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Generating by denoising

Distinguish corruption during learning from progressive generation.

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Scientific sources
Definition

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.

Assumptions and notation

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.

Limits of interpretation

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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Transformer · Recurrent network
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Scientific sources

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

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