feat: add proxyWidgetSelector to subgraph blueprints

.ci/windows_amd_base_files/run_amd_gpu_disable_smart_memory.bat

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

.ci/windows_intel_base_files/run_intel_gpu.bat

@@ -1,2 +0,0 @@
-.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build
-pause

.github/workflows/openapi-lint.yml

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

.github/workflows/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/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",
-                "shape": 7,
-                "type": "FLOAT,INT",
-                "link": 19
-              },
-              {
-                "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": [
-                  13
-                ]
-              },
-              {
-                "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,
-            "target_id": 2,
-            "target_slot": 1,
-            "type": "IMAGE"
-          },
-          {
-            "id": 8,
-            "origin_id": 9,
-            "origin_slot": 0,
-            "target_id": 2,
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-            "type": "INT"
-          },
-          {
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-            "origin_id": 2,
-            "origin_slot": 0,
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-            "type": "IMAGE"
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-          {
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-            "target_id": 5,
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-            "type": "AUDIO"
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-            "origin_id": 10,
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-          },
-          {
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-            "origin_id": 3,
-            "origin_slot": 2,
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-            "type": "FLOAT"
-          },
-          {
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-            "origin_id": 11,
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-          {
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-            "origin_slot": 0,
-            "target_id": 10,
-            "target_slot": 2,
-            "type": "BOOLEAN"
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-          {
-            "id": 18,
-            "origin_id": 3,
-            "origin_slot": 2,
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-          {
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-            "target_slot": 0,
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-            "origin_id": -10,
-            "origin_slot": 1,
-            "target_id": 9,
-            "target_slot": 0,
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-          {
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-            "target_slot": 0,
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-          },
-          {
-            "id": 24,
-            "origin_id": -10,
-            "origin_slot": 3,
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-            "target_slot": 0,
-            "type": "COMBO"
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-          {
-            "id": 26,
-            "origin_id": 5,
-            "origin_slot": 0,
-            "target_id": -20,
-            "target_slot": 0,
-            "type": "VIDEO"
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-          {
-            "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
-        },
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-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": []
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-      ],
-      "title": "Get Any Video Frame",
-      "properties": {
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-        "extra": {},
-        "category": "Video Tools",
-        "description": "Extracts one image frame from a video at a chosen index, with optional trim and FPS control."
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blueprints/Image Channels.json

