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Diffusion Models (DMs), also referred to as score-based diffusion models, utilize neural networks to specify score functions.
Monte carlo sampling methods using markov chains and their applications
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Gareth O Roberts and Richard L Tweedie · 1996
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Optimal scaling of discrete approximations to langevin diffusions
Gareth O Roberts and Jeffrey S Rosenthal · 1998
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Filters, random fields and maximum entropy (frame): Towards a unified theory for texture modeling
Song-Chun Zhu, Ying Nian Wu, and David Mumford · 1998
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Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen · 2005
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Denoising diffusion probabilistic models
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A tutorial on energy-based learning
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
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Mcmc using hamiltonian dynamics
Radford M Neal et al · 2011
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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 · 2011
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Narain Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2011
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A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
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Generating sequences with recurrent neural networks
Alex Graves · 2013
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Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014
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Langevin diffusions and the metropolis-adjusted langevin algorithm
Tatiana Xifara, Chris Sherlock, Samuel Livingstone, Simon Byrne, and Mark Girolami · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 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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Densely connected convolutional networks
Gao Huang, Zhuang Liu, and Kilian Q. Weinberger · 2016
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Exploring the limits of language modeling
Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu · 2016
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Pixel recurrent neural networks
Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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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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Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
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Parallel wavenet: Fast high-fidelity speech synthesis
Aaron Oord, Yazhe Li, Igor Babuschkin, Karen Simonyan, Oriol Vinyals, Koray Kavukcuoglu, George Driessche, Edward Lockhart, Luis Cobo, Florian Stimberg, et al · 2018
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Analyzing and improving the image quality of stylegan
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
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Refining generative process with discriminator guidance in score-based diffusion models
Dongjun Kim, Yeongmin Kim, Wanmo Kang, and Il-Chul Moon · 2022
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Magic3d: High-resolution text-to-3d content creation
Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin · 2022
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On distillation of guided diffusion models
Chenlin Meng, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, and Tim Salimans · 2022
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Sliced score matching: A scalable approach to density and score estimation
Yang Song, Sahaj Garg, Jiaxin Shi, and Stefano Ermon · 2019
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Wavegrad: Estimating gradients for waveform generation
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan · 2020
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Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng · 2020
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Efficient learning of generative models via finite-difference score matching
Tianyu Pang, Kun Xu, Chongxuan Li, Yang Song, Stefano Ermon, and Jun Zhu · 2020
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Label-efficient semantic segmentation with diffusion models
Dmitry Baranchuk, Ivan Rubachev, Andrey Voynov, Valentin Khrulkov, and Artem Babenko · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Gotta go fast when generating data with score-based models
Alexia Jolicoeur-Martineau, Ke Li, Remi Piche-Taillefer, Tal Kachman, and Ioannis Mitliagkas · 2021
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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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Accelerating diffusion sampling with classifier-based feature distillation
Wujie Sun, Defang Chen, Can Wang, Deshi Ye, Yan Feng, and Chun Chen · 2022
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Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation
Haochen Wang, Xiaodan Du, Jiahao Li, Raymond A. Yeh, and Greg Shakhnarovich · 2022
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Learning fast samplers for diffusion models by differentiating through sample quality
Daniel Watson, William Chan, Jonathan Ho, and Mohammad Norouzi · 2022
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Fast point cloud generation with straight flows
Lemeng Wu, Dilin Wang, Chengyue Gong, Xingchao Liu, Yunyang Xiong, Rakesh Ranjan, Raghuraman Krishnamoorthi, Vikas Chandra, and Qiang Liu · 2022
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Neurallift-360: Lifting an in-the-wild 2d photo to a 3d object with 360° views
Dejia Xu, Yifan Jiang, Peihao Wang, Zhiwen Fan, Yi Wang, and Zhangyang Wang · 2022
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Fast sampling of diffusion models with exponential integrator
Qinsheng Zhang and Yongxin Chen · 2022
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Fast sampling of diffusion models via operator learning
Hongkai Zheng, Weili Nie, Arash Vahdat, Kamyar Azizzadenesheli, and Anima Anandkumar · 2022
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Fast inference in denoising diffusion models via mmd finetuning
Emanuele Aiello, Diego Valsesia, and Enrico Magli · 2023
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Riemannian flow matching on general geometries
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Optimizing ddpm sampling with shortcut fine-tuning
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Minimizing trajectory curvature of ode-based generative models
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Dreamix: Video diffusion models are general video editors
Eyal Molad, Eliahu Horwitz, Dani Valevski, Alex Rav Acha, Yossi Matias, Yael Pritch, Yaniv Leviathan, and Yedid Hoshen · 2023
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Text-to-4d dynamic scene generation
Uriel Singer, Shelly Sheynin, Adam Polyak, Oron Ashual, Iurii Makarov, Filippos Kokkinos, Naman Goyal, Andrea Vedaldi, Devi Parikh, Justin Johnson, and Yaniv Taigman · 2023
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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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Unipc: A unified predictor-corrector framework for fast sampling of diffusion models
Wenliang Zhao, Lujia Bai, Yongming Rao, Jie Zhou, and Jiwen Lu · 2023
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