upsample3D(nn.Module):
-    """3D upsampling with optional temporal decompression."""
-    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, compress_time=False):
-        super().__init__()
-        self.conv = ops.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
-        self.compress_time = compress_time
-
-    def forward(self, x):
-        if self.compress_time:
-            if x.shape[2] > 1 and x.shape[2] % 2 == 1:
-                x_first, x_rest = x[:, :, 0], x[:, :, 1:]
-                x_first = F.interpolate(x_first, scale_factor=2.0)
-                x_rest = F.interpolate(x_rest, scale_factor=2.0)
-                x = torch.cat([x_first[:, :, None, :, :], x_rest], dim=2)
-            elif x.shape[2] > 1:
-                x = F.interpolate(x, scale_factor=2.0)
-            else:
-                x = x.squeeze(2)
-                x = F.interpolate(x, scale_factor=2.0)
-                x = x[:, :, None, :, :]
-        else:
-            b, c, t, h, w = x.shape
-            x = x.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
-            x = F.interpolate(x, scale_factor=2.0)
-            x = x.reshape(b, t, c, *x.shape[2:]).permute(0, 2, 1, 3, 4)
-
-        b, c, t, h, w = x.shape
-        x = x.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
-        x = self.conv(x)
-        x = x.reshape(b, t, *x.shape[1:]).permute(0, 2, 1, 3, 4)
-        return x
-
-
-class DownBlock3D(nn.Module):
-    def __init__(self, in_channels, out_channels, temb_channels=0, num_layers=1,
-                 eps=1e-6, act_fn="silu", groups=32, add_downsample=True,
-                 compress_time=False, pad_mode="first"):
-        super().__init__()
-        self.resnets = nn.ModuleList([
-            ResnetBlock3D(
-                in_channels=in_channels if i == 0 else out_channels,
-                out_channels=out_channels,
-                temb_channels=temb_channels,
-                groups=groups, eps=eps, act_fn=act_fn, pad_mode=pad_mode,
-            )
-            for i in range(num_layers)
-        ])
-        self.downsamplers = nn.ModuleList([Downsample3D(out_channels, out_channels, compress_time=compress_time)]) if add_downsample else None
-
-    def forward(self, x, temb=None, zq=None, conv_cache=None):
-        new_cache = {}
-        conv_cache = conv_cache or {}
-        for i, resnet in enumerate(self.resnets):
-            x, new_cache[f"resnet_{i}"] = resnet(x, temb, zq, conv_cache=conv_cache.get(f"resnet_{i}"))
-        if self.downsamplers is not None:
-            for ds in self.downsamplers:
-                x = ds(x)
-        return x, new_cache
-
-
-class MidBlock3D(nn.Module):
-    def __init__(self, in_channels, temb_channels=0, num_layers=1,
-                 eps=1e-6, act_fn="silu", groups=32, spatial_norm_dim=None, pad_mode="first"):
-        super().__init__()
-        self.resnets = nn.ModuleList([
-            ResnetBlock3D(
-                in_channels=in_channels, out_channels=in_channels,
-                temb_channels=temb_channels, groups=groups, eps=eps,
-                act_fn=act_fn, spatial_norm_dim=spatial_norm_dim, pad_mode=pad_mode,
-            )
-            for _ in range(num_layers)
-        ])
-
-    def forward(self, x, temb=None, zq=None, conv_cache=None):
-        new_cache = {}
-        conv_cache = conv_cache or {}
-        for i, resnet in enumerate(self.resnets):
-            x, new_cache[f"resnet_{i}"] = resnet(x, temb, zq, conv_cache=conv_cache.get(f"resnet_{i}"))
-        return x, new_cache
-
-
-class UpBlock3D(nn.Module):
-    def __init__(self, in_channels, out_channels, temb_channels=0, num_layers=1,
-                 eps=1e-6, act_fn="silu", groups=32, spatial_norm_dim=16,
-                 add_upsample=True, compress_time=False, pad_mode="first"):
-        super().__init__()
-        self.resnets = nn.ModuleList([
-            ResnetBlock3D(
-                in_channels=in_channels if i == 0 else out_channels,
-                out_channels=out_channels,
-                temb_channels=temb_channels, groups=groups, eps=eps,
-                act_fn=act_fn, spatial_norm_dim=spatial_norm_dim, pad_mode=pad_mode,
-            )
-            for i in range(num_layers)
-        ])
-        self.upsamplers = nn.ModuleList([Upsample3D(out_channels, out_channels, compress_time=compress_time)]) if add_upsample else None
-
-    def forward(self, x, temb=None, zq=None, conv_cache=None):
-        new_cache = {}
-        conv_cache = conv_cache or {}
-        for i, resnet in enumerate(self.resnets):
-            x, new_cache[f"resnet_{i}"] = resnet(x, temb, zq, conv_cache=conv_cache.get(f"resnet_{i}"))
-        if self.upsamplers is not None:
-            for us in self.upsamplers:
-                x = us(x)
-        return x, new_cache
-
-
-class Encoder3D(nn.Module):
-    def __init__(self, in_channels=3, out_channels=16,
-                 block_out_channels=(128, 256, 256, 512),
-                 layers_per_block=3, act_fn="silu",
-                 eps=1e-6, groups=32, pad_mode="first",
-                 temporal_compression_ratio=4):
-        super().__init__()
-        temporal_compress_level = int(np.log2(temporal_compression_ratio))
-
-        self.conv_in = CausalConv3d(in_channels, block_out_channels[0], kernel_size=3, pad_mode=pad_mode)
-
-        self.down_blocks = nn.ModuleList()
-        output_channel = block_out_channels[0]
-        for i in range(len(block_out_channels)):
-            input_channel = output_channel
-            output_channel = block_out_channels[i]
-            is_final = i == len(block_out_channels) - 1
-            compress_time = i < temporal_compress_level
-
-            self.down_blocks.append(DownBlock3D(
-                in_channels=input_channel, out_channels=output_channel,
-                temb_channels=0, num_layers=layers_per_block,
-                eps=eps, act_fn=act_fn, groups=groups,
-                add_downsample=not is_final, compress_time=compress_time,
-            ))
-
-        self.mid_block = MidBlock3D(
-            in_channels=block_out_channels[-1], temb_channels=0,
-            num_layers=2, eps=eps, act_fn=act_fn, groups=groups, pad_mode=pad_mode,
-        )
-
-        self.norm_out = ops.GroupNorm(groups, block_out_channels[-1], eps=1e-6)
-        self.conv_act = nn.SiLU()
-        self.conv_out = CausalConv3d(block_out_channels[-1], 2 * out_channels, kernel_size=3, pad_mode=pad_mode)
-
-    def forward(self, x, conv_cache=None):
-        new_cache = {}
-        conv_cache = conv_cache or {}
-
-        x, new_cache["conv_in"] = self.conv_in(x, conv_cache=conv_cache.get("conv_in"))
-
-        for i, block in enumerate(self.down_blocks):
-            key = f"down_block_{i}"
-            x, new_cache[key] = block(x, None, None, conv_cache.get(key))
-
-        x, new_cache["mid_block"] = self.mid_block(x, None, None, conv_cache=conv_cache.get("mid_block"))
-
-        x = self.norm_out(x)
-        x = self.conv_act(x)
-        x, new_cache["conv_out"] = self.conv_out(x, conv_cache=conv_cache.get("conv_out"))
-
-        return x, new_cache
-
-
-class Decoder3D(nn.Module):
-    def __init__(self, in_channels=16, out_channels=3,
-                 block_out_channels=(128, 256, 256, 512),
-                 layers_per_block=3, act_fn="silu",
-                 eps=1e-6, groups=32, pad_mode="first",
-                 temporal_compression_ratio=4):
-        super().__init__()
-        reversed_channels = list(reversed(block_out_channels))
-        temporal_compress_level = int(np.log2(temporal_compression_ratio))
-
-        self.conv_in = CausalConv3d(in_channels, reversed_channels[0], kernel_size=3, pad_mode=pad_mode)
-
-        self.mid_block = MidBlock3D(
-            in_channels=reversed_channels[0], temb_channels=0,
-            num_layers=2, eps=eps, act_fn=act_fn, groups=groups,
-            spatial_norm_dim=in_channels, pad_mode=pad_mode,
-        )
-
-        self.up_blocks = nn.ModuleList()
-        output_channel = reversed_channels[0]
-        for i in range(len(block_out_channels)):
-            prev_channel = output_channel
-            output_channel = reversed_channels[i]
-            is_final = i == len(block_out_channels) - 1
-            compress_time = i < temporal_compress_level
-
-            self.up_blocks.append(UpBlock3D(
-                in_channels=prev_channel, out_channels=output_channel,
