Papers › PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding

PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding

7 Dec 2023CVPR 2024 1arXiv:2312.04461archive 2025-07-28

Zhen Li, Mingdeng Cao, Xintao Wang, Zhongang Qi, Ming-Ming Cheng, Ying Shan

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/

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Code

Syntology Ran 5 of 5 code samples harvested from 1 repository linked to this paper; 0 have no recorded run. Of those that ran: 3 ran · our draft was wrong; 2 ran with no contract checked.

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TencentARC/PhotoMaker officialpytorchNOASSERTION report

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3ran · our draft was wrong
2ran

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FeedForward TencentARC/PhotoMaker/photomaker/resampler.py official repository ran · our draft was wrong no licence file found · pointer only · 5105747c2711b1cb · report
masked_mean TencentARC/PhotoMaker/photomaker/resampler.py official repository ran no licence file found · pointer only · 230588e8f1237737 · report
rescale_noise_cfg TencentARC/PhotoMaker/photomaker/pipeline_controlnet.py official repository ran · our draft was wrong fingerprinted no licence file found · pointer only · bea2d776a332f2b0 · report
reshape_tensor TencentARC/PhotoMaker/photomaker/resampler.py official repository ran fingerprinted no licence file found · pointer only · 4cb2e2a2ca0bec9f · report
retrieve_timesteps TencentARC/PhotoMaker/photomaker/pipeline_controlnet.py official repository ran · our draft was wrong no licence file found · pointer only · 22b1f260da28f6a5 · report

Tasks

Diffusion PersonalizationDiffusion Personalization Tuning FreeImage GenerationText to Image GenerationText-to-Image Generation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Diffusion Personalization Tuning Free AgeDB PhotoMaker Cosine Similarity 0.287 #6 of 7 Archive leaderboard report
Diffusion Personalization Tuning Free AgeDB PhotoMaker FID 8.410 #6 of 7 Archive leaderboard report
Diffusion Personalization Tuning Free AgeDB PhotoMaker LPIPS 0.424 #6 of 7 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

SPEED

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