title: AnyStory: Towards Unified Single and Multiple Subject Personalization in
    Text-to-Image Generation
summary: 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/ .
paper_authors._id: 6789e8178a14e7d6ea183dd3
paper_authors.name: Junjie He
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paper_authors.fullname: Junjie He
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paper_authors.name: Yuxiang Tuo
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paper_authors.fullname: tuoyuxiang
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paper_authors.name: Binghui Chen
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paper_authors.fullname: ashui
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paper_authors.user: ashui
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paper_authors.name: Chongyang Zhong
paper_authors._id: 6789e8178a14e7d6ea183dd7
paper_authors.name: Yifeng Geng
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paper_authors.fullname: gengyifeng
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paper_authors.user: gengyifeng
paper_authors._id: 6789e8178a14e7d6ea183dd8
paper_authors.name: Liefeng Bo
paper_authors.status: admin_assigned
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paper_authors.fullname: Bo
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paper_authors.user: Liefeng
paper_title: AnyStory: Towards Unified Single and Multiple Subject Personalization in
    Text-to-Image Generation
paper_summary: 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/ .
paper_ai_keywords: large-scale generative models text-to-image generation high-fidelity images personalized images personalized subject generation single subjects multiple subjects subject fidelity encode-then-route manner universal and powerful image encoder ReferenceNet CLIP vision encoder subject features instance-aware subject router latent space subject conditions subject details text extensions