.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

.ci/windows_intel_base_files/run_intel_gpu.bat

@@ -1,2 +0,0 @@
-.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build
-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/tag-dispatch-cloud.yml

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

.gitignore

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

.spectral.yaml

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

CODEOWNERS

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

QUANTIZATION.md

@@ -139,9 +139,9 @@ Example:
   "_quantization_metadata": {
     "format_version": "1.0",
     "layers": {
-      "model.layers.0.mlp.up_proj": {"format": "float8_e4m3fn"},
-      "model.layers.0.mlp.down_proj": {"format": "float8_e4m3fn"},
-      "model.layers.1.mlp.up_proj": {"format": "float8_e4m3fn"}
+      "model.layers.0.mlp.up_proj": "float8_e4m3fn",
+      "model.layers.0.mlp.down_proj": "float8_e4m3fn",
+      "model.layers.1.mlp.up_proj": "float8_e4m3fn"
     }
   }
 }
@@ -165,4 +165,4 @@ Activation quantization (e.g., for FP8 Tensor Core operations) requires `input_s
 3. **Compute scales**: Derive `input_scale` from collected statistics
 4. **Store in checkpoint**: Save `input_scale` parameters alongside weights
 
-The calibration dataset should be representative of your target use case. For diffusion models, this typically means a diverse set of prompts and generation parameters.
+The calibration dataset should be representative of your target use case. For diffusion models, this typically means a diverse set of prompts and generation parameters.

README.md

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

api_server/routes/internal/internal_routes.py

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

app/user_manager.py

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

blueprints/.glsl/Glow_30.frag

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

blueprints/.glsl/Image_Blur_1.frag

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

blueprints/.glsl/Sharpen_23.frag

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

blueprints/.glsl/Unsharp_Mask_26.frag

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

blueprints/Image Channels.json

@@ -1,322 +1 @@
-{
-  "revision": 0,
-  "last_node_id": 29,
-  "last_link_id": 0,
-  "nodes": [
-    {
-      "id": 29,
-      "type": "4c9d6ea4-b912-40e5-8766-6793a9758c53",
-      "pos": [
-        1970,
-        -230
-      ],
-      "size": [
-        180,
-        86
-      ],
-      "flags": {},
-      "order": 5,
-      "mode": 0,
-      "inputs": [
-        {
-          "label": "image",
-          "localized_name": "images.image0",
-          "name": "images.image0",
-          "type": "IMAGE",
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "label": "R",
-          "localized_name": "IMAGE0",
-          "name": "IMAGE0",
-          "type": "IMAGE",
-          "links": []
-        },
-        {
-          "label": "G",
-          "localized_name": "IMAGE1",
-          "name": "IMAGE1",
-          "type": "IMAGE",
-          "links": []
-        },
-        {
-          "label": "B",
-          "localized_name": "IMAGE2",
-          "name": "IMAGE2",
-          "type": "IMAGE",
-          "links": []
-        },
-        {
-          "label": "A",
-          "localized_name": "IMAGE3",
-          "name": "IMAGE3",
-          "type": "IMAGE",
-          "links": []
-        }
-      ],
-      "title": "Image Channels",
-      "properties": {
-        "proxyWidgets": []
-      },
-      "widgets_values": []
-    }
-  ],
-  "links": [],
-  "version": 0.4,
-  "definitions": {
-    "subgraphs": [
-      {
-        "id": "4c9d6ea4-b912-40e5-8766-6793a9758c53",
-        "version": 1,
-        "state": {
-          "lastGroupId": 0,
-          "lastNodeId": 28,
-          "lastLinkId": 39,
-          "lastRerouteId": 0
-        },
-        "revision": 0,
-        "config": {},
-        "name": "Image Channels",
-        "inputNode": {
-          "id": -10,
-          "bounding": [
-            1820,
-            -185,
-            120,
-            60
-          ]
-        },
-        "outputNode": {
-          "id": -20,
-          "bounding": [
-            2460,
-            -215,
-            120,
-            120
-          ]
-        },
-        "inputs": [
-          {
-            "id": "3522932b-2d86-4a1f-a02a-cb29f3a9d7fe",
-            "name": "images.image0",
-            "type": "IMAGE",
-            "linkIds": [
-              39
-            ],
-            "localized_name": "images.image0",
-            "label": "image",
-            "pos": [
-              1920,
-              -165
-            ]
-          }
-        ],
-        "outputs": [
-          {
-            "id": "605cb9c3-b065-4d9b-81d2-3ec331889b2b",
-            "name": "IMAGE0",
-            "type": "IMAGE",
-            "linkIds": [
-              26
-            ],
-            "localized_name": "IMAGE0",
-            "label": "R",
-            "pos": [
-              2480,
-              -195
-            ]
-          },
-          {
-            "id": "fb44a77e-0522-43e9-9527-82e7465b3596",
-            "name": "IMAGE1",
-            "type": "IMAGE",
-            "linkIds": [
-              27
-            ],
-            "localized_name": "IMAGE1",
-            "label": "G",
-            "pos": [
-              2480,
-              -175
-            ]
-          },
-          {
-            "id": "81460ee6-0131-402a-874f-6bf3001fc4ff",
-            "name": "IMAGE2",
-            "type": "IMAGE",
-            "linkIds": [
-              28
-            ],
-            "localized_name": "IMAGE2",
-            "label": "B",
-            "pos": [
-              2480,
-              -155
-            ]
-          },
-          {
-            "id": "ae690246-80d4-4951-b1d9-9306d8a77417",
-            "name": "IMAGE3",
-            "type": "IMAGE",
-            "linkIds": [
-              29
-            ],
-            "localized_name": "IMAGE3",
-            "label": "A",
-            "pos": [
-              2480,
-              -135
-            ]
-          }
-        ],
-        "widgets": [],
-        "nodes": [
-          {
-            "id": 23,
-            "type": "GLSLShader",
-            "pos": [
-              2000,
-              -330
-            ],
-            "size": [
-              400,
-              172
-            ],
-            "flags": {},
-            "order": 0,
-            "mode": 0,
-            "inputs": [
-              {
-                "label": "image",
-                "localized_name": "images.image0",
-                "name": "images.image0",
-                "type": "IMAGE",
-                "link": 39
-              },
-              {
-                "localized_name": "fragment_shader",
-                "name": "fragment_shader",
-                "type": "STRING",
-                "widget": {
-                  "name": "fragment_shader"
-                },
-                "link": null
-              },
-              {
-                "localized_name": "size_mode",
-                "name": "size_mode",
-                "type": "COMFY_DYNAMICCOMBO_V3",
-                "widget": {
-                  "name": "size_mode"
-                },
-                "link": null
-              },
-              {
-                "label": "image1",
-                "localized_name": "images.image1",
-                "name": "images.image1",
-                "shape": 7,
-                "type": "IMAGE",
-                "link": null
-              }
-            ],
-            "outputs": [
-              {
-                "label": "R",
-                "localized_name": "IMAGE0",
-                "name": "IMAGE0",
-                "type": "IMAGE",
-                "links": [
-                  26
-                ]
-              },
-              {
-                "label": "G",
-                "localized_name": "IMAGE1",
-                "name": "IMAGE1",
-                "type": "IMAGE",
-                "links": [
-                  27
-                ]
-              },
-              {
-                "label": "B",
-                "localized_name": "IMAGE2",
-                "name": "IMAGE2",
-                "type": "IMAGE",
-                "links": [
-                  28
-                ]
-              },
-              {
-                "label": "A",
-                "localized_name": "IMAGE3",
-                "name": "IMAGE3",
-                "type": "IMAGE",
-                "links": [
-                  29
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "GLSLShader"
-            },
-            "widgets_values": [
-              "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\nlayout(location = 1) out vec4 fragColor1;\nlayout(location = 2) out vec4 fragColor2;\nlayout(location = 3) out vec4 fragColor3;\n\nvoid main() {\n  vec4 color = texture(u_image0, v_texCoord);\n  // Output each channel as grayscale to separate render targets\n  fragColor0 = vec4(vec3(color.r), 1.0);  // Red channel\n  fragColor1 = vec4(vec3(color.g), 1.0);  // Green channel\n  fragColor2 = vec4(vec3(color.b), 1.0);  // Blue channel\n  fragColor3 = vec4(vec3(color.a), 1.0);  // Alpha channel\n}\n",
-              "from_input"
-            ]
-          }
-        ],
-        "groups": [],
-        "links": [
-          {
-            "id": 39,
-            "origin_id": -10,
-            "origin_slot": 0,
-            "target_id": 23,
-            "target_slot": 0,
-            "type": "IMAGE"
-          },
-          {
-            "id": 26,
-            "origin_id": 23,
-            "origin_slot": 0,
-            "target_id": -20,
-            "target_slot": 0,
-            "type": "IMAGE"
-          },
-          {
-            "id": 27,
-            "origin_id": 23,
-            "origin_slot": 1,
-            "target_id": -20,
-            "target_slot": 1,
-            "type": "IMAGE"
-          },
-          {
-            "id": 28,
-            "origin_id": 23,
-            "origin_slot": 2,
-            "target_id": -20,
-            "target_slot": 2,
-            "type": "IMAGE"
-          },
-          {
-            "id": 29,
-            "origin_id": 23,
-            "origin_slot": 3,
-            "target_id": -20,
-            "target_slot": 3,
-            "type": "IMAGE"
-          }
-        ],
-        "extra": {
-          "workflowRendererVersion": "LG"
-        },
-        "category": "Image Tools/Color adjust"
-      }
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blueprints/Prompt Enhance.json

@@ -1,278 +1 @@
-{
-  "revision": 0,
-  "last_node_id": 15,
-  "last_link_id": 0,
-  "nodes": [
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-      "type": "24d8bbfd-39d4-4774-bff0-3de40cc7a471",
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-      "order": 0,
-      "mode": 0,
-      "inputs": [
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-          "type": "STRING",
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-          "link": null
-        },
-        {
-          "label": "reference images",
-          "name": "images",
-          "type": "IMAGE",
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "name": "STRING",
-          "type": "STRING",
-          "links": null
-        }
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-      "title": "Prompt Enhance",
-      "properties": {
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-            "prompt"
-          ]
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-        "cnr_id": "comfy-core",
-        "ver": "0.14.1"
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-      "widgets_values": [
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-                "name": "audio",
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-                "type": "AUDIO",
-                "link": null
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-                "type": "VIDEO",
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-                "type": "STRING",
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-                "type": "COMBO",
-                "widget": {
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-              {
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-                "name": "seed",
-                "type": "INT",
-                "widget": {
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-                "name": "system_prompt",
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-                "type": "STRING",
-                "widget": {
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-                "name": "STRING",
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blueprints/Sharpen.json

@@ -1,309 +1 @@
-{
-  "revision": 0,
-  "last_node_id": 25,
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-          "localized_name": "images.image0",
-          "name": "images.image0",
-          "type": "IMAGE",
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "label": "IMAGE",
-          "localized_name": "IMAGE0",
-          "name": "IMAGE0",
-          "type": "IMAGE",
-          "links": []
-        }
-      ],
-      "title": "Sharpen",
-      "properties": {
-        "proxyWidgets": [
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-            "24",
-            "value"
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-      "widgets_values": []
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-              "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform float u_float0;  // strength [0.0 – 2.0] typical: 0.3–1.0\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nvoid main() {\n    vec2 texel = 1.0 / vec2(textureSize(u_image0, 0));\n    \n    // Sample center and neighbors\n    vec4 center = texture(u_image0, v_texCoord);\n    vec4 top    = texture(u_image0, v_texCoord + vec2( 0.0, -texel.y));\n    vec4 bottom = texture(u_image0, v_texCoord + vec2( 0.0,  texel.y));\n    vec4 left   = texture(u_image0, v_texCoord + vec2(-texel.x,  0.0));\n    vec4 right  = texture(u_image0, v_texCoord + vec2( texel.x,  0.0));\n    \n    // Edge enhancement (Laplacian)\n    vec4 edges = center * 4.0 - top - bottom - left - right;\n    \n    // Add edges back scaled by strength\n    vec4 sharpened = center + edges * u_float0;\n    \n    fragColor0 = vec4(clamp(sharpened.rgb, 0.0, 1.0), center.a);\n}",
-              "from_input"
-            ]
-          }
-        ],
-        "groups": [],
-        "links": [
-          {
-            "id": 36,
-            "origin_id": 24,
-            "origin_slot": 0,
-            "target_id": 23,
-            "target_slot": 2,
-            "type": "FLOAT"
-          },
-          {
-            "id": 34,
-            "origin_id": -10,
-            "origin_slot": 0,
-            "target_id": 23,
-            "target_slot": 0,
-            "type": "IMAGE"
-          },
-          {
-            "id": 35,
-            "origin_id": 23,
-            "origin_slot": 0,
-            "target_id": -20,
-            "target_slot": 0,
-            "type": "IMAGE"
-          }
-        ],
-        "extra": {
-          "workflowRendererVersion": "LG"
-        },
-        "category": "Image Tools/Sharpen"
-      }
-    ]
-  }
-}
+{"revision": 0, "last_node_id": 25, "last_link_id": 0, "nodes": [{"id": 25, "type": "621ba4e2-22a8-482d-a369-023753198b7b", "pos": [4610, -790], "size": [230, 58], "flags": {}, "order": 4, "mode": 0, "inputs": [{"label": "image", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": null}], "outputs": [{"label": "IMAGE", "localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": []}], "title": "Sharpen", "properties": {"proxyWidgets": [["24", "value"]]}, "widgets_values": []}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "621ba4e2-22a8-482d-a369-023753198b7b", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 24, "lastLinkId": 36, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Sharpen", "inputNode": {"id": -10, "bounding": [4090, -825, 120, 60]}, "outputNode": {"id": -20, "bounding": [5150, -825, 120, 60]}, "inputs": [{"id": "37011fb7-14b7-4e0e-b1a0-6a02e8da1fd7", "name": "images.image0", "type": "IMAGE", "linkIds": [34], "localized_name": "images.image0", "label": "image", "pos": [4190, -805]}], "outputs": [{"id": "e9182b3f-635c-4cd4-a152-4b4be17ae4b9", "name": "IMAGE0", "type": "IMAGE", "linkIds": [35], "localized_name": "IMAGE0", "label": "IMAGE", "pos": [5170, -805]}], "widgets": [], "nodes": [{"id": 24, "type": "PrimitiveFloat", "pos": [4280, -1240], "size": [270, 58], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "strength", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [36]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 3, "precision": 2, "step": 0.05}, "widgets_values": [0.5]}, {"id": 23, "type": "GLSLShader", "pos": [4570, -1240], "size": [370, 192], "flags": {}, "order": 1, "mode": 0, "inputs": [{"label": "image0", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": 34}, {"label": "image1", "localized_name": "images.image1", "name": "images.image1", "shape": 7, "type": "IMAGE", "link": null}, {"label": "u_float0", "localized_name": "floats.u_float0", "name": "floats.u_float0", "shape": 7, "type": "FLOAT", "link": 36}, {"label": "u_float1", "localized_name": "floats.u_float1", "name": "floats.u_float1", "shape": 7, "type": "FLOAT", "link": null}, {"label": "u_int0", "localized_name": "ints.u_int0", "name": "ints.u_int0", "shape": 7, "type": "INT", "link": null}, {"localized_name": "fragment_shader", "name": "fragment_shader", "type": "STRING", "widget": {"name": "fragment_shader"}, "link": null}, {"localized_name": "size_mode", "name": "size_mode", "type": "COMFY_DYNAMICCOMBO_V3", "widget": {"name": "size_mode"}, "link": null}], "outputs": [{"localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": [35]}, {"localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": null}], "properties": {"Node name for S&R": "GLSLShader"}, "widgets_values": ["#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform float u_float0;  // strength [0.0 – 2.0] typical: 0.3–1.0\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nvoid main() {\n    vec2 texel = 1.0 / u_resolution;\n    \n    // Sample center and neighbors\n    vec4 center = texture(u_image0, v_texCoord);\n    vec4 top    = texture(u_image0, v_texCoord + vec2( 0.0, -texel.y));\n    vec4 bottom = texture(u_image0, v_texCoord + vec2( 0.0,  texel.y));\n    vec4 left   = texture(u_image0, v_texCoord + vec2(-texel.x,  0.0));\n    vec4 right  = texture(u_image0, v_texCoord + vec2( texel.x,  0.0));\n    \n    // Edge enhancement (Laplacian)\n    vec4 edges = center * 4.0 - top - bottom - left - right;\n    \n    // Add edges back scaled by strength\n    vec4 sharpened = center + edges * u_float0;\n    \n    fragColor0 = vec4(clamp(sharpened.rgb, 0.0, 1.0), center.a);\n}", "from_input"]}], "groups": [], "links": [{"id": 36, "origin_id": 24, "origin_slot": 0, "target_id": 23, "target_slot": 2, "type": "FLOAT"}, {"id": 34, "origin_id": -10, "origin_slot": 0, "target_id": 23, "target_slot": 0, "type": "IMAGE"}, {"id": 35, "origin_id": 23, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Image Tools/Sharpen"}]}}

