Make Load3D model_file optional by adding "none" choice

.ci/windows_amd_base_files/run_amd_gpu_disable_smart_memory.bat

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

.github/workflows/openapi-lint.yml

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

.github/workflows/stable-release.yml

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

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

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

.gitignore

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

.spectral.yaml

@@ -1,100 +0,0 @@
-extends:
-  - spectral:oas
-
-# Severity levels: error, warn, info, hint, off
-# Rules from the built-in "spectral:oas" ruleset are active by default.
-# Below we tune severity and add custom rules for our conventions.
-#
-# This ruleset mirrors Comfy-Org/cloud/.spectral.yaml so specs across the
-# organization are linted against a single consistent standard.
-
-rules:
-  # -----------------------------------------------------------------------
-  # Built-in rule severity overrides
-  # -----------------------------------------------------------------------
-  operation-operationId: error
-  operation-description: warn
-  operation-tag-defined: error
-  info-contact: off
-  info-description: warn
-  no-eval-in-markdown: error
-  no-$ref-siblings: error
-
-  # -----------------------------------------------------------------------
-  # Custom rules: naming conventions
-  # -----------------------------------------------------------------------
-
-  # Property names should be snake_case
-  property-name-snake-case:
-    description: Property names must be snake_case
-    severity: warn
-    given: "$.components.schemas.*.properties[*]~"
-    then:
-      function: pattern
-      functionOptions:
-        match: "^[a-z][a-z0-9]*(_[a-z0-9]+)*$"
-
-  # Operation IDs should be camelCase
-  operation-id-camel-case:
-    description: Operation IDs must be camelCase
-    severity: warn
-    given: "$.paths.*.*.operationId"
-    then:
-      function: pattern
-      functionOptions:
-        match: "^[a-z][a-zA-Z0-9]*$"
-
-  # -----------------------------------------------------------------------
-  # Custom rules: response conventions
-  # -----------------------------------------------------------------------
-
-  # Error responses (4xx, 5xx) should use a consistent shape
-  error-response-schema:
-    description: Error responses should reference a standard error schema
-    severity: hint
-    given: "$.paths.*.*.responses[?(@property >= '400' && @property < '600')].content['application/json'].schema"
-    then:
-      field: "$ref"
-      function: truthy
-
-  # All 2xx responses with JSON body should have a schema
-  response-schema-defined:
-    description: Success responses with JSON content should define a schema
-    severity: warn
-    given: "$.paths.*.*.responses[?(@property >= '200' && @property < '300')].content['application/json']"
-    then:
-      field: schema
-      function: truthy
-
-  # -----------------------------------------------------------------------
-  # Custom rules: best practices
-  # -----------------------------------------------------------------------
-
-  # Path parameters must have a description
-  path-param-description:
-    description: Path parameters should have a description
-    severity: warn
-    given:
-      - "$.paths.*.parameters[?(@.in == 'path')]"
-      - "$.paths.*.*.parameters[?(@.in == 'path')]"
-    then:
-      field: description
-      function: truthy
-
-  # Schemas should have a description
-  schema-description:
-    description: Component schemas should have a description
-    severity: hint
-    given: "$.components.schemas.*"
-    then:
-      field: description
-      function: truthy
-
-overrides:
-  # /ws uses HTTP 101 (Switching Protocols) — a legitimate response for a
-  # WebSocket upgrade, but not a 2xx, so operation-success-response fires
-  # as a false positive. OpenAPI 3.x has no native WebSocket support.
-  - files:
-      - "openapi.yaml#/paths/~1ws"
-    rules:
-      operation-success-response: off

CODEOWNERS

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

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](https://www.comfy.org/download), our [portable install](#installing) or on our [cloud](https://www.comfy.org/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?
 
@@ -429,8 +418,6 @@ Use `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app w
 
 See also: [https://www.comfy.org/](https://www.comfy.org/)
 
-> _psst — we're hiring!_ Help build ComfyUI: [comfy.org/careers](https://www.comfy.org/careers)
-
 ## Frontend Development
 
 As of August 15, 2024, we have transitioned to a new frontend, which is now hosted in a separate repository: [ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend). This repository now hosts the compiled JS (from TS/Vue) under the `web/` directory.

SECURITY.md

@@ -1,44 +0,0 @@
-# Security Policy
-
-## Scope
-
-ComfyUI is designed to run locally. By default, the server binds to `127.0.0.1`, meaning only the user's own machine can reach it. Our threat model assumes:
-
-- The user installed ComfyUI through a supported channel: the desktop application, the portable build, or a manual install following the README.
-- The user has not installed untrusted custom nodes. Custom nodes are arbitrary Python code and are trusted as much as any other software the user chooses to install.
-- Anyone with access to the ComfyUI URL is trusted (a direct consequence of the localhost-only default).
-- PyTorch and other dependencies are at the versions we ship or recommend in the README.
-
-A report is in scope only if it affects a user operating within this threat model.
-
-## What We Consider a Vulnerability
-
-We want to hear about issues where a **reasonable user** — someone who does not install random untrusted nodes and who reads UI prompts and warnings before clicking through them — can be harmed by ComfyUI itself.
-
-The clearest example: a workflow file that such a user might plausibly load and run, using only built-in nodes, that results in **untrusted code execution, arbitrary file read/write outside expected directories, or credential/data exfiltration**.
-
-When submitting a report, please include a clear description of *why this is a problem for a typical local ComfyUI user*. Reports without this context are difficult to act on.
-
-## What We Do Not Consider a Security Vulnerability
-
-Please report the following through our regular [GitHub issues](https://github.com/comfyanonymous/ComfyUI/issues) instead. Filing them as security reports will likely cause them to be deprioritized or closed.
-
-- **Issues requiring `--listen` or any non-default network exposure.** ComfyUI binds to localhost by default. If a remote attacker needs to reach the server for the attack to work, the user has chosen to expose it and is responsible for securing that deployment (firewall, reverse proxy, authentication, etc.). These are bugs, not vulnerabilities.
-- **`torch.load` and related deserialization issues in old PyTorch versions.** These are upstream PyTorch issues. Our distributions ship with — and our documentation recommends — recent PyTorch versions where these are addressed.
-- **Vulnerabilities that depend on outdated library versions** that we neither ship nor recommend (e.g., requiring PyTorch 2.6 or older).
-- **Issues that require a specific custom node to be installed.** Custom nodes are third-party code. Report these to the maintainer of that node.
-- **Crashes, hangs, or resource exhaustion from a loaded workflow.** Annoying, but not a security issue in our model. File a regular bug.
-- **Social-engineering scenarios** where the user is expected to ignore an explicit UI warning or prompt.
-
-## Reporting
-
-If you believe you have found an issue that falls within the scope above, please report it privately via GitHub's [Report a vulnerability](https://github.com/comfyanonymous/ComfyUI/security/advisories/new) feature rather than opening a public issue.
-
-Please include:
-
-1. A description of the vulnerability and the affected component.
-2. Reproduction steps, ideally with a minimal workflow file or proof-of-concept.
-3. The ComfyUI version, install method (desktop / portable / manual), and OS.
-4. An explanation of how this affects a typical local user as described in the threat model.
-
-We will acknowledge valid reports and coordinate a fix and disclosure timeline with you.

api_server/routes/internal/internal_routes.py

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

app/frontend_management.py

@@ -27,7 +27,7 @@ def frontend_install_warning_message():
     return f"""
 {get_missing_requirements_message()}
 
-The ComfyUI frontend is shipped in a pip package so it needs to be updated separately from the ComfyUI code.
+This error is happening because the ComfyUI frontend is no longer shipped as part of the main repo but as a pip package instead.
 """.strip()
 
 def parse_version(version: str) -> tuple[int, int, int]:
@@ -38,54 +38,40 @@ def is_valid_version(version: str) -> bool:
     pattern = r"^(\d+)\.(\d+)\.(\d+)$"
     return bool(re.match(pattern, version))
 
+def get_installed_frontend_version():
+    """Get the currently installed frontend package version."""
+    frontend_version_str = version("comfyui-frontend-package")
+    return frontend_version_str
+
+
 def get_required_frontend_version():
     return get_required_packages_versions().get("comfyui-frontend-package", None)
 
 
-COMFY_PACKAGE_VERSIONS = []
-def get_comfy_package_versions():
-    """List installed/required versions for every comfy* package in requirements.txt."""
-    if COMFY_PACKAGE_VERSIONS:
-        return COMFY_PACKAGE_VERSIONS.copy()
-    out = COMFY_PACKAGE_VERSIONS
-    for name, required in (get_required_packages_versions() or {}).items():
-        if not name.startswith("comfy"):
-            continue
-        try:
-            installed = version(name)
-        except Exception:
-            installed = None
-        out.append({"name": name, "installed": installed, "required": required})
-    return out.copy()
+def check_frontend_version():
+    """Check if the frontend version is up to date."""
 
-
-def check_comfy_packages_versions():
-    """Warn for every comfy* package whose installed version is below requirements.txt."""
-    from packaging.version import InvalidVersion, parse as parse_pep440
-    for pkg in get_comfy_package_versions():
-        installed_str = pkg["installed"]
-        required_str = pkg["required"]
-        if not installed_str or not required_str:
-            continue
-        try:
-            outdated = parse_pep440(installed_str) < parse_pep440(required_str)
-        except InvalidVersion as e:
-            logging.error(f"Failed to check {pkg['name']} version: {e}")
-            continue
-        if outdated:
+    try:
+        frontend_version_str = get_installed_frontend_version()
+        frontend_version = parse_version(frontend_version_str)
+        required_frontend_str = get_required_frontend_version()
+        required_frontend = parse_version(required_frontend_str)
+        if frontend_version < required_frontend:
             app.logger.log_startup_warning(
                 f"""
 ________________________________________________________________________
 WARNING WARNING WARNING WARNING WARNING
 
-Installed {pkg["name"]} version {installed_str} is lower than the recommended version {required_str}.
+Installed frontend version {".".join(map(str, frontend_version))} is lower than the recommended version {".".join(map(str, required_frontend))}.
 
-{get_missing_requirements_message()}
+{frontend_install_warning_message()}
 ________________________________________________________________________
 """.strip()
             )
         else:
-            logging.info("{} version: {}".format(pkg["name"], installed_str))
+            logging.info("ComfyUI frontend version: {}".format(frontend_version_str))
+    except Exception as e:
+        logging.error(f"Failed to check frontend version: {e}")
 
 
 REQUEST_TIMEOUT = 10  # seconds
@@ -215,11 +201,6 @@ class FrontendManager:
     def get_required_templates_version(cls) -> str:
         return get_required_packages_versions().get("comfyui-workflow-templates", None)
 
-    @classmethod
-    def get_comfy_package_versions(cls):
-        """List installed/required versions for every comfy* package in requirements.txt."""
-        return get_comfy_package_versions()
-
     @classmethod
     def default_frontend_path(cls) -> str:
         try:
@@ -360,7 +341,7 @@ comfyui-workflow-templates is not installed.
             main error source might be request timeout or invalid URL.
         """
         if version_string == DEFAULT_VERSION_STRING:
-            check_comfy_packages_versions()
+            check_frontend_version()
             return cls.default_frontend_path()
 
         repo_owner, repo_name, version = cls.parse_version_string(version_string)
@@ -422,7 +403,7 @@ comfyui-workflow-templates is not installed.
         except Exception as e:
             logging.error("Failed to initialize frontend: %s", e)
             logging.info("Falling back to the default frontend.")
-            check_comfy_packages_versions()
+            check_frontend_version()
             return cls.default_frontend_path()
     @classmethod
     def template_asset_handler(cls):