-                temb_channels=0, num_layers=layers_per_block + 1,
-                eps=eps, act_fn=act_fn, groups=groups,
-                spatial_norm_dim=in_channels,
-                add_upsample=not is_final, compress_time=compress_time,
-            ))
-
-        self.norm_out = SpatialNorm3D(reversed_channels[-1], in_channels, groups=groups)
-        self.conv_act = nn.SiLU()
-        self.conv_out = CausalConv3d(reversed_channels[-1], out_channels, kernel_size=3, pad_mode=pad_mode)
-
-    def forward(self, sample, conv_cache=None):
-        new_cache = {}
-        conv_cache = conv_cache or {}
-
-        x, new_cache["conv_in"] = self.conv_in(sample, conv_cache=conv_cache.get("conv_in"))
-
-        x, new_cache["mid_block"] = self.mid_block(x, None, sample, conv_cache=conv_cache.get("mid_block"))
-
-        for i, block in enumerate(self.up_blocks):
-            key = f"up_block_{i}"
-            x, new_cache[key] = block(x, None, sample, conv_cache=conv_cache.get(key))
-
-        x, new_cache["norm_out"] = self.norm_out(x, sample, conv_cache=conv_cache.get("norm_out"))
-        x = self.conv_act(x)
-        x, new_cache["conv_out"] = self.conv_out(x, conv_cache=conv_cache.get("conv_out"))
-
-        return x, new_cache
-
-
-
-class AutoencoderKLCogVideoX(nn.Module):
-    """CogVideoX VAE. Spatial tiling/slicing handled by ComfyUI's VAE wrapper.
-
-    Uses rolling temporal decode: conv_in + mid_block + temporal up_blocks run
-    on the full (low-res) tensor, then the expensive spatial-only up_blocks +
-    norm_out + conv_out are processed in small temporal chunks with conv_cache
-    carrying causal state between chunks. This keeps peak VRAM proportional to
-    chunk_size rather than total frame count.
-    """
-
-    def __init__(self,
-                 in_channels=3, out_channels=3,
-                 block_out_channels=(128, 256, 256, 512),
-                 latent_channels=16, layers_per_block=3,
-                 act_fn="silu", eps=1e-6, groups=32,
-                 temporal_compression_ratio=4,
-                 ):
-        super().__init__()
-        self.latent_channels = latent_channels
-        self.temporal_compression_ratio = temporal_compression_ratio
-
-        self.encoder = Encoder3D(
-            in_channels=in_channels, out_channels=latent_channels,
-            block_out_channels=block_out_channels, layers_per_block=layers_per_block,
-            act_fn=act_fn, eps=eps, groups=groups,
-            temporal_compression_ratio=temporal_compression_ratio,
-        )
-        self.decoder = Decoder3D(
-            in_channels=latent_channels, out_channels=out_channels,
-            block_out_channels=block_out_channels, layers_per_block=layers_per_block,
-            act_fn=act_fn, eps=eps, groups=groups,
-            temporal_compression_ratio=temporal_compression_ratio,
-        )
-
-        self.num_latent_frames_batch_size = 2
-        self.num_sample_frames_batch_size = 8
-
-    def encode(self, x):
-        t = x.shape[2]
-        frame_batch = self.num_sample_frames_batch_size
-        remainder = t % frame_batch
-        conv_cache = None
-        enc = []
-
-        # Process remainder frames first so only the first chunk can have an
-        # odd temporal dimension — where Downsample3D's first-frame-special
-        # handling in temporal compression is actually correct.
-        if remainder > 0:
-            chunk, conv_cache = self.encoder(x[:, :, :remainder], conv_cache=conv_cache)
-            enc.append(chunk.to(x.device))
-
-        for start in range(remainder, t, frame_batch):
-            chunk, conv_cache = self.encoder(x[:, :, start:start + frame_batch], conv_cache=conv_cache)
-            enc.append(chunk.to(x.device))
-
-        enc = torch.cat(enc, dim=2)
-        mean, _ = enc.chunk(2, dim=1)
-        return mean
-
-    def decode(self, z):
-        return self._decode_rolling(z)
-
-    def _decode_batched(self, z):
-        """Original batched decode - processes 2 latent frames through full decoder."""
-        t = z.shape[2]
-        frame_batch = self.num_latent_frames_batch_size
-        num_batches = max(t // frame_batch, 1)
-        conv_cache = None
-        dec = []
-        for i in range(num_batches):
-            remaining = t % frame_batch
-            start = frame_batch * i + (0 if i == 0 else remaining)
-            end = frame_batch * (i + 1) + remaining
-            chunk, conv_cache = self.decoder(z[:, :, start:end], conv_cache=conv_cache)
-            dec.append(chunk.cpu())
-        return torch.cat(dec, dim=2).to(z.device)
-
-    def _decode_rolling(self, z):
-        """Rolling decode - processes low-res layers on full tensor, then rolls
-        through expensive high-res layers in temporal chunks."""
-        decoder = self.decoder
-        device = z.device
-
-        # Determine which up_blocks have temporal upsample vs spatial-only.
-        # Temporal up_blocks are cheap (low res), spatial-only are expensive.
-        temporal_compress_level = int(np.log2(self.temporal_compression_ratio))
-        split_at = temporal_compress_level  # first N up_blocks do temporal upsample
-
-        # Phase 1: conv_in + mid_block + temporal up_blocks on full tensor (low/medium res)
-        x, _ = decoder.conv_in(z)
-        x, _ = decoder.mid_block(x, None, z)
-
-        for i in range(split_at):
-            x, _ = decoder.up_blocks[i](x, None, z)
-
-        # Phase 2: remaining spatial-only up_blocks + norm_out + conv_out in temporal chunks
-        remaining_blocks = list(range(split_at, len(decoder.up_blocks)))
-        chunk_size = 4  # pixel frames per chunk through high-res layers
-        t_expanded = x.shape[2]
-
-        if t_expanded <= chunk_size or len(remaining_blocks) == 0:
-            # Small enough to process in one go
-            for i in remaining_blocks:
-                x, _ = decoder.up_blocks[i](x, None, z)
-            x, _ = decoder.norm_out(x, z)
-            x = decoder.conv_act(x)
-            x, _ = decoder.conv_out(x)
-            return x
-
-        # Expand z temporally once to match Phase 2's time dimension.
-        # z stays at latent spatial resolution so this is small (~16 MB vs ~1.3 GB
-        # for the old approach of pre-interpolating to every pixel resolution).
-        z_time_expanded = _interpolate_zq(z, (t_expanded, z.shape[3], z.shape[4]))
-
-        # Process in temporal chunks, interpolating spatially per-chunk to avoid
-        # allocating full [B, C, t_expanded, H, W] tensors at each resolution.
-        dec_out = []
-        conv_caches = {}
-
-        for chunk_start in range(0, t_expanded, chunk_size):
-            chunk_end = min(chunk_start + chunk_size, t_expanded)
-            x_chunk = x[:, :, chunk_start:chunk_end]
-            z_t_chunk = z_time_expanded[:, :, chunk_start:chunk_end]
-            z_spatial_cache = {}
-
-            for i in remaining_blocks:
-                block = decoder.up_blocks[i]
-                cache_key = f"up_block_{i}"
-                hw_key = (x_chunk.shape[3], x_chunk.shape[4])
-                if hw_key not in z_spatial_cache:
-                    if z_t_chunk.shape[3] == hw_key[0] and z_t_chunk.shape[4] == hw_key[1]:
-                        z_spatial_cache[hw_key] = z_t_chunk
-                    else:
-                        z_spatial_cache[hw_key] = F.interpolate(z_t_chunk, size=(z_t_chunk.shape[2], hw_key[0], hw_key[1]))
-                x_chunk, new_cache = block(x_chunk, None, z_spatial_cache[hw_key], conv_cache=conv_caches.get(cache_key))
-                conv_caches[cache_key] = new_cache
-
-            hw_key = (x_chunk.shape[3], x_chunk.shape[4])
-            if hw_key not in z_spatial_cache:
-                z_spatial_cache[hw_key] = F.interpolate(z_t_chunk, size=(z_t_chunk.shape[2], hw_key[0], hw_key[1]))
-            x_chunk, new_cache = decoder.norm_out(x_chunk, z_spatial_cache[hw_key], conv_cache=conv_caches.get("norm_out"))
-            conv_caches["norm_out"] = new_cache
-            x_chunk = decoder.conv_act(x_chunk)
-            x_chunk, new_cache = decoder.conv_out(x_chunk, conv_cache=conv_caches.get("conv_out"))
-            conv_caches["conv_out"] = new_cache
-
-            dec_out.append(x_chunk.cpu())
-            del z_spatial_cache
-
-        del x, z_time_expanded
-        return torch.cat(dec_out, dim=2).to(device)

