Edicho：實現野外環境下的一致性影像編輯

Edicho: Consistent Image Editing in the Wild

December 30, 2024

作者: Qingyan Bai, Hao Ouyang, Yinghao Xu, Qiuyu Wang, Ceyuan Yang, Ka Leong Cheng, Yujun Shen, Qifeng Chen

cs.AI

摘要

作為一項已驗證的需求，在真實場景圖像中實現一致性編輯仍是技術挑戰，這源於諸多不可控因素，如物體姿態、光照條件和拍攝環境。Edicho提出了一種基於擴散模型的免訓練解決方案，其核心設計原理是利用顯式圖像對應關係來指導編輯。具體而言，關鍵組件包括注意力操控模組和精心優化的無分類器引導（CFG）去噪策略，兩者均考量了預先估算的對應關係。這種推理階段的演算法具備即插即用特性，可兼容多數基於擴散的編輯方法（如ControlNet和BrushNet）。大量實驗結果證實Edicho在多種設定下實現跨圖像一致性編輯的有效性。我們將公開程式碼以促進後續研究。

English

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.