title: InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity
summary: Achieving flexible and high-fidelity identity-preserved image generation
  remains formidable, particularly with advanced Diffusion Transformers (DiTs)
  like FLUX. We introduce InfiniteYou (InfU), one of the earliest robust
  frameworks leveraging DiTs for this task. InfU addresses significant issues of
  existing methods, such as insufficient identity similarity, poor text-image
  alignment, and low generation quality and aesthetics. Central to InfU is
  InfuseNet, a component that injects identity features into the DiT base model
  via residual connections, enhancing identity similarity while maintaining
  generation capabilities. A multi-stage training strategy, including pretraining
  and supervised fine-tuning (SFT) with synthetic single-person-multiple-sample
  (SPMS) data, further improves text-image alignment, ameliorates image quality,
  and alleviates face copy-pasting. Extensive experiments demonstrate that InfU
  achieves state-of-the-art performance, surpassing existing baselines. In
  addition, the plug-and-play design of InfU ensures compatibility with various
  existing methods, offering a valuable contribution to the broader community.
paper_authors._id: 67dcd0fe1f94b594ef4f3e8e
paper_authors.name: Liming Jiang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-21T13:03:33.323Z
paper_authors._id: 632ea8a92a6ef6fb4acd6403
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1664002049861-noauth.png
paper_authors.fullname: Liming Jiang
paper_authors.type: user
paper_authors.user: EndlessSora
paper_authors._id: 67dcd0fe1f94b594ef4f3e8f
paper_authors.name: Qing Yan
paper_authors._id: 67dcd0fe1f94b594ef4f3e90
paper_authors.name: Yumin Jia
paper_authors._id: 67dcd0fe1f94b594ef4f3e91
paper_authors.name: Zichuan Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-21T13:04:10.048Z
paper_authors._id: 642f487cee432fcf2e9170c2
paper_authors.avatarUrl: /avatars/aa03bf5f546dd732084f7ff7738a94ee.svg
paper_authors.fullname: Zichuan Liu
paper_authors.type: user
paper_authors.user: sun8878232
paper_authors._id: 67dcd0fe1f94b594ef4f3e92
paper_authors.name: Hao Kang
paper_authors._id: 67dcd0fe1f94b594ef4f3e93
paper_authors.name: Xin Lu
paper_title: InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity
paper_summary: Achieving flexible and high-fidelity identity-preserved image generation
  remains formidable, particularly with advanced Diffusion Transformers (DiTs)
  like FLUX. We introduce InfiniteYou (InfU), one of the earliest robust
  frameworks leveraging DiTs for this task. InfU addresses significant issues of
  existing methods, such as insufficient identity similarity, poor text-image
  alignment, and low generation quality and aesthetics. Central to InfU is
  InfuseNet, a component that injects identity features into the DiT base model
  via residual connections, enhancing identity similarity while maintaining
  generation capabilities. A multi-stage training strategy, including pretraining
  and supervised fine-tuning (SFT) with synthetic single-person-multiple-sample
  (SPMS) data, further improves text-image alignment, ameliorates image quality,
  and alleviates face copy-pasting. Extensive experiments demonstrate that InfU
  achieves state-of-the-art performance, surpassing existing baselines. In
  addition, the plug-and-play design of InfU ensures compatibility with various
  existing methods, offering a valuable contribution to the broader community.
paper_ai_summary: InfiniteYou leverages Diffusion Transformers to generate high-fidelity identity-preserved images, addressing issues of identity similarity, text-image alignment, and quality through a novel framework and training strategy.
paper_ai_keywords: Diffusion Transformers DiTs InfiniteYou InfU InfuseNet residual connections pretraining supervised fine-tuning SFT synthetic single-person-multiple-sample SPMS state-of-the-art performance