Diffusion models have emerged as a powerful class of generative models and demonstrated astonishing results, in particular in image synthesis. However, training high-resolution diffusion models in pixel space can be highly expensive. Overcoming these limitations, Latent Diffusion Models (LDMs) first map high-resolution data into a compressed, typically lower-dimensional latent space using an autoencoder, and then train a diffusion model in that latent space more efficiently. Thereby, LDMs enable high-quality image synthesis while avoiding excessive compute demands. Furthermore, the LDM paradigm with an autoencoder, which can be tailored to specific problems and data, and a separate diffusion model in latent space offers significant flexibility with respect to architecture and model design. This has allowed LDMs to be successfully extended to various tasks beyond image generation, such as video synthesis, 3D object and scene generation, language modeling, and more. Most prominently, the well-known text-to-image model Stable Diffusion leverages the LDM framework. LDMs have become very popular and widely used in the generative modeling literature.

In this tutorial, we aim to provide an introduction to LDMs. While the literature on diffusion models has become broad, the LDM paradigm stands out as a particularly powerful approach due to its flexibility and excellent trade-off with respect to performance and compute demands. We aim to present a tutorial on LDMs that will benefit researchers interested in efficient and flexible, yet expressive generative modeling frameworks. We will also highlight advanced techniques for accelerated sampling and controllability, and discuss various applications of LDMs beyond image synthesis. Moreover, a panel discussion will provide diverse perspectives on this dynamic field and offer an outlook for future research on LDMs.