ComfyUI LTS

A vanilla, up-to-date fork of ComfyUI intended for long term support (LTS) from AppMana and Hidden Switch.

New Features

To run, just type comfyui in your command line and press enter.
Installable via pip: pip install comfyui[withtorch]@git+https://github.com/hiddenswitch/ComfyUI.git.
Large Language Models with multi-modal support included.
Automatic model downloading to save you disk space and time.
Distributed with support for multiple GPUs, multiple backends and frontends, including in containers, using RabbitMQ.
Installable custom nodes via pip, including LTS support for forked nodes from the community.
New configuration options for directories, models and metrics.
API support, using the vanilla ComfyUI API and new API endpoints.
Embed ComfyUI as a library inside your Python application. No server or frontend needed.
Containers for running on Linux, Windows and Kubernetes with CUDA acceleration.
Automated tests for new features.

Upstream Features

Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
Fully supports SD1.x, SD2.x, SDXL, Stable Video Diffusion, Stable Cascade, SD3 and Stable Audio
LTX-Video
Flux
Mochi
Asynchronous Queue system
Many optimizations: Only re-executes the parts of the workflow that changes between executions.
Smart memory management: can automatically run models on GPUs with as low as 1GB vram.
Works even if you don't have a GPU with: --cpu (slow)
Can load ckpt, safetensors and diffusers models/checkpoints. Standalone VAEs and CLIP models.
Embeddings/Textual inversion
Loras (regular, locon and loha)
Hypernetworks
Loading full workflows (with seeds) from generated PNG, WebP and FLAC files.
Saving/Loading workflows as Json files.
Nodes interface can be used to create complex workflows like one for Hires fix or much more advanced ones.
Area Composition
Inpainting with both regular and inpainting models.
ControlNet and T2I-Adapter
Upscale Models (ESRGAN, ESRGAN variants, SwinIR, Swin2SR, etc...)
unCLIP Models
GLIGEN
Model Merging
LCM models and Loras
SDXL Turbo
AuraFlow
HunyuanDiT
Latent previews with TAESD
Starts up very fast.
Works fully offline: will never download anything.
Config file to set the search paths for models.

Getting Started

Installing

You must have Python 3.10, 3.11 or 3.12 installed. On Windows, download the latest Python from the Python website.

On macOS, you will need Python 3.10, 3.11 or 3.12, which is easy to install using brew: brew install [email protected]. You can check which version of Python you have installed using python --version.

When using Windows, open the Windows Powershell app. Then observe you are at a command line, and it is printing "where" you are in your file system: your user directory (e.g., C:\Users\doctorpangloss). This is where a bunch of files will go. If you want files to go somewhere else, consult a chat bot for the basics of using command lines, because it is beyond the scope of this document. Then:

Create a virtual environment:
```
python -m venv venv
```

Activate it on Windows (PowerShell):

Set-ExecutionPolicy Unrestricted -Scope Process
& .\venv\Scripts\activate.ps1

Linux and macOS

source ./venv/bin/activate

Run the following command to install comfyui into your current environment. This will correctly select the version of torch that matches the GPU on your machine (NVIDIA or CPU on Windows, NVIDIA, Intel, AMD or CPU on Linux, CPU on macOS):
```
pip install "comfyui[withtorch]@git+https://github.com/hiddenswitch/ComfyUI.git"
```
Recommended: Currently, torch 2.5.0 is the latest version that xformers is compatible with. On Windows, install it first, along with xformers, for maximum compatibility and the best performance without advanced techniques in ComfyUI:
```
pip install torch==2.5.1+cu121 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
pip install --no-build-isolation --no-deps xformers==0.0.28.post3 --index-url https://download.pytorch.org/whl/
pip install comfyui@git+https://github.com/hiddenswitch/ComfyUI.git
```
To enable torchaudio support on Windows, install it directly:
```
pip install torchaudio==2.5.0+cu121 --index-url https://download.pytorch.org/whl/cu121
```
Advanced: If you are running in Google Collab or another environment which has already installed torch for you; or, if you are an application developer:
```
# You will need wheel, which isn't included in Python 3.11 or later
pip install wheel
pip install --no-build-isolation comfyui@git+https://github.com/hiddenswitch/ComfyUI.git
```
This will use your pre-installed torch. This is also the appropriate dependency for packages, and is the one published to pypi. To automatically install with torch nightlies, use:
```
pip install comfyui[withtorchnightlies]@git+https://github.com/hiddenswitch/ComfyUI.git
```
Create the directories you can fill with checkpoints:
```
comfyui --create-directories
```
Your current working directory is wherever you started running comfyui. You don't need to clone this repository, observe it is omitted from the instructions. You can cd into a different directory containing models/, or if the models are located somehwere else, like C:/some directory/models, do:
```
comfyui --cwd="C:/some directory/"
```
You can see all the command line options with hints using comfyui --help.
To run the web server:
```
comfyui
```
When you run workflows that use well-known models, this will download them automatically.

To make it accessible over the network:
```
comfyui --listen
```

On Windows, you will need to open PowerShell and activate your virtual environment whenever you want to run comfyui.

& .\venv\Scripts\activate.ps1
comfyui

Upgrades are delivered frequently and automatically. To force one immediately, run pip upgrade like so:

pip install --no-build-isolation --no-deps --upgrade comfyui@git+https://github.com/hiddenswitch/ComfyUI.git

Advanced: Using uv:

uv is a significantly faster and improved Python package manager. On Windows, use the following commands to install the package from scratch about 6x faster than vanilla pip:

uv venv --seed
& .\venv\Scripts\activate.ps1
uv pip install comfyui[withtorch]@git+https://github.com/hiddenswitch/ComfyUI.git
python -m comfy.cmd.main

LTS Custom Nodes

These packages have been adapted to be installable with pip and download models to the correct places:

ELLA T5 Text Conditioning for SD1.5: pip install git+https://github.com/AppMana/appmana-comfyui-nodes-ella.git
IP Adapter: pip install git+https://github.com/AppMana/appmana-comfyui-nodes-ipadapter-plus
ControlNet Auxiliary Preprocessors: pip install git+https://github.com/AppMana/appmana-comfyui-nodes-controlnet-aux.git.
LayerDiffuse Alpha Channel Diffusion: pip install git+https://github.com/AppMana/appmana-comfyui-nodes-layerdiffuse.git.
BRIA Background Removal: pip install git+https://github.com/AppMana/appmana-comfyui-nodes-bria-bg-removal.git
Video Frame Interpolation: pip install git+https://github.com/AppMana/appmana-comfyui-nodes-video-frame-interpolation
Video Helper Suite: pip install git+https://github.com/AppMana/appmana-comfyui-nodes-video-helper-suite
AnimateDiff Evolved: pip install git+https://github.com/AppMana/appmana-comfyui-nodes-animatediff-evolved
Impact Pack: pip install git+https://github.com/AppMana/appmana-comfyui-nodes-impact-pack
TensorRT: pip install git+https://github.com/AppMAna/appmana-comfyui-nodes-tensorrt

Custom nodes are generally supported by this fork. Use these for a bug-free experience.

