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Text-to-image diffusion models are a class of deep generative models that have demonstrated an impressive capacity for high-quality image generation.
Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David McAllester, Satinder Singh, and Yishay Mansour · 1999
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 2004
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Deep unsupervised learning using nonequilibrium thermodynamics, 2015
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Decoupled weight decay regularization, 2019
Ilya Loshchilov and Frank Hutter · 2019
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Fair generative modeling via weak supervision, 2020
Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, and Stefano Ermon · 2020
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Denoising diffusion probabilistic models, 2020
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Monte carlo gradient estimation in machine learning, 2020
Shakir Mohamed, Mihaela Rosca, Michael Figurnov, and Andriy Mnih · 2020
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A general language assistant as a laboratory for alignment, 2021
Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy Jones, Nicholas Joseph, Ben Mann, Nova DasSarma, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Jackson Kernion, Kamal Ndousse, Catherine Olsson, Dario Amodei, Tom Brown, Jack Clark, Sam McCandlish, Chris Olah, and Jared Kaplan · 2021
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Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts, 2021
Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut · 2021
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Learning transferable visual models from natural language supervision, 2021
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
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Measuring fairness in generative models, 2021
Christopher T. H Teo and Ngai-Man Cheung · 2021
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Training a helpful and harmless assistant with reinforcement learning from human feedback, 2022
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Nicholas Joseph, Saurav Kadavath, Jackson Kernion, Tom Conerly, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, Dario Amodei, Tom Brown, Jack Clark, Sam McCandlish, Chris Olah, Ben Mann, and Jared Kaplan · 2022
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Classifier-free diffusion guidance, 2022
Jonathan Ho and Tim Salimans · 2022
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Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation, 2022
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi · 2022
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Compositional visual generation with composable diffusion models
Nan Liu, Shuang Li, Yilun Du, Antonio Torralba, and Joshua B Tenenbaum · 2022
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Webgpt: Browser-assisted question-answering with human feedback, 2022
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, and John Schulman · 2022
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Training language models to follow instructions with human feedback, 2022
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe · 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
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Laion-5b: An open large-scale dataset for training next generation image-text models, 2022
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev · 2022
Cited alongside, same era.
Emu: Enhancing image generation models using photogenic needles in a haystack, 2023
Xiaoliang Dai, Ji Hou, Chih-Yao Ma, Sam Tsai, Jialiang Wang, Rui Wang, Peizhao Zhang, Simon Vandenhende, Xiaofang Wang, Abhimanyu Dubey, Matthew Yu, Abhishek Kadian, Filip Radenovic, Dhruv Mahajan, Kunpeng Li, Yue Zhao, Vladan Petrovic, Mitesh Kumar Singh, Simran Motwani, Yi Wen, Yiwen Song, Roshan Sumbaly, Vignesh Ramanathan, Zijian He, Peter Vajda, and Devi Parikh · 2023
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Raft: Reward ranked finetuning for generative foundation model alignment, 2023
Hanze Dong, Wei Xiong, Deepanshu Goyal, Yihan Zhang, Winnie Chow, Rui Pan, Shizhe Diao, Jipeng Zhang, Kashun Shum, and Tong Zhang · 2023
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Reduce, reuse, recycle: Compositional generation with energy-based diffusion models and mcmc
Yilun Du, Conor Durkan, Robin Strudel, Joshua B Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, and Will Sussman Grathwohl · 2023
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Mitigating stereotypical biases in text to image generative systems, 2023
Piero Esposito, Parmida Atighehchian, Anastasis Germanidis, and Deepti Ghadiyaram · 2023
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Dpok: Reinforcement learning for fine-tuning text-to-image diffusion models, 2023
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Jiaming Song, Chenlin Meng, and Stefano Ermon · 2022
Cited alongside, same era.
DiffusionDB: A large-scale prompt gallery dataset for text-to-image generative models
Zijie J. Wang, Evan Montoya, David Munechika, Haoyang Yang, Benjamin Hoover, and Duen Horng Chau · 2022
Cited alongside, same era.
Scaling autoregressive models for content-rich text-to-image generation, 2022
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.
Simple multi-dataset detection, 2022
Xingyi Zhou, Vladlen Koltun, and Philipp Krähenbühl · 2022
Cited alongside, same era.
Hrs-bench: Holistic, reliable and scalable benchmark for text-to-image models, 2023
Eslam Mohamed Bakr, Pengzhan Sun, Xiaoqian Shen, Faizan Farooq Khan, Li Erran Li, and Mohamed Elhoseiny · 2023
Cited alongside, same era.
Easily accessible text-to-image generation amplifies demographic stereotypes at large scale
Federico Bianchi, Pratyusha Kalluri, Esin Durmus, Faisal Ladhak, Myra Cheng, Debora Nozza, Tatsunori Hashimoto, Dan Jurafsky, James Zou, and Aylin Caliskan · 2023
Cited alongside, same era.
Training diffusion models with reinforcement learning, 2023
Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine · 2023
Cited alongside, same era.
Attend-and-excite: Attention-based semantic guidance for text-to-image diffusion models, 2023
Hila Chefer, Yuval Alaluf, Yael Vinker, Lior Wolf, and Daniel Cohen-Or · 2023
Cited alongside, same era.
Ying Fan, Olivia Watkins, Yuqing Du, Hao Liu, Moonkyung Ryu, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh, Kangwook Lee, and Kimin Lee · 2023
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Fair diffusion: Instructing text-to-image generation models on fairness, 2023
Felix Friedrich, Manuel Brack, Lukas Struppek, Dominik Hintersdorf, Patrick Schramowski, Sasha Luccioni, and Kristian Kersting · 2023
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Benchmarking spatial relationships in text-to-image generation, 2023
Tejas Gokhale, Hamid Palangi, Besmira Nushi, Vibhav Vineet, Eric Horvitz, Ece Kamar, Chitta Baral, and Yezhou Yang · 2023
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T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation, 2023
Kaiyi Huang, Kaiyue Sun, Enze Xie, Zhenguo Li, and Xihui Liu · 2023
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Aligning text-to-image models using human feedback, 2023
Kimin Lee, Hao Liu, Moonkyung Ryu, Olivia Watkins, Yuqing Du, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh, and Shixiang Shane Gu · 2023
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Gligen: Open-set grounded text-to-image generation, 2023
Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jianwei Yang, Jianfeng Gao, Chunyuan Li, and Yong Jae Lee · 2023
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Llm-grounded diffusion: Enhancing prompt understanding of text-to-image diffusion models with large language models, 2023
Long Lian, Boyi Li, Adam Yala, and Trevor Darrell · 2023
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Stable bias: Analyzing societal representations in diffusion models, 2023
Alexandra Sasha Luccioni, Christopher Akiki, Margaret Mitchell, and Yacine Jernite · 2023
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Social biases through the text-to-image generation lens, 2023
Ranjita Naik and Besmira Nushi · 2023
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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 · 2023
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Balancing the picture: Debiasing vision-language datasets with synthetic contrast sets, 2023
Brandon Smith, Miguel Farinha, Siobhan Mackenzie Hall, Hannah Rose Kirk, Aleksandar Shtedritski, and Max Bain · 2023
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Imagereward: Learning and evaluating human preferences for text-to-image generation, 2023
Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong · 2023
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