PuLID: Pure and Lightning ID Customization via Contrastive Alignment
We propose Pure and Lightning ID customization (PuLID), a novel tuning-free
ID customization method for text-to-image generation. By incorporating a
Lightning T2I branch with a standard diffusion one, PuLID introduces both
contrastive alignment loss and accurate ID loss, minimizing disruption to the
original model and ensuring high ID fidelity. Experiments show that PuLID
achieves superior performance in both ID fidelity and editability. Another
attractive property of PuLID is that the image elements (e.g., background,
lighting, composition, and style) before and after the ID insertion are kept as
consistent as possible. Codes and models will be available at
https://github.com/ToTheBeginning/PuLID
662a71ec309bb68eb4c9efe9
Zinan Guo
662a71ec309bb68eb4c9efea
Yanze Wu
extracted_confirmed
2024-04-25T15:18:37.744Z
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Yanze Wu
user
yanze
662a71ec309bb68eb4c9efeb
Zhuowei Chen
admin_assigned
2024-04-25T18:07:40.993Z
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/avatars/3fd4a9a62b0ef98db2573411463a9247.svg
Zhuowei_Chen
user
ZhuoweiChen
662a71ec309bb68eb4c9efec
Lang Chen
662a71ec309bb68eb4c9efed
Qian He
PuLID: Pure and Lightning ID Customization via Contrastive Alignment
We propose Pure and Lightning ID customization (PuLID), a novel tuning-free
ID customization method for text-to-image generation. By incorporating a
Lightning T2I branch with a standard diffusion one, PuLID introduces both
contrastive alignment loss and accurate ID loss, minimizing disruption to the
original model and ensuring high ID fidelity. Experiments show that PuLID
achieves superior performance in both ID fidelity and editability. Another
attractive property of PuLID is that the image elements (e.g., background,
lighting, composition, and style) before and after the ID insertion are kept as
consistent as possible. Codes and models will be available at
https://github.com/ToTheBeginning/PuLID
PuLID combines a Lightning T2I branch with a diffusion model to provide ID customization in text-to-image generation, maintaining ID fidelity and consistency in image elements.
Lightning T2I diffusion model contrastive alignment loss accurate ID loss ID fidelity editability image elements consistency