DEADiff: An Efficient Stylization Diffusion Model with Disentangled Representations
<|reference_start|>DEADiff: An Efficient Stylization Diffusion Model with Disentangled Representations: The diffusion-based text-to-image model harbors immense potential in transferring reference style. However, current encoder-based approaches significantly impair the text controllability of text-to-image models while transferring styles. In this paper, we introduce DEADiff to address this issue using the following two strategies: 1) a mechanism to decouple the style and semantics of reference images. The decoupled feature representations are first extracted by Q-Formers which are instructed by different text descriptions. Then they are injected into mutually exclusive subsets of cross-attention layers for better disentanglement. 2) A non-reconstructive learning method. The Q-Formers are trained using paired images rather than the identical target, in which the reference image and the ground-truth image are with the same style or semantics. We show that DEADiff attains the best visual stylization results and optimal balance between the text controllability inherent in the text-to-image model and style similarity to the reference image, as demonstrated both quantitatively and qualitatively. Our project page is https://tianhao-qi.github.io/DEADiff/.<|reference_end|>
@article{qi2024deadiff:,
    title={DEADiff: An Efficient Stylization Diffusion Model with Disentangled
  Representations},
    author={Tianhao Qi, Shancheng Fang, Yanze Wu, Hongtao Xie, Jiawei Liu, Lang
  Chen, Qian He, Yongdong Zhang},
    journal={arXiv preprint arXiv:2403.06951},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06951},
    primaryClass={cs.CV}
}