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We propose an inference-time scaling approach for pretrained flow models.
Reverse-time diffusion equation models
Brian D.O. Anderson · 1982
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An Empirical Bayes Approach to Statistics
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Sequential Monte Carlo methods in practice
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Deep unsupervised learning using nonequilibrium thermodynamics
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Reinforcement learning and control as probabilistic inference: Tutorial and review
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
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Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
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Tomasz Korbak, Hady Elsahar, Germán Kruszewski, and Marc Dymetmant · 2022
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Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Tim Salimans and Jonathan Ho · 2022
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Laion aesthetics
C. Schuhmann · 2022
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Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2023
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Diffusion posterior sampling for general noisy inverse problems
Hyungjin Chung, Jeongsol Kim, Michael Thompson Mccann, Marc Louis Klasky, and Jong Chul Ye · 2023
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Instructblip: Towards general-purpose vision-language models with instruction tuning
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Segment anything
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick · 2023
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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
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Flow matching for generative modeling
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le · 2023
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Flow straight and fast: Learning to generate and transfer data with rectified flow
Xingchao Liu, Chengyue Gong, and Qiang Liu · 2023
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Aligning text-to-image diffusion models with reward backpropagation
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Direct preference optimization: Your language model is secretly a reward model
Genai arena: An open evaluation platform for generative models
Dongfu Jiang, Max Ku, Tianle Li, Yuansheng Ni, Shizhuo Sun, Rongqi Fan, and Wenhu Chen · 2024
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Derivative-free guidance in continuous and discrete diffusion models with soft value-based decoding
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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Yaron Lipman, Marton Havasi, Peter Holderrieth, Neta Shaul, Matt Le, Brian Karrer, Ricky TQ Chen, David Lopez-Paz, Heli Ben-Hamu, and Itai Gat · 2024
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Grounding dino: Marrying dino with grounded pre-training for open-set object detection
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End-to-end diffusion latent optimization improves classifier guidance
Bram Wallace, Akash Gokul, Stefano Ermon, and Nikhil Naik · 2023
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Practical and asymptotically exact conditional sampling in diffusion models
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Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers
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Fine-tuning of continuous-time diffusion models as entropy-regularized control
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Using human feedback to fine-tune diffusion models without any reward model
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Open-sora: Democratizing efficient video production for all, March 2024
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