Neural denoising diffusion models

Denoising diffusion probabilistic models (DDPMs), score-based generative models, generative diffusion processes, neural energy models…

November 11, 2021 — December 6, 2023

Figure 1

Placeholder.

AFAICS, generative models using score-matching to learn and Langevin MCMC to sample. There are various tricks needed to to do it with successive denoising steps and interpretation in terms of diffusion SDEs. I am vaguely aware that this oversimplifies a rich and interesting history of convergence of many useful techniques, but have not invested enough time to claim actual expertise.

1 Training: score matching

Denoising score matching Hyvärinen (2005). See score matching or McAllester (2023) for an introduction to the general idea.

2 Sampling: Langevin dynamics

See Langevin samplers.

3 Image generation in particular

See image generation with diffusion.

Figure 2

4 Latent

4.1 Generic

4.2 CLIP

Radford et al. (2021)

5 Diffusion on weird spaces

5.1 Proteins

Baker Lab (Torres et al. 2022; Watson et al. 2022)

6 Incoming

Suggestive connection to thermodynamics (Sohl-Dickstein et al. 2015).

Figure 3

7 References

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Dhariwal, and Nichol. 2021. Diffusion Models Beat GANs on Image Synthesis.” arXiv:2105.05233 [Cs, Stat].
Dockhorn, Vahdat, and Kreis. 2022. GENIE: Higher-Order Denoising Diffusion Solvers.” In.
Dutordoir, Saul, Ghahramani, et al. 2022. Neural Diffusion Processes.”
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