Upload 42 files

.ci/update_windows/update.py

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+import pygit2
+from datetime import datetime
+import sys
+import os
+import shutil
+import filecmp
+
+def pull(repo, remote_name='origin', branch='master'):
+    for remote in repo.remotes:
+        if remote.name == remote_name:
+            remote.fetch()
+            remote_master_id = repo.lookup_reference('refs/remotes/origin/%s' % (branch)).target
+            merge_result, _ = repo.merge_analysis(remote_master_id)
+            # Up to date, do nothing
+            if merge_result & pygit2.GIT_MERGE_ANALYSIS_UP_TO_DATE:
+                return
+            # We can just fastforward
+            elif merge_result & pygit2.GIT_MERGE_ANALYSIS_FASTFORWARD:
+                repo.checkout_tree(repo.get(remote_master_id))
+                try:
+                    master_ref = repo.lookup_reference('refs/heads/%s' % (branch))
+                    master_ref.set_target(remote_master_id)
+                except KeyError:
+                    repo.create_branch(branch, repo.get(remote_master_id))
+                repo.head.set_target(remote_master_id)
+            elif merge_result & pygit2.GIT_MERGE_ANALYSIS_NORMAL:
+                repo.merge(remote_master_id)
+
+                if repo.index.conflicts is not None:
+                    for conflict in repo.index.conflicts:
+                        print('Conflicts found in:', conflict[0].path)  # noqa: T201
+                    raise AssertionError('Conflicts, ahhhhh!!')
+
+                user = repo.default_signature
+                tree = repo.index.write_tree()
+                repo.create_commit('HEAD',
+                                    user,
+                                    user,
+                                    'Merge!',
+                                    tree,
+                                    [repo.head.target, remote_master_id])
+                # We need to do this or git CLI will think we are still merging.
+                repo.state_cleanup()
+            else:
+                raise AssertionError('Unknown merge analysis result')
+
+pygit2.option(pygit2.GIT_OPT_SET_OWNER_VALIDATION, 0)
+repo_path = str(sys.argv[1])
+repo = pygit2.Repository(repo_path)
+ident = pygit2.Signature('comfyui', 'comfy@ui')
+try:
+    print("stashing current changes")  # noqa: T201
+    repo.stash(ident)
+except KeyError:
+    print("nothing to stash")  # noqa: T201
+backup_branch_name = 'backup_branch_{}'.format(datetime.today().strftime('%Y-%m-%d_%H_%M_%S'))
+print("creating backup branch: {}".format(backup_branch_name))  # noqa: T201
+try:
+    repo.branches.local.create(backup_branch_name, repo.head.peel())
+except:
+    pass
+
+print("checking out master branch")  # noqa: T201
+branch = repo.lookup_branch('master')
+if branch is None:
+    ref = repo.lookup_reference('refs/remotes/origin/master')
+    repo.checkout(ref)
+    branch = repo.lookup_branch('master')
+    if branch is None:
+        repo.create_branch('master', repo.get(ref.target))
+else:
+    ref = repo.lookup_reference(branch.name)
+    repo.checkout(ref)
+
+print("pulling latest changes")  # noqa: T201
+pull(repo)
+
+if "--stable" in sys.argv:
+    def latest_tag(repo):
+        versions = []
+        for k in repo.references:
+            try:
+                prefix = "refs/tags/v"
+                if k.startswith(prefix):
+                    version = list(map(int, k[len(prefix):].split(".")))
+                    versions.append((version[0] * 10000000000 + version[1] * 100000 + version[2], k))
+            except:
+                pass
+        versions.sort()
+        if len(versions) > 0:
+            return versions[-1][1]
+        return None
+    latest_tag = latest_tag(repo)
+    if latest_tag is not None:
+        repo.checkout(latest_tag)
+
+print("Done!")  # noqa: T201
+
+self_update = True
+if len(sys.argv) > 2:
+    self_update = '--skip_self_update' not in sys.argv
+
+update_py_path = os.path.realpath(__file__)
+repo_update_py_path = os.path.join(repo_path, ".ci/update_windows/update.py")
+
+cur_path = os.path.dirname(update_py_path)
+
+
+req_path = os.path.join(cur_path, "current_requirements.txt")
+repo_req_path = os.path.join(repo_path, "requirements.txt")
+
+
+def files_equal(file1, file2):
+    try:
+        return filecmp.cmp(file1, file2, shallow=False)
+    except:
+        return False
+
+def file_size(f):
+    try:
+        return os.path.getsize(f)
+    except:
+        return 0
+
+
+if self_update and not files_equal(update_py_path, repo_update_py_path) and file_size(repo_update_py_path) > 10:
+    shutil.copy(repo_update_py_path, os.path.join(cur_path, "update_new.py"))
+    exit()
+
+if not os.path.exists(req_path) or not files_equal(repo_req_path, req_path):
+    import subprocess
+    try:
+        subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'install', '-r', repo_req_path])
+        shutil.copy(repo_req_path, req_path)
+    except:
+        pass
+
+
+stable_update_script = os.path.join(repo_path, ".ci/update_windows/update_comfyui_stable.bat")
+stable_update_script_to = os.path.join(cur_path, "update_comfyui_stable.bat")
+
+try:
+    if not file_size(stable_update_script_to) > 10:
+        shutil.copy(stable_update_script, stable_update_script_to)
+except:
+    pass

.ci/update_windows/update_comfyui.bat

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+@echo off
+..\python_embeded\python.exe .\update.py ..\ComfyUI\
+if exist update_new.py (
+  move /y update_new.py update.py
+  echo Running updater again since it got updated.
+  ..\python_embeded\python.exe .\update.py ..\ComfyUI\ --skip_self_update
+)
+if "%~1"=="" pause

.ci/update_windows/update_comfyui_stable.bat

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+@echo off
+..\python_embeded\python.exe .\update.py ..\ComfyUI\ --stable
+if exist update_new.py (
+  move /y update_new.py update.py
+  echo Running updater again since it got updated.
+  ..\python_embeded\python.exe .\update.py ..\ComfyUI\ --skip_self_update --stable
+)
+if "%~1"=="" pause

.ci/windows_base_files/README_VERY_IMPORTANT.txt

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+HOW TO RUN:
+
+if you have a NVIDIA gpu:
+
+run_nvidia_gpu.bat
+
+
+
+To run it in slow CPU mode:
+
+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/Comfy-Org/stable-diffusion-v1-5-archive/blob/main/v1-5-pruned-emaonly-fp16.safetensors
+
+
+RECOMMENDED WAY TO UPDATE:
+To update the ComfyUI code: update\update_comfyui.bat
+
+
+
+To update ComfyUI with the python dependencies, note that you should ONLY run this if you have issues with python dependencies.
+update\update_comfyui_and_python_dependencies.bat
+
+
+TO SHARE MODELS BETWEEN COMFYUI AND ANOTHER UI:
+In the ComfyUI directory you will find a file: extra_model_paths.yaml.example
+Rename this file to: extra_model_paths.yaml and edit it with your favorite text editor.

.ci/windows_base_files/run_cpu.bat

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+.\python_embeded\python.exe -s ComfyUI\main.py --cpu --windows-standalone-build
+pause

.ci/windows_base_files/run_nvidia_gpu.bat

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+.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build
+pause

.ci/windows_nightly_base_files/run_nvidia_gpu_fast.bat

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+.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --fast
+pause

CODEOWNERS

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+# Admins
+* @comfyanonymous
+
+# Note: Github teams syntax cannot be used here as the repo is not owned by Comfy-Org.
+# Inlined the team members for now.
+
+# Maintainers
+*.md @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink
+/tests/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink
+/tests-unit/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink
+/notebooks/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink
+/script_examples/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink
+/.github/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink
+
+# Python web server
+/api_server/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata
+/app/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata
+
+# Frontend assets
+/web/ @huchenlei @webfiltered @pythongosssss @yoland68 @robinjhuang
+
+# Extra nodes
+/comfy_extras/ @yoland68 @robinjhuang @huchenlei @pythongosssss @ltdrdata @Kosinkadink

CONTRIBUTING.md

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+# Contributing to ComfyUI
+
+Welcome, and thank you for your interest in contributing to ComfyUI!
+
+There are several ways in which you can contribute, beyond writing code. The goal of this document is to provide a high-level overview of how you can get involved.
+
+## Asking Questions
+
+Have a question? Instead of opening an issue, please ask on [Discord](https://comfy.org/discord) or [Matrix](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) channels. Our team and the community will help you.
+
+## Providing Feedback
+
+Your comments and feedback are welcome, and the development team is available via a handful of different channels.
+
+See the `#bug-report`, `#feature-request` and `#feedback` channels on Discord.
+
+## Reporting Issues
+
+Have you identified a reproducible problem in ComfyUI? Do you have a feature request? We want to hear about it! Here's how you can report your issue as effectively as possible.
+
+
+### Look For an Existing Issue
+
+Before you create a new issue, please do a search in [open issues](https://github.com/comfyanonymous/ComfyUI/issues) to see if the issue or feature request has already been filed.
+
+If you find your issue already exists, make relevant comments and add your [reaction](https://github.com/blog/2119-add-reactions-to-pull-requests-issues-and-comments). Use a reaction in place of a "+1" comment:
+
+* 👍 - upvote
+* 👎 - downvote
+
+If you cannot find an existing issue that describes your bug or feature, create a new issue. We have an issue template in place to organize new issues.
+
+
+### Creating Pull Requests
+
+* Please refer to the article on [creating pull requests](https://github.com/comfyanonymous/ComfyUI/wiki/How-to-Contribute-Code) and contributing to this project.
+
+
+## Thank You
+
+Your contributions to open source, large or small, make great projects like this possible. Thank you for taking the time to contribute.

api_server/routes/internal/README.md

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+# ComfyUI Internal Routes
+
+All routes under the `/internal` path are designated for **internal use by ComfyUI only**. These routes are not intended for use by external applications may change at any time without notice.

api_server/routes/internal/internal_routes.py

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+from aiohttp import web
+from typing import Optional
+from folder_paths import models_dir, user_directory, output_directory, folder_names_and_paths
+from api_server.services.file_service import FileService
+from api_server.services.terminal_service import TerminalService
+import app.logger
+
+class InternalRoutes:
+    '''
+    The top level web router for internal routes: /internal/*
+    The endpoints here should NOT be depended upon. It is for ComfyUI frontend use only.
+    Check README.md for more information.
+    '''
+
+    def __init__(self, prompt_server):
+        self.routes: web.RouteTableDef = web.RouteTableDef()
+        self._app: Optional[web.Application] = None
+        self.file_service = FileService({
+            "models": models_dir,
+            "user": user_directory,
+            "output": output_directory
+        })
+        self.prompt_server = prompt_server
+        self.terminal_service = TerminalService(prompt_server)
+
+    def setup_routes(self):
+        @self.routes.get('/files')
+        async def list_files(request):
+            directory_key = request.query.get('directory', '')
+            try:
+                file_list = self.file_service.list_files(directory_key)
+                return web.json_response({"files": file_list})
+            except ValueError as e:
+                return web.json_response({"error": str(e)}, status=400)
+            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("".join([(l["t"] + " - " + l["m"]) for l in app.logger.get_logs()]))
+
+        @self.routes.get('/logs/raw')
+        async def get_raw_logs(request):
+            self.terminal_service.update_size()
+            return web.json_response({
+                "entries": list(app.logger.get_logs()),
+                "size": {"cols": self.terminal_service.cols, "rows": self.terminal_service.rows}
+            })
+
+        @self.routes.patch('/logs/subscribe')
+        async def subscribe_logs(request):
+            json_data = await request.json()
+            client_id = json_data["clientId"]
+            enabled = json_data["enabled"]
+            if enabled:
+                self.terminal_service.subscribe(client_id)
+            else:
+                self.terminal_service.unsubscribe(client_id)
+
+            return web.Response(status=200)
+
+
+        @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:
+            self._app = web.Application()
+            self.setup_routes()
+            self._app.add_routes(self.routes)
+        return self._app

api_server/services/file_service.py

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+from typing import Dict, List, Optional
+from api_server.utils.file_operations import FileSystemOperations, FileSystemItem
+
+class FileService:
+    def __init__(self, allowed_directories: Dict[str, str], file_system_ops: Optional[FileSystemOperations] = None):
+        self.allowed_directories: Dict[str, str] = allowed_directories
+        self.file_system_ops: FileSystemOperations = file_system_ops or FileSystemOperations()
+
+    def list_files(self, directory_key: str) -> List[FileSystemItem]:
+        if directory_key not in self.allowed_directories:
+            raise ValueError("Invalid directory key")
+        directory_path: str = self.allowed_directories[directory_key]
+        return self.file_system_ops.walk_directory(directory_path)

api_server/services/terminal_service.py

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+from app.logger import on_flush
+import os
+import shutil
+
+
+class TerminalService:
+    def __init__(self, server):
+        self.server = server
+        self.cols = None
+        self.rows = None
+        self.subscriptions = set()
+        on_flush(self.send_messages)
+
+    def get_terminal_size(self):
+        try:
+            size = os.get_terminal_size()
+            return (size.columns, size.lines)
+        except OSError:
+            try:
+                size = shutil.get_terminal_size()
+                return (size.columns, size.lines)
+            except OSError:
+                return (80, 24)  # fallback to 80x24
+
+    def update_size(self):
+        columns, lines = self.get_terminal_size()
+        changed = False
+
+        if columns != self.cols:
+            self.cols = columns
+            changed = True
+
+        if lines != self.rows:
+            self.rows = lines
+            changed = True
+
+        if changed:
+            return {"cols": self.cols, "rows": self.rows}
+
+        return None
+
+    def subscribe(self, client_id):
+        self.subscriptions.add(client_id)
+
+    def unsubscribe(self, client_id):
+        self.subscriptions.discard(client_id)
+
+    def send_messages(self, entries):
+        if not len(entries) or not len(self.subscriptions):
+            return
+
+        new_size = self.update_size()
+
+        for client_id in self.subscriptions.copy(): # prevent: Set changed size during iteration
+            if client_id not in self.server.sockets:
+                # Automatically unsub if the socket has disconnected
+                self.unsubscribe(client_id)
+                continue
+
+            self.server.send_sync("logs", {"entries": entries, "size": new_size}, client_id)

