Explain the Forward and Reverse Processes of Diffusion Models
Mistral AISoftware Engineer · Technical Screen
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Explain Gaussian diffusion corruption, timestep-conditioned denoising, noise-prediction training, iterative reverse sampling, latent representations, and guidance tradeoffs.
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?
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?
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