Score-Based-Generative-modeling-Gaussian-Data-Distribution

This is an implementation for Section 3.2.1 of the paper "On diffusion-based generative models and their error bounds: The log-concave case with full convergence estimates" accepted on Transactions on Machine Learning Research (TMLR). Using score-based generative models, we are able to generate new data from an approximate distribution that is close to a multivariate Gaussian distribution with unknown mean and identity covariance.