👁️HardMultimodal ModelsPREMIUM

Diffusion Models & Image Generation

Master the mathematics and architecture of Diffusion Models, from the forward noising process to U-Net denoising, Classifier-Free Guidance, and Latent Diffusion scaling.

What you'll master

Forward diffusion: adding Gaussian noise

Reverse process: learned denoising

U-Net architecture for noise prediction

Classifier-free guidance mechanism

Latent diffusion (Stable Diffusion architecture)

DDIM sampling and deterministic inversion

VAE compression trade-offs

CLIP text encoding integration

Noise schedule (beta/alpha) math

Reparameterization trick (closed form sampling)

Hard45 min readIncludes code examples, architecture diagrams, and expert-level follow-up questions.

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Code examplesArchitecture diagramsModel answersScoring rubricCommon pitfallsFollow-up Q&A

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