title: PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding
summary: Recent advances in text-to-image generation have made remarkable progress in
  synthesizing realistic human photos conditioned on given text prompts. However,
  existing personalized generation methods cannot simultaneously satisfy the
  requirements of high efficiency, promising identity (ID) fidelity, and flexible
  text controllability. In this work, we introduce PhotoMaker, an efficient
  personalized text-to-image generation method, which mainly encodes an arbitrary
  number of input ID images into a stack ID embedding for preserving ID
  information. Such an embedding, serving as a unified ID representation, can not
  only encapsulate the characteristics of the same input ID comprehensively, but
  also accommodate the characteristics of different IDs for subsequent
  integration. This paves the way for more intriguing and practically valuable
  applications. Besides, to drive the training of our PhotoMaker, we propose an
  ID-oriented data construction pipeline to assemble the training data. Under the
  nourishment of the dataset constructed through the proposed pipeline, our
  PhotoMaker demonstrates better ID preservation ability than test-time
  fine-tuning based methods, yet provides significant speed improvements,
  high-quality generation results, strong generalization capabilities, and a wide
  range of applications. Our project page is available at
  https://photo-maker.github.io/
paper_authors._id: 65728d88cca2247f3f2acf96
paper_authors.name: Zhen Li
paper_authors.status: claimed_verified
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paper_authors.fullname: Zhen Li
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paper_authors.user: Paper99
paper_authors._id: 65728d88cca2247f3f2acf97
paper_authors.name: Mingdeng Cao
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paper_authors.fullname: Mingdeng Cao
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paper_authors.user: Ljzycmd
paper_authors._id: 65728d88cca2247f3f2acf98
paper_authors.name: Xintao Wang
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paper_authors.fullname: Xintao Wang
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paper_authors.user: Xintao
paper_authors._id: 65728d88cca2247f3f2acf99
paper_authors.name: Zhongang Qi
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paper_authors.fullname: Zhongang Qi
paper_authors.type: user
paper_authors.user: phoenixqza
paper_authors._id: 65728d88cca2247f3f2acf9a
paper_authors.name: Ming-Ming Cheng
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paper_authors.fullname: chengmingming
paper_authors.type: user
paper_authors.user: mingming8688
paper_authors._id: 65728d88cca2247f3f2acf9b
paper_authors.name: Ying Shan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-08T10:21:31.705Z
paper_authors._id: 63ca3ddc04c979828310bfcb
paper_authors.avatarUrl: /avatars/615e0d8622950b4408b40d550f02a894.svg
paper_authors.fullname: Ying Shan
paper_authors.type: user
paper_authors.user: yshan2u
paper_title: PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding
paper_summary: Recent advances in text-to-image generation have made remarkable progress in
  synthesizing realistic human photos conditioned on given text prompts. However,
  existing personalized generation methods cannot simultaneously satisfy the
  requirements of high efficiency, promising identity (ID) fidelity, and flexible
  text controllability. In this work, we introduce PhotoMaker, an efficient
  personalized text-to-image generation method, which mainly encodes an arbitrary
  number of input ID images into a stack ID embedding for preserving ID
  information. Such an embedding, serving as a unified ID representation, can not
  only encapsulate the characteristics of the same input ID comprehensively, but
  also accommodate the characteristics of different IDs for subsequent
  integration. This paves the way for more intriguing and practically valuable
  applications. Besides, to drive the training of our PhotoMaker, we propose an
  ID-oriented data construction pipeline to assemble the training data. Under the
  nourishment of the dataset constructed through the proposed pipeline, our
  PhotoMaker demonstrates better ID preservation ability than test-time
  fine-tuning based methods, yet provides significant speed improvements,
  high-quality generation results, strong generalization capabilities, and a wide
  range of applications. Our project page is available at
  https://photo-maker.github.io/
paper_ai_summary: PhotoMaker is an efficient personalized text-to-image generation method that encodes ID images into a unified embedding, achieving high ID fidelity, speed improvements, and generalization capabilities with an ID-oriented data pipeline.
paper_ai_keywords: ID embedding text-to-image generation ID fidelity test-time fine-tuning ID-oriented data construction pipeline