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arXiv · 2405.14677

RectifID: Personalizing Rectified Flow with Anchored Classifier Guidance

Zhicheng Sun·Zhenhao Yang·Yang Jin·Haozhe Chi·Kun Xu·Liwei Chen·Hao Jiang·Yang Song·Kun Gai·Yadong Mu
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Abstract

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.

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Zhicheng Sun, Zhenhao Yang, Yang Jin, Haozhe Chi, Kun Xu, Liwei Chen, Hao Jiang, Yang Song, Kun Gai, Yadong Mu. 2024-05-23. RectifID: Personalizing Rectified Flow with Anchored Classifier Guidance. https://arxiv.org/abs/2405.14677

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