Request first-class, LTS support for more nodes by creating a new issue. Remember, ordinary custom nodes from the ComfyUI ecosystem work in this fork. Create an issue if you experience a bug or if you think something needs more attention.

Running with TLS

To serve with https:// on Windows easily, use Caddy. Extract caddy.exe to a directory, then run it:

caddy reverse-proxy --from localhost:443 --to localhost:8188 --tls self_signed

Notes for AMD Users

Until a workaround is found, specify these variables:

RDNA 3 (RX 7600 and later)

export HSA_OVERRIDE_GFX_VERSION=11.0.0
comfyui

RDNA 2 (RX 6600 and others)

export HSA_OVERRIDE_GFX_VERSION=10.3.0
comfyui

Model Downloading

ComfyUI LTS supports downloading models on demand. Its list of known models includes the most notable and common Stable Diffusion architecture checkpoints, slider LoRAs, all the notable ControlNets for SD1.5 and SDXL, and a small selection of LLM models. Additionally, all other supported LTS nodes will download models using the same mechanisms. This means that you will save storage space and time: you won't have to ever figure out the "right name" for a model, where to download it from, or where to put it ever again.

Known models will be downloaded from Hugging Face or CivitAI. Hugging Face has a thoughtful approach to file downloading and organization. This means you do not have to toil about having one, or many, files, or worry about where to put them.

On Windows platforms, symbolic links should be enabled to minimize the amount of space used: Enable Developer Mode in the Windows Settings, then reboot your computer. This way, Hugging Face can download models into a common place for all your apps, and place small "link" files that ComfyUI and others can read instead of whole copies of models.

To disable model downloading, start with the command line argument --disable-known-models: comfyui --disable-known-models. However, this will generally only increase your toil for no return.

Manual Install (Windows, Linux, macOS) For Development

Clone this repo:

git clone https://github.com/hiddenswitch/ComfyUI.git
cd ComfyUI

Create a virtual environment:

Create an environment:
```
python -m virtualenv venv
```

Activate it:

Windows (PowerShell):

Set-ExecutionPolicy Unrestricted -Scope Process
& .\venv\Scripts\activate.ps1

Linux and macOS

source ./venv/bin/activate

Then, run the following command to install comfyui into your current environment. This will correctly select the version of pytorch that matches the GPU on your machine (NVIDIA or CPU on Windows, NVIDIA AMD or CPU on Linux):
```
pip install -e ".[dev]"
```
To run the web server:
```
comfyui
```
To run tests:
```
pytest -v tests/
```
You can use comfyui as an API. Visit the OpenAPI specification. This file can be used to generate typed clients for your preferred language.

To create the standalone binary:

python -m PyInstaller --onefile --noupx -n ComfyUI --add-data="comfy/;comfy/" --paths $(pwd) --paths comfy/cmd main.py

Because pip installs the package as editable with pip install -e ., any changes you make to the repository will affect the next launch of comfy. In IDEA based editors like PyCharm and IntelliJ, the Relodium plugin supports modifying your custom nodes or similar code while the server is running.

Linux Development Dependencies

apt install -y git build-essential clang python3-dev python3-venv

Large Language Models

ComfyUI LTS supports text and multi-modal LLM models from the transformers ecosystem. This means all the LLaMA family models, LLAVA-NEXT, Phi-3, etc. are supported out-of-the-box with no configuration necessary.

In this example, LLAVA-NEXT (LLAVA 1.6) is prompted to describe an image.

You can try the LLAVA-NEXT, Phi-3, and two translation workflows.

SVG Conversion and String Saving

ComfyUI LTS supports powerful SVG conversion capabilities using vtracer and Skia, along with enhanced string saving functionality. This allows for seamless conversion between raster images and SVG format, as well as flexible string saving options.

In this example, a raster image is converted to SVG, potentially modified, and then converted back to a raster image. The resulting image and SVG code can be saved.

You can try the SVG Conversion Workflow to explore these features.

Video Workflows

ComfyUI LTS supports video workflows with AnimateDiff Evolved.

First, install this package using the Installation Instructions.

Then, install the custom nodes packages that support video creation workflows:

pip install git+https://github.com/AppMana/appmana-comfyui-nodes-video-frame-interpolation
pip install git+https://github.com/AppMana/appmana-comfyui-nodes-video-helper-suite
pip install git+https://github.com/AppMana/appmana-comfyui-nodes-animatediff-evolved
pip install git+https://github.com/AppMana/appmana-comfyui-nodes-controlnet-aux.git

Start creating an AnimateDiff workflow. When using these packages, the appropriate models will download automatically.

SageAttention

Improve the performance of your Mochi model video generation using Sage Attention:

Device	PyTorch 2.5.1	SageAttention	S.A. + TorchCompileModel
A5000	7.52s/it	5.81s/it	5.00s/it (but corrupted)

Use the default Mochi Workflow. This does not require any custom nodes or any change to your workflow.

Install the dependencies for Windows or Linux using the withtriton component, or install the specific dependencies you need from requirements-triton.txt:

pip install "comfyui[withtriton]@git+https://github.com/hiddenswitch/ComfyUI.git"

On Windows, you will need the CUDA Toolkit and Visual Studio 2022. If you do not already have this, use chocolatey:

# install chocolatey
Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))
choco install -y visualstudio2022buildtools
# purposefully executed separately
choco install -y visualstudio2022-workload-vctools
choco install -y vcredist2010 vcredist2013 vcredist140
choco install -y cuda

If you have xformers installed, disable it, as it will be preferred over Sage Attention:

comfyui --disable-xformers

Sage Attention is not compatible with Flux. It does not appear to be compatible with Mochi when using torch.compile

With SageAttention

With PyTorch Attention

Custom Nodes

Custom Nodes can be added to ComfyUI by copying and pasting Python files into your ./custom_nodes directory.

Installing Custom Nodes

There are two kinds of custom nodes: vanilla custom nodes, which generally expect to be dropped into the custom_nodes directory and managed by a tool called the ComfyUI Extension manager ("vanilla" custom nodes) and this repository's opinionated, installable custom nodes ("installable").

Vanilla Custom Nodes

Clone the repository containing the custom nodes into custom_nodes/ in your working directory. Currently, this is not known to be compatible with ComfyUI Node Manager.

Run pip install git+https://github.com/owner/repository, replacing the git repository with the installable custom nodes URL. This is just the GitHub URL.

Authoring Custom Nodes

These instructions will allow you to quickly author installable custom nodes.

