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Diffusion transformers have been widely adopted for text-to-image synthesis.
Exploring the limits of transfer learning with a unified text-to-text transformer, 2023
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 1910
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Logistic-normal distributions:Some properties and uses
J. Atchison and S.M. Shen · 1980
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Denoising diffusion probabilistic models, 2020
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2006
Earlier work this paper cites.
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, and Zhifeng Chen · 2006
Earlier work this paper cites.
Improving image captioning with better use of captions, 2020
Zhan Shi, Xu Zhou, Xipeng Qiu, and Xiaodan Zhu · 2006
Earlier work this paper cites.
Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. coursera: Neural networks for machine learning, 4, 26-31., 2012
Geoffrey Hinton · 2012
Earlier work this paper cites.
Microsoft coco: Common objects in context, 2015
Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, and Piotr Dollár · 2015
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.
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
Earlier work this paper cites.
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
William Fedus, Barret Zoph, and Noam Shazeer · 2021
Earlier work this paper cites.
eDiff-I: Text-to-Image Diffusion Models with an 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
Earlier work this paper cites.
Glam: Efficient scaling of language models with mixture-of-experts
Nan Du, Yanping Huang, Andrew Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten Bosma, Zongwei Zhou, Tao Wang, Yu Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathleen Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc V Le, Yonghui Wu, Zhifeng Chen, and Claire Cui · 2022
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ERNIE-ViLG 2.0: Improving Text-to-Image Diffusion Model with Knowledge-Enhanced Mixture-of-Denoising-Experts
Zhida Feng, Zhenyu Zhang, Xintong Yu, Yewei Fang, Lanxin Li, Xuyi Chen, Yuxiang Lu, Jiaxiang Liu, Weichong Yin, Shikun Feng, Yu Sun, Li Chen, Hao Tian, Hua Wu, and Haifeng Wang · 2022
Earlier work this paper cites.
Make-a-scene: Scene-based text-to-image generation with human priors, 2022
Oran Gafni, Adam Polyak, Oron Ashual, Shelly Sheynin, Devi Parikh, and Yaniv Taigman · 2022
Earlier work this paper cites.
Clipscore: A reference-free evaluation metric for image captioning, 2022
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi · 2022
Earlier work this paper cites.
Flow Matching for Generative Modeling
Yaron Lipman, Ricky T Q Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le · 2022
Cited alongside, same era.
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
Xingchao Liu, Chengyue Gong, and Qiang Liu · 2022
Cited alongside, same era.
Hierarchical text-conditional image generation with clip latents, 2022
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models, 2022
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Cited alongside, same era.
Photorealistic text-to-image diffusion models with deep language understanding, 2022
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, Tim Salimans, Jonathan Ho, David J Fleet, and Mohammad Norouzi · 2022
Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach · 2023
Later among the works it cites.
From Sparse to Soft Mixtures of Experts
Joan Puigcerver, Carlos Riquelme, Basil Mustafa, and Neil Houlsby · 2023
Later among the works it cites.
Attention is all you need, 2023
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2023
Later among the works it cites.
RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths
Zeyue Xue, Guanglu Song, Qiushan Guo, Boxiao Liu, Zhuofan Zong, Yu Liu, and Ping Luo · 2023
Later among the works it cites.
Fast Training of Diffusion Models with Masked Transformers
Hongkai Zheng, Weili Nie, Arash Vahdat, and Anima Anandkumar · 2023
Later among the works it cites.
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Cited alongside, same era.
Scaling autoregressive models for content-rich text-to-image generation
Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, Ben Hutchinson, Wei Han, Zarana Parekh, Xin Li, Han Zhang, Jason Baldridge, and Yonghui Wu · 2022
Cited alongside, same era.
Mixture-of-experts with expert choice routing, 2022
Yanqi Zhou, Tao Lei, Hanxiao Liu, Nan Du, Yanping Huang, Vincent Zhao, Andrew Dai, Zhifeng Chen, Quoc Le, and James Laudon · 2022
Cited alongside, same era.
ST-MoE: Designing Stable and Transferable Sparse Expert Models
Barret Zoph, Irwan Bello, Sameer Kumar, Nan Du, Yanping Huang, Jeff Dean, Noam Shazeer, and William Fedus · 2022
Cited alongside, same era.
Geneval: An object-focused framework for evaluating text-to-image alignment, 2023
Dhruba Ghosh, Hanna Hajishirzi, and Ludwig Schmidt · 2023
Cited alongside, same era.
Sparsectrl: Adding sparse controls to text-to-video diffusion models, 2023
Yuwei Guo, Ceyuan Yang, Anyi Rao, Maneesh Agrawala, Dahua Lin, and Bo Dai · 2023
Cited alongside, same era.
Veclip: Improving clip training via visual-enriched captions, 2023
Zhengfeng Lai, Haotian Zhang, Bowen Zhang, Wentao Wu, Haoping Bai, Aleksei Timofeev, Xianzhi Du, Zhe Gan, Jiulong Shan, Chen-Nee Chuah, Yinfei Yang, and Meng Cao · 2023
Cited alongside, same era.
Multi-Architecture Multi-Expert Diffusion Models
Yunsung Lee, Jin-Young Kim, Hyojun Go, Myeongho Jeong, Shinhyeok Oh, and Seungtaek Choi · 2023
Cited alongside, same era.
Announcing black forest labs, 2024
Black Forest Labs · 2024
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Jaemin Cho, Yushi Hu, Roopal Garg, Peter Anderson, Ranjay Krishna, Jason Baldridge, Mohit Bansal, Jordi Pont-Tuset, and Su Wang · 2024
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Scaling rectified flow transformers for high-resolution image synthesis, 2024
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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Scaling Diffusion Transformers to 16 Billion Parameters
Zhengcong Fei, Mingyuan Fan, Changqian Yu, Debang Li, and Junshi Huang · 2024
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MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts
Xi Victoria Lin, Akshat Shrivastava, Liang Luo, Srinivasan Iyer, Mike Lewis, Gargi Gosh, Luke Zettlemoyer, and Armen Aghajanyan · 2024
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Mm1: Methods, analysis & insights from multimodal llm pre-training
Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier, Sam Dodge, Bowen Zhang, Philipp Dufter, Dhruti Shah, Xianzhi Du, Futang Peng, Floris Weers, Anton Belyi, Haotian Zhang, Karanjeet Singh, Doug Kang, Ankur Jain, Hongyu Hè, Max Schwarzer, Tom Gunter, Xiang Kong, Aonan Zhang, Jianyu Wang, Chong Wang, Nan Du, Tao Lei, Sam Wiseman, Guoli Yin, Mark Lee, Zirui Wang, Ruoming Pang, Peter Grasch, Alexander Toshev, and Yinfei Yang · 2024
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Switch Diffusion Transformer: Synergizing Denoising Tasks with Sparse Mixture-of-Experts
Byeongjun Park, Hyojun Go, Jin-Young Kim, Sangmin Woo, Seokil Ham, and Changick Kim · 2024
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Chameleon: Mixed-modal early-fusion foundation models, 2024
Chameleon Team · 2024
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Transfusion: Predict the next token and diffuse images with one multi-modal model, 2024
Chunting Zhou, Lili Yu, Arun Babu, Kushal Tirumala, Michihiro Yasunaga, Leonid Shamis, Jacob Kahn, Xuezhe Ma, Luke Zettlemoyer, and Omer Levy · 2024
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