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Diffusion-based generative models have demonstrated their powerful performance across various tasks, but this comes at a cost of the slow sampling speed.
Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen · 2005
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Interpretation and generalization of score matching
Siwei Lyu · 2009
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Stochastic differential equations: an introduction with applications
Bernt Oksendal · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Fei-Fei Li · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 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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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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On the variance of the adaptive learning rate and beyond
Liyuan Liu, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Jiawei Han · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Reliable fidelity and diversity metrics for generative models
Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, and Jaejun Yoo · 2020
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Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2021
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Knowledge distillation in iterative generative models for improved sampling speed
Eric Luhman and Troy Luhman · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
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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 · 2021
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Genie: Higher-order denoising diffusion solvers
Tim Dockhorn, Arash Vahdat, and Karsten Kreis · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Video diffusion models
Jonathan Ho, Tim Salimans, Alexey A. Gritsenko, William Chan, Mohammad Norouzi, and David J. Fleet · 2022
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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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Pseudo numerical methods for diffusion models on manifolds
Luping Liu, Yi Ren, Zhijie Lin, and Zhou Zhao · 2022
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Flow straight and fast: Learning to generate and transfer data with rectified flow
Xingchao Liu, Chengyue Gong, and Qiang Liu · 2022
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Latent consistency models: Synthesizing high-resolution images with few-step inference
Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao · 2023
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On distillation of guided diffusion models
Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik Kingma, Stefano Ermon, Jonathan Ho, and Tim Salimans · 2023
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Swiftbrush: One-step text-to-image diffusion model with variational score distillation
Thuan Hoang Nguyen and Anh Tran · 2023
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Efficient integrators for diffusion generative models
Kushagra Pandey, Maja Rudolph, and Stephan Mandt · 2023
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Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman · 2023
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Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu · 2022
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Dpm-solver++: Fast solver for guided sampling of diffusion probabilistic models
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu · 2022
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Dreamfusion: Text-to-3d using 2d diffusion
Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall · 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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Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al · 2022
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Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 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, et al · 2022
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Adversarial diffusion distillation
Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach · 2023
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Improved techniques for training consistency models
Yang Song and Prafulla Dhariwal · 2023
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Consistency models
Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever · 2023
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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 · 2023
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Ufogen: You forward once large scale text-to-image generation via diffusion gans
Yanwu Xu, Yang Zhao, Zhisheng Xiao, and Tingbo Hou · 2023
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One-step diffusion with distribution matching distillation
Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Fredo Durand, William T Freeman, and Taesung Park · 2023
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Fast sampling of diffusion models with exponential integrator
Qinsheng Zhang and Yongxin Chen · 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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Fast sampling of diffusion models via operator learning
Hongkai Zheng, Weili Nie, Arash Vahdat, Kamyar Azizzadenesheli, and Anima Anandkumar · 2023
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Dpm-solver-v3: Improved diffusion ode solver with empirical model statistics
Kaiwen Zheng, Cheng Lu, Jianfei Chen, and Jun Zhu · 2023
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On the trajectory regularity of ODE-based diffusion sampling
Defang Chen, Zhenyu Zhou, Can Wang, Chunhua Shen, and Siwei Lyu · 2024
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Relational diffusion distillation for efficient image generation
Weilun Feng, Chuanguang Yang, Zhulin An, Libo Huang, Boyu Diao, Fei Wang, and Yongjun Xu · 2024
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Seeds: Exponential sde solvers for fast high-quality sampling from diffusion models
Martin Gonzalez, Nelson Fernandez Pinto, Thuy Tran, Hatem Hajri, Nader Masmoudi, et al · 2024
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Snapfusion: Text-to-image diffusion model on mobile devices within two seconds
Yanyu Li, Huan Wang, Qing Jin, Ju Hu, Pavlo Chemerys, Yun Fu, Yanzhi Wang, Sergey Tulyakov, and Jian Ren · 2024
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Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation
Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu · 2024
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Fast ode-based sampling for diffusion models in around 5 steps
Zhenyu Zhou, Defang Chen, Can Wang, and Chun Chen · 2024
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