Diffusion Models Explained — DDPM, Stable Diffusion & How AI Art Works (Deep Learning EP43)

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EP42 built a DCGAN — two networks in adversarial competition.
EP43 covers diffusion models: the current state of the art in image generation.
No discriminator. No adversarial game. Just a network that learns to remove noise,
one tiny step at a time — and that's enough to generate anything.
We derive DDPM from scratch, build the U-Net denoiser, and run Stable Diffusion
in 10 lines via Hugging Face Diffusers.

✅ The diffusion idea — forward noising process destroys information gradually
✅ Reverse process — a network learns to denoise one step at a time
✅ DDPM math — Beta schedule, reparameterisation trick, noise prediction loss
✅ U-Net architecture — skip connections, self-attention, timestep conditioning
✅ Score matching — the connection between diffusion and score-based models
✅ Classifier-free guidance (CFG) — how text prompts steer generation
✅ Stable Diffusion architecture — VAE latent space + U-Net + CLIP text encoder
✅ DALL-E and Imagen — how the family tree extends
✅ Hugging Face Diffusers — Stable Diffusion in 10 lines of Python

⏱ CHAPTERS:
0:00 From EP42 GANs to the diffusion idea
1:05 Forward process — adding noise over T timesteps
2:15 DDPM math — Beta schedule and reparameterisation
3:30 Reverse process — denoising network predicts the noise
4:45 U-Net — skip connections, attention, timestep embedding
5:55 Score matching — the theoretical connection
7:00 Classifier-free guidance — how prompts steer generation
8:10 Stable Diffusion — latent diffusion, CLIP, VAE decoder
9:20 Hugging Face Diffusers — Stable Diffusion in 10 lines
10:40 Recap + challenge
11:30 Outro → EP44 Build a Text-to-Image App

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