Using `pyproject.toml` for projects with existing `requirements.txt`

Suppose your custom nodes called my_comfyui_nodes has a folder layout that looks like this:

__init__.py
some_python_file.py
requirements.txt
LICENSE.txt
some_directory/some_code.py

First, add an __init__.py to some_directory, so that it is a Python package:

__init__.py
some_python_file.py
requirements.txt
LICENSE.txt
some_directory/__init__.py
some_directory/some_code.py

Then, if your NODE_CLASS_MAPPINGS are declared in __init__.py, use the following as a pyproject.toml, substituting your actual project name:

pyproject.toml

[project]
name = "my_comfyui_nodes"
description = "My nodes description."
version = "1.0.0"
license = { file = "LICENSE.txt" }
dynamic = ["dependencies"]

[project.urls]
Repository = "https://github.com/your-github-username/my-comfyui-nodes"
#  Used by Comfy Registry https://comfyregistry.org

[tool.comfy]
PublisherId = "your-github-username"
DisplayName = "my_comfyui_nodes"
Icon = ""

[build-system]
requires = ["setuptools", "wheel"]
build-backend = "setuptools.build_meta"

[tool.setuptools]
packages = ["my_comfyui_nodes", "my_comfyui_nodes.some_directory"]
package-dir = { "my_comfyui_nodes" = ".", "my_comfyui_nodes.some_directory" = "some_directory" }

[tool.setuptools.dynamic]
dependencies = { file = ["requirements.txt"] }

[project.entry-points."comfyui.custom_nodes"]
my_comfyui_nodes = "my_comfyui_nodes"

Observe that the directory should now be listed as a package in the packages and package-dir statement.

Using `setup.py`

Create a requirements.txt:

comfyui

Observe comfyui is now a requirement for using your custom nodes. This will ensure you will be able to access comfyui as a library. For example, your code will now be able to import the folder paths using from comfyui.cmd import folder_paths. Because you will be using my fork, use this:

comfyui @ git+https://github.com/hiddenswitch/ComfyUI.git

Additionally, create a pyproject.toml:

[build-system]
requires = ["setuptools", "wheel", "pip"]
build-backend = "setuptools.build_meta"

This ensures you will be compatible with later versions of Python.

Finally, move your nodes to a directory with an empty __init__.py, i.e., a package. You should have a file structure like this:

# the root of your git repository
/.git
/pyproject.toml
/requirements.txt
/mypackage_custom_nodes/__init__.py
/mypackage_custom_nodes/some_nodes.py

Finally, create a setup.py at the root of your custom nodes package / repository. Here is an example:

setup.py

from setuptools import setup, find_packages
import os.path

setup(
    name="mypackage",
    version="0.0.1",
    packages=find_packages(),
    install_requires=open(os.path.join(os.path.dirname(__file__), "requirements.txt")).readlines(),
    author='',
    author_email='',
    description='',
    entry_points={
        'comfyui.custom_nodes': [
            'mypackage = mypackage_custom_nodes',
        ],
    },
)

All .py files located in the package specified by the entrypoint with your package's name will be scanned for node class mappings declared like this:

some_nodes.py:

from comfy.nodes.package_typing import CustomNode


class Binary_Preprocessor(CustomNode):
    ...


NODE_CLASS_MAPPINGS = {
    "BinaryPreprocessor": Binary_Preprocessor
}
NODE_DISPLAY_NAME_MAPPINGS = {
    "BinaryPreprocessor": "Binary Lines"
}

These packages will be scanned recursively.

Extending the comfy.nodes.package_typing.CustomNode provides type hints for authoring nodes.

Adding Custom Configuration

Declare an entry point for configuration hooks in your setup.py that defines a function that takes and returns an configargparser.ArgParser object:

setup.py

setup(
    name="mypackage",
    ...
entry_points = {
    'comfyui.custom_nodes': [
        'mypackage = mypackage_custom_nodes',
    ],
    'comfyui.custom_config': [
        'mypackage = mypackage_custom_config:add_configuration',
    ]
},
)

mypackage_custom_config.py:

import configargparse


def add_configuration(parser: configargparse.ArgParser) -> configargparse.ArgParser:
    parser.add_argument("--openai-api-key",
                        required=False,
                        type=str,
                        help="Configures the OpenAI API Key for the OpenAI nodes", env_var="OPENAI_API_KEY")
    return parser

You can now see your configuration option at the bottom of the --help command along with hints for how to use it:

$ comfyui --help
usage: comfyui.exe [-h] [-c CONFIG_FILE] [--write-out-config-file CONFIG_OUTPUT_PATH] [-w CWD] [-H [IP]] [--port PORT]
                   [--enable-cors-header [ORIGIN]] [--max-upload-size MAX_UPLOAD_SIZE] [--extra-model-paths-config PATH [PATH ...]]
...
                   [--openai-api-key OPENAI_API_KEY]

options:
  -h, --help            show this help message and exit
  -c CONFIG_FILE, --config CONFIG_FILE
                        config file path
  --write-out-config-file CONFIG_OUTPUT_PATH
                        takes the current command line args and writes them out to a config file at the given path, then exits
  -w CWD, --cwd CWD     Specify the working directory. If not set, this is the current working directory. models/, input/, output/ and other
                        directories will be located here by default. [env var: COMFYUI_CWD]
  -H [IP], --listen [IP]
                        Specify the IP address to listen on (default: 127.0.0.1). If --listen is provided without an argument, it defaults to
                        0.0.0.0. (listens on all) [env var: COMFYUI_LISTEN]
  --port PORT           Set the listen port. [env var: COMFYUI_PORT]
...
  --distributed-queue-name DISTRIBUTED_QUEUE_NAME
                        This name will be used by the frontends and workers to exchange prompt requests and replies. Progress updates will be
                        prefixed by the queue name, followed by a '.', then the user ID [env var: COMFYUI_DISTRIBUTED_QUEUE_NAME]
  --external-address EXTERNAL_ADDRESS
                        Specifies a base URL for external addresses reported by the API, such as for image paths. [env var:
                        COMFYUI_EXTERNAL_ADDRESS]
  --openai-api-key OPENAI_API_KEY
                        Configures the OpenAI API Key for the OpenAI nodes [env var: OPENAI_API_KEY]

You can now start comfyui with:

comfyui --openai-api-key=abcdefg12345

or set the environment variable you specified:

export OPENAI_API_KEY=abcdefg12345
comfyui

or add it to your config file:

config.yaml:

openapi-api-key: abcdefg12345

comfyui --config config.yaml

Since comfyui looks for a config.yaml in your current working directory by default, you can omit the argument if config.yaml is located in your current working directory:

comfyui

Your entry point for adding configuration options should not import your nodes. This gives you the opportunity to use the configuration you added in your nodes; otherwise, if you imported your nodes in your configuration entry point, the nodes will potentially be initialized without any configuration.

