feat: add model download API gated behind --enable-download-api

Add a new server-side download API that allows frontends and desktop apps
to download models directly into ComfyUI's models directory, eliminating
the need for DOM scraping of the frontend UI.

New files:
- app/download_manager.py: Async download manager with streaming downloads,
  pause/resume/cancel, manual redirect following with per-hop host validation,
  sidecar metadata for safe resume, and concurrency limiting.

API endpoints (all under /download/, also mirrored at /api/download/):
- POST /download/model - Start a download (url, directory, filename)
- GET /download/status - List all downloads (filterable by client_id)
- GET /download/status/{id} - Get single download status
- POST /download/pause/{id} - Pause (cancels transfer, keeps temp)
- POST /download/resume/{id} - Resume (new request with Range header)
- POST /download/cancel/{id} - Cancel and clean up temp files

Security:
- Gated behind --enable-download-api CLI flag (403 if disabled)
- HTTPS-only with exact host allowlist (huggingface.co, civitai.com + CDNs)
- Manual redirect following with per-hop host validation (no SSRF)
- Path traversal protection via realpath + commonpath
- Extension allowlist (.safetensors, .sft)
- Filename sanitization (no separators, .., control chars)
- Destination re-checked before final rename
- Progress events scoped to initiating client_id

Closes Comfy-Org/ComfyUI-Desktop-2.0-Beta#293

Amp-Thread-ID: https://ampcode.com/threads/T-019d2344-139e-77a5-9f24-1cbb3b26a8ec
Co-authored-by: Amp <amp@ampcode.com>

.ci/windows_amd_base_files/run_amd_gpu_disable_smart_memory.bat

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

.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/release-stable-all.yml

@@ -20,12 +20,29 @@ jobs:
       git_tag: ${{ inputs.git_tag }}
       cache_tag: "cu130"
       python_minor: "13"
-      python_patch: "12"
+      python_patch: "11"
       rel_name: "nvidia"
       rel_extra_name: ""
       test_release: true
     secrets: inherit
 
+  release_nvidia_cu128:
+    permissions:
+      contents: "write"
+      packages: "write"
+      pull-requests: "read"
+    name: "Release NVIDIA cu128"
+    uses: ./.github/workflows/stable-release.yml
+    with:
+      git_tag: ${{ inputs.git_tag }}
+      cache_tag: "cu128"
+      python_minor: "12"
+      python_patch: "10"
+      rel_name: "nvidia"
+      rel_extra_name: "_cu128"
+      test_release: true
+    secrets: inherit
+
   release_nvidia_cu126:
     permissions:
       contents: "write"
@@ -59,20 +76,3 @@ jobs:
       rel_extra_name: ""
       test_release: false
     secrets: inherit
-
-  release_xpu:
-    permissions:
-      contents: "write"
-      packages: "write"
-      pull-requests: "read"
-    name: "Release Intel XPU"
-    uses: ./.github/workflows/stable-release.yml
-    with:
-      git_tag: ${{ inputs.git_tag }}
-      cache_tag: "xpu"
-      python_minor: "13"
-      python_patch: "12"
-      rel_name: "intel"
-      rel_extra_name: ""
-      test_release: true
-    secrets: inherit

.github/workflows/stable-release.yml

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

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

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

.gitignore

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

.spectral.yaml

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

CODEOWNERS

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

QUANTIZATION.md

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

README.md

@@ -1,7 +1,7 @@
 <div align="center">
 
 # ComfyUI
-**The most powerful and modular AI engine for content creation.**
+**The most powerful and modular visual AI engine and application.**
 
 
 [![Website][website-shield]][website-url]
@@ -31,16 +31,10 @@
 [github-downloads-latest-shield]: https://img.shields.io/github/downloads/comfyanonymous/ComfyUI/latest/total?style=flat&label=downloads%40latest
 [github-downloads-link]: https://github.com/comfyanonymous/ComfyUI/releases
 
-<img width="1590" height="795" alt="ComfyUI Screenshot" src="https://github.com/user-attachments/assets/36e065e0-bfae-4456-8c7f-8369d5ea48a2" />
-<br>
+![ComfyUI Screenshot](https://github.com/user-attachments/assets/7ccaf2c1-9b72-41ae-9a89-5688c94b7abe)
 </div>
 
-ComfyUI is the AI creation engine for visual professionals who demand control over every model, every parameter, and every output. Its powerful and modular node graph interface empowers creatives to generate images, videos, 3D models, audio, and more...
-- ComfyUI natively supports the latest open-source state of the art models.
-- API nodes provide access to the best closed source models such as Nano Banana, Seedance, Hunyuan3D, etc.
-- It is available on Windows, Linux, and macOS, locally with our desktop application or on our cloud.
-- The most sophisticated workflows can be exposed through a simple UI thanks to App Mode.
-- It integrates seamlessly into production pipelines with our API endpoints.
+ComfyUI lets you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. Available on Windows, Linux, and macOS.
 
 ## Get Started
 
@@ -67,7 +61,6 @@ See what ComfyUI can do with the [newer template workflows](https://comfy.org/wo
 
 ## Features
 - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
-- NOTE: There are many more models supported than the list below, if you want to see what is supported see our templates list inside ComfyUI.
 - Image Models
    - SD1.x, SD2.x ([unCLIP](https://comfyanonymous.github.io/ComfyUI_examples/unclip/))
    - [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/)
@@ -83,7 +76,6 @@ 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 +125,7 @@ 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.
@@ -144,7 +136,7 @@ ComfyUI follows a weekly release cycle targeting Monday but this regularly chang
    - Builds a new release using the latest stable core version
 
 3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)**
-   - Every 2+ weeks frontend updates are merged into the core repository
+   - Weekly frontend updates are merged into the core repository
    - Features are frozen for the upcoming core release
    - Development continues for the next release cycle
 
@@ -200,15 +192,11 @@ 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?
 
@@ -244,7 +232,7 @@ Put your VAE in: models/vae
 
 AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
 
-```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.2```
+```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.1```
 
 This is the command to install the nightly with ROCm 7.2 which might have some performance improvements:
 
@@ -287,7 +275,7 @@ Nvidia users should install stable pytorch using this command:
 
 This is the command to install pytorch nightly instead which might have performance improvements.
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu132```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu130```
 
 #### Troubleshooting
 

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/assets/services/path_utils.py

@@ -93,13 +93,12 @@ def compute_relative_filename(file_path: str) -> str | None:
 
 def get_asset_category_and_relative_path(
     file_path: str,
-) -> tuple[Literal["input", "output", "temp", "models"], str]:
+) -> tuple[Literal["input", "output", "models"], str]:
     """Determine which root category a file path belongs to.
 
