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Aligning diffusion models to downstream tasks often requires finetuning new models or gradient-based guidance at inference time to enable sampling from the reward-tilted posterior.
Denoising Diffusion Implicit Models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
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Tweedie’s formula and selection bias
Bradley Efron · 2011
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Bayesian learning via stochastic gradient Langevin dynamics
Max Welling and Yee W Teh · 2011
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Image style transfer using convolutional neural networks
Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 2016
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Joint face detection and alignment using multitask cascaded convolutional networks
Kaipeng Zhang, Zhanpeng Zhang, Zhifeng Li, and Yu Qiao · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium, 2017
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Generative Modeling by Estimating Gradients of the Data Distribution
Yang Song and Stefano Ermon · 2019
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Denoising Diffusion Probabilistic Models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Improving style transfer with calibrated metrics
Mao-Chuang Yeh, Shuai Tang, Anand Bhattad, Chuhang Zou, and David Forsyth · 2020
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Diffusion Models Beat GANs on Image Synthesis
Prafulla Dhariwal and Alex Nichol · 2021
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Clipscore: A reference-free evaluation metric for image captioning
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi · 2021
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Classifier-Free Diffusion Guidance
Jonathan Ho, Google Research, and Tim Salimans · 2021
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SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations
Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun Yan Zhu, and Stefano Ermon · 2021
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Improved Denoising Diffusion Probabilistic Models, 7 2021
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Laion-400m: Open dataset of clip-filtered 400 million image-text pairs, 2021
Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki · 2021
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FUDGE: Controlled Text Generation With Future Discriminators
Kevin Yang and Dan Klein · 2021
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Improving diffusion models for inverse problems using manifold constraints
Hyungjin Chung, Byeongsu Sim, Dohoon Ryu, and Jong Chul Ye · 2022
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Scaling Laws for Reward Model Overoptimization
Leo Gao, John Schulman, and Jacob Hilton · 2022
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RL with KL penalties is better viewed as Bayesian inference
Tomasz Korbak, Ethan Perez, and Christopher L. Buckley · 2022
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High-Resolution Image Synthesis with Latent Diffusion Models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjorn Ommer · 2022
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Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al · 2022
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Diffusion-based molecule generation with informative prior bridges
Lemeng Wu, Chengyue Gong, Xingchao Liu, Mao Ye, and Qiang Liu · 2022
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Training Diffusion Models with Reinforcement Learning
Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine · 2023
Cited alongside, same era.
Monte Carlo guided Diffusion for Bayesian linear inverse problems
Theoretical guarantees on the best-of-n alignment policy
Ahmad Beirami, Alekh Agarwal, Jonathan Berant, Jacob Eisenstein, Chirag Nagpal, Ananda Theertha Suresh, Google Research, and Google DeepMind · 2024
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Large language monkeys: Scaling inference compute with repeated sampling
Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V Le, Christopher Ré, and Azalia Mirhoseini · 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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Diffusion-rpo: Aligning diffusion models through relative preference optimization, 2024
Yi Gu, Zhendong Wang, Yueqin Yin, Yujia Xie, and Mingyuan Zhou · 2024
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BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling
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Gabriel Cardoso, Yazid Janati, E L Idrissi, Sylvain Le Corff, and Eric Moulines · 2023
Cited alongside, same era.
Adaptively-realistic image generation from stroke and sketch with diffusion model
Shin-I Cheng, Yu-Jie Chen, Wei-Chen Chiu, Hung-Yu Tseng, and Hsin-Ying Lee · 2023
Cited alongside, same era.
Diffusion Posterior Sampling for General Noisy Inverse Problems
Hyungjin Chung, Jeongsol Kim, Michael T. Mccann, Marc L. Klasky, and Jong Chul Ye · 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.
Leveraging early-stage robustness in diffusion models for efficient and high-quality image synthesis
Yulhwa Kim, Dongwon Jo, Hyesung Jeon, Taesu Kim, Daehyun Ahn, Hyungjun Kim, and jae-joon kim · 2023
Cited alongside, same era.
