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Diffusion models have demonstrated empirical successes in various applications and can be adapted to task-specific needs via guidance.
Reverse-time diffusion equation models
Brian DO Anderson · 1982
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Nonlinear dimensionality reduction by locally linear embedding
Sam T Roweis and Lawrence K Saul · 2000
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A global geometric framework for nonlinear dimensionality reduction
Joshua B Tenenbaum, Vin de Silva, and John C Langford · 2000
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
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Tweedie’s formula and selection bias
Bradley Efron · 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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Convex optimization: Algorithms and complexity
Sébastien Bubeck et al · 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 residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Generative modeling with denoising auto-encoders and langevin sampling
Adam Block, Youssef Mroueh, and Alexander Rakhlin · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2020
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Diffusion schrödinger bridge with applications to score-based generative modeling
Valentin De Bortoli, James Thornton, Jeremy Heng, and Arnaud Doucet · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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The intrinsic dimension of images and its impact on learning
Phillip Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, and Tom Goldstein · 2021
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Is conditional generative modeling all you need for decision-making?
Anurag Ajay, Yilun Du, Abhi Gupta, Joshua Tenenbaum, Tommi Jaakkola, and Pulkit Agrawal · 2022
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Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, and Anru R Zhang · 2022
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Convergence of denoising diffusion models under the manifold hypothesis
Valentin De Bortoli · 2022
Cited alongside, same era.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
Cited alongside, same era.
Denoising diffusion restoration models
Bahjat Kawar, Michael Elad, Stefano Ermon, and Jiaming Song · 2022
Cited alongside, same era.
Let us build bridges: Understanding and extending diffusion generative models
Xingchao Liu, Lemeng Wu, Mao Ye, and Qiang Liu · 2022
Cited alongside, same era.
Repaint: Inpainting using denoising diffusion probabilistic models
Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool · 2022
Cited alongside, same era.
Diffusion models are minimax optimal distribution estimators
Kazusato Oko, Shunta Akiyama, and Taiji Suzuki · 2023
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Aligning text-to-image diffusion models with reward backpropagation
Mihir Prabhudesai, Anirudh Goyal, Deepak Pathak, and Katerina Fragkiadaki · 2023
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Loss-guided diffusion models for plug-and-play controllable generation
Jiaming Song, Qinsheng Zhang, Hongxu Yin, Morteza Mardani, Ming-Yu Liu, Jan Kautz, Yongxin Chen, and Arash Vahdat · 2023
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End-to-end diffusion latent optimization improves classifier guidance
Bram Wallace, Akash Gokul, Stefano Ermon, and Nikhil Naik · 2023
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De novo design of protein structure and function with rfdiffusion
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, et al · 2023
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Cited alongside, same era.
Zero-shot image restoration using denoising diffusion null-space model
Yinhuai Wang, Jiwen Yu, and Jian Zhang · 2022
Cited alongside, same era.
Stochastic interpolants: A unifying framework for flows and diffusions
Michael S Albergo, Nicholas M Boffi, and Eric Vanden-Eijnden · 2023
Cited alongside, same era.
Universal guidance for diffusion models
Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2023
Cited alongside, same era.
Linear convergence bounds for diffusion models via stochastic localization
Joe Benton, Valentin De Bortoli, Arnaud Doucet, and George Deligiannidis · 2023
Cited alongside, same era.
Training diffusion models with reinforcement learning
Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine · 2023
Cited alongside, same era.
Directly fine-tuning diffusion models on differentiable rewards
Kevin Clark, Paul Vicol, Kevin Swersky, and David J Fleet · 2023
Cited alongside, same era.
Later among the works it cites.
Imagereward: Learning and evaluating human preferences for text-to-image generation
Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong · 2023
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Diffusion models: A comprehensive survey of methods and applications
Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Wentao Zhang, Bin Cui, and Ming-Hsuan Yang · 2023
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Freedom: Training-free energy-guided conditional diffusion model
Jiwen Yu, Yinhuai Wang, Chen Zhao, Bernard Ghanem, and Jian Zhang · 2023
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Reward-directed conditional diffusion: Provable distribution estimation and reward improvement
Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Minshuo Chen, and Mengdi Wang · 2023
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D-flow: Differentiating through flows for controlled generation
Heli Ben-Hamu, Omri Puny, Itai Gat, Brian Karrer, Uriel Singer, and Yaron Lipman · 2024
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An overview of diffusion models: Applications, guided generation, statistical rates and optimization
Minshuo Chen, Song Mei, Jianqing Fan, and Mengdi Wang · 2024
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Unveil conditional diffusion models with classifier-free guidance: A sharp statistical theory
Hengyu Fu, Zhuoran Yang, Mengdi Wang, and Minshuo Chen · 2024
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Image restoration by denoising diffusion models with iteratively preconditioned guidance
Tomer Garber and Tom Tirer · 2024
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Optimizing diffusion noise can serve as universal motion priors
Korrawe Karunratanakul, Konpat Preechakul, Emre Aksan, Thabo Beeler, Supasorn Suwajanakorn, and Siyu Tang · 2024
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Sora: A review on background, technology, limitations, and opportunities of large vision models
Yixin Liu, Kai Zhang, Yuan Li, Zhiling Yan, Chujie Gao, Ruoxi Chen, Zhengqing Yuan, Yue Huang, Hanchi Sun, Jianfeng Gao, et al · 2024
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Implicit diffusion: Efficient optimization through stochastic sampling
Pierre Marion, Anna Korba, Peter Bartlett, Mathieu Blondel, Valentin De Bortoli, Arnaud Doucet, Felipe Llinares-López, Courtney Paquette, and Quentin Berthet · 2024
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Tuning-free alignment of diffusion models with direct noise optimization
Zhiwei Tang, Jiangweizhi Peng, Jiasheng Tang, Mingyi Hong, Fan Wang, and Tsung-Hui Chang · 2024
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Fine-tuning of continuous-time diffusion models as entropy-regularized control
Masatoshi Uehara, Yulai Zhao, Kevin Black, Ehsan Hajiramezanali, Gabriele Scalia, Nathaniel Lee Diamant, Alex M Tseng, Tommaso Biancalani, and Sergey Levine · 2024
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Optimal score estimation via empirical bayes smoothing
Andre Wibisono, Yihong Wu, and Kaylee Yingxi Yang · 2024
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