Edicho: Consistent Image Editing in the Wild
Qingyan Bai, Hao Ouyang, Yinghao Xu, Qiuyu Wang, Ceyuan Yang, Ka Leong Cheng, Yujun Shen, Qifeng Chen
摘要
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. Edicho11“Edicho” is an abbreviation of “edit echo”, implying that the edit is echoed across images. 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 () denoising strategy, both of which take into account the preestimated 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. Project page can be found here.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper4
- Group Editing: Edit Multiple Images in One GoYue Ma, Xinyu Wang, Qianli Ma, Qinghe Wang 等CVPR 2026 · 被引用 15 次
- Mind-the-Glitch: Visual Correspondence for Detecting Inconsistencies in Subject-Driven GenerationAbdelrahman Eldesokey, Aleksandar Cvejic, Bernard Ghanem, Peter WonkaNeurIPS 2025 · 被引用 6 次
- RewardFlow: Generate Images by Optimizing What You RewardOnkar Susladkar, Dong-Hwan Jang, Tushar Prakash, Adheesh Sunil Juvekar 等CVPR 2026 · 被引用 2 次
- Match-and-Fuse: Consistent Generation from Unstructured Image SetsKate Feingold, Omri Kaduri, Tali DekelCVPR 2026
它引用的顶会 Paper45
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh 等ICML 2021 · 被引用 47,906 次
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 被引用 35,902 次
- LoRA: Low-Rank Adaptation of Large Language ModelsEdward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu 等ICLR 2022 · 被引用 18,833 次
- Diffusion Models Beat GANs on Image SynthesisPrafulla Dhariwal, Alexander Quinn NicholNeurIPS 2021 · 被引用 13,211 次
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser 等CVPR 2022 · 被引用 13,123 次
相关 Paper
- PixelMan: Consistent Object Editing with Diffusion Models via Pixel Manipulation and GenerationLiyao Jiang, Negar Hassanpour, Mohammad Salameh, Mohammadreza Samadi 等AAAI 2025 · 被引用 8 次
- TweezeEdit: Consistent and Efficient Image Editing with Path RegularizationJianda Mao, Kaibo Wang, Yang Xiang, Kani ChenAAAI 2026 · 被引用 2 次
- Inversion-Free Image Editing with Language-Guided Diffusion ModelsSihan Xu, Yidong Huang, Jiayi Pan, Ziqiao Ma 等CVPR 2024 · 被引用 12 次
- PostEdit: Posterior Sampling for Efficient Zero-Shot Image EditingFeng Tian, Yixuan Li, Yichao Yan, Shanyan Guan 等ICLR 2025
- Training-Free Text-Guided Color Editing with Multi-Modal Diffusion TransformerZixin Yin, Xili Dai, Ling-Hao Chen, Deyu Zhou 等ICLR 2026 · 被引用 6 次
