PuLID：通过对比对齐实现纯净和快速身份验证定制

PuLID: Pure and Lightning ID Customization via Contrastive Alignment

April 24, 2024

作者: Zinan Guo, Yanze Wu, Zhuowei Chen, Lang Chen, Qian He

cs.AI

摘要

我们提出了一种名为纯净闪电ID定制（PuLID）的新型无调整ID定制方法，用于文本到图像生成。通过将一个闪电T2I分支与一个标准扩散分支结合，PuLID引入了对比对齐损失和准确的ID损失，最大程度地减少对原始模型的干扰，并确保高度ID保真度。实验表明，PuLID在ID保真度和可编辑性方面均取得了优越的性能。PuLID的另一个吸引人之处在于，在ID插入前后，图像元素（如背景、光照、构图和风格）尽可能保持一致。代码和模型将在https://github.com/ToTheBeginning/PuLID 上提供。

English

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