DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, Kfir Aberman
2023Year
1,526Top-tier citations
Abstract
Figure 1 . With just a few images (typically 3-5) of a subject (left), DreamBooth-our AI-powered photo booth-can generate a myriad of images of the subject in different contexts (right), using the guidance of a text prompt. The results exhibit natural interactions with the environment, as well as novel articulations and variation in lighting conditions, all while maintaining high fidelity to the key visual features of the subject.
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Cited by top-tier papers1,526
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- Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video GenerationJay Zhangjie Wu, Yixiao Ge, Xintao Wang, Stan Weixian Lei et al.ICCV 2023 · 1,113 citations
- MVDream: Multi-view Diffusion for 3D GenerationYichun Shi, Peng Wang, Jianglong Ye, Long Mai et al.ICLR 2024 · 973 citations
- PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image SynthesisJunsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao et al.ICLR 2024 · 831 citations
Builds on37
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh et al.ICML 2021 · 47,906 citations
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 35,902 citations
- Diffusion Models Beat GANs on Image SynthesisPrafulla Dhariwal, Alexander Quinn NicholNeurIPS 2021 · 13,211 citations
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser et al.CVPR 2022 · 13,123 citations
- Denoising Diffusion Implicit ModelsJiaming Song, Chenlin Meng, Stefano ErmonICLR 2021 · 11,743 citations
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