Merge branch 'master' into range-type

.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

@@ -1,100 +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
-
-overrides:
-  # /ws uses HTTP 101 (Switching Protocols) — a legitimate response for a
-  # WebSocket upgrade, but not a 2xx, so operation-success-response fires
-  # as a false positive. OpenAPI 3.x has no native WebSocket support.
-  - files:
-      - "openapi.yaml#/paths/~1ws"
-    rules:
-      operation-success-response: off

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 +193,13 @@ 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)
 
-[Portable for Intel GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_intel.7z)
+[Experimental 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?
 

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/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": [
-        {
-          "label": "VIDEO",
-          "name": "VIDEO_1",
-          "type": "VIDEO",
-          "links": []
-        },
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": null
-        }
-      ],
-      "properties": {
-        "proxyWidgets": [
-          [
-            "9",
-            "value"
-          ],
-          [
-            "13",
-            "value"
-          ],
-          [
-            "1",
-            "model_name"
-          ]
-        ],
-        "enableTabs": false,
-        "tabWidth": 65,
-        "tabXOffset": 10,
-        "hasSecondTab": false,
-        "secondTabText": "Send Back",
-        "secondTabOffset": 80,
-        "secondTabWidth": 65,
-        "cnr_id": "comfy-core",
-        "ver": "0.19.3"
-      },
-      "widgets_values": [],
-      "title": "Frame Interpolation"
-    }
-  ],
-  "links": [],
-  "version": 0.4,
-  "definitions": {
-    "subgraphs": [
-      {
-        "id": "022693be-2baa-4009-870a-28921508a7ef",
-        "version": 1,
-        "state": {
-          "lastGroupId": 0,
-          "lastNodeId": 17,
-          "lastLinkId": 28,
-          "lastRerouteId": 0
-        },
-        "revision": 0,
-        "config": {},
-        "name": "Frame Interpolation",
-        "inputNode": {
-          "id": -10,
-          "bounding": [
-            -2810,
-            -3070,
-            159.7421875,
-            120
-          ]
-        },
-        "outputNode": {
-          "id": -20,
-          "bounding": [
-            -1270,
-            -3075,
-            120,
-            80
-          ]
-        },
-        "inputs": [
-          {
-            "id": "05e31c51-dcb6-4a1e-9651-1b9ad4f7a287",
-            "name": "video",
-            "type": "VIDEO",
-            "linkIds": [
-              2
-            ],
-            "localized_name": "video",
-            "pos": [
-              -2670.2578125,
-              -3050
-            ]
-          },
-          {
-            "id": "feecb409-7d1c-4a99-9c63-50c5fecdd3c9",
-            "name": "value",
-            "type": "INT",
-            "linkIds": [
-              22
-            ],
-            "label": "multiplier",
-            "pos": [
-              -2670.2578125,
-              -3030
-            ]
-          },
-          {
-            "id": "0b8a861b-b581-4068-9e8c-f8d15daf1ca6",
-            "name": "value_1",
-            "type": "BOOLEAN",
-            "linkIds": [
-              23
-            ],
-            "label": "enable_fps_multiplier",
-            "pos": [
-              -2670.2578125,
-              -3010
-            ]
-          },
-          {
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blueprints/Get Any Video Frame.json

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blueprints/Glow.json

@@ -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

@@ -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"
       }
     ]
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-}
+}

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)",
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@@ -1472,8 +1472,7 @@
         "extra": {
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         },
-        "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"
       },
       {
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@@ -1822,8 +1821,7 @@
         ],
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-        },
-        "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": {
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@@ -1468,8 +1468,7 @@
           "VHS_MetadataImage": true,
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         },
-        "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"
       }
     ]
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@@ -1490,4 +1489,4 @@
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   "version": 0.4
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+}

blueprints/Image Inpainting (Qwen-image).json

@@ -124,7 +124,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "Image Inpainting (Qwen-image)",
+        "name": "local-Image Inpainting (Qwen-image)",
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@@ -1548,8 +1548,7 @@
         "extra": {
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         },
-        "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"
       },
       {
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@@ -1908,8 +1907,7 @@
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-        },
-        "description": "Expands and softens mask edges to reduce visible seams after image processing."
+        }
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@@ -1925,4 +1923,4 @@
     "workflowRendererVersion": "LG"
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+}

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"
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+}

blueprints/Image Outpainting (Qwen-Image).json

@@ -204,7 +204,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "Image Outpainting (Qwen-Image)",
+        "name": "local-Image Outpainting (Qwen-Image)",
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@@ -1919,8 +1919,7 @@
         "extra": {
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         },
-        "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"
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@@ -2279,8 +2278,7 @@
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       {
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@@ -2735,8 +2733,7 @@
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-        },
-        "description": "Scales both image and mask together while preserving alignment for editing workflows."
+        }
       }
     ]
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@@ -2752,4 +2749,4 @@
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+}

blueprints/Image Segmentation (SAM3).json

@@ -1,714 +0,0 @@
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blueprints/Image Upscale(Z-image-Turbo).json

@@ -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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@@ -592,18 +480,19 @@
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               "tabXOffset": 10,
@@ -903,7 +769,8 @@
               "secondTabText": "Send Back",
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               "secondTabWidth": 65
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+            "widgets_values": []
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@@ -913,11 +780,11 @@
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+            "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": {
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@@ -976,14 +838,14 @@
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+            "order": 5,
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@@ -1017,7 +879,7 @@
                 "widget": {
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-                "link": 377
+                "link": null
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               {
                 "localized_name": "steps",
@@ -1077,14 +939,9 @@
               }
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             "properties": {
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               "cnr_id": "comfy-core",
               "ver": "0.5.1",
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@@ -1107,12 +964,12 @@
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+              136
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             "flags": {},
             "order": 12,
@@ -1150,14 +1007,9 @@
               }
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             "properties": {
+              "Node name for S&R": "GetImageSize",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
-              "ue_properties": {
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@@ -1165,23 +1017,23 @@
               "secondTabText": "Send Back",
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               "secondTabWidth": 65
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           {
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             "type": "EmptyQwenImageLayeredLatentImage",
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-            "showAdvanced": true,
             "inputs": [
               {
                 "localized_name": "width",
@@ -1231,14 +1083,9 @@
               }
             ],
             "properties": {
+              "Node name for S&R": "EmptyQwenImageLayeredLatentImage",
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               "ver": "0.5.1",
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@@ -1262,11 +1109,11 @@
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@@ -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",
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@@ -1349,8 +1191,8 @@
             "bounding": [
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+              421.6
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@@ -1549,48 +1391,16 @@
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         },
-        "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": {
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@@ -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": {
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           "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": {
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@@ -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 @@
-{
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-  "last_link_id": 0,
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-      "title": "Remove Background (BiRefNet)"
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-        "name": "Remove Background (BiRefNet)",
-        "description": "Removes or replaces image backgrounds using BiRefNet segmentation and alpha compositing.",
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-                "name": "bg_removal_model",
-                "type": "BACKGROUND_REMOVAL",
-                "link": 3
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-                "type": "MASK",
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-                "name": "bg_model",
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-                "type": "IMAGE",
-                "links": [
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-        "extra": {},
-        "category": "Image generation and editing/Background Removal"
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-  "extra": {}
-}

blueprints/Sharpen.json

@@ -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": {
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           "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,
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@@ -43,22 +44,6 @@
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         {
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           "type": "COMBO",
@@ -95,15 +80,15 @@
       "properties": {
         "proxyWidgets": [
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           ],
           [
@@ -112,23 +97,19 @@
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           [
             "3",
-            "steps"
+            "control_after_generate"
           ],
           [
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+            "-1",
             "unet_name"
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           [
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+            "-1",
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         "cnr_id": "comfy-core",
@@ -141,40 +122,48 @@
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@@ -288,8 +253,8 @@
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@@ -350,9 +315,9 @@
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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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@@ -436,12 +401,12 @@
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@@ -465,9 +430,9 @@
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@@ -475,21 +440,22 @@
               "secondTabText": "Send Back",
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@@ -527,21 +493,22 @@
               "secondTabText": "Send Back",
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                 "widget": {
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@@ -574,9 +541,9 @@
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@@ -601,15 +568,15 @@
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@@ -659,15 +626,15 @@
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@@ -727,77 +694,19 @@
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@@ -831,7 +740,7 @@
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@@ -840,7 +749,7 @@
                 "widget": {
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@@ -891,9 +800,9 @@
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@@ -905,23 +814,81 @@
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         "groups": [
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@@ -929,12 +896,12 @@
           },
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             "id": 3,
-            "title": "Step3 - Prompt",
+            "title": "Prompt",
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@@ -942,12 +909,12 @@
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+            "title": "Models",
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@@ -1060,41 +1027,25 @@
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@@ -1103,10 +1054,25 @@
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-        "description": "Generates images from text prompts using Z-Image-Turbo, Alibaba's distilled 6B DiT model."
+        "category": "Image generation and editing/Text to image"
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+}

blueprints/Text to Video (Wan 2.2).json

@@ -1572,8 +1572,7 @@
         "extra": {
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         },
-        "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"
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@@ -1587,4 +1586,4 @@
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+}

blueprints/Unsharp Mask.json

@@ -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"
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     ]
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-}
+}

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"
       }
     ]
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blueprints/Video Inpaint(Wan2.1 VACE).json

@@ -165,7 +165,7 @@
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         "config": {},
-        "name": "Video Inpaint (Wan 2.1 VACE)",
+        "name": "local-Video Inpaint(Wan2.1 VACE)",
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@@ -2368,8 +2368,7 @@
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-        "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"
       }
     ]
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blueprints/Video Segmentation (SAM3).json

@@ -1,827 +0,0 @@
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blueprints/Video Stitch.json

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               "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

@@ -242,7 +242,6 @@ def sample_euler_ancestral_RF(model, x, sigmas, extra_args=None, callback=None,
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
-    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
     s_in = x.new_ones([x.shape[0]])
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
@@ -374,7 +373,6 @@ def sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args=None, callback=None,
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
-    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
     s_in = x.new_ones([x.shape[0]])
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
@@ -688,7 +686,6 @@ def sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args=None, callback=Non
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
-    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
     s_in = x.new_ones([x.shape[0]])
     sigma_fn = lambda lbda: (lbda.exp() + 1) ** -1
     lambda_fn = lambda sigma: ((1-sigma)/sigma).log()
@@ -750,7 +747,6 @@ def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=N
     sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
-    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
 
