title: RelationAdapter: Learning and Transferring Visual Relation with
    Diffusion Transformers
summary: 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.
paper_authors._id: 683fb8bc3bcb592f18f5b866
paper_authors.name: Yan Gong
paper_authors._id: 683fb8bc3bcb592f18f5b867
paper_authors.name: Yiren Song
paper_authors._id: 683fb8bc3bcb592f18f5b868
paper_authors.name: Yicheng Li
paper_authors._id: 683fb8bc3bcb592f18f5b869
paper_authors.name: Chenglin Li
paper_authors._id: 683fb8bc3bcb592f18f5b86a
paper_authors.name: Yin Zhang
paper_title: RelationAdapter: Learning and Transferring Visual Relation with
    Diffusion Transformers
paper_summary: 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.
paper_ai_summary: RelationAdapter, a lightweight module, enhances Diffusion Transformer models to capture and apply visual transformations effectively using source-target image pairs, improving editing performance and generalization on diverse tasks.
paper_ai_keywords: RelationAdapter Diffusion Transformer DiT visual prompt-based image editing content-aware editing intent Relation252K visual transformations editing intent generation quality overall editing performance