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

• 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.

• Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
• Image Models
  • SD1.x, SD2.x,
  • SDXL, SDXL Turbo
  • Stable Cascade
  • SD3 and SD3.5
  • Pixart Alpha and Sigma
  • AuraFlow
  • HunyuanDiT
  • Flux
• Video Models
  • Stable Video Diffusion
  • Mochi
  • LTX-Video
  • Hunyuan Video
• Stable Audio
• 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
• Latent previews with TAESD
• Starts up very fast.
• Works fully offline: will never download anything.
• Config file to set the search paths for models.

• Workflows
• Installing
• Configuration
• Installing Custom Nodes
• Authoring Custom Nodes
• API
• Distributed

These instructions will install an interactive ComfyUI using the command line.

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:

1. Install Python 3.12, 3.11 or 3.10. You can do this from the Python website; or, you can use chocolatey, a Windows package manager:

   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 python --version 3.12.6

2. Install uv, which makes subsequent installation of Python packages much faster:

   choco install -y uv

3. Switch into a directory that you want to store your outputs, custom nodes and models in. This is your ComfyUI workspace. For example, if you want to store your workspace in a directory called ComfyUI_Workspace in your Documents folder:

   mkdir ~/Documents/ComfyUI_Workspace
   cd ~/Documents/ComfyUI_Workspace

4. Create a virtual environment:

   uv venv --seed --python 3.12

5. Activate it on Windows (PowerShell):

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

6. 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):

   uv pip install setuptools wheel
   uv pip install "comfyui[withtorch]@git+https://github.com/hiddenswitch/ComfyUI.git"

   Recommended: Install xformers:

   uv pip install --no-build-isolation --no-deps xformers==0.0.28.post3 --index-url https://download.pytorch.org/whl/

   To enable torchaudio support on Windows, install it directly:

   uv pip install torchaudio==2.5.1+cu121 --index-url https://download.pytorch.org/whl/cu121

7. 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

Running

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

cd ~\Documents\ComfyUI_Workspace\
& .venv\Scripts\activate.ps1
comfyui

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

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

1. Install Python 3.10, 3.11 or 3.12. This should be achieved by installing brew, a macOS package manager, first:

   /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

   Then, install python and uv:

   HOMEBREW_NO_AUTO_UPDATE=1 brew install python@3.12 uv

2. Switch into a directory that you want to store your outputs, custom nodes and models in. This is your ComfyUI workspace. For example, if you want to store your workspace in a directory called ComfyUI_Workspace in your Documents folder:

   mkdir -pv ~/Documents/ComfyUI_Workspace
   cd ~/Documents/ComfyUI_Workspace

3. Create a virtual environment:

   uv venv --seed --python 3.12

4. Activate it on macOS

   source .venv/bin/activate

5. 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):

   uv pip install setuptools wheel
   uv pip install "comfyui[withtorch]@git+https://github.com/hiddenswitch/ComfyUI.git"

   To enable torchaudio support, install it directly:

   uv pip install torchaudio --index-url https://download.pytorch.org/whl/

6. 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

Running

On macOS, you will need to open the terminal and activate your virtual environment whenever you want to run comfyui.

cd ~/Documents/ComfyUI_Workspace/
source .venv/bin/activate
comfyui

ComfyUI LTS supports downloading models on demand.

Known models will be downloaded from Hugging Face or CivitAI.

To support licensed models like Flux, you will need to login to Hugging Face from the command line.

1. Activate your Python environment by cd followed by your workspace directory. For example, if your workspace is located in ~/Documents/ComfyUI_Workspace, do:

cd ~/Documents/ComfyUI_Workspace

Then, on Windows: & .venv/scripts/activate.ps1; on macOS: source .venv/bin/activate.

2. Login with Huggingface:

uv pip install huggingface-cli
huggingface-cli login

3. Agree to the terms for a repository. For example, visit https://huggingface.co/black-forest-labs/FLUX.1-dev, login with your HuggingFace account, then choose Agree.

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.

To save space, you will need to 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.

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.

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

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

You can enable experimental memory efficient attention on pytorch 2.5 in ComfyUI on RDNA3 and potentially other AMD GPUs using this command:

TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 python main.py --use-pytorch-cross-attention

You can also try setting this env variable PYTORCH_TUNABLEOP_ENABLED=1 which might speed things up at the cost of a very slow initial run.

These instructions from upstream have not yet been validated.

For models compatible with Ascend Extension for PyTorch (torch_npu). To get started, ensure your environment meets the prerequisites outlined on the installation page. Here's a step-by-step guide tailored to your platform and installation method:

1. Begin by installing the recommended or newer kernel version for Linux as specified in the Installation page of torch-npu, if necessary.
2. Proceed with the installation of Ascend Basekit, which includes the driver, firmware, and CANN, following the instructions provided for your specific platform.
3. Next, install the necessary packages for torch-npu by adhering to the platform-specific instructions on the Installation page.
4. Finally, adhere to the ComfyUI manual installation guide for Linux. Once all components are installed, you can run ComfyUI as described earlier.

1. Clone this repo:

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

2. Create a virtual environment:

   1. Create an environment:

      python -m virtualenv venv

   2. Activate it:

      Windows (PowerShell):

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

      Linux and macOS

      source ./venv/bin/activate

3. 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]"

4. 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.

5. 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.

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

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.

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.

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.

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

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

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").

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.

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

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.

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.

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

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.

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

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

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.

Ctrl can also be replaced with Cmd instead for macOS users

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

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.

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:guest@127.0.0.1" - 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.

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

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.

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.

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.

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.

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.

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.

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.

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:guest@10.1.0.100"

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:guest@10.1.0.100"

Starting a Frontend:

comfyui --listen --distributed-queue-connection-uri="amqp://guest:guest@10.1.0.100" --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:guest@10.1.0.100" --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.

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.

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

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.

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.

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

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

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

1. For the latest daily release, launch ComfyUI with this command line argument:

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

2. For a specific version, replace latest with the desired version number:

   --front-end-version Comfy-Org/ComfyUI_frontend@1.2.2
   

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.

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.

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

Please visit the Issues tab for documented known issues.