Diffusion Models
Diffusion Models is a generative modeling framework that belongs to the class of probabilistic latent variable models, distinguished by its sequential process of gradually transforming data into noise through a forward diffusion process and subsequently learning to reverse this process for data generation.
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205.9K
Citations
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Authors
1.8K
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Publications per year
2017–2026
1.3K
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Denoising Diffusion Probabilistic Models
arXiv (Cornell University) · 2020 · 5.6K citations · Full text
Adding Conditional Control to Text-to-Image Diffusion Models
Lvmin Zhang, Anyi Rao, Maneesh Agrawala · 2023 · 3.6K citations
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Korea Advanced Institute of Science and Technology, South Korea
14 publications · 1.6K citations
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Universidade Federal de Campina Grande, Brazil
14 publications · 481 citations
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Stanford University
Stanford, United States
33 h-index
106 publications
25.1K citations
Tsinghua University
Beijing, China
24 h-index
82 publications
6.4K citations
National University of Singapore
Singapore, Singapore
23 h-index
53 publications
6.1K citations
University of California, Berkeley
Berkeley, United States
22 h-index
42 publications
11.5K citations

Centre National de la Recherche Scientifique
Paris, France
22 h-index
69 publications
3K citations
Google (United States)
Mountain View, United States
21 h-index
100 publications
27.7K citations

Chinese Academy of Sciences
Beijing, China
18 h-index
73 publications
4.3K citations
The University of Texas at Austin
Austin, United States
18 h-index
33 publications
3.1K citations
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Repository · 375 publications · 32.7K citations
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Conference · 29 publications · 1.9K citations
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22 publications · 1.7K citations
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16 publications · 1.4K citations
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15 publications · 905 citations
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