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Generating by denoising

Distinguish corruption during learning from progressive generation.

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Scientific sources

How it works

Diffusion

In DDPM, a forward process progressively adds noise to data. A network learns information for reversing this corruption. Sampling begins with noise and applies denoising steps to generate a new sample.

Equation

xₜ = √ᾱₜ x₀ + √(1 − ᾱₜ) ε, ε ∼ N(0,I)

x₀ is data, ε Gaussian noise and ᾱₜ the product of retention factors through t. This equation describes DDPM forward corruption, not every generative variant.

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A diffusion method and a Transformer architecture can be combined.

Takeaway

The network, objective and sampling schedule are separate choices. Data and compute also affect quality. Denoising does not necessarily recover a memorised original image.

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Scientific sources

  • Ho, Jain & Abbeel · 2020Denoising Diffusion Probabilistic Models
    Original publication

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