Foundation Models

Latent Diffusion

Latent diffusion is a generative modeling approach that runs the diffusion denoising process in the compressed latent space of a pretrained autoencoder rather than in raw pixel space. Introduced by Rombach et al. in 2022 and popularized by Stable Diffusion, it dramatically reduces the compute needed for high-resolution image and video generation while preserving quality. Conditioning mechanisms allow generation guided by text, images, or other modalities.

Why it matters for physical AI

Efficient generative models power video prediction, world models, and synthetic data pipelines for robotics, where generating plausible future observations at practical compute budgets is essential for planning and large-scale training.