@@ -1,323 +1 @@
-{
-  "revision": 0,
-  "last_node_id": 29,
-  "last_link_id": 0,
-  "nodes": [
-    {
-      "id": 29,
-      "type": "4c9d6ea4-b912-40e5-8766-6793a9758c53",
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-      "size": [
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-      "flags": {},
-      "order": 5,
-      "mode": 0,
-      "inputs": [
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-          "label": "image",
-          "localized_name": "images.image0",
-          "name": "images.image0",
-          "type": "IMAGE",
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "label": "R",
-          "localized_name": "IMAGE0",
-          "name": "IMAGE0",
-          "type": "IMAGE",
-          "links": []
-        },
-        {
-          "label": "G",
-          "localized_name": "IMAGE1",
-          "name": "IMAGE1",
-          "type": "IMAGE",
-          "links": []
-        },
-        {
-          "label": "B",
-          "localized_name": "IMAGE2",
-          "name": "IMAGE2",
-          "type": "IMAGE",
-          "links": []
-        },
-        {
-          "label": "A",
-          "localized_name": "IMAGE3",
-          "name": "IMAGE3",
-          "type": "IMAGE",
-          "links": []
-        }
-      ],
-      "title": "Image Channels",
-      "properties": {
-        "proxyWidgets": []
-      },
-      "widgets_values": []
-    }
-  ],
-  "links": [],
-  "version": 0.4,
-  "definitions": {
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-        "version": 1,
-        "state": {
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-          "lastNodeId": 28,
-          "lastLinkId": 39,
-          "lastRerouteId": 0
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-        "revision": 0,
-        "config": {},
-        "name": "Image Channels",
-        "inputNode": {
-          "id": -10,
-          "bounding": [
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-            -185,
-            120,
-            60
-          ]
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-            "name": "images.image0",
-            "type": "IMAGE",
-            "linkIds": [
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-            "localized_name": "images.image0",
-            "label": "image",
-            "pos": [
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-        ],
-        "outputs": [
-          {
-            "id": "605cb9c3-b065-4d9b-81d2-3ec331889b2b",
-            "name": "IMAGE0",
-            "type": "IMAGE",
-            "linkIds": [
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-            "localized_name": "IMAGE0",
-            "label": "R",
-            "pos": [
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-              -195
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-          {
-            "id": "fb44a77e-0522-43e9-9527-82e7465b3596",
-            "name": "IMAGE1",
-            "type": "IMAGE",
-            "linkIds": [
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-            "localized_name": "IMAGE1",
-            "label": "G",
-            "pos": [
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-          },
-          {
-            "id": "81460ee6-0131-402a-874f-6bf3001fc4ff",
-            "name": "IMAGE2",
-            "type": "IMAGE",
-            "linkIds": [
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-            ],
-            "localized_name": "IMAGE2",
-            "label": "B",
-            "pos": [
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-              -155
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-          {
-            "id": "ae690246-80d4-4951-b1d9-9306d8a77417",
-            "name": "IMAGE3",
-            "type": "IMAGE",
-            "linkIds": [
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-            ],
-            "localized_name": "IMAGE3",
-            "label": "A",
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-              -135
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-            "type": "GLSLShader",
-            "pos": [
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-              -330
-            ],
-            "size": [
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-              172
-            ],
-            "flags": {},
-            "order": 0,
-            "mode": 0,
-            "inputs": [
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-                "label": "image",
-                "localized_name": "images.image0",
-                "name": "images.image0",
-                "type": "IMAGE",
-                "link": 39
-              },
-              {
-                "localized_name": "fragment_shader",
-                "name": "fragment_shader",
-                "type": "STRING",
-                "widget": {
-                  "name": "fragment_shader"
-                },
-                "link": null
-              },
-              {
-                "localized_name": "size_mode",
-                "name": "size_mode",
-                "type": "COMFY_DYNAMICCOMBO_V3",
-                "widget": {
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-                },
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-              },
-              {
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-                "localized_name": "images.image1",
-                "name": "images.image1",
-                "shape": 7,
-                "type": "IMAGE",
-                "link": null
-              }
-            ],
-            "outputs": [
-              {
-                "label": "R",
-                "localized_name": "IMAGE0",
-                "name": "IMAGE0",
-                "type": "IMAGE",
-                "links": [
-                  26
-                ]
-              },
-              {
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-                "name": "IMAGE1",
-                "type": "IMAGE",
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-                ]
-              },
-              {
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-                "links": [
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-                "name": "IMAGE3",
-                "type": "IMAGE",
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-                  29
-                ]
-              }
-            ],
-            "properties": {
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-            },
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-            "type": "IMAGE"
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-            "target_slot": 2,
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-            "target_slot": 3,
-            "type": "IMAGE"
-          }
-        ],
-        "extra": {
-          "workflowRendererVersion": "LG"
-        },
-        "category": "Image Tools/Color adjust",
-        "description": "Manipulates individual RGBA channels for masking, compositing, and channel effects."
-      }
-    ]
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blueprints/Image to Pose Map (SDPose-OOD).json

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blueprints/Prompt Enhance.json

@@ -1,279 +1 @@
-{
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blueprints/Remove Background (BiRefNet).json

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blueprints/Select Per-Line Text by Index.json

