Diffusion powers modern image, audio and video generation: destroy data with a fixed noise schedule (the forward process — no learning), then train a network to reverse one small step of that destruction. Chain the reversals and pure noise walks back into a picture. From there: fewer steps (DDIM), steering (classifier-free guidance), compression (latent diffusion), words (CLIP) — and your own fine-tuned Stable Diffusion, shipped.
Fine-tune Stable Diffusion on a custom concept (a style, an object) and ship a Gradio demo on HF Spaces — and/or train a small DDPM from scratch (fast.ai Part 2 style) to prove you own the math. Read the DDPM or Latent Diffusion paper with your research-skills method. You've got diffusion when you can explain the forward/reverse process, why a U-Net denoises, what guidance trades, and why latent diffusion is efficient — and you've generated images both from scratch and with Stable Diffusion.
Guides: module-b guide ↗ · BYO-12 generative ↗