     Categories:
       - 'input': under folder_paths.get_input_directory()
       - 'output': under folder_paths.get_output_directory()
-      - 'temp': under folder_paths.get_temp_directory()
       - 'models': under any base path from get_comfy_models_folders()
 
     Returns:
@@ -130,12 +129,7 @@ def get_asset_category_and_relative_path(
     if _check_is_within(fp_abs, output_base):
         return "output", _compute_relative(fp_abs, output_base)
 
-    # 3) temp
-    temp_base = os.path.abspath(folder_paths.get_temp_directory())
-    if _check_is_within(fp_abs, temp_base):
-        return "temp", _compute_relative(fp_abs, temp_base)
-
-    # 4) models (check deepest matching base to avoid ambiguity)
+    # 3) models (check deepest matching base to avoid ambiguity)
     best: tuple[int, str, str] | None = None  # (base_len, bucket, rel_inside_bucket)
     for bucket, bases in get_comfy_models_folders():
         for b in bases:
@@ -152,7 +146,7 @@ def get_asset_category_and_relative_path(
         return "models", os.path.relpath(os.path.join(os.sep, combined), os.sep)
 
     raise ValueError(
-        f"Path is not within input, output, temp, or configured model bases: {file_path}"
+        f"Path is not within input, output, or configured model bases: {file_path}"
     )
 
 

