Diffusion Models 1 — all 50 problems

Implement the mathematics that surrounds a diffusion model's neural network: noise schedules, the closed-form forward process, SNR-based loss weighting, the DDPM posterior, DDIM sampling, classifier-free guidance and latent scaling. Every exercise takes the network's prediction as a given array, so you write exactly the code that lives outside the U-Net in a real sampler.