title: Edicho: Consistent Image Editing in the Wild
summary: As a verified need, consistent editing across in-the-wild images remains a
  technical challenge arising from various unmanageable factors, like object
  poses, lighting conditions, and photography environments. Edicho steps in with
  a training-free solution based on diffusion models, featuring a fundamental
  design principle of using explicit image correspondence to direct editing.
  Specifically, the key components include an attention manipulation module and a
  carefully refined classifier-free guidance (CFG) denoising strategy, both of
  which take into account the pre-estimated correspondence. Such an
  inference-time algorithm enjoys a plug-and-play nature and is compatible to
  most diffusion-based editing methods, such as ControlNet and BrushNet.
  Extensive results demonstrate the efficacy of Edicho in consistent cross-image
  editing under diverse settings. We will release the code to facilitate future
  studies.
paper_authors._id: 67736096b272a4f186d161f9
paper_authors.name: Qingyan Bai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-01T20:12:57.160Z
paper_authors._id: 63f0baf66309c84d5f4a2226
paper_authors.avatarUrl: /avatars/a122f7d92441bd2feef7d4eda993fab7.svg
paper_authors.fullname: Qingyan Bai
paper_authors.type: user
paper_authors.user: Meme145
paper_authors._id: 67736096b272a4f186d161fa
paper_authors.name: Hao Ouyang
paper_authors._id: 67736096b272a4f186d161fb
paper_authors.name: Yinghao Xu
paper_authors._id: 67736096b272a4f186d161fc
paper_authors.name: Qiuyu Wang
paper_authors._id: 67736096b272a4f186d161fd
paper_authors.name: Ceyuan Yang
paper_authors._id: 67736096b272a4f186d161fe
paper_authors.name: Ka Leong Cheng
paper_authors._id: 67736096b272a4f186d161ff
paper_authors.name: Yujun Shen
paper_authors._id: 67736096b272a4f186d16200
paper_authors.name: Qifeng Chen
paper_title: Edicho: Consistent Image Editing in the Wild
paper_summary: As a verified need, consistent editing across in-the-wild images remains a
  technical challenge arising from various unmanageable factors, like object
  poses, lighting conditions, and photography environments. Edicho steps in with
  a training-free solution based on diffusion models, featuring a fundamental
  design principle of using explicit image correspondence to direct editing.
  Specifically, the key components include an attention manipulation module and a
  carefully refined classifier-free guidance (CFG) denoising strategy, both of
  which take into account the pre-estimated correspondence. Such an
  inference-time algorithm enjoys a plug-and-play nature and is compatible to
  most diffusion-based editing methods, such as ControlNet and BrushNet.
  Extensive results demonstrate the efficacy of Edicho in consistent cross-image
  editing under diverse settings. We will release the code to facilitate future
  studies.
paper_ai_keywords: diffusion models attention manipulation module classifier-free guidance (CFG) denoising strategy image correspondence ControlNet BrushNet cross-image editing