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Step1X-Edit: A Practical Framework for General Image Editing

Paper · 2025 · Shiyu Liu, Yucheng Han, Peng Xing et al.

Summary

Proprietary systems such as GPT-4o, Gemini2 Flash and Doubao now handle natural-language image editing very well. Open-source alternatives like OmniGen and ACE++ still lag in edit accuracy, generalization and output quality. The StepFun team presents Step1X-Edit to close this gap. In its design, a multimodal LLM (e.g., Qwen-VL) jointly reads the source image and the user's instruction. It produces a latent conditioning signal that guides a DiT-style diffusion decoder, which is initialised from a text-to-image model. The authors also built a data pipeline around 11 edit categories identified from web-collected examples. The pipeline produced about 20M source–instruction–target triplets, filtered down to more than 1M high-quality ones. To measure real-world performance, they introduce GEdit-Bench, built from genuine user editing requests. On this benchmark, Step1X-Edit clearly beats open-source baselines and comes close to the commercial systems. This matters because the model is openly released, which gives researchers a reproducible, near-proprietary-quality editing system plus a practical evaluation resource.

Key points

  • The 11 edit types include subject addition, removal and replacement, background change, style transfer, text modification, color, material, tone, motion change and portrait beautification.
  • Roughly 20M triplets were generated and about 1M were kept (Step1X-Edit-HQ, about 20:1 filtering). Filtering used step-1o and human annotators. Tooling included Florence-2, SAM-2, ObjectRemovalAlpha and GPT-4o.
  • GEdit-Bench reflects real user requests, and the paper shows both English and Chinese instructions.
  • A blinded user study with 55 participants scored the full set as follows: GPT-4o 7.134, Step1X-Edit 6.939, Gemini 6.603, Doubao 5.678. On the intersection subset, Gemini led with 7.109.
  • Code and model are released at github.com/stepfun-ai/Step1X-Edit.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 2The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksComputer Vision and Pattern Recognition · 2,869 works

Cite

Cite as Shiyu Liu, Yucheng Han, Peng Xing, Fukun Yin, Rui Wang, Wei Cheng, Jiaqi Liao, Yingming Wang, Honghao Fu, Chunrui Han, Guopeng Li, Yuang Peng, Quan Sun, Jingwei Wu, Yan Cai, Zheng Ge, Ranchen Ming, Lei Xia, Xianfang Zeng, Yibo Zhu, Binxing Jiao, Xiangyu Zhang, Gang Yu and Daxin Jiang. 2025. Step1X-Edit: A Practical Framework for General Image Editing. arXiv:2504.17761.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2504.17761 · arXiv 2504.17761

@misc{liu2025step1x,
  title = {{Step1X-Edit: A Practical Framework for General Image Editing}},
  author = {Shiyu Liu, Yucheng Han, Peng Xing, Fukun Yin, Rui Wang, Wei Cheng, Jiaqi Liao, Yingming Wang, Honghao Fu, Chunrui Han, Guopeng Li, Yuang Peng, Quan Sun, Jingwei Wu, Yan Cai, Zheng Ge, Ranchen Ming, Lei Xia, Xianfang Zeng, Yibo Zhu, Binxing Jiao, Xiangyu Zhang, Gang Yu and Daxin Jiang},
  year = {2025},
  doi = {10.48550/arxiv.2504.17761},
  eprint = {2504.17761},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2504.17761}
}

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