blueprints/Video Upscale(GAN x4).json

@@ -1,420 +1 @@
-{
-  "revision": 0,
-  "last_node_id": 13,
-  "last_link_id": 0,
-  "nodes": [
-    {
-      "id": 13,
-      "type": "cf95b747-3e17-46cb-8097-cac60ff9b2e1",
-      "pos": [
-        1120,
-        330
-      ],
-      "size": [
-        240,
-        58
-      ],
-      "flags": {},
-      "order": 3,
-      "mode": 0,
-      "inputs": [
-        {
-          "localized_name": "video",
-          "name": "video",
-          "type": "VIDEO",
-          "link": null
-        },
-        {
-          "name": "model_name",
-          "type": "COMBO",
-          "widget": {
-            "name": "model_name"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "localized_name": "VIDEO",
-          "name": "VIDEO",
-          "type": "VIDEO",
-          "links": []
-        }
-      ],
-      "title": "Video Upscale(GAN x4)",
-      "properties": {
-        "proxyWidgets": [
-          [
-            "-1",
-            "model_name"
-          ]
-        ],
-        "cnr_id": "comfy-core",
-        "ver": "0.14.1"
-      },
-      "widgets_values": [
-        "RealESRGAN_x4plus.safetensors"
-      ]
-    }
-  ],
-  "links": [],
-  "version": 0.4,
-  "definitions": {
-    "subgraphs": [
-      {
-        "id": "cf95b747-3e17-46cb-8097-cac60ff9b2e1",
-        "version": 1,
-        "state": {
-          "lastGroupId": 0,
-          "lastNodeId": 13,
-          "lastLinkId": 19,
-          "lastRerouteId": 0
-        },
-        "revision": 0,
-        "config": {},
-        "name": "Video Upscale(GAN x4)",
-        "inputNode": {
-          "id": -10,
-          "bounding": [
-            550,
-            460,
-            120,
-            80
-          ]
-        },
-        "outputNode": {
-          "id": -20,
-          "bounding": [
-            1490,
-            460,
-            120,
-            60
-          ]
-        },
-        "inputs": [
-          {
-            "id": "666d633e-93e7-42dc-8d11-2b7b99b0f2a6",
-            "name": "video",
-            "type": "VIDEO",
-            "linkIds": [
-              10
-            ],
-            "localized_name": "video",
-            "pos": [
-              650,
-              480
-            ]
-          },
-          {
-            "id": "2e23a087-caa8-4d65-99e6-662761aa905a",
-            "name": "model_name",
-            "type": "COMBO",
-            "linkIds": [
-              19
-            ],
-            "pos": [
-              650,
-              500
-            ]
-          }
-        ],
-        "outputs": [
-          {
-            "id": "0c1768ea-3ec2-412f-9af6-8e0fa36dae70",
-            "name": "VIDEO",
-            "type": "VIDEO",
-            "linkIds": [
-              15
-            ],
-            "localized_name": "VIDEO",
-            "pos": [
-              1510,
-              480
-            ]
-          }
-        ],
-        "widgets": [],
-        "nodes": [
-          {
-            "id": 2,
-            "type": "ImageUpscaleWithModel",
-            "pos": [
-              1110,
-              450
-            ],
-            "size": [
-              320,
-              46
-            ],
-            "flags": {},
-            "order": 1,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "upscale_model",
-                "name": "upscale_model",
-                "type": "UPSCALE_MODEL",
-                "link": 1
-              },
-              {
-                "localized_name": "image",
-                "name": "image",
-                "type": "IMAGE",
-                "link": 14
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "IMAGE",
-                "name": "IMAGE",
-                "type": "IMAGE",
-                "links": [
-                  13
-                ]
-              }
-            ],
-            "properties": {
-              "cnr_id": "comfy-core",
-              "ver": "0.10.0",
-              "Node name for S&R": "ImageUpscaleWithModel"
-            }
-          },
-          {
-            "id": 11,
-            "type": "CreateVideo",
-            "pos": [
-              1110,
-              550
-            ],
-            "size": [
-              320,
-              78
-            ],
-            "flags": {},
-            "order": 3,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "images",
-                "name": "images",
-                "type": "IMAGE",
-                "link": 13
-              },
-              {
-                "localized_name": "audio",
-                "name": "audio",
-                "shape": 7,
-                "type": "AUDIO",
-                "link": 16
-              },
-              {
-                "localized_name": "fps",
-                "name": "fps",
-                "type": "FLOAT",
-                "widget": {
-                  "name": "fps"
-                },
-                "link": 12
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "VIDEO",
-                "name": "VIDEO",
-                "type": "VIDEO",
-                "links": [
-                  15
-                ]
-              }
-            ],
-            "properties": {
-              "cnr_id": "comfy-core",
-              "ver": "0.10.0",
-              "Node name for S&R": "CreateVideo"
-            },
-            "widgets_values": [
-              30
-            ]
-          },
-          {
-            "id": 10,
-            "type": "GetVideoComponents",
-            "pos": [
-              1110,
-              330
-            ],
-            "size": [
-              320,
-              70
-            ],
-            "flags": {},
-            "order": 2,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "video",
-                "name": "video",
-                "type": "VIDEO",
-                "link": 10
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "images",
-                "name": "images",
-                "type": "IMAGE",
-                "links": [
-                  14
-                ]
-              },
-              {
-                "localized_name": "audio",
-                "name": "audio",
-                "type": "AUDIO",
-                "links": [
-                  16
-                ]
-              },
-              {
-                "localized_name": "fps",
-                "name": "fps",
-                "type": "FLOAT",
-                "links": [
-                  12
-                ]
-              }
-            ],
-            "properties": {
-              "cnr_id": "comfy-core",
-              "ver": "0.10.0",
-              "Node name for S&R": "GetVideoComponents"
-            }
-          },
-          {
-            "id": 1,
-            "type": "UpscaleModelLoader",
-            "pos": [
-              750,
-              450
-            ],
-            "size": [
-              280,
-              60
-            ],
-            "flags": {},
-            "order": 0,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "model_name",
-                "name": "model_name",
-                "type": "COMBO",
-                "widget": {
-                  "name": "model_name"
-                },
-                "link": 19
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "UPSCALE_MODEL",
-                "name": "UPSCALE_MODEL",
-                "type": "UPSCALE_MODEL",
-                "links": [
-                  1
-                ]
-              }
-            ],
-            "properties": {
-              "cnr_id": "comfy-core",
-              "ver": "0.10.0",
-              "Node name for S&R": "UpscaleModelLoader",
-              "models": [
-                {
-                  "name": "RealESRGAN_x4plus.safetensors",
-                  "url": "https://huggingface.co/Comfy-Org/Real-ESRGAN_repackaged/resolve/main/RealESRGAN_x4plus.safetensors",
-                  "directory": "upscale_models"
-                }
-              ]
-            },
-            "widgets_values": [
-              "RealESRGAN_x4plus.safetensors"
-            ]
-          }
-        ],
-        "groups": [],
-        "links": [
-          {
-            "id": 1,
-            "origin_id": 1,
-            "origin_slot": 0,
-            "target_id": 2,
-            "target_slot": 0,
-            "type": "UPSCALE_MODEL"
-          },
-          {
-            "id": 14,
-            "origin_id": 10,
-            "origin_slot": 0,
-            "target_id": 2,
-            "target_slot": 1,
-            "type": "IMAGE"
-          },
-          {
-            "id": 13,
-            "origin_id": 2,
-            "origin_slot": 0,
-            "target_id": 11,
-            "target_slot": 0,
-            "type": "IMAGE"
-          },
-          {
-            "id": 16,
-            "origin_id": 10,
-            "origin_slot": 1,
-            "target_id": 11,
-            "target_slot": 1,
-            "type": "AUDIO"
-          },
-          {
-            "id": 12,
-            "origin_id": 10,
-            "origin_slot": 2,
-            "target_id": 11,
-            "target_slot": 2,
-            "type": "FLOAT"
-          },
-          {
-            "id": 10,
-            "origin_id": -10,
-            "origin_slot": 0,
-            "target_id": 10,
-            "target_slot": 0,
-            "type": "VIDEO"
-          },
-          {
-            "id": 15,
-            "origin_id": 11,
-            "origin_slot": 0,
-            "target_id": -20,
-            "target_slot": 0,
-            "type": "VIDEO"
-          },
-          {
-            "id": 19,
-            "origin_id": -10,
-            "origin_slot": 1,
-            "target_id": 1,
-            "target_slot": 0,
-            "type": "COMBO"
-          }
-        ],
-        "extra": {
-          "workflowRendererVersion": "LG"
-        },
-        "category": "Video generation and editing/Enhance video"
-      }
-    ]
-  },
-  "extra": {}
-}
+{"revision": 0, "last_node_id": 13, "last_link_id": 0, "nodes": [{"id": 13, "type": "cf95b747-3e17-46cb-8097-cac60ff9b2e1", "pos": [1120, 330], "size": [240, 58], "flags": {}, "order": 3, "mode": 0, "inputs": [{"localized_name": "video", "name": "video", "type": "VIDEO", "link": null}, {"name": "model_name", "type": "COMBO", "widget": {"name": "model_name"}, "link": null}], "outputs": [{"localized_name": "VIDEO", "name": "VIDEO", "type": "VIDEO", "links": []}], "title": "Video Upscale(GAN x4)", "properties": {"proxyWidgets": [["-1", "model_name"]], "cnr_id": "comfy-core", "ver": "0.14.1"}, "widgets_values": ["RealESRGAN_x4plus.safetensors"]}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "cf95b747-3e17-46cb-8097-cac60ff9b2e1", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 13, "lastLinkId": 19, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Video Upscale(GAN x4)", "inputNode": {"id": -10, "bounding": [550, 460, 120, 80]}, "outputNode": {"id": -20, "bounding": [1490, 460, 120, 60]}, "inputs": [{"id": "666d633e-93e7-42dc-8d11-2b7b99b0f2a6", "name": "video", "type": "VIDEO", "linkIds": [10], "localized_name": "video", "pos": [650, 480]}, {"id": "2e23a087-caa8-4d65-99e6-662761aa905a", "name": "model_name", "type": "COMBO", "linkIds": [19], "pos": [650, 500]}], "outputs": [{"id": "0c1768ea-3ec2-412f-9af6-8e0fa36dae70", "name": "VIDEO", "type": "VIDEO", "linkIds": [15], "localized_name": "VIDEO", "pos": [1510, 480]}], "widgets": [], "nodes": [{"id": 2, "type": "ImageUpscaleWithModel", "pos": [1110, 450], "size": [320, 46], "flags": {}, "order": 1, "mode": 0, "inputs": [{"localized_name": "upscale_model", "name": "upscale_model", "type": "UPSCALE_MODEL", "link": 1}, {"localized_name": "image", "name": "image", "type": "IMAGE", "link": 14}], "outputs": [{"localized_name": "IMAGE", "name": "IMAGE", "type": "IMAGE", "links": [13]}], "properties": {"cnr_id": "comfy-core", "ver": "0.10.0", "Node name for S&R": "ImageUpscaleWithModel"}}, {"id": 11, "type": "CreateVideo", "pos": [1110, 550], "size": [320, 78], "flags": {}, "order": 3, "mode": 0, "inputs": [{"localized_name": "images", "name": "images", "type": "IMAGE", "link": 13}, {"localized_name": "audio", "name": "audio", "shape": 7, "type": "AUDIO", "link": 16}, {"localized_name": "fps", "name": "fps", "type": "FLOAT", "widget": {"name": "fps"}, "link": 12}], "outputs": [{"localized_name": "VIDEO", "name": "VIDEO", "type": "VIDEO", "links": [15]}], "properties": {"cnr_id": "comfy-core", "ver": "0.10.0", "Node name for S&R": "CreateVideo"}, "widgets_values": [30]}, {"id": 10, "type": "GetVideoComponents", "pos": [1110, 330], "size": [320, 70], "flags": {}, "order": 2, "mode": 0, "inputs": [{"localized_name": "video", "name": "video", "type": "VIDEO", "link": 10}], "outputs": [{"localized_name": "images", "name": "images", "type": "IMAGE", "links": [14]}, {"localized_name": "audio", "name": "audio", "type": "AUDIO", "links": [16]}, {"localized_name": "fps", "name": "fps", "type": "FLOAT", "links": [12]}], "properties": {"cnr_id": "comfy-core", "ver": "0.10.0", "Node name for S&R": "GetVideoComponents"}}, {"id": 1, "type": "UpscaleModelLoader", "pos": [750, 450], "size": [280, 60], "flags": {}, "order": 0, "mode": 0, "inputs": [{"localized_name": "model_name", "name": "model_name", "type": "COMBO", "widget": {"name": "model_name"}, "link": 19}], "outputs": [{"localized_name": "UPSCALE_MODEL", "name": "UPSCALE_MODEL", "type": "UPSCALE_MODEL", "links": [1]}], "properties": {"cnr_id": "comfy-core", "ver": "0.10.0", "Node name for S&R": "UpscaleModelLoader", "models": [{"name": "RealESRGAN_x4plus.safetensors", "url": "https://huggingface.co/Comfy-Org/Real-ESRGAN_repackaged/resolve/main/RealESRGAN_x4plus.safetensors", "directory": "upscale_models"}]}, "widgets_values": ["RealESRGAN_x4plus.safetensors"]}], "groups": [], "links": [{"id": 1, "origin_id": 1, "origin_slot": 0, "target_id": 2, "target_slot": 0, "type": "UPSCALE_MODEL"}, {"id": 14, "origin_id": 10, "origin_slot": 0, "target_id": 2, "target_slot": 1, "type": "IMAGE"}, {"id": 13, "origin_id": 2, "origin_slot": 0, "target_id": 11, "target_slot": 0, "type": "IMAGE"}, {"id": 16, "origin_id": 10, "origin_slot": 1, "target_id": 11, "target_slot": 1, "type": "AUDIO"}, {"id": 12, "origin_id": 10, "origin_slot": 2, "target_id": 11, "target_slot": 2, "type": "FLOAT"}, {"id": 10, "origin_id": -10, "origin_slot": 0, "target_id": 10, "target_slot": 0, "type": "VIDEO"}, {"id": 15, "origin_id": 11, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "VIDEO"}, {"id": 19, "origin_id": -10, "origin_slot": 1, "target_id": 1, "target_slot": 0, "type": "COMBO"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Video generation and editing/Enhance video"}]}, "extra": {}}

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/k_diffusion/sampling.py

@@ -1810,102 +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
-    num_sigma_steps = len(sigmas) - 1
-    total_real_steps = num_blocks * num_sigma_steps
-    step_count = 0
-
-    try:
-        for block_idx in trange(num_blocks, disable=disable):
-            bf = min(num_frame_per_block, lat_t - current_start_frame)
-            fs, fe = current_start_frame, current_start_frame + bf
-            noisy_input = x[:, :, fs:fe]
-
-            ar_state = {
-                "start_frame": current_start_frame,
-                "kv_caches": kv_caches,
-                "crossattn_caches": crossattn_caches,
-            }
-            transformer_options["ar_state"] = ar_state
-
-            for i in range(num_sigma_steps):
-                denoised = model(noisy_input, sigmas[i] * s_in, **extra_args)
-
-                if callback is not None:
-                    scaled_i = step_count * num_sigma_steps // total_real_steps
-                    callback({"x": noisy_input, "i": scaled_i, "sigma": sigmas[i],
-                              "sigma_hat": sigmas[i], "denoised": denoised})
-
-                if sigmas[i + 1] == 0:
-                    noisy_input = denoised
-                else:
-                    sigma_next = sigmas[i + 1]
-                    torch.manual_seed(seed + block_idx * 1000 + i)
-                    fresh_noise = torch.randn_like(denoised)
-                    noisy_input = (1.0 - sigma_next) * denoised + sigma_next * fresh_noise
-
-                    for cache in kv_caches:
-                        cache["end"] -= bf * frame_seq_len
-
-                step_count += 1
-
-            output[:, :, fs:fe] = noisy_input
-
-            for cache in kv_caches:
-                cache["end"] -= bf * frame_seq_len
-            zero_sigma = sigmas.new_zeros([1])
-            _ = model(noisy_input, zero_sigma * s_in, **extra_args)
-
-            current_start_frame += bf
-    finally:
-        transformer_options.pop("ar_state", None)
-
-    return output

comfy/latent_formats.py

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

comfy/ldm/ace/ace_step15.py

@@ -611,7 +611,6 @@ class AceStepDiTModel(nn.Module):
         intermediate_size,
         patch_size,
         audio_acoustic_hidden_dim,
-        condition_dim=None,
         layer_types=None,
         sliding_window=128,
         rms_norm_eps=1e-6,
@@ -641,7 +640,7 @@ class AceStepDiTModel(nn.Module):
 
         self.time_embed = TimestepEmbedding(256, hidden_size, dtype=dtype, device=device, operations=operations)
         self.time_embed_r = TimestepEmbedding(256, hidden_size, dtype=dtype, device=device, operations=operations)
-        self.condition_embedder = Linear(condition_dim, hidden_size, dtype=dtype, device=device)
+        self.condition_embedder = Linear(hidden_size, hidden_size, dtype=dtype, device=device)
 
         if layer_types is None:
             layer_types = ["full_attention"] * num_layers
@@ -1036,9 +1035,6 @@ class AceStepConditionGenerationModel(nn.Module):
         fsq_dim=2048,
         fsq_levels=[8, 8, 8, 5, 5, 5],
         fsq_input_num_quantizers=1,
-        encoder_hidden_size=2048,
-        encoder_intermediate_size=6144,
-        encoder_num_heads=16,
         audio_model=None,
         dtype=None,
         device=None,
@@ -1058,24 +1054,24 @@ class AceStepConditionGenerationModel(nn.Module):
 
         self.decoder = AceStepDiTModel(
             in_channels, hidden_size, num_dit_layers, num_heads, num_kv_heads, head_dim,
-            intermediate_size, patch_size, audio_acoustic_hidden_dim, condition_dim=encoder_hidden_size,
+            intermediate_size, patch_size, audio_acoustic_hidden_dim,
             layer_types=layer_types, sliding_window=sliding_window, rms_norm_eps=rms_norm_eps,
             dtype=dtype, device=device, operations=operations
         )
         self.encoder = AceStepConditionEncoder(
-            text_hidden_dim, timbre_hidden_dim, encoder_hidden_size, num_lyric_layers, num_timbre_layers,
-            encoder_num_heads, num_kv_heads, head_dim, encoder_intermediate_size, rms_norm_eps,
+            text_hidden_dim, timbre_hidden_dim, hidden_size, num_lyric_layers, num_timbre_layers,
+            num_heads, num_kv_heads, head_dim, intermediate_size, rms_norm_eps,
             dtype=dtype, device=device, operations=operations
         )
         self.tokenizer = AceStepAudioTokenizer(
-            audio_acoustic_hidden_dim, encoder_hidden_size, pool_window_size, fsq_dim=fsq_dim, fsq_levels=fsq_levels, fsq_input_num_quantizers=fsq_input_num_quantizers, num_layers=num_tokenizer_layers, head_dim=head_dim, rms_norm_eps=rms_norm_eps,
+            audio_acoustic_hidden_dim, hidden_size, pool_window_size, fsq_dim=fsq_dim, fsq_levels=fsq_levels, fsq_input_num_quantizers=fsq_input_num_quantizers, num_layers=num_tokenizer_layers, head_dim=head_dim, rms_norm_eps=rms_norm_eps,
             dtype=dtype, device=device, operations=operations
         )
         self.detokenizer = AudioTokenDetokenizer(
-            encoder_hidden_size, pool_window_size, audio_acoustic_hidden_dim, num_layers=2, head_dim=head_dim,
+            hidden_size, pool_window_size, audio_acoustic_hidden_dim, num_layers=2, head_dim=head_dim,
             dtype=dtype, device=device, operations=operations
         )
-        self.null_condition_emb = nn.Parameter(torch.empty(1, 1, encoder_hidden_size, dtype=dtype, device=device))
+        self.null_condition_emb = nn.Parameter(torch.empty(1, 1, hidden_size, dtype=dtype, device=device))
 