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
@@ -836,7 +832,6 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
     model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
-    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
 
     old_denoised = None
     h, h_last = None, None
@@ -894,7 +889,6 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
     model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
-    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
 
     denoised_1, denoised_2 = None, None
     h, h_1, h_2 = None, None, None
@@ -1012,39 +1006,23 @@ def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None,
     return generic_step_sampler(model, x, sigmas, extra_args, callback, disable, noise_sampler, DDPMSampler_step)
 
 @torch.no_grad()
-def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, s_noise=1.0, s_noise_end=None, noise_clip_std=0.0):
-
-    # s_noise / s_noise_end: per-step noise multiplier, linearly interpolated across steps
-    # noise_clip_std: clamp injected noise to +/- N stddevs (0 disables).
-
+def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
-    n_steps = max(1, len(sigmas) - 1)
-    model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
-
-    s_start = float(s_noise)
-    s_end = s_start if s_noise_end is None else float(s_noise_end)
-    for i in trange(n_steps, disable=disable):
+    for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
         if callback is not None:
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
 
         x = denoised
         if sigmas[i + 1] > 0:
-            noise = noise_sampler(sigmas[i], sigmas[i + 1])
-            if noise_clip_std > 0:
-                clip_val = noise_clip_std * noise.std()
-                noise = noise.clamp(min=-clip_val, max=clip_val)
-            t = (i / (n_steps - 1)) if n_steps > 1 else 0.0
-            s_noise_i = s_start + (s_end - s_start) * t
-            if s_noise_i != 1.0:
-                noise = noise * s_noise_i
-            x = model_sampling.noise_scaling(sigmas[i + 1], noise, x)
+            x = model.inner_model.inner_model.model_sampling.noise_scaling(sigmas[i + 1], noise_sampler(sigmas[i], sigmas[i + 1]), x)
     return x
 
 
+
 @torch.no_grad()
 def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
     # From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/
@@ -1271,7 +1249,6 @@ def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=No
 
     model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling")
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
-    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
 
     uncond_denoised = None
 
@@ -1319,7 +1296,6 @@ def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
-    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
 
     temp = [0]
     def post_cfg_function(args):
@@ -1395,7 +1371,6 @@ def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
-    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
     s_in = x.new_ones([x.shape[0]])
     sigma_fn = lambda t: t.neg().exp()
     t_fn = lambda sigma: sigma.log().neg()
@@ -1529,7 +1504,6 @@ def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
-    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
     s_in = x.new_ones([x.shape[0]])
 
     def default_er_sde_noise_scaler(x):
@@ -1600,10 +1574,9 @@ def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=Non
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
+    inject_noise = eta > 0 and s_noise > 0
 
     model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
-    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
-    inject_noise = eta > 0 and s_noise > 0
     sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
@@ -1672,10 +1645,9 @@ def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, disable=Non
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
+    inject_noise = eta > 0 and s_noise > 0
 
     model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
-    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
-    inject_noise = eta > 0 and s_noise > 0
     sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
@@ -1741,7 +1713,6 @@ def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=F
     s_in = x.new_ones([x.shape[0]])
 
     model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling")
-    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
     lambdas = sigma_to_half_log_snr(sigmas, model_sampling=model_sampling)
 
@@ -1839,119 +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"
 
@@ -791,36 +783,3 @@ class ZImagePixelSpace(ChromaRadiance):
     No VAE encoding/decoding — the model operates directly on RGB pixels.
     """
     pass
-
-
-class HiDreamO1Pixel(ChromaRadiance):
-    """Pixel-space latent format for HiDream-O1.
-    No VAE — model patches/unpatches raw RGB internally with patch_size=32.
-    """
-    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/hidream_o1/attention.py

@@ -1,41 +0,0 @@
-"""HiDream-O1 two-pass attention: tokens [0, ar_len) are causal, [ar_len, T)
-attend full K/V. Splitting Q at the boundary avoids the (B, 1, T, T) additive
-mask the general-purpose path would build (~500 MB at T~16K) and lets the
-gen half hit the user's preferred backend via optimized_attention.
-"""
-
-import torch
-
-import comfy.ops
-from comfy.ldm.modules.attention import optimized_attention
-
-
-def make_two_pass_attention(ar_len: int, transformer_options=None):
-    """Build a two-pass attention callable. AR pass uses SDPA-causal directly, gen pass routes through optimized_attention.
-    The AR pass goes through SDPA directand bypasses wrappers, it is only ~1% of T at typical edit sizes.
-    """
-
-    def two_pass_attention(q, k, v, heads, **kwargs):
-        B, H, T, D = q.shape
-
-        if T < k.shape[2]: # KV-cache hot path: Q is shorter than K/V (cached AR prefix is in K/V only), all fresh Q positions are in the gen region, single full-attention call
-            out = optimized_attention(q, k, v, heads, mask=None, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options)
-        elif ar_len >= T:
-            out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=True)
-        elif ar_len <= 0:
-            out = optimized_attention(q, k, v, heads, mask=None, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options)
-        else:
-            out_ar = comfy.ops.scaled_dot_product_attention(
-                q[:, :, :ar_len], k[:, :, :ar_len], v[:, :, :ar_len],
-                attn_mask=None, dropout_p=0.0, is_causal=True,
-            )
-            out_gen = optimized_attention(
-                q[:, :, ar_len:], k, v, heads,
-                mask=None, skip_reshape=True, skip_output_reshape=True,
-                transformer_options=transformer_options,
-            )
-            out = torch.cat([out_ar, out_gen], dim=2)
-
-        return out.transpose(1, 2).reshape(B, T, H * D)
-
-    return two_pass_attention

