ComfyUI

The most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface.

64,238

6,876

64,238

1,961

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Invoke is a leading creative engine for Stable Diffusion models, empowering professionals, artists, and enthusiasts to generate and create visual media using the latest AI-driven technologies. The solution offers an industry leading WebUI, and serves as the foundation for multiple commercial products.

InvokeAI

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stablediffusion

39,825

High-Resolution Image Synthesis with Latent Diffusion Models

Quick Overview

ComfyUI is a powerful and modular UI system for Stable Diffusion, designed to be both user-friendly and highly customizable. It provides a node-based interface for creating complex image generation workflows, allowing users to experiment with various AI models and techniques in a visual and intuitive manner.

Pros

Highly modular and extensible architecture
User-friendly node-based interface for creating complex workflows
Supports a wide range of Stable Diffusion models and techniques
Active community and frequent updates

Cons

Steeper learning curve compared to some simpler Stable Diffusion interfaces
May require more computational resources for complex workflows
Documentation can be sparse for some advanced features
Limited built-in presets for beginners

Getting Started

To get started with ComfyUI:

Clone the repository:

git clone https://github.com/comfyanonymous/ComfyUI.git

Install the required dependencies:
```
pip install -r requirements.txt
```
Run the application:
```
python main.py
```
Open your web browser and navigate to http://localhost:8188 to access the ComfyUI interface.
Start building your workflows by dragging and connecting nodes in the interface.

For more detailed instructions and advanced usage, refer to the project's documentation and community resources.

Competitor Comparisons

stable-diffusion-webui

149,793

Stable Diffusion web UI

Pros of stable-diffusion-webui

More user-friendly interface with a simpler learning curve
Extensive built-in features and extensions ecosystem
Active community support and frequent updates

Cons of stable-diffusion-webui

Less flexibility for custom workflows and advanced node-based operations
Higher resource consumption, especially for complex tasks
Potential performance bottlenecks with large batch processing

Code Comparison

stable-diffusion-webui:

def process_images(p):
    processed = Processed(p, images_list, p.seed, "", all_prompts, all_negative_prompts, all_seeds, all_subseeds)
    return processed

ComfyUI:

class LoadImage:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"image": ("IMAGE",)}}
    
    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "load_image"

    def load_image(self, image):
        return (image,)

The code snippets highlight the different approaches:

stable-diffusion-webui uses a more traditional function-based approach
ComfyUI employs a node-based system with class definitions for each operation

Both repositories offer powerful tools for working with Stable Diffusion, but they cater to different user needs and preferences. stable-diffusion-webui provides a more accessible experience for beginners, while ComfyUI offers greater customization and flexibility for advanced users.

InvokeAI

24,210

Pros of InvokeAI

More user-friendly interface, suitable for beginners
Extensive documentation and active community support
Built-in image editing features and advanced prompt engineering tools

Cons of InvokeAI

Less flexible for custom workflows compared to ComfyUI
Potentially slower processing times for complex operations
Limited node-based editing capabilities

Code Comparison

InvokeAI example:

from invokeai.app.invocations.baseinvocation import BaseInvocation

class CustomInvocation(BaseInvocation):
    def invoke(self, context):
        # Custom logic here

ComfyUI example:

class CustomNode:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"input": ("STRING", {})}}
    
    RETURN_TYPES = ("STRING",)
    FUNCTION = "process"

    def process(self, input):
        # Custom logic here
        return (result,)

The code comparison shows that ComfyUI offers a more node-centric approach, allowing for greater customization and flexibility in creating complex workflows. InvokeAI, on the other hand, provides a more structured and guided approach to creating custom invocations, which may be easier for beginners but less flexible for advanced users.

