MultiEdit: Advancing Instruction-based Image Editing on Diverse and
  Challenging Tasks
Current instruction-based image editing (IBIE) methods struggle with
challenging editing tasks, as both editing types and sample counts of existing
datasets are limited. Moreover, traditional dataset construction often contains
noisy image-caption pairs, which may introduce biases and limit model
capabilities in complex editing scenarios. To address these limitations, we
introduce MultiEdit, a comprehensive dataset featuring over 107K high-quality
image editing samples. It encompasses 6 challenging editing tasks through a
diverse collection of 18 non-style-transfer editing types and 38 style transfer
operations, covering a spectrum from sophisticated style transfer to complex
semantic operations like person reference editing and in-image text editing. We
employ a novel dataset construction pipeline that utilizes two multi-modal
large language models (MLLMs) to generate visual-adaptive editing instructions
and produce high-fidelity edited images, respectively. Extensive experiments
demonstrate that fine-tuning foundational open-source models with our
MultiEdit-Train set substantially improves models' performance on sophisticated
editing tasks in our proposed MultiEdit-Test benchmark, while effectively
preserving their capabilities on the standard editing benchmark. We believe
MultiEdit provides a valuable resource for advancing research into more diverse
and challenging IBIE capabilities. Our dataset is available at
https://huggingface.co/datasets/inclusionAI/MultiEdit.
68ccb51f3df9ac65e93dc5dc
Mingsong Li
claimed_verified
2025-09-19T06:48:51.131Z
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Mingsong_Li
user
Mingsong07
68ccb51f3df9ac65e93dc5dd
Lin Liu
68ccb51f3df9ac65e93dc5de
Hongjun Wang
admin_assigned
2025-09-19T10:38:35.453Z
649bb8158f4b425bdeb13b77
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Hongjun Wang
user
dreamzz5
68ccb51f3df9ac65e93dc5df
Haoxing Chen
admin_assigned
2025-09-19T10:38:44.187Z
63898c562a897944ea5f07a2
https://cdn-avatars.huggingface.co/v1/production/uploads/63898c562a897944ea5f07a2/oG1ia44ICpAk1tReIaX0G.jpeg
Haoxing chen
user
HaoxingChen
68ccb51f3df9ac65e93dc5e0
Xijun Gu
68ccb51f3df9ac65e93dc5e1
Shizhan Liu
68ccb51f3df9ac65e93dc5e2
Dong Gong
68ccb51f3df9ac65e93dc5e3
Junbo Zhao
68ccb51f3df9ac65e93dc5e4
Zhenzhong Lan
68ccb51f3df9ac65e93dc5e5
Jianguo Li
MultiEdit: Advancing Instruction-based Image Editing on Diverse and
  Challenging Tasks
Current instruction-based image editing (IBIE) methods struggle with
challenging editing tasks, as both editing types and sample counts of existing
datasets are limited. Moreover, traditional dataset construction often contains
noisy image-caption pairs, which may introduce biases and limit model
capabilities in complex editing scenarios. To address these limitations, we
introduce MultiEdit, a comprehensive dataset featuring over 107K high-quality
image editing samples. It encompasses 6 challenging editing tasks through a
diverse collection of 18 non-style-transfer editing types and 38 style transfer
operations, covering a spectrum from sophisticated style transfer to complex
semantic operations like person reference editing and in-image text editing. We
employ a novel dataset construction pipeline that utilizes two multi-modal
large language models (MLLMs) to generate visual-adaptive editing instructions
and produce high-fidelity edited images, respectively. Extensive experiments
demonstrate that fine-tuning foundational open-source models with our
MultiEdit-Train set substantially improves models' performance on sophisticated
editing tasks in our proposed MultiEdit-Test benchmark, while effectively
preserving their capabilities on the standard editing benchmark. We believe
MultiEdit provides a valuable resource for advancing research into more diverse
and challenging IBIE capabilities. Our dataset is available at
https://huggingface.co/datasets/inclusionAI/MultiEdit.
MultiEdit, a comprehensive dataset with over 107K high-quality image editing samples, improves performance on sophisticated editing tasks using a novel pipeline with multi-modal large language models.
instruction-based image editing IBIE editing tasks sample counts noisy image-caption pairs biases dataset construction editing types style transfer semantic operations person reference editing in-image text editing multi-modal large language models MLLMs visual-adaptive editing instructions high-fidelity edited images fine-tuning foundational open-source models MultiEdit-Train MultiEdit-Test benchmark standard editing benchmark