2024/04/24 by Zinan Guo, Yanze Wu, Guo, Zinan +10 · 1 voice · 60 citations
Computer Science · Decision Sciences · #Data Quality and Management #Graph Theory and Algorithms #Web Data Mining and Analysis #cs.CV
paper · pdf · doi:10.48550/arxiv.2404.16022
openalex publication_date 2024/04/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 are available at https://github.com/ToTheBeginning/PuLID