MultiEdit: Advancing Instruction-based Image Editing on Diverse and
  Challenging Tasks
Authors: name: Mingsong Li; name: Lin Liu; name: Hongjun Wang; name: Haoxing Chen; name: Xijun Gu; name: Shizhan Liu; name: Dong Gong; name: Junbo Zhao; name: Zhenzhong Lan; name: Jianguo Li
Published: 2025-09-18 05:33:38

Abstract: 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.