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The use of guidance in diffusion models was originally motivated by the premise that the guidance-modified score is that of the data distribution tilted by a conditional likelihood raised to some power.
A family of embedded runge-kutta formulae
J. Dormand and P. Prince · 1980
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Deep unsupervised learning using nonequilibrium thermodynamics
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli · 2015
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Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
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JAX: composable transformations of Python+NumPy programs, 2018
J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. VanderPlas, S. Wanderman-Milne, and Q. Zhang · 2018
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Generative modeling by estimating gradients of the data distribution
Y. Song and S. Ermon · 2019
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Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
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Diffusion models beat GANs on image synthesis
P. Dhariwal and A. Nichol · 2021
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Diffusion models beat gans on image synthesis
P. Dhariwal and A. Nichol · 2021
Earlier work this paper cites.
Maximum likelihood training of score-based diffusion models
Y. Song, C. Durkan, I. Murray, and S. Ermon · 2021
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Score-based generative modeling through stochastic differential equations
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole · 2021
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Score-based generative modeling in latent space
A. Vahdat, K. Kreis, and J. Kautz · 2021
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Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
S. Chen, S. Chewi, J. Li, Y. Li, A. Salim, and A. R. Zhang · 2022
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Classifier-free diffusion guidance
J. Ho and T. Salimans · 2022
Cited alongside, same era.
Hierarchical text-conditional image generation with CLIP latents
A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen · 2022
Cited alongside, same era.
Denoising diffusion implicit models, 2022
J. Song, C. Meng, and S. Ermon · 2022
Cited alongside, same era.
Linear convergence bounds for diffusion models via stochastic localization
J. Benton, V. De Bortoli, A. Doucet, and G. Deligiannidis · 2023
Cited alongside, same era.
Improved analysis of score-based generative modeling: User-friendly bounds under minimal smoothness assumptions
H. Chen, H. Lee, and J. Lu · 2023
Cited alongside, same era.
The probability flow ode is provably fast
S. Chen, S. Chewi, H. Lee, Y. Li, J. Lu, and A. Salim · 2024
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Learning general gaussian mixtures with efficient score matching
S. Chen, V. Kontonis, and K. Shah · 2024
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Unveil conditional diffusion models with classifier-free guidance: A sharp statistical theory
H. Fu, Z. Yang, M. Wang, and M. Chen · 2024
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Learning mixtures of gaussians using diffusion models
K. Gatmiry, J. Kelner, and H. Lee · 2024
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Diffusion posterior sampling is computationally intractable
S. Gupta, A. Jalal, A. Parulekar, E. Price, and Z. Xun · 2024
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M. Chen, K. Huang, T. Zhao, and M. Wang · 2023
Cited alongside, same era.
Score diffusion models without early stopping: finite fisher information is all you need
G. Conforti, A. Durmus, and M. G. Silveri · 2023
Cited alongside, same era.
Analysis of learning a flow-based generative model from limited sample complexity
H. Cui, F. Krzakala, E. Vanden-Eijnden, and L. Zdeborová · 2023
Cited alongside, same era.
Reduce, reuse, recycle: Compositional generation with energy-based diffusion models and mcmc
Y. Du, C. Durkan, R. Strudel, J. B. Tenenbaum, S. Dieleman, R. Fergus, J. Sohl-Dickstein, A. Doucet, and W. S. Grathwohl · 2023
Cited alongside, same era.
Convergence of score-based generative modeling for general data distributions
H. Lee, J. Lu, and Y. Tan · 2023
Cited alongside, same era.
Towards faster non-asymptotic convergence for diffusion-based generative models
G. Li, Y. Wei, Y. Chen, and Y. Chi · 2023
Cited alongside, same era.
Learning mixtures of gaussians using the ddpm objective
K. Shah, S. Chen, and A. Klivans · 2023
Cited alongside, same era.
Guiding a diffusion model with a bad version of itself
T. Karras, M. Aittala, T. Kynkäänniemi, J. Lehtinen, T. Aila, and S. Laine · 2024
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Accelerating convergence of score-based diffusion models, provably
G. Li, Y. Huang, T. Efimov, Y. Wei, Y. Chi, and Y. Chen · 2024
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A sharp convergence theory for the probability flow odes of diffusion models
G. Li, Y. Wei, Y. Chi, and Y. Chen · 2024
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Critical windows: non-asymptotic theory for feature emergence in diffusion models
M. Li and S. Chen · 2024
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Sora: Creating video from text, 2024
OpenAI · 2024
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TeaPearce/Conditional_Diffusion_MNIST
T. Pearce, H. H. Tan, M. Zeraatkar, and X. Zhao · 2024
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Theoretical insights for diffusion guidance: A case study for gaussian mixture models
Y. Wu, M. Chen, Z. Li, M. Wang, and Y. Wei · 2024
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