Optimizations to --fast and scaled fp8.

This change allows supports for diffusion models where all the linears are
scaled fp8 while the other weights are the original precision.

.ci/windows_base_files/README_VERY_IMPORTANT.txt

@@ -14,7 +14,7 @@ run_cpu.bat
 
 IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\models\checkpoints
 
-You can download the stable diffusion 1.5 one from: https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt
+You can download the stable diffusion 1.5 one from: https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/blob/main/v1-5-pruned-emaonly-fp16.safetensors
 
 
 RECOMMENDED WAY TO UPDATE:

.github/workflows/pullrequest-ci-run.yml

@@ -23,7 +23,7 @@ jobs:
             runner_label: [self-hosted, Linux]
             flags: ""
           - os: windows
-            runner_label: [self-hosted, win]
+            runner_label: [self-hosted, Windows]
             flags: ""
     runs-on: ${{ matrix.runner_label }}
     steps:

.github/workflows/stable-release.yml

@@ -12,17 +12,17 @@ on:
         description: 'CUDA version'
         required: true
         type: string
-        default: "121"
+        default: "124"
       python_minor:
         description: 'Python minor version'
         required: true
         type: string
-        default: "11"
+        default: "12"
       python_patch:
         description: 'Python patch version'
         required: true
         type: string
-        default: "9"
+        default: "7"
 
 
 jobs:

.github/workflows/stale-issues.yml

@@ -0,0 +1,21 @@
+name: 'Close stale issues'
+on:
+  schedule:
+    # Run daily at 430 am PT
+    - cron: '30 11 * * *'
+permissions:
+  issues: write
+
+jobs:
+  stale:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/stale@v9
+        with:
+          stale-issue-message: "This issue is being marked stale because it has not had any activity for 30 days. Reply below within 7 days if your issue still isn't solved, and it will be left open. Otherwise, the issue will be closed automatically."
+          days-before-stale: 30
+          days-before-close: 7
+          stale-issue-label: 'Stale'
+          only-labels: 'User Support'
+          exempt-all-assignees: true
+          exempt-all-milestones: true

.github/workflows/test-ci.yml

@@ -32,7 +32,7 @@ jobs:
             runner_label: [self-hosted, Linux]
             flags: ""
           - os: windows
-            runner_label: [self-hosted, win]
+            runner_label: [self-hosted, Windows]
             flags: ""
     runs-on: ${{ matrix.runner_label }}
     steps:
@@ -55,7 +55,7 @@ jobs:
         torch_version: ["nightly"]
         include:
           - os: windows
-            runner_label: [self-hosted, win]
+            runner_label: [self-hosted, Windows]
             flags: ""
     runs-on: ${{ matrix.runner_label }}
     steps:

.github/workflows/test-unit.yml

@@ -0,0 +1,30 @@
+name: Unit Tests
+
+on:
+  push:
+    branches: [ main, master ]
+  pull_request:
+    branches: [ main, master ]
+
+jobs:
+  test:
+    strategy:
+      matrix:
+        os: [ubuntu-latest, windows-latest, macos-latest]
+    runs-on: ${{ matrix.os }}
+    continue-on-error: true
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python      
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.10'
+    - name: Install requirements
+      run: |
+        python -m pip install --upgrade pip
+        pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+        pip install -r requirements.txt
+    - name: Run Unit Tests
+      run: |
+        pip install -r tests-unit/requirements.txt
+        python -m pytest tests-unit

.github/workflows/windows_release_dependencies.yml

@@ -12,7 +12,7 @@ on:
         description: 'extra dependencies'
         required: false
         type: string
-        default: "\"numpy<2\""
+        default: ""
       cu:
         description: 'cuda version'
         required: true
@@ -23,13 +23,13 @@ on:
         description: 'python minor version'
         required: true
         type: string
-        default: "11"
+        default: "12"
 
       python_patch:
         description: 'python patch version'
         required: true
         type: string
-        default: "9"
+        default: "7"
 #  push:
 #    branches:
 #      - master

.github/workflows/windows_release_package.yml

@@ -13,13 +13,13 @@ on:
         description: 'python minor version'
         required: true
         type: string
-        default: "11"
+        default: "12"
 
       python_patch:
         description: 'python patch version'
         required: true
         type: string
-        default: "9"
+        default: "7"
 #  push:
 #    branches:
 #      - master

.gitignore

@@ -12,6 +12,7 @@ extra_model_paths.yaml
 .vscode/
 .idea/
 venv/
+.venv/
 /web/extensions/*
 !/web/extensions/logging.js.example
 !/web/extensions/core/

README.md

@@ -1,7 +1,7 @@
 <div align="center">
 
 # ComfyUI
-**The most powerful and modular stable diffusion GUI and backend.**
+**The most powerful and modular diffusion model GUI and backend.**
 
 
 [![Website][website-shield]][website-url]
@@ -94,6 +94,8 @@ Workflow examples can be found on the [Examples page](https://comfyanonymous.git
 | Alt + `+`                          | Canvas Zoom in                                                                                                     |
 | Alt + `-`                          | Canvas Zoom out                                                                                                    |
 | Ctrl + Shift + LMB + Vertical drag | Canvas Zoom in/out                                                                                                 |
+| P                                  | Pin/Unpin selected nodes                                                                                           |
+| Ctrl + G                           | Group selected nodes                                                                                               |
 | Q                                  | Toggle visibility of the queue                                                                                     |
 | H                                  | Toggle visibility of history                                                                                       |
 | R                                  | Refresh graph                                                                                                      |
@@ -125,6 +127,8 @@ To run it on services like paperspace, kaggle or colab you can use my [Jupyter N
 
 ## Manual Install (Windows, Linux)
 
+Note that some dependencies do not yet support python 3.13 so using 3.12 is recommended.
+
 Git clone this repo.
 
 Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints
@@ -135,17 +139,17 @@ Put your VAE in: models/vae
 ### AMD GPUs (Linux only)
 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/rocm6.0```
+```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.1```
 
-This is the command to install the nightly with ROCm 6.0 which might have some performance improvements:
+This is the command to install the nightly with ROCm 6.2 which might have some performance improvements:
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.1```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.2```
 
 ### NVIDIA
 
 Nvidia users should install stable pytorch using this command:
 
-```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu121```
+```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu124```
 
 This is the command to install pytorch nightly instead which might have performance improvements:
 
@@ -230,7 +234,7 @@ To use a textual inversion concepts/embeddings in a text prompt put them in the
 
 Use ```--preview-method auto``` to enable previews.
 
-The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_decoder.pth](https://github.com/madebyollin/taesd/raw/main/taesd_decoder.pth) (for SD1.x and SD2.x) and [taesdxl_decoder.pth](https://github.com/madebyollin/taesd/raw/main/taesdxl_decoder.pth) (for SDXL) models and place them in the `models/vae_approx` folder. Once they're installed, restart ComfyUI to enable high-quality previews.
+The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_decoder.pth, taesdxl_decoder.pth, taesd3_decoder.pth and taef1_decoder.pth](https://github.com/madebyollin/taesd/) and place them in the `models/vae_approx` folder. Once they're installed, restart ComfyUI and launch it with `--preview-method taesd` to enable high-quality previews.
 
 ## How to use TLS/SSL?
 Generate a self-signed certificate (not appropriate for shared/production use) and key by running the command: `openssl req -x509 -newkey rsa:4096 -keyout key.pem -out cert.pem -sha256 -days 3650 -nodes -subj "/C=XX/ST=StateName/L=CityName/O=CompanyName/OU=CompanySectionName/CN=CommonNameOrHostname"`

api_server/routes/internal/internal_routes.py

@@ -1,7 +1,8 @@
 from aiohttp import web
 from typing import Optional
-from folder_paths import models_dir, user_directory, output_directory
+from folder_paths import models_dir, user_directory, output_directory, folder_names_and_paths
 from api_server.services.file_service import FileService
+import app.logger
 
 class InternalRoutes:
     '''
@@ -31,6 +32,16 @@ class InternalRoutes:
             except Exception as e:
                 return web.json_response({"error": str(e)}, status=500)
 
+        @self.routes.get('/logs')
+        async def get_logs(request):
+            return web.json_response(app.logger.get_logs())
+
+        @self.routes.get('/folder_paths')
+        async def get_folder_paths(request):
+            response = {}
+            for key in folder_names_and_paths:
+                response[key] = folder_names_and_paths[key][0]
+            return web.json_response(response)
 
     def get_app(self):
         if self._app is None:

app/frontend_management.py

@@ -8,7 +8,7 @@ import zipfile
 from dataclasses import dataclass
 from functools import cached_property
 from pathlib import Path
-from typing import TypedDict
+from typing import TypedDict, Optional
 
 import requests
 from typing_extensions import NotRequired
@@ -132,12 +132,13 @@ class FrontendManager:
         return match_result.group(1), match_result.group(2), match_result.group(3)
 
     @classmethod
-    def init_frontend_unsafe(cls, version_string: str) -> str:
+    def init_frontend_unsafe(cls, version_string: str, provider: Optional[FrontEndProvider] = None) -> str:
         """
         Initializes the frontend for the specified version.
 
         Args:
             version_string (str): The version string.
+            provider (FrontEndProvider, optional): The provider to use. Defaults to None.
 
         Returns:
             str: The path to the initialized frontend.
@@ -150,7 +151,16 @@ class FrontendManager:
             return cls.DEFAULT_FRONTEND_PATH
 
         repo_owner, repo_name, version = cls.parse_version_string(version_string)
-        provider = FrontEndProvider(repo_owner, repo_name)
+
+        if version.startswith("v"):
+            expected_path = str(Path(cls.CUSTOM_FRONTENDS_ROOT) / f"{repo_owner}_{repo_name}" / version.lstrip("v"))
+            if os.path.exists(expected_path):
+                logging.info(f"Using existing copy of specific frontend version tag: {repo_owner}/{repo_name}@{version}")
+                return expected_path
+
+        logging.info(f"Initializing frontend: {repo_owner}/{repo_name}@{version}, requesting version details from GitHub...")
+
+        provider = provider or FrontEndProvider(repo_owner, repo_name)
         release = provider.get_release(version)
 
         semantic_version = release["tag_name"].lstrip("v")
@@ -158,15 +168,25 @@ class FrontendManager:
             Path(cls.CUSTOM_FRONTENDS_ROOT) / provider.folder_name / semantic_version
         )
         if not os.path.exists(web_root):
-            os.makedirs(web_root, exist_ok=True)
+            # Use tmp path until complete to avoid path exists check passing from interrupted downloads
-            logging.info(
+            tmp_path = web_root + ".tmp"
-                "Downloading frontend(%s) version(%s) to (%s)",
+            try:
-                provider.folder_name,
+                os.makedirs(tmp_path, exist_ok=True)
-                semantic_version,
+                logging.info(
-                web_root,
+                    "Downloading frontend(%s) version(%s) to (%s)",
-            )
+                    provider.folder_name,
-            logging.debug(release)
+                    semantic_version,
-            download_release_asset_zip(release, destination_path=web_root)
+                    tmp_path,
+                )
+                logging.debug(release)
+                download_release_asset_zip(release, destination_path=tmp_path)
+                if os.listdir(tmp_path):
+                    os.rename(tmp_path, web_root)
+            finally:
+                # Clean up the directory if it is empty, i.e. the download failed
+                if not os.listdir(web_root):
+                    os.rmdir(web_root)
+
         return web_root
 
     @classmethod

app/logger.py

@@ -0,0 +1,31 @@
+import logging
+from logging.handlers import MemoryHandler
+from collections import deque
+
+logs = None
+formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
+
+
+def get_logs():
+    return "\n".join([formatter.format(x) for x in logs])
+
+
+def setup_logger(log_level: str = 'INFO', capacity: int = 300):
+    global logs
+    if logs:
+        return
+
+    # Setup default global logger
+    logger = logging.getLogger()
+    logger.setLevel(log_level)
+
+    stream_handler = logging.StreamHandler()
+    stream_handler.setFormatter(logging.Formatter("%(message)s"))
+    logger.addHandler(stream_handler)
+
+    # Create a memory handler with a deque as its buffer
+    logs = deque(maxlen=capacity)
+    memory_handler = MemoryHandler(capacity, flushLevel=logging.INFO)
+    memory_handler.buffer = logs
+    memory_handler.setFormatter(formatter)
+    logger.addHandler(memory_handler)

app/user_manager.py

@@ -5,17 +5,17 @@ import uuid
 import glob
 import shutil
 from aiohttp import web
+from urllib import parse
 from comfy.cli_args import args
-from folder_paths import user_directory
+import folder_paths
 from .app_settings import AppSettings
 
 default_user = "default"
-users_file = os.path.join(user_directory, "users.json")
 
 
 class UserManager():
     def __init__(self):
-        global user_directory
+        user_directory = folder_paths.get_user_directory()
 
         self.settings = AppSettings(self)
         if not os.path.exists(user_directory):
@@ -25,14 +25,17 @@ class UserManager():
                 print("****** For multi-user setups add the --multi-user CLI argument to enable multiple user profiles. ******")
 
         if args.multi_user:
-            if os.path.isfile(users_file):
+            if os.path.isfile(self.get_users_file()):
-                with open(users_file) as f:
+                with open(self.get_users_file()) as f:
                     self.users = json.load(f)
             else:
                 self.users = {}
         else:
             self.users = {"default": "default"}
 
+    def get_users_file(self):
+        return os.path.join(folder_paths.get_user_directory(), "users.json")
+
     def get_request_user_id(self, request):
         user = "default"
         if args.multi_user and "comfy-user" in request.headers:
@@ -44,7 +47,7 @@ class UserManager():
         return user
 
     def get_request_user_filepath(self, request, file, type="userdata", create_dir=True):
-        global user_directory
+        user_directory = folder_paths.get_user_directory()
 
         if type == "userdata":
             root_dir = user_directory
@@ -59,6 +62,10 @@ class UserManager():
             return None
 
         if file is not None:
+            # Check if filename is url encoded
+            if "%" in file:
+                file = parse.unquote(file)
+
             # prevent leaving /{type}/{user}
             path = os.path.abspath(os.path.join(user_root, file))
             if os.path.commonpath((user_root, path)) != user_root:
@@ -80,8 +87,7 @@ class UserManager():
 
         self.users[user_id] = name
 
-        global users_file
+        with open(self.get_users_file(), "w") as f:
-        with open(users_file, "w") as f:
             json.dump(self.users, f)
 
         return user_id
@@ -112,25 +118,69 @@ class UserManager():
 
         @routes.get("/userdata")
         async def listuserdata(request):
+            """
+            List user data files in a specified directory.
+
+            This endpoint allows listing files in a user's data directory, with options for recursion,
+            full file information, and path splitting.
+
+            Query Parameters:
+            - dir (required): The directory to list files from.
+            - recurse (optional): If "true", recursively list files in subdirectories.
+            - full_info (optional): If "true", return detailed file information (path, size, modified time).
+            - split (optional): If "true", split file paths into components (only applies when full_info is false).
+
+            Returns:
+            - 400: If 'dir' parameter is missing.
+            - 403: If the requested path is not allowed.
+            - 404: If the requested directory does not exist.
+            - 200: JSON response with the list of files or file information.
+
+            The response format depends on the query parameters:
+            - Default: List of relative file paths.
+            - full_info=true: List of dictionaries with file details.
+            - split=true (and full_info=false): List of lists, each containing path components.
+            """
             directory = request.rel_url.query.get('dir', '')
             if not directory:
-                return web.Response(status=400)
+                return web.Response(status=400, text="Directory not provided")
 
             path = self.get_request_user_filepath(request, directory)
             if not path:
-                return web.Response(status=403)
+                return web.Response(status=403, text="Invalid directory")
 
             if not os.path.exists(path):
-                return web.Response(status=404)
+                return web.Response(status=404, text="Directory not found")
 
             recurse = request.rel_url.query.get('recurse', '').lower() == "true"
-            results = glob.glob(os.path.join(
+            full_info = request.rel_url.query.get('full_info', '').lower() == "true"
-                glob.escape(path), '**/*'), recursive=recurse)
+
-            results = [os.path.relpath(x, path) for x in results if os.path.isfile(x)]
+            # Use different patterns based on whether we're recursing or not
+            if recurse:
+                pattern = os.path.join(glob.escape(path), '**', '*')
+            else:
+                pattern = os.path.join(glob.escape(path), '*')
+
+            results = glob.glob(pattern, recursive=recurse)
+
+            if full_info:
+                results = [
+                    {
+                        'path': os.path.relpath(x, path).replace(os.sep, '/'),
+                        'size': os.path.getsize(x),
+                        'modified': os.path.getmtime(x)
+                    } for x in results if os.path.isfile(x)
+                ]
+            else:
+                results = [
+                    os.path.relpath(x, path).replace(os.sep, '/')
+                    for x in results
+                    if os.path.isfile(x)
+                ]
 
             split_path = request.rel_url.query.get('split', '').lower() == "true"
-            if split_path:
+            if split_path and not full_info:
-                results = [[x] + x.split(os.sep) for x in results]
+                results = [[x] + x.split('/') for x in results]
 
             return web.json_response(results)
 

comfy/cldm/mmdit.py

@@ -6,6 +6,7 @@ class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT):
     def __init__(
         self,
         num_blocks = None,
+        control_latent_channels = None,
         dtype = None,
         device = None,
         operations = None,
@@ -17,10 +18,13 @@ class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT):
         for _ in range(len(self.joint_blocks)):
             self.controlnet_blocks.append(operations.Linear(self.hidden_size, self.hidden_size, device=device, dtype=dtype))
 
+        if control_latent_channels is None:
+            control_latent_channels = self.in_channels
+
         self.pos_embed_input = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed(
             None,
             self.patch_size,
-            self.in_channels,
+            control_latent_channels,
             self.hidden_size,
             bias=True,
             strict_img_size=False,

comfy/cli_args.py

@@ -36,7 +36,7 @@ class EnumAction(argparse.Action):
 
 parser = argparse.ArgumentParser()
 
-parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0", help="Specify the IP address to listen on (default: 127.0.0.1). If --listen is provided without an argument, it defaults to 0.0.0.0. (listens on all)")
+parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0,::", help="Specify the IP address to listen on (default: 127.0.0.1). You can give a list of ip addresses by separating them with a comma like: 127.2.2.2,127.3.3.3 If --listen is provided without an argument, it defaults to 0.0.0.0,:: (listens on all ipv4 and ipv6)")
 parser.add_argument("--port", type=int, default=8188, help="Set the listen port.")
 parser.add_argument("--tls-keyfile", type=str, help="Path to TLS (SSL) key file. Enables TLS, makes app accessible at https://... requires --tls-certfile to function")
 parser.add_argument("--tls-certfile", type=str, help="Path to TLS (SSL) certificate file. Enables TLS, makes app accessible at https://... requires --tls-keyfile to function")
@@ -92,6 +92,8 @@ class LatentPreviewMethod(enum.Enum):
 
 parser.add_argument("--preview-method", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help="Default preview method for sampler nodes.", action=EnumAction)
 
+parser.add_argument("--preview-size", type=int, default=512, help="Sets the maximum preview size for sampler nodes.")
+
 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.")
@@ -134,7 +136,7 @@ parser.add_argument("--disable-all-custom-nodes", action="store_true", help="Dis
 
 parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.")
 
-parser.add_argument("--verbose", action="store_true", help="Enables more debug prints.")
+parser.add_argument("--verbose", default='INFO', const='DEBUG', nargs="?", choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], help='Set the logging level')
 
 # The default built-in provider hosted under web/
 DEFAULT_VERSION_STRING = "comfyanonymous/ComfyUI@latest"
@@ -169,6 +171,8 @@ parser.add_argument(
     help="The local filesystem path to the directory where the frontend is located. Overrides --front-end-version.",
 )
 
+parser.add_argument("--user-directory", type=is_valid_directory, default=None, help="Set the ComfyUI user directory with an absolute path.")
+
 if comfy.options.args_parsing:
     args = parser.parse_args()
 else:
@@ -179,10 +183,3 @@ if args.windows_standalone_build:
 
 if args.disable_auto_launch:
     args.auto_launch = False
-
-import logging
-logging_level = logging.INFO
-if args.verbose:
-    logging_level = logging.DEBUG
-
-logging.basicConfig(format="%(message)s", level=logging_level)

comfy/clip_vision.py

@@ -109,8 +109,7 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
     keys = list(sd.keys())
     for k in keys:
         if k not in u:
-            t = sd.pop(k)
+            sd.pop(k)
-            del t
     return clip
 
 def load(ckpt_path):

comfy/controlnet.py

@@ -34,7 +34,7 @@ import comfy.t2i_adapter.adapter
 import comfy.ldm.cascade.controlnet
 import comfy.cldm.mmdit
 import comfy.ldm.hydit.controlnet
-import comfy.ldm.flux.controlnet_xlabs
+import comfy.ldm.flux.controlnet
 
 
 def broadcast_image_to(tensor, target_batch_size, batched_number):
@@ -79,13 +79,21 @@ class ControlBase:
         self.previous_controlnet = None
         self.extra_conds = []
         self.strength_type = StrengthType.CONSTANT
+        self.concat_mask = False
+        self.extra_concat_orig = []
+        self.extra_concat = None
 
-    def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None):
+    def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None, extra_concat=[]):
         self.cond_hint_original = cond_hint
         self.strength = strength
         self.timestep_percent_range = timestep_percent_range
         if self.latent_format is not None:
+            if vae is None:
+                logging.warning("WARNING: no VAE provided to the controlnet apply node when this controlnet requires one.")
             self.vae = vae
+        self.extra_concat_orig = extra_concat.copy()
+        if self.concat_mask and len(self.extra_concat_orig) == 0:
+            self.extra_concat_orig.append(torch.tensor([[[[1.0]]]]))
         return self
 
     def pre_run(self, model, percent_to_timestep_function):
@@ -100,9 +108,9 @@ class ControlBase:
     def cleanup(self):
         if self.previous_controlnet is not None:
             self.previous_controlnet.cleanup()
-        if self.cond_hint is not None:
+
-            del self.cond_hint
+        self.cond_hint = None
-            self.cond_hint = None
+        self.extra_concat = None
         self.timestep_range = None
 
     def get_models(self):
@@ -123,6 +131,8 @@ class ControlBase:
         c.vae = self.vae
         c.extra_conds = self.extra_conds.copy()
         c.strength_type = self.strength_type
+        c.concat_mask = self.concat_mask
+        c.extra_concat_orig = self.extra_concat_orig.copy()
 
     def inference_memory_requirements(self, dtype):
         if self.previous_controlnet is not None:
@@ -148,7 +158,7 @@ class ControlBase:
                         elif self.strength_type == StrengthType.LINEAR_UP:
                             x *= (self.strength ** float(len(control_output) - i))
 
-                    if x.dtype != output_dtype:
+                    if output_dtype is not None and x.dtype != output_dtype:
                         x = x.to(output_dtype)
 
                 out[key].append(x)
@@ -175,7 +185,7 @@ class ControlBase:
 
 
 class ControlNet(ControlBase):
-    def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, latent_format=None, device=None, load_device=None, manual_cast_dtype=None, extra_conds=["y"], strength_type=StrengthType.CONSTANT):
+    def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, latent_format=None, device=None, load_device=None, manual_cast_dtype=None, extra_conds=["y"], strength_type=StrengthType.CONSTANT, concat_mask=False):
         super().__init__(device)
         self.control_model = control_model
         self.load_device = load_device
@@ -189,6 +199,7 @@ class ControlNet(ControlBase):
         self.latent_format = latent_format
         self.extra_conds += extra_conds
         self.strength_type = strength_type
+        self.concat_mask = concat_mask
 
     def get_control(self, x_noisy, t, cond, batched_number):
         control_prev = None
@@ -206,7 +217,6 @@ class ControlNet(ControlBase):
         if self.manual_cast_dtype is not None:
             dtype = self.manual_cast_dtype
 
-        output_dtype = x_noisy.dtype
         if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]:
             if self.cond_hint is not None:
                 del self.cond_hint
@@ -214,6 +224,9 @@ class ControlNet(ControlBase):
             compression_ratio = self.compression_ratio
             if self.vae is not None:
                 compression_ratio *= self.vae.downscale_ratio
+            else:
+                if self.latent_format is not None:
+                    raise ValueError("This Controlnet needs a VAE but none was provided, please use a ControlNetApply node with a VAE input and connect it.")
             self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * compression_ratio, x_noisy.shape[2] * compression_ratio, self.upscale_algorithm, "center")
             if self.vae is not None:
                 loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
@@ -221,6 +234,14 @@ class ControlNet(ControlBase):
                 comfy.model_management.load_models_gpu(loaded_models)
             if self.latent_format is not None:
                 self.cond_hint = self.latent_format.process_in(self.cond_hint)
+            if len(self.extra_concat_orig) > 0:
+                to_concat = []
+                for c in self.extra_concat_orig:
+                    c = c.to(self.cond_hint.device)
+                    c = comfy.utils.common_upscale(c, self.cond_hint.shape[3], self.cond_hint.shape[2], self.upscale_algorithm, "center")
+                    to_concat.append(comfy.utils.repeat_to_batch_size(c, self.cond_hint.shape[0]))
+                self.cond_hint = torch.cat([self.cond_hint] + to_concat, dim=1)
+
             self.cond_hint = self.cond_hint.to(device=self.device, dtype=dtype)
         if x_noisy.shape[0] != self.cond_hint.shape[0]:
             self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
@@ -236,7 +257,7 @@ class ControlNet(ControlBase):
         x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)
 
         control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.to(dtype), context=context.to(dtype), **extra)
-        return self.control_merge(control, control_prev, output_dtype)
+        return self.control_merge(control, control_prev, output_dtype=None)
 
     def copy(self):
         c = ControlNet(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)
@@ -320,7 +341,7 @@ class ControlLoraOps:
 
 
 class ControlLora(ControlNet):
-    def __init__(self, control_weights, global_average_pooling=False, device=None):
+    def __init__(self, control_weights, global_average_pooling=False, device=None, model_options={}): #TODO? model_options
         ControlBase.__init__(self, device)
         self.control_weights = control_weights
         self.global_average_pooling = global_average_pooling
@@ -377,21 +398,28 @@ class ControlLora(ControlNet):
     def inference_memory_requirements(self, dtype):
         return comfy.utils.calculate_parameters(self.control_weights) * comfy.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype)
 
-def controlnet_config(sd):
+def controlnet_config(sd, model_options={}):
     model_config = comfy.model_detection.model_config_from_unet(sd, "", True)
 
-    supported_inference_dtypes = model_config.supported_inference_dtypes
+    unet_dtype = model_options.get("dtype", None)
+    if unet_dtype is None:
+        weight_dtype = comfy.utils.weight_dtype(sd)
+
+        supported_inference_dtypes = list(model_config.supported_inference_dtypes)
+        if weight_dtype is not None:
+            supported_inference_dtypes.append(weight_dtype)
+
+        unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes)
 
-    controlnet_config = model_config.unet_config
-    unet_dtype = comfy.model_management.unet_dtype(supported_dtypes=supported_inference_dtypes)
     load_device = comfy.model_management.get_torch_device()
     manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
-    if manual_cast_dtype is not None:
-        operations = comfy.ops.manual_cast
-    else:
-        operations = comfy.ops.disable_weight_init
 
-    return model_config, operations, load_device, unet_dtype, manual_cast_dtype
+    operations = model_options.get("custom_operations", None)
+    if operations is None:
+        operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype, disable_fast_fp8=True)
+
+    offload_device = comfy.model_management.unet_offload_device()
+    return model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device
 
 def controlnet_load_state_dict(control_model, sd):
     missing, unexpected = control_model.load_state_dict(sd, strict=False)
@@ -403,26 +431,31 @@ def controlnet_load_state_dict(control_model, sd):
         logging.debug("unexpected controlnet keys: {}".format(unexpected))
     return control_model
 
-def load_controlnet_mmdit(sd):
+def load_controlnet_mmdit(sd, model_options={}):
     new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "")
-    model_config, operations, load_device, unet_dtype, manual_cast_dtype = controlnet_config(new_sd)
+    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd, model_options=model_options)
     num_blocks = comfy.model_detection.count_blocks(new_sd, 'joint_blocks.{}.')
     for k in sd:
         new_sd[k] = sd[k]
 
-    control_model = comfy.cldm.mmdit.ControlNet(num_blocks=num_blocks, operations=operations, device=load_device, dtype=unet_dtype, **model_config.unet_config)
+    concat_mask = False
+    control_latent_channels = new_sd.get("pos_embed_input.proj.weight").shape[1]
+    if control_latent_channels == 17: #inpaint controlnet
+        concat_mask = True
+
+    control_model = comfy.cldm.mmdit.ControlNet(num_blocks=num_blocks, control_latent_channels=control_latent_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
     control_model = controlnet_load_state_dict(control_model, new_sd)
 
     latent_format = comfy.latent_formats.SD3()
     latent_format.shift_factor = 0 #SD3 controlnet weirdness
-    control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
+    control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
     return control
 
 
-def load_controlnet_hunyuandit(controlnet_data):
+def load_controlnet_hunyuandit(controlnet_data, model_options={}):
-    model_config, operations, load_device, unet_dtype, manual_cast_dtype = controlnet_config(controlnet_data)
+    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(controlnet_data, model_options=model_options)
 
-    control_model = comfy.ldm.hydit.controlnet.HunYuanControlNet(operations=operations, device=load_device, dtype=unet_dtype)
+    control_model = comfy.ldm.hydit.controlnet.HunYuanControlNet(operations=operations, device=offload_device, dtype=unet_dtype)
     control_model = controlnet_load_state_dict(control_model, controlnet_data)
 
     latent_format = comfy.latent_formats.SDXL()
@@ -430,22 +463,49 @@ def load_controlnet_hunyuandit(controlnet_data):
     control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds, strength_type=StrengthType.CONSTANT)
     return control
 
-def load_controlnet_flux_xlabs(sd):
+def load_controlnet_flux_xlabs_mistoline(sd, mistoline=False, model_options={}):
-    model_config, operations, load_device, unet_dtype, manual_cast_dtype = controlnet_config(sd)
+    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(sd, model_options=model_options)
-    control_model = comfy.ldm.flux.controlnet_xlabs.ControlNetFlux(operations=operations, device=load_device, dtype=unet_dtype, **model_config.unet_config)
+    control_model = comfy.ldm.flux.controlnet.ControlNetFlux(mistoline=mistoline, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
     control_model = controlnet_load_state_dict(control_model, sd)
     extra_conds = ['y', 'guidance']
     control = ControlNet(control_model, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)
     return control
 
+def load_controlnet_flux_instantx(sd, model_options={}):
+    new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "")
+    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd, model_options=model_options)
+    for k in sd:
+        new_sd[k] = sd[k]
 
-def load_controlnet(ckpt_path, model=None):
+    num_union_modes = 0
-    controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
+    union_cnet = "controlnet_mode_embedder.weight"
+    if union_cnet in new_sd:
+        num_union_modes = new_sd[union_cnet].shape[0]
+
+    control_latent_channels = new_sd.get("pos_embed_input.weight").shape[1] // 4
+    concat_mask = False
+    if control_latent_channels == 17:
+        concat_mask = True
+
+    control_model = comfy.ldm.flux.controlnet.ControlNetFlux(latent_input=True, num_union_modes=num_union_modes, control_latent_channels=control_latent_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
+    control_model = controlnet_load_state_dict(control_model, new_sd)
+
+    latent_format = comfy.latent_formats.Flux()
+    extra_conds = ['y', 'guidance']
+    control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)
+    return control
+
+def convert_mistoline(sd):
+    return comfy.utils.state_dict_prefix_replace(sd, {"single_controlnet_blocks.": "controlnet_single_blocks."})
+
+
+def load_controlnet_state_dict(state_dict, model=None, model_options={}):
+    controlnet_data = state_dict
     if 'after_proj_list.18.bias' in controlnet_data.keys(): #Hunyuan DiT
-        return load_controlnet_hunyuandit(controlnet_data)
+        return load_controlnet_hunyuandit(controlnet_data, model_options=model_options)
 
     if "lora_controlnet" in controlnet_data:
-        return ControlLora(controlnet_data)
+        return ControlLora(controlnet_data, model_options=model_options)
 
     controlnet_config = None
     supported_inference_dtypes = None
@@ -500,11 +560,15 @@ def load_controlnet(ckpt_path, model=None):
         if len(leftover_keys) > 0:
             logging.warning("leftover keys: {}".format(leftover_keys))
         controlnet_data = new_sd
-    elif "controlnet_blocks.0.weight" in controlnet_data: #SD3 diffusers format
+    elif "controlnet_blocks.0.weight" in controlnet_data:
         if "double_blocks.0.img_attn.norm.key_norm.scale" in controlnet_data:
-            return load_controlnet_flux_xlabs(controlnet_data)
+            return load_controlnet_flux_xlabs_mistoline(controlnet_data, model_options=model_options)
-        else:
+        elif "pos_embed_input.proj.weight" in controlnet_data:
-            return load_controlnet_mmdit(controlnet_data)
+            return load_controlnet_mmdit(controlnet_data, model_options=model_options) #SD3 diffusers controlnet
+        elif "controlnet_x_embedder.weight" in controlnet_data:
+            return load_controlnet_flux_instantx(controlnet_data, model_options=model_options)
+    elif "controlnet_blocks.0.linear.weight" in controlnet_data: #mistoline flux
+        return load_controlnet_flux_xlabs_mistoline(convert_mistoline(controlnet_data), mistoline=True, model_options=model_options)
 
     pth_key = 'control_model.zero_convs.0.0.weight'
     pth = False
@@ -516,26 +580,38 @@ def load_controlnet(ckpt_path, model=None):
     elif key in controlnet_data:
         prefix = ""
     else:
-        net = load_t2i_adapter(controlnet_data)
+        net = load_t2i_adapter(controlnet_data, model_options=model_options)
         if net is None:
-            logging.error("error checkpoint does not contain controlnet or t2i adapter data {}".format(ckpt_path))
+            logging.error("error could not detect control model type.")
         return net
 
     if controlnet_config is None:
         model_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, True)
-        supported_inference_dtypes = model_config.supported_inference_dtypes
+        supported_inference_dtypes = list(model_config.supported_inference_dtypes)
         controlnet_config = model_config.unet_config
 
+    unet_dtype = model_options.get("dtype", None)
+    if unet_dtype is None:
+        weight_dtype = comfy.utils.weight_dtype(controlnet_data)
+
+        if supported_inference_dtypes is None:
+            supported_inference_dtypes = [comfy.model_management.unet_dtype()]
+
+        if weight_dtype is not None:
+            supported_inference_dtypes.append(weight_dtype)
+
+        unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes)
+
     load_device = comfy.model_management.get_torch_device()
-    if supported_inference_dtypes is None:
-        unet_dtype = comfy.model_management.unet_dtype()
-    else:
-        unet_dtype = comfy.model_management.unet_dtype(supported_dtypes=supported_inference_dtypes)
 
     manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
-    if manual_cast_dtype is not None:
+    operations = model_options.get("custom_operations", None)
-        controlnet_config["operations"] = comfy.ops.manual_cast
+    if operations is None:
+        operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype)
+
+    controlnet_config["operations"] = operations
     controlnet_config["dtype"] = unet_dtype
+    controlnet_config["device"] = comfy.model_management.unet_offload_device()
     controlnet_config.pop("out_channels")
     controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1]
     control_model = comfy.cldm.cldm.ControlNet(**controlnet_config)
@@ -569,14 +645,21 @@ def load_controlnet(ckpt_path, model=None):
     if len(unexpected) > 0:
         logging.debug("unexpected controlnet keys: {}".format(unexpected))
 
-    global_average_pooling = False
+    global_average_pooling = model_options.get("global_average_pooling", False)
-    filename = os.path.splitext(ckpt_path)[0]
-    if filename.endswith("_shuffle") or filename.endswith("_shuffle_fp16"): #TODO: smarter way of enabling global_average_pooling
-        global_average_pooling = True
-
     control = ControlNet(control_model, global_average_pooling=global_average_pooling, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
     return control
 
+def load_controlnet(ckpt_path, model=None, model_options={}):
+    if "global_average_pooling" not in model_options:
+        filename = os.path.splitext(ckpt_path)[0]
+        if filename.endswith("_shuffle") or filename.endswith("_shuffle_fp16"): #TODO: smarter way of enabling global_average_pooling
+            model_options["global_average_pooling"] = True
+
+    cnet = load_controlnet_state_dict(comfy.utils.load_torch_file(ckpt_path, safe_load=True), model=model, model_options=model_options)
+    if cnet is None:
+        logging.error("error checkpoint does not contain controlnet or t2i adapter data {}".format(ckpt_path))
+    return cnet
+
 class T2IAdapter(ControlBase):
     def __init__(self, t2i_model, channels_in, compression_ratio, upscale_algorithm, device=None):
         super().__init__(device)
@@ -632,7 +715,7 @@ class T2IAdapter(ControlBase):
         self.copy_to(c)
         return c
 
