This repository provides the official implementation of Latent Diffusion Models (LDM), a method that learns to generate images by reversing a diffusion process in a compressed latent space. It includes training code, pre-trained model weights, and inference utilities for text-to-image synthesis and class-conditional generation. The approach significantly reduces computational cost by operating in latent space rather than pixel space while maintaining generation quality.