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Diffusion Models (DMs) have achieved great success in image generation and other fields.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Generative adversarial networks, 2014
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift, 2015
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation, 2015
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition, 2015
Karen Simonyan and Andrew Zisserman · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics, 2015
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
Improved techniques for training gans, 2016
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium, 2018
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2018
Earlier work this paper cites.
Which training methods for gans do actually converge?, 2018
Lars Mescheder, Andreas Geiger, and Sebastian Nowozin · 2018
Earlier work this paper cites.
The unreasonable effectiveness of deep features as a perceptual metric, 2018
Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang · 2018
Earlier work this paper cites.
Large scale gan training for high fidelity natural image synthesis, 2019
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
Earlier work this paper cites.
A style-based generator architecture for generative adversarial networks, 2019
Tero Karras, Samuli Laine, and Timo Aila · 2019
Earlier work this paper cites.
Improved precision and recall metric for assessing generative models, 2019
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2019
Earlier work this paper cites.
Score identity distillation: Exponentially fast distillation of pretrained diffusion models for one-step generation, 2024
Mingyuan Zhou, Huangjie Zheng, Zhendong Wang, Mingzhang Yin, and Hai Huang · 2019
Earlier work this paper cites.
Stargan v2: Diverse image synthesis for multiple domains, 2020
Yunjey Choi, Youngjung Uh, Jaejun Yoo, and Jung-Woo Ha · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models, 2020
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Efficientnet: Rethinking model scaling for convolutional neural networks, 2020
Mingxing Tan and Quoc V. Le · 2020
Cited alongside, same era.
Differentiable augmentation for data-efficient gan training, 2020
Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han · 2020
Cited alongside, same era.
Diffusion models beat gans on image synthesis, 2021
Prafulla Dhariwal and Alex Nichol · 2021
Cited alongside, same era.
Taming transformers for high-resolution image synthesis, 2021
Patrick Esser, Robin Rombach, and Björn Ommer · 2021
Cited alongside, same era.
Progressive distillation for fast sampling of diffusion models, 2022
Tim Salimans and Jonathan Ho · 2022
Later among the works it cites.
Stylegan-xl: Scaling stylegan to large diverse datasets, 2022
Axel Sauer, Katja Schwarz, and Andreas Geiger · 2022
Later among the works it cites.
Denoising diffusion implicit models, 2022
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2022
Later among the works it cites.
Boot: Data-free distillation of denoising diffusion models with bootstrapping, 2023
Jiatao Gu, Shuangfei Zhai, Yizhe Zhang, Lingjie Liu, and Josh Susskind · 2023
Later among the works it cites.
Scalable adaptive computation for iterative generation, 2023
Allan Jabri, David Fleet, and Ting Chen · 2023
Later among the works it cites.
Mage: Masked generative encoder to unify representation learning and image synthesis, 2023
Tianhong Li, Huiwen Chang, Shlok Kumar Mishra, Han Zhang, Dina Katabi, and Dilip Krishnan · 2023
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Score-based generative modeling through stochastic differential equations, 2021
Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
Cited alongside, same era.
Training data-efficient image transformers & distillation through attention, 2021
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
Cited alongside, same era.
Maskgit: Masked generative image transformer, 2022
Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T. Freeman · 2022
Cited alongside, same era.
Elucidating the design space of diffusion-based generative models, 2022
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
Cited alongside, same era.
Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps, 2022
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu · 2022
Cited alongside, same era.
Glide: Towards photorealistic image generation and editing with text-guided diffusion models, 2022
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2022
Cited alongside, same era.
Later among the works it cites.
Scalable diffusion models with transformers, 2023
William Peebles and Saining Xie · 2023
Later among the works it cites.
Improved techniques for training consistency models
Yang Song and Prafulla Dhariwal · 2023
Later among the works it cites.
Consistency models, 2023
Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever · 2023
Later among the works it cites.
Attention is all you need, 2023
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2023
Later among the works it cites.
One-step diffusion with distribution matching distillation, 2023
Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Fredo Durand, William T. Freeman, and Taesung Park · 2023
Later among the works it cites.
Fast sampling of diffusion models via operator learning, 2023
Hongkai Zheng, Weili Nie, Arash Vahdat, Kamyar Azizzadenesheli, and Anima Anandkumar · 2023
Later among the works it cites.
Consistency trajectory models: Learning probability flow ode trajectory of diffusion, 2024
Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Yutong He, Yuki Mitsufuji, and Stefano Ermon · 2024
Closest in time.
Sdxl-lightning: Progressive adversarial diffusion distillation, 2024
Shanchuan Lin, Anran Wang, and Xiao Yang · 2024
Closest in time.