Edicho: Consistent Image Editing in the Wild
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.
67736096b272a4f186d161f9
Qingyan Bai
claimed_verified
2025-01-01T20:12:57.160Z
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Meme155
user
Meme145
67736096b272a4f186d161fa
Hao Ouyang
67736096b272a4f186d161fb
Yinghao Xu
67736096b272a4f186d161fc
Qiuyu Wang
67736096b272a4f186d161fd
Ceyuan Yang
67736096b272a4f186d161fe
Ka Leong Cheng
67736096b272a4f186d161ff
Yujun Shen
67736096b272a4f186d16200
Qifeng Chen
Edicho: Consistent Image Editing in the Wild
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.
Edicho, a training-free diffusion-based editing method, uses explicit image correspondence and a classifier-free guidance denoising strategy for consistent editing across diverse images.
diffusion models attention manipulation module classifier-free guidance (CFG) image correspondence ControlNet BrushNet