-def load_t2i_adapter(t2i_data):
+def load_t2i_adapter(t2i_data, model_options={}): #TODO: model_options
     compression_ratio = 8
     upscale_algorithm = 'nearest-exact'
 

comfy/float.py

@@ -1,7 +1,17 @@
 import torch
 
+def calc_mantissa(abs_x, exponent, normal_mask, MANTISSA_BITS, EXPONENT_BIAS, generator=None):
+    mantissa_scaled = torch.where(
+        normal_mask,
+        (abs_x / (2.0 ** (exponent - EXPONENT_BIAS)) - 1.0) * (2**MANTISSA_BITS),
+        (abs_x / (2.0 ** (-EXPONENT_BIAS + 1 - MANTISSA_BITS)))
+    )
+
+    mantissa_scaled += torch.rand(mantissa_scaled.size(), dtype=mantissa_scaled.dtype, layout=mantissa_scaled.layout, device=mantissa_scaled.device, generator=generator)
+    return mantissa_scaled.floor() / (2**MANTISSA_BITS)
+
 #Not 100% sure about this
-def manual_stochastic_round_to_float8(x, dtype):
+def manual_stochastic_round_to_float8(x, dtype, generator=None):
     if dtype == torch.float8_e4m3fn:
         EXPONENT_BITS, MANTISSA_BITS, EXPONENT_BIAS = 4, 3, 7
     elif dtype == torch.float8_e5m2:
@@ -9,44 +19,35 @@ def manual_stochastic_round_to_float8(x, dtype):
     else:
         raise ValueError("Unsupported dtype")
 
+    x = x.half()
     sign = torch.sign(x)
     abs_x = x.abs()
+    sign = torch.where(abs_x == 0, 0, sign)
 
     # Combine exponent calculation and clamping
     exponent = torch.clamp(
-        torch.floor(torch.log2(abs_x)).to(torch.int32) + EXPONENT_BIAS,
+        torch.floor(torch.log2(abs_x)) + EXPONENT_BIAS,
         0, 2**EXPONENT_BITS - 1
     )
 
     # Combine mantissa calculation and rounding
-    # min_normal = 2.0 ** (-EXPONENT_BIAS + 1)
-    # zero_mask = (abs_x == 0)
-    # subnormal_mask = (exponent == 0) & (abs_x != 0)
     normal_mask = ~(exponent == 0)
 
-    mantissa_scaled = torch.where(
+    abs_x[:] = calc_mantissa(abs_x, exponent, normal_mask, MANTISSA_BITS, EXPONENT_BIAS, generator=generator)
+
+    sign *= torch.where(
         normal_mask,
-        (abs_x / (2.0 ** (exponent - EXPONENT_BIAS)) - 1.0) * (2**MANTISSA_BITS),
+        (2.0 ** (exponent - EXPONENT_BIAS)) * (1.0 + abs_x),
-        (abs_x / (2.0 ** (-EXPONENT_BIAS + 1 - MANTISSA_BITS)))
+        (2.0 ** (-EXPONENT_BIAS + 1)) * abs_x
-    )
-    mantissa_floor = mantissa_scaled.floor()
-    mantissa = torch.where(
-        torch.rand_like(mantissa_scaled) < (mantissa_scaled - mantissa_floor),
-        (mantissa_floor + 1) / (2**MANTISSA_BITS),
-        mantissa_floor / (2**MANTISSA_BITS)
-    )
-    result = torch.where(
-        normal_mask,
-        sign * (2.0 ** (exponent - EXPONENT_BIAS)) * (1.0 + mantissa),
-        sign * (2.0 ** (-EXPONENT_BIAS + 1)) * mantissa
     )
 
-    result = torch.where(abs_x == 0, 0, result)
+    inf = torch.finfo(dtype)
-    return result.to(dtype=dtype)
+    torch.clamp(sign, min=inf.min, max=inf.max, out=sign)
+    return sign
 
 
 
-def stochastic_rounding(value, dtype):
+def stochastic_rounding(value, dtype, seed=0):
     if dtype == torch.float32:
         return value.to(dtype=torch.float32)
     if dtype == torch.float16:
@@ -54,6 +55,13 @@ def stochastic_rounding(value, dtype):
     if dtype == torch.bfloat16:
         return value.to(dtype=torch.bfloat16)
     if dtype == torch.float8_e4m3fn or dtype == torch.float8_e5m2:
-        return manual_stochastic_round_to_float8(value, dtype)
+        generator = torch.Generator(device=value.device)
+        generator.manual_seed(seed)
+        output = torch.empty_like(value, dtype=dtype)
+        num_slices = max(1, (value.numel() / (4096 * 4096)))
+        slice_size = max(1, round(value.shape[0] / num_slices))
+        for i in range(0, value.shape[0], slice_size):
+            output[i:i+slice_size].copy_(manual_stochastic_round_to_float8(value[i:i+slice_size], dtype, generator=generator))
+        return output
 
     return value.to(dtype=dtype)

comfy/k_diffusion/sampling.py

@@ -44,6 +44,17 @@ def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'):
     return append_zero(sigmas)
 
 
+def get_sigmas_laplace(n, sigma_min, sigma_max, mu=0., beta=0.5, device='cpu'):
+    """Constructs the noise schedule proposed by Tiankai et al. (2024). """
+    epsilon = 1e-5 # avoid log(0)
+    x = torch.linspace(0, 1, n, device=device)
+    clamp = lambda x: torch.clamp(x, min=sigma_min, max=sigma_max)
+    lmb = mu - beta * torch.sign(0.5-x) * torch.log(1 - 2 * torch.abs(0.5-x) + epsilon)
+    sigmas = clamp(torch.exp(lmb))
+    return sigmas
+
+
+
 def to_d(x, sigma, denoised):
     """Converts a denoiser output to a Karras ODE derivative."""
     return (x - denoised) / utils.append_dims(sigma, x.ndim)
@@ -1069,7 +1080,6 @@ def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disabl
         d = to_d(x, sigma_hat, temp[0])
         if callback is not None:
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
-        dt = sigmas[i + 1] - sigma_hat
         # Euler method
         x = denoised + d * sigmas[i + 1]
     return x
@@ -1096,8 +1106,81 @@ def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=No
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
         d = to_d(x, sigmas[i], temp[0])
         # Euler method
-        dt = sigma_down - sigmas[i]
         x = denoised + d * sigma_down
         if sigmas[i + 1] > 0:
             x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
     return x
+@torch.no_grad()
+def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+    """Ancestral sampling with DPM-Solver++(2S) second-order steps."""
+    extra_args = {} if extra_args is None else extra_args
+    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+
+    temp = [0]
+    def post_cfg_function(args):
+        temp[0] = args["uncond_denoised"]
+        return args["denoised"]
+
+    model_options = extra_args.get("model_options", {}).copy()
+    extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+    s_in = x.new_ones([x.shape[0]])
+    sigma_fn = lambda t: t.neg().exp()
+    t_fn = lambda sigma: sigma.log().neg()
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        if sigma_down == 0:
+            # Euler method
+            d = to_d(x, sigmas[i], temp[0])
+            x = denoised + d * sigma_down
+        else:
+            # DPM-Solver++(2S)
+            t, t_next = t_fn(sigmas[i]), t_fn(sigma_down)
+            # r = torch.sinh(1 + (2 - eta) * (t_next - t) / (t - t_fn(sigma_up))) works only on non-cfgpp, weird
+            r = 1 / 2
+            h = t_next - t
+            s = t + r * h
+            x_2 = (sigma_fn(s) / sigma_fn(t)) * (x + (denoised - temp[0])) - (-h * r).expm1() * denoised
+            denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
+            x = (sigma_fn(t_next) / sigma_fn(t)) * (x + (denoised - temp[0])) - (-h).expm1() * denoised_2
+        # Noise addition
+        if sigmas[i + 1] > 0:
+            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+    return x
+
+@torch.no_grad()
+def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    """DPM-Solver++(2M)."""
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    t_fn = lambda sigma: sigma.log().neg()
+
+    old_uncond_denoised = None
+    uncond_denoised = None
+    def post_cfg_function(args):
+        nonlocal uncond_denoised
+        uncond_denoised = args["uncond_denoised"]
+        return args["denoised"]
+    
+    model_options = extra_args.get("model_options", {}).copy()
+    extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
+        h = t_next - t
+        if old_uncond_denoised is None or sigmas[i + 1] == 0:
+            denoised_mix = -torch.exp(-h) * uncond_denoised
+        else:
+            h_last = t - t_fn(sigmas[i - 1])
+            r = h_last / h
+            denoised_mix = -torch.exp(-h) * uncond_denoised - torch.expm1(-h) * (1 / (2 * r)) * (denoised - old_uncond_denoised)
+        x = denoised + denoised_mix + torch.exp(-h) * x
+        old_uncond_denoised = uncond_denoised
+    return x

comfy/latent_formats.py

@@ -4,6 +4,7 @@ class LatentFormat:
     scale_factor = 1.0
     latent_channels = 4
     latent_rgb_factors = None
+    latent_rgb_factors_bias = None
     taesd_decoder_name = None
 
     def process_in(self, latent):
@@ -30,11 +31,13 @@ class SDXL(LatentFormat):
     def __init__(self):
         self.latent_rgb_factors = [
                     #   R        G        B
-                    [ 0.3920,  0.4054,  0.4549],
+                    [ 0.3651,  0.4232,  0.4341],
-                    [-0.2634, -0.0196,  0.0653],
+                    [-0.2533, -0.0042,  0.1068],
-                    [ 0.0568,  0.1687, -0.0755],
+                    [ 0.1076,  0.1111, -0.0362],
-                    [-0.3112, -0.2359, -0.2076]
+                    [-0.3165, -0.2492, -0.2188]
                 ]
+        self.latent_rgb_factors_bias = [ 0.1084, -0.0175, -0.0011]
+
         self.taesd_decoder_name = "taesdxl_decoder"
 
 class SDXL_Playground_2_5(LatentFormat):
@@ -112,23 +115,24 @@ class SD3(LatentFormat):
         self.scale_factor = 1.5305
         self.shift_factor = 0.0609
         self.latent_rgb_factors = [
-            [-0.0645,  0.0177,  0.1052],
+            [-0.0922, -0.0175,  0.0749],
-            [ 0.0028,  0.0312,  0.0650],
+            [ 0.0311,  0.0633,  0.0954],
-            [ 0.1848,  0.0762,  0.0360],
+            [ 0.1994,  0.0927,  0.0458],
-            [ 0.0944,  0.0360,  0.0889],
+            [ 0.0856,  0.0339,  0.0902],
-            [ 0.0897,  0.0506, -0.0364],
+            [ 0.0587,  0.0272, -0.0496],
-            [-0.0020,  0.1203,  0.0284],
+            [-0.0006,  0.1104,  0.0309],
-            [ 0.0855,  0.0118,  0.0283],
+            [ 0.0978,  0.0306,  0.0427],
-            [-0.0539,  0.0658,  0.1047],
+            [-0.0042,  0.1038,  0.1358],
-            [-0.0057,  0.0116,  0.0700],
+            [-0.0194,  0.0020,  0.0669],
-            [-0.0412,  0.0281, -0.0039],
+            [-0.0488,  0.0130, -0.0268],
-            [ 0.1106,  0.1171,  0.1220],
+            [ 0.0922,  0.0988,  0.0951],
-            [-0.0248,  0.0682, -0.0481],
+            [-0.0278,  0.0524, -0.0542],
-            [ 0.0815,  0.0846,  0.1207],
+            [ 0.0332,  0.0456,  0.0895],
-            [-0.0120, -0.0055, -0.0867],
+            [-0.0069, -0.0030, -0.0810],
-            [-0.0749, -0.0634, -0.0456],
+            [-0.0596, -0.0465, -0.0293],
-            [-0.1418, -0.1457, -0.1259]
+            [-0.1448, -0.1463, -0.1189]
         ]
+        self.latent_rgb_factors_bias = [0.2394, 0.2135, 0.1925]
         self.taesd_decoder_name = "taesd3_decoder"
 
     def process_in(self, latent):
@@ -146,23 +150,24 @@ class Flux(SD3):
         self.scale_factor = 0.3611
         self.shift_factor = 0.1159
         self.latent_rgb_factors =[
-            [-0.0404,  0.0159,  0.0609],
+            [-0.0346,  0.0244,  0.0681],
-            [ 0.0043,  0.0298,  0.0850],
+            [ 0.0034,  0.0210,  0.0687],
-            [ 0.0328, -0.0749, -0.0503],
+            [ 0.0275, -0.0668, -0.0433],
-            [-0.0245,  0.0085,  0.0549],
+            [-0.0174,  0.0160,  0.0617],
-            [ 0.0966,  0.0894,  0.0530],
+            [ 0.0859,  0.0721,  0.0329],
-            [ 0.0035,  0.0399,  0.0123],
+            [ 0.0004,  0.0383,  0.0115],
-            [ 0.0583,  0.1184,  0.1262],
+            [ 0.0405,  0.0861,  0.0915],
-            [-0.0191, -0.0206, -0.0306],
+            [-0.0236, -0.0185, -0.0259],
-            [-0.0324,  0.0055,  0.1001],
+            [-0.0245,  0.0250,  0.1180],
-            [ 0.0955,  0.0659, -0.0545],
+            [ 0.1008,  0.0755, -0.0421],
-            [-0.0504,  0.0231, -0.0013],
+            [-0.0515,  0.0201,  0.0011],
-            [ 0.0500, -0.0008, -0.0088],
+            [ 0.0428, -0.0012, -0.0036],
-            [ 0.0982,  0.0941,  0.0976],
+            [ 0.0817,  0.0765,  0.0749],
-            [-0.1233, -0.0280, -0.0897],
+            [-0.1264, -0.0522, -0.1103],
-            [-0.0005, -0.0530, -0.0020],
+            [-0.0280, -0.0881, -0.0499],
-            [-0.1273, -0.0932, -0.0680]
+            [-0.1262, -0.0982, -0.0778]
         ]
+        self.latent_rgb_factors_bias = [-0.0329, -0.0718, -0.0851]
         self.taesd_decoder_name = "taef1_decoder"
 
     def process_in(self, latent):

comfy/ldm/common_dit.py

@@ -1,4 +1,5 @@
 import torch
+import comfy.ops
 
 def pad_to_patch_size(img, patch_size=(2, 2), padding_mode="circular"):
     if padding_mode == "circular" and torch.jit.is_tracing() or torch.jit.is_scripting():
@@ -6,3 +7,15 @@ def pad_to_patch_size(img, patch_size=(2, 2), padding_mode="circular"):
     pad_h = (patch_size[0] - img.shape[-2] % patch_size[0]) % patch_size[0]
     pad_w = (patch_size[1] - img.shape[-1] % patch_size[1]) % patch_size[1]
     return torch.nn.functional.pad(img, (0, pad_w, 0, pad_h), mode=padding_mode)
+
+try:
+    rms_norm_torch = torch.nn.functional.rms_norm
+except:
+    rms_norm_torch = None
+
+def rms_norm(x, weight, eps=1e-6):
+    if rms_norm_torch is not None and not (torch.jit.is_tracing() or torch.jit.is_scripting()):
+        return rms_norm_torch(x, weight.shape, weight=comfy.ops.cast_to(weight, dtype=x.dtype, device=x.device), eps=eps)
+    else:
+        rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + eps)
+        return (x * rrms) * comfy.ops.cast_to(weight, dtype=x.dtype, device=x.device)

comfy/ldm/flux/controlnet.py

@@ -0,0 +1,205 @@
+#Original code can be found on: https://github.com/XLabs-AI/x-flux/blob/main/src/flux/controlnet.py
+#modified to support different types of flux controlnets
+
+import torch
+import math
+from torch import Tensor, nn
+from einops import rearrange, repeat
+
+from .layers import (DoubleStreamBlock, EmbedND, LastLayer,
+                                 MLPEmbedder, SingleStreamBlock,
+                                 timestep_embedding)
+
+from .model import Flux
+import comfy.ldm.common_dit
+
+class MistolineCondDownsamplBlock(nn.Module):
+    def __init__(self, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.encoder = nn.Sequential(
+            operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device)
+        )
+
+    def forward(self, x):
+        return self.encoder(x)
+
+class MistolineControlnetBlock(nn.Module):
+    def __init__(self, hidden_size, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.linear = operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device)
+        self.act = nn.SiLU()
+
+    def forward(self, x):
+        return self.act(self.linear(x))
+
+
+class ControlNetFlux(Flux):
+    def __init__(self, latent_input=False, num_union_modes=0, mistoline=False, control_latent_channels=None, image_model=None, dtype=None, device=None, operations=None, **kwargs):
+        super().__init__(final_layer=False, dtype=dtype, device=device, operations=operations, **kwargs)
+
+        self.main_model_double = 19
+        self.main_model_single = 38
+
+        self.mistoline = mistoline
+        # add ControlNet blocks
+        if self.mistoline:
+            control_block = lambda : MistolineControlnetBlock(self.hidden_size, dtype=dtype, device=device, operations=operations)
+        else:
+            control_block = lambda : operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)
+
+        self.controlnet_blocks = nn.ModuleList([])
+        for _ in range(self.params.depth):
+            self.controlnet_blocks.append(control_block())
+
+        self.controlnet_single_blocks = nn.ModuleList([])
+        for _ in range(self.params.depth_single_blocks):
+            self.controlnet_single_blocks.append(control_block())
+
+        self.num_union_modes = num_union_modes
+        self.controlnet_mode_embedder = None
+        if self.num_union_modes > 0:
+            self.controlnet_mode_embedder = operations.Embedding(self.num_union_modes, self.hidden_size, dtype=dtype, device=device)
+
+        self.gradient_checkpointing = False
+        self.latent_input = latent_input
+        if control_latent_channels is None:
+            control_latent_channels = self.in_channels
+        else:
+            control_latent_channels *= 2 * 2 #patch size
+
+        self.pos_embed_input = operations.Linear(control_latent_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
+        if not self.latent_input:
+            if self.mistoline:
+                self.input_cond_block = MistolineCondDownsamplBlock(dtype=dtype, device=device, operations=operations)
+            else:
+                self.input_hint_block = nn.Sequential(
+                    operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device),
+                    nn.SiLU(),
+                    operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+                    nn.SiLU(),
+                    operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+                    nn.SiLU(),
+                    operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+                    nn.SiLU(),
+                    operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+                    nn.SiLU(),
+                    operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+                    nn.SiLU(),
+                    operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+                    nn.SiLU(),
+                    operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device)
+                )
+
+    def forward_orig(
+        self,
+        img: Tensor,
+        img_ids: Tensor,
+        controlnet_cond: Tensor,
+        txt: Tensor,
+        txt_ids: Tensor,
+        timesteps: Tensor,
+        y: Tensor,
+        guidance: Tensor = None,
+        control_type: Tensor = None,
+    ) -> Tensor:
+        if img.ndim != 3 or txt.ndim != 3:
+            raise ValueError("Input img and txt tensors must have 3 dimensions.")
+
+        # running on sequences img
+        img = self.img_in(img)
+
+        controlnet_cond = self.pos_embed_input(controlnet_cond)
+        img = img + controlnet_cond
+        vec = self.time_in(timestep_embedding(timesteps, 256))
+        if self.params.guidance_embed:
+            vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
+        vec = vec + self.vector_in(y)
+        txt = self.txt_in(txt)
+
+        if self.controlnet_mode_embedder is not None and len(control_type) > 0:
+            control_cond = self.controlnet_mode_embedder(torch.tensor(control_type, device=img.device), out_dtype=img.dtype).unsqueeze(0).repeat((txt.shape[0], 1, 1))
+            txt = torch.cat([control_cond, txt], dim=1)
+            txt_ids = torch.cat([txt_ids[:,:1], txt_ids], dim=1)
+
+        ids = torch.cat((txt_ids, img_ids), dim=1)
+        pe = self.pe_embedder(ids)
+
+        controlnet_double = ()
+
+        for i in range(len(self.double_blocks)):
+            img, txt = self.double_blocks[i](img=img, txt=txt, vec=vec, pe=pe)
+            controlnet_double = controlnet_double + (self.controlnet_blocks[i](img),)
+
+        img = torch.cat((txt, img), 1)
+
+        controlnet_single = ()
+
+        for i in range(len(self.single_blocks)):
+            img = self.single_blocks[i](img, vec=vec, pe=pe)
+            controlnet_single = controlnet_single + (self.controlnet_single_blocks[i](img[:, txt.shape[1] :, ...]),)
+
+        repeat = math.ceil(self.main_model_double / len(controlnet_double))
+        if self.latent_input:
+            out_input = ()
+            for x in controlnet_double:
+                    out_input += (x,) * repeat
+        else:
+            out_input = (controlnet_double * repeat)
+
+        out = {"input": out_input[:self.main_model_double]}
+        if len(controlnet_single) > 0:
+            repeat = math.ceil(self.main_model_single / len(controlnet_single))
+            out_output = ()
+            if self.latent_input:
+                for x in controlnet_single:
+                        out_output += (x,) * repeat
+            else:
+                out_output = (controlnet_single * repeat)
+            out["output"] = out_output[:self.main_model_single]
+        return out
+
+    def forward(self, x, timesteps, context, y, guidance=None, hint=None, **kwargs):
+        patch_size = 2
+        if self.latent_input:
+            hint = comfy.ldm.common_dit.pad_to_patch_size(hint, (patch_size, patch_size))
+        elif self.mistoline:
+            hint = hint * 2.0 - 1.0
+            hint = self.input_cond_block(hint)
+        else:
+            hint = hint * 2.0 - 1.0
+            hint = self.input_hint_block(hint)
+
+        hint = rearrange(hint, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
+
+        bs, c, h, w = x.shape
+        x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))
+
+        img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
+
+        h_len = ((h + (patch_size // 2)) // patch_size)
+        w_len = ((w + (patch_size // 2)) // patch_size)
+        img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
+        img_ids[..., 1] = img_ids[..., 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype)[:, None]
+        img_ids[..., 2] = img_ids[..., 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype)[None, :]
+        img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
+
+        txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
+        return self.forward_orig(img, img_ids, hint, context, txt_ids, timesteps, y, guidance, control_type=kwargs.get("control_type", []))

comfy/ldm/flux/controlnet_xlabs.py

@@ -1,104 +0,0 @@
-#Original code can be found on: https://github.com/XLabs-AI/x-flux/blob/main/src/flux/controlnet.py
-
-import torch
-from torch import Tensor, nn
-from einops import rearrange, repeat
-
-from .layers import (DoubleStreamBlock, EmbedND, LastLayer,
-                                 MLPEmbedder, SingleStreamBlock,
-                                 timestep_embedding)
-
-from .model import Flux
-import comfy.ldm.common_dit
-
-
-class ControlNetFlux(Flux):
-    def __init__(self, image_model=None, dtype=None, device=None, operations=None, **kwargs):
-        super().__init__(final_layer=False, dtype=dtype, device=device, operations=operations, **kwargs)
-
-        # add ControlNet blocks
-        self.controlnet_blocks = nn.ModuleList([])
-        for _ in range(self.params.depth):
-            controlnet_block = operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)
-            # controlnet_block = zero_module(controlnet_block)
-            self.controlnet_blocks.append(controlnet_block)
-        self.pos_embed_input = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
-        self.gradient_checkpointing = False
-        self.input_hint_block = nn.Sequential(
-            operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device),
-            nn.SiLU(),
-            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
-            nn.SiLU(),
-            operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
-            nn.SiLU(),
-            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
-            nn.SiLU(),
-            operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
-            nn.SiLU(),
-            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
-            nn.SiLU(),
-            operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
-            nn.SiLU(),
-            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device)
-        )
-
-    def forward_orig(
-        self,
-        img: Tensor,
-        img_ids: Tensor,
-        controlnet_cond: Tensor,
-        txt: Tensor,
-        txt_ids: Tensor,
-        timesteps: Tensor,
-        y: Tensor,
-        guidance: Tensor = None,
-    ) -> Tensor:
-        if img.ndim != 3 or txt.ndim != 3:
-            raise ValueError("Input img and txt tensors must have 3 dimensions.")
-
-        # running on sequences img
-        img = self.img_in(img)
-        controlnet_cond = self.input_hint_block(controlnet_cond)
-        controlnet_cond = rearrange(controlnet_cond, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
-        controlnet_cond = self.pos_embed_input(controlnet_cond)
-        img = img + controlnet_cond
-        vec = self.time_in(timestep_embedding(timesteps, 256))
-        if self.params.guidance_embed:
-            vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
-        vec = vec + self.vector_in(y)
-        txt = self.txt_in(txt)
-
-        ids = torch.cat((txt_ids, img_ids), dim=1)
-        pe = self.pe_embedder(ids)
-
-        block_res_samples = ()
-
-        for block in self.double_blocks:
-            img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
-            block_res_samples = block_res_samples + (img,)
-
-        controlnet_block_res_samples = ()
-        for block_res_sample, controlnet_block in zip(block_res_samples, self.controlnet_blocks):
-            block_res_sample = controlnet_block(block_res_sample)
-            controlnet_block_res_samples = controlnet_block_res_samples + (block_res_sample,)
-
-        return {"input": (controlnet_block_res_samples * 10)[:19]}
-
-    def forward(self, x, timesteps, context, y, guidance=None, hint=None, **kwargs):
-        hint = hint * 2.0 - 1.0
-
-        bs, c, h, w = x.shape
-        patch_size = 2
-        x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))
-
-        img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
-
-        h_len = ((h + (patch_size // 2)) // patch_size)
-        w_len = ((w + (patch_size // 2)) // patch_size)
-        img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
-        img_ids[..., 1] = img_ids[..., 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype)[:, None]
-        img_ids[..., 2] = img_ids[..., 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype)[None, :]
-        img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
-
-        txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
-        return self.forward_orig(img, img_ids, hint, context, txt_ids, timesteps, y, guidance)

comfy/ldm/flux/layers.py

@@ -6,6 +6,7 @@ from torch import Tensor, nn
 
 from .math import attention, rope
 import comfy.ops
+import comfy.ldm.common_dit
 
 
 class EmbedND(nn.Module):
@@ -63,10 +64,7 @@ class RMSNorm(torch.nn.Module):
         self.scale = nn.Parameter(torch.empty((dim), dtype=dtype, device=device))
 
     def forward(self, x: Tensor):
-        x_dtype = x.dtype
+        return comfy.ldm.common_dit.rms_norm(x, self.scale, 1e-6)
-        x = x.float()
-        rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6)
-        return (x * rrms).to(dtype=x_dtype) * comfy.ops.cast_to(self.scale, dtype=x_dtype, device=x.device)
 
 
 class QKNorm(torch.nn.Module):

comfy/ldm/flux/model.py

@@ -108,7 +108,7 @@ class Flux(nn.Module):
                 raise ValueError("Didn't get guidance strength for guidance distilled model.")
             vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
 
-        vec = vec + self.vector_in(y)
+        vec = vec + self.vector_in(y[:,:self.params.vec_in_dim])
         txt = self.txt_in(txt)
 
         ids = torch.cat((txt_ids, img_ids), dim=1)
@@ -151,8 +151,8 @@ class Flux(nn.Module):
         h_len = ((h + (patch_size // 2)) // patch_size)
         w_len = ((w + (patch_size // 2)) // patch_size)
         img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
-        img_ids[..., 1] = img_ids[..., 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype)[:, None]
+        img_ids[:, :, 1] = torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
-        img_ids[..., 2] = img_ids[..., 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype)[None, :]
+        img_ids[:, :, 2] = torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
         img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
 
         txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)

comfy/ldm/hydit/models.py

@@ -372,7 +372,7 @@ class HunYuanDiT(nn.Module):
         for layer, block in enumerate(self.blocks):
             if layer > self.depth // 2:
                 if controls is not None:
-                    skip = skips.pop() + controls.pop()
+                    skip = skips.pop() + controls.pop().to(dtype=x.dtype)
                 else:
                     skip = skips.pop()
                 x = block(x, c, text_states, freqs_cis_img, skip)   # (N, L, D)

comfy/ldm/modules/diffusionmodules/mmdit.py

@@ -355,29 +355,9 @@ class RMSNorm(torch.nn.Module):
         else:
             self.register_parameter("weight", None)
 
-    def _norm(self, x):
-        """
-        Apply the RMSNorm normalization to the input tensor.
-        Args:
-            x (torch.Tensor): The input tensor.
-        Returns:
-            torch.Tensor: The normalized tensor.
-        """
-        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
-
     def forward(self, x):
-        """
+        return comfy.ldm.common_dit.rms_norm(x, self.weight, self.eps)
-        Forward pass through the RMSNorm layer.
+
-        Args:
-            x (torch.Tensor): The input tensor.
-        Returns:
-            torch.Tensor: The output tensor after applying RMSNorm.
-        """
-        x = self._norm(x)
-        if self.learnable_scale:
-            return x * self.weight.to(device=x.device, dtype=x.dtype)
-        else:
-            return x
 
 
 class SwiGLUFeedForward(nn.Module):

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -842,6 +842,11 @@ class UNetModel(nn.Module):
         t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
         emb = self.time_embed(t_emb)
 
+        if "emb_patch" in transformer_patches:
+            patch = transformer_patches["emb_patch"]
+            for p in patch:
+                emb = p(emb, self.model_channels, transformer_options)
+
         if self.num_classes is not None:
             assert y.shape[0] == x.shape[0]
             emb = emb + self.label_emb(y)

comfy/lora.py

@@ -16,6 +16,7 @@
     along with this program.  If not, see <https://www.gnu.org/licenses/>.
 """
 
+from __future__ import annotations
 import comfy.utils
 import comfy.model_management
 import comfy.model_base
@@ -200,9 +201,13 @@ def load_lora(lora, to_load):
 
 def model_lora_keys_clip(model, key_map={}):
     sdk = model.state_dict().keys()
+    for k in sdk:
+        if k.endswith(".weight"):
+            key_map["text_encoders.{}".format(k[:-len(".weight")])] = k #generic lora format without any weird key names
 
     text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}"
     clip_l_present = False
+    clip_g_present = False
     for b in range(32): #TODO: clean up
         for c in LORA_CLIP_MAP:
             k = "clip_h.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
@@ -226,6 +231,7 @@ def model_lora_keys_clip(model, key_map={}):
 
             k = "clip_g.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
             if k in sdk:
+                clip_g_present = True
                 if clip_l_present:
                     lora_key = "lora_te2_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base
                     key_map[lora_key] = k
@@ -241,10 +247,18 @@ def model_lora_keys_clip(model, key_map={}):
 
     for k in sdk:
         if k.endswith(".weight"):
-            if k.startswith("t5xxl.transformer."):#OneTrainer SD3 lora
+            if k.startswith("t5xxl.transformer."):#OneTrainer SD3 and Flux lora
                 l_key = k[len("t5xxl.transformer."):-len(".weight")]
-                lora_key = "lora_te3_{}".format(l_key.replace(".", "_"))
+                t5_index = 1
-                key_map[lora_key] = k
+                if clip_g_present:
+                    t5_index += 1
+                if clip_l_present:
+                    t5_index += 1
+                    if t5_index == 2:
+                        key_map["lora_te{}_{}".format(t5_index, l_key.replace(".", "_"))] = k #OneTrainer Flux
+                        t5_index += 1
+
+                key_map["lora_te{}_{}".format(t5_index, l_key.replace(".", "_"))] = k
             elif k.startswith("hydit_clip.transformer.bert."): #HunyuanDiT Lora
                 l_key = k[len("hydit_clip.transformer.bert."):-len(".weight")]
                 lora_key = "lora_te1_{}".format(l_key.replace(".", "_"))
@@ -280,6 +294,7 @@ def model_lora_keys_unet(model, key_map={}):
             unet_key = "diffusion_model.{}".format(diffusers_keys[k])
             key_lora = k[:-len(".weight")].replace(".", "_")
             key_map["lora_unet_{}".format(key_lora)] = unet_key
+            key_map["lycoris_{}".format(key_lora)] = unet_key #simpletuner lycoris format
 
             diffusers_lora_prefix = ["", "unet."]
             for p in diffusers_lora_prefix:
@@ -323,14 +338,15 @@ def model_lora_keys_unet(model, key_map={}):
                 to = diffusers_keys[k]
                 key_map["transformer.{}".format(k[:-len(".weight")])] = to #simpletrainer and probably regular diffusers flux lora format
                 key_map["lycoris_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #simpletrainer lycoris
+                key_map["lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #onetrainer
 
     return key_map
 
 
-def weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype):
+def weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function):
     dora_scale = comfy.model_management.cast_to_device(dora_scale, weight.device, intermediate_dtype)
     lora_diff *= alpha
-    weight_calc = weight + lora_diff.type(weight.dtype)
+    weight_calc = weight + function(lora_diff).type(weight.dtype)
     weight_norm = (
         weight_calc.transpose(0, 1)
         .reshape(weight_calc.shape[1], -1)
@@ -347,6 +363,39 @@ def weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediat
         weight[:] = weight_calc
     return weight
 
+def pad_tensor_to_shape(tensor: torch.Tensor, new_shape: list[int]) -> torch.Tensor:
+    """
+    Pad a tensor to a new shape with zeros.
+
+    Args:
+        tensor (torch.Tensor): The original tensor to be padded.
+        new_shape (List[int]): The desired shape of the padded tensor.
+
+    Returns:
+        torch.Tensor: A new tensor padded with zeros to the specified shape.
+
+    Note:
+        If the new shape is smaller than the original tensor in any dimension,
+        the original tensor will be truncated in that dimension.
+    """
+    if any([new_shape[i] < tensor.shape[i] for i in range(len(new_shape))]):
+        raise ValueError("The new shape must be larger than the original tensor in all dimensions")
+
+    if len(new_shape) != len(tensor.shape):
+        raise ValueError("The new shape must have the same number of dimensions as the original tensor")
+
+    # Create a new tensor filled with zeros
+    padded_tensor = torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device)
+
+    # Create slicing tuples for both tensors
+    orig_slices = tuple(slice(0, dim) for dim in tensor.shape)
+    new_slices = tuple(slice(0, dim) for dim in tensor.shape)
+
+    # Copy the original tensor into the new tensor
+    padded_tensor[new_slices] = tensor[orig_slices]
+
+    return padded_tensor
+
 def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32):
     for p in patches:
         strength = p[0]
@@ -366,7 +415,7 @@ def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32):
             weight *= strength_model
 
         if isinstance(v, list):
-            v = (calculate_weight(v[1:], v[0].clone(), key, intermediate_dtype=intermediate_dtype), )
+            v = (calculate_weight(v[1:], v[0][1](comfy.model_management.cast_to_device(v[0][0], weight.device, intermediate_dtype, copy=True), inplace=True), key, intermediate_dtype=intermediate_dtype), )
 
         if len(v) == 1:
             patch_type = "diff"
@@ -375,12 +424,18 @@ def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32):
             v = v[1]
 
         if patch_type == "diff":
-            w1 = v[0]
+            diff: torch.Tensor = v[0]
+            # An extra flag to pad the weight if the diff's shape is larger than the weight
+            do_pad_weight = len(v) > 1 and v[1]['pad_weight']
+            if do_pad_weight and diff.shape != weight.shape:
+                logging.info("Pad weight {} from {} to shape: {}".format(key, weight.shape, diff.shape))
+                weight = pad_tensor_to_shape(weight, diff.shape)
+
             if strength != 0.0:
-                if w1.shape != weight.shape:
+                if diff.shape != weight.shape:
-                    logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
+                    logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, diff.shape, weight.shape))
                 else:
-                    weight += function(strength * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype))
+                    weight += function(strength * comfy.model_management.cast_to_device(diff, weight.device, weight.dtype))
         elif patch_type == "lora": #lora/locon
             mat1 = comfy.model_management.cast_to_device(v[0], weight.device, intermediate_dtype)
             mat2 = comfy.model_management.cast_to_device(v[1], weight.device, intermediate_dtype)
@@ -398,7 +453,7 @@ def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32):
             try:
                 lora_diff = torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)).reshape(weight.shape)
                 if dora_scale is not None:
-                    weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
+                    weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function)
                 else:
                     weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
             except Exception as e:
@@ -444,7 +499,7 @@ def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32):
             try:
                 lora_diff = torch.kron(w1, w2).reshape(weight.shape)
                 if dora_scale is not None:
-                    weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
+                    weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function)
                 else:
                     weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
             except Exception as e:
@@ -481,28 +536,48 @@ def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32):
             try:
                 lora_diff = (m1 * m2).reshape(weight.shape)
                 if dora_scale is not None:
-                    weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
+                    weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function)
                 else:
                     weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
             except Exception as e:
                 logging.error("ERROR {} {} {}".format(patch_type, key, e))
         elif patch_type == "glora":
-            if v[4] is not None:
-                alpha = v[4] / v[0].shape[0]
-            else:
-                alpha = 1.0
-
             dora_scale = v[5]
 