api_server/utils/file_operations.py

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+import os
+from typing import List, Union, TypedDict, Literal
+from typing_extensions import TypeGuard
+class FileInfo(TypedDict):
+    name: str
+    path: str
+    type: Literal["file"]
+    size: int
+
+class DirectoryInfo(TypedDict):
+    name: str
+    path: str
+    type: Literal["directory"]
+
+FileSystemItem = Union[FileInfo, DirectoryInfo]
+
+def is_file_info(item: FileSystemItem) -> TypeGuard[FileInfo]:
+    return item["type"] == "file"
+
+class FileSystemOperations:
+    @staticmethod
+    def walk_directory(directory: str) -> List[FileSystemItem]:
+        file_list: List[FileSystemItem] = []
+        for root, dirs, files in os.walk(directory):
+            for name in files:
+                file_path = os.path.join(root, name)
+                relative_path = os.path.relpath(file_path, directory)
+                file_list.append({
+                    "name": name,
+                    "path": relative_path,
+                    "type": "file",
+                    "size": os.path.getsize(file_path)
+                })
+            for name in dirs:
+                dir_path = os.path.join(root, name)
+                relative_path = os.path.relpath(dir_path, directory)
+                file_list.append({
+                    "name": name,
+                    "path": relative_path,
+                    "type": "directory"
+                })
+        return file_list

app.py

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+#########################
+# app.py for ZeroGPU    #
+#########################
+
+import os
+import sys
+import random
+import torch
+import gradio as gr
+import spaces  # for ZeroGPU usage
+from typing import Sequence, Mapping, Any, Union
+
+# 1) Load your token from environment (make sure you set HF_TOKEN in Space settings)
+token = os.environ["HF_TOKEN"]
+
+# 2) We'll use huggingface_hub to download each gated model
+from huggingface_hub import hf_hub_download
+
+import shutil
+import pathlib
+
+# Create the directories we need (mirroring your ComfyUI structure)
+pathlib.Path("ComfyUI/models/vae").mkdir(parents=True, exist_ok=True)
+pathlib.Path("ComfyUI/models/clip").mkdir(parents=True, exist_ok=True)
+pathlib.Path("ComfyUI/models/clip_vision").mkdir(parents=True, exist_ok=True)
+pathlib.Path("ComfyUI/models/unet").mkdir(parents=True, exist_ok=True)
+pathlib.Path("ComfyUI/models/loras").mkdir(parents=True, exist_ok=True)
+pathlib.Path("ComfyUI/models/style_models").mkdir(parents=True, exist_ok=True)
+
+# Download each gated model into the correct local folder
+
+hf_hub_download(
+    repo_id="black-forest-labs/FLUX.1-dev",
+    filename="ae.safetensors",
+    local_dir="ComfyUI/models/vae",
+    use_auth_token=token
+)
+
+hf_hub_download(
+    repo_id="comfyanonymous/flux_text_encoders",
+    filename="t5xxl_fp16.safetensors",
+    local_dir="ComfyUI/models/clip",
+    use_auth_token=token
+)
+
+hf_hub_download(
+    repo_id="comfyanonymous/flux_text_encoders",
+    filename="clip_l.safetensors",
+    local_dir="ComfyUI/models/clip",
+    use_auth_token=token
+)
+
+hf_hub_download(
+    repo_id="black-forest-labs/FLUX.1-Fill-dev",
+    filename="flux1-fill-dev.safetensors",
+    local_dir="ComfyUI/models/unet",
+    use_auth_token=token
+)
+
+hf_hub_download(
+    repo_id="zhengchong/CatVTON",
+    filename="flux-lora/pytorch_lora_weights.safetensors",
+    local_dir="ComfyUI/models/loras",
+    # rename so it matches your code reference:
+    local_fname="catvton-flux-lora.safetensors",
+    use_auth_token=token
+)
+
+hf_hub_download(
+    repo_id="alimama-creative/FLUX.1-Turbo-Alpha",
+    filename="diffusion_pytorch_model.safetensors",
+    local_dir="ComfyUI/models/loras",
+    # rename so it matches your code reference:
+    local_fname="alimama-flux-turbo-alpha.safetensors",
+    use_auth_token=token
+)
+
+hf_hub_download(
+    repo_id="Comfy-Org/sigclip_vision_384",
+    filename="sigclip_vision_patch14_384.safetensors",
+    local_dir="ComfyUI/models/clip_vision",
+    use_auth_token=token
+)
+
+hf_hub_download(
+    repo_id="black-forest-labs/FLUX.1-Redux-dev",
+    filename="flux1-redux-dev.safetensors",
+    local_dir="ComfyUI/models/style_models",
+    use_auth_token=token
+)
+
+#############################
+# ComfyUI support functions
+#############################
+
+def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any:
+    """Returns the value at the given index of a sequence or mapping."""
+    try:
+        return obj[index]
+    except KeyError:
+        return obj["result"][index]
+
+def find_path(name: str, path: str = None) -> str:
+    import os
+    if path is None:
+        path = os.getcwd()
+
+    if name in os.listdir(path):
+        path_name = os.path.join(path, name)
+        print(f"{name} found: {path_name}")
+        return path_name
+
+    parent_directory = os.path.dirname(path)
+    if parent_directory == path:
+        return None
+
+    return find_path(name, parent_directory)
+
+def add_comfyui_directory_to_sys_path() -> None:
+    comfyui_path = find_path("ComfyUI")
+    if comfyui_path is not None and os.path.isdir(comfyui_path):
+        sys.path.append(comfyui_path)
+        print(f"'{comfyui_path}' added to sys.path")
+
+def add_extra_model_paths() -> None:
+    try:
+        from main import load_extra_path_config
+    except ImportError:
+        print("Could not import load_extra_path_config from main.py. Looking in utils.extra_config instead.")
+        from utils.extra_config import load_extra_path_config
+
+    extra_model_paths = find_path("extra_model_paths.yaml")
+    if extra_model_paths is not None:
+        load_extra_path_config(extra_model_paths)
+    else:
+        print("Could not find the extra_model_paths config file.")
+
+add_comfyui_directory_to_sys_path()
+add_extra_model_paths()
+
+def import_custom_nodes() -> None:
+    import asyncio
+    import execution
+    from nodes import init_extra_nodes
+    import server
+
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    server_instance = server.PromptServer(loop)
+    execution.PromptQueue(server_instance)
+    init_extra_nodes()
+
+##########################
+# Import node mappings
+##########################
+from nodes import NODE_CLASS_MAPPINGS
+
+
+#############################################
+# MAIN PIPELINE with ZeroGPU Decorator
+#############################################
+@spaces.GPU(duration=90)  # 90s of GPU usage; adjust as needed
+def generate_images(user_image_path):
+    """
+    This function runs your node-based pipeline, 
+    using user_image_path for loadimage_264 and 
+    returning the final saveimage_295 path.
+    """
+    import_custom_nodes()
+    with torch.inference_mode():
+        loadimage = NODE_CLASS_MAPPINGS["LoadImage"]()
+        loadimage_116 = loadimage.load_image(image="assets_black_tshirt.png")
+
+        loadautomasker = NODE_CLASS_MAPPINGS["LoadAutoMasker"]()
+        loadautomasker_120 = loadautomasker.load(catvton_path="zhengchong/CatVTON")
+
+        loadcatvtonpipeline = NODE_CLASS_MAPPINGS["LoadCatVTONPipeline"]()
+        loadcatvtonpipeline_123 = loadcatvtonpipeline.load(
+            sd15_inpaint_path="runwayml/stable-diffusion-inpainting",
+            catvton_path="zhengchong/CatVTON",
+            mixed_precision="bf16",
+        )
+
+        loadimage_264 = loadimage.load_image(
+            image=user_image_path
+        )
+
+        randomnoise = NODE_CLASS_MAPPINGS["RandomNoise"]()
+        randomnoise_273 = randomnoise.get_noise(noise_seed=random.randint(1, 2**64))
+
+        downloadandloadflorence2model = NODE_CLASS_MAPPINGS["DownloadAndLoadFlorence2Model"]()
+        downloadandloadflorence2model_274 = downloadandloadflorence2model.loadmodel(
+            model="gokaygokay/Florence-2-Flux-Large", precision="fp16", attention="sdpa"
+        )
+
+        automasker = NODE_CLASS_MAPPINGS["AutoMasker"]()
+        automasker_119 = automasker.generate(
+            cloth_type="overall",
+            pipe=get_value_at_index(loadautomasker_120, 0),
+            target_image=get_value_at_index(loadimage_264, 0),
+        )
+
+        catvton = NODE_CLASS_MAPPINGS["CatVTON"]()
+        catvton_121 = catvton.generate(
+            seed=random.randint(1, 2**64),
+            steps=50,
+            cfg=2.5,
+            pipe=get_value_at_index(loadcatvtonpipeline_123, 0),
+            target_image=get_value_at_index(loadimage_264, 0),
+            refer_image=get_value_at_index(loadimage_116, 0),
+            mask_image=get_value_at_index(automasker_119, 0),
+        )
+
+        florence2run = NODE_CLASS_MAPPINGS["Florence2Run"]()
+        florence2run_275 = florence2run.encode(
+            text_input="Haircut",
+            task="caption_to_phrase_grounding",
+            fill_mask=True,
+            keep_model_loaded=False,
+            max_new_tokens=1024,
+            num_beams=3,
+            do_sample=True,
+            output_mask_select="",
+            seed=random.randint(1, 2**64),
+            image=get_value_at_index(catvton_121, 0),
+            florence2_model=get_value_at_index(downloadandloadflorence2model_274, 0),
+        )
+
+        downloadandloadsam2model = NODE_CLASS_MAPPINGS["DownloadAndLoadSAM2Model"]()
+        downloadandloadsam2model_277 = downloadandloadsam2model.loadmodel(
+            model="sam2.1_hiera_large.safetensors",
+            segmentor="single_image",
+            device="cuda",
+            precision="fp16",
+        )
+
+        dualcliploadergguf = NODE_CLASS_MAPPINGS["DualCLIPLoaderGGUF"]()
+        dualcliploadergguf_284 = dualcliploadergguf.load_clip(
+            clip_name1="t5xxl_fp16.safetensors",
+            clip_name2="clip_l.safetensors",
+            type="flux",
+        )
+
+        cliptextencode = NODE_CLASS_MAPPINGS["CLIPTextEncode"]()
+        cliptextencode_279 = cliptextencode.encode(
+            text="Br0k0L8, Broccoli haircut with voluminous, textured curls on top resembling broccoli florets, contrasted by closely shaved tapered sides",
+            clip=get_value_at_index(dualcliploadergguf_284, 0),
+        )
+
+        clipvisionloader = NODE_CLASS_MAPPINGS["CLIPVisionLoader"]()
+        clipvisionloader_281 = clipvisionloader.load_clip(
+            clip_name="sigclip_vision_patch14_384.safetensors"
+        )
+
+        loadimage_289 = loadimage.load_image(image="assets_broc_ref.jpg")
+
+        clipvisionencode = NODE_CLASS_MAPPINGS["CLIPVisionEncode"]()
+        clipvisionencode_282 = clipvisionencode.encode(
+            crop="center",
+            clip_vision=get_value_at_index(clipvisionloader_281, 0),
+            image=get_value_at_index(loadimage_289, 0),
+        )
+
+        vaeloader = NODE_CLASS_MAPPINGS["VAELoader"]()
+        vaeloader_285 = vaeloader.load_vae(vae_name="ae.safetensors")
+
+        stylemodelloader = NODE_CLASS_MAPPINGS["StyleModelLoader"]()
+        stylemodelloader_292 = stylemodelloader.load_style_model(
+            style_model_name="flux1-redux-dev.safetensors"
+        )
+
+        stylemodelapply = NODE_CLASS_MAPPINGS["StyleModelApply"]()
+        stylemodelapply_280 = stylemodelapply.apply_stylemodel(
+            strength=1,
+            strength_type="multiply",
+            conditioning=get_value_at_index(cliptextencode_279, 0),
+            style_model=get_value_at_index(stylemodelloader_292, 0),
+            clip_vision_output=get_value_at_index(clipvisionencode_282, 0),
+        )
+
+        fluxguidance = NODE_CLASS_MAPPINGS["FluxGuidance"]()
+        fluxguidance_288 = fluxguidance.append(
+            guidance=30, conditioning=get_value_at_index(stylemodelapply_280, 0)
+        )
+
+        conditioningzeroout = NODE_CLASS_MAPPINGS["ConditioningZeroOut"]()
+        conditioningzeroout_287 = conditioningzeroout.zero_out(
+            conditioning=get_value_at_index(fluxguidance_288, 0)
+        )
+
+        florence2tocoordinates = NODE_CLASS_MAPPINGS["Florence2toCoordinates"]()
+        florence2tocoordinates_276 = florence2tocoordinates.segment(
+            index="", batch=False, data=get_value_at_index(florence2run_275, 3)
+        )
+
+        sam2segmentation = NODE_CLASS_MAPPINGS["Sam2Segmentation"]()
+        sam2segmentation_278 = sam2segmentation.segment(
+            keep_model_loaded=False,
+            individual_objects=False,
+            sam2_model=get_value_at_index(downloadandloadsam2model_277, 0),
+            image=get_value_at_index(florence2run_275, 0),
+            bboxes=get_value_at_index(florence2tocoordinates_276, 1),
+        )
+
+        growmask = NODE_CLASS_MAPPINGS["GrowMask"]()
+        growmask_299 = growmask.expand_mask(
+            expand=35,
+            tapered_corners=True,
+            mask=get_value_at_index(sam2segmentation_278, 0),
+        )
+
+        layermask_segformerb2clothesultra = NODE_CLASS_MAPPINGS["LayerMask: SegformerB2ClothesUltra"]()
+        layermask_segformerb2clothesultra_300 = layermask_segformerb2clothesultra.segformer_ultra(
+            face=True,
+            hair=False,
+            hat=False,
+            sunglass=False,
+            left_arm=False,
+            right_arm=False,
+            left_leg=False,
+            right_leg=False,
+            upper_clothes=True,
+            skirt=False,
+            pants=False,
+            dress=False,
+            belt=False,
+            shoe=False,
+            bag=False,
+            scarf=True,
+            detail_method="VITMatte",
+            detail_erode=12,
+            detail_dilate=6,
+            black_point=0.15,
+            white_point=0.99,
+            process_detail=True,
+            device="cuda",
+            max_megapixels=2,
+            image=get_value_at_index(catvton_121, 0),
+        )
+
+        masks_subtract = NODE_CLASS_MAPPINGS["Masks Subtract"]()
+        masks_subtract_296 = masks_subtract.subtract_masks(
+            masks_a=get_value_at_index(growmask_299, 0),
+            masks_b=get_value_at_index(layermask_segformerb2clothesultra_300, 1),
+        )
+
+        inpaintmodelconditioning = NODE_CLASS_MAPPINGS["InpaintModelConditioning"]()
+        inpaintmodelconditioning_286 = inpaintmodelconditioning.encode(
+            noise_mask=True,
+            positive=get_value_at_index(fluxguidance_288, 0),
+            negative=get_value_at_index(conditioningzeroout_287, 0),
+            vae=get_value_at_index(vaeloader_285, 0),
+            pixels=get_value_at_index(catvton_121, 0),
+            mask=get_value_at_index(masks_subtract_296, 0),
+        )
+
+        unetloader = NODE_CLASS_MAPPINGS["UNETLoader"]()
+        unetloader_291 = unetloader.load_unet(
+            unet_name="flux1-fill-dev.safetensors", weight_dtype="default"
+        )
+
+        loraloadermodelonly = NODE_CLASS_MAPPINGS["LoraLoaderModelOnly"]()
+        loraloadermodelonly_290 = loraloadermodelonly.load_lora_model_only(
+            lora_name="alimama-flux-turbo-alpha.safetensors",
+            strength_model=1,
+            model=get_value_at_index(unetloader_291, 0),
+        )
+
+        ksampler = NODE_CLASS_MAPPINGS["KSampler"]()
+        vaedecode = NODE_CLASS_MAPPINGS["VAEDecode"]()
+        saveimage = NODE_CLASS_MAPPINGS["SaveImage"]()
+
+        # We'll do a single pass
+        for q in range(1):
+            ksampler_283 = ksampler.sample(
+                seed=random.randint(1, 2**64),
+                steps=10,
+                cfg=1,
+                sampler_name="dpmpp_2m",
+                scheduler="sgm_uniform",
+                denoise=1,
+                model=get_value_at_index(loraloadermodelonly_290, 0),
+                positive=get_value_at_index(inpaintmodelconditioning_286, 0),
+                negative=get_value_at_index(inpaintmodelconditioning_286, 1),
+                latent_image=get_value_at_index(inpaintmodelconditioning_286, 2),
+            )
+
+            vaedecode_294 = vaedecode.decode(
+                samples=get_value_at_index(ksampler_283, 0),
+                vae=get_value_at_index(vaeloader_285, 0),
+            )
+
+            saveimage_295 = saveimage.save_images(
+                filename_prefix="The_Broccolator_",
+                images=get_value_at_index(vaedecode_294, 0),
+            )
+
+        # final_output_path is the only one we return
+        final_output_path = f"output/{saveimage_295['ui']['images'][0]['filename']}"
+        return final_output_path
+
+
+###################################
+# A simple Gradio interface
+###################################
+with gr.Blocks() as demo:
+    gr.Markdown("## The Broccolator 🥦\nUpload an image for `loadimage_264` and see final output.")
+    with gr.Row():
+        with gr.Column():
+            user_input_image = gr.Image(type="filepath", label="Input Image")
+            btn_generate = gr.Button("Generate")
+        with gr.Column():
+            final_image = gr.Image(label="Final output (saveimage_295)")
+
+    btn_generate.click(
+        fn=generate_images,
+        inputs=user_input_image,
+        outputs=final_image
+    )
+
+demo.launch(debug=True)

