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Recent advances in text-to-image diffusion models have achieved impressive image generation capabilities.
Brain mechanisms in conditioning and learning
Robert B Livingston · 1966
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Microsoft coco captions: Data collection and evaluation server
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Deep unsupervised learning using nonequilibrium thermodynamics
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The impact of studying brain plasticity
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Wenhui Wang, Hangbo Bao, Shaohan Huang, Li Dong, and Furu Wei · 2020
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Flow network based generative models for non-iterative diverse candidate generation
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Implicit under-parameterization inhibits data-efficient deep reinforcement learning
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Learning transferable visual models from natural language supervision
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Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
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Pseudo numerical methods for diffusion models on manifolds
Luping Liu, Yi Ren, Zhijie Lin, and Zhou Zhao · 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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Understanding and preventing capacity loss in reinforcement learning
Clare Lyle, Mark Rowland, and Will Dabney · 2022
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Nikolay Malkin, Moksh Jain, Emmanuel Bengio, Chen Sun, and Yoshua Bengio · 2022
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Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine · 2024
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Directly fine-tuning diffusion models on differentiable rewards
Kevin Clark, Paul Vicol, Kevin Swersky, and David J Fleet · 2024
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Discrete probabilistic inference as control in multi-path environments
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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
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Reinforcement learning for fine-tuning text-to-image diffusion models
Ying Fan, Olivia Watkins, Yuqing Du, Hao Liu, Moonkyung Ryu, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh, Kangwook Lee, and Kimin Lee · 2024
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Photorealistic text-to-image diffusion models with deep language understanding
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Generative flow networks for discrete probabilistic modeling
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Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J Hu, Mo Tiwari, and Emmanuel Bengio · 2023
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Sample-efficient reinforcement learning by breaking the replay ratio barrier
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A theory of continuous generative flow networks
Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-García, Léna Néhale Ezzine, Yoshua Bengio, and Nikolay Malkin · 2023
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Progressive knowledge distillation of stable diffusion xl using layer level loss, 2024
Yatharth Gupta, Vishnu V. Jaddipal, Harish Prabhala, Sayak Paul, and Patrick Von Platen · 2024
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Optimizing prompts for text-to-image generation
Yaru Hao, Zewen Chi, Li Dong, and Furu Wei · 2024
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Amortizing intractable inference in large language models
Edward J Hu, Moksh Jain, Eric Elmoznino, Younesse Kaddar, Guillaume Lajoie, Yoshua Bengio, and Nikolay Malkin · 2024
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Qgfn: Controllable greediness with action values
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Pre-training and fine-tuning generative flow networks
Ling Pan, Moksh Jain, Kanika Madan, and Yoshua Bengio · 2024
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Generative flow networks as entropy-regularized rl
Daniil Tiapkin, Nikita Morozov, Alexey Naumov, and Dmitry P Vetrov · 2024
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Amortizing intractable inference in diffusion models for vision, language, and control
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On discrete prompt optimization for diffusion models
Ruochen Wang, Ting Liu, Cho-Jui Hsieh, and Boqing Gong · 2024
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Imagereward: Learning and evaluating human preferences for text-to-image generation
Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong · 2024
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Probabilistic inference in language models via twisted sequential monte carlo
Stephen Zhao, Rob Brekelmans, Alireza Makhzani, and Roger Grosse · 2024
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Adversarial diffusion distillation
Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach · 2025
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