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
this https URL

我们提出了纯净和闪电身份自定义（PuLID），一种用于文本到图像生成的新型无需调整的自定义方法。通过将 Lightning T2I 分支与标准扩散模型相结合，PuLID 引入了对比对齐损失和准确的身份损失，最大程度地减少对原始模型的干扰，并确保高度身份的保真度。实验表明，PuLID 在身份的保真度和可编辑性上均实现了卓越的性能。PuLID 的另一个吸引人的特性是，在身份插入前后，图像元素（如背景、照明、构图和风格）尽可能保持一致。对应的代码和模型将可在此 https URL 获得。

PuLID：通过对比对齐实现纯净且高效的身份验证定制

PuLID: Pure and Lightning ID Customization via Contrastive Alignment

Recent advances in text-to-image generation have made remarkable progress in
synthesizing realistic human photos conditioned on given text prompts. However,
existing personalized generation methods cannot simultaneously satisfy the
requirements of high efficiency, promising identity (ID) fidelity, and flexible
text controllability. In this work, we introduce PhotoMaker, an efficient
personalized text-to-image generation method, which mainly encodes an arbitrary
number of input ID images into a stack ID embedding for preserving ID
information. Such an embedding, serving as a unified ID representation, can not
only encapsulate the characteristics of the same input ID comprehensively, but
also accommodate the characteristics of different IDs for subsequent
integration. This paves the way for more intriguing and practically valuable
applications. Besides, to drive the training of our PhotoMaker, we propose an
ID-oriented data construction pipeline to assemble the training data. Under the
nourishment of the dataset constructed through the proposed pipeline, our
PhotoMaker demonstrates better ID preservation ability than test-time
fine-tuning based methods, yet provides significant speed improvements,
high-quality generation results, strong generalization capabilities, and a wide
range of applications. Our project page is available at
this https URL

研究对文本到图像的生成进行了进一步的提升，在保证高效率、有辨识度的身份和灵活文本控制性等要求的前提下，提出了 PhotoMaker 方法。通过将输入的身份图像编码为堆叠的 ID 嵌入来维护身份信息，该嵌入不仅可以全面地表达相同输入身份的特征，还可以适应不同身份的特征进行融合，从而实现更具吸引力和实用价值的应用。