Fetching the paper…
Reading the bibliography…
Diffusion or flow-based models are powerful generative paradigms that are notoriously hard to sample as samples are defined as solutions to high-dimensional Ordinary or Stochastic Differential Equations (ODEs/SDEs) which require a large Number of Function Evaluations (NFE) to approximate well.
Some practical runge-kutta formulas
Lawrence F Shampine · 1986
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li · 2009
Earlier work this paper cites.
A first course in the numerical analysis of differential equations
Arieh Iserles · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2011
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
A downsampled variant of imagenet as an alternative to the cifar datasets
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Adam: A method for stochastic optimization, 2017
Diederik P. Kingma and Jimmy Ba · 2017
Earlier work this paper cites.
torchdiffeq, 2018
Ricky T. Q. Chen · 2018
Earlier work this paper cites.
Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2020
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
Variational diffusion models
Diederik Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
Cited alongside, same era.
Bilateral denoising diffusion models
Max WY Lam, Jun Wang, Rongjie Huang, Dan Su, and Dong Yu · 2021
Cited alongside, same era.
Knowledge distillation in iterative generative models for improved sampling speed
Flow matching for generative modeling
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le · 2022
Later among the works it cites.
Flow straight and fast: Learning to generate and transfer data with rectified flow
Xingchao Liu, Chengyue Gong, and Qiang Liu · 2022
Later among the works it cites.
Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 2022
Later among the works it cites.
Fast sampling of diffusion models with exponential integrator
Qinsheng Zhang and Yongxin Chen · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Eric Luhman and Troy Luhman · 2021
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models, 2021
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2021
Cited alongside, same era.
Learning fast samplers for diffusion models by differentiating through sample quality
Daniel Watson, William Chan, Jonathan Ho, and Mohammad Norouzi · 2021
Cited alongside, same era.
Building normalizing flows with stochastic interpolants, 2022
Michael S. Albergo and Eric Vanden-Eijnden · 2022
Cited alongside, same era.
Genie: Higher-order denoising diffusion solvers
Tim Dockhorn, Arash Vahdat, and Karsten Kreis · 2022
Cited alongside, same era.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
Cited alongside, same era.
Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
Cited alongside, same era.
Zhongjie Duan, Chengyu Wang, Cen Chen, Jun Huang, and Weining Qian · 2023
Closest in time.
Voicebox: Text-guided multilingual universal speech generation at scale
Matthew Le, Apoorv Vyas, Bowen Shi, Brian Karrer, Leda Sari, Rashel Moritz, Mary Williamson, Vimal Manohar, Yossi Adi, Jay Mahadeokar, et al · 2023
Closest in time.
On distillation of guided diffusion models
Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik Kingma, Stefano Ermon, Jonathan Ho, and Tim Salimans · 2023
Closest in time.
Consistency models
Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever · 2023
Closest in time.
Diffusion probabilistic model made slim
Xingyi Yang, Daquan Zhou, Jiashi Feng, and Xinchao Wang · 2023
Closest in time.
Improved order analysis and design of exponential integrator for diffusion models sampling
Qinsheng Zhang, Jiaming Song, and Yongxin Chen · 2023
Closest in time.
Fast sampling of diffusion models via operator learning
Hongkai Zheng, Weili Nie, Arash Vahdat, Kamyar Azizzadenesheli, and Anima Anandkumar · 2023
Closest in time.