PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding
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
https://photo-maker.github.io/
65728d88cca2247f3f2acf96
Zhen Li
claimed_verified
2023-12-11T08:16:38.158Z
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Zhen Li
user
Paper99
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Mingdeng Cao
admin_assigned
2023-12-08T10:22:21.729Z
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Mingdeng Cao
user
Ljzycmd
65728d88cca2247f3f2acf98
Xintao Wang
admin_assigned
2023-12-08T10:20:56.158Z
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Xintao Wang
user
Xintao
65728d88cca2247f3f2acf99
Zhongang Qi
claimed_verified
2024-11-19T08:40:50.194Z
660cc64f1b14f691990c0ea0
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Zhongang Qi
user
phoenixqza
65728d88cca2247f3f2acf9a
Ming-Ming Cheng
admin_assigned
2023-12-08T10:21:24.317Z
64e496ae0195913c7fa91c66
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chengmingming
user
mingming8688
65728d88cca2247f3f2acf9b
Ying Shan
admin_assigned
2023-12-08T10:21:31.705Z
63ca3ddc04c979828310bfcb
/avatars/615e0d8622950b4408b40d550f02a894.svg
Ying Shan
user
yshan2u
PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding
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
https://photo-maker.github.io/
PhotoMaker is an efficient personalized text-to-image generation method that encodes ID images into a unified embedding, achieving high ID fidelity, speed improvements, and generalization capabilities with an ID-oriented data pipeline.
ID embedding text-to-image generation ID fidelity test-time fine-tuning ID-oriented data construction pipeline