Access your configuration from cli_args:

from comfy.cli_args import args
from comfy.cli_args_types import Configuration
from typing import Optional


# Add type hints when accessing args
class CustomConfiguration(Configuration):
    def __init__(self):
        super().__init__()
        self.openai_api_key: Optional[str] = None


args: CustomConfiguration


class OpenAINode(CustomNode):
    ...

    def execute(self):
        openai_api_key = args.open_api_key

Troubleshooting

I see a message like RuntimeError: '"upsample_bilinear2d_channels_last" not implemented for 'Half''

You must use Python 3.11 on macOS devices, and update to at least Ventura.

I see a message like Error while deserializing header: HeaderTooLarge

Download your model file again.

Using the Editor

Notes

Only parts of the graph that have an output with all the correct inputs will be executed.

Only parts of the graph that change from each execution to the next will be executed, if you submit the same graph twice only the first will be executed. If you change the last part of the graph only the part you changed and the part that depends on it will be executed.

Dragging a generated png on the webpage or loading one will give you the full workflow including seeds that were used to create it.

You can use () to change emphasis of a word or phrase like: (good code:1.2) or (bad code:0.8). The default emphasis for () is 1.1. To use () characters in your actual prompt escape them like \( or \).

You can use {day|night}, for wildcard/dynamic prompts. With this syntax "{wild|card|test}" will be randomly replaced by either "wild", "card" or "test" by the frontend every time you queue the prompt. To use {} characters in your actual prompt escape them like: \{ or \}.

Dynamic prompts also support C-style comments, like // comment or /* comment */.

To use a textual inversion concepts/embeddings in a text prompt put them in the models/embeddings directory and use them in the CLIPTextEncode node like this (you can omit the .pt extension):

embedding:embedding_filename.pt

How to increase generation speed?

Make sure you use the regular loaders/Load Checkpoint node to load checkpoints. It will auto pick the right settings depending on your GPU.

You can set this command line setting to disable the upcasting to fp32 in some cross attention operations which will increase your speed. Note that this will very likely give you black images on SD2.x models. If you use xformers or pytorch attention this option does not do anything.

--dont-upcast-attention

How to show high-quality previews?

Use --preview-method auto to enable previews.

The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with TAESD, download the taesd_decoder.pth (for SD1.x and SD2.x) and taesdxl_decoder.pth (for SDXL) models and place them in the models/vae_approx folder. Once they're installed, restart ComfyUI to enable high-quality previews.

Keyboard Shortcuts

Keybind	Explanation
Ctrl + Enter	Queue up current graph for generation
Ctrl + Shift + Enter	Queue up current graph as first for generation
Ctrl + Alt + Enter	Cancel current generation
Ctrl + Z/Ctrl + Y	Undo/Redo
Ctrl + S	Save workflow
Ctrl + O	Load workflow
Ctrl + A	Select all nodes
Alt + C	Collapse/uncollapse selected nodes
Ctrl + M	Mute/unmute selected nodes
Ctrl + B	Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through)
Delete/Backspace	Delete selected nodes
Ctrl + Backspace	Delete the current graph
Space	Move the canvas around when held and moving the cursor
Ctrl/Shift + Click	Add clicked node to selection
Ctrl + C/Ctrl + V	Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes)
Ctrl + C/Ctrl + Shift + V	Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes)
Shift + Drag	Move multiple selected nodes at the same time
Ctrl + D	Load default graph
Alt + `+`	Canvas Zoom in
Alt + `-`	Canvas Zoom out
Ctrl + Shift + LMB + Vertical drag	Canvas Zoom in/out
P	Pin/Unpin selected nodes
Ctrl + G	Group selected nodes
Q	Toggle visibility of the queue
H	Toggle visibility of history
R	Refresh graph
Double-Click LMB	Open node quick search palette
Shift + Drag	Move multiple wires at once
Ctrl + Alt + LMB	Disconnect all wires from clicked slot

Ctrl can also be replaced with Cmd instead for macOS users

Configuration

This supports configuration with command line arguments, the environment and a configuration file.

Configuration File

First, run comfyui --help for all supported configuration and arguments.

Args that start with -- can also be set in a config file (config.yaml or config.json or specified via -c). Config file syntax allows: key=value, flag=true, stuff=[a,b,c] (for details, see syntax here). In general, command-line values override environment variables which override config file values which override defaults.

Extra Model Paths

Copy docs/examples/configuration/extra_model_paths.yaml to your working directory, and modify the folder paths to match your folder structure.

You can pass additional extra model path configurations with one or more copies of --extra-model-paths-config=some_configuration.yaml.

Command Line Arguments

usage: comfyui.exe [-h] [-c CONFIG_FILE] [--write-out-config-file CONFIG_OUTPUT_PATH] [-w CWD] [--base-paths BASE_PATHS [BASE_PATHS ...]] [-H [IP]] [--port PORT]
                   [--enable-cors-header [ORIGIN]] [--max-upload-size MAX_UPLOAD_SIZE] [--extra-model-paths-config PATH [PATH ...]]
                   [--output-directory OUTPUT_DIRECTORY] [--temp-directory TEMP_DIRECTORY] [--input-directory INPUT_DIRECTORY] [--auto-launch] [--disable-auto-launch]
                   [--cuda-device DEVICE_ID] [--cuda-malloc | --disable-cuda-malloc] [--force-fp32 | --force-fp16 | --force-bf16]
                   [--bf16-unet | --fp16-unet | --fp8_e4m3fn-unet | --fp8_e5m2-unet] [--fp16-vae | --fp32-vae | --bf16-vae] [--cpu-vae]
                   [--fp8_e4m3fn-text-enc | --fp8_e5m2-text-enc | --fp16-text-enc | --fp32-text-enc] [--directml [DIRECTML_DEVICE]] [--disable-ipex-optimize]
                   [--preview-method [none,auto,latent2rgb,taesd]] [--preview-size PREVIEW_SIZE] [--cache-lru CACHE_LRU]
                   [--use-split-cross-attention | --use-quad-cross-attention | --use-pytorch-cross-attention] [--disable-xformers] [--disable-flash-attn]
                   [--disable-sage-attention] [--force-upcast-attention | --dont-upcast-attention]
                   [--gpu-only | --highvram | --normalvram | --lowvram | --novram | --cpu] [--reserve-vram RESERVE_VRAM]
                   [--default-hashing-function {md5,sha1,sha256,sha512}] [--disable-smart-memory] [--deterministic] [--fast] [--dont-print-server]
                   [--quick-test-for-ci] [--windows-standalone-build] [--disable-metadata] [--disable-all-custom-nodes] [--multi-user] [--create-directories]
                   [--plausible-analytics-base-url PLAUSIBLE_ANALYTICS_BASE_URL] [--plausible-analytics-domain PLAUSIBLE_ANALYTICS_DOMAIN]
                   [--analytics-use-identity-provider] [--distributed-queue-connection-uri DISTRIBUTED_QUEUE_CONNECTION_URI] [--distributed-queue-worker]
                   [--distributed-queue-frontend] [--distributed-queue-name DISTRIBUTED_QUEUE_NAME] [--external-address EXTERNAL_ADDRESS]
                   [--logging-level {DEBUG,INFO,WARNING,ERROR,CRITICAL}] [--disable-known-models] [--max-queue-size MAX_QUEUE_SIZE]
                   [--otel-service-name OTEL_SERVICE_NAME] [--otel-service-version OTEL_SERVICE_VERSION] [--otel-exporter-otlp-endpoint OTEL_EXPORTER_OTLP_ENDPOINT]
                   [--force-channels-last] [--force-hf-local-dir-mode] [--front-end-version FRONT_END_VERSION] [--front-end-root FRONT_END_ROOT]
                   [--executor-factory EXECUTOR_FACTORY] [--openai-api-key OPENAI_API_KEY] [--user-directory USER_DIRECTORY] [--blip-model-url BLIP_MODEL_URL]
                   [--blip-model-vqa-url BLIP_MODEL_VQA_URL] [--sam-model-vith-url SAM_MODEL_VITH_URL] [--sam-model-vitl-url SAM_MODEL_VITL_URL]
                   [--sam-model-vitb-url SAM_MODEL_VITB_URL] [--history-display-limit HISTORY_DISPLAY_LIMIT] [--ffmpeg-bin-path FFMPEG_BIN_PATH]
                   [--ffmpeg-extra-codecs FFMPEG_EXTRA_CODECS] [--wildcards-path WILDCARDS_PATH] [--wildcard-api WILDCARD_API] [--photoprism-host PHOTOPRISM_HOST]
                   [--immich-host IMMICH_HOST] [--ideogram-session-cookie IDEOGRAM_SESSION_COOKIE] [--annotator-ckpts-path ANNOTATOR_CKPTS_PATH] [--use-symlinks]
                   [--ort-providers ORT_PROVIDERS] [--vfi-ops-backend VFI_OPS_BACKEND] [--dependency-version DEPENDENCY_VERSION] [--mmdet-skip] [--sam-editor-cpu]
                   [--sam-editor-model SAM_EDITOR_MODEL] [--custom-wildcards CUSTOM_WILDCARDS] [--disable-gpu-opencv]