comfy/ldm/hidream_o1/conditioning.py

@@ -1,230 +0,0 @@
-"""HiDream-O1 conditioning prep — ref-image dual path + extra_conds assembly.
-
-Each ref image goes through two paths: a 32x32 patchified stream concatenated
-to the noised target, and a Qwen3-VL ViT path producing tokens that scatter
-into input_ids at <|image_pad|> positions.
-"""
-
-from typing import List
-
-import torch
-
-import comfy.utils
-from comfy.text_encoders.qwen_vl import process_qwen2vl_images
-
-from .utils import (PATCH_SIZE, calculate_dimensions, cond_image_size, ref_max_size, resize_tensor)
-
-# Qwen3-VL ViT preprocessing constants (preprocessor_config.json).
-VIT_PATCH = 16
-VIT_MERGE = 2
-VIT_IMAGE_MEAN = [0.5, 0.5, 0.5]
-VIT_IMAGE_STD = [0.5, 0.5, 0.5]
-
-
-def prepare_ref_images(
-    ref_images: List[torch.Tensor],
-    target_h: int,
-    target_w: int,
-    device: torch.device,
-    dtype: torch.dtype,
-):
-    """Build the dual-path tensors for K reference images at (target_h, target_w).
-
-    Returns None for K=0, else a dict with ref_patches, ref_pixel_values,
-    ref_image_grid_thw, per_ref_vit_tokens, per_ref_patch_grids.
-    """
-    K = len(ref_images)
-    if K == 0:
-        return None
-    max_size = ref_max_size(max(target_h, target_w), K)
-    cis = cond_image_size(K)
-
-    refs_t = [img[0].clamp(0, 1).permute(2, 0, 1).unsqueeze(0).contiguous().float() for img in ref_images]
-    refs_t = [resize_tensor(t, max_size, PATCH_SIZE) for t in refs_t]
-
-    # 32-patch path.
-    ref_patches_per = []
-    per_ref_patch_grids = []
-    for t in refs_t:
-        t_norm = (t.squeeze(0) - 0.5) / 0.5  # (3, H, W) in [-1, 1]
-        h_p, w_p = t_norm.shape[-2] // PATCH_SIZE, t_norm.shape[-1] // PATCH_SIZE
-        per_ref_patch_grids.append((h_p, w_p))
-        patches = (
-            t_norm.reshape(3, h_p, PATCH_SIZE, w_p, PATCH_SIZE)
-            .permute(1, 3, 0, 2, 4)
-            .reshape(h_p * w_p, 3 * PATCH_SIZE * PATCH_SIZE)
-        )
-        ref_patches_per.append(patches)
-    ref_patches = torch.cat(ref_patches_per, dim=0).unsqueeze(0).to(device=device, dtype=dtype)
-
-    # ViT path.
-    refs_vlm_t = []
-    for t in refs_t:
-        _, _, h, w = t.shape
-        cond_w, cond_h = calculate_dimensions(cis, w / h)
-        cond_w = max(cond_w, VIT_PATCH * VIT_MERGE)
-        cond_h = max(cond_h, VIT_PATCH * VIT_MERGE)
-        refs_vlm_t.append(comfy.utils.common_upscale(t, cond_w, cond_h, "lanczos", "disabled"))
-
-    pv_list, grid_list, per_ref_vit_tokens = [], [], []
-    for t_v in refs_vlm_t:
-        pv, grid_thw = process_qwen2vl_images(
-            t_v.permute(0, 2, 3, 1),
-            min_pixels=0, max_pixels=10**12,
-            patch_size=VIT_PATCH, merge_size=VIT_MERGE,
-            image_mean=VIT_IMAGE_MEAN, image_std=VIT_IMAGE_STD,
-        )
-        grid_thw = grid_thw[0]
-        pv_list.append(pv.to(device=device, dtype=dtype))
-        grid_list.append(grid_thw.to(device=device))
-        # Post-merge token count = number of <|image_pad|> tokens this image expands to in input_ids.
-        gh, gw = int(grid_thw[1].item()), int(grid_thw[2].item())
-        per_ref_vit_tokens.append((gh // VIT_MERGE) * (gw // VIT_MERGE))
-
-    return {
-        "ref_patches": ref_patches,
-        "ref_pixel_values": torch.cat(pv_list, dim=0),
-        "ref_image_grid_thw": torch.stack(grid_list, dim=0),
-        "per_ref_vit_tokens": per_ref_vit_tokens,
-        "per_ref_patch_grids": per_ref_patch_grids,
-    }
-
-
-def build_ref_input_ids(
-    text_input_ids: torch.Tensor,
-    per_ref_vit_tokens: List[int],
-    image_token_id: int,
-    vision_start_id: int,
-    vision_end_id: int,
-):
-    """Splice [vision_start, image_pad*N, vision_end] blocks into input_ids
-    after the [im_start, user, \\n] prefix (matches original chat template).
-    """
-    ids = text_input_ids[0].tolist()
-    inserted = []
-    for n_pad in per_ref_vit_tokens:
-        inserted.extend([vision_start_id] + [image_token_id] * n_pad + [vision_end_id])
-    new_ids = ids[:3] + inserted + ids[3:]  # 3 = len([im_start, user, \n])
-    return torch.tensor([new_ids], dtype=text_input_ids.dtype, device=text_input_ids.device)
-
-
-def build_extra_conds(
-    text_input_ids: torch.Tensor,
-    noise: torch.Tensor,
-    ref_images: List[torch.Tensor] = None,
-    target_patch_size: int = 32,
-):
-    """Assemble all conditioning tensors for HiDreamO1Transformer.forward:
-    input_ids (with ref-vision tokens spliced in for the edit/IP path),
-    position_ids (MRoPE), token_types, vinput_mask, plus the ref
-    dual-path tensors when refs are provided.
-    """
-    from .utils import get_rope_index_fix_point
-    from comfy.text_encoders.hidream_o1 import (
-        IMAGE_TOKEN_ID, VISION_START_ID, VISION_END_ID,
-    )
-
-    if text_input_ids.dim() == 1:
-        text_input_ids = text_input_ids.unsqueeze(0)
-    text_input_ids = text_input_ids.long().to(noise.device)
-    B = noise.shape[0]
-    if text_input_ids.shape[0] == 1 and B > 1:
-        text_input_ids = text_input_ids.expand(B, -1)
-
-    H, W = noise.shape[-2], noise.shape[-1]
-    h_p, w_p = H // target_patch_size, W // target_patch_size
-    image_len = h_p * w_p
-    image_grid_thw_tgt = torch.tensor(
-        [[1, h_p, w_p]], dtype=torch.long, device=text_input_ids.device,
-    )
-
-    out = {}
-    if ref_images:
-        ref = prepare_ref_images(ref_images, H, W, device=noise.device, dtype=noise.dtype)
-        text_input_ids = build_ref_input_ids(
-            text_input_ids, ref["per_ref_vit_tokens"],
-            IMAGE_TOKEN_ID, VISION_START_ID, VISION_END_ID,
-        )
-        new_txt_len = text_input_ids.shape[1]
-
-        # Each ref's patchified stream gets a [vision_start, image_pad*N-1]
-        # block in the position-id stream after the noised target.
-        ref_grid_lengths = [hp * wp for (hp, wp) in ref["per_ref_patch_grids"]]
-        tgt_vision = torch.full((1, image_len), IMAGE_TOKEN_ID,
-                                dtype=text_input_ids.dtype, device=text_input_ids.device)
-        tgt_vision[:, 0] = VISION_START_ID
-        ref_vision_blocks = []
-        for rl in ref_grid_lengths:
-            blk = torch.full((1, rl), IMAGE_TOKEN_ID,
-                             dtype=text_input_ids.dtype, device=text_input_ids.device)
-            blk[:, 0] = VISION_START_ID
-            ref_vision_blocks.append(blk)
-        ref_vision_cat = torch.cat([tgt_vision] + ref_vision_blocks, dim=1)
-        input_ids_pad = torch.cat([text_input_ids, ref_vision_cat], dim=-1)
-        total_ref_patches_len = sum(ref_grid_lengths)
-        total_len = new_txt_len + image_len + total_ref_patches_len
-
-        # K (ViT, post-merge) + 1 (target) + K (ref-patches) image grids.
-        K = len(ref_images)
-        igthw_cond = ref["ref_image_grid_thw"].clone()
-        igthw_cond[:, 1] //= 2
-        igthw_cond[:, 2] //= 2
-        image_grid_thw_ref = torch.tensor(
-            [[1, hp, wp] for (hp, wp) in ref["per_ref_patch_grids"]],
-            dtype=torch.long, device=text_input_ids.device,
-        )
-        igthw_all = torch.cat([
-            igthw_cond.to(text_input_ids.device),
-            image_grid_thw_tgt,
-            image_grid_thw_ref,
-        ], dim=0)
-        position_ids, _ = get_rope_index_fix_point(
-            spatial_merge_size=1,
-            image_token_id=IMAGE_TOKEN_ID,
-            vision_start_token_id=VISION_START_ID,
-            input_ids=input_ids_pad, image_grid_thw=igthw_all,
-            attention_mask=None,
-            skip_vision_start_token=[0] * K + [1] + [1] * K,
-            fix_point=4096,
-        )
-
-        # tms + target_image + ref_patches are all gen.
-        tms_pos = new_txt_len - 1
-        ar_len = tms_pos
-        token_types = torch.zeros(B, total_len, dtype=torch.long, device=noise.device)
-        token_types[:, tms_pos:] = 1
-        vinput_mask = torch.zeros(B, total_len, dtype=torch.bool, device=noise.device)
-        vinput_mask[:, new_txt_len:] = True
-
-        # Leading batch dim sidesteps CONDRegular.process_cond's repeat_to_batch_size truncation
-        out["ref_pixel_values"] = ref["ref_pixel_values"].unsqueeze(0)
-        out["ref_image_grid_thw"] = ref["ref_image_grid_thw"].unsqueeze(0)
-        out["ref_patches"] = ref["ref_patches"]
-    else:
-        # T2I: text + noised target only, vision_start replaces the first image token
-        txt_len = text_input_ids.shape[1]
-        total_len = txt_len + image_len
-        vision_tokens = torch.full((B, image_len), IMAGE_TOKEN_ID,
-                                   dtype=text_input_ids.dtype, device=text_input_ids.device)
-        vision_tokens[:, 0] = VISION_START_ID
-        input_ids_pad = torch.cat([text_input_ids, vision_tokens], dim=-1)
-        position_ids, _ = get_rope_index_fix_point(
-            spatial_merge_size=1,
-            image_token_id=IMAGE_TOKEN_ID,
-            vision_start_token_id=VISION_START_ID,
-            input_ids=input_ids_pad, image_grid_thw=image_grid_thw_tgt,
-            attention_mask=None,
-            skip_vision_start_token=[1],
-        )
-        ar_len = txt_len - 1
-        token_types = torch.zeros(B, total_len, dtype=torch.long, device=noise.device)
-        token_types[:, ar_len:] = 1
-        vinput_mask = torch.zeros(B, total_len, dtype=torch.bool, device=noise.device)
-        vinput_mask[:, txt_len:] = True
-
-    out["input_ids"] = text_input_ids
-    out["position_ids"] = position_ids[:, 0].unsqueeze(0) # Collapse position_ids batch and add a leading dim so CONDRegular's batch-resize doesn't truncate the 3-axis MRoPE dim
-    out["token_types"] = token_types
-    out["vinput_mask"] = vinput_mask
-    out["ar_len"] = ar_len
-    return out

