title: Step1X-Edit: A Practical Framework for General Image Editing
summary: In recent years, image editing models have witnessed remarkable and rapid
  development. The recent unveiling of cutting-edge multimodal models such as
  GPT-4o and Gemini2 Flash has introduced highly promising image editing
  capabilities. These models demonstrate an impressive aptitude for fulfilling a
  vast majority of user-driven editing requirements, marking a significant
  advancement in the field of image manipulation. However, there is still a large
  gap between the open-source algorithm with these closed-source models. Thus, in
  this paper, we aim to release a state-of-the-art image editing model, called
  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 the 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.
paper_authors._id: 680af2df3b93130c9b2b90a7
paper_authors.name: Shiyu Liu
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paper_authors.name: Yucheng Han
paper_authors._id: 680af2df3b93130c9b2b90a9
paper_authors.name: Peng Xing
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paper_authors.name: Fukun Yin
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paper_authors.name: Rui Wang
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paper_authors.name: Wei Cheng
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paper_authors.fullname: Wei Cheng
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paper_authors.user: wchengad
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paper_authors.name: Jiaqi Liao
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paper_authors.name: Yingming Wang
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paper_authors.name: Honghao Fu
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paper_authors.name: Chunrui Han
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paper_authors.name: Guopeng Li
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paper_authors.name: Yuang Peng
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paper_authors.fullname: Yuang Peng
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paper_authors.user: yuangpeng
paper_authors._id: 680af2df3b93130c9b2b90b3
paper_authors.name: Quan Sun
paper_authors._id: 680af2df3b93130c9b2b90b4
paper_authors.name: Jingwei Wu
paper_authors._id: 680af2df3b93130c9b2b90b5
paper_authors.name: Yan Cai
paper_authors._id: 680af2df3b93130c9b2b90b6
paper_authors.name: Zheng Ge
paper_authors._id: 680af2df3b93130c9b2b90b7
paper_authors.name: Ranchen Ming
paper_authors._id: 680af2df3b93130c9b2b90b8
paper_authors.name: Lei Xia
paper_authors._id: 680af2df3b93130c9b2b90b9
paper_authors.name: Xianfang Zeng
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paper_authors.name: Yibo Zhu
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paper_authors.name: Binxing Jiao
paper_authors._id: 680af2df3b93130c9b2b90bc
paper_authors.name: Xiangyu Zhang
paper_authors._id: 680af2df3b93130c9b2b90bd
paper_authors.name: Gang Yu
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paper_authors.fullname: Gang Yu
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paper_authors.name: Daxin Jiang
paper_title: Step1X-Edit: A Practical Framework for General Image Editing
paper_summary: In recent years, image editing models have witnessed remarkable and rapid
  development. The recent unveiling of cutting-edge multimodal models such as
  GPT-4o and Gemini2 Flash has introduced highly promising image editing
  capabilities. These models demonstrate an impressive aptitude for fulfilling a
  vast majority of user-driven editing requirements, marking a significant
  advancement in the field of image manipulation. However, there is still a large
  gap between the open-source algorithm with these closed-source models. Thus, in
  this paper, we aim to release a state-of-the-art image editing model, called
  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 the 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.
paper_ai_summary: Step1X-Edit, an image editing model that uses multimodal LLM and diffusion image decoder, outperforms open-source models and approaches proprietary models in quality.
paper_ai_keywords: Multimodal LLM diffusion image decoder latent embedding GEdit-Bench