title: IDAdapter: Learning Mixed Features for Tuning-Free Personalization of
    Text-to-Image Models
summary: Leveraging Stable Diffusion for the generation of personalized portraits has
  emerged as a powerful and noteworthy tool, enabling users to create
  high-fidelity, custom character avatars based on their specific prompts.
  However, existing personalization methods face challenges, including test-time
  fine-tuning, the requirement of multiple input images, low preservation of
  identity, and limited diversity in generated outcomes. To overcome these
  challenges, we introduce IDAdapter, a tuning-free approach that enhances the
  diversity and identity preservation in personalized image generation from a
  single face image. IDAdapter integrates a personalized concept into the
  generation process through a combination of textual and visual injections and a
  face identity loss. During the training phase, we incorporate mixed features
  from multiple reference images of a specific identity to enrich
  identity-related content details, guiding the model to generate images with
  more diverse styles, expressions, and angles compared to previous works.
  Extensive evaluations demonstrate the effectiveness of our method, achieving
  both diversity and identity fidelity in generated images.
paper_authors._id: 65fb9d3d04769daf217987a2
paper_authors.name: Siying Cui
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-21T10:05:41.265Z
paper_authors._id: 65b61966a0dc6ea5995b53e3
paper_authors.avatarUrl: /avatars/c712b4ed8e4b5144f4be9b8b5e528ca7.svg
paper_authors.fullname: Siying Cui
paper_authors.type: user
paper_authors.user: hycsy2024
paper_authors._id: 65fb9d3d04769daf217987a3
paper_authors.name: Jiankang Deng
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-03-21T02:36:47.176Z
paper_authors._id: 62cc7a38376917c0223dd24b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1657566065867-noauth.png
paper_authors.fullname: JiankangDeng
paper_authors.type: user
paper_authors.user: JiankangDeng
paper_authors._id: 65fb9d3d04769daf217987a4
paper_authors.name: Jia Guo
paper_authors._id: 65fb9d3d04769daf217987a5
paper_authors.name: Xiang An
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-27T09:48:26.584Z
paper_authors._id: 6478679d7b370854241b2ad8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6478679d7b370854241b2ad8/dBczWYYdfEt9tQcnVGhQk.jpeg
paper_authors.fullname: xiangan
paper_authors.type: user
paper_authors.user: xiangan
paper_authors._id: 65fb9d3d04769daf217987a6
paper_authors.name: Yongle Zhao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-21T10:06:28.451Z
paper_authors._id: 6433e9e4ea46c00990460ab0
paper_authors.avatarUrl: /avatars/3a4bec74eeec94713e8eb5e290d00765.svg
paper_authors.fullname: yonglezhao
paper_authors.type: user
paper_authors.user: yonglezhao
paper_authors._id: 65fb9d3d04769daf217987a7
paper_authors.name: Xinyu Wei
paper_authors._id: 65fb9d3d04769daf217987a8
paper_authors.name: Ziyong Feng
paper_title: IDAdapter: Learning Mixed Features for Tuning-Free Personalization of
    Text-to-Image Models
paper_summary: Leveraging Stable Diffusion for the generation of personalized portraits has
  emerged as a powerful and noteworthy tool, enabling users to create
  high-fidelity, custom character avatars based on their specific prompts.
  However, existing personalization methods face challenges, including test-time
  fine-tuning, the requirement of multiple input images, low preservation of
  identity, and limited diversity in generated outcomes. To overcome these
  challenges, we introduce IDAdapter, a tuning-free approach that enhances the
  diversity and identity preservation in personalized image generation from a
  single face image. IDAdapter integrates a personalized concept into the
  generation process through a combination of textual and visual injections and a
  face identity loss. During the training phase, we incorporate mixed features
  from multiple reference images of a specific identity to enrich
  identity-related content details, guiding the model to generate images with
  more diverse styles, expressions, and angles compared to previous works.
  Extensive evaluations demonstrate the effectiveness of our method, achieving
  both diversity and identity fidelity in generated images.
paper_ai_keywords: Stable Diffusion personalized portraits custom character avatars test-time fine-tuning identity preservation IDAdapter tuning-free textual and visual injections face identity loss mixed features hash tables diversity identity fidelity