     def prepare_condition(
         self,

comfy/ldm/cogvideo/model.py

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

comfy/ldm/cogvideo/vae.py

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

comfy/ldm/ernie/model.py

@@ -1,301 +0,0 @@
-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.model_management
-
-def rope(pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor:
-    assert dim % 2 == 0
-    if not comfy.model_management.supports_fp64(pos.device):
-        device = torch.device("cpu")
-    else:
-        device = pos.device
-
-    scale = torch.arange(0, dim, 2, dtype=torch.float64, device=device) / dim
-    omega = 1.0 / (theta**scale)
-    out = torch.einsum("...n,d->...nd", pos.to(device), omega)
-    out = torch.stack([torch.cos(out), torch.sin(out)], dim=0)
-    return out.to(dtype=torch.float32, device=pos.device)
-
-def apply_rotary_emb(x_in: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
-    rot_dim = freqs_cis.shape[-1]
-    x, x_pass = x_in[..., :rot_dim], x_in[..., rot_dim:]
-    cos_ = freqs_cis[0]
-    sin_ = freqs_cis[1]
-    x1, x2 = x.chunk(2, dim=-1)
-    x_rotated = torch.cat((-x2, x1), dim=-1)
-    return torch.cat((x * cos_ + x_rotated * sin_, x_pass), dim=-1)
-
-class ErnieImageEmbedND3(nn.Module):
-    def __init__(self, dim: int, theta: int, axes_dim: tuple):
-        super().__init__()
-        self.dim = dim
-        self.theta = theta
-        self.axes_dim = list(axes_dim)
-
-    def forward(self, ids: torch.Tensor) -> torch.Tensor:
-        emb = torch.cat([rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(3)], dim=-1)
-        emb = emb.unsqueeze(3)  # [2, B, S, 1, head_dim//2]
-        return torch.stack([emb, emb], dim=-1).reshape(*emb.shape[:-1], -1)  # [B, S, 1, head_dim]
-
-class ErnieImagePatchEmbedDynamic(nn.Module):
-    def __init__(self, in_channels: int, embed_dim: int, patch_size: int, operations, device=None, dtype=None):
-        super().__init__()
-        self.patch_size = patch_size
-        self.proj = operations.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size, bias=True, device=device, dtype=dtype)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.proj(x)
-        batch_size, dim, height, width = x.shape
-        return x.reshape(batch_size, dim, height * width).transpose(1, 2).contiguous()
-
-class Timesteps(nn.Module):
-    def __init__(self, num_channels: int, flip_sin_to_cos: bool = False):
-        super().__init__()
-        self.num_channels = num_channels
-        self.flip_sin_to_cos = flip_sin_to_cos
-
-    def forward(self, timesteps: torch.Tensor) -> torch.Tensor:
-        half_dim = self.num_channels // 2
-        exponent = -math.log(10000) * torch.arange(half_dim, dtype=torch.float32, device=timesteps.device) / half_dim
-        emb = torch.exp(exponent)
-        emb = timesteps[:, None].float() * emb[None, :]
-        if self.flip_sin_to_cos:
-            emb = torch.cat([torch.cos(emb), torch.sin(emb)], dim=-1)
-        else:
-            emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
-        return emb
-
-class TimestepEmbedding(nn.Module):
-    def __init__(self, in_channels: int, time_embed_dim: int, operations, device=None, dtype=None):
-        super().__init__()
-        Linear = operations.Linear
-        self.linear_1 = Linear(in_channels, time_embed_dim, bias=True, device=device, dtype=dtype)
-        self.act = nn.SiLU()
-        self.linear_2 = Linear(time_embed_dim, time_embed_dim, bias=True, device=device, dtype=dtype)
-
-    def forward(self, sample: torch.Tensor) -> torch.Tensor:
-        sample = self.linear_1(sample)
-        sample = self.act(sample)
-        sample = self.linear_2(sample)
-        return sample
-
-class ErnieImageAttention(nn.Module):
-    def __init__(self, query_dim: int, heads: int, dim_head: int, eps: float = 1e-6, operations=None, device=None, dtype=None):
-        super().__init__()
-        self.heads = heads
-        self.head_dim = dim_head
-        self.inner_dim = heads * dim_head
-
-        Linear = operations.Linear
-        RMSNorm = operations.RMSNorm
-
-        self.to_q = Linear(query_dim, self.inner_dim, bias=False, device=device, dtype=dtype)
-        self.to_k = Linear(query_dim, self.inner_dim, bias=False, device=device, dtype=dtype)
-        self.to_v = Linear(query_dim, self.inner_dim, bias=False, device=device, dtype=dtype)
-
-        self.norm_q = RMSNorm(dim_head, eps=eps, elementwise_affine=True, device=device, dtype=dtype)
-        self.norm_k = RMSNorm(dim_head, eps=eps, elementwise_affine=True, device=device, dtype=dtype)
-
-        self.to_out = nn.ModuleList([Linear(self.inner_dim, query_dim, bias=False, device=device, dtype=dtype)])
-
-    def forward(self, x: torch.Tensor, attention_mask: torch.Tensor = None, image_rotary_emb: torch.Tensor = None) -> torch.Tensor:
-        B, S, _ = x.shape
-
-        q_flat = self.to_q(x)
-        k_flat = self.to_k(x)
-        v_flat = self.to_v(x)
-
-        query = q_flat.view(B, S, self.heads, self.head_dim)
-        key = k_flat.view(B, S, self.heads, self.head_dim)
-
-        query = self.norm_q(query)
-        key = self.norm_k(key)
-
-        if image_rotary_emb is not None:
-            query = apply_rotary_emb(query, image_rotary_emb)
-            key = apply_rotary_emb(key, image_rotary_emb)
-
-        q_flat = query.reshape(B, S, -1)
-        k_flat = key.reshape(B, S, -1)
-
-        hidden_states = optimized_attention(q_flat, k_flat, v_flat, self.heads, mask=attention_mask)
-
-        return self.to_out[0](hidden_states)
-
-class ErnieImageFeedForward(nn.Module):
-    def __init__(self, hidden_size: int, ffn_hidden_size: int, operations, device=None, dtype=None):
-        super().__init__()
-        Linear = operations.Linear
-        self.gate_proj = Linear(hidden_size, ffn_hidden_size, bias=False, device=device, dtype=dtype)
-        self.up_proj = Linear(hidden_size, ffn_hidden_size, bias=False, device=device, dtype=dtype)
-        self.linear_fc2 = Linear(ffn_hidden_size, hidden_size, bias=False, device=device, dtype=dtype)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return self.linear_fc2(self.up_proj(x) * F.gelu(self.gate_proj(x)))
-
-class ErnieImageSharedAdaLNBlock(nn.Module):
-    def __init__(self, hidden_size: int, num_heads: int, ffn_hidden_size: int, eps: float = 1e-6, operations=None, device=None, dtype=None):
-        super().__init__()
-        RMSNorm = operations.RMSNorm
-
-        self.adaLN_sa_ln = RMSNorm(hidden_size, eps=eps, device=device, dtype=dtype)
-        self.self_attention = ErnieImageAttention(
-            query_dim=hidden_size,
-            dim_head=hidden_size // num_heads,
-            heads=num_heads,
-            eps=eps,
-            operations=operations,
-            device=device,
-            dtype=dtype
-        )
-        self.adaLN_mlp_ln = RMSNorm(hidden_size, eps=eps, device=device, dtype=dtype)
-        self.mlp = ErnieImageFeedForward(hidden_size, ffn_hidden_size, operations=operations, device=device, dtype=dtype)
-
-    def forward(self, x, rotary_pos_emb, temb, attention_mask=None):
-        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = temb
-
-        residual = x
-        x_norm = self.adaLN_sa_ln(x)
-        x_norm = x_norm * (1 + scale_msa) + shift_msa
-
-        attn_out = self.self_attention(x_norm, attention_mask=attention_mask, image_rotary_emb=rotary_pos_emb)
-        x = residual + gate_msa * attn_out
-
-        residual = x
-        x_norm = self.adaLN_mlp_ln(x)
-        x_norm = x_norm * (1 + scale_mlp) + shift_mlp
-
-        return residual + gate_mlp * self.mlp(x_norm)
-
-class ErnieImageAdaLNContinuous(nn.Module):
-    def __init__(self, hidden_size: int, eps: float = 1e-6, operations=None, device=None, dtype=None):
-        super().__init__()
-        LayerNorm = operations.LayerNorm
-        Linear = operations.Linear
-        self.norm = LayerNorm(hidden_size, elementwise_affine=False, eps=eps, device=device, dtype=dtype)
-        self.linear = Linear(hidden_size, hidden_size * 2, device=device, dtype=dtype)
-
-    def forward(self, x: torch.Tensor, conditioning: torch.Tensor) -> torch.Tensor:
-        scale, shift = self.linear(conditioning).chunk(2, dim=-1)
-        x = self.norm(x)
-        x = torch.addcmul(shift.unsqueeze(1), x, 1 + scale.unsqueeze(1))
-        return x
-
-class ErnieImageModel(nn.Module):
-    def __init__(
-        self,
-        hidden_size: int = 4096,
-        num_attention_heads: int = 32,
-        num_layers: int = 36,
-        ffn_hidden_size: int = 12288,
-        in_channels: int = 128,
-        out_channels: int = 128,
-        patch_size: int = 1,
-        text_in_dim: int = 3072,
-        rope_theta: int = 256,
-        rope_axes_dim: tuple = (32, 48, 48),
-        eps: float = 1e-6,
-        qk_layernorm: bool = True,
-        device=None,
-        dtype=None,
-        operations=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.dtype = dtype
-        self.hidden_size = hidden_size
-        self.num_heads = num_attention_heads
-        self.head_dim = hidden_size // num_attention_heads
-        self.patch_size = patch_size
-        self.out_channels = out_channels
-
-        Linear = operations.Linear
-
-        self.x_embedder = ErnieImagePatchEmbedDynamic(in_channels, hidden_size, patch_size, operations, device, dtype)
-        self.text_proj = Linear(text_in_dim, hidden_size, bias=False, device=device, dtype=dtype) if text_in_dim != hidden_size else None
-
-        self.time_proj = Timesteps(hidden_size, flip_sin_to_cos=False)
-        self.time_embedding = TimestepEmbedding(hidden_size, hidden_size, operations, device, dtype)
-
-        self.pos_embed = ErnieImageEmbedND3(dim=self.head_dim, theta=rope_theta, axes_dim=rope_axes_dim)
-
-        self.adaLN_modulation = nn.Sequential(
-            nn.SiLU(),
-            Linear(hidden_size, 6 * hidden_size, device=device, dtype=dtype)
-        )
-
-        self.layers = nn.ModuleList([
-            ErnieImageSharedAdaLNBlock(hidden_size, num_attention_heads, ffn_hidden_size, eps, operations, device, dtype)
-            for _ in range(num_layers)
-        ])
-
-        self.final_norm = ErnieImageAdaLNContinuous(hidden_size, eps, operations, device, dtype)
-        self.final_linear = Linear(hidden_size, patch_size * patch_size * out_channels, device=device, dtype=dtype)
-
-    def forward(self, x, timesteps, context, **kwargs):
-        device, dtype = x.device, x.dtype
-        B, C, H, W = x.shape
-        p, Hp, Wp = self.patch_size, H // self.patch_size, W // self.patch_size
-        N_img = Hp * Wp
-
-        img_bsh = self.x_embedder(x)
-
-        text_bth = context
-        if self.text_proj is not None and text_bth.numel() > 0:
-            text_bth = self.text_proj(text_bth)
-        Tmax = text_bth.shape[1]
-
-        hidden_states = torch.cat([img_bsh, text_bth], dim=1)
-
-        text_ids = torch.zeros((B, Tmax, 3), device=device, dtype=torch.float32)
-        text_ids[:, :, 0] = torch.linspace(0, Tmax - 1, steps=Tmax, device=x.device, dtype=torch.float32)
-        index = float(Tmax)
-
-        transformer_options = kwargs.get("transformer_options", {})
-        rope_options = transformer_options.get("rope_options", None)
-
-        h_len, w_len = float(Hp), float(Wp)
-        h_offset, w_offset = 0.0, 0.0
-
-        if rope_options is not None:
-            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
-            index += rope_options.get("shift_t", 0.0)
-            h_offset += rope_options.get("shift_y", 0.0)
-            w_offset += rope_options.get("shift_x", 0.0)
-
-        image_ids = torch.zeros((Hp, Wp, 3), device=device, dtype=torch.float32)
-        image_ids[:, :, 0] = image_ids[:, :, 1] + index
-        image_ids[:, :, 1] = image_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=Hp, device=device, dtype=torch.float32).unsqueeze(1)
-        image_ids[:, :, 2] = image_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=Wp, device=device, dtype=torch.float32).unsqueeze(0)
-
-        image_ids = image_ids.view(1, N_img, 3).expand(B, -1, -1)
-
-        rotary_pos_emb = self.pos_embed(torch.cat([image_ids, text_ids], dim=1)).to(x.dtype)
-        del image_ids, text_ids
-
-        sample = self.time_proj(timesteps).to(dtype)
-        c = self.time_embedding(sample)
-
-        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = [
-            t.unsqueeze(1).contiguous() for t in self.adaLN_modulation(c).chunk(6, dim=-1)
-        ]
-
-        temb = [shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp]
-        for layer in self.layers:
-            hidden_states = layer(hidden_states, rotary_pos_emb, temb)
-
-        hidden_states = self.final_norm(hidden_states, c).type_as(hidden_states)
-
-        patches = self.final_linear(hidden_states)[:, :N_img, :]
-        output = (
-            patches.view(B, Hp, Wp, p, p, self.out_channels)
-            .permute(0, 5, 1, 3, 2, 4)
-            .contiguous()
-            .view(B, self.out_channels, H, W)
-        )
-
-        return output

comfy/ldm/flux/math.py

@@ -16,7 +16,7 @@ def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transforme
 
 def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     assert dim % 2 == 0
-    if not comfy.model_management.supports_fp64(pos.device):
+    if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu() or comfy.model_management.is_directml_enabled():
         device = torch.device("cpu")
     else:
         device = pos.device

comfy/ldm/lightricks/av_model.py

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

comfy/ldm/lightricks/vae/audio_vae.py

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

comfy/ldm/models/autoencoder.py

@@ -155,7 +155,6 @@ class AutoencodingEngineLegacy(AutoencodingEngine):
     def __init__(self, embed_dim: int, **kwargs):
         self.max_batch_size = kwargs.pop("max_batch_size", None)
         ddconfig = kwargs.pop("ddconfig")
-        decoder_ddconfig = kwargs.pop("decoder_ddconfig", ddconfig)
         super().__init__(
             encoder_config={
                 "target": "comfy.ldm.modules.diffusionmodules.model.Encoder",
@@ -163,7 +162,7 @@ class AutoencodingEngineLegacy(AutoencodingEngine):
             },
             decoder_config={
                 "target": "comfy.ldm.modules.diffusionmodules.model.Decoder",
-                "params": decoder_ddconfig,
+                "params": ddconfig,
             },
             **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/openaimodel.py

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

comfy/ldm/modules/sdpose.py

@@ -90,7 +90,7 @@ class HeatmapHead(torch.nn.Module):
                 origin_max = np.max(hm[k])
                 dr = np.zeros((H + 2 * border, W + 2 * border), dtype=np.float32)
                 dr[border:-border, border:-border] = hm[k].copy()
-                dr = gaussian_filter(dr, sigma=2.0, truncate=2.5)
+                dr = gaussian_filter(dr, sigma=2.0)
                 hm[k] = dr[border:-border, border:-border].copy()
                 cur_max = np.max(hm[k])
                 if cur_max > 0:

