RectifID: Personalizing Rectified Flow with Anchored Classifier Guidance
<|reference_start|>RectifID: Personalizing Rectified Flow with Anchored Classifier Guidance: Customizing diffusion models to generate identity-preserving images from user-provided reference images is an intriguing new problem. The prevalent approaches typically require training on extensive domain-specific images to achieve identity preservation, which lacks flexibility across different use cases. To address this issue, we exploit classifier guidance, a training-free technique that steers diffusion models using an existing classifier, for personalized image generation. Our study shows that based on a recent rectified flow framework, the major limitation of vanilla classifier guidance in requiring a special classifier can be resolved with a simple fixed-point solution, allowing flexible personalization with off-the-shelf image discriminators. Moreover, its solving procedure proves to be stable when anchored to a reference flow trajectory, with a convergence guarantee. The derived method is implemented on rectified flow with different off-the-shelf image discriminators, delivering advantageous personalization results for human faces, live subjects, and certain objects. Code is available at https://github.com/feifeiobama/RectifID.<|reference_end|>
@article{sun2024rectifid:,
    title={RectifID: Personalizing Rectified Flow with Anchored Classifier Guidance},
    author={Zhicheng Sun, Zhenhao Yang, Yang Jin, Haozhe Chi, Kun Xu, Kun Xu,
  Liwei Chen, Hao Jiang, Yang Song, Kun Gai, Yadong Mu},
    journal={arXiv preprint arXiv:2405.14677},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14677},
    primaryClass={cs.CV cs.LG}
}