comfy/ldm/hidream_o1/model.py

@@ -1,306 +0,0 @@
-"""HiDream-O1-Image transformer.
-
-Pixel-space DiT built on Qwen3-VL: the vision tower (Qwen35VisionModel)
-encodes ref images, the Qwen3-VL-8B decoder (Llama2_ with interleaved MRoPE)
-processes a unified text+image sequence, and 32x32 patch embed/unembed
-shims map raw RGB in and out of LLM hidden space. The Qwen3-VL deepstack
-mergers go unused — their weights are dropped at load.
-"""
-
-from dataclasses import dataclass, field
-from typing import List, Optional
-
-import einops
-import torch
-import torch.nn as nn
-
-import comfy.patcher_extension
-from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder
-from comfy.text_encoders.llama import Llama2_
-from comfy.text_encoders.qwen35 import Qwen35VisionModel
-
-from .attention import make_two_pass_attention
-
-
-IMAGE_TOKEN_ID = 151655   # Qwen3-VL <|image_pad|>
-TMS_TOKEN_ID = 151673     # HiDream-O1 <|tms_token|>
-PATCH_SIZE = 32
-
-
-@dataclass
-class HiDreamO1TextConfig:
-    """Qwen3-VL-8B text-decoder dims (matches public Qwen3-VL-8B-Instruct)."""
-    vocab_size: int = 151936
-    hidden_size: int = 4096
-    intermediate_size: int = 12288
-    num_hidden_layers: int = 36
-    num_attention_heads: int = 32
-    num_key_value_heads: int = 8
-    head_dim: int = 128
-    max_position_embeddings: int = 128000
-    rms_norm_eps: float = 1e-6
-    rope_theta: float = 5000000.0
-    rope_scale: Optional[float] = None
-    rope_dims: List[int] = field(default_factory=lambda: [24, 20, 20])
-    interleaved_mrope: bool = True
-    transformer_type: str = "llama"
-    rms_norm_add: bool = False
-    mlp_activation: str = "silu"
-    qkv_bias: bool = False
-    q_norm: str = "gemma3"
-    k_norm: str = "gemma3"
-    final_norm: bool = True
-    lm_head: bool = False
-    stop_tokens: List[int] = field(default_factory=lambda: [151643, 151645])
-
-
-QWEN3VL_VISION_DEFAULTS = dict(
-    hidden_size=1152,
-    num_heads=16,
-    intermediate_size=4304,
-    depth=27,
-    patch_size=16,
-    temporal_patch_size=2,
-    in_channels=3,
-    spatial_merge_size=2,
-    num_position_embeddings=2304,
-    deepstack_visual_indexes=(8, 16, 24),
-    out_hidden_size=4096,  # final merger projects directly into LLM hidden
-)
-
-
-class BottleneckPatchEmbed(nn.Module):
-    # 3072 -> 1024 -> 4096 (raw 32x32 RGB patch -> bottleneck -> LLM hidden).
-    def __init__(self, patch_size=32, in_chans=3, pca_dim=1024, embed_dim=4096, bias=True, device=None, dtype=None, ops=None):
-        super().__init__()
-        self.proj1 = ops.Linear(patch_size * patch_size * in_chans, pca_dim, bias=False, device=device, dtype=dtype)
-        self.proj2 = ops.Linear(pca_dim, embed_dim, bias=bias, device=device, dtype=dtype)
-
-    def forward(self, x):
-        return self.proj2(self.proj1(x))
-
-
-class FinalLayer(nn.Module):
-    # 4096 -> 3072 (LLM hidden -> flat pixel patch).
-    def __init__(self, hidden_size, patch_size=32, out_channels=3, device=None, dtype=None, ops=None):
-        super().__init__()
-        self.linear = ops.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, device=device, dtype=dtype)
-
-    def forward(self, x):
-        return self.linear(x)
-
-
-class HiDreamO1Transformer(nn.Module):
-    """HiDream-O1 unified pixel-level transformer."""
-
-    def __init__(self, image_model=None, dtype=None, device=None, operations=None,
-                 text_config_overrides=None, vision_config_overrides=None, **kwargs):
-        super().__init__()
-        self.dtype = dtype
-
-        text_cfg = HiDreamO1TextConfig(**(text_config_overrides or {}))
-        vision_cfg = dict(QWEN3VL_VISION_DEFAULTS)
-        if vision_config_overrides:
-            vision_cfg.update(vision_config_overrides)
-        vision_cfg["out_hidden_size"] = text_cfg.hidden_size
-
-        self.text_config = text_cfg
-        self.vision_config = vision_cfg
-        self.hidden_size = text_cfg.hidden_size
-        self.patch_size = PATCH_SIZE
-        self.in_channels = 3
-        self.tms_token_id = TMS_TOKEN_ID
-
-        self.visual = Qwen35VisionModel(vision_cfg, device=device, dtype=dtype, ops=operations)
-        self.language_model = Llama2_(text_cfg, device=device, dtype=dtype, ops=operations)
-        self.t_embedder1 = TimestepEmbedder(
-            text_cfg.hidden_size, device=device, dtype=dtype, operations=operations,
-        )
-        self.x_embedder = BottleneckPatchEmbed(
-            patch_size=self.patch_size, in_chans=self.in_channels,
-            pca_dim=text_cfg.hidden_size // 4, embed_dim=text_cfg.hidden_size,
-            bias=True, device=device, dtype=dtype, ops=operations,
-        )
-        self.final_layer2 = FinalLayer(
-            text_cfg.hidden_size, patch_size=self.patch_size,
-            out_channels=self.in_channels, device=device, dtype=dtype, ops=operations,
-        )
-
-        self._visual_cache = None
-        self._kv_cache_entries = []
-
-    def clear_kv_cache(self):
-        self._kv_cache_entries = []
-        self._visual_cache = None
-
-    def forward(self, x, timesteps, context=None, transformer_options={}, **kwargs):
-        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, timesteps, context, transformer_options, **kwargs)
-
-    def _forward(self, x, timesteps, context=None, transformer_options={}, input_ids=None, attention_mask=None, position_ids=None,
-                 vinput_mask=None, ar_len=None, ref_pixel_values=None, ref_image_grid_thw=None, ref_patches=None, **kwargs):
-        """Returns flow-match velocity (x - x_pred) / sigma"""
-
-        if input_ids is None or position_ids is None:
-            raise ValueError("HiDreamO1Transformer requires input_ids and position_ids in conditioning")
-
-        B, _, H, W = x.shape
-        h_p, w_p = H // self.patch_size, W // self.patch_size
-        tgt_image_len = h_p * w_p
-
-        z = einops.rearrange(
-            x, 'B C (H p1) (W p2) -> B (H W) (C p1 p2)',
-            p1=self.patch_size, p2=self.patch_size,
-        )
-        vinputs = torch.cat([z, ref_patches.to(z.dtype)], dim=1) if ref_patches is not None else z
-
-        inputs_embeds = self.language_model.embed_tokens(input_ids).to(x.dtype)
-
-        if ref_pixel_values is not None and ref_image_grid_thw is not None:
-            # ViT output is constant across sampling steps within a generation
-            # identity-key by the input tensor so refs don't recompute every step.
-            cached = self._visual_cache
-            if cached is not None and cached[0] is ref_pixel_values:
-                image_embeds = cached[1]
-            else:
-                ref_pv = ref_pixel_values.to(inputs_embeds.device)
-                ref_grid = ref_image_grid_thw.to(inputs_embeds.device).long()
-                # extra_conds wraps with a leading batch dim; refs are model-level so [0] always recovers them.
-                if ref_pv.dim() == 3:
-                    ref_pv = ref_pv[0]
-                if ref_grid.dim() == 3:
-                    ref_grid = ref_grid[0]
-                image_embeds = self.visual(ref_pv, ref_grid).to(inputs_embeds.dtype)
-                self._visual_cache = (ref_pixel_values, image_embeds)
-            # image_pad positions identical across batch (input_ids shared cond/uncond).
-            image_idx = (input_ids[0] == IMAGE_TOKEN_ID).nonzero(as_tuple=True)[0]
-            if image_idx.shape[0] != image_embeds.shape[0]:
-                raise ValueError(
-                    f"Image-token count {image_idx.shape[0]} != ViT output count "
-                    f"{image_embeds.shape[0]}; check tokenizer/processor alignment."
-                )
-            inputs_embeds[:, image_idx] = image_embeds.unsqueeze(0).expand(B, -1, -1)
-
-        sigma = timesteps.float() / 1000.0
-        t_pixeldit = 1.0 - sigma
-        t_emb = self.t_embedder1(t_pixeldit * 1000, inputs_embeds.dtype)
-        tms_mask_3d = (input_ids == self.tms_token_id).unsqueeze(-1).expand_as(inputs_embeds)
-        inputs_embeds = torch.where(tms_mask_3d, t_emb.unsqueeze(1).expand_as(inputs_embeds), inputs_embeds)
-
-        vinputs_embedded = self.x_embedder(vinputs.to(inputs_embeds.dtype))
-        inputs_embeds = torch.cat([inputs_embeds, vinputs_embedded], dim=1)
-
-        # extra_conds stores position_ids as (1, 3, T); process_cond repeats dim 0 to B. Take row 0.
-        freqs_cis = self.language_model.compute_freqs_cis(position_ids[0].to(x.device), x.device)
-        freqs_cis = tuple(t.to(x.dtype) for t in freqs_cis)
-
-        two_pass_attn = make_two_pass_attention(ar_len, transformer_options=transformer_options)
-        patches_replace = transformer_options.get("patches_replace", {})
-        blocks_replace = patches_replace.get("dit", {})
-        transformer_options["total_blocks"] = len(self.language_model.layers)
-        transformer_options["block_type"] = "double"
-
-        # Cache prefix K/V across steps. Key includes input_ids (prompt), ref_id
-        # (refs scatter into inputs_embeds), and position_ids (RoPE baked into cached K).
-        can_cache = not blocks_replace and ar_len > 0
-        cache_len = ar_len if can_cache else 0
-        ref_id = id(ref_pixel_values) if ref_pixel_values is not None else None
-        pos_ids_key = position_ids[..., :cache_len] if can_cache else position_ids
-        cache_entries = self._kv_cache_entries
-        # Drop stale entries from a previous device (model was unloaded and reloaded).
-        if cache_entries and cache_entries[0]["input_ids"].device != input_ids.device:
-            cache_entries = []
-            self._kv_cache_entries = []
-        kv_cache = None
-        if can_cache:
-            for entry in cache_entries:
-                ck = entry["input_ids"]
-                ep = entry["position_ids"]
-                if (entry["cache_len"] == cache_len
-                        and ck.shape == input_ids.shape and torch.equal(ck, input_ids)
-                        and entry["ref_id"] == ref_id
-                        and ep.shape == pos_ids_key.shape and torch.equal(ep, pos_ids_key)):
-                    kv_cache = entry
-                    break
-
-        if kv_cache is not None:
-            # Hot path: project Q/K/V only for fresh positions; past_key_value prepends cached AR K/V.
-            hidden_states = inputs_embeds[:, cache_len:]
-            sliced_freqs = tuple(t[..., cache_len:, :] for t in freqs_cis)
-            for i, layer in enumerate(self.language_model.layers):
-                transformer_options["block_index"] = i
-                K_i, V_i = kv_cache["kv"][i]
-                hidden_states, _ = layer(
-                    x=hidden_states, attention_mask=None, freqs_cis=sliced_freqs, optimized_attention=two_pass_attn,
-                    past_key_value=(K_i, V_i, cache_len),
-                )
-        else:
-            # Cold path: run full sequence; if cacheable, snapshot K/V at AR positions.
-            snapshots = [] if can_cache else None
-            past_kv_cold = () if can_cache else None
-            hidden_states = inputs_embeds
-            for i, layer in enumerate(self.language_model.layers):
-                transformer_options["block_index"] = i
-                if ("double_block", i) in blocks_replace:
-                    def block_wrap(args, _layer=layer):
-                        out = {}
-                        out["x"], _ = _layer(
-                            x=args["x"], attention_mask=args.get("attention_mask"),
-                            freqs_cis=args["freqs_cis"], optimized_attention=args["optimized_attention"],
-                            past_key_value=None,
-                        )
-                        return out
-                    out = blocks_replace[("double_block", i)](
-                        {"x": hidden_states, "attention_mask": None,
-                         "freqs_cis": freqs_cis, "optimized_attention": two_pass_attn,
-                         "transformer_options": transformer_options},
-                        {"original_block": block_wrap},
-                    )
-                    hidden_states = out["x"]
-                else:
-                    hidden_states, present_kv = layer(
-                        x=hidden_states, attention_mask=None,
-                        freqs_cis=freqs_cis, optimized_attention=two_pass_attn,
-                        past_key_value=past_kv_cold,
-                    )
-                    if snapshots is not None:
-                        K, V, _ = present_kv
-                        snapshots.append((K[:, :, :cache_len].contiguous(),
-                                          V[:, :, :cache_len].contiguous()))
-            if snapshots is not None:
-                # Cap at 2 entries (cond + uncond). Multi-cond workflows LRU-evict.
-                new_entry = {
-                    "input_ids": input_ids.clone(),
-                    "cache_len": cache_len,
-                    "kv": snapshots,
-                    "ref_id": ref_id,
-                    "position_ids": pos_ids_key.clone(),
-                }
-                self._kv_cache_entries = (cache_entries + [new_entry])[-2:]
-
-        if self.language_model.norm is not None:
-            hidden_states = self.language_model.norm(hidden_states)
-
-        # Slice target-image positions before the final projection so the Linear only runs on tgt_image_len tokens.
-        # In the hot path hidden_states starts at original position cache_len, so masks/indices shift by cache_len.
-        sliced_offset = cache_len if kv_cache is not None else 0
-        if vinput_mask is not None:
-            vmask = vinput_mask.to(x.device).bool()
-            if sliced_offset > 0:
-                vmask = vmask[:, sliced_offset:]
-            target_hidden = hidden_states[vmask].view(B, -1, hidden_states.shape[-1])[:, :tgt_image_len]
-        else:
-            txt_seq_len = input_ids.shape[1]
-            start = txt_seq_len - sliced_offset
-            target_hidden = hidden_states[:, start:start + tgt_image_len]
-        x_pred_tgt = self.final_layer2(target_hidden)
-
-        # fp32 final subtraction, bf16 here noticeably degrades samples.
-        x_pred_img = einops.rearrange(
-            x_pred_tgt, 'B (H W) (C p1 p2) -> B C (H p1) (W p2)',
-            H=h_p, W=w_p, p1=self.patch_size, p2=self.patch_size,
-        )
-        return (x.float() - x_pred_img.float()) / sigma.view(B, 1, 1, 1).clamp_min(1e-3)

