title: ImgEdit: A Unified Image Editing Dataset and Benchmark
summary: Recent advancements in generative models have enabled high-fidelity
  text-to-image generation. However, open-source image-editing models still lag
  behind their proprietary counterparts, primarily due to limited high-quality
  data and insufficient benchmarks. To overcome these limitations, we introduce
  ImgEdit, a large-scale, high-quality image-editing dataset comprising 1.2
  million carefully curated edit pairs, which contain both novel and complex
  single-turn edits, as well as challenging multi-turn tasks. To ensure the data
  quality, we employ a multi-stage pipeline that integrates a cutting-edge
  vision-language model, a detection model, a segmentation model, alongside
  task-specific in-painting procedures and strict post-processing. ImgEdit
  surpasses existing datasets in both task novelty and data quality. Using
  ImgEdit, we train ImgEdit-E1, an editing model using Vision Language Model to
  process the reference image and editing prompt, which outperforms existing
  open-source models on multiple tasks, highlighting the value of ImgEdit and
  model design. For comprehensive evaluation, we introduce ImgEdit-Bench, a
  benchmark designed to evaluate image editing performance in terms of
  instruction adherence, editing quality, and detail preservation. It includes a
  basic testsuite, a challenging single-turn suite, and a dedicated multi-turn
  suite. We evaluate both open-source and proprietary models, as well as
  ImgEdit-E1, providing deep analysis and actionable insights into the current
  behavior of image-editing models. The source data are publicly available on
  https://github.com/PKU-YuanGroup/ImgEdit.
paper_authors._id: 68366fd72ae719660435220b
paper_authors.name: Yang Ye
paper_authors._id: 68366fd72ae719660435220c
paper_authors.name: Xianyi He
paper_authors._id: 68366fd72ae719660435220d
paper_authors.name: Zongjian Li
paper_authors._id: 68366fd72ae719660435220e
paper_authors.name: Bin Lin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-04T09:04:14.116Z
paper_authors._id: 6367a8175bb06007ea099b8f
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paper_authors.fullname: linbin
paper_authors.type: user
paper_authors.user: LanguageBind
paper_authors._id: 68366fd72ae719660435220f
paper_authors.name: Shenghai Yuan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T08:58:05.860Z
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paper_authors.fullname: YSH
paper_authors.type: user
paper_authors.user: BestWishYsh
paper_authors._id: 68366fd72ae7196604352210
paper_authors.name: Zhiyuan Yan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T08:58:03.482Z
paper_authors._id: 67dd44d52599dbcecfb4cb9c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/9yZaPuSMY-evu25DPT0o5.png
paper_authors.fullname: Zhiyuan Yan
paper_authors.type: user
paper_authors.user: zhiyuanyan1
paper_authors._id: 68366fd72ae7196604352211
paper_authors.name: Bohan Hou
paper_authors._id: 68366fd72ae7196604352212
paper_authors.name: Li Yuan
paper_title: ImgEdit: A Unified Image Editing Dataset and Benchmark
paper_summary: Recent advancements in generative models have enabled high-fidelity
  text-to-image generation. However, open-source image-editing models still lag
  behind their proprietary counterparts, primarily due to limited high-quality
  data and insufficient benchmarks. To overcome these limitations, we introduce
  ImgEdit, a large-scale, high-quality image-editing dataset comprising 1.2
  million carefully curated edit pairs, which contain both novel and complex
  single-turn edits, as well as challenging multi-turn tasks. To ensure the data
  quality, we employ a multi-stage pipeline that integrates a cutting-edge
  vision-language model, a detection model, a segmentation model, alongside
  task-specific in-painting procedures and strict post-processing. ImgEdit
  surpasses existing datasets in both task novelty and data quality. Using
  ImgEdit, we train ImgEdit-E1, an editing model using Vision Language Model to
  process the reference image and editing prompt, which outperforms existing
  open-source models on multiple tasks, highlighting the value of ImgEdit and
  model design. For comprehensive evaluation, we introduce ImgEdit-Bench, a
  benchmark designed to evaluate image editing performance in terms of
  instruction adherence, editing quality, and detail preservation. It includes a
  basic testsuite, a challenging single-turn suite, and a dedicated multi-turn
  suite. We evaluate both open-source and proprietary models, as well as
  ImgEdit-E1, providing deep analysis and actionable insights into the current
  behavior of image-editing models. The source data are publicly available on
  https://github.com/PKU-YuanGroup/ImgEdit.
paper_ai_summary: ImgEdit, a comprehensive image-editing dataset and benchmark, improves open-source text-to-image editing models by providing high-quality data and evaluation metrics.
paper_ai_keywords: Vision Language Model detection model segmentation model in-painting image-editing model ImgEdit-Bench instruction adherence editing quality detail preservation challenging single-turn suite dedicated multi-turn suite