标题： 关系适配器：使用扩散变换学习和迁移视觉关系 显示英文标题

标题： RelationAdapter: Learning and Transferring Visual Relation with Diffusion Transformers

摘要： 受大型语言模型（LLMs）上下文学习机制的启发，一种新的可泛化的基于视觉提示的图像编辑范式正在兴起。现有的单参考方法通常侧重于样式或外观调整，并且难以处理非刚性变换。为了解决这些局限性，我们提出利用源目标图像对来提取并将内容感知的编辑意图转移到新的查询图像上。为此，我们引入了RelationAdapter，这是一种轻量级模块，可以使基于Diffusion Transformer (DiT) 的模型有效地捕获和应用来自最小样本的视觉变换。我们还引入了Relation252K，这是一个包含218种多样化编辑任务的综合数据集，用于评估模型在基于视觉提示场景中的泛化能力和适应性。Relation252K上的实验表明，RelationAdapter显著提高了模型理解并转移编辑意图的能力，从而在生成质量和整体编辑性能方面取得了显著提升。 显示英文摘要

摘要： Inspired by the in-context learning mechanism of large language models (LLMs), a new paradigm of generalizable visual prompt-based image editing is emerging. Existing single-reference methods typically focus on style or appearance adjustments and struggle with non-rigid transformations. To address these limitations, we propose leveraging source-target image pairs to extract and transfer content-aware editing intent to novel query images. To this end, we introduce RelationAdapter, a lightweight module that enables Diffusion Transformer (DiT) based models to effectively capture and apply visual transformations from minimal examples. We also introduce Relation252K, a comprehensive dataset comprising 218 diverse editing tasks, to evaluate model generalization and adaptability in visual prompt-driven scenarios. Experiments on Relation252K show that RelationAdapter significantly improves the model's ability to understand and transfer editing intent, leading to notable gains in generation quality and overall editing performance.