ComfyUI

The most powerful and modular AI engine for content creation.

ComfyUI is the AI creation engine for visual professionals who demand control over every model, every parameter, and every output. Its powerful and modular node graph interface empowers creatives to generate images, videos, 3D models, audio, and more...

• ComfyUI natively supports the latest open-source state of the art models.
• API nodes provide access to the best closed source models such as Nano Banana, Seedance, Hunyuan3D, etc.
• It is available on Windows, Linux, and macOS, locally with our desktop application, our portable install or on our cloud.
• The most sophisticated workflows can be exposed through a simple UI thanks to App Mode.
• It integrates seamlessly into production pipelines with our API endpoints.

Get Started

Local

[Desktop Application](https://www.comfy.org/download)

• The easiest way to get started.
• Available on Windows & macOS.

[Windows Portable Package](#installing)

• Get the latest commits and completely portable.
• Available on Windows.

[Manual Install](#manual-install-windows-linux)

Supports all operating systems and GPU types (NVIDIA, AMD, Intel, Apple Silicon, Ascend).

Cloud

[Comfy Cloud](https://www.comfy.org/cloud)

• Our official paid cloud version for those who can't afford local hardware.

Examples

See what ComfyUI can do with the newer template workflows or old example workflows.

Features

• Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
• NOTE: There are many more models supported than the list below, if you want to see what is supported see our templates list inside ComfyUI.
• Image Models
  • SD1.x, SD2.x (unCLIP)
  • SDXL, SDXL Turbo
  • Stable Cascade
  • SD3 and SD3.5
  • Pixart Alpha and Sigma
  • AuraFlow
  • HunyuanDiT
  • Flux
  • Lumina Image 2.0
  • HiDream
  • Qwen Image
  • Hunyuan Image 2.1
  • Flux 2
  • Z Image
  • Ernie Image
• Image Editing Models
  • Omnigen 2
  • Flux Kontext
  • HiDream E1.1
  • Qwen Image Edit
• Video Models
  • Stable Video Diffusion
  • Mochi
  • LTX-Video
  • Hunyuan Video
  • Wan 2.1
  • Wan 2.2
  • Hunyuan Video 1.5
• Audio Models
  • Stable Audio
  • ACE Step
• 3D Models
  • Hunyuan3D 2.0
• Asynchronous Queue system
• Many optimizations: Only re-executes the parts of the workflow that changes between executions.
• Smart memory management: can automatically run large models on GPUs with as low as 1GB vram with smart offloading.
• Works even if you don't have a GPU with: --cpu (slow)
• Can load ckpt and safetensors: All in one checkpoints or standalone diffusion models, VAEs and CLIP models.
• Safe loading of ckpt, pt, pth, etc.. files.
• 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...)
• GLIGEN
• Model Merging
• LCM models and Loras
• Latent previews with TAESD
• Works fully offline: core will never download anything unless you want to.
• Optional API nodes to use paid models from external providers through the online Comfy API disable with: --disable-api-nodes
• Config file to set the search paths for models.

Workflow examples can be found on the Examples page

Release Process

ComfyUI follows a weekly release cycle targeting Monday but this regularly changes because of model releases or large changes to the codebase. There are three interconnected repositories:

1. ComfyUI Core

   • Releases a new major stable version (e.g., v0.7.0) roughly every 2 weeks.
   • Starting from v0.4.0 patch versions will be used for fixes backported onto the current stable release.
   • Minor versions will be used for releases off the master branch.
   • Patch versions may still be used for releases on the master branch in cases where a backport would not make sense.
   • Commits outside of the stable release tags may be very unstable and break many custom nodes.
   • Serves as the foundation for the desktop release

2. ComfyUI Desktop

   • Builds a new release using the latest stable core version

3. ComfyUI Frontend

   • Every 2+ weeks frontend updates are merged into the core repository
   • Features are frozen for the upcoming core release
   • Development continues for the next release cycle

Shortcuts

Ctrl can also be replaced with Cmd instead for macOS users

Installing

Windows Portable

There is a portable standalone build for Windows that should work for running on Nvidia GPUs or for running on your CPU only on the releases page.

[Direct link to download](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia.7z)

Simply download, extract with 7-Zip or with the windows explorer on recent windows versions and run. For smaller models you normally only need to put the checkpoints (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints but many of the larger models have multiple files. Make sure to follow the instructions to know which subfolder to put them in ComfyUI\models\

If you have trouble extracting it, right click the file -> properties -> unblock

The portable above currently comes with python 3.13 and pytorch cuda 13.0. Update your Nvidia drivers if it doesn't start.

All Official Portable Downloads:

Portable for AMD GPUs

Portable for Intel GPUs

Portable for Nvidia GPUs (supports 20 series and above).

Portable for Nvidia GPUs with pytorch cuda 12.6 and python 3.12 (Supports Nvidia 10 series and older GPUs).

How do I share models between another UI and ComfyUI?

See the Config file to set the search paths for models. In the standalone windows build you can find this file in the ComfyUI directory. Rename this file to extra_model_paths.yaml and edit it with your favorite text editor.

[comfy-cli](https://docs.comfy.org/comfy-cli/getting-started)

You can install and start ComfyUI using comfy-cli:

pip install comfy-cli
comfy install

Manual Install (Windows, Linux)

Python 3.14 works but some custom nodes may have issues. The free threaded variant works but some dependencies will enable the GIL so it's not fully supported.

Python 3.13 is very well supported. If you have trouble with some custom node dependencies on 3.13 you can try 3.12

torch 2.4 and above is supported but some features and optimizations might only work on newer versions. We generally recommend using the latest major version of pytorch with the latest cuda version unless it is less than 2 weeks old.

