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Diffusion models have emerged as the mainstream approach for visual generation.
Adaptive Mixtures of Local Experts
Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan, and Geoffrey E. Hinton · 1991
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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
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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 unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 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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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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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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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer, 2017
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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Assessing generative models via precision and recall
Mehdi SM Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, and Sylvain Gelly · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Improved techniques for training score-based generative models
Yang Song and Stefano Ermon · 2020
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Dynamicvit: Efficient vision transformers with dynamic token sparsification
Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, and Cho-Jui Hsieh · 2021
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Continuous conditional generative adversarial networks: Novel empirical losses and label input mechanisms
Xin Ding, Yongwei Wang, Zuheng Xu, William J Welch, and Z Jane Wang · 2022
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Adaptive token sampling for efficient vision transformers
Mohsen Fayyaz, Soroush Abbasi Kouhpayegani, Farnoush Rezaei Jafari, Eric Sommerlade, Hamid Reza Vaezi Joze, Hamed Pirsiavash, and Juergen Gall · 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
Cited alongside, same era.
Maskvit: Masked visual pre-training for video prediction, 2022
Agrim Gupta, Stephen Tian, Yunzhi Zhang, Jiajun Wu, Roberto Martín-Martín, and Li Fei-Fei · 2022
Cited alongside, same era.
Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al · 2022
Cited alongside, same era.
Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
Cited alongside, same era.
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, Dustin Podell, Tim Dockhorn, Zion English, Kyle Lacey, Alex Goodwin, Yannik Marek, and Robin Rombach · 2024
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Diffusion models and representation learning: A survey
Michael Fuest, Pingchuan Ma, Ming Gui, Johannes S Fischer, Vincent Tao Hu, and Bjorn Ommer · 2024
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Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2024
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What matters in transformers? not all attention is needed
Shwai He, Guoheng Sun, Zheyu Shen, and Ang Li · 2024
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Applying guidance in a limited interval improves sample and distribution quality in diffusion models
Tuomas Kynkäänniemi, Miika Aittala, Tero Karras, Samuli Laine, Timo Aila, and Jaakko Lehtinen · 2024
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Adavit: Adaptive vision transformers for efficient image recognition
Lingchen Meng, Hengduo Li, Bor-Chun Chen, Shiyi Lan, Zuxuan Wu, Yu-Gang Jiang, and Ser-Nam Lim · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Cited alongside, same era.
Building normalizing flows with stochastic interpolants
Michael S Albergo and Eric Vanden-Eijnden · 2023
Cited alongside, same era.
Self-supervised learning from images with a joint-embedding predictive architecture, 2023
Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, and Nicolas Ballas · 2023
Cited alongside, same era.
Align your latents: High-resolution video synthesis with latent diffusion models
Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis · 2023
Cited alongside, same era.
Masked diffusion transformer is a strong image synthesizer
Shanghua Gao, Pan Zhou, Ming-Ming Cheng, and Shuicheng Yan · 2023
Cited alongside, same era.
Self-guided diffusion models
Vincent Tao Hu, David W Zhang, Yuki M Asano, Gertjan J Burghouts, and Cees GM Snoek · 2023
Cited alongside, same era.
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Videomamba: State space model for efficient video understanding
Kunchang Li, Xinhao Li, Yi Wang, Yinan He, Yali Wang, Limin Wang, and Yu Qiao · 2024
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Scaling laws for diffusion transformers, 2024
Zhengyang Liang, Hao He, Ceyuan Yang, and Bo Dai · 2024
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Switch diffusion transformer: Synergizing denoising tasks with sparse mixture-of-experts, 2024
Byeongjun Park, Hyojun Go, Jin-Young Kim, Sangmin Woo, Seokil Ham, and Changick Kim · 2024
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Moe-mamba: Efficient selective state space models with mixture of experts, 2024
Maciej Pióro, Kamil Ciebiera, Krystian Król, Jan Ludziejewski, Michał Krutul, Jakub Krajewski, Szymon Antoniak, Piotr Miłoś, Marek Cygan, and Sebastian Jaszczur · 2024
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Mixture-of-depths: Dynamically allocating compute in transformer-based language models
David Raposo, Sam Ritter, Blake Richards, Timothy Lillicrap, Peter Conway Humphreys, and Adam Santoro · 2024
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Boosting latent diffusion with flow matching
Johannes Schusterbauer, Ming Gui, Pingchuan Ma, Nick Stracke, Stefan A. Baumann, Vincent Tao Hu, and Björn Ommer · 2024
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Ec-dit: Scaling diffusion transformers with adaptive expert-choice routing, 2024
Haotian Sun, Tao Lei, Bowen Zhang, Yanghao Li, Haoshuo Huang, Ruoming Pang, Bo Dai, and Nan Du · 2024
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Dim: Diffusion mamba for efficient high-resolution image synthesis, 2024
Yao Teng, Yue Wu, Han Shi, Xuefei Ning, Guohao Dai, Yu Wang, Zhenguo Li, and Xihui Liu · 2024
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Improving and generalizing flow-based generative models with minibatch optimal transport
Alexander Tong, Kilian FATRAS, Nikolay Malkin, Guillaume Huguet, Yanlei Zhang, Jarrid Rector-Brooks, Guy Wolf, and Yoshua Bengio · 2024
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Attention-driven training-free efficiency enhancement of diffusion models, 2024
Hongjie Wang, Difan Liu, Yan Kang, Yijun Li, Zhe Lin, Niraj K. Jha, and Yuchen Liu · 2024
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Cache me if you can: Accelerating diffusion models through block caching
Felix Wimbauer, Bichen Wu, Edgar Schoenfeld, Xiaoliang Dai, Ji Hou, Zijian He, Artsiom Sanakoyeu, Peizhao Zhang, Sam Tsai, Jonas Kohler, et al · 2024
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Representation alignment for generation: Training diffusion transformers is easier than you think
Sihyun Yu, Sangkyung Kwak, Huiwon Jang, Jongheon Jeong, Jonathan Huang, Jinwoo Shin, and Saining Xie · 2024
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Exploring token pruning in vision state space models
Zheng Zhan, Zhenglun Kong, Yifan Gong, Yushu Wu, Zichong Meng, Hangyu Zheng, Xuan Shen, Stratis Ioannidis, Wei Niu, Pu Zhao, and Yanzhi Wang · 2024
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Fast training of diffusion models with masked transformers
Hongkai Zheng, Weili Nie, Arash Vahdat, and Anima Anandkumar · 2024
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Sd-dit: Unleashing the power of self-supervised discrimination in diffusion transformer
Rui Zhu, Yingwei Pan, Yehao Li, Ting Yao, Zhenglong Sun, Tao Mei, and Chang Wen Chen · 2024
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Depthfm: Fast monocular depth estimation with flow matching
Ming Gui, Johannes S. Fischer, Ulrich Prestel, Pingchuan Ma, Olga Grebenkova Dmytro Kotovenko, Stefan Andreas Baumann, Vincent Tao Hu, and Björn Ommer · 2025
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Reconstruction vs. generation: Taming optimization dilemma in latent diffusion models
Jingfeng Yao, Bin Yang, and Xinggang Wang · 2025
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