comfy_execution/caching.py

@@ -0,0 +1,318 @@
+import itertools
+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 = {}
+        self.subcache_keys = {}
+
+    def add_keys(self, node_ids):
+        raise NotImplementedError()
+
+    def all_node_ids(self):
+        return set(self.keys.keys())
+
+    def get_used_keys(self):
+        return self.keys.values()
+
+    def get_used_subcache_keys(self):
+        return self.subcache_keys.values()
+
+    def get_data_key(self, node_id):
+        return self.keys.get(node_id, None)
+
+    def get_subcache_key(self, node_id):
+        return self.subcache_keys.get(node_id, None)
+
+class Unhashable:
+    def __init__(self):
+        self.value = float("NaN")
+
+def to_hashable(obj):
+    # So that we don't infinitely recurse since frozenset and tuples
+    # are Sequences.
+    if isinstance(obj, (int, float, str, bool, type(None))):
+        return obj
+    elif isinstance(obj, Mapping):
+        return frozenset([(to_hashable(k), to_hashable(v)) for k, v in sorted(obj.items())])
+    elif isinstance(obj, Sequence):
+        return frozenset(zip(itertools.count(), [to_hashable(i) for i in obj]))
+    else:
+        # TODO - Support other objects like tensors?
+        return Unhashable()
+
+class CacheKeySetID(CacheKeySet):
+    def __init__(self, dynprompt, node_ids, is_changed_cache):
+        super().__init__(dynprompt, node_ids, is_changed_cache)
+        self.dynprompt = dynprompt
+        self.add_keys(node_ids)
+
+    def add_keys(self, node_ids):
+        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"])
+
+class CacheKeySetInputSignature(CacheKeySet):
+    def __init__(self, dynprompt, node_ids, is_changed_cache):
+        super().__init__(dynprompt, node_ids, is_changed_cache)
+        self.dynprompt = dynprompt
+        self.is_changed_cache = is_changed_cache
+        self.add_keys(node_ids)
+
+    def include_node_id_in_input(self) -> bool:
+        return False
+
+    def add_keys(self, node_ids):
+        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"])
+
+    def get_node_signature(self, dynprompt, node_id):
+        signature = []
+        ancestors, order_mapping = self.get_ordered_ancestry(dynprompt, node_id)
+        signature.append(self.get_immediate_node_signature(dynprompt, node_id, order_mapping))
+        for ancestor_id in ancestors:
+            signature.append(self.get_immediate_node_signature(dynprompt, ancestor_id, order_mapping))
+        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) or include_unique_id_in_input(class_type):
+            signature.append(node_id)
+        inputs = node["inputs"]
+        for key in sorted(inputs.keys()):
+            if is_link(inputs[key]):
+                (ancestor_id, ancestor_socket) = inputs[key]
+                ancestor_index = ancestor_order_mapping[ancestor_id]
+                signature.append((key,("ANCESTOR", ancestor_index, ancestor_socket)))
+            else:
+                signature.append((key, inputs[key]))
+        return signature
+
+    # This function returns a list of all ancestors of the given node. The order of the list is
+    # deterministic based on which specific inputs the ancestor is connected by.
+    def get_ordered_ancestry(self, dynprompt, node_id):
+        ancestors = []
+        order_mapping = {}
+        self.get_ordered_ancestry_internal(dynprompt, node_id, ancestors, order_mapping)
+        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:
+            if is_link(inputs[key]):
+                ancestor_id = inputs[key][0]
+                if ancestor_id not in order_mapping:
+                    ancestors.append(ancestor_id)
+                    order_mapping[ancestor_id] = len(ancestors) - 1
+                    self.get_ordered_ancestry_internal(dynprompt, ancestor_id, ancestors, order_mapping)
+
+class BasicCache:
+    def __init__(self, key_class):
+        self.key_class = key_class
+        self.initialized = False
+        self.dynprompt: DynamicPrompt
+        self.cache_key_set: CacheKeySet
+        self.cache = {}
+        self.subcaches = {}
+
+    def set_prompt(self, dynprompt, node_ids, is_changed_cache):
+        self.dynprompt = dynprompt
+        self.cache_key_set = self.key_class(dynprompt, node_ids, is_changed_cache)
+        self.is_changed_cache = is_changed_cache
+        self.initialized = True
+
+    def all_node_ids(self):
+        assert self.initialized
+        node_ids = self.cache_key_set.all_node_ids()
+        for subcache in self.subcaches.values():
+            node_ids = node_ids.union(subcache.all_node_ids())
+        return node_ids
+
+    def _clean_cache(self):
+        preserve_keys = set(self.cache_key_set.get_used_keys())
+        to_remove = []
+        for key in self.cache:
+            if key not in preserve_keys:
+                to_remove.append(key)
+        for key in to_remove:
+            del self.cache[key]
+
+    def _clean_subcaches(self):
+        preserve_subcaches = set(self.cache_key_set.get_used_subcache_keys())
+
+        to_remove = []
+        for key in self.subcaches:
+            if key not in preserve_subcaches:
+                to_remove.append(key)
+        for key in to_remove:
+            del self.subcaches[key]
+
+    def clean_unused(self):
+        assert self.initialized
+        self._clean_cache()
+        self._clean_subcaches()
+
+    def _set_immediate(self, node_id, value):
+        assert self.initialized
+        cache_key = self.cache_key_set.get_data_key(node_id)
+        self.cache[cache_key] = value
+
+    def _get_immediate(self, node_id):
+        if not self.initialized:
+            return None
+        cache_key = self.cache_key_set.get_data_key(node_id)
+        if cache_key in self.cache:
+            return self.cache[cache_key]
+        else:
+            return None
+
+    def _ensure_subcache(self, node_id, children_ids):
+        subcache_key = self.cache_key_set.get_subcache_key(node_id)
+        subcache = self.subcaches.get(subcache_key, None)
+        if subcache is None:
+            subcache = BasicCache(self.key_class)
+            self.subcaches[subcache_key] = subcache
+        subcache.set_prompt(self.dynprompt, children_ids, self.is_changed_cache)
+        return subcache
+
+    def _get_subcache(self, node_id):
+        assert self.initialized
+        subcache_key = self.cache_key_set.get_subcache_key(node_id)
+        if subcache_key in self.subcaches:
+            return self.subcaches[subcache_key]
+        else:
+            return None
+
+    def recursive_debug_dump(self):
+        result = []
+        for key in self.cache:
+            result.append({"key": key, "value": self.cache[key]})
+        for key in self.subcaches:
+            result.append({"subcache_key": key, "subcache": self.subcaches[key].recursive_debug_dump()})
+        return result
+
+class HierarchicalCache(BasicCache):
+    def __init__(self, key_class):
+        super().__init__(key_class)
+
+    def _get_cache_for(self, node_id):
+        assert self.dynprompt is not None
+        parent_id = self.dynprompt.get_parent_node_id(node_id)
+        if parent_id is None:
+            return self
+
+        hierarchy = []
+        while parent_id is not None:
+            hierarchy.append(parent_id)
+            parent_id = self.dynprompt.get_parent_node_id(parent_id)
+
+        cache = self
+        for parent_id in reversed(hierarchy):
+            cache = cache._get_subcache(parent_id)
+            if cache is None:
+                return None
+        return cache
+
+    def get(self, node_id):
+        cache = self._get_cache_for(node_id)
+        if cache is None:
+            return None
+        return cache._get_immediate(node_id)
+
+    def set(self, node_id, value):
+        cache = self._get_cache_for(node_id)
+        assert cache is not None
+        cache._set_immediate(node_id, value)
+
+    def ensure_subcache_for(self, node_id, children_ids):
+        cache = self._get_cache_for(node_id)
+        assert cache is not None
+        return cache._ensure_subcache(node_id, children_ids)
+
+class LRUCache(BasicCache):
+    def __init__(self, key_class, max_size=100):
+        super().__init__(key_class)
+        self.max_size = max_size
+        self.min_generation = 0
+        self.generation = 0
+        self.used_generation = {}
+        self.children = {}
+
+    def set_prompt(self, dynprompt, node_ids, is_changed_cache):
+        super().set_prompt(dynprompt, node_ids, is_changed_cache)
+        self.generation += 1
+        for node_id in node_ids:
+            self._mark_used(node_id)
+
+    def clean_unused(self):
+        while len(self.cache) > self.max_size and self.min_generation < self.generation:
+            self.min_generation += 1
+            to_remove = [key for key in self.cache if self.used_generation[key] < self.min_generation]
+            for key in to_remove:
+                del self.cache[key]
+                del self.used_generation[key]
+                if key in self.children:
+                    del self.children[key]
+        self._clean_subcaches()
+
+    def get(self, node_id):
+        self._mark_used(node_id)
+        return self._get_immediate(node_id)
+
+    def _mark_used(self, node_id):
+        cache_key = self.cache_key_set.get_data_key(node_id)
+        if cache_key is not None:
+            self.used_generation[cache_key] = self.generation
+
+    def set(self, node_id, value):
+        self._mark_used(node_id)
+        return self._set_immediate(node_id, value)
+
+    def ensure_subcache_for(self, node_id, children_ids):
+        # Just uses subcaches for tracking 'live' nodes
+        super()._ensure_subcache(node_id, children_ids)
+
+        self.cache_key_set.add_keys(children_ids)
+        self._mark_used(node_id)
+        cache_key = self.cache_key_set.get_data_key(node_id)
+        self.children[cache_key] = []
+        for child_id in children_ids:
+            self._mark_used(child_id)
+            self.children[cache_key].append(self.cache_key_set.get_data_key(child_id))
+        return self
+

