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How do diffusion generative models convert pure noise into meaningful images?We argue that generation involves first committing to an outline, and then tofiner and finer details. The corresponding reverse diffusion process can bemodeled by dynamics on a (time-dependent) high-dimensional landscape full ofGaussian-like modes, which makes the following predictions: (i) individualtrajectories tend to be very low-dimensional; (ii) scene elements that varymore within training data tend to emerge earlier; and (iii) early perturbationssubstantially change image content more often than late perturbations. We showthat the behavior of a variety of trained unconditional and conditionaldiffusion models like Stable Diffusion is consistent with these predictions.Finally, we use our theory to search for the latent image manifold of diffusionmodels, and propose a new way to generate interpretable image variations. Ourviewpoint suggests generation by GANs and diffusion models have unexpectedsimilarities.

Diffusion Models Generate Images Like Painters: an Analytical Theory of Outline First, Details Later