Explain the Forward and Reverse Processes of Diffusion Models

Describe the forward and reverse steps of a diffusion generative model. Outline a typical training objective and contrast generation with training.

Constraints & Assumptions

Adopt a continuous Gaussian diffusion as a concrete example. Noise schedules, prediction parameterizations, and samplers may differ; specify the choices you make.

Clarifying Questions

Are we modeling pixels, latent representations, or some other continuous signal? Is the generation conditional? Does the model predict noise, clean data, or a different parameterization?

What a Strong Answer Covers

Explain the progressively corrupted training data, timestep conditioning, the denoising objective, and iterative reverse sampling, while distinguishing the learned reverse process from merely undoing known noise.

Follow-up Questions

Why can training directly sample an arbitrary timestep? Why is generation typically iterative? How do conditioning and guidance influence quality, diversity, and sampling cost?

Overview: Cover Gaussian diffusion corruption, timestep-conditioned denoising, noise-prediction training, iterative reverse sampling, latent representations, and guidance trade-offs.

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