comfy_execution/graph.py

@@ -0,0 +1,270 @@
+import nodes
+
+from comfy_execution.graph_utils import is_link
+
+class DependencyCycleError(Exception):
+    pass
+
+class NodeInputError(Exception):
+    pass
+
+class NodeNotFoundError(Exception):
+    pass
+
+class DynamicPrompt:
+    def __init__(self, original_prompt):
+        # The original prompt provided by the user
+        self.original_prompt = original_prompt
+        # Any extra pieces of the graph created during execution
+        self.ephemeral_prompt = {}
+        self.ephemeral_parents = {}
+        self.ephemeral_display = {}
+
+    def get_node(self, node_id):
+        if node_id in self.ephemeral_prompt:
+            return self.ephemeral_prompt[node_id]
+        if node_id in self.original_prompt:
+            return self.original_prompt[node_id]
+        raise NodeNotFoundError(f"Node {node_id} not found")
+
+    def has_node(self, node_id):
+        return node_id in self.original_prompt or node_id in self.ephemeral_prompt
+
+    def add_ephemeral_node(self, node_id, node_info, parent_id, display_id):
+        self.ephemeral_prompt[node_id] = node_info
+        self.ephemeral_parents[node_id] = parent_id
+        self.ephemeral_display[node_id] = display_id
+
+    def get_real_node_id(self, node_id):
+        while node_id in self.ephemeral_parents:
+            node_id = self.ephemeral_parents[node_id]
+        return node_id
+
+    def get_parent_node_id(self, node_id):
+        return self.ephemeral_parents.get(node_id, None)
+
+    def get_display_node_id(self, node_id):
+        while node_id in self.ephemeral_display:
+            node_id = self.ephemeral_display[node_id]
+        return node_id
+
+    def all_node_ids(self):
+        return set(self.original_prompt.keys()).union(set(self.ephemeral_prompt.keys()))
+
+    def get_original_prompt(self):
+        return self.original_prompt
+
+def get_input_info(class_def, input_name, valid_inputs=None):
+    valid_inputs = valid_inputs or class_def.INPUT_TYPES()
+    input_info = None
+    input_category = None
+    if "required" in valid_inputs and input_name in valid_inputs["required"]:
+        input_category = "required"
+        input_info = valid_inputs["required"][input_name]
+    elif "optional" in valid_inputs and input_name in valid_inputs["optional"]:
+        input_category = "optional"
+        input_info = valid_inputs["optional"][input_name]
+    elif "hidden" in valid_inputs and input_name in valid_inputs["hidden"]:
+        input_category = "hidden"
+        input_info = valid_inputs["hidden"][input_name]
+    if input_info is None:
+        return None, None, None
+    input_type = input_info[0]
+    if len(input_info) > 1:
+        extra_info = input_info[1]
+    else:
+        extra_info = {}
+    return input_type, input_category, extra_info
+
+class TopologicalSort:
+    def __init__(self, dynprompt):
+        self.dynprompt = dynprompt
+        self.pendingNodes = {}
+        self.blockCount = {} # Number of nodes this node is directly blocked by
+        self.blocking = {} # Which nodes are blocked by this node
+
+    def get_input_info(self, unique_id, input_name):
+        class_type = self.dynprompt.get_node(unique_id)["class_type"]
+        class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+        return get_input_info(class_def, input_name)
+
+    def make_input_strong_link(self, to_node_id, to_input):
+        inputs = self.dynprompt.get_node(to_node_id)["inputs"]
+        if to_input not in inputs:
+            raise NodeInputError(f"Node {to_node_id} says it needs input {to_input}, but there is no input to that node at all")
+        value = inputs[to_input]
+        if not is_link(value):
+            raise NodeInputError(f"Node {to_node_id} says it needs input {to_input}, but that value is a constant")
+        from_node_id, from_socket = value
+        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):
+        if not self.is_cached(from_node_id):
+            self.add_node(from_node_id)
+            if to_node_id not in self.blocking[from_node_id]:
+                self.blocking[from_node_id][to_node_id] = {}
+                self.blockCount[to_node_id] += 1
+            self.blocking[from_node_id][to_node_id][from_socket] = True
+
+    def add_node(self, node_unique_id, include_lazy=False, subgraph_nodes=None):
+        node_ids = [node_unique_id]
+        links = []
+
+        while len(node_ids) > 0:
+            unique_id = node_ids.pop()
+            if unique_id in self.pendingNodes:
+                continue
+
+            self.pendingNodes[unique_id] = True
+            self.blockCount[unique_id] = 0
+            self.blocking[unique_id] = {}
+
+            inputs = self.dynprompt.get_node(unique_id)["inputs"]
+            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]
+
+    def pop_node(self, unique_id):
+        del self.pendingNodes[unique_id]
+        for blocked_node_id in self.blocking[unique_id]:
+            self.blockCount[blocked_node_id] -= 1
+        del self.blocking[unique_id]
+
+    def is_empty(self):
+        return len(self.pendingNodes) == 0
+
+class ExecutionList(TopologicalSort):
+    """
+    ExecutionList implements a topological dissolve of the graph. After a node is staged for execution,
+    it can still be returned to the graph after having further dependencies added.
+    """
+    def __init__(self, dynprompt, output_cache):
+        super().__init__(dynprompt)
+        self.output_cache = output_cache
+        self.staged_node_id = None
+
+    def is_cached(self, node_id):
+        return self.output_cache.get(node_id) is not None
+
+    def stage_node_execution(self):
+        assert self.staged_node_id is None
+        if self.is_empty():
+            return None, None, None
+        available = self.get_ready_nodes()
+        if len(available) == 0:
+            cycled_nodes = self.get_nodes_in_cycle()
+            # Because cycles composed entirely of static nodes are caught during initial validation,
+            # we will 'blame' the first node in the cycle that is not a static node.
+            blamed_node = cycled_nodes[0]
+            for node_id in cycled_nodes:
+                display_node_id = self.dynprompt.get_display_node_id(node_id)
+                if display_node_id != node_id:
+                    blamed_node = display_node_id
+                    break
+            ex = DependencyCycleError("Dependency cycle detected")
+            error_details = {
+                "node_id": blamed_node,
+                "exception_message": str(ex),
+                "exception_type": "graph.DependencyCycleError",
+                "traceback": [],
+                "current_inputs": []
+            }
+            return None, error_details, ex
+
+        self.staged_node_id = self.ux_friendly_pick_node(available)
+        return self.staged_node_id, None, None
+
+    def ux_friendly_pick_node(self, node_list):
+        # If an output node is available, do that first.
+        # Technically this has no effect on the overall length of execution, but it feels better as a user
+        # for a PreviewImage to display a result as soon as it can
+        # Some other heuristics could probably be used here to improve the UX further.
+        def is_output(node_id):
+            class_type = self.dynprompt.get_node(node_id)["class_type"]
+            class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+            if hasattr(class_def, 'OUTPUT_NODE') and class_def.OUTPUT_NODE == True:
+                return True
+            return False
+
+        for node_id in node_list:
+            if is_output(node_id):
+                return node_id
+
+        #This should handle the VAEDecode -> preview case
+        for node_id in node_list:
+            for blocked_node_id in self.blocking[node_id]:
+                if is_output(blocked_node_id):
+                    return node_id
+
+        #This should handle the VAELoader -> VAEDecode -> preview case
+        for node_id in node_list:
+            for blocked_node_id in self.blocking[node_id]:
+                for blocked_node_id1 in self.blocking[blocked_node_id]:
+                    if is_output(blocked_node_id1):
+                        return node_id
+
+        #TODO: this function should be improved
+        return node_list[0]
+
+    def unstage_node_execution(self):
+        assert self.staged_node_id is not None
+        self.staged_node_id = None
+
+    def complete_node_execution(self):
+        node_id = self.staged_node_id
+        self.pop_node(node_id)
+        self.staged_node_id = None
+
+    def get_nodes_in_cycle(self):
+        # We'll dissolve the graph in reverse topological order to leave only the nodes in the cycle.
+        # We're skipping some of the performance optimizations from the original TopologicalSort to keep
+        # the code simple (and because having a cycle in the first place is a catastrophic error)
+        blocked_by = { node_id: {} for node_id in self.pendingNodes }
+        for from_node_id in self.blocking:
+            for to_node_id in self.blocking[from_node_id]:
+                if True in self.blocking[from_node_id][to_node_id].values():
+                    blocked_by[to_node_id][from_node_id] = True
+        to_remove = [node_id for node_id in blocked_by if len(blocked_by[node_id]) == 0]
+        while len(to_remove) > 0:
+            for node_id in to_remove:
+                for to_node_id in blocked_by:
+                    if node_id in blocked_by[to_node_id]:
+                        del blocked_by[to_node_id][node_id]
+                del blocked_by[node_id]
+            to_remove = [node_id for node_id in blocked_by if len(blocked_by[node_id]) == 0]
+        return list(blocked_by.keys())
+
+class ExecutionBlocker:
+    """
+    Return this from a node and any users will be blocked with the given error message.
+    If the message is None, execution will be blocked silently instead.
+    Generally, you should avoid using this functionality unless absolutely necessary. Whenever it's
+    possible, a lazy input will be more efficient and have a better user experience.
+    This functionality is useful in two cases:
+    1. You want to conditionally prevent an output node from executing. (Particularly a built-in node
+       like SaveImage. For your own output nodes, I would recommend just adding a BOOL input and using
+       lazy evaluation to let it conditionally disable itself.)
+    2. You have a node with multiple possible outputs, some of which are invalid and should not be used.
+       (I would recommend not making nodes like this in the future -- instead, make multiple nodes with
+       different outputs. Unfortunately, there are several popular existing nodes using this pattern.)
+    """
+    def __init__(self, message):
+        self.message = message
+

comfy_execution/graph_utils.py

@@ -0,0 +1,139 @@
+def is_link(obj):
+    if not isinstance(obj, list):
+        return False
+    if len(obj) != 2:
+        return False
+    if not isinstance(obj[0], str):
+        return False
+    if not isinstance(obj[1], int) and not isinstance(obj[1], float):
+        return False
+    return True
+
+# The GraphBuilder is just a utility class that outputs graphs in the form expected by the ComfyUI back-end
+class GraphBuilder:
+    _default_prefix_root = ""
+    _default_prefix_call_index = 0
+    _default_prefix_graph_index = 0
+
+    def __init__(self, prefix = None):
+        if prefix is None:
+            self.prefix = GraphBuilder.alloc_prefix()
+        else:
+            self.prefix = prefix
+        self.nodes = {}
+        self.id_gen = 1
+
+    @classmethod
+    def set_default_prefix(cls, prefix_root, call_index, graph_index = 0):
+        cls._default_prefix_root = prefix_root
+        cls._default_prefix_call_index = call_index
+        cls._default_prefix_graph_index = graph_index
+
+    @classmethod
+    def alloc_prefix(cls, root=None, call_index=None, graph_index=None):
+        if root is None:
+            root = GraphBuilder._default_prefix_root
+        if call_index is None:
+            call_index = GraphBuilder._default_prefix_call_index
+        if graph_index is None:
+            graph_index = GraphBuilder._default_prefix_graph_index
+        result = f"{root}.{call_index}.{graph_index}."
+        GraphBuilder._default_prefix_graph_index += 1
+        return result
+
+    def node(self, class_type, id=None, **kwargs):
+        if id is None:
+            id = str(self.id_gen)
+            self.id_gen += 1
+        id = self.prefix + id
+        if id in self.nodes:
+            return self.nodes[id]
+
+        node = Node(id, class_type, kwargs)
+        self.nodes[id] = node
+        return node
+
+    def lookup_node(self, id):
+        id = self.prefix + id
+        return self.nodes.get(id)
+
+    def finalize(self):
+        output = {}
+        for node_id, node in self.nodes.items():
+            output[node_id] = node.serialize()
+        return output
+
+    def replace_node_output(self, node_id, index, new_value):
+        node_id = self.prefix + node_id
+        to_remove = []
+        for node in self.nodes.values():
+            for key, value in node.inputs.items():
+                if is_link(value) and value[0] == node_id and value[1] == index:
+                    if new_value is None:
+                        to_remove.append((node, key))
+                    else:
+                        node.inputs[key] = new_value
+        for node, key in to_remove:
+            del node.inputs[key]
+
+    def remove_node(self, id):
+        id = self.prefix + id
+        del self.nodes[id]
+
+class Node:
+    def __init__(self, id, class_type, inputs):
+        self.id = id
+        self.class_type = class_type
+        self.inputs = inputs
+        self.override_display_id = None
+
+    def out(self, index):
+        return [self.id, index]
+
+    def set_input(self, key, value):
+        if value is None:
+            if key in self.inputs:
+                del self.inputs[key]
+        else:
+            self.inputs[key] = value
+
+    def get_input(self, key):
+        return self.inputs.get(key)
+
+    def set_override_display_id(self, override_display_id):
+        self.override_display_id = override_display_id
+
+    def serialize(self):
+        serialized = {
+            "class_type": self.class_type,
+            "inputs": self.inputs
+        }
+        if self.override_display_id is not None:
+            serialized["override_display_id"] = self.override_display_id
+        return serialized
+
+def add_graph_prefix(graph, outputs, prefix):
+    # Change the node IDs and any internal links
+    new_graph = {}
+    for node_id, node_info in graph.items():
+        # Make sure the added nodes have unique IDs
+        new_node_id = prefix + node_id
+        new_node = { "class_type": node_info["class_type"], "inputs": {} }
+        for input_name, input_value in node_info.get("inputs", {}).items():
+            if is_link(input_value):
+                new_node["inputs"][input_name] = [prefix + input_value[0], input_value[1]]
+            else:
+                new_node["inputs"][input_name] = input_value
+        new_graph[new_node_id] = new_node
+
+    # Change the node IDs in the outputs
+    new_outputs = []
+    for n in range(len(outputs)):
+        output = outputs[n]
+        if is_link(output):
+            new_outputs.append([prefix + output[0], output[1]])
+        else:
+            new_outputs.append(output)
+
+    return new_graph, tuple(new_outputs)
+