comfy/ldm/ernie/model.py

@@ -118,6 +118,8 @@ class ErnieImageAttention(nn.Module):
             query = apply_rotary_emb(query, image_rotary_emb)
             key = apply_rotary_emb(key, image_rotary_emb)
 
+        query, key = query.to(x.dtype), key.to(x.dtype)
+
         q_flat = query.reshape(B, S, -1)
         k_flat = key.reshape(B, S, -1)
 
@@ -159,16 +161,16 @@ class ErnieImageSharedAdaLNBlock(nn.Module):
 
         residual = x
         x_norm = self.adaLN_sa_ln(x)
-        x_norm = x_norm * (1 + scale_msa) + shift_msa
+        x_norm = (x_norm.float() * (1 + scale_msa.float()) + shift_msa.float()).to(x.dtype)
 
         attn_out = self.self_attention(x_norm, attention_mask=attention_mask, image_rotary_emb=rotary_pos_emb)
-        x = residual + gate_msa * attn_out
+        x = residual + (gate_msa.float() * attn_out.float()).to(x.dtype)
 
         residual = x
         x_norm = self.adaLN_mlp_ln(x)
-        x_norm = x_norm * (1 + scale_mlp) + shift_mlp
+        x_norm = (x_norm.float() * (1 + scale_mlp.float()) + shift_mlp.float()).to(x.dtype)
 
-        return residual + gate_mlp * self.mlp(x_norm)
+        return residual + (gate_mlp.float() * self.mlp(x_norm).float()).to(x.dtype)
 
 class ErnieImageAdaLNContinuous(nn.Module):
     def __init__(self, hidden_size: int, eps: float = 1e-6, operations=None, device=None, dtype=None):
@@ -181,7 +183,7 @@ class ErnieImageAdaLNContinuous(nn.Module):
     def forward(self, x: torch.Tensor, conditioning: torch.Tensor) -> torch.Tensor:
         scale, shift = self.linear(conditioning).chunk(2, dim=-1)
         x = self.norm(x)
-        x = torch.addcmul(shift.unsqueeze(1), x, 1 + scale.unsqueeze(1))
+        x = x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
         return x
 
 class ErnieImageModel(nn.Module):

comfy/ldm/lightricks/av_model.py

@@ -16,7 +16,6 @@ from comfy.ldm.lightricks.model import (
 from comfy.ldm.lightricks.symmetric_patchifier import AudioPatchifier
 from comfy.ldm.lightricks.embeddings_connector import Embeddings1DConnector
 import comfy.ldm.common_dit
-import comfy.model_prefetch
 
 class CompressedTimestep:
     """Store video timestep embeddings in compressed form using per-frame indexing."""
@@ -908,11 +907,9 @@ class LTXAVModel(LTXVModel):
         """Process transformer blocks for LTXAV."""
         patches_replace = transformer_options.get("patches_replace", {})
         blocks_replace = patches_replace.get("dit", {})
-        prefetch_queue = comfy.model_prefetch.make_prefetch_queue(list(self.transformer_blocks), vx.device, transformer_options)
 
         # Process transformer blocks
         for i, block in enumerate(self.transformer_blocks):
-            comfy.model_prefetch.prefetch_queue_pop(prefetch_queue, vx.device, block)
             if ("double_block", i) in blocks_replace:
 
                 def block_wrap(args):
@@ -985,8 +982,6 @@ class LTXAVModel(LTXVModel):
                     a_prompt_timestep=a_prompt_timestep,
                 )
 
-        comfy.model_prefetch.prefetch_queue_pop(prefetch_queue, vx.device, None)
-
         return [vx, ax]
 
     def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs):

comfy/ldm/lightricks/vae/audio_vae.py

@@ -4,6 +4,9 @@ import math
 import torch
 import torchaudio
 
+import comfy.model_management
+import comfy.model_patcher
+import comfy.utils as utils
 from comfy.ldm.mmaudio.vae.distributions import DiagonalGaussianDistribution
 from comfy.ldm.lightricks.symmetric_patchifier import AudioPatchifier
 from comfy.ldm.lightricks.vae.causal_audio_autoencoder import (
@@ -40,6 +43,30 @@ class AudioVAEComponentConfig:
 
         return cls(autoencoder=audio_config, vocoder=vocoder_config)
 
+
+class ModelDeviceManager:
+    """Manages device placement and GPU residency for the composed model."""
+
+    def __init__(self, module: torch.nn.Module):
+        load_device = comfy.model_management.get_torch_device()
+        offload_device = comfy.model_management.vae_offload_device()
+        self.patcher = comfy.model_patcher.ModelPatcher(module, load_device, offload_device)
+
+    def ensure_model_loaded(self) -> None:
+        comfy.model_management.free_memory(
+            self.patcher.model_size(),
+            self.patcher.load_device,
+        )
+        comfy.model_management.load_model_gpu(self.patcher)
+
+    def move_to_load_device(self, tensor: torch.Tensor) -> torch.Tensor:
+        return tensor.to(self.patcher.load_device)
+
+    @property
+    def load_device(self):
+        return self.patcher.load_device
+
+
 class AudioLatentNormalizer:
     """Applies per-channel statistics in patch space and restores original layout."""
 
@@ -105,17 +132,23 @@ class AudioPreprocessor:
 class AudioVAE(torch.nn.Module):
     """High-level Audio VAE wrapper exposing encode and decode entry points."""
 
-    def __init__(self, metadata: dict):
+    def __init__(self, state_dict: dict, metadata: dict):
         super().__init__()
 
         component_config = AudioVAEComponentConfig.from_metadata(metadata)
 
+        vae_sd = utils.state_dict_prefix_replace(state_dict, {"audio_vae.": ""}, filter_keys=True)
+        vocoder_sd = utils.state_dict_prefix_replace(state_dict, {"vocoder.": ""}, filter_keys=True)
+
         self.autoencoder = CausalAudioAutoencoder(config=component_config.autoencoder)
         if "bwe" in component_config.vocoder:
             self.vocoder = VocoderWithBWE(config=component_config.vocoder)
         else:
             self.vocoder = Vocoder(config=component_config.vocoder)
 
+        self.autoencoder.load_state_dict(vae_sd, strict=False)
+        self.vocoder.load_state_dict(vocoder_sd, strict=False)
+
         autoencoder_config = self.autoencoder.get_config()
         self.normalizer = AudioLatentNormalizer(
             AudioPatchifier(
@@ -135,12 +168,18 @@ class AudioVAE(torch.nn.Module):
             n_fft=autoencoder_config["n_fft"],
         )
 
-    def encode(self, audio, sample_rate=44100) -> torch.Tensor:
+        self.device_manager = ModelDeviceManager(self)
+
+    def encode(self, audio: dict) -> torch.Tensor:
         """Encode a waveform dictionary into normalized latent tensors."""
 
