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

TL;DR
Edicho introduces a training-free, diffusion-model-based method for consistent image editing across diverse in-the-wild images by leveraging explicit image correspondence and novel guidance strategies.
Contribution
It presents a novel, training-free approach that uses explicit correspondence and refined guidance to achieve consistent editing in complex, real-world images.
Findings
Effective in maintaining consistency across diverse images
Compatible with existing diffusion-based editing methods
Demonstrates superior performance in various settings
Abstract
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…
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Taxonomy
TopicsModular Robots and Swarm Intelligence · Advanced Image and Video Retrieval Techniques
MethodsSoftmax · Attention Is All You Need · Diffusion