comfy/ldm/hidream_o1/utils.py

@@ -1,173 +0,0 @@
-"""HiDream-O1 input-prep helpers: image/resolution math and unified-sequence
-RoPE position-id assembly. The fix_point offset in get_rope_index_fix_point
-lets the target image and patchified ref images share spatial RoPE positions
-despite living at different sequence indices — same 2D image plane.
-"""
-
-import math
-from typing import Optional
-
-import torch
-
-
-PATCH_SIZE = 32
-CONDITION_IMAGE_SIZE = 384  # ViT-side base size for ref images
-
-
-def resize_tensor(img_t, image_size, patch_size=16):
-    """img_t: (1, 3, H, W) float [0, 1]. Fit to image_size**2 area, patch-aligned, center-cropped."""
-
-    while min(img_t.shape[-2], img_t.shape[-1]) >= 2 * image_size: # Pre-halves with 2x2 box averaging while the image is still very large
-        img_t = torch.nn.functional.avg_pool2d(img_t, kernel_size=2, stride=2)
-
-    _, _, height, width = img_t.shape
-    m = patch_size
-    s_max = image_size * image_size
-    scale = math.sqrt(s_max / (width * height))
-
-    candidates = [
-        (round(width * scale) // m * m, round(height * scale) // m * m),
-        (round(width * scale) // m * m, math.floor(height * scale) // m * m),
-        (math.floor(width * scale) // m * m, round(height * scale) // m * m),
-        (math.floor(width * scale) // m * m, math.floor(height * scale) // m * m),
-    ]
-    candidates = sorted(candidates, key=lambda x: x[0] * x[1], reverse=True)
-    new_size = candidates[-1]
-    for c in candidates:
-        if c[0] * c[1] <= s_max:
-            new_size = c
-            break
-
-    new_w, new_h = new_size
-    s1 = width / new_w
-    s2 = height / new_h
-    if s1 < s2:
-        resize_w, resize_h = new_w, round(height / s1)
-    else:
-        resize_w, resize_h = round(width / s2), new_h
-    img_t = torch.nn.functional.interpolate(img_t, size=(resize_h, resize_w), mode="bicubic")
-    top = (resize_h - new_h) // 2
-    left = (resize_w - new_w) // 2
-    return img_t[..., top:top + new_h, left:left + new_w]
-
-
-def calculate_dimensions(max_size, ratio):
-    """(W, H) for an aspect ratio fitting in max_size**2 area, 32-aligned."""
-    width = math.sqrt(max_size * max_size * ratio)
-    height = width / ratio
-    width = int(width / 32) * 32
-    height = int(height / 32) * 32
-    return width, height
-
-
-def ref_max_size(target_max_dim, k):
-    """K-dependent ref-image max dim before patchifying."""
-    if k == 1:
-        return target_max_dim
-    if k == 2:
-        return target_max_dim * 48 // 64
-    if k <= 4:
-        return target_max_dim // 2
-    if k <= 8:
-        return target_max_dim * 24 // 64
-    return target_max_dim // 4
-
-
-def cond_image_size(k):
-    """K-dependent ViT-side image size."""
-    if k <= 4:
-        return CONDITION_IMAGE_SIZE
-    if k <= 8:
-        return CONDITION_IMAGE_SIZE * 48 // 64
-    return CONDITION_IMAGE_SIZE // 2
-
-
-def get_rope_index_fix_point(
-    spatial_merge_size: int,
-    image_token_id: int,
-    vision_start_token_id: int,
-    input_ids: Optional[torch.LongTensor] = None,
-    image_grid_thw: Optional[torch.LongTensor] = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    skip_vision_start_token=None,
-    fix_point: int = 4096,
-):
-    mrope_position_deltas = []
-    if input_ids is not None and image_grid_thw is not None:
-        total_input_ids = input_ids
-        if attention_mask is None:
-            attention_mask = torch.ones_like(total_input_ids)
-        position_ids = torch.ones(
-            3, input_ids.shape[0], input_ids.shape[1],
-            dtype=input_ids.dtype, device=input_ids.device,
-        )
-        attention_mask = attention_mask.to(total_input_ids.device)
-        for i, input_ids_b in enumerate(total_input_ids):
-            fp = fix_point
-            image_index = 0
-            input_ids_b = input_ids_b[attention_mask[i] == 1]
-            vision_start_indices = torch.argwhere(input_ids_b == vision_start_token_id).squeeze(1)
-            vision_tokens = input_ids_b[vision_start_indices + 1]
-            image_nums = (vision_tokens == image_token_id).sum()
-            input_tokens = input_ids_b.tolist()
-            llm_pos_ids_list = []
-            st = 0
-            remain_images = image_nums
-            for _ in range(image_nums):
-                if image_token_id in input_tokens and remain_images > 0:
-                    ed = input_tokens.index(image_token_id, st)
-                else:
-                    ed = len(input_tokens) + 1
-                t = image_grid_thw[image_index][0]
-                h = image_grid_thw[image_index][1]
-                w = image_grid_thw[image_index][2]
-                image_index += 1
-                remain_images -= 1
-                llm_grid_t = t.item()
-                llm_grid_h = h.item() // spatial_merge_size
-                llm_grid_w = w.item() // spatial_merge_size
-                text_len = ed - st
-                text_len -= skip_vision_start_token[image_index - 1]
-                text_len = max(0, text_len)
-                st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
-                llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
-
-                t_index = torch.arange(llm_grid_t).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w).flatten()
-                h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(llm_grid_t, -1, llm_grid_w).flatten()
-                w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(llm_grid_t, llm_grid_h, -1).flatten()
-
-                if skip_vision_start_token[image_index - 1]:
-                    if fp > 0:
-                        fp = fp - st_idx
-                    llm_pos_ids_list.append(torch.stack([t_index, h_index, w_index]) + fp + st_idx)
-                    fp = 0
-                else:
-                    llm_pos_ids_list.append(torch.stack([t_index, h_index, w_index]) + text_len + st_idx)
-                st = ed + llm_grid_t * llm_grid_h * llm_grid_w
-
-            if st < len(input_tokens):
-                st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
-                text_len = len(input_tokens) - st
-                llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
-
-            llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
-            position_ids[..., i, attention_mask[i] == 1] = llm_positions.to(position_ids.device)
-            mrope_position_deltas.append(llm_positions.max() + 1 - len(total_input_ids[i]))
-        mrope_position_deltas = torch.tensor(mrope_position_deltas, device=input_ids.device).unsqueeze(1)
-        return position_ids, mrope_position_deltas
-
-    if attention_mask is not None:
-        position_ids = attention_mask.long().cumsum(-1) - 1
-        position_ids.masked_fill_(attention_mask == 0, 1)
-        position_ids = position_ids.unsqueeze(0).expand(3, -1, -1).to(attention_mask.device)
-        max_position_ids = position_ids.max(0, keepdim=False)[0].max(-1, keepdim=True)[0]
-        mrope_position_deltas = max_position_ids + 1 - attention_mask.shape[-1]
-    else:
-        position_ids = (
-            torch.arange(input_ids.shape[1], device=input_ids.device)
-            .view(1, 1, -1).expand(3, input_ids.shape[0], -1)
-        )
-        mrope_position_deltas = torch.zeros(
-            [input_ids.shape[0], 1], device=input_ids.device, dtype=input_ids.dtype,
-        )
-    return position_ids, mrope_position_deltas

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/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/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

@@ -561,8 +561,7 @@ class SAM3Model(nn.Module):
         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):
+                       new_det_thresh=0.5, max_objects=0, detect_interval=1):
         """Track video with optional per-frame text-prompted detection."""
         bb = self.detector.backbone["vision_backbone"]
 
@@ -590,10 +589,8 @@ class SAM3Model(nn.Module):
             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)
+                detect_interval=detect_interval, backbone_obj=bb, pbar=pbar)
         # 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)
+        return self.tracker.track_video(backbone_fn, images, initial_masks, pbar=pbar, backbone_obj=bb)

comfy/ldm/sam3/tracker.py

@@ -200,13 +200,8 @@ def pack_masks(masks):
 
 def unpack_masks(packed):
     """Unpack bit-packed [*, H, W//8] uint8 to bool [*, H, W*8]."""
-    bits = torch.tensor([1, 2, 4, 8, 16, 32, 64, 128], dtype=torch.uint8, device=packed.device)
-    return (packed.unsqueeze(-1) & bits).bool().view(*packed.shape[:-1], -1)
-
-
-def _prep_frame(images, idx, device, dt, size):
-    """Slice CPU full-res frames, transfer to GPU in target dtype, and resize to (size, size)."""
-    return comfy.utils.common_upscale(images[idx].to(device=device, dtype=dt), size, size, "bicubic", crop="disabled")
+    shifts = torch.arange(8, device=packed.device)
+    return ((packed.unsqueeze(-1) >> shifts) & 1).view(*packed.shape[:-1], -1).bool()
 
 
 def _compute_backbone(backbone_fn, frame, frame_idx=None):
@@ -1083,19 +1078,16 @@ class SAM3Tracker(nn.Module):
         # SAM3: drop last FPN level
         return vision_feats[:-1], vision_pos[:-1], feat_sizes[:-1]
 
-    def _track_single_object(self, backbone_fn, images, initial_mask, pbar=None,
-                             target_device=None, target_dtype=None):
+    def _track_single_object(self, backbone_fn, images, initial_mask, pbar=None):
         """Track one object, computing backbone per frame to save VRAM."""
         N = images.shape[0]
-        device = target_device if target_device is not None else images.device
-        dt = target_dtype if target_dtype is not None else images.dtype
-        size = self.image_size
+        device, dt = images.device, images.dtype
         output_dict = {"cond_frame_outputs": {}, "non_cond_frame_outputs": {}}
         all_masks = []
 
         for frame_idx in tqdm(range(N), desc="tracking"):
             vision_feats, vision_pos, feat_sizes = self._compute_backbone_frame(
-                backbone_fn, _prep_frame(images, slice(frame_idx, frame_idx + 1), device, dt, size), frame_idx=frame_idx)
+                backbone_fn, images[frame_idx:frame_idx + 1], frame_idx=frame_idx)
             mask_input = None
             if frame_idx == 0:
                 mask_input = F.interpolate(initial_mask.to(device=device, dtype=dt),
@@ -1122,13 +1114,12 @@ class SAM3Tracker(nn.Module):
 
         return torch.cat(all_masks, dim=0)  # [N, 1, H, W]
 
-    def track_video(self, backbone_fn, images, initial_masks, pbar=None,
-                    target_device=None, target_dtype=None, **kwargs):
+    def track_video(self, backbone_fn, images, initial_masks, pbar=None, **kwargs):
         """Track one or more objects across video frames.
 