+            old_glora = False
+            if v[3].shape[1] == v[2].shape[0] == v[0].shape[0] == v[1].shape[1]:
+                rank = v[0].shape[0]
+                old_glora = True
+
+            if v[3].shape[0] == v[2].shape[1] == v[0].shape[1] == v[1].shape[0]:
+                if old_glora and v[1].shape[0] == weight.shape[0] and weight.shape[0] == weight.shape[1]:
+                    pass
+                else:
+                    old_glora = False
+                    rank = v[1].shape[0]
+
             a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, intermediate_dtype)
             a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, intermediate_dtype)
             b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, intermediate_dtype)
             b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, intermediate_dtype)
 
+            if v[4] is not None:
+                alpha = v[4] / rank
+            else:
+                alpha = 1.0
+
             try:
-                lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)).reshape(weight.shape)
+                if old_glora:
+                    lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1).to(dtype=intermediate_dtype), a2), a1)).reshape(weight.shape) #old lycoris glora
+                else:
+                    if weight.dim() > 2:
+                        lora_diff = torch.einsum("o i ..., i j -> o j ...", torch.einsum("o i ..., i j -> o j ...", weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape)
+                    else:
+                        lora_diff = torch.mm(torch.mm(weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape)
+                    lora_diff += torch.mm(b1, b2).reshape(weight.shape)
+
                 if dora_scale is not None:
-                    weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
+                    weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function)
                 else:
                     weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
             except Exception as e:

comfy/model_base.py

@@ -96,7 +96,8 @@ class BaseModel(torch.nn.Module):
 
         if not unet_config.get("disable_unet_model_creation", False):
             if model_config.custom_operations is None:
-                operations = comfy.ops.pick_operations(unet_config.get("dtype", None), self.manual_cast_dtype)
+                fp8 = model_config.optimizations.get("fp8", model_config.scaled_fp8 is not None)
+                operations = comfy.ops.pick_operations(unet_config.get("dtype", None), self.manual_cast_dtype, fp8_optimizations=fp8, scaled_fp8=model_config.scaled_fp8)
             else:
                 operations = model_config.custom_operations
             self.diffusion_model = unet_model(**unet_config, device=device, operations=operations)
@@ -244,6 +245,10 @@ class BaseModel(torch.nn.Module):
             extra_sds.append(self.model_config.process_clip_vision_state_dict_for_saving(clip_vision_state_dict))
 
         unet_state_dict = self.diffusion_model.state_dict()
+
+        if self.model_config.scaled_fp8 is not None:
+            unet_state_dict["scaled_fp8"] = torch.tensor([], dtype=self.model_config.scaled_fp8)
+
         unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict)
 
         if self.model_type == ModelType.V_PREDICTION:

comfy/model_detection.py

@@ -286,9 +286,15 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal
         return None
     model_config = model_config_from_unet_config(unet_config, state_dict)
     if model_config is None and use_base_if_no_match:
-        return comfy.supported_models_base.BASE(unet_config)
+        model_config = comfy.supported_models_base.BASE(unet_config)
-    else:
+
-        return model_config
+    scaled_fp8_weight = state_dict.get("{}scaled_fp8".format(unet_key_prefix), None)
+    if scaled_fp8_weight is not None:
+        model_config.scaled_fp8 = scaled_fp8_weight.dtype
+        if model_config.scaled_fp8 == torch.float32:
+            model_config.scaled_fp8 = torch.float8_e4m3fn
+
+    return model_config
 
 def unet_prefix_from_state_dict(state_dict):
     candidates = ["model.diffusion_model.", #ldm/sgm models

comfy/model_management.py

@@ -45,6 +45,7 @@ cpu_state = CPUState.GPU
 total_vram = 0
 
 xpu_available = False
+torch_version = ""
 try:
     torch_version = torch.version.__version__
     xpu_available = (int(torch_version[0]) < 2 or (int(torch_version[0]) == 2 and int(torch_version[2]) <= 4)) and torch.xpu.is_available()
@@ -144,7 +145,7 @@ total_ram = psutil.virtual_memory().total / (1024 * 1024)
 logging.info("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram))
 
 try:
-    logging.info("pytorch version: {}".format(torch.version.__version__))
+    logging.info("pytorch version: {}".format(torch_version))
 except:
     pass
 
@@ -325,7 +326,7 @@ class LoadedModel:
                 self.model_unload()
                 raise e
 
-        if is_intel_xpu() and not args.disable_ipex_optimize and self.real_model is not None:
+        if is_intel_xpu() and not args.disable_ipex_optimize and 'ipex' in globals() and self.real_model is not None:
             with torch.no_grad():
                 self.real_model = ipex.optimize(self.real_model.eval(), inplace=True, graph_mode=True, concat_linear=True)
 
@@ -369,12 +370,11 @@ def offloaded_memory(loaded_models, device):
             offloaded_mem += m.model_offloaded_memory()
     return offloaded_mem
 
-def minimum_inference_memory():
+WINDOWS = any(platform.win32_ver())
-    return (1024 * 1024 * 1024) * 1.2
 
-EXTRA_RESERVED_VRAM = 200 * 1024 * 1024
+EXTRA_RESERVED_VRAM = 400 * 1024 * 1024
-if any(platform.win32_ver()):
+if WINDOWS:
-    EXTRA_RESERVED_VRAM = 500 * 1024 * 1024 #Windows is higher because of the shared vram issue
+    EXTRA_RESERVED_VRAM = 600 * 1024 * 1024 #Windows is higher because of the shared vram issue
 
 if args.reserve_vram is not None:
     EXTRA_RESERVED_VRAM = args.reserve_vram * 1024 * 1024 * 1024
@@ -383,6 +383,9 @@ if args.reserve_vram is not None:
 def extra_reserved_memory():
     return EXTRA_RESERVED_VRAM
 
+def minimum_inference_memory():
+    return (1024 * 1024 * 1024) * 0.8 + extra_reserved_memory()
+
 def unload_model_clones(model, unload_weights_only=True, force_unload=True):
     to_unload = []
     for i in range(len(current_loaded_models)):
@@ -405,6 +408,8 @@ def unload_model_clones(model, unload_weights_only=True, force_unload=True):
     if not force_unload:
         if unload_weights_only and unload_weight == False:
             return None
+    else:
+        unload_weight = True
 
     for i in to_unload:
         logging.debug("unload clone {} {}".format(i, unload_weight))
@@ -421,7 +426,7 @@ def free_memory(memory_required, device, keep_loaded=[]):
         shift_model = current_loaded_models[i]
         if shift_model.device == device:
             if shift_model not in keep_loaded:
-                can_unload.append((sys.getrefcount(shift_model.model), shift_model.model_memory(), i))
+                can_unload.append((-shift_model.model_offloaded_memory(), sys.getrefcount(shift_model.model), shift_model.model_memory(), i))
                 shift_model.currently_used = False
 
     for x in sorted(can_unload):
@@ -621,6 +626,8 @@ def maximum_vram_for_weights(device=None):
     return (get_total_memory(device) * 0.88 - minimum_inference_memory())
 
 def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]):
+    if model_params < 0:
+        model_params = 1000000000000000000000
     if args.bf16_unet:
         return torch.bfloat16
     if args.fp16_unet:
@@ -640,6 +647,9 @@ def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, tor
         pass
 
     if fp8_dtype is not None:
+        if supports_fp8_compute(device): #if fp8 compute is supported the casting is most likely not expensive
+            return fp8_dtype
+
         free_model_memory = maximum_vram_for_weights(device)
         if model_params * 2 > free_model_memory:
             return fp8_dtype
@@ -833,27 +843,21 @@ def force_channels_last():
     #TODO
     return False
 
+def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False):
+    if device is None or weight.device == device:
+        if not copy:
+            if dtype is None or weight.dtype == dtype:
+                return weight
+        return weight.to(dtype=dtype, copy=copy)
+
+    r = torch.empty_like(weight, dtype=dtype, device=device)
+    r.copy_(weight, non_blocking=non_blocking)
+    return r
+
 def cast_to_device(tensor, device, dtype, copy=False):
-    device_supports_cast = False
+    non_blocking = device_supports_non_blocking(device)
-    if tensor.dtype == torch.float32 or tensor.dtype == torch.float16:
+    return cast_to(tensor, dtype=dtype, device=device, non_blocking=non_blocking, copy=copy)
-        device_supports_cast = True
-    elif tensor.dtype == torch.bfloat16:
-        if hasattr(device, 'type') and device.type.startswith("cuda"):
-            device_supports_cast = True
-        elif is_intel_xpu():
-            device_supports_cast = True
 
-    non_blocking = device_should_use_non_blocking(device)
-
-    if device_supports_cast:
-        if copy:
-            if tensor.device == device:
-                return tensor.to(dtype, copy=copy, non_blocking=non_blocking)
-            return tensor.to(device, copy=copy, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking)
-        else:
-            return tensor.to(device, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking)
-    else:
-        return tensor.to(device, dtype, copy=copy, non_blocking=non_blocking)
 
 def xformers_enabled():
     global directml_enabled
@@ -892,7 +896,7 @@ def force_upcast_attention_dtype():
     upcast = args.force_upcast_attention
     try:
         macos_version = tuple(int(n) for n in platform.mac_ver()[0].split("."))
-        if (14, 5) <= macos_version < (14, 7):  # black image bug on recent versions of MacOS
+        if (14, 5) <= macos_version <= (15, 0, 1):  # black image bug on recent versions of macOS
             upcast = True
     except:
         pass
@@ -999,7 +1003,10 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma
     nvidia_10_series = ["1080", "1070", "titan x", "p3000", "p3200", "p4000", "p4200", "p5000", "p5200", "p6000", "1060", "1050", "p40", "p100", "p6", "p4"]
     for x in nvidia_10_series:
         if x in props.name.lower():
-            return True
+            if WINDOWS or manual_cast:
+                return True
+            else:
+                return False #weird linux behavior where fp32 is faster
 
     if manual_cast:
         free_model_memory = maximum_vram_for_weights(device)
@@ -1055,6 +1062,9 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
     return False
 
 def supports_fp8_compute(device=None):
+    if not is_nvidia():
+        return False
+
     props = torch.cuda.get_device_properties(device)
     if props.major >= 9:
         return True
@@ -1062,6 +1072,14 @@ def supports_fp8_compute(device=None):
         return False
     if props.minor < 9:
         return False
+
+    if int(torch_version[0]) < 2 or (int(torch_version[0]) == 2 and int(torch_version[2]) < 3):
+        return False
+
+    if WINDOWS:
+        if (int(torch_version[0]) == 2 and int(torch_version[2]) < 4):
+            return False
+
     return True
 
 def soft_empty_cache(force=False):

comfy/model_patcher.py

@@ -28,8 +28,20 @@ import comfy.utils
 import comfy.float
 import comfy.model_management
 import comfy.lora
-from comfy.types import UnetWrapperFunction
+from comfy.comfy_types import UnetWrapperFunction
 
+def string_to_seed(data):
+    crc = 0xFFFFFFFF
+    for byte in data:
+        if isinstance(byte, str):
+            byte = ord(byte)
+        crc ^= byte
+        for _ in range(8):
+            if crc & 1:
+                crc = (crc >> 1) ^ 0xEDB88320
+            else:
+                crc >>= 1
+    return crc ^ 0xFFFFFFFF
 
 def set_model_options_patch_replace(model_options, patch, name, block_name, number, transformer_index=None):
     to = model_options["transformer_options"].copy()
@@ -76,7 +88,36 @@ class LowVramPatch:
         self.key = key
         self.patches = patches
     def __call__(self, weight):
-        return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=weight.dtype)
+        intermediate_dtype = weight.dtype
+        if intermediate_dtype not in [torch.float32, torch.float16, torch.bfloat16]: #intermediate_dtype has to be one that is supported in math ops
+            intermediate_dtype = torch.float32
+            return comfy.float.stochastic_rounding(comfy.lora.calculate_weight(self.patches[self.key], weight.to(intermediate_dtype), self.key, intermediate_dtype=intermediate_dtype), weight.dtype, seed=string_to_seed(self.key))
+
+        return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=intermediate_dtype)
+
+def get_key_weight(model, key):
+    set_func = None
+    convert_func = None
+    op_keys = key.rsplit('.', 1)
+    if len(op_keys) < 2:
+        weight = comfy.utils.get_attr(model, key)
+    else:
+        op = comfy.utils.get_attr(model, op_keys[0])
+        try:
+            set_func = getattr(op, "set_{}".format(op_keys[1]))
+        except AttributeError:
+            pass
+
+        try:
+            convert_func = getattr(op, "convert_{}".format(op_keys[1]))
+        except AttributeError:
+            pass
+
+        weight = getattr(op, op_keys[1])
+        if convert_func is not None:
+            weight = comfy.utils.get_attr(model, key)
+
+    return weight, set_func, convert_func
 
 class ModelPatcher:
     def __init__(self, model, load_device, offload_device, size=0, weight_inplace_update=False):
@@ -271,17 +312,23 @@ class ModelPatcher:
         return list(p)
 
     def get_key_patches(self, filter_prefix=None):
-        comfy.model_management.unload_model_clones(self)
         model_sd = self.model_state_dict()
         p = {}
         for k in model_sd:
             if filter_prefix is not None:
                 if not k.startswith(filter_prefix):
                     continue
+            bk = self.backup.get(k, None)
+            weight, set_func, convert_func = get_key_weight(self.model, k)
+            if bk is not None:
+                weight = bk.weight
+            if convert_func is None:
+                convert_func = lambda a, **kwargs: a
+
             if k in self.patches:
-                p[k] = [model_sd[k]] + self.patches[k]
+                p[k] = [(weight, convert_func)] + self.patches[k]
             else:
-                p[k] = (model_sd[k],)
+                p[k] = [(weight, convert_func)]
         return p
 
     def model_state_dict(self, filter_prefix=None):
@@ -297,8 +344,7 @@ class ModelPatcher:
         if key not in self.patches:
             return
 
-        weight = comfy.utils.get_attr(self.model, key)
+        weight, set_func, convert_func = get_key_weight(self.model, key)
-
         inplace_update = self.weight_inplace_update or inplace_update
 
         if key not in self.backup:
@@ -308,23 +354,38 @@ class ModelPatcher:
             temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
         else:
             temp_weight = weight.to(torch.float32, copy=True)
+        if convert_func is not None:
+            temp_weight = convert_func(temp_weight, inplace=True)
+
         out_weight = comfy.lora.calculate_weight(self.patches[key], temp_weight, key)
-        out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype)
+        if set_func is None:
-        if inplace_update:
+            out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=string_to_seed(key))
-            comfy.utils.copy_to_param(self.model, key, out_weight)
+            if inplace_update:
+                comfy.utils.copy_to_param(self.model, key, out_weight)
+            else:
+                comfy.utils.set_attr_param(self.model, key, out_weight)
         else:
-            comfy.utils.set_attr_param(self.model, key, out_weight)
+            set_func(out_weight, inplace_update=inplace_update, seed=string_to_seed(key))
 
     def load(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False, full_load=False):
         mem_counter = 0
         patch_counter = 0
         lowvram_counter = 0
-        load_completely = []
+        loading = []
         for n, m in self.model.named_modules():
+            if hasattr(m, "comfy_cast_weights") or hasattr(m, "weight"):
+                loading.append((comfy.model_management.module_size(m), n, m))
+
+        load_completely = []
+        loading.sort(reverse=True)
+        for x in loading:
+            n = x[1]
+            m = x[2]
+            module_mem = x[0]
+
             lowvram_weight = False
 
             if not full_load and hasattr(m, "comfy_cast_weights"):
-                module_mem = comfy.model_management.module_size(m)
                 if mem_counter + module_mem >= lowvram_model_memory:
                     lowvram_weight = True
                     lowvram_counter += 1
@@ -356,9 +417,8 @@ class ModelPatcher:
                         wipe_lowvram_weight(m)
 
                 if hasattr(m, "weight"):
-                    mem_used = comfy.model_management.module_size(m)
+                    mem_counter += module_mem
-                    mem_counter += mem_used
+                    load_completely.append((module_mem, n, m))
-                    load_completely.append((mem_used, n, m))
 
         load_completely.sort(reverse=True)
         for x in load_completely:

comfy/ops.py

@@ -19,16 +19,12 @@
 import torch
 import comfy.model_management
 from comfy.cli_args import args
+import comfy.float
 
-def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=True):
+cast_to = comfy.model_management.cast_to #TODO: remove once no more references
-    if not copy and (dtype is None or weight.dtype == dtype) and (device is None or weight.device == device):
-        return weight
-    r = torch.empty_like(weight, dtype=dtype, device=device)
-    r.copy_(weight, non_blocking=non_blocking)
-    return r
 
 def cast_to_input(weight, input, non_blocking=False, copy=True):
-    return cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy)
+    return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy)
 
 def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
     if input is not None:
@@ -43,12 +39,12 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
     non_blocking = comfy.model_management.device_supports_non_blocking(device)
     if s.bias is not None:
         has_function = s.bias_function is not None
-        bias = cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=has_function)
+        bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=has_function)
         if has_function:
             bias = s.bias_function(bias)
 
     has_function = s.weight_function is not None
-    weight = cast_to(s.weight, dtype, device, non_blocking=non_blocking, copy=has_function)
+    weight = comfy.model_management.cast_to(s.weight, dtype, device, non_blocking=non_blocking, copy=has_function)
     if has_function:
         weight = s.weight_function(weight)
     return weight, bias
@@ -254,20 +250,25 @@ def fp8_linear(self, input):
     if dtype not in [torch.float8_e4m3fn]:
         return None
 
+    tensor_2d = False
+    if len(input.shape) == 2:
+        tensor_2d = True
+        input = input.unsqueeze(1)
+
+
     if len(input.shape) == 3:
-        inn = input.reshape(-1, input.shape[2]).to(dtype)
-        non_blocking = comfy.model_management.device_supports_non_blocking(input.device)
         w, bias = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input.dtype)
         w = w.t()
 
         scale_weight = self.scale_weight
         scale_input = self.scale_input
         if scale_weight is None:
-            scale_weight = torch.ones((1), device=input.device, dtype=torch.float32)
+            scale_weight = torch.ones((), device=input.device, dtype=torch.float32)
-            if scale_input is None:
-                scale_input = scale_weight
         if scale_input is None:
-            scale_input = torch.ones((1), device=input.device, dtype=torch.float32)
+            scale_input = torch.ones((), device=input.device, dtype=torch.float32)
+            inn = input.reshape(-1, input.shape[2]).to(dtype)
+        else:
+            inn = (input * (1.0 / scale_input).to(input.dtype)).reshape(-1, input.shape[2]).to(dtype)
 
         if bias is not None:
             o = torch._scaled_mm(inn, w, out_dtype=input.dtype, bias=bias, scale_a=scale_input, scale_b=scale_weight)
@@ -277,7 +278,11 @@ def fp8_linear(self, input):
         if isinstance(o, tuple):
             o = o[0]
 
+        if tensor_2d:
+            return o.reshape(input.shape[0], -1)
+
         return o.reshape((-1, input.shape[1], self.weight.shape[0]))
+
     return None
 
 class fp8_ops(manual_cast):
@@ -295,11 +300,63 @@ class fp8_ops(manual_cast):
             weight, bias = cast_bias_weight(self, input)
             return torch.nn.functional.linear(input, weight, bias)
 
+def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None):
+    class scaled_fp8_op(manual_cast):
+        class Linear(manual_cast.Linear):
+            def __init__(self, *args, **kwargs):
+                if override_dtype is not None:
+                    kwargs['dtype'] = override_dtype
+                super().__init__(*args, **kwargs)
+
+            def reset_parameters(self):
+                if not hasattr(self, 'scale_weight'):
+                    self.scale_weight = torch.nn.parameter.Parameter(data=torch.ones((), device=self.weight.device, dtype=torch.float32), requires_grad=False)
+
+                if not scale_input:
+                    self.scale_input = None
+
+                if not hasattr(self, 'scale_input'):
+                    self.scale_input = torch.nn.parameter.Parameter(data=torch.ones((), device=self.weight.device, dtype=torch.float32), requires_grad=False)
+                return None
+
+            def forward_comfy_cast_weights(self, input):
+                if fp8_matrix_mult:
+                    out = fp8_linear(self, input)
+                    if out is not None:
+                        return out
+
+                weight, bias = cast_bias_weight(self, input)
+
+                if weight.numel() < input.numel(): #TODO: optimize
+                    return torch.nn.functional.linear(input, weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype), bias)
+                else:
+                    return torch.nn.functional.linear(input * self.scale_weight.to(device=weight.device, dtype=weight.dtype), weight, bias)
+
+            def convert_weight(self, weight, inplace=False, **kwargs):
+                if inplace:
+                    weight *= self.scale_weight.to(device=weight.device, dtype=weight.dtype)
+                    return weight
+                else:
+                    return weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype)
+
+            def set_weight(self, weight, inplace_update=False, seed=None, **kwargs):
+                weight = comfy.float.stochastic_rounding(weight / self.scale_weight.to(device=weight.device, dtype=weight.dtype), self.weight.dtype, seed=seed)
+                if inplace_update:
+                    self.weight.data.copy_(weight)
+                else:
+                    self.weight = torch.nn.Parameter(weight, requires_grad=False)
+
+    return scaled_fp8_op
+
+def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, scaled_fp8=None):
+    fp8_compute = comfy.model_management.supports_fp8_compute(load_device)
+    if scaled_fp8 is not None:
+        return scaled_fp8_ops(fp8_matrix_mult=fp8_compute, scale_input=True, override_dtype=scaled_fp8)
+
+    if fp8_compute and (fp8_optimizations or args.fast) and not disable_fast_fp8:
+        return fp8_ops
 
-def pick_operations(weight_dtype, compute_dtype, load_device=None):
     if compute_dtype is None or weight_dtype == compute_dtype:
         return disable_weight_init
-    if args.fast:
+
-        if comfy.model_management.supports_fp8_compute(load_device):
-            return fp8_ops
     return manual_cast

comfy/samplers.py

@@ -6,7 +6,7 @@ from comfy import model_management
 import math
 import logging
 import comfy.sampler_helpers
-import scipy
+import scipy.stats
 import numpy
 
 def get_area_and_mult(conds, x_in, timestep_in):
@@ -358,8 +358,11 @@ def beta_scheduler(model_sampling, steps, alpha=0.6, beta=0.6):
     ts = numpy.rint(scipy.stats.beta.ppf(ts, alpha, beta) * total_timesteps)
 
     sigs = []
+    last_t = -1
     for t in ts:
-        sigs += [float(model_sampling.sigmas[int(t)])]
+        if t != last_t:
+            sigs += [float(model_sampling.sigmas[int(t)])]
+        last_t = t
     sigs += [0.0]
     return torch.FloatTensor(sigs)
 
@@ -570,8 +573,8 @@ class Sampler:
         return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma
 
 KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
-                  "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
+                  "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu",
-                  "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
+                  "dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
                   "ipndm", "ipndm_v", "deis"]
 
 class KSAMPLER(Sampler):

comfy/sd.py

@@ -29,7 +29,6 @@ import comfy.text_encoders.long_clipl
 import comfy.model_patcher
 import comfy.lora
 import comfy.t2i_adapter.adapter
-import comfy.supported_models_base
 import comfy.taesd.taesd
 
 def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
@@ -70,14 +69,14 @@ class CLIP:
         clip = target.clip
         tokenizer = target.tokenizer
 
-        load_device = model_management.text_encoder_device()
+        load_device = model_options.get("load_device", model_management.text_encoder_device())
-        offload_device = model_management.text_encoder_offload_device()
+        offload_device = model_options.get("offload_device", model_management.text_encoder_offload_device())
         dtype = model_options.get("dtype", None)
         if dtype is None:
             dtype = model_management.text_encoder_dtype(load_device)
 
         params['dtype'] = dtype
-        params['device'] = model_management.text_encoder_initial_device(load_device, offload_device, parameters * model_management.dtype_size(dtype))
+        params['device'] = model_options.get("initial_device", model_management.text_encoder_initial_device(load_device, offload_device, parameters * model_management.dtype_size(dtype)))
         params['model_options'] = model_options
 
         self.cond_stage_model = clip(**(params))
@@ -348,7 +347,7 @@ class VAE:
             memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)
             model_management.load_models_gpu([self.patcher], memory_required=memory_used)
             free_memory = model_management.get_free_memory(self.device)
-            batch_number = int(free_memory / memory_used)
+            batch_number = int(free_memory / max(1, memory_used))
             batch_number = max(1, batch_number)
             samples = torch.empty((pixel_samples.shape[0], self.latent_channels) + tuple(map(lambda a: a // self.downscale_ratio, pixel_samples.shape[2:])), device=self.output_device)
             for x in range(0, pixel_samples.shape[0], batch_number):
@@ -406,8 +405,47 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
         clip_data.append(comfy.utils.load_torch_file(p, safe_load=True))
     return load_text_encoder_state_dicts(clip_data, embedding_directory=embedding_directory, clip_type=clip_type, model_options=model_options)
 
+
+class TEModel(Enum):
+    CLIP_L = 1
+    CLIP_H = 2
+    CLIP_G = 3
+    T5_XXL = 4
+    T5_XL = 5
+    T5_BASE = 6
+
+def detect_te_model(sd):
+    if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
+        return TEModel.CLIP_G
+    if "text_model.encoder.layers.22.mlp.fc1.weight" in sd:
+        return TEModel.CLIP_H
+    if "text_model.encoder.layers.0.mlp.fc1.weight" in sd:
+        return TEModel.CLIP_L
+    if "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in sd:
+        weight = sd["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"]
+        if weight.shape[-1] == 4096:
+            return TEModel.T5_XXL
+        elif weight.shape[-1] == 2048:
+            return TEModel.T5_XL
+    if "encoder.block.0.layer.0.SelfAttention.k.weight" in sd:
+        return TEModel.T5_BASE
+    return None
+
+
+def t5xxl_detect(clip_data):
+    weight_name = "encoder.block.23.layer.1.DenseReluDense.wi_1.weight"
+
+    dtype_t5 = None
+    for sd in clip_data:
+        if weight_name in sd:
+            return comfy.text_encoders.sd3_clip.t5_xxl_detect(sd)
+
+    return {}
+
+
 def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
     clip_data = state_dicts
+
     class EmptyClass:
         pass
 
@@ -421,64 +459,61 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
     clip_target = EmptyClass()
     clip_target.params = {}
     if len(clip_data) == 1:
-        if "text_model.encoder.layers.30.mlp.fc1.weight" in clip_data[0]:
+        te_model = detect_te_model(clip_data[0])
+        if te_model == TEModel.CLIP_G:
             if clip_type == CLIPType.STABLE_CASCADE:
                 clip_target.clip = sdxl_clip.StableCascadeClipModel
                 clip_target.tokenizer = sdxl_clip.StableCascadeTokenizer
+            elif clip_type == CLIPType.SD3:
+                clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=True, t5=False)
+                clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
             else:
                 clip_target.clip = sdxl_clip.SDXLRefinerClipModel
                 clip_target.tokenizer = sdxl_clip.SDXLTokenizer
-        elif "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data[0]:
+        elif te_model == TEModel.CLIP_H:
             clip_target.clip = comfy.text_encoders.sd2_clip.SD2ClipModel
             clip_target.tokenizer = comfy.text_encoders.sd2_clip.SD2Tokenizer
-        elif "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in clip_data[0]:
+        elif te_model == TEModel.T5_XXL:
-            weight = clip_data[0]["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"]
+            clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, **t5xxl_detect(clip_data))
-            dtype_t5 = weight.dtype
+            clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
-            if weight.shape[-1] == 4096:
+        elif te_model == TEModel.T5_XL:
-                clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=dtype_t5)
+            clip_target.clip = comfy.text_encoders.aura_t5.AuraT5Model
-                clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
+            clip_target.tokenizer = comfy.text_encoders.aura_t5.AuraT5Tokenizer
-            elif weight.shape[-1] == 2048:
+        elif te_model == TEModel.T5_BASE:
-                clip_target.clip = comfy.text_encoders.aura_t5.AuraT5Model
-                clip_target.tokenizer = comfy.text_encoders.aura_t5.AuraT5Tokenizer
-        elif "encoder.block.0.layer.0.SelfAttention.k.weight" in clip_data[0]:
             clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model
             clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer
         else:
-            w = clip_data[0].get("text_model.embeddings.position_embedding.weight", None)
+            if clip_type == CLIPType.SD3:
-            if w is not None and w.shape[0] == 248:
+                clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=False, t5=False)
-                clip_target.clip = comfy.text_encoders.long_clipl.LongClipModel
+                clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
-                clip_target.tokenizer = comfy.text_encoders.long_clipl.LongClipTokenizer
             else:
                 clip_target.clip = sd1_clip.SD1ClipModel
                 clip_target.tokenizer = sd1_clip.SD1Tokenizer
     elif len(clip_data) == 2:
         if clip_type == CLIPType.SD3:
-            clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=True, t5=False)
+            te_models = [detect_te_model(clip_data[0]), detect_te_model(clip_data[1])]
+            clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=TEModel.CLIP_L in te_models, clip_g=TEModel.CLIP_G in te_models, t5=TEModel.T5_XXL in te_models, **t5xxl_detect(clip_data))
             clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
         elif clip_type == CLIPType.HUNYUAN_DIT:
             clip_target.clip = comfy.text_encoders.hydit.HyditModel
             clip_target.tokenizer = comfy.text_encoders.hydit.HyditTokenizer
         elif clip_type == CLIPType.FLUX:
-            weight_name = "encoder.block.23.layer.1.DenseReluDense.wi_1.weight"
+            clip_target.clip = comfy.text_encoders.flux.flux_clip(**t5xxl_detect(clip_data))
-            weight = clip_data[0].get(weight_name, clip_data[1].get(weight_name, None))
-            dtype_t5 = None
-            if weight is not None:
-                dtype_t5 = weight.dtype
-
-            clip_target.clip = comfy.text_encoders.flux.flux_clip(dtype_t5=dtype_t5)
             clip_target.tokenizer = comfy.text_encoders.flux.FluxTokenizer
         else:
             clip_target.clip = sdxl_clip.SDXLClipModel
             clip_target.tokenizer = sdxl_clip.SDXLTokenizer
     elif len(clip_data) == 3:
-        clip_target.clip = comfy.text_encoders.sd3_clip.SD3ClipModel
+        clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(**t5xxl_detect(clip_data))
         clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
 
     parameters = 0
+    tokenizer_data = {}
     for c in clip_data:
         parameters += comfy.utils.calculate_parameters(c)
+        tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options)
 
-    clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, model_options=model_options)
+    clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, model_options=model_options)
     for c in clip_data:
         m, u = clip.load_sd(c)
         if len(m) > 0:
@@ -544,11 +579,11 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
         return None
 
     unet_weight_dtype = list(model_config.supported_inference_dtypes)
-    if weight_dtype is not None:
+    if weight_dtype is not None and model_config.scaled_fp8 is None:
         unet_weight_dtype.append(weight_dtype)
 
     model_config.custom_operations = model_options.get("custom_operations", None)
-    unet_dtype = model_options.get("weight_dtype", None)
+    unet_dtype = model_options.get("dtype", model_options.get("weight_dtype", None))
 
     if unet_dtype is None:
         unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype)
@@ -562,7 +597,6 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
 
     if output_model:
         inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype)
-        offload_device = model_management.unet_offload_device()
         model = model_config.get_model(sd, diffusion_model_prefix, device=inital_load_device)
         model.load_model_weights(sd, diffusion_model_prefix)
 
@@ -614,6 +648,8 @@ def load_diffusion_model_state_dict(sd, model_options={}): #load unet in diffuse
         sd = temp_sd
 
     parameters = comfy.utils.calculate_parameters(sd)
+    weight_dtype = comfy.utils.weight_dtype(sd)
+
     load_device = model_management.get_torch_device()
     model_config = model_detection.model_config_from_unet(sd, "")
 
@@ -640,14 +676,21 @@ def load_diffusion_model_state_dict(sd, model_options={}): #load unet in diffuse
                     logging.warning("{} {}".format(diffusers_keys[k], k))
 
     offload_device = model_management.unet_offload_device()
+    unet_weight_dtype = list(model_config.supported_inference_dtypes)
+    if weight_dtype is not None and model_config.scaled_fp8 is None:
+        unet_weight_dtype.append(weight_dtype)
+
     if dtype is None:
-        unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
+        unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype)
     else:
         unet_dtype = dtype
 
     manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
     model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
-    model_config.custom_operations = model_options.get("custom_operations", None)
+    model_config.custom_operations = model_options.get("custom_operations", model_config.custom_operations)
+    if model_options.get("fp8_optimizations", False):
+        model_config.optimizations["fp8"] = True
+
     model = model_config.get_model(new_sd, "")
     model = model.to(offload_device)
     model.load_model_weights(new_sd, "")

comfy/sd1_clip.py

@@ -80,7 +80,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         "pooled",
         "hidden"
     ]
-    def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77,
+    def __init__(self, device="cpu", max_length=77,
                  freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=comfy.clip_model.CLIPTextModel,
                  special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False,
                  return_projected_pooled=True, return_attention_masks=False, model_options={}):  # clip-vit-base-patch32
@@ -94,11 +94,20 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
             config = json.load(f)
 
         operations = model_options.get("custom_operations", None)
+        scaled_fp8 = None
+
         if operations is None:
-            operations = comfy.ops.manual_cast
+            scaled_fp8 = model_options.get("scaled_fp8", None)
+            if scaled_fp8 is not None:
+                operations = comfy.ops.scaled_fp8_ops(fp8_matrix_mult=False, override_dtype=scaled_fp8)
+            else:
+                operations = comfy.ops.manual_cast
 
         self.operations = operations
         self.transformer = model_class(config, dtype, device, self.operations)
+        if scaled_fp8 is not None:
+            self.transformer.scaled_fp8 = torch.nn.Parameter(torch.tensor([], dtype=scaled_fp8))
+
         self.num_layers = self.transformer.num_layers
 
         self.max_length = max_length
@@ -542,6 +551,7 @@ class SD1Tokenizer:
     def __init__(self, embedding_directory=None, tokenizer_data={}, clip_name="l", tokenizer=SDTokenizer):
         self.clip_name = clip_name
         self.clip = "clip_{}".format(self.clip_name)
+        tokenizer = tokenizer_data.get("{}_tokenizer_class".format(self.clip), tokenizer)
         setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data))
 
     def tokenize_with_weights(self, text:str, return_word_ids=False):
@@ -570,6 +580,7 @@ class SD1ClipModel(torch.nn.Module):
             self.clip_name = clip_name
             self.clip = "clip_{}".format(self.clip_name)
 
+        clip_model = model_options.get("{}_class".format(self.clip), clip_model)
         setattr(self, self.clip, clip_model(device=device, dtype=dtype, model_options=model_options, **kwargs))
 
         self.dtypes = set()

comfy/sdxl_clip.py

@@ -22,7 +22,8 @@ class SDXLClipGTokenizer(sd1_clip.SDTokenizer):
 
 class SDXLTokenizer:
     def __init__(self, embedding_directory=None, tokenizer_data={}):
-        self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)
+        clip_l_tokenizer_class = tokenizer_data.get("clip_l_tokenizer_class", sd1_clip.SDTokenizer)
+        self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory)
         self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory)
 
     def tokenize_with_weights(self, text:str, return_word_ids=False):
@@ -40,7 +41,8 @@ class SDXLTokenizer:
 class SDXLClipModel(torch.nn.Module):
     def __init__(self, device="cpu", dtype=None, model_options={}):
         super().__init__()
-        self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, model_options=model_options)
+        clip_l_class = model_options.get("clip_l_class", sd1_clip.SDClipModel)
+        self.clip_l = clip_l_class(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, model_options=model_options)
         self.clip_g = SDXLClipG(device=device, dtype=dtype, model_options=model_options)
         self.dtypes = set([dtype])
 
@@ -57,7 +59,8 @@ class SDXLClipModel(torch.nn.Module):
         token_weight_pairs_l = token_weight_pairs["l"]
         g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
         l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
-        return torch.cat([l_out, g_out], dim=-1), g_pooled
+        cut_to = min(l_out.shape[1], g_out.shape[1])
+        return torch.cat([l_out[:,:cut_to], g_out[:,:cut_to]], dim=-1), g_pooled
 
     def load_sd(self, sd):
         if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:

comfy/supported_models.py

@@ -529,12 +529,11 @@ class SD3(supported_models_base.BASE):
             clip_l = True
         if "{}clip_g.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict:
             clip_g = True
-        t5_key = "{}t5xxl.transformer.encoder.final_layer_norm.weight".format(pref)
+        t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
-        if t5_key in state_dict:
+        if "dtype_t5" in t5_detect:
             t5 = True
-            dtype_t5 = state_dict[t5_key].dtype
 
