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Diffusion Models Course

by Jonathan Whitaker & Hugging Face Team · Hugging Face

4.6
(1,100 reviews)
60K+ enrolledSelf-pacedUpdated 2024-09
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Our Verdict

Worth it — with caveats

The Hugging Face Diffusion Models Course is a free, four-unit, code-first introduction to diffusion models built around the open-source Diffusers library, authored by Jonathan Whitaker (answer.ai) and Lewis Tunstall (Hugging Face). It is genuinely strong for intermediate practitioners who already know Python, PyTorch, and deep-learning basics and want hands-on reps: you train an unconditional model from scratch, fine-tune with guidance, dig into Stable Diffusion's latent pipeline, and build custom pipelines. The biggest honest caveat is staleness: the final unit shipped in January 2023 and the notebooks have not been kept in lockstep with the fast-moving Diffusers library, so learners in 2024-2025 hit real dependency and import errors (notably a pinned pyarrow==9.0.0 that breaks on Google Colab). There is no certificate, and the catalog's 4.6 star figure could not be verified against any public rating source. Treat it as a high-quality, free supplementary course you can finish in about a month, not a polished turnkey product.

Excellent free, hands-on material from credible authors and arguably the best free diffusion-specific course available, but it is conditional because (a) it assumes solid PyTorch and deep-learning prerequisites, (b) the content is frozen at early-2023 and the notebooks ship known dependency/import errors that you must fix yourself, and (c) there is no certificate. Take it if you can debug environment issues and want the practical skills; otherwise pick a more actively maintained option.

Best for: Intermediate ML practitioners and engineers who are already comfortable with Python and PyTorch and want a free, code-first path to actually training, fine-tuning, and building pipelines for diffusion models (including Stable Diffusion). It suits people who learn by running notebooks, are comfortable using a GPU via Colab/Kaggle, and don't mind patching outdated dependencies. It is also a good companion to fast.ai-style practical learning given Whitaker's background.

Skip if: Complete beginners or non-coders, anyone without a grounding in deep learning and PyTorch (the course explicitly states this prerequisite), learners who need a certificate or credential, and people who want a fully maintained, plug-and-play experience where every notebook runs first try. If you get stuck on pip/dependency errors and have no way to troubleshoot, skip it or use a newer course.

About This Course

Hands-on course covering diffusion model theory, fine-tuning Stable Diffusion, guidance, and building custom pipelines.

What You'll Learn

The theory behind diffusion models, including the iterative noise-and-denoise process and the DDPM training loop (add noise, predict denoising, update weights)
How to create, train, and sample your own unconditional diffusion model using the Hugging Face Diffusers library
A from-scratch PyTorch implementation of the core components so you can recognize the key ideas (noise scheduling, the UNet model, sampling) in any codebase
Fine-tuning existing diffusion models on new datasets and adding conditioning and guidance to steer outputs
How Stable Diffusion works as a text-conditioned latent diffusion model, and how to use its pipeline (text-to-image and inpainting)
Building custom diffusion pipelines and applying advanced techniques covered in Unit 4 ('Doing More with Diffusion')
Pushing models and results to the Hugging Face Hub and sharing training recipes with the community

Curriculum

Unit 1: An Introduction to Diffusion Models

Theory of how diffusion models generate data by iteratively denoising random noise, plus two notebooks: 'Introduction to Diffusers' (create, train, and sample your own model with the Diffusers library) and 'Diffusion Models from Scratch' (a minimal PyTorch implementation comparing toy code to the library version). Released Nov 28, 2022.

Unit 2: Fine-Tuning, Guidance and Conditioning

Fine-tuning a pretrained diffusion model on new data and adding control over outputs through guidance and extra conditioning (e.g., class labels). Released Dec 12, 2022.

Unit 3: Stable Diffusion

Exploring Stable Diffusion as a powerful text-conditioned latent diffusion model; using its pipelines for text-to-image and inpainting. Released Dec 21, 2022.

Unit 4: Doing More with Diffusion (Going Further)

Advanced techniques for pushing diffusion models further and building custom pipelines. Released Jan 2023 (final unit).

