image generation
AI, ML & GenAI interview questions tagged image generation, across every topic.
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Concepts behind "image generation"
The curriculum that explains the ideas these questions test.
Core
Diffusion ModelsDiffusion 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 begins 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. AI, ML, and GenAI interviews probe it because it is the basis of image generation systems and explains their cost, latency, and the role of guidance.🧠 Foundations of LLMs & GenAISign in
Core
Diffusion Control and Fast SamplingA 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.🧠 Foundations of LLMs & GenAISign in