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Training diffusion models is always a computation-intensive task.
Exponential smoothing: The state of the art
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Equilibrium free-energy differences from nonequilibrium measurements: A master-equation approach
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Imagenet: A large-scale hierarchical image database
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Auto-encoding variational bayes
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Nice: Non-linear independent components estimation
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Adam: A method for stochastic optimization
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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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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 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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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 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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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 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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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Training generative adversarial networks with limited data
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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Dynamic neural networks: A survey
Yizeng Han, Gao Huang, Shiji Song, Le Yang, Honghui Wang, and Yulin Wang · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 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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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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Animatediff: Animate your personalized text-to-image diffusion models without specific tuning
Yuwei Guo, Ceyuan Yang, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, and Bo Dai · 2023
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Efficient diffusion training via min-snr weighting strategy
Tiankai Hang, Shuyang Gu, Chen Li, Jianmin Bao, Dong Chen, Han Hu, Xin Geng, and Baining Guo · 2023
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Consistency trajectory models: Learning probability flow ode trajectory of diffusion
Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Yutong He, Yuki Mitsufuji, and Stefano Ermon · 2023
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Instaflow: One step is enough for high-quality diffusion-based text-to-image generation
Xingchao Liu, Xiwen Zhang, Jianzhu Ma, Jian Peng, et al · 2023
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Dalle-2, 2023
OpenAI · 2023
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Fan Bao, Chongxuan Li, Jun Zhu, and Bo Zhang · 2022
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Perception prioritized training of diffusion models
Jooyoung Choi, Jungbeom Lee, Chaehun Shin, Sungwon Kim, Hyunwoo Kim, and Sungroh Yoon · 2022
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Latent video diffusion models for high-fidelity long video generation
Yingqing He, Tianyu Yang, Yong Zhang, Ying Shan, and Qifeng Chen · 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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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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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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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
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Infobatch: Lossless training speed up by unbiased dynamic data pruning
Ziheng Qin, Kai Wang, Zangwei Zheng, Jianyang Gu, Xiangyu Peng, Zhaopan Xu, Daquan Zhou, Lei Shang, Baigui Sun, Xuansong Xie, et al · 2023
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Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever · 2023
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Videocomposer: Compositional video synthesis with motion controllability
Xiang* Wang, Hangjie* Yuan, Shiwei* Zhang, Dayou* Chen, Jiuniu Wang, Yingya Zhang, Yujun Shen, Deli Zhao, and Jingren Zhou · 2023
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Zike Wu, Pan Zhou, Kenji Kawaguchi, and Hanwang Zhang · 2023
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Fast training of diffusion models with masked transformers
Hongkai Zheng, Weili Nie, Arash Vahdat, and Anima Anandkumar · 2023
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Video generation models as world simulators
Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang, and Aditya Ramesh · 2024
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Scaling rectified flow transformers for high-resolution image synthesis
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al · 2024
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Equivariant 3d-conditional diffusion model for molecular linker design
Ilia Igashov, Hannes Stärk, Clément Vignac, Arne Schneuing, Victor Garcia Satorras, Pascal Frossard, Max Welling, Michael Bronstein, and Bruno Correia · 2024
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Denoising task difficulty-based curriculum for training diffusion models
Jin-Young Kim, Hyojun Go, Soonwoo Kwon, and Hyun-Gyoon Kim · 2024
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PKU-Yuan Lab and Tuzhan AI etc · 2024
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Latte: Latent diffusion transformer for video generation
Xin Ma, Yaohui Wang, Gengyun Jia, Xinyuan Chen, Ziwei Liu, Yuan-Fang Li, Cunjian Chen, and Yu Qiao · 2024
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Sora, 2024
OpenAI · 2024
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Towards faster training of diffusion models: An inspiration of a consistency phenomenon
Tianshuo Xu, Peng Mi, Ruilin Wang, and Yingcong Chen · 2024
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Unmasking bias in diffusion model training
Hu Yu, Li Shen, Jie Huang, Hongsheng Li, and Feng Zhao · 2024
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Non-uniform timestep sampling: Towards faster diffusion model training
Tianyi Zheng, Cong Geng, Peng-Tao Jiang, Ben Wan, Hao Zhang, Jinwei Chen, Jia Wang, and Bo Li · 2024
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Famim: A novel frequency-domain augmentation masked image model framework for domain generalizable face anti-spoofing
Tianyi Zheng, Qinji Yu, Zhaoyu Chen, and Jia Wang · 2024
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