options:
  -h, --help            show this help message and exit
  -c CONFIG_FILE, --config CONFIG_FILE
                        config file path
  --write-out-config-file CONFIG_OUTPUT_PATH
                        takes the current command line args and writes them out to a config file at the given path, then exits
  -w CWD, --cwd CWD     Specify the working directory. If not set, this is the current working directory. models/, input/, output/ and other directories will be
                        located here by default. [env var: COMFYUI_CWD]
  --base-paths BASE_PATHS [BASE_PATHS ...]
                        Additional base paths for custom nodes, models and inputs. [env var: COMFYUI_BASE_PATHS]
  -H [IP], --listen [IP]
                        Specify the IP address to listen on (default: 127.0.0.1). You can give a list of ip addresses by separating them with a comma like:
                        127.2.2.2,127.3.3.3 If --listen is provided without an argument, it defaults to 0.0.0.0,:: (listens on all ipv4 and ipv6) [env var:
                        COMFYUI_LISTEN]
  --port PORT           Set the listen port. [env var: COMFYUI_PORT]
  --enable-cors-header [ORIGIN]
                        Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'. [env var: COMFYUI_ENABLE_CORS_HEADER]
  --max-upload-size MAX_UPLOAD_SIZE
                        Set the maximum upload size in MB. [env var: COMFYUI_MAX_UPLOAD_SIZE]
  --extra-model-paths-config PATH [PATH ...]
                        Load one or more extra_model_paths.yaml files. [env var: COMFYUI_EXTRA_MODEL_PATHS_CONFIG]
  --output-directory OUTPUT_DIRECTORY
                        Set the ComfyUI output directory. [env var: COMFYUI_OUTPUT_DIRECTORY]
  --temp-directory TEMP_DIRECTORY
                        Set the ComfyUI temp directory (default is in the ComfyUI directory). [env var: COMFYUI_TEMP_DIRECTORY]
  --input-directory INPUT_DIRECTORY
                        Set the ComfyUI input directory. [env var: COMFYUI_INPUT_DIRECTORY]
  --auto-launch         Automatically launch ComfyUI in the default browser. [env var: COMFYUI_AUTO_LAUNCH]
  --disable-auto-launch
                        Disable auto launching the browser. [env var: COMFYUI_DISABLE_AUTO_LAUNCH]
  --cuda-device DEVICE_ID
                        Set the id of the cuda device this instance will use. [env var: COMFYUI_CUDA_DEVICE]
  --cuda-malloc         Enable cudaMallocAsync (enabled by default for torch 2.0 and up). [env var: COMFYUI_CUDA_MALLOC]
  --disable-cuda-malloc
                        Disable cudaMallocAsync. [env var: COMFYUI_DISABLE_CUDA_MALLOC]
  --force-fp32          Force fp32 (If this makes your GPU work better please report it). [env var: COMFYUI_FORCE_FP32]
  --force-fp16          Force fp16. [env var: COMFYUI_FORCE_FP16]
  --force-bf16          Force bf16. [env var: COMFYUI_FORCE_BF16]
  --bf16-unet           Run the UNET in bf16. This should only be used for testing stuff. [env var: COMFYUI_BF16_UNET]
  --fp16-unet           Store unet weights in fp16. [env var: COMFYUI_FP16_UNET]
  --fp8_e4m3fn-unet     Store unet weights in fp8_e4m3fn. [env var: COMFYUI_FP8_E4M3FN_UNET]
  --fp8_e5m2-unet       Store unet weights in fp8_e5m2. [env var: COMFYUI_FP8_E5M2_UNET]
  --fp16-vae            Run the VAE in fp16, might cause black images. [env var: COMFYUI_FP16_VAE]
  --fp32-vae            Run the VAE in full precision fp32. [env var: COMFYUI_FP32_VAE]
  --bf16-vae            Run the VAE in bf16. [env var: COMFYUI_BF16_VAE]
  --cpu-vae             Run the VAE on the CPU. [env var: COMFYUI_CPU_VAE]
  --fp8_e4m3fn-text-enc
                        Store text encoder weights in fp8 (e4m3fn variant). [env var: COMFYUI_FP8_E4M3FN_TEXT_ENC]
  --fp8_e5m2-text-enc   Store text encoder weights in fp8 (e5m2 variant). [env var: COMFYUI_FP8_E5M2_TEXT_ENC]
  --fp16-text-enc       Store text encoder weights in fp16. [env var: COMFYUI_FP16_TEXT_ENC]
  --fp32-text-enc       Store text encoder weights in fp32. [env var: COMFYUI_FP32_TEXT_ENC]
  --directml [DIRECTML_DEVICE]
                        Use torch-directml. [env var: COMFYUI_DIRECTML]
  --disable-ipex-optimize
                        Disables ipex.optimize when loading models with Intel GPUs. [env var: COMFYUI_DISABLE_IPEX_OPTIMIZE]
  --preview-method [none,auto,latent2rgb,taesd]
                        Default preview method for sampler nodes. [env var: COMFYUI_PREVIEW_METHOD]
  --preview-size PREVIEW_SIZE
                        Sets the maximum preview size for sampler nodes. [env var: COMFYUI_PREVIEW_SIZE]
  --cache-lru CACHE_LRU
                        Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM. [env var: COMFYUI_CACHE_LRU]
  --use-split-cross-attention
                        Use the split cross attention optimization. Ignored when xformers is used. [env var: COMFYUI_USE_SPLIT_CROSS_ATTENTION]
  --use-quad-cross-attention
                        Use the sub-quadratic cross attention optimization . Ignored when xformers is used. [env var: COMFYUI_USE_QUAD_CROSS_ATTENTION]
  --use-pytorch-cross-attention
                        Use the new pytorch 2.0 cross attention function. [env var: COMFYUI_USE_PYTORCH_CROSS_ATTENTION]
  --disable-xformers    Disable xformers. [env var: COMFYUI_DISABLE_XFORMERS]
  --disable-flash-attn  Disable Flash Attention [env var: COMFYUI_DISABLE_FLASH_ATTN]
  --disable-sage-attention
                        Disable Sage Attention [env var: COMFYUI_DISABLE_SAGE_ATTENTION]
  --force-upcast-attention
                        Force enable attention upcasting, please report if it fixes black images. [env var: COMFYUI_FORCE_UPCAST_ATTENTION]
  --dont-upcast-attention
                        Disable all upcasting of attention. Should be unnecessary except for debugging. [env var: COMFYUI_DONT_UPCAST_ATTENTION]
  --gpu-only            Store and run everything (text encoders/CLIP models, etc... on the GPU). [env var: COMFYUI_GPU_ONLY]
  --highvram            By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory. [env var: COMFYUI_HIGHVRAM]
  --normalvram          Used to force normal vram use if lowvram gets automatically enabled. [env var: COMFYUI_NORMALVRAM]
  --lowvram             Split the unet in parts to use less vram. [env var: COMFYUI_LOWVRAM]
  --novram              When lowvram isn't enough. [env var: COMFYUI_NOVRAM]
  --cpu                 To use the CPU for everything (slow). [env var: COMFYUI_CPU]
  --reserve-vram RESERVE_VRAM
                        Set the amount of vram in GB you want to reserve for use by your OS/other software. By default some amount is reserved depending on your OS.