app/download_manager.py

@@ -0,0 +1,507 @@
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import os
+import time
+import uuid
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Optional, TYPE_CHECKING
+from urllib.parse import urlsplit
+
+import aiohttp
+from yarl import URL
+
+import folder_paths
+
+if TYPE_CHECKING:
+    from server import PromptServer
+
+logger = logging.getLogger(__name__)
+
+ALLOWED_HTTPS_HOSTS = frozenset({
+    "huggingface.co",
+    "cdn-lfs.huggingface.co",
+    "cdn-lfs-us-1.huggingface.co",
+    "cdn-lfs-eu-1.huggingface.co",
+    "civitai.com",
+    "api.civitai.com",
+})
+
+ALLOWED_EXTENSIONS = frozenset({".safetensors", ".sft"})
+
+MAX_CONCURRENT_DOWNLOADS = 3
+MAX_TERMINAL_TASKS = 50
+MAX_REDIRECTS = 10
+
+DOWNLOAD_TEMP_SUFFIX = ".download_tmp"
+DOWNLOAD_META_SUFFIX = ".download_meta"
+
+
+class DownloadStatus(str, Enum):
+    PENDING = "pending"
+    DOWNLOADING = "downloading"
+    PAUSED = "paused"
+    COMPLETED = "completed"
+    ERROR = "error"
+    CANCELLED = "cancelled"
+
+
+ACTIVE_STATUSES = frozenset({
+    DownloadStatus.PENDING,
+    DownloadStatus.DOWNLOADING,
+    DownloadStatus.PAUSED,
+})
+
+TERMINAL_STATUSES = frozenset({
+    DownloadStatus.COMPLETED,
+    DownloadStatus.ERROR,
+    DownloadStatus.CANCELLED,
+})
+
+
+@dataclass
+class DownloadTask:
+    id: str
+    url: str
+    filename: str
+    directory: str
+    save_path: str
+    temp_path: str
+    meta_path: str
+    status: DownloadStatus = DownloadStatus.PENDING
+    progress: float = 0.0
+    received_bytes: int = 0
+    total_bytes: int = 0
+    speed_bytes_per_sec: float = 0.0
+    eta_seconds: float = 0.0
+    error: Optional[str] = None
+    created_at: float = field(default_factory=time.time)
+    client_id: Optional[str] = None
+    _worker: Optional[asyncio.Task] = field(default=None, repr=False)
+    _stop_reason: Optional[str] = field(default=None, repr=False)
+
+    def to_dict(self) -> dict:
+        return {
+            "id": self.id,
+            "url": self.url,
+            "filename": self.filename,
+            "directory": self.directory,
+            "status": self.status.value,
+            "progress": self.progress,
+            "received_bytes": self.received_bytes,
+            "total_bytes": self.total_bytes,
+            "speed_bytes_per_sec": self.speed_bytes_per_sec,
+            "eta_seconds": self.eta_seconds,
+            "error": self.error,
+            "created_at": self.created_at,
+        }
+
+
+class DownloadManager:
+    def __init__(self, server: PromptServer):
+        self.server = server
+        self.tasks: dict[str, DownloadTask] = {}
+        self._session: Optional[aiohttp.ClientSession] = None
+        self._semaphore = asyncio.Semaphore(MAX_CONCURRENT_DOWNLOADS)
+
+    async def _get_session(self) -> aiohttp.ClientSession:
+        if self._session is None or self._session.closed:
+            timeout = aiohttp.ClientTimeout(total=None, connect=30, sock_read=60)
+            self._session = aiohttp.ClientSession(timeout=timeout)
+        return self._session
+
+    async def close(self):
+        workers = [t._worker for t in self.tasks.values() if t._worker and not t._worker.done()]
+        for w in workers:
+            w.cancel()
+        if workers:
+            await asyncio.gather(*workers, return_exceptions=True)
+        if self._session and not self._session.closed:
+            await self._session.close()
+
+    # -- Validation --
+
+    @staticmethod
+    def _validate_url(url: str) -> Optional[str]:
+        try:
+            parts = urlsplit(url)
+        except Exception:
+            return "Invalid URL"
+
+        if parts.username or parts.password:
+            return "Credentials in URL are not allowed"
+
+        host = (parts.hostname or "").lower()
+        scheme = parts.scheme.lower()
+
+        if scheme != "https":
+            return "Only HTTPS URLs are allowed"
+
+        if host not in ALLOWED_HTTPS_HOSTS:
+            return f"Host '{host}' is not in the allowed list"
+
+        if parts.port not in (None, 443):
+            return "Custom ports are not allowed for remote downloads"
+
+        return None
+
+    @staticmethod
+    def _validate_filename(filename: str) -> Optional[str]:
+        if not filename:
+            return "Filename must not be empty"
+        ext = os.path.splitext(filename)[1].lower()
+        if ext not in ALLOWED_EXTENSIONS:
+            return f"File extension '{ext}' not allowed. Allowed: {', '.join(sorted(ALLOWED_EXTENSIONS))}"
+        if os.path.sep in filename or (os.path.altsep and os.path.altsep in filename):
+            return "Filename must not contain path separators"
+        if ".." in filename:
+            return "Filename must not contain '..'"
+        for ch in filename:
+            if ord(ch) < 32:
+                return "Filename must not contain control characters"
+        return None
+
+    @staticmethod
+    def _validate_directory(directory: str) -> Optional[str]:
+        if directory not in folder_paths.folder_names_and_paths:
+            valid = ', '.join(sorted(folder_paths.folder_names_and_paths.keys()))
+            return f"Unknown model directory '{directory}'. Valid directories: {valid}"
+        return None
+
+    @staticmethod
+    def _resolve_save_path(directory: str, filename: str) -> tuple[str, str, str]:
+        """Returns (save_path, temp_path, meta_path) for a download."""
+        paths = folder_paths.folder_names_and_paths[directory][0]
+        base_dir = paths[0]
+        os.makedirs(base_dir, exist_ok=True)
+
+        save_path = os.path.join(base_dir, filename)
+        temp_path = save_path + DOWNLOAD_TEMP_SUFFIX
+        meta_path = save_path + DOWNLOAD_META_SUFFIX
+
+        real_save = os.path.realpath(save_path)
+        real_base = os.path.realpath(base_dir)
+        if os.path.commonpath([real_save, real_base]) != real_base:
+            raise ValueError("Resolved path escapes the model directory")
+
+        return save_path, temp_path, meta_path
+
+    # -- Sidecar metadata for resume validation --
+
+    @staticmethod
+    def _write_meta(meta_path: str, url: str, task_id: str):
+        try:
+            with open(meta_path, "w") as f:
+                json.dump({"url": url, "task_id": task_id}, f)
+        except OSError:
+            pass
+
+    @staticmethod
+    def _read_meta(meta_path: str) -> Optional[dict]:
+        try:
+            with open(meta_path, "r") as f:
+                return json.load(f)
+        except (OSError, json.JSONDecodeError):
+            return None
+
+    @staticmethod
+    def _cleanup_files(*paths: str):
+        for p in paths:
+            try:
+                if os.path.exists(p):
+                    os.remove(p)
+            except OSError:
+                pass
+
+    # -- Task management --
+
+    def _prune_terminal_tasks(self):
+        terminal = [
+            (tid, t) for tid, t in self.tasks.items()
+            if t.status in TERMINAL_STATUSES
+        ]
+        if len(terminal) > MAX_TERMINAL_TASKS:
+            terminal.sort(key=lambda x: x[1].created_at)
+            to_remove = len(terminal) - MAX_TERMINAL_TASKS
+            for tid, _ in terminal[:to_remove]:
+                del self.tasks[tid]
+
+    async def start_download(
+        self, url: str, directory: str, filename: str, client_id: Optional[str] = None
+    ) -> tuple[Optional[DownloadTask], Optional[str]]:
+        err = self._validate_url(url)
+        if err:
+            return None, err
+
+        err = self._validate_filename(filename)
+        if err:
+            return None, err
+
+        err = self._validate_directory(directory)
+        if err:
+            return None, err
+
+        try:
+            save_path, temp_path, meta_path = self._resolve_save_path(directory, filename)
+        except ValueError as e:
+            return None, str(e)
+
+        if os.path.exists(save_path):
+            return None, f"File already exists: {directory}/{filename}"
+
+        # Reject duplicate active download by URL
+        for task in self.tasks.values():
+            if task.url == url and task.status in ACTIVE_STATUSES:
+                return None, f"Download already in progress for this URL (id: {task.id})"
+
+        # Reject duplicate active download by destination path (#4)
+        for task in self.tasks.values():
+            if task.save_path == save_path and task.status in ACTIVE_STATUSES:
+                return None, f"Download already in progress for {directory}/{filename} (id: {task.id})"
+
+        # Clean stale temp/meta if no active task owns them (#9)
+        existing_meta = self._read_meta(meta_path)
+        if existing_meta:
+            owning_task = self.tasks.get(existing_meta.get("task_id", ""))
+            if not owning_task or owning_task.status in TERMINAL_STATUSES:
+                if existing_meta.get("url") != url:
+                    self._cleanup_files(temp_path, meta_path)
+
+        task = DownloadTask(
+            id=uuid.uuid4().hex[:12],
+            url=url,
+            filename=filename,
+            directory=directory,
+            save_path=save_path,
+            temp_path=temp_path,
+            meta_path=meta_path,
+            client_id=client_id,
+        )
+        self.tasks[task.id] = task
+        self._prune_terminal_tasks()
+
+        task._worker = asyncio.create_task(self._run_download(task))
+        return task, None
+
+    # -- Redirect-safe fetch (#1, #2, #3) --
+
+    async def _fetch_with_validated_redirects(
+        self, session: aiohttp.ClientSession, url: str, headers: dict
+    ) -> aiohttp.ClientResponse:
+        """Follow redirects manually, validating each hop against the allowlist."""
+        current_url = url
+        for _ in range(MAX_REDIRECTS + 1):
+            resp = await session.get(current_url, headers=headers, allow_redirects=False)
+            if resp.status not in (301, 302, 303, 307, 308):
+                return resp
+
+            location = resp.headers.get("Location")
+            await resp.release()
+            if not location:
+                raise ValueError("Redirect without Location header")
+
+            resolved = URL(current_url).join(URL(location))
+            current_url = str(resolved)
+
+            # Validate the redirect target host
+            parts = urlsplit(current_url)
+            host = (parts.hostname or "").lower()
+            scheme = parts.scheme.lower()
+
+            if scheme != "https":
+                raise ValueError(f"Redirect to non-HTTPS URL: {current_url}")
+            if host not in ALLOWED_HTTPS_HOSTS:
+                # Allow CDN hosts that HuggingFace/CivitAI commonly redirect to
+                raise ValueError(f"Redirect to disallowed host: {host}")
+
+            # 303 means GET with no Range
+            if resp.status == 303:
+                headers = {k: v for k, v in headers.items() if k.lower() != "range"}
+
+        raise ValueError(f"Too many redirects (>{MAX_REDIRECTS})")
+
+    # -- Download worker --
+
+    async def _run_download(self, task: DownloadTask):
+        try:
+            async with self._semaphore:
+                await self._run_download_inner(task)
+        except asyncio.CancelledError:
+            if task._stop_reason == "pause":
+                task.status = DownloadStatus.PAUSED
+                task.speed_bytes_per_sec = 0
+                task.eta_seconds = 0
+                await self._send_progress(task)
+            else:
+                task.status = DownloadStatus.CANCELLED
+                await self._send_progress(task)
+                self._cleanup_files(task.temp_path, task.meta_path)
+        except Exception as e:
+            task.status = DownloadStatus.ERROR
+            task.error = str(e)
+            await self._send_progress(task)
+            logger.exception("Download error for %s", task.url)
+
+    async def _run_download_inner(self, task: DownloadTask):
+        session = await self._get_session()
+        headers = {}
+
+        # Resume support with sidecar validation (#9)
+        if os.path.exists(task.temp_path):
+            meta = self._read_meta(task.meta_path)
+            if meta and meta.get("url") == task.url:
+                existing_size = os.path.getsize(task.temp_path)
+                if existing_size > 0:
+                    headers["Range"] = f"bytes={existing_size}-"
+                    task.received_bytes = existing_size
+            else:
+                self._cleanup_files(task.temp_path, task.meta_path)
+
+        self._write_meta(task.meta_path, task.url, task.id)
+        task.status = DownloadStatus.DOWNLOADING
+        await self._send_progress(task)
+
+        resp = await self._fetch_with_validated_redirects(session, task.url, headers)
+        try:
+            if resp.status == 416:
+                content_range = resp.headers.get("Content-Range", "")
+                if content_range:
+                    total_str = content_range.split("/")[-1]
+                    if total_str != "*":
+                        total = int(total_str)
+                        if task.received_bytes >= total:
+                            if not os.path.exists(task.save_path):
+                                os.rename(task.temp_path, task.save_path)
+                                self._cleanup_files(task.meta_path)
+                            task.status = DownloadStatus.COMPLETED
+                            task.progress = 1.0
+                            task.total_bytes = total
+                            await self._send_progress(task)
+                            return
+                raise ValueError(f"HTTP 416 Range Not Satisfiable")
+
+            if resp.status not in (200, 206):
+                task.status = DownloadStatus.ERROR
+                task.error = f"HTTP {resp.status}"
+                await self._send_progress(task)
+                return
+
+            if resp.status == 200:
+                task.received_bytes = 0
+
+            content_length = resp.content_length
+            if resp.status == 206 and content_length:
+                task.total_bytes = task.received_bytes + content_length
+            elif resp.status == 200 and content_length:
+                task.total_bytes = content_length
+
+            mode = "ab" if resp.status == 206 else "wb"
+            speed_window_start = time.monotonic()
+            speed_window_bytes = 0
+            last_progress_time = 0.0
+
+            with open(task.temp_path, mode) as f:
+                async for chunk in resp.content.iter_chunked(1024 * 64):
+                    f.write(chunk)
+                    task.received_bytes += len(chunk)
+                    speed_window_bytes += len(chunk)
+
+                    now = time.monotonic()
+                    elapsed = now - speed_window_start
+                    if elapsed > 0.5:
+                        task.speed_bytes_per_sec = speed_window_bytes / elapsed
+                        if task.total_bytes > 0 and task.speed_bytes_per_sec > 0:
+                            remaining = task.total_bytes - task.received_bytes
+                            task.eta_seconds = remaining / task.speed_bytes_per_sec
+                        speed_window_start = now
+                        speed_window_bytes = 0
+
+                    if task.total_bytes > 0:
+                        task.progress = task.received_bytes / task.total_bytes
+
+                    if now - last_progress_time >= 0.25:
+                        await self._send_progress(task)
+                        last_progress_time = now
+        finally:
+            resp.release()
+
+        # Final cancel check before committing (#7)
+        if task._stop_reason is not None:
+            raise asyncio.CancelledError()
+
+        # Re-check destination before finalizing (#10)
+        if os.path.exists(task.save_path):
+            task.status = DownloadStatus.ERROR
+            task.error = f"Destination file appeared during download: {task.directory}/{task.filename}"
+            await self._send_progress(task)
+            return
+
+        os.replace(task.temp_path, task.save_path)
+        self._cleanup_files(task.meta_path)
+        task.status = DownloadStatus.COMPLETED
+        task.progress = 1.0
+        task.speed_bytes_per_sec = 0
+        task.eta_seconds = 0
+        await self._send_progress(task)
+        logger.info("Download complete: %s/%s", task.directory, task.filename)
+
+    # -- Progress (#8, #14) --
+
+    async def _send_progress(self, task: DownloadTask):
+        try:
+            self.server.send_sync("download_progress", task.to_dict(), task.client_id)
+        except Exception:
+            logger.exception("Failed to send download progress event")
+
+    # -- Control operations (#5, #6, #13) --
+
+    def pause_download(self, task_id: str) -> Optional[str]:
+        task = self.tasks.get(task_id)
+        if not task:
+            return "Download not found"
+        if task.status not in (DownloadStatus.PENDING, DownloadStatus.DOWNLOADING):
+            return f"Cannot pause download in state '{task.status.value}'"
+        task._stop_reason = "pause"
+        if task._worker and not task._worker.done():
+            task._worker.cancel()
+        return None
+
+    def resume_download(self, task_id: str) -> Optional[str]:
+        task = self.tasks.get(task_id)
+        if not task:
+            return "Download not found"
+        if task.status != DownloadStatus.PAUSED:
+            return f"Cannot resume download in state '{task.status.value}'"
+        task._stop_reason = None
+        task.status = DownloadStatus.PENDING
+        task._worker = asyncio.create_task(self._run_download(task))
+        return None
+
+    def cancel_download(self, task_id: str) -> Optional[str]:
+        task = self.tasks.get(task_id)
+        if not task:
+            return "Download not found"
+        if task.status in TERMINAL_STATUSES:
+            return f"Cannot cancel download in state '{task.status.value}'"
+        task._stop_reason = "cancel"
+        if task._worker and not task._worker.done():
+            task._worker.cancel()
+        else:
+            task.status = DownloadStatus.CANCELLED
+            self._cleanup_files(task.temp_path, task.meta_path)
+        return None
+
+    # -- Query --
+
+    def get_all_tasks(self, client_id: Optional[str] = None) -> list[dict]:
+        tasks = self.tasks.values()
+        if client_id is not None:
+            tasks = [t for t in tasks if t.client_id == client_id]
+        return [t.to_dict() for t in tasks]
+
+    def get_task(self, task_id: str) -> Optional[dict]:
+        task = self.tasks.get(task_id)
+        return task.to_dict() if task else None

