Foundation Models

Diffusion Model

Diffusion Model is a generative model that learns to reverse a gradual noising process, generating samples by iteratively denoising from pure noise. Formalized in denoising diffusion probabilistic models (Ho et al., 2020) and score-based frameworks, diffusion models achieve state-of-the-art image, video, and trajectory synthesis with stable training and strong mode coverage. Sampling acceleration techniques such as DDIM and distillation reduce the number of denoising steps required.

Why it matters for physical AI

Beyond powering diffusion policies for control, these models generate synthetic training scenes, forecast video futures for world models, and synthesize trajectories, making them a core generative engine across the physical AI stack.