Many applications can benefit from personalized image generation models,
including image enhancement, video conferences, just to name a few. Existing
works achieved personalization by fine-tuning one model for each person. While
being successful, this approach incurs additional computation and storage
overhead for each new identity. Furthermore, it usually expects tens or
hundreds of examples per identity to achieve the best performance. To overcome
these challenges, we propose an encoder-based approach for personalization. We
learn an identity encoder which can extract an identity representation from a
set of reference images of a subject, together with a diffusion generator that
can generate new images of the subject conditioned on the identity
representation. Once being trained, the model can be used to generate images of
arbitrary identities given a few examples even if the model hasn't been trained
on the identity. Our approach greatly reduces the overhead for personalized
image generation and is more applicable in many potential applications.
Empirical results show that our approach consistently outperforms existing
fine-tuning based approach in both image generation and reconstruction, and the
outputs is preferred by users more than 95% of the time compared with the best
performing baseline.

论文探讨了使用编码器和扩散生成器来进行个性化图像生成的方法，并比较了该方法与 Fine-tuning 等已有方法的性能，实验结果表明，该方法在图像生成和重建上表现更好，且适用于更多应用场景。