Genie: Generative hard negative images through diffusion, 2023
Soroush Abbasi Koohpayegani, Anuj Singh, K L Navaneet, Hadi Jamali-Rad, and Hamed Pirsiavash · 2023
Cited alongside, same era.
Gligen: Open-set grounded text-to-image generation
Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jianwei Yang, Jianfeng Gao, Chunyuan Li, and Yong Jae Lee · 2023
Cited alongside, same era.
FreeControl: Training-Free Spatial Control of Any Text-to-Image Diffusion Model with Any Condition
Sicheng Mo, Fangzhou Mu, Kuan Heng Lin, Yanli Liu, Bochen Guan, Yin Li, and Bolei Zhou · 2023
Cited alongside, same era.
Lin Gui, Cristina Gârbacea, and Victor Veitch · 2024
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Gradient Guidance for Diffusion Models: An Optimization Perspective
Yingqing Guo, Hui Yuan, Yukang Yang, Minshuo Chen, and Mengdi Wang · 2024
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Manifold preserving guided diffusion
Yutong He, Naoki Murata, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Dongjun Kim, Wei-Hsiang Liao, Yuki Mitsufuji, J Zico Kolter, Ruslan Salakhutdinov, and Stefano Ermon · 2024
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Rethinking fid: Towards a better evaluation metric for image generation
Sadeep Jayasumana, Srikumar Ramalingam, Andreas Veit, Daniel Glasner, Ayan Chakrabarti, and Sanjiv Kumar · 2024
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Elucidating optimal reward-diversity tradeoffs in text-to-image diffusion models, 2024
Rohit Jena, Ali Taghibakhshi, Sahil Jain, Gerald Shen, Nima Tajbakhsh, and Arash Vahdat · 2024
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Direct consistency optimization for compositional text-to-image personalization, 2024
Kyungmin Lee, Sangkyung Kwak, Kihyuk Sohn, and Jinwoo Shin · 2024
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Xiner Li, Yulai Zhao, Chenyu Wang, Gabriele Scalia, Gokcen Eraslan, Surag Nair, Tommaso Biancalani, Aviv Regev, Sergey Levine, and Masatoshi Uehara · 2024
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T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models
Chong Mou, Xintao Wang, Liangbin Xie, Yanze Wu, Jian Zhang, Zhongang Qi, and Ying Shan · 2024
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Information theoretic guarantees for policy alignment in large language models
Youssef Mroueh · 2024
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Controlled Decoding from Language Models
Sidharth Mudgal, Jong Lee, Harish Ganapathy, YaGuang Li, Tao Wang, Yanping Huang, Zhifeng Chen, Heng-Tze Cheng, Michael Collins, Trevor Strohman, Jilin Chen, Alex Beutel, and Ahmad Beirami · 2024
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Particle Denoising Diffusion Sampler
Angus Phillips, Hai Dang Dau, Michael John Hutchinson, Valentin De Bortoli, George Deligiannidis, and Arnaud Doucet · 2024
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CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling
Seyedmorteza Sadat, Jakob Buhmann, Derek Bradley, Otmar Hilliges, and Romann M. Weber · 2024
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Scaling llm test-time compute optimally can be more effective than scaling model parameters
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar · 2024
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Measuring Style Similarity in Diffusion Models
Gowthami Somepalli, Anubhav Gupta, Kamal Gupta, Shramay Palta, Micah Goldblum, Jonas Geiping, Abhinav Shrivastava, and Tom Goldstein · 2024
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Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models
Yuchen Wu, Minshuo Chen, Zihao Li, Mengdi Wang, and Yuting Wei · 2024
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Asymptotics of language model alignment
Joy Qiping Yang, Salman Salamatian, Ziteng Sun, Ananda Theertha Suresh, and Ahmad Beirami · 2024
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TFG: Unified Training-Free Guidance for Diffusion Models
Haotian Ye, Haowei Lin, Jiaqi Han, Minkai Xu, Sheng Liu, Yitao Liang, Jianzhu Ma, James Zou, and Stefano Ermon · 2024
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A phase transition in diffusion models reveals the hierarchical nature of data
Antonio Sclocchi, Alessandro Favero, and Matthieu Wyart · 2025
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