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Diffusion models are a powerful class of generative models that iteratively denoise samples to produce data.
Generative modeling by estimating gradients of the data distribution, 2019
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Denoising diffusion probabilistic models, 2020
J. Ho, A. Jain, and P. Abbeel · 2020
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Score-based generative modeling through stochastic differential equations, 2020
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole · 2020
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Subspace diffusion generative models
B. Jing, G. Corso, R. Berlinghieri, and T. Jaakkola · 2022
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Diffusion models beat GANs on image synthesis
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An image is worth 16x16 words: Transformers for image recognition at scale
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Classifier-free diffusion guidance
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Cascaded diffusion models for high fidelity image generation
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Generating images with sparse representations, 2021
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