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.
6650143d6f16bbd9a17f2423
Zhicheng Sun
admin_assigned
2024-05-24T10:38:08.415Z
62fc758172a7ab50b4b89c8c
https://cdn-avatars.huggingface.co/v1/production/uploads/1677251053045-62fc758172a7ab50b4b89c8c.jpeg
Zhicheng Sun
user
feifeiobama
6650143d6f16bbd9a17f2424
Zhenhao Yang
6650143d6f16bbd9a17f2425
Yang Jin
claimed_verified
2024-10-10T10:59:56.375Z
64b79ec0a8c39dc078897430
/avatars/8339f3ab9bb7baaf69bf174eafb7282c.svg
Yang Jin
user
rain1011
6650143d6f16bbd9a17f2426
Haozhe Chi
6650143d6f16bbd9a17f2427
Kun Xu
6650143d6f16bbd9a17f2428
Kun Xu
6650143d6f16bbd9a17f2429
Liwei Chen
6650143d6f16bbd9a17f242a
Hao Jiang
6650143d6f16bbd9a17f242b
Di Zhang
6650143d6f16bbd9a17f242c
Yang Song
6650143d6f16bbd9a17f242d
Kun Gai
6650143d6f16bbd9a17f242e
Yadong Mu
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.
A classifier guidance method resolves limitations in diffusion models for identity-preserving image generation using off-the-shelf image discriminators, enabling flexible personalization across various subjects.
classifier guidance diffusion models identity preservation rectified flow fixed-point solution reference flow trajectory convergence guarantee