MultiEdit: Advancing Instruction-based Image Editing on Diverse and Challenging Tasks

• URL: http://arxiv.org/abs/2509.14638v1
• Date: Thu, 18 Sep 2025 05:33:38 GMT
• Title: MultiEdit: Advancing Instruction-based Image Editing on Diverse and Challenging Tasks
• Authors: Mingsong Li, Lin Liu, Hongjun Wang, Haoxing Chen, Xijun Gu, Shizhan Liu, Dong Gong, Junbo Zhao, Zhenzhong Lan, Jianguo Li,
• Abstract summary: MultiEdit is a comprehensive dataset featuring over 107K high-quality image editing samples.<n>It encompasses 6 challenging editing tasks through a diverse collection of 18 non-style-transfer editing types and 38 style transfer operations.<n>We employ a novel dataset construction pipeline that utilizes two multi-modal large language models (MLLMs) to generate visual-adaptive editing instructions.
• Score: 46.87912659985628
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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