Train a DDPM on Fashion-MNIST

Build a denoising diffusion model end to end. Implement the closed-form forward noising process and watch clothing dissolve into Gaussian noise, train a small diffusers UNet2DModel to predict that noise (mixed precision and an EMA copy of the weights), then write the reverse sampling loop yourself with both the 1000-step DDPM sampler and a 50-step DDIM sampler. Finish with a grid of generated garments and a strip showing one sample emerging from noise. Training is capped at about two minutes on a T4.