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# edicho | ||
# Edicho: Consistent Image Editing in the Wild | ||
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> **Edicho: Consistent Image Editing in the Wild** <br> | ||
> Qingyan Bai, Hao Ouyang, Yinghao Xu, Qiuyu Wang, Ceyuan Yang, Ka Leong Cheng, Yujun Shen, Qifeng Chen <br> | ||
<div align=center> | ||
<img src="./docs/assets/teaser.jpg" width=850px> | ||
</div> | ||
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**Figure:** Given two images in the wild, Edicho generates consistent editing versions of them in a zero-shot manner. Our approach | ||
achieves precise consistency for editing parts (left), objects (middle), and the entire images (right) by leveraging explicit correspondence. | ||
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**[**[**Paper**](https://arxiv.org/pdf/2412.XXXXX)**]** | ||
**[**[**Project Page**](https://ezioby.github.io/edicho/)**]** | ||
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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. | ||
<i>Edicho</i>, embodying "echoing the editing effect", steps in with a training-free solution based on diffusion models, featuring a fundamental design principle of using <b>explicit</b> 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 <i>Edicho</i> in consistent cross-image editing under diverse settings. | ||
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<div align=center> | ||
<img src="./docs/assets/pipeline.jpg" width=900px> | ||
</div> | ||
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**Figure:** Method overview. To achieve consistent editing, we propose a training-free and plug-and-play method that injects the pre-computed correspondence into the pre-trained diffusion models and guides the denoising in the two levels of (a) attention features and (b) noisy latents in classifier-free guidance (CFG). | ||
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## BibTeX | ||
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If you find our work helpful for your research, please consider to cite: | ||
```bibtex | ||
@inproceedings{bai2024edicho, | ||
title = {Edicho: Consistent Image Editing in the Wild}, | ||
author = {Bai, Qingyan and Ouyang, Hao and Xu, Yinghao and Wang, Qiuyu and Yang, Ceyuan and Cheng, Ka Leong and Shen, Yujun and Chen, Qifeng}, | ||
booktitle = {arXiv preprint arXiv:2412.XXXXX}, | ||
year = {2024} | ||
} | ||
``` |
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