This repository provides an implementation of improved denoising diffusion probabilistic models as described in the associated research paper. It includes training scripts for class-conditional image generation on datasets like ImageNet, LSUN, and CIFAR-10. The codebase implements model architectures, diffusion process scheduling (linear and cosine noise schedules), learned sigma estimation, and sampling procedures for generating images from the trained models.