ComfyUI

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

53,273

5,645

53,273

1,750

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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

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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

144,415

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

23,395

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

23,397

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

38,794

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 diffusion model GUI and backend.

ComfyUI Screenshot

This ui will let you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. For some workflow examples and see what ComfyUI can do you can check out:

ComfyUI Examples

Installing ComfyUI

Features

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

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

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

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

Manual Install (Windows, Linux)

Note that some dependencies do not yet support python 3.13 so using 3.12 is recommended.

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

This is the command to install the nightly with ROCm 6.2 which might have some performance improvements:

pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.2

NVIDIA

Nvidia users should install stable pytorch using this command:

pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu124

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/cu124

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:

Intel GPUs

Intel GPU support is available for all Intel GPUs supported by Intel's Extension for Pytorch (IPEX) with the support requirements listed in the Installation page. Choose your platform and method of install and follow the instructions. The steps are as follows:

Start by installing the drivers or kernel listed or newer in the Installation page of IPEX linked above for Windows and Linux if needed.
Follow the instructions to install Intel's oneAPI Basekit for your platform.
Install the packages for IPEX using the instructions provided in the Installation page for your platform.
Follow the ComfyUI manual installation instructions for Windows and Linux and run ComfyUI normally as described above after everything is installed.

Additional discussion and help can be found here.

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

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

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 weekly.
Daily releases are available in the separate frontend repository.

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

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

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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