-        waveform = audio
-        waveform_sample_rate = sample_rate
+        waveform = audio["waveform"]
+        waveform_sample_rate = audio["sample_rate"]
         input_device = waveform.device
+        # Ensure that Audio VAE is loaded on the correct device.
+        self.device_manager.ensure_model_loaded()
+
+        waveform = self.device_manager.move_to_load_device(waveform)
         expected_channels = self.autoencoder.encoder.in_channels
         if waveform.shape[1] != expected_channels:
             if waveform.shape[1] == 1:
@@ -151,7 +190,7 @@ class AudioVAE(torch.nn.Module):
                 )
 
         mel_spec = self.preprocessor.waveform_to_mel(
-            waveform, waveform_sample_rate, device=waveform.device
+            waveform, waveform_sample_rate, device=self.device_manager.load_device
         )
 
         latents = self.autoencoder.encode(mel_spec)
@@ -165,13 +204,17 @@ class AudioVAE(torch.nn.Module):
         """Decode normalized latent tensors into an audio waveform."""
         original_shape = latents.shape
 
+        # Ensure that Audio VAE is loaded on the correct device.
+        self.device_manager.ensure_model_loaded()
+
+        latents = self.device_manager.move_to_load_device(latents)
         latents = self.normalizer.denormalize(latents)
 
         target_shape = self.target_shape_from_latents(original_shape)
         mel_spec = self.autoencoder.decode(latents, target_shape=target_shape)
 
         waveform = self.run_vocoder(mel_spec)
-        return waveform
+        return self.device_manager.move_to_load_device(waveform)
 
     def target_shape_from_latents(self, latents_shape):
         batch, _, time, _ = latents_shape

comfy/ldm/modules/attention.py

@@ -14,8 +14,6 @@ from .sub_quadratic_attention import efficient_dot_product_attention
 
 from comfy import model_management
 
-TORCH_HAS_GQA = model_management.torch_version_numeric >= (2, 5)
-
 if model_management.xformers_enabled():
     import xformers
     import xformers.ops
@@ -152,12 +150,7 @@ def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
         b, _, dim_head = q.shape
         dim_head //= heads
 
-    if kwargs.get("enable_gqa", False) and q.shape[-3] != k.shape[-3]:
-        n_rep = q.shape[-3] // k.shape[-3]
-        k = k.repeat_interleave(n_rep, dim=-3)
-        v = v.repeat_interleave(n_rep, dim=-3)
-
-    scale = kwargs.get("scale", dim_head ** -0.5)
+    scale = dim_head ** -0.5
 
     h = heads
     if skip_reshape:
@@ -226,10 +219,6 @@ def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None,
         b, _, dim_head = query.shape
         dim_head //= heads
 
-    if "scale" in kwargs:
-        # Pre-scale query to match requested scale (cancels internal 1/sqrt(dim_head))
-        query = query * (kwargs["scale"] * dim_head ** 0.5)
-
     if skip_reshape:
         query = query.reshape(b * heads, -1, dim_head)
         value = value.reshape(b * heads, -1, dim_head)
@@ -301,7 +290,7 @@ def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
         b, _, dim_head = q.shape
         dim_head //= heads
 
-    scale = kwargs.get("scale", dim_head ** -0.5)
+    scale = dim_head ** -0.5
 
     if skip_reshape:
          q, k, v = map(
@@ -511,13 +500,8 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
         if mask.ndim == 3:
             mask = mask.unsqueeze(1)
 
-    # Pass through extra SDPA kwargs (scale, enable_gqa) if provided
-    # enable_gqa requires PyTorch 2.5+; older versions use manual KV expansion above
-    sdpa_keys = ("scale", "enable_gqa") if TORCH_HAS_GQA else ("scale",)
-    sdpa_extra = {k: v for k, v in kwargs.items() if k in sdpa_keys}
-
     if SDP_BATCH_LIMIT >= b:
-        out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False, **sdpa_extra)
+        out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
         if not skip_output_reshape:
             out = (
                 out.transpose(1, 2).reshape(b, -1, heads * dim_head)
@@ -535,7 +519,7 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
                 k[i : i + SDP_BATCH_LIMIT],
                 v[i : i + SDP_BATCH_LIMIT],
                 attn_mask=m,
-                dropout_p=0.0, is_causal=False, **sdpa_extra
+                dropout_p=0.0, is_causal=False
             ).transpose(1, 2).reshape(-1, q.shape[2], heads * dim_head)
     return out
 

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -34,16 +34,6 @@ class TimestepBlock(nn.Module):
 #This is needed because accelerate makes a copy of transformer_options which breaks "transformer_index"
 def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None, time_context=None, num_video_frames=None, image_only_indicator=None):
     for layer in ts:
-        if "patches" in transformer_options and "forward_timestep_embed_patch" in transformer_options["patches"]:
-            found_patched = False
-            for class_type, handler in transformer_options["patches"]["forward_timestep_embed_patch"]:
-                if isinstance(layer, class_type):
-                    x = handler(layer, x, emb, context, transformer_options, output_shape, time_context, num_video_frames, image_only_indicator)
-                    found_patched = True
-                    break
-            if found_patched:
-                continue
-
         if isinstance(layer, VideoResBlock):
             x = layer(x, emb, num_video_frames, image_only_indicator)
         elif isinstance(layer, TimestepBlock):
@@ -59,6 +49,15 @@ def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, out
         elif isinstance(layer, Upsample):
             x = layer(x, output_shape=output_shape)
         else:
+            if "patches" in transformer_options and "forward_timestep_embed_patch" in transformer_options["patches"]:
+                found_patched = False
+                for class_type, handler in transformer_options["patches"]["forward_timestep_embed_patch"]:
+                    if isinstance(layer, class_type):
+                        x = handler(layer, x, emb, context, transformer_options, output_shape, time_context, num_video_frames, image_only_indicator)
+                        found_patched = True
+                        break
+                if found_patched:
+                    continue
             x = layer(x)
     return x
 
@@ -895,12 +894,6 @@ class UNetModel(nn.Module):
             h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)
         h = apply_control(h, control, 'middle')
 
-        if "middle_block_after_patch" in transformer_patches:
-            patch = transformer_patches["middle_block_after_patch"]
-            for p in patch:
-                out = p({"h": h, "x": x, "emb": emb, "context": context, "y": y,
-                         "timesteps": timesteps, "transformer_options": transformer_options})
-                h = out["h"]
 
         for id, module in enumerate(self.output_blocks):
             transformer_options["block"] = ("output", id)
@@ -912,9 +905,8 @@ class UNetModel(nn.Module):
                 for p in patch:
                     h, hsp = p(h, hsp, transformer_options)
 
-            if hsp is not None:
-                h = th.cat([h, hsp], dim=1)
-                del hsp
+            h = th.cat([h, hsp], dim=1)
+            del hsp
             if len(hs) > 0:
                 output_shape = hs[-1].shape
             else:

comfy/ldm/modules/diffusionmodules/util.py

@@ -140,7 +140,7 @@ def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
     alphas = alphacums[ddim_timesteps]
     alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
 
