Edicho: Consistent Image Editing in the Wild
Authors: name: Qingyan Bai, name: Hao Ouyang, name: Yinghao Xu, name: Qiuyu Wang, name: Ceyuan Yang, name: Ka Leong Cheng, name: Yujun Shen, name: Qifeng Chen

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.