                        [env var: COMFYUI_RESERVE_VRAM]
  --default-hashing-function {md5,sha1,sha256,sha512}
                        Allows you to choose the hash function to use for duplicate filename / contents comparison. Default is sha256. [env var:
                        COMFYUI_DEFAULT_HASHING_FUNCTION]
  --disable-smart-memory
                        Force ComfyUI to agressively offload to regular ram instead of keeping models in vram when it can. [env var: COMFYUI_DISABLE_SMART_MEMORY]
  --deterministic       Make pytorch use slower deterministic algorithms when it can. Note that this might not make images deterministic in all cases. [env var:
                        COMFYUI_DETERMINISTIC]
  --fast                Enable some untested and potentially quality deteriorating optimizations. [env var: COMFYUI_FAST]
  --dont-print-server   Don't print server output. [env var: COMFYUI_DONT_PRINT_SERVER]
  --quick-test-for-ci   Quick test for CI. Raises an error if nodes cannot be imported, [env var: COMFYUI_QUICK_TEST_FOR_CI]
  --windows-standalone-build
                        Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening
                        the page on startup). [env var: COMFYUI_WINDOWS_STANDALONE_BUILD]
  --disable-metadata    Disable saving prompt metadata in files. [env var: COMFYUI_DISABLE_METADATA]
  --disable-all-custom-nodes
                        Disable loading all custom nodes. [env var: COMFYUI_DISABLE_ALL_CUSTOM_NODES]
  --multi-user          Enables per-user storage. [env var: COMFYUI_MULTI_USER]
  --create-directories  Creates the default models/, input/, output/ and temp/ directories, then exits. [env var: COMFYUI_CREATE_DIRECTORIES]
  --plausible-analytics-base-url PLAUSIBLE_ANALYTICS_BASE_URL
                        Enables server-side analytics events sent to the provided URL. [env var: COMFYUI_PLAUSIBLE_ANALYTICS_BASE_URL]
  --plausible-analytics-domain PLAUSIBLE_ANALYTICS_DOMAIN
                        Specifies the domain name for analytics events. [env var: COMFYUI_PLAUSIBLE_ANALYTICS_DOMAIN]
  --analytics-use-identity-provider
                        Uses platform identifiers for unique visitor analytics. [env var: COMFYUI_ANALYTICS_USE_IDENTITY_PROVIDER]
  --distributed-queue-connection-uri DISTRIBUTED_QUEUE_CONNECTION_URI
                        EXAMPLE: "amqp://guest:[email protected]" - Servers and clients will connect to this AMPQ URL to form a distributed queue and exchange prompt
                        execution requests and progress updates. [env var: COMFYUI_DISTRIBUTED_QUEUE_CONNECTION_URI]
  --distributed-queue-worker
                        Workers will pull requests off the AMQP URL. [env var: COMFYUI_DISTRIBUTED_QUEUE_WORKER]
  --distributed-queue-frontend
                        Frontends will start the web UI and connect to the provided AMQP URL to submit prompts. [env var: COMFYUI_DISTRIBUTED_QUEUE_FRONTEND]
  --distributed-queue-name DISTRIBUTED_QUEUE_NAME
                        This name will be used by the frontends and workers to exchange prompt requests and replies. Progress updates will be prefixed by the queue
                        name, followed by a '.', then the user ID [env var: COMFYUI_DISTRIBUTED_QUEUE_NAME]
  --external-address EXTERNAL_ADDRESS
                        Specifies a base URL for external addresses reported by the API, such as for image paths. [env var: COMFYUI_EXTERNAL_ADDRESS]
  --logging-level {DEBUG,INFO,WARNING,ERROR,CRITICAL}
                        Set the logging level [env var: COMFYUI_LOGGING_LEVEL]
  --disable-known-models
                        Disables automatic downloads of known models and prevents them from appearing in the UI. [env var: COMFYUI_DISABLE_KNOWN_MODELS]
  --max-queue-size MAX_QUEUE_SIZE
                        The API will reject prompt requests if the queue's size exceeds this value. [env var: COMFYUI_MAX_QUEUE_SIZE]
  --otel-service-name OTEL_SERVICE_NAME
                        The name of the service or application that is generating telemetry data. [env var: OTEL_SERVICE_NAME]
  --otel-service-version OTEL_SERVICE_VERSION
                        The version of the service or application that is generating telemetry data. [env var: OTEL_SERVICE_VERSION]
  --otel-exporter-otlp-endpoint OTEL_EXPORTER_OTLP_ENDPOINT
                        A base endpoint URL for any signal type, with an optionally-specified port number. Helpful for when you're sending more than one signal to the
                        same endpoint and want one environment variable to control the endpoint. [env var: OTEL_EXPORTER_OTLP_ENDPOINT]
  --force-channels-last
                        Force channels last format when inferencing the models. [env var: COMFYUI_FORCE_CHANNELS_LAST]
  --force-hf-local-dir-mode
                        Download repos from huggingface.co to the models/huggingface directory with the "local_dir" argument instead of models/huggingface_cache with
                        the "cache_dir" argument, recreating the traditional file structure. [env var: COMFYUI_FORCE_HF_LOCAL_DIR_MODE]
  --front-end-version FRONT_END_VERSION
                        Specifies the version of the frontend to be used. This command needs internet connectivity to query and download available frontend
                        implementations from GitHub releases. The version string should be in the format of: [repoOwner]/[repoName]@[version] where version is one of:
                        "latest" or a valid version number (e.g. "1.0.0") [env var: COMFYUI_FRONT_END_VERSION]
  --front-end-root FRONT_END_ROOT
                        The local filesystem path to the directory where the frontend is located. Overrides --front-end-version. [env var: COMFYUI_FRONT_END_ROOT]
  --executor-factory EXECUTOR_FACTORY
                        When running ComfyUI as a distributed worker, this specifies the kind of executor that should be used to run the actual ComfyUI workflow
                        worker. A ThreadPoolExecutor is the default. A ProcessPoolExecutor results in better memory management, since the process will be closed and
                        large, contiguous blocks of CUDA memory can be freed. [env var: COMFYUI_EXECUTOR_FACTORY]
  --openai-api-key OPENAI_API_KEY
                        Configures the OpenAI API Key for the OpenAI nodes [env var: OPENAI_API_KEY]
  --user-directory USER_DIRECTORY
                        Set the ComfyUI user directory with an absolute path. [env var: COMFYUI_USER_DIRECTORY]

