Infinite-ID: Identity-preserved Personalization via ID-semantics
  Decoupling Paradigm
Yi Wu Ziqiang Li Heliang Zheng Chaoyue Wang Bin Li
Drawing on recent advancements in diffusion models for text-to-image
generation, identity-preserved personalization has made significant progress in
accurately capturing specific identities with just a single reference image.
However, existing methods primarily integrate reference images within the text
embedding space, leading to a complex entanglement of image and text
information, which poses challenges for preserving both identity fidelity and
semantic consistency. To tackle this challenge, we propose Infinite-ID, an
ID-semantics decoupling paradigm for identity-preserved personalization.
Specifically, we introduce identity-enhanced training, incorporating an
additional image cross-attention module to capture sufficient ID information
while deactivating the original text cross-attention module of the diffusion
model. This ensures that the image stream faithfully represents the identity
provided by the reference image while mitigating interference from textual
input. Additionally, we introduce a feature interaction mechanism that combines
a mixed attention module with an AdaIN-mean operation to seamlessly merge the
two streams. This mechanism not only enhances the fidelity of identity and
semantic consistency but also enables convenient control over the styles of the
generated images. Extensive experimental results on both raw photo generation
and style image generation demonstrate the superior performance of our proposed
method.