comfy/ldm/sam3/detector.py

@@ -1,596 +0,0 @@
-# SAM3 detector: transformer encoder-decoder, segmentation head, geometry encoder, scoring.
-
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torchvision.ops import roi_align
-
-from comfy.ldm.modules.attention import optimized_attention
-from comfy.ldm.sam3.tracker import SAM3Tracker, SAM31Tracker
-from comfy.ldm.sam3.sam import SAM3VisionBackbone  # noqa: used in __init__
-from comfy.ldm.sam3.sam import MLP, PositionEmbeddingSine
-
-TRACKER_CLASSES = {"SAM3": SAM3Tracker, "SAM31": SAM31Tracker}
-from comfy.ops import cast_to_input
-
-
-def box_cxcywh_to_xyxy(x):
-    cx, cy, w, h = x.unbind(-1)
-    return torch.stack([cx - 0.5 * w, cy - 0.5 * h, cx + 0.5 * w, cy + 0.5 * h], dim=-1)
-
-
-def gen_sineembed_for_position(pos_tensor, num_feats=256):
-    """Per-coordinate sinusoidal embedding: (..., N) -> (..., N * num_feats)."""
-    assert num_feats % 2 == 0
-    hdim = num_feats // 2
-    freqs = 10000.0 ** (2 * (torch.arange(hdim, dtype=torch.float32, device=pos_tensor.device) // 2) / hdim)
-    embeds = []
-    for c in range(pos_tensor.shape[-1]):
-        raw = (pos_tensor[..., c].float() * 2 * math.pi).unsqueeze(-1) / freqs
-        embeds.append(torch.stack([raw[..., 0::2].sin(), raw[..., 1::2].cos()], dim=-1).flatten(-2))
-    return torch.cat(embeds, dim=-1).to(pos_tensor.dtype)
-
-
-class SplitMHA(nn.Module):
-    """Multi-head attention with separate Q/K/V projections (split from fused in_proj_weight)."""
-    def __init__(self, d_model, num_heads=8, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.num_heads = num_heads
-        self.q_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype)
-        self.k_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype)
-        self.v_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype)
-        self.out_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype)
-
-    def forward(self, q_input, k_input=None, v_input=None, mask=None):
-        q = self.q_proj(q_input)
-        if k_input is None:
-            k = self.k_proj(q_input)
-            v = self.v_proj(q_input)
-        else:
-            k = self.k_proj(k_input)
-            v = self.v_proj(v_input if v_input is not None else k_input)
-        if mask is not None and mask.ndim == 2:
-            mask = mask[:, None, None, :]  # [B, T] -> [B, 1, 1, T] for SDPA broadcast
-        dtype = q.dtype  # manual_cast may produce mixed dtypes
-        out = optimized_attention(q, k.to(dtype), v.to(dtype), self.num_heads, mask=mask, low_precision_attention=False)
-        return self.out_proj(out)
-
-
-class MLPWithNorm(nn.Module):
-    """MLP with residual connection and output LayerNorm."""
-    def __init__(self, input_dim, hidden_dim, output_dim, num_layers, residual=True, device=None, dtype=None, operations=None):
-        super().__init__()
-        dims = [input_dim] + [hidden_dim] * (num_layers - 1) + [output_dim]
-        self.layers = nn.ModuleList([
-            operations.Linear(dims[i], dims[i + 1], device=device, dtype=dtype)
-            for i in range(num_layers)
-        ])
-        self.out_norm = operations.LayerNorm(output_dim, device=device, dtype=dtype)
-        self.residual = residual and (input_dim == output_dim)
-
-    def forward(self, x):
-        orig = x
-        for i, layer in enumerate(self.layers):
-            x = layer(x)
-            if i < len(self.layers) - 1:
-                x = F.relu(x)
-        if self.residual:
-            x = x + orig
-        return self.out_norm(x)
-
-
-class EncoderLayer(nn.Module):
-    def __init__(self, d_model=256, num_heads=8, dim_ff=2048, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.self_attn = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations)
-        self.cross_attn_image = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations)
-        self.linear1 = operations.Linear(d_model, dim_ff, device=device, dtype=dtype)
-        self.linear2 = operations.Linear(dim_ff, d_model, device=device, dtype=dtype)
-        self.norm1 = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.norm2 = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.norm3 = operations.LayerNorm(d_model, device=device, dtype=dtype)
-
-    def forward(self, x, pos, text_memory=None, text_mask=None):
-        normed = self.norm1(x)
-        q_k = normed + pos
-        x = x + self.self_attn(q_k, q_k, normed)
-        if text_memory is not None:
-            normed = self.norm2(x)
-            x = x + self.cross_attn_image(normed, text_memory, text_memory, mask=text_mask)
-        normed = self.norm3(x)
-        x = x + self.linear2(F.relu(self.linear1(normed)))
-        return x
-
-
-class TransformerEncoder(nn.Module):
-    """Checkpoint: transformer.encoder.layers.N.*"""
-    def __init__(self, d_model=256, num_heads=8, dim_ff=2048, num_layers=6, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.layers = nn.ModuleList([
-            EncoderLayer(d_model, num_heads, dim_ff, device=device, dtype=dtype, operations=operations)
-            for _ in range(num_layers)
-        ])
-
-    def forward(self, x, pos, text_memory=None, text_mask=None):
-        for layer in self.layers:
-            x = layer(x, pos, text_memory, text_mask)
-        return x
-
-
-class DecoderLayer(nn.Module):
-    def __init__(self, d_model=256, num_heads=8, dim_ff=2048, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.self_attn = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations)
-        self.cross_attn = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations)
-        self.ca_text = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations)
-        self.norm1 = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.norm2 = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.norm3 = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.catext_norm = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.linear1 = operations.Linear(d_model, dim_ff, device=device, dtype=dtype)
-        self.linear2 = operations.Linear(dim_ff, d_model, device=device, dtype=dtype)
-
-    def forward(self, x, memory, x_pos, memory_pos, text_memory=None, text_mask=None, cross_attn_bias=None):
-        q_k = x + x_pos
-        x = self.norm2(x + self.self_attn(q_k, q_k, x))
-        if text_memory is not None:
-            x = self.catext_norm(x + self.ca_text(x + x_pos, text_memory, text_memory, mask=text_mask))
-        x = self.norm1(x + self.cross_attn(x + x_pos, memory + memory_pos, memory, mask=cross_attn_bias))
-        x = self.norm3(x + self.linear2(F.relu(self.linear1(x))))
-        return x
-
-
-class TransformerDecoder(nn.Module):
-    def __init__(self, d_model=256, num_heads=8, dim_ff=2048, num_layers=6,
-                 num_queries=200, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.d_model = d_model
-        self.num_queries = num_queries
-
-        self.layers = nn.ModuleList([
-            DecoderLayer(d_model, num_heads, dim_ff, device=device, dtype=dtype, operations=operations)
-            for _ in range(num_layers)
-        ])
-        self.norm = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.query_embed = operations.Embedding(num_queries, d_model, device=device, dtype=dtype)
-        self.reference_points = operations.Embedding(num_queries, 4, device=device, dtype=dtype) # Reference points: Embedding(num_queries, 4) — learned anchor boxes
-        self.ref_point_head = MLP(d_model * 2, d_model, d_model, 2, device=device, dtype=dtype, operations=operations) # ref_point_head input: 512 (4 coords * 128 sine features each)
-        self.bbox_embed = MLP(d_model, d_model, 4, 3, device=device, dtype=dtype, operations=operations)
-
-        self.boxRPB_embed_x = MLP(2, d_model, num_heads, 2, device=device, dtype=dtype, operations=operations)
-        self.boxRPB_embed_y = MLP(2, d_model, num_heads, 2, device=device, dtype=dtype, operations=operations)
-
-        self.presence_token = operations.Embedding(1, d_model, device=device, dtype=dtype)
-        self.presence_token_head = MLP(d_model, d_model, 1, 3, device=device, dtype=dtype, operations=operations)
-        self.presence_token_out_norm = operations.LayerNorm(d_model, device=device, dtype=dtype)
-
-    @staticmethod
-    def _inverse_sigmoid(x):
-        return torch.log(x / (1 - x + 1e-6) + 1e-6)
-
-    def _compute_box_rpb(self, ref_points, H, W):
-        """Box rotary position bias: (B, Q, 4) cxcywh -> (B, n_heads, Q+1, H*W) bias."""
-        boxes_xyxy = box_cxcywh_to_xyxy(ref_points)
-        B, Q, _ = boxes_xyxy.shape
-        coords_h = torch.arange(H, device=ref_points.device, dtype=torch.float32) / H
-        coords_w = torch.arange(W, device=ref_points.device, dtype=torch.float32) / W
-        deltas_x = coords_w.view(1, 1, -1, 1) - boxes_xyxy[:, :, None, 0:3:2]
-        deltas_y = coords_h.view(1, 1, -1, 1) - boxes_xyxy[:, :, None, 1:4:2]
-
-        log2_8 = float(math.log2(8))
-        def log_scale(d):
-            return torch.sign(d * 8) * torch.log2(torch.abs(d * 8) + 1.0) / log2_8
-
-        rpb_x = self.boxRPB_embed_x(log_scale(deltas_x).to(ref_points.dtype))
-        rpb_y = self.boxRPB_embed_y(log_scale(deltas_y).to(ref_points.dtype))
-
-        bias = (rpb_y.unsqueeze(3) + rpb_x.unsqueeze(2)).flatten(2, 3).permute(0, 3, 1, 2)
-        pres_bias = torch.zeros(B, bias.shape[1], 1, bias.shape[3], device=bias.device, dtype=bias.dtype)
-        return torch.cat([pres_bias, bias], dim=2)
-
-    def forward(self, memory, memory_pos, text_memory=None, text_mask=None, H=72, W=72):
-        B = memory.shape[0]
-        tgt = cast_to_input(self.query_embed.weight, memory).unsqueeze(0).expand(B, -1, -1)
-        presence_out = cast_to_input(self.presence_token.weight, memory)[None].expand(B, -1, -1)
-        ref_points = cast_to_input(self.reference_points.weight, memory).unsqueeze(0).expand(B, -1, -1).sigmoid()
-
-        for layer_idx, layer in enumerate(self.layers):
-            query_pos = self.ref_point_head(gen_sineembed_for_position(ref_points, self.d_model))
-            tgt_with_pres = torch.cat([presence_out, tgt], dim=1)
-            pos_with_pres = torch.cat([torch.zeros_like(presence_out), query_pos], dim=1)
-            tgt_with_pres = layer(tgt_with_pres, memory, pos_with_pres, memory_pos,
-                                  text_memory, text_mask, self._compute_box_rpb(ref_points, H, W))
-            presence_out, tgt = tgt_with_pres[:, :1], tgt_with_pres[:, 1:]
-            if layer_idx < len(self.layers) - 1:
-                ref_inv = self._inverse_sigmoid(ref_points)
-                ref_points = (ref_inv + self.bbox_embed(self.norm(tgt))).sigmoid().detach()
-
-        query_out = self.norm(tgt)
-        ref_inv = self._inverse_sigmoid(ref_points)
-        boxes = (ref_inv + self.bbox_embed(query_out)).sigmoid()
-        presence = self.presence_token_head(self.presence_token_out_norm(presence_out)).squeeze(-1)
-        return {"decoder_output": query_out, "pred_boxes": boxes, "presence": presence}
-
-
-class Transformer(nn.Module):
-    def __init__(self, d_model=256, num_heads=8, dim_ff=2048, enc_layers=6, dec_layers=6,
-                 num_queries=200, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.encoder = TransformerEncoder(d_model, num_heads, dim_ff, enc_layers, device=device, dtype=dtype, operations=operations)
-        self.decoder = TransformerDecoder(d_model, num_heads, dim_ff, dec_layers, num_queries, device=device, dtype=dtype, operations=operations)
-
-
-class GeometryEncoder(nn.Module):
-    def __init__(self, d_model=256, num_heads=8, num_layers=3, roi_size=7, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.d_model = d_model
-        self.roi_size = roi_size
-        self.pos_enc = PositionEmbeddingSine(num_pos_feats=d_model, normalize=True)
-        self.points_direct_project = operations.Linear(2, d_model, device=device, dtype=dtype)
-        self.points_pool_project = operations.Linear(d_model, d_model, device=device, dtype=dtype)
-        self.points_pos_enc_project = operations.Linear(d_model, d_model, device=device, dtype=dtype)
-        self.boxes_direct_project = operations.Linear(4, d_model, device=device, dtype=dtype)
-        self.boxes_pool_project = operations.Conv2d(d_model, d_model, kernel_size=roi_size, device=device, dtype=dtype)
-        self.boxes_pos_enc_project = operations.Linear(d_model + 2, d_model, device=device, dtype=dtype)
-        self.label_embed = operations.Embedding(2, d_model, device=device, dtype=dtype)
-        self.cls_embed = operations.Embedding(1, d_model, device=device, dtype=dtype)
-        self.norm = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.img_pre_norm = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.encode = nn.ModuleList([
-            EncoderLayer(d_model, num_heads, 2048, device=device, dtype=dtype, operations=operations)
-            for _ in range(num_layers)
-        ])
-        self.encode_norm = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.final_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype)
-
-    def _encode_points(self, coords, labels, img_feat_2d):
-        """Encode point prompts: direct + pool + pos_enc + label. coords: [B, N, 2] normalized."""
-        B, N, _ = coords.shape
-        embed = self.points_direct_project(coords)
-        # Pool features from backbone at point locations via grid_sample
-        grid = (coords * 2 - 1).unsqueeze(2)  # [B, N, 1, 2] in [-1, 1]
-        sampled = F.grid_sample(img_feat_2d, grid, align_corners=False)  # [B, C, N, 1]
-        embed = embed + self.points_pool_project(sampled.squeeze(-1).permute(0, 2, 1))  # [B, N, C]
-        # Positional encoding of coordinates
-        x, y = coords[:, :, 0], coords[:, :, 1]  # [B, N]
-        pos_x, pos_y = self.pos_enc._encode_xy(x.flatten(), y.flatten())
-        enc = torch.cat([pos_x, pos_y], dim=-1).view(B, N, -1)
-        embed = embed + self.points_pos_enc_project(cast_to_input(enc, embed))
-        embed = embed + cast_to_input(self.label_embed(labels.long()), embed)
-        return embed
-
-    def _encode_boxes(self, boxes, labels, img_feat_2d):
-        """Encode box prompts: direct + pool + pos_enc + label. boxes: [B, N, 4] normalized cxcywh."""
-        B, N, _ = boxes.shape
-        embed = self.boxes_direct_project(boxes)
-        # ROI align from backbone at box regions
-        H, W = img_feat_2d.shape[-2:]
-        boxes_xyxy = box_cxcywh_to_xyxy(boxes)
-        scale = torch.tensor([W, H, W, H], dtype=boxes_xyxy.dtype, device=boxes_xyxy.device)
-        boxes_scaled = boxes_xyxy * scale
-        sampled = roi_align(img_feat_2d, boxes_scaled.view(-1, 4).split(N), self.roi_size)
-        proj = self.boxes_pool_project(sampled).view(B, N, -1)  # Conv2d(roi_size) -> [B*N, C, 1, 1] -> [B, N, C]
-        embed = embed + proj
-        # Positional encoding of box center + size
-        cx, cy, w, h = boxes[:, :, 0], boxes[:, :, 1], boxes[:, :, 2], boxes[:, :, 3]
-        enc = self.pos_enc.encode_boxes(cx.flatten(), cy.flatten(), w.flatten(), h.flatten())
-        enc = enc.view(B, N, -1)
-        embed = embed + self.boxes_pos_enc_project(cast_to_input(enc, embed))
-        embed = embed + cast_to_input(self.label_embed(labels.long()), embed)
-        return embed
-
-    def forward(self, points=None, boxes=None, image_features=None):
-        """Encode geometry prompts. image_features: [B, HW, C] flattened backbone features."""
-        # Prepare 2D image features for pooling
-        img_feat_2d = None
-        if image_features is not None:
-            B = image_features.shape[0]
-            HW, C = image_features.shape[1], image_features.shape[2]
-            hw = int(math.sqrt(HW))
-            img_normed = self.img_pre_norm(image_features)
-            img_feat_2d = img_normed.permute(0, 2, 1).view(B, C, hw, hw)
-
-        embeddings = []
-        if points is not None:
-            coords, labels = points
-            embeddings.append(self._encode_points(coords, labels, img_feat_2d))
-        if boxes is not None:
-            B = boxes.shape[0]
-            box_labels = torch.ones(B, boxes.shape[1], dtype=torch.long, device=boxes.device)
-            embeddings.append(self._encode_boxes(boxes, box_labels, img_feat_2d))
-        if not embeddings:
-            return None
-        geo = torch.cat(embeddings, dim=1)
-        geo = self.norm(geo)
-        if image_features is not None:
-            for layer in self.encode:
-                geo = layer(geo, torch.zeros_like(geo), image_features)
-        geo = self.encode_norm(geo)
-        return self.final_proj(geo)
-
-
-class PixelDecoder(nn.Module):
-    """Top-down FPN pixel decoder with GroupNorm + ReLU + nearest interpolation."""
-    def __init__(self, d_model=256, num_stages=3, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.conv_layers = nn.ModuleList([operations.Conv2d(d_model, d_model, kernel_size=3, padding=1, device=device, dtype=dtype) for _ in range(num_stages)])
-        self.norms = nn.ModuleList([operations.GroupNorm(8, d_model, device=device, dtype=dtype) for _ in range(num_stages)])
-
-    def forward(self, backbone_features):
-        prev = backbone_features[-1]
-        for i, feat in enumerate(backbone_features[:-1][::-1]):
-            prev = F.relu(self.norms[i](self.conv_layers[i](feat + F.interpolate(prev, size=feat.shape[-2:], mode="nearest"))))
-        return prev
-
-
-class MaskPredictor(nn.Module):
-    def __init__(self, d_model=256, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.mask_embed = MLP(d_model, d_model, d_model, 3, device=device, dtype=dtype, operations=operations)
-
-    def forward(self, query_embeddings, pixel_features):
-        mask_embed = self.mask_embed(query_embeddings)
-        return torch.einsum("bqc,bchw->bqhw", mask_embed, pixel_features)
-
-
-class SegmentationHead(nn.Module):
-    def __init__(self, d_model=256, num_heads=8, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.d_model = d_model
-        self.pixel_decoder = PixelDecoder(d_model, 3, device=device, dtype=dtype, operations=operations)
-        self.mask_predictor = MaskPredictor(d_model, device=device, dtype=dtype, operations=operations)
-        self.cross_attend_prompt = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations)
-        self.cross_attn_norm = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.instance_seg_head = operations.Conv2d(d_model, d_model, kernel_size=1, device=device, dtype=dtype)
-        self.semantic_seg_head = operations.Conv2d(d_model, 1, kernel_size=1, device=device, dtype=dtype)
-
-    def forward(self, query_embeddings, backbone_features, encoder_hidden_states=None, prompt=None, prompt_mask=None):
-        if encoder_hidden_states is not None and prompt is not None:
-            enc_normed = self.cross_attn_norm(encoder_hidden_states)
-            enc_cross = self.cross_attend_prompt(enc_normed, prompt, prompt, mask=prompt_mask)
-            encoder_hidden_states = enc_cross + encoder_hidden_states
-
-        if encoder_hidden_states is not None:
-            B, H, W = encoder_hidden_states.shape[0], backbone_features[-1].shape[-2], backbone_features[-1].shape[-1]
-            encoder_visual = encoder_hidden_states[:, :H * W].permute(0, 2, 1).view(B, self.d_model, H, W)
-            backbone_features = list(backbone_features)
-            backbone_features[-1] = encoder_visual
-
-        pixel_features = self.pixel_decoder(backbone_features)
-        instance_features = self.instance_seg_head(pixel_features)
-        masks = self.mask_predictor(query_embeddings, instance_features)
-        return masks
-
-
-class DotProductScoring(nn.Module):
-    def __init__(self, d_model=256, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.hs_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype)
-        self.prompt_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype)
-        self.prompt_mlp = MLPWithNorm(d_model, 2048, d_model, 2, device=device, dtype=dtype, operations=operations)
-        self.scale = 1.0 / (d_model ** 0.5)
-
-    def forward(self, query_embeddings, prompt_embeddings, prompt_mask=None):
-        prompt = self.prompt_mlp(prompt_embeddings)
-        if prompt_mask is not None:
-            weight = prompt_mask.unsqueeze(-1).to(dtype=prompt.dtype)
-            pooled = (prompt * weight).sum(dim=1) / weight.sum(dim=1).clamp(min=1)
-        else:
-            pooled = prompt.mean(dim=1)
-        hs = self.hs_proj(query_embeddings)
-        pp = self.prompt_proj(pooled).unsqueeze(-1).to(hs.dtype)
-        scores = torch.matmul(hs, pp)
-        return (scores * self.scale).clamp(-12.0, 12.0).squeeze(-1)
-
-
-class SAM3Detector(nn.Module):
-    def __init__(self, d_model=256, embed_dim=1024, num_queries=200, device=None, dtype=None, operations=None, **kwargs):
-        super().__init__()
-        image_model = kwargs.pop("image_model", "SAM3")
-        for k in ("num_heads", "num_head_channels"):
-            kwargs.pop(k, None)
-        multiplex = image_model == "SAM31"
-        # SAM3: 4 FPN levels, drop last (scalp=1); SAM3.1: 3 levels, use all (scalp=0)
-        self.scalp = 0 if multiplex else 1
-        self.backbone = nn.ModuleDict({
-            "vision_backbone": SAM3VisionBackbone(embed_dim=embed_dim, d_model=d_model, multiplex=multiplex, device=device, dtype=dtype, operations=operations, **kwargs),
-            "language_backbone": nn.ModuleDict({"resizer": operations.Linear(embed_dim, d_model, device=device, dtype=dtype)}),
-        })
-        self.transformer = Transformer(d_model=d_model, num_queries=num_queries, device=device, dtype=dtype, operations=operations)
-        self.segmentation_head = SegmentationHead(d_model=d_model, device=device, dtype=dtype, operations=operations)
-        self.geometry_encoder = GeometryEncoder(d_model=d_model, device=device, dtype=dtype, operations=operations)
-        self.dot_prod_scoring = DotProductScoring(d_model=d_model, device=device, dtype=dtype, operations=operations)
-
-    def _get_backbone_features(self, images):
-        """Run backbone and return (detector_features, detector_positions, tracker_features, tracker_positions)."""
-        bb = self.backbone["vision_backbone"]
-        if bb.multiplex:
-            all_f, all_p, tf, tp = bb(images, tracker_mode="propagation")
-        else:
-            all_f, all_p, tf, tp = bb(images, need_tracker=True)
-        return all_f, all_p, tf, tp
-
-    @staticmethod
-    def _run_geo_layer(layer, x, memory, memory_pos):
-        x = x + layer.self_attn(layer.norm1(x))
-        x = x + layer.cross_attn_image(layer.norm2(x), memory + memory_pos, memory)
-        x = x + layer.linear2(F.relu(layer.linear1(layer.norm3(x))))
-        return x
-
-    def _detect(self, features, positions, text_embeddings=None, text_mask=None,
-                points=None, boxes=None):
-        """Shared detection: geometry encoding, transformer, scoring, segmentation."""
-        B = features[0].shape[0]
-        # Scalp for encoder (use top-level feature), but keep all levels for segmentation head
-        seg_features = features
-        if self.scalp > 0:
-            features = features[:-self.scalp]
-            positions = positions[:-self.scalp]
-        enc_feat, enc_pos = features[-1], positions[-1]
-        _, _, H, W = enc_feat.shape
-        img_flat = enc_feat.flatten(2).permute(0, 2, 1)
-        pos_flat = enc_pos.flatten(2).permute(0, 2, 1)
-
-        has_prompts = text_embeddings is not None or points is not None or boxes is not None
-        if has_prompts:
-            geo_enc = self.geometry_encoder
-            geo_prompts = geo_enc(points=points, boxes=boxes, image_features=img_flat)
-            geo_cls = geo_enc.norm(geo_enc.final_proj(cast_to_input(geo_enc.cls_embed.weight, img_flat).view(1, 1, -1).expand(B, -1, -1)))
-            for layer in geo_enc.encode:
-                geo_cls = self._run_geo_layer(layer, geo_cls, img_flat, pos_flat)
-            geo_cls = geo_enc.encode_norm(geo_cls)
-            if text_embeddings is not None and text_embeddings.shape[0] != B:
-                text_embeddings = text_embeddings.expand(B, -1, -1)
-            if text_mask is not None and text_mask.shape[0] != B:
-                text_mask = text_mask.expand(B, -1)
-            parts = [t for t in [text_embeddings, geo_prompts, geo_cls] if t is not None]
-            text_embeddings = torch.cat(parts, dim=1)
-            n_new = text_embeddings.shape[1] - (text_mask.shape[1] if text_mask is not None else 0)
-            if text_mask is not None:
-                text_mask = torch.cat([text_mask, torch.ones(B, n_new, dtype=torch.bool, device=text_mask.device)], dim=1)
-            else:
-                text_mask = torch.ones(B, text_embeddings.shape[1], dtype=torch.bool, device=text_embeddings.device)
-
-        memory = self.transformer.encoder(img_flat, pos_flat, text_embeddings, text_mask)
-        dec_out = self.transformer.decoder(memory, pos_flat, text_embeddings, text_mask, H, W)
-        query_out, pred_boxes = dec_out["decoder_output"], dec_out["pred_boxes"]
-
-        if text_embeddings is not None:
-            scores = self.dot_prod_scoring(query_out, text_embeddings, text_mask)
-        else:
-            scores = torch.zeros(B, query_out.shape[1], device=query_out.device)
-
-        masks = self.segmentation_head(query_out, seg_features, encoder_hidden_states=memory, prompt=text_embeddings, prompt_mask=text_mask)
-        return box_cxcywh_to_xyxy(pred_boxes), scores, masks, dec_out
-
-    def forward(self, images, text_embeddings=None, text_mask=None, points=None, boxes=None, threshold=0.3, orig_size=None):
-        features, positions, _, _ = self._get_backbone_features(images)
-
-        if text_embeddings is not None:
-            text_embeddings = self.backbone["language_backbone"]["resizer"](text_embeddings)
-            if text_mask is not None:
-                text_mask = text_mask.bool()
-
-        boxes_xyxy, scores, masks, dec_out = self._detect(
-            features, positions, text_embeddings, text_mask, points, boxes)
-
-        if orig_size is not None:
-            oh, ow = orig_size
-            boxes_xyxy = boxes_xyxy * torch.tensor([ow, oh, ow, oh], device=boxes_xyxy.device, dtype=boxes_xyxy.dtype)
-            masks = F.interpolate(masks, size=orig_size, mode="bilinear", align_corners=False)
-
-        return {
-            "boxes": boxes_xyxy,
-            "scores": scores,
-            "masks": masks,
-            "presence": dec_out.get("presence"),
-        }
-
-    def forward_from_trunk(self, trunk_out, text_embeddings, text_mask):
-        """Run detection using a pre-computed ViTDet trunk output.
-
-        text_embeddings must already be resized through language_backbone.resizer.
-        Returns dict with boxes (normalized xyxy), scores, masks at detector resolution.
-        """
-        bb = self.backbone["vision_backbone"]
-        features = [conv(trunk_out) for conv in bb.convs]
-        positions = [cast_to_input(bb.position_encoding(f), f) for f in features]
-
-        if text_mask is not None:
-            text_mask = text_mask.bool()
-
-        boxes_xyxy, scores, masks, _ = self._detect(features, positions, text_embeddings, text_mask)
-        return {"boxes": boxes_xyxy, "scores": scores, "masks": masks}
-
-
-class SAM3Model(nn.Module):
-    def __init__(self, device=None, dtype=None, operations=None, **kwargs):
-        super().__init__()
-        self.dtype = dtype
-        image_model = kwargs.get("image_model", "SAM3")
-        tracker_cls = TRACKER_CLASSES[image_model]
-        self.detector = SAM3Detector(device=device, dtype=dtype, operations=operations, **kwargs)
-        self.tracker = tracker_cls(device=device, dtype=dtype, operations=operations, **kwargs)
-
-    def forward(self, images, **kwargs):
-        return self.detector(images, **kwargs)
-
-    def forward_segment(self, images, point_inputs=None, box_inputs=None, mask_inputs=None):
-        """Interactive segmentation using SAM decoder with point/box/mask prompts.
-
-        Args:
-            images: [B, 3, 1008, 1008] preprocessed images
-            point_inputs: {"point_coords": [B, N, 2], "point_labels": [B, N]} in 1008x1008 pixel space
-            box_inputs: [B, 2, 2] box corners (top-left, bottom-right) in 1008x1008 pixel space
-            mask_inputs: [B, 1, H, W] coarse mask logits to refine
-        Returns:
-            [B, 1, image_size, image_size] high-res mask logits
-        """
-        bb = self.detector.backbone["vision_backbone"]
-        if bb.multiplex:
-            _, _, tracker_features, tracker_positions = bb(images, tracker_mode="interactive")
-        else:
-            _, _, tracker_features, tracker_positions = bb(images, need_tracker=True)
-            if self.detector.scalp > 0:
-                tracker_features = tracker_features[:-self.detector.scalp]
-                tracker_positions = tracker_positions[:-self.detector.scalp]
-
-        high_res = list(tracker_features[:-1])
-        backbone_feat = tracker_features[-1]
-        B, C, H, W = backbone_feat.shape
-        # Add no-memory embedding (init frame path)
-        no_mem = getattr(self.tracker, 'interactivity_no_mem_embed', None)
-        if no_mem is None:
-            no_mem = getattr(self.tracker, 'no_mem_embed', None)
-        if no_mem is not None:
-            feat_flat = backbone_feat.flatten(2).permute(0, 2, 1)
-            feat_flat = feat_flat + cast_to_input(no_mem, feat_flat)
-            backbone_feat = feat_flat.view(B, H, W, C).permute(0, 3, 1, 2)
-
-        num_pts = 0 if point_inputs is None else point_inputs["point_labels"].size(1)
-        _, high_res_masks, _, _ = self.tracker._forward_sam_heads(
-            backbone_features=backbone_feat,
-            point_inputs=point_inputs,
-            mask_inputs=mask_inputs,
-            box_inputs=box_inputs,
-            high_res_features=high_res,
-            multimask_output=(0 < num_pts <= 1),
-        )
-        return high_res_masks
-
-    def forward_video(self, images, initial_masks, pbar=None, text_prompts=None,
-                       new_det_thresh=0.5, max_objects=0, detect_interval=1):
-        """Track video with optional per-frame text-prompted detection."""
-        bb = self.detector.backbone["vision_backbone"]
-
-        def backbone_fn(frame, frame_idx=None):
-            trunk_out = bb.trunk(frame)
-            if bb.multiplex:
-                _, _, tf, tp = bb(frame, tracker_mode="propagation", cached_trunk=trunk_out, tracker_only=True)
-            else:
-                _, _, tf, tp = bb(frame, need_tracker=True, cached_trunk=trunk_out, tracker_only=True)
-            return tf, tp, trunk_out
-
-        detect_fn = None
-        if text_prompts:
-            resizer = self.detector.backbone["language_backbone"]["resizer"]
-            resized = [(resizer(emb), m.bool() if m is not None else None) for emb, m in text_prompts]
-            def detect_fn(trunk_out):
-                all_scores, all_masks = [], []
-                for emb, mask in resized:
-                    det = self.detector.forward_from_trunk(trunk_out, emb, mask)
-                    all_scores.append(det["scores"])
-                    all_masks.append(det["masks"])
-                return {"scores": torch.cat(all_scores, dim=1), "masks": torch.cat(all_masks, dim=1)}
-
-        if hasattr(self.tracker, 'track_video_with_detection'):
-            return self.tracker.track_video_with_detection(
-                backbone_fn, images, initial_masks, detect_fn,
-                new_det_thresh=new_det_thresh, max_objects=max_objects,
-                detect_interval=detect_interval, backbone_obj=bb, pbar=pbar)
-        # 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)

