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PuLID: Pure and Lightning ID Customization via Contrastive Alignment

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Content provided by Rob. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Rob or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ro.player.fm/legal.
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
2024: Zinan Guo, Yanze Wu, Zhuowei Chen, Lang Chen, Qian He
https://arxiv.org/pdf/2404.16022v1
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298 episoade

Artwork
iconDistribuie
 
Manage episode 441394547 series 2954468
Content provided by Rob. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Rob or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ro.player.fm/legal.
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
2024: Zinan Guo, Yanze Wu, Zhuowei Chen, Lang Chen, Qian He
https://arxiv.org/pdf/2404.16022v1
  continue reading

298 episoade

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