ImgEdit: A Unified Image Editing Dataset and Benchmark
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.
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Yang Ye
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Xianyi He
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Zongjian Li
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Bin Lin
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linbin
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LanguageBind
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Shenghai Yuan
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2025-05-28T08:58:05.860Z
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YSH
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Zhiyuan Yan
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Zhiyuan Yan
user
zhiyuanyan1
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Bohan Hou
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Li Yuan
ImgEdit: A Unified Image Editing Dataset and Benchmark
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.
ImgEdit, a comprehensive image-editing dataset and benchmark, improves open-source text-to-image editing models by providing high-quality data and evaluation metrics.
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