title: Infinite-ID: Identity-preserved Personalization via ID-semantics
    Decoupling Paradigm
summary: Drawing on recent advancements in diffusion models for text-to-image
  generation, identity-preserved personalization has made significant progress in
  accurately capturing specific identities with just a single reference image.
  However, existing methods primarily integrate reference images within the text
  embedding space, leading to a complex entanglement of image and text
  information, which poses challenges for preserving both identity fidelity and
  semantic consistency. To tackle this challenge, we propose Infinite-ID, an
  ID-semantics decoupling paradigm for identity-preserved personalization.
  Specifically, we introduce identity-enhanced training, incorporating an
  additional image cross-attention module to capture sufficient ID information
  while deactivating the original text cross-attention module of the diffusion
  model. This ensures that the image stream faithfully represents the identity
  provided by the reference image while mitigating interference from textual
  input. Additionally, we introduce a feature interaction mechanism that combines
  a mixed attention module with an AdaIN-mean operation to seamlessly merge the
  two streams. This mechanism not only enhances the fidelity of identity and
  semantic consistency but also enables convenient control over the styles of the
  generated images. Extensive experimental results on both raw photo generation
  and style image generation demonstrate the superior performance of our proposed
  method.
paper_authors._id: 65f907e75df5183c9a7ded6b
paper_authors.name: Yi Wu
paper_authors._id: 65f907e75df5183c9a7ded6c
paper_authors.name: Ziqiang Li
paper_authors._id: 65f907e75df5183c9a7ded6d
paper_authors.name: Heliang Zheng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-19T09:07:42.017Z
paper_authors._id: 640ef1b2c025ddf61895c31a
paper_authors.avatarUrl: /avatars/444de5bf32b1daa4dda1a7c17a328578.svg
paper_authors.fullname: Heliang Zheng
paper_authors.type: user
paper_authors.user: ilcve21
paper_authors._id: 65f907e75df5183c9a7ded6e
paper_authors.name: Chaoyue Wang
paper_authors._id: 65f907e75df5183c9a7ded6f
paper_authors.name: Bin Li
paper_title: Infinite-ID: Identity-preserved Personalization via ID-semantics
    Decoupling Paradigm
paper_summary: Drawing on recent advancements in diffusion models for text-to-image
  generation, identity-preserved personalization has made significant progress in
  accurately capturing specific identities with just a single reference image.
  However, existing methods primarily integrate reference images within the text
  embedding space, leading to a complex entanglement of image and text
  information, which poses challenges for preserving both identity fidelity and
  semantic consistency. To tackle this challenge, we propose Infinite-ID, an
  ID-semantics decoupling paradigm for identity-preserved personalization.
  Specifically, we introduce identity-enhanced training, incorporating an
  additional image cross-attention module to capture sufficient ID information
  while deactivating the original text cross-attention module of the diffusion
  model. This ensures that the image stream faithfully represents the identity
  provided by the reference image while mitigating interference from textual
  input. Additionally, we introduce a feature interaction mechanism that combines
  a mixed attention module with an AdaIN-mean operation to seamlessly merge the
  two streams. This mechanism not only enhances the fidelity of identity and
  semantic consistency but also enables convenient control over the styles of the
  generated images. Extensive experimental results on both raw photo generation
  and style image generation demonstrate the superior performance of our proposed
  method.
paper_ai_summary: Infinite-ID decouples identity and semantics in image generation, using enhanced training with image cross-attention and feature interaction to preserve identity fidelity and enhance semantic consistency.
paper_ai_keywords: diffusion models text-to-image identity-preserved personalization text embedding space identity-enhanced training image cross-attention text cross-attention feature interaction mechanism mixed attention module AdaIN-mean operation identity fidelity semantic consistency style image generation