comfy/ldm/sam3/sam.py

@@ -1,425 +0,0 @@
-# SAM3 shared components: primitives, ViTDet backbone, FPN neck, position encodings.
-
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from comfy.ldm.modules.attention import optimized_attention
-from comfy.ldm.flux.math import apply_rope
-from comfy.ldm.flux.layers import EmbedND
-from comfy.ops import cast_to_input
-
-
-class MLP(nn.Module):
-    def __init__(self, input_dim, hidden_dim, output_dim, num_layers, sigmoid_output=False, device=None, dtype=None, operations=None):
-        super().__init__()
-        dims = [input_dim] + [hidden_dim] * (num_layers - 1) + [output_dim]
-        self.layers = nn.ModuleList([operations.Linear(dims[i], dims[i + 1], device=device, dtype=dtype) for i in range(num_layers)])
-        self.sigmoid_output = sigmoid_output
-
-    def forward(self, x):
-        for i, layer in enumerate(self.layers):
-            x = F.relu(layer(x)) if i < len(self.layers) - 1 else layer(x)
-        return torch.sigmoid(x) if self.sigmoid_output else x
-
-
-class SAMAttention(nn.Module):
-    def __init__(self, embedding_dim, num_heads, downsample_rate=1, kv_in_dim=None, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.num_heads = num_heads
-        internal_dim = embedding_dim // downsample_rate
-        kv_dim = kv_in_dim if kv_in_dim is not None else embedding_dim
-        self.q_proj = operations.Linear(embedding_dim, internal_dim, device=device, dtype=dtype)
-        self.k_proj = operations.Linear(kv_dim, internal_dim, device=device, dtype=dtype)
-        self.v_proj = operations.Linear(kv_dim, internal_dim, device=device, dtype=dtype)
-        self.out_proj = operations.Linear(internal_dim, embedding_dim, device=device, dtype=dtype)
-
-    def forward(self, q, k, v):
-        q = self.q_proj(q)
-        k = self.k_proj(k)
-        v = self.v_proj(v)
-        return self.out_proj(optimized_attention(q, k, v, self.num_heads, low_precision_attention=False))
-
-
-class TwoWayAttentionBlock(nn.Module):
-    def __init__(self, embedding_dim, num_heads, mlp_dim=2048, attention_downsample_rate=2, skip_first_layer_pe=False, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.skip_first_layer_pe = skip_first_layer_pe
-        self.self_attn = SAMAttention(embedding_dim, num_heads, device=device, dtype=dtype, operations=operations)
-        self.cross_attn_token_to_image = SAMAttention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate, device=device, dtype=dtype, operations=operations)
-        self.cross_attn_image_to_token = SAMAttention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate, device=device, dtype=dtype, operations=operations)
-        self.mlp = nn.Sequential(operations.Linear(embedding_dim, mlp_dim, device=device, dtype=dtype), nn.ReLU(), operations.Linear(mlp_dim, embedding_dim, device=device, dtype=dtype))
-        self.norm1 = operations.LayerNorm(embedding_dim, device=device, dtype=dtype)
-        self.norm2 = operations.LayerNorm(embedding_dim, device=device, dtype=dtype)
-        self.norm3 = operations.LayerNorm(embedding_dim, device=device, dtype=dtype)
-        self.norm4 = operations.LayerNorm(embedding_dim, device=device, dtype=dtype)
-
-    def forward(self, queries, keys, query_pe, key_pe):
-        if self.skip_first_layer_pe:
-            queries = self.norm1(self.self_attn(queries, queries, queries))
-        else:
-            q = queries + query_pe
-            queries = self.norm1(queries + self.self_attn(q, q, queries))
-        q, k = queries + query_pe, keys + key_pe
-        queries = self.norm2(queries + self.cross_attn_token_to_image(q, k, keys))
-        queries = self.norm3(queries + self.mlp(queries))
-        q, k = queries + query_pe, keys + key_pe
-        keys = self.norm4(keys + self.cross_attn_image_to_token(k, q, queries))
-        return queries, keys
-
-
-class TwoWayTransformer(nn.Module):
-    def __init__(self, depth=2, embedding_dim=256, num_heads=8, mlp_dim=2048, attention_downsample_rate=2, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.layers = nn.ModuleList([
-            TwoWayAttentionBlock(embedding_dim, num_heads, mlp_dim, attention_downsample_rate,
-                                 skip_first_layer_pe=(i == 0), device=device, dtype=dtype, operations=operations)
-            for i in range(depth)
-        ])
-        self.final_attn_token_to_image = SAMAttention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate, device=device, dtype=dtype, operations=operations)
-        self.norm_final = operations.LayerNorm(embedding_dim, device=device, dtype=dtype)
-
-    def forward(self, image_embedding, image_pe, point_embedding):
-        queries, keys = point_embedding, image_embedding
-        for layer in self.layers:
-            queries, keys = layer(queries, keys, point_embedding, image_pe)
-        q, k = queries + point_embedding, keys + image_pe
-        queries = self.norm_final(queries + self.final_attn_token_to_image(q, k, keys))
-        return queries, keys
-
-
-class PositionEmbeddingRandom(nn.Module):
-    """Fourier feature positional encoding with random gaussian projection."""
-    def __init__(self, num_pos_feats=64, scale=None):
-        super().__init__()
-        self.register_buffer("positional_encoding_gaussian_matrix", (scale or 1.0) * torch.randn(2, num_pos_feats))
-
-    def _encode(self, normalized_coords):
-        """Map normalized [0,1] coordinates to fourier features via random projection. Computes in fp32."""
-        orig_dtype = normalized_coords.dtype
-        proj_matrix = self.positional_encoding_gaussian_matrix.to(device=normalized_coords.device, dtype=torch.float32)
-        projected = 2 * math.pi * (2 * normalized_coords.float() - 1) @ proj_matrix
-        return torch.cat([projected.sin(), projected.cos()], dim=-1).to(orig_dtype)
-
-    def forward(self, size, device=None):
-        h, w = size
-        dev = device if device is not None else self.positional_encoding_gaussian_matrix.device
-        ones = torch.ones((h, w), device=dev, dtype=torch.float32)
-        norm_xy = torch.stack([(ones.cumsum(1) - 0.5) / w, (ones.cumsum(0) - 0.5) / h], dim=-1)
-        return self._encode(norm_xy).permute(2, 0, 1).unsqueeze(0)
-
-    def forward_with_coords(self, pixel_coords, image_size):
-        norm = pixel_coords.clone()
-        norm[:, :, 0] /= image_size[1]
-        norm[:, :, 1] /= image_size[0]
-        return self._encode(norm)
-
-
-# ViTDet backbone + FPN neck
-
-def window_partition(x: torch.Tensor, window_size: int):
-    B, H, W, C = x.shape
-    pad_h = (window_size - H % window_size) % window_size
-    pad_w = (window_size - W % window_size) % window_size
-    if pad_h > 0 or pad_w > 0:
-        x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
-    Hp, Wp = H + pad_h, W + pad_w
-    x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows, (Hp, Wp)
-
-
-def window_unpartition(windows: torch.Tensor, window_size: int, pad_hw, hw):
-    Hp, Wp = pad_hw
-    H, W = hw
-    B = windows.shape[0] // (Hp * Wp // window_size // window_size)
-    x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
-    if Hp > H or Wp > W:
-        x = x[:, :H, :W, :].contiguous()
-    return x
-
-
-def rope_2d(end_x: int, end_y: int, dim: int, theta: float = 10000.0, scale_pos: float = 1.0):
-    """Generate 2D axial RoPE using flux EmbedND. Returns [1, 1, HW, dim//2, 2, 2]."""
-    t = torch.arange(end_x * end_y, dtype=torch.float32)
-    ids = torch.stack([(t % end_x) * scale_pos,
-                       torch.div(t, end_x, rounding_mode="floor") * scale_pos], dim=-1)
-    return EmbedND(dim=dim, theta=theta, axes_dim=[dim // 2, dim // 2])(ids.unsqueeze(0))
-
-
-class _ViTMLP(nn.Module):
-    def __init__(self, dim, mlp_ratio=4.0, device=None, dtype=None, operations=None):
-        super().__init__()
-        hidden = int(dim * mlp_ratio)
-        self.fc1 = operations.Linear(dim, hidden, device=device, dtype=dtype)
-        self.act = nn.GELU()
-        self.fc2 = operations.Linear(hidden, dim, device=device, dtype=dtype)
-
-    def forward(self, x):
-        return self.fc2(self.act(self.fc1(x)))
-
-
-class Attention(nn.Module):
-    """ViTDet multi-head attention with fused QKV projection."""
-
-    def __init__(self, dim, num_heads=8, qkv_bias=True, use_rope=False, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.num_heads = num_heads
-        self.head_dim = dim // num_heads
-        self.use_rope = use_rope
-        self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, device=device, dtype=dtype)
-        self.proj = operations.Linear(dim, dim, device=device, dtype=dtype)
-
-    def forward(self, x, freqs_cis=None):
-        B, N, C = x.shape
-        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim)
-        q, k, v = qkv.permute(2, 0, 3, 1, 4).unbind(dim=0)
-        if self.use_rope and freqs_cis is not None:
-            q, k = apply_rope(q, k, freqs_cis)
-        return self.proj(optimized_attention(q, k, v, self.num_heads, skip_reshape=True, low_precision_attention=False))
-
-
-class Block(nn.Module):
-    def __init__(self, dim, num_heads, mlp_ratio=4.0, qkv_bias=True, window_size=0, use_rope=False, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.window_size = window_size
-        self.norm1 = operations.LayerNorm(dim, device=device, dtype=dtype)
-        self.attn = Attention(dim, num_heads, qkv_bias, use_rope, device=device, dtype=dtype, operations=operations)
-        self.norm2 = operations.LayerNorm(dim, device=device, dtype=dtype)
-        self.mlp = _ViTMLP(dim, mlp_ratio, device=device, dtype=dtype, operations=operations)
-
-    def forward(self, x, freqs_cis=None):
-        shortcut = x
-        x = self.norm1(x)
-        if self.window_size > 0:
-            H, W = x.shape[1], x.shape[2]
-            x, pad_hw = window_partition(x, self.window_size)
-            x = x.view(x.shape[0], self.window_size * self.window_size, -1)
-            x = self.attn(x, freqs_cis=freqs_cis)
-            x = x.view(-1, self.window_size, self.window_size, x.shape[-1])
-            x = window_unpartition(x, self.window_size, pad_hw, (H, W))
-        else:
-            B, H, W, C = x.shape
-            x = x.view(B, H * W, C)
-            x = self.attn(x, freqs_cis=freqs_cis)
-            x = x.view(B, H, W, C)
-        x = shortcut + x
-        x = x + self.mlp(self.norm2(x))
-        return x
-
-
-class PatchEmbed(nn.Module):
-    def __init__(self, patch_size=14, in_chans=3, embed_dim=1024, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.proj = operations.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=False, device=device, dtype=dtype)
-
-    def forward(self, x):
-        return self.proj(x)
-
-
-class ViTDet(nn.Module):
-    def __init__(self, img_size=1008, patch_size=14, embed_dim=1024, depth=32, num_heads=16, mlp_ratio=4.625, qkv_bias=True, window_size=24,
-                 global_att_blocks=(7, 15, 23, 31), use_rope=True, pretrain_img_size=336, device=None, dtype=None, operations=None, **kwargs):
-        super().__init__()
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.global_att_blocks = set(global_att_blocks)
-
-        self.patch_embed = PatchEmbed(patch_size, 3, embed_dim, device=device, dtype=dtype, operations=operations)
-
-        num_patches = (pretrain_img_size // patch_size) ** 2 + 1  # +1 for cls token
-        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim, device=device, dtype=dtype))
-
-        self.ln_pre = operations.LayerNorm(embed_dim, device=device, dtype=dtype)
-
-        grid_size = img_size // patch_size
-        pretrain_grid = pretrain_img_size // patch_size
-
-        self.blocks = nn.ModuleList()
-        for i in range(depth):
-            is_global = i in self.global_att_blocks
-            self.blocks.append(Block(
-                embed_dim, num_heads, mlp_ratio, qkv_bias,
-                window_size=0 if is_global else window_size,
-                use_rope=use_rope,
-                device=device, dtype=dtype, operations=operations,
-            ))
-
-        if use_rope:
-            rope_scale = pretrain_grid / grid_size
-            self.register_buffer("freqs_cis", rope_2d(grid_size, grid_size, embed_dim // num_heads, scale_pos=rope_scale), persistent=False)
-            self.register_buffer("freqs_cis_window", rope_2d(window_size, window_size, embed_dim // num_heads), persistent=False)
-        else:
-            self.freqs_cis = None
-            self.freqs_cis_window = None
-
-    def _get_pos_embed(self, num_tokens):
-        pos = self.pos_embed
-        if pos.shape[1] == num_tokens:
-            return pos
-        cls_pos = pos[:, :1]
-        spatial_pos = pos[:, 1:]
-        old_size = int(math.sqrt(spatial_pos.shape[1]))
-        new_size = int(math.sqrt(num_tokens - 1)) if num_tokens > 1 else old_size
-        spatial_2d = spatial_pos.reshape(1, old_size, old_size, -1).permute(0, 3, 1, 2)
-        tiles_h = new_size // old_size + 1
-        tiles_w = new_size // old_size + 1
-        tiled = spatial_2d.tile([1, 1, tiles_h, tiles_w])[:, :, :new_size, :new_size]
-        tiled = tiled.permute(0, 2, 3, 1).reshape(1, new_size * new_size, -1)
-        return torch.cat([cls_pos, tiled], dim=1)
-
-    def forward(self, x):
-        x = self.patch_embed(x)
-        B, C, Hp, Wp = x.shape
-        x = x.permute(0, 2, 3, 1).reshape(B, Hp * Wp, C)
-
-        pos = cast_to_input(self._get_pos_embed(Hp * Wp + 1), x)
-        x = x + pos[:, 1:Hp * Wp + 1]
-
-        x = x.view(B, Hp, Wp, C)
-        x = self.ln_pre(x)
-
-        freqs_cis_global = self.freqs_cis
-        freqs_cis_win = self.freqs_cis_window
-        if freqs_cis_global is not None:
-            freqs_cis_global = cast_to_input(freqs_cis_global, x)
-        if freqs_cis_win is not None:
-            freqs_cis_win = cast_to_input(freqs_cis_win, x)
-
-        for block in self.blocks:
-            fc = freqs_cis_win if block.window_size > 0 else freqs_cis_global
-            x = block(x, freqs_cis=fc)
-
-        return x.permute(0, 3, 1, 2)
-
-
-class FPNScaleConv(nn.Module):
-    def __init__(self, in_dim, out_dim, scale, device=None, dtype=None, operations=None):
-        super().__init__()
-        if scale == 4.0:
-            self.dconv_2x2_0 = operations.ConvTranspose2d(in_dim, in_dim // 2, kernel_size=2, stride=2, device=device, dtype=dtype)
-            self.dconv_2x2_1 = operations.ConvTranspose2d(in_dim // 2, in_dim // 4, kernel_size=2, stride=2, device=device, dtype=dtype)
-            proj_in = in_dim // 4
-        elif scale == 2.0:
-            self.dconv_2x2 = operations.ConvTranspose2d(in_dim, in_dim // 2, kernel_size=2, stride=2, device=device, dtype=dtype)
-            proj_in = in_dim // 2
-        elif scale == 1.0:
-            proj_in = in_dim
-        elif scale == 0.5:
-            self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
-            proj_in = in_dim
-        self.scale = scale
-        self.conv_1x1 = operations.Conv2d(proj_in, out_dim, kernel_size=1, device=device, dtype=dtype)
-        self.conv_3x3 = operations.Conv2d(out_dim, out_dim, kernel_size=3, padding=1, device=device, dtype=dtype)
-
-    def forward(self, x):
-        if self.scale == 4.0:
-            x = F.gelu(self.dconv_2x2_0(x))
-            x = self.dconv_2x2_1(x)
-        elif self.scale == 2.0:
-            x = self.dconv_2x2(x)
-        elif self.scale == 0.5:
-            x = self.pool(x)
-        x = self.conv_1x1(x)
-        x = self.conv_3x3(x)
-        return x
-
-
-class PositionEmbeddingSine(nn.Module):
-    """2D sinusoidal position encoding (DETR-style) with result caching."""
-    def __init__(self, num_pos_feats=256, temperature=10000.0, normalize=True, scale=None):
-        super().__init__()
-        assert num_pos_feats % 2 == 0
-        self.half_dim = num_pos_feats // 2
-        self.temperature = temperature
-        self.normalize = normalize
-        self.scale = scale if scale is not None else 2 * math.pi
-        self._cache = {}
-
-    def _sincos(self, vals):
-        """Encode 1D values to interleaved sin/cos features."""
-        freqs = self.temperature ** (2 * (torch.arange(self.half_dim, dtype=torch.float32, device=vals.device) // 2) / self.half_dim)
-        raw = vals[..., None] * self.scale / freqs
-        return torch.stack((raw[..., 0::2].sin(), raw[..., 1::2].cos()), dim=-1).flatten(-2)
-
-    def _encode_xy(self, x, y):
-        """Encode normalized x, y coordinates to sinusoidal features. Returns (pos_x, pos_y) each [N, half_dim]."""
-        dim_t = self.temperature ** (2 * (torch.arange(self.half_dim, dtype=torch.float32, device=x.device) // 2) / self.half_dim)
-        pos_x = x[:, None] * self.scale / dim_t
-        pos_y = y[:, None] * self.scale / dim_t
-        pos_x = torch.stack((pos_x[:, 0::2].sin(), pos_x[:, 1::2].cos()), dim=2).flatten(1)
-        pos_y = torch.stack((pos_y[:, 0::2].sin(), pos_y[:, 1::2].cos()), dim=2).flatten(1)
-        return pos_x, pos_y
-
-    def encode_boxes(self, cx, cy, w, h):
-        """Encode box center + size to [N, d_model+2] features."""
-        pos_x, pos_y = self._encode_xy(cx, cy)
-        return torch.cat((pos_y, pos_x, h[:, None], w[:, None]), dim=1)
-
-    def forward(self, x):
-        B, C, H, W = x.shape
-        key = (H, W, x.device)
-        if key not in self._cache:
-            gy = torch.arange(H, dtype=torch.float32, device=x.device)
-            gx = torch.arange(W, dtype=torch.float32, device=x.device)
-            if self.normalize:
-                gy, gx = gy / (H - 1 + 1e-6), gx / (W - 1 + 1e-6)
-            yy, xx = torch.meshgrid(gy, gx, indexing="ij")
-            self._cache[key] = torch.cat((self._sincos(yy), self._sincos(xx)), dim=-1).permute(2, 0, 1).unsqueeze(0)
-        return self._cache[key].expand(B, -1, -1, -1)
-
-
-class SAM3VisionBackbone(nn.Module):
-    def __init__(self, embed_dim=1024, d_model=256, multiplex=False, device=None, dtype=None, operations=None, **kwargs):
-        super().__init__()
-        self.trunk = ViTDet(embed_dim=embed_dim, device=device, dtype=dtype, operations=operations, **kwargs)
-        self.position_encoding = PositionEmbeddingSine(num_pos_feats=d_model, normalize=True)
-        self.multiplex = multiplex
-
-        fpn_args = dict(device=device, dtype=dtype, operations=operations)
-        if multiplex:
-            scales = [4.0, 2.0, 1.0]
-            self.convs = nn.ModuleList([FPNScaleConv(embed_dim, d_model, s, **fpn_args) for s in scales])
-            self.propagation_convs = nn.ModuleList([FPNScaleConv(embed_dim, d_model, s, **fpn_args) for s in scales])
-            self.interactive_convs = nn.ModuleList([FPNScaleConv(embed_dim, d_model, s, **fpn_args) for s in scales])
-        else:
-            scales = [4.0, 2.0, 1.0, 0.5]
-            self.convs = nn.ModuleList([FPNScaleConv(embed_dim, d_model, s, **fpn_args) for s in scales])
-            self.sam2_convs = nn.ModuleList([FPNScaleConv(embed_dim, d_model, s, **fpn_args) for s in scales])
-
-    def forward(self, images, need_tracker=False, tracker_mode=None, cached_trunk=None, tracker_only=False):
-        backbone_out = cached_trunk if cached_trunk is not None else self.trunk(images)
-
-        if tracker_only:
-            # Skip detector FPN when only tracker features are needed (video tracking)
-            if self.multiplex:
-                tracker_convs = self.propagation_convs if tracker_mode == "propagation" else self.interactive_convs
-            else:
-                tracker_convs = self.sam2_convs
-            tracker_features = [conv(backbone_out) for conv in tracker_convs]
-            tracker_positions = [cast_to_input(self.position_encoding(f), f) for f in tracker_features]
-            return None, None, tracker_features, tracker_positions
-
-        features = [conv(backbone_out) for conv in self.convs]
-        positions = [cast_to_input(self.position_encoding(f), f) for f in features]
-
-        if self.multiplex:
-            if tracker_mode == "propagation":
-                tracker_convs = self.propagation_convs
-            elif tracker_mode == "interactive":
-                tracker_convs = self.interactive_convs
-            else:
-                return features, positions, None, None
-        elif need_tracker:
-            tracker_convs = self.sam2_convs
-        else:
-            return features, positions, None, None
-
-        tracker_features = [conv(backbone_out) for conv in tracker_convs]
-        tracker_positions = [cast_to_input(self.position_encoding(f), f) for f in tracker_features]
-        return features, positions, tracker_features, tracker_positions

