title: Edit Transfer: Learning Image Editing via Vision In-Context Relations
summary: We introduce a new setting, Edit Transfer, where a model learns a
  transformation from just a single source-target example and applies it to a new
  query image. While text-based methods excel at semantic manipulations through
  textual prompts, they often struggle with precise geometric details (e.g.,
  poses and viewpoint changes). Reference-based editing, on the other hand,
  typically focuses on style or appearance and fails at non-rigid
  transformations. By explicitly learning the editing transformation from a
  source-target pair, Edit Transfer mitigates the limitations of both text-only
  and appearance-centric references. Drawing inspiration from in-context learning
  in large language models, we propose a visual relation in-context learning
  paradigm, building upon a DiT-based text-to-image model. We arrange the edited
  example and the query image into a unified four-panel composite, then apply
  lightweight LoRA fine-tuning to capture complex spatial transformations from
  minimal examples. Despite using only 42 training samples, Edit Transfer
  substantially outperforms state-of-the-art TIE and RIE methods on diverse
  non-rigid scenarios, demonstrating the effectiveness of few-shot visual
  relation learning.
paper_authors._id: 67d8e00f0922c3dc8866520c
paper_authors.name: Lan Chen
paper_authors.status: admin_assigned
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paper_authors.fullname: Lan Chen
paper_authors.type: user
paper_authors.user: Orannue
paper_authors._id: 67d8e00f0922c3dc8866520d
paper_authors.name: Qi Mao
paper_authors.status: claimed_verified
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paper_authors.fullname: Qi Mao
paper_authors.type: user
paper_authors.user: HelenMao
paper_authors._id: 67d8e00f0922c3dc8866520e
paper_authors.name: Yuchao Gu
paper_authors.status: admin_assigned
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paper_authors._id: 63021630a35b21bd8a53305a
paper_authors.avatarUrl: /avatars/7a7e8b39749eda61e57d8a1908726558.svg
paper_authors.fullname: Gu Yuchao
paper_authors.type: user
paper_authors.user: guyuchao
paper_authors._id: 67d8e00f0922c3dc8866520f
paper_authors.name: Mike Zheng Shou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-18T09:21:29.754Z
paper_authors._id: 661ab3da2b14565c7acccf5c
paper_authors.avatarUrl: /avatars/fa4fc03664803e02aede4d4c3d50b393.svg
paper_authors.fullname: Mike Zheng Shou
paper_authors.type: user
paper_authors.user: AnalMom
paper_title: Edit Transfer: Learning Image Editing via Vision In-Context Relations
paper_summary: We introduce a new setting, Edit Transfer, where a model learns a
  transformation from just a single source-target example and applies it to a new
  query image. While text-based methods excel at semantic manipulations through
  textual prompts, they often struggle with precise geometric details (e.g.,
  poses and viewpoint changes). Reference-based editing, on the other hand,
  typically focuses on style or appearance and fails at non-rigid
  transformations. By explicitly learning the editing transformation from a
  source-target pair, Edit Transfer mitigates the limitations of both text-only
  and appearance-centric references. Drawing inspiration from in-context learning
  in large language models, we propose a visual relation in-context learning
  paradigm, building upon a DiT-based text-to-image model. We arrange the edited
  example and the query image into a unified four-panel composite, then apply
  lightweight LoRA fine-tuning to capture complex spatial transformations from
  minimal examples. Despite using only 42 training samples, Edit Transfer
  substantially outperforms state-of-the-art TIE and RIE methods on diverse
  non-rigid scenarios, demonstrating the effectiveness of few-shot visual
  relation learning.
paper_ai_keywords: Edit Transfer visual relation in-context learning DiT-based text-to-image model four-panel composite LoRA fine-tuning few-shot visual relation learning non-rigid transformations TIE methods RIE methods