-        return supported_models_base.ClipTarget(comfy.text_encoders.sd3_clip.SD3Tokenizer, comfy.text_encoders.sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5))
+        return supported_models_base.ClipTarget(comfy.text_encoders.sd3_clip.SD3Tokenizer, comfy.text_encoders.sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, **t5_detect))
 
 class StableAudio(supported_models_base.BASE):
     unet_config = {
@@ -653,11 +652,8 @@ class Flux(supported_models_base.BASE):
 
     def clip_target(self, state_dict={}):
         pref = self.text_encoder_key_prefix[0]
-        t5_key = "{}t5xxl.transformer.encoder.final_layer_norm.weight".format(pref)
+        t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
-        dtype_t5 = None
+        return supported_models_base.ClipTarget(comfy.text_encoders.flux.FluxTokenizer, comfy.text_encoders.flux.flux_clip(**t5_detect))
-        if t5_key in state_dict:
-            dtype_t5 = state_dict[t5_key].dtype
-        return supported_models_base.ClipTarget(comfy.text_encoders.flux.FluxTokenizer, comfy.text_encoders.flux.flux_clip(dtype_t5=dtype_t5))
 
 class FluxSchnell(Flux):
     unet_config = {

comfy/supported_models_base.py

@@ -49,6 +49,8 @@ class BASE:
 
     manual_cast_dtype = None
     custom_operations = None
+    scaled_fp8 = None
+    optimizations = {"fp8": False}
 
     @classmethod
     def matches(s, unet_config, state_dict=None):
@@ -71,6 +73,7 @@ class BASE:
         self.unet_config = unet_config.copy()
         self.sampling_settings = self.sampling_settings.copy()
         self.latent_format = self.latent_format()
+        self.optimizations = self.optimizations.copy()
         for x in self.unet_extra_config:
             self.unet_config[x] = self.unet_extra_config[x]
 

comfy/text_encoders/flux.py

@@ -1,24 +1,21 @@
 from comfy import sd1_clip
 import comfy.text_encoders.t5
+import comfy.text_encoders.sd3_clip
 import comfy.model_management
 from transformers import T5TokenizerFast
 import torch
 import os
 
-class T5XXLModel(sd1_clip.SDClipModel):
-    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}):
-        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json")
-        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, model_options=model_options)
-
 class T5XXLTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
-        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256)
+        super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256)
 
 
 class FluxTokenizer:
     def __init__(self, embedding_directory=None, tokenizer_data={}):
-        self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)
+        clip_l_tokenizer_class = tokenizer_data.get("clip_l_tokenizer_class", sd1_clip.SDTokenizer)
+        self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory)
         self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory)
 
     def tokenize_with_weights(self, text:str, return_word_ids=False):
@@ -38,8 +35,9 @@ class FluxClipModel(torch.nn.Module):
     def __init__(self, dtype_t5=None, device="cpu", dtype=None, model_options={}):
         super().__init__()
         dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)
-        self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options)
+        clip_l_class = model_options.get("clip_l_class", sd1_clip.SDClipModel)
-        self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options)
+        self.clip_l = clip_l_class(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options)
+        self.t5xxl = comfy.text_encoders.sd3_clip.T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options)
         self.dtypes = set([dtype, dtype_t5])
 
     def set_clip_options(self, options):
@@ -64,8 +62,11 @@ class FluxClipModel(torch.nn.Module):
         else:
             return self.t5xxl.load_sd(sd)
 
-def flux_clip(dtype_t5=None):
+def flux_clip(dtype_t5=None, t5xxl_scaled_fp8=None):
     class FluxClipModel_(FluxClipModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):
+            if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+                model_options = model_options.copy()
+                model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
             super().__init__(dtype_t5=dtype_t5, device=device, dtype=dtype, model_options=model_options)
     return FluxClipModel_

comfy/text_encoders/long_clipl.py

@@ -6,9 +6,9 @@ class LongClipTokenizer_(sd1_clip.SDTokenizer):
         super().__init__(max_length=248, embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
 
 class LongClipModel_(sd1_clip.SDClipModel):
-    def __init__(self, device="cpu", dtype=None, model_options={}):
+    def __init__(self, *args, **kwargs):
         textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "long_clipl.json")
-        super().__init__(device=device, textmodel_json_config=textmodel_json_config, return_projected_pooled=False, dtype=dtype, model_options=model_options)
+        super().__init__(*args, textmodel_json_config=textmodel_json_config, **kwargs)
 
 class LongClipTokenizer(sd1_clip.SD1Tokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
@@ -17,3 +17,14 @@ class LongClipTokenizer(sd1_clip.SD1Tokenizer):
 class LongClipModel(sd1_clip.SD1ClipModel):
     def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):
         super().__init__(device=device, dtype=dtype, model_options=model_options, clip_model=LongClipModel_, **kwargs)
+
+def model_options_long_clip(sd, tokenizer_data, model_options):
+    w = sd.get("clip_l.text_model.embeddings.position_embedding.weight", None)
+    if w is None:
+        w = sd.get("text_model.embeddings.position_embedding.weight", None)
+    if w is not None and w.shape[0] == 248:
+        tokenizer_data = tokenizer_data.copy()
+        model_options = model_options.copy()
+        tokenizer_data["clip_l_tokenizer_class"] = LongClipTokenizer_
+        model_options["clip_l_class"] = LongClipModel_
+    return tokenizer_data, model_options

comfy/text_encoders/sd3_clip.py

@@ -8,9 +8,27 @@ import comfy.model_management
 import logging
 
 class T5XXLModel(sd1_clip.SDClipModel):
-    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=False, model_options={}):
         textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json")
-        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, model_options=model_options)
+        t5xxl_scaled_fp8 = model_options.get("t5xxl_scaled_fp8", None)
+        if t5xxl_scaled_fp8 is not None:
+            model_options = model_options.copy()
+            model_options["scaled_fp8"] = t5xxl_scaled_fp8
+
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
+
+
+def t5_xxl_detect(state_dict, prefix=""):
+    out = {}
+    t5_key = "{}encoder.final_layer_norm.weight".format(prefix)
+    if t5_key in state_dict:
+        out["dtype_t5"] = state_dict[t5_key].dtype
+
+    scaled_fp8_key = "{}scaled_fp8".format(prefix)
+    if scaled_fp8_key in state_dict:
+        out["t5xxl_scaled_fp8"] = state_dict[scaled_fp8_key].dtype
+
+    return out
 
 class T5XXLTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
@@ -20,7 +38,8 @@ class T5XXLTokenizer(sd1_clip.SDTokenizer):
 
 class SD3Tokenizer:
     def __init__(self, embedding_directory=None, tokenizer_data={}):
-        self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)
+        clip_l_tokenizer_class = tokenizer_data.get("clip_l_tokenizer_class", sd1_clip.SDTokenizer)
+        self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory)
         self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory)
         self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory)
 
@@ -38,11 +57,12 @@ class SD3Tokenizer:
         return {}
 
 class SD3ClipModel(torch.nn.Module):
-    def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, device="cpu", dtype=None, model_options={}):
+    def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5_attention_mask=False, device="cpu", dtype=None, model_options={}):
         super().__init__()
         self.dtypes = set()
         if clip_l:
-            self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False, model_options=model_options)
+            clip_l_class = model_options.get("clip_l_class", sd1_clip.SDClipModel)
+            self.clip_l = clip_l_class(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False, model_options=model_options)
             self.dtypes.add(dtype)
         else:
             self.clip_l = None
@@ -55,7 +75,8 @@ class SD3ClipModel(torch.nn.Module):
 
         if t5:
             dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)
-            self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options)
+            self.t5_attention_mask = t5_attention_mask
+            self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options, attention_mask=self.t5_attention_mask)
             self.dtypes.add(dtype_t5)
         else:
             self.t5xxl = None
@@ -85,6 +106,7 @@ class SD3ClipModel(torch.nn.Module):
         lg_out = None
         pooled = None
         out = None
+        extra = {}
 
         if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0:
             if self.clip_l is not None:
@@ -95,7 +117,8 @@ class SD3ClipModel(torch.nn.Module):
             if self.clip_g is not None:
                 g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
                 if lg_out is not None:
-                    lg_out = torch.cat([lg_out, g_out], dim=-1)
+                    cut_to = min(lg_out.shape[1], g_out.shape[1])
+                    lg_out = torch.cat([lg_out[:,:cut_to], g_out[:,:cut_to]], dim=-1)
                 else:
                     lg_out = torch.nn.functional.pad(g_out, (768, 0))
             else:
@@ -108,7 +131,11 @@ class SD3ClipModel(torch.nn.Module):
             pooled = torch.cat((l_pooled, g_pooled), dim=-1)
 
         if self.t5xxl is not None:
-            t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
+            t5_output = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
+            t5_out, t5_pooled = t5_output[:2]
+            if self.t5_attention_mask:
+                extra["attention_mask"] = t5_output[2]["attention_mask"]
+
             if lg_out is not None:
                 out = torch.cat([lg_out, t5_out], dim=-2)
             else:
@@ -120,7 +147,7 @@ class SD3ClipModel(torch.nn.Module):
         if pooled is None:
             pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device())
 
-        return out, pooled
+        return out, pooled, extra
 
     def load_sd(self, sd):
         if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
@@ -130,8 +157,11 @@ class SD3ClipModel(torch.nn.Module):
         else:
             return self.t5xxl.load_sd(sd)
 
-def sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None):
+def sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5xxl_scaled_fp8=None, t5_attention_mask=False):
     class SD3ClipModel_(SD3ClipModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):
-            super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, device=device, dtype=dtype, model_options=model_options)
+            if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+                model_options = model_options.copy()
+                model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+            super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, t5_attention_mask=t5_attention_mask, device=device, dtype=dtype, model_options=model_options)
     return SD3ClipModel_

comfy/utils.py

@@ -68,7 +68,7 @@ def weight_dtype(sd, prefix=""):
     for k in sd.keys():
         if k.startswith(prefix):
             w = sd[k]
-            dtypes[w.dtype] = dtypes.get(w.dtype, 0) + 1
+            dtypes[w.dtype] = dtypes.get(w.dtype, 0) + w.numel()
 
     if len(dtypes) == 0:
         return None
@@ -528,6 +528,8 @@ def flux_to_diffusers(mmdit_config, output_prefix=""):
         ("guidance_in.out_layer.weight", "time_text_embed.guidance_embedder.linear_2.weight"),
         ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift),
         ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift),
+        ("pos_embed_input.bias", "controlnet_x_embedder.bias"),
+        ("pos_embed_input.weight", "controlnet_x_embedder.weight"),
     }
 
     for k in MAP_BASIC:
@@ -711,7 +713,9 @@ def common_upscale(samples, width, height, upscale_method, crop):
             return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)
 
 def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):
-    return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap)))
+    rows = 1 if height <= tile_y else math.ceil((height - overlap) / (tile_y - overlap))
+    cols = 1 if width <= tile_x else math.ceil((width - overlap) / (tile_x - overlap))
+    return rows * cols
 
 @torch.inference_mode()
 def tiled_scale_multidim(samples, function, tile=(64, 64), overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
@@ -720,10 +724,20 @@ def tiled_scale_multidim(samples, function, tile=(64, 64), overlap = 8, upscale_
 
     for b in range(samples.shape[0]):
         s = samples[b:b+1]
+
+        # handle entire input fitting in a single tile
+        if all(s.shape[d+2] <= tile[d] for d in range(dims)):
+            output[b:b+1] = function(s).to(output_device)
+            if pbar is not None:
+                pbar.update(1)
+            continue
+
         out = torch.zeros([s.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), s.shape[2:])), device=output_device)
         out_div = torch.zeros([s.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), s.shape[2:])), device=output_device)
 
-        for it in itertools.product(*map(lambda a: range(0, a[0], a[1] - overlap), zip(s.shape[2:], tile))):
+        positions = [range(0, s.shape[d+2], tile[d] - overlap) if s.shape[d+2] > tile[d] else [0] for d in range(dims)]
+
+        for it in itertools.product(*positions):
             s_in = s
             upscaled = []
 
@@ -732,15 +746,16 @@ def tiled_scale_multidim(samples, function, tile=(64, 64), overlap = 8, upscale_
                 l = min(tile[d], s.shape[d + 2] - pos)
                 s_in = s_in.narrow(d + 2, pos, l)
                 upscaled.append(round(pos * upscale_amount))
+
             ps = function(s_in).to(output_device)
             mask = torch.ones_like(ps)
             feather = round(overlap * upscale_amount)
+
             for t in range(feather):
                 for d in range(2, dims + 2):
-                    m = mask.narrow(d, t, 1)
+                    a = (t + 1) / feather
-                    m *= ((1.0/feather) * (t + 1))
+                    mask.narrow(d, t, 1).mul_(a)
-                    m = mask.narrow(d, mask.shape[d] -1 -t, 1)
+                    mask.narrow(d, mask.shape[d] - 1 - t, 1).mul_(a)
-                    m *= ((1.0/feather) * (t + 1))
 
             o = out
             o_d = out_div
@@ -748,8 +763,8 @@ def tiled_scale_multidim(samples, function, tile=(64, 64), overlap = 8, upscale_
                 o = o.narrow(d + 2, upscaled[d], mask.shape[d + 2])
                 o_d = o_d.narrow(d + 2, upscaled[d], mask.shape[d + 2])
 
-            o += ps * mask
+            o.add_(ps * mask)
-            o_d += mask
+            o_d.add_(mask)
 
             if pbar is not None:
                 pbar.update(1)

comfy_execution/caching.py

@@ -1,11 +1,21 @@
 import itertools
-from typing import Sequence, Mapping
+from typing import Sequence, Mapping, Dict
 from comfy_execution.graph import DynamicPrompt
 
 import nodes
 
 from comfy_execution.graph_utils import is_link
 
+NODE_CLASS_CONTAINS_UNIQUE_ID: Dict[str, bool] = {}
+
+
+def include_unique_id_in_input(class_type: str) -> bool:
+    if class_type in NODE_CLASS_CONTAINS_UNIQUE_ID:
+        return NODE_CLASS_CONTAINS_UNIQUE_ID[class_type]
+    class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+    NODE_CLASS_CONTAINS_UNIQUE_ID[class_type] = "UNIQUE_ID" in class_def.INPUT_TYPES().get("hidden", {}).values()
+    return NODE_CLASS_CONTAINS_UNIQUE_ID[class_type]
+
 class CacheKeySet:
     def __init__(self, dynprompt, node_ids, is_changed_cache):
         self.keys = {}
@@ -56,6 +66,8 @@ class CacheKeySetID(CacheKeySet):
         for node_id in node_ids:
             if node_id in self.keys:
                 continue
+            if not self.dynprompt.has_node(node_id):
+                continue
             node = self.dynprompt.get_node(node_id)
             self.keys[node_id] = (node_id, node["class_type"])
             self.subcache_keys[node_id] = (node_id, node["class_type"])
@@ -74,6 +86,8 @@ class CacheKeySetInputSignature(CacheKeySet):
         for node_id in node_ids:
             if node_id in self.keys:
                 continue
+            if not self.dynprompt.has_node(node_id):
+                continue
             node = self.dynprompt.get_node(node_id)
             self.keys[node_id] = self.get_node_signature(self.dynprompt, node_id)
             self.subcache_keys[node_id] = (node_id, node["class_type"])
@@ -87,11 +101,14 @@ class CacheKeySetInputSignature(CacheKeySet):
         return to_hashable(signature)
 
     def get_immediate_node_signature(self, dynprompt, node_id, ancestor_order_mapping):
+        if not dynprompt.has_node(node_id):
+            # This node doesn't exist -- we can't cache it.
+            return [float("NaN")]
         node = dynprompt.get_node(node_id)
         class_type = node["class_type"]
         class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
         signature = [class_type, self.is_changed_cache.get(node_id)]
-        if self.include_node_id_in_input() or (hasattr(class_def, "NOT_IDEMPOTENT") and class_def.NOT_IDEMPOTENT):
+        if self.include_node_id_in_input() or (hasattr(class_def, "NOT_IDEMPOTENT") and class_def.NOT_IDEMPOTENT) or include_unique_id_in_input(class_type):
             signature.append(node_id)
         inputs = node["inputs"]
         for key in sorted(inputs.keys()):
@@ -112,6 +129,8 @@ class CacheKeySetInputSignature(CacheKeySet):
         return ancestors, order_mapping
 
     def get_ordered_ancestry_internal(self, dynprompt, node_id, ancestors, order_mapping):
+        if not dynprompt.has_node(node_id):
+            return
         inputs = dynprompt.get_node(node_id)["inputs"]
         input_keys = sorted(inputs.keys())
         for key in input_keys:

comfy_execution/graph.py

@@ -99,30 +99,44 @@ class TopologicalSort:
         self.add_strong_link(from_node_id, from_socket, to_node_id)
 
     def add_strong_link(self, from_node_id, from_socket, to_node_id):
-        self.add_node(from_node_id)
+        if not self.is_cached(from_node_id):
-        if to_node_id not in self.blocking[from_node_id]:
+            self.add_node(from_node_id)
-            self.blocking[from_node_id][to_node_id] = {}
+            if to_node_id not in self.blocking[from_node_id]:
-            self.blockCount[to_node_id] += 1
+                self.blocking[from_node_id][to_node_id] = {}
-        self.blocking[from_node_id][to_node_id][from_socket] = True
+                self.blockCount[to_node_id] += 1
+            self.blocking[from_node_id][to_node_id][from_socket] = True
 
-    def add_node(self, unique_id, include_lazy=False, subgraph_nodes=None):
+    def add_node(self, node_unique_id, include_lazy=False, subgraph_nodes=None):
-        if unique_id in self.pendingNodes:
+        node_ids = [node_unique_id]
-            return
+        links = []
-        self.pendingNodes[unique_id] = True
-        self.blockCount[unique_id] = 0
-        self.blocking[unique_id] = {}
 
-        inputs = self.dynprompt.get_node(unique_id)["inputs"]
+        while len(node_ids) > 0:
-        for input_name in inputs:
+            unique_id = node_ids.pop()
-            value = inputs[input_name]
+            if unique_id in self.pendingNodes:
-            if is_link(value):
+                continue
-                from_node_id, from_socket = value
+
-                if subgraph_nodes is not None and from_node_id not in subgraph_nodes:
+            self.pendingNodes[unique_id] = True
-                    continue
+            self.blockCount[unique_id] = 0
-                input_type, input_category, input_info = self.get_input_info(unique_id, input_name)
+            self.blocking[unique_id] = {}
-                is_lazy = input_info is not None and "lazy" in input_info and input_info["lazy"]
+
-                if include_lazy or not is_lazy:
+            inputs = self.dynprompt.get_node(unique_id)["inputs"]
-                    self.add_strong_link(from_node_id, from_socket, unique_id)
+            for input_name in inputs:
+                value = inputs[input_name]
+                if is_link(value):
+                    from_node_id, from_socket = value
+                    if subgraph_nodes is not None and from_node_id not in subgraph_nodes:
+                        continue
+                    input_type, input_category, input_info = self.get_input_info(unique_id, input_name)
+                    is_lazy = input_info is not None and "lazy" in input_info and input_info["lazy"]
+                    if (include_lazy or not is_lazy) and not self.is_cached(from_node_id):
+                        node_ids.append(from_node_id)
+                        links.append((from_node_id, from_socket, unique_id))
+                        
+        for link in links:
+            self.add_strong_link(*link)
+
+    def is_cached(self, node_id):
+        return False
 
     def get_ready_nodes(self):
         return [node_id for node_id in self.pendingNodes if self.blockCount[node_id] == 0]
@@ -146,11 +160,8 @@ class ExecutionList(TopologicalSort):
         self.output_cache = output_cache
         self.staged_node_id = None
 
-    def add_strong_link(self, from_node_id, from_socket, to_node_id):
+    def is_cached(self, node_id):
-        if self.output_cache.get(from_node_id) is not None:
+        return self.output_cache.get(node_id) is not None
-            # Nothing to do
-            return
-        super().add_strong_link(from_node_id, from_socket, to_node_id)
 
     def stage_node_execution(self):
         assert self.staged_node_id is None

comfy_extras/nodes_audio.py

@@ -16,14 +16,15 @@ class EmptyLatentAudio:
 
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": {"seconds": ("FLOAT", {"default": 47.6, "min": 1.0, "max": 1000.0, "step": 0.1})}}
+        return {"required": {"seconds": ("FLOAT", {"default": 47.6, "min": 1.0, "max": 1000.0, "step": 0.1}),
+                             "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}),
+                             }}
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "generate"
 
     CATEGORY = "latent/audio"
 
-    def generate(self, seconds):
+    def generate(self, seconds, batch_size):
-        batch_size = 1
         length = round((seconds * 44100 / 2048) / 2) * 2
         latent = torch.zeros([batch_size, 64, length], device=self.device)
         return ({"samples":latent, "type": "audio"}, )
@@ -58,6 +59,9 @@ class VAEDecodeAudio:
 
     def decode(self, vae, samples):
         audio = vae.decode(samples["samples"]).movedim(-1, 1)
+        std = torch.std(audio, dim=[1,2], keepdim=True) * 5.0
+        std[std < 1.0] = 1.0
+        audio /= std
         return ({"waveform": audio, "sample_rate": 44100}, )
 
 
@@ -183,17 +187,10 @@ class PreviewAudio(SaveAudio):
                 }
 
 class LoadAudio:
-    SUPPORTED_FORMATS = ('.wav', '.mp3', '.ogg', '.flac', '.aiff', '.aif')
-
     @classmethod
     def INPUT_TYPES(s):
         input_dir = folder_paths.get_input_directory()
-        files = [
+        files = folder_paths.filter_files_content_types(os.listdir(input_dir), ["audio", "video"])
-            f for f in os.listdir(input_dir)
-            if (os.path.isfile(os.path.join(input_dir, f))
-                and f.endswith(LoadAudio.SUPPORTED_FORMATS)
-            )
-        ]
         return {"required": {"audio": (sorted(files), {"audio_upload": True})}}
 
     CATEGORY = "audio"

comfy_extras/nodes_controlnet.py

@@ -1,4 +1,6 @@
 from comfy.cldm.control_types import UNION_CONTROLNET_TYPES
+import nodes
+import comfy.utils
 
 class SetUnionControlNetType:
     @classmethod
@@ -22,6 +24,37 @@ class SetUnionControlNetType:
 
         return (control_net,)
 
+class ControlNetInpaintingAliMamaApply(nodes.ControlNetApplyAdvanced):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"positive": ("CONDITIONING", ),
+                             "negative": ("CONDITIONING", ),
+                             "control_net": ("CONTROL_NET", ),
+                             "vae": ("VAE", ),
+                             "image": ("IMAGE", ),
+                             "mask": ("MASK", ),
+                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                             "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                             "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
+                             }}
+
+    FUNCTION = "apply_inpaint_controlnet"
+
+    CATEGORY = "conditioning/controlnet"
+
+    def apply_inpaint_controlnet(self, positive, negative, control_net, vae, image, mask, strength, start_percent, end_percent):
+        extra_concat = []
+        if control_net.concat_mask:
+            mask = 1.0 - mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1]))
+            mask_apply = comfy.utils.common_upscale(mask, image.shape[2], image.shape[1], "bilinear", "center").round()
+            image = image * mask_apply.movedim(1, -1).repeat(1, 1, 1, image.shape[3])
+            extra_concat = [mask]
+
+        return self.apply_controlnet(positive, negative, control_net, image, strength, start_percent, end_percent, vae=vae, extra_concat=extra_concat)
+
+
+
 NODE_CLASS_MAPPINGS = {
     "SetUnionControlNetType": SetUnionControlNetType,
+    "ControlNetInpaintingAliMamaApply": ControlNetInpaintingAliMamaApply,
 }

comfy_extras/nodes_custom_sampler.py

@@ -90,6 +90,27 @@ class PolyexponentialScheduler:
         sigmas = k_diffusion_sampling.get_sigmas_polyexponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho)
         return (sigmas, )
 
+class LaplaceScheduler:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                     "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
+                     "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
+                     "mu": ("FLOAT", {"default": 0.0, "min": -10.0, "max": 10.0, "step":0.1, "round": False}),
+                     "beta": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step":0.1, "round": False}),
+                    }
+               }
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "sampling/custom_sampling/schedulers"
+
+    FUNCTION = "get_sigmas"
+
+    def get_sigmas(self, steps, sigma_max, sigma_min, mu, beta):
+        sigmas = k_diffusion_sampling.get_sigmas_laplace(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, mu=mu, beta=beta)
+        return (sigmas, )
+
+
 class SDTurboScheduler:
     @classmethod
     def INPUT_TYPES(s):
@@ -673,6 +694,7 @@ NODE_CLASS_MAPPINGS = {
     "KarrasScheduler": KarrasScheduler,
     "ExponentialScheduler": ExponentialScheduler,
     "PolyexponentialScheduler": PolyexponentialScheduler,
+    "LaplaceScheduler": LaplaceScheduler,
     "VPScheduler": VPScheduler,
     "BetaSamplingScheduler": BetaSamplingScheduler,
     "SDTurboScheduler": SDTurboScheduler,

comfy_extras/nodes_hypernetwork.py

@@ -107,7 +107,7 @@ class HypernetworkLoader:
     CATEGORY = "loaders"
 
     def load_hypernetwork(self, model, hypernetwork_name, strength):
-        hypernetwork_path = folder_paths.get_full_path("hypernetworks", hypernetwork_name)
+        hypernetwork_path = folder_paths.get_full_path_or_raise("hypernetworks", hypernetwork_name)
         model_hypernetwork = model.clone()
         patch = load_hypernetwork_patch(hypernetwork_path, strength)
         if patch is not None:

comfy_extras/nodes_latent.py

@@ -1,4 +1,5 @@
 import comfy.utils
+import comfy_extras.nodes_post_processing
 import torch
 
 def reshape_latent_to(target_shape, latent):
@@ -145,6 +146,131 @@ class LatentBatchSeedBehavior:
 
         return (samples_out,)
 
+class LatentApplyOperation:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples": ("LATENT",),
+                             "operation": ("LATENT_OPERATION",),
+                             }}
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "op"
+
+    CATEGORY = "latent/advanced/operations"
+    EXPERIMENTAL = True
+
+    def op(self, samples, operation):
+        samples_out = samples.copy()
+
+        s1 = samples["samples"]
+        samples_out["samples"] = operation(latent=s1)
+        return (samples_out,)
+
+class LatentApplyOperationCFG:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                             "operation": ("LATENT_OPERATION",),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "latent/advanced/operations"
+    EXPERIMENTAL = True
+
+    def patch(self, model, operation):
+        m = model.clone()
+
+        def pre_cfg_function(args):
+            conds_out = args["conds_out"]
+            if len(conds_out) == 2:
+                conds_out[0] = operation(latent=(conds_out[0] - conds_out[1])) + conds_out[1]
+            else:
+                conds_out[0] = operation(latent=conds_out[0])
+            return conds_out
+
+        m.set_model_sampler_pre_cfg_function(pre_cfg_function)
+        return (m, )
+
+class LatentOperationTonemapReinhard:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
+                              }}
+
+    RETURN_TYPES = ("LATENT_OPERATION",)
+    FUNCTION = "op"
+
+    CATEGORY = "latent/advanced/operations"
+    EXPERIMENTAL = True
+
+    def op(self, multiplier):
+        def tonemap_reinhard(latent, **kwargs):
+            latent_vector_magnitude = (torch.linalg.vector_norm(latent, dim=(1)) + 0.0000000001)[:,None]
+            normalized_latent = latent / latent_vector_magnitude
+
+            mean = torch.mean(latent_vector_magnitude, dim=(1,2,3), keepdim=True)
+            std = torch.std(latent_vector_magnitude, dim=(1,2,3), keepdim=True)
+
+            top = (std * 5 + mean) * multiplier
+
+            #reinhard
+            latent_vector_magnitude *= (1.0 / top)
+            new_magnitude = latent_vector_magnitude / (latent_vector_magnitude + 1.0)
+            new_magnitude *= top
+
+            return normalized_latent * new_magnitude
+        return (tonemap_reinhard,)
+
+class LatentOperationSharpen:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                "sharpen_radius": ("INT", {
+                    "default": 9,
+                    "min": 1,
+                    "max": 31,
+                    "step": 1
+                }),
+                "sigma": ("FLOAT", {
+                    "default": 1.0,
+                    "min": 0.1,
+                    "max": 10.0,
+                    "step": 0.1
+                }),
+                "alpha": ("FLOAT", {
+                    "default": 0.1,
+                    "min": 0.0,
+                    "max": 5.0,
+                    "step": 0.01
+                }),
+                              }}
+
+    RETURN_TYPES = ("LATENT_OPERATION",)
+    FUNCTION = "op"
+
+    CATEGORY = "latent/advanced/operations"
+    EXPERIMENTAL = True
+
+    def op(self, sharpen_radius, sigma, alpha):
+        def sharpen(latent, **kwargs):
+            luminance = (torch.linalg.vector_norm(latent, dim=(1)) + 1e-6)[:,None]
+            normalized_latent = latent / luminance
+            channels = latent.shape[1]
+
+            kernel_size = sharpen_radius * 2 + 1
+            kernel = comfy_extras.nodes_post_processing.gaussian_kernel(kernel_size, sigma, device=luminance.device)
+            center = kernel_size // 2
+
+            kernel *= alpha * -10
+            kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0
+
+            padded_image = torch.nn.functional.pad(normalized_latent, (sharpen_radius,sharpen_radius,sharpen_radius,sharpen_radius), 'reflect')
+            sharpened = torch.nn.functional.conv2d(padded_image, kernel.repeat(channels, 1, 1).unsqueeze(1), padding=kernel_size // 2, groups=channels)[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius]
+
+            return luminance * sharpened
+        return (sharpen,)
+
 NODE_CLASS_MAPPINGS = {
     "LatentAdd": LatentAdd,
     "LatentSubtract": LatentSubtract,
@@ -152,4 +278,8 @@ NODE_CLASS_MAPPINGS = {
     "LatentInterpolate": LatentInterpolate,
     "LatentBatch": LatentBatch,
     "LatentBatchSeedBehavior": LatentBatchSeedBehavior,
+    "LatentApplyOperation": LatentApplyOperation,
+    "LatentApplyOperationCFG": LatentApplyOperationCFG,
+    "LatentOperationTonemapReinhard": LatentOperationTonemapReinhard,
+    "LatentOperationSharpen": LatentOperationSharpen,
 }

comfy_extras/nodes_lora_extract.py

@@ -0,0 +1,119 @@
+import torch
+import comfy.model_management
+import comfy.utils
+import folder_paths
+import os
+import logging
+from enum import Enum
+
+CLAMP_QUANTILE = 0.99
+
+def extract_lora(diff, rank):
+    conv2d = (len(diff.shape) == 4)
+    kernel_size = None if not conv2d else diff.size()[2:4]
+    conv2d_3x3 = conv2d and kernel_size != (1, 1)
+    out_dim, in_dim = diff.size()[0:2]
+    rank = min(rank, in_dim, out_dim)
+
+    if conv2d:
+        if conv2d_3x3:
+            diff = diff.flatten(start_dim=1)
+        else:
+            diff = diff.squeeze()
+
+
+    U, S, Vh = torch.linalg.svd(diff.float())
+    U = U[:, :rank]
+    S = S[:rank]
+    U = U @ torch.diag(S)
+    Vh = Vh[:rank, :]
+
+    dist = torch.cat([U.flatten(), Vh.flatten()])
+    hi_val = torch.quantile(dist, CLAMP_QUANTILE)
+    low_val = -hi_val
+
+    U = U.clamp(low_val, hi_val)
+    Vh = Vh.clamp(low_val, hi_val)
+    if conv2d:
+        U = U.reshape(out_dim, rank, 1, 1)
+        Vh = Vh.reshape(rank, in_dim, kernel_size[0], kernel_size[1])
+    return (U, Vh)
+
+class LORAType(Enum):
+    STANDARD = 0
+    FULL_DIFF = 1
+
+LORA_TYPES = {"standard": LORAType.STANDARD,
+              "full_diff": LORAType.FULL_DIFF}
+
+def calc_lora_model(model_diff, rank, prefix_model, prefix_lora, output_sd, lora_type, bias_diff=False):
+    comfy.model_management.load_models_gpu([model_diff], force_patch_weights=True)
+    sd = model_diff.model_state_dict(filter_prefix=prefix_model)
+
+    for k in sd:
+        if k.endswith(".weight"):
+            weight_diff = sd[k]
+            if lora_type == LORAType.STANDARD:
+                if weight_diff.ndim < 2:
+                    if bias_diff:
+                        output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().half().cpu()
+                    continue
+                try:
+                    out = extract_lora(weight_diff, rank)
+                    output_sd["{}{}.lora_up.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[0].contiguous().half().cpu()
+                    output_sd["{}{}.lora_down.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[1].contiguous().half().cpu()
+                except:
+                    logging.warning("Could not generate lora weights for key {}, is the weight difference a zero?".format(k))
+            elif lora_type == LORAType.FULL_DIFF:
+                output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().half().cpu()
+
+        elif bias_diff and k.endswith(".bias"):
+            output_sd["{}{}.diff_b".format(prefix_lora, k[len(prefix_model):-5])] = sd[k].contiguous().half().cpu()
+    return output_sd
+
+class LoraSave:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"filename_prefix": ("STRING", {"default": "loras/ComfyUI_extracted_lora"}),
+                              "rank": ("INT", {"default": 8, "min": 1, "max": 4096, "step": 1}),
+                              "lora_type": (tuple(LORA_TYPES.keys()),),
+                              "bias_diff": ("BOOLEAN", {"default": True}),
+                            },
+                "optional": {"model_diff": ("MODEL", {"tooltip": "The ModelSubtract output to be converted to a lora."}),
+                             "text_encoder_diff": ("CLIP", {"tooltip": "The CLIPSubtract output to be converted to a lora."})},
+    }
+    RETURN_TYPES = ()
+    FUNCTION = "save"
+    OUTPUT_NODE = True
+
+    CATEGORY = "_for_testing"
+
+    def save(self, filename_prefix, rank, lora_type, bias_diff, model_diff=None, text_encoder_diff=None):
+        if model_diff is None and text_encoder_diff is None:
+            return {}
+
+        lora_type = LORA_TYPES.get(lora_type)
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+
+        output_sd = {}
+        if model_diff is not None:
+            output_sd = calc_lora_model(model_diff, rank, "diffusion_model.", "diffusion_model.", output_sd, lora_type, bias_diff=bias_diff)
+        if text_encoder_diff is not None:
+            output_sd = calc_lora_model(text_encoder_diff.patcher, rank, "", "text_encoders.", output_sd, lora_type, bias_diff=bias_diff)
+
+        output_checkpoint = f"{filename}_{counter:05}_.safetensors"
+        output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
+
+        comfy.utils.save_torch_file(output_sd, output_checkpoint, metadata=None)
+        return {}
+
+NODE_CLASS_MAPPINGS = {
+    "LoraSave": LoraSave
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LoraSave": "Extract and Save Lora"
+}

comfy_extras/nodes_model_downscale.py

@@ -17,7 +17,7 @@ class PatchModelAddDownscale:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "_for_testing"
+    CATEGORY = "model_patches/unet"
 
     def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method):
         model_sampling = model.get_model_object("model_sampling")

comfy_extras/nodes_perpneg.py

@@ -26,6 +26,7 @@ class PerpNeg:
     FUNCTION = "patch"
 
     CATEGORY = "_for_testing"
+    DEPRECATED = True
 
     def patch(self, model, empty_conditioning, neg_scale):
         m = model.clone()

comfy_extras/nodes_photomaker.py

@@ -126,7 +126,7 @@ class PhotoMakerLoader:
     CATEGORY = "_for_testing/photomaker"
 
     def load_photomaker_model(self, photomaker_model_name):
-        photomaker_model_path = folder_paths.get_full_path("photomaker", photomaker_model_name)
+        photomaker_model_path = folder_paths.get_full_path_or_raise("photomaker", photomaker_model_name)
         photomaker_model = PhotoMakerIDEncoder()
         data = comfy.utils.load_torch_file(photomaker_model_path, safe_load=True)
         if "id_encoder" in data:

comfy_extras/nodes_sd3.py

@@ -15,9 +15,9 @@ class TripleCLIPLoader:
     CATEGORY = "advanced/loaders"
 
     def load_clip(self, clip_name1, clip_name2, clip_name3):
-        clip_path1 = folder_paths.get_full_path("clip", clip_name1)
+        clip_path1 = folder_paths.get_full_path_or_raise("clip", clip_name1)
-        clip_path2 = folder_paths.get_full_path("clip", clip_name2)
+        clip_path2 = folder_paths.get_full_path_or_raise("clip", clip_name2)
-        clip_path3 = folder_paths.get_full_path("clip", clip_name3)
+        clip_path3 = folder_paths.get_full_path_or_raise("clip", clip_name3)
         clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3], embedding_directory=folder_paths.get_folder_paths("embeddings"))
         return (clip,)
 