-    # according to the formula provided in https://arxiv.org/abs/2010.02502
+    # according the the formula provided in https://arxiv.org/abs/2010.02502
     sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
     if verbose:
         logging.info(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')

comfy/ldm/sam3/detector.py

@@ -1,599 +0,0 @@
-# SAM3 detector: transformer encoder-decoder, segmentation head, geometry encoder, scoring.
-
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torchvision.ops import roi_align
-
-from comfy.ldm.modules.attention import optimized_attention
-from comfy.ldm.sam3.tracker import SAM3Tracker, SAM31Tracker
-from comfy.ldm.sam3.sam import SAM3VisionBackbone  # noqa: used in __init__
-from comfy.ldm.sam3.sam import MLP, PositionEmbeddingSine
-
-TRACKER_CLASSES = {"SAM3": SAM3Tracker, "SAM31": SAM31Tracker}
-from comfy.ops import cast_to_input
-
-
-def box_cxcywh_to_xyxy(x):
-    cx, cy, w, h = x.unbind(-1)
-    return torch.stack([cx - 0.5 * w, cy - 0.5 * h, cx + 0.5 * w, cy + 0.5 * h], dim=-1)
-
-
-def gen_sineembed_for_position(pos_tensor, num_feats=256):
-    """Per-coordinate sinusoidal embedding: (..., N) -> (..., N * num_feats)."""
-    assert num_feats % 2 == 0
-    hdim = num_feats // 2
-    freqs = 10000.0 ** (2 * (torch.arange(hdim, dtype=torch.float32, device=pos_tensor.device) // 2) / hdim)
-    embeds = []
-    for c in range(pos_tensor.shape[-1]):
-        raw = (pos_tensor[..., c].float() * 2 * math.pi).unsqueeze(-1) / freqs
-        embeds.append(torch.stack([raw[..., 0::2].sin(), raw[..., 1::2].cos()], dim=-1).flatten(-2))
-    return torch.cat(embeds, dim=-1).to(pos_tensor.dtype)
-
-
-class SplitMHA(nn.Module):
-    """Multi-head attention with separate Q/K/V projections (split from fused in_proj_weight)."""
-    def __init__(self, d_model, num_heads=8, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.num_heads = num_heads
-        self.q_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype)
-        self.k_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype)
-        self.v_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype)
-        self.out_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype)
-
-    def forward(self, q_input, k_input=None, v_input=None, mask=None):
-        q = self.q_proj(q_input)
-        if k_input is None:
-            k = self.k_proj(q_input)
-            v = self.v_proj(q_input)
-        else:
-            k = self.k_proj(k_input)
-            v = self.v_proj(v_input if v_input is not None else k_input)
-        if mask is not None and mask.ndim == 2:
-            mask = mask[:, None, None, :]  # [B, T] -> [B, 1, 1, T] for SDPA broadcast
-        dtype = q.dtype  # manual_cast may produce mixed dtypes
-        out = optimized_attention(q, k.to(dtype), v.to(dtype), self.num_heads, mask=mask, low_precision_attention=False)
-        return self.out_proj(out)
-
-
-class MLPWithNorm(nn.Module):
-    """MLP with residual connection and output LayerNorm."""
-    def __init__(self, input_dim, hidden_dim, output_dim, num_layers, residual=True, device=None, dtype=None, operations=None):
-        super().__init__()
-        dims = [input_dim] + [hidden_dim] * (num_layers - 1) + [output_dim]
-        self.layers = nn.ModuleList([
-            operations.Linear(dims[i], dims[i + 1], device=device, dtype=dtype)
-            for i in range(num_layers)
-        ])
-        self.out_norm = operations.LayerNorm(output_dim, device=device, dtype=dtype)
-        self.residual = residual and (input_dim == output_dim)
-
-    def forward(self, x):
-        orig = x
-        for i, layer in enumerate(self.layers):
-            x = layer(x)
-            if i < len(self.layers) - 1:
-                x = F.relu(x)
-        if self.residual:
-            x = x + orig
-        return self.out_norm(x)
-
-
-class EncoderLayer(nn.Module):
-    def __init__(self, d_model=256, num_heads=8, dim_ff=2048, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.self_attn = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations)
-        self.cross_attn_image = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations)
-        self.linear1 = operations.Linear(d_model, dim_ff, device=device, dtype=dtype)
-        self.linear2 = operations.Linear(dim_ff, d_model, device=device, dtype=dtype)
-        self.norm1 = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.norm2 = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.norm3 = operations.LayerNorm(d_model, device=device, dtype=dtype)
-
-    def forward(self, x, pos, text_memory=None, text_mask=None):
-        normed = self.norm1(x)
-        q_k = normed + pos
-        x = x + self.self_attn(q_k, q_k, normed)
-        if text_memory is not None:
-            normed = self.norm2(x)
-            x = x + self.cross_attn_image(normed, text_memory, text_memory, mask=text_mask)
-        normed = self.norm3(x)
-        x = x + self.linear2(F.relu(self.linear1(normed)))
-        return x
-
-
-class TransformerEncoder(nn.Module):
-    """Checkpoint: transformer.encoder.layers.N.*"""
-    def __init__(self, d_model=256, num_heads=8, dim_ff=2048, num_layers=6, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.layers = nn.ModuleList([
-            EncoderLayer(d_model, num_heads, dim_ff, device=device, dtype=dtype, operations=operations)
-            for _ in range(num_layers)
-        ])
-
-    def forward(self, x, pos, text_memory=None, text_mask=None):
-        for layer in self.layers:
-            x = layer(x, pos, text_memory, text_mask)
-        return x
-
-
-class DecoderLayer(nn.Module):
-    def __init__(self, d_model=256, num_heads=8, dim_ff=2048, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.self_attn = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations)
-        self.cross_attn = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations)
-        self.ca_text = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations)
-        self.norm1 = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.norm2 = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.norm3 = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.catext_norm = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.linear1 = operations.Linear(d_model, dim_ff, device=device, dtype=dtype)
-        self.linear2 = operations.Linear(dim_ff, d_model, device=device, dtype=dtype)
-
-    def forward(self, x, memory, x_pos, memory_pos, text_memory=None, text_mask=None, cross_attn_bias=None):
-        q_k = x + x_pos
-        x = self.norm2(x + self.self_attn(q_k, q_k, x))
-        if text_memory is not None:
-            x = self.catext_norm(x + self.ca_text(x + x_pos, text_memory, text_memory, mask=text_mask))
-        x = self.norm1(x + self.cross_attn(x + x_pos, memory + memory_pos, memory, mask=cross_attn_bias))
-        x = self.norm3(x + self.linear2(F.relu(self.linear1(x))))
-        return x
-
-
-class TransformerDecoder(nn.Module):
-    def __init__(self, d_model=256, num_heads=8, dim_ff=2048, num_layers=6,
-                 num_queries=200, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.d_model = d_model
-        self.num_queries = num_queries
-
-        self.layers = nn.ModuleList([
-            DecoderLayer(d_model, num_heads, dim_ff, device=device, dtype=dtype, operations=operations)
-            for _ in range(num_layers)
-        ])
-        self.norm = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.query_embed = operations.Embedding(num_queries, d_model, device=device, dtype=dtype)
-        self.reference_points = operations.Embedding(num_queries, 4, device=device, dtype=dtype) # Reference points: Embedding(num_queries, 4) — learned anchor boxes
-        self.ref_point_head = MLP(d_model * 2, d_model, d_model, 2, device=device, dtype=dtype, operations=operations) # ref_point_head input: 512 (4 coords * 128 sine features each)
-        self.bbox_embed = MLP(d_model, d_model, 4, 3, device=device, dtype=dtype, operations=operations)
-
-        self.boxRPB_embed_x = MLP(2, d_model, num_heads, 2, device=device, dtype=dtype, operations=operations)
