Latent diffusion models work directly with pixel space and full-size images.

The main advantage of latent diffusion models is their ability to work sequentially on the entire image, leading to faster training and inference times.

Latent diffusion models use random noise as inputs, which can be conditioned with text or images.

Working in a compressed image representation allows latent diffusion models to handle different modalities like text and images.