1 code implementation • 22 Oct 2023 • Yang song, Prafulla Dhariwal
Consistency models are a nascent family of generative models that can sample high quality data in one step without the need for adversarial training.
4 code implementations • 2 Mar 2023 • Yang song, Prafulla Dhariwal, Mark Chen, Ilya Sutskever
Through extensive experiments, we demonstrate that they outperform existing distillation techniques for diffusion models in one- and few-step sampling, achieving the new state-of-the-art FID of 3. 55 on CIFAR-10 and 6. 20 on ImageNet 64x64 for one-step generation.
Ranked #9 on
Image Generation
on ImageNet 64x64
1 code implementation • 16 Dec 2022 • Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, Mark Chen
This is in stark contrast to state-of-the-art generative image models, which produce samples in a number of seconds or minutes.
7 code implementations • 13 Apr 2022 • Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen
Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style.
Ranked #27 on
Text-to-Image Generation
on COCO
(using extra training data)
Conditional Image Generation
Zero-Shot Text-to-Image Generation
2 code implementations • 20 Dec 2021 • Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, Mark Chen
Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity.
Ranked #32 on
Text-to-Image Generation
on COCO
(using extra training data)
17 code implementations • NeurIPS 2021 • Prafulla Dhariwal, Alex Nichol
Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3. 94 on ImageNet 256$\times$256 and 3. 85 on ImageNet 512$\times$512.
Ranked #1 on
Image Generation
on LSUN Bedroom 256 x 256
(FD metric)
9 code implementations • 18 Feb 2021 • Alex Nichol, Prafulla Dhariwal
Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples.
Ranked #4 on
Image Generation
on CIFAR-10
(FD metric)
no code implementations • 28 Oct 2020 • Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B. Brown, Prafulla Dhariwal, Scott Gray, Chris Hallacy, Benjamin Mann, Alec Radford, Aditya Ramesh, Nick Ryder, Daniel M. Ziegler, John Schulman, Dario Amodei, Sam McCandlish
The optimal model size also depends on the compute budget through a power-law, with exponents that are nearly universal across all data domains.
4 code implementations • ICML 2020 • Mark Chen, Alec Radford, Rewon Child, Jeff Wu, Heewoo Jun, Prafulla Dhariwal, David Luan, Ilya Sutskever
Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images.
Ranked #14 on
Image Classification
on STL-10
(using extra training data)
Representation Learning
Self-Supervised Image Classification
40 code implementations • NeurIPS 2020 • Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei
By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
12 code implementations • Preprint 2020 • Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever
We introduce Jukebox, a model that generates music with singing in the raw audio domain.
27 code implementations • NeurIPS 2018 • Diederik P. Kingma, Prafulla Dhariwal
Flow-based generative models (Dinh et al., 2014) are conceptually attractive due to tractability of the exact log-likelihood, tractability of exact latent-variable inference, and parallelizability of both training and synthesis.
Ranked #10 on
Image Generation
on CelebA 256x256
1 code implementation • ICLR 2019 • Daniel Huang, Prafulla Dhariwal, Dawn Song, Ilya Sutskever
In this paper, we introduce a system called GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof assistant.
168 code implementations • 20 Jul 2017 • John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov
We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective function using stochastic gradient ascent.
10 code implementations • ICLR 2018 • Matthias Plappert, Rein Houthooft, Prafulla Dhariwal, Szymon Sidor, Richard Y. Chen, Xi Chen, Tamim Asfour, Pieter Abbeel, Marcin Andrychowicz
Combining parameter noise with traditional RL methods allows to combine the best of both worlds.
no code implementations • 8 Nov 2016 • Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel
Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification.