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Diffusion models are powerful generative models that allow for precise control over the characteristics of the generated samples.
Reverse-time diffusion equation models
Anderson, B. D. (1982) · 1982
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Diffusions, Markov processes and martingales: Volume 2, Itô calculus
Rogers, L. C. G. and D. Williams (2000) · 2000
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Stochastic calculus for finance II: Continuous-time models
Shreve, S. E. et al. (2004) · 2004
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Denoising diffusion implicit models
Song, J., C. Meng, and S. Ermon (2020) · 2010
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Score-based generative modeling through stochastic differential equations
Song, Y., J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole (2020) · 2011
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No-reference image quality assessment in the spatial domain
Mittal, A., A. K. Moorthy, and A. C. Bovik (2012) · 2012
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Ava: A large-scale database for aesthetic visual analysis
Murray, N., L. Marchesotti, and F. Perronnin (2012) · 2012
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., E. Weiss, N. Maheswaranathan, and S. Ganguli (2015) · 2015
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Training deep nets with sublinear memory cost
Chen, T., B. Xu, C. Zhang, and C. Guestrin (2016) · 2016
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Memory-efficient backpropagation through time
Gruslys, A., R. Munos, I. Danihelka, M. Lanctot, and A. Graves (2016) · 2016
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On calibration of modern neural networks
Guo, C., G. Pleiss, Y. Sun, and K. Q. Weinberger (2017) · 2017
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Proximal policy optimization algorithms
Schulman, J., F. Wolski, P. Dhariwal, A. Radford, and O. Klimov (2017) · 2017
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Decoupled weight decay regularization
Loshchilov, I. and F. Hutter (2019) · 2019
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High-dimensional statistics: A non-asymptotic viewpoint
Wainwright, M. J. (2019) · 2019
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Denoising diffusion probabilistic models
Ho, J., A. Jain, and P. Abbeel (2020) · 2020
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Diffusion models beat gans on image synthesis
Dhariwal, P. and A. Nichol (2021) · 2021
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Lora: Low-rank adaptation of large language models
Hu, E. J., Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen (2021) · 2021
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Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development
Huang, K., T. Fu, W. Gao, Y. Zhao, Y. H. Roohani, J. Leskovec, C. W. Coley, C. Xiao, J. Sun, and M. Zitnik (2021) · 2021
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Nichol, A., P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. McGrew, I. Sutskever, and M. Chen (2021) · 2021
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Learning transferable visual models from natural language supervision
Radford, A., J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever (2021) · 2021
Cited alongside, same era.
Zero-shot text-to-image generation
Ramesh, A., M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever (2021) · 2021
Cited alongside, same era.
Diffusion posterior sampling for general noisy inverse problems
Chung, H., J. Kim, M. T. Mccann, M. L. Klasky, and J. C. Ye (2022) · 2022
Cited alongside, same era.
Card: Classification and regression diffusion models
Han, X., H. Zheng, and M. Zhou (2022) · 2022
Cited alongside, same era.
Classifier-free diffusion guidance
Ho, J. and T. Salimans (2022) · 2022
Cited alongside, same era.
Better aligning text-to-image models with human preference
Wu, X., K. Sun, F. Zhu, R. Zhao, and H. Li (2023) · 2023
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Difffit: Unlocking transferability of large diffusion models via simple parameter-efficient fine-tuning
Xie, E., L. Yao, H. Shi, Z. Liu, D. Zhou, Z. Liu, J. Li, and Z. Li (2023) · 2023
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Imagereward: Learning and evaluating human preferences for text-to-image generation
Xu, J., X. Liu, Y. Wu, Y. Tong, Q. Li, M. Ding, J. Tang, and Y. Dong (2023) · 2023
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Adding conditional control to text-to-image diffusion models
Zhang, L., A. Rao, and M. Agrawala (2023) · 2023
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Sine: Single image editing with text-to-image diffusion models
Zhang, Z., L. Han, A. Ghosh, D. N. Metaxas, and J. Ren (2023) · 2023
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Video diffusion models
Ho, J., T. Salimans, A. Gritsenko, W. Chan, M. Norouzi, and D. J. Fleet (2022) · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Rombach, R., A. Blattmann, D. Lorenz, P. Esser, and B. Ommer (2022) · 2022
Cited alongside, same era.
Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans, et al. (2022) · 2022
Cited alongside, same era.
Pseudoinverse-guided diffusion models for inverse problems
Song, J., A. Vahdat, M. Mardani, and J. Kautz (2022) · 2022
Cited alongside, same era.
Universal guidance for diffusion models
Bansal, A., H.-M. Chu, A. Schwarzschild, S. Sengupta, M. Goldblum, J. Geiping, and T. Goldstein (2023) · 2023
Cited alongside, same era.
Training diffusion models with reinforcement learning
Black, K., M. Janner, Y. Du, I. Kostrikov, and S. Levine (2023) · 2023
Cited alongside, same era.
Instructpix2pix: Learning to follow image editing instructions
Brooks, T., A. Holynski, and A. A. Efros (2023) · 2023
Cited alongside, same era.
clip-score: CLIP Score for PyTorch
Zhengwentai, S. (2023, March) · 2023
Later among the works it cites.
Aligning optimization trajectories with diffusion models for constrained design generation
Giannone, G., A. Srivastava, O. Winther, and F. Ahmed (2024) · 2024
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Gradient guidance for diffusion models: An optimization perspective
Guo, Y., H. Yuan, Y. Yang, M. Chen, and M. Wang (2024) · 2024
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Manifold preserving guided diffusion
He, Y., N. Murata, C.-H. Lai, Y. Takida, T. Uesaka, D. Kim, W.-H. Liao, Y. Mitsufuji, J. Z. Kolter, R. Salakhutdinov, and S. Ermon (2024) · 2024
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Derivative-free guidance in continuous and discrete diffusion models with soft value-based decoding
Li, X., Y. Zhao, C. Wang, G. Scalia, G. Eraslan, S. Nair, T. Biancalani, S. Ji, A. Regev, S. Levine, et al. (2024) · 2024
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Particle denoising diffusion sampler
Phillips, A., H.-D. Dau, M. J. Hutchinson, V. De Bortoli, G. Deligiannidis, and A. Doucet (2024) · 2024
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Understanding reinforcement learning-based fine-tuning of diffusion models: A tutorial and review
Uehara, M., Y. Zhao, T. Biancalani, and S. Levine (2024) · 2024
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Fine-tuning of continuous-time diffusion models as entropy-regularized control
Uehara, M., Y. Zhao, K. Black, E. Hajiramezanali, G. Scalia, N. L. Diamant, A. M. Tseng, T. Biancalani, and S. Levine (2024) · 2024
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Feedback efficient online fine-tuning of diffusion models
Uehara, M., Y. Zhao, K. Black, E. Hajiramezanali, G. Scalia, N. L. Diamant, A. M. Tseng, S. Levine, and T. Biancalani (2024, 21–27 Jul) · 2024
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Bridging model-based optimization and generative modeling via conservative fine-tuning of diffusion models
Uehara, M., Y. Zhao, E. Hajiramezanali, G. Scalia, G. Eraslan, A. Lal, S. Levine, and T. Biancalani (2024) · 2024
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Practical and asymptotically exact conditional sampling in diffusion models
Wu, L., B. Trippe, C. Naesseth, D. Blei, and J. P. Cunningham (2024) · 2024
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Pathldm: Text conditioned latent diffusion model for histopathology
Yellapragada, S., A. Graikos, P. Prasanna, T. Kurc, J. Saltz, and D. Samaras (2024) · 2024
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Uni-controlnet: All-in-one control to text-to-image diffusion models
Zhao, S., D. Chen, Y.-C. Chen, J. Bao, S. Hao, L. Yuan, and K.-Y. K. Wong (2024) · 2024
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Deft: Efficient fine-tuning of diffusion models by learning the generalised h h -transform
Denker, A., F. Vargas, S. Padhy, K. Didi, S. Mathis, R. Barbano, V. Dutordoir, E. Mathieu, U. J. Komorowska, and P. Lio (2025) · 2025
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