app/node_replace_manager.py

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

app/user_manager.py

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

blueprints/.glsl/Glow_30.frag

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

blueprints/.glsl/Image_Blur_1.frag

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

blueprints/.glsl/Sharpen_23.frag

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

blueprints/.glsl/Unsharp_Mask_26.frag

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

blueprints/Brightness and Contrast.json

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

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

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

blueprints/Canny to Video (LTX 2.0).json

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

blueprints/Chromatic Aberration.json

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

blueprints/Color Adjustment.json

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

blueprints/Color Balance.json

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

blueprints/Color Curves.json

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

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

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

blueprints/Depth to Video (ltx 2.0).json

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

blueprints/Edge-Preserving Blur.json

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

blueprints/Film Grain.json

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

blueprints/Frame Interpolation.json

@@ -1,858 +0,0 @@
-{
-  "revision": 0,
-  "last_node_id": 16,
-  "last_link_id": 0,
-  "nodes": [
-    {
-      "id": 16,
-      "type": "022693be-2baa-4009-870a-28921508a7ef",
-      "pos": [
-        -2990,
-        -3240
-      ],
-      "size": [
-        410,
-        200
-      ],
-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "inputs": [
-        {
-          "localized_name": "video",
-          "name": "video",
-          "type": "VIDEO",
-          "link": null
-        },
-        {
-          "label": "multiplier",
-          "name": "value",
-          "type": "INT",
-          "widget": {
-            "name": "value"
-          },
-          "link": null
-        },
-        {
-          "label": "enable_fps_multiplier",
-          "name": "value_1",
-          "type": "BOOLEAN",
-          "widget": {
-            "name": "value_1"
-          },
-          "link": null
-        },
-        {
-          "name": "model_name",
-          "type": "COMBO",
-          "widget": {
-            "name": "model_name"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "label": "VIDEO",
-          "name": "VIDEO_1",
-          "type": "VIDEO",
-          "links": []
-        },
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": null
-        }
-      ],
-      "properties": {
-        "proxyWidgets": [
-          [
-            "9",
-            "value"
-          ],
-          [
-            "13",
-            "value"
-          ],
-          [
-            "1",
-            "model_name"
-          ]
-        ],
-        "enableTabs": false,
-        "tabWidth": 65,
-        "tabXOffset": 10,
-        "hasSecondTab": false,
-        "secondTabText": "Send Back",
-        "secondTabOffset": 80,
-        "secondTabWidth": 65,
-        "cnr_id": "comfy-core",
-        "ver": "0.19.3"
-      },
-      "widgets_values": [],
-      "title": "Frame Interpolation"
-    }
-  ],
-  "links": [],
-  "version": 0.4,
-  "definitions": {
-    "subgraphs": [
-      {
-        "id": "022693be-2baa-4009-870a-28921508a7ef",
-        "version": 1,
-        "state": {
-          "lastGroupId": 0,
-          "lastNodeId": 17,
-          "lastLinkId": 28,
-          "lastRerouteId": 0
-        },
-        "revision": 0,
-        "config": {},
-        "name": "Frame Interpolation",
-        "inputNode": {
-          "id": -10,
-          "bounding": [
-            -2810,
-            -3070,
-            159.7421875,
-            120
-          ]
-        },
-        "outputNode": {
-          "id": -20,
-          "bounding": [
-            -1270,
-            -3075,
-            120,
-            80
-          ]
-        },
-        "inputs": [
-          {
-            "id": "05e31c51-dcb6-4a1e-9651-1b9ad4f7a287",
-            "name": "video",
-            "type": "VIDEO",
-            "linkIds": [
-              2
-            ],
-            "localized_name": "video",
-            "pos": [
-              -2670.2578125,
-              -3050
-            ]
-          },
-          {
-            "id": "feecb409-7d1c-4a99-9c63-50c5fecdd3c9",
-            "name": "value",
-            "type": "INT",
-            "linkIds": [
-              22
-            ],
-            "label": "multiplier",
-            "pos": [
-              -2670.2578125,
-              -3030
-            ]
-          },
-          {
-            "id": "0b8a861b-b581-4068-9e8c-f8d15daf1ca6",
-            "name": "value_1",
-            "type": "BOOLEAN",
-            "linkIds": [
-              23
-            ],
-            "label": "enable_fps_multiplier",
-            "pos": [
-              -2670.2578125,
-              -3010
-            ]
-          },
-          {
-            "id": "a22b101e-8773-4e17-a297-7ee3aae09162",
-            "name": "model_name",
-            "type": "COMBO",
-            "linkIds": [
-              24
-            ],
-            "pos": [
-              -2670.2578125,
-              -2990
-            ]
-          }
-        ],
-        "outputs": [
-          {
-            "id": "ef2ada05-d5aa-492a-9394-6c3e71e39ebb",
-            "name": "VIDEO_1",
-            "type": "VIDEO",
-            "linkIds": [
-              26
-            ],
-            "label": "VIDEO",
-            "pos": [
-              -1250,
-              -3055
-            ]
-          },
-          {
-            "id": "5aacc622-2a07-4983-b31c-e04461f7f953",
-            "name": "IMAGE",
-            "type": "IMAGE",
-            "linkIds": [
-              28
-            ],
-            "pos": [
-              -1250,
-              -3035
-            ]
-          }
-        ],
-        "widgets": [],
-        "nodes": [
-          {
-            "id": 1,
-            "type": "FrameInterpolationModelLoader",
-            "pos": [
-              -2510,
-              -3370
-            ],
-            "size": [
-              370,
-              90
-            ],
-            "flags": {},
-            "order": 0,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "model_name",
-                "name": "model_name",
-                "type": "COMBO",
-                "widget": {
-                  "name": "model_name"
-                },
-                "link": 24
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "INTERP_MODEL",
-                "name": "INTERP_MODEL",
-                "type": "INTERP_MODEL",
-                "links": [
-                  1
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "FrameInterpolationModelLoader",
-              "enableTabs": false,
-              "tabWidth": 65,
-              "tabXOffset": 10,
-              "hasSecondTab": false,
-              "secondTabText": "Send Back",
-              "secondTabOffset": 80,
-              "secondTabWidth": 65,
-              "cnr_id": "comfy-core",
-              "ver": "0.19.3",
-              "models": [
-                {
-                  "name": "film_net_fp16.safetensors",
-                  "url": "https://huggingface.co/Comfy-Org/frame_interpolation/resolve/main/frame_interpolation/film_net_fp16.safetensors",
-                  "directory": "frame_interpolation"
-                }
-              ]
-            },
-            "widgets_values": [
-              "film_net_fp16.safetensors"
-            ]
-          },
-          {
-            "id": 2,
-            "type": "FrameInterpolate",
-            "pos": [
-              -2040,
-              -3370
-            ],
-            "size": [
-              270,
-              110
-            ],
-            "flags": {},
-            "order": 1,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "interp_model",
-                "name": "interp_model",
-                "type": "INTERP_MODEL",
-                "link": 1
-              },
-              {
-                "localized_name": "images",
-                "name": "images",
-                "type": "IMAGE",
-                "link": 3
-              },
-              {
-                "localized_name": "multiplier",
-                "name": "multiplier",
-                "type": "INT",
-                "widget": {
-                  "name": "multiplier"
-                },
-                "link": 8
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "IMAGE",
-                "name": "IMAGE",
-                "type": "IMAGE",
-                "links": [
-                  4,
-                  28
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "FrameInterpolate",
-              "enableTabs": false,
-              "tabWidth": 65,
-              "tabXOffset": 10,
-              "hasSecondTab": false,
-              "secondTabText": "Send Back",
-              "secondTabOffset": 80,
-              "secondTabWidth": 65,
-              "cnr_id": "comfy-core",
-              "ver": "0.19.3"
-            },
-            "widgets_values": [
-              2
-            ]
-          },
-          {
-            "id": 5,
-            "type": "CreateVideo",
-            "pos": [
-              -1600,
-              -3370
-            ],
-            "size": [
-              270,
-              110
-            ],
-            "flags": {},
-            "order": 3,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "images",
-                "name": "images",
-                "type": "IMAGE",
-                "link": 4
-              },
-              {
-                "localized_name": "audio",
-                "name": "audio",
-                "shape": 7,
-                "type": "AUDIO",
-                "link": 5
-              },
-              {
-                "localized_name": "fps",
-                "name": "fps",
-                "type": "FLOAT",
-                "widget": {
-                  "name": "fps"
-                },
-                "link": 12
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "VIDEO",
-                "name": "VIDEO",
-                "type": "VIDEO",
-                "links": [
-                  26
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "CreateVideo",
-              "enableTabs": false,
-              "tabWidth": 65,
-              "tabXOffset": 10,
-              "hasSecondTab": false,
-              "secondTabText": "Send Back",
-              "secondTabOffset": 80,
-              "secondTabWidth": 65,
-              "cnr_id": "comfy-core",
-              "ver": "0.19.3"
-            },
-            "widgets_values": [
-              30
-            ]
-          },
-          {
-            "id": 9,
-            "type": "PrimitiveInt",
-            "pos": [
-              -2500,
-              -2970
-            ],
-            "size": [
-              270,
-              90
-            ],
-            "flags": {},
-            "order": 4,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "value",
-                "name": "value",
-                "type": "INT",
-                "widget": {
-                  "name": "value"
-                },
-                "link": 22
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "INT",
-                "name": "INT",
-                "type": "INT",
-                "links": [
-                  8,
-                  19
-                ]
-              }
-            ],
-            "title": "Int (Multiplier)",
-            "properties": {
-              "Node name for S&R": "PrimitiveInt",
-              "enableTabs": false,
-              "tabWidth": 65,
-              "tabXOffset": 10,
-              "hasSecondTab": false,
-              "secondTabText": "Send Back",
-              "secondTabOffset": 80,
-              "secondTabWidth": 65,
-              "cnr_id": "comfy-core",
-              "ver": "0.19.3"
-            },
-            "widgets_values": [
-              2,
-              "fixed"
-            ]
-          },
-          {
-            "id": 10,
-            "type": "ComfySwitchNode",
-            "pos": [
-              -1610,
-              -3120
-            ],
-            "size": [
-              270,
-              130
-            ],
-            "flags": {},
-            "order": 5,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "on_false",
-                "name": "on_false",
-                "type": "*",
-                "link": 11
-              },
-              {
-                "localized_name": "on_true",
-                "name": "on_true",
-                "type": "*",
-                "link": 13
-              },
-              {
-                "localized_name": "switch",
-                "name": "switch",
-                "type": "BOOLEAN",
-                "widget": {
-                  "name": "switch"
-                },
-                "link": 15
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "output",
-                "name": "output",
-                "type": "*",
-                "links": [
-                  12
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "ComfySwitchNode",
-              "enableTabs": false,
-              "tabWidth": 65,
-              "tabXOffset": 10,
-              "hasSecondTab": false,
-              "secondTabText": "Send Back",
-              "secondTabOffset": 80,
-              "secondTabWidth": 65,
-              "cnr_id": "comfy-core",
-              "ver": "0.19.3"
-            },
-            "widgets_values": [
-              true
-            ]
-          },
-          {
-            "id": 13,
-            "type": "PrimitiveBoolean",
-            "pos": [
-              -2500,
-              -2770
-            ],
-            "size": [
-              310,
-              90
-            ],
-            "flags": {},
-            "order": 7,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "value",
-                "name": "value",
-                "type": "BOOLEAN",
-                "widget": {
-                  "name": "value"
-                },
-                "link": 23
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "BOOLEAN",
-                "name": "BOOLEAN",
-                "type": "BOOLEAN",
-                "links": [
-                  15
-                ]
-              }
-            ],
-            "title": "Boolean (Apply multiplier to FPS?)",
-            "properties": {
-              "Node name for S&R": "PrimitiveBoolean",
-              "enableTabs": false,
-              "tabWidth": 65,
-              "tabXOffset": 10,
-              "hasSecondTab": false,
-              "secondTabText": "Send Back",
-              "secondTabOffset": 80,
-              "secondTabWidth": 65,
-              "cnr_id": "comfy-core",
-              "ver": "0.19.3"
-            },
-            "widgets_values": [
-              true
-            ]
-          },
-          {
-            "id": 3,
-            "type": "GetVideoComponents",
-            "pos": [
-              -2500,
-              -3170
-            ],
-            "size": [
-              230,
-              100
-            ],
-            "flags": {},
-            "order": 2,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "video",
-                "name": "video",
-                "type": "VIDEO",
-                "link": 2
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "images",
-                "name": "images",
-                "type": "IMAGE",
-                "links": [
-                  3
-                ]
-              },
-              {
-                "localized_name": "audio",
-                "name": "audio",
-                "type": "AUDIO",
-                "links": [
-                  5
-                ]
-              },
-              {
-                "localized_name": "fps",
-                "name": "fps",
-                "type": "FLOAT",
-                "links": [
-                  11,
-                  18
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "GetVideoComponents",
-              "enableTabs": false,
-              "tabWidth": 65,
-              "tabXOffset": 10,
-              "hasSecondTab": false,
-              "secondTabText": "Send Back",
-              "secondTabOffset": 80,
-              "secondTabWidth": 65,
-              "cnr_id": "comfy-core",
-              "ver": "0.19.3"
-            }
-          },
-          {
-            "id": 11,
-            "type": "ComfyMathExpression",
-            "pos": [
-              -2090,
-              -3070
-            ],
-            "size": [
-              400,
-              210
-            ],
-            "flags": {
-              "collapsed": false
-            },
-            "order": 6,
-            "mode": 0,
-            "inputs": [
-              {
-                "label": "a",
-                "localized_name": "values.a",
-                "name": "values.a",
-                "type": "FLOAT,INT",
-                "link": 18
-              },
-              {
-                "label": "b",
-                "localized_name": "values.b",
-                "name": "values.b",
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-                "type": "FLOAT,INT",
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-                "type": "STRING",
-                "widget": {
-                  "name": "expression"
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-                "link": null
-              }
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-            "outputs": [
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-                "name": "FLOAT",
-                "type": "FLOAT",
-                "links": [
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-                ]
-              },
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-                "localized_name": "INT",
-                "name": "INT",
-                "type": "INT",
-                "links": null
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "ComfyMathExpression",
-              "enableTabs": false,
-              "tabWidth": 65,
-              "tabXOffset": 10,
-              "hasSecondTab": false,
-              "secondTabText": "Send Back",
-              "secondTabOffset": 80,
-              "secondTabWidth": 65,
-              "cnr_id": "comfy-core",
-              "ver": "0.19.3"
-            },
-            "widgets_values": [
-              "min(abs(b), 16) * a"
-            ]
-          }
-        ],
-        "groups": [],
-        "links": [
-          {
-            "id": 1,
-            "origin_id": 1,
-            "origin_slot": 0,
-            "target_id": 2,
-            "target_slot": 0,
-            "type": "INTERP_MODEL"
-          },
-          {
-            "id": 3,
-            "origin_id": 3,
-            "origin_slot": 0,
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-            "target_slot": 1,
-            "type": "IMAGE"
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-            "origin_slot": 1,
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-            "target_slot": 1,
-            "type": "AUDIO"
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-            "type": "FLOAT"
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-            "type": "FLOAT"
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-            "id": 13,
-            "origin_id": 11,
-            "origin_slot": 0,
-            "target_id": 10,
-            "target_slot": 1,
-            "type": "FLOAT"
-          },
-          {
-            "id": 15,
-            "origin_id": 13,
-            "origin_slot": 0,
-            "target_id": 10,
-            "target_slot": 2,
-            "type": "BOOLEAN"
-          },
-          {
-            "id": 18,
-            "origin_id": 3,
-            "origin_slot": 2,
-            "target_id": 11,
-            "target_slot": 0,
-            "type": "FLOAT"
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-            "id": 19,
-            "origin_id": 9,
-            "origin_slot": 0,
-            "target_id": 11,
-            "target_slot": 1,
-            "type": "INT"
-          },
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-            "id": 2,
-            "origin_id": -10,
-            "origin_slot": 0,
-            "target_id": 3,
-            "target_slot": 0,
-            "type": "VIDEO"
-          },
-          {
-            "id": 22,
-            "origin_id": -10,
-            "origin_slot": 1,
-            "target_id": 9,
-            "target_slot": 0,
-            "type": "INT"
-          },
-          {
-            "id": 23,
-            "origin_id": -10,
-            "origin_slot": 2,
-            "target_id": 13,
-            "target_slot": 0,
-            "type": "BOOLEAN"
-          },
-          {
-            "id": 24,
-            "origin_id": -10,
-            "origin_slot": 3,
-            "target_id": 1,
-            "target_slot": 0,
-            "type": "COMBO"
-          },
-          {
-            "id": 26,
-            "origin_id": 5,
-            "origin_slot": 0,
-            "target_id": -20,
-            "target_slot": 0,
-            "type": "VIDEO"
-          },
-          {
-            "id": 28,
-            "origin_id": 2,
-            "origin_slot": 0,
-            "target_id": -20,
-            "target_slot": 1,
-            "type": "IMAGE"
-          }
-        ],
-        "extra": {},
-        "category": "Video Tools",
-        "description": "Increases video frame rate by synthesizing intermediate frames with a frame interpolation model."
-      }
-    ]
-  },
-  "extra": {}
-}

blueprints/Get Any Video Frame.json

@@ -1,485 +0,0 @@
-{
-  "revision": 0,
-  "last_node_id": 98,
-  "last_link_id": 0,
-  "nodes": [
-    {
-      "id": 98,
-      "type": "dca6e78d-fb06-421e-97f7-6ce17a665260",
-      "pos": [
-        -410,
-        -2230
-      ],
-      "size": [
-        270,
-        104
-      ],
-      "flags": {},
-      "order": 7,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "video",
-          "type": "VIDEO",
-          "link": null
-        },
-        {
-          "label": "frame_index",
-          "name": "value",
-          "type": "INT",
-          "widget": {
-            "name": "value"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": []
-        }
-      ],
-      "title": "Get Any Video Frame",
-      "properties": {
-        "proxyWidgets": [
-          [
-            "100",
-            "value"
-          ]
-        ]
-      },
-      "widgets_values": []
-    }
-  ],
-  "links": [],
-  "version": 0.4,
-  "definitions": {
-    "subgraphs": [
-      {
-        "id": "dca6e78d-fb06-421e-97f7-6ce17a665260",
-        "version": 1,
-        "state": {
-          "lastGroupId": 1,
-          "lastNodeId": 136,
-          "lastLinkId": 302,
-          "lastRerouteId": 0
-        },
-        "revision": 0,
-        "config": {},
-        "name": "Get Any Video Frame",
-        "inputNode": {
-          "id": -10,
-          "bounding": [
-            380,
-            -57,
-            120,
-            80
-          ]
-        },
-        "outputNode": {
-          "id": -20,
-          "bounding": [
-            1460,
-            -57,
-            120,
-            60
-          ]
-        },
-        "inputs": [
-          {
-            "id": "2ceec378-8dcf-4340-8570-155967f59a93",
-            "name": "video",
-            "type": "VIDEO",
-            "linkIds": [
-              4
-            ],
-            "pos": [
-              480,
-              -37
-            ]
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-          {
-            "id": "819955f6-c686-4896-8032-ff2d0059109a",
-            "name": "value",
-            "type": "INT",
-            "linkIds": [
-              283
-            ],
-            "label": "frame_index",
-            "pos": [
-              480,
-              -17
-            ]
-          }
-        ],
-        "outputs": [
-          {
-            "id": "1ab0684d-6a44-45b6-8aa4-a0b971a1d41e",
-            "name": "IMAGE",
-            "type": "IMAGE",
-            "linkIds": [
-              5
-            ],
-            "pos": [
-              1480,
-              -37
-            ]
-          }
-        ],
-        "widgets": [],
-        "nodes": [
-          {
-            "id": 1,
-            "type": "GetVideoComponents",
-            "pos": [
-              560,
-              -150
-            ],
-            "size": [
-              230,
-              120
-            ],
-            "flags": {},
-            "order": 0,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "video",
-                "name": "video",
-                "type": "VIDEO",
-                "link": 4
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "images",
-                "name": "images",
-                "type": "IMAGE",
-                "links": [
-                  1,
-                  2
-                ]
-              },
-              {
-                "localized_name": "audio",
-                "name": "audio",
-                "type": "AUDIO",
-                "links": null
-              },
-              {
-                "localized_name": "fps",
-                "name": "fps",
-                "type": "FLOAT",
-                "links": null
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "GetVideoComponents"
-            }
-          },
-          {
-            "id": 2,
-            "type": "GetImageSize",
-            "pos": [
-              560,
-              50
-            ],
-            "size": [
-              230,
-              120
-            ],
-            "flags": {},
-            "order": 1,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "image",
-                "name": "image",
-                "type": "IMAGE",
-                "link": 1
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "width",
-                "name": "width",
-                "type": "INT",
-                "links": null
-              },
-              {
-                "localized_name": "height",
-                "name": "height",
-                "type": "INT",
-                "links": null
-              },
-              {
-                "localized_name": "batch_size",
-                "name": "batch_size",
-                "type": "INT",
-                "links": [
-                  285
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "GetImageSize"
-            }
-          },
-          {
-            "id": 3,
-            "type": "ImageFromBatch",
-            "pos": [
-              1130,
-              -150
-            ],
-            "size": [
-              270,
-              140
-            ],
-            "flags": {},
-            "order": 2,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "image",
-                "name": "image",
-                "type": "IMAGE",
-                "link": 2
-              },
-              {
-                "localized_name": "batch_index",
-                "name": "batch_index",
-                "type": "INT",
-                "widget": {
-                  "name": "batch_index"
-                },
-                "link": 286
-              },
-              {
-                "localized_name": "length",
-                "name": "length",
-                "type": "INT",
-                "widget": {
-                  "name": "length"
-                },
-                "link": null
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "IMAGE",
-                "name": "IMAGE",
-                "type": "IMAGE",
-                "links": [
-                  5
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "ImageFromBatch"
-            },
-            "widgets_values": [
-              0,
-              1
-            ]
-          },
-          {
-            "id": 99,
-            "type": "ComfyMathExpression",
-            "pos": [
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-              100
-            ],
-            "size": [
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-              200
-            ],
-            "flags": {},
-            "order": 3,
-            "mode": 0,
-            "inputs": [
-              {
-                "label": "a",
-                "localized_name": "values.a",
-                "name": "values.a",
-                "type": "FLOAT,INT",
-                "link": 284
-              },
-              {
-                "label": "b",
-                "localized_name": "values.b",
-                "name": "values.b",
-                "shape": 7,
-                "type": "FLOAT,INT",
-                "link": 285
-              },
-              {
-                "label": "c",
-                "localized_name": "values.c",
-                "name": "values.c",
-                "shape": 7,
-                "type": "FLOAT,INT",
-                "link": null
-              },
-              {
-                "localized_name": "expression",
-                "name": "expression",
-                "type": "STRING",
-                "widget": {
-                  "name": "expression"
-                },
-                "link": null
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "FLOAT",
-                "name": "FLOAT",
-                "type": "FLOAT",
-                "links": null
-              },
-              {
-                "localized_name": "INT",
-                "name": "INT",
-                "type": "INT",
-                "links": [
-                  286
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "ComfyMathExpression"
-            },
-            "widgets_values": [
-              "min(max(int(a if a >= 0 else b + a), 0), b - 1)"
-            ]
-          },
-          {
-            "id": 100,
-            "type": "PrimitiveInt",
-            "pos": [
-              560,
-              250
-            ],
-            "size": [
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-              110
-            ],
-            "flags": {},
-            "order": 4,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "value",
-                "name": "value",
-                "type": "INT",
-                "widget": {
-                  "name": "value"
-                },
-                "link": 283
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "INT",
-                "name": "INT",
-                "type": "INT",
-                "links": [
-                  284
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "PrimitiveInt"
-            },
-            "widgets_values": [
-              0,
-              "fixed"
-            ]
-          }
-        ],
-        "groups": [],
-        "links": [
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-            "origin_id": 1,
-            "origin_slot": 0,
-            "target_id": 2,
-            "target_slot": 0,
-            "type": "IMAGE"
-          },
-          {
-            "id": 2,
-            "origin_id": 1,
-            "origin_slot": 0,
-            "target_id": 3,
-            "target_slot": 0,
-            "type": "IMAGE"
-          },
-          {
-            "id": 4,
-            "origin_id": -10,
-            "origin_slot": 0,
-            "target_id": 1,
-            "target_slot": 0,
-            "type": "VIDEO"
-          },
-          {
-            "id": 5,
-            "origin_id": 3,
-            "origin_slot": 0,
-            "target_id": -20,
-            "target_slot": 0,
-            "type": "IMAGE"
-          },
-          {
-            "id": 283,
-            "origin_id": -10,
-            "origin_slot": 1,
-            "target_id": 100,
-            "target_slot": 0,
-            "type": "INT"
-          },
-          {
-            "id": 284,
-            "origin_id": 100,
-            "origin_slot": 0,
-            "target_id": 99,
-            "target_slot": 0,
-            "type": "INT"
-          },
-          {
-            "id": 285,
-            "origin_id": 2,
-            "origin_slot": 2,
-            "target_id": 99,
-            "target_slot": 1,
-            "type": "INT"
-          },
-          {
-            "id": 286,
-            "origin_id": 99,
-            "origin_slot": 1,
-            "target_id": 3,
-            "target_slot": 1,
-            "type": "INT"
-          }
-        ],
-        "extra": {},
-        "category": "Video Tools",
-        "description": "Extracts one image frame from a video at a chosen index, with optional trim and FPS control."
-      }
-    ]
-  },
-  "extra": {
-    "ds": {
-      "scale": 1.197015527856339,
-      "offset": [
-        -168.76833554248222,
-        540.6638955283997
-      ]
-    },
-    "frontendVersion": "1.42.8"
-  }
-}

