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- Google AI Blog: High Fidelity Image Generation Using Diffusion Models
- What are Diffusion Models?
- Yang Song, Generative Modeling by Estimating Gradients of the Data Distribution
- Diffusion models are autoencoders – Sander Dieleman
Suggestive connection to thermodynamics (Sohl-Dickstein et al. 2015).
References
Dhariwal, Prafulla, and Alex Nichol. 2021. “Diffusion Models Beat GANs on Image Synthesis.” arXiv:2105.05233 [Cs, Stat], June.
Ho, Jonathan, Ajay Jain, and Pieter Abbeel. 2020. “Denoising Diffusion Probabilistic Models.” arXiv:2006.11239 [Cs, Stat], December.
Hoogeboom, Emiel, Alexey A. Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, and Tim Salimans. 2021. “Autoregressive Diffusion Models.” arXiv:2110.02037 [Cs, Stat], October.
Nichol, Alex, and Prafulla Dhariwal. 2021. “Improved Denoising Diffusion Probabilistic Models.” arXiv:2102.09672 [Cs, Stat], February.
Sohl-Dickstein, Jascha, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. “Deep Unsupervised Learning Using Nonequilibrium Thermodynamics.” arXiv:1503.03585 [Cond-Mat, q-Bio, Stat], November.
Song, Jiaming, Chenlin Meng, and Stefano Ermon. 2021. “Denoising Diffusion Implicit Models.” arXiv:2010.02502 [Cs], November.
Song, Yang, and Stefano Ermon. 2020a. “Generative Modeling by Estimating Gradients of the Data Distribution.” arXiv:1907.05600 [Cs, Stat], October.
———. 2020b. “Improved Techniques for Training Score-Based Generative Models.” arXiv:2006.09011 [Cs, Stat], October.
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