comfy_execution/validation.py

@@ -0,0 +1,39 @@
+from __future__ import annotations
+
+
+def validate_node_input(
+    received_type: str, input_type: str, strict: bool = False
+) -> bool:
+    """
+    received_type and input_type are both strings of the form "T1,T2,...".
+
+    If strict is True, the input_type must contain the received_type.
+      For example, if received_type is "STRING" and input_type is "STRING,INT",
+      this will return True. But if received_type is "STRING,INT" and input_type is
+      "INT", this will return False.
+
+    If strict is False, the input_type must have overlap with the received_type.
+      For example, if received_type is "STRING,BOOLEAN" and input_type is "STRING,INT",
+      this will return True.
+
+    Supports pre-union type extension behaviour of ``__ne__`` overrides.
+    """
+    # If the types are exactly the same, we can return immediately
+    # Use pre-union behaviour: inverse of `__ne__`
+    if not received_type != input_type:
+        return True
+
+    # Not equal, and not strings
+    if not isinstance(received_type, str) or not isinstance(input_type, str):
+        return False
+
+    # Split the type strings into sets for comparison
+    received_types = set(t.strip() for t in received_type.split(","))
+    input_types = set(t.strip() for t in input_type.split(","))
+
+    if strict:
+        # In strict mode, all received types must be in the input types
+        return received_types.issubset(input_types)
+    else:
+        # In non-strict mode, there must be at least one type in common
+        return len(received_types.intersection(input_types)) > 0

comfyui_version.py

@@ -0,0 +1,3 @@
+# This file is automatically generated by the build process when version is
+# updated in pyproject.toml.
+__version__ = "0.3.12"

cuda_malloc.py

@@ -0,0 +1,90 @@
+import os
+import importlib.util
+from comfy.cli_args import args
+import subprocess
+
+#Can't use pytorch to get the GPU names because the cuda malloc has to be set before the first import.
+def get_gpu_names():
+    if os.name == 'nt':
+        import ctypes
+
+        # Define necessary C structures and types
+        class DISPLAY_DEVICEA(ctypes.Structure):
+            _fields_ = [
+                ('cb', ctypes.c_ulong),
+                ('DeviceName', ctypes.c_char * 32),
+                ('DeviceString', ctypes.c_char * 128),
+                ('StateFlags', ctypes.c_ulong),
+                ('DeviceID', ctypes.c_char * 128),
+                ('DeviceKey', ctypes.c_char * 128)
+            ]
+
+        # Load user32.dll
+        user32 = ctypes.windll.user32
+
+        # Call EnumDisplayDevicesA
+        def enum_display_devices():
+            device_info = DISPLAY_DEVICEA()
+            device_info.cb = ctypes.sizeof(device_info)
+            device_index = 0
+            gpu_names = set()
+
+            while user32.EnumDisplayDevicesA(None, device_index, ctypes.byref(device_info), 0):
+                device_index += 1
+                gpu_names.add(device_info.DeviceString.decode('utf-8'))
+            return gpu_names
+        return enum_display_devices()
+    else:
+        gpu_names = set()
+        out = subprocess.check_output(['nvidia-smi', '-L'])
+        for l in out.split(b'\n'):
+            if len(l) > 0:
+                gpu_names.add(l.decode('utf-8').split(' (UUID')[0])
+        return gpu_names
+
+blacklist = {"GeForce GTX TITAN X", "GeForce GTX 980", "GeForce GTX 970", "GeForce GTX 960", "GeForce GTX 950", "GeForce 945M",
+                "GeForce 940M", "GeForce 930M", "GeForce 920M", "GeForce 910M", "GeForce GTX 750", "GeForce GTX 745", "Quadro K620",
+                "Quadro K1200", "Quadro K2200", "Quadro M500", "Quadro M520", "Quadro M600", "Quadro M620", "Quadro M1000",
+                "Quadro M1200", "Quadro M2000", "Quadro M2200", "Quadro M3000", "Quadro M4000", "Quadro M5000", "Quadro M5500", "Quadro M6000",
+                "GeForce MX110", "GeForce MX130", "GeForce 830M", "GeForce 840M", "GeForce GTX 850M", "GeForce GTX 860M",
+                "GeForce GTX 1650", "GeForce GTX 1630", "Tesla M4", "Tesla M6", "Tesla M10", "Tesla M40", "Tesla M60"
+                }
+
+def cuda_malloc_supported():
+    try:
+        names = get_gpu_names()
+    except:
+        names = set()
+    for x in names:
+        if "NVIDIA" in x:
+            for b in blacklist:
+                if b in x:
+                    return False
+    return True
+
+
+if not args.cuda_malloc:
+    try:
+        version = ""
+        torch_spec = importlib.util.find_spec("torch")
+        for folder in torch_spec.submodule_search_locations:
+            ver_file = os.path.join(folder, "version.py")
+            if os.path.isfile(ver_file):
+                spec = importlib.util.spec_from_file_location("torch_version_import", ver_file)
+                module = importlib.util.module_from_spec(spec)
+                spec.loader.exec_module(module)
+                version = module.__version__
+        if int(version[0]) >= 2: #enable by default for torch version 2.0 and up
+            args.cuda_malloc = cuda_malloc_supported()
+    except:
+        pass
+
+
+if args.cuda_malloc and not args.disable_cuda_malloc:
+    env_var = os.environ.get('PYTORCH_CUDA_ALLOC_CONF', None)
+    if env_var is None:
+        env_var = "backend:cudaMallocAsync"
+    else:
+        env_var += ",backend:cudaMallocAsync"
+
+    os.environ['PYTORCH_CUDA_ALLOC_CONF'] = env_var

extra_model_paths.yaml.example

@@ -0,0 +1,47 @@
+#Rename this to extra_model_paths.yaml and ComfyUI will load it
+
+
+#config for a1111 ui
+#all you have to do is change the base_path to where yours is installed
+a111:
+    base_path: path/to/stable-diffusion-webui/
+
+    checkpoints: models/Stable-diffusion
+    configs: models/Stable-diffusion
+    vae: models/VAE
+    loras: |
+         models/Lora
+         models/LyCORIS
+    upscale_models: |
+                  models/ESRGAN
+                  models/RealESRGAN
+                  models/SwinIR
+    embeddings: embeddings
+    hypernetworks: models/hypernetworks
+    controlnet: models/ControlNet
+
+#config for comfyui
+#your base path should be either an existing comfy install or a central folder where you store all of your models, loras, etc.
+
+#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/
+#     vae: models/vae/
+
+#other_ui:
+#    base_path: path/to/ui
+#    checkpoints: models/checkpoints
+#    gligen: models/gligen
+#    custom_nodes: path/custom_nodes

fix_torch.py

@@ -0,0 +1,28 @@
+import importlib.util
+import shutil
+import os
+import ctypes
+import logging
+
+
+def fix_pytorch_libomp():
+    """
+    Fix PyTorch libomp DLL issue on Windows by copying the correct DLL file if needed.
+    """
+    torch_spec = importlib.util.find_spec("torch")
+    for folder in torch_spec.submodule_search_locations:
+        lib_folder = os.path.join(folder, "lib")
+        test_file = os.path.join(lib_folder, "fbgemm.dll")
+        dest = os.path.join(lib_folder, "libomp140.x86_64.dll")
+        if os.path.exists(dest):
+            break
+
+        with open(test_file, "rb") as f:
+            contents = f.read()
+            if b"libomp140.x86_64.dll" not in contents:
+                break
+        try:
+            ctypes.cdll.LoadLibrary(test_file)
+        except FileNotFoundError:
+            logging.warning("Detected pytorch version with libomp issue, patching.")
+            shutil.copyfile(os.path.join(lib_folder, "libiomp5md.dll"), dest)

folder_paths.py

@@ -0,0 +1,385 @@
+from __future__ import annotations
+
+import os
+import time
+import mimetypes
+import logging
+from typing import Literal
+from collections.abc import Collection
+
+supported_pt_extensions: set[str] = {'.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl', '.sft'}
+
+folder_names_and_paths: dict[str, tuple[list[str], set[str]]] = {}
+
+base_path = os.path.dirname(os.path.realpath(__file__))
+models_dir = os.path.join(base_path, "models")
+folder_names_and_paths["checkpoints"] = ([os.path.join(models_dir, "checkpoints")], supported_pt_extensions)
+folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".yaml"])
+
+folder_names_and_paths["loras"] = ([os.path.join(models_dir, "loras")], supported_pt_extensions)
+folder_names_and_paths["vae"] = ([os.path.join(models_dir, "vae")], supported_pt_extensions)
+folder_names_and_paths["text_encoders"] = ([os.path.join(models_dir, "text_encoders"), os.path.join(models_dir, "clip")], supported_pt_extensions)
+folder_names_and_paths["diffusion_models"] = ([os.path.join(models_dir, "unet"), os.path.join(models_dir, "diffusion_models")], supported_pt_extensions)
+folder_names_and_paths["clip_vision"] = ([os.path.join(models_dir, "clip_vision")], supported_pt_extensions)
+folder_names_and_paths["style_models"] = ([os.path.join(models_dir, "style_models")], supported_pt_extensions)
+folder_names_and_paths["embeddings"] = ([os.path.join(models_dir, "embeddings")], supported_pt_extensions)
+folder_names_and_paths["diffusers"] = ([os.path.join(models_dir, "diffusers")], ["folder"])
+folder_names_and_paths["vae_approx"] = ([os.path.join(models_dir, "vae_approx")], supported_pt_extensions)
+
+folder_names_and_paths["controlnet"] = ([os.path.join(models_dir, "controlnet"), os.path.join(models_dir, "t2i_adapter")], supported_pt_extensions)
+folder_names_and_paths["gligen"] = ([os.path.join(models_dir, "gligen")], supported_pt_extensions)
+
+folder_names_and_paths["upscale_models"] = ([os.path.join(models_dir, "upscale_models")], supported_pt_extensions)
+
+folder_names_and_paths["custom_nodes"] = ([os.path.join(base_path, "custom_nodes")], set())
+
+folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetworks")], supported_pt_extensions)
+
+folder_names_and_paths["photomaker"] = ([os.path.join(models_dir, "photomaker")], supported_pt_extensions)
+
+folder_names_and_paths["classifiers"] = ([os.path.join(models_dir, "classifiers")], {""})
+
+output_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output")
+temp_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "temp")
+input_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input")
+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",
+              "clip": "text_encoders"}
+    return legacy.get(folder_name, folder_name)
+
+if not os.path.exists(input_directory):
+    try:
+        os.makedirs(input_directory)
+    except:
+        logging.error("Failed to create input directory")
+
+def set_output_directory(output_dir: str) -> None:
+    global output_directory
+    output_directory = output_dir
+
+def set_temp_directory(temp_dir: str) -> None:
+    global temp_directory
+    temp_directory = temp_dir
+
+def set_input_directory(input_dir: str) -> None:
+    global input_directory
+    input_directory = input_dir
+
+def get_output_directory() -> str:
+    global output_directory
+    return output_directory
+
+def get_temp_directory() -> str:
+    global temp_directory
+    return temp_directory
+
+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:
+    if type_name == "output":
+        return get_output_directory()
+    if type_name == "temp":
+        return get_temp_directory()
+    if type_name == "input":
+        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
+def annotated_filepath(name: str) -> tuple[str, str | None]:
+    if name.endswith("[output]"):
+        base_dir = get_output_directory()
+        name = name[:-9]
+    elif name.endswith("[input]"):
+        base_dir = get_input_directory()
+        name = name[:-8]
+    elif name.endswith("[temp]"):
+        base_dir = get_temp_directory()
+        name = name[:-7]
+    else:
+        return name, None
+
+    return name, base_dir
+
+
+def get_annotated_filepath(name: str, default_dir: str | None=None) -> str:
+    name, base_dir = annotated_filepath(name)
+
+    if base_dir is None:
+        if default_dir is not None:
+            base_dir = default_dir
+        else:
+            base_dir = get_input_directory()  # fallback path
+
+    return os.path.join(base_dir, name)
+
+
+def exists_annotated_filepath(name) -> bool:
+    name, base_dir = annotated_filepath(name)
+
+    if base_dir is None:
+        base_dir = get_input_directory()  # fallback path
+
+    filepath = os.path.join(base_dir, name)
+    return os.path.exists(filepath)
+
+
+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:
+        paths, _exts = folder_names_and_paths[folder_name]
+        if full_folder_path in paths:
+            if is_default and paths[0] != full_folder_path:
+                # If the path to the folder is not the first in the list, move it to the beginning.
+                paths.remove(full_folder_path)
+                paths.insert(0, full_folder_path)
+        else:
+            if is_default:
+                paths.insert(0, full_folder_path)
+            else:
+                paths.append(full_folder_path)
+    else:
+        folder_names_and_paths[folder_name] = ([full_folder_path], set())
+
+def get_folder_paths(folder_name: str) -> list[str]:
+    folder_name = map_legacy(folder_name)
+    return folder_names_and_paths[folder_name][0][:]
+
+def recursive_search(directory: str, excluded_dir_names: list[str] | None=None) -> tuple[list[str], dict[str, float]]:
+    if not os.path.isdir(directory):
+        return [], {}
+
+    if excluded_dir_names is None:
+        excluded_dir_names = []
+
+    result = []
+    dirs = {}
+
+    # Attempt to add the initial directory to dirs with error handling
+    try:
+        dirs[directory] = os.path.getmtime(directory)
+    except FileNotFoundError:
+        logging.warning(f"Warning: Unable to access {directory}. Skipping this path.")
+
+    logging.debug("recursive file list on directory {}".format(directory))
+    dirpath: str
+    subdirs: list[str]
+    filenames: list[str]
+
+    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:
+            try:
+                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)
+            try:
+                dirs[path] = os.path.getmtime(path)
+            except FileNotFoundError:
+                logging.warning(f"Warning: Unable to access {path}. Skipping this path.")
+                continue
+    logging.debug("found {} files".format(len(result)))
+    return result, dirs
+
+def filter_files_extensions(files: Collection[str], extensions: Collection[str]) -> list[str]:
+    return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions or len(extensions) == 0, files)))
+
+
+
+def get_full_path(folder_name: str, filename: str) -> str | None:
+    global folder_names_and_paths
+    folder_name = map_legacy(folder_name)
+    if folder_name not in folder_names_and_paths:
+        return None
+    folders = folder_names_and_paths[folder_name]
+    filename = os.path.relpath(os.path.join("/", filename), "/")
+    for x in folders[0]:
+        full_path = os.path.join(x, filename)
+        if os.path.isfile(full_path):
+            return full_path
+        elif os.path.islink(full_path):
+            logging.warning("WARNING path {} exists but doesn't link anywhere, skipping.".format(full_path))
+
+    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
+    output_list = set()
+    folders = folder_names_and_paths[folder_name]
+    output_folders = {}
+    for x in folders[0]:
+        files, folders_all = recursive_search(x, excluded_dir_names=[".git"])
+        output_list.update(filter_files_extensions(files, folders[1]))
+        output_folders = {**output_folders, **folders_all}
+
+    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)
+    if folder_name not in filename_list_cache:
+        return None
+    out = filename_list_cache[folder_name]
+
+    for x in out[1]:
+        time_modified = out[1][x]
+        folder = x
+        if os.path.getmtime(folder) != time_modified:
+            return None
+
+    folders = folder_names_and_paths[folder_name]
+    for x in folders[0]:
+        if os.path.isdir(x):
+            if x not in out[1]:
+                return None
+
+    return out
+
+def get_filename_list(folder_name: str) -> list[str]:
+    folder_name = map_legacy(folder_name)
+    out = cached_filename_list_(folder_name)
+    if out is None:
+        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]:
+    def map_filename(filename: str) -> tuple[int, str]:
+        prefix_len = len(os.path.basename(filename_prefix))
+        prefix = filename[:prefix_len + 1]
+        try:
+            digits = int(filename[prefix_len + 1:].split('_')[0])
+        except:
+            digits = 0
+        return digits, prefix
+
+    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
+
+    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))
+
+    full_output_folder = os.path.join(output_dir, subfolder)
+
+    if os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) != output_dir:
+        err = "**** ERROR: Saving image outside the output folder is not allowed." + \
+              "\n full_output_folder: " + os.path.abspath(full_output_folder) + \
+              "\n         output_dir: " + output_dir + \
+              "\n         commonpath: " + os.path.commonpath((output_dir, os.path.abspath(full_output_folder)))
+        logging.error(err)
+        raise Exception(err)
+
+    try:
+        counter = max(filter(lambda a: os.path.normcase(a[1][:-1]) == os.path.normcase(filename) and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1
+    except ValueError:
+        counter = 1
+    except FileNotFoundError:
+        os.makedirs(full_output_folder, exist_ok=True)
+        counter = 1
+    return full_output_folder, filename, counter, subfolder, filename_prefix

