Infinite-ID: Identity-preserved Personalization via ID-semantics
  Decoupling Paradigm
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.
65f907e75df5183c9a7ded6b
Yi Wu
65f907e75df5183c9a7ded6c
Ziqiang Li
65f907e75df5183c9a7ded6d
Heliang Zheng
admin_assigned
2024-03-19T09:07:42.017Z
640ef1b2c025ddf61895c31a
/avatars/444de5bf32b1daa4dda1a7c17a328578.svg
Heliang Zheng
user
ilcve21
65f907e75df5183c9a7ded6e
Chaoyue Wang
65f907e75df5183c9a7ded6f
Bin Li
Infinite-ID: Identity-preserved Personalization via ID-semantics
  Decoupling Paradigm
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.
Infinite-ID decouples identity and semantics in image generation, using enhanced training with image cross-attention and feature interaction to preserve identity fidelity and enhance semantic consistency.
diffusion models text-to-image identity-preserved personalization text embedding space identity-enhanced training image cross-attention text cross-attention feature interaction mechanism mixed attention module AdaIN-mean operation identity fidelity semantic consistency style image generation