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. The flexible LDM paradigm has become very popular in the literature and has been successfully extended also to video synthesis, 3D generation, and more. Most prominently, the state-of-the-art text-to-image model Stable Diffusion, which has attracted worldwide attention, leverages the LDM framework. 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 propose a tutorial on LDMs that will benefit researchers interested in efficient and flexible, yet expressive generative modeling frameworks. Moreover, a panel discussion with well-known experts will provide diverse perspectives on this dynamic field and offer an outlook for future research on LDMs.