How do energy-based models relate to modern diffusion models?

Both define implicit probability distributions through learned energy or score functions.

Energy-based models assign scalar energy to configurations, while diffusion models learn gradients of data density (score functions). Both avoid explicit likelihood computation and instead define implicit distributions. Diffusion models can be interpreted as learning a time-dependent energy landscape where noise is gradually removed.