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Diffusion Probabilistic Models (DPMs) have achieved considerable success in generation tasks.
B. D. Anderson, “Reverse-time diffusion equation models,” Stochastic Processes and their Applications , vol. 12, no. 3, pp. 313–326, 1982
1982
Earlier work this paper cites.
P. E. Kloeden and E. Platen, Numerical Solution of Stochastic Differential Equations . Springer, 1992
1992
Earlier work this paper cites.
E. Buckwar and R. Winkler, “Multistep methods for sdes and their application to problems with small noise,” SIAM Journal on Numerical Analysis , 2006
2006
Earlier work this paper cites.
E. Buckwar and R. Winkler, “Improved linear multi-step methods for stochastic ordinary differential equations,” Journal of Computational and Applied Mathematics , 2007
2007
Earlier work this paper cites.
A. Krizhevsky, “Learning multiple layers of features from tiny images,” Tech. Rep., 2009
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition . IEEE, 2009, pp. 248–255
2009
Earlier work this paper cites.
K. Atkinson, W. Han, and D. E. Stewart, Numerical solution of ordinary differential equations . John Wiley & Sons, 2011, vol. 108
2011
Earlier work this paper cites.
A. Roberts, “Modify the improved euler scheme to integrate stochastic differential equations,” 2012
2012
Earlier work this paper cites.
B. Oksendal, Stochastic differential equations: an introduction with applications . Springer Science & Business Media, 2013
2013
Earlier work this paper cites.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems , vol. 27, 2014, pp. 2672–2680
2014
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in International Conference on Learning Representations , 2014
2014
Earlier work this paper cites.
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in International Conference on Machine Learning . PMLR, 2015, pp. 2256–2265
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “GANs trained by a two time-scale update rule converge to a local Nash equilibrium,” in Advances in Neural Information Processing Systems , I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan, and R. Garnett, Eds., vol. 30, 2017, pp. 6626–6637
2017
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” in Advances in Neural Information Processing Systems , vol. 33, 2020, pp. 6840–6851
2020
Earlier work this paper cites.
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, “Score-based generative modeling through stochastic differential equations,” in International Conference on Learning Representations , 2021
2021
Cited alongside, same era.
P. Dhariwal and A. Q. Nichol, “Diffusion models beat GANs on image synthesis,” in Advances in Neural Information Processing Systems , vol. 34, 2021, pp. 8780–8794
2021
Cited alongside, same era.
2021
Cited alongside, same era.
J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” in International Conference on Learning Representations , 2021
2021
Cited alongside, same era.
C. Meng, R. Gao, D. P. Kingma, S. Ermon, J. Ho, and T. Salimans, “On distillation of guided diffusion models,” in NeurIPS 2022 Workshop on Score-Based Methods , 2022
2022
Later among the works it cites.
D. Watson, W. Chan, J. Ho, and M. Norouzi, “Learning fast samplers for diffusion models by differentiating through sample quality,” in International Conference on Learning Representations , 2022
2022
Later among the works it cites.
Z. Xiao, K. Kreis, and A. Vahdat, “Tackling the generative learning trilemma with denoising diffusion GANs,” in International Conference on Learning Representations , 2022
2022
Later among the works it cites.
C. Lu, Y. Zhou, F. Bao, J. Chen, C. LI, and J. Zhu, “Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps,” in Advances in Neural Information Processing Systems , S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds., vol. 35, 2022, pp. 5775–5787
2022
Later among the works it cites.
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2021
Cited alongside, same era.
D. P. Kingma, T. Salimans, B. Poole, and J. Ho, “Variational diffusion models,” in Advances in Neural Information Processing Systems , 2021
2021
Cited alongside, same era.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2022, pp. 10 684–10 695
2022
Cited alongside, same era.
J. Ho, C. Saharia, W. Chan, D. J. Fleet, M. Norouzi, and T. Salimans, “Cascaded diffusion models for high fidelity image generation,” Journal of Machine Learning Research , vol. 23, no. 47, pp. 1–33, 2022
2022
Cited alongside, same era.
J. Ho, T. Salimans, A. Gritsenko, W. Chan, M. Norouzi, and D. J. Fleet, “Video diffusion models,” in Advances in Neural Information Processing Systems , 2022
2022
Cited alongside, same era.
A. Q. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. McGrew, I. Sutskever, and M. Chen, “GLIDE: towards photorealistic image generation and editing with text-guided diffusion models,” in International Conference on Machine Learning (ICML), 2022
2022
Cited alongside, same era.
A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen, “Hierarchical text-conditional image generation with clip latents,” 2022
2022
Cited alongside, same era.
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans, J. Ho, D. J. Fleet, and M. Norouzi, “Photorealistic text-to-image diffusion models with deep language understanding,” in Advances in Neural Information Processing Systems , 2022
2022
Cited alongside, same era.
F. Bao, C. Li, J. Zhu, and B. Zhang, “Analytic-DPM: An analytic estimate of the optimal reverse variance in diffusion probabilistic models,” in International Conference on Learning Representations , 2022
2022
Later among the works it cites.
T. Karras, M. Aittala, T. Aila, and S. Laine, “Elucidating the design space of diffusion-based generative models,” in Proc. NeurIPS , 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
A. Blattmann, R. Rombach, H. Ling, T. Dockhorn, S. W. Kim, S. Fidler, and K. Kreis, “Align your latents: High-resolution video synthesis with latent diffusion models,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2023
2023
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Z. Wang, H. Zheng, P. He, W. Chen, and M. Zhou, “Diffusion-GAN: Training GANs with diffusion,” in The Eleventh International Conference on Learning Representations , 2023
2023
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Q. Zhang and Y. Chen, “Fast sampling of diffusion models with exponential integrator,” in The Eleventh International Conference on Learning Representations , 2023
2023
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C. Lu, Y. Zhou, F. Bao, J. Chen, C. Li, and J. Zhu, “Dpm-solver++: Fast solver for guided sampling of diffusion probabilistic models,” 2023
2023
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S. Li, L. Liu, Z. Chai, R. Li, and X. Tan, “Era-solver: Error-robust adams solver for fast sampling of diffusion probabilistic models,” 2023
2023
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