AnyStory: Towards Unified Single and Multiple Subject Personalization in
  Text-to-Image Generation
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/ .
6789e8178a14e7d6ea183dd3
Junjie He
claimed_verified
2025-02-06T14:15:25.079Z
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Junjie He
user
Junjie96
6789e8178a14e7d6ea183dd4
Yuxiang Tuo
admin_assigned
2025-01-17T10:47:02.023Z
64130f4014c9a170ae873bd8
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tuoyuxiang
user
tuoyuxiang
6789e8178a14e7d6ea183dd5
Binghui Chen
extracted_confirmed
2025-02-28T03:05:45.786Z
63b66df8889aa6707f167f5d
/avatars/62248a70698a95f74ce7267ca42cacef.svg
ashui
user
ashui
6789e8178a14e7d6ea183dd6
Chongyang Zhong
6789e8178a14e7d6ea183dd7
Yifeng Geng
admin_assigned
2025-01-17T10:47:21.325Z
659caf6b0030e8faffb00c41
/avatars/dde52bcf694120e59307b4ab8a7eeb33.svg
gengyifeng
user
gengyifeng
6789e8178a14e7d6ea183dd8
Liefeng Bo
admin_assigned
2025-01-17T10:47:29.725Z
63d0cc736b985b0f25d0412c
/avatars/3eb8c79f9a7c4c819038ea7b04e323dd.svg
Bo
user
Liefeng
AnyStory: Towards Unified Single and Multiple Subject Personalization in
  Text-to-Image Generation
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/ .
AnyStory uses a unified approach with ReferenceNet and CLIP for encoding and routing subject features to achieve high-fidelity personalized text-to-image generation for multiple subjects.
generative models text-to-image generation high-fidelity personalization encode-then-route ReferenceNet CLIP vision encoder instance-aware subject router latent space subject conditions experimental results