InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity
Authors: name: Liming Jiang, name: Qing Yan, name: Yumin Jia, name: Zichuan Liu, name: Hao Kang, name: Xin Lu

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.