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Despite their strong performances on many generative tasks, diffusion models require a large number of sampling steps in order to generate realistic samples.
A Connection Between Score Matching and Denoising Autoencoders
Pascal Vincent · 2011
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The CIFAR-10 Dataset
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2014
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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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Wavenet: A generative model for raw audio
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 2016
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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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Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
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Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
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Residual flows for invertible generative modeling
Ricky TQ Chen, Jens Behrmann, David K Duvenaud, and Jörn-Henrik Jacobsen · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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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 · 2020
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Feature quantization improves gan training
Yang Zhao, Chunyuan Li, Ping Yu, Jianfeng Gao, and Changyou Chen · 2020
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A multi-class hinge loss for conditional gans
Ilya Kavalerov, Wojciech Czaja, and Rama Chellappa · 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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Sdedit: Image synthesis and editing with stochastic differential equations
Chenlin Meng, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon · 2021
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Improved denoising diffusion probabilistic models
Alex Nichol and Prafulla Dhariwal · 2021
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Tackling the generative learning trilemma with denoising diffusion gans
Zhisheng Xiao, Karsten Kreis, and Arash Vahdat · 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, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, Tero Karras, and Ming-Yu Liu · 2022
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Diffedit: Diffusion-based semantic image editing with mask guidance
Guillaume Couairon, Jakob Verbeek, Holger Schwenk, and Matthieu Cord · 2022
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An image is worth one word: Personalizing text-to-image generation using textual inversion
Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or · 2022
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Prompt-to-prompt image editing with cross attention control
Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or · 2022
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Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet · 2022
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Equivariant diffusion for molecule generation in 3d
Emiel Hoogeboom, Vıctor Garcia Satorras, Clément Vignac, and Max Welling · 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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On distillation of guided diffusion models
Chenlin Meng, Ruiqi Gao, Diederik P Kingma, Stefano Ermon, Jonathan Ho, and Tim Salimans · 2022
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Scalable diffusion models with transformers
William Peebles and Saining Xie · 2022
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Deep equilibrium approaches to diffusion models
Ashwini Pokle, Zhengyang Geng, and J Zico Kolter · 2022
Null-text inversion for editing real images using guided diffusion models
Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or · 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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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 · 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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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 Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, 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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Stylegan-xl: Scaling stylegan to large diverse datasets
Axel Sauer, Katja Schwarz, and Andreas Geiger · 2022
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Diffusion-gan: Training gans with diffusion
Zhendong Wang, Huangjie Zheng, Pengcheng He, Weizhu Chen, and Mingyuan Zhou · 2022
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Fast sampling of diffusion models with exponential integrator
Qinsheng Zhang and Yongxin Chen · 2022
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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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Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever · 2023
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Patch diffusion: Faster and more data-efficient training of diffusion models
Zhendong Wang, Yifan Jiang, Huangjie Zheng, Peihao Wang, Pengcheng He, Zhangyang "Atlas" Wang, Weizhu Chen, and Mingyuan Zhou · 2023
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Smart: Improving gans with score matching regularity
Mengfei Xia, Yujun Shen, Ceyuan Yang, Ran Yi, Wenping Wang, and Yong-jin Liu · 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
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Truncated diffusion probabilistic models and diffusion-based adversarial auto-encoders
Huangjie Zheng, Pengcheng He, Weizhu Chen, and Mingyuan Zhou · 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
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Zhengyang Geng, Ashwini Pokle, William Luo, Justin Lin, and J Zico Kolter · 2024
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Sdxl-lightning: Progressive adversarial diffusion distillation
Shanchuan Lin, Anran Wang, and Xiao Yang · 2024
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Scott: Accelerating diffusion models with stochastic consistency distillation
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Data prediction denoising models: The pupil outdoes the master, 2024
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Hyper-sd: Trajectory segmented consistency model for efficient image synthesis
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Sdxs: Real-time one-step latent diffusion models with image conditions
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Yifei Wang, Weimin Bai, Weijian Luo, Wenzheng Chen, and He Sun · 2024
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Perflow: Piecewise rectified flow as universal plug-and-play accelerator
Hanshu Yan, Xingchao Liu, Jiachun Pan, Jun Hao Liew, Qiang Liu, and Jiashi Feng · 2024
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Trajectory consistency distillation
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