RelationAdapter: Learning and Transferring Visual Relation with
  Diffusion Transformers
Authors: name: Yan Gong, name: Yiren Song, name: Yicheng Li, name: Chenglin Li, name: Yin Zhang

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.