         Args:
             backbone_fn: callable that returns (sam2_features, sam2_positions, trunk_out) for a frame
-            images: [N, 3, H, W] CPU full-res video frames (resized per-frame to self.image_size)
+            images: [N, 3, 1008, 1008] video frames
             initial_masks: [N_obj, 1, H, W] binary masks for first frame (one per object)
             pbar: optional progress bar
 
@@ -1139,8 +1130,7 @@ class SAM3Tracker(nn.Module):
         per_object = []
         for obj_idx in range(N_obj):
             obj_masks = self._track_single_object(
-                backbone_fn, images, initial_masks[obj_idx:obj_idx + 1], pbar=pbar,
-                target_device=target_device, target_dtype=target_dtype)
+                backbone_fn, images, initial_masks[obj_idx:obj_idx + 1], pbar=pbar)
             per_object.append(obj_masks)
 
         return torch.cat(per_object, dim=1)  # [N, N_obj, H, W]
@@ -1642,18 +1632,11 @@ class SAM31Tracker(nn.Module):
             return det_scores[new_dets].tolist() if det_scores is not None else [0.0] * new_dets.sum().item()
         return []
 
-    INTERNAL_MAX_OBJECTS = 64  # Hard ceiling on accumulated tracks; max_objects=0 or any value above this is clamped here.
-
     def track_video_with_detection(self, backbone_fn, images, initial_masks, detect_fn=None,
                                    new_det_thresh=0.5, max_objects=0, detect_interval=1,
-                                   backbone_obj=None, pbar=None, target_device=None, target_dtype=None):
+                                   backbone_obj=None, pbar=None):
         """Track with optional per-frame detection. Returns [N, max_N_obj, H, W] mask logits."""
-        if max_objects <= 0 or max_objects > self.INTERNAL_MAX_OBJECTS:
-            max_objects = self.INTERNAL_MAX_OBJECTS
-        N = images.shape[0]
-        device = target_device if target_device is not None else images.device
-        dt = target_dtype if target_dtype is not None else images.dtype
-        size = self.image_size
+        N, device, dt = images.shape[0], images.device, images.dtype
         output_dict = {"cond_frame_outputs": {}, "non_cond_frame_outputs": {}}
         all_masks = []
         idev = comfy.model_management.intermediate_device()
@@ -1673,7 +1656,7 @@ class SAM31Tracker(nn.Module):
                 prefetch = True
             except RuntimeError:
                 pass
-        cur_bb = self._compute_backbone_frame(backbone_fn, _prep_frame(images, slice(0, 1), device, dt, size), frame_idx=0)
+        cur_bb = self._compute_backbone_frame(backbone_fn, images[0:1], frame_idx=0)
 
         for frame_idx in tqdm(range(N), desc="tracking"):
             vision_feats, vision_pos, feat_sizes, high_res_prop, trunk_out = cur_bb
@@ -1683,7 +1666,7 @@ class SAM31Tracker(nn.Module):
                 backbone_stream.wait_stream(torch.cuda.current_stream(device))
                 with torch.cuda.stream(backbone_stream):
                     next_bb = self._compute_backbone_frame(
-                        backbone_fn, _prep_frame(images, slice(frame_idx + 1, frame_idx + 2), device, dt, size), frame_idx=frame_idx + 1)
+                        backbone_fn, images[frame_idx + 1:frame_idx + 2], frame_idx=frame_idx + 1)
 
             # Per-frame detection with NMS (skip if no detect_fn, or interval/max not met)
             det_masks = torch.empty(0, device=device)
@@ -1704,7 +1687,7 @@ class SAM31Tracker(nn.Module):
                 current_out = self._condition_with_masks(
                     initial_masks.to(device=device, dtype=dt), frame_idx, vision_feats, vision_pos,
                     feat_sizes, high_res_prop, output_dict, N, mux_state, backbone_obj,
-                    _prep_frame(images, slice(frame_idx, frame_idx + 1), device, dt, size), trunk_out)
+                    images[frame_idx:frame_idx + 1], trunk_out)
                 last_occluded = torch.full((mux_state.total_valid_entries,), -1, device=device, dtype=torch.long)
                 obj_scores = [1.0] * mux_state.total_valid_entries
                 if keep_alive is not None:
@@ -1719,7 +1702,7 @@ class SAM31Tracker(nn.Module):
                     current_out = self._condition_with_masks(
                         det_masks, frame_idx, vision_feats, vision_pos, feat_sizes, high_res_prop,
                         output_dict, N, mux_state, backbone_obj,
-                        _prep_frame(images, slice(frame_idx, frame_idx + 1), device, dt, size), trunk_out, threshold=0.0)
+                        images[frame_idx:frame_idx + 1], trunk_out, threshold=0.0)
                     last_occluded = torch.full((mux_state.total_valid_entries,), -1, device=device, dtype=torch.long)
                     obj_scores = det_scores[:mux_state.total_valid_entries].tolist()
                     if keep_alive is not None:
@@ -1735,7 +1718,7 @@ class SAM31Tracker(nn.Module):
                             torch.cuda.current_stream(device).wait_stream(backbone_stream)
                             cur_bb = next_bb
                         else:
-                            cur_bb = self._compute_backbone_frame(backbone_fn, _prep_frame(images, slice(frame_idx + 1, frame_idx + 2), device, dt, size), frame_idx=frame_idx + 1)
+                            cur_bb = self._compute_backbone_frame(backbone_fn, images[frame_idx + 1:frame_idx + 2], frame_idx=frame_idx + 1)
                     continue
             else:
                 N_obj = mux_state.total_valid_entries
@@ -1785,7 +1768,7 @@ class SAM31Tracker(nn.Module):
                     torch.cuda.current_stream(device).wait_stream(backbone_stream)
                     cur_bb = next_bb
                 else:
-                    cur_bb = self._compute_backbone_frame(backbone_fn, _prep_frame(images, slice(frame_idx + 1, frame_idx + 2), device, dt, size), frame_idx=frame_idx + 1)
+                    cur_bb = self._compute_backbone_frame(backbone_fn, images[frame_idx + 1:frame_idx + 2], frame_idx=frame_idx + 1)
 
         if not all_masks or all(m is None for m in all_masks):
             return {"packed_masks": None, "n_frames": N, "scores": []}

