RectifID: Personalizing Rectified Flow with Anchored Classifier Guidance

Understand

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.