blueprints/Glow.json

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

blueprints/Hue and Saturation.json

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

blueprints/Image Blur.json

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

blueprints/Image Captioning (gemini).json

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

blueprints/Image Channels.json

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

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

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

blueprints/Image Edit (Qwen 2511).json

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

blueprints/Image Inpainting (Qwen-image).json

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

blueprints/Image Levels.json

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

blueprints/Image Outpainting (Qwen-Image).json

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

blueprints/Image Segmentation (SAM3).json

@@ -1,714 +0,0 @@
-{
-  "revision": 0,
-  "last_node_id": 99,
-  "last_link_id": 0,
-  "nodes": [
-    {
-      "id": 99,
-      "type": "6e7ab3ea-96aa-470f-9b94-3d9d0e01f481",
-      "pos": [
-        -1630,
-        -3270
-      ],
-      "size": [
-        290,
-        370
-      ],
-      "flags": {},
-      "order": 3,
-      "mode": 0,
-      "inputs": [
-        {
-          "label": "image",
-          "localized_name": "image",
-          "name": "image",
-          "type": "IMAGE",
-          "link": null
-        },
-        {
-          "label": "object",
-          "name": "text",
-          "type": "STRING",
-          "widget": {
-            "name": "text"
-          },
-          "link": null
-        },
-        {
-          "name": "bboxes",
-          "type": "BOUNDING_BOX",
-          "link": null
-        },
-        {
-          "name": "positive_coords",
-          "type": "STRING",
-          "link": null
-        },
-        {
-          "name": "negative_coords",
-          "type": "STRING",
-          "link": null
-        },
-        {
-          "name": "threshold",
-          "type": "FLOAT",
-          "widget": {
-            "name": "threshold"
-          },
-          "link": null
-        },
-        {
-          "name": "refine_iterations",
-          "type": "INT",
-          "widget": {
-            "name": "refine_iterations"
-          },
-          "link": null
-        },
-        {
-          "name": "individual_masks",
-          "type": "BOOLEAN",
-          "widget": {
-            "name": "individual_masks"
-          },
-          "link": null
-        },
-        {
-          "name": "ckpt_name",
-          "type": "COMBO",
-          "widget": {
-            "name": "ckpt_name"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "localized_name": "masks",
-          "name": "masks",
-          "type": "MASK",
-          "links": []
-        },
-        {
-          "localized_name": "bboxes",
-          "name": "bboxes",
-          "type": "BOUNDING_BOX",
-          "links": []
-        }
-      ],
-      "properties": {
-        "proxyWidgets": [
-          [
-            "78",
-            "text"
-          ],
-          [
-            "75",
-            "threshold"
-          ],
-          [
-            "75",
-            "refine_iterations"
-          ],
-          [
-            "75",
-            "individual_masks"
-          ],
-          [
-            "77",
-            "ckpt_name"
-          ]
-        ],
-        "ue_properties": {
-          "widget_ue_connectable": {
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blueprints/Image Upscale(Z-image-Turbo).json

@@ -141,7 +141,7 @@
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blueprints/Image to Depth Map (Lotus).json

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             "type": "CLIPLoader",
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             "size": [
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+              106
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@@ -340,7 +248,7 @@
                 "widget": {
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-                "link": 379
+                "link": null
               },
               {
                 "localized_name": "type",
@@ -375,14 +283,9 @@
               }
             ],
             "properties": {
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               "cnr_id": "comfy-core",
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@@ -409,14 +312,14 @@
             "type": "VAELoader",
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             "inputs": [
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@@ -426,7 +329,7 @@
                 "widget": {
                   "name": "vae_name"
                 },
-                "link": 380
+                "link": null
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             "outputs": [
@@ -442,14 +345,9 @@
               }
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             "properties": {
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               "ver": "0.5.1",
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@@ -477,11 +375,11 @@
               420
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             "mode": 0,
             "inputs": [
               {
@@ -513,14 +411,9 @@
             ],
             "title": "CLIP Text Encode (Negative Prompt)",
             "properties": {
+              "Node name for S&R": "CLIPTextEncode",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
-              "ue_properties": {
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@@ -539,12 +432,12 @@
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             "type": "ReferenceLatent",
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+              46
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@@ -577,14 +470,9 @@
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               "cnr_id": "comfy-core",
               "ver": "0.5.1",
-              "ue_properties": {
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               "tabXOffset": 10,
@@ -592,18 +480,19 @@
               "secondTabText": "Send Back",
               "secondTabOffset": 80,
               "secondTabWidth": 65
-            }
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           {
             "id": 69,
             "type": "ReferenceLatent",
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+              46
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               "collapsed": true
@@ -636,14 +525,9 @@
               }
             ],
             "properties": {
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               "cnr_id": "comfy-core",
               "ver": "0.5.1",
-              "ue_properties": {
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               "tabXOffset": 10,
@@ -651,7 +535,8 @@
               "secondTabText": "Send Back",
               "secondTabOffset": 80,
               "secondTabWidth": 65
-            }
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+            "widgets_values": []
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           {
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@@ -662,10 +547,10 @@
             ],
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-              110
+              58
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             "flags": {},
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+            "order": 4,
             "mode": 0,
             "inputs": [
               {
@@ -695,14 +580,9 @@
               }
             ],
             "properties": {
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               "cnr_id": "comfy-core",
               "ver": "0.5.1",
-              "ue_properties": {
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               "tabXOffset": 10,
@@ -720,11 +600,11 @@
             "type": "LatentCutToBatch",
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               270,
-              140
+              82
             ],
             "flags": {},
             "order": 11,
@@ -766,14 +646,9 @@
               }
             ],
             "properties": {
+              "Node name for S&R": "LatentCutToBatch",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
-              "ue_properties": {
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@@ -791,12 +666,12 @@
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             "type": "VAEEncode",
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-              100
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+              46
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             "flags": {
               "collapsed": false
@@ -829,14 +704,9 @@
               }
             ],
             "properties": {
+              "Node name for S&R": "VAEEncode",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
-              "ue_properties": {
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@@ -844,23 +714,24 @@
               "secondTabText": "Send Back",
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               "secondTabWidth": 65
-            }
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           {
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             "type": "VAEDecode",
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             "mode": 0,
             "inputs": [
               {
@@ -888,14 +759,9 @@
               }
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             "properties": {
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               "cnr_id": "comfy-core",
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@@ -903,7 +769,8 @@
               "secondTabText": "Send Back",
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               "secondTabWidth": 65
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+            "widgets_values": []
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           {
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@@ -913,11 +780,11 @@
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             "mode": 0,
             "inputs": [
               {
@@ -949,14 +816,9 @@
             ],
             "title": "CLIP Text Encode (Positive Prompt)",
             "properties": {
+              "Node name for S&R": "CLIPTextEncode",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
-              "ue_properties": {
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@@ -976,14 +838,14 @@
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               400
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@@ -1017,7 +879,7 @@
                 "widget": {
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+                "link": null
               },
               {
                 "localized_name": "steps",
@@ -1077,14 +939,9 @@
               }
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               "ver": "0.5.1",
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@@ -1107,12 +964,12 @@
             "id": 78,
             "type": "GetImageSize",
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+              136
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             "order": 12,
@@ -1150,14 +1007,9 @@
               }
             ],
             "properties": {
+              "Node name for S&R": "GetImageSize",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
-              "ue_properties": {
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               "tabXOffset": 10,
@@ -1165,23 +1017,23 @@
               "secondTabText": "Send Back",
               "secondTabOffset": 80,
               "secondTabWidth": 65
-            }
+            },
+            "widgets_values": []
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           {
             "id": 83,
             "type": "EmptyQwenImageLayeredLatentImage",
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-            "showAdvanced": true,
             "inputs": [
               {
                 "localized_name": "width",
@@ -1231,14 +1083,9 @@
               }
             ],
             "properties": {
+              "Node name for S&R": "EmptyQwenImageLayeredLatentImage",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
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               "tabXOffset": 10,
@@ -1262,11 +1109,11 @@
               180
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@@ -1276,7 +1123,7 @@
                 "widget": {
                   "name": "unet_name"
                 },
-                "link": 378
+                "link": null
               },
               {
                 "localized_name": "weight_dtype",
@@ -1300,14 +1147,9 @@
               }
             ],
             "properties": {
+              "Node name for S&R": "UNETLoader",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
-              "ue_properties": {
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@@ -1349,8 +1191,8 @@
             "bounding": [
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+              421.6
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@@ -1549,48 +1391,16 @@
             "target_id": 83,
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         "extra": {
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         },
-        "category": "Image generation and editing/Image to layers",
-        "description": "Decomposes an image into variable-resolution RGBA layers for independent editing using Qwen-Image-Layered."
+        "category": "Image generation and editing/Image to layers"
       }
     ]
   },
+  "config": {},
   "extra": {
     "ds": {
       "scale": 1.14,
@@ -1599,6 +1409,7 @@
         6.855893974423647
       ]
     },
-    "ue_links": []
-  }
-}
+    "workflowRendererVersion": "LG"
+  },
+  "version": 0.4
+}

blueprints/Image to Model (Hunyuan3d 2.1).json

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

blueprints/Image to Video (Wan 2.2).json

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

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

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

blueprints/Prompt Enhance.json

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

blueprints/Remove Background (BiRefNet).json

@@ -1,397 +0,0 @@
-{
-  "revision": 0,
-  "last_node_id": 19,
-  "last_link_id": 0,
-  "nodes": [
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-          "type": "COMBO",
-          "widget": {
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-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": []
-        },
-        {
-          "name": "mask",
-          "type": "MASK",
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-      "properties": {
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-            "bg_removal_name"
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-      "widgets_values": [],
-      "title": "Remove Background (BiRefNet)"
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-          "lastRerouteId": 0
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-        "revision": 0,
-        "config": {},
-        "name": "Remove Background (BiRefNet)",
-        "description": "Removes or replaces image backgrounds using BiRefNet segmentation and alpha compositing.",
-        "inputNode": {
-          "id": -10,
-          "bounding": [
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-        "outputs": [
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-            "name": "IMAGE",
-            "type": "IMAGE",
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-                "name": "bg_removal_model",
-                "type": "BACKGROUND_REMOVAL",
-                "link": 3
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-            "outputs": [
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-                "localized_name": "mask",
-                "name": "mask",
-                "type": "MASK",
-                "links": [
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-            "type": "LoadBackgroundRemovalModel",
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-        "extra": {},
-        "category": "Image generation and editing/Background Removal"
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blueprints/Sharpen.json

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

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

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

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

@@ -1,21 +1,22 @@
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+  "id": "1c3eaa76-5cfa-4dc7-8571-97a570324e01",
   "revision": 0,
-  "last_node_id": 57,
-  "last_link_id": 0,
+  "last_node_id": 34,
+  "last_link_id": 40,
   "nodes": [
     {
-      "id": 57,
-      "type": "f2fdebf6-dfaf-43b6-9eb2-7f70613cfdc1",
+      "id": 5,
+      "type": "dfe9eb32-97c0-43a5-90d5-4fd37768d91b",
       "pos": [
-        130,
-        200
+        -2.5766491043910378e-05,
+        1229.999928629805
       ],
       "size": [
         400,
         470
       ],
       "flags": {},
-      "order": 1,
+      "order": 0,
       "mode": 0,
       "inputs": [
         {
@@ -43,22 +44,6 @@
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           "link": null
         },
-        {
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-          "type": "INT",
-          "widget": {
-            "name": "seed"
-          },
-          "link": null
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-        {
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-          "type": "INT",
-          "widget": {
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-          },
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           "name": "unet_name",
           "type": "COMBO",
@@ -95,15 +80,15 @@
       "properties": {
         "proxyWidgets": [
           [
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+            "-1",
             "text"
           ],
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+            "-1",
             "width"
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-            "13",
+            "-1",
             "height"
           ],
           [
@@ -112,23 +97,19 @@
           ],
           [
             "3",
-            "steps"
+            "control_after_generate"
           ],
           [
-            "28",
+            "-1",
             "unet_name"
           ],
           [
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+            "-1",
             "clip_name"
           ],
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+            "-1",
             "vae_name"
-          ],
-          [
-            "3",
-            "control_after_generate"
           ]
         ],
         "cnr_id": "comfy-core",
@@ -141,40 +122,48 @@
         "secondTabOffset": 80,
         "secondTabWidth": 65
       },
-      "widgets_values": [],
-      "title": "Text to Image (Z-Image-Turbo)"
+      "widgets_values": [
+        "",
+        1024,
+        1024,
+        null,
+        null,
+        "z_image_turbo_bf16.safetensors",
+        "qwen_3_4b.safetensors",
+        "ae.safetensors"
+      ]
     }
   ],
   "links": [],
-  "version": 0.4,
+  "groups": [],
   "definitions": {
     "subgraphs": [
       {
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+        "id": "dfe9eb32-97c0-43a5-90d5-4fd37768d91b",
         "version": 1,
         "state": {
           "lastGroupId": 4,
-          "lastNodeId": 61,
-          "lastLinkId": 75,
+          "lastNodeId": 34,
+          "lastLinkId": 40,
           "lastRerouteId": 0
         },
         "revision": 0,
         "config": {},
-        "name": "Text to Image (Z-Image-Turbo)",
+        "name": "local-Text to Image (Z-Image-Turbo)",
         "inputNode": {
           "id": -10,
           "bounding": [
-            -560,
-            480,
+            -80,
+            425,
             120,
-            200
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         },
         "outputNode": {
           "id": -20,
           "bounding": [
-            1670,
-            320,
+            1490,
+            415,
             120,
             60
           ]
@@ -189,8 +178,8 @@
             ],
             "label": "prompt",
             "pos": [
-              -460,
-              500
+              20,
+              445
             ]
           },
           {
@@ -201,8 +190,8 @@
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             ],
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@@ -213,68 +202,44 @@
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             ],
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-              580
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-          {
-            "id": "a20a1b30-785f-4a04-bb6d-3d61adab9764",
+            "id": "23087d15-8412-4fbd-b71e-9b6d7ef76de1",
             "name": "unet_name",
             "type": "COMBO",
             "linkIds": [
-              73
+              38
             ],
             "pos": [
-              -460,
-              600
+              20,
+              505
             ]
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           {
-            "id": "4af8fc2b-4655-4086-8240-45f8cb38c6f6",
+            "id": "0677f5c3-2a3f-43d4-98ac-a4c56d5efdc0",
             "name": "clip_name",
             "type": "COMBO",
             "linkIds": [
-              74
+              39
             ],
             "pos": [
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-              620
+              20,
+              525
             ]
           },
           {
-            "id": "4d518693-2807-439c-9cb6-cffd23ccba2c",
+            "id": "c85c0445-2641-48b1-bbca-95057edf2fcf",
             "name": "vae_name",
             "type": "COMBO",
             "linkIds": [
-              75
+              40
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             "pos": [
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+              20,
+              545
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@@ -288,8 +253,8 @@
             ],
             "localized_name": "IMAGE",
             "pos": [
-              1690,
-              340
+              1510,
+              435
             ]
           }
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@@ -299,15 +264,15 @@
             "id": 30,
             "type": "CLIPLoader",
             "pos": [
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+              329.99999029608756
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             "size": [
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+              269.9869791666667,
+              106
             ],
             "flags": {},
-            "order": 7,
+            "order": 0,
             "mode": 0,
             "inputs": [
               {
@@ -317,7 +282,7 @@
                 "widget": {
                   "name": "clip_name"
                 },
-                "link": 74
+                "link": 39
               },
               {
                 "localized_name": "type",
@@ -350,9 +315,9 @@
               }
             ],
             "properties": {
-              "Node name for S&R": "CLIPLoader",
               "cnr_id": "comfy-core",
               "ver": "0.3.73",
+              "Node name for S&R": "CLIPLoader",
               "models": [
                 {
                   "name": "qwen_3_4b.safetensors",
@@ -378,15 +343,15 @@
             "id": 29,
             "type": "VAELoader",
             "pos": [
-              30,
-              650
+              109.99997264844609,
+              479.9999847172637
             ],
             "size": [
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-              110
+              269.9869791666667,
+              58
             ],
             "flags": {},
-            "order": 6,
+            "order": 1,
             "mode": 0,
             "inputs": [
               {
@@ -396,7 +361,7 @@
                 "widget": {
                   "name": "vae_name"
                 },
-                "link": 75
+                "link": 40
               }
             ],
             "outputs": [
@@ -410,9 +375,9 @@
               }
             ],
             "properties": {
-              "Node name for S&R": "VAELoader",
               "cnr_id": "comfy-core",
               "ver": "0.3.73",
+              "Node name for S&R": "VAELoader",
               "models": [
                 {
                   "name": "ae.safetensors",
@@ -436,12 +401,12 @@
             "id": 33,
             "type": "ConditioningZeroOut",
             "pos": [
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+              639.9999103333332,
+              620.0000271257795
             ],
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+              26
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             "order": 8,
@@ -465,9 +430,9 @@
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             ],
             "properties": {
-              "Node name for S&R": "ConditioningZeroOut",
               "cnr_id": "comfy-core",
               "ver": "0.3.73",
+              "Node name for S&R": "ConditioningZeroOut",
               "enableTabs": false,
               "tabWidth": 65,
               "tabXOffset": 10,
@@ -475,21 +440,22 @@
               "secondTabText": "Send Back",
               "secondTabOffset": 80,
               "secondTabWidth": 65
-            }
+            },
+            "widgets_values": []
           },
           {
             "id": 8,
             "type": "VAEDecode",
             "pos": [
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+              46
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+            "order": 5,
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             "inputs": [
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@@ -517,9 +483,9 @@
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             ],
             "properties": {
-              "Node name for S&R": "VAEDecode",
               "cnr_id": "comfy-core",
               "ver": "0.3.64",
+              "Node name for S&R": "VAEDecode",
               "enableTabs": false,
               "tabWidth": 65,
               "tabXOffset": 10,
@@ -527,21 +493,22 @@
               "secondTabText": "Send Back",
               "secondTabOffset": 80,
               "secondTabWidth": 65
-            }
+            },
+            "widgets_values": []
           },
           {
             "id": 28,
             "type": "UNETLoader",
             "pos": [
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+              109.99997264844609,
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             "size": [
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+              82
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-            "order": 5,
+            "order": 2,
             "mode": 0,
             "inputs": [
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@@ -551,7 +518,7 @@
                 "widget": {
                   "name": "unet_name"
                 },
-                "link": 73
+                "link": 38
               },
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                 "localized_name": "weight_dtype",
@@ -574,9 +541,9 @@
               }
             ],
             "properties": {
-              "Node name for S&R": "UNETLoader",
               "cnr_id": "comfy-core",
               "ver": "0.3.73",
+              "Node name for S&R": "UNETLoader",
               "models": [
                 {
                   "name": "z_image_turbo_bf16.safetensors",
@@ -601,15 +568,15 @@
             "id": 27,
             "type": "CLIPTextEncode",
             "pos": [
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+              429.99997828947767,
+              200.0000502647102
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+              319.9869791666667
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+            "order": 7,
             "mode": 0,
             "inputs": [
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@@ -640,9 +607,9 @@
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             ],
             "properties": {
-              "Node name for S&R": "CLIPTextEncode",
               "cnr_id": "comfy-core",
               "ver": "0.3.73",
+              "Node name for S&R": "CLIPTextEncode",
               "enableTabs": false,
               "tabWidth": 65,
               "tabXOffset": 10,
@@ -659,15 +626,15 @@
             "id": 13,
             "type": "EmptySD3LatentImage",
             "pos": [
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@@ -710,9 +677,9 @@
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             ],
             "properties": {
-              "Node name for S&R": "EmptySD3LatentImage",
               "cnr_id": "comfy-core",
               "ver": "0.3.64",
+              "Node name for S&R": "EmptySD3LatentImage",
               "enableTabs": false,
               "tabWidth": 65,
               "tabXOffset": 10,
@@ -727,77 +694,19 @@
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-            "type": "ModelSamplingAuraFlow",
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-                "name": "shift",
-                "type": "FLOAT",
-                "widget": {
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-              }
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-            "outputs": [
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-                "name": "MODEL",
-                "type": "MODEL",
-                "slot_index": 0,
-                "links": [
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-                ]
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-              "enableTabs": false,
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-              "tabXOffset": 10,
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-              "secondTabText": "Send Back",
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-          },
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             "type": "KSampler",
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             "inputs": [
               {
@@ -831,7 +740,7 @@
                 "widget": {
                   "name": "seed"
                 },
-                "link": 71
+                "link": null
               },
               {
                 "localized_name": "steps",
@@ -840,7 +749,7 @@
                 "widget": {
                   "name": "steps"
                 },
-                "link": 72
+                "link": null
               },
               {
                 "localized_name": "cfg",
@@ -891,9 +800,9 @@
               }
             ],
             "properties": {
-              "Node name for S&R": "KSampler",
               "cnr_id": "comfy-core",
               "ver": "0.3.64",
+              "Node name for S&R": "KSampler",
               "enableTabs": false,
               "tabWidth": 65,
               "tabXOffset": 10,
@@ -905,23 +814,81 @@
             "widgets_values": [
               0,
               "randomize",
-              8,
+              4,
               1,
               "res_multistep",
               "simple",
               1
             ]
+          },
+          {
+            "id": 11,
+            "type": "ModelSamplingAuraFlow",
+            "pos": [
+              879.9999615530063,
+              160.00009184959066
+            ],
+            "size": [
+              309.9869791666667,
+              58
+            ],
+            "flags": {},
+            "order": 3,
+            "mode": 0,
+            "inputs": [
+              {
+                "localized_name": "model",
+                "name": "model",
+                "type": "MODEL",
+                "link": 26
+              },
+              {
+                "localized_name": "shift",
+                "name": "shift",
+                "type": "FLOAT",
+                "widget": {
+                  "name": "shift"
+                },
+                "link": null
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "MODEL",
+                "name": "MODEL",
+                "type": "MODEL",
+                "slot_index": 0,
+                "links": [
+                  13
+                ]
+              }
+            ],
+            "properties": {
+              "cnr_id": "comfy-core",
+              "ver": "0.3.64",
+              "Node name for S&R": "ModelSamplingAuraFlow",
+              "enableTabs": false,
+              "tabWidth": 65,
+              "tabXOffset": 10,
+              "hasSecondTab": false,
+              "secondTabText": "Send Back",
+              "secondTabOffset": 80,
+              "secondTabWidth": 65
+            },
+            "widgets_values": [
+              3
+            ]
           }
         ],
         "groups": [
           {
             "id": 2,
-            "title": "Step2 - Image size",
+            "title": "Image size",
             "bounding": [
-              10,
-              820,
-              320,
-              280
+              100,
+              560,
+              290,
+              200
             ],
             "color": "#3f789e",
             "font_size": 24,
@@ -929,12 +896,12 @@
           },
           {
             "id": 3,
-            "title": "Step3 - Prompt",
+            "title": "Prompt",
             "bounding": [
-              360,
+              410,
               130,
-              530,
-              970
+              450,
+              540
             ],
             "color": "#3f789e",
             "font_size": 24,
@@ -942,12 +909,12 @@
           },
           {
             "id": 4,
-            "title": "Step1 - Load models",
+            "title": "Models",
             "bounding": [
-              0,
+              100,
               130,
-              330,
-              660
+              290,
+              413.6
             ],
             "color": "#3f789e",
             "font_size": 24,
@@ -1060,41 +1027,25 @@
             "type": "INT"
           },
           {
-            "id": 71,
+            "id": 38,
             "origin_id": -10,
             "origin_slot": 3,
-            "target_id": 3,
-            "target_slot": 4,
-            "type": "INT"
-          },
-          {
-            "id": 72,
-            "origin_id": -10,
-            "origin_slot": 4,
-            "target_id": 3,
-            "target_slot": 5,
-            "type": "INT"
-          },
-          {
-            "id": 73,
-            "origin_id": -10,
-            "origin_slot": 5,
             "target_id": 28,
             "target_slot": 0,
             "type": "COMBO"
           },
           {
-            "id": 74,
+            "id": 39,
             "origin_id": -10,
-            "origin_slot": 6,
+            "origin_slot": 4,
             "target_id": 30,
             "target_slot": 0,
             "type": "COMBO"
           },
           {
-            "id": 75,
+            "id": 40,
             "origin_id": -10,
-            "origin_slot": 7,
+            "origin_slot": 5,
             "target_id": 29,
             "target_slot": 0,
             "type": "COMBO"
@@ -1103,10 +1054,25 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Text to image",
-        "description": "Generates images from text prompts using Z-Image-Turbo, Alibaba's distilled 6B DiT model."
+        "category": "Image generation and editing/Text to image"
       }
     ]
   },
-  "extra": {}
-}
+  "config": {},
+  "extra": {
+    "frontendVersion": "1.37.10",
+    "workflowRendererVersion": "LG",
+    "VHS_latentpreview": false,
+    "VHS_latentpreviewrate": 0,
+    "VHS_MetadataImage": true,
+    "VHS_KeepIntermediate": true,
+    "ds": {
+      "scale": 0.8401370345180755,
+      "offset": [
+        940.0587067393087,
+        -830.7121087564725
+      ]
+    }
+  },
+  "version": 0.4
+}

