Machine Learning Advanced Pro

Text-to-Image Generation with Diffusion Models

Design a DALL-E/Imagen/Stable Diffusion-style system — noise schedules, U-Net and DiT cross-attention, classifier-free guidance, DDIM, and the CLIP-based evaluation vocabulary the rest of the generative-image track leans on

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The deepest subject in this track's generative-image sequence: why diffusion, not autoregressive generation, is the default choice for text-to-image quality and a tunable steps-vs-quality dial; caption engineering at 500M-pair scale (BLIP-style re-captioning, CLIP-score filtering); the U-Net (downsampling/upsampling blocks with cross-attention) and DiT (patchify -> Transformer -> unpatchify) architectures; the forward/backward diffusion process and the predict-the-noise training objective at interview altitude; classifier-free guidance and DDIM step reduction as the two sampling techniques worth having ready; CLIP and CLIPScore introduced properly, once, for this subject and its two upcoming siblings to reuse; and the full production system — data, training, optimization, and inference pipelines, including the prompt-safety-to-super-resolution inference chain.

Part of the AI Engineer Interview path → Part of the ML System Design Interview path → Part of the Generative Vision & Image AI System Design path →

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Practice questions (6)

Choosing Diffusion Over Autoregressive for a Consumer T2I Product

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Debugging a U-Net That Ignores the Prompt

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CFG and DDIM: What Each One Actually Buys You

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CLIP and CLIPScore: One Model, Two Jobs in the Same Pipeline

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Why the Inference Chain Is Ordered the Way It Is

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Quizzes (1)

Text-to-Image Diffusion Models Quiz

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