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Core
Diffusion Models
Diffusion models generate images (and audio/video) by learning to reverse a noising process: training corrupts data into noise step by step, and the model learns to denoise, so at generation it starts from pure noise and iteratively denoises into a sample. Text conditioning (via cross-attention to text embeddings) steers what gets generated, and latent diffusion denoises in a compressed space for efficiency. Applied-AI interviews probe it because it is the basis of image generation systems and explains their cost, latency, and the role of guidance.
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PRACTICE THIS IN REAL QUESTIONS
System Design for AI in ProductionDesign a text-to-image generation service (Midjourney/DALL-E-like) at scale.→Machine Learning & Data ScienceHow do diffusion models work, and what do the VAE and U-Net do in latent diffusion (Stable Diffusion)?→Machine Learning & Data ScienceHow do GANs work, why is training unstable, and why did diffusion overtake them?→Machine Learning & Data ScienceYour generative image model produces low-diversity or garbled samples. How do you diagnose and fix it?→System Design for AI in ProductionDesign a text-to-SQL feature: let users ask questions in natural language over a real database.→System Design for AI in ProductionDesign a music generation service (Suno-like).→
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