-        self.boxRPB_embed_y = MLP(2, d_model, num_heads, 2, device=device, dtype=dtype, operations=operations)
-
-        self.presence_token = operations.Embedding(1, d_model, device=device, dtype=dtype)
-        self.presence_token_head = MLP(d_model, d_model, 1, 3, device=device, dtype=dtype, operations=operations)
-        self.presence_token_out_norm = operations.LayerNorm(d_model, device=device, dtype=dtype)
-
-    @staticmethod
-    def _inverse_sigmoid(x):
-        return torch.log(x / (1 - x + 1e-6) + 1e-6)
-
-    def _compute_box_rpb(self, ref_points, H, W):
-        """Box rotary position bias: (B, Q, 4) cxcywh -> (B, n_heads, Q+1, H*W) bias."""
-        boxes_xyxy = box_cxcywh_to_xyxy(ref_points)
-        B, Q, _ = boxes_xyxy.shape
-        coords_h = torch.arange(H, device=ref_points.device, dtype=torch.float32) / H
-        coords_w = torch.arange(W, device=ref_points.device, dtype=torch.float32) / W
-        deltas_x = coords_w.view(1, 1, -1, 1) - boxes_xyxy[:, :, None, 0:3:2]
-        deltas_y = coords_h.view(1, 1, -1, 1) - boxes_xyxy[:, :, None, 1:4:2]
-
-        log2_8 = float(math.log2(8))
-        def log_scale(d):
-            return torch.sign(d * 8) * torch.log2(torch.abs(d * 8) + 1.0) / log2_8
-
-        rpb_x = self.boxRPB_embed_x(log_scale(deltas_x).to(ref_points.dtype))
-        rpb_y = self.boxRPB_embed_y(log_scale(deltas_y).to(ref_points.dtype))
-
-        bias = (rpb_y.unsqueeze(3) + rpb_x.unsqueeze(2)).flatten(2, 3).permute(0, 3, 1, 2)
-        pres_bias = torch.zeros(B, bias.shape[1], 1, bias.shape[3], device=bias.device, dtype=bias.dtype)
-        return torch.cat([pres_bias, bias], dim=2)
-
-    def forward(self, memory, memory_pos, text_memory=None, text_mask=None, H=72, W=72):
-        B = memory.shape[0]
-        tgt = cast_to_input(self.query_embed.weight, memory).unsqueeze(0).expand(B, -1, -1)
-        presence_out = cast_to_input(self.presence_token.weight, memory)[None].expand(B, -1, -1)
-        ref_points = cast_to_input(self.reference_points.weight, memory).unsqueeze(0).expand(B, -1, -1).sigmoid()
-
-        for layer_idx, layer in enumerate(self.layers):
-            query_pos = self.ref_point_head(gen_sineembed_for_position(ref_points, self.d_model))
-            tgt_with_pres = torch.cat([presence_out, tgt], dim=1)
-            pos_with_pres = torch.cat([torch.zeros_like(presence_out), query_pos], dim=1)
-            tgt_with_pres = layer(tgt_with_pres, memory, pos_with_pres, memory_pos,
-                                  text_memory, text_mask, self._compute_box_rpb(ref_points, H, W))
-            presence_out, tgt = tgt_with_pres[:, :1], tgt_with_pres[:, 1:]
-            if layer_idx < len(self.layers) - 1:
-                ref_inv = self._inverse_sigmoid(ref_points)
-                ref_points = (ref_inv + self.bbox_embed(self.norm(tgt))).sigmoid().detach()
-
-        query_out = self.norm(tgt)
-        ref_inv = self._inverse_sigmoid(ref_points)
-        boxes = (ref_inv + self.bbox_embed(query_out)).sigmoid()
-        presence = self.presence_token_head(self.presence_token_out_norm(presence_out)).squeeze(-1)
-        return {"decoder_output": query_out, "pred_boxes": boxes, "presence": presence}
-
-
-class Transformer(nn.Module):
-    def __init__(self, d_model=256, num_heads=8, dim_ff=2048, enc_layers=6, dec_layers=6,
-                 num_queries=200, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.encoder = TransformerEncoder(d_model, num_heads, dim_ff, enc_layers, device=device, dtype=dtype, operations=operations)
-        self.decoder = TransformerDecoder(d_model, num_heads, dim_ff, dec_layers, num_queries, device=device, dtype=dtype, operations=operations)
-
-
-class GeometryEncoder(nn.Module):
-    def __init__(self, d_model=256, num_heads=8, num_layers=3, roi_size=7, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.d_model = d_model
-        self.roi_size = roi_size
-        self.pos_enc = PositionEmbeddingSine(num_pos_feats=d_model, normalize=True)
-        self.points_direct_project = operations.Linear(2, d_model, device=device, dtype=dtype)
-        self.points_pool_project = operations.Linear(d_model, d_model, device=device, dtype=dtype)
-        self.points_pos_enc_project = operations.Linear(d_model, d_model, device=device, dtype=dtype)
-        self.boxes_direct_project = operations.Linear(4, d_model, device=device, dtype=dtype)
-        self.boxes_pool_project = operations.Conv2d(d_model, d_model, kernel_size=roi_size, device=device, dtype=dtype)
-        self.boxes_pos_enc_project = operations.Linear(d_model + 2, d_model, device=device, dtype=dtype)
-        self.label_embed = operations.Embedding(2, d_model, device=device, dtype=dtype)
-        self.cls_embed = operations.Embedding(1, d_model, device=device, dtype=dtype)
-        self.norm = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.img_pre_norm = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.encode = nn.ModuleList([
-            EncoderLayer(d_model, num_heads, 2048, device=device, dtype=dtype, operations=operations)
-            for _ in range(num_layers)
-        ])
-        self.encode_norm = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.final_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype)
-
-    def _encode_points(self, coords, labels, img_feat_2d):
-        """Encode point prompts: direct + pool + pos_enc + label. coords: [B, N, 2] normalized."""
-        B, N, _ = coords.shape
-        embed = self.points_direct_project(coords)
-        # Pool features from backbone at point locations via grid_sample
-        grid = (coords * 2 - 1).unsqueeze(2)  # [B, N, 1, 2] in [-1, 1]
-        sampled = F.grid_sample(img_feat_2d, grid, align_corners=False)  # [B, C, N, 1]
-        embed = embed + self.points_pool_project(sampled.squeeze(-1).permute(0, 2, 1))  # [B, N, C]
-        # Positional encoding of coordinates
-        x, y = coords[:, :, 0], coords[:, :, 1]  # [B, N]
-        pos_x, pos_y = self.pos_enc._encode_xy(x.flatten(), y.flatten())
-        enc = torch.cat([pos_x, pos_y], dim=-1).view(B, N, -1)
-        embed = embed + self.points_pos_enc_project(cast_to_input(enc, embed))
-        embed = embed + cast_to_input(self.label_embed(labels.long()), embed)
-        return embed
-
-    def _encode_boxes(self, boxes, labels, img_feat_2d):
-        """Encode box prompts: direct + pool + pos_enc + label. boxes: [B, N, 4] normalized cxcywh."""
-        B, N, _ = boxes.shape
-        embed = self.boxes_direct_project(boxes)
-        # ROI align from backbone at box regions
-        H, W = img_feat_2d.shape[-2:]
-        boxes_xyxy = box_cxcywh_to_xyxy(boxes)
-        scale = torch.tensor([W, H, W, H], dtype=boxes_xyxy.dtype, device=boxes_xyxy.device)
-        boxes_scaled = boxes_xyxy * scale
-        sampled = roi_align(img_feat_2d, boxes_scaled.view(-1, 4).split(N), self.roi_size)
-        proj = self.boxes_pool_project(sampled).view(B, N, -1)  # Conv2d(roi_size) -> [B*N, C, 1, 1] -> [B, N, C]
-        embed = embed + proj
-        # Positional encoding of box center + size
-        cx, cy, w, h = boxes[:, :, 0], boxes[:, :, 1], boxes[:, :, 2], boxes[:, :, 3]
-        enc = self.pos_enc.encode_boxes(cx.flatten(), cy.flatten(), w.flatten(), h.flatten())
-        enc = enc.view(B, N, -1)
-        embed = embed + self.boxes_pos_enc_project(cast_to_input(enc, embed))
-        embed = embed + cast_to_input(self.label_embed(labels.long()), embed)
-        return embed
-
-    def forward(self, points=None, boxes=None, image_features=None):
-        """Encode geometry prompts. image_features: [B, HW, C] flattened backbone features."""
-        # Prepare 2D image features for pooling
-        img_feat_2d = None
-        if image_features is not None:
-            B = image_features.shape[0]
-            HW, C = image_features.shape[1], image_features.shape[2]
-            hw = int(math.sqrt(HW))
-            img_normed = self.img_pre_norm(image_features)
-            img_feat_2d = img_normed.permute(0, 2, 1).view(B, C, hw, hw)
-
-        embeddings = []
-        if points is not None:
-            coords, labels = points
-            embeddings.append(self._encode_points(coords, labels, img_feat_2d))
