Core

Diffusion Control and Fast Sampling

A text prompt is a weak handle on an image model. Real control comes from structural conditioning: ControlNet for layout and pose, IP-Adapter for identity and style, masked inpainting for local edits. Speed comes from attacking three separate factors: the number of steps, the cost per step, and the passes per step. AI, ML, and GenAI interviews probe this because shipping an image product means hitting a latency budget and giving users control that a prompt alone cannot deliver.

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RELATED CONCEPTS

Diffusion Models LoRA and Parameter-Efficient Fine-Tuning Knowledge Distillation GPU Memory and the Serving Stack Latency Budgets and Streaming

PRACTICE THIS IN REAL QUESTIONS

LLM & GenAI FundamentalsYour diffusion model ignores the precise layout you asked for. How do you make image generation controllable?→ML Infrastructure & GPUsYour diffusion model takes too long per image. How do you speed up sampling?→System Design for AI in ProductionDesign a text-to-image generation service (Midjourney/DALL-E-like) at scale.→MLOps & ML EngineeringYour model's p99 inference latency is too high. How do you bring it down without retraining?→ML Infrastructure & GPUsExplain speculative decoding and the other main levers for cutting LLM generation latency.→LLM & GenAI FundamentalsExplain temperature, top-k, and top-p (nucleus) sampling. When do you use greedy vs sampling?→

COMPANIES THAT ASSUME THIS

OpenAI Anthropic Google Microsoft Scale AI Decagon

NEXT IN FOUNDATIONS OF LLMS & GENAIMultimodal Fusion Architectures→