Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matching
Zijing Ou Mingtian Zhang Andi Zhang Tim Z. Xiao Yingzhen Li David Barber
The probabilistic diffusion model has become highly effective across various domains. Typically, sampling from a diffusion model involves using a denoising distribution characterized by a Gaussian with a learned mean and either fixed or learned covariances. In this paper, we leverage the recently proposed covariance moment matching technique and introduce a novel method for learning the diagonal covariances. Unlike traditional data-driven covariance approximation approaches, our method involves directly regressing the optimal analytic covariance using a new, unbiased objective named Optimal Covariance Matching (OCM). This approach can significantly reduce the approximation error in covariance prediction. We demonstrate how our method can substantially enhance the sampling efficiency, recall rate and likelihood of both diffusion models and latent diffusion models.
Diffusion Model, Generative Model, Probalistic Modelling
We introduce Optimal Covariance Matching (OCM), a novel method that improves sampling efficiency and accuracy in diffusion models by directly regressing optimal analytic covariances.