app/frontend_management.py

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

app/node_replace_manager.py

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

app/user_manager.py

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

blueprints/.glsl/Color_Balance_15.frag

@@ -1,90 +0,0 @@
-#version 300 es
-precision highp float;
-
-uniform sampler2D u_image0;
-uniform float u_float0;
-uniform float u_float1;
-uniform float u_float2;
-uniform float u_float3;
-uniform float u_float4;
-uniform float u_float5;
-uniform float u_float6;
-uniform float u_float7;
-uniform float u_float8;
-uniform bool u_bool0;
-
-in vec2 v_texCoord;
-out vec4 fragColor;
-
-vec3 rgb2hsl(vec3 c) {
-    float maxC = max(c.r, max(c.g, c.b));
-    float minC = min(c.r, min(c.g, c.b));
-    float l = (maxC + minC) * 0.5;
-    if (maxC == minC) return vec3(0.0, 0.0, l);
-    float d = maxC - minC;
-    float s = l > 0.5 ? d / (2.0 - maxC - minC) : d / (maxC + minC);
-    float h;
-    if (maxC == c.r) {
-        h = (c.g - c.b) / d + (c.g < c.b ? 6.0 : 0.0);
-    } else if (maxC == c.g) {
-        h = (c.b - c.r) / d + 2.0;
-    } else {
-        h = (c.r - c.g) / d + 4.0;
-    }
-    h /= 6.0;
-    return vec3(h, s, l);
-}
-
-float hue2rgb(float p, float q, float t) {
-    if (t < 0.0) t += 1.0;
-    if (t > 1.0) t -= 1.0;
-    if (t < 1.0 / 6.0) return p + (q - p) * 6.0 * t;
-    if (t < 1.0 / 2.0) return q;
-    if (t < 2.0 / 3.0) return p + (q - p) * (2.0 / 3.0 - t) * 6.0;
-    return p;
-}
-
-vec3 hsl2rgb(vec3 hsl) {
-    float h = hsl.x, s = hsl.y, l = hsl.z;
-    if (s == 0.0) return vec3(l);
-    float q = l < 0.5 ? l * (1.0 + s) : l + s - l * s;
-    float p = 2.0 * l - q;
-    return vec3(
-        hue2rgb(p, q, h + 1.0 / 3.0),
-        hue2rgb(p, q, h),
-        hue2rgb(p, q, h - 1.0 / 3.0)
-    );
-}
-
-void main() {
-    vec4 tex = texture(u_image0, v_texCoord);
-    vec3 color = tex.rgb;
-
-    vec3 shadows = vec3(u_float0, u_float1, u_float2) * 0.01;
-    vec3 midtones = vec3(u_float3, u_float4, u_float5) * 0.01;
-    vec3 highlights = vec3(u_float6, u_float7, u_float8) * 0.01;
-
-    float maxC = max(color.r, max(color.g, color.b));
-    float minC = min(color.r, min(color.g, color.b));
-    float lightness = (maxC + minC) * 0.5;
-
-    // GIMP weight curves: linear ramps with constants a=0.25, b=0.333, scale=0.7
-    const float a = 0.25;
-    const float b = 0.333;
-    const float scale = 0.7;
-
-    float sw = clamp((lightness - b) / -a + 0.5, 0.0, 1.0) * scale;
-    float mw = clamp((lightness - b) / a + 0.5, 0.0, 1.0) *
-               clamp((lightness + b - 1.0) / -a + 0.5, 0.0, 1.0) * scale;
-    float hw = clamp((lightness + b - 1.0) / a + 0.5, 0.0, 1.0) * scale;
-
-    color += sw * shadows + mw * midtones + hw * highlights;
-
-    if (u_bool0) {
-        vec3 hsl = rgb2hsl(clamp(color, 0.0, 1.0));
-        hsl.z = lightness;
-        color = hsl2rgb(hsl);
-    }
-
-    fragColor = vec4(clamp(color, 0.0, 1.0), tex.a);
-}