blueprints/Text to Video (Wan 2.2).json

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

blueprints/Unsharp Mask.json

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

blueprints/Video Captioning (Gemini).json

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

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

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

blueprints/Video Segmentation (SAM3).json

@@ -1,827 +0,0 @@
-{
-  "revision": 0,
-  "last_node_id": 130,
-  "last_link_id": 0,
-  "nodes": [
-    {
-      "id": 130,
-      "type": "7937cf78-b52b-40a3-93b2-b4e2e5f98df1",
-      "pos": [
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-        -2780
-      ],
-      "size": [
-        300,
-        370
-      ],
-      "flags": {},
-      "order": 3,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "video",
-          "type": "VIDEO",
-          "link": null
-        },
-        {
-          "name": "text",
-          "type": "STRING",
-          "widget": {
-            "name": "text"
-          },
-          "link": null
-        },
-        {
-          "name": "bboxes",
-          "type": "BOUNDING_BOX",
-          "link": null
-        },
-        {
-          "name": "positive_coords",
-          "type": "STRING",
-          "link": null
-        },
-        {
-          "name": "negative_coords",
-          "type": "STRING",
-          "link": null
-        },
-        {
-          "name": "threshold",
-          "type": "FLOAT",
-          "widget": {
-            "name": "threshold"
-          },
-          "link": null
-        },
-        {
-          "name": "refine_iterations",
-          "type": "INT",
-          "widget": {
-            "name": "refine_iterations"
-          },
-          "link": null
-        },
-        {
-          "name": "individual_masks",
-          "type": "BOOLEAN",
-          "widget": {
-            "name": "individual_masks"
-          },
-          "link": null
-        },
-        {
-          "name": "ckpt_name",
-          "type": "COMBO",
-          "widget": {
-            "name": "ckpt_name"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "localized_name": "masks",
-          "name": "masks",
-          "type": "MASK",
-          "links": []
-        },
-        {
-          "localized_name": "bboxes",
-          "name": "bboxes",
-          "type": "BOUNDING_BOX",
-          "links": []
-        },
-        {
-          "name": "audio",
-          "type": "AUDIO",
-          "links": null
-        },
-        {
-          "name": "fps",
-          "type": "FLOAT",
-          "links": null
-        }
-      ],
-      "properties": {
-        "proxyWidgets": [
-          [
-            "125",
-            "text"
-          ],
-          [
-            "126",
-            "threshold"
-          ],
-          [
-            "126",
-            "refine_iterations"
-          ],
-          [
-            "126",
-            "individual_masks"
-          ],
-          [
-            "127",
-            "ckpt_name"
-          ]
-        ],
-        "cnr_id": "comfy-core",
-        "ver": "0.19.3",
-        "enableTabs": false,
-        "tabWidth": 65,
-        "tabXOffset": 10,
-        "hasSecondTab": false,
-        "secondTabText": "Send Back",
-        "secondTabOffset": 80,
-        "secondTabWidth": 65
-      },
-      "widgets_values": [],
-      "title": "Video Segmentation (SAM3)"
-    }
-  ],
-  "links": [],
-  "version": 0.4,
-  "definitions": {
-    "subgraphs": [
-      {
-        "id": "7937cf78-b52b-40a3-93b2-b4e2e5f98df1",
-        "version": 1,
-        "state": {
-          "lastGroupId": 0,
-          "lastNodeId": 130,
-          "lastLinkId": 299,
-          "lastRerouteId": 0
-        },
-        "revision": 0,
-        "config": {},
-        "name": "Video Segmentation (SAM3)",
-        "inputNode": {
-          "id": -10,
-          "bounding": [
-            -2260,
-            -3450,
-            136.369140625,
-            220
-          ]
-        },
-        "outputNode": {
-          "id": -20,
-          "bounding": [
-            -1050,
-            -3510,
-            120,
-            120
-          ]
-        },
-        "inputs": [
-          {
-            "id": "680ffd88-32fe-48be-88d6-91ea44d5eaee",
-            "name": "video",
-            "type": "VIDEO",
-            "linkIds": [
-              252
-            ],
-            "pos": [
-              -2143.630859375,
-              -3430
-            ]
-          },
-          {
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-            "type": "STRING",
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-              -3410
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-            "name": "bboxes",
-            "type": "BOUNDING_BOX",
-            "linkIds": [
-              255
-            ],
-            "pos": [
-              -2143.630859375,
-              -3390
-            ]
-          },
-          {
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-            "name": "positive_coords",
-            "type": "STRING",
-            "linkIds": [
-              256
-            ],
-            "pos": [
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-              -3370
-            ]
-          },
-          {
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-            "name": "negative_coords",
-            "type": "STRING",
-            "linkIds": [
-              257
-            ],
-            "pos": [
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-              -3350
-            ]
-          },
-          {
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-            "name": "threshold",
-            "type": "FLOAT",
-            "linkIds": [
-              261
-            ],
-            "pos": [
-              -2143.630859375,
-              -3330
-            ]
-          },
-          {
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-              -3310
-            ]
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-          {
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-            "name": "individual_masks",
-            "type": "BOOLEAN",
-            "linkIds": [
-              263
-            ],
-            "pos": [
-              -2143.630859375,
-              -3290
-            ]
-          },
-          {
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-            "name": "ckpt_name",
-            "type": "COMBO",
-            "linkIds": [
-              273
-            ],
-            "pos": [
-              -2143.630859375,
-              -3270
-            ]
-          }
-        ],
-        "outputs": [
-          {
-            "id": "ff50da09-1e59-4a58-9b7f-be1a00aa5913",
-            "name": "masks",
-            "type": "MASK",
-            "linkIds": [
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-            ],
-            "localized_name": "masks",
-            "pos": [
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-              -3490
-            ]
-          },
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-            "name": "audio",
-            "type": "AUDIO",
-            "linkIds": [
-              259
-            ],
-            "pos": [
-              -1030,
-              -3450
-            ]
-          },
-          {
-            "id": "82c1cddc-ab11-44eb-9e2f-1a5c7ea5645b",
-            "name": "fps",
-            "type": "FLOAT",
-            "linkIds": [
-              260
-            ],
-            "pos": [
-              -1030,
-              -3430
-            ]
-          }
-        ],
-        "widgets": [],
-        "nodes": [
-          {
-            "id": 125,
-            "type": "CLIPTextEncode",
-            "pos": [
-              -2010,
-              -3040
-            ],
-            "size": [
-              400,
-              200
-            ],
-            "flags": {},
-            "order": 1,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "clip",
-                "name": "clip",
-                "type": "CLIP",
-                "link": 240
-              },
-              {
-                "localized_name": "text",
-                "name": "text",
-                "type": "STRING",
-                "widget": {
-                  "name": "text"
-                },
-                "link": 254
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "CONDITIONING",
-                "name": "CONDITIONING",
-                "type": "CONDITIONING",
-                "links": [
-                  200
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "CLIPTextEncode",
-              "cnr_id": "comfy-core",
-              "ver": "0.19.3",
-              "enableTabs": false,
-              "tabWidth": 65,
-              "tabXOffset": 10,
-              "hasSecondTab": false,
-              "secondTabText": "Send Back",
-              "secondTabOffset": 80,
-              "secondTabWidth": 65
-            },
-            "widgets_values": [
-              ""
-            ]
-          },
-          {
-            "id": 126,
-            "type": "SAM3_Detect",
-            "pos": [
-              -1520,
-              -3520
-            ],
-            "size": [
-              270,
-              290
-            ],
-            "flags": {},
-            "order": 2,
-            "mode": 0,
-            "inputs": [
-              {
-                "label": "model",
-                "localized_name": "model",
-                "name": "model",
-                "type": "MODEL",
-                "link": 237
-              },
-              {
-                "label": "image",
-                "localized_name": "image",
-                "name": "image",
-                "type": "IMAGE",
-                "link": 253
-              },
-              {
-                "label": "conditioning",
-                "localized_name": "conditioning",
-                "name": "conditioning",
-                "shape": 7,
-                "type": "CONDITIONING",
-                "link": 200
-              },
-              {
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-                "localized_name": "bboxes",
-                "name": "bboxes",
-                "shape": 7,
-                "type": "BOUNDING_BOX",
-                "link": 255
-              },
-              {
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-                "localized_name": "positive_coords",
-                "name": "positive_coords",
-                "shape": 7,
-                "type": "STRING",
-                "link": 256
-              },
-              {
-                "label": "negative_coords",
-                "localized_name": "negative_coords",
-                "name": "negative_coords",
-                "shape": 7,
-                "type": "STRING",
-                "link": 257
-              },
-              {
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-                "type": "FLOAT",
-                "widget": {
-                  "name": "threshold"
-                },
-                "link": 261
-              },
-              {
-                "localized_name": "refine_iterations",
-                "name": "refine_iterations",
-                "type": "INT",
-                "widget": {
-                  "name": "refine_iterations"
-                },
-                "link": 262
-              },
-              {
-                "localized_name": "individual_masks",
-                "name": "individual_masks",
-                "type": "BOOLEAN",
-                "widget": {
-                  "name": "individual_masks"
-                },
-                "link": 263
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "masks",
-                "name": "masks",
-                "type": "MASK",
-                "links": [
-                  231
-                ]
-              },
-              {
-                "localized_name": "bboxes",
-                "name": "bboxes",
-                "type": "BOUNDING_BOX",
-                "links": [
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-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "SAM3_Detect",
-              "cnr_id": "comfy-core",
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-            "inputs": [
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-                "localized_name": "ckpt_name",
-                "name": "ckpt_name",
-                "type": "COMBO",
-                "widget": {
-                  "name": "ckpt_name"
-                },
-                "link": 273
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "MODEL",
-                "name": "MODEL",
-                "type": "MODEL",
-                "links": [
-                  237
-                ]
-              },
-              {
-                "localized_name": "CLIP",
-                "name": "CLIP",
-                "type": "CLIP",
-                "links": [
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-                ]
-              },
-              {
-                "localized_name": "VAE",
-                "name": "VAE",
-                "type": "VAE",
-                "links": null
-              }
-            ],
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-              "Node name for S&R": "CheckpointLoaderSimple",
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-              "tabXOffset": 10,
-              "hasSecondTab": false,
-              "secondTabText": "Send Back",
-              "secondTabOffset": 80,
-              "secondTabWidth": 65,
-              "models": [
-                {
-                  "name": "sam3.1_multiplex_fp16.safetensors",
-                  "url": "https://huggingface.co/Comfy-Org/sam3.1/resolve/main/checkpoints/sam3.1_multiplex_fp16.safetensors",
-                  "directory": "checkpoints"
-                }
-              ]
-            },
-            "widgets_values": [
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-          },
-          {
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-            "type": "GetVideoComponents",
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-              -3540
-            ],
-            "size": [
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-              120
-            ],
-            "flags": {},
-            "order": 4,
-            "mode": 0,
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-              {
-                "localized_name": "video",
-                "name": "video",
-                "type": "VIDEO",
-                "link": 252
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "images",
-                "name": "images",
-                "type": "IMAGE",
-                "links": [
-                  253
-                ]
-              },
-              {
-                "localized_name": "audio",
-                "name": "audio",
-                "type": "AUDIO",
-                "links": [
-                  259
-                ]
-              },
-              {
-                "localized_name": "fps",
-                "name": "fps",
-                "type": "FLOAT",
-                "links": [
-                  260
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "GetVideoComponents",
-              "cnr_id": "comfy-core",
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-              "enableTabs": false,
-              "tabWidth": 65,
-              "tabXOffset": 10,
-              "hasSecondTab": false,
-              "secondTabText": "Send Back",
-              "secondTabOffset": 80,
-              "secondTabWidth": 65
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-            ],
-            "flags": {},
-            "order": 0,
-            "mode": 0,
-            "inputs": [],
-            "outputs": [],
-            "title": "Note: Prompt format",
-            "properties": {},
-            "widgets_values": [
-              "Max tokens for this model is only 32, to separately prompt multiple subjects you can separate prompts with comma, and set the max amount of objects detected for each prompt with :N\n\nFor example above test prompt finds 2 cakes, one apron, 4 window panels"
-            ],
-            "color": "#432",
-            "bgcolor": "#653"
-          }
-        ],
-        "groups": [],
-        "links": [
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-            "target_slot": 0,
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-            "target_slot": 0,
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-            "type": "BOUNDING_BOX"
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-            "target_id": 128,
-            "target_slot": 0,
-            "type": "VIDEO"
-          },
-          {
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-            "origin_slot": 0,
-            "target_id": 126,
-            "target_slot": 1,
-            "type": "IMAGE"
-          },
-          {
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-            "origin_id": -10,
-            "origin_slot": 1,
-            "target_id": 125,
-            "target_slot": 1,
-            "type": "STRING"
-          },
-          {
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-            "origin_id": -10,
-            "origin_slot": 2,
-            "target_id": 126,
-            "target_slot": 3,
-            "type": "BOUNDING_BOX"
-          },
-          {
-            "id": 256,
-            "origin_id": -10,
-            "origin_slot": 3,
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-            "target_slot": 4,
-            "type": "STRING"
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-            "target_slot": 5,
-            "type": "STRING"
-          },
-          {
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-            "origin_id": 128,
-            "origin_slot": 1,
-            "target_id": -20,
-            "target_slot": 2,
-            "type": "AUDIO"
-          },
-          {
-            "id": 260,
-            "origin_id": 128,
-            "origin_slot": 2,
-            "target_id": -20,
-            "target_slot": 3,
-            "type": "FLOAT"
-          },
-          {
-            "id": 261,
-            "origin_id": -10,
-            "origin_slot": 5,
-            "target_id": 126,
-            "target_slot": 6,
-            "type": "FLOAT"
-          },
-          {
-            "id": 262,
-            "origin_id": -10,
-            "origin_slot": 6,
-            "target_id": 126,
-            "target_slot": 7,
-            "type": "INT"
-          },
-          {
-            "id": 263,
-            "origin_id": -10,
-            "origin_slot": 7,
-            "target_id": 126,
-            "target_slot": 8,
-            "type": "BOOLEAN"
-          },
-          {
-            "id": 273,
-            "origin_id": -10,
-            "origin_slot": 8,
-            "target_id": 127,
-            "target_slot": 0,
-            "type": "COMBO"
-          }
-        ],
-        "extra": {},
-        "category": "Video Tools",
-        "description": "Segments video into temporally consistent masks using Meta SAM3 from text or interactive prompts."
-      }
-    ]
-  },
-  "extra": {}
-}