Args that start with '--' can also be set in a config file (config.yaml or config.json or specified via -c). Config file syntax allows: key=value, flag=true, stuff=[a,b,c] (for details, see syntax at
https://goo.gl/R74nmi). In general, command-line values override environment variables which override config file values which override defaults.

Using ComfyUI as an API / Programmatically

There are multiple ways to use this ComfyUI package to run workflows programmatically:

Embedded

Start ComfyUI by creating an ordinary Python object. This does not create a web server. It runs ComfyUI as a library, like any other package you are familiar with:

from comfy.client.embedded_comfy_client import EmbeddedComfyClient

async with EmbeddedComfyClient() as client:
    # This will run your prompt
    # To get the prompt JSON, visit the ComfyUI interface, design your workflow and click **Save (API Format)**. This JSON is what you will use as your workflow.
    outputs = await client.queue_prompt(prompt)
    # At this point, your prompt is finished and all the outputs, like saving images, have been completed.
    # Now the outputs will contain the same thing that the Web UI expresses: a file path for each output.
    # Let's find the node ID of the first SaveImage node. This will work when you change your workflow JSON from
    # the example above.
    save_image_node_id = next(key for key in prompt if prompt[key].class_type == "SaveImage")
    # Now let's print the absolute path to the image.
    print(outputs[save_image_node_id]["images"][0]["abs_path"])
# At this point, all the models have been unloaded from VRAM, and everything has been cleaned up.

See script_examples/basic_api_example.py for a complete example.

Remote

Visit the ComfyUI interface, design your workflow and click Save (API Format). This JSON is what you will use as your workflow.

You can use the built-in Python client library by installing this package without its dependencies.

pip install aiohttp
pip install --no-deps git+https://github.com/hiddenswitch/ComfyUI.git

Then the following idiomatic pattern is available:

from comfy.client.aio_client import AsyncRemoteComfyClient

client = AsyncRemoteComfyClient(server_address="http://localhost:8188")
# Now let's get the bytes of the PNG image saved by the SaveImage node:
png_image_bytes = await client.queue_prompt(prompt)
# You can save these bytes wherever you need!
with open("image.png", "rb") as f:
    f.write(png_image_bytes)

See script_examples/remote_api_example.py for a complete example.

REST API

First, install this package using the Installation Instructions. Then, run comfyui.

Visit the ComfyUI interface, design your workflow and click Save (API Format). This JSON is what you will use as your workflow.

Then, send a request to api/v1/prompts. Here are some examples:

curl:

curl -X POST "http://localhost:8188/api/v1/prompts" \
     -H "Content-Type: application/json" \
     -H "Accept: image/png" \
     -o output.png \
     -d '{
       "prompt": {
         # ... (include the rest of the workflow)
       }
     }'

Python:

import requests

url = "http://localhost:8188/api/v1/prompts"
headers = {
    "Content-Type": "application/json",
    "Accept": "image/png"
}
workflow = {
    "4": {
        "inputs": {
            "ckpt_name": "sd_xl_base_1.0.safetensors"
        },
        "class_type": "CheckpointLoaderSimple"
    },
    # ... (include the rest of the workflow)
}

payload = {"prompt": workflow}

response = requests.post(url, json=payload, headers=headers)

Javascript (Browser):

async function generateImage() {
    const prompt = "a man walking on the beach";
    const workflow = {
        "4": {
            "inputs": {
                "ckpt_name": "sd_xl_base_1.0.safetensors"
            },
            "class_type": "CheckpointLoaderSimple"
        },
        // ... (include the rest of the workflow)
    };

    const response = await fetch('http://localhost:8188/api/v1/prompts', {
        method: 'POST',
        headers: {
            'Content-Type': 'application/json',
            'Accept': 'image/png'
        },
        body: JSON.stringify({prompt: workflow})
    });

    const blob = await response.blob();
    const imageUrl = URL.createObjectURL(blob);
    const img = document.createElement('img');
    // load image into the DOM
    img.src = imageUrl;
    document.body.appendChild(img);
}

generateImage().catch(console.error);

You can use the OpenAPI specification file to learn more about all the supported API methods.

OpenAPI Spec for Vanilla API, Typed Clients

Use a typed, generated API client for your programming language and access ComfyUI server remotely as an API.

You can generate the client from comfy/api/openapi.yaml.

RabbitMQ / AMQP Support

Submit jobs directly to a distributed work queue. This package supports AMQP message queues like RabbitMQ. You can submit workflows to the queue, including from the web using RabbitMQ's STOMP support, and receive realtime progress updates from multiple workers. Continue to the next section for more details.

Distributed, Multi-Process and Multi-GPU Comfy

This package supports multi-processing across machines using RabbitMQ. This means you can launch multiple ComfyUI backend workers and queue prompts against them from multiple frontends.