blueprints/.glsl/Color_Curves_8.frag

@@ -1,49 +0,0 @@
-#version 300 es
-precision highp float;
-
-uniform sampler2D u_image0;
-uniform sampler2D u_curve0;  // RGB master curve (256x1 LUT)
-uniform sampler2D u_curve1;  // Red channel curve
-uniform sampler2D u_curve2;  // Green channel curve
-uniform sampler2D u_curve3;  // Blue channel curve
-
-in vec2 v_texCoord;
-layout(location = 0) out vec4 fragColor0;
-
-// GIMP-compatible curve lookup with manual linear interpolation.
-// Matches gimp_curve_map_value_inline() from gimpcurve-map.c:
-//   index = value * (n_samples - 1)
-//   f = fract(index)
-//   result = (1-f) * samples[floor] + f * samples[ceil]
-//
-// Uses texelFetch (NEAREST) to avoid GPU half-texel offset issues
-// that occur with texture() + GL_LINEAR on small 256x1 LUTs.
-float applyCurve(sampler2D curve, float value) {
-    value = clamp(value, 0.0, 1.0);
-
-    float pos = value * 255.0;
-    int lo = int(floor(pos));
-    int hi = min(lo + 1, 255);
-    float f = pos - float(lo);
-
-    float a = texelFetch(curve, ivec2(lo, 0), 0).r;
-    float b = texelFetch(curve, ivec2(hi, 0), 0).r;
-
-    return a + f * (b - a);
-}
-
-void main() {
-    vec4 color = texture(u_image0, v_texCoord);
-
-    // GIMP order: per-channel curves first, then RGB master curve.
-    // See gimp_curve_map_pixels() default case in gimpcurve-map.c:
-    //   dest = colors_curve( channel_curve( src ) )
-    float tmp_r = applyCurve(u_curve1, color.r);
-    float tmp_g = applyCurve(u_curve2, color.g);
-    float tmp_b = applyCurve(u_curve3, color.b);
-    color.r = applyCurve(u_curve0, tmp_r);
-    color.g = applyCurve(u_curve0, tmp_g);
-    color.b = applyCurve(u_curve0, tmp_b);
-
-    fragColor0 = vec4(color.rgb, color.a);
-}

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/Color Curves.json

@@ -1,616 +0,0 @@
-{
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-  "last_node_id": 10,
-  "last_link_id": 0,
-  "nodes": [
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-          "localized_name": "images.image0",
-          "name": "images.image0",
-          "type": "IMAGE",
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "label": "IMAGE",
-          "localized_name": "IMAGE0",
-          "name": "IMAGE0",
-          "type": "IMAGE",
-          "links": []
-        }
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-      "properties": {
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-            "curve"
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-      },
-      "widgets_values": [],
-      "title": "Color Curves"
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-            "name": "IMAGE0",
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-            "id": 4,
-            "type": "CurveEditor",
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-              -4500
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-            "size": [
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-            "flags": {},
-            "order": 0,
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-                "label": "curve",
-                "localized_name": "curve",
-                "name": "curve",
-                "type": "CURVE",
-                "widget": {
-                  "name": "curve"
-                },
-                "link": null
-              },
-              {
-                "label": "histogram",
-                "localized_name": "histogram",
-                "name": "histogram",
-                "type": "HISTOGRAM",
-                "shape": 7,
-                "link": 35
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "CURVE",
-                "name": "CURVE",
-                "type": "CURVE",
-                "links": [
-                  30
-                ]
-              }
-            ],
-            "title": "RGB Master",
-            "properties": {
-              "Node name for S&R": "CurveEditor"
-            },
-            "widgets_values": []
-          },
-          {
-            "id": 5,
-            "type": "CurveEditor",
-            "pos": [
-              3060,
-              -4250
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-            "size": [
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-                "label": "curve",
-                "localized_name": "curve",
-                "name": "curve",
-                "type": "CURVE",
-                "widget": {
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-                },
-                "link": null
-              },
-              {
-                "label": "histogram",
-                "localized_name": "histogram",
-                "name": "histogram",
-                "type": "HISTOGRAM",
-                "shape": 7,
-                "link": 36
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "CURVE",
-                "name": "CURVE",
-                "type": "CURVE",
-                "links": [
-                  31
-                ]
-              }
-            ],
-            "title": "Red",
-            "properties": {
-              "Node name for S&R": "CurveEditor"
-            },
-            "widgets_values": []
-          },
-          {
-            "id": 6,
-            "type": "CurveEditor",
-            "pos": [
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-            "size": [
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-            "flags": {},
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-            "inputs": [
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-                "label": "curve",
-                "localized_name": "curve",
-                "name": "curve",
-                "type": "CURVE",
-                "widget": {
-                  "name": "curve"
-                },
-                "link": null
-              },
-              {
-                "label": "histogram",
-                "localized_name": "histogram",
-                "name": "histogram",
-                "type": "HISTOGRAM",
-                "shape": 7,
-                "link": 37
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "CURVE",
-                "name": "CURVE",
-                "type": "CURVE",
-                "links": [
-                  32
-                ]
-              }
-            ],
-            "title": "Green",
-            "properties": {
-              "Node name for S&R": "CurveEditor"
-            },
-            "widgets_values": []
-          },
-          {
-            "id": 7,
-            "type": "CurveEditor",
-            "pos": [
-              3060,
-              -3750
-            ],
-            "size": [
-              270,
-              200
-            ],
-            "flags": {},
-            "order": 3,
-            "mode": 0,
-            "inputs": [
-              {
-                "label": "curve",
-                "localized_name": "curve",
-                "name": "curve",
-                "type": "CURVE",
-                "widget": {
-                  "name": "curve"
-                },
-                "link": null
-              },
-              {
-                "label": "histogram",
-                "localized_name": "histogram",
-                "name": "histogram",
-                "type": "HISTOGRAM",
-                "shape": 7,
-                "link": 38
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "CURVE",
-                "name": "CURVE",
-                "type": "CURVE",
-                "links": [
-                  33
-                ]
-              }
-            ],
-            "title": "Blue",
-            "properties": {
-              "Node name for S&R": "CurveEditor"
-            },
-            "widgets_values": []
-          },
-          {
-            "id": 8,
-            "type": "GLSLShader",
-            "pos": [
-              3590,
-              -4500
-            ],
-            "size": [
-              420,
-              500
-            ],
-            "flags": {},
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-            "mode": 0,
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-                "label": "image0",
-                "localized_name": "images.image0",
-                "name": "images.image0",
-                "type": "IMAGE",
-                "link": 29
-              },
-              {
-                "label": "image1",
-                "localized_name": "images.image1",
-                "name": "images.image1",
-                "shape": 7,
-                "type": "IMAGE",
-                "link": null
-              },
-              {
-                "label": "u_curve0",
-                "localized_name": "curves.u_curve0",
-                "name": "curves.u_curve0",
-                "shape": 7,
-                "type": "CURVE",
-                "link": 30
-              },
-              {
-                "label": "u_curve1",
-                "localized_name": "curves.u_curve1",
-                "name": "curves.u_curve1",
-                "shape": 7,
-                "type": "CURVE",
-                "link": 31
-              },
-              {
-                "label": "u_curve2",
-                "localized_name": "curves.u_curve2",
-                "name": "curves.u_curve2",
-                "shape": 7,
-                "type": "CURVE",
-                "link": 32
-              },
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-                "localized_name": "curves.u_curve3",
-                "name": "curves.u_curve3",
-                "shape": 7,
-                "type": "CURVE",
-                "link": 33
-              },
-              {
-                "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": [
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-                "localized_name": "IMAGE0",
-                "name": "IMAGE0",
-                "type": "IMAGE",
-                "links": [
-                  28
-                ]
-              },
-              {
-                "localized_name": "IMAGE1",
-                "name": "IMAGE1",
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-                "links": null
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-                "localized_name": "IMAGE2",
-                "name": "IMAGE2",
-                "type": "IMAGE",
-                "links": null
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-                "name": "IMAGE3",
-                "type": "IMAGE",
-                "links": null
-              }
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-              "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform sampler2D u_curve0;  // RGB master curve (256x1 LUT)\nuniform sampler2D u_curve1;  // Red channel curve\nuniform sampler2D u_curve2;  // Green channel curve\nuniform sampler2D u_curve3;  // Blue channel curve\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\n// GIMP-compatible curve lookup with manual linear interpolation.\n// Matches gimp_curve_map_value_inline() from gimpcurve-map.c:\n//   index = value * (n_samples - 1)\n//   f = fract(index)\n//   result = (1-f) * samples[floor] + f * samples[ceil]\n//\n// Uses texelFetch (NEAREST) to avoid GPU half-texel offset issues\n// that occur with texture() + GL_LINEAR on small 256x1 LUTs.\nfloat applyCurve(sampler2D curve, float value) {\n    value = clamp(value, 0.0, 1.0);\n\n    float pos = value * 255.0;\n    int lo = int(floor(pos));\n    int hi = min(lo + 1, 255);\n    float f = pos - float(lo);\n\n    float a = texelFetch(curve, ivec2(lo, 0), 0).r;\n    float b = texelFetch(curve, ivec2(hi, 0), 0).r;\n\n    return a + f * (b - a);\n}\n\nvoid main() {\n    vec4 color = texture(u_image0, v_texCoord);\n\n    // GIMP order: per-channel curves first, then RGB master curve.\n    // See gimp_curve_map_pixels() default case in gimpcurve-map.c:\n    //   dest = colors_curve( channel_curve( src ) )\n    float tmp_r = applyCurve(u_curve1, color.r);\n    float tmp_g = applyCurve(u_curve2, color.g);\n    float tmp_b = applyCurve(u_curve3, color.b);\n    color.r = applyCurve(u_curve0, tmp_r);\n    color.g = applyCurve(u_curve0, tmp_g);\n    color.b = applyCurve(u_curve0, tmp_b);\n\n    fragColor0 = vec4(color.rgb, color.a);\n}\n",
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-                "type": "IMAGE",
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-                "localized_name": "HISTOGRAM",
-                "name": "rgb",
-                "type": "HISTOGRAM",
-                "links": [
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-                ]
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-              {
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-            "origin_id": 9,
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-            "type": "HISTOGRAM"
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-            "target_id": 7,
-            "target_slot": 1,
-            "type": "HISTOGRAM"
-          }
-        ],
-        "extra": {
-          "workflowRendererVersion": "LG"
-        },
-        "category": "Image Tools/Color adjust",
-        "description": "Fine-tunes tone and color with per-channel curve adjustments using a real-time GPU fragment shader."
-      }
-    ]
-  }
-}

