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Sampling from diffusion models involves a slow iterative process that hinders their practical deployment, especially for interactive applications.
On measures of entropy and information
Alfréd Rényi · 1961
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Pattern recognition and machine learning (information science and statistics)
Christopher M. Bishop · 2006
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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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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
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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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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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f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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Improved generator objectives for gans
Ben Poole, Alexander A. Alemi, Jascha Narain Sohl-Dickstein, and Anelia Angelova · 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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Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
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Improved precision and recall metric for assessing generative models
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Denoising Diffusion Probabilistic Models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Non-saturating gan training as divergence minimization
Matt Shannon, Ben Poole, Soroosh Mariooryad, Tom Bagby, Eric Battenberg, David Kao, Daisy Stanton, and R. J. Skerry-Ryan · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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f-divergence variational inference
Neng Wan, Dapeng Li, and NAIRA HOVAKIMYAN · 2020
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Training deep energy-based models with f-divergence minimization
Lantao Yu, Yang Song, Jiaming Song, and Stefano Ermon · 2020
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Emerging properties in self-supervised vision transformers
M. Caron, H. Touvron, I. Misra, H. Jegou, J. Mairal, P. Bojanowski, and A. Joulin · 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
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Knowledge distillation in iterative generative models for improved sampling speed
Eric Luhman and Troy Luhman · 2021
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Diffusion probabilistic models for 3d point cloud generation
Shitong Luo and Wei Hu · 2021
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Score-based Generative Modeling in Latent Space
Arash Vahdat, Karsten Kreis, and Jan Kautz · 2021
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eDiff-I: Text-to-Image Diffusion Models with Ensemble of Expert Denoisers
Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Qinsheng Zhang, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, Tero Karras, and Ming-Yu Liu · 2022
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Coyo-700m: Image-text pair dataset
Minwoo Byeon, Beomhee Park, Haecheon Kim, Sungjun Lee, Woonhyuk Baek, and Saehoon Kim · 2022
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Video diffusion models
Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet · 2022
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Scalable adaptive computation for iterative generation
A. Jabri, David J. Fleet, and Ting Chen · 2022
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Elucidating the design space of diffusion-based generative models
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 · 2023
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Physdiff: Physics-guided human motion diffusion model
Ye Yuan, Jiaming Song, Umar Iqbal, Arash Vahdat, and Jan Kautz · 2023
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Hipa: Enabling one-step text-to-image diffusion models via high-frequency-promoting adaptation
Yifan Zhang and Bryan Hooi · 2023
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Particle guidance: non-iid diverse sampling with diffusion models
Corso et al · 2024
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Zhengyang Geng, Ashwini Pokle, William Luo, Justin Lin, and J Zico Kolter · 2024
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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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Hierarchical Text-Conditional Image Generation with CLIP Latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 2022
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Stylegan-xl: Scaling stylegan to large diverse datasets
Axel Sauer, Katja Schwarz, and Andreas Geiger · 2022
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LION: Latent Point Diffusion Models for 3D Shape Generation
Xiaohui Zeng, Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, and Karsten Kreis · 2022
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Motiondiffuse: Text-driven human motion generation with diffusion model
Mingyuan Zhang, Zhongang Cai, Liang Pan, Fangzhou Hong, Xinying Guo, Lei Yang, and Ziwei Liu · 2022
Cited alongside, same era.
Guiding a diffusion model with a bad version of itself
Tero Karras, Miika Aittala, Tuomas Kynkäänniemi, Jaakko Lehtinen, Timo Aila, and Samuli Laine · 2024
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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 · 2024
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Sangyun Lee, Yilun Xu, Tomas Geffner, Giulia Fanti, Karsten Kreis, Arash Vahdat, and Weili Nie · 2024
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Simplifying, stabilizing and scaling continuous-time consistency models
Cheng Lu and Yang Song · 2024
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Swiftbrush: One-step text-to-image diffusion model with variational score distillation
Thuan Hoang Nguyen and Anh Tran · 2024
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SDXL: Improving latent diffusion models for high-resolution image synthesis
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach · 2024
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Multistep distillation of diffusion models via moment matching
Tim Salimans, Thomas Mensink, Jonathan Heek, and Emiel Hoogeboom · 2024
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Fast high-resolution image synthesis with latent adversarial diffusion distillation
Axel Sauer, Frederic Boesel, Tim Dockhorn, Andreas Blattmann, Patrick Esser, and Robin Rombach · 2024
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Fine-tuning of diffusion models via stochastic control: entropy regularization and beyond
Wenpin Tang · 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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Em distillation for one-step diffusion models
Sirui Xie, Zhisheng Xiao, Diederik P Kingma, Tingbo Hou, Ying Nian Wu, Kevin Patrick Murphy, Tim Salimans, Ben Poole, and Ruiqi Gao · 2024
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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 · 2024
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Diffusion models are innate one-step generators
Bowen Zheng and Tianming Yang · 2024
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Dpm-solver-v3: Improved diffusion ode solver with empirical model statistics
Kaiwen Zheng, Cheng Lu, Jianfei Chen, and Jun Zhu · 2024
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Score identity distillation: Exponentially fast distillation of pretrained diffusion models for one-step generation
Mingyuan Zhou, Huangjie Zheng, Zhendong Wang, Mingzhang Yin, and Hai Huang · 2024
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Swiftbrush v2: Make your one-step diffusion model better than its teacher
Trung Dao, Thuan Hoang Nguyen, Thanh Le, Duc Vu, Khoi Nguyen, Cuong Pham, and Anh Tran · 2025
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