InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity
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.
67dcd0fe1f94b594ef4f3e8e
Liming Jiang
admin_assigned
2025-03-21T13:03:33.323Z
632ea8a92a6ef6fb4acd6403
https://cdn-avatars.huggingface.co/v1/production/uploads/1664002049861-noauth.png
Liming Jiang
user
EndlessSora
67dcd0fe1f94b594ef4f3e8f
Qing Yan
67dcd0fe1f94b594ef4f3e90
Yumin Jia
67dcd0fe1f94b594ef4f3e91
Zichuan Liu
admin_assigned
2025-03-21T13:04:10.048Z
642f487cee432fcf2e9170c2
/avatars/aa03bf5f546dd732084f7ff7738a94ee.svg
Zichuan Liu
user
sun8878232
67dcd0fe1f94b594ef4f3e92
Hao Kang
67dcd0fe1f94b594ef4f3e93
Xin Lu
InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity
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.
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.
Diffusion Transformers DiTs InfiniteYou InfU InfuseNet residual connections pretraining supervised fine-tuning SFT synthetic single-person-multiple-sample SPMS state-of-the-art performance