@@ -36,7 +36,7 @@ class EmptySD3LatentImage:
     CATEGORY = "latent/sd3"
 
     def generate(self, width, height, batch_size=1):
-        latent = torch.ones([batch_size, 16, height // 8, width // 8], device=self.device) * 0.0609
+        latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=self.device)
         return ({"samples":latent}, )
 
 class CLIPTextEncodeSD3:
@@ -93,6 +93,7 @@ class ControlNetApplySD3(nodes.ControlNetApplyAdvanced):
                              "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
                              }}
     CATEGORY = "conditioning/controlnet"
+    DEPRECATED = True
 
 NODE_CLASS_MAPPINGS = {
     "TripleCLIPLoader": TripleCLIPLoader,
@@ -103,5 +104,5 @@ NODE_CLASS_MAPPINGS = {
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     # Sampling
-    "ControlNetApplySD3": "ControlNetApply SD3 and HunyuanDiT",
+    "ControlNetApplySD3": "Apply Controlnet with VAE",
 }

comfy_extras/nodes_stable_cascade.py

@@ -116,6 +116,7 @@ class StableCascade_SuperResolutionControlnet:
     RETURN_NAMES = ("controlnet_input", "stage_c", "stage_b")
     FUNCTION = "generate"
 
+    EXPERIMENTAL = True
     CATEGORY = "_for_testing/stable_cascade"
 
     def generate(self, image, vae):

comfy_extras/nodes_tomesd.py

@@ -154,7 +154,7 @@ class TomePatchModel:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "_for_testing"
+    CATEGORY = "model_patches/unet"
 
     def patch(self, model, ratio):
         self.u = None

comfy_extras/nodes_torch_compile.py

@@ -0,0 +1,22 @@
+import torch
+
+class TorchCompileModel:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                             "backend": (["inductor", "cudagraphs"],),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "_for_testing"
+    EXPERIMENTAL = True
+
+    def patch(self, model, backend):
+        m = model.clone()
+        m.add_object_patch("diffusion_model", torch.compile(model=m.get_model_object("diffusion_model"), backend=backend))
+        return (m, )
+
+NODE_CLASS_MAPPINGS = {
+    "TorchCompileModel": TorchCompileModel,
+}

comfy_extras/nodes_upscale_model.py

@@ -25,7 +25,7 @@ class UpscaleModelLoader:
     CATEGORY = "loaders"
 
     def load_model(self, model_name):
-        model_path = folder_paths.get_full_path("upscale_models", model_name)
+        model_path = folder_paths.get_full_path_or_raise("upscale_models", model_name)
         sd = comfy.utils.load_torch_file(model_path, safe_load=True)
         if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd:
             sd = comfy.utils.state_dict_prefix_replace(sd, {"module.":""})

comfy_extras/nodes_video_model.py

@@ -17,7 +17,7 @@ class ImageOnlyCheckpointLoader:
     CATEGORY = "loaders/video_models"
 
     def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
-        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
+        ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
         out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=False, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
         return (out[0], out[3], out[2])
 
@@ -107,7 +107,7 @@ class VideoTriangleCFGGuidance:
         return (m, )
 
 class ImageOnlyCheckpointSave(comfy_extras.nodes_model_merging.CheckpointSave):
-    CATEGORY = "_for_testing"
+    CATEGORY = "advanced/model_merging"
 
     @classmethod
     def INPUT_TYPES(s):

custom_nodes/websocket_image_save.py

@@ -37,6 +37,7 @@ class SaveImageWebsocket:
 
         return {}
 
+    @classmethod
     def IS_CHANGED(s, images):
         return time.time()
 

execution.py

@@ -179,7 +179,13 @@ def merge_result_data(results, obj):
     # merge node execution results
     for i, is_list in zip(range(len(results[0])), output_is_list):
         if is_list:
-            output.append([x for o in results for x in o[i]])
+            value = []
+            for o in results:
+                if isinstance(o[i], ExecutionBlocker):
+                    value.append(o[i])
+                else:
+                    value.extend(o[i])
+            output.append(value)
         else:
             output.append([o[i] for o in results])
     return output

extra_model_paths.yaml.example

@@ -25,11 +25,16 @@ a111:
 
 #comfyui:
 #     base_path: path/to/comfyui/
+#     # You can use is_default to mark that these folders should be listed first, and used as the default dirs for eg downloads
+#     #is_default: true
 #     checkpoints: models/checkpoints/
 #     clip: models/clip/
 #     clip_vision: models/clip_vision/
 #     configs: models/configs/
 #     controlnet: models/controlnet/
+#     diffusion_models: |
+#                  models/diffusion_models
+#                  models/unet
 #     embeddings: models/embeddings/
 #     loras: models/loras/
 #     upscale_models: models/upscale_models/

folder_paths.py

@@ -2,7 +2,9 @@ from __future__ import annotations
 
 import os
 import time
+import mimetypes
 import logging
+from typing import Set, List, Dict, Tuple, Literal
 from collections.abc import Collection
 
 supported_pt_extensions: set[str] = {'.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl', '.sft'}
@@ -44,6 +46,40 @@ user_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "user
 
 filename_list_cache: dict[str, tuple[list[str], dict[str, float], float]] = {}
 
+class CacheHelper:
+    """
+    Helper class for managing file list cache data.
+    """
+    def __init__(self):
+        self.cache: dict[str, tuple[list[str], dict[str, float], float]] = {}
+        self.active = False
+
+    def get(self, key: str, default=None) -> tuple[list[str], dict[str, float], float]:
+        if not self.active:
+            return default
+        return self.cache.get(key, default)
+    
+    def set(self, key: str, value: tuple[list[str], dict[str, float], float]) -> None:
+        if self.active:
+            self.cache[key] = value
+
+    def clear(self):
+        self.cache.clear()
+
+    def __enter__(self):
+        self.active = True
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.active = False
+        self.clear()
+
+cache_helper = CacheHelper()
+
+extension_mimetypes_cache = {
+    "webp" : "image",
+}
+
 def map_legacy(folder_name: str) -> str:
     legacy = {"unet": "diffusion_models"}
     return legacy.get(folder_name, folder_name)
@@ -78,6 +114,13 @@ def get_input_directory() -> str:
     global input_directory
     return input_directory
 
+def get_user_directory() -> str:
+    return user_directory
+
+def set_user_directory(user_dir: str) -> None:
+    global user_directory
+    user_directory = user_dir
+
 
 #NOTE: used in http server so don't put folders that should not be accessed remotely
 def get_directory_by_type(type_name: str) -> str | None:
@@ -89,6 +132,28 @@ def get_directory_by_type(type_name: str) -> str | None:
         return get_input_directory()
     return None
 
+def filter_files_content_types(files: List[str], content_types: Literal["image", "video", "audio"]) -> List[str]:
+    """
+    Example:
+        files = os.listdir(folder_paths.get_input_directory())
+        filter_files_content_types(files, ["image", "audio", "video"])
+    """
+    global extension_mimetypes_cache
+    result = []
+    for file in files:
+        extension = file.split('.')[-1]
+        if extension not in extension_mimetypes_cache:
+            mime_type, _ = mimetypes.guess_type(file, strict=False)
+            if not mime_type:
+                continue
+            content_type = mime_type.split('/')[0]
+            extension_mimetypes_cache[extension] = content_type
+        else:
+            content_type = extension_mimetypes_cache[extension]
+
+        if content_type in content_types:
+            result.append(file)
+    return result
 
 # determine base_dir rely on annotation if name is 'filename.ext [annotation]' format
 # otherwise use default_path as base_dir
@@ -130,11 +195,14 @@ def exists_annotated_filepath(name) -> bool:
     return os.path.exists(filepath)
 
 
-def add_model_folder_path(folder_name: str, full_folder_path: str) -> None:
+def add_model_folder_path(folder_name: str, full_folder_path: str, is_default: bool = False) -> None:
     global folder_names_and_paths
     folder_name = map_legacy(folder_name)
     if folder_name in folder_names_and_paths:
-        folder_names_and_paths[folder_name][0].append(full_folder_path)
+        if is_default:
+            folder_names_and_paths[folder_name][0].insert(0, full_folder_path)
+        else:
+            folder_names_and_paths[folder_name][0].append(full_folder_path)
     else:
         folder_names_and_paths[folder_name] = ([full_folder_path], set())
 
@@ -166,8 +234,12 @@ def recursive_search(directory: str, excluded_dir_names: list[str] | None=None)
     for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True):
         subdirs[:] = [d for d in subdirs if d not in excluded_dir_names]
         for file_name in filenames:
-            relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory)
+            try:
-            result.append(relative_path)
+                relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory)
+                result.append(relative_path)
+            except:
+                logging.warning(f"Warning: Unable to access {file_name}. Skipping this file.")
+                continue
 
         for d in subdirs:
             path: str = os.path.join(dirpath, d)
@@ -200,6 +272,14 @@ def get_full_path(folder_name: str, filename: str) -> str | None:
 
     return None
 
+
+def get_full_path_or_raise(folder_name: str, filename: str) -> str:
+    full_path = get_full_path(folder_name, filename)
+    if full_path is None:
+        raise FileNotFoundError(f"Model in folder '{folder_name}' with filename '{filename}' not found.")
+    return full_path
+
+
 def get_filename_list_(folder_name: str) -> tuple[list[str], dict[str, float], float]:
     folder_name = map_legacy(folder_name)
     global folder_names_and_paths
@@ -214,6 +294,10 @@ def get_filename_list_(folder_name: str) -> tuple[list[str], dict[str, float], f
     return sorted(list(output_list)), output_folders, time.perf_counter()
 
 def cached_filename_list_(folder_name: str) -> tuple[list[str], dict[str, float], float] | None:
+    strong_cache = cache_helper.get(folder_name)
+    if strong_cache is not None:
+        return strong_cache
+    
     global filename_list_cache
     global folder_names_and_paths
     folder_name = map_legacy(folder_name)
@@ -242,6 +326,7 @@ def get_filename_list(folder_name: str) -> list[str]:
         out = get_filename_list_(folder_name)
         global filename_list_cache
         filename_list_cache[folder_name] = out
+    cache_helper.set(folder_name, out)
     return list(out[0])
 
 def get_save_image_path(filename_prefix: str, output_dir: str, image_width=0, image_height=0) -> tuple[str, str, int, str, str]:
@@ -257,9 +342,17 @@ def get_save_image_path(filename_prefix: str, output_dir: str, image_width=0, im
     def compute_vars(input: str, image_width: int, image_height: int) -> str:
         input = input.replace("%width%", str(image_width))
         input = input.replace("%height%", str(image_height))
+        now = time.localtime()
+        input = input.replace("%year%", str(now.tm_year))
+        input = input.replace("%month%", str(now.tm_mon).zfill(2))
+        input = input.replace("%day%", str(now.tm_mday).zfill(2))
+        input = input.replace("%hour%", str(now.tm_hour).zfill(2))
+        input = input.replace("%minute%", str(now.tm_min).zfill(2))
+        input = input.replace("%second%", str(now.tm_sec).zfill(2))
         return input
 
-    filename_prefix = compute_vars(filename_prefix, image_width, image_height)
+    if "%" in filename_prefix:
+        filename_prefix = compute_vars(filename_prefix, image_width, image_height)
 
     subfolder = os.path.dirname(os.path.normpath(filename_prefix))
     filename = os.path.basename(os.path.normpath(filename_prefix))

latent_preview.py

@@ -9,7 +9,7 @@ import folder_paths
 import comfy.utils
 import logging
 
-MAX_PREVIEW_RESOLUTION = 512
+MAX_PREVIEW_RESOLUTION = args.preview_size
 
 def preview_to_image(latent_image):
         latents_ubyte = (((latent_image + 1.0) / 2.0).clamp(0, 1)  # change scale from -1..1 to 0..1
@@ -36,12 +36,20 @@ class TAESDPreviewerImpl(LatentPreviewer):
 
 
 class Latent2RGBPreviewer(LatentPreviewer):
-    def __init__(self, latent_rgb_factors):
+    def __init__(self, latent_rgb_factors, latent_rgb_factors_bias=None):
-        self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu")
+        self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu").transpose(0, 1)
+        self.latent_rgb_factors_bias = None
+        if latent_rgb_factors_bias is not None:
+            self.latent_rgb_factors_bias = torch.tensor(latent_rgb_factors_bias, device="cpu")
 
     def decode_latent_to_preview(self, x0):
         self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device)
-        latent_image = x0[0].permute(1, 2, 0) @ self.latent_rgb_factors
+        if self.latent_rgb_factors_bias is not None:
+            self.latent_rgb_factors_bias = self.latent_rgb_factors_bias.to(dtype=x0.dtype, device=x0.device)
+
+        latent_image = torch.nn.functional.linear(x0[0].permute(1, 2, 0), self.latent_rgb_factors, bias=self.latent_rgb_factors_bias)
+        # latent_image = x0[0].permute(1, 2, 0) @ self.latent_rgb_factors
+
         return preview_to_image(latent_image)
 
 
@@ -71,7 +79,7 @@ def get_previewer(device, latent_format):
 
         if previewer is None:
             if latent_format.latent_rgb_factors is not None:
-                previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors)
+                previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors, latent_format.latent_rgb_factors_bias)
     return previewer
 
 def prepare_callback(model, steps, x0_output_dict=None):

main.py

@@ -6,6 +6,10 @@ import importlib.util
 import folder_paths
 import time
 from comfy.cli_args import args
+from app.logger import setup_logger
+
+
+setup_logger(log_level=args.verbose)
 
 
 def execute_prestartup_script():
@@ -59,6 +63,7 @@ import threading
 import gc
 
 import logging
+import utils.extra_config
 
 if os.name == "nt":
     logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
@@ -81,7 +86,6 @@ if args.windows_standalone_build:
         pass
 
 import comfy.utils
-import yaml
 
 import execution
 import server
@@ -156,7 +160,10 @@ def prompt_worker(q, server):
                 need_gc = False
 
 async def run(server, address='', port=8188, verbose=True, call_on_start=None):
-    await asyncio.gather(server.start(address, port, verbose, call_on_start), server.publish_loop())
+    addresses = []
+    for addr in address.split(","):
+        addresses.append((addr, port))
+    await asyncio.gather(server.start_multi_address(addresses, call_on_start), server.publish_loop())
 
 
 def hijack_progress(server):
@@ -176,27 +183,6 @@ def cleanup_temp():
         shutil.rmtree(temp_dir, ignore_errors=True)
 
 
-def load_extra_path_config(yaml_path):
-    with open(yaml_path, 'r') as stream:
-        config = yaml.safe_load(stream)
-    for c in config:
-        conf = config[c]
-        if conf is None:
-            continue
-        base_path = None
-        if "base_path" in conf:
-            base_path = conf.pop("base_path")
-        for x in conf:
-            for y in conf[x].split("\n"):
-                if len(y) == 0:
-                    continue
-                full_path = y
-                if base_path is not None:
-                    full_path = os.path.join(base_path, full_path)
-                logging.info("Adding extra search path {} {}".format(x, full_path))
-                folder_paths.add_model_folder_path(x, full_path)
-
-
 if __name__ == "__main__":
     if args.temp_directory:
         temp_dir = os.path.join(os.path.abspath(args.temp_directory), "temp")
@@ -218,11 +204,11 @@ if __name__ == "__main__":
 
     extra_model_paths_config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "extra_model_paths.yaml")
     if os.path.isfile(extra_model_paths_config_path):
-        load_extra_path_config(extra_model_paths_config_path)
+        utils.extra_config.load_extra_path_config(extra_model_paths_config_path)
 
     if args.extra_model_paths_config:
         for config_path in itertools.chain(*args.extra_model_paths_config):
-            load_extra_path_config(config_path)
+            utils.extra_config.load_extra_path_config(config_path)
 
     nodes.init_extra_nodes(init_custom_nodes=not args.disable_all_custom_nodes)
 
@@ -243,21 +229,30 @@ if __name__ == "__main__":
     folder_paths.add_model_folder_path("clip", os.path.join(folder_paths.get_output_directory(), "clip"))
     folder_paths.add_model_folder_path("vae", os.path.join(folder_paths.get_output_directory(), "vae"))
     folder_paths.add_model_folder_path("diffusion_models", os.path.join(folder_paths.get_output_directory(), "diffusion_models"))
+    folder_paths.add_model_folder_path("loras", os.path.join(folder_paths.get_output_directory(), "loras"))
 
     if args.input_directory:
         input_dir = os.path.abspath(args.input_directory)
         logging.info(f"Setting input directory to: {input_dir}")
         folder_paths.set_input_directory(input_dir)
     
+    if args.user_directory:
+        user_dir = os.path.abspath(args.user_directory)
+        logging.info(f"Setting user directory to: {user_dir}")
+        folder_paths.set_user_directory(user_dir)
+
     if args.quick_test_for_ci:
         exit(0)
 
+    os.makedirs(folder_paths.get_temp_directory(), exist_ok=True)
     call_on_start = None
     if args.auto_launch:
         def startup_server(scheme, address, port):
             import webbrowser
             if os.name == 'nt' and address == '0.0.0.0':
                 address = '127.0.0.1'
+            if ':' in address:
+                address = "[{}]".format(address)
             webbrowser.open(f"{scheme}://{address}:{port}")
         call_on_start = startup_server
 

model_filemanager/__init__.py

@@ -1,2 +1,2 @@
 # model_manager/__init__.py
-from .download_models import download_model, DownloadModelStatus, DownloadStatusType, create_model_path, check_file_exists, track_download_progress, validate_model_subdirectory, validate_filename
+from .download_models import download_model, DownloadModelStatus, DownloadStatusType, create_model_path, check_file_exists, track_download_progress, validate_filename

model_filemanager/download_models.py

@@ -1,9 +1,10 @@
+#NOTE: This was an experiment and WILL BE REMOVED
 from __future__ import annotations
 import aiohttp
 import os
 import traceback
 import logging
-from folder_paths import models_dir
+from folder_paths import folder_names_and_paths, get_folder_paths
 import re
 from typing import Callable, Any, Optional, Awaitable, Dict
 from enum import Enum
@@ -17,6 +18,7 @@ class DownloadStatusType(Enum):
     COMPLETED = "completed"
     ERROR = "error"
 
+
 @dataclass
 class DownloadModelStatus():
     status: str
@@ -38,10 +40,12 @@ class DownloadModelStatus():
             "already_existed": self.already_existed
         }
 
+
 async def download_model(model_download_request: Callable[[str], Awaitable[aiohttp.ClientResponse]],
                          model_name: str,
                          model_url: str,
-                         model_sub_directory: str,
+                         model_directory: str,
+                         folder_path: str,
                          progress_callback: Callable[[str, DownloadModelStatus], Awaitable[Any]],
                          progress_interval: float = 1.0) -> DownloadModelStatus:
     """
@@ -54,23 +58,17 @@ async def download_model(model_download_request: Callable[[str], Awaitable[aioht
             The name of the model file to be downloaded. This will be the filename on disk.
         model_url (str):
             The URL from which to download the model.
-        model_sub_directory (str): 
+        model_directory (str):
             The subdirectory within the main models directory where the model
             should be saved (e.g., 'checkpoints', 'loras', etc.).
         progress_callback (Callable[[str, DownloadModelStatus], Awaitable[Any]]):
             An asynchronous function to call with progress updates.
+        folder_path (str);
+            Path to which model folder should be used as the root.
 
     Returns:
         DownloadModelStatus: The result of the download operation.
     """
-    if not validate_model_subdirectory(model_sub_directory):
-        return DownloadModelStatus(
-            DownloadStatusType.ERROR, 
-            0,
-            "Invalid model subdirectory", 
-            False
-        )
-
     if not validate_filename(model_name):
         return DownloadModelStatus(
             DownloadStatusType.ERROR,
@@ -79,52 +77,67 @@ async def download_model(model_download_request: Callable[[str], Awaitable[aioht
             False
         )
 
-    file_path, relative_path = create_model_path(model_name, model_sub_directory, models_dir)
+    if not model_directory in folder_names_and_paths:
-    existing_file = await check_file_exists(file_path, model_name, progress_callback, relative_path)
+        return DownloadModelStatus(
+            DownloadStatusType.ERROR,
+            0,
+            "Invalid or unrecognized model directory. model_directory must be a known model type (eg 'checkpoints'). If you are seeing this error for a custom model type, ensure the relevant custom nodes are installed and working.",
+            False
+        )
+
+    if not folder_path in get_folder_paths(model_directory):
+        return DownloadModelStatus(
+            DownloadStatusType.ERROR,
+            0,
+            f"Invalid folder path '{folder_path}', does not match the list of known directories ({get_folder_paths(model_directory)}). If you're seeing this in the downloader UI, you may need to refresh the page.",
+            False
+        )
+
+    file_path = create_model_path(model_name, folder_path)
+    existing_file = await check_file_exists(file_path, model_name, progress_callback)
     if existing_file:
         return existing_file
 
     try:
+        logging.info(f"Downloading {model_name} from {model_url}")
         status = DownloadModelStatus(DownloadStatusType.PENDING, 0, f"Starting download of {model_name}", False)
-        await progress_callback(relative_path, status)
+        await progress_callback(model_name, status)
 
         response = await model_download_request(model_url)
         if response.status != 200:
             error_message = f"Failed to download {model_name}. Status code: {response.status}"
             logging.error(error_message)
             status = DownloadModelStatus(DownloadStatusType.ERROR, 0, error_message, False)
-            await progress_callback(relative_path, status)
+            await progress_callback(model_name, status)
             return DownloadModelStatus(DownloadStatusType.ERROR, 0, error_message, False)
 
-        return await track_download_progress(response, file_path, model_name, progress_callback, relative_path, progress_interval)
+        return await track_download_progress(response, file_path, model_name, progress_callback, progress_interval)
 
     except Exception as e:
         logging.error(f"Error in downloading model: {e}")
-        return await handle_download_error(e, model_name, progress_callback, relative_path)
+        return await handle_download_error(e, model_name, progress_callback)
 
 
-def create_model_path(model_name: str, model_directory: str, models_base_dir: str) -> tuple[str, str]:
+def create_model_path(model_name: str, folder_path: str) -> tuple[str, str]:
-    full_model_dir = os.path.join(models_base_dir, model_directory)
+    os.makedirs(folder_path, exist_ok=True)
-    os.makedirs(full_model_dir, exist_ok=True)
+    file_path = os.path.join(folder_path, model_name)
-    file_path = os.path.join(full_model_dir, model_name)
 
     # Ensure the resulting path is still within the base directory
     abs_file_path = os.path.abspath(file_path)
-    abs_base_dir = os.path.abspath(str(models_base_dir))
+    abs_base_dir = os.path.abspath(folder_path)
     if os.path.commonprefix([abs_file_path, abs_base_dir]) != abs_base_dir:
-        raise Exception(f"Invalid model directory: {model_directory}/{model_name}")
+        raise Exception(f"Invalid model directory: {folder_path}/{model_name}")
 
+    return file_path
 
-    relative_path = '/'.join([model_directory, model_name])
-    return file_path, relative_path
 
 async def check_file_exists(file_path: str,
                             model_name: str,
-                            progress_callback: Callable[[str, DownloadModelStatus], Awaitable[Any]], 
+                            progress_callback: Callable[[str, DownloadModelStatus], Awaitable[Any]]
-                            relative_path: str) -> Optional[DownloadModelStatus]:
+                            ) -> Optional[DownloadModelStatus]:
     if os.path.exists(file_path):
         status = DownloadModelStatus(DownloadStatusType.COMPLETED, 100, f"{model_name} already exists", True)
-        await progress_callback(relative_path, status)
+        await progress_callback(model_name, status)
         return status
     return None
 
@@ -133,7 +146,6 @@ async def track_download_progress(response: aiohttp.ClientResponse,
                                   file_path: str,
                                   model_name: str,
                                   progress_callback: Callable[[str, DownloadModelStatus], Awaitable[Any]],
-                                  relative_path: str, 
                                   interval: float = 1.0) -> DownloadModelStatus:
     try:
         total_size = int(response.headers.get('Content-Length', 0))
@@ -144,10 +156,11 @@ async def track_download_progress(response: aiohttp.ClientResponse,
             nonlocal last_update_time
             progress = (downloaded / total_size) * 100 if total_size > 0 else 0
             status = DownloadModelStatus(DownloadStatusType.IN_PROGRESS, progress, f"Downloading {model_name}", False)
-            await progress_callback(relative_path, status)
+            await progress_callback(model_name, status)
             last_update_time = time.time()
 
-        with open(file_path, 'wb') as f:
+        temp_file_path = file_path + '.tmp'
+        with open(temp_file_path, 'wb') as f:
             chunk_iterator = response.content.iter_chunked(8192)
             while True:
                 try:
@@ -160,49 +173,30 @@ async def track_download_progress(response: aiohttp.ClientResponse,
                 if time.time() - last_update_time >= interval:
                     await update_progress()
 
+        os.rename(temp_file_path, file_path)
+
         await update_progress()
 
         logging.info(f"Successfully downloaded {model_name}. Total downloaded: {downloaded}")
         status = DownloadModelStatus(DownloadStatusType.COMPLETED, 100, f"Successfully downloaded {model_name}", False)
-        await progress_callback(relative_path, status)
+        await progress_callback(model_name, status)
 
         return status
     except Exception as e:
         logging.error(f"Error in track_download_progress: {e}")
         logging.error(traceback.format_exc())
-        return await handle_download_error(e, model_name, progress_callback, relative_path)
+        return await handle_download_error(e, model_name, progress_callback)
+
 
 async def handle_download_error(e: Exception,
                                 model_name: str,
-                                progress_callback: Callable[[str, DownloadModelStatus], Any], 
+                                progress_callback: Callable[[str, DownloadModelStatus], Any]
-                                relative_path: str) -> DownloadModelStatus:
+                                ) -> DownloadModelStatus:
     error_message = f"Error downloading {model_name}: {str(e)}"
     status = DownloadModelStatus(DownloadStatusType.ERROR, 0, error_message, False)
-    await progress_callback(relative_path, status)
+    await progress_callback(model_name, status)
     return status
 
-def validate_model_subdirectory(model_subdirectory: str) -> bool:
-    """
-    Validate that the model subdirectory is safe to install into. 
-    Must not contain relative paths, nested paths or special characters
-    other than underscores and hyphens.
-
-    Args:
-        model_subdirectory (str): The subdirectory for the specific model type.
-
-    Returns:
-        bool: True if the subdirectory is safe, False otherwise.
-    """
-    if len(model_subdirectory) > 50:
-        return False
-
-    if '..' in model_subdirectory or '/' in model_subdirectory:
-        return False
-
-    if not re.match(r'^[a-zA-Z0-9_-]+$', model_subdirectory):
-        return False
-
-    return True
 
 def validate_filename(filename: str)-> bool:
     """

nodes.py

@@ -511,10 +511,11 @@ class CheckpointLoader:
     FUNCTION = "load_checkpoint"
 
     CATEGORY = "advanced/loaders"
+    DEPRECATED = True
 
     def load_checkpoint(self, config_name, ckpt_name):
         config_path = folder_paths.get_full_path("configs", config_name)
-        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
+        ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
         return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
 
 class CheckpointLoaderSimple:
@@ -535,7 +536,7 @@ class CheckpointLoaderSimple:
     DESCRIPTION = "Loads a diffusion model checkpoint, diffusion models are used to denoise latents."
 
     def load_checkpoint(self, ckpt_name):
-        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
+        ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
         out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
         return out[:3]
 
@@ -577,7 +578,7 @@ class unCLIPCheckpointLoader:
     CATEGORY = "loaders"
 
     def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
-        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
+        ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
         out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
         return out
 
@@ -624,7 +625,7 @@ class LoraLoader:
         if strength_model == 0 and strength_clip == 0:
             return (model, clip)
 
-        lora_path = folder_paths.get_full_path("loras", lora_name)
+        lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
         lora = None
         if self.loaded_lora is not None:
             if self.loaded_lora[0] == lora_path:
@@ -703,11 +704,11 @@ class VAELoader:
         encoder = next(filter(lambda a: a.startswith("{}_encoder.".format(name)), approx_vaes))
         decoder = next(filter(lambda a: a.startswith("{}_decoder.".format(name)), approx_vaes))
 
-        enc = comfy.utils.load_torch_file(folder_paths.get_full_path("vae_approx", encoder))
+        enc = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("vae_approx", encoder))
         for k in enc:
             sd["taesd_encoder.{}".format(k)] = enc[k]
 
-        dec = comfy.utils.load_torch_file(folder_paths.get_full_path("vae_approx", decoder))
+        dec = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("vae_approx", decoder))
         for k in dec:
             sd["taesd_decoder.{}".format(k)] = dec[k]
 
@@ -738,7 +739,7 @@ class VAELoader:
         if vae_name in ["taesd", "taesdxl", "taesd3", "taef1"]:
             sd = self.load_taesd(vae_name)
         else:
-            vae_path = folder_paths.get_full_path("vae", vae_name)
+            vae_path = folder_paths.get_full_path_or_raise("vae", vae_name)
             sd = comfy.utils.load_torch_file(vae_path)
         vae = comfy.sd.VAE(sd=sd)
         return (vae,)
@@ -754,7 +755,7 @@ class ControlNetLoader:
     CATEGORY = "loaders"
 
     def load_controlnet(self, control_net_name):
-        controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
+        controlnet_path = folder_paths.get_full_path_or_raise("controlnet", control_net_name)
         controlnet = comfy.controlnet.load_controlnet(controlnet_path)
         return (controlnet,)
 
@@ -770,7 +771,7 @@ class DiffControlNetLoader:
     CATEGORY = "loaders"
 
     def load_controlnet(self, model, control_net_name):
-        controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
+        controlnet_path = folder_paths.get_full_path_or_raise("controlnet", control_net_name)
         controlnet = comfy.controlnet.load_controlnet(controlnet_path, model)
         return (controlnet,)
 
@@ -786,6 +787,7 @@ class ControlNetApply:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "apply_controlnet"
 
+    DEPRECATED = True
     CATEGORY = "conditioning/controlnet"
 
     def apply_controlnet(self, conditioning, control_net, image, strength):
@@ -815,7 +817,10 @@ class ControlNetApplyAdvanced:
                              "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
                              "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
                              "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
-                             }}
+                             },
+                "optional": {"vae": ("VAE", ),
+                             }
+    }
 
     RETURN_TYPES = ("CONDITIONING","CONDITIONING")
     RETURN_NAMES = ("positive", "negative")
@@ -823,7 +828,7 @@ class ControlNetApplyAdvanced:
 
     CATEGORY = "conditioning/controlnet"
 
-    def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent, vae=None):
+    def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent, vae=None, extra_concat=[]):
         if strength == 0:
             return (positive, negative)
 
@@ -840,7 +845,7 @@ class ControlNetApplyAdvanced:
                 if prev_cnet in cnets:
                     c_net = cnets[prev_cnet]
                 else:
-                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent), vae)
+                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent), vae=vae, extra_concat=extra_concat)
                     c_net.set_previous_controlnet(prev_cnet)
                     cnets[prev_cnet] = c_net
 
@@ -856,7 +861,7 @@ class UNETLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "unet_name": (folder_paths.get_filename_list("diffusion_models"), ),
-                              "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e5m2"],)
+                              "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],)
                              }}
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "load_unet"
@@ -867,10 +872,13 @@ class UNETLoader:
         model_options = {}
         if weight_dtype == "fp8_e4m3fn":
             model_options["dtype"] = torch.float8_e4m3fn
+        elif weight_dtype == "fp8_e4m3fn_fast":
+            model_options["dtype"] = torch.float8_e4m3fn
+            model_options["fp8_optimizations"] = True
         elif weight_dtype == "fp8_e5m2":
             model_options["dtype"] = torch.float8_e5m2
 
-        unet_path = folder_paths.get_full_path("diffusion_models", unet_name)
+        unet_path = folder_paths.get_full_path_or_raise("diffusion_models", unet_name)
         model = comfy.sd.load_diffusion_model(unet_path, model_options=model_options)
         return (model,)
 
@@ -895,7 +903,7 @@ class CLIPLoader:
         else:
             clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
 
-        clip_path = folder_paths.get_full_path("clip", clip_name)
+        clip_path = folder_paths.get_full_path_or_raise("clip", clip_name)
         clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type)
         return (clip,)
 
@@ -912,8 +920,8 @@ class DualCLIPLoader:
     CATEGORY = "advanced/loaders"
 
     def load_clip(self, clip_name1, clip_name2, type):
-        clip_path1 = folder_paths.get_full_path("clip", clip_name1)
+        clip_path1 = folder_paths.get_full_path_or_raise("clip", clip_name1)
-        clip_path2 = folder_paths.get_full_path("clip", clip_name2)
+        clip_path2 = folder_paths.get_full_path_or_raise("clip", clip_name2)
         if type == "sdxl":
             clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
         elif type == "sd3":
@@ -935,7 +943,7 @@ class CLIPVisionLoader:
     CATEGORY = "loaders"
 
     def load_clip(self, clip_name):
-        clip_path = folder_paths.get_full_path("clip_vision", clip_name)
+        clip_path = folder_paths.get_full_path_or_raise("clip_vision", clip_name)
         clip_vision = comfy.clip_vision.load(clip_path)
         return (clip_vision,)
 
@@ -965,7 +973,7 @@ class StyleModelLoader:
     CATEGORY = "loaders"
 
     def load_style_model(self, style_model_name):
-        style_model_path = folder_paths.get_full_path("style_models", style_model_name)
+        style_model_path = folder_paths.get_full_path_or_raise("style_models", style_model_name)
         style_model = comfy.sd.load_style_model(style_model_path)
         return (style_model,)
 
@@ -1030,7 +1038,7 @@ class GLIGENLoader:
     CATEGORY = "loaders"
 
     def load_gligen(self, gligen_name):
-        gligen_path = folder_paths.get_full_path("gligen", gligen_name)
+        gligen_path = folder_paths.get_full_path_or_raise("gligen", gligen_name)
         gligen = comfy.sd.load_gligen(gligen_path)
         return (gligen,)
 
@@ -1916,8 +1924,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ConditioningSetArea": "Conditioning (Set Area)",
     "ConditioningSetAreaPercentage": "Conditioning (Set Area with Percentage)",
     "ConditioningSetMask": "Conditioning (Set Mask)",
-    "ControlNetApply": "Apply ControlNet",
+    "ControlNetApply": "Apply ControlNet (OLD)",
-    "ControlNetApplyAdvanced": "Apply ControlNet (Advanced)",
+    "ControlNetApplyAdvanced": "Apply ControlNet",
     # Latent
     "VAEEncodeForInpaint": "VAE Encode (for Inpainting)",
     "SetLatentNoiseMask": "Set Latent Noise Mask",
@@ -2101,6 +2109,8 @@ def init_builtin_extra_nodes():
         "nodes_controlnet.py",
         "nodes_hunyuan.py",
         "nodes_flux.py",
+        "nodes_lora_extract.py",
+        "nodes_torch_compile.py",
     ]
 
     import_failed = []
@@ -2129,3 +2139,5 @@ def init_extra_nodes(init_custom_nodes=True):
         else:
             logging.warning("Please do a: pip install -r requirements.txt")
         logging.warning("")
+    
+    return import_failed

notebooks/comfyui_colab.ipynb

@@ -79,7 +79,7 @@
         "#!wget -c https://huggingface.co/comfyanonymous/clip_vision_g/resolve/main/clip_vision_g.safetensors -P ./models/clip_vision/\n",
         "\n",
         "# SD1.5\n",
-        "!wget -c https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -P ./models/checkpoints/\n",
+        "!wget -c https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/resolve/main/v1-5-pruned-emaonly-fp16.safetensors -P ./models/checkpoints/\n",
         "\n",
         "# SD2\n",
         "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.safetensors -P ./models/checkpoints/\n",

script_examples/basic_api_example.py

@@ -43,7 +43,7 @@ prompt_text = """
     "4": {
         "class_type": "CheckpointLoaderSimple",
         "inputs": {
-            "ckpt_name": "v1-5-pruned-emaonly.ckpt"
+            "ckpt_name": "v1-5-pruned-emaonly.safetensors"
         }
     },
     "5": {

script_examples/websockets_api_example.py

@@ -38,18 +38,20 @@ def get_images(ws, prompt):
                 if data['node'] is None and data['prompt_id'] == prompt_id:
                     break #Execution is done
         else:
+            # If you want to be able to decode the binary stream for latent previews, here is how you can do it:
+            # bytesIO = BytesIO(out[8:])
+            # preview_image = Image.open(bytesIO) # This is your preview in PIL image format, store it in a global
             continue #previews are binary data
 
     history = get_history(prompt_id)[prompt_id]
-    for o in history['outputs']:
+    for node_id in history['outputs']:
-        for node_id in history['outputs']:
+        node_output = history['outputs'][node_id]
-            node_output = history['outputs'][node_id]
+        images_output = []
-            if 'images' in node_output:
+        if 'images' in node_output:
-                images_output = []
+            for image in node_output['images']:
-                for image in node_output['images']:
+                image_data = get_image(image['filename'], image['subfolder'], image['type'])
-                    image_data = get_image(image['filename'], image['subfolder'], image['type'])
+                images_output.append(image_data)
-                    images_output.append(image_data)
+        output_images[node_id] = images_output
-            output_images[node_id] = images_output
 
     return output_images
 
@@ -85,7 +87,7 @@ prompt_text = """
     "4": {
         "class_type": "CheckpointLoaderSimple",
         "inputs": {
-            "ckpt_name": "v1-5-pruned-emaonly.ckpt"
+            "ckpt_name": "v1-5-pruned-emaonly.safetensors"
         }
     },
     "5": {
@@ -152,7 +154,7 @@ prompt["3"]["inputs"]["seed"] = 5
 ws = websocket.WebSocket()
 ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
 images = get_images(ws, prompt)
-
+ws.close() # for in case this example is used in an environment where it will be repeatedly called, like in a Gradio app. otherwise, you'll randomly receive connection timeouts
 #Commented out code to display the output images:
 
