Course Description
From first principles to working image generation. The forward diffusion process (adding noise), reverse denoising, DDPM, DDIM sampling. The score function and score matching. Latent diffusion: encoding to latent space, UNet denoising, decoding. CLIP text conditioning. Classifier-free guidance: intuition and implementation. FLUX architecture: flow matching and improved efficiency. Students generate their first images and analyze quality metrics.