latent_preview.py

@@ -0,0 +1,105 @@
+import torch
+from PIL import Image
+from comfy.cli_args import args, LatentPreviewMethod
+from comfy.taesd.taesd import TAESD
+import comfy.model_management
+import folder_paths
+import comfy.utils
+import logging
+
+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
+                            .mul(0xFF)  # to 0..255
+                            ).to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device))
+
+        return Image.fromarray(latents_ubyte.numpy())
+
+class LatentPreviewer:
+    def decode_latent_to_preview(self, x0):
+        pass
+
+    def decode_latent_to_preview_image(self, preview_format, x0):
+        preview_image = self.decode_latent_to_preview(x0)
+        return ("JPEG", preview_image, MAX_PREVIEW_RESOLUTION)
+
+class TAESDPreviewerImpl(LatentPreviewer):
+    def __init__(self, taesd):
+        self.taesd = taesd
+
+    def decode_latent_to_preview(self, x0):
+        x_sample = self.taesd.decode(x0[:1])[0].movedim(0, 2)
+        return preview_to_image(x_sample)
+
+
+class Latent2RGBPreviewer(LatentPreviewer):
+    def __init__(self, latent_rgb_factors, latent_rgb_factors_bias=None):
+        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)
+        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)
+
+        if x0.ndim == 5:
+            x0 = x0[0, :, 0]
+        else:
+            x0 = x0[0]
+
+        latent_image = torch.nn.functional.linear(x0.movedim(0, -1), 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)
+
+
+def get_previewer(device, latent_format):
+    previewer = None
+    method = args.preview_method
+    if method != LatentPreviewMethod.NoPreviews:
+        # TODO previewer methods
+        taesd_decoder_path = None
+        if latent_format.taesd_decoder_name is not None:
+            taesd_decoder_path = next(
+                (fn for fn in folder_paths.get_filename_list("vae_approx")
+                    if fn.startswith(latent_format.taesd_decoder_name)),
+                ""
+            )
+            taesd_decoder_path = folder_paths.get_full_path("vae_approx", taesd_decoder_path)
+
+        if method == LatentPreviewMethod.Auto:
+            method = LatentPreviewMethod.Latent2RGB
+
+        if method == LatentPreviewMethod.TAESD:
+            if taesd_decoder_path:
+                taesd = TAESD(None, taesd_decoder_path, latent_channels=latent_format.latent_channels).to(device)
+                previewer = TAESDPreviewerImpl(taesd)
+            else:
+                logging.warning("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name))
+
+        if previewer is None:
+            if latent_format.latent_rgb_factors is not None:
+                previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors, latent_format.latent_rgb_factors_bias)
+    return previewer
+
+def prepare_callback(model, steps, x0_output_dict=None):
+    preview_format = "JPEG"
+    if preview_format not in ["JPEG", "PNG"]:
+        preview_format = "JPEG"
+
+    previewer = get_previewer(model.load_device, model.model.latent_format)
+
+    pbar = comfy.utils.ProgressBar(steps)
+    def callback(step, x0, x, total_steps):
+        if x0_output_dict is not None:
+            x0_output_dict["x0"] = x0
+
+        preview_bytes = None
+        if previewer:
+            preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0)
+        pbar.update_absolute(step + 1, total_steps, preview_bytes)
+    return callback
+

main.py

@@ -0,0 +1,301 @@
+import comfy.options
+comfy.options.enable_args_parsing()
+
+import os
+import importlib.util
+import folder_paths
+import time
+from comfy.cli_args import args
+from app.logger import setup_logger
+import itertools
+import utils.extra_config
+import logging
+
+if __name__ == "__main__":
+    #NOTE: These do not do anything on core ComfyUI which should already have no communication with the internet, they are for custom nodes.
+    os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1'
+    os.environ['DO_NOT_TRACK'] = '1'
+
+
+setup_logger(log_level=args.verbose, use_stdout=args.log_stdout)
+
+def apply_custom_paths():
+    # extra model paths
+    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):
+        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):
+            utils.extra_config.load_extra_path_config(config_path)
+
+    # --output-directory, --input-directory, --user-directory
+    if args.output_directory:
+        output_dir = os.path.abspath(args.output_directory)
+        logging.info(f"Setting output directory to: {output_dir}")
+        folder_paths.set_output_directory(output_dir)
+
+    # These are the default folders that checkpoints, clip and vae models will be saved to when using CheckpointSave, etc.. nodes
+    folder_paths.add_model_folder_path("checkpoints", os.path.join(folder_paths.get_output_directory(), "checkpoints"))
+    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)
+
+
+def execute_prestartup_script():
+    def execute_script(script_path):
+        module_name = os.path.splitext(script_path)[0]
+        try:
+            spec = importlib.util.spec_from_file_location(module_name, script_path)
+            module = importlib.util.module_from_spec(spec)
+            spec.loader.exec_module(module)
+            return True
+        except Exception as e:
+            logging.error(f"Failed to execute startup-script: {script_path} / {e}")
+        return False
+
+    if args.disable_all_custom_nodes:
+        return
+
+    node_paths = folder_paths.get_folder_paths("custom_nodes")
+    for custom_node_path in node_paths:
+        possible_modules = os.listdir(custom_node_path)
+        node_prestartup_times = []
+
+        for possible_module in possible_modules:
+            module_path = os.path.join(custom_node_path, possible_module)
+            if os.path.isfile(module_path) or module_path.endswith(".disabled") or module_path == "__pycache__":
+                continue
+
+            script_path = os.path.join(module_path, "prestartup_script.py")
+            if os.path.exists(script_path):
+                time_before = time.perf_counter()
+                success = execute_script(script_path)
+                node_prestartup_times.append((time.perf_counter() - time_before, module_path, success))
+    if len(node_prestartup_times) > 0:
+        logging.info("\nPrestartup times for custom nodes:")
+        for n in sorted(node_prestartup_times):
+            if n[2]:
+                import_message = ""
+            else:
+                import_message = " (PRESTARTUP FAILED)"
+            logging.info("{:6.1f} seconds{}: {}".format(n[0], import_message, n[1]))
+        logging.info("")
+
+apply_custom_paths()
+execute_prestartup_script()
+
+
+# Main code
+import asyncio
+import shutil
+import threading
+import gc
+
+
+if os.name == "nt":
+    logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
+
+if __name__ == "__main__":
+    if args.cuda_device is not None:
+        os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda_device)
+        os.environ['HIP_VISIBLE_DEVICES'] = str(args.cuda_device)
+        logging.info("Set cuda device to: {}".format(args.cuda_device))
+
+    if args.oneapi_device_selector is not None:
+        os.environ['ONEAPI_DEVICE_SELECTOR'] = args.oneapi_device_selector
+        logging.info("Set oneapi device selector to: {}".format(args.oneapi_device_selector))
+
+    if args.deterministic:
+        if 'CUBLAS_WORKSPACE_CONFIG' not in os.environ:
+            os.environ['CUBLAS_WORKSPACE_CONFIG'] = ":4096:8"
+
+    import cuda_malloc
+
+if args.windows_standalone_build:
+    try:
+        from fix_torch import fix_pytorch_libomp
+        fix_pytorch_libomp()
+    except:
+        pass
+
+import comfy.utils
+
+import execution
+import server
+from server import BinaryEventTypes
+import nodes
+import comfy.model_management
+
+def cuda_malloc_warning():
+    device = comfy.model_management.get_torch_device()
+    device_name = comfy.model_management.get_torch_device_name(device)
+    cuda_malloc_warning = False
+    if "cudaMallocAsync" in device_name:
+        for b in cuda_malloc.blacklist:
+            if b in device_name:
+                cuda_malloc_warning = True
+        if cuda_malloc_warning:
+            logging.warning("\nWARNING: this card most likely does not support cuda-malloc, if you get \"CUDA error\" please run ComfyUI with: --disable-cuda-malloc\n")
+
+
+def prompt_worker(q, server_instance):
+    current_time: float = 0.0
+    e = execution.PromptExecutor(server_instance, lru_size=args.cache_lru)
+    last_gc_collect = 0
+    need_gc = False
+    gc_collect_interval = 10.0
+
+    while True:
+        timeout = 1000.0
+        if need_gc:
+            timeout = max(gc_collect_interval - (current_time - last_gc_collect), 0.0)
+
+        queue_item = q.get(timeout=timeout)
+        if queue_item is not None:
+            item, item_id = queue_item
+            execution_start_time = time.perf_counter()
+            prompt_id = item[1]
+            server_instance.last_prompt_id = prompt_id
+
+            e.execute(item[2], prompt_id, item[3], item[4])
+            need_gc = True
+            q.task_done(item_id,
+                        e.history_result,
+                        status=execution.PromptQueue.ExecutionStatus(
+                            status_str='success' if e.success else 'error',
+                            completed=e.success,
+                            messages=e.status_messages))
+            if server_instance.client_id is not None:
+                server_instance.send_sync("executing", {"node": None, "prompt_id": prompt_id}, server_instance.client_id)
+
+            current_time = time.perf_counter()
+            execution_time = current_time - execution_start_time
+            logging.info("Prompt executed in {:.2f} seconds".format(execution_time))
+
+        flags = q.get_flags()
+        free_memory = flags.get("free_memory", False)
+
+        if flags.get("unload_models", free_memory):
+            comfy.model_management.unload_all_models()
+            need_gc = True
+            last_gc_collect = 0
+
+        if free_memory:
+            e.reset()
+            need_gc = True
+            last_gc_collect = 0
+
+        if need_gc:
+            current_time = time.perf_counter()
+            if (current_time - last_gc_collect) > gc_collect_interval:
+                gc.collect()
+                comfy.model_management.soft_empty_cache()
+                last_gc_collect = current_time
+                need_gc = False
+
+
+async def run(server_instance, address='', port=8188, verbose=True, call_on_start=None):
+    addresses = []
+    for addr in address.split(","):
+        addresses.append((addr, port))
+    await asyncio.gather(
+        server_instance.start_multi_address(addresses, call_on_start, verbose), server_instance.publish_loop()
+    )
+
+
+def hijack_progress(server_instance):
+    def hook(value, total, preview_image):
+        comfy.model_management.throw_exception_if_processing_interrupted()
+        progress = {"value": value, "max": total, "prompt_id": server_instance.last_prompt_id, "node": server_instance.last_node_id}
+
+        server_instance.send_sync("progress", progress, server_instance.client_id)
+        if preview_image is not None:
+            server_instance.send_sync(BinaryEventTypes.UNENCODED_PREVIEW_IMAGE, preview_image, server_instance.client_id)
+
+    comfy.utils.set_progress_bar_global_hook(hook)
+
+
+def cleanup_temp():
+    temp_dir = folder_paths.get_temp_directory()
+    if os.path.exists(temp_dir):
+        shutil.rmtree(temp_dir, ignore_errors=True)
+
+
+def start_comfyui(asyncio_loop=None):
+    """
+    Starts the ComfyUI server using the provided asyncio event loop or creates a new one.
+    Returns the event loop, server instance, and a function to start the server asynchronously.
+    """
+    if args.temp_directory:
+        temp_dir = os.path.join(os.path.abspath(args.temp_directory), "temp")
+        logging.info(f"Setting temp directory to: {temp_dir}")
+        folder_paths.set_temp_directory(temp_dir)
+    cleanup_temp()
+
+    if args.windows_standalone_build:
+        try:
+            import new_updater
+            new_updater.update_windows_updater()
+        except:
+            pass
+
+    if not asyncio_loop:
+        asyncio_loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(asyncio_loop)
+    prompt_server = server.PromptServer(asyncio_loop)
+    q = execution.PromptQueue(prompt_server)
+
+    nodes.init_extra_nodes(init_custom_nodes=not args.disable_all_custom_nodes)
+
+    cuda_malloc_warning()
+
+    prompt_server.add_routes()
+    hijack_progress(prompt_server)
+
+    threading.Thread(target=prompt_worker, daemon=True, args=(q, prompt_server,)).start()
+
+    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
+
+    async def start_all():
+        await prompt_server.setup()
+        await run(prompt_server, address=args.listen, port=args.port, verbose=not args.dont_print_server, call_on_start=call_on_start)
+
+    # Returning these so that other code can integrate with the ComfyUI loop and server
+    return asyncio_loop, prompt_server, start_all
+
+
+if __name__ == "__main__":
+    # Running directly, just start ComfyUI.
+    event_loop, _, start_all_func = start_comfyui()
+    try:
+        event_loop.run_until_complete(start_all_func())
+    except KeyboardInterrupt:
+        logging.info("\nStopped server")
+
+    cleanup_temp()

