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 this https URL.
Abstract (translated)
当前基于指令的图像编辑（IBIE）方法在处理复杂的编辑任务时面临挑战，因为现有数据集中的编辑类型和样本数量都有限。此外，传统的数据集构建方式通常包含有噪声的图片-描述对，这可能会引入偏差并限制模型在复杂编辑场景中的能力。为了解决这些局限性，我们推出了MultiEdit，这是一个全面的数据集，包含了超过107,000个高质量的图像编辑样本。该数据集涵盖了6项具有挑战性的编辑任务，包括通过多样化的18种非风格迁移编辑类型和38种风格转移操作收集而成的一系列复杂语义操作，例如人物参考编辑和图片内文本编辑等。 我们采用了一种新颖的数据构建管道，利用两种多模态大型语言模型（MLLMs）来生成适应视觉的编辑指令，并产生高保真的编辑图像。通过广泛的实验表明，在我们的MultiEdit-Test基准测试中对基础开源模型使用MultiEdit-Train数据集进行微调能够显著提高模型在复杂编辑任务上的表现，同时有效保持它们在标准编辑基准测试中的能力。 我们认为，MultiEdit为推进研究以实现更多样化和具有挑战性的IBIE功能提供了宝贵资源。我们的数据集可以在以下链接获取：[此处插入实际的URL]。
URL
https://arxiv.org/abs/2509.14638
