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DeltaEdit: Exploring Text-free Training for Text-Driven Image Manipulation
Yueming Lyu; Tianwei Lin; Fu Li; Dongliang He; Jing Dong; Tieniu Tan
Conference NameIEEE Conference on Computer Vision and Pattern Recognition
Conference Date2023-06
Conference PlaceVancouver, Canada

Text-driven image manipulation remains challenging in training or inference flexibility. Conditional generative models depend heavily on expensive annotated training data. Meanwhile, recent frameworks, which leverage pre-trained vision-language models, are limited by either per text-prompt optimization or inference-time hyperparameters tuning. In this work, we propose a novel framework named DeltaEdit to address these problems. Our key idea is to investigate and identify a space, namely delta image and text space that has well-aligned distribution between CLIP visual feature differences of two images and CLIP textual embedding differences of source and target texts. Based on the CLIP delta space, the DeltaEdit network is designed to map the CLIP visual features differences to the editing directions of StyleGAN at training phase. Then, in inference phase, DeltaEdit predicts the StyleGAN’s editing directions from the differences of the CLIP textual features. In this way, DeltaEdit is trained in a text-free manner. Once trained, it can well generalize to various text prompts for zero-shot inference without bells and whistles. Code is available at

Sub direction classification图像视频处理与分析
planning direction of the national heavy laboratory视觉信息处理
Paper associated data
Document Type会议论文
Corresponding AuthorJing Dong
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.Baidu Inc
4.Nanjing University
Recommended Citation
GB/T 7714
Yueming Lyu,Tianwei Lin,Fu Li,et al. DeltaEdit: Exploring Text-free Training for Text-Driven Image Manipulation[C],2023.
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