 # for node_id in images:

script_examples/websockets_api_example_ws_images.py

@@ -81,7 +81,7 @@ prompt_text = """
     "4": {
         "class_type": "CheckpointLoaderSimple",
         "inputs": {
-            "ckpt_name": "v1-5-pruned-emaonly.ckpt"
+            "ckpt_name": "v1-5-pruned-emaonly.safetensors"
         }
     },
     "5": {
@@ -147,7 +147,7 @@ prompt["3"]["inputs"]["seed"] = 5
 ws = websocket.WebSocket()
 ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
 images = get_images(ws, prompt)
-
+ws.close() # for in case this example is used in an environment where it will be repeatedly called, like in a Gradio app. otherwise, you'll randomly receive connection timeouts
 #Commented out code to display the output images:
 
 # for node_id in images:

server.py

@@ -12,6 +12,8 @@ import json
 import glob
 import struct
 import ssl
+import socket
+import ipaddress
 from PIL import Image, ImageOps
 from PIL.PngImagePlugin import PngInfo
 from io import BytesIO
@@ -31,7 +33,6 @@ from model_filemanager import download_model, DownloadModelStatus
 from typing import Optional
 from api_server.routes.internal.internal_routes import InternalRoutes
 
-
 class BinaryEventTypes:
     PREVIEW_IMAGE = 1
     UNENCODED_PREVIEW_IMAGE = 2
@@ -39,9 +40,24 @@ class BinaryEventTypes:
 async def send_socket_catch_exception(function, message):
     try:
         await function(message)
-    except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError) as err:
+    except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError, BrokenPipeError, ConnectionError) as err:
         logging.warning("send error: {}".format(err))
 
+def get_comfyui_version():
+    comfyui_version = "unknown"
+    repo_path = os.path.dirname(os.path.realpath(__file__))
+    try:
+        import pygit2
+        repo = pygit2.Repository(repo_path)
+        comfyui_version = repo.describe(describe_strategy=pygit2.GIT_DESCRIBE_TAGS)
+    except Exception:
+        try:
+            import subprocess
+            comfyui_version = subprocess.check_output(["git", "describe", "--tags"], cwd=repo_path).decode('utf-8')
+        except Exception as e:
+            logging.warning(f"Failed to get ComfyUI version: {e}")
+    return comfyui_version.strip()
+
 @web.middleware
 async def cache_control(request: web.Request, handler):
     response: web.Response = await handler(request)
@@ -66,6 +82,68 @@ def create_cors_middleware(allowed_origin: str):
 
     return cors_middleware
 
+def is_loopback(host):
+    if host is None:
+        return False
+    try:
+        if ipaddress.ip_address(host).is_loopback:
+            return True
+        else:
+            return False
+    except:
+        pass
+
+    loopback = False
+    for family in (socket.AF_INET, socket.AF_INET6):
+        try:
+            r = socket.getaddrinfo(host, None, family, socket.SOCK_STREAM)
+            for family, _, _, _, sockaddr in r:
+                if not ipaddress.ip_address(sockaddr[0]).is_loopback:
+                    return loopback
+                else:
+                    loopback = True
+        except socket.gaierror:
+            pass
+
+    return loopback
+
+
+def create_origin_only_middleware():
+    @web.middleware
+    async def origin_only_middleware(request: web.Request, handler):
+        #this code is used to prevent the case where a random website can queue comfy workflows by making a POST to 127.0.0.1 which browsers don't prevent for some dumb reason.
+        #in that case the Host and Origin hostnames won't match
+        #I know the proper fix would be to add a cookie but this should take care of the problem in the meantime
+        if 'Host' in request.headers and 'Origin' in request.headers:
+            host = request.headers['Host']
+            origin = request.headers['Origin']
+            host_domain = host.lower()
+            parsed = urllib.parse.urlparse(origin)
+            origin_domain = parsed.netloc.lower()
+            host_domain_parsed = urllib.parse.urlsplit('//' + host_domain)
+
+            #limit the check to when the host domain is localhost, this makes it slightly less safe but should still prevent the exploit
+            loopback = is_loopback(host_domain_parsed.hostname)
+
+            if parsed.port is None: #if origin doesn't have a port strip it from the host to handle weird browsers, same for host
+                host_domain = host_domain_parsed.hostname
+            if host_domain_parsed.port is None:
+                origin_domain = parsed.hostname
+
+            if loopback and host_domain is not None and origin_domain is not None and len(host_domain) > 0 and len(origin_domain) > 0:
+                if host_domain != origin_domain:
+                    logging.warning("WARNING: request with non matching host and origin {} != {}, returning 403".format(host_domain, origin_domain))
+                    return web.Response(status=403)
+
+        if request.method == "OPTIONS":
+            response = web.Response()
+        else:
+            response = await handler(request)
+
+        return response
+
+    return origin_only_middleware
+
 class PromptServer():
     def __init__(self, loop):
         PromptServer.instance = self
@@ -85,6 +163,8 @@ class PromptServer():
         middlewares = [cache_control]
         if args.enable_cors_header:
             middlewares.append(create_cors_middleware(args.enable_cors_header))
+        else:
+            middlewares.append(create_origin_only_middleware())
 
         max_upload_size = round(args.max_upload_size * 1024 * 1024)
         self.app = web.Application(client_max_size=max_upload_size, middlewares=middlewares)
@@ -142,6 +222,12 @@ class PromptServer():
             embeddings = folder_paths.get_filename_list("embeddings")
             return web.json_response(list(map(lambda a: os.path.splitext(a)[0], embeddings)))
         
+        @routes.get("/models")
+        def list_model_types(request):
+            model_types = list(folder_paths.folder_names_and_paths.keys())
+
+            return web.json_response(model_types)
+
         @routes.get("/models/{folder}")
         async def get_models(request):
             folder = request.match_info.get("folder", None)
@@ -401,16 +487,25 @@ class PromptServer():
             return web.json_response(dt["__metadata__"])
 
         @routes.get("/system_stats")
-        async def get_queue(request):
+        async def system_stats(request):
             device = comfy.model_management.get_torch_device()
             device_name = comfy.model_management.get_torch_device_name(device)
+            cpu_device = comfy.model_management.torch.device("cpu")
+            ram_total = comfy.model_management.get_total_memory(cpu_device)
+            ram_free = comfy.model_management.get_free_memory(cpu_device)
             vram_total, torch_vram_total = comfy.model_management.get_total_memory(device, torch_total_too=True)
             vram_free, torch_vram_free = comfy.model_management.get_free_memory(device, torch_free_too=True)
+
             system_stats = {
                 "system": {
                     "os": os.name,
+                    "ram_total": ram_total,
+                    "ram_free": ram_free,
+                    "comfyui_version": get_comfyui_version(),
                     "python_version": sys.version,
-                    "embedded_python": os.path.split(os.path.split(sys.executable)[0])[1] == "python_embeded"
+                    "pytorch_version": comfy.model_management.torch_version,
+                    "embedded_python": os.path.split(os.path.split(sys.executable)[0])[1] == "python_embeded",
+                    "argv": sys.argv
                 },
                 "devices": [
                     {
@@ -462,14 +557,15 @@ class PromptServer():
 
         @routes.get("/object_info")
         async def get_object_info(request):
-            out = {}
+            with folder_paths.cache_helper:
-            for x in nodes.NODE_CLASS_MAPPINGS:
+                out = {}
-                try:
+                for x in nodes.NODE_CLASS_MAPPINGS:
-                    out[x] = node_info(x)
+                    try:
-                except Exception as e:
+                        out[x] = node_info(x)
-                    logging.error(f"[ERROR] An error occurred while retrieving information for the '{x}' node.")
+                    except Exception as e:
-                    logging.error(traceback.format_exc())
+                        logging.error(f"[ERROR] An error occurred while retrieving information for the '{x}' node.")
-            return web.json_response(out)
+                        logging.error(traceback.format_exc())
+                return web.json_response(out)
 
         @routes.get("/object_info/{node_class}")
         async def get_object_info_node(request):
@@ -583,18 +679,22 @@ class PromptServer():
         
         # Internal route. Should not be depended upon and is subject to change at any time.
         # TODO(robinhuang): Move to internal route table class once we refactor PromptServer to pass around Websocket.
+        # NOTE: This was an experiment and WILL BE REMOVED
         @routes.post("/internal/models/download")
         async def download_handler(request):
             async def report_progress(filename: str, status: DownloadModelStatus):
-                await self.send_json("download_progress", status.to_dict())
+                payload = status.to_dict()
+                payload['download_path'] = filename
+                await self.send_json("download_progress", payload)
 
             data = await request.json()
             url = data.get('url')
             model_directory = data.get('model_directory')
+            folder_path = data.get('folder_path')
             model_filename = data.get('model_filename')
             progress_interval = data.get('progress_interval', 1.0) # In seconds, how often to report download progress.
 
-            if not url or not model_directory or not model_filename:
+            if not url or not model_directory or not model_filename or not folder_path:
                 return web.json_response({"status": "error", "message": "Missing URL or folder path or filename"}, status=400)
 
             session = self.client_session
@@ -602,7 +702,7 @@ class PromptServer():
                 logging.error("Client session is not initialized")
                 return web.Response(status=500)
             
-            task = asyncio.create_task(download_model(lambda url: session.get(url), model_filename, url, model_directory, report_progress, progress_interval))
+            task = asyncio.create_task(download_model(lambda url: session.get(url), model_filename, url, model_directory, folder_path, report_progress, progress_interval))
             await task
 
             return web.json_response(task.result().to_dict())
@@ -719,6 +819,9 @@ class PromptServer():
             await self.send(*msg)
 
     async def start(self, address, port, verbose=True, call_on_start=None):
+        await self.start_multi_address([(address, port)], call_on_start=call_on_start)
+
+    async def start_multi_address(self, addresses, call_on_start=None):
         runner = web.AppRunner(self.app, access_log=None)
         await runner.setup()
         ssl_ctx = None
@@ -729,17 +832,26 @@ class PromptServer():
                                 keyfile=args.tls_keyfile)
                 scheme = "https"
 
-        site = web.TCPSite(runner, address, port, ssl_context=ssl_ctx)
+        logging.info("Starting server\n")
-        await site.start()
+        for addr in addresses:
+            address = addr[0]
+            port = addr[1]
+            site = web.TCPSite(runner, address, port, ssl_context=ssl_ctx)
+            await site.start()
 
-        self.address = address
+            if not hasattr(self, 'address'):
-        self.port = port
+                self.address = address #TODO: remove this
+                self.port = port
+
+            if ':' in address:
+                address_print = "[{}]".format(address)
+            else:
+                address_print = address
+
+            logging.info("To see the GUI go to: {}://{}:{}".format(scheme, address_print, port))
 
-        if verbose:
-            logging.info("Starting server\n")
-            logging.info("To see the GUI go to: {}://{}:{}".format(scheme, address, port))
         if call_on_start is not None:
-            call_on_start(scheme, address, port)
+            call_on_start(scheme, self.address, self.port)
 
     def add_on_prompt_handler(self, handler):
         self.on_prompt_handlers.append(handler)

tests-unit/README.md

@@ -2,7 +2,7 @@
 
 ## Install test dependencies
 
-`pip install -r tests-units/requirements.txt`
+`pip install -r tests-unit/requirements.txt`
 
 ## Run tests
-`pytest tests-units/`
+`pytest tests-unit/`

tests-unit/app_test/frontend_manager_test.py

@@ -1,6 +1,7 @@
 import argparse
 import pytest
 from requests.exceptions import HTTPError
+from unittest.mock import patch
 
 from app.frontend_management import (
     FrontendManager,
@@ -83,6 +84,35 @@ def test_init_frontend_invalid_provider():
     with pytest.raises(HTTPError):
         FrontendManager.init_frontend_unsafe(version_string)
 
+@pytest.fixture
+def mock_os_functions():
+    with patch('app.frontend_management.os.makedirs') as mock_makedirs, \
+         patch('app.frontend_management.os.listdir') as mock_listdir, \
+         patch('app.frontend_management.os.rmdir') as mock_rmdir:
+        mock_listdir.return_value = []  # Simulate empty directory
+        yield mock_makedirs, mock_listdir, mock_rmdir
+
+@pytest.fixture
+def mock_download():
+    with patch('app.frontend_management.download_release_asset_zip') as mock:
+        mock.side_effect = Exception("Download failed")  # Simulate download failure
+        yield mock
+
+def test_finally_block(mock_os_functions, mock_download, mock_provider):
+    # Arrange
+    mock_makedirs, mock_listdir, mock_rmdir = mock_os_functions
+    version_string = 'test-owner/test-repo@1.0.0'
+
+    # Act & Assert
+    with pytest.raises(Exception):
+        FrontendManager.init_frontend_unsafe(version_string, mock_provider)
+
+    # Assert
+    mock_makedirs.assert_called_once()
+    mock_download.assert_called_once()
+    mock_listdir.assert_called_once()
+    mock_rmdir.assert_called_once()
+
 
 def test_parse_version_string():
     version_string = "owner/repo@1.0.0"

tests-unit/comfy_test/folder_path_test.py

@@ -0,0 +1,66 @@
+### 🗻 This file is created through the spirit of Mount Fuji at its peak
+# TODO(yoland): clean up this after I get back down
+import pytest
+import os
+import tempfile
+from unittest.mock import patch
+
+import folder_paths
+
+@pytest.fixture
+def temp_dir():
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        yield tmpdirname
+
+
+def test_get_directory_by_type():
+    test_dir = "/test/dir"
+    folder_paths.set_output_directory(test_dir)
+    assert folder_paths.get_directory_by_type("output") == test_dir
+    assert folder_paths.get_directory_by_type("invalid") is None
+
+def test_annotated_filepath():
+    assert folder_paths.annotated_filepath("test.txt") == ("test.txt", None)
+    assert folder_paths.annotated_filepath("test.txt [output]") == ("test.txt", folder_paths.get_output_directory())
+    assert folder_paths.annotated_filepath("test.txt [input]") == ("test.txt", folder_paths.get_input_directory())
+    assert folder_paths.annotated_filepath("test.txt [temp]") == ("test.txt", folder_paths.get_temp_directory())
+
+def test_get_annotated_filepath():
+    default_dir = "/default/dir"
+    assert folder_paths.get_annotated_filepath("test.txt", default_dir) == os.path.join(default_dir, "test.txt")
+    assert folder_paths.get_annotated_filepath("test.txt [output]") == os.path.join(folder_paths.get_output_directory(), "test.txt")
+
+def test_add_model_folder_path():
+    folder_paths.add_model_folder_path("test_folder", "/test/path")
+    assert "/test/path" in folder_paths.get_folder_paths("test_folder")
+
+def test_recursive_search(temp_dir):
+    os.makedirs(os.path.join(temp_dir, "subdir"))
+    open(os.path.join(temp_dir, "file1.txt"), "w").close()
+    open(os.path.join(temp_dir, "subdir", "file2.txt"), "w").close()
+
+    files, dirs = folder_paths.recursive_search(temp_dir)
+    assert set(files) == {"file1.txt", os.path.join("subdir", "file2.txt")}
+    assert len(dirs) == 2  # temp_dir and subdir
+
+def test_filter_files_extensions():
+    files = ["file1.txt", "file2.jpg", "file3.png", "file4.txt"]
+    assert folder_paths.filter_files_extensions(files, [".txt"]) == ["file1.txt", "file4.txt"]
+    assert folder_paths.filter_files_extensions(files, [".jpg", ".png"]) == ["file2.jpg", "file3.png"]
+    assert folder_paths.filter_files_extensions(files, []) == files
+
+@patch("folder_paths.recursive_search")
+@patch("folder_paths.folder_names_and_paths")
+def test_get_filename_list(mock_folder_names_and_paths, mock_recursive_search):
+    mock_folder_names_and_paths.__getitem__.return_value = (["/test/path"], {".txt"})
+    mock_recursive_search.return_value = (["file1.txt", "file2.jpg"], {})
+    assert folder_paths.get_filename_list("test_folder") == ["file1.txt"]
+
+def test_get_save_image_path(temp_dir):
+    with patch("folder_paths.output_directory", temp_dir):
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path("test", temp_dir, 100, 100)
+        assert os.path.samefile(full_output_folder, temp_dir)
+        assert filename == "test"
+        assert counter == 1
+        assert subfolder == ""
+        assert filename_prefix == "test"

tests-unit/folder_paths_test/filter_by_content_types_test.py

@@ -0,0 +1,52 @@
+import pytest
+import os
+import tempfile
+from folder_paths import filter_files_content_types
+
+@pytest.fixture(scope="module")
+def file_extensions():
+    return {
+        'image': ['gif', 'heif', 'ico', 'jpeg', 'jpg', 'png', 'pnm', 'ppm', 'svg', 'tiff', 'webp', 'xbm', 'xpm'], 
+        'audio': ['aif', 'aifc', 'aiff', 'au', 'flac', 'm4a', 'mp2', 'mp3', 'ogg', 'snd', 'wav'], 
+        'video': ['avi', 'm2v', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ogv', 'qt', 'webm', 'wmv']
+    }
+
+
+@pytest.fixture(scope="module")
+def mock_dir(file_extensions):
+    with tempfile.TemporaryDirectory() as directory:
+        for content_type, extensions in file_extensions.items():
+            for extension in extensions:
+                with open(f"{directory}/sample_{content_type}.{extension}", "w") as f:
+                    f.write(f"Sample {content_type} file in {extension} format")
+        yield directory
+
+
+def test_categorizes_all_correctly(mock_dir, file_extensions):
+    files = os.listdir(mock_dir)
+    for content_type, extensions in file_extensions.items():
+        filtered_files = filter_files_content_types(files, [content_type])
+        for extension in extensions:
+            assert f"sample_{content_type}.{extension}" in filtered_files
+
+
+def test_categorizes_all_uniquely(mock_dir, file_extensions):
+    files = os.listdir(mock_dir)
+    for content_type, extensions in file_extensions.items():
+        filtered_files = filter_files_content_types(files, [content_type])
+        assert len(filtered_files) == len(extensions)
+
+
+def test_handles_bad_extensions():
+    files = ["file1.txt", "file2.py", "file3.example", "file4.pdf", "file5.ini", "file6.doc", "file7.md"]
+    assert filter_files_content_types(files, ["image", "audio", "video"]) == []
+
+
+def test_handles_no_extension():
+    files = ["file1", "file2", "file3", "file4", "file5", "file6", "file7"]
+    assert filter_files_content_types(files, ["image", "audio", "video"]) == []
+
+
+def test_handles_no_files():
+    files = []
+    assert filter_files_content_types(files, ["image", "audio", "video"]) == []

tests-unit/prompt_server_test/download_models_test.py

@@ -1,10 +1,17 @@
 import pytest
+import tempfile
 import aiohttp
 from aiohttp import ClientResponse
 import itertools
 import os
 from unittest.mock import AsyncMock, patch, MagicMock
-from model_filemanager import download_model, validate_model_subdirectory, track_download_progress, create_model_path, check_file_exists, DownloadStatusType, DownloadModelStatus, validate_filename
+from model_filemanager import download_model, track_download_progress, create_model_path, check_file_exists, DownloadStatusType, DownloadModelStatus, validate_filename
+import folder_paths
+
+@pytest.fixture
+def temp_dir():
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        yield tmpdirname
 
 class AsyncIteratorMock:
     """
@@ -42,7 +49,7 @@ class ContentMock:
         return AsyncIteratorMock(self.chunks)
 
 @pytest.mark.asyncio
-async def test_download_model_success():
+async def test_download_model_success(temp_dir):
     mock_response = AsyncMock(spec=aiohttp.ClientResponse)
     mock_response.status = 200
     mock_response.headers = {'Content-Length': '1000'}
@@ -53,15 +60,13 @@ async def test_download_model_success():
     mock_make_request = AsyncMock(return_value=mock_response)
     mock_progress_callback = AsyncMock()
 
-    # Mock file operations
-    mock_open = MagicMock()
-    mock_file = MagicMock()
-    mock_open.return_value.__enter__.return_value = mock_file
     time_values = itertools.count(0, 0.1)
 
-    with patch('model_filemanager.create_model_path', return_value=('models/checkpoints/model.sft', 'checkpoints/model.sft')), \
+    fake_paths = {'checkpoints': ([temp_dir], folder_paths.supported_pt_extensions)}
+
+    with patch('model_filemanager.create_model_path', return_value=('models/checkpoints/model.sft', 'model.sft')), \
          patch('model_filemanager.check_file_exists', return_value=None), \
-         patch('builtins.open', mock_open), \
+         patch('folder_paths.folder_names_and_paths', fake_paths), \
          patch('time.time', side_effect=time_values):  # Simulate time passing
 
         result = await download_model(
@@ -69,6 +74,7 @@ async def test_download_model_success():
             'model.sft',
             'http://example.com/model.sft',
             'checkpoints',
+            temp_dir,
             mock_progress_callback
         )
 
@@ -83,44 +89,48 @@ async def test_download_model_success():
     
     # Check initial call
     mock_progress_callback.assert_any_call(
-        'checkpoints/model.sft',
+        'model.sft',
         DownloadModelStatus(DownloadStatusType.PENDING, 0, "Starting download of model.sft", False)
     )
 
     # Check final call
     mock_progress_callback.assert_any_call(
-        'checkpoints/model.sft',
+        'model.sft',
         DownloadModelStatus(DownloadStatusType.COMPLETED, 100, "Successfully downloaded model.sft", False)
     )
 
-    # Verify file writing
+    mock_file_path = os.path.join(temp_dir, 'model.sft')
-    mock_file.write.assert_any_call(b'a' * 500)
+    assert os.path.exists(mock_file_path)
-    mock_file.write.assert_any_call(b'b' * 300)
+    with open(mock_file_path, 'rb') as mock_file:
-    mock_file.write.assert_any_call(b'c' * 200)
+        assert mock_file.read() == b''.join(chunks)
+    os.remove(mock_file_path)
 
     # Verify request was made
     mock_make_request.assert_called_once_with('http://example.com/model.sft')
 
 @pytest.mark.asyncio
-async def test_download_model_url_request_failure():
+async def test_download_model_url_request_failure(temp_dir):
     # Mock dependencies
     mock_response = AsyncMock(spec=ClientResponse)
     mock_response.status = 404  # Simulate a "Not Found" error
     mock_get = AsyncMock(return_value=mock_response)
     mock_progress_callback = AsyncMock()
     
+    fake_paths = {'checkpoints': ([temp_dir], folder_paths.supported_pt_extensions)}
+
     # Mock the create_model_path function
-    with patch('model_filemanager.create_model_path', return_value=('/mock/path/model.safetensors', 'mock/path/model.safetensors')):
+    with patch('model_filemanager.create_model_path', return_value='/mock/path/model.safetensors'), \
-        # Mock the check_file_exists function to return None (file doesn't exist)
+         patch('model_filemanager.check_file_exists', return_value=None), \
-        with patch('model_filemanager.check_file_exists', return_value=None):
+         patch('folder_paths.folder_names_and_paths', fake_paths):
-            # Call the function
+        # Call the function
-            result = await download_model(
+        result = await download_model(
-                mock_get,
+            mock_get,
-                'model.safetensors',
+            'model.safetensors',
-                'http://example.com/model.safetensors',
+            'http://example.com/model.safetensors',
-                'mock_directory',
+            'checkpoints',
-                mock_progress_callback
+            temp_dir,
-            )
+            mock_progress_callback
+        )
 
     # Assert the expected behavior
     assert isinstance(result, DownloadModelStatus)
@@ -130,7 +140,7 @@ async def test_download_model_url_request_failure():
 
     # Check that progress_callback was called with the correct arguments
     mock_progress_callback.assert_any_call(
-        'mock_directory/model.safetensors',
+        'model.safetensors',
         DownloadModelStatus(
             status=DownloadStatusType.PENDING,
             progress_percentage=0,
@@ -139,7 +149,7 @@ async def test_download_model_url_request_failure():
         )
     )
     mock_progress_callback.assert_called_with(
-        'mock_directory/model.safetensors',
+        'model.safetensors',
         DownloadModelStatus(
             status=DownloadStatusType.ERROR,
             progress_percentage=0,
@@ -153,40 +163,59 @@ async def test_download_model_url_request_failure():
 
 @pytest.mark.asyncio
 async def test_download_model_invalid_model_subdirectory():
-    
     mock_make_request = AsyncMock()
     mock_progress_callback = AsyncMock()
 
-    
     result = await download_model(
         mock_make_request,
         'model.sft',
         'http://example.com/model.sft',
         '../bad_path',
+        '../bad_path',
         mock_progress_callback
     )
 
     # Assert the result
     assert isinstance(result, DownloadModelStatus)
-    assert result.message == 'Invalid model subdirectory'
+    assert result.message.startswith('Invalid or unrecognized model directory')
     assert result.status == 'error'
     assert result.already_existed is False
 
+@pytest.mark.asyncio
+async def test_download_model_invalid_folder_path():
+    mock_make_request = AsyncMock()
+    mock_progress_callback = AsyncMock()
+
+    result = await download_model(
+        mock_make_request,
+        'model.sft',
+        'http://example.com/model.sft',
+        'checkpoints',
+        'invalid_path',
+        mock_progress_callback
+    )
+
+    # Assert the result
+    assert isinstance(result, DownloadModelStatus)
+    assert result.message.startswith("Invalid folder path")
+    assert result.status == 'error'
+    assert result.already_existed is False
 
-# For create_model_path function
 def test_create_model_path(tmp_path, monkeypatch):
-    mock_models_dir = tmp_path / "models"
+    model_name = "model.safetensors"
-    monkeypatch.setattr('folder_paths.models_dir', str(mock_models_dir))
+    folder_path = os.path.join(tmp_path, "mock_dir")
 
-    model_name = "test_model.sft"
+    file_path = create_model_path(model_name, folder_path)
-    model_directory = "test_dir"
 
-    file_path, relative_path = create_model_path(model_name, model_directory, mock_models_dir)
+    assert file_path == os.path.join(folder_path, "model.safetensors")
-    
-    assert file_path == str(mock_models_dir / model_directory / model_name)
-    assert relative_path == f"{model_directory}/{model_name}"
     assert os.path.exists(os.path.dirname(file_path))
 
+    with pytest.raises(Exception, match="Invalid model directory"):
+        create_model_path("../path_traversal.safetensors", folder_path)
+
+    with pytest.raises(Exception, match="Invalid model directory"):
+        create_model_path("/etc/some_root_path", folder_path)
+
 
 @pytest.mark.asyncio
 async def test_check_file_exists_when_file_exists(tmp_path):
@@ -195,7 +224,7 @@ async def test_check_file_exists_when_file_exists(tmp_path):
 
     mock_callback = AsyncMock()
 
-    result = await check_file_exists(str(file_path), "existing_model.sft", mock_callback, "test/existing_model.sft")
+    result = await check_file_exists(str(file_path), "existing_model.sft", mock_callback)
 
     assert result is not None
     assert result.status == "completed"
@@ -203,7 +232,7 @@ async def test_check_file_exists_when_file_exists(tmp_path):
     assert result.already_existed is True
 
     mock_callback.assert_called_once_with(
-        "test/existing_model.sft",
+        "existing_model.sft",
         DownloadModelStatus(DownloadStatusType.COMPLETED, 100, "existing_model.sft already exists", already_existed=True)
     )
 
@@ -213,38 +242,46 @@ async def test_check_file_exists_when_file_does_not_exist(tmp_path):
 
     mock_callback = AsyncMock()
 
-    result = await check_file_exists(str(file_path), "non_existing_model.sft", mock_callback, "test/non_existing_model.sft")
+    result = await check_file_exists(str(file_path), "non_existing_model.sft", mock_callback)
 
     assert result is None
     mock_callback.assert_not_called()
 
 @pytest.mark.asyncio
-async def test_track_download_progress_no_content_length():
+async def test_track_download_progress_no_content_length(temp_dir):
     mock_response = AsyncMock(spec=aiohttp.ClientResponse)
     mock_response.headers = {}  # No Content-Length header
-    mock_response.content.iter_chunked.return_value = AsyncIteratorMock([b'a' * 500, b'b' * 500])
+    chunks = [b'a' * 500, b'b' * 500]
+    mock_response.content.iter_chunked.return_value = AsyncIteratorMock(chunks)
 
     mock_callback = AsyncMock()
-    mock_open = MagicMock(return_value=MagicMock())
 
-    with patch('builtins.open', mock_open):
+    full_path = os.path.join(temp_dir, 'model.sft')
-        result = await track_download_progress(
+
-            mock_response, '/mock/path/model.sft', 'model.sft',
+    result = await track_download_progress(
-            mock_callback, 'models/model.sft', interval=0.1
+        mock_response, full_path, 'model.sft',
-        )
+        mock_callback, interval=0.1
+    )
 
     assert result.status == "completed"
+
+    assert os.path.exists(full_path)
+    with open(full_path, 'rb') as f:
+        assert f.read() == b''.join(chunks)
+    os.remove(full_path)
+
     # Check that progress was reported even without knowing the total size
     mock_callback.assert_any_call(
-        'models/model.sft',
+        'model.sft',
         DownloadModelStatus(DownloadStatusType.IN_PROGRESS, 0, "Downloading model.sft", already_existed=False)
     )
 
 @pytest.mark.asyncio
-async def test_track_download_progress_interval():
+async def test_track_download_progress_interval(temp_dir):
     mock_response = AsyncMock(spec=aiohttp.ClientResponse)
     mock_response.headers = {'Content-Length': '1000'}
-    mock_response.content.iter_chunked.return_value = AsyncIteratorMock([b'a' * 100] * 10)
+    chunks = [b'a' * 100] * 10
+    mock_response.content.iter_chunked.return_value = AsyncIteratorMock(chunks)
 
     mock_callback = AsyncMock()
     mock_open = MagicMock(return_value=MagicMock())
@@ -253,18 +290,18 @@ async def test_track_download_progress_interval():
     mock_time = MagicMock()
     mock_time.side_effect = [i * 0.5 for i in range(30)]  # This should be enough for 10 chunks
 
-    with patch('builtins.open', mock_open), \
+    full_path = os.path.join(temp_dir, 'model.sft')
-         patch('time.time', mock_time):
+
+    with patch('time.time', mock_time):
         await track_download_progress(
-            mock_response, '/mock/path/model.sft', 'model.sft',
+            mock_response, full_path, 'model.sft',
-            mock_callback, 'models/model.sft', interval=1.0
+            mock_callback, interval=1.0
         )
     
-    # Print out the actual call count and the arguments of each call for debugging
+    assert os.path.exists(full_path)
-    print(f"mock_callback was called {mock_callback.call_count} times")
+    with open(full_path, 'rb') as f:
-    for i, call in enumerate(mock_callback.call_args_list):
+        assert f.read() == b''.join(chunks)
-        args, kwargs = call
+    os.remove(full_path)
-        print(f"Call {i + 1}: {args[1].status}, Progress: {args[1].progress_percentage:.2f}%")
 
     # Assert that progress was updated at least 3 times (start, at least one interval, and end)
     assert mock_callback.call_count >= 3, f"Expected at least 3 calls, but got {mock_callback.call_count}"
@@ -279,27 +316,6 @@ async def test_track_download_progress_interval():
     assert last_call[0][1].status == "completed"
     assert last_call[0][1].progress_percentage == 100
 
