4 Best Hugging Face Diffusion Models Course Alternatives in 2026 (Open Source)
Hugging Face Diffusion Models Course — Materials for the Hugging Face Diffusion Models Course. Official Hugging Face free course teaching diffusion models from theory to practice with hands-on notebooks and community support
These 4 open-source tools do the same job. They are ordered by how closely they match Hugging Face Diffusion Models Course, with live GitHub data so you can see which projects are actively maintained.
| Tool | GitHub stars | Stars / 30d | Last commit |
|---|---|---|---|
| Hugging Face Diffusion Models Course(original) | 4.4k | +10 | 2026-09-17 |
| Anthropic courses | 22.9k | +468 | 2025-11-13 |
| Large-Language-Model-Notebooks-Course | 1.8k | +7 | 2026-09-29 |
| Intro to the course | 3.4k | +4 | 2024-12-09 |
| Generative AI on Google Cloud | 17.8k | +206 | 2026-09-30 |
1. Anthropic courses
Anthropic's educational courses
What sets it apart: vs generic prompt engineering guides: official Anthropic courses with hands-on Claude API exercises, covering fundamentals through production evaluation
Best for: Developers learning Anthropic Claude API from scratch; Teams establishing prompt engineering best practices
2. Large-Language-Model-Notebooks-Course
Practical course about Large Language Models.
What sets it apart: Comprehensive free hands-on LLM course with 30+ Jupyter notebooks covering the full stack from prompting to fine-tuning to enterprise architecture — backed by an Apress published book for deeper coverage
Best for: Developers learning LLM application development through hands-on practice; Engineers wanting structured progression from basics to enterprise patterns
3. Intro to the course
🦖 𝗟𝗲𝗮𝗿𝗻 about 𝗟𝗟𝗠𝘀, 𝗟𝗟𝗠𝗢𝗽𝘀, and 𝘃𝗲𝗰𝘁𝗼𝗿 𝗗𝗕𝘀 for free by designing, training, and deploying a real-time financial advisor LLM system ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 𝘷𝘪𝘥𝘦𝘰 & 𝘳𝘦
What sets it apart: vs generic LLM tutorials: 3-pipeline production architecture (training + streaming + inference) with real financial data — teaches QLoRA fine-tuning, real-time embeddings, and RAG deployment end-to-end
Best for: ML engineers wanting to learn production LLM deployment end-to-end; Practitioners building real-time RAG systems with streaming data; Teams learning QLoRA fine-tuning with LLMOps best practices
4. Generative AI on Google Cloud
Sample code and notebooks for Generative AI on Google Cloud, with Gemini on Vertex AI
What sets it apart: Google's official sample repository for Generative AI on Google Cloud — the most comprehensive collection of Gemini, Imagen, and Vertex AI notebooks, unlike third-party tutorials it's maintained by Google and always reflects latest APIs
Best for: Learning Google Cloud's generative AI capabilities with hands-on examples; Teams already on Google Cloud wanting to integrate Gemini/Vertex AI