blueprints/Video Stitch.json

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

blueprints/Video Upscale(GAN x4).json

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

comfy/background_removal/birefnet.json

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

comfy/background_removal/birefnet.py

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

comfy/bg_removal_model.py

@@ -1,85 +0,0 @@
-from .utils import load_torch_file
-import os
-import json
-import torch
-import logging
-
-import comfy.ops
-import comfy.model_patcher
-import comfy.model_management
-import comfy.clip_model
-import comfy.background_removal.birefnet
-
-BG_REMOVAL_MODELS = {
-    "birefnet": comfy.background_removal.birefnet.BiRefNet
-}
-
-class BackgroundRemovalModel():
-    def __init__(self, json_config):
-        with open(json_config) as f:
-            config = json.load(f)
-
-        self.image_size = config.get("image_size", 1024)
-        self.image_mean = config.get("image_mean", [0.0, 0.0, 0.0])
-        self.image_std = config.get("image_std", [1.0, 1.0, 1.0])
-        self.model_type = config.get("model_type", "birefnet")
-        self.config = config.copy()
-        model_class = BG_REMOVAL_MODELS.get(self.model_type)
-
-        self.load_device = comfy.model_management.text_encoder_device()
-        offload_device = comfy.model_management.text_encoder_offload_device()
-        self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)
-        self.model = model_class(config, self.dtype, offload_device, comfy.ops.manual_cast)
-        self.model.eval()
-
-        self.patcher = comfy.model_patcher.CoreModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
-
-    def load_sd(self, sd):
-        return self.model.load_state_dict(sd, strict=False, assign=self.patcher.is_dynamic())
-
-    def get_sd(self):
-        return self.model.state_dict()
-
-    def encode_image(self, image):
-        comfy.model_management.load_model_gpu(self.patcher)
-        H, W = image.shape[1], image.shape[2]
-        pixel_values = comfy.clip_model.clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=False)
-
-        if pixel_values.shape[0] > 1:
-            out = torch.cat([
-                self.model(pixel_values=pixel_values[i:i+1])
-                for i in range(pixel_values.shape[0])
-            ], dim=0)
-        else:
-            out = self.model(pixel_values=pixel_values)
-        out = torch.nn.functional.interpolate(out, size=(H, W), mode="bicubic", antialias=False)
-
-        mask = out.sigmoid().to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
-        if mask.ndim == 3:
-            mask = mask.unsqueeze(0)
-        if mask.shape[1] != 1:
-            mask = mask.movedim(-1, 1)
-
-        return mask
-
-
-def load_background_removal_model(sd):
-    if "bb.layers.1.blocks.0.attn.relative_position_index" in sd:
-        json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "background_removal"), "birefnet.json")
-    else:
-        return None
-
-    bg_model = BackgroundRemovalModel(json_config)
-    m, u = bg_model.load_sd(sd)
-    if len(m) > 0:
-        logging.warning("missing background removal: {}".format(m))
-    u = set(u)
-    keys = list(sd.keys())
-    for k in keys:
-        if k not in u:
-            sd.pop(k)
-    return bg_model
-
-def load(ckpt_path):
-    sd = load_torch_file(ckpt_path)
-    return load_background_removal_model(sd)

comfy/cli_args.py

@@ -90,8 +90,8 @@ parser.add_argument("--force-channels-last", action="store_true", help="Force ch
 parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
 
 parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.")
+parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize default when loading models with Intel's Extension for Pytorch.")
 parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.")
-parser.add_argument("--enable-triton-backend", action="store_true", help="ComfyUI will enable the use of Triton backend in comfy-kitchen. Is disabled at launch by default.")
 
 class LatentPreviewMethod(enum.Enum):
     NoPreviews = "none"
@@ -141,7 +141,8 @@ manager_group.add_argument("--enable-manager-legacy-ui", action="store_true", he
 vram_group = parser.add_mutually_exclusive_group()
 vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).")
 vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.")
-vram_group.add_argument("--lowvram", action="store_true", help="Doesn't do anything if dynamic vram is enabled. If dynamic vram isn't being used this option makes the text encoders run on the CPU.")
+vram_group.add_argument("--normalvram", action="store_true", help="Used to force normal vram use if lowvram gets automatically enabled.")
+vram_group.add_argument("--lowvram", action="store_true", help="Split the unet in parts to use less vram.")
 vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.")
 vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).")
 
@@ -237,8 +238,6 @@ database_default_path = os.path.abspath(
 )
 parser.add_argument("--database-url", type=str, default=f"sqlite:///{database_default_path}", help="Specify the database URL, e.g. for an in-memory database you can use 'sqlite:///:memory:'.")
 parser.add_argument("--enable-assets", action="store_true", help="Enable the assets system (API routes, database synchronization, and background scanning).")
-parser.add_argument("--feature-flag", type=str, action='append', default=[], metavar="KEY[=VALUE]", help="Set a server feature flag. Use KEY=VALUE to set an explicit value, or bare KEY to set it to true. Can be specified multiple times. Boolean values (true/false) and numbers are auto-converted. Examples: --feature-flag show_signin_button=true  or  --feature-flag show_signin_button")
-parser.add_argument("--list-feature-flags", action="store_true", help="Print the registry of known CLI-settable feature flags as JSON and exit.")
 
 if comfy.options.args_parsing:
     args = parser.parse_args()

comfy/context_windows.py

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

comfy/deploy_environment.py

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

comfy/hooks.py

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

comfy/image_encoders/dino2.py

@@ -106,7 +106,6 @@ class Dino2Encoder(torch.nn.Module):
 class Dino2PatchEmbeddings(torch.nn.Module):
     def __init__(self, dim, num_channels=3, patch_size=14, image_size=518, dtype=None, device=None, operations=None):
         super().__init__()
-        self.patch_size = patch_size
         self.projection = operations.Conv2d(
             in_channels=num_channels,
             out_channels=dim,
@@ -126,37 +125,17 @@ class Dino2Embeddings(torch.nn.Module):
         super().__init__()
         patch_size = 14
         image_size = 518
-        self.patch_size = patch_size
 
         self.patch_embeddings = Dino2PatchEmbeddings(dim, patch_size=patch_size, image_size=image_size, dtype=dtype, device=device, operations=operations)
         self.position_embeddings = torch.nn.Parameter(torch.empty(1, (image_size // patch_size) ** 2 + 1, dim, dtype=dtype, device=device))
-        self.cls_token = torch.nn.Parameter(torch.empty(1, 1, dim, dtype=dtype, device=device)) # mask_token is a pre-training param, kept only so strict loading accepts the key.
+        self.cls_token = torch.nn.Parameter(torch.empty(1, 1, dim, dtype=dtype, device=device))
         self.mask_token = torch.nn.Parameter(torch.empty(1, dim, dtype=dtype, device=device))
 
-    def interpolate_pos_encoding(self, x, h_pixels, w_pixels):
-        pos_embed = comfy.model_management.cast_to_device(self.position_embeddings, x.device, torch.float32)
-
-        class_pos = pos_embed[:, 0:1]
-        patch_pos = pos_embed[:, 1:]
-        N = patch_pos.shape[1]
-        M = int(N ** 0.5)
-        h0 = h_pixels // self.patch_size
-        w0 = w_pixels // self.patch_size
-        scale_factor = ((h0 + 0.1) / M, (w0 + 0.1) / M)  # +0.1 matches upstream DINOv2's FP-rounding workaround so the interpolate output size lands on (h0, w0).
-
-        patch_pos = patch_pos.reshape(1, M, M, -1).permute(0, 3, 1, 2)
-        patch_pos = torch.nn.functional.interpolate(patch_pos, scale_factor=scale_factor, mode="bicubic", antialias=False)
-        patch_pos = patch_pos.permute(0, 2, 3, 1).flatten(1, 2)
-        return torch.cat((class_pos, patch_pos), dim=1).to(x.dtype)
-
     def forward(self, pixel_values):
         x = self.patch_embeddings(pixel_values)
+        # TODO: mask_token?
         x = torch.cat((self.cls_token.to(device=x.device, dtype=x.dtype).expand(x.shape[0], -1, -1), x), dim=1)
-        if x.shape[1] - 1 == self.position_embeddings.shape[1] - 1:
-            x = x + comfy.model_management.cast_to_device(self.position_embeddings, x.device, x.dtype)
-        else:
-            h, w = pixel_values.shape[-2:]
-            x = x + self.interpolate_pos_encoding(x, h, w)
+        x = x + comfy.model_management.cast_to_device(self.position_embeddings, x.device, x.dtype)
         return x
 
 
@@ -179,21 +158,3 @@ class Dinov2Model(torch.nn.Module):
         x = self.layernorm(x)
         pooled_output = x[:, 0, :]
         return x, i, pooled_output, None
-
-    def get_intermediate_layers(self, pixel_values, indices, apply_norm=True):
-        x = self.embeddings(pixel_values)
-        optimized_attention = optimized_attention_for_device(x.device, False, small_input=True)
-        n_layers = len(self.encoder.layer)
-        resolved = [(i if i >= 0 else n_layers + i) for i in indices]
-        target = set(resolved)
-        max_idx = max(resolved)
-        n_skip = 1  # skip cls token
-        cache = {}
-        for i, layer in enumerate(self.encoder.layer):
-            x = layer(x, optimized_attention)
-            if i in target:
-                normed = self.layernorm(x) if apply_norm else x
-                cache[i] = (normed[:, n_skip:], normed[:, 0])
-            if i >= max_idx:
-                break
-        return [cache[i] for i in resolved]

comfy/k_diffusion/sampling.py

@@ -242,7 +242,6 @@ def sample_euler_ancestral_RF(model, x, sigmas, extra_args=None, callback=None,
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
-    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
     s_in = x.new_ones([x.shape[0]])
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
@@ -374,7 +373,6 @@ def sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args=None, callback=None,
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
-    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
     s_in = x.new_ones([x.shape[0]])
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
@@ -688,7 +686,6 @@ def sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args=None, callback=Non
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
-    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
     s_in = x.new_ones([x.shape[0]])
     sigma_fn = lambda lbda: (lbda.exp() + 1) ** -1
     lambda_fn = lambda sigma: ((1-sigma)/sigma).log()
@@ -750,7 +747,6 @@ def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=N
     sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
-    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
 
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
@@ -836,7 +832,6 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
     model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
-    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
 
     old_denoised = None
     h, h_last = None, None
@@ -894,7 +889,6 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
     model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
-    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
 
     denoised_1, denoised_2 = None, None
     h, h_1, h_2 = None, None, None
@@ -1012,39 +1006,23 @@ def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None,
     return generic_step_sampler(model, x, sigmas, extra_args, callback, disable, noise_sampler, DDPMSampler_step)
 
 @torch.no_grad()
-def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, s_noise=1.0, s_noise_end=None, noise_clip_std=0.0):
-
-    # s_noise / s_noise_end: per-step noise multiplier, linearly interpolated across steps
-    # noise_clip_std: clamp injected noise to +/- N stddevs (0 disables).
-
+def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
-    n_steps = max(1, len(sigmas) - 1)
-    model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
-
-    s_start = float(s_noise)
-    s_end = s_start if s_noise_end is None else float(s_noise_end)
-    for i in trange(n_steps, disable=disable):
+    for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
         if callback is not None:
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
 
         x = denoised
         if sigmas[i + 1] > 0:
-            noise = noise_sampler(sigmas[i], sigmas[i + 1])
-            if noise_clip_std > 0:
-                clip_val = noise_clip_std * noise.std()
-                noise = noise.clamp(min=-clip_val, max=clip_val)
-            t = (i / (n_steps - 1)) if n_steps > 1 else 0.0
-            s_noise_i = s_start + (s_end - s_start) * t
-            if s_noise_i != 1.0:
-                noise = noise * s_noise_i
-            x = model_sampling.noise_scaling(sigmas[i + 1], noise, x)
+            x = model.inner_model.inner_model.model_sampling.noise_scaling(sigmas[i + 1], noise_sampler(sigmas[i], sigmas[i + 1]), x)
     return x
 
 
+
 @torch.no_grad()
 def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
     # From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/
@@ -1271,7 +1249,6 @@ def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=No
 
     model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling")
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
-    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
 
     uncond_denoised = None
 
@@ -1319,7 +1296,6 @@ def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
-    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
 
     temp = [0]
     def post_cfg_function(args):
@@ -1395,7 +1371,6 @@ def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
-    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
     s_in = x.new_ones([x.shape[0]])
     sigma_fn = lambda t: t.neg().exp()
     t_fn = lambda sigma: sigma.log().neg()
@@ -1529,7 +1504,6 @@ def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
-    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
     s_in = x.new_ones([x.shape[0]])
 
     def default_er_sde_noise_scaler(x):
@@ -1600,10 +1574,9 @@ def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=Non
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
+    inject_noise = eta > 0 and s_noise > 0
 
     model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
-    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
-    inject_noise = eta > 0 and s_noise > 0
     sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
@@ -1672,10 +1645,9 @@ def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, disable=Non
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
+    inject_noise = eta > 0 and s_noise > 0
 
     model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
-    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
-    inject_noise = eta > 0 and s_noise > 0
     sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
@@ -1741,7 +1713,6 @@ def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=F
     s_in = x.new_ones([x.shape[0]])
 
     model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling")
-    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
     lambdas = sigma_to_half_log_snr(sigmas, model_sampling=model_sampling)
 
