Explain the Forward and Reverse Processes of Diffusion Models

Explain the forward and reverse processes of a diffusion generative model. Describe a common training objective and how generation differs from training. ### Constraints & Assumptions Use a continuous Gaussian diffusion formulation as a concrete example. Noise schedules, prediction parameterizations, and samplers can vary; identify the choices you use. ### Clarifying Questions Are we modeling pixels, latent representations, or another continuous signal? Is generation conditional? Does the model predict noise, clean data, or another parameterization? ### What a Strong Answer Covers Explain progressively corrupted training data, timestep conditioning, the denoising objective, and iterative reverse sampling without confusing the learned reverse process with simply undoing known noise. ### Follow-up Questions Why can training sample an arbitrary timestep directly? Why is generation often iterative? How can conditioning and guidance affect quality, diversity, and sampling cost?