blueprints/Frame Interpolation.json

@@ -1,858 +0,0 @@
-{
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-  "last_node_id": 16,
-  "last_link_id": 0,
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-          "name": "video",
-          "type": "VIDEO",
-          "link": null
-        },
-        {
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-          "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",
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blueprints/Get Any Video Frame.json

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-            "target_id": 1,
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-            "target_id": -20,
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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",
-      "pos": [
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-        -230
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-      "size": [
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-      "flags": {},
-      "order": 5,
-      "mode": 0,
-      "inputs": [
-        {
-          "label": "image",
-          "localized_name": "images.image0",
-          "name": "images.image0",
-          "type": "IMAGE",
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "label": "R",
-          "localized_name": "IMAGE0",
-          "name": "IMAGE0",
-          "type": "IMAGE",
-          "links": []
-        },
-        {
-          "label": "G",
-          "localized_name": "IMAGE1",
-          "name": "IMAGE1",
-          "type": "IMAGE",
-          "links": []
-        },
-        {
-          "label": "B",
-          "localized_name": "IMAGE2",
-          "name": "IMAGE2",
-          "type": "IMAGE",
-          "links": []
-        },
-        {
-          "label": "A",
-          "localized_name": "IMAGE3",
-          "name": "IMAGE3",
-          "type": "IMAGE",
-          "links": []
-        }
-      ],
-      "title": "Image Channels",
-      "properties": {
-        "proxyWidgets": []
-      },
-      "widgets_values": []
-    }
-  ],
-  "links": [],
-  "version": 0.4,
-  "definitions": {
-    "subgraphs": [
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-        "id": "4c9d6ea4-b912-40e5-8766-6793a9758c53",
-        "version": 1,
-        "state": {
-          "lastGroupId": 0,
-          "lastNodeId": 28,
-          "lastLinkId": 39,
-          "lastRerouteId": 0
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-        "revision": 0,
-        "config": {},
-        "name": "Image Channels",
-        "inputNode": {
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-          "bounding": [
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-            120,
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-            "id": "3522932b-2d86-4a1f-a02a-cb29f3a9d7fe",
-            "name": "images.image0",
-            "type": "IMAGE",
-            "linkIds": [
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-            "localized_name": "images.image0",
-            "label": "image",
-            "pos": [
-              1920,
-              -165
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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": [
-              2480,
-              -175
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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": [
-              2480,
-              -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",
-            "pos": [
-              2480,
-              -135
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-            "type": "GLSLShader",
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-            "flags": {},
-            "order": 0,
-            "mode": 0,
-            "inputs": [
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-                "label": "image",
-                "localized_name": "images.image0",
-                "name": "images.image0",
-                "type": "IMAGE",
-                "link": 39
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-              {
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-                "name": "fragment_shader",
-                "type": "STRING",
-                "widget": {
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-                "link": null
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-                "shape": 7,
-                "type": "IMAGE",
-                "link": null
-              }
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-                "localized_name": "IMAGE0",
-                "name": "IMAGE0",
-                "type": "IMAGE",
-                "links": [
-                  26
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-                "name": "IMAGE3",
-                "type": "IMAGE",
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-            "properties": {
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-            "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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-}
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blueprints/Image Segmentation (SAM3).json

@@ -1,714 +0,0 @@
-{
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-  "last_link_id": 0,
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-          "link": null
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-          "type": "STRING",
-          "widget": {
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-          "type": "BOUNDING_BOX",
-          "link": null
-        },
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-          "type": "STRING",
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-          "type": "INT",
-          "widget": {
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-          "link": null
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-        {
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-          "type": "BOOLEAN",
-          "widget": {
-            "name": "individual_masks"
-          },
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-          "type": "COMBO",
-          "widget": {
-            "name": "ckpt_name"
-          },
-          "link": null
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-      "outputs": [
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-          "name": "masks",
-          "type": "MASK",
-          "links": []
-        },
-        {
-          "localized_name": "bboxes",
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-      "title": "Image Segmentation (SAM3)"
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blueprints/Prompt Enhance.json

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blueprints/Remove Background (BiRefNet).json

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-          "type": "COMBO",
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blueprints/Sharpen.json

@@ -1,310 +1 @@
-{
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-          "localized_name": "IMAGE0",
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-          "type": "IMAGE",
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-        }
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blueprints/Video Segmentation (SAM3).json