new_updater.py

@@ -0,0 +1,35 @@
+import os
+import shutil
+
+base_path = os.path.dirname(os.path.realpath(__file__))
+
+
+def update_windows_updater():
+    top_path = os.path.dirname(base_path)
+    updater_path = os.path.join(base_path, ".ci/update_windows/update.py")
+    bat_path = os.path.join(base_path, ".ci/update_windows/update_comfyui.bat")
+
+    dest_updater_path = os.path.join(top_path, "update/update.py")
+    dest_bat_path = os.path.join(top_path, "update/update_comfyui.bat")
+    dest_bat_deps_path = os.path.join(top_path, "update/update_comfyui_and_python_dependencies.bat")
+
+    try:
+        with open(dest_bat_path, 'rb') as f:
+            contents = f.read()
+    except:
+        return
+
+    if not contents.startswith(b"..\\python_embeded\\python.exe .\\update.py"):
+        return
+
+    shutil.copy(updater_path, dest_updater_path)
+    try:
+        with open(dest_bat_deps_path, 'rb') as f:
+            contents = f.read()
+            contents = contents.replace(b'..\\python_embeded\\python.exe .\\update.py ..\\ComfyUI\\', b'call update_comfyui.bat nopause')
+        with open(dest_bat_deps_path, 'wb') as f:
+            f.write(contents)
+    except:
+        pass
+    shutil.copy(bat_path, dest_bat_path)
+    print("Updated the windows standalone package updater.")  # noqa: T201

node_helpers.py

@@ -0,0 +1,37 @@
+import hashlib
+
+from comfy.cli_args import args
+
+from PIL import ImageFile, UnidentifiedImageError
+
+def conditioning_set_values(conditioning, values={}):
+    c = []
+    for t in conditioning:
+        n = [t[0], t[1].copy()]
+        for k in values:
+            n[1][k] = values[k]
+        c.append(n)
+
+    return c
+
+def pillow(fn, arg):
+    prev_value = None
+    try:
+        x = fn(arg)
+    except (OSError, UnidentifiedImageError, ValueError): #PIL issues #4472 and #2445, also fixes ComfyUI issue #3416
+        prev_value = ImageFile.LOAD_TRUNCATED_IMAGES
+        ImageFile.LOAD_TRUNCATED_IMAGES = True
+        x = fn(arg)
+    finally:
+        if prev_value is not None:
+            ImageFile.LOAD_TRUNCATED_IMAGES = prev_value
+    return x
+
+def hasher():
+    hashfuncs = {
+        "md5": hashlib.md5,
+        "sha1": hashlib.sha1,
+        "sha256": hashlib.sha256,
+        "sha512": hashlib.sha512
+    }
+    return hashfuncs[args.default_hashing_function]

notebooks/comfyui_colab.ipynb

@@ -0,0 +1,322 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "aaaaaaaaaa"
+   },
+   "source": [
+    "Git clone the repo and install the requirements. (ignore the pip errors about protobuf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "bbbbbbbbbb"
+   },
+   "outputs": [],
+   "source": [
+    "#@title Environment Setup\n",
+    "\n",
+    "\n",
+    "OPTIONS = {}\n",
+    "\n",
+    "USE_GOOGLE_DRIVE = False  #@param {type:\"boolean\"}\n",
+    "UPDATE_COMFY_UI = True  #@param {type:\"boolean\"}\n",
+    "WORKSPACE = 'ComfyUI'\n",
+    "OPTIONS['USE_GOOGLE_DRIVE'] = USE_GOOGLE_DRIVE\n",
+    "OPTIONS['UPDATE_COMFY_UI'] = UPDATE_COMFY_UI\n",
+    "\n",
+    "if OPTIONS['USE_GOOGLE_DRIVE']:\n",
+    "    !echo \"Mounting Google Drive...\"\n",
+    "    %cd /\n",
+    "    \n",
+    "    from google.colab import drive\n",
+    "    drive.mount('/content/drive')\n",
+    "\n",
+    "    WORKSPACE = \"/content/drive/MyDrive/ComfyUI\"\n",
+    "    %cd /content/drive/MyDrive\n",
+    "\n",
+    "![ ! -d $WORKSPACE ] && echo -= Initial setup ComfyUI =- && git clone https://github.com/comfyanonymous/ComfyUI\n",
+    "%cd $WORKSPACE\n",
+    "\n",
+    "if OPTIONS['UPDATE_COMFY_UI']:\n",
+    "  !echo -= Updating ComfyUI =-\n",
+    "  !git pull\n",
+    "\n",
+    "!echo -= Install dependencies =-\n",
+    "!pip install xformers!=0.0.18 -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu121 --extra-index-url https://download.pytorch.org/whl/cu118 --extra-index-url https://download.pytorch.org/whl/cu117"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cccccccccc"
+   },
+   "source": [
+    "Download some models/checkpoints/vae or custom comfyui nodes (uncomment the commands for the ones you want)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "dddddddddd"
+   },
+   "outputs": [],
+   "source": [
+    "# Checkpoints\n",
+    "\n",
+    "### SDXL\n",
+    "### I recommend these workflow examples: https://comfyanonymous.github.io/ComfyUI_examples/sdxl/\n",
+    "\n",
+    "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors -P ./models/checkpoints/\n",
+    "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0.safetensors -P ./models/checkpoints/\n",
+    "\n",
+    "# SDXL ReVision\n",
+    "#!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/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",
+    "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors -P ./models/checkpoints/\n",
+    "\n",
+    "# Some SD1.5 anime style\n",
+    "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_hard.safetensors -P ./models/checkpoints/\n",
+    "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1_orangemixs.safetensors -P ./models/checkpoints/\n",
+    "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A3_orangemixs.safetensors -P ./models/checkpoints/\n",
+    "#!wget -c https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/anything-v3-fp16-pruned.safetensors -P ./models/checkpoints/\n",
+    "\n",
+    "# Waifu Diffusion 1.5 (anime style SD2.x 768-v)\n",
+    "#!wget -c https://huggingface.co/waifu-diffusion/wd-1-5-beta3/resolve/main/wd-illusion-fp16.safetensors -P ./models/checkpoints/\n",
+    "\n",
+    "\n",
+    "# unCLIP models\n",
+    "#!wget -c https://huggingface.co/comfyanonymous/illuminatiDiffusionV1_v11_unCLIP/resolve/main/illuminatiDiffusionV1_v11-unclip-h-fp16.safetensors -P ./models/checkpoints/\n",
+    "#!wget -c https://huggingface.co/comfyanonymous/wd-1.5-beta2_unCLIP/resolve/main/wd-1-5-beta2-aesthetic-unclip-h-fp16.safetensors -P ./models/checkpoints/\n",
+    "\n",
+    "\n",
+    "# VAE\n",
+    "!wget -c https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors -P ./models/vae/\n",
+    "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt -P ./models/vae/\n",
+    "#!wget -c https://huggingface.co/hakurei/waifu-diffusion-v1-4/resolve/main/vae/kl-f8-anime2.ckpt -P ./models/vae/\n",
+    "\n",
+    "\n",
+    "# Loras\n",
+    "#!wget -c https://civitai.com/api/download/models/10350 -O ./models/loras/theovercomer8sContrastFix_sd21768.safetensors #theovercomer8sContrastFix SD2.x 768-v\n",
+    "#!wget -c https://civitai.com/api/download/models/10638 -O ./models/loras/theovercomer8sContrastFix_sd15.safetensors #theovercomer8sContrastFix SD1.x\n",
+    "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_offset_example-lora_1.0.safetensors -P ./models/loras/ #SDXL offset noise lora\n",
+    "\n",
+    "\n",
+    "# T2I-Adapter\n",
+    "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_depth_sd14v1.pth -P ./models/controlnet/\n",
+    "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_seg_sd14v1.pth -P ./models/controlnet/\n",
+    "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_sketch_sd14v1.pth -P ./models/controlnet/\n",
+    "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_keypose_sd14v1.pth -P ./models/controlnet/\n",
+    "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_openpose_sd14v1.pth -P ./models/controlnet/\n",
+    "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_color_sd14v1.pth -P ./models/controlnet/\n",
+    "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_canny_sd14v1.pth -P ./models/controlnet/\n",
+    "\n",
+    "# T2I Styles Model\n",
+    "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_style_sd14v1.pth -P ./models/style_models/\n",
+    "\n",
+    "# CLIPVision model (needed for styles model)\n",
+    "#!wget -c https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/pytorch_model.bin -O ./models/clip_vision/clip_vit14.bin\n",
+    "\n",
+    "\n",
+    "# ControlNet\n",
+    "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_ip2p_fp16.safetensors -P ./models/controlnet/\n",
+    "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_shuffle_fp16.safetensors -P ./models/controlnet/\n",
+    "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_canny_fp16.safetensors -P ./models/controlnet/\n",
+    "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors -P ./models/controlnet/\n",
+    "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors -P ./models/controlnet/\n",
+    "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_lineart_fp16.safetensors -P ./models/controlnet/\n",
+    "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_mlsd_fp16.safetensors -P ./models/controlnet/\n",
+    "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_normalbae_fp16.safetensors -P ./models/controlnet/\n",
+    "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_openpose_fp16.safetensors -P ./models/controlnet/\n",
+    "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_scribble_fp16.safetensors -P ./models/controlnet/\n",
+    "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_seg_fp16.safetensors -P ./models/controlnet/\n",
+    "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_softedge_fp16.safetensors -P ./models/controlnet/\n",
+    "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors -P ./models/controlnet/\n",
+    "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11u_sd15_tile_fp16.safetensors -P ./models/controlnet/\n",
+    "\n",
+    "# ControlNet SDXL\n",
+    "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-canny-rank256.safetensors -P ./models/controlnet/\n",
+    "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-depth-rank256.safetensors -P ./models/controlnet/\n",
+    "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-recolor-rank256.safetensors -P ./models/controlnet/\n",
+    "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-sketch-rank256.safetensors -P ./models/controlnet/\n",
+    "\n",
+    "# Controlnet Preprocessor nodes by Fannovel16\n",
+    "#!cd custom_nodes && git clone https://github.com/Fannovel16/comfy_controlnet_preprocessors; cd comfy_controlnet_preprocessors && python install.py\n",
+    "\n",
+    "\n",
+    "# GLIGEN\n",
+    "#!wget -c https://huggingface.co/comfyanonymous/GLIGEN_pruned_safetensors/resolve/main/gligen_sd14_textbox_pruned_fp16.safetensors -P ./models/gligen/\n",
+    "\n",
+    "\n",
+    "# ESRGAN upscale model\n",
+    "#!wget -c https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P ./models/upscale_models/\n",
+    "#!wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x2.pth -P ./models/upscale_models/\n",
+    "#!wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x4.pth -P ./models/upscale_models/\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kkkkkkkkkkkkkkk"
+   },
+   "source": [
+    "### Run ComfyUI with cloudflared (Recommended Way)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "jjjjjjjjjjjjjj"
+   },
+   "outputs": [],
+   "source": [
+    "!wget https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64.deb\n",
+    "!dpkg -i cloudflared-linux-amd64.deb\n",
+    "\n",
+    "import subprocess\n",
+    "import threading\n",
+    "import time\n",
+    "import socket\n",
+    "import urllib.request\n",
+    "\n",
+    "def iframe_thread(port):\n",
+    "  while True:\n",
+    "      time.sleep(0.5)\n",
+    "      sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n",
+    "      result = sock.connect_ex(('127.0.0.1', port))\n",
+    "      if result == 0:\n",
+    "        break\n",
+    "      sock.close()\n",
+    "  print(\"\\nComfyUI finished loading, trying to launch cloudflared (if it gets stuck here cloudflared is having issues)\\n\")\n",
+    "\n",
+    "  p = subprocess.Popen([\"cloudflared\", \"tunnel\", \"--url\", \"http://127.0.0.1:{}\".format(port)], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n",
+    "  for line in p.stderr:\n",
+    "    l = line.decode()\n",
+    "    if \"trycloudflare.com \" in l:\n",
+    "      print(\"This is the URL to access ComfyUI:\", l[l.find(\"http\"):], end='')\n",
+    "    #print(l, end='')\n",
+    "\n",
+    "\n",
+    "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n",
+    "\n",
+    "!python main.py --dont-print-server"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kkkkkkkkkkkkkk"
+   },
+   "source": [
+    "### Run ComfyUI with localtunnel\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "jjjjjjjjjjjjj"
+   },
+   "outputs": [],
+   "source": [
+    "!npm install -g localtunnel\n",
+    "\n",
+    "import threading\n",
+    "\n",
+    "def iframe_thread(port):\n",
+    "  while True:\n",
+    "      time.sleep(0.5)\n",
+    "      sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n",
+    "      result = sock.connect_ex(('127.0.0.1', port))\n",
+    "      if result == 0:\n",
+    "        break\n",
+    "      sock.close()\n",
+    "  print(\"\\nComfyUI finished loading, trying to launch localtunnel (if it gets stuck here localtunnel is having issues)\\n\")\n",
+    "\n",
+    "  print(\"The password/enpoint ip for localtunnel is:\", urllib.request.urlopen('https://ipv4.icanhazip.com').read().decode('utf8').strip(\"\\n\"))\n",
+    "  p = subprocess.Popen([\"lt\", \"--port\", \"{}\".format(port)], stdout=subprocess.PIPE)\n",
+    "  for line in p.stdout:\n",
+    "    print(line.decode(), end='')\n",
+    "\n",
+    "\n",
+    "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n",
+    "\n",
+    "!python main.py --dont-print-server"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "gggggggggg"
+   },
+   "source": [
+    "### Run ComfyUI with colab iframe (use only in case the previous way with localtunnel doesn't work)\n",
+    "\n",
+    "You should see the ui appear in an iframe. If you get a 403 error, it's your firefox settings or an extension that's messing things up.\n",
+    "\n",
+    "If you want to open it in another window use the link.\n",
+    "\n",
+    "Note that some UI features like live image previews won't work because the colab iframe blocks websockets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "hhhhhhhhhh"
+   },
+   "outputs": [],
+   "source": [
+    "import threading\n",
+    "def iframe_thread(port):\n",
+    "  while True:\n",
+    "      time.sleep(0.5)\n",
+    "      sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n",
+    "      result = sock.connect_ex(('127.0.0.1', port))\n",
+    "      if result == 0:\n",
+    "        break\n",
+    "      sock.close()\n",
+    "  from google.colab import output\n",
+    "  output.serve_kernel_port_as_iframe(port, height=1024)\n",
+    "  print(\"to open it in a window you can open this link here:\")\n",
+    "  output.serve_kernel_port_as_window(port)\n",
+    "\n",
+    "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n",
+    "\n",
+    "!python main.py --dont-print-server"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "provenance": []
+  },
+  "gpuClass": "standard",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

