Diffusion Model Theory Instruction

via “progressive diffusion model theory instruction with hands-on implementation”

Python materials for the online course on diffusion models by [@huggingface](https://github.com/huggingface).

Unique: Combines theoretical exposition with implementation-from-scratch exercises using Hugging Face's Diffusers library as a reference, allowing learners to understand both low-level diffusion mechanics and high-level API abstractions. The four-unit progression explicitly scaffolds from basic noise-to-image generation through text-conditioning to advanced techniques like DreamBooth personalization.

vs others: More comprehensive than blog posts or papers because it provides executable code alongside theory; more accessible than academic papers because it prioritizes intuition and practical implementation over mathematical rigor.