Instructions:

Git clone this repo.

Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints

Put your VAE in: models/vae

AMD GPUs (Linux)

AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.2

This is the command to install the nightly with ROCm 7.2 which might have some performance improvements:

pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.2

AMD GPUs (Experimental: Windows and Linux), RDNA 3, 3.5 and 4 only.

These have less hardware support than the builds above but they work on windows. You also need to install the pytorch version specific to your hardware.

RDNA 3 (RX 7000 series):

pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx110X-all/

RDNA 3.5 (Strix halo/Ryzen AI Max+ 365):

pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx1151/

RDNA 4 (RX 9000 series):

pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx120X-all/

Intel GPUs (Windows and Linux)

Intel Arc GPU users can install native PyTorch with torch.xpu support using pip. More information can be found here

1. To install PyTorch xpu, use the following command:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/xpu

This is the command to install the Pytorch xpu nightly which might have some performance improvements:

pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu

NVIDIA

Nvidia users should install stable pytorch using this command:

pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130

This is the command to install pytorch nightly instead which might have performance improvements.

pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu132

Troubleshooting

If you get the "Torch not compiled with CUDA enabled" error, uninstall torch with:

pip uninstall torch

And install it again with the command above.

Dependencies

Install the dependencies by opening your terminal inside the ComfyUI folder and:

pip install -r requirements.txt

After this you should have everything installed and can proceed to running ComfyUI.

Others:

Apple Mac silicon

You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS version.

1. Install pytorch nightly. For instructions, read the Accelerated PyTorch training on Mac Apple Developer guide (make sure to install the latest pytorch nightly).
2. Follow the ComfyUI manual installation instructions for Windows and Linux.
3. Install the ComfyUI dependencies. If you have another Stable Diffusion UI you might be able to reuse the dependencies.
4. Launch ComfyUI by running python main.py

Note: Remember to add your models, VAE, LoRAs etc. to the corresponding Comfy folders, as discussed in ComfyUI manual installation.

Ascend NPUs

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.

Cambricon MLUs

For models compatible with Cambricon Extension for PyTorch (torch_mlu). Here's a step-by-step guide tailored to your platform and installation method:

1. Install the Cambricon CNToolkit by adhering to the platform-specific instructions on the Installation
2. Next, install the PyTorch(torch_mlu) following the instructions on the Installation
3. Launch ComfyUI by running python main.py

Iluvatar Corex

For models compatible with Iluvatar Extension for PyTorch. Here's a step-by-step guide tailored to your platform and installation method:

1. Install the Iluvatar Corex Toolkit by adhering to the platform-specific instructions on the Installation
2. Launch ComfyUI by running python main.py

[ComfyUI-Manager](https://github.com/Comfy-Org/ComfyUI-Manager/tree/manager-v4)

ComfyUI-Manager is an extension that allows you to easily install, update, and manage custom nodes for ComfyUI.

Setup

1. Install the manager dependencies:

   bash Copy

   pip install -r manager_requirements.txt

2. Enable the manager with the --enable-manager flag when running ComfyUI:

   bash Copy

   python main.py --enable-manager

Command Line Options

Running

python main.py

For AMD cards not officially supported by ROCm

Try running it with this command if you have issues:

For 6700, 6600 and maybe other RDNA2 or older: HSA_OVERRIDE_GFX_VERSION=10.3.0 python main.py

For AMD 7600 and maybe other RDNA3 cards: HSA_OVERRIDE_GFX_VERSION=11.0.0 python main.py

AMD ROCm Tips

You can enable experimental memory efficient attention on recent pytorch in ComfyUI on some AMD GPUs using this command, it should already be enabled by default on RDNA3. If this improves speed for you on latest pytorch on your GPU please report it so that I can enable it by default.

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.

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 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, taesdxl_decoder.pth, taesd3_decoder.pth and taef1_decoder.pth and place them in the models/vae_approx folder. Once they're installed, restart ComfyUI and launch it with --preview-method taesd to enable high-quality previews.

How to use TLS/SSL?

Generate a self-signed certificate (not appropriate for shared/production use) and key by running the command: openssl req -x509 -newkey rsa:4096 -keyout key.pem -out cert.pem -sha256 -days 3650 -nodes -subj "/C=XX/ST=StateName/L=CityName/O=CompanyName/OU=CompanySectionName/CN=CommonNameOrHostname"

Use --tls-keyfile key.pem --tls-certfile cert.pem to enable TLS/SSL, the app will now be accessible with https://... instead of http://....

Note: Windows users can use alexisrolland/docker-openssl or one of the 3rd party binary distributions to run the command example above.

If you use a container, note that the volume mount -v can be a relative path so ... -v ".\:/openssl-certs" ... would create the key & cert files in the current directory of your command prompt or powershell terminal.

Support and dev channel

Discord: Try the #help or #feedback channels.

Matrix space: #comfyui_space:matrix.org (it's like discord but open source).

See also: https://www.comfy.org/

psst — we're hiring! Help build ComfyUI: comfy.org/careers

Frontend Development

As of August 15, 2024, we have transitioned to a new frontend, which is now hosted in a separate repository: ComfyUI Frontend. The compiled JS files (from TS/Vue) are published to pypi and installed as a dependency in ComfyUI.

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:

1. The frontend in the main ComfyUI repository is updated fortnightly.
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:

   text Copy

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

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

   text Copy

   --front-end-version Comfy-Org/[email protected]

This approach allows you to easily switch between the stable fortnightly 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.

QA

Which GPU should I buy for this?

See this page for some recommendations

🚀 Quick Start

pip install comfy-cli
comfy install