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We provide the first convergence guarantees for the Consistency Models (CMs), a newly emerging type of one-step generative models that can generate comparable samples to those generated by Diffusion Models.
Brownian dynamics as smart monte carlo simulation
Peter J. Rossky, Jimmie D. Doll, and Harold L. Friedman · 1978
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Jascha Narain Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Convergence of langevin mcmc in kl-divergence
Xiang Cheng and Peter L. Bartlett · 2017
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Xiang Cheng, Niladri S. Chatterji, Peter L. Bartlett, and Michael I. Jordan · 2017
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Score-based generative modeling through stochastic differential equations
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Vadim Popov, Ivan Vovk, Vladimir Gogoryan, Tasnima Sadekova, and Mikhail A. Kudinov · 2021
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Maximum likelihood training of score-based diffusion models
Yang Song, Conor Durkan, Iain Murray, and Stefano Ermon · 2021
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Convergence of denoising diffusion models under the manifold hypothesis
Valentin De Bortoli · 2022
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Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
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Maximum likelihood training for score-based diffusion odes by high-order denoising score matching
Chengjiang Lu, Kaiwen Zheng, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu · 2022
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Yang Song, Jascha Narain Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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The probability flow ode is provably fast
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Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J. Fleet
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Photorealistic text-to-image diffusion models with deep language understanding
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Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever · 2023
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