Getting Started

ComfyUI has two roles: worker and frontend. An unlimited number of workers can consume and execute workflows (prompts) in parallel; and an unlimited number of frontends can submit jobs. All of the frontends' API calls will operate transparently against your collection of workers, including progress notifications from the websocket.

To share work among multiple workers and frontends, ComfyUI uses RabbitMQ or any AMQP-compatible message queue like SQS or Kafka.

Example with RabbitMQ and File Share

On a machine in your local network, install Docker and run RabbitMQ:

docker run -it --rm --name rabbitmq -p 5672:5672 rabbitmq:latest

Find the machine's main LAN IP address:

Windows (PowerShell):

Get-NetIPConfiguration | Where-Object { $_.InterfaceAlias -like '*Ethernet*' -and $_.IPv4DefaultGateway -ne $null } | ForEach-Object { $_.IPv4Address.IPAddress }

Linux

ip -4 addr show $(ip route show default | awk '/default/ {print $5}') | grep -oP 'inet \K[\d.]+'

macOS

ifconfig $(route get default | grep interface | awk '{print $2}') | awk '/inet / {print $2; exit}'

On my machine, this prints 10.1.0.100, which is a local LAN IP that other hosts on my network can reach.

On this machine, you can also set up a file share for models, outputs and inputs.

Once you have installed this Python package following the installation steps, you can start a worker using:

Starting a Worker:

# you must replace the IP address with the one you printed above
comfyui-worker --distributed-queue-connection-uri="amqp://guest:[email protected]"

All the normal command line arguments are supported. This means you can use --cwd to point to a file share containing the models/ directory:

comfyui-worker --cwd //10.1.0.100/shared/workspace --distributed-queue-connection-uri="amqp://guest:[email protected]"

Starting a Frontend:

comfyui --listen --distributed-queue-connection-uri="amqp://guest:[email protected]" --distributed-queue-frontend

However, the frontend will not be able to find the output images or models to show the client by default. You must specify a place where the frontend can find the same outputs and models that are available to the backends:

comfyui --cwd //10.1.0.100/shared/workspace --listen --distributed-queue-connection-uri="amqp://guest:[email protected]" --distributed-queue-frontend

You can carefully mount network directories into outputs/ and inputs/ such that they are shared among workers and frontends; you can store the models/ on each machine, or serve them over a file share too.

Operating

The frontend expects to find the referenced output images in its --output-directory or in the default outputs/ under --cwd (aka the "workspace").

This means that workers and frontends do not have to have the same argument to --cwd. The paths that are passed to the frontend, such as the inputs/ and outputs/ directories, must have the same contents as the paths passed as those directories to the workers.

Since reading models like large checkpoints over the network can be slow, you can use --extra-model-paths-config to specify additional model paths. Or, you can use --cwd some/path, where some/path is a local directory, and, and mount some/path/outputs to a network directory.

Known models listed in model_downloader.py are downloaded using huggingface_hub with the default cache_dir. This means you can mount a read-write-many volume, like an SMB share, into the default cache directory. Read more about this here.

Containers

Build the Dockerfile:

docker build . -t hiddenswitch/comfyui

To run:

docker run -it -v ./output:/workspace/output -v ./models:/workspace/models --gpus=all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --rm hiddenswitch/comfyui

Frontend Development

As of August 15, 2024, we have transitioned to a new frontend, which is now hosted in a separate repository: ComfyUI Frontend. This repository now hosts the compiled JS (from TS/Vue) under the web/ directory.

Reporting Issues and Requesting Features

For any bugs, issues, or feature requests related to the frontend, please use the ComfyUI Frontend repository. This will help us manage and address frontend-specific concerns more efficiently.

Using the Latest Frontend

The new frontend is now the default for ComfyUI. However, please note:

The frontend in the main ComfyUI repository is updated weekly.
Daily releases are available in the separate frontend repository.

To use the most up-to-date frontend version:

For the latest daily release, launch ComfyUI with this command line argument:
```
--front-end-version Comfy-Org/ComfyUI_frontend@latest
```
For a specific version, replace latest with the desired version number:
```
--front-end-version Comfy-Org/[email protected]
```

This approach allows you to easily switch between the stable weekly release and the cutting-edge daily updates, or even specific versions for testing purposes.

Accessing the Legacy Frontend

If you need to use the legacy frontend for any reason, you can access it using the following command line argument:

--front-end-version Comfy-Org/ComfyUI_legacy_frontend@latest

This will use a snapshot of the legacy frontend preserved in the ComfyUI Legacy Frontend repository.

Community

Chat on Matrix: #comfyui_space:matrix.org, an alternative to Discord.

Known Issues

Please visit the Issues tab for documented known issues.

Name		Name	Last commit message	Last commit date
Latest commit History 3,459 Commits
.github		.github
comfy		comfy
comfy_extras		comfy_extras
custom_nodes		custom_nodes
docs		docs
input		input
models		models
output		output
tests		tests
.editorconfig		.editorconfig
.gitattributes		.gitattributes
.gitignore		.gitignore
.pylintrc		.pylintrc
CODEOWNERS		CODEOWNERS
CONTRIBUTING.md		CONTRIBUTING.md
Dockerfile		Dockerfile
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.md		README.md
amd.Dockerfile		amd.Dockerfile
intel.Dockerfile		intel.Dockerfile
main.py		main.py
mypy.ini		mypy.ini
pyproject.toml		pyproject.toml
pytest.ini		pytest.ini
requirements-dev.txt		requirements-dev.txt
requirements-triton.txt		requirements-triton.txt
requirements.txt		requirements.txt
setup.py		setup.py

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hiddenswitch/ComfyUI

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ComfyUI LTS

New Features

Upstream Features

Table of Contents

Getting Started

Installing

LTS Custom Nodes

Running with TLS

Notes for AMD Users

Model Downloading

Manual Install (Windows, Linux, macOS) For Development

Linux Development Dependencies

Large Language Models

SVG Conversion and String Saving

Video Workflows

SageAttention

Custom Nodes

Installing Custom Nodes

Vanilla Custom Nodes

Authoring Custom Nodes

Using pyproject.toml for projects with existing requirements.txt

Using setup.py

Adding Custom Configuration

Troubleshooting

Using the Editor

Notes

How to increase generation speed?

How to show high-quality previews?

Keyboard Shortcuts

Configuration

Configuration File

Extra Model Paths

Command Line Arguments

Using ComfyUI as an API / Programmatically

Embedded

Remote

REST API

OpenAPI Spec for Vanilla API, Typed Clients

RabbitMQ / AMQP Support

Distributed, Multi-Process and Multi-GPU Comfy

Getting Started

Example with RabbitMQ and File Share

Operating

Containers

Frontend Development

Reporting Issues and Requesting Features

Using the Latest Frontend

Accessing the Legacy Frontend

Community

Known Issues

About

Resources

License

Stars

Watchers

Forks

Releases

Packages 0

Languages

Using `pyproject.toml` for projects with existing `requirements.txt`

Using `setup.py`

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