@@ -1,484 +0,0 @@
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+{"revision": 0, "last_node_id": 25, "last_link_id": 0, "nodes": [{"id": 25, "type": "621ba4e2-22a8-482d-a369-023753198b7b", "pos": [4610, -790], "size": [230, 58], "flags": {}, "order": 4, "mode": 0, "inputs": [{"label": "image", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": null}], "outputs": [{"label": "IMAGE", "localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": []}], "title": "Sharpen", "properties": {"proxyWidgets": [["24", "value"]]}, "widgets_values": []}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "621ba4e2-22a8-482d-a369-023753198b7b", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 24, "lastLinkId": 36, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Sharpen", "inputNode": {"id": -10, "bounding": [4090, -825, 120, 60]}, "outputNode": {"id": -20, "bounding": [5150, -825, 120, 60]}, "inputs": [{"id": "37011fb7-14b7-4e0e-b1a0-6a02e8da1fd7", "name": "images.image0", "type": "IMAGE", "linkIds": [34], "localized_name": "images.image0", "label": "image", "pos": [4190, -805]}], "outputs": [{"id": "e9182b3f-635c-4cd4-a152-4b4be17ae4b9", "name": "IMAGE0", "type": "IMAGE", "linkIds": [35], "localized_name": "IMAGE0", "label": "IMAGE", "pos": [5170, -805]}], "widgets": [], "nodes": [{"id": 24, "type": "PrimitiveFloat", "pos": [4280, -1240], "size": [270, 58], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "strength", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [36]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 3, "precision": 2, "step": 0.05}, "widgets_values": [0.5]}, {"id": 23, "type": "GLSLShader", "pos": [4570, -1240], "size": [370, 192], "flags": {}, "order": 1, "mode": 0, "inputs": [{"label": "image0", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": 34}, {"label": "image1", "localized_name": "images.image1", "name": "images.image1", "shape": 7, "type": "IMAGE", "link": null}, {"label": "u_float0", "localized_name": "floats.u_float0", "name": "floats.u_float0", "shape": 7, "type": "FLOAT", "link": 36}, {"label": "u_float1", "localized_name": "floats.u_float1", "name": "floats.u_float1", "shape": 7, "type": "FLOAT", "link": null}, {"label": "u_int0", "localized_name": "ints.u_int0", "name": "ints.u_int0", "shape": 7, "type": "INT", "link": null}, {"localized_name": "fragment_shader", "name": "fragment_shader", "type": "STRING", "widget": {"name": "fragment_shader"}, "link": null}, {"localized_name": "size_mode", "name": "size_mode", "type": "COMFY_DYNAMICCOMBO_V3", "widget": {"name": "size_mode"}, "link": null}], "outputs": [{"localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": [35]}, {"localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": null}], "properties": {"Node name for S&R": "GLSLShader"}, "widgets_values": ["#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform float u_float0;  // strength [0.0 – 2.0] typical: 0.3–1.0\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nvoid main() {\n    vec2 texel = 1.0 / u_resolution;\n    \n    // Sample center and neighbors\n    vec4 center = texture(u_image0, v_texCoord);\n    vec4 top    = texture(u_image0, v_texCoord + vec2( 0.0, -texel.y));\n    vec4 bottom = texture(u_image0, v_texCoord + vec2( 0.0,  texel.y));\n    vec4 left   = texture(u_image0, v_texCoord + vec2(-texel.x,  0.0));\n    vec4 right  = texture(u_image0, v_texCoord + vec2( texel.x,  0.0));\n    \n    // Edge enhancement (Laplacian)\n    vec4 edges = center * 4.0 - top - bottom - left - right;\n    \n    // Add edges back scaled by strength\n    vec4 sharpened = center + edges * u_float0;\n    \n    fragColor0 = vec4(clamp(sharpened.rgb, 0.0, 1.0), center.a);\n}", "from_input"]}], "groups": [], "links": [{"id": 36, "origin_id": 24, "origin_slot": 0, "target_id": 23, "target_slot": 2, "type": "FLOAT"}, {"id": 34, "origin_id": -10, "origin_slot": 0, "target_id": 23, "target_slot": 0, "type": "IMAGE"}, {"id": 35, "origin_id": 23, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Image Tools/Sharpen"}]}}

