Diffusion and Generative Media

Diffusion models turn noise into images (or audio, or video) by learning the reverse of a noising process. In practice, you’ll use latent diffusion: a VAE encodes images to a compact latent space, a UNet denoiser learns to remove noise step by step, and a text encoder (CLIP/Transformer) conditions the denoiser on your prompt. The ◻ diffusers library wraps all of this into pipelines you can compose, fine-tune, and ship. This chapter shows how to stand up text-to-image, add structure with ControlNet, make it fast with consistency distillation, and keep it safe with guardrails that actually run in production.