Fetching the paper…
Reading the bibliography…
Recent studies have demonstrated the effectiveness of directly aligning diffusion models with human preferences using differentiable reward.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
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 · 2011
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
Earlier work this paper cites.
Flow matching for generative modeling
Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le · 2022
Earlier work this paper cites.
Flow straight and fast: Learning to generate and transfer data with rectified flow
Xingchao Liu, Chengyue Gong, and Qiang Liu · 2022
Earlier work this paper cites.
The effects of reward misspecification: Mapping and mitigating misaligned models
Alexander Pan, Kush Bhatia, and Jacob Steinhardt · 2022
Earlier work this paper cites.
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
Earlier work this paper cites.
Stochastic interpolants: A unifying framework for flows and diffusions
Michael S Albergo, Nicholas M Boffi, and Eric Vanden-Eijnden · 2023
Earlier work this paper cites.
Training diffusion models with reinforcement learning
Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine · 2023
Earlier work this paper cites.
Directly fine-tuning diffusion models on differentiable rewards
Kevin Clark, Paul Vicol, Kevin Swersky, and David J Fleet · 2023
Earlier work this paper cites.
Optimizing ddpm sampling with shortcut fine-tuning
Ying Fan and Kangwook Lee · 2023
Cited alongside, same era.
Dpok: 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 · 2023
Cited alongside, same era.
Geneval: An object-focused framework for evaluating text-to-image alignment
Dhruba Ghosh, Hannaneh Hajishirzi, and Ludwig Schmidt · 2023
Cited alongside, same era.
Pick-a-pic: An open dataset of user preferences for text-to-image generation
Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, and Omer Levy · 2023
Cited alongside, same era.
Aligning text-to-image models using human feedback
Kimin Lee, Hao Liu, Moonkyung Ryu, Olivia Watkins, Yuqing Du, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh, and Shixiang Shane Gu · 2023
Video diffusion alignment via reward gradients
Mihir Prabhudesai, Russell Mendonca, Zheyang Qin, Katerina Fragkiadaki, and Deepak Pathak · 2024
Later among the works it cites.
Learning multi-dimensional human preference for text-to-image generation
Sixian Zhang, Bohan Wang, Junqiang Wu, Yan Li, Tingting Gao, Di Zhang, and Zhongyuan Wang · 2024
Later among the works it cites.
https://github.com/discus0434/aesthetic-predictor-v2-5 , 2025
Aesthetic predictor v2.5 · 2025
Closest in time.
Enhancing reward models for high-quality image generation: Beyond text-image alignment
Ying Ba, Tianyu Zhang, Yalong Bai, Wenyi Mo, Tao Liang, Bing Su, and Ji-Rong Wen · 2025
Closest in time.
Flux.1 krea [dev]
Sangwu Lee, Titus Ebbecke, Erwann Millon, Will Beddow, Le Zhuo, Iker García-Ferrero, Liam Esparraguera, Mihai Petrescu, Gian Saß, Gabriel Menezes, and Victor Perez · 2025
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Aligning text-to-image diffusion models with reward backpropagation
Mihir Prabhudesai, Anirudh Goyal, Deepak Pathak, and Katerina Fragkiadaki · 2023
Cited alongside, same era.
Laion-aesthetics
Christoph Schuhmann · 2023
Cited alongside, same era.
Human preference score: Better aligning text-to-image models with human preference
Xiaoshi Wu, Keqiang Sun, Feng Zhu, Rui Zhao, and Hongsheng Li · 2023
Cited alongside, same era.
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 · 2023
Cited alongside, same era.
Carles Domingo-Enrich, Michal Drozdzal, Brian Karrer, and Ricky TQ Chen · 2024
Cited alongside, same era.
Zhimin Li, Jianwei Zhang, Qin Lin, Jiangfeng Xiong, Yanxin Long, Xinchi Deng, Yingfang Zhang, Xingchao Liu, Minbin Huang, Zedong Xiao, et al · 2024
Cited alongside, same era.
Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers
Nanye Ma, Mark Goldstein, Michael S Albergo, Nicholas M Boffi, Eric Vanden-Eijnden, and Saining Xie · 2024
Cited alongside, same era.
Aesthetic post-training diffusion models from generic preferences with step-by-step preference optimization
Zhanhao Liang, Yuhui Yuan, Shuyang Gu, Bohan Chen, Tiankai Hang, Mingxi Cheng, Ji Li, and Liang Zheng · 2025
Closest in time.
Flow-grpo: Training flow matching models via online rl
Jie Liu, Gongye Liu, Jiajun Liang, Yangguang Li, Jiaheng Liu, Xintao Wang, Pengfei Wan, Di Zhang, and Wanli Ouyang · 2025
Closest in time.
Hpsv3: Towards wide-spectrum human preference score
Yuhang Ma, Xiaoshi Wu, Keqiang Sun, and Hongsheng Li · 2025
Closest in time.
Pref-grpo: Pairwise preference reward-based grpo for stable text-to-image reinforcement learning
Yibin Wang, Zhimin Li, Yuhang Zang, Yujie Zhou, Jiazi Bu, Chunyu Wang, Qinglin Lu, Cheng Jin, and Jiaqi Wang · 2025
Closest in time.
Dancegrpo: Unleashing grpo on visual generation
Zeyue Xue, Jie Wu, Yu Gao, Fangyuan Kong, Lingting Zhu, Mengzhao Chen, Zhiheng Liu, Wei Liu, Qiushan Guo, Weilin Huang, et al · 2025
Closest in time.
Teaching large language models to regress accurate image quality scores using score distribution
Zhiyuan You, Xin Cai, Jinjin Gu, Tianfan Xue, and Chao Dong · 2025
Closest in time.
Diffusion model as a noise-aware latent reward model for step-level preference optimization
Tao Zhang, Cheng Da, Kun Ding, Huan Yang, Kun Jin, Yan Li, Tingting Gao, Di Zhang, Shiming Xiang, and Chunhong Pan · 2025
Closest in time.