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-            "origin_id": 126,
-            "origin_slot": 0,
-            "target_id": -20,
-            "target_slot": 0,
-            "type": "MASK"
-          },
-          {
-            "id": 232,
-            "origin_id": 126,
-            "origin_slot": 1,
-            "target_id": -20,
-            "target_slot": 1,
-            "type": "BOUNDING_BOX"
-          },
-          {
-            "id": 252,
-            "origin_id": -10,
-            "origin_slot": 0,
-            "target_id": 128,
-            "target_slot": 0,
-            "type": "VIDEO"
-          },
-          {
-            "id": 253,
-            "origin_id": 128,
-            "origin_slot": 0,
-            "target_id": 126,
-            "target_slot": 1,
-            "type": "IMAGE"
-          },
-          {
-            "id": 254,
-            "origin_id": -10,
-            "origin_slot": 1,
-            "target_id": 125,
-            "target_slot": 1,
-            "type": "STRING"
-          },
-          {
-            "id": 255,
-            "origin_id": -10,
-            "origin_slot": 2,
-            "target_id": 126,
-            "target_slot": 3,
-            "type": "BOUNDING_BOX"
-          },
-          {
-            "id": 256,
-            "origin_id": -10,
-            "origin_slot": 3,
-            "target_id": 126,
-            "target_slot": 4,
-            "type": "STRING"
-          },
-          {
-            "id": 257,
-            "origin_id": -10,
-            "origin_slot": 4,
-            "target_id": 126,
-            "target_slot": 5,
-            "type": "STRING"
-          },
-          {
-            "id": 259,
-            "origin_id": 128,
-            "origin_slot": 1,
-            "target_id": -20,
-            "target_slot": 2,
-            "type": "AUDIO"
-          },
-          {
-            "id": 260,
-            "origin_id": 128,
-            "origin_slot": 2,
-            "target_id": -20,
-            "target_slot": 3,
-            "type": "FLOAT"
-          },
-          {
-            "id": 261,
-            "origin_id": -10,
-            "origin_slot": 5,
-            "target_id": 126,
-            "target_slot": 6,
-            "type": "FLOAT"
-          },
-          {
-            "id": 262,
-            "origin_id": -10,
-            "origin_slot": 6,
-            "target_id": 126,
-            "target_slot": 7,
-            "type": "INT"
-          },
-          {
-            "id": 263,
-            "origin_id": -10,
-            "origin_slot": 7,
-            "target_id": 126,
-            "target_slot": 8,
-            "type": "BOOLEAN"
-          },
-          {
-            "id": 273,
-            "origin_id": -10,
-            "origin_slot": 8,
-            "target_id": 127,
-            "target_slot": 0,
-            "type": "COMBO"
-          }
-        ],
-        "extra": {},
-        "category": "Video Tools",
-        "description": "Segments video into temporally consistent masks using Meta SAM3 from text or interactive prompts."
-      }
-    ]
-  },
-  "extra": {}
-}

blueprints/Video Upscale(GAN x4).json

@@ -1,421 +1 @@
-{
-  "revision": 0,
-  "last_node_id": 13,
-  "last_link_id": 0,
-  "nodes": [
-    {
-      "id": 13,
-      "type": "cf95b747-3e17-46cb-8097-cac60ff9b2e1",
-      "pos": [
-        1120,
-        330
-      ],
-      "size": [
-        240,
-        58
-      ],
-      "flags": {},
-      "order": 3,
-      "mode": 0,
-      "inputs": [
-        {
-          "localized_name": "video",
-          "name": "video",
-          "type": "VIDEO",
-          "link": null
-        },
-        {
-          "name": "model_name",
-          "type": "COMBO",
-          "widget": {
-            "name": "model_name"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "localized_name": "VIDEO",
-          "name": "VIDEO",
-          "type": "VIDEO",
-          "links": []
-        }
-      ],
-      "title": "Video Upscale(GAN x4)",
-      "properties": {
-        "proxyWidgets": [
-          [
-            "-1",
-            "model_name"
-          ]
-        ],
-        "cnr_id": "comfy-core",
-        "ver": "0.14.1"
-      },
-      "widgets_values": [
-        "RealESRGAN_x4plus.safetensors"
-      ]
-    }
-  ],
-  "links": [],
-  "version": 0.4,
-  "definitions": {
-    "subgraphs": [
-      {
-        "id": "cf95b747-3e17-46cb-8097-cac60ff9b2e1",
-        "version": 1,
-        "state": {
-          "lastGroupId": 0,
-          "lastNodeId": 13,
-          "lastLinkId": 19,
-          "lastRerouteId": 0
-        },
-        "revision": 0,
-        "config": {},
-        "name": "Video Upscale(GAN x4)",
-        "inputNode": {
-          "id": -10,
-          "bounding": [
-            550,
-            460,
-            120,
-            80
-          ]
-        },
-        "outputNode": {
-          "id": -20,
-          "bounding": [
-            1490,
-            460,
-            120,
-            60
-          ]
-        },
-        "inputs": [
-          {
-            "id": "666d633e-93e7-42dc-8d11-2b7b99b0f2a6",
-            "name": "video",
-            "type": "VIDEO",
-            "linkIds": [
-              10
-            ],
-            "localized_name": "video",
-            "pos": [
-              650,
-              480
-            ]
-          },
-          {
-            "id": "2e23a087-caa8-4d65-99e6-662761aa905a",
-            "name": "model_name",
-            "type": "COMBO",
-            "linkIds": [
-              19
-            ],
-            "pos": [
-              650,
-              500
-            ]
-          }
-        ],
-        "outputs": [
-          {
-            "id": "0c1768ea-3ec2-412f-9af6-8e0fa36dae70",
-            "name": "VIDEO",
-            "type": "VIDEO",
-            "linkIds": [
-              15
-            ],
-            "localized_name": "VIDEO",
-            "pos": [
-              1510,
-              480
-            ]
-          }
-        ],
-        "widgets": [],
-        "nodes": [
-          {
-            "id": 2,
-            "type": "ImageUpscaleWithModel",
-            "pos": [
-              1110,
-              450
-            ],
-            "size": [
-              320,
-              46
-            ],
-            "flags": {},
-            "order": 1,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "upscale_model",
-                "name": "upscale_model",
-                "type": "UPSCALE_MODEL",
-                "link": 1
-              },
-              {
-                "localized_name": "image",
-                "name": "image",
-                "type": "IMAGE",
-                "link": 14
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "IMAGE",
-                "name": "IMAGE",
-                "type": "IMAGE",
-                "links": [
-                  13
-                ]
-              }
-            ],
-            "properties": {
-              "cnr_id": "comfy-core",
-              "ver": "0.10.0",
-              "Node name for S&R": "ImageUpscaleWithModel"
-            }
-          },
-          {
-            "id": 11,
-            "type": "CreateVideo",
-            "pos": [
-              1110,
-              550
-            ],
-            "size": [
-              320,
-              78
-            ],
-            "flags": {},
-            "order": 3,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "images",
-                "name": "images",
-                "type": "IMAGE",
-                "link": 13
-              },
-              {
-                "localized_name": "audio",
-                "name": "audio",
-                "shape": 7,
-                "type": "AUDIO",
-                "link": 16
-              },
-              {
-                "localized_name": "fps",
-                "name": "fps",
-                "type": "FLOAT",
-                "widget": {
-                  "name": "fps"
-                },
-                "link": 12
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "VIDEO",
-                "name": "VIDEO",
-                "type": "VIDEO",
-                "links": [
-                  15
-                ]
-              }
-            ],
-            "properties": {
-              "cnr_id": "comfy-core",
-              "ver": "0.10.0",
-              "Node name for S&R": "CreateVideo"
-            },
-            "widgets_values": [
-              30
-            ]
-          },
-          {
-            "id": 10,
-            "type": "GetVideoComponents",
-            "pos": [
-              1110,
-              330
-            ],
-            "size": [
-              320,
-              70
-            ],
-            "flags": {},
-            "order": 2,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "video",
-                "name": "video",
-                "type": "VIDEO",
-                "link": 10
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "images",
-                "name": "images",
-                "type": "IMAGE",
-                "links": [
-                  14
-                ]
-              },
-              {
-                "localized_name": "audio",
-                "name": "audio",
-                "type": "AUDIO",
-                "links": [
-                  16
-                ]
-              },
-              {
-                "localized_name": "fps",
-                "name": "fps",
-                "type": "FLOAT",
-                "links": [
-                  12
-                ]
-              }
-            ],
-            "properties": {
-              "cnr_id": "comfy-core",
-              "ver": "0.10.0",
-              "Node name for S&R": "GetVideoComponents"
-            }
-          },
-          {
-            "id": 1,
-            "type": "UpscaleModelLoader",
-            "pos": [
-              750,
-              450
-            ],
-            "size": [
-              280,
-              60
-            ],
-            "flags": {},
-            "order": 0,
-            "mode": 0,
-            "inputs": [
-              {
-                "localized_name": "model_name",
-                "name": "model_name",
-                "type": "COMBO",
-                "widget": {
-                  "name": "model_name"
-                },
-                "link": 19
-              }
-            ],
-            "outputs": [
-              {
-                "localized_name": "UPSCALE_MODEL",
-                "name": "UPSCALE_MODEL",
-                "type": "UPSCALE_MODEL",
-                "links": [
-                  1
-                ]
-              }
-            ],
-            "properties": {
-              "cnr_id": "comfy-core",
-              "ver": "0.10.0",
-              "Node name for S&R": "UpscaleModelLoader",
-              "models": [
-                {
-                  "name": "RealESRGAN_x4plus.safetensors",
-                  "url": "https://huggingface.co/Comfy-Org/Real-ESRGAN_repackaged/resolve/main/RealESRGAN_x4plus.safetensors",
-                  "directory": "upscale_models"
-                }
-              ]
-            },
-            "widgets_values": [
-              "RealESRGAN_x4plus.safetensors"
-            ]
-          }
-        ],
-        "groups": [],
-        "links": [
-          {
-            "id": 1,
-            "origin_id": 1,
-            "origin_slot": 0,
-            "target_id": 2,
-            "target_slot": 0,
-            "type": "UPSCALE_MODEL"
-          },
-          {
-            "id": 14,
-            "origin_id": 10,
-            "origin_slot": 0,
-            "target_id": 2,
-            "target_slot": 1,
-            "type": "IMAGE"
-          },
-          {
-            "id": 13,
-            "origin_id": 2,
-            "origin_slot": 0,
-            "target_id": 11,
-            "target_slot": 0,
-            "type": "IMAGE"
-          },
-          {
-            "id": 16,
-            "origin_id": 10,
-            "origin_slot": 1,
-            "target_id": 11,
-            "target_slot": 1,
-            "type": "AUDIO"
-          },
-          {
-            "id": 12,
-            "origin_id": 10,
-            "origin_slot": 2,
-            "target_id": 11,
-            "target_slot": 2,
-            "type": "FLOAT"
-          },
-          {
-            "id": 10,
-            "origin_id": -10,
-            "origin_slot": 0,
-            "target_id": 10,
-            "target_slot": 0,
-            "type": "VIDEO"
-          },
-          {
-            "id": 15,
-            "origin_id": 11,
-            "origin_slot": 0,
-            "target_id": -20,
-            "target_slot": 0,
-            "type": "VIDEO"
-          },
-          {
-            "id": 19,
-            "origin_id": -10,
-            "origin_slot": 1,
-            "target_id": 1,
-            "target_slot": 0,
-            "type": "COMBO"
-          }
-        ],
-        "extra": {
-          "workflowRendererVersion": "LG"
-        },
-        "category": "Video generation and editing/Enhance video",
-        "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