@@ -1839,119 +1810,3 @@ def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=F
 def sample_sa_solver_pece(model, x, sigmas, extra_args=None, callback=None, disable=False, tau_func=None, s_noise=1.0, noise_sampler=None, predictor_order=3, corrector_order=4, simple_order_2=False):
     """Stochastic Adams Solver with PECE (Predict–Evaluate–Correct–Evaluate) mode (NeurIPS 2023)."""
     return sample_sa_solver(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, tau_func=tau_func, s_noise=s_noise, noise_sampler=noise_sampler, predictor_order=predictor_order, corrector_order=corrector_order, use_pece=True, simple_order_2=simple_order_2)
-
-
-@torch.no_grad()
-def sample_ar_video(model, x, sigmas, extra_args=None, callback=None, disable=None,
-                    num_frame_per_block=1):
-    """
-    Autoregressive video sampler: block-by-block denoising with KV cache
-    and flow-match re-noising for Causal Forcing / Self-Forcing models.
-
-    Requires a Causal-WAN compatible model (diffusion_model must expose
-    init_kv_caches / init_crossattn_caches) and 5-D latents [B,C,T,H,W].
-
-    All AR-loop parameters are passed via the SamplerARVideo node, not read
-    from the checkpoint or transformer_options.
-    """
-    extra_args = {} if extra_args is None else extra_args
-    model_options = extra_args.get("model_options", {})
-    transformer_options = model_options.get("transformer_options", {})
-
-    if x.ndim != 5:
-        raise ValueError(
-            f"ar_video sampler requires 5-D video latents [B,C,T,H,W], got {x.ndim}-D tensor with shape {x.shape}. "
-            "This sampler is only compatible with autoregressive video models (e.g. Causal-WAN)."
-        )
-
-    inner_model = model.inner_model.inner_model
-    causal_model = inner_model.diffusion_model
-
-    if not (hasattr(causal_model, "init_kv_caches") and hasattr(causal_model, "init_crossattn_caches")):
-        raise TypeError(
-            "ar_video sampler requires a Causal-WAN compatible model whose diffusion_model "
-            "exposes init_kv_caches() and init_crossattn_caches(). The loaded checkpoint "
-            "does not support this interface — choose a different sampler."
-        )
-
-    seed = extra_args.get("seed", 0)
-
-    bs, c, lat_t, lat_h, lat_w = x.shape
-    frame_seq_len = -(-lat_h // 2) * -(-lat_w // 2) # ceiling division
-    num_blocks = -(-lat_t // num_frame_per_block)   # ceiling division
-    device = x.device
-    model_dtype = inner_model.get_dtype()
-
-    kv_caches = causal_model.init_kv_caches(bs, lat_t * frame_seq_len, device, model_dtype)
-    crossattn_caches = causal_model.init_crossattn_caches(bs, device, model_dtype)
-
-    output = torch.zeros_like(x)
-    s_in = x.new_ones([x.shape[0]])
-    current_start_frame = 0
-
-    # I2V: seed KV cache with the initial image latent before the denoising loop
-    initial_latent = transformer_options.get("ar_config", {}).get("initial_latent", None)
-    if initial_latent is not None:
-        initial_latent = inner_model.process_latent_in(initial_latent).to(device=device, dtype=model_dtype)
-        n_init = initial_latent.shape[2]
-        output[:, :, :n_init] = initial_latent
-
-        ar_state = {"start_frame": 0, "kv_caches": kv_caches, "crossattn_caches": crossattn_caches}
-        transformer_options["ar_state"] = ar_state
-        zero_sigma = sigmas.new_zeros([1])
-        _ = model(initial_latent, zero_sigma * s_in, **extra_args)
-
-        current_start_frame = n_init
-        remaining = lat_t - n_init
-        num_blocks = -(-remaining // num_frame_per_block)
-
-    num_sigma_steps = len(sigmas) - 1
-    total_real_steps = num_blocks * num_sigma_steps
-    step_count = 0
-
-    try:
-        for block_idx in trange(num_blocks, disable=disable):
-            bf = min(num_frame_per_block, lat_t - current_start_frame)
-            fs, fe = current_start_frame, current_start_frame + bf
-            noisy_input = x[:, :, fs:fe]
-
-            ar_state = {
-                "start_frame": current_start_frame,
-                "kv_caches": kv_caches,
-                "crossattn_caches": crossattn_caches,
-            }
-            transformer_options["ar_state"] = ar_state
-
-            for i in range(num_sigma_steps):
-                denoised = model(noisy_input, sigmas[i] * s_in, **extra_args)
-
-                if callback is not None:
-                    scaled_i = step_count * num_sigma_steps // total_real_steps
-                    callback({"x": noisy_input, "i": scaled_i, "sigma": sigmas[i],
-                              "sigma_hat": sigmas[i], "denoised": denoised})
-
-                if sigmas[i + 1] == 0:
-                    noisy_input = denoised
-                else:
-                    sigma_next = sigmas[i + 1]
-                    torch.manual_seed(seed + block_idx * 1000 + i)
-                    fresh_noise = torch.randn_like(denoised)
-                    noisy_input = (1.0 - sigma_next) * denoised + sigma_next * fresh_noise
-
-                    for cache in kv_caches:
-                        cache["end"] -= bf * frame_seq_len
-
-                step_count += 1
-
-            output[:, :, fs:fe] = noisy_input
-
-            for cache in kv_caches:
-                cache["end"] -= bf * frame_seq_len
-            zero_sigma = sigmas.new_zeros([1])
-            _ = model(noisy_input, zero_sigma * s_in, **extra_args)
-
-            current_start_frame += bf
-    finally:
-        transformer_options.pop("ar_state", None)
-
-    return output

comfy/latent_formats.py

@@ -9,7 +9,6 @@ class LatentFormat:
     latent_rgb_factors_reshape = None
     taesd_decoder_name = None
     spacial_downscale_ratio = 8
-    temporal_downscale_ratio = 1
 
     def process_in(self, latent):
         return latent * self.scale_factor
@@ -150,12 +149,6 @@ class SD3(LatentFormat):
 class StableAudio1(LatentFormat):
     latent_channels = 64
     latent_dimensions = 1
-    temporal_downscale_ratio = 2048
-
-class StableAudio3(LatentFormat):
-    latent_channels = 256
-    latent_dimensions = 1
-    temporal_downscale_ratio = 4096
 
 class Flux(SD3):
     latent_channels = 16
@@ -231,7 +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
@@ -242,7 +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
@@ -286,7 +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 = [
@@ -430,7 +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],
@@ -457,7 +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],
@@ -483,7 +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],
@@ -746,7 +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"
 
@@ -772,7 +758,6 @@ class ACEAudio(LatentFormat):
 class ACEAudio15(LatentFormat):
     latent_channels = 64
     latent_dimensions = 1
-    temporal_downscale_ratio = 1764
 
 class ChromaRadiance(LatentFormat):
     latent_channels = 3
@@ -798,36 +783,3 @@ class ZImagePixelSpace(ChromaRadiance):
     No VAE encoding/decoding — the model operates directly on RGB pixels.
     """
     pass
-
-
-class HiDreamO1Pixel(ChromaRadiance):
-    """Pixel-space latent format for HiDream-O1.
-    No VAE — model patches/unpatches raw RGB internally with patch_size=32.
-    """
-    pass
-
-class CogVideoX(LatentFormat):
-    """Latent format for CogVideoX-2b (THUDM/CogVideoX-2b).
-
-    scale_factor matches the vae/config.json scaling_factor for the 2b variant.
-    The 5b-class checkpoints (CogVideoX-5b, CogVideoX-1.5-5B, CogVideoX-Fun-V1.5-*)
-    use a different value; see CogVideoX1_5 below.
-    """
-    latent_channels = 16
-    latent_dimensions = 3
-    temporal_downscale_ratio = 4
-
-    def __init__(self):
-        self.scale_factor = 1.15258426
-
-
-class CogVideoX1_5(CogVideoX):
-    """Latent format for 5b-class CogVideoX checkpoints.
-
-    Covers THUDM/CogVideoX-5b, THUDM/CogVideoX-1.5-5B, and the CogVideoX-Fun
-    V1.5-5b family (including VOID inpainting). All of these have
-    scaling_factor=0.7 in their vae/config.json. Auto-selected in
-    supported_models.CogVideoX_T2V based on transformer hidden dim.
-    """
-    def __init__(self):
-        self.scale_factor = 0.7

comfy/ldm/audio/dit.py

@@ -10,17 +10,6 @@ from torch import nn
 from torch.nn import functional as F
 import math
 import comfy.ops
-from .embedders import ExpoFourierFeatures
-
-
-def _left_pad_to_match(emb, target_len):
-    emb_len = emb.shape[-2]
-    if emb_len < target_len:
-        return F.pad(emb, (0, 0, target_len - emb_len, 0), value=0.)
-    elif emb_len > target_len:
-        return emb[:, -target_len:, :]
-    return emb
-
 
 class FourierFeatures(nn.Module):
     def __init__(self, in_features, out_features, std=1., dtype=None, device=None):
@@ -33,7 +22,6 @@ class FourierFeatures(nn.Module):
         f = 2 * math.pi * input @ comfy.ops.cast_to_input(self.weight.T, input)
         return torch.cat([f.cos(), f.sin()], dim=-1)
 
-
 # norms
 class LayerNorm(nn.Module):
     def __init__(self, dim, bias=False, fix_scale=False, dtype=None, device=None):
@@ -55,16 +43,6 @@ class LayerNorm(nn.Module):
             beta = comfy.ops.cast_to_input(beta, x)
         return F.layer_norm(x, x.shape[-1:], weight=comfy.ops.cast_to_input(self.gamma, x), bias=beta)
 
-
-class RMSNorm(nn.Module):
-    def __init__(self, dim, dtype=None, device=None):
-        super().__init__()
-        self.gamma = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
-
-    def forward(self, x):
-        return F.rms_norm(x, x.shape[-1:], weight=comfy.ops.cast_to_input(self.gamma, x))
-
-
 class GLU(nn.Module):
     def __init__(
         self,
@@ -258,6 +236,13 @@ class FeedForward(nn.Module):
 
         linear_out = operations.Linear(inner_dim, dim_out, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(inner_dim, dim_out, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device)
 
+        # # init last linear layer to 0
+        # if zero_init_output:
+        #     nn.init.zeros_(linear_out.weight)
+        #     if not no_bias:
+        #         nn.init.zeros_(linear_out.bias)
+
+
         self.ff = nn.Sequential(
             linear_in,
             rearrange('b d n -> b n d') if use_conv else nn.Identity(),
@@ -276,10 +261,8 @@ class Attention(nn.Module):
         dim_context = None,
         causal = False,
         zero_init_output=True,
-        qk_norm = "none",
-        differential = False,
+        qk_norm = False,
         natten_kernel_size = None,
-        feat_scale = False,
         dtype=None,
         device=None,
         operations=None,
@@ -288,7 +271,6 @@ class Attention(nn.Module):
         self.dim = dim
         self.dim_heads = dim_heads
         self.causal = causal
-        self.differential = differential
 
         dim_kv = dim_context if dim_context is not None else dim
 
@@ -296,37 +278,18 @@ class Attention(nn.Module):
         self.kv_heads = dim_kv // dim_heads
 
         if dim_context is not None:
-            if differential:
-                self.to_q = operations.Linear(dim, dim * 2, bias=False, dtype=dtype, device=device)
-                self.to_kv = operations.Linear(dim_kv, dim_kv * 3, bias=False, dtype=dtype, device=device)
-            else:
-                self.to_q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
-                self.to_kv = operations.Linear(dim_kv, dim_kv * 2, bias=False, dtype=dtype, device=device)
+            self.to_q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+            self.to_kv = operations.Linear(dim_kv, dim_kv * 2, bias=False, dtype=dtype, device=device)
         else:
-            if differential:
-                self.to_qkv = operations.Linear(dim, dim * 5, bias=False, dtype=dtype, device=device)
-            else:
-                self.to_qkv = operations.Linear(dim, dim * 3, bias=False, dtype=dtype, device=device)
+            self.to_qkv = operations.Linear(dim, dim * 3, bias=False, dtype=dtype, device=device)
 
         self.to_out = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
 
-        # Accept bool for backward compat
-        if isinstance(qk_norm, bool):
-            qk_norm = "l2" if qk_norm else "none"
+        # if zero_init_output:
+        #     nn.init.zeros_(self.to_out.weight)
+
         self.qk_norm = qk_norm
 
-        if self.qk_norm == "ln":
-            self.q_norm = operations.LayerNorm(dim_heads, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
-            self.k_norm = operations.LayerNorm(dim_heads, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
-        elif self.qk_norm == "rms":
-            self.q_norm = RMSNorm(dim_heads, dtype=dtype, device=device)
-            self.k_norm = RMSNorm(dim_heads, dtype=dtype, device=device)
-
-        self.feat_scale = feat_scale
-
-        if self.feat_scale:
-            self.lambda_dc = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
-            self.lambda_hf = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
 
     def forward(
         self,
@@ -343,51 +306,22 @@ class Attention(nn.Module):
         kv_input = context if has_context else x
 
         if hasattr(self, 'to_q'):
-            if self.differential:
-                # cross-attention differential: to_q → (q, q_diff), to_kv → (k, k_diff, v)
-                q, q_diff = self.to_q(x).chunk(2, dim=-1)
-                q      = rearrange(q,      'b n (h d) -> b h n d', h=h)
-                q_diff = rearrange(q_diff, 'b n (h d) -> b h n d', h=h)
-                q = torch.stack([q, q_diff], dim=1)  # (B, 2, H, N, D)
-                k, k_diff, v = self.to_kv(kv_input).chunk(3, dim=-1)
-                k      = rearrange(k,      'b n (h d) -> b h n d', h=kv_h)
-                k_diff = rearrange(k_diff, 'b n (h d) -> b h n d', h=kv_h)
-                v      = rearrange(v,      'b n (h d) -> b h n d', h=kv_h)
-                k = torch.stack([k, k_diff], dim=1)  # (B, 2, H, M, D)
-            else:
-                # Use separate linear projections for q and k/v
-                q = self.to_q(x)
-                q = rearrange(q, 'b n (h d) -> b h n d', h = h)
+            # Use separate linear projections for q and k/v
+            q = self.to_q(x)
+            q = rearrange(q, 'b n (h d) -> b h n d', h = h)
 
-                k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+            k, v = self.to_kv(kv_input).chunk(2, dim=-1)
 
-                k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = kv_h), (k, v))
+            k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = kv_h), (k, v))
         else:
-            if self.differential:
-                # self-attention differential: to_qkv → (q, k, v, q_diff, k_diff)
-                q, k, v, q_diff, k_diff = self.to_qkv(x).chunk(5, dim=-1)
-                q, k, v, q_diff, k_diff = map(
-                    lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h),
-                    (q, k, v, q_diff, k_diff)
-                )
-                q = torch.stack([q, q_diff], dim=1)  # (B, 2, H, N, D)
-                k = torch.stack([k, k_diff], dim=1)
-            else:
-                # Use fused linear projection
-                q, k, v = self.to_qkv(x).chunk(3, dim=-1)
-                q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v))
+            # Use fused linear projection
+            q, k, v = self.to_qkv(x).chunk(3, dim=-1)
+            q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v))
 
         # Normalize q and k for cosine sim attention
-        if self.qk_norm == "l2":
+        if self.qk_norm:
             q = F.normalize(q, dim=-1)
             k = F.normalize(k, dim=-1)
-        elif self.qk_norm == "rms":
-            q_type, k_type = q.dtype, k.dtype
-            q = self.q_norm(q).to(q_type)
-            k = self.k_norm(k).to(k_type)
-        elif self.qk_norm != 'none':
-            q = self.q_norm(q)
-            k = self.k_norm(k)
 
         if rotary_pos_emb is not None and not has_context:
             freqs, _ = rotary_pos_emb
@@ -430,24 +364,9 @@ class Attention(nn.Module):
             heads_per_kv_head = h // kv_h
             k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v))
 
-        if self.differential:
-            q, q_diff = q.unbind(dim=1)
-            k, k_diff = k.unbind(dim=1)
-            out      = optimized_attention(q,      k,      v, h, skip_reshape=True, transformer_options=transformer_options)
-            out_diff = optimized_attention(q_diff, k_diff, v, h, skip_reshape=True, transformer_options=transformer_options)
-            out = out - out_diff
-        else:
-            out = optimized_attention(q, k, v, h, skip_reshape=True, transformer_options=transformer_options)
-
+        out = optimized_attention(q, k, v, h, skip_reshape=True, transformer_options=transformer_options)
         out = self.to_out(out)
 
-        if self.feat_scale:
-            out_dc = out.mean(dim=-2, keepdim=True)
-            out_hf = out - out_dc
-
-            # Selectively modulate DC and high frequency components
-            out = out + comfy.ops.cast_to_input(self.lambda_dc, out) * out_dc + comfy.ops.cast_to_input(self.lambda_hf, out) * out_hf
-
         if mask is not None:
             mask = rearrange(mask, 'b n -> b n 1')
             out = out.masked_fill(~mask, 0.)
@@ -498,14 +417,11 @@ class TransformerBlock(nn.Module):
             cross_attend = False,
             dim_context = None,
             global_cond_dim = None,
-            global_cond_shared_embed = False,
-            local_add_cond_dim = None,
             causal = False,
             zero_init_branch_outputs = True,
             conformer = False,
             layer_ix = -1,
             remove_norms = False,
-            norm_type = "layer_norm",
             attn_kwargs = {},
             ff_kwargs = {},
             norm_kwargs = {},
@@ -520,20 +436,8 @@ class TransformerBlock(nn.Module):
         self.cross_attend = cross_attend
         self.dim_context = dim_context
         self.causal = causal
-        self.global_cond_shared_embed = global_cond_shared_embed
 
-        norm_layer_map = {
-            "layer_norm": LayerNorm,
-            "rms_norm": RMSNorm,
-        }
-        norm_cls = norm_layer_map.get(norm_type, LayerNorm)
-
-        def make_norm():
-            if remove_norms:
-                return nn.Identity()
-            return norm_cls(dim, dtype=dtype, device=device, **norm_kwargs)
-
-        self.pre_norm = make_norm()
+        self.pre_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
 
         self.self_attn = Attention(
             dim,
@@ -547,7 +451,7 @@ class TransformerBlock(nn.Module):
         )
 
         if cross_attend:
-            self.cross_attend_norm = make_norm()
+            self.cross_attend_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
             self.cross_attn = Attention(
                 dim,
                 dim_heads = dim_heads,
@@ -560,56 +464,37 @@ class TransformerBlock(nn.Module):
                 **attn_kwargs
             )
 
-        self.ff_norm = make_norm()
-        self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs, dtype=dtype, device=device, operations=operations, **ff_kwargs)
+        self.ff_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
+        self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs, dtype=dtype, device=device, operations=operations,**ff_kwargs)
 
         self.layer_ix = layer_ix
 
         self.conformer = ConformerModule(dim, norm_kwargs=norm_kwargs) if conformer else None
 
-        # Global conditioning
-        self.has_global_cond = (global_cond_dim is not None) or global_cond_shared_embed
+        self.global_cond_dim = global_cond_dim
 
-        if global_cond_shared_embed:
-            # SA3 style: learnable per-block additive bias; global_cond is pre-projected to (B, dim*6)
-            self.to_scale_shift_gate = nn.Parameter(torch.empty(dim * 6, device=device, dtype=dtype))
-        elif global_cond_dim is not None:
-            # SA1 style: per-block MLP projects global_cond → (B, dim*6)
+        if global_cond_dim is not None:
             self.to_scale_shift_gate = nn.Sequential(
                 nn.SiLU(),
-                operations.Linear(global_cond_dim, dim * 6, bias=False, device=device, dtype=dtype)
+                nn.Linear(global_cond_dim, dim * 6, bias=False)
             )
 
-        # Local additive conditioning (e.g. inpaint mask + masked latent)
-        self.local_add_cond_dim = local_add_cond_dim
-        if local_add_cond_dim is not None:
-            self.to_local_embed = nn.Sequential(
-                operations.Linear(local_add_cond_dim, dim, bias=True, dtype=dtype, device=device),
-                nn.SiLU(),
-                operations.Linear(dim, dim, bias=True, dtype=dtype, device=device),
-            )
-        else:
-            self.to_local_embed = None
+            nn.init.zeros_(self.to_scale_shift_gate[1].weight)
+            #nn.init.zeros_(self.to_scale_shift_gate_self[1].bias)
 
     def forward(
         self,
         x,
         context = None,
         global_cond=None,
-        local_add_cond=None,
         mask = None,
         context_mask = None,
         rotary_pos_emb = None,
         transformer_options={}
     ):
-        if self.has_global_cond and global_cond is not None:
-            if self.global_cond_shared_embed:
-                # global_cond already has shape (B, dim*6)
-                ssg = (comfy.ops.cast_to_input(self.to_scale_shift_gate, global_cond) + global_cond).unsqueeze(1)
-            else:
-                ssg = self.to_scale_shift_gate(global_cond).unsqueeze(1)
+        if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None:
 
