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Generative processes that involve solving differential equations, such as diffusion models, frequently necessitate balancing speed and quality.
Reverse-time diffusion equation models
Brian DO Anderson · 1982
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Computer methods for ordinary differential equations and differential-algebraic equations
Uri M. Ascher and Linda R. Petzold · 1998
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, K. Li, and Li Fei-Fei · 2009
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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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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
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Handbook of Brownian motion-facts and formulae
Andrei N Borodin and Paavo Salminen · 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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User-friendly guarantees for the langevin monte carlo with inaccurate gradient
Arnak S Dalalyan and Avetik Karagulyan · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and P. Abbeel · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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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 · 2020
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Clipscore: A reference-free evaluation metric for image captioning
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Joseph Le Bras, and Yejin Choi · 2021
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Openclip
Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, Cade Gordon, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, Hongseok Namkoong, John Miller, Hannaneh Hajishirzi, Ali Farhadi, and Ludwig Schmidt · 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
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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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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Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L. Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, Seyedeh Sara Mahdavi, Raphael Gontijo Lopes, Tim Salimans, Jonathan Ho, David J. Fleet, and Mohammad Norouzi · 2022
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Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev · 2022
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
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High-resolution image synthesis with latent diffusion models
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Classifier-free diffusion guidance
Jonathan Ho · 2022
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Elucidating the design space of diffusion-based generative models
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Laion-aesthetics predictor v2
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Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models
Joseph L. Watson, David Juergens, Nathaniel R. Bennett, Brian L. Trippe, Jason Yim, Helen E. Eisenach, Woody Ahern, Andrew J. Borst, Robert J. Ragotte, Lukas F. Milles, Basile I. M. Wicky, Nikita Hanikel, Samuel J. Pellock, Alexis Courbet, William Sheffler, Jue Wang, Preetham Venkatesh, Isaac Sappington, Susana Vázquez Torres, Anna Lauko, Valentin De Bortoli, Emile Mathieu, Regina Barzilay, T. Jaakkola, Frank DiMaio, Minkyung Baek, and David Baker · 2022
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Poisson flow generative models
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Pf-abgen: A reliable and efficient antibody generator via poisson flow
Chutian Huang, Zijing Liu, Shengyuan Bai, Linwei Zhang, Chencheng Xu, Zhe Wang, Yang Xiang, and Yuanpeng Xiong · 2023
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Pfgm++: Unlocking the potential of physics-inspired generative models
Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, and T. Jaakkola · 2023
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