- Apr 25, 2025: 👋 We release the evaluation code and benchmark data of Step1X-Edit. Download GEdit-Bench
- Apr 25, 2025: 👋 We release the inference code and model weights of Step1X-Edit. ModelScope & HuggingFace models.
- Apr 25, 2025: 🎉 We have made our technical report available as open source. Read
Step1X-Edit: a unified image editing model performs impressively on various genuine user instructions.
We release a state-of-the-art image editing model, Step1X-Edit, which can provide comparable performance against the closed-source models like GPT-4o and Gemini2 Flash. More specifically, we adopt the Multimodal LLM to process the reference image and user's editing instruction. A latent embedding has been extracted and integrated with a diffusion image decoder to obtain the target image. To train the model, we build a data generation pipeline to produce a high-quality dataset. For evaluation, we develop the GEdit-Bench, a novel benchmark rooted in real-world user instructions. Experimental results on GEdit-Bench demonstrate that Step1X-Edit outperforms existing open-source baselines by a substantial margin and approaches the performance of leading proprietary models, thereby making significant contributions to the field of image editing. More details please refer to our technical report.
The following table shows the requirements for running Step1X-Edit model (batch size = 1, w/o cfg distillation) to edit images:
| Model | height/width | Peak GPU Memory | 28 steps w flash-attn |
|---|---|---|---|
| Step1X-Edit | 512x512 | 42.5 GB | 5 s |
| Step1X-Edit | 768x768 | 46.5 GB | 11 s |
| Step1X-Edit | 1024x1024 | 49.8 GB | 22 s |
- The model is tested on one H800 GPUs.
- Tested operating system: Linux
- We recommend to use GPUs with 80GB of memory for better generation quality.
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.txtInstall flash-attn, here we provide a script to help find the pre-built wheel suitable for your system.
python scripts/get_flash_attn.pyThe 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.
After downloading the model weights, you can use the following scripts to edit images:
bash scripts/run_examples.sh
This script runs the inference code on example inputs. The results will look like:
We release GEdit-Bench as a new benchmark, grounded in real-world usages is developed to support more authentic and comprehensive evaluation. This benchmark, which is carefully curated to reflect actual user editing needs and a wide range of editing scenarios, enables more authentic and comprehensive evaluations of image editing models. The evaluation process and related code can be found in GEdit-Bench/EVAL.md. Part results of the benchmark are shown below:
@article{liu2025step1x-edit,
title={Step1X-Edit: A Practical Framework for General Image Editing},
author={Shiyu Liu and Yucheng Han and Peng Xing and Fukun Yin and Rui Wang and Wei Cheng and Jiaqi Liao and Yingming Wang and Honghao Fu and Chunrui Han and Guopeng Li and Yuang Peng and Quan Sun and Jingwei Wu and Yan Cai and Zheng Ge and Ranchen Ming and Lei Xia and Xianfang Zeng and Yibo Zhu and Binxing Jiao and Xiangyu Zhang and Gang Yu and Daxin Jiang},
journal={arXiv preprint arXiv:2504.17761},
year={2025}
}
We would like to express our sincere thanks to the contributors of SD3, FLUX, Qwen, diffusers and HuggingFace teams, for their open research and exploration.
The results produced by this image editing model are entirely determined by user input and actions. The development team and this open-source project are not responsible for any outcomes or consequences arising from its use.