comfy/ldm/wan/ar_model.py

@@ -1,276 +0,0 @@
-"""
-CausalWanModel: Wan 2.1 backbone with KV-cached causal self-attention for
-autoregressive (frame-by-frame) video generation via Causal Forcing.
-
-Weight-compatible with the standard WanModel -- same layer names, same shapes.
-The difference is purely in the forward pass: this model processes one temporal
-block at a time and maintains a KV cache across blocks.
-
-Reference: https://github.com/thu-ml/Causal-Forcing
-"""
-
-import torch
-import torch.nn as nn
-
-from comfy.ldm.modules.attention import optimized_attention
-from comfy.ldm.flux.math import apply_rope1
-from comfy.ldm.wan.model import (
-    sinusoidal_embedding_1d,
-    repeat_e,
-    WanModel,
-    WanAttentionBlock,
-)
-import comfy.ldm.common_dit
-import comfy.model_management
-
-
-class CausalWanSelfAttention(nn.Module):
-    """Self-attention with KV cache support for autoregressive inference."""
-
-    def __init__(self, dim, num_heads, window_size=(-1, -1), qk_norm=True,
-                 eps=1e-6, operation_settings={}):
-        assert dim % num_heads == 0
-        super().__init__()
-        self.dim = dim
-        self.num_heads = num_heads
-        self.head_dim = dim // num_heads
-        self.qk_norm = qk_norm
-        self.eps = eps
-
-        ops = operation_settings.get("operations")
-        device = operation_settings.get("device")
-        dtype = operation_settings.get("dtype")
-
-        self.q = ops.Linear(dim, dim, device=device, dtype=dtype)
-        self.k = ops.Linear(dim, dim, device=device, dtype=dtype)
-        self.v = ops.Linear(dim, dim, device=device, dtype=dtype)
-        self.o = ops.Linear(dim, dim, device=device, dtype=dtype)
-        self.norm_q = ops.RMSNorm(dim, eps=eps, elementwise_affine=True, device=device, dtype=dtype) if qk_norm else nn.Identity()
-        self.norm_k = ops.RMSNorm(dim, eps=eps, elementwise_affine=True, device=device, dtype=dtype) if qk_norm else nn.Identity()
-
-    def forward(self, x, freqs, kv_cache=None, transformer_options={}):
-        b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
-
-        q = apply_rope1(self.norm_q(self.q(x)).view(b, s, n, d), freqs)
-        k = apply_rope1(self.norm_k(self.k(x)).view(b, s, n, d), freqs)
-        v = self.v(x).view(b, s, n, d)
-
-        if kv_cache is None:
-            x = optimized_attention(
-                q.view(b, s, n * d),
-                k.view(b, s, n * d),
-                v.view(b, s, n * d),
-                heads=self.num_heads,
-                transformer_options=transformer_options,
-            )
-        else:
-            end = kv_cache["end"]
-            new_end = end + s
-
-            # Roped K and plain V go into cache
-            kv_cache["k"][:, end:new_end] = k
-            kv_cache["v"][:, end:new_end] = v
-            kv_cache["end"] = new_end
-
-            x = optimized_attention(
-                q.view(b, s, n * d),
-                kv_cache["k"][:, :new_end].view(b, new_end, n * d),
-                kv_cache["v"][:, :new_end].view(b, new_end, n * d),
-                heads=self.num_heads,
-                transformer_options=transformer_options,
-            )
-
-        x = self.o(x)
-        return x
-
-
-class CausalWanAttentionBlock(WanAttentionBlock):
-    """Transformer block with KV-cached self-attention and cross-attention caching."""
-
-    def __init__(self, cross_attn_type, dim, ffn_dim, num_heads,
-                 window_size=(-1, -1), qk_norm=True, cross_attn_norm=False,
-                 eps=1e-6, operation_settings={}):
-        super().__init__(cross_attn_type, dim, ffn_dim, num_heads,
-                         window_size, qk_norm, cross_attn_norm, eps,
-                         operation_settings=operation_settings)
-        self.self_attn = CausalWanSelfAttention(
-            dim, num_heads, window_size, qk_norm, eps,
-            operation_settings=operation_settings)
-
-    def forward(self, x, e, freqs, context, context_img_len=257,
-                kv_cache=None, crossattn_cache=None, transformer_options={}):
-        if e.ndim < 4:
-            e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1)
-        else:
-            e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e).unbind(2)
-
-        # Self-attention with optional KV cache
-        x = x.contiguous()
-        y = self.self_attn(
-            torch.addcmul(repeat_e(e[0], x), self.norm1(x), 1 + repeat_e(e[1], x)),
-            freqs, kv_cache=kv_cache, transformer_options=transformer_options)
-        x = torch.addcmul(x, y, repeat_e(e[2], x))
-        del y
-
-        # Cross-attention with optional caching
-        if crossattn_cache is not None and crossattn_cache.get("is_init"):
-            q = self.cross_attn.norm_q(self.cross_attn.q(self.norm3(x)))
-            x_ca = optimized_attention(
-                q, crossattn_cache["k"], crossattn_cache["v"],
-                heads=self.num_heads, transformer_options=transformer_options)
-            x = x + self.cross_attn.o(x_ca)
-        else:
-            x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len, transformer_options=transformer_options)
-            if crossattn_cache is not None:
-                crossattn_cache["k"] = self.cross_attn.norm_k(self.cross_attn.k(context))
-                crossattn_cache["v"] = self.cross_attn.v(context)
-                crossattn_cache["is_init"] = True
-
-        # FFN
-        y = self.ffn(torch.addcmul(repeat_e(e[3], x), self.norm2(x), 1 + repeat_e(e[4], x)))
-        x = torch.addcmul(x, y, repeat_e(e[5], x))
-        return x
-
-
-class CausalWanModel(WanModel):
-    """
-    Wan 2.1 diffusion backbone with causal KV-cache support.
-
-    Same weight structure as WanModel -- loads identical state dicts.
-    Adds forward_block() for frame-by-frame autoregressive inference.
-    """
-
-    def __init__(self,
-                 model_type='t2v',
-                 patch_size=(1, 2, 2),
-                 text_len=512,
-                 in_dim=16,
-                 dim=2048,
-                 ffn_dim=8192,
-                 freq_dim=256,
-                 text_dim=4096,
-                 out_dim=16,
-                 num_heads=16,
-                 num_layers=32,
-                 window_size=(-1, -1),
-                 qk_norm=True,
-                 cross_attn_norm=True,
-                 eps=1e-6,
-                 image_model=None,
-                 device=None,
-                 dtype=None,
-                 operations=None):
-        super().__init__(
-            model_type=model_type, patch_size=patch_size, text_len=text_len,
-            in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim,
-            text_dim=text_dim, out_dim=out_dim, num_heads=num_heads,
-            num_layers=num_layers, window_size=window_size, qk_norm=qk_norm,
-            cross_attn_norm=cross_attn_norm, eps=eps, image_model=image_model,
-            wan_attn_block_class=CausalWanAttentionBlock,
-            device=device, dtype=dtype, operations=operations)
-
-    def forward_block(self, x, timestep, context, start_frame,
-                      kv_caches, crossattn_caches, clip_fea=None):
-        """
-        Forward one temporal block for autoregressive inference.
-
-        Args:
-            x: [B, C, block_frames, H, W] input latent for the current block
-            timestep: [B, block_frames] per-frame timesteps
-            context: [B, L, text_dim] raw text embeddings (pre-text_embedding)
-            start_frame: temporal frame index for RoPE offset
-            kv_caches: list of per-layer KV cache dicts
-            crossattn_caches: list of per-layer cross-attention cache dicts
-            clip_fea: optional CLIP features for I2V
-
-        Returns:
-            flow_pred: [B, C_out, block_frames, H, W] flow prediction
-        """
-        x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)
-        bs, c, t, h, w = x.shape
-
-        x = self.patch_embedding(x.float()).to(x.dtype)
-        grid_sizes = x.shape[2:]
-        x = x.flatten(2).transpose(1, 2)
-
-        # Per-frame time embedding
-        e = self.time_embedding(
-            sinusoidal_embedding_1d(self.freq_dim, timestep.flatten()).to(dtype=x.dtype))
-        e = e.reshape(timestep.shape[0], -1, e.shape[-1])
-        e0 = self.time_projection(e).unflatten(2, (6, self.dim))
-
-        # Text embedding (reuses crossattn_cache after first block)
-        context = self.text_embedding(context)
-
-        context_img_len = None
-        if clip_fea is not None and self.img_emb is not None:
-            context_clip = self.img_emb(clip_fea)
-            context = torch.concat([context_clip, context], dim=1)
-            context_img_len = clip_fea.shape[-2]
-
-        # RoPE for current block's temporal position
-        freqs = self.rope_encode(t, h, w, t_start=start_frame, device=x.device, dtype=x.dtype)
-
-        # Transformer blocks
-        for i, block in enumerate(self.blocks):
-            x = block(x, e=e0, freqs=freqs, context=context,
-                      context_img_len=context_img_len,
-                      kv_cache=kv_caches[i],
-                      crossattn_cache=crossattn_caches[i])
-
-        # Head
-        x = self.head(x, e)
-
-        # Unpatchify
-        x = self.unpatchify(x, grid_sizes)
-        return x[:, :, :t, :h, :w]
-
-    def init_kv_caches(self, batch_size, max_seq_len, device, dtype):
-        """Create fresh KV caches for all layers."""
-        caches = []
-        for _ in range(self.num_layers):
-            caches.append({
-                "k": torch.zeros(batch_size, max_seq_len, self.num_heads, self.head_dim, device=device, dtype=dtype),
-                "v": torch.zeros(batch_size, max_seq_len, self.num_heads, self.head_dim, device=device, dtype=dtype),
-                "end": 0,
-            })
-        return caches
-
-    def init_crossattn_caches(self, batch_size, device, dtype):
-        """Create fresh cross-attention caches for all layers."""
-        caches = []
-        for _ in range(self.num_layers):
-            caches.append({"is_init": False})
-        return caches
-
-    def reset_kv_caches(self, kv_caches):
-        """Reset KV caches to empty (reuse allocated memory)."""
-        for cache in kv_caches:
-            cache["end"] = 0
-
-    def reset_crossattn_caches(self, crossattn_caches):
-        """Reset cross-attention caches."""
-        for cache in crossattn_caches:
-            cache["is_init"] = False
-
-    @property
-    def head_dim(self):
-        return self.dim // self.num_heads
-
-    def forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, **kwargs):
-        ar_state = transformer_options.get("ar_state")
-        if ar_state is not None:
-            bs = x.shape[0]
-            block_frames = x.shape[2]
-            t_per_frame = timestep.unsqueeze(1).expand(bs, block_frames)
-            return self.forward_block(
-                x=x, timestep=t_per_frame, context=context,
-                start_frame=ar_state["start_frame"],
-                kv_caches=ar_state["kv_caches"],
-                crossattn_caches=ar_state["crossattn_caches"],
-                clip_fea=clip_fea,
-            )
-
-        return super().forward(x, timestep, context, clip_fea=clip_fea,
-                               time_dim_concat=time_dim_concat,
-                               transformer_options=transformer_options, **kwargs)

comfy/ldm/wan/model.py

@@ -1135,7 +1135,7 @@ class AudioInjector_WAN(nn.Module):
                 self.injector_adain_output_layers = nn.ModuleList(
                     [operations.Linear(dim, dim, dtype=dtype, device=device) for _ in range(audio_injector_id)])
 
-    def forward(self, x, block_id, audio_emb, audio_emb_global, seq_len, scale=1.0):
+    def forward(self, x, block_id, audio_emb, audio_emb_global, seq_len):
         audio_attn_id = self.injected_block_id.get(block_id, None)
         if audio_attn_id is None:
             return x
@@ -1148,15 +1148,12 @@ class AudioInjector_WAN(nn.Module):
             attn_hidden_states = adain_hidden_states
         else:
             attn_hidden_states = self.injector_pre_norm_feat[audio_attn_id](input_hidden_states)
-
-        if audio_emb.dim() == 3: # WanDancer case
-            attn_audio_emb = rearrange(audio_emb, "b t c -> (b t) 1 c", t=num_frames)
-        else: # S2V case
-            attn_audio_emb = rearrange(audio_emb, "b t n c -> (b t) n c", t=num_frames)
-
+        audio_emb = rearrange(audio_emb, "b t n c -> (b t) n c", t=num_frames)
+        attn_audio_emb = audio_emb
         residual_out = self.injector[audio_attn_id](x=attn_hidden_states, context=attn_audio_emb)
-        residual_out = rearrange(residual_out, "(b t) n c -> b (t n) c", t=num_frames)
-        x[:, :seq_len] = x[:, :seq_len] + residual_out * scale
+        residual_out = rearrange(
+            residual_out, "(b t) n c -> b (t n) c", t=num_frames)
+        x[:, :seq_len] = x[:, :seq_len] + residual_out
         return x
 