comfy/ldm/supir/supir_modules.py

@@ -1,226 +0,0 @@
-import torch
-import torch.nn as nn
-
-from comfy.ldm.modules.diffusionmodules.util import timestep_embedding
-from comfy.ldm.modules.diffusionmodules.openaimodel import Downsample, TimestepEmbedSequential, ResBlock, SpatialTransformer
-from comfy.ldm.modules.attention import optimized_attention
-
-
-class ZeroSFT(nn.Module):
-    def __init__(self, label_nc, norm_nc, concat_channels=0, dtype=None, device=None, operations=None):
-        super().__init__()
-
-        ks = 3
-        pw = ks // 2
-
-        self.param_free_norm = operations.GroupNorm(32, norm_nc + concat_channels, dtype=dtype, device=device)
-
-        nhidden = 128
-
-        self.mlp_shared = nn.Sequential(
-            operations.Conv2d(label_nc, nhidden, kernel_size=ks, padding=pw, dtype=dtype, device=device),
-            nn.SiLU()
-        )
-        self.zero_mul = operations.Conv2d(nhidden, norm_nc + concat_channels, kernel_size=ks, padding=pw, dtype=dtype, device=device)
-        self.zero_add = operations.Conv2d(nhidden, norm_nc + concat_channels, kernel_size=ks, padding=pw, dtype=dtype, device=device)
-
-        self.zero_conv = operations.Conv2d(label_nc, norm_nc, 1, 1, 0, dtype=dtype, device=device)
-        self.pre_concat = bool(concat_channels != 0)
-
-    def forward(self, c, h, h_ori=None, control_scale=1):
-        if h_ori is not None and self.pre_concat:
-            h_raw = torch.cat([h_ori, h], dim=1)
-        else:
-            h_raw = h
-
-        h = h + self.zero_conv(c)
-        if h_ori is not None and self.pre_concat:
-            h = torch.cat([h_ori, h], dim=1)
-        actv = self.mlp_shared(c)
-        gamma = self.zero_mul(actv)
-        beta = self.zero_add(actv)
-        h = self.param_free_norm(h)
-        h = torch.addcmul(h + beta, h, gamma)
-        if h_ori is not None and not self.pre_concat:
-            h = torch.cat([h_ori, h], dim=1)
-        return torch.lerp(h_raw, h, control_scale)
-
-
-class _CrossAttnInner(nn.Module):
-    """Inner cross-attention module matching the state_dict layout of the original CrossAttention."""
-    def __init__(self, query_dim, context_dim, heads, dim_head, dtype=None, device=None, operations=None):
-        super().__init__()
-        inner_dim = dim_head * heads
-        self.heads = heads
-        self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.to_out = nn.Sequential(
-            operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
-        )
-
-    def forward(self, x, context):
-        q = self.to_q(x)
-        k = self.to_k(context)
-        v = self.to_v(context)
-        return self.to_out(optimized_attention(q, k, v, self.heads))
-
-
-class ZeroCrossAttn(nn.Module):
-    def __init__(self, context_dim, query_dim, dtype=None, device=None, operations=None):
-        super().__init__()
-        heads = query_dim // 64
-        dim_head = 64
-        self.attn = _CrossAttnInner(query_dim, context_dim, heads, dim_head, dtype=dtype, device=device, operations=operations)
-        self.norm1 = operations.GroupNorm(32, query_dim, dtype=dtype, device=device)
-        self.norm2 = operations.GroupNorm(32, context_dim, dtype=dtype, device=device)
-
-    def forward(self, context, x, control_scale=1):
-        b, c, h, w = x.shape
-        x_in = x
-
-        x = self.attn(
-            self.norm1(x).flatten(2).transpose(1, 2),
-            self.norm2(context).flatten(2).transpose(1, 2),
-        ).transpose(1, 2).unflatten(2, (h, w))
-
-        return x_in + x * control_scale
-
-
-class GLVControl(nn.Module):
-    """SUPIR's Guided Latent Vector control encoder. Truncated UNet (input + middle blocks only)."""
-    def __init__(
-        self,
-        in_channels=4,
-        model_channels=320,
-        num_res_blocks=2,
-        attention_resolutions=(4, 2),
-        channel_mult=(1, 2, 4),
-        num_head_channels=64,
-        transformer_depth=(1, 2, 10),
-        context_dim=2048,
-        adm_in_channels=2816,
-        use_linear_in_transformer=True,
-        use_checkpoint=False,
-        dtype=None,
-        device=None,
-        operations=None,
-        **kwargs,
-    ):
-        super().__init__()
-        self.model_channels = model_channels
-        time_embed_dim = model_channels * 4
-
-        self.time_embed = nn.Sequential(
-            operations.Linear(model_channels, time_embed_dim, dtype=dtype, device=device),
-            nn.SiLU(),
-            operations.Linear(time_embed_dim, time_embed_dim, dtype=dtype, device=device),
-        )
-
-        self.label_emb = nn.Sequential(
-            nn.Sequential(
-                operations.Linear(adm_in_channels, time_embed_dim, dtype=dtype, device=device),
-                nn.SiLU(),
-                operations.Linear(time_embed_dim, time_embed_dim, dtype=dtype, device=device),
-            )
-        )
-
-        self.input_blocks = nn.ModuleList([
-            TimestepEmbedSequential(
-                operations.Conv2d(in_channels, model_channels, 3, padding=1, dtype=dtype, device=device)
-            )
-        ])
-        ch = model_channels
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for nr in range(num_res_blocks):
-                layers = [
-                    ResBlock(ch, time_embed_dim, 0, out_channels=mult * model_channels,
-                             dtype=dtype, device=device, operations=operations)
-                ]
-                ch = mult * model_channels
-                if ds in attention_resolutions:
-                    num_heads = ch // num_head_channels
-                    layers.append(
-                        SpatialTransformer(ch, num_heads, num_head_channels,
-                                           depth=transformer_depth[level], context_dim=context_dim,
-                                           use_linear=use_linear_in_transformer,
-                                           use_checkpoint=use_checkpoint,
-                                           dtype=dtype, device=device, operations=operations)
-                    )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-            if level != len(channel_mult) - 1:
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        Downsample(ch, True, out_channels=ch, dtype=dtype, device=device, operations=operations)
-                    )
-                )
-                ds *= 2
-
-        num_heads = ch // num_head_channels
-        self.middle_block = TimestepEmbedSequential(
-            ResBlock(ch, time_embed_dim, 0, dtype=dtype, device=device, operations=operations),
-            SpatialTransformer(ch, num_heads, num_head_channels,
-                               depth=transformer_depth[-1], context_dim=context_dim,
-                               use_linear=use_linear_in_transformer,
-                               use_checkpoint=use_checkpoint,
-                               dtype=dtype, device=device, operations=operations),
-            ResBlock(ch, time_embed_dim, 0, dtype=dtype, device=device, operations=operations),
-        )
-
-        self.input_hint_block = TimestepEmbedSequential(
-            operations.Conv2d(in_channels, model_channels, 3, padding=1, dtype=dtype, device=device)
-        )
-
-    def forward(self, x, timesteps, xt, context=None, y=None, **kwargs):
-        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
-        emb = self.time_embed(t_emb) + self.label_emb(y)
-
-        guided_hint = self.input_hint_block(x, emb, context)
-
-        hs = []
-        h = xt
-        for module in self.input_blocks:
-            if guided_hint is not None:
-                h = module(h, emb, context)
-                h += guided_hint
-                guided_hint = None
-            else:
-                h = module(h, emb, context)
-            hs.append(h)
-        h = self.middle_block(h, emb, context)
-        hs.append(h)
-        return hs
-
-
-class SUPIR(nn.Module):
-    """
-    SUPIR model containing GLVControl (control encoder) and project_modules (adapters).
-    State dict keys match the original SUPIR checkpoint layout:
-      control_model.*           -> GLVControl
-      project_modules.*         -> nn.ModuleList of ZeroSFT/ZeroCrossAttn
-    """
-    def __init__(self, device=None, dtype=None, operations=None):
-        super().__init__()
-
-        self.control_model = GLVControl(dtype=dtype, device=device, operations=operations)
-
-        project_channel_scale = 2
-        cond_output_channels = [320] * 4 + [640] * 3 + [1280] * 3
-        project_channels = [int(c * project_channel_scale) for c in [160] * 4 + [320] * 3 + [640] * 3]
-        concat_channels = [320] * 2 + [640] * 3 + [1280] * 4 + [0]
-        cross_attn_insert_idx = [6, 3]
-
-        self.project_modules = nn.ModuleList()
-        for i in range(len(cond_output_channels)):
-            self.project_modules.append(ZeroSFT(
-                project_channels[i], cond_output_channels[i],
-                concat_channels=concat_channels[i],
-                dtype=dtype, device=device, operations=operations,
-            ))
-
-        for i in cross_attn_insert_idx:
-            self.project_modules.insert(i, ZeroCrossAttn(
-                cond_output_channels[i], concat_channels[i],
-                dtype=dtype, device=device, operations=operations,
-            ))

comfy/ldm/supir/supir_patch.py

@@ -1,103 +0,0 @@
-import torch
-from comfy.ldm.modules.diffusionmodules.openaimodel import Upsample
-
-
-class SUPIRPatch:
-    """
-    Holds GLVControl (control encoder) + project_modules (ZeroSFT/ZeroCrossAttn adapters).
-    Runs GLVControl lazily on first patch invocation per step, applies adapters through
-    middle_block_after_patch, output_block_merge_patch, and forward_timestep_embed_patch.
-    """
-    SIGMA_MAX = 14.6146
-
-    def __init__(self, model_patch, project_modules, hint_latent, strength_start, strength_end):
-        self.model_patch = model_patch           # CoreModelPatcher wrapping GLVControl
-        self.project_modules = project_modules   # nn.ModuleList of ZeroSFT/ZeroCrossAttn
-        self.hint_latent = hint_latent           # encoded LQ image latent
-        self.strength_start = strength_start
-        self.strength_end = strength_end
-        self.cached_features = None
-        self.adapter_idx = 0
-        self.control_idx = 0
-        self.current_control_idx = 0
-        self.active = True
-
-    def _ensure_features(self, kwargs):
-        """Run GLVControl on first call per step, cache results."""
-        if self.cached_features is not None:
-            return
-        x = kwargs["x"]
-        b = x.shape[0]
-        hint = self.hint_latent.to(device=x.device, dtype=x.dtype)
-        if hint.shape[0] != b:
-            hint = hint.expand(b, -1, -1, -1) if hint.shape[0] == 1 else hint.repeat((b + hint.shape[0] - 1) // hint.shape[0], 1, 1, 1)[:b]
-        self.cached_features = self.model_patch.model.control_model(
-            hint, kwargs["timesteps"], x,
-            kwargs["context"], kwargs["y"]
-        )
-        self.adapter_idx = len(self.project_modules) - 1
-        self.control_idx = len(self.cached_features) - 1
-
-    def _get_control_scale(self, kwargs):
-        if self.strength_start == self.strength_end:
-            return self.strength_end
-        sigma = kwargs["transformer_options"].get("sigmas")
-        if sigma is None:
-            return self.strength_end
-        s = sigma[0].item() if sigma.dim() > 0 else sigma.item()
-        t = min(s / self.SIGMA_MAX, 1.0)
-        return t * (self.strength_start - self.strength_end) + self.strength_end
-
-    def middle_after(self, kwargs):
-        """middle_block_after_patch: run GLVControl lazily, apply last adapter after middle block."""
-        self.cached_features = None  # reset from previous step
-        self.current_scale = self._get_control_scale(kwargs)
-        self.active = self.current_scale > 0
-        if not self.active:
-            return {"h": kwargs["h"]}
-        self._ensure_features(kwargs)
-        h = kwargs["h"]
-        h = self.project_modules[self.adapter_idx](
-            self.cached_features[self.control_idx], h, control_scale=self.current_scale
-        )
-        self.adapter_idx -= 1
-        self.control_idx -= 1
-        return {"h": h}
-
-    def output_block(self, h, hsp, transformer_options):
-        """output_block_patch: ZeroSFT adapter fusion replaces cat([h, hsp]). Returns (h, None) to skip cat."""
-        if not self.active:
-            return h, hsp
-        self.current_control_idx = self.control_idx
-        h = self.project_modules[self.adapter_idx](
-            self.cached_features[self.control_idx], hsp, h, control_scale=self.current_scale
-        )
-        self.adapter_idx -= 1
-        self.control_idx -= 1
-        return h, None
-
-    def pre_upsample(self, layer, x, emb, context, transformer_options, output_shape, *args, **kw):
-        """forward_timestep_embed_patch for Upsample: extra cross-attn adapter before upsample."""
-        block_type, _ = transformer_options["block"]
-        if block_type == "output" and self.active and self.cached_features is not None:
-            x = self.project_modules[self.adapter_idx](
-                self.cached_features[self.current_control_idx], x, control_scale=self.current_scale
-            )
-            self.adapter_idx -= 1
-        return layer(x, output_shape=output_shape)
-
-    def to(self, device_or_dtype):
-        if isinstance(device_or_dtype, torch.device):
-            self.cached_features = None
-            if self.hint_latent is not None:
-                self.hint_latent = self.hint_latent.to(device_or_dtype)
-        return self
-
-    def models(self):
-        return [self.model_patch]
-
-    def register(self, model_patcher):
-        """Register all patches on a cloned model patcher."""
-        model_patcher.set_model_patch(self.middle_after, "middle_block_after_patch")
-        model_patcher.set_model_output_block_patch(self.output_block)
-        model_patcher.set_model_patch((Upsample, self.pre_upsample), "forward_timestep_embed_patch")

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/lora.py

@@ -17,7 +17,6 @@
 """
 
 from __future__ import annotations
-import comfy.memory_management
 import comfy.utils
 import comfy.model_management
 import comfy.model_base
@@ -343,12 +342,6 @@ def model_lora_keys_unet(model, key_map={}):
                 key_map["base_model.model.{}".format(key_lora)] = k  # Official base model loras
                 key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = k  # LyCORIS/LoKR format
 
-    if isinstance(model, comfy.model_base.ErnieImage):
-        for k in sdk:
-            if k.startswith("diffusion_model.") and k.endswith(".weight"):
-                key_lora = k[len("diffusion_model."):-len(".weight")]
-                key_map["transformer.{}".format(key_lora)] = k
-
     return key_map
 
 
@@ -474,17 +467,3 @@ def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32, ori
             weight = old_weight
 
     return weight
-
-def prefetch_prepared_value(value, allocate_buffer, stream):
-    if isinstance(value, torch.Tensor):
-        dest = allocate_buffer(comfy.memory_management.vram_aligned_size(value))
-        comfy.model_management.cast_to_gathered([value], dest, non_blocking=True, stream=stream)
-        return comfy.memory_management.interpret_gathered_like([value], dest)[0]
-    elif isinstance(value, weight_adapter.WeightAdapterBase):
-        return type(value)(value.loaded_keys, prefetch_prepared_value(value.weights, allocate_buffer, stream))
-    elif isinstance(value, tuple):
-        return tuple(prefetch_prepared_value(item, allocate_buffer, stream) for item in value)
-    elif isinstance(value, list):
-        return [prefetch_prepared_value(item, allocate_buffer, stream) for item in value]
-
-    return value

comfy/model_base.py

@@ -42,7 +42,6 @@ import comfy.ldm.cosmos.predict2
 import comfy.ldm.lumina.model
 import comfy.ldm.wan.model
 import comfy.ldm.wan.model_animate
-import comfy.ldm.wan.ar_model
 import comfy.ldm.hunyuan3d.model
 import comfy.ldm.hidream.model
 import comfy.ldm.chroma.model
@@ -53,10 +52,7 @@ import comfy.ldm.qwen_image.model
 import comfy.ldm.kandinsky5.model
 import comfy.ldm.anima.model
 import comfy.ldm.ace.ace_step15
-import comfy.ldm.cogvideo.model
 import comfy.ldm.rt_detr.rtdetr_v4
-import comfy.ldm.ernie.model
-import comfy.ldm.sam3.detector
 
 import comfy.model_management
 import comfy.patcher_extension
@@ -83,7 +79,6 @@ class ModelType(Enum):
     IMG_TO_IMG = 9
     FLOW_COSMOS = 10
     IMG_TO_IMG_FLOW = 11
-    V_PREDICTION_DDPM = 12
 
 
 def model_sampling(model_config, model_type):
@@ -118,8 +113,6 @@ def model_sampling(model_config, model_type):
         s = comfy.model_sampling.ModelSamplingCosmosRFlow
     elif model_type == ModelType.IMG_TO_IMG_FLOW:
         c = comfy.model_sampling.IMG_TO_IMG_FLOW
-    elif model_type == ModelType.V_PREDICTION_DDPM:
-        c = comfy.model_sampling.V_PREDICTION_DDPM
 
     class ModelSampling(s, c):
         pass
@@ -215,11 +208,6 @@ class BaseModel(torch.nn.Module):
         if "latent_shapes" in extra_conds:
             xc = utils.unpack_latents(xc, extra_conds.pop("latent_shapes"))
 
-        transformer_options = transformer_options.copy()
-        transformer_options["prefetch_dynamic_vbars"] = (
-            self.current_patcher is not None and self.current_patcher.is_dynamic()
-        )
-
         model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds)
         if len(model_output) > 1 and not torch.is_tensor(model_output):
             model_output, _ = utils.pack_latents(model_output)
@@ -589,8 +577,8 @@ class Stable_Zero123(BaseModel):
     def __init__(self, model_config, model_type=ModelType.EPS, device=None, cc_projection_weight=None, cc_projection_bias=None):
         super().__init__(model_config, model_type, device=device)
         self.cc_projection = comfy.ops.manual_cast.Linear(cc_projection_weight.shape[1], cc_projection_weight.shape[0], dtype=self.get_dtype(), device=device)
-        self.cc_projection.weight = torch.nn.Parameter(cc_projection_weight.clone())
-        self.cc_projection.bias = torch.nn.Parameter(cc_projection_bias.clone())
+        self.cc_projection.weight.copy_(cc_projection_weight)
+        self.cc_projection.bias.copy_(cc_projection_bias)
 
     def extra_conds(self, **kwargs):
         out = {}
@@ -1366,13 +1354,6 @@ class WAN21(BaseModel):
         return out
 