-        if boxes is not None:
-            B = boxes.shape[0]
-            box_labels = torch.ones(B, boxes.shape[1], dtype=torch.long, device=boxes.device)
-            embeddings.append(self._encode_boxes(boxes, box_labels, img_feat_2d))
-        if not embeddings:
-            return None
-        geo = torch.cat(embeddings, dim=1)
-        geo = self.norm(geo)
-        if image_features is not None:
-            for layer in self.encode:
-                geo = layer(geo, torch.zeros_like(geo), image_features)
-        geo = self.encode_norm(geo)
-        return self.final_proj(geo)
-
-
-class PixelDecoder(nn.Module):
-    """Top-down FPN pixel decoder with GroupNorm + ReLU + nearest interpolation."""
-    def __init__(self, d_model=256, num_stages=3, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.conv_layers = nn.ModuleList([operations.Conv2d(d_model, d_model, kernel_size=3, padding=1, device=device, dtype=dtype) for _ in range(num_stages)])
-        self.norms = nn.ModuleList([operations.GroupNorm(8, d_model, device=device, dtype=dtype) for _ in range(num_stages)])
-
-    def forward(self, backbone_features):
-        prev = backbone_features[-1]
-        for i, feat in enumerate(backbone_features[:-1][::-1]):
-            prev = F.relu(self.norms[i](self.conv_layers[i](feat + F.interpolate(prev, size=feat.shape[-2:], mode="nearest"))))
-        return prev
-
-
-class MaskPredictor(nn.Module):
-    def __init__(self, d_model=256, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.mask_embed = MLP(d_model, d_model, d_model, 3, device=device, dtype=dtype, operations=operations)
-
-    def forward(self, query_embeddings, pixel_features):
-        mask_embed = self.mask_embed(query_embeddings)
-        return torch.einsum("bqc,bchw->bqhw", mask_embed, pixel_features)
-
-
-class SegmentationHead(nn.Module):
-    def __init__(self, d_model=256, num_heads=8, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.d_model = d_model
-        self.pixel_decoder = PixelDecoder(d_model, 3, device=device, dtype=dtype, operations=operations)
-        self.mask_predictor = MaskPredictor(d_model, device=device, dtype=dtype, operations=operations)
-        self.cross_attend_prompt = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations)
-        self.cross_attn_norm = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.instance_seg_head = operations.Conv2d(d_model, d_model, kernel_size=1, device=device, dtype=dtype)
-        self.semantic_seg_head = operations.Conv2d(d_model, 1, kernel_size=1, device=device, dtype=dtype)
-
-    def forward(self, query_embeddings, backbone_features, encoder_hidden_states=None, prompt=None, prompt_mask=None):
-        if encoder_hidden_states is not None and prompt is not None:
-            enc_normed = self.cross_attn_norm(encoder_hidden_states)
-            enc_cross = self.cross_attend_prompt(enc_normed, prompt, prompt, mask=prompt_mask)
-            encoder_hidden_states = enc_cross + encoder_hidden_states
-
-        if encoder_hidden_states is not None:
-            B, H, W = encoder_hidden_states.shape[0], backbone_features[-1].shape[-2], backbone_features[-1].shape[-1]
-            encoder_visual = encoder_hidden_states[:, :H * W].permute(0, 2, 1).view(B, self.d_model, H, W)
-            backbone_features = list(backbone_features)
-            backbone_features[-1] = encoder_visual
-
-        pixel_features = self.pixel_decoder(backbone_features)
-        instance_features = self.instance_seg_head(pixel_features)
-        masks = self.mask_predictor(query_embeddings, instance_features)
-        return masks
-
-
-class DotProductScoring(nn.Module):
-    def __init__(self, d_model=256, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.hs_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype)
-        self.prompt_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype)
-        self.prompt_mlp = MLPWithNorm(d_model, 2048, d_model, 2, device=device, dtype=dtype, operations=operations)
-        self.scale = 1.0 / (d_model ** 0.5)
-
-    def forward(self, query_embeddings, prompt_embeddings, prompt_mask=None):
-        prompt = self.prompt_mlp(prompt_embeddings)
-        if prompt_mask is not None:
-            weight = prompt_mask.unsqueeze(-1).to(dtype=prompt.dtype)
-            pooled = (prompt * weight).sum(dim=1) / weight.sum(dim=1).clamp(min=1)
-        else:
-            pooled = prompt.mean(dim=1)
-        hs = self.hs_proj(query_embeddings)
-        pp = self.prompt_proj(pooled).unsqueeze(-1).to(hs.dtype)
-        scores = torch.matmul(hs, pp)
-        return (scores * self.scale).clamp(-12.0, 12.0).squeeze(-1)
-
-
-class SAM3Detector(nn.Module):
-    def __init__(self, d_model=256, embed_dim=1024, num_queries=200, device=None, dtype=None, operations=None, **kwargs):
-        super().__init__()
-        image_model = kwargs.pop("image_model", "SAM3")
-        for k in ("num_heads", "num_head_channels"):
-            kwargs.pop(k, None)
-        multiplex = image_model == "SAM31"
-        # SAM3: 4 FPN levels, drop last (scalp=1); SAM3.1: 3 levels, use all (scalp=0)
-        self.scalp = 0 if multiplex else 1
-        self.backbone = nn.ModuleDict({
-            "vision_backbone": SAM3VisionBackbone(embed_dim=embed_dim, d_model=d_model, multiplex=multiplex, device=device, dtype=dtype, operations=operations, **kwargs),
-            "language_backbone": nn.ModuleDict({"resizer": operations.Linear(embed_dim, d_model, device=device, dtype=dtype)}),
-        })
-        self.transformer = Transformer(d_model=d_model, num_queries=num_queries, device=device, dtype=dtype, operations=operations)
-        self.segmentation_head = SegmentationHead(d_model=d_model, device=device, dtype=dtype, operations=operations)
-        self.geometry_encoder = GeometryEncoder(d_model=d_model, device=device, dtype=dtype, operations=operations)
-        self.dot_prod_scoring = DotProductScoring(d_model=d_model, device=device, dtype=dtype, operations=operations)
-
-    def _get_backbone_features(self, images):
-        """Run backbone and return (detector_features, detector_positions, tracker_features, tracker_positions)."""
-        bb = self.backbone["vision_backbone"]
-        if bb.multiplex:
-            all_f, all_p, tf, tp = bb(images, tracker_mode="propagation")
-        else:
-            all_f, all_p, tf, tp = bb(images, need_tracker=True)
-        return all_f, all_p, tf, tp
-
-    @staticmethod
-    def _run_geo_layer(layer, x, memory, memory_pos):
-        x = x + layer.self_attn(layer.norm1(x))
-        x = x + layer.cross_attn_image(layer.norm2(x), memory + memory_pos, memory)
-        x = x + layer.linear2(F.relu(layer.linear1(layer.norm3(x))))
-        return x
-
-    def _detect(self, features, positions, text_embeddings=None, text_mask=None,
-                points=None, boxes=None):
-        """Shared detection: geometry encoding, transformer, scoring, segmentation."""
-        B = features[0].shape[0]
-        # Scalp for encoder (use top-level feature), but keep all levels for segmentation head
-        seg_features = features
-        if self.scalp > 0:
-            features = features[:-self.scalp]
-            positions = positions[:-self.scalp]
-        enc_feat, enc_pos = features[-1], positions[-1]
-        _, _, H, W = enc_feat.shape
-        img_flat = enc_feat.flatten(2).permute(0, 2, 1)
-        pos_flat = enc_pos.flatten(2).permute(0, 2, 1)
-
-        has_prompts = text_embeddings is not None or points is not None or boxes is not None
-        if has_prompts:
-            geo_enc = self.geometry_encoder
-            geo_prompts = geo_enc(points=points, boxes=boxes, image_features=img_flat)
-            geo_cls = geo_enc.norm(geo_enc.final_proj(cast_to_input(geo_enc.cls_embed.weight, img_flat).view(1, 1, -1).expand(B, -1, -1)))
-            for layer in geo_enc.encode:
-                geo_cls = self._run_geo_layer(layer, geo_cls, img_flat, pos_flat)
-            geo_cls = geo_enc.encode_norm(geo_cls)
-            if text_embeddings is not None and text_embeddings.shape[0] != B:
-                text_embeddings = text_embeddings.expand(B, -1, -1)