InvokeAI

24,216

Pros of InvokeAI

More user-friendly interface, suitable for beginners
Extensive documentation and active community support
Built-in image editing features and advanced prompt engineering tools

Cons of InvokeAI

Less flexible for custom workflows compared to ComfyUI
Potentially slower processing times for complex operations
Limited node-based editing capabilities

Code Comparison

InvokeAI example:

from invokeai.app.invocations.baseinvocation import BaseInvocation

class CustomInvocation(BaseInvocation):
    def invoke(self, context):
        # Custom logic here

ComfyUI example:

class CustomNode:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"input": ("STRING", {})}}
    
    RETURN_TYPES = ("STRING",)
    FUNCTION = "process"

    def process(self, input):
        # Custom logic here
        return (result,)

stablediffusion

39,825

High-Resolution Image Synthesis with Latent Diffusion Models

Pros of stablediffusion

More comprehensive and feature-rich, offering a wider range of functionalities
Better documentation and community support due to its popularity
Directly supported by Stability AI, ensuring regular updates and improvements

Cons of stablediffusion

Steeper learning curve for beginners due to its complexity
Requires more computational resources to run effectively
Less flexible for custom workflows compared to ComfyUI's node-based approach

Code Comparison

stablediffusion:

from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
image = pipe("A beautiful sunset over the ocean").images[0]
image.save("sunset.png")

ComfyUI:

import comfy

workflow = comfy.load_workflow("sunset_workflow.json")
result = comfy.run_workflow(workflow, {"prompt": "A beautiful sunset over the ocean"})
comfy.save_image(result["output"], "sunset.png")

Both repositories offer powerful tools for working with Stable Diffusion models, but they cater to different user needs. stablediffusion provides a more comprehensive solution with extensive features, while ComfyUI offers a more flexible, node-based approach that may be preferable for custom workflows and experimentation.

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README

ComfyUI

The most powerful and modular visual AI engine and application.

ComfyUI Screenshot

ComfyUI lets you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. Available on Windows, Linux, and macOS.

Get Started

Desktop Application

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

Windows Portable Package

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

Manual Install

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

Examples

See what ComfyUI can do with the example workflows.

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
- Lumina Image 2.0
Video Models
- Stable Video Diffusion
- Mochi
- LTX-Video
- Hunyuan Video
- Nvidia Cosmos
- Wan 2.1
3D Models
- Hunyuan3D 2.0
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.

Workflow examples can be found on the Examples page

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
`F`	Show/Hide menu
`.`	Fit view to selection (Whole graph when nothing is selected)
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

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

Simply download, extract with 7-Zip and run. Make sure you put your Stable Diffusion checkpoints/models (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints

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

If you have a 50 series Blackwell card like a 5090 or 5080 see this discussion thread

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.

Jupyter Notebook

To run it on services like paperspace, kaggle or colab you can use my Jupyter Notebook

comfy-cli

You can install and start ComfyUI using comfy-cli:

pip install comfy-cli
comfy install

Manual Install (Windows, Linux)

python 3.13 is supported but using 3.12 is recommended because some custom nodes and their dependencies might not support it yet.

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 only)

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

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2.4

This is the command to install the nightly with ROCm 6.3 which might have some performance improvements:

pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.3

Intel GPUs (Windows and Linux)

(Option 1) Intel Arc GPU users can install native PyTorch with torch.xpu support using pip (currently available in PyTorch nightly builds). More information can be found here

To install PyTorch nightly, use the following command:

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

Launch ComfyUI by running python main.py

(Option 2) Alternatively, Intel GPUs supported by Intel Extension for PyTorch (IPEX) can leverage IPEX for improved performance.

For IntelÂ® Arcâ¢ A-Series Graphics utilizing IPEX, create a conda environment and use the commands below:

conda install libuv
pip install torch==2.3.1.post0+cxx11.abi torchvision==0.18.1.post0+cxx11.abi torchaudio==2.3.1.post0+cxx11.abi intel-extension-for-pytorch==2.3.110.post0+xpu --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/

For other supported Intel GPUs with IPEX, visit Installation for more information.

Additional discussion and help can be found here.

NVIDIA

Nvidia users should install stable pytorch using this command:

pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu126

This is the command to install pytorch nightly instead which supports the new blackwell 50xx series GPUs and might have performance improvements.

pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128

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.

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

DirectML (AMD Cards on Windows)

pip install torch-directml Then you can launch ComfyUI with: python main.py --directml

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:

Begin by installing the recommended or newer kernel version for Linux as specified in the Installation page of torch-npu, if necessary.
Proceed with the installation of Ascend Basekit, which includes the driver, firmware, and CANN, following the instructions provided for your specific platform.
Next, install the necessary packages for torch-npu by adhering to the platform-specific instructions on the Installation page.
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:

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

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 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.

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).

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 fortnightly.
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/ComfyUI_frontend@1.2.2
```

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

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