blueprints/Split Image Grid to Tiles.json

@@ -1,713 +0,0 @@
-{
-  "revision": 0,
-  "last_node_id": 251,
-  "last_link_id": 0,
-  "nodes": [
-    {
-      "id": 251,
-      "type": "609e1fd1-b731-4b78-89ac-d19b1156b025",
-      "pos": [
-        -1490,
-        130
-      ],
-      "size": [
-        230,
-        164
-      ],
-      "flags": {},
-      "order": 1,
-      "mode": 0,
-      "inputs": [
-        {
-          "localized_name": "source_image",
-          "name": "source_image",
-          "type": "IMAGE",
-          "link": null
-        },
-        {
-          "localized_name": "columns",
-          "name": "columns",
-          "type": "INT",
-          "widget": {
-            "name": "columns"
-          },
-          "link": null
-        },
-        {
-          "localized_name": "rows",
-          "name": "rows",
-          "type": "INT",
-          "widget": {
-            "name": "rows"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "localized_name": "tiles",
-          "name": "tiles",
-          "type": "IMAGE",
-          "links": []
-        }
-      ],
-      "properties": {
-        "proxyWidgets": [
-          [
-            "228",
-            "value"
-          ],
-          [
-            "252",
-            "value"
-          ]
-        ],
-        "cnr_id": "comfy-core",
-        "ver": "0.20.1",
-        "enableTabs": false,
-        "tabWidth": 65,
-        "tabXOffset": 10,
-        "hasSecondTab": false,
-        "secondTabText": "Send Back",
-        "secondTabOffset": 80,
-        "secondTabWidth": 65
-      },
-      "widgets_values": [],
-      "title": "Split Image Grid to Tiles"
-    }
-  ],
-  "links": [],
-  "version": 0.4,
-  "definitions": {
-    "subgraphs": [
-      {
-        "id": "609e1fd1-b731-4b78-89ac-d19b1156b025",
-        "version": 1,
-        "state": {
-          "lastGroupId": 9,
-          "lastNodeId": 252,
-          "lastLinkId": 429,
-          "lastRerouteId": 0
-        },
-        "revision": 0,
-        "config": {},
-        "name": "Split Image Grid to Tiles",
-        "inputNode": {
-          "id": -10,
-          "bounding": [
-            -1690,
-            260,
-            128,
-            108
-          ]
-        },
-        "outputNode": {
-          "id": -20,
-          "bounding": [
-            -510,
-            590,
-            128,
-            68
-          ]
-        },
-        "inputs": [
-          {
-            "id": "866ac798-cfbc-450a-b755-e704f86404d9",
-            "name": "source_image",
-            "type": "IMAGE",
-            "linkIds": [
-              386,
-              389
-            ],
-            "localized_name": "source_image",
-            "pos": [
-              -1586,
-              284
-            ]
-          },
-          {
-            "id": "bc37b1f8-8ab2-4f19-bd00-75d4fbc4feb3",
-            "name": "columns",
-            "type": "INT",
-            "linkIds": [
-              427
-            ],
-            "localized_name": "columns",
-            "pos": [
-              -1586,
-              304
-            ]
-          },
-          {
-            "id": "d45915da-e848-43dd-9ccc-e3161e9c99d9",
-            "name": "rows",
-            "type": "INT",
-            "linkIds": [
-              428
-            ],
-            "localized_name": "rows",
-            "pos": [
-              -1586,
-              324
-            ]
-          }
-        ],
-        "outputs": [
-          {
-            "id": "18bc780f-064b-4038-87c6-67dba71deb08",
-            "name": "tiles",
-            "type": "IMAGE",
-            "linkIds": [
-              394
-            ],
-            "localized_name": "tiles",
-            "shape": 6,
-            "pos": [
-              -486,
-              614
-            ]
-          }
-        ],
-        "widgets": [],
-        "nodes": [
-          {
-            "id": 225,
-            "type": "SplitImageToTileList",
-            "pos": [
-              -1010,
-              620
-            ],
-            "size": [
-              290,
-              170
-            ],
-            "flags": {},
-            "order": 0,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "image",
-                "name": "image",
-                "type": "IMAGE",
-                "link": 386
-              },
-              {
-                "localized_name": "tile_width",
-                "name": "tile_width",
-                "type": "INT",
-                "widget": {
-                  "name": "tile_width"
-                },
-                "link": 403
-              },
-              {
-                "localized_name": "tile_height",
-                "name": "tile_height",
-                "type": "INT",
-                "widget": {
-                  "name": "tile_height"
-                },
-                "link": 404
-              },
-              {
-                "localized_name": "overlap",
-                "name": "overlap",
-                "type": "INT",
-                "widget": {
-                  "name": "overlap"
-                },
-                "link": null
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "IMAGE",
-                "name": "IMAGE",
-                "shape": 6,
-                "type": "IMAGE",
-                "links": [
-                  394
-                ]
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "SplitImageToTileList",
-              "cnr_id": "comfy-core",
-              "ver": "0.20.1",
-              "enableTabs": false,
-              "tabWidth": 65,
-              "tabXOffset": 10,
-              "hasSecondTab": false,
-              "secondTabText": "Send Back",
-              "secondTabOffset": 80,
-              "secondTabWidth": 65
-            },
-            "widgets_values": [
-              1024,