-            scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = ssg.chunk(6, dim = -1)
+            scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = self.to_scale_shift_gate(global_cond).unsqueeze(1).chunk(6, dim = -1)
 
             # self-attention with adaLN
             residual = x
@@ -625,9 +510,6 @@ class TransformerBlock(nn.Module):
             if self.conformer is not None:
                 x = x + self.conformer(x)
 
-            if local_add_cond is not None and self.to_local_embed is not None:
-                x = x + _left_pad_to_match(self.to_local_embed(local_add_cond), x.shape[-2])
-
             # feedforward with adaLN
             residual = x
             x = self.ff_norm(x)
@@ -645,9 +527,6 @@ class TransformerBlock(nn.Module):
             if self.conformer is not None:
                 x = x + self.conformer(x)
 
-            if local_add_cond is not None and self.to_local_embed is not None:
-                x = x + _left_pad_to_match(self.to_local_embed(local_add_cond), x.shape[-2])
-
             x = x + self.ff(self.ff_norm(x))
 
         return x
@@ -664,8 +543,6 @@ class ContinuousTransformer(nn.Module):
         cross_attend=False,
         cond_token_dim=None,
         global_cond_dim=None,
-        global_cond_shared_embed=False,
-        local_add_cond_dim=None,
         causal=False,
         rotary_pos_emb=True,
         zero_init_branch_outputs=True,
@@ -673,7 +550,6 @@ class ContinuousTransformer(nn.Module):
         use_sinusoidal_emb=False,
         use_abs_pos_emb=False,
         abs_pos_emb_max_length=10000,
-        num_memory_tokens=0,
         dtype=None,
         device=None,
         operations=None,
@@ -686,8 +562,6 @@ class ContinuousTransformer(nn.Module):
         self.depth = depth
         self.causal = causal
         self.layers = nn.ModuleList([])
-        self.num_memory_tokens = num_memory_tokens
-        self.global_cond_shared_embed = global_cond_shared_embed
 
         self.project_in = operations.Linear(dim_in, dim, bias=False, dtype=dtype, device=device) if dim_in is not None else nn.Identity()
         self.project_out = operations.Linear(dim, dim_out, bias=False, dtype=dtype, device=device) if dim_out is not None else nn.Identity()
@@ -703,22 +577,7 @@ class ContinuousTransformer(nn.Module):
 
         self.use_abs_pos_emb = use_abs_pos_emb
         if use_abs_pos_emb:
-            self.pos_emb = AbsolutePositionalEmbedding(dim, abs_pos_emb_max_length + num_memory_tokens)
-
-        if num_memory_tokens > 0:
-            self.memory_tokens = nn.Parameter(torch.empty(num_memory_tokens, dim, device=device, dtype=dtype))
-
-        # Shared global-cond embedder (SA3 style): projects (B, global_cond_dim) → (B, dim*6)
-        self.global_cond_embedder = None
-        if global_cond_shared_embed and global_cond_dim is not None:
-            self.global_cond_embedder = nn.Sequential(
-                operations.Linear(global_cond_dim, dim, bias=True, dtype=dtype, device=device),
-                nn.SiLU(),
-                operations.Linear(dim, dim * 6, bias=True, dtype=dtype, device=device),
-            )
-
-        # When using shared embed, TransformerBlocks use per-block Parameter (not per-block MLP)
-        block_global_cond_dim = None if global_cond_shared_embed else global_cond_dim
+            self.pos_emb = AbsolutePositionalEmbedding(dim, abs_pos_emb_max_length)
 
         for i in range(depth):
             self.layers.append(
@@ -727,9 +586,7 @@ class ContinuousTransformer(nn.Module):
                     dim_heads = dim_heads,
                     cross_attend = cross_attend,
                     dim_context = cond_token_dim,
-                    global_cond_dim = block_global_cond_dim,
-                    global_cond_shared_embed = global_cond_shared_embed,
-                    local_add_cond_dim = local_add_cond_dim,
+                    global_cond_dim = global_cond_dim,
                     causal = causal,
                     zero_init_branch_outputs = zero_init_branch_outputs,
                     conformer=conformer,
@@ -748,7 +605,6 @@ class ContinuousTransformer(nn.Module):
         prepend_embeds = None,
         prepend_mask = None,
         global_cond = None,
-        local_add_cond = None,
         return_info = False,
         **kwargs
     ):
@@ -776,9 +632,7 @@ class ContinuousTransformer(nn.Module):
 
                 mask = torch.cat((prepend_mask, mask), dim = -1)
 
-        if self.num_memory_tokens > 0:
-            memory_tokens = comfy.ops.cast_to_input(self.memory_tokens, x).expand(batch, -1, -1)
-            x = torch.cat((memory_tokens, x), dim=1)
+        # Attention layers
 
         if self.rotary_pos_emb is not None:
             rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=torch.float, device=x.device)
@@ -788,10 +642,6 @@ class ContinuousTransformer(nn.Module):
         if self.use_sinusoidal_emb or self.use_abs_pos_emb:
             x = x + self.pos_emb(x)
 
-        # Project global_cond once (SA3 shared-embed path)
-        if global_cond is not None and self.global_cond_embedder is not None:
-            global_cond = self.global_cond_embedder(global_cond)
-
         blocks_replace = patches_replace.get("dit", {})
         # Iterate over the transformer layers
         for i, layer in enumerate(self.layers):
@@ -804,17 +654,12 @@ class ContinuousTransformer(nn.Module):
                 out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": global_cond, "pe": rotary_pos_emb, "transformer_options": transformer_options}, {"original_block": block_wrap})
                 x = out["img"]
             else:
-                x = layer(x, rotary_pos_emb=rotary_pos_emb, global_cond=global_cond,
-                          local_add_cond=local_add_cond, context=context,
-                          transformer_options=transformer_options)
+                x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, context=context, transformer_options=transformer_options)
+            # x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)
 
             if return_info:
                 info["hidden_states"].append(x)
 
-        # Strip memory tokens before projecting out
-        if self.num_memory_tokens > 0:
-            x = x[:, self.num_memory_tokens:, :]
-
         x = self.project_out(x)
 
         if return_info:
@@ -837,7 +682,6 @@ class AudioDiffusionTransformer(nn.Module):
         num_heads=24,
         transformer_type: tp.Literal["continuous_transformer"] = "continuous_transformer",
         global_cond_type: tp.Literal["prepend", "adaLN"] = "prepend",
-        timestep_features_type: str = "learned",
         audio_model="",
         dtype=None,
         device=None,
@@ -852,10 +696,7 @@ class AudioDiffusionTransformer(nn.Module):
         # Timestep embeddings
         timestep_features_dim = 256
 
-        if timestep_features_type == "expo":
-            self.timestep_features = ExpoFourierFeatures(timestep_features_dim, 0.5, 10000.0)
-        else:
-            self.timestep_features = FourierFeatures(1, timestep_features_dim, dtype=dtype, device=device)
+        self.timestep_features = FourierFeatures(1, timestep_features_dim, dtype=dtype, device=device)
 
         self.to_timestep_embed = nn.Sequential(
             operations.Linear(timestep_features_dim, embed_dim, bias=True, dtype=dtype, device=device),
@@ -940,7 +781,6 @@ class AudioDiffusionTransformer(nn.Module):
         cross_attn_cond=None,
         cross_attn_cond_mask=None,
         input_concat_cond=None,
-        local_add_cond=None,
         global_embed=None,
         prepend_cond=None,
         prepend_cond_mask=None,
@@ -962,13 +802,9 @@ class AudioDiffusionTransformer(nn.Module):
             prepend_cond = self.to_prepend_embed(prepend_cond)
 
             prepend_inputs = prepend_cond
-            prepend_length = prepend_cond.shape[1]
             if prepend_cond_mask is not None:
                 prepend_mask = prepend_cond_mask
 
-        if local_add_cond is not None and local_add_cond.dim() == 3:
-            local_add_cond = local_add_cond.permute(0, 2, 1)
-
         if input_concat_cond is not None:
 
             # Interpolate input_concat_cond to the same length as x
@@ -1014,7 +850,7 @@ class AudioDiffusionTransformer(nn.Module):
         if self.transformer_type == "x-transformers":
             output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, **extra_args, **kwargs)
         elif self.transformer_type == "continuous_transformer":
-            output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, return_info=return_info, local_add_cond=local_add_cond, **extra_args, **kwargs)
+            output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, return_info=return_info, **extra_args, **kwargs)
 
             if return_info:
                 output, info = output
@@ -1040,7 +876,6 @@ class AudioDiffusionTransformer(nn.Module):
         context=None,
         context_mask=None,
         input_concat_cond=None,
-        local_add_cond=None,
         global_embed=None,
         negative_global_embed=None,
         prepend_cond=None,
@@ -1055,7 +890,6 @@ class AudioDiffusionTransformer(nn.Module):
                 cross_attn_cond=context,
                 cross_attn_cond_mask=context_mask,
                 input_concat_cond=input_concat_cond,
-                local_add_cond=local_add_cond,
                 global_embed=global_embed,
                 prepend_cond=prepend_cond,
                 prepend_cond_mask=prepend_cond_mask,

comfy/ldm/audio/embedders.py

@@ -31,39 +31,15 @@ def TimePositionalEmbedding(dim: int, out_features: int) -> nn.Module:
     )
 
 
-class ExpoFourierFeatures(nn.Module):
-    """Exponentially-spaced Fourier features (no learnable parameters)."""
-    def __init__(self, dim, min_freq=0.5, max_freq=10000.0):
-        super().__init__()
-        self.dim = dim
-        self.min_freq = min_freq
-        self.max_freq = max_freq
-
-    def forward(self, t):
-        in_dtype = t.dtype
-        t = t.float()
-        if t.dim() == 1:
-            t = t.unsqueeze(-1)
-        half_dim = self.dim // 2
-        ramp = torch.linspace(0, 1, half_dim, device=t.device, dtype=torch.float32)
-        freqs = torch.exp(ramp * (math.log(self.max_freq) - math.log(self.min_freq)) + math.log(self.min_freq))
-        args = t * freqs * 2 * math.pi
-        return torch.cat([args.cos(), args.sin()], dim=-1).to(in_dtype)
-
-
 class NumberEmbedder(nn.Module):
     def __init__(
         self,
         features: int,
         dim: int = 256,
-        fourier_features_type="learned",
     ):
         super().__init__()
         self.features = features
-        if fourier_features_type == "expo":
-            self.embedding = nn.Sequential(ExpoFourierFeatures(dim=dim), comfy.ops.manual_cast.Linear(in_features=dim, out_features=features))
-        else:
-            self.embedding = TimePositionalEmbedding(dim=dim, out_features=features)
+        self.embedding = TimePositionalEmbedding(dim=dim, out_features=features)
 
     def forward(self, x: Union[List[float], Tensor]) -> Tensor:
         if not torch.is_tensor(x):
@@ -101,15 +77,14 @@ class NumberConditioner(Conditioner):
     def __init__(self,
                 output_dim: int,
                 min_val: float=0,
-                max_val: float=1,
-                fourier_features_type: str = "learned",
+                max_val: float=1
                 ):
         super().__init__(output_dim, output_dim)
 
         self.min_val = min_val
         self.max_val = max_val
 
-        self.embedder = NumberEmbedder(features=output_dim, fourier_features_type=fourier_features_type)
+        self.embedder = NumberEmbedder(features=output_dim)
 