 

comfy/ldm/wan/model_wandancer.py

@@ -1,251 +0,0 @@
-import torch
-import torch.nn as nn
-import comfy
-from comfy.ldm.modules.attention import optimized_attention
-from comfy.ldm.flux.math import apply_rope1
-from comfy.ldm.flux.layers import EmbedND
-
-from .model import AudioInjector_WAN, WanModel, MLPProj, Head, sinusoidal_embedding_1d
-
-
-class MusicSelfAttention(nn.Module):
-    def __init__(self, dim, num_heads, device=None, dtype=None, operations=None):
-        assert dim % num_heads == 0
-        super().__init__()
-        self.embed_dim = dim
-        self.num_heads = num_heads
-        self.head_dim = dim // num_heads
-
-        self.q_proj = operations.Linear(dim, dim, device=device, dtype=dtype)
-        self.k_proj = operations.Linear(dim, dim, device=device, dtype=dtype)
-        self.v_proj = operations.Linear(dim, dim, device=device, dtype=dtype)
-        self.out_proj = operations.Linear(dim, dim, device=device, dtype=dtype)
-
-    def forward(self, x, freqs):
-        b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
-
-        q = self.q_proj(x).view(b, s, n, d)
-        q = apply_rope1(q, freqs)
-
-        k = self.k_proj(x).view(b, s, n, d)
-        k = apply_rope1(k, freqs)
-
-        x = optimized_attention(
-            q.view(b, s, n * d),
-            k.view(b, s, n * d),
-            self.v_proj(x).view(b, s, n * d),
-            heads=self.num_heads,
-        )
-
-        return self.out_proj(x)
-
-
-class MusicEncoderLayer(nn.Module):
-    def __init__(self, dim: int, num_heads: int, ffn_dim: int, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.self_attn = MusicSelfAttention(dim, num_heads, device=device, dtype=dtype, operations=operations)
-
-        self.linear1 = operations.Linear(dim, ffn_dim, device=device, dtype=dtype)
-        self.linear2 = operations.Linear(ffn_dim, dim, device=device, dtype=dtype)
-
-        self.norm1 = operations.LayerNorm(dim, device=device, dtype=dtype)
-        self.norm2 = operations.LayerNorm(dim, device=device, dtype=dtype)
-
-    def forward(self, x: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
-        x = x + self.self_attn(self.norm1(x), freqs=freqs)
-        x = x + self.linear2(torch.nn.functional.gelu(self.linear1(self.norm2(x)))) # ffn
-        return x
-
-
-class WanDancerModel(WanModel):
-    def __init__(self,
-                 model_type='wandancer',
-                 patch_size=(1, 2, 2),
-                 text_len=512,
-                 in_dim=16,
-                 dim=5120,
-                 ffn_dim=8192,
-                 freq_dim=256,
-                 text_dim=4096,
-                 out_dim=16,
-                 num_heads=16,
-                 num_layers=40,
-                 window_size=(-1, -1),
-                 qk_norm=True,
-                 cross_attn_norm=True,
-                 eps=1e-6,
-                 in_dim_ref_conv=None,
-                 image_model=None,
-                 device=None, dtype=None, operations=None,
-                 audio_inject_layers=[0, 4, 8, 12, 16, 20, 24, 27],
-                 music_dim = 256,
-                 music_heads = 4,
-                 music_feature_dim = 35,
-                 music_latent_dim = 256
-                 ):
-
-        super().__init__(model_type='i2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim,
-                         num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, image_model=image_model, in_dim_ref_conv=in_dim_ref_conv,
-                         device=device, dtype=dtype, operations=operations)
-
-        self.dtype = dtype
-        operation_settings = {"operations": operations, "device": device, "dtype": dtype}
-
-        self.patch_embedding_global = operations.Conv3d(in_dim, dim, kernel_size=patch_size, stride=patch_size, device=operation_settings.get("device"), dtype=torch.float32)
-        self.img_emb_refimage = MLPProj(1280, dim, operation_settings=operation_settings)
-        self.head_global = Head(dim, out_dim, patch_size, eps, operation_settings=operation_settings)
-
-        self.music_injector = AudioInjector_WAN(
-            dim=self.dim,
-            num_heads=self.num_heads,
-            inject_layer=audio_inject_layers,
-            root_net=self,
-            enable_adain=False,
-            dtype=dtype, device=device, operations=operations
-        )
-
-        self.music_projection = operations.Linear(music_feature_dim, music_latent_dim, device=device, dtype=dtype)
-        self.music_encoder = nn.ModuleList([MusicEncoderLayer(dim=music_dim, num_heads=music_heads, ffn_dim=1024, device=device, dtype=dtype, operations=operations) for _ in range(2)])
-        music_head_dim = music_dim // music_heads
-        self.music_rope_embedder = EmbedND(dim=music_head_dim, theta=10000.0, axes_dim=[music_head_dim])
-
-    def forward_orig(self, x, t, context, clip_fea=None, clip_fea_ref=None, freqs=None, audio_embed=None, fps=30, audio_inject_scale=1.0, transformer_options={}, **kwargs):
-        # embeddings
-        if int(fps + 0.5) != 30:
-            x = self.patch_embedding_global(x.float()).to(x.dtype)
-        else:
-            x = self.patch_embedding(x.float()).to(x.dtype)
-
-        grid_sizes = x.shape[2:]
-        latent_frames = grid_sizes[0]
-        transformer_options["grid_sizes"] = grid_sizes
-        x = x.flatten(2).transpose(1, 2)
-        seq_len = x.size(1)
-
-        # time embeddings
-        e = self.time_embedding(sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype))
-        e = e.reshape(t.shape[0], -1, e.shape[-1])
-        e0 = self.time_projection(e).unflatten(2, (6, self.dim))
-
-        full_ref = None
-        if self.ref_conv is not None: # model has the weight, but this wasn't used in the original pipeline
-            full_ref = kwargs.get("reference_latent", None)
-            if full_ref is not None:
-                full_ref = self.ref_conv(full_ref).flatten(2).transpose(1, 2)
-                x = torch.concat((full_ref, x), dim=1)
-
-        # context
-        context = self.text_embedding(context)
-
-        audio_emb = None
-        if audio_embed is not None: # encode music feature，[1, frame_num, 35] -> [1, F*8, dim]
-            music_feature = self.music_projection(audio_embed)
-
-            music_seq_len = music_feature.shape[1]
-            music_ids = torch.arange(music_seq_len, device=music_feature.device, dtype=music_feature.dtype).reshape(1, -1, 1) # create 1D position IDs
-            music_freqs = self.music_rope_embedder(music_ids).movedim(1, 2)
-
-            # apply encoder layers
-            for layer in self.music_encoder:
-                music_feature = layer(music_feature, music_freqs)
-
-            # interpolate
-            audio_emb = torch.nn.functional.interpolate(music_feature.unsqueeze(1), size=(latent_frames * 8, self.dim), mode='bilinear').squeeze(1)
-
-        context_img_len = 0
-        if self.img_emb is not None and clip_fea is not None:
-            context_clip = self.img_emb(clip_fea)  # bs x 257 x dim
-            context = torch.cat([context_clip, context], dim=1)
-            context_img_len += clip_fea.shape[-2]
-        if self.img_emb_refimage is not None and clip_fea_ref is not None:
-            context_clip_ref = self.img_emb_refimage(clip_fea_ref)
-            context = torch.cat([context_clip_ref, context], dim=1)
-            context_img_len += clip_fea_ref.shape[-2]
-
-        patches_replace = transformer_options.get("patches_replace", {})
-        blocks_replace = patches_replace.get("dit", {})
-        transformer_options["total_blocks"] = len(self.blocks)
-        transformer_options["block_type"] = "double"
-        for i, block in enumerate(self.blocks):
-            transformer_options["block_index"] = i
-            if ("double_block", i) in blocks_replace:
-                def block_wrap(args):
-                    out = {}
-                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"])
-                    return out
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})
-                x = out["img"]
-            else:
-                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)
-            if audio_emb is not None:
-                x = self.music_injector(x, i, audio_emb, audio_emb_global=None, seq_len=seq_len, scale=audio_inject_scale)
-
-        # head
-        if int(fps + 0.5) != 30:
-            x = self.head_global(x, e)
-        else:
-            x = self.head(x, e)
-
-        if full_ref is not None:
-            x = x[:, full_ref.shape[1]:]
-
-        # unpatchify
-        x = self.unpatchify(x, grid_sizes)
-        return x
-
-    def _forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, clip_fea_ref=None, fps=30, audio_inject_scale=1.0, **kwargs):
-        bs, c, t, h, w = x.shape
-        x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)
-
-        t_len = t
-        if time_dim_concat is not None:
-            time_dim_concat = comfy.ldm.common_dit.pad_to_patch_size(time_dim_concat, self.patch_size)
-            x = torch.cat([x, time_dim_concat], dim=2)
-            t_len = x.shape[2]
-
-        freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype, fps=fps, transformer_options=transformer_options)
-        return self.forward_orig(x, timestep, context, clip_fea=clip_fea, clip_fea_ref=clip_fea_ref, freqs=freqs, fps=fps, audio_inject_scale=audio_inject_scale, transformer_options=transformer_options, **kwargs)[:, :, :t, :h, :w]
-
-    def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, fps=30, device=None, dtype=None, transformer_options={}):
-        patch_size = self.patch_size
-        t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
-        h_len = ((h + (patch_size[1] // 2)) // patch_size[1])
-        w_len = ((w + (patch_size[2] // 2)) // patch_size[2])
-
-        if steps_t is None:
-            steps_t = t_len
-        if steps_h is None:
-            steps_h = h_len
-        if steps_w is None:
-            steps_w = w_len
-
-        h_start = 0
-        w_start = 0
-        rope_options = transformer_options.get("rope_options", None)
-        if rope_options is not None:
-            t_len = (t_len - 1.0) * rope_options.get("scale_t", 1.0) + 1.0
-            h_len = (h_len - 1.0) * rope_options.get("scale_y", 1.0) + 1.0
-            w_len = (w_len - 1.0) * rope_options.get("scale_x", 1.0) + 1.0
-
-            t_start += rope_options.get("shift_t", 0.0)
-            h_start += rope_options.get("shift_y", 0.0)
-            w_start += rope_options.get("shift_x", 0.0)
-
-        img_ids = torch.zeros((steps_t, steps_h, steps_w, 3), device=device, dtype=dtype)
-
-        if int(fps + 0.5) != 30:
-            time_scale = 30.0 / fps # how many time units each frame represents relative to 30fps
-            positions_new = torch.arange(steps_t, device=device, dtype=dtype) * time_scale + t_start
-            total_frames_at_30fps = int(time_scale * steps_t + 0.5)
-            positions_new[-1] = t_start + (total_frames_at_30fps - 1)
-
-            img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + positions_new.reshape(-1, 1, 1)
-        else:
-            img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(t_start, t_start + (t_len - 1), steps=steps_t, device=device, dtype=dtype).reshape(-1, 1, 1)
-
-        img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(h_start, h_start + (h_len - 1), steps=steps_h, device=device, dtype=dtype).reshape(1, -1, 1)
-        img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(w_start, w_start + (w_len - 1), steps=steps_w, device=device, dtype=dtype).reshape(1, 1, -1)
-        img_ids = img_ids.reshape(1, -1, img_ids.shape[-1])
-
-        freqs = self.rope_embedder(img_ids).movedim(1, 2)
-        return freqs