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Zero-shot Image-to-Image Translation

Authors:

Krishna Kumar Singh,

Jun-Yan ZhuAuthors Info & Claims

SIGGRAPH '23: ACM SIGGRAPH 2023 Conference Proceedings

Article No.: 11, Pages 1 - 11

https://doi.org/10.1145/3588432.3591513

Published: 23 July 2023 Publication History

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Abstract

Large-scale text-to-image generative models have shown their remarkable ability to synthesize diverse, high-quality images. However, directly applying these models for real image editing remains challenging for two reasons. First, it is hard for users to craft a perfect text prompt depicting every visual detail in the input image. Second, while existing models can introduce desirable changes in certain regions, they often dramatically alter the input content and introduce unexpected changes in unwanted regions. In this work, we introduce pix2pix-zero, an image-to-image translation method that can preserve the original image’s content without manual prompting. We first automatically discover editing directions that reflect desired edits in the text embedding space. To preserve the content structure, we propose cross-attention guidance, which aims to retain the cross-attention maps of the input image throughout the diffusion process. Finally, to enable interactive editing, we distill the diffusion model into a fast conditional GAN. We conduct extensive experiments and show that our method outperforms existing and concurrent works for both real and synthetic image editing. In addition, our method does not need additional training for these edits and can directly use the existing pre-trained text-to-image diffusion model.

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Index Terms

Zero-shot Image-to-Image Translation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Image manipulation

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cover image ACM Conferences

SIGGRAPH '23: ACM SIGGRAPH 2023 Conference Proceedings

July 2023

911 pages

ISBN:9798400701597

DOI:10.1145/3588432

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 23 July 2023

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Cited By

Huang WTu SXu L(2025)PFB-Diff: Progressive Feature Blending diffusion for text-driven image editingNeural Networks10.1016/j.neunet.2024.106777181(106777)Online publication date: Jan-2025
https://doi.org/10.1016/j.neunet.2024.106777
Zubair MHakak S(2025)Exploring the Landscape of Compressed DeepFakes: Generation, Dataset and DetectionNeurocomputing10.1016/j.neucom.2024.129116619(129116)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129116
Zhang YLi LWang JYang XZhou HHe JXie YJiang YSun WZhang XZhou GZhang Z(2025)Texture-preserving diffusion model for CBCT-to-CT synthesisMedical Image Analysis10.1016/j.media.2024.10336299(103362)Online publication date: Jan-2025
https://doi.org/10.1016/j.media.2024.103362
Zhang JGuo SDong PZhang JLiu ZYu YWu XSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Easing concept bleeding in diffusion via entity localization and anchoringProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694503(58955-58966)Online publication date: 21-Jul-2024
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Xu MLi CZhang DSu DLiang WYu DSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Prompt-guided precise audio editing with diffusion modelsProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694341(55126-55143)Online publication date: 21-Jul-2024
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Wang ZSehwag VChen CLyu LMetaxas DMa SSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)How to trace latent generative model generated images without artificial watermark?Proceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694178(51396-51414)Online publication date: 21-Jul-2024
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Tian JWu JChen HMa M(2024)MapGen-Diff: An End-to-End Remote Sensing Image to Map Generator via Denoising Diffusion Bridge ModelRemote Sensing10.3390/rs1619371616:19(3716)Online publication date: 6-Oct-2024
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