-def test_valid_subdirectory():
-    assert validate_model_subdirectory("valid-model123") is True
-
-def test_subdirectory_too_long():
-    assert validate_model_subdirectory("a" * 51) is False
-
-def test_subdirectory_with_double_dots():
-    assert validate_model_subdirectory("model/../unsafe") is False
-
-def test_subdirectory_with_slash():
-    assert validate_model_subdirectory("model/unsafe") is False
-
-def test_subdirectory_with_special_characters():
-    assert validate_model_subdirectory("model@unsafe") is False
-
-def test_subdirectory_with_underscore_and_dash():
-    assert validate_model_subdirectory("valid_model-name") is True
-
-def test_empty_subdirectory():
-    assert validate_model_subdirectory("") is False
-
 @pytest.mark.parametrize("filename, expected", [
     ("valid_model.safetensors", True),
     ("valid_model.sft", True),

tests-unit/prompt_server_test/user_manager_test.py

@@ -0,0 +1,120 @@
+import pytest
+import os
+from aiohttp import web
+from app.user_manager import UserManager
+from unittest.mock import patch
+
+pytestmark = (
+    pytest.mark.asyncio
+)  # This applies the asyncio mark to all test functions in the module
+
+
+@pytest.fixture
+def user_manager(tmp_path):
+    um = UserManager()
+    um.get_request_user_filepath = lambda req, file, **kwargs: os.path.join(
+        tmp_path, file
+    )
+    return um
+
+
+@pytest.fixture
+def app(user_manager):
+    app = web.Application()
+    routes = web.RouteTableDef()
+    user_manager.add_routes(routes)
+    app.add_routes(routes)
+    return app
+
+
+async def test_listuserdata_empty_directory(aiohttp_client, app, tmp_path):
+    client = await aiohttp_client(app)
+    resp = await client.get("/userdata?dir=test_dir")
+    assert resp.status == 404
+
+
+async def test_listuserdata_with_files(aiohttp_client, app, tmp_path):
+    os.makedirs(tmp_path / "test_dir")
+    with open(tmp_path / "test_dir" / "file1.txt", "w") as f:
+        f.write("test content")
+
+    client = await aiohttp_client(app)
+    resp = await client.get("/userdata?dir=test_dir")
+    assert resp.status == 200
+    assert await resp.json() == ["file1.txt"]
+
+
+async def test_listuserdata_recursive(aiohttp_client, app, tmp_path):
+    os.makedirs(tmp_path / "test_dir" / "subdir")
+    with open(tmp_path / "test_dir" / "file1.txt", "w") as f:
+        f.write("test content")
+    with open(tmp_path / "test_dir" / "subdir" / "file2.txt", "w") as f:
+        f.write("test content")
+
+    client = await aiohttp_client(app)
+    resp = await client.get("/userdata?dir=test_dir&recurse=true")
+    assert resp.status == 200
+    assert set(await resp.json()) == {"file1.txt", "subdir/file2.txt"}
+
+
+async def test_listuserdata_full_info(aiohttp_client, app, tmp_path):
+    os.makedirs(tmp_path / "test_dir")
+    with open(tmp_path / "test_dir" / "file1.txt", "w") as f:
+        f.write("test content")
+
+    client = await aiohttp_client(app)
+    resp = await client.get("/userdata?dir=test_dir&full_info=true")
+    assert resp.status == 200
+    result = await resp.json()
+    assert len(result) == 1
+    assert result[0]["path"] == "file1.txt"
+    assert "size" in result[0]
+    assert "modified" in result[0]
+
+
+async def test_listuserdata_split_path(aiohttp_client, app, tmp_path):
+    os.makedirs(tmp_path / "test_dir" / "subdir")
+    with open(tmp_path / "test_dir" / "subdir" / "file1.txt", "w") as f:
+        f.write("test content")
+
+    client = await aiohttp_client(app)
+    resp = await client.get("/userdata?dir=test_dir&recurse=true&split=true")
+    assert resp.status == 200
+    assert await resp.json() == [
+        ["subdir/file1.txt", "subdir", "file1.txt"]
+    ]
+
+
+async def test_listuserdata_invalid_directory(aiohttp_client, app):
+    client = await aiohttp_client(app)
+    resp = await client.get("/userdata?dir=")
+    assert resp.status == 400
+
+
+async def test_listuserdata_normalized_separator(aiohttp_client, app, tmp_path):
+    os_sep = "\\"
+    with patch("os.sep", os_sep):
+        with patch("os.path.sep", os_sep):
+            os.makedirs(tmp_path / "test_dir" / "subdir")
+            with open(tmp_path / "test_dir" / "subdir" / "file1.txt", "w") as f:
+                f.write("test content")
+
+            client = await aiohttp_client(app)
+            resp = await client.get("/userdata?dir=test_dir&recurse=true")
+            assert resp.status == 200
+            result = await resp.json()
+            assert len(result) == 1
+            assert "/" in result[0]  # Ensure forward slash is used
+            assert "\\" not in result[0]  # Ensure backslash is not present
+            assert result[0] == "subdir/file1.txt"
+
+            # Test with full_info
+            resp = await client.get(
+                "/userdata?dir=test_dir&recurse=true&full_info=true"
+            )
+            assert resp.status == 200
+            result = await resp.json()
+            assert len(result) == 1
+            assert "/" in result[0]["path"]  # Ensure forward slash is used
+            assert "\\" not in result[0]["path"]  # Ensure backslash is not present
+            assert result[0]["path"] == "subdir/file1.txt"

tests-unit/utils/extra_config_test.py

@@ -0,0 +1,126 @@
+import pytest
+import yaml
+import os
+from unittest.mock import Mock, patch, mock_open
+
+from utils.extra_config import load_extra_path_config
+import folder_paths
+
+@pytest.fixture
+def mock_yaml_content():
+    return {
+        'test_config': {
+            'base_path': '~/App/',
+            'checkpoints': 'subfolder1',
+        }
+    }
+
+@pytest.fixture
+def mock_expanded_home():
+    return '/home/user'
+
+@pytest.fixture
+def yaml_config_with_appdata():
+    return """
+    test_config:
+      base_path: '%APPDATA%/ComfyUI'
+      checkpoints: 'models/checkpoints'
+    """
+
+@pytest.fixture
+def mock_yaml_content_appdata(yaml_config_with_appdata):
+    return yaml.safe_load(yaml_config_with_appdata)
+
+@pytest.fixture
+def mock_expandvars_appdata():
+    mock = Mock()
+    mock.side_effect = lambda path: path.replace('%APPDATA%', 'C:/Users/TestUser/AppData/Roaming')
+    return mock
+
+@pytest.fixture
+def mock_add_model_folder_path():
+    return Mock()
+
+@pytest.fixture
+def mock_expanduser(mock_expanded_home):
+    def _expanduser(path):
+        if path.startswith('~/'):
+            return os.path.join(mock_expanded_home, path[2:])
+        return path
+    return _expanduser
+
+@pytest.fixture
+def mock_yaml_safe_load(mock_yaml_content):
+    return Mock(return_value=mock_yaml_content)
+
+@patch('builtins.open', new_callable=mock_open, read_data="dummy file content")
+def test_load_extra_model_paths_expands_userpath(
+    mock_file,
+    monkeypatch,
+    mock_add_model_folder_path, 
+    mock_expanduser, 
+    mock_yaml_safe_load,
+    mock_expanded_home
+):
+    # Attach mocks used by load_extra_path_config
+    monkeypatch.setattr(folder_paths, 'add_model_folder_path', mock_add_model_folder_path)
+    monkeypatch.setattr(os.path, 'expanduser', mock_expanduser)
+    monkeypatch.setattr(yaml, 'safe_load', mock_yaml_safe_load)
+
+    dummy_yaml_file_name = 'dummy_path.yaml'
+    load_extra_path_config(dummy_yaml_file_name)
+
+    expected_calls = [
+        ('checkpoints', os.path.join(mock_expanded_home, 'App', 'subfolder1'), False),
+    ]
+
+    assert mock_add_model_folder_path.call_count == len(expected_calls)
+    
+    # Check if add_model_folder_path was called with the correct arguments
+    for actual_call, expected_call in zip(mock_add_model_folder_path.call_args_list, expected_calls):
+        assert actual_call.args[0] == expected_call[0] 
+        assert os.path.normpath(actual_call.args[1]) == os.path.normpath(expected_call[1])  # Normalize and check the path to check on multiple OS.
+        assert actual_call.args[2] == expected_call[2]
+
+    # Check if yaml.safe_load was called
+    mock_yaml_safe_load.assert_called_once()
+
+    # Check if open was called with the correct file path
+    mock_file.assert_called_once_with(dummy_yaml_file_name, 'r')
+
+@patch('builtins.open', new_callable=mock_open)
+def test_load_extra_model_paths_expands_appdata(
+    mock_file,
+    monkeypatch,
+    mock_add_model_folder_path,
+    mock_expandvars_appdata,
+    yaml_config_with_appdata,
+    mock_yaml_content_appdata
+):
+    # Set the mock_file to return yaml with appdata as a variable 
+    mock_file.return_value.read.return_value = yaml_config_with_appdata
+
+    # Attach mocks
+    monkeypatch.setattr(folder_paths, 'add_model_folder_path', mock_add_model_folder_path)
+    monkeypatch.setattr(os.path, 'expandvars', mock_expandvars_appdata)
+    monkeypatch.setattr(yaml, 'safe_load', Mock(return_value=mock_yaml_content_appdata))
+
+    # Mock expanduser to do nothing (since we're not testing it here)
+    monkeypatch.setattr(os.path, 'expanduser', lambda x: x)
+
+    dummy_yaml_file_name = 'dummy_path.yaml'
+    load_extra_path_config(dummy_yaml_file_name)
+
+    expected_base_path = 'C:/Users/TestUser/AppData/Roaming/ComfyUI'
+    expected_calls = [
+        ('checkpoints', os.path.join(expected_base_path, 'models/checkpoints'), False),
+    ]
+
+    assert mock_add_model_folder_path.call_count == len(expected_calls)
+    
+    # Check the base path variable was expanded
+    for actual_call, expected_call in zip(mock_add_model_folder_path.call_args_list, expected_calls):
+        assert actual_call.args == expected_call
+
+    # Verify that expandvars was called
+    assert mock_expandvars_appdata.called

tests/inference/test_execution.py

@@ -95,17 +95,16 @@ class ComfyClient:
                     pass # Probably want to store this off for testing
 
         history = self.get_history(prompt_id)[prompt_id]
-        for o in history['outputs']:
+        for node_id in history['outputs']:
-            for node_id in history['outputs']:
+            node_output = history['outputs'][node_id]
-                node_output = history['outputs'][node_id]
+            result.outputs[node_id] = node_output
-                result.outputs[node_id] = node_output
+            images_output = []
-                if 'images' in node_output:
+            if 'images' in node_output:
-                    images_output = []
+                for image in node_output['images']:
-                    for image in node_output['images']:
+                    image_data = self.get_image(image['filename'], image['subfolder'], image['type'])
-                        image_data = self.get_image(image['filename'], image['subfolder'], image['type'])
+                    image_obj = Image.open(BytesIO(image_data))
-                        image_obj = Image.open(BytesIO(image_data))
+                    images_output.append(image_obj)
-                        images_output.append(image_obj)
+                node_output['image_objects'] = images_output
-                    node_output['image_objects'] = images_output
 
         return result
 
@@ -357,6 +356,25 @@ class TestExecution:
             assert 'prompt_id' in e.args[0], f"Did not get back a proper error message: {e}"
             assert e.args[0]['node_id'] == generator.id, "Error should have been on the generator node"
 
+    def test_missing_node_error(self, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
+        input2 = g.node("StubImage", id="removeme", content="WHITE", height=512, width=512, batch_size=1)
+        input3 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
+        mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1)
+        mix1 = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0))
+        mix2 = g.node("TestLazyMixImages", image1=input1.out(0), image2=input3.out(0), mask=mask.out(0))
+        # We have multiple outputs. The first is invalid, but the second is valid
+        g.node("SaveImage", images=mix1.out(0))
+        g.node("SaveImage", images=mix2.out(0))
+        g.remove_node("removeme")
+        
+        client.run(g)
+
+        # Add back in the missing node to make sure the error doesn't break the server
+        input2 = g.node("StubImage", id="removeme", content="WHITE", height=512, width=512, batch_size=1)
+        client.run(g)
+
     def test_custom_is_changed(self, client: ComfyClient, builder: GraphBuilder):
         g = builder
         # Creating the nodes in this specific order previously caused a bug
@@ -450,8 +468,8 @@ class TestExecution:
         g = builder
         input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
 
-        output1 = g.node("PreviewImage", images=input1.out(0))
+        output1 = g.node("SaveImage", images=input1.out(0))
-        output2 = g.node("PreviewImage", images=input1.out(0))
+        output2 = g.node("SaveImage", images=input1.out(0))
 
         result = client.run(g)
         images1 = result.get_images(output1)
@@ -478,3 +496,29 @@ class TestExecution:
         assert len(images) == 1, "Should have 1 image"
         assert numpy.array(images[0]).min() == 63 and numpy.array(images[0]).max() == 63, "Image should have value 0.25"
         assert not result.did_run(test_node), "The execution should have been cached"
+
+    # This tests that nodes with OUTPUT_IS_LIST function correctly when they receive an ExecutionBlocker
+    # as input. We also test that when that list (containing an ExecutionBlocker) is passed to a node,
+    # only that one entry in the list is blocked.
+    def test_execution_block_list_output(self, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
+        image2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
+        image3 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
+        image_list = g.node("TestMakeListNode", value1=image1.out(0), value2=image2.out(0), value3=image3.out(0))
+        int1 = g.node("StubInt", value=1)
+        int2 = g.node("StubInt", value=2)
+        int3 = g.node("StubInt", value=3)
+        int_list = g.node("TestMakeListNode", value1=int1.out(0), value2=int2.out(0), value3=int3.out(0))
+        compare = g.node("TestIntConditions", a=int_list.out(0), b=2, operation="==")
+        blocker = g.node("TestExecutionBlocker", input=image_list.out(0), block=compare.out(0), verbose=False)
+        
+        list_output = g.node("TestMakeListNode", value1=blocker.out(0))
+        output = g.node("PreviewImage", images=list_output.out(0))
+
+        result = client.run(g)
+        assert result.did_run(output), "The execution should have run"
+        images = result.get_images(output)
+        assert len(images) == 2, "Should have 2 images"
+        assert numpy.array(images[0]).min() == 0 and numpy.array(images[0]).max() == 0, "First image should be black"
+        assert numpy.array(images[1]).min() == 0 and numpy.array(images[1]).max() == 0, "Second image should also be black"

tests/inference/test_inference.py

@@ -109,15 +109,14 @@ class ComfyClient:
                 continue #previews are binary data
 
         history = self.get_history(prompt_id)[prompt_id]
-        for o in history['outputs']:
+        for node_id in history['outputs']:
-            for node_id in history['outputs']:
+            node_output = history['outputs'][node_id]
-                node_output = history['outputs'][node_id]
+            images_output = []
-                if 'images' in node_output:
+            if 'images' in node_output:
-                    images_output = []
+                for image in node_output['images']:
-                    for image in node_output['images']:
+                    image_data = self.get_image(image['filename'], image['subfolder'], image['type'])
-                        image_data = self.get_image(image['filename'], image['subfolder'], image['type'])
+                    images_output.append(image_data)
-                        images_output.append(image_data)
+            output_images[node_id] = images_output
-                output_images[node_id] = images_output
 
         return output_images
 

utils/extra_config.py

@@ -0,0 +1,28 @@
+import os
+import yaml
+import folder_paths
+import logging
+
+def load_extra_path_config(yaml_path):
+    with open(yaml_path, 'r') as stream:
+        config = yaml.safe_load(stream)
+    for c in config:
+        conf = config[c]
+        if conf is None:
+            continue
+        base_path = None
+        if "base_path" in conf:
+            base_path = conf.pop("base_path")
+            base_path = os.path.expandvars(os.path.expanduser(base_path))
+        is_default = False
+        if "is_default" in conf:
+            is_default = conf.pop("is_default")
+        for x in conf:
+            for y in conf[x].split("\n"):
+                if len(y) == 0:
+                    continue
+                full_path = y
+                if base_path is not None:
+                    full_path = os.path.join(base_path, full_path)
+                logging.info("Adding extra search path {} {}".format(x, full_path))
+                folder_paths.add_model_folder_path(x, full_path, is_default)

web/assets/CREDIT.txt

@@ -0,0 +1 @@
+Thanks to OpenArt (https://openart.ai) for providing the sorted-custom-node-map data, captured in September 2024.

web/assets/GraphView-BGt8GmeB.css

@@ -0,0 +1,792 @@
+
+.editable-text[data-v-54da6fc9] {
+  display: inline;
+}
+.editable-text input[data-v-54da6fc9] {
+  width: 100%;
+  box-sizing: border-box;
+}
+
+.group-title-editor.node-title-editor[data-v-fc3f26e3] {
+  z-index: 9999;
+  padding: 0.25rem;
+}
+[data-v-fc3f26e3] .editable-text {
+  width: 100%;
+  height: 100%;
+}
+[data-v-fc3f26e3] .editable-text input {
+  width: 100%;
+  height: 100%;
+  /* Override the default font size */
+  font-size: inherit;
+}
+
+.side-bar-button-icon {
+  font-size: var(--sidebar-icon-size) !important;
+}
+.side-bar-button-selected .side-bar-button-icon {
+  font-size: var(--sidebar-icon-size) !important;
+  font-weight: bold;
+}
+
+.side-bar-button[data-v-caa3ee9c] {
+  width: var(--sidebar-width);
+  height: var(--sidebar-width);
+  border-radius: 0;
+}
+.comfyui-body-left .side-bar-button.side-bar-button-selected[data-v-caa3ee9c],
+.comfyui-body-left .side-bar-button.side-bar-button-selected[data-v-caa3ee9c]:hover {
+  border-left: 4px solid var(--p-button-text-primary-color);
+}
+.comfyui-body-right .side-bar-button.side-bar-button-selected[data-v-caa3ee9c],
+.comfyui-body-right .side-bar-button.side-bar-button-selected[data-v-caa3ee9c]:hover {
+  border-right: 4px solid var(--p-button-text-primary-color);
+}
+
+:root {
+  --sidebar-width: 64px;
+  --sidebar-icon-size: 1.5rem;
+}
+:root .small-sidebar {
+  --sidebar-width: 40px;
+  --sidebar-icon-size: 1rem;
+}
+
+.side-tool-bar-container[data-v-4da64512] {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+
+  pointer-events: auto;
+
+  width: var(--sidebar-width);
+  height: 100%;
+
+  background-color: var(--comfy-menu-bg);
+  color: var(--fg-color);
+}
+.side-tool-bar-end[data-v-4da64512] {
+  align-self: flex-end;
+  margin-top: auto;
+}
+.sidebar-content-container[data-v-4da64512] {
+  height: 100%;
+  overflow-y: auto;
+}
+
+.p-splitter-gutter {
+  pointer-events: auto;
+}
+.gutter-hidden {
+  display: none !important;
+}
+
+.side-bar-panel[data-v-b9df3042] {
+  background-color: var(--bg-color);
+  pointer-events: auto;
+}
+.splitter-overlay[data-v-b9df3042] {
+  width: 100%;
+  height: 100%;
+  position: absolute;
+  top: 0;
+  left: 0;
+  background-color: transparent;
+  pointer-events: none;
+  /* Set it the same as the ComfyUI menu */
+  /* Note: Lite-graph DOM widgets have the same z-index as the node id, so
+  999 should be sufficient to make sure splitter overlays on node's DOM
+  widgets */
+  z-index: 999;
+  border: none;
+}
+
+._content[data-v-e7b35fd9] {
+
+    display: flex;
+
+    flex-direction: column
+}
+._content[data-v-e7b35fd9] > :not([hidden]) ~ :not([hidden]) {
+
+    --tw-space-y-reverse: 0;
+
+    margin-top: calc(0.5rem * calc(1 - var(--tw-space-y-reverse)));
+
+    margin-bottom: calc(0.5rem * var(--tw-space-y-reverse))
+}
+._footer[data-v-e7b35fd9] {
+
+    display: flex;
+
+    flex-direction: column;
+
+    align-items: flex-end;
+
+    padding-top: 1rem
+}
+
+[data-v-37f672ab] .highlight {
+  background-color: var(--p-primary-color);
+  color: var(--p-primary-contrast-color);
+  font-weight: bold;
+  border-radius: 0.25rem;
+  padding: 0rem 0.125rem;
+  margin: -0.125rem 0.125rem;
+}
+
+.slot_row[data-v-ff07c900] {
+  padding: 2px;
+}
+
+/* Original N-Sidebar styles */
+._sb_dot[data-v-ff07c900] {
+  width: 8px;
+  height: 8px;
+  border-radius: 50%;
+  background-color: grey;
+}
+.node_header[data-v-ff07c900] {
+  line-height: 1;
+  padding: 8px 13px 7px;
+  margin-bottom: 5px;
+  font-size: 15px;
+  text-wrap: nowrap;
+  overflow: hidden;
+  display: flex;
+  align-items: center;
+}
+.headdot[data-v-ff07c900] {
+  width: 10px;
+  height: 10px;
+  float: inline-start;
+  margin-right: 8px;
+}
+.IMAGE[data-v-ff07c900] {
+  background-color: #64b5f6;
+}
+.VAE[data-v-ff07c900] {
+  background-color: #ff6e6e;
+}
+.LATENT[data-v-ff07c900] {
+  background-color: #ff9cf9;
+}
+.MASK[data-v-ff07c900] {
+  background-color: #81c784;
+}
+.CONDITIONING[data-v-ff07c900] {
+  background-color: #ffa931;
+}
+.CLIP[data-v-ff07c900] {
+  background-color: #ffd500;
+}
+.MODEL[data-v-ff07c900] {
+  background-color: #b39ddb;
+}
+.CONTROL_NET[data-v-ff07c900] {
+  background-color: #a5d6a7;
+}
+._sb_node_preview[data-v-ff07c900] {
+  background-color: var(--comfy-menu-bg);
+  font-family: 'Open Sans', sans-serif;
+  font-size: small;
+  color: var(--descrip-text);
+  border: 1px solid var(--descrip-text);
+  min-width: 300px;
+  width: -moz-min-content;
+  width: min-content;
+  height: -moz-fit-content;
+  height: fit-content;
+  z-index: 9999;
+  border-radius: 12px;
+  overflow: hidden;
+  font-size: 12px;
+  padding-bottom: 10px;
+}
+._sb_node_preview ._sb_description[data-v-ff07c900] {
+  margin: 10px;
+  padding: 6px;
+  background: var(--border-color);
+  border-radius: 5px;
+  font-style: italic;
+  font-weight: 500;
+  font-size: 0.9rem;
+  word-break: break-word;
+}
+._sb_table[data-v-ff07c900] {
+  display: grid;
+
+  grid-column-gap: 10px;
+  /* Spazio tra le colonne */
+  width: 100%;
+  /* Imposta la larghezza della tabella al 100% del contenitore */
+}
+._sb_row[data-v-ff07c900] {
+  display: grid;
+  grid-template-columns: 10px 1fr 1fr 1fr 10px;
+  grid-column-gap: 10px;
+  align-items: center;
+  padding-left: 9px;
+  padding-right: 9px;
+}
+._sb_row_string[data-v-ff07c900] {
+  grid-template-columns: 10px 1fr 1fr 10fr 1fr;
+}
+._sb_col[data-v-ff07c900] {
+  border: 0px solid #000;
+  display: flex;
+  align-items: flex-end;
+  flex-direction: row-reverse;
+  flex-wrap: nowrap;
+  align-content: flex-start;
+  justify-content: flex-end;
+}
+._sb_inherit[data-v-ff07c900] {
+  display: inherit;
+}
+._long_field[data-v-ff07c900] {
+  background: var(--bg-color);
+  border: 2px solid var(--border-color);
+  margin: 5px 5px 0 5px;
+  border-radius: 10px;
+  line-height: 1.7;
+  text-wrap: nowrap;
+}
+._sb_arrow[data-v-ff07c900] {
+  color: var(--fg-color);
+}
+._sb_preview_badge[data-v-ff07c900] {
+  text-align: center;
+  background: var(--comfy-input-bg);
+  font-weight: bold;
+  color: var(--error-text);
+}
+
+.comfy-vue-node-search-container[data-v-2d409367] {
+  display: flex;
+  width: 100%;
+  min-width: 26rem;
+  align-items: center;
+  justify-content: center;
+}
+.comfy-vue-node-search-container[data-v-2d409367] * {
+  pointer-events: auto;
+}
+.comfy-vue-node-preview-container[data-v-2d409367] {
+  position: absolute;
+  left: -350px;
+  top: 50px;
+}
+.comfy-vue-node-search-box[data-v-2d409367] {
+  z-index: 10;
+  flex-grow: 1;
+}
+._filter-button[data-v-2d409367] {
+  z-index: 10;
+}
+._dialog[data-v-2d409367] {
+  min-width: 26rem;
+}
+
+.invisible-dialog-root {
+  width: 60%;
+  min-width: 24rem;
+  max-width: 48rem;
+  border: 0 !important;
+  background-color: transparent !important;
+  margin-top: 25vh;
+  margin-left: 400px;
+}
+@media all and (max-width: 768px) {
+.invisible-dialog-root {
+    margin-left: 0px;
+}
+}
+.node-search-box-dialog-mask {
+  align-items: flex-start !important;
+}
+
+.node-tooltip[data-v-0a4402f9] {
+  background: var(--comfy-input-bg);
+  border-radius: 5px;
+  box-shadow: 0 0 5px rgba(0, 0, 0, 0.4);
+  color: var(--input-text);
+  font-family: sans-serif;
+  left: 0;
+  max-width: 30vw;
+  padding: 4px 8px;
+  position: absolute;
+  top: 0;
+  transform: translate(5px, calc(-100% - 5px));
+  white-space: pre-wrap;
+  z-index: 99999;
+}
+
+.p-buttongroup-vertical[data-v-ce8bd6ac] {
+  display: flex;
+  flex-direction: column;
+  border-radius: var(--p-button-border-radius);
+  overflow: hidden;
+  border: 1px solid var(--p-panel-border-color);
+}
+.p-buttongroup-vertical .p-button[data-v-ce8bd6ac] {
+  margin: 0;
+  border-radius: 0;
+}
+
+.comfy-image-wrap[data-v-9bc23daf] {
+  display: contents;
+}
+.comfy-image-blur[data-v-9bc23daf] {
+  position: absolute;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  -o-object-fit: cover;
+     object-fit: cover;
+}
+.comfy-image-main[data-v-9bc23daf] {
+  width: 100%;
+  height: 100%;
+  -o-object-fit: cover;
+     object-fit: cover;
+  -o-object-position: center;
+     object-position: center;
+  z-index: 1;
+}
+.contain .comfy-image-wrap[data-v-9bc23daf] {
+  position: relative;
+  width: 100%;
+  height: 100%;
+}
+.contain .comfy-image-main[data-v-9bc23daf] {
+  -o-object-fit: contain;
+     object-fit: contain;
+  -webkit-backdrop-filter: blur(10px);
+          backdrop-filter: blur(10px);
+  position: absolute;
+}
+.broken-image-placeholder[data-v-9bc23daf] {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  width: 100%;
+  height: 100%;
+  margin: 2rem;
+}
+.broken-image-placeholder i[data-v-9bc23daf] {
+  font-size: 3rem;
+  margin-bottom: 0.5rem;
+}
+
+.result-container[data-v-d9c060ae] {
+  width: 100%;
+  height: 100%;
+  aspect-ratio: 1 / 1;
+  overflow: hidden;
+  position: relative;
+  display: flex;
+  justify-content: center;
+  align-items: center;
+}
+.image-preview-mask[data-v-d9c060ae] {
+  position: absolute;
+  left: 50%;
+  top: 50%;
+  transform: translate(-50%, -50%);
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  opacity: 0;
+  transition: opacity 0.3s ease;
+  z-index: 1;
+}
+.result-container:hover .image-preview-mask[data-v-d9c060ae] {
+  opacity: 1;
+}
+
+.task-result-preview[data-v-d4c8a1fe] {
+  aspect-ratio: 1 / 1;
+  overflow: hidden;
+  display: flex;
+  justify-content: center;
+  align-items: center;
+  width: 100%;
+  height: 100%;
+}
+.task-result-preview i[data-v-d4c8a1fe],
+.task-result-preview span[data-v-d4c8a1fe] {
+  font-size: 2rem;
+}
+.task-item[data-v-d4c8a1fe] {
+  display: flex;
+  flex-direction: column;
+  border-radius: 4px;
+  overflow: hidden;
+  position: relative;
+}
+.task-item-details[data-v-d4c8a1fe] {
+  position: absolute;
+  bottom: 0;
+  padding: 0.6rem;
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  width: 100%;
+  z-index: 1;
+}
+.task-node-link[data-v-d4c8a1fe] {
+  padding: 2px;
+}
+
+/* In dark mode, transparent background color for tags is not ideal for tags that
+are floating on top of images. */
+.tag-wrapper[data-v-d4c8a1fe] {
+  background-color: var(--p-primary-contrast-color);
+  border-radius: 6px;
+  display: inline-flex;
+}
+.node-name-tag[data-v-d4c8a1fe] {
+  word-break: break-all;
+}
+.status-tag-group[data-v-d4c8a1fe] {
+  display: flex;
+  flex-direction: column;
+}
+.progress-preview-img[data-v-d4c8a1fe] {
+  width: 100%;
+  height: 100%;
+  -o-object-fit: cover;
+     object-fit: cover;
+  -o-object-position: center;
+     object-position: center;
+}
+
+/* PrimeVue's galleria teleports the fullscreen gallery out of subtree so we
+cannot use scoped style here. */
+img.galleria-image {
+  max-width: 100vw;
+  max-height: 100vh;
+  -o-object-fit: contain;
+     object-fit: contain;
+}
+.p-galleria-close-button {
+  /* Set z-index so the close button doesn't get hidden behind the image when image is large */
+  z-index: 1;
+}
+
+.comfy-vue-side-bar-container[data-v-1b0a8fe3] {
+  display: flex;
+  flex-direction: column;
+  height: 100%;
+  overflow: hidden;
+}
+.comfy-vue-side-bar-header[data-v-1b0a8fe3] {
+  flex-shrink: 0;
+  border-left: none;
+  border-right: none;
+  border-top: none;
+  border-radius: 0;
+  padding: 0.25rem 1rem;
+  min-height: 2.5rem;
+}
+.comfy-vue-side-bar-header-span[data-v-1b0a8fe3] {
+  font-size: small;
+}
+.comfy-vue-side-bar-body[data-v-1b0a8fe3] {
+  flex-grow: 1;
+  overflow: auto;
+  scrollbar-width: thin;
+  scrollbar-color: transparent transparent;
+}
+.comfy-vue-side-bar-body[data-v-1b0a8fe3]::-webkit-scrollbar {
+  width: 1px;
+}
+.comfy-vue-side-bar-body[data-v-1b0a8fe3]::-webkit-scrollbar-thumb {
+  background-color: transparent;
+}
+
+.scroll-container[data-v-08fa89b1] {
+  height: 100%;
+  overflow-y: auto;
+}
+.queue-grid[data-v-08fa89b1] {
+  display: grid;
+  grid-template-columns: repeat(auto-fill, minmax(200px, 1fr));
+  padding: 0.5rem;
+  gap: 0.5rem;
+}
+
+.tree-node[data-v-633e27ab] {
+  width: 100%;
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+}
+.leaf-count-badge[data-v-633e27ab] {
+  margin-left: 0.5rem;
+}
+.node-content[data-v-633e27ab] {
+  display: flex;
+  align-items: center;
+  flex-grow: 1;
+}
+.leaf-label[data-v-633e27ab] {
+  margin-left: 0.5rem;
+}
+[data-v-633e27ab] .editable-text span {
+  word-break: break-all;
+}
+
+[data-v-bd7bae90] .tree-explorer-node-label {
+  width: 100%;
+  display: flex;
+  align-items: center;
+  margin-left: var(--p-tree-node-gap);
+  flex-grow: 1;
+}
+
+/*
+ * The following styles are necessary to avoid layout shift when dragging nodes over folders.
+ * By setting the position to relative on the parent and using an absolutely positioned pseudo-element,
+ * we can create a visual indicator for the drop target without affecting the layout of other elements.
+ */
+[data-v-bd7bae90] .p-tree-node-content:has(.tree-folder) {
+  position: relative;
+}
+[data-v-bd7bae90] .p-tree-node-content:has(.tree-folder.can-drop)::after {
+  content: '';
+  position: absolute;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  border: 1px solid var(--p-content-color);
+  pointer-events: none;
+}
+
+.node-lib-node-container[data-v-90dfee08] {
+    height: 100%;
+    width: 100%
+}
+
+.p-selectbutton .p-button[data-v-91077f2a] {
+  padding: 0.5rem;
+}
+.p-selectbutton .p-button .pi[data-v-91077f2a] {
+  font-size: 1.5rem;
+}
+.field[data-v-91077f2a] {
+  display: flex;
+  flex-direction: column;
+  gap: 0.5rem;
+}
+.color-picker-container[data-v-91077f2a] {
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+}
+
+.node-lib-filter-popup {
+  margin-left: -13px;
+}
+
+[data-v-f6a7371a] .comfy-vue-side-bar-body {
+  background: var(--p-tree-background);
+}
+[data-v-f6a7371a] .node-lib-bookmark-tree-explorer {
+  padding-bottom: 2px;
+}
+[data-v-f6a7371a] .p-divider {
+  margin: var(--comfy-tree-explorer-item-padding) 0px;
+}
+
+.model_preview[data-v-32e6c4d9] {
+  background-color: var(--comfy-menu-bg);
+  font-family: 'Open Sans', sans-serif;
+  color: var(--descrip-text);
+  border: 1px solid var(--descrip-text);
+  min-width: 300px;
+  max-width: 500px;
+  width: -moz-fit-content;
+  width: fit-content;
+  height: -moz-fit-content;
+  height: fit-content;
+  z-index: 9999;
+  border-radius: 12px;
+  overflow: hidden;
+  font-size: 12px;
+  padding: 10px;
+}
+.model_preview_image[data-v-32e6c4d9] {
+  margin: auto;
+  width: -moz-fit-content;
+  width: fit-content;
+}
+.model_preview_image img[data-v-32e6c4d9] {
+  max-width: 100%;
+  max-height: 150px;
+  -o-object-fit: contain;
+     object-fit: contain;
+}
+.model_preview_title[data-v-32e6c4d9] {
+  font-weight: bold;
+  text-align: center;
+  font-size: 14px;
+}
+.model_preview_top_container[data-v-32e6c4d9] {
+  text-align: center;
+  line-height: 0.5;
+}
+.model_preview_filename[data-v-32e6c4d9],
+.model_preview_author[data-v-32e6c4d9],
+.model_preview_architecture[data-v-32e6c4d9] {
+  display: inline-block;
+  text-align: center;
+  margin: 5px;
+  font-size: 10px;
+}
+.model_preview_prefix[data-v-32e6c4d9] {
+  font-weight: bold;
+}
+
+.model-lib-model-icon-container[data-v-70b69131] {
+  display: inline-block;
+  position: relative;
+  left: 0;
+  height: 1.5rem;
+  vertical-align: top;
+  width: 0px;
+}
+.model-lib-model-icon[data-v-70b69131] {
+  background-size: cover;
+  background-position: center;
+  display: inline-block;
+  position: relative;
+  left: -2.5rem;
+  height: 2rem;
+  width: 2rem;
+  vertical-align: top;
+}
+
+.pi-fake-spacer {
+  height: 1px;
+  width: 16px;
+}
+
+[data-v-74b01bce] .comfy-vue-side-bar-body {
+  background: var(--p-tree-background);
+}
+
+[data-v-d2d58252] .comfy-vue-side-bar-body {
+  background: var(--p-tree-background);
+}
+
+[data-v-84e785b8] .p-togglebutton::before {
+  display: none
+}
+[data-v-84e785b8] .p-togglebutton {
+  position: relative;
+  flex-shrink: 0;
+  border-radius: 0px;
+  background-color: transparent;
+  padding-left: 0.5rem;
+  padding-right: 0.5rem
+}
+[data-v-84e785b8] .p-togglebutton.p-togglebutton-checked {
+  border-bottom-width: 2px;
+  border-bottom-color: var(--p-button-text-primary-color)
+}
+[data-v-84e785b8] .p-togglebutton-checked .close-button,[data-v-84e785b8] .p-togglebutton:hover .close-button {
+  visibility: visible
+}
+.status-indicator[data-v-84e785b8] {
+  position: absolute;
+  font-weight: 700;
+  font-size: 1.5rem;
+  top: 50%;
+  left: 50%;
+  transform: translate(-50%, -50%)
+}
+[data-v-84e785b8] .p-togglebutton:hover .status-indicator {
+  display: none
+}
+[data-v-84e785b8] .p-togglebutton .close-button {
+  visibility: hidden
+}
+
+.top-menubar[data-v-2ec1b620] .p-menubar-item-link svg {
+  display: none;
+}
+[data-v-2ec1b620] .p-menubar-submenu.dropdown-direction-up {
+  top: auto;
+  bottom: 100%;
+  flex-direction: column-reverse;
+}
+.keybinding-tag[data-v-2ec1b620] {
+  background: var(--p-content-hover-background);
+  border-color: var(--p-content-border-color);
+  border-style: solid;
+}
+
+[data-v-713442be] .p-inputtext {
+  border-top-left-radius: 0;
+  border-bottom-left-radius: 0;
+}
+
+.comfyui-queue-button[data-v-fcd3efcd] .p-splitbutton-dropdown {
+  border-top-right-radius: 0;
+  border-bottom-right-radius: 0;
+}
+
+.actionbar[data-v-bc6c78dd] {
+  pointer-events: all;
+  position: fixed;
+  z-index: 1000;
+}
+.actionbar.is-docked[data-v-bc6c78dd] {
+  position: static;
+  border-style: none;
+  background-color: transparent;
+  padding: 0px;
+}
+.actionbar.is-dragging[data-v-bc6c78dd] {
+  -webkit-user-select: none;
+     -moz-user-select: none;
+          user-select: none;
+}
+[data-v-bc6c78dd] .p-panel-content {
+  padding: 0.25rem;
+}
+[data-v-bc6c78dd] .p-panel-header {
+  display: none;
+}
+
+.comfyui-menu[data-v-b13fdc92] {
+  width: 100vw;
+  background: var(--comfy-menu-bg);
+  color: var(--fg-color);
+  font-family: Arial, Helvetica, sans-serif;
+  font-size: 0.8em;
+  box-sizing: border-box;
+  z-index: 1000;
+  order: 0;
+  grid-column: 1/-1;
+  max-height: 90vh;
+}
+.comfyui-menu.dropzone[data-v-b13fdc92] {
+  background: var(--p-highlight-background);
+}
+.comfyui-menu.dropzone-active[data-v-b13fdc92] {
+  background: var(--p-highlight-background-focus);
+}
+.comfyui-logo[data-v-b13fdc92] {
+  font-size: 1.2em;
+  -webkit-user-select: none;
+     -moz-user-select: none;
+          user-select: none;
+  cursor: default;
+}