-            if text_mask is not None and text_mask.shape[0] != B:
-                text_mask = text_mask.expand(B, -1)
-            parts = [t for t in [text_embeddings, geo_prompts, geo_cls] if t is not None]
-            text_embeddings = torch.cat(parts, dim=1)
-            n_new = text_embeddings.shape[1] - (text_mask.shape[1] if text_mask is not None else 0)
-            if text_mask is not None:
-                text_mask = torch.cat([text_mask, torch.ones(B, n_new, dtype=torch.bool, device=text_mask.device)], dim=1)
-            else:
-                text_mask = torch.ones(B, text_embeddings.shape[1], dtype=torch.bool, device=text_embeddings.device)
-
-        memory = self.transformer.encoder(img_flat, pos_flat, text_embeddings, text_mask)
-        dec_out = self.transformer.decoder(memory, pos_flat, text_embeddings, text_mask, H, W)
-        query_out, pred_boxes = dec_out["decoder_output"], dec_out["pred_boxes"]
-
-        if text_embeddings is not None:
-            scores = self.dot_prod_scoring(query_out, text_embeddings, text_mask)
-        else:
-            scores = torch.zeros(B, query_out.shape[1], device=query_out.device)
-
-        masks = self.segmentation_head(query_out, seg_features, encoder_hidden_states=memory, prompt=text_embeddings, prompt_mask=text_mask)
-        return box_cxcywh_to_xyxy(pred_boxes), scores, masks, dec_out
-
-    def forward(self, images, text_embeddings=None, text_mask=None, points=None, boxes=None, threshold=0.3, orig_size=None):
-        features, positions, _, _ = self._get_backbone_features(images)
-
-        if text_embeddings is not None:
-            text_embeddings = self.backbone["language_backbone"]["resizer"](text_embeddings)
-            if text_mask is not None:
-                text_mask = text_mask.bool()
-
-        boxes_xyxy, scores, masks, dec_out = self._detect(
-            features, positions, text_embeddings, text_mask, points, boxes)
-
-        if orig_size is not None:
-            oh, ow = orig_size
-            boxes_xyxy = boxes_xyxy * torch.tensor([ow, oh, ow, oh], device=boxes_xyxy.device, dtype=boxes_xyxy.dtype)
-            masks = F.interpolate(masks, size=orig_size, mode="bilinear", align_corners=False)
-
-        return {
-            "boxes": boxes_xyxy,
-            "scores": scores,
-            "masks": masks,
-            "presence": dec_out.get("presence"),
-        }
-
-    def forward_from_trunk(self, trunk_out, text_embeddings, text_mask):
-        """Run detection using a pre-computed ViTDet trunk output.
-
-        text_embeddings must already be resized through language_backbone.resizer.
-        Returns dict with boxes (normalized xyxy), scores, masks at detector resolution.
-        """
-        bb = self.backbone["vision_backbone"]
-        features = [conv(trunk_out) for conv in bb.convs]
-        positions = [cast_to_input(bb.position_encoding(f), f) for f in features]
-
-        if text_mask is not None:
-            text_mask = text_mask.bool()
-
-        boxes_xyxy, scores, masks, _ = self._detect(features, positions, text_embeddings, text_mask)
-        return {"boxes": boxes_xyxy, "scores": scores, "masks": masks}
-
-
-class SAM3Model(nn.Module):
-    def __init__(self, device=None, dtype=None, operations=None, **kwargs):
-        super().__init__()
-        self.dtype = dtype
-        image_model = kwargs.get("image_model", "SAM3")
-        tracker_cls = TRACKER_CLASSES[image_model]
-        self.detector = SAM3Detector(device=device, dtype=dtype, operations=operations, **kwargs)
-        self.tracker = tracker_cls(device=device, dtype=dtype, operations=operations, **kwargs)
-
-    def forward(self, images, **kwargs):
-        return self.detector(images, **kwargs)
-
-    def forward_segment(self, images, point_inputs=None, box_inputs=None, mask_inputs=None):
-        """Interactive segmentation using SAM decoder with point/box/mask prompts.
-
-        Args:
-            images: [B, 3, 1008, 1008] preprocessed images
-            point_inputs: {"point_coords": [B, N, 2], "point_labels": [B, N]} in 1008x1008 pixel space
-            box_inputs: [B, 2, 2] box corners (top-left, bottom-right) in 1008x1008 pixel space
-            mask_inputs: [B, 1, H, W] coarse mask logits to refine
-        Returns:
-            [B, 1, image_size, image_size] high-res mask logits
-        """
-        bb = self.detector.backbone["vision_backbone"]
-        if bb.multiplex:
-            _, _, tracker_features, tracker_positions = bb(images, tracker_mode="interactive")
-        else:
-            _, _, tracker_features, tracker_positions = bb(images, need_tracker=True)
-            if self.detector.scalp > 0:
-                tracker_features = tracker_features[:-self.detector.scalp]
-                tracker_positions = tracker_positions[:-self.detector.scalp]
-
-        high_res = list(tracker_features[:-1])
-        backbone_feat = tracker_features[-1]
-        B, C, H, W = backbone_feat.shape
-        # Add no-memory embedding (init frame path)
-        no_mem = getattr(self.tracker, 'interactivity_no_mem_embed', None)
-        if no_mem is None:
-            no_mem = getattr(self.tracker, 'no_mem_embed', None)
-        if no_mem is not None:
-            feat_flat = backbone_feat.flatten(2).permute(0, 2, 1)
-            feat_flat = feat_flat + cast_to_input(no_mem, feat_flat)
-            backbone_feat = feat_flat.view(B, H, W, C).permute(0, 3, 1, 2)
-
-        num_pts = 0 if point_inputs is None else point_inputs["point_labels"].size(1)
-        _, high_res_masks, _, _ = self.tracker._forward_sam_heads(
-            backbone_features=backbone_feat,
-            point_inputs=point_inputs,
-            mask_inputs=mask_inputs,
-            box_inputs=box_inputs,
-            high_res_features=high_res,
-            multimask_output=(0 < num_pts <= 1),
-        )
-        return high_res_masks
-
-    def forward_video(self, images, initial_masks, pbar=None, text_prompts=None,
-                       new_det_thresh=0.5, max_objects=0, detect_interval=1,
-                       target_device=None, target_dtype=None):
-        """Track video with optional per-frame text-prompted detection."""
-        bb = self.detector.backbone["vision_backbone"]
-
-        def backbone_fn(frame, frame_idx=None):
-            trunk_out = bb.trunk(frame)
-            if bb.multiplex:
-                _, _, tf, tp = bb(frame, tracker_mode="propagation", cached_trunk=trunk_out, tracker_only=True)
-            else:
-                _, _, tf, tp = bb(frame, need_tracker=True, cached_trunk=trunk_out, tracker_only=True)
-            return tf, tp, trunk_out
-
-        detect_fn = None
-        if text_prompts:
-            resizer = self.detector.backbone["language_backbone"]["resizer"]
-            resized = [(resizer(emb), m.bool() if m is not None else None) for emb, m in text_prompts]
-            def detect_fn(trunk_out):
-                all_scores, all_masks = [], []
-                for emb, mask in resized:
-                    det = self.detector.forward_from_trunk(trunk_out, emb, mask)
-                    all_scores.append(det["scores"])
-                    all_masks.append(det["masks"])
-                return {"scores": torch.cat(all_scores, dim=1), "masks": torch.cat(all_masks, dim=1)}
-
-        if hasattr(self.tracker, 'track_video_with_detection'):
-            return self.tracker.track_video_with_detection(
-                backbone_fn, images, initial_masks, detect_fn,
-                new_det_thresh=new_det_thresh, max_objects=max_objects,
-                detect_interval=detect_interval, backbone_obj=bb, pbar=pbar,
-                target_device=target_device, target_dtype=target_dtype)
-        # SAM3 (non-multiplex) — no detection support, requires initial masks
-        if initial_masks is None:
-            raise ValueError("SAM3 (non-multiplex) requires initial_mask for video tracking")
-        return self.tracker.track_video(backbone_fn, images, initial_masks, pbar=pbar, backbone_obj=bb,
-                                         target_device=target_device, target_dtype=target_dtype)