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
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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。