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Diffusion models suffer from slow sample generation at inference time.
On the product of semi-groups of operators
H. F. Trotter · 1959
Earlier work this paper cites.
Computer ”experiments” on classical fluids. i. thermodynamical properties of lennard-jones molecules
Loup Verlet · 1967
Earlier work this paper cites.
A family of embedded runge-kutta formulae
J.R. Dormand and P.J. Prince · 1980
Earlier work this paper cites.
Reverse-time diffusion equation models
Brian D.O. Anderson · 1982
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Construction of higher order symplectic integrators
Haruo Yoshida · 1990
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Numerical Solution of Stochastic Differential Equations
Peter E. Kloeden and Eckhard Platen · 1992
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Solving Ordinary Differential Equations I (2nd Revised. Ed.): Nonstiff Problems
E. Hairer, S. P. Nørsett, and G. Wanner · 1993
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
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Molecular dynamics : with deterministic and stochastic numerical methods / Ben Leimkuhler, Charles Matthews
B. Leimkuhler · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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torchdiffeq, 2018
Ricky T. Q. Chen · 2018
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Making convolutional networks shift-invariant again
Richard Zhang · 2019
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Stargan v2: Diverse image synthesis for multiple domains
Yunjey Choi, Youngjung Uh, Jaejun Yoo, and Jung-Woo Ha · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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High-fidelity performance metrics for generative models in pytorch, 2020
Anton Obukhov, Maximilian Seitzer, Po-Wei Wu, Semen Zhydenko, Jonathan Kyl, and Elvis Yu-Jing Lin · 2020
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Pseudo numerical methods for diffusion models on manifolds
Luping Liu, Yi Ren, Zhijie Lin, and Zhou Zhao · 2022
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Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu · 2022
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Hierarchical text-conditional image generation with clip latents, 2022
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 2022
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Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors · 2020
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Wavegrad: Estimating gradients for waveform generation
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Bddm: Bilateral denoising diffusion models for fast and high-quality speech synthesis
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Knowledge distillation in iterative generative models for improved sampling speed
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
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Learning to efficiently sample from diffusion probabilistic models
Daniel Watson, Jonathan Ho, Mohammad Norouzi, and William Chan · 2021
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Ruihan Yang, Prakhar Srivastava, and Stephan Mandt · 2022
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gddim: Generalized denoising diffusion implicit models
Qinsheng Zhang, Molei Tao, and Yongxin Chen · 2022
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Seeds: Exponential sde solvers for fast high-quality sampling from diffusion models, 2023
Martin Gonzalez, Nelson Fernandez, Thuy Tran, Elies Gherbi, Hatem Hajri, and Nader Masmoudi · 2023
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On distillation of guided diffusion models
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Generative diffusions in augmented spaces: A complete recipe, 2023
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Where to diffuse, how to diffuse, and how to get back: Automated learning for multivariate diffusions
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Consistency models
Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever · 2023
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Accelerating guided diffusion sampling with splitting numerical methods
Suttisak Wizadwongsa and Supasorn Suwajanakorn · 2023
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Sa-solver: Stochastic adams solver for fast sampling of diffusion models, 2023
Shuchen Xue, Mingyang Yi, Weijian Luo, Shifeng Zhang, Jiacheng Sun, Zhenguo Li, and Zhi-Ming Ma · 2023
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Fast sampling of diffusion models with exponential integrator
Qinsheng Zhang and Yongxin Chen · 2023
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