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
Organization: inclusionAI

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.