AnyStory: Towards Unified Single and Multiple Subject Personalization in
  Text-to-Image Generation
Authors: name: Junjie He, name: Yuxiang Tuo, name: Binghui Chen, name: Chongyang Zhong, name: Yifeng Geng, name: Liefeng Bo

Recently, large-scale generative models have demonstrated outstanding
text-to-image generation capabilities. However, generating high-fidelity
personalized images with specific subjects still presents challenges,
especially in cases involving multiple subjects. In this paper, we propose
AnyStory, a unified approach for personalized subject generation. AnyStory not
only achieves high-fidelity personalization for single subjects, but also for
multiple subjects, without sacrificing subject fidelity. Specifically, AnyStory
models the subject personalization problem in an "encode-then-route" manner. In
the encoding step, AnyStory utilizes a universal and powerful image encoder,
i.e., ReferenceNet, in conjunction with CLIP vision encoder to achieve
high-fidelity encoding of subject features. In the routing step, AnyStory
utilizes a decoupled instance-aware subject router to accurately perceive and
predict the potential location of the corresponding subject in the latent
space, and guide the injection of subject conditions. Detailed experimental
results demonstrate the excellent performance of our method in retaining
subject details, aligning text descriptions, and personalizing for multiple
subjects. The project page is at https://aigcdesigngroup.github.io/AnyStory/ .