web/assets/colorPalette-D5oi2-2V.js

@@ -0,0 +1,865 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { k as app, aP as LGraphCanvas, bO as useToastStore, ca as $el, z as LiteGraph } from "./index-DGAbdBYF.js";
+const colorPalettes = {
+  dark: {
+    id: "dark",
+    name: "Dark (Default)",
+    colors: {
+      node_slot: {
+        CLIP: "#FFD500",
+        // bright yellow
+        CLIP_VISION: "#A8DADC",
+        // light blue-gray
+        CLIP_VISION_OUTPUT: "#ad7452",
+        // rusty brown-orange
+        CONDITIONING: "#FFA931",
+        // vibrant orange-yellow
+        CONTROL_NET: "#6EE7B7",
+        // soft mint green
+        IMAGE: "#64B5F6",
+        // bright sky blue
+        LATENT: "#FF9CF9",
+        // light pink-purple
+        MASK: "#81C784",
+        // muted green
+        MODEL: "#B39DDB",
+        // light lavender-purple
+        STYLE_MODEL: "#C2FFAE",
+        // light green-yellow
+        VAE: "#FF6E6E",
+        // bright red
+        NOISE: "#B0B0B0",
+        // gray
+        GUIDER: "#66FFFF",
+        // cyan
+        SAMPLER: "#ECB4B4",
+        // very soft red
+        SIGMAS: "#CDFFCD",
+        // soft lime green
+        TAESD: "#DCC274"
+        // cheesecake
+      },
+      litegraph_base: {
+        BACKGROUND_IMAGE: "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAGQAAABkCAIAAAD/gAIDAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAAQBJREFUeNrs1rEKwjAUhlETUkj3vP9rdmr1Ysammk2w5wdxuLgcMHyptfawuZX4pJSWZTnfnu/lnIe/jNNxHHGNn//HNbbv+4dr6V+11uF527arU7+u63qfa/bnmh8sWLBgwYJlqRf8MEptXPBXJXa37BSl3ixYsGDBMliwFLyCV/DeLIMFCxYsWLBMwSt4Be/NggXLYMGCBUvBK3iNruC9WbBgwYJlsGApeAWv4L1ZBgsWLFiwYJmCV/AK3psFC5bBggULloJX8BpdwXuzYMGCBctgwVLwCl7Be7MMFixYsGDBsu8FH1FaSmExVfAxBa/gvVmwYMGCZbBg/W4vAQYA5tRF9QYlv/QAAAAASUVORK5CYII=",
+        CLEAR_BACKGROUND_COLOR: "#222",
+        NODE_TITLE_COLOR: "#999",
+        NODE_SELECTED_TITLE_COLOR: "#FFF",
+        NODE_TEXT_SIZE: 14,
+        NODE_TEXT_COLOR: "#AAA",
+        NODE_SUBTEXT_SIZE: 12,
+        NODE_DEFAULT_COLOR: "#333",
+        NODE_DEFAULT_BGCOLOR: "#353535",
+        NODE_DEFAULT_BOXCOLOR: "#666",
+        NODE_DEFAULT_SHAPE: "box",
+        NODE_BOX_OUTLINE_COLOR: "#FFF",
+        NODE_BYPASS_BGCOLOR: "#FF00FF",
+        DEFAULT_SHADOW_COLOR: "rgba(0,0,0,0.5)",
+        DEFAULT_GROUP_FONT: 24,
+        WIDGET_BGCOLOR: "#222",
+        WIDGET_OUTLINE_COLOR: "#666",
+        WIDGET_TEXT_COLOR: "#DDD",
+        WIDGET_SECONDARY_TEXT_COLOR: "#999",
+        LINK_COLOR: "#9A9",
+        EVENT_LINK_COLOR: "#A86",
+        CONNECTING_LINK_COLOR: "#AFA",
+        BADGE_FG_COLOR: "#FFF",
+        BADGE_BG_COLOR: "#0F1F0F"
+      },
+      comfy_base: {
+        "fg-color": "#fff",
+        "bg-color": "#202020",
+        "comfy-menu-bg": "#353535",
+        "comfy-input-bg": "#222",
+        "input-text": "#ddd",
+        "descrip-text": "#999",
+        "drag-text": "#ccc",
+        "error-text": "#ff4444",
+        "border-color": "#4e4e4e",
+        "tr-even-bg-color": "#222",
+        "tr-odd-bg-color": "#353535",
+        "content-bg": "#4e4e4e",
+        "content-fg": "#fff",
+        "content-hover-bg": "#222",
+        "content-hover-fg": "#fff"
+      }
+    }
+  },
+  light: {
+    id: "light",
+    name: "Light",
+    colors: {
+      node_slot: {
+        CLIP: "#FFA726",
+        // orange
+        CLIP_VISION: "#5C6BC0",
+        // indigo
+        CLIP_VISION_OUTPUT: "#8D6E63",
+        // brown
+        CONDITIONING: "#EF5350",
+        // red
+        CONTROL_NET: "#66BB6A",
+        // green
+        IMAGE: "#42A5F5",
+        // blue
+        LATENT: "#AB47BC",
+        // purple
+        MASK: "#9CCC65",
+        // light green
+        MODEL: "#7E57C2",
+        // deep purple
+        STYLE_MODEL: "#D4E157",
+        // lime
+        VAE: "#FF7043"
+        // deep orange
+      },
+      litegraph_base: {
+        BACKGROUND_IMAGE: "data:image/gif;base64,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",
+        CLEAR_BACKGROUND_COLOR: "lightgray",
+        NODE_TITLE_COLOR: "#222",
+        NODE_SELECTED_TITLE_COLOR: "#000",
+        NODE_TEXT_SIZE: 14,
+        NODE_TEXT_COLOR: "#444",
+        NODE_SUBTEXT_SIZE: 12,
+        NODE_DEFAULT_COLOR: "#F7F7F7",
+        NODE_DEFAULT_BGCOLOR: "#F5F5F5",
+        NODE_DEFAULT_BOXCOLOR: "#CCC",
+        NODE_DEFAULT_SHAPE: "box",
+        NODE_BOX_OUTLINE_COLOR: "#000",
+        NODE_BYPASS_BGCOLOR: "#FF00FF",
+        DEFAULT_SHADOW_COLOR: "rgba(0,0,0,0.1)",
+        DEFAULT_GROUP_FONT: 24,
+        WIDGET_BGCOLOR: "#D4D4D4",
+        WIDGET_OUTLINE_COLOR: "#999",
+        WIDGET_TEXT_COLOR: "#222",
+        WIDGET_SECONDARY_TEXT_COLOR: "#555",
+        LINK_COLOR: "#4CAF50",
+        EVENT_LINK_COLOR: "#FF9800",
+        CONNECTING_LINK_COLOR: "#2196F3",
+        BADGE_FG_COLOR: "#000",
+        BADGE_BG_COLOR: "#FFF"
+      },
+      comfy_base: {
+        "fg-color": "#222",
+        "bg-color": "#DDD",
+        "comfy-menu-bg": "#F5F5F5",
+        "comfy-input-bg": "#C9C9C9",
+        "input-text": "#222",
+        "descrip-text": "#444",
+        "drag-text": "#555",
+        "error-text": "#F44336",
+        "border-color": "#888",
+        "tr-even-bg-color": "#f9f9f9",
+        "tr-odd-bg-color": "#fff",
+        "content-bg": "#e0e0e0",
+        "content-fg": "#222",
+        "content-hover-bg": "#adadad",
+        "content-hover-fg": "#222"
+      }
+    }
+  },
+  solarized: {
+    id: "solarized",
+    name: "Solarized",
+    colors: {
+      node_slot: {
+        CLIP: "#2AB7CA",
+        // light blue
+        CLIP_VISION: "#6c71c4",
+        // blue violet
+        CLIP_VISION_OUTPUT: "#859900",
+        // olive green
+        CONDITIONING: "#d33682",
+        // magenta
+        CONTROL_NET: "#d1ffd7",
+        // light mint green
+        IMAGE: "#5940bb",
+        // deep blue violet
+        LATENT: "#268bd2",
+        // blue
+        MASK: "#CCC9E7",
+        // light purple-gray
+        MODEL: "#dc322f",
+        // red
+        STYLE_MODEL: "#1a998a",
+        // teal
+        UPSCALE_MODEL: "#054A29",
+        // dark green
+        VAE: "#facfad"
+        // light pink-orange
+      },
+      litegraph_base: {
+        NODE_TITLE_COLOR: "#fdf6e3",
+        // Base3
+        NODE_SELECTED_TITLE_COLOR: "#A9D400",
+        NODE_TEXT_SIZE: 14,
+        NODE_TEXT_COLOR: "#657b83",
+        // Base00
+        NODE_SUBTEXT_SIZE: 12,
+        NODE_DEFAULT_COLOR: "#094656",
+        NODE_DEFAULT_BGCOLOR: "#073642",
+        // Base02
+        NODE_DEFAULT_BOXCOLOR: "#839496",
+        // Base0
+        NODE_DEFAULT_SHAPE: "box",
+        NODE_BOX_OUTLINE_COLOR: "#fdf6e3",
+        // Base3
+        NODE_BYPASS_BGCOLOR: "#FF00FF",
+        DEFAULT_SHADOW_COLOR: "rgba(0,0,0,0.5)",
+        DEFAULT_GROUP_FONT: 24,
+        WIDGET_BGCOLOR: "#002b36",
+        // Base03
+        WIDGET_OUTLINE_COLOR: "#839496",
+        // Base0
+        WIDGET_TEXT_COLOR: "#fdf6e3",
+        // Base3
+        WIDGET_SECONDARY_TEXT_COLOR: "#93a1a1",
+        // Base1
+        LINK_COLOR: "#2aa198",
+        // Solarized Cyan
+        EVENT_LINK_COLOR: "#268bd2",
+        // Solarized Blue
+        CONNECTING_LINK_COLOR: "#859900"
+        // Solarized Green
+      },
+      comfy_base: {
+        "fg-color": "#fdf6e3",
+        // Base3
+        "bg-color": "#002b36",
+        // Base03
+        "comfy-menu-bg": "#073642",
+        // Base02
+        "comfy-input-bg": "#002b36",
+        // Base03
+        "input-text": "#93a1a1",
+        // Base1
+        "descrip-text": "#586e75",
+        // Base01
+        "drag-text": "#839496",
+        // Base0
+        "error-text": "#dc322f",
+        // Solarized Red
+        "border-color": "#657b83",
+        // Base00
+        "tr-even-bg-color": "#002b36",
+        "tr-odd-bg-color": "#073642",
+        "content-bg": "#657b83",
+        "content-fg": "#fdf6e3",
+        "content-hover-bg": "#002b36",
+        "content-hover-fg": "#fdf6e3"
+      }
+    }
+  },
+  arc: {
+    id: "arc",
+    name: "Arc",
+    colors: {
+      node_slot: {
+        BOOLEAN: "",
+        CLIP: "#eacb8b",
+        CLIP_VISION: "#A8DADC",
+        CLIP_VISION_OUTPUT: "#ad7452",
+        CONDITIONING: "#cf876f",
+        CONTROL_NET: "#00d78d",
+        CONTROL_NET_WEIGHTS: "",
+        FLOAT: "",
+        GLIGEN: "",
+        IMAGE: "#80a1c0",
+        IMAGEUPLOAD: "",
+        INT: "",
+        LATENT: "#b38ead",
+        LATENT_KEYFRAME: "",
+        MASK: "#a3bd8d",
+        MODEL: "#8978a7",
+        SAMPLER: "",
+        SIGMAS: "",
+        STRING: "",
+        STYLE_MODEL: "#C2FFAE",
+        T2I_ADAPTER_WEIGHTS: "",
+        TAESD: "#DCC274",
+        TIMESTEP_KEYFRAME: "",
+        UPSCALE_MODEL: "",
+        VAE: "#be616b"
+      },
+      litegraph_base: {
+        BACKGROUND_IMAGE: "data:image/png;base64,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",
+        CLEAR_BACKGROUND_COLOR: "#2b2f38",
+        NODE_TITLE_COLOR: "#b2b7bd",
+        NODE_SELECTED_TITLE_COLOR: "#FFF",
+        NODE_TEXT_SIZE: 14,
+        NODE_TEXT_COLOR: "#AAA",
+        NODE_SUBTEXT_SIZE: 12,
+        NODE_DEFAULT_COLOR: "#2b2f38",
+        NODE_DEFAULT_BGCOLOR: "#242730",
+        NODE_DEFAULT_BOXCOLOR: "#6e7581",
+        NODE_DEFAULT_SHAPE: "box",
+        NODE_BOX_OUTLINE_COLOR: "#FFF",
+        NODE_BYPASS_BGCOLOR: "#FF00FF",
+        DEFAULT_SHADOW_COLOR: "rgba(0,0,0,0.5)",
+        DEFAULT_GROUP_FONT: 22,
+        WIDGET_BGCOLOR: "#2b2f38",
+        WIDGET_OUTLINE_COLOR: "#6e7581",
+        WIDGET_TEXT_COLOR: "#DDD",
+        WIDGET_SECONDARY_TEXT_COLOR: "#b2b7bd",
+        LINK_COLOR: "#9A9",
+        EVENT_LINK_COLOR: "#A86",
+        CONNECTING_LINK_COLOR: "#AFA"
+      },
+      comfy_base: {
+        "fg-color": "#fff",
+        "bg-color": "#2b2f38",
+        "comfy-menu-bg": "#242730",
+        "comfy-input-bg": "#2b2f38",
+        "input-text": "#ddd",
+        "descrip-text": "#b2b7bd",
+        "drag-text": "#ccc",
+        "error-text": "#ff4444",
+        "border-color": "#6e7581",
+        "tr-even-bg-color": "#2b2f38",
+        "tr-odd-bg-color": "#242730",
+        "content-bg": "#6e7581",
+        "content-fg": "#fff",
+        "content-hover-bg": "#2b2f38",
+        "content-hover-fg": "#fff"
+      }
+    }
+  },
+  nord: {
+    id: "nord",
+    name: "Nord",
+    colors: {
+      node_slot: {
+        BOOLEAN: "",
+        CLIP: "#eacb8b",
+        CLIP_VISION: "#A8DADC",
+        CLIP_VISION_OUTPUT: "#ad7452",
+        CONDITIONING: "#cf876f",
+        CONTROL_NET: "#00d78d",
+        CONTROL_NET_WEIGHTS: "",
+        FLOAT: "",
+        GLIGEN: "",
+        IMAGE: "#80a1c0",
+        IMAGEUPLOAD: "",
+        INT: "",
+        LATENT: "#b38ead",
+        LATENT_KEYFRAME: "",
+        MASK: "#a3bd8d",
+        MODEL: "#8978a7",
+        SAMPLER: "",
+        SIGMAS: "",
+        STRING: "",
+        STYLE_MODEL: "#C2FFAE",
+        T2I_ADAPTER_WEIGHTS: "",
+        TAESD: "#DCC274",
+        TIMESTEP_KEYFRAME: "",
+        UPSCALE_MODEL: "",
+        VAE: "#be616b"
+      },
+      litegraph_base: {
+        BACKGROUND_IMAGE: "data:image/png;base64,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",
+        CLEAR_BACKGROUND_COLOR: "#212732",
+        NODE_TITLE_COLOR: "#999",
+        NODE_SELECTED_TITLE_COLOR: "#e5eaf0",
+        NODE_TEXT_SIZE: 14,
+        NODE_TEXT_COLOR: "#bcc2c8",
+        NODE_SUBTEXT_SIZE: 12,
+        NODE_DEFAULT_COLOR: "#2e3440",
+        NODE_DEFAULT_BGCOLOR: "#161b22",
+        NODE_DEFAULT_BOXCOLOR: "#545d70",
+        NODE_DEFAULT_SHAPE: "box",
+        NODE_BOX_OUTLINE_COLOR: "#e5eaf0",
+        NODE_BYPASS_BGCOLOR: "#FF00FF",
+        DEFAULT_SHADOW_COLOR: "rgba(0,0,0,0.5)",
+        DEFAULT_GROUP_FONT: 24,
+        WIDGET_BGCOLOR: "#2e3440",
+        WIDGET_OUTLINE_COLOR: "#545d70",
+        WIDGET_TEXT_COLOR: "#bcc2c8",
+        WIDGET_SECONDARY_TEXT_COLOR: "#999",
+        LINK_COLOR: "#9A9",
+        EVENT_LINK_COLOR: "#A86",
+        CONNECTING_LINK_COLOR: "#AFA"
+      },
+      comfy_base: {
+        "fg-color": "#e5eaf0",
+        "bg-color": "#2e3440",
+        "comfy-menu-bg": "#161b22",
+        "comfy-input-bg": "#2e3440",
+        "input-text": "#bcc2c8",
+        "descrip-text": "#999",
+        "drag-text": "#ccc",
+        "error-text": "#ff4444",
+        "border-color": "#545d70",
+        "tr-even-bg-color": "#2e3440",
+        "tr-odd-bg-color": "#161b22",
+        "content-bg": "#545d70",
+        "content-fg": "#e5eaf0",
+        "content-hover-bg": "#2e3440",
+        "content-hover-fg": "#e5eaf0"
+      }
+    }
+  },
+  github: {
+    id: "github",
+    name: "Github",
+    colors: {
+      node_slot: {
+        BOOLEAN: "",
+        CLIP: "#eacb8b",
+        CLIP_VISION: "#A8DADC",
+        CLIP_VISION_OUTPUT: "#ad7452",
+        CONDITIONING: "#cf876f",
+        CONTROL_NET: "#00d78d",
+        CONTROL_NET_WEIGHTS: "",
+        FLOAT: "",
+        GLIGEN: "",
+        IMAGE: "#80a1c0",
+        IMAGEUPLOAD: "",
+        INT: "",
+        LATENT: "#b38ead",
+        LATENT_KEYFRAME: "",
+        MASK: "#a3bd8d",
+        MODEL: "#8978a7",
+        SAMPLER: "",
+        SIGMAS: "",
+        STRING: "",
+        STYLE_MODEL: "#C2FFAE",
+        T2I_ADAPTER_WEIGHTS: "",
+        TAESD: "#DCC274",
+        TIMESTEP_KEYFRAME: "",
+        UPSCALE_MODEL: "",
+        VAE: "#be616b"
+      },
+      litegraph_base: {
+        BACKGROUND_IMAGE: "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAGQAAABkCAIAAAD/gAIDAAAACXBIWXMAAAsTAAALEwEAmpwYAAAGlmlUWHRYTUw6Y29tLmFkb2JlLnhtcAAAAAAAPD94cGFja2V0IGJlZ2luPSLvu78iIGlkPSJXNU0wTXBDZWhpSHpyZVN6TlRjemtjOWQiPz4gPHg6eG1wbWV0YSB4bWxuczp4PSJhZG9iZTpuczptZXRhLyIgeDp4bXB0az0iQWRvYmUgWE1QIENvcmUgOS4xLWMwMDEgNzkuMTQ2Mjg5OSwgMjAyMy8wNi8yNS0yMDowMTo1NSAgICAgICAgIj4gPHJkZjpSREYgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj4gPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIgeG1sbnM6eG1wPSJodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvIiB4bWxuczpkYz0iaHR0cDovL3B1cmwub3JnL2RjL2VsZW1lbnRzLzEuMS8iIHhtbG5zOnBob3Rvc2hvcD0iaHR0cDovL25zLmFkb2JlLmNvbS9waG90b3Nob3AvMS4wLyIgeG1sbnM6eG1wTU09Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC9tbS8iIHhtbG5zOnN0RXZ0PSJodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvc1R5cGUvUmVzb3VyY2VFdmVudCMiIHhtcDpDcmVhdG9yVG9vbD0iQWRvYmUgUGhvdG9zaG9wIDI1LjEgKFdpbmRvd3MpIiB4bXA6Q3JlYXRlRGF0ZT0iMjAyMy0xMS0xM1QwMDoxODowMiswMTowMCIgeG1wOk1vZGlmeURhdGU9IjIwMjMtMTEtMTVUMDI6MDQ6NTkrMDE6MDAiIHhtcDpNZXRhZGF0YURhdGU9IjIwMjMtMTEtMTVUMDI6MDQ6NTkrMDE6MDAiIGRjOmZvcm1hdD0iaW1hZ2UvcG5nIiBwaG90b3Nob3A6Q29sb3JNb2RlPSIzIiB4bXBNTTpJbnN0YW5jZUlEPSJ4bXAuaWlkOmIyYzRhNjA5LWJmYTctYTg0MC1iOGFlLTk3MzE2ZjM1ZGIyNyIgeG1wTU06RG9jdW1lbnRJRD0iYWRvYmU6ZG9jaWQ6cGhvdG9zaG9wOjk0ZmNlZGU4LTE1MTctZmQ0MC04ZGU3LWYzOTgxM2E3ODk5ZiIgeG1wTU06T3JpZ2luYWxEb2N1bWVudElEPSJ4bXAuZGlkOjIzMWIxMGIwLWI0ZmItMDI0ZS1iMTJlLTMwNTMwM2NkMDdjOCI+IDx4bXBNTTpIaXN0b3J5PiA8cmRmOlNlcT4gPHJkZjpsaSBzdEV2dDphY3Rpb249ImNyZWF0ZWQiIHN0RXZ0Omluc3RhbmNlSUQ9InhtcC5paWQ6MjMxYjEwYjAtYjRmYi0wMjRlLWIxMmUtMzA1MzAzY2QwN2M4IiBzdEV2dDp3aGVuPSIyMDIzLTExLTEzVDAwOjE4OjAyKzAxOjAwIiBzdEV2dDpzb2Z0d2FyZUFnZW50PSJBZG9iZSBQaG90b3Nob3AgMjUuMSAoV2luZG93cykiLz4gPHJkZjpsaSBzdEV2dDphY3Rpb249InNhdmVkIiBzdEV2dDppbnN0YW5jZUlEPSJ4bXAuaWlkOjQ4OWY1NzlmLTJkNjUtZWQ0Zi04OTg0LTA4NGE2MGE1ZTMzNSIgc3RFdnQ6d2hlbj0iMjAyMy0xMS0xNVQwMjowNDo1OSswMTowMCIgc3RFdnQ6c29mdHdhcmVBZ2VudD0iQWRvYmUgUGhvdG9zaG9wIDI1LjEgKFdpbmRvd3MpIiBzdEV2dDpjaGFuZ2VkPSIvIi8+IDxyZGY6bGkgc3RFdnQ6YWN0aW9uPSJzYXZlZCIgc3RFdnQ6aW5zdGFuY2VJRD0ieG1wLmlpZDpiMmM0YTYwOS1iZmE3LWE4NDAtYjhhZS05NzMxNmYzNWRiMjciIHN0RXZ0OndoZW49IjIwMjMtMTEtMTVUMDI6MDQ6NTkrMDE6MDAiIHN0RXZ0OnNvZnR3YXJlQWdlbnQ9IkFkb2JlIFBob3Rvc2hvcCAyNS4xIChXaW5kb3dzKSIgc3RFdnQ6Y2hhbmdlZD0iLyIvPiA8L3JkZjpTZXE+IDwveG1wTU06SGlzdG9yeT4gPC9yZGY6RGVzY3JpcHRpb24+IDwvcmRmOlJERj4gPC94OnhtcG1ldGE+IDw/eHBhY2tldCBlbmQ9InIiPz4OTe6GAAAAx0lEQVR42u3WMQoAIQxFwRzJys77X8vSLiRgITif7bYbgrwYc/mKXyBoY4VVBgsWLFiwYFmOlTv+9jfDOjHmr8u6eVkGCxYsWLBgmc5S8ApewXvgYRksWLBgKXidpeBdloL3wMOCBctgwVLwCl7BuyyDBQsWLFiwTGcpeAWv4D3wsAwWLFiwFLzOUvAuS8F74GHBgmWwYCl4Ba/gXZbBggULFixYprMUvIJX8B54WAYLFixYCl5nKXiXpeA98LBgwTJYsGC9tg1o8f4TTtqzNQAAAABJRU5ErkJggg==",
+        CLEAR_BACKGROUND_COLOR: "#040506",
+        NODE_TITLE_COLOR: "#999",
+        NODE_SELECTED_TITLE_COLOR: "#e5eaf0",
+        NODE_TEXT_SIZE: 14,
+        NODE_TEXT_COLOR: "#bcc2c8",
+        NODE_SUBTEXT_SIZE: 12,
+        NODE_DEFAULT_COLOR: "#161b22",
+        NODE_DEFAULT_BGCOLOR: "#13171d",
+        NODE_DEFAULT_BOXCOLOR: "#30363d",
+        NODE_DEFAULT_SHAPE: "box",
+        NODE_BOX_OUTLINE_COLOR: "#e5eaf0",
+        NODE_BYPASS_BGCOLOR: "#FF00FF",
+        DEFAULT_SHADOW_COLOR: "rgba(0,0,0,0.5)",
+        DEFAULT_GROUP_FONT: 24,
+        WIDGET_BGCOLOR: "#161b22",
+        WIDGET_OUTLINE_COLOR: "#30363d",
+        WIDGET_TEXT_COLOR: "#bcc2c8",
+        WIDGET_SECONDARY_TEXT_COLOR: "#999",
+        LINK_COLOR: "#9A9",
+        EVENT_LINK_COLOR: "#A86",
+        CONNECTING_LINK_COLOR: "#AFA"
+      },
+      comfy_base: {
+        "fg-color": "#e5eaf0",
+        "bg-color": "#161b22",
+        "comfy-menu-bg": "#13171d",
+        "comfy-input-bg": "#161b22",
+        "input-text": "#bcc2c8",
+        "descrip-text": "#999",
+        "drag-text": "#ccc",
+        "error-text": "#ff4444",
+        "border-color": "#30363d",
+        "tr-even-bg-color": "#161b22",
+        "tr-odd-bg-color": "#13171d",
+        "content-bg": "#30363d",
+        "content-fg": "#e5eaf0",
+        "content-hover-bg": "#161b22",
+        "content-hover-fg": "#e5eaf0"
+      }
+    }
+  }
+};
+const id = "Comfy.ColorPalette";
+const idCustomColorPalettes = "Comfy.CustomColorPalettes";
+const defaultColorPaletteId = "dark";
+const els = {
+  select: null
+};
+const getCustomColorPalettes = /* @__PURE__ */ __name(() => {
+  return app.ui.settings.getSettingValue(idCustomColorPalettes, {});
+}, "getCustomColorPalettes");
+const setCustomColorPalettes = /* @__PURE__ */ __name((customColorPalettes) => {
+  return app.ui.settings.setSettingValue(
+    idCustomColorPalettes,
+    customColorPalettes
+  );
+}, "setCustomColorPalettes");
+const defaultColorPalette = colorPalettes[defaultColorPaletteId];
+const getColorPalette = /* @__PURE__ */ __name((colorPaletteId) => {
+  if (!colorPaletteId) {
+    colorPaletteId = app.ui.settings.getSettingValue(id, defaultColorPaletteId);
+  }
+  if (colorPaletteId.startsWith("custom_")) {
+    colorPaletteId = colorPaletteId.substr(7);
+    let customColorPalettes = getCustomColorPalettes();
+    if (customColorPalettes[colorPaletteId]) {
+      return customColorPalettes[colorPaletteId];
+    }
+  }
+  return colorPalettes[colorPaletteId];
+}, "getColorPalette");
+const setColorPalette = /* @__PURE__ */ __name((colorPaletteId) => {
+  app.ui.settings.setSettingValue(id, colorPaletteId);
+}, "setColorPalette");
+app.registerExtension({
+  name: id,
+  init() {
+    LGraphCanvas.prototype.updateBackground = function(image, clearBackgroundColor) {
+      this._bg_img = new Image();
+      this._bg_img.name = image;
+      this._bg_img.src = image;
+      this._bg_img.onload = () => {
+        this.draw(true, true);
+      };
+      this.background_image = image;
+      this.clear_background = true;
+      this.clear_background_color = clearBackgroundColor;
+      this._pattern = null;
+    };
+  },
+  addCustomNodeDefs(node_defs) {
+    const sortObjectKeys = /* @__PURE__ */ __name((unordered) => {
+      return Object.keys(unordered).sort().reduce((obj, key) => {
+        obj[key] = unordered[key];
+        return obj;
+      }, {});
+    }, "sortObjectKeys");
+    function getSlotTypes() {
+      var types = [];
+      const defs = node_defs;
+      for (const nodeId in defs) {
+        const nodeData = defs[nodeId];
+        var inputs = nodeData["input"]["required"];
+        if (nodeData["input"]["optional"] !== void 0) {
+          inputs = Object.assign(
+            {},
+            nodeData["input"]["required"],
+            nodeData["input"]["optional"]
+          );
+        }
+        for (const inputName in inputs) {
+          const inputData = inputs[inputName];
+          const type = inputData[0];
+          if (!Array.isArray(type)) {
+            types.push(type);
+          }
+        }
+        for (const o in nodeData["output"]) {
+          const output = nodeData["output"][o];
+          types.push(output);
+        }
+      }
+      return types;
+    }
+    __name(getSlotTypes, "getSlotTypes");
+    function completeColorPalette(colorPalette) {
+      var types = getSlotTypes();
+      for (const type of types) {
+        if (!colorPalette.colors.node_slot[type]) {
+          colorPalette.colors.node_slot[type] = "";
+        }
+      }
+      colorPalette.colors.node_slot = sortObjectKeys(
+        colorPalette.colors.node_slot
+      );
+      return colorPalette;
+    }
+    __name(completeColorPalette, "completeColorPalette");
+    const getColorPaletteTemplate = /* @__PURE__ */ __name(async () => {
+      let colorPalette = {
+        id: "my_color_palette_unique_id",
+        name: "My Color Palette",
+        colors: {
+          node_slot: {},
+          litegraph_base: {},
+          comfy_base: {}
+        }
+      };
+      const defaultColorPalette2 = colorPalettes[defaultColorPaletteId];
+      for (const key in defaultColorPalette2.colors.litegraph_base) {
+        if (!colorPalette.colors.litegraph_base[key]) {
+          colorPalette.colors.litegraph_base[key] = "";
+        }
+      }
+      for (const key in defaultColorPalette2.colors.comfy_base) {
+        if (!colorPalette.colors.comfy_base[key]) {
+          colorPalette.colors.comfy_base[key] = "";
+        }
+      }
+      return completeColorPalette(colorPalette);
+    }, "getColorPaletteTemplate");
+    const addCustomColorPalette = /* @__PURE__ */ __name(async (colorPalette) => {
+      if (typeof colorPalette !== "object") {
+        useToastStore().addAlert("Invalid color palette.");
+        return;
+      }
+      if (!colorPalette.id) {
+        useToastStore().addAlert("Color palette missing id.");
+        return;
+      }
+      if (!colorPalette.name) {
+        useToastStore().addAlert("Color palette missing name.");
+        return;
+      }
+      if (!colorPalette.colors) {
+        useToastStore().addAlert("Color palette missing colors.");
+        return;
+      }
+      if (colorPalette.colors.node_slot && typeof colorPalette.colors.node_slot !== "object") {
+        useToastStore().addAlert("Invalid color palette colors.node_slot.");
+        return;
+      }
+      const customColorPalettes = getCustomColorPalettes();
+      customColorPalettes[colorPalette.id] = colorPalette;
+      setCustomColorPalettes(customColorPalettes);
+      for (const option of els.select.childNodes) {
+        if (option.value === "custom_" + colorPalette.id) {
+          els.select.removeChild(option);
+        }
+      }
+      els.select.append(
+        $el("option", {
+          textContent: colorPalette.name + " (custom)",
+          value: "custom_" + colorPalette.id,
+          selected: true
+        })
+      );
+      setColorPalette("custom_" + colorPalette.id);
+      await loadColorPalette(colorPalette);
+    }, "addCustomColorPalette");
+    const deleteCustomColorPalette = /* @__PURE__ */ __name(async (colorPaletteId) => {
+      const customColorPalettes = getCustomColorPalettes();
+      delete customColorPalettes[colorPaletteId];
+      setCustomColorPalettes(customColorPalettes);
+      for (const opt of els.select.childNodes) {
+        const option = opt;
+        if (option.value === defaultColorPaletteId) {
+          option.selected = true;
+        }
+        if (option.value === "custom_" + colorPaletteId) {
+          els.select.removeChild(option);
+        }
+      }
+      setColorPalette(defaultColorPaletteId);
+      await loadColorPalette(getColorPalette());
+    }, "deleteCustomColorPalette");
+    const loadColorPalette = /* @__PURE__ */ __name(async (colorPalette) => {
+      colorPalette = await completeColorPalette(colorPalette);
+      if (colorPalette.colors) {
+        if (colorPalette.colors.node_slot) {
+          Object.assign(
+            app.canvas.default_connection_color_byType,
+            colorPalette.colors.node_slot
+          );
+          Object.assign(
+            LGraphCanvas.link_type_colors,
+            colorPalette.colors.node_slot
+          );
+        }
+        if (colorPalette.colors.litegraph_base) {
+          app.canvas.node_title_color = colorPalette.colors.litegraph_base.NODE_TITLE_COLOR;
+          app.canvas.default_link_color = colorPalette.colors.litegraph_base.LINK_COLOR;
+          for (const key in colorPalette.colors.litegraph_base) {
+            if (colorPalette.colors.litegraph_base.hasOwnProperty(key) && LiteGraph.hasOwnProperty(key)) {
+              LiteGraph[key] = colorPalette.colors.litegraph_base[key];
+            }
+          }
+        }
+        if (colorPalette.colors.comfy_base) {
+          const rootStyle = document.documentElement.style;
+          for (const key in colorPalette.colors.comfy_base) {
+            rootStyle.setProperty(
+              "--" + key,
+              colorPalette.colors.comfy_base[key]
+            );
+          }
+        }
+        if (colorPalette.colors.litegraph_base.NODE_BYPASS_BGCOLOR) {
+          app.bypassBgColor = colorPalette.colors.litegraph_base.NODE_BYPASS_BGCOLOR;
+        }
+        app.canvas.draw(true, true);
+      }
+    }, "loadColorPalette");
+    const fileInput = $el("input", {
+      type: "file",
+      accept: ".json",
+      style: { display: "none" },
+      parent: document.body,
+      onchange: /* @__PURE__ */ __name(() => {
+        const file = fileInput.files[0];
+        if (file.type === "application/json" || file.name.endsWith(".json")) {
+          const reader = new FileReader();
+          reader.onload = async () => {
+            await addCustomColorPalette(JSON.parse(reader.result));
+          };
+          reader.readAsText(file);
+        }
+      }, "onchange")
+    });
+    app.ui.settings.addSetting({
+      id,
+      category: ["Comfy", "ColorPalette"],
+      name: "Color Palette",
+      type: /* @__PURE__ */ __name((name, setter, value) => {
+        const options = [
+          ...Object.values(colorPalettes).map(
+            (c) => $el("option", {
+              textContent: c.name,
+              value: c.id,
+              selected: c.id === value
+            })
+          ),
+          ...Object.values(getCustomColorPalettes()).map(
+            (c) => $el("option", {
+              textContent: `${c.name} (custom)`,
+              value: `custom_${c.id}`,
+              selected: `custom_${c.id}` === value
+            })
+          )
+        ];
+        els.select = $el(
+          "select",
+          {
+            style: {
+              marginBottom: "0.15rem",
+              width: "100%"
+            },
+            onchange: /* @__PURE__ */ __name((e) => {
+              setter(e.target.value);
+            }, "onchange")
+          },
+          options
+        );
+        return $el("tr", [
+          $el("td", [
+            els.select,
+            $el(
+              "div",
+              {
+                style: {
+                  display: "grid",
+                  gap: "4px",
+                  gridAutoFlow: "column"
+                }
+              },
+              [
+                $el("input", {
+                  type: "button",
+                  value: "Export",
+                  onclick: /* @__PURE__ */ __name(async () => {
+                    const colorPaletteId = app.ui.settings.getSettingValue(
+                      id,
+                      defaultColorPaletteId
+                    );
+                    const colorPalette = await completeColorPalette(
+                      getColorPalette(colorPaletteId)
+                    );
+                    const json = JSON.stringify(colorPalette, null, 2);
+                    const blob = new Blob([json], { type: "application/json" });
+                    const url = URL.createObjectURL(blob);
+                    const a = $el("a", {
+                      href: url,
+                      download: colorPaletteId + ".json",
+                      style: { display: "none" },
+                      parent: document.body
+                    });
+                    a.click();
+                    setTimeout(function() {
+                      a.remove();
+                      window.URL.revokeObjectURL(url);
+                    }, 0);
+                  }, "onclick")
+                }),
+                $el("input", {
+                  type: "button",
+                  value: "Import",
+                  onclick: /* @__PURE__ */ __name(() => {
+                    fileInput.click();
+                  }, "onclick")
+                }),
+                $el("input", {
+                  type: "button",
+                  value: "Template",
+                  onclick: /* @__PURE__ */ __name(async () => {
+                    const colorPalette = await getColorPaletteTemplate();
+                    const json = JSON.stringify(colorPalette, null, 2);
+                    const blob = new Blob([json], { type: "application/json" });
+                    const url = URL.createObjectURL(blob);
+                    const a = $el("a", {
+                      href: url,
+                      download: "color_palette.json",
+                      style: { display: "none" },
+                      parent: document.body
+                    });
+                    a.click();
+                    setTimeout(function() {
+                      a.remove();
+                      window.URL.revokeObjectURL(url);
+                    }, 0);
+                  }, "onclick")
+                }),
+                $el("input", {
+                  type: "button",
+                  value: "Delete",
+                  onclick: /* @__PURE__ */ __name(async () => {
+                    let colorPaletteId = app.ui.settings.getSettingValue(
+                      id,
+                      defaultColorPaletteId
+                    );
+                    if (colorPalettes[colorPaletteId]) {
+                      useToastStore().addAlert(
+                        "You cannot delete a built-in color palette."
+                      );
+                      return;
+                    }
+                    if (colorPaletteId.startsWith("custom_")) {
+                      colorPaletteId = colorPaletteId.substr(7);
+                    }
+                    await deleteCustomColorPalette(colorPaletteId);
+                  }, "onclick")
+                })
+              ]
+            )
+          ])
+        ]);
+      }, "type"),
+      defaultValue: defaultColorPaletteId,
+      async onChange(value) {
+        if (!value) {
+          return;
+        }
+        let palette = colorPalettes[value];
+        if (palette) {
+          await loadColorPalette(palette);
+        } else if (value.startsWith("custom_")) {
+          value = value.substr(7);
+          let customColorPalettes = getCustomColorPalettes();
+          if (customColorPalettes[value]) {
+            palette = customColorPalettes[value];
+            await loadColorPalette(customColorPalettes[value]);
+          }
+        }
+        let { BACKGROUND_IMAGE, CLEAR_BACKGROUND_COLOR } = palette.colors.litegraph_base;
+        if (BACKGROUND_IMAGE === void 0 || CLEAR_BACKGROUND_COLOR === void 0) {
+          const base = colorPalettes["dark"].colors.litegraph_base;
+          BACKGROUND_IMAGE = base.BACKGROUND_IMAGE;
+          CLEAR_BACKGROUND_COLOR = base.CLEAR_BACKGROUND_COLOR;
+        }
+        app.canvas.updateBackground(BACKGROUND_IMAGE, CLEAR_BACKGROUND_COLOR);
+      }
+    });
+  }
+});
+window.comfyAPI = window.comfyAPI || {};
+window.comfyAPI.colorPalette = window.comfyAPI.colorPalette || {};
+window.comfyAPI.colorPalette.defaultColorPalette = defaultColorPalette;
+window.comfyAPI.colorPalette.getColorPalette = getColorPalette;
+export {
+  defaultColorPalette as d,
+  getColorPalette as g
+};
+//# sourceMappingURL=colorPalette-D5oi2-2V.js.map