-              1024,
-              0
-            ]
-          },
-          {
-            "id": 231,
-            "type": "ComfyMathExpression",
-            "pos": [
-              -1080,
-              330
-            ],
-            "size": [
-              370,
-              190
-            ],
-            "flags": {},
-            "order": 4,
-            "mode": 0,
-            "inputs": [
-              {
-                "label": "a",
-                "localized_name": "values.a",
-                "name": "values.a",
-                "type": "FLOAT,INT,BOOLEAN",
-                "link": 390
-              },
-              {
-                "label": "b",
-                "localized_name": "values.b",
-                "name": "values.b",
-                "shape": 7,
-                "type": "FLOAT,INT,BOOLEAN",
-                "link": 429
-              },
-              {
-                "label": "c",
-                "localized_name": "values.c",
-                "name": "values.c",
-                "shape": 7,
-                "type": "FLOAT,INT,BOOLEAN",
-                "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": [
-                  404
-                ]
-              },
-              {
-                "localized_name": "BOOL",
-                "name": "BOOL",
-                "type": "BOOLEAN",
-                "links": null
-              }
-            ],
-            "title": "Math Expression （Height）",
-            "properties": {
-              "Node name for S&R": "ComfyMathExpression",
-              "cnr_id": "comfy-core",
-              "ver": "0.18.1",
-              "enableTabs": false,
-              "tabWidth": 65,
-              "tabXOffset": 10,
-              "hasSecondTab": false,
-              "secondTabText": "Send Back",
-              "secondTabOffset": 80,
-              "secondTabWidth": 65,
-              "ue_properties": {
-                "widget_ue_connectable": {},
-                "version": "7.7",
-                "input_ue_unconnectable": {}
-              }
-            },
-            "widgets_values": [
-              "max(1, (int(a) + int(b) - 1) // int(b))"
-            ]
-          },
-          {
-            "id": 229,
-            "type": "ComfyMathExpression",
-            "pos": [
-              -1090,
-              -30
-            ],
-            "size": [
-              370,
-              190
-            ],
-            "flags": {},
-            "order": 2,
-            "mode": 0,
-            "inputs": [
-              {
-                "label": "a",
-                "localized_name": "values.a",
-                "name": "values.a",
-                "type": "FLOAT,INT,BOOLEAN",
-                "link": 387
-              },
-              {
-                "label": "b",
-                "localized_name": "values.b",
-                "name": "values.b",
-                "shape": 7,
-                "type": "FLOAT,INT,BOOLEAN",
-                "link": 388
-              },
-              {
-                "label": "c",
-                "localized_name": "values.c",
-                "name": "values.c",
-                "shape": 7,
-                "type": "FLOAT,INT,BOOLEAN",
-                "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": [
-                  403
-                ]
-              },
-              {
-                "localized_name": "BOOL",
-                "name": "BOOL",
-                "type": "BOOLEAN",
-                "links": null
-              }
-            ],
-            "title": "Math Expression （Width）",
-            "properties": {
-              "Node name for S&R": "ComfyMathExpression",
-              "cnr_id": "comfy-core",
-              "ver": "0.18.1",
-              "enableTabs": false,
-              "tabWidth": 65,
-              "tabXOffset": 10,
-              "hasSecondTab": false,
-              "secondTabText": "Send Back",
-              "secondTabOffset": 80,
-              "secondTabWidth": 65,
-              "ue_properties": {
-                "widget_ue_connectable": {},
-                "version": "7.7",
-                "input_ue_unconnectable": {}
-              }
-            },
-            "widgets_values": [
-              "max(1, (int(a) + int(b) - 1) // int(b))"
-            ]
-          },
-          {
-            "id": 228,
-            "type": "PrimitiveInt",
-            "pos": [
-              -1380,
-              90
-            ],
-            "size": [
-              230,
-              110
-            ],
-            "flags": {},
-            "order": 1,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "value",
-                "name": "value",
-                "type": "INT",
-                "widget": {
-                  "name": "value"
-                },
-                "link": 427
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "INT",
-                "name": "INT",
-                "type": "INT",
-                "links": [
-                  388
-                ]
-              }
-            ],
-            "title": "Int (grid columns)",
-            "properties": {
-              "Node name for S&R": "Int (grid columns)",
-              "cnr_id": "comfy-core",
-              "ver": "0.18.1",
-              "enableTabs": false,
-              "tabWidth": 65,
-              "tabXOffset": 10,
-              "hasSecondTab": false,
-              "secondTabText": "Send Back",
-              "secondTabOffset": 80,
-              "secondTabWidth": 65,
-              "ue_properties": {
-                "widget_ue_connectable": {},
-                "version": "7.7",
-                "input_ue_unconnectable": {}
-              }
-            },
-            "widgets_values": [
-              2,
-              "fixed"
-            ]
-          },
-          {
