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Diffusion models are relatively easy to train but require many steps to generate samples.
ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
J. Sohl-Dickstein, E. A. Weiss, N. Maheswaranathan, and S. Ganguli · 2015
Earlier work this paper cites.
Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
Earlier work this paper cites.
Gotta go fast when generating data with score-based models
A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas · 2021
Earlier work this paper cites.
D. P. Kingma, T. Salimans, B. Poole, and J. Ho · 2021
Earlier work this paper cites.
DiffWave: A versatile diffusion model for audio synthesis
Z. Kong, W. Ping, J. Huang, K. Zhao, and B. Catanzaro · 2021
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole · 2021
Earlier work this paper cites.
Classifier-free diffusion guidance
J. Ho and T. Salimans · 2022
Earlier work this paper cites.
Elucidating the design space of diffusion-based generative models
T. Karras, M. Aittala, T. Aila, and S. Laine · 2022
Earlier work this paper cites.
On distillation of guided diffusion models
C. Meng, R. Gao, D. P. Kingma, S. Ermon, J. Ho, and T. Salimans · 2022
Earlier work this paper cites.
High-resolution image synthesis with latent diffusion models
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer · 2022
Cited alongside, same era.
Photorealistic text-to-image diffusion models with deep language understanding
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. Denton, S. K. S. Ghasemipour, B. K. Ayan, S. S. Mahdavi, R. G. Lopes, T. Salimans, J. Ho, D. J. Fleet, and M. Norouzi · 2022
Cited alongside, same era.
Progressive distillation for fast sampling of diffusion models
T. Salimans and J. Ho · 2022
Cited alongside, same era.
TRACT: denoising diffusion models with transitive closure time-distillation
D. Berthelot, A. Autef, J. Lin, D. A. Yap, S. Zhai, S. Hu, D. Zheng, W. Talbott, and E. Gu · 2023
Cited alongside, same era.
simple diffusion: End-to-end diffusion for high resolution images
E. Hoogeboom, J. Heek, and T. Salimans · 2023
Cited alongside, same era.
Power hungry processing: Watts driving the cost of ai deployment?
A. S. Luccioni, Y. Jernite, and E. Strubell · 2023
Later among the works it cites.
Diff-instruct: A universal approach for transferring knowledge from pre-trained diffusion models
W. Luo, T. Hu, S. Zhang, J. Sun, Z. Li, and Z. Zhang · 2023
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Improved techniques for training consistency models
Y. Song and P. Dhariwal · 2023
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Consistency models
Y. Song, P. Dhariwal, M. Chen, and I. Sutskever · 2023
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Ufogen: You forward once large scale text-to-image generation via diffusion gans
Y. Xu, Y. Zhao, Z. Xiao, and T. Hou · 2023
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Consistency trajectory models: Learning probability flow ODE trajectory of diffusion
D. Kim, C. Lai, W. Liao, N. Murata, Y. Takida, T. Uesaka, Y. He, Y. Mitsufuji, and S. Ermon · 2023
Cited alongside, same era.
Understanding the diffusion objective as a weighted integral of elbos
D. P. Kingma and R. Gao · 2023
Cited alongside, same era.
Flow matching for generative modeling
Y. Lipman, R. T. Q. Chen, H. Ben-Hamu, M. Nickel, and M. Le · 2023
Cited alongside, same era.
Flow straight and fast: Learning to generate and transfer data with rectified flow
X. Liu, C. Gong, and Q. Liu · 2023
Cited alongside, same era.
Instaflow: One step is enough for high-quality diffusion-based text-to-image generation
X. Liu, X. Zhang, J. Ma, J. Peng, and Q. Liu
Cited in the paper.
Denoising diffusion implicit models
J. Song, C. Meng, and S. Ermon
Cited in the paper.
Later among the works it cites.
One-step diffusion with distribution matching distillation
T. Yin, M. Gharbi, R. Zhang, E. Shechtman, F. Durand, W. T. Freeman, and T. Park · 2023
Later among the works it cites.
Fast sampling of diffusion models via operator learning
H. Zheng, W. Nie, A. Vahdat, K. Azizzadenesheli, and A. Anandkumar · 2023
Later among the works it cites.
Common diffusion noise schedules and sample steps are flawed, 2024
S. Lin, B. Liu, J. Li, and X. Yang · 2024
Closest in time.
Perflow: Piecewise rectified flow as universal plug-and-play accelerator
H. Yan, X. Liu, J. Pan, J. H. Liew, Q. Liu, and J. Feng · 2024
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