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

comfy/cli_args.py

@@ -90,8 +90,8 @@ parser.add_argument("--force-channels-last", action="store_true", help="Force ch
 parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
 
 parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.")
+parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize default when loading models with Intel's Extension for Pytorch.")
 parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.")
-parser.add_argument("--enable-triton-backend", action="store_true", help="ComfyUI will enable the use of Triton backend in comfy-kitchen. Is disabled at launch by default.")
 
 class LatentPreviewMethod(enum.Enum):
     NoPreviews = "none"
@@ -110,13 +110,11 @@ parser.add_argument("--preview-method", type=LatentPreviewMethod, default=Latent
 
 parser.add_argument("--preview-size", type=int, default=512, help="Sets the maximum preview size for sampler nodes.")
 
-CACHE_RAM_AUTO_GB = -1.0
-
 cache_group = parser.add_mutually_exclusive_group()
 cache_group.add_argument("--cache-classic", action="store_true", help="Use the old style (aggressive) caching.")
 cache_group.add_argument("--cache-lru", type=int, default=0, help="Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.")
 cache_group.add_argument("--cache-none", action="store_true", help="Reduced RAM/VRAM usage at the expense of executing every node for each run.")
-cache_group.add_argument("--cache-ram", nargs='?', const=CACHE_RAM_AUTO_GB, type=float, default=0, help="Use RAM pressure caching with the specified headroom threshold. If available RAM drops below the threshold the cache removes large items to free RAM. Default (when no value is provided): 25%% of system RAM (min 4GB, max 32GB).")
+cache_group.add_argument("--cache-ram", nargs='?', const=4.0, type=float, default=0, help="Use RAM pressure caching with the specified headroom threshold. If available RAM drops below the threhold the cache remove large items to free RAM. Default 4GB")
 
 attn_group = parser.add_mutually_exclusive_group()
 attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.")
@@ -226,6 +224,8 @@ parser.add_argument("--user-directory", type=is_valid_directory, default=None, h
 
 parser.add_argument("--enable-compress-response-body", action="store_true", help="Enable compressing response body.")
 
+parser.add_argument("--enable-download-api", action="store_true", help="Enable the model download API. When set, ComfyUI exposes endpoints that allow downloading model files directly into the models directory. Only HTTPS downloads from allowed hosts (huggingface.co, civitai.com) are permitted.")
+
 parser.add_argument(
     "--comfy-api-base",
     type=str,
@@ -238,8 +238,6 @@ database_default_path = os.path.abspath(
 )
 parser.add_argument("--database-url", type=str, default=f"sqlite:///{database_default_path}", help="Specify the database URL, e.g. for an in-memory database you can use 'sqlite:///:memory:'.")
 parser.add_argument("--enable-assets", action="store_true", help="Enable the assets system (API routes, database synchronization, and background scanning).")
-parser.add_argument("--feature-flag", type=str, action='append', default=[], metavar="KEY[=VALUE]", help="Set a server feature flag. Use KEY=VALUE to set an explicit value, or bare KEY to set it to true. Can be specified multiple times. Boolean values (true/false) and numbers are auto-converted. Examples: --feature-flag show_signin_button=true  or  --feature-flag show_signin_button")
-parser.add_argument("--list-feature-flags", action="store_true", help="Print the registry of known CLI-settable feature flags as JSON and exit.")
 
 if comfy.options.args_parsing:
     args = parser.parse_args()

comfy/context_windows.py

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

comfy/deploy_environment.py

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

comfy/hooks.py

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

comfy/k_diffusion/sampling.py

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

comfy/latent_formats.py

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

comfy/ldm/ace/ace_step15.py

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