-            "id": 230,
-            "type": "GetImageSize",
-            "pos": [
-              -1380,
-              290
-            ],
-            "size": [
-              230,
-              100
-            ],
-            "flags": {},
-            "order": 3,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "image",
-                "name": "image",
-                "type": "IMAGE",
-                "link": 389
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "width",
-                "name": "width",
-                "type": "INT",
-                "links": [
-                  387
-                ]
-              },
-              {
-                "localized_name": "height",
-                "name": "height",
-                "type": "INT",
-                "links": [
-                  390
-                ]
-              },
-              {
-                "localized_name": "batch_size",
-                "name": "batch_size",
-                "type": "INT",
-                "links": null
-              }
-            ],
-            "properties": {
-              "Node name for S&R": "GetImageSize",
-              "cnr_id": "comfy-core",
-              "ver": "0.18.1",
-              "enableTabs": false,
-              "tabWidth": 65,
-              "tabXOffset": 10,
-              "hasSecondTab": false,
-              "secondTabText": "Send Back",
-              "secondTabOffset": 80,
-              "secondTabWidth": 65,
-              "ue_properties": {
-                "widget_ue_connectable": {},
-                "version": "7.7",
-                "input_ue_unconnectable": {}
-              }
-            }
-          },
-          {
-            "id": 252,
-            "type": "PrimitiveInt",
-            "pos": [
-              -1380,
-              470
-            ],
-            "size": [
-              230,
-              110
-            ],
-            "flags": {},
-            "order": 5,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "value",
-                "name": "value",
-                "type": "INT",
-                "widget": {
-                  "name": "value"
-                },
-                "link": 428
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "INT",
-                "name": "INT",
-                "type": "INT",
-                "links": [
-                  429
-                ]
-              }
-            ],
-            "title": "Int (grid rows)",
-            "properties": {
-              "Node name for S&R": "Int (grid rows)",
-              "cnr_id": "comfy-core",
-              "ver": "0.18.1",
-              "enableTabs": false,
-              "tabWidth": 65,
-              "tabXOffset": 10,
-              "hasSecondTab": false,
-              "secondTabText": "Send Back",
-              "secondTabOffset": 80,
-              "secondTabWidth": 65,
-              "ue_properties": {
-                "widget_ue_connectable": {},
-                "version": "7.7",
-                "input_ue_unconnectable": {}
-              }
-            },
-            "widgets_values": [
-              3,
-              "fixed"
-            ]
-          }
-        ],
-        "groups": [],
-        "links": [
-          {
-            "id": 403,
-            "origin_id": 229,
-            "origin_slot": 1,
-            "target_id": 225,
-            "target_slot": 1,
-            "type": "INT"
-          },
-          {
-            "id": 404,
-            "origin_id": 231,
-            "origin_slot": 1,
-            "target_id": 225,
-            "target_slot": 2,
-            "type": "INT"
-          },
-          {
-            "id": 390,
-            "origin_id": 230,
-            "origin_slot": 1,
-            "target_id": 231,
-            "target_slot": 0,
-            "type": "INT"
-          },
-          {
-            "id": 387,
-            "origin_id": 230,
-            "origin_slot": 0,
-            "target_id": 229,
-            "target_slot": 0,
-            "type": "INT"
-          },
-          {
-            "id": 388,
-            "origin_id": 228,
-            "origin_slot": 0,
-            "target_id": 229,
-            "target_slot": 1,
-            "type": "INT"
-          },
-          {
-            "id": 386,
-            "origin_id": -10,
-            "origin_slot": 0,
-            "target_id": 225,
-            "target_slot": 0,
-            "type": "IMAGE"
-          },
-          {
-            "id": 389,
-            "origin_id": -10,
-            "origin_slot": 0,
-            "target_id": 230,
-            "target_slot": 0,
-            "type": "IMAGE"
-          },
-          {
-            "id": 394,
-            "origin_id": 225,
-            "origin_slot": 0,
-            "target_id": -20,
-            "target_slot": 0,
-            "type": "IMAGE"
-          },
-          {
-            "id": 427,
-            "origin_id": -10,
-            "origin_slot": 1,
-            "target_id": 228,
-            "target_slot": 0,
-            "type": "INT"
-          },
-          {
-            "id": 428,
-            "origin_id": -10,
-            "origin_slot": 2,
-            "target_id": 252,
-            "target_slot": 0,
-            "type": "INT"
-          },
-          {
-            "id": 429,
-            "origin_id": 252,
-            "origin_slot": 0,
-            "target_id": 231,
-            "target_slot": 1,
-            "type": "INT"
-          }
-        ],
-        "extra": {},
-        "category": "Image Tools/Crop"
-      }
-    ]
-  },
-  "extra": {}
-}

blueprints/Video Segmentation (SAM3).json

@@ -1,827 +0,0 @@
-{
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blueprints/Video Upscale(GAN x4).json

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

comfy/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]