ComfyUI Extension: ComfyUI-Step1X-Edit

Authored by Yuan-ManX

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Make Step1X-Edit avialbe in ComfyUI.

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    README

    ComfyUI-Step1X-Edit

    Make Step1X-Edit avialbe in ComfyUI.

    Step1X-Edit: A Practical Framework for General Image Editing. A SOTA open-source image editing model, which aims to provide comparable performance against the closed-source models like GPT-4o and Gemini 2 Flash.

    Installation

    1. Make sure you have ComfyUI installed

    2. Clone this repository into your ComfyUI's custom_nodes directory:

    cd ComfyUI/custom_nodes
git clone https://github.com/Yuan-ManX/ComfyUI-Step1X-Edit.git
    1. Install dependencies:
    cd ComfyUI-Step1X-Edit
pip install -r requirements.txt

    Model

    šŸ‘‹ We release the inference code and model weights of Step1X-Edit. ModelScope & HuggingFace models.

    šŸ‘‹ We have made our technical report available as open source. Read

    šŸŽ‰ With community support, we update the inference code and model weights of Step1X-Edit-FP8. meimeilook/Step1X-Edit-FP8 & rkfg/Step1X-Edit-FP8.

    2.1 Requirements

    The following table shows the requirements for running Step1X-Edit model (batch size = 1, w/o cfg distillation) to edit images:

    | Model | Peak GPU Memory (512 / 786 / 1024) | 28 steps w flash-attn(512 / 786 / 1024) | |:------------:|:------------:|:------------:| | Step1X-Edit | 42.5GB / 46.5GB / 49.8GB | 5s / 11s / 22s | | Step1X-Edit-FP8 | 31GB / 31.5GB / 34GB | 6.8s / 13.5s / 25s | | Step1X-Edit + offload | 25.9GB / 27.3GB / 29.1GB | 49.6s / 54.1s / 63.2s | | Step1X-Edit-FP8 + offload | 18GB / 18GB / 18GB | 35s / 40s / 51s |

    • The model is tested on one H800 GPUs.
    • We recommend to use GPUs with 80GB of memory for better generation quality and efficiency.
    • The Step1X-Edit-FP8 model we tested comes from meimeilook/Step1X-Edit-FP8.

    2.2 Dependencies and Installation

    python >=3.10.0 and install torch >= 2.2 with cuda toolkit and corresponding torchvision. We test our model using torch==2.3.1 and torch==2.5.1 with cuda-12.1.

    Install requirements:

    pip install -r requirements.txt

    Install flash-attn, here we provide a script to help find the pre-built wheel suitable for your system.

    python src/scripts/get_flash_attn.py

    The script will generate a wheel name like flash_attn-2.7.2.post1+cu12torch2.5cxx11abiFALSE-cp310-cp310-linux_x86_64.whl, which could be found in the release page of flash-attn.

    Then you can download the corresponding pre-built wheel and install it following the instructions in flash-attn.