 
-class WAN21_CausalAR(WAN21):
-    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
-        super(WAN21, self).__init__(model_config, model_type, device=device,
-                                    unet_model=comfy.ldm.wan.ar_model.CausalWanModel)
-        self.image_to_video = False
-
-
 class WAN21_Vace(WAN21):
     def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
         super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.VaceWanModel)
@@ -1981,74 +1962,3 @@ class Kandinsky5Image(Kandinsky5):
 class RT_DETR_v4(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.rt_detr.rtdetr_v4.RTv4)
-
-class ErnieImage(BaseModel):
-    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
-        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.ernie.model.ErnieImageModel)
-
-    def extra_conds(self, **kwargs):
-        out = super().extra_conds(**kwargs)
-        cross_attn = kwargs.get("cross_attn", None)
-        if cross_attn is not None:
-            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
-        return out
-
-class SAM3(BaseModel):
-    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
-        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.sam3.detector.SAM3Model)
-
-class CogVideoX(BaseModel):
-    def __init__(self, model_config, model_type=ModelType.V_PREDICTION_DDPM, image_to_video=False, device=None):
-        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.cogvideo.model.CogVideoXTransformer3DModel)
-        self.image_to_video = image_to_video
-
-    def concat_cond(self, **kwargs):
-        noise = kwargs.get("noise", None)
-        # Detect extra channels needed (e.g. 32 - 16 = 16 for ref latent)
-        extra_channels = self.diffusion_model.in_channels - noise.shape[1]
-        if extra_channels == 0:
-            return None
-
-        image = kwargs.get("concat_latent_image", None)
-        device = kwargs["device"]
-
-        if image is None:
-            shape = list(noise.shape)
-            shape[1] = extra_channels
-            return torch.zeros(shape, dtype=noise.dtype, layout=noise.layout, device=noise.device)
-
-        latent_dim = self.latent_format.latent_channels
-        image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
-
-        if noise.ndim == 5 and image.ndim == 5:
-            if image.shape[-3] < noise.shape[-3]:
-                image = torch.nn.functional.pad(image, (0, 0, 0, 0, 0, noise.shape[-3] - image.shape[-3]), "constant", 0)
-            elif image.shape[-3] > noise.shape[-3]:
-                image = image[:, :, :noise.shape[-3]]
-
-        for i in range(0, image.shape[1], latent_dim):
-            image[:, i:i + latent_dim] = self.process_latent_in(image[:, i:i + latent_dim])
-        image = utils.resize_to_batch_size(image, noise.shape[0])
-
-        if image.shape[1] > extra_channels:
-            image = image[:, :extra_channels]
-        elif image.shape[1] < extra_channels:
-            repeats = extra_channels // image.shape[1]
-            remainder = extra_channels % image.shape[1]
-            parts = [image] * repeats
-            if remainder > 0:
-                parts.append(image[:, :remainder])
-            image = torch.cat(parts, dim=1)
-
-        return image
-
-    def extra_conds(self, **kwargs):
-        out = super().extra_conds(**kwargs)
-        # OFS embedding (CogVideoX 1.5 I2V), default 2.0 as used by SparkVSR
-        if self.diffusion_model.ofs_proj_dim is not None:
-            ofs = kwargs.get("ofs", None)
-            if ofs is None:
-                noise = kwargs.get("noise", None)
-                ofs = torch.full((noise.shape[0],), 2.0, device=noise.device, dtype=noise.dtype)
-            out['ofs'] = comfy.conds.CONDRegular(ofs)
-        return out

comfy/model_detection.py

@@ -490,54 +490,6 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
 
         return dit_config
 
-    if '{}blocks.0.norm1.linear.weight'.format(key_prefix) in state_dict_keys:  # CogVideoX
-        dit_config = {}
-        dit_config["image_model"] = "cogvideox"
-
-        # Extract config from weight shapes
-        norm1_weight = state_dict['{}blocks.0.norm1.linear.weight'.format(key_prefix)]
-        time_embed_dim = norm1_weight.shape[1]
-        dim = norm1_weight.shape[0] // 6
-
-        dit_config["num_attention_heads"] = dim // 64
-        dit_config["attention_head_dim"] = 64
-        dit_config["time_embed_dim"] = time_embed_dim
-        dit_config["num_layers"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.')
-
-        # Detect in_channels from patch_embed
-        patch_proj_key = '{}patch_embed.proj.weight'.format(key_prefix)
-        if patch_proj_key in state_dict_keys:
-            w = state_dict[patch_proj_key]
-            if w.ndim == 4:
-                # Conv2d: [out, in, kh, kw] — CogVideoX 1.0
-                dit_config["in_channels"] = w.shape[1]
-                dit_config["patch_size"] = w.shape[2]
-            elif w.ndim == 2:
-                # Linear: [out, in_channels * patch_size * patch_size * patch_size_t] — CogVideoX 1.5
-                dit_config["patch_size"] = 2
-                dit_config["patch_size_t"] = 2
-                dit_config["in_channels"] = w.shape[1] // (2 * 2 * 2)  # 256 // 8 = 32
-
-        text_proj_key = '{}patch_embed.text_proj.weight'.format(key_prefix)
-        if text_proj_key in state_dict_keys:
-            dit_config["text_embed_dim"] = state_dict[text_proj_key].shape[1]
-
-        # Detect OFS embedding
-        ofs_key = '{}ofs_embedding_linear_1.weight'.format(key_prefix)
-        if ofs_key in state_dict_keys:
-            dit_config["ofs_embed_dim"] = state_dict[ofs_key].shape[1]
-
-        # Detect positional embedding type
-        pos_key = '{}patch_embed.pos_embedding'.format(key_prefix)
-        if pos_key in state_dict_keys:
-            dit_config["use_learned_positional_embeddings"] = True
-            dit_config["use_rotary_positional_embeddings"] = False
-        else:
-            dit_config["use_learned_positional_embeddings"] = False
-            dit_config["use_rotary_positional_embeddings"] = True
-
-        return dit_config
-
     if '{}head.modulation'.format(key_prefix) in state_dict_keys:  # Wan 2.1
         dit_config = {}
         dit_config["image_model"] = "wan2.1"
@@ -744,15 +696,6 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
     if '{}encoder.lyric_encoder.layers.0.input_layernorm.weight'.format(key_prefix) in state_dict_keys:
         dit_config = {}
         dit_config["audio_model"] = "ace1.5"
-        head_dim = 128
-        dit_config["hidden_size"] = state_dict['{}decoder.layers.0.self_attn_norm.weight'.format(key_prefix)].shape[0]
-        dit_config["intermediate_size"] = state_dict['{}decoder.layers.0.mlp.gate_proj.weight'.format(key_prefix)].shape[0]
-        dit_config["num_heads"] = state_dict['{}decoder.layers.0.self_attn.q_proj.weight'.format(key_prefix)].shape[0] // head_dim
-
-        dit_config["encoder_hidden_size"] = state_dict['{}encoder.lyric_encoder.layers.0.input_layernorm.weight'.format(key_prefix)].shape[0]
-        dit_config["encoder_num_heads"] = state_dict['{}encoder.lyric_encoder.layers.0.self_attn.q_proj.weight'.format(key_prefix)].shape[0] // head_dim
-        dit_config["encoder_intermediate_size"] = state_dict['{}encoder.lyric_encoder.layers.0.mlp.gate_proj.weight'.format(key_prefix)].shape[0]
-        dit_config["num_dit_layers"] = count_blocks(state_dict_keys, '{}decoder.layers.'.format(key_prefix) + '{}.')
         return dit_config
 
     if '{}encoder.pan_blocks.1.cv4.conv.weight'.format(key_prefix) in state_dict_keys: # RT-DETR_v4
@@ -761,19 +704,6 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config["enc_h"] = state_dict['{}encoder.pan_blocks.1.cv4.conv.weight'.format(key_prefix)].shape[0]
         return dit_config
 
-    if '{}layers.0.mlp.linear_fc2.weight'.format(key_prefix) in state_dict_keys: # Ernie Image
-        dit_config = {}
-        dit_config["image_model"] = "ernie"
-        return dit_config
-
-    if 'detector.backbone.vision_backbone.trunk.blocks.0.attn.qkv.weight' in state_dict_keys: # SAM3 / SAM3.1
-        if 'detector.transformer.decoder.query_embed.weight' in state_dict_keys:
-            dit_config = {}
-            dit_config["image_model"] = "SAM3"
-            if 'detector.backbone.vision_backbone.propagation_convs.0.conv_1x1.weight' in state_dict_keys:
-                dit_config["image_model"] = "SAM31"
-            return dit_config
-
     if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
         return None
 
@@ -929,10 +859,6 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal
     return model_config
 
 def unet_prefix_from_state_dict(state_dict):
-    # SAM3: detector.* and tracker.* at top level, no common prefix
-    if any(k.startswith("detector.") for k in state_dict) and any(k.startswith("tracker.") for k in state_dict):
-        return ""
-
     candidates = ["model.diffusion_model.", #ldm/sgm models
                   "model.model.", #audio models
                   "net.", #cosmos

comfy/model_management.py

@@ -31,7 +31,6 @@ from contextlib import nullcontext
 import comfy.memory_management
 import comfy.utils
 import comfy.quant_ops
-import comfy_aimdo.vram_buffer
 
 class VRAMState(Enum):
     DISABLED = 0    #No vram present: no need to move models to vram
@@ -113,6 +112,10 @@ if args.directml is not None:
     # torch_directml.disable_tiled_resources(True)
     lowvram_available = False #TODO: need to find a way to get free memory in directml before this can be enabled by default.
 
+try:
+    import intel_extension_for_pytorch as ipex  # noqa: F401
+except:
+    pass
 
 try:
     _ = torch.xpu.device_count()
@@ -580,6 +583,9 @@ class LoadedModel:
 
         real_model = self.model.model
 
+        if is_intel_xpu() and not args.disable_ipex_optimize and 'ipex' in globals() and real_model is not None:
+            with torch.no_grad():
+                real_model = ipex.optimize(real_model.eval(), inplace=True, graph_mode=True, concat_linear=True)
 
         self.real_model = weakref.ref(real_model)
         self.model_finalizer = weakref.finalize(real_model, cleanup_models)
@@ -657,7 +663,6 @@ def minimum_inference_memory():
 
 def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, pins_required=0, ram_required=0):
     cleanup_models_gc()
-    comfy.memory_management.extra_ram_release(max(pins_required, ram_required))
     unloaded_model = []
     can_unload = []
     unloaded_models = []
@@ -721,15 +726,13 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
     else:
         minimum_memory_required = max(inference_memory, minimum_memory_required + extra_reserved_memory())
 
-    # Order-preserving dedup. A plain set() would randomize iteration order across runs
-    models_temp = {}
+    models_temp = set()
     for m in models:
-        models_temp[m] = None
+        models_temp.add(m)
         for mm in m.model_patches_models():
-            models_temp[mm] = None
+            models_temp.add(mm)
 
-    models = list(models_temp)
-    models.reverse()
+    models = models_temp
 
     models_to_load = []
 
@@ -1178,10 +1181,6 @@ stream_counters = {}
 
 STREAM_CAST_BUFFERS = {}
 LARGEST_CASTED_WEIGHT = (None, 0)
-STREAM_AIMDO_CAST_BUFFERS = {}
-LARGEST_AIMDO_CASTED_WEIGHT = (None, 0)
-
-DEFAULT_AIMDO_CAST_BUFFER_RESERVATION_SIZE = 16 * 1024 ** 3
 
 def get_cast_buffer(offload_stream, device, size, ref):
     global LARGEST_CASTED_WEIGHT
@@ -1215,26 +1214,13 @@ def get_cast_buffer(offload_stream, device, size, ref):
 
     return cast_buffer
 
-def get_aimdo_cast_buffer(offload_stream, device):
-    cast_buffer = STREAM_AIMDO_CAST_BUFFERS.get(offload_stream, None)
-    if cast_buffer is None:
-        cast_buffer = comfy_aimdo.vram_buffer.VRAMBuffer(DEFAULT_AIMDO_CAST_BUFFER_RESERVATION_SIZE, device.index)
-        STREAM_AIMDO_CAST_BUFFERS[offload_stream] = cast_buffer
-
-    return cast_buffer
 def reset_cast_buffers():
     global LARGEST_CASTED_WEIGHT
-    global LARGEST_AIMDO_CASTED_WEIGHT
-
     LARGEST_CASTED_WEIGHT = (None, 0)
-    LARGEST_AIMDO_CASTED_WEIGHT = (None, 0)
-    for offload_stream in set(STREAM_CAST_BUFFERS) | set(STREAM_AIMDO_CAST_BUFFERS):
-        if offload_stream is not None:
-            offload_stream.synchronize()
+    for offload_stream in STREAM_CAST_BUFFERS:
+        offload_stream.synchronize()
     synchronize()
-
     STREAM_CAST_BUFFERS.clear()
-    STREAM_AIMDO_CAST_BUFFERS.clear()
     soft_empty_cache()
 
 def get_offload_stream(device):
@@ -1594,7 +1580,10 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma
         return False
 
     if is_intel_xpu():
-        return torch.xpu.get_device_properties(device).has_fp16
+        if torch_version_numeric < (2, 3):
+            return True
+        else:
+            return torch.xpu.get_device_properties(device).has_fp16
 
     if is_ascend_npu():
         return True
@@ -1660,7 +1649,10 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
         return False
 
     if is_intel_xpu():
-        return torch.xpu.is_bf16_supported()
+        if torch_version_numeric < (2, 3):
+            return True
+        else:
+            return torch.xpu.is_bf16_supported()
 
     if is_ascend_npu():
         return True
@@ -1740,21 +1732,6 @@ def supports_mxfp8_compute(device=None):
 
     return True
 
-def supports_fp64(device=None):
-    if is_device_mps(device):
-        return False
-
-    if is_intel_xpu():
-        return False
-
-    if is_directml_enabled():
-        return False
-
-    if is_ixuca():
-        return False
-
-    return True
-
 def extended_fp16_support():
     # TODO: check why some models work with fp16 on newer torch versions but not on older
     if torch_version_numeric < (2, 7):
@@ -1791,7 +1768,6 @@ def soft_empty_cache(force=False):
     if cpu_state == CPUState.MPS:
         torch.mps.empty_cache()
     elif is_intel_xpu():
-        torch.xpu.synchronize()
         torch.xpu.empty_cache()
     elif is_ascend_npu():
         torch.npu.empty_cache()
@@ -1810,7 +1786,7 @@ def debug_memory_summary():
         return torch.cuda.memory.memory_summary()
     return ""
 
-class InterruptProcessingException(BaseException):
+class InterruptProcessingException(Exception):
     pass
 
 interrupt_processing_mutex = threading.RLock()

comfy/model_patcher.py

@@ -31,7 +31,6 @@ import comfy.float
 import comfy.hooks
 import comfy.lora
 import comfy.model_management
-import comfy.ops
 import comfy.patcher_extension
 import comfy.utils
 from comfy.comfy_types import UnetWrapperFunction
@@ -121,20 +120,9 @@ class LowVramPatch:
         self.patches = patches
         self.convert_func = convert_func # TODO: remove
         self.set_func = set_func
-        self.prepared_patches = None
-
-    def prepare(self, allocate_buffer, stream):
-        self.prepared_patches = [
-            (patch[0], comfy.lora.prefetch_prepared_value(patch[1], allocate_buffer, stream), patch[2], patch[3], patch[4])
-            for patch in self.patches[self.key]
-        ]
-
-    def clear_prepared(self):
-        self.prepared_patches = None
 
     def __call__(self, weight):
-        patches = self.prepared_patches if self.prepared_patches is not None else self.patches[self.key]
-        return comfy.lora.calculate_weight(patches, weight, self.key, intermediate_dtype=weight.dtype)
+        return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=weight.dtype)
 
 LOWVRAM_PATCH_ESTIMATE_MATH_FACTOR = 2
 
@@ -518,10 +506,6 @@ class ModelPatcher:
     def set_model_noise_refiner_patch(self, patch):
         self.set_model_patch(patch, "noise_refiner")
 
-    def set_model_middle_block_after_patch(self, patch):
-        self.set_model_patch(patch, "middle_block_after_patch")
-
-
     def set_model_rope_options(self, scale_x, shift_x, scale_y, shift_y, scale_t, shift_t, **kwargs):
         rope_options = self.model_options["transformer_options"].get("rope_options", {})
         rope_options["scale_x"] = scale_x
@@ -697,9 +681,9 @@ class ModelPatcher:
                         sd.pop(k)
             return sd
 
-    def patch_weight_to_device(self, key, device_to=None, inplace_update=False, return_weight=False, force_cast=False):
+    def patch_weight_to_device(self, key, device_to=None, inplace_update=False, return_weight=False):
         weight, set_func, convert_func = get_key_weight(self.model, key)
-        if key not in self.patches and not force_cast:
+        if key not in self.patches:
             return weight
 
         inplace_update = self.weight_inplace_update or inplace_update
@@ -707,7 +691,7 @@ class ModelPatcher:
         if key not in self.backup and not return_weight:
             self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(weight.to(device=self.offload_device, copy=inplace_update), inplace_update)
 
-        temp_dtype = comfy.model_management.lora_compute_dtype(device_to) if key in self.patches else None
+        temp_dtype = comfy.model_management.lora_compute_dtype(device_to)
         if device_to is not None:
             temp_weight = comfy.model_management.cast_to_device(weight, device_to, temp_dtype, copy=True)
         else:
@@ -715,10 +699,9 @@ class ModelPatcher:
         if convert_func is not None:
             temp_weight = convert_func(temp_weight, inplace=True)
 
-        out_weight = comfy.lora.calculate_weight(self.patches[key], temp_weight, key) if key in self.patches else temp_weight
+        out_weight = comfy.lora.calculate_weight(self.patches[key], temp_weight, key)
         if set_func is None:
-            if key in self.patches:
-                out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=comfy.utils.string_to_seed(key))
+            out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=comfy.utils.string_to_seed(key))
             if return_weight:
                 return out_weight
             elif inplace_update:
@@ -868,9 +851,7 @@ class ModelPatcher:
                     if m.comfy_patched_weights == True:
                         continue
 
-                for param, param_value in params.items():
-                    if hasattr(m, "comfy_cast_weights") and getattr(param_value, "is_meta", False):
-                        comfy.ops.disable_weight_init._zero_init_parameter(m, param)
+                for param in params:
                     key = key_param_name_to_key(n, param)
                     self.unpin_weight(key)
                     self.patch_weight_to_device(key, device_to=device_to)
@@ -1599,7 +1580,7 @@ class ModelPatcherDynamic(ModelPatcher):
                     key = key_param_name_to_key(n, param_key)
                     if key in self.backup:
                         comfy.utils.set_attr_param(self.model, key, self.backup[key].weight)
-                    self.patch_weight_to_device(key, device_to=device_to, force_cast=True)
+                    self.patch_weight_to_device(key, device_to=device_to)
                     weight, _, _ = get_key_weight(self.model, key)
                     if weight is not None:
                         self.model.model_loaded_weight_memory += weight.numel() * weight.element_size()
@@ -1624,10 +1605,6 @@ class ModelPatcherDynamic(ModelPatcher):
                             m._v = vbar.alloc(v_weight_size)
                         allocated_size += v_weight_size
 