     def forward(self, floats, device=None):
             # Cast the inputs to floats

comfy/ldm/audio/vae_sa3.py

@@ -1,533 +0,0 @@
-import torch
-import torch.nn as nn
-
-import comfy.ops
-import comfy.model_management
-from comfy.ldm.modules.attention import optimized_attention
-from comfy.ldm.audio.autoencoder import WNConv1d
-
-ops = comfy.ops.disable_weight_init
-
-class Transpose(nn.Module):
-    def forward(self, x, **kwargs):
-        return x.transpose(-2, -1)
-
-
-def _zero_pad_modulo_sequence(x, size, dim=-2):
-    input_len = x.shape[dim]
-    pad_len = (size - input_len % size) % size
-    if pad_len > 0:
-        pad_shape = list(x.shape)
-        pad_shape[dim] = pad_len
-        x = torch.cat([x, torch.zeros(pad_shape, device=x.device, dtype=x.dtype)], dim=dim)
-    return x
-
-
-def _sliding_window_mask(seq_len, window, device, dtype):
-    """Additive attention mask enforcing a ±window local window (matches flash_attn window_size)."""
-    i = torch.arange(seq_len, device=device).unsqueeze(1)
-    j = torch.arange(seq_len, device=device).unsqueeze(0)
-    out_of_window = (j - i).abs() > window
-    return torch.where(
-        out_of_window,
-        torch.full((1,), torch.finfo(dtype).min / 4, device=device, dtype=dtype),
-        torch.zeros(1, device=device, dtype=dtype),
-    )
-
-
-class DynamicTanh(nn.Module):
-    def __init__(self, dim, init_alpha=4.0, dtype=None, device=None, **kwargs):
-        super().__init__()
-        self.alpha = nn.Parameter(torch.empty(1, dtype=dtype, device=device))
-        self.gamma = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
-        self.beta = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
-
-    def forward(self, x):
-        alpha = comfy.ops.cast_to_input(self.alpha, x)
-        gamma = comfy.ops.cast_to_input(self.gamma, x)
-        beta = comfy.ops.cast_to_input(self.beta, x)
-        return gamma * torch.tanh(alpha * x) + beta
-
-
-class RotaryEmbedding(nn.Module):
-    def __init__(self, dim, base=10000, base_rescale_factor=1., dtype=None, device=None):
-        super().__init__()
-        base = base * base_rescale_factor ** (dim / (dim - 2))
-        self.register_buffer("inv_freq", torch.empty(dim // 2, dtype=dtype, device=device))
-
-    def forward_from_seq_len(self, seq_len, device, dtype=None):
-        t = torch.arange(seq_len, device=device, dtype=torch.float32)
-        return self.forward(t)
-
-    def forward(self, t):
-        freqs = torch.outer(t.float(), comfy.model_management.cast_to(self.inv_freq, dtype=torch.float32, device=t.device))
-        freqs = torch.cat((freqs, freqs), dim=-1)
-        return freqs, 1.
-
-
-def _rotate_half(x):
-    d = x.shape[-1] // 2
-    return torch.cat((-x[..., d:], x[..., :d]), dim=-1)
-
-
-def _apply_rotary_pos_emb(t, freqs):
-    out_dtype = t.dtype
-    rot_dim = freqs.shape[-1]
-    seq_len = t.shape[-2]
-    freqs = freqs[-seq_len:]
-    t_rot, t_pass = t[..., :rot_dim], t[..., rot_dim:]
-    t_rot = t_rot * freqs.cos() + _rotate_half(t_rot) * freqs.sin()
-    return torch.cat((t_rot.to(out_dtype), t_pass.to(out_dtype)), dim=-1)
-
-
-class Attention(nn.Module):
-    def __init__(self, dim, dim_heads=64, qk_norm="none", qk_norm_eps=1e-6,
-                 differential=False, zero_init_output=True,
-                 dtype=None, device=None, operations=None, **kwargs):
-        super().__init__()
-        self.num_heads = dim // dim_heads
-        self.differential = differential
-        self.qk_norm = qk_norm
-
-        self.to_qkv = operations.Linear(
-            dim, dim * (5 if differential else 3), bias=False, dtype=dtype, device=device)
-        self.to_out = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
-
-        if qk_norm == "dyt":
-            self.q_norm = DynamicTanh(dim_heads, dtype=dtype, device=device)
-            self.k_norm = DynamicTanh(dim_heads, dtype=dtype, device=device)
-        elif qk_norm == "rms":
-            self.q_norm = operations.RMSNorm(dim_heads, eps=qk_norm_eps, dtype=dtype, device=device)
-            self.k_norm = operations.RMSNorm(dim_heads, eps=qk_norm_eps, dtype=dtype, device=device)
-
-    def forward(self, x, rotary_pos_emb=None, mask=None, **kwargs):
-        B, N, _ = x.shape
-        h = self.num_heads
-
-        qkv = self.to_qkv(x)
-        if self.differential:
-            q, k, v, q_diff, k_diff = qkv.chunk(5, dim=-1)
-            del qkv
-            q = q.view(B, N, h, -1).transpose(1, 2)
-            k = k.view(B, N, h, -1).transpose(1, 2)
-            v = v.view(B, N, h, -1).transpose(1, 2)
-            q_diff = q_diff.view(B, N, h, -1).transpose(1, 2)
-            k_diff = k_diff.view(B, N, h, -1).transpose(1, 2)
-        else:
-            q, k, v = qkv.chunk(3, dim=-1)
-            del qkv
-            q = q.view(B, N, h, -1).transpose(1, 2)
-            k = k.view(B, N, h, -1).transpose(1, 2)
-            v = v.view(B, N, h, -1).transpose(1, 2)
-
-        if self.qk_norm != "none":
-            q_dtype, k_dtype = q.dtype, k.dtype
-            q = self.q_norm(q).to(q_dtype)
-            k = self.k_norm(k).to(k_dtype)
-            if self.differential:
-                q_diff = self.q_norm(q_diff).to(q_dtype)
-                k_diff = self.k_norm(k_diff).to(k_dtype)
-
-        if rotary_pos_emb is not None:
-            freqs, _ = rotary_pos_emb
-            q_dtype, k_dtype = q.dtype, k.dtype
-            q = _apply_rotary_pos_emb(q.float(), freqs).to(q_dtype)
-            k = _apply_rotary_pos_emb(k.float(), freqs).to(k_dtype)
-            if self.differential:
-                q_diff = _apply_rotary_pos_emb(q_diff.float(), freqs).to(q_dtype)
-                k_diff = _apply_rotary_pos_emb(k_diff.float(), freqs).to(k_dtype)
-
-        if self.differential:
-            out = (optimized_attention(q, k, v, h, mask=mask, skip_reshape=True)
-                   - optimized_attention(q_diff, k_diff, v, h, mask=mask, skip_reshape=True))
-            del q, k, v, q_diff, k_diff
-        else:
-            out = optimized_attention(q, k, v, h, mask=mask, skip_reshape=True)
-            del q, k, v
-
-        return self.to_out(out)
-
-
-class _Sin(nn.Module):
-    def forward(self, x):
-        return torch.sin(3.14159265359 * x)
-
-
-class _GLU(nn.Module):
-    def __init__(self, dim_in, dim_out, activation, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.act = activation
-        self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device)
-
-    def forward(self, x):
-        x = self.proj(x)
-        x, gate = x.chunk(2, dim=-1)
-        return x * self.act(gate)
-
-
-class FeedForward(nn.Module):
-    def __init__(self, dim, mult=4, no_bias=False, zero_init_output=True,
-                 sinusoidal=False, dtype=None, device=None, operations=None, **kwargs):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        act = _Sin() if sinusoidal else nn.SiLU()
-        self.ff = nn.Sequential(
-            _GLU(dim, inner_dim, act, dtype=dtype, device=device, operations=operations),
-            nn.Identity(),
-            operations.Linear(inner_dim, dim, bias=not no_bias, dtype=dtype, device=device),
-            nn.Identity(),
-        )
-
-    def forward(self, x, **kwargs):
-        return self.ff(x)
-
-
-class TransformerBlock(nn.Module):
-    def __init__(self, dim, dim_heads=64, causal=False, zero_init_branch_outputs=True,
-                 norm_type="dyt", add_rope=False, attn_kwargs=None, ff_kwargs=None,
-                 norm_kwargs=None, dtype=None, device=None, operations=None, **kwargs):
-        super().__init__()
-        if attn_kwargs is None:
-            attn_kwargs = {}
-        if ff_kwargs is None:
-            ff_kwargs = {}
-        if norm_kwargs is None:
-            norm_kwargs = {}
-        dim_heads = min(dim_heads, dim)
-
-        Norm = DynamicTanh if norm_type == "dyt" else operations.RMSNorm
-        norm_kw = {**norm_kwargs, "dtype": dtype, "device": device}
-
-        self.pre_norm = Norm(dim, **norm_kw)
-        self.self_attn = Attention(dim, dim_heads=dim_heads,
-                                   zero_init_output=zero_init_branch_outputs,
-                                   dtype=dtype, device=device, operations=operations,
-                                   **attn_kwargs)
-        self.ff_norm = Norm(dim, **norm_kw)
-        self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs,
-                              dtype=dtype, device=device, operations=operations, **ff_kwargs)
-        self.rope = RotaryEmbedding(dim_heads // 2, dtype=dtype, device=device) if add_rope else None
-
-    def forward(self, x, mask=None, **kwargs):
-        rope = self.rope.forward_from_seq_len(x.shape[-2], device=x.device) \
-               if self.rope is not None else None
-        x = x + self.self_attn(self.pre_norm(x), rotary_pos_emb=rope, mask=mask)
-        x = x + self.ff(self.ff_norm(x))
-        return x
-
-
-class TransformerResamplingBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, stride, type="encoder",
-                 transformer_depth=3, dim_heads=128, differential=True,
-                 sliding_window=None, chunk_size=128, chunk_midpoint_shift=False,
-                 dyt=True, ff_mult=3, mapping_bias=True, variable_stride=False,
-                 sinusoidal_blocks=0, conv_mapping=False, dtype=None, device=None, operations=None, **kwargs):
-        super().__init__()
-        if type not in ("encoder", "decoder"):
-            raise ValueError(f"type must be 'encoder' or 'decoder', got {type!r}")
-
-        self.type = type
-        self.stride = stride
-        self.chunk_size = chunk_size
-        self.chunk_midpoint_shift = chunk_midpoint_shift
-        self.variable_stride = variable_stride
-        self.transformer_depth = transformer_depth
-
-        transformer_dim = out_channels if type == "encoder" else in_channels
-
-        self.mapping = (WNConv1d(in_channels, out_channels, 3 if conv_mapping else 1, padding="same", bias=mapping_bias)
-                        if in_channels != out_channels else nn.Identity())
-
-        self.sliding_window_latents = sliding_window
-        self.sliding_window_seq = self._get_sliding_window_size(sliding_window, stride)
-        self.input_seg_size, self.output_seg_size, self.sub_chunk_size = self._get_seg_sizes(stride)
-
-        token_seq = 1 if variable_stride else self.output_seg_size
-        self.new_tokens = nn.Parameter(torch.empty(1, token_seq, transformer_dim, dtype=dtype, device=device))
-
-        norm_type = "dyt" if dyt else "rms_norm"
-        attn_kwargs = {"qk_norm": "dyt" if dyt else "rms", "qk_norm_eps": 1e-3,
-                       "differential": differential}
-        norm_kwargs = {"eps": 1e-3}
-        transformers = []
-        for i in range(transformer_depth):
-            sinusoidal = (transformer_depth - i) < sinusoidal_blocks
-            transformers.append(TransformerBlock(
-                transformer_dim,
-                dim_heads=dim_heads,
-                causal=False,
-                zero_init_branch_outputs=True,
-                norm_type=norm_type,
-                add_rope=True,
-                attn_kwargs=attn_kwargs,
-                ff_kwargs={"mult": ff_mult, "no_bias": False, "sinusoidal": sinusoidal},
-                norm_kwargs=norm_kwargs,
-                dtype=dtype, device=device, operations=operations,
-            ))
-        self.transformers = nn.ModuleList(transformers)
-
-    def _get_sliding_window_size(self, window, stride, prepend_cond_length=0):
-        if window is None:
-            return None
-        return [w * (stride + 1 + prepend_cond_length) for w in window]
-
-    def _get_seg_sizes(self, stride, prepend_cond_length=0):
-        sub_chunk_size = stride + 1 + prepend_cond_length
-        input_seg_size = stride if self.type == "encoder" else 1
-        output_seg_size = 1 if self.type == "encoder" else stride
-        return input_seg_size, output_seg_size, sub_chunk_size
-
-    def forward(self, x, stride=None, **kwargs):
-        B = x.shape[0]
-
-        if stride is None:
-            input_seg = self.input_seg_size
-            output_seg = self.output_seg_size
-            sub_chunk = self.sub_chunk_size
-            sliding_window = self.sliding_window_seq
-        else:
-            input_seg, output_seg, sub_chunk = self._get_seg_sizes(stride)
-            sliding_window = self._get_sliding_window_size(self.sliding_window_latents, stride)
-
-        if self.type == "encoder":
-            if self.transformer_depth > 0:
-                pad_mod = self.chunk_size if sliding_window is None else input_seg
-                x = _zero_pad_modulo_sequence(x, pad_mod, dim=-1)
-            x = self.mapping(x)
-
-        if self.transformer_depth > 0:
-            x = x.permute(0, 2, 1)
-
-            if self.type != "encoder":
-                pad_mod = 1 if sliding_window is not None else (
-                    self.chunk_size // (stride if stride is not None else self.stride))
-                x = _zero_pad_modulo_sequence(x, pad_mod)
-
-            C = x.shape[2]
-            x = x.reshape(-1, input_seg, C)
-
-            new_tokens = self.new_tokens.expand(x.shape[0], output_seg, -1)
-            x = torch.cat([x, comfy.ops.cast_to_input(new_tokens, x)], dim=-2)
-            del new_tokens
-
-            x = x.reshape(B, -1, C)
-
-            if sliding_window is None:
-                eff_chunk = self.chunk_size + self.chunk_size // (stride if stride is not None else self.stride)
-
-            if sliding_window is None and self.chunk_midpoint_shift:
-                split = self.transformer_depth // 2
-                shift = eff_chunk // 2
-
-                x = x.reshape(-1, eff_chunk, C)
-                for layer in self.transformers[:split]:
-                    x = layer(x)
-                x = x.reshape(B, -1, C)
-
-                shifted = torch.cat([x[:, :shift, :], x, x[:, -shift:, :]], dim=1)
-                del x
-                x = shifted.reshape(-1, eff_chunk, C)
-                del shifted
-                for layer in self.transformers[split:]:
-                    x = layer(x)
-                x = x.reshape(B, -1, C)
-                x = x[:, shift:-shift, :]
-            elif sliding_window is None:
-                x = x.reshape(-1, eff_chunk, C)
-                for layer in self.transformers:
-                    x = layer(x)
-                x = x.reshape(B, -1, C)
-            else:
-                attn_mask = _sliding_window_mask(x.shape[1], sliding_window[0], x.device, x.dtype)
-                for layer in self.transformers:
-                    x = layer(x, mask=attn_mask)
-
-            x = x.reshape(-1, sub_chunk, C)
-            x = x[:, -output_seg:, :]
-            x = x.reshape(B, -1, C).transpose(1, 2)
-
-        if self.type == "decoder":
-            x = self.mapping(x)
-
-        return x
-
-
-class SAMEEncoder(nn.Module):
-    def __init__(self, in_channels=2, channels=128, latent_dim=32,
-                 c_mults=(1, 2, 4, 8), strides=(2, 4, 8, 8),
-                 transformer_depths=(3, 3, 3, 3),
-                 dtype=None, device=None, operations=None, **kwargs):
-        super().__init__()
-        channel_dims = [in_channels] + [channels * c for c in c_mults]
-        layers = []
-        for i in range(len(c_mults)):
-            layers.append(TransformerResamplingBlock(
-                in_channels=channel_dims[i], out_channels=channel_dims[i + 1],
-                stride=strides[i], type="encoder",
-                transformer_depth=transformer_depths[i],
-                dtype=dtype, device=device, operations=operations, **kwargs))
-        layers += [
-            Transpose(),
-            operations.Linear(channel_dims[-1], latent_dim, dtype=dtype, device=device),
-            Transpose(),
-        ]
-        self.layers = nn.ModuleList(layers)
-
-    def forward(self, x, **kwargs):
-        for layer in self.layers:
-            x = layer(x)
-        return x
-
-
-class SAMEDecoder(nn.Module):
-    def __init__(self, out_channels=2, channels=128, latent_dim=32,
-                 c_mults=(1, 2, 4, 8), strides=(2, 4, 8, 8),
-                 transformer_depths=(3, 3, 3, 3), sinusoidal_blocks=None,
-                 dtype=None, device=None, operations=None, **kwargs):
-        super().__init__()
-        if sinusoidal_blocks is None:
-            sinusoidal_blocks = [0] * len(c_mults)
-        channel_dims = [out_channels] + [channels * c for c in c_mults]
-        layers = [
-            Transpose(),
-            operations.Linear(latent_dim, channel_dims[-1], dtype=dtype, device=device),
-            Transpose(),
-        ]
-        for i in range(len(c_mults) - 1, -1, -1):
-            layers.append(TransformerResamplingBlock(
-                in_channels=channel_dims[i + 1], out_channels=channel_dims[i],
-                stride=strides[i], type="decoder",
-                transformer_depth=transformer_depths[i],
-                sinusoidal_blocks=sinusoidal_blocks[i],
-                dtype=dtype, device=device, operations=operations, **kwargs))
-        self.layers = nn.ModuleList(layers)
-
-    def forward(self, x, **kwargs):
-        for layer in self.layers:
-            x = layer(x)
-        return x
-
-
-class SoftNormBottleneck(nn.Module):
-    def __init__(self, dim=32, noise_augment_dim=0, noise_regularize=False,
-                 auto_scale=False, freeze=False, dtype=None, device=None, **kwargs):
-        super().__init__()
-        self.noise_augment_dim = noise_augment_dim
-        self.noise_regularize = noise_regularize
-        self.scaling_factor = nn.Parameter(torch.empty(1, dim, 1, dtype=dtype, device=device))
-        self.bias = nn.Parameter(torch.empty(1, dim, 1, dtype=dtype, device=device))
-        self.noise_scaling_factor = nn.Parameter(torch.empty(1, noise_augment_dim, 1, dtype=dtype, device=device))
-        if auto_scale:
-            self.register_parameter("running_std", nn.Parameter(
-                torch.empty(1, dtype=dtype, device=device), requires_grad=False))
-        if freeze:
-            for p in self.parameters():
-                p.requires_grad = False
-
-    def encode(self, x, return_info=False, **kwargs):
-        x = x * comfy.ops.cast_to_input(self.scaling_factor, x) \
-              + comfy.ops.cast_to_input(self.bias, x)
-        if hasattr(self, "running_std"):
-            x = x / comfy.ops.cast_to_input(self.running_std, x)
-        if return_info:
-            return x, {}
-        return x
-
-    def decode(self, x, **kwargs):
-        if hasattr(self, "running_std"):
-            x = x * comfy.ops.cast_to_input(self.running_std, x)
-        if self.noise_regularize:
-            scaling = self.running_std if hasattr(self, "running_std") \
-                      else x.std(dim=-1, keepdim=True)
-            noise = torch.randn_like(x) * comfy.ops.cast_to_input(scaling, x) * 1e-3
-            x = x + noise
-        if self.noise_augment_dim > 0:
-            noise = comfy.ops.cast_to_input(self.noise_scaling_factor, x) * torch.randn(
-                x.shape[0], self.noise_augment_dim, x.shape[-1], device=x.device, dtype=x.dtype)
-            x = torch.cat([x, noise], dim=1)
-        return x
-
-
-class PatchedPretransform(nn.Module):
-    def __init__(self, channels, patch_size, **kwargs):
-        super().__init__()
-        self.channels = channels
-        self.patch_size = patch_size
-        self.enable_grad = False
-
-    def _pad(self, x):
-        pad_len = (self.patch_size - x.shape[-1] % self.patch_size) % self.patch_size
-        if pad_len > 0:
-            x = torch.cat([x, torch.zeros_like(x[:, :, :pad_len])], dim=-1)
-        return x
-
-    def encode(self, x):
-        x = self._pad(x)
-        B, C, T = x.shape
-        h = self.patch_size
-        L = T // h
-        # b c (l h) -> b (c h) l
-        return x.reshape(B, C, L, h).permute(0, 1, 3, 2).reshape(B, C * h, L)
-
-    def decode(self, x):
-        B, Ch, L = x.shape
-        h = self.patch_size
-        C = Ch // h
-        # b (c h) l -> b c (l h)
-        return x.reshape(B, C, h, L).permute(0, 1, 3, 2).reshape(B, C, L * h)
-
-
-class SA3AudioVAE(nn.Module):
-    """SA3 VAE. State dict keys match checkpoint after stripping 'pretransform.model.'"""
-
-    def __init__(self, channels=256, transformer_depths=12, sinusoidal_blocks=8,
-                 sliding_window=None, decoder_conv_mapping=False,
-                 chunk_size=128, chunk_midpoint_shift=False,
-                 dtype=None, device=None, operations=None):
-        super().__init__()
-        if operations is None:
-            operations = ops
-
-        self.pretransform = PatchedPretransform(channels=2, patch_size=256)
-
-        common_kwargs = dict(
-            differential=True, dyt=True, dim_heads=64,
-            sliding_window=sliding_window, variable_stride=True,
-            chunk_size=chunk_size, chunk_midpoint_shift=chunk_midpoint_shift,
-            dtype=dtype, device=device, operations=operations,
-        )
-        self.encoder = SAMEEncoder(
-            in_channels=512, channels=channels, c_mults=[6], strides=[16],
-            latent_dim=256, transformer_depths=[transformer_depths],
-            conv_mapping=False, **common_kwargs,
-        )
-        self.decoder = SAMEDecoder(
-            out_channels=512, channels=channels, c_mults=[6], strides=[16],
-            latent_dim=256, transformer_depths=[transformer_depths], sinusoidal_blocks=[sinusoidal_blocks],
-            conv_mapping=decoder_conv_mapping, **common_kwargs,
-        )
-        self.bottleneck = SoftNormBottleneck(
-            dim=256, noise_augment_dim=0, noise_regularize=True,
-            auto_scale=True, freeze=True,
-            dtype=dtype, device=device,
-        )
-
-    @torch.no_grad()
-    def _pretransform_encode(self, x):
-        return self.pretransform.encode(x)
-
-    @torch.no_grad()
-    def _pretransform_decode(self, x):
-        return self.pretransform.decode(x)
-
-    def encode(self, x):
-        x = self._pretransform_encode(x)
-        x = self.encoder(x)
-        x = self.bottleneck.encode(x)
-        return x
-
-    def decode(self, x):
-        x = self.bottleneck.decode(x)
-        x = self.decoder(x)
-        x = self._pretransform_decode(x)
-        return x

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

@@ -15,7 +15,7 @@ def rope(pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor:
 
     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.einsum("...n,d->...nd", pos, omega)
     out = torch.stack([torch.cos(out), torch.sin(out)], dim=0)
     return out.to(dtype=torch.float32, device=pos.device)
 
@@ -118,6 +118,8 @@ class ErnieImageAttention(nn.Module):
             query = apply_rotary_emb(query, image_rotary_emb)
             key = apply_rotary_emb(key, image_rotary_emb)
 
+        query, key = query.to(x.dtype), key.to(x.dtype)
+
         q_flat = query.reshape(B, S, -1)
         k_flat = key.reshape(B, S, -1)
 
@@ -159,16 +161,16 @@ class ErnieImageSharedAdaLNBlock(nn.Module):
 
         residual = x
         x_norm = self.adaLN_sa_ln(x)
-        x_norm = x_norm * (1 + scale_msa) + shift_msa
+        x_norm = (x_norm.float() * (1 + scale_msa.float()) + shift_msa.float()).to(x.dtype)
 
         attn_out = self.self_attention(x_norm, attention_mask=attention_mask, image_rotary_emb=rotary_pos_emb)
-        x = residual + gate_msa * attn_out
+        x = residual + (gate_msa.float() * attn_out.float()).to(x.dtype)
 
         residual = x
         x_norm = self.adaLN_mlp_ln(x)
-        x_norm = x_norm * (1 + scale_mlp) + shift_mlp
+        x_norm = (x_norm.float() * (1 + scale_mlp.float()) + shift_mlp.float()).to(x.dtype)
 
-        return residual + gate_mlp * self.mlp(x_norm)
+        return residual + (gate_mlp.float() * self.mlp(x_norm).float()).to(x.dtype)
 
 class ErnieImageAdaLNContinuous(nn.Module):
     def __init__(self, hidden_size: int, eps: float = 1e-6, operations=None, device=None, dtype=None):
@@ -181,7 +183,7 @@ class ErnieImageAdaLNContinuous(nn.Module):
     def forward(self, x: torch.Tensor, conditioning: torch.Tensor) -> torch.Tensor:
         scale, shift = self.linear(conditioning).chunk(2, dim=-1)
         x = self.norm(x)
-        x = torch.addcmul(shift.unsqueeze(1), x, 1 + scale.unsqueeze(1))
+        x = x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
         return x
 
 class ErnieImageModel(nn.Module):
@@ -277,7 +279,7 @@ class ErnieImageModel(nn.Module):
         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)
+        sample = self.time_proj(timesteps.to(dtype)).to(self.time_embedding.linear_1.weight.dtype)
         c = self.time_embedding(sample)
 
         shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = [