Fetching the paper…
Reading the bibliography…
The diffusion model performs remarkable in generating high-dimensional content but is computationally intensive, especially during training.
On the total nonnegativity of the hurwitz matrix
Bernard A Asner, Jr · 1970
Earlier work this paper cites.
Animating rotation with quaternion curves
Ken Shoemake · 1985
Earlier work this paper cites.
Stability theory of dynamical systems
Nam Parshad Bhatia and Giorgio P Szegö · 2002
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
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
Earlier work this paper cites.
Log-concavity and strong log-concavity: a review
Adrien Saumard and Jon A Wellner · 2014
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Sergey Zagoruyko and Nikos Komodakis · 2016
Earlier work this paper cites.
Yfcc100m: The new data in multimedia research
Bart Thomee, David A Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li · 2016
Earlier work this paper cites.
Improved techniques for training gans
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
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Earlier work this paper cites.
Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
Earlier work this paper cites.
Gradient descent gan optimization is locally stable
Vaishnavh Nagarajan and J Zico Kolter · 2017
Earlier work this paper cites.
The numerics of gans
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2017
Earlier work this paper cites.
Which training methods for gans do actually converge?
Lars Mescheder, Andreas Geiger, and Sebastian Nowozin · 2018
Earlier work this paper cites.
The mechanics of n-player differentiable games
David Balduzzi, Sebastien Racaniere, James Martens, Jakob Foerster, Karl Tuyls, and Thore Graepel · 2018
Earlier work this paper cites.
Global convergence to the equilibrium of gans using variational inequalities
Ian Gemp and Sridhar Mahadevan · 2018
Earlier work this paper cites.
A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
Earlier work this paper cites.
Improved precision and recall metric for assessing generative models
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2019
Earlier work this paper cites.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
Earlier work this paper cites.
On the variance of the adaptive learning rate and beyond
Liyuan Liu, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Jiawei Han · 2019
Earlier work this paper cites.
A solvable high-dimensional model of gan
Chuang Wang, Hong Hu, and Yue Lu · 2019
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
Earlier work this paper cites.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Earlier work this paper cites.
Scaling laws for autoregressive generative modeling
Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B Brown, Prafulla Dhariwal, Scott Gray, et al · 2020
Cited alongside, same era.
Differentiable augmentation for data-efficient gan training
Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han · 2020
Cited alongside, same era.
Training generative adversarial networks by solving ordinary differential equations
Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andy Brock, Jeff Donahue, Timothy Lillicrap, and Pushmeet Kohli · 2020
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
Knowledge distillation in iterative generative models for improved sampling speed
Eric Luhman and Troy Luhman · 2021
Cited alongside, same era.
Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever · 2023
Later among the works it cites.
Würstchen: An efficient architecture for large-scale text-to-image diffusion models
Pablo Pernias, Dominic Rampas, Mats Leon Richter, Christopher Pal, and Marc Aubreville · 2023
Later among the works it cites.
Scaling up gans for text-to-image synthesis
Minguk Kang, Jun-Yan Zhu, Richard Zhang, Jaesik Park, Eli Shechtman, Sylvain Paris, and Taesung Park · 2023
Later among the works it cites.
Stylegan-t: Unlocking the power of gans for fast large-scale text-to-image synthesis
Axel Sauer, Tero Karras, Samuli Laine, Andreas Geiger, and Timo Aila · 2023
Later among the works it cites.
Adversarial diffusion distillation
Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A contrastive learning approach for training variational autoencoder priors
Jyoti Aneja, Alex Schwing, Jan Kautz, and Arash Vahdat · 2021
Cited alongside, same era.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Taming transformers for high-resolution image synthesis
Patrick Esser, Robin Rombach, and Bjorn Ommer · 2021
Cited alongside, same era.
Conceptual 12M: Pushing web-scale image-text pre-training to recognize long-tail visual concepts
Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, A. Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
Cited alongside, same era.
Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 2021
Cited alongside, same era.
On distillation of guided diffusion models
Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik Kingma, Stefano Ermon, Jonathan Ho, and Tim Salimans · 2023
Later among the works it cites.
simple diffusion: End-to-end diffusion for high resolution images
Emiel Hoogeboom, Jonathan Heek, and Tim Salimans · 2023
Later among the works it cites.
Understanding diffusion objectives as the elbo with simple data augmentation
Diederik P Kingma and Ruiqi Gao · 2023
Later among the works it cites.
Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
Later among the works it cites.
Analyzing and improving the training dynamics of diffusion models
Tero Karras, Miika Aittala, Jaakko Lehtinen, Janne Hellsten, Timo Aila, and Samuli Laine · 2023
Later among the works it cites.
Adding conditional control to text-to-image diffusion models
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala · 2023
Later among the works it cites.
Manifold preserving guided diffusion
Yutong He, Naoki Murata, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Dongjun Kim, Wei-Hsiang Liao, Yuki Mitsufuji, J Zico Kolter, Ruslan Salakhutdinov, et al · 2023
Later among the works it cites.
If by deepfloyd lab at stabilityai
DeepFloyd Lab · 2023
Later among the works it cites.
Latent consistency models: Synthesizing high-resolution images with few-step inference
Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao · 2023
Later among the works it cites.
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
Later among the works it cites.
Ufogen: You forward once large scale text-to-image generation via diffusion gans
Yanwu Xu, Yang Zhao, Zhisheng Xiao, and Tingbo Hou · 2023
Later among the works it cites.
Pixart- α \alpha : Fast training of diffusion transformer for photorealistic text-to-image synthesis
Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, et al · 2023
Later among the works it cites.
One-step diffusion with distribution matching distillation
Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Fredo Durand, William T Freeman, and Taesung Park · 2023
Later among the works it cites.
Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al · 2023
Later among the works it cites.
Improving image generation with better captions
James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al · 2023
Later among the works it cites.
Sampling is as easy as keeping the consistency: convergence guarantee for consistency models
Junlong Lyu, Zhitang Chen, and Shoubo Feng · 2023
Later among the works it cites.
Wasserstein convergence guarantees for a general class of score-based generative models
Xuefeng Gao, Hoang M Nguyen, and Lingjiong Zhu · 2023
Later among the works it cites.
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
Closest in time.
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
Closest in time.
Fast high-resolution image synthesis with latent adversarial diffusion distillation
Axel Sauer, Frederic Boesel, Tim Dockhorn, Andreas Blattmann, Patrick Esser, and Robin Rombach · 2024
Closest in time.
Scott: Accelerating diffusion models with stochastic consistency distillation
Hongjian Liu, Qingsong Xie, Zhijie Deng, Chen Chen, Shixiang Tang, Fueyang Fu, Zheng-jun Zha, and Haonan Lu · 2024
Closest in time.
Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2024
Closest in time.
Contractive diffusion probabilistic models
Wenpin Tang and Hanyang Zhao · 2024
Closest in time.