pyproject.toml

@@ -0,0 +1,23 @@
+[project]
+name = "ComfyUI"
+version = "0.3.12"
+readme = "README.md"
+license = { file = "LICENSE" }
+requires-python = ">=3.9"
+
+[project.urls]
+homepage = "https://www.comfy.org/"
+repository = "https://github.com/comfyanonymous/ComfyUI"
+documentation = "https://docs.comfy.org/"
+
+[tool.ruff]
+lint.select = [
+  "S307", # suspicious-eval-usage
+  "S102", # exec
+  "T",    # print-usage
+  "W",
+  # The "F" series in Ruff stands for "Pyflakes" rules, which catch various Python syntax errors and undefined names.
+  # See all rules here: https://docs.astral.sh/ruff/rules/#pyflakes-f
+  "F",
+]
+exclude = ["*.ipynb"]

pytest.ini

@@ -0,0 +1,9 @@
+[pytest]
+markers = 
+  inference: mark as inference test (deselect with '-m "not inference"')
+  execution: mark as execution test (deselect with '-m "not execution"')
+testpaths =
+  tests
+  tests-unit
+addopts = -s
+pythonpath = .

requirements.txt

@@ -0,0 +1,29 @@
+torch
+torchsde
+torchvision
+torchaudio
+numpy>=1.25.0
+einops
+transformers>=4.28.1
+tokenizers>=0.13.3
+sentencepiece
+safetensors>=0.4.2
+aiohttp
+pyyaml
+Pillow>=9.5.0
+scipy
+tqdm
+psutil
+gradio 
+huggingface_hub
+# Base Detectron2
+git+https://github.com/facebookresearch/[email protected]
+# DensePose (part of Detectron2 projects)
+git+https://github.com/facebookresearch/[email protected]#subdirectory=projects/DensePose
+
+# Other dependencies, except conflicting ones, should be manually added
+# If you have a modified requirements file, include its contents here, excluding detectron2-related dependencies.
+#non essential dependencies:
+kornia>=0.7.1
+spandrel
+soundfile

script_examples/basic_api_example.py

@@ -0,0 +1,119 @@
+import json
+from urllib import request
+
+#This is the ComfyUI api prompt format.
+
+#If you want it for a specific workflow you can "enable dev mode options"
+#in the settings of the UI (gear beside the "Queue Size: ") this will enable
+#a button on the UI to save workflows in api format.
+
+#keep in mind ComfyUI is pre alpha software so this format will change a bit.
+
+#this is the one for the default workflow
+prompt_text = """
+{
+    "3": {
+        "class_type": "KSampler",
+        "inputs": {
+            "cfg": 8,
+            "denoise": 1,
+            "latent_image": [
+                "5",
+                0
+            ],
+            "model": [
+                "4",
+                0
+            ],
+            "negative": [
+                "7",
+                0
+            ],
+            "positive": [
+                "6",
+                0
+            ],
+            "sampler_name": "euler",
+            "scheduler": "normal",
+            "seed": 8566257,
+            "steps": 20
+        }
+    },
+    "4": {
+        "class_type": "CheckpointLoaderSimple",
+        "inputs": {
+            "ckpt_name": "v1-5-pruned-emaonly.safetensors"
+        }
+    },
+    "5": {
+        "class_type": "EmptyLatentImage",
+        "inputs": {
+            "batch_size": 1,
+            "height": 512,
+            "width": 512
+        }
+    },
+    "6": {
+        "class_type": "CLIPTextEncode",
+        "inputs": {
+            "clip": [
+                "4",
+                1
+            ],
+            "text": "masterpiece best quality girl"
+        }
+    },
+    "7": {
+        "class_type": "CLIPTextEncode",
+        "inputs": {
+            "clip": [
+                "4",
+                1
+            ],
+            "text": "bad hands"
+        }
+    },
+    "8": {
+        "class_type": "VAEDecode",
+        "inputs": {
+            "samples": [
+                "3",
+                0
+            ],
+            "vae": [
+                "4",
+                2
+            ]
+        }
+    },
+    "9": {
+        "class_type": "SaveImage",
+        "inputs": {
+            "filename_prefix": "ComfyUI",
+            "images": [
+                "8",
+                0
+            ]
+        }
+    }
+}
+"""
+
+def queue_prompt(prompt):
+    p = {"prompt": prompt}
+    data = json.dumps(p).encode('utf-8')
+    req =  request.Request("http://127.0.0.1:8188/prompt", data=data)
+    request.urlopen(req)
+
+
+prompt = json.loads(prompt_text)
+#set the text prompt for our positive CLIPTextEncode
+prompt["6"]["inputs"]["text"] = "masterpiece best quality man"
+
+#set the seed for our KSampler node
+prompt["3"]["inputs"]["seed"] = 5
+
+
+queue_prompt(prompt)
+
+

script_examples/websockets_api_example.py

@@ -0,0 +1,166 @@
+#This is an example that uses the websockets api to know when a prompt execution is done
+#Once the prompt execution is done it downloads the images using the /history endpoint
+
+import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client)
+import uuid
+import json
+import urllib.request
+import urllib.parse
+
+server_address = "127.0.0.1:8188"
+client_id = str(uuid.uuid4())
+
+def queue_prompt(prompt):
+    p = {"prompt": prompt, "client_id": client_id}
+    data = json.dumps(p).encode('utf-8')
+    req =  urllib.request.Request("http://{}/prompt".format(server_address), data=data)
+    return json.loads(urllib.request.urlopen(req).read())
+
+def get_image(filename, subfolder, folder_type):
+    data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
+    url_values = urllib.parse.urlencode(data)
+    with urllib.request.urlopen("http://{}/view?{}".format(server_address, url_values)) as response:
+        return response.read()
+
+def get_history(prompt_id):
+    with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response:
+        return json.loads(response.read())
+
+def get_images(ws, prompt):
+    prompt_id = queue_prompt(prompt)['prompt_id']
+    output_images = {}
+    while True:
+        out = ws.recv()
+        if isinstance(out, str):
+            message = json.loads(out)
+            if message['type'] == 'executing':
+                data = message['data']
+                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 node_id in history['outputs']:
+        node_output = history['outputs'][node_id]
+        images_output = []
+        if 'images' in node_output:
+            for image in node_output['images']:
+                image_data = get_image(image['filename'], image['subfolder'], image['type'])
+                images_output.append(image_data)
+        output_images[node_id] = images_output
+
+    return output_images
+
+prompt_text = """
+{
+    "3": {
+        "class_type": "KSampler",
+        "inputs": {
+            "cfg": 8,
+            "denoise": 1,
+            "latent_image": [
+                "5",
+                0
+            ],
+            "model": [
+                "4",
+                0
+            ],
+            "negative": [
+                "7",
+                0
+            ],
+            "positive": [
+                "6",
+                0
+            ],
+            "sampler_name": "euler",
+            "scheduler": "normal",
+            "seed": 8566257,
+            "steps": 20
+        }
+    },
+    "4": {
+        "class_type": "CheckpointLoaderSimple",
+        "inputs": {
+            "ckpt_name": "v1-5-pruned-emaonly.safetensors"
+        }
+    },
+    "5": {
+        "class_type": "EmptyLatentImage",
+        "inputs": {
+            "batch_size": 1,
+            "height": 512,
+            "width": 512
+        }
+    },
+    "6": {
+        "class_type": "CLIPTextEncode",
+        "inputs": {
+            "clip": [
+                "4",
+                1
+            ],
+            "text": "masterpiece best quality girl"
+        }
+    },
+    "7": {
+        "class_type": "CLIPTextEncode",
+        "inputs": {
+            "clip": [
+                "4",
+                1
+            ],
+            "text": "bad hands"
+        }
+    },
+    "8": {
+        "class_type": "VAEDecode",
+        "inputs": {
+            "samples": [
+                "3",
+                0
+            ],
+            "vae": [
+                "4",
+                2
+            ]
+        }
+    },
+    "9": {
+        "class_type": "SaveImage",
+        "inputs": {
+            "filename_prefix": "ComfyUI",
+            "images": [
+                "8",
+                0
+            ]
+        }
+    }
+}
+"""
+
+prompt = json.loads(prompt_text)
+#set the text prompt for our positive CLIPTextEncode
+prompt["6"]["inputs"]["text"] = "masterpiece best quality man"
+
+#set the seed for our KSampler node
+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:
+#     for image_data in images[node_id]:
+#         from PIL import Image
+#         import io
+#         image = Image.open(io.BytesIO(image_data))
+#         image.show()
+

script_examples/websockets_api_example_ws_images.py

@@ -0,0 +1,159 @@
+#This is an example that uses the websockets api and the SaveImageWebsocket node to get images directly without
+#them being saved to disk
+
+import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client)
+import uuid
+import json
+import urllib.request
+import urllib.parse
+
+server_address = "127.0.0.1:8188"
+client_id = str(uuid.uuid4())
+
+def queue_prompt(prompt):
+    p = {"prompt": prompt, "client_id": client_id}
+    data = json.dumps(p).encode('utf-8')
+    req =  urllib.request.Request("http://{}/prompt".format(server_address), data=data)
+    return json.loads(urllib.request.urlopen(req).read())
+
+def get_image(filename, subfolder, folder_type):
+    data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
+    url_values = urllib.parse.urlencode(data)
+    with urllib.request.urlopen("http://{}/view?{}".format(server_address, url_values)) as response:
+        return response.read()
+
+def get_history(prompt_id):
+    with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response:
+        return json.loads(response.read())
+
+def get_images(ws, prompt):
+    prompt_id = queue_prompt(prompt)['prompt_id']
+    output_images = {}
+    current_node = ""
+    while True:
+        out = ws.recv()
+        if isinstance(out, str):
+            message = json.loads(out)
+            if message['type'] == 'executing':
+                data = message['data']
+                if data['prompt_id'] == prompt_id:
+                    if data['node'] is None:
+                        break #Execution is done
+                    else:
+                        current_node = data['node']
+        else:
+            if current_node == 'save_image_websocket_node':
+                images_output = output_images.get(current_node, [])
+                images_output.append(out[8:])
+                output_images[current_node] = images_output
+
+    return output_images
+
+prompt_text = """
+{
+    "3": {
+        "class_type": "KSampler",
+        "inputs": {
+            "cfg": 8,
+            "denoise": 1,
+            "latent_image": [
+                "5",
+                0
+            ],
+            "model": [
+                "4",
+                0
+            ],
+            "negative": [
+                "7",
+                0
+            ],
+            "positive": [
+                "6",
+                0
+            ],
+            "sampler_name": "euler",
+            "scheduler": "normal",
+            "seed": 8566257,
+            "steps": 20
+        }
+    },
+    "4": {
+        "class_type": "CheckpointLoaderSimple",
+        "inputs": {
+            "ckpt_name": "v1-5-pruned-emaonly.safetensors"
+        }
+    },
+    "5": {
+        "class_type": "EmptyLatentImage",
+        "inputs": {
+            "batch_size": 1,
+            "height": 512,
+            "width": 512
+        }
+    },
+    "6": {
+        "class_type": "CLIPTextEncode",
+        "inputs": {
+            "clip": [
+                "4",
+                1
+            ],
+            "text": "masterpiece best quality girl"
+        }
+    },
+    "7": {
+        "class_type": "CLIPTextEncode",
+        "inputs": {
+            "clip": [
+                "4",
+                1
+            ],
+            "text": "bad hands"
+        }
+    },
+    "8": {
+        "class_type": "VAEDecode",
+        "inputs": {
+            "samples": [
+                "3",
+                0
+            ],
+            "vae": [
+                "4",
+                2
+            ]
+        }
+    },
+    "save_image_websocket_node": {
+        "class_type": "SaveImageWebsocket",
+        "inputs": {
+            "images": [
+                "8",
+                0
+            ]
+        }
+    }
+}
+"""
+
+prompt = json.loads(prompt_text)
+#set the text prompt for our positive CLIPTextEncode
+prompt["6"]["inputs"]["text"] = "masterpiece best quality man"
+
+#set the seed for our KSampler node
+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:
+#     for image_data in images[node_id]:
+#         from PIL import Image
+#         import io
+#         image = Image.open(io.BytesIO(image_data))
+#         image.show()
+

utils/extra_config.py

@@ -0,0 +1,33 @@
+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)
+    yaml_dir = os.path.dirname(os.path.abspath(yaml_path))
+    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))
+            if not os.path.isabs(base_path):
+                base_path = os.path.abspath(os.path.join(yaml_dir, 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:
+                    full_path = os.path.join(base_path, full_path)
+                elif not os.path.isabs(full_path):
+                    full_path = os.path.abspath(os.path.join(yaml_dir, y))
+                logging.info("Adding extra search path {} {}".format(x, full_path))
+                folder_paths.add_model_folder_path(x, full_path, is_default)