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About this paper
Eric Heitz, Laurent Belcour, Thomas Chambon
Iterative Alpha-(de)Blending (IADB) presents a deterministic diffusion model grounded in linear interpolation, simplifying the theoretical foundation of generative models. This approach demonstrates improved performance over DDIM across various image datasets, achieving up to 30% better FID scores, and can generalize to map between arbitrary finite-variance distributions.
Key insights
- Deterministic diffusion models can be fundamentally derived from the intuitive concept of linear interpolation (alpha-blending) between samples, simplifying complex stochastic calculus.
- Training a neural network to predict the difference vector between posterior samples (x R ₁ - x R ₀) leads to superior numerical stability and improved performance compared to learning individual posteriors.
- The Iterative Alpha-(de)Blending (IADB) method is theoretically equivalent to Denoising Diffusion Implicit Models (DDIM) under Gaussian assumptions, but offers a more general and accessible derivation.
Summary by alphaXiv.
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