-                    for param in params:
-                        if param not in ("weight", "bias"):
-                            force_load_param(self, param, device_to)
-
                 else:
                     for param in params:
                         key = key_param_name_to_key(n, param)

comfy/model_prefetch.py

@@ -1,66 +0,0 @@
-import comfy_aimdo.model_vbar
-import comfy.model_management
-import comfy.ops
-
-PREFETCH_QUEUES = []
-
-def cleanup_prefetched_modules(comfy_modules):
-    for s in comfy_modules:
-        prefetch = getattr(s, "_prefetch", None)
-        if prefetch is None:
-            continue
-        for param_key in ("weight", "bias"):
-            lowvram_fn = getattr(s, param_key + "_lowvram_function", None)
-            if lowvram_fn is not None:
-                lowvram_fn.clear_prepared()
-        if prefetch["signature"] is not None:
-            comfy_aimdo.model_vbar.vbar_unpin(s._v)
-        delattr(s, "_prefetch")
-
-def cleanup_prefetch_queues():
-    global PREFETCH_QUEUES
-
-    for queue in PREFETCH_QUEUES:
-        for entry in queue:
-            if entry is None or not isinstance(entry, tuple):
-                continue
-            _, prefetch_state = entry
-            comfy_modules = prefetch_state[1]
-            if comfy_modules is not None:
-                cleanup_prefetched_modules(comfy_modules)
-    PREFETCH_QUEUES = []
-
-def prefetch_queue_pop(queue, device, module):
-    if queue is None:
-        return
-
-    consumed = queue.pop(0)
-    if consumed is not None:
-        offload_stream, prefetch_state = consumed
-        if offload_stream is not None:
-            offload_stream.wait_stream(comfy.model_management.current_stream(device))
-        _, comfy_modules = prefetch_state
-        if comfy_modules is not None:
-            cleanup_prefetched_modules(comfy_modules)
-
-    prefetch = queue[0]
-    if prefetch is not None:
-        comfy_modules = []
-        for s in prefetch.modules():
-            if hasattr(s, "_v"):
-                comfy_modules.append(s)
-
-        offload_stream = comfy.ops.cast_modules_with_vbar(comfy_modules, None, device, None, True)
-        comfy.model_management.sync_stream(device, offload_stream)
-        queue[0] = (offload_stream, (prefetch, comfy_modules))
-
-def make_prefetch_queue(queue, device, transformer_options):
-    if (not transformer_options.get("prefetch_dynamic_vbars", False)
-        or comfy.model_management.NUM_STREAMS == 0
-        or comfy.model_management.is_device_cpu(device)
-        or not comfy.model_management.device_supports_non_blocking(device)):
-        return None
-
-    queue = [None] + queue + [None]
-    PREFETCH_QUEUES.append(queue)
-    return queue

comfy/model_sampling.py

@@ -54,30 +54,6 @@ class V_PREDICTION(EPS):
         sigma = reshape_sigma(sigma, model_output.ndim)
         return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
 
-class V_PREDICTION_DDPM:
-    """CogVideoX v-prediction: model receives raw x_t (unscaled), predicts velocity v.
-    x_0 = sqrt(alpha) * x_t - sqrt(1-alpha) * v
-        = x_t / sqrt(sigma^2 + 1) - v * sigma / sqrt(sigma^2 + 1)
-    """
-    def calculate_input(self, sigma, noise):
-        return noise
-
-    def calculate_denoised(self, sigma, model_output, model_input):
-        sigma = reshape_sigma(sigma, model_output.ndim)
-        return model_input / (sigma ** 2 + 1.0) ** 0.5 - model_output * sigma / (sigma ** 2 + 1.0) ** 0.5
-
-    def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
-        sigma = reshape_sigma(sigma, noise.ndim)
-        if max_denoise:
-            noise = noise * torch.sqrt(1.0 + sigma ** 2.0)
-        else:
-            noise = noise * sigma
-        noise += latent_image
-        return noise
-
-    def inverse_noise_scaling(self, sigma, latent):
-        return latent
-
 class EDM(V_PREDICTION):
     def calculate_denoised(self, sigma, model_output, model_input):
         sigma = reshape_sigma(sigma, model_output.ndim)

comfy/ops.py

@@ -79,68 +79,37 @@ def cast_to_input(weight, input, non_blocking=False, copy=True):
     return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy)
 
 
-def materialize_meta_param(s, param_keys):
-    for param_key in param_keys:
-        param = getattr(s, param_key, None)
-        if param is not None and getattr(param, "is_meta", False):
-            setattr(s, param_key, torch.nn.Parameter(torch.zeros(param.shape, dtype=param.dtype), requires_grad=param.requires_grad))
+def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compute_dtype, want_requant):
 
+    #vbar doesn't support CPU weights, but some custom nodes have weird paths
+    #that might switch the layer to the CPU and expect it to work. We have to take
+    #a clone conservatively as we are mmapped and some SFT files are packed misaligned
+    #If you are a custom node author reading this, please move your layer to the GPU
+    #or declare your ModelPatcher as CPU in the first place.
+    if comfy.model_management.is_device_cpu(device):
+        weight = s.weight.to(dtype=dtype, copy=True)
+        if isinstance(weight, QuantizedTensor):
+            weight = weight.dequantize()
+        bias = None
+        if s.bias is not None:
+            bias = s.bias.to(dtype=bias_dtype, copy=True)
+        return weight, bias, (None, None, None)
 
-# FIXME: add n=1 cache hit fast path
-def cast_modules_with_vbar(comfy_modules, dtype, device, bias_dtype, non_blocking):
     offload_stream = None
-    cast_buffer = None
-    cast_buffer_offset = 0
-
-    def ensure_offload_stream(module, required_size, check_largest):
-        nonlocal offload_stream
-        nonlocal cast_buffer
-
-        if offload_stream is None:
-            offload_stream = comfy.model_management.get_offload_stream(device)
-        if offload_stream is None or not check_largest or len(comfy_modules) != 1:
-            return
-
-        current_size = 0 if cast_buffer is None else cast_buffer.size()
-        if current_size < required_size and module is comfy.model_management.LARGEST_AIMDO_CASTED_WEIGHT[0]:
-            offload_stream = comfy.model_management.get_offload_stream(device)
-            cast_buffer = None
-        if required_size > comfy.model_management.LARGEST_AIMDO_CASTED_WEIGHT[1]:
-            comfy.model_management.LARGEST_AIMDO_CASTED_WEIGHT = (module, required_size)
-
-    def get_cast_buffer(buffer_size):
-        nonlocal offload_stream
-        nonlocal cast_buffer
-        nonlocal cast_buffer_offset
-
-        if buffer_size == 0:
-            return None
-
-        if offload_stream is None:
-            return torch.empty((buffer_size,), dtype=torch.uint8, device=device)
-
-        cast_buffer = comfy.model_management.get_aimdo_cast_buffer(offload_stream, device)
-        buffer = comfy_aimdo.torch.aimdo_to_tensor(cast_buffer.get(buffer_size, cast_buffer_offset), device)
-        cast_buffer_offset += buffer_size
-        return buffer
-
-    for s in comfy_modules:
-        signature = comfy_aimdo.model_vbar.vbar_fault(s._v)
-        resident = comfy_aimdo.model_vbar.vbar_signature_compare(signature, s._v_signature)
-        prefetch = {
-            "signature": signature,
-            "resident": resident,
-        }
+    xfer_dest = None
 
+    signature = comfy_aimdo.model_vbar.vbar_fault(s._v)
+    resident = comfy_aimdo.model_vbar.vbar_signature_compare(signature, s._v_signature)
+    if signature is not None:
         if resident:
-            s._prefetch = prefetch
-            continue
+            weight = s._v_weight
+            bias = s._v_bias
+        else:
+            xfer_dest = comfy_aimdo.torch.aimdo_to_tensor(s._v, device)
 
-        materialize_meta_param(s, ["weight", "bias"])
-        xfer_dest = comfy_aimdo.torch.aimdo_to_tensor(s._v, device) if signature is not None else None
+    if not resident:
         cast_geometry = comfy.memory_management.tensors_to_geometries([ s.weight, s.bias ])
         cast_dest = None
-        needs_cast = False
 
         xfer_source = [ s.weight, s.bias ]
 
@@ -152,15 +121,22 @@ def cast_modules_with_vbar(comfy_modules, dtype, device, bias_dtype, non_blockin
             if data is None:
                 continue
             if data.dtype != geometry.dtype:
-                needs_cast = True
                 cast_dest = xfer_dest
+                if cast_dest is None:
+                    cast_dest = torch.empty((comfy.memory_management.vram_aligned_size(cast_geometry),), dtype=torch.uint8, device=device)
                 xfer_dest = None
                 break
 
         dest_size = comfy.memory_management.vram_aligned_size(xfer_source)
-        ensure_offload_stream(s, dest_size if xfer_dest is None else 0, True)
+        offload_stream = comfy.model_management.get_offload_stream(device)
+        if xfer_dest is None and offload_stream is not None:
+                xfer_dest = comfy.model_management.get_cast_buffer(offload_stream, device, dest_size, s)
+                if xfer_dest is None:
+                    offload_stream = comfy.model_management.get_offload_stream(device)
+                    xfer_dest = comfy.model_management.get_cast_buffer(offload_stream, device, dest_size, s)
         if xfer_dest is None:
-            xfer_dest = get_cast_buffer(dest_size)
+            xfer_dest = torch.empty((dest_size,), dtype=torch.uint8, device=device)
+            offload_stream = None
 
         if signature is None and pin is None:
             comfy.pinned_memory.pin_memory(s)
@@ -173,54 +149,27 @@ def cast_modules_with_vbar(comfy_modules, dtype, device, bias_dtype, non_blockin
             xfer_source = [ pin ]
         #send it over
         comfy.model_management.cast_to_gathered(xfer_source, xfer_dest, non_blocking=non_blocking, stream=offload_stream)
+        comfy.model_management.sync_stream(device, offload_stream)
 
-        for param_key in ("weight", "bias"):
-            lowvram_fn = getattr(s, param_key + "_lowvram_function", None)
-            if lowvram_fn is not None:
-                ensure_offload_stream(s, cast_buffer_offset, False)
-                lowvram_fn.prepare(lambda size: get_cast_buffer(size), offload_stream)
-
-        prefetch["xfer_dest"] = xfer_dest
-        prefetch["cast_dest"] = cast_dest
-        prefetch["cast_geometry"] = cast_geometry
-        prefetch["needs_cast"] = needs_cast
-        s._prefetch = prefetch
-
-    return offload_stream
-
-
-def resolve_cast_module_with_vbar(s, dtype, device, bias_dtype, compute_dtype, want_requant):
-
-    prefetch = getattr(s, "_prefetch", None)
-
-    if prefetch["resident"]:
-        weight = s._v_weight
-        bias = s._v_bias
-    else:
-        xfer_dest = prefetch["xfer_dest"]
-        if prefetch["needs_cast"]:
-            cast_dest = prefetch["cast_dest"] if prefetch["cast_dest"] is not None else torch.empty((comfy.memory_management.vram_aligned_size(prefetch["cast_geometry"]),), dtype=torch.uint8, device=device)
+        if cast_dest is not None:
             for pre_cast, post_cast in zip(comfy.memory_management.interpret_gathered_like([s.weight, s.bias ], xfer_dest),
-                                           comfy.memory_management.interpret_gathered_like(prefetch["cast_geometry"], cast_dest)):
+                                           comfy.memory_management.interpret_gathered_like(cast_geometry, cast_dest)):
                 if post_cast is not None:
                     post_cast.copy_(pre_cast)
             xfer_dest = cast_dest
 
-        params = comfy.memory_management.interpret_gathered_like(prefetch["cast_geometry"], xfer_dest)
+        params = comfy.memory_management.interpret_gathered_like(cast_geometry, xfer_dest)
         weight = params[0]
         bias = params[1]
-        if prefetch["signature"] is not None:
+        if signature is not None:
             s._v_weight = weight
             s._v_bias = bias
-        s._v_signature = prefetch["signature"]
+        s._v_signature=signature
 
     def post_cast(s, param_key, x, dtype, resident, update_weight):
         lowvram_fn = getattr(s, param_key + "_lowvram_function", None)
         fns = getattr(s, param_key + "_function", [])
 
-        if x is None:
-            return None
-
         orig = x
 
         def to_dequant(tensor, dtype):
@@ -248,15 +197,14 @@ def resolve_cast_module_with_vbar(s, dtype, device, bias_dtype, compute_dtype, w
             x = f(x)
         return x
 
-    update_weight = prefetch["signature"] is not None
-    weight = post_cast(s, "weight", weight, dtype, prefetch["resident"], update_weight)
-    if bias is not None:
-        bias = post_cast(s, "bias", bias, bias_dtype, prefetch["resident"], update_weight)
+    update_weight = signature is not None
 
-    if prefetch["signature"] is not None:
-        prefetch["resident"] = True
+    weight = post_cast(s, "weight", weight, dtype, resident, update_weight)
+    if s.bias is not None:
+        bias = post_cast(s, "bias", bias, bias_dtype, resident, update_weight)
 
-    return weight, bias
+    #FIXME: weird offload return protocol
+    return weight, bias, (offload_stream, device if signature is not None else None, None)
 
 
 def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, offloadable=False, compute_dtype=None, want_requant=False):
@@ -274,46 +222,10 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
         if device is None:
             device = input.device
 
-    def format_return(result, offloadable):
-        weight, bias, offload_stream = result
-        return (weight, bias, offload_stream) if offloadable else (weight, bias)
-
     non_blocking = comfy.model_management.device_supports_non_blocking(device)
 
     if hasattr(s, "_v"):
-
-        #vbar doesn't support CPU weights, but some custom nodes have weird paths
-        #that might switch the layer to the CPU and expect it to work. We have to take
-        #a clone conservatively as we are mmapped and some SFT files are packed misaligned
-        #If you are a custom node author reading this, please move your layer to the GPU
-        #or declare your ModelPatcher as CPU in the first place.
-        if comfy.model_management.is_device_cpu(device):
-            materialize_meta_param(s, ["weight", "bias"])
-            weight = s.weight.to(dtype=dtype, copy=True)
-            if isinstance(weight, QuantizedTensor):
-                weight = weight.dequantize()
-            bias = s.bias.to(dtype=bias_dtype, copy=True) if s.bias is not None else None
-            return format_return((weight, bias, (None, None, None)), offloadable)
-
-        prefetched = hasattr(s, "_prefetch")
-        offload_stream = None
-        offload_device = None
-        if not prefetched:
-            offload_stream = cast_modules_with_vbar([s], dtype, device, bias_dtype, non_blocking)
-            comfy.model_management.sync_stream(device, offload_stream)
-
-        weight, bias = resolve_cast_module_with_vbar(s, dtype, device, bias_dtype, compute_dtype, want_requant)
-
-        if not prefetched:
-            if getattr(s, "_prefetch")["signature"] is not None:
-                offload_device = device
-            for param_key in ("weight", "bias"):
-                lowvram_fn = getattr(s, param_key + "_lowvram_function", None)
-                if lowvram_fn is not None:
-                    lowvram_fn.clear_prepared()
-            delattr(s, "_prefetch")
-        return format_return((weight, bias, (offload_stream, offload_device, None)), offloadable)
-
+        return cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compute_dtype, want_requant)
 
     if offloadable and (device != s.weight.device or
                         (s.bias is not None and device != s.bias.device)):
@@ -360,7 +272,11 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
         for f in s.weight_function:
             weight = f(weight)
 
-    return format_return((weight, bias, (offload_stream, weight_a, bias_a)), offloadable)
+    if offloadable:
+        return weight, bias, (offload_stream, weight_a, bias_a)
+    else:
+        #Legacy function signature
+        return weight, bias
 
 
 def uncast_bias_weight(s, weight, bias, offload_stream):
@@ -390,12 +306,6 @@ class CastWeightBiasOp:
     bias_function = []
 
 class disable_weight_init:
-    @staticmethod
-    def _zero_init_parameter(module, name):
-        param = getattr(module, name)
-        device = None if getattr(param, "is_meta", False) else param.device
-        setattr(module, name, torch.nn.Parameter(torch.zeros(param.shape, device=device, dtype=param.dtype), requires_grad=False))
-
     @staticmethod
     def _lazy_load_from_state_dict(module, state_dict, prefix, local_metadata,
                                    missing_keys, unexpected_keys, weight_shape,
@@ -1241,7 +1151,7 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                     if param is None:
                         continue
                     p = fn(param)
-                    if (not torch.is_inference_mode_enabled()) and p.is_inference():
+                    if p.is_inference():
                         p = p.clone()
                     self.register_parameter(key, torch.nn.Parameter(p, requires_grad=False))
                 for key, buf in self._buffers.items():
@@ -1249,93 +1159,6 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                         self._buffers[key] = fn(buf)
                 return self
 
-        class Embedding(manual_cast.Embedding):
-            def _load_from_state_dict(self, state_dict, prefix, local_metadata,
-                                    strict, missing_keys, unexpected_keys, error_msgs):
-                weight_key = f"{prefix}weight"
-                layer_conf = state_dict.pop(f"{prefix}comfy_quant", None)
-                if layer_conf is not None:
-                    layer_conf = json.loads(layer_conf.numpy().tobytes())
-
-                # Only fp8 makes sense for embeddings (per-row dequant via index select).
-                # Block-scaled formats (NVFP4, MXFP8) can't do per-row lookup efficiently.
-                quant_format = layer_conf.get("format", None) if layer_conf is not None else None
-                if quant_format in ["float8_e4m3fn", "float8_e5m2"] and weight_key in state_dict:
-                    self.quant_format = quant_format
-                    qconfig = QUANT_ALGOS[quant_format]
-                    layout_cls = get_layout_class(qconfig["comfy_tensor_layout"])
-                    weight = state_dict.pop(weight_key)
-                    manually_loaded_keys = [weight_key]
-
-                    scale_key = f"{prefix}weight_scale"
-                    scale = state_dict.pop(scale_key, None)
-                    if scale is not None:
-                        scale = scale.float()
-                        manually_loaded_keys.append(scale_key)
-
-                    params = layout_cls.Params(
-                        scale=scale if scale is not None else torch.ones((), dtype=torch.float32),
-                        orig_dtype=MixedPrecisionOps._compute_dtype,
-                        orig_shape=(self.num_embeddings, self.embedding_dim),
-                    )
-                    self.weight = torch.nn.Parameter(
-                        QuantizedTensor(weight.to(dtype=qconfig["storage_t"]), qconfig["comfy_tensor_layout"], params),
-                        requires_grad=False)
-
-                    super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
-                    for k in manually_loaded_keys:
-                        if k in missing_keys:
-                            missing_keys.remove(k)
-                else:
-                    if layer_conf is not None:
-                        state_dict[f"{prefix}comfy_quant"] = torch.tensor(list(json.dumps(layer_conf).encode('utf-8')), dtype=torch.uint8)
-                    super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
-
-            def state_dict(self, *args, destination=None, prefix="", **kwargs):
-                if destination is not None:
-                    sd = destination
-                else:
-                    sd = {}
-
-                if not hasattr(self, 'weight') or self.weight is None:
-                    return sd
-
-                if isinstance(self.weight, QuantizedTensor):
-                    sd_out = self.weight.state_dict("{}weight".format(prefix))
-                    for k in sd_out:
-                        sd[k] = sd_out[k]
-
-                    quant_conf = {"format": self.quant_format}
-                    sd["{}comfy_quant".format(prefix)] = torch.tensor(list(json.dumps(quant_conf).encode('utf-8')), dtype=torch.uint8)
-                else:
-                    sd["{}weight".format(prefix)] = self.weight
-                return sd
-
-            def forward_comfy_cast_weights(self, input, out_dtype=None):
-                weight = self.weight
-
-                # Optimized path: lookup in fp8, dequantize only the selected rows.
-                if isinstance(weight, QuantizedTensor) and len(self.weight_function) == 0:
-                    qdata, _, offload_stream = cast_bias_weight(self, device=input.device, dtype=weight.dtype, offloadable=True)
-                    if isinstance(qdata, QuantizedTensor):
-                        scale = qdata._params.scale
-                        qdata = qdata._qdata
-                    else:
-                        scale = None
-
-                    x = torch.nn.functional.embedding(
-                        input, qdata, self.padding_idx, self.max_norm,
-                        self.norm_type, self.scale_grad_by_freq, self.sparse)
-                    uncast_bias_weight(self, qdata, None, offload_stream)
-                    target_dtype = out_dtype if out_dtype is not None else weight._params.orig_dtype
-                    x = x.to(dtype=target_dtype)
-                    if scale is not None and scale != 1.0:
-                        x = x * scale.to(dtype=target_dtype)
-                    return x
-
-                # Fallback for non-quantized or weight_function (LoRA) case
-                return super().forward_comfy_cast_weights(input, out_dtype=out_dtype)
-
     return MixedPrecisionOps
 
 def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, model_config=None):

comfy/pinned_memory.py

@@ -2,6 +2,7 @@ import comfy.model_management
 import comfy.memory_management
 import comfy_aimdo.host_buffer
 import comfy_aimdo.torch
+import psutil
 
 from comfy.cli_args import args
 
@@ -11,6 +12,11 @@ def get_pin(module):
 def pin_memory(module):
     if module.pin_failed or args.disable_pinned_memory or get_pin(module) is not None:
         return
+    #FIXME: This is a RAM cache trigger event
+    ram_headroom = comfy.memory_management.RAM_CACHE_HEADROOM
+    #we split the difference and assume half the RAM cache headroom is for us
+    if ram_headroom > 0 and psutil.virtual_memory().available < (ram_headroom * 0.5):
+        comfy.memory_management.extra_ram_release(ram_headroom)
 
     size = comfy.memory_management.vram_aligned_size([ module.weight, module.bias ])