Prerequisites

  • Solid Python proficiency
  • Grounding in deep learning fundamentals
  • Working knowledge of PyTorch
  • A free Hugging Face account (required to push trained models to the Hub)
  • Access to a GPU runtime such as Google Colab, Kaggle, Paperspace Gradient, or SageMaker Studio Lab

Instructor

Jonathan Whitaker & Hugging Face Team

Instructor · Hugging Face

Pros & Cons

Pros

  • Completely free and open-source (Apache 2.0 license), with theory plus two hands-on notebooks per unit and ready-to-run badges for Colab, Kaggle, Paperspace Gradient, and SageMaker Studio Lab
  • Authored by credible practitioners: Jonathan Whitaker (answer.ai, generative-AI focus) and Lewis Tunstall (Hugging Face ML engineer and co-author of O'Reilly's 'NLP with Transformers')
  • Genuinely code-first and practical: you train a model from scratch, see a minimal from-scratch PyTorch version, fine-tune with guidance, and work directly with Stable Diffusion rather than only reading theory
  • Strong community standing as a reference: the companion GitHub repo has roughly 4.3k stars and ~491 forks, with community translations into Chinese, Japanese, and Korean, plus an active #diffusion-models-class Discord channel and responsive maintainers on the HF forums
  • Curated 'additional resources' (The Annotated Diffusion Model, DDPM papers, video walkthroughs) make it a good launchpad for deeper study

Cons

  • Content is frozen in early 2023 (last unit Jan 2023), so it predates many later diffusion advances and the notebooks have not kept pace with the rapidly evolving Diffusers library
  • Real, documented dependency and import errors: GitHub issue #90 (opened Oct 24, 2024, still open) and a HF forum thread (May 20, 2025) report that the pinned pyarrow==9.0.0 fails to build on Google Colab and breaks 'from datasets import load_dataset', forcing learners to manually upgrade packages to get past the very first setup cell
  • No certificate of completion is offered (Hugging Face says a certification program is 'in the works' but none exists for this course)
  • Steep prerequisites: it explicitly requires existing deep-learning and PyTorch knowledge and a GPU, so it is unforgiving for beginners and offers little hand-holding on environment setup

Alternatives To Consider

Practical Deep Learning for Coders

fast.ai

View course

PyTorch for Deep Learning & Machine Learning

Udemy

View course

NLP Course

Hugging Face

View course

Frequently Asked Questions

Is Diffusion Models Course free?

Yes — Diffusion Models Course is free to access. Free and open-source (Apache 2.0). There is no paid tier and no certificate. The only real cost is GPU compute for training/fine-tuning: the free tiers of Google Colab, Kaggle, Paperspace Gradient, or SageMaker Studio Lab are sufficient for the notebooks, and a free Hugging Face account is needed to push models to the Hub.

Who is Diffusion Models Course for?

Intermediate ML practitioners and engineers who are already comfortable with Python and PyTorch and want a free, code-first path to actually training, fine-tuning, and building pipelines for diffusion models (including Stable Diffusion). It suits people who learn by running notebooks, are comfortable using a GPU via Colab/Kaggle, and don't mind patching outdated dependencies. It is also a good companion to fast.ai-style practical learning given Whitaker's background.

What will you learn in Diffusion Models Course?

The theory behind diffusion models, including the iterative noise-and-denoise process and the DDPM training loop (add noise, predict denoising, update weights); How to create, train, and sample your own unconditional diffusion model using the Hugging Face Diffusers library; A from-scratch PyTorch implementation of the core components so you can recognize the key ideas (noise scheduling, the UNet model, sampling) in any codebase; Fine-tuning existing diffusion models on new datasets and adding conditioning and guidance to steer outputs.

What are the prerequisites for Diffusion Models Course?

Solid Python proficiency; Grounding in deep learning fundamentals; Working knowledge of PyTorch; A free Hugging Face account (required to push trained models to the Hub); Access to a GPU runtime such as Google Colab, Kaggle, Paperspace Gradient, or SageMaker Studio Lab.

Is Diffusion Models Course worth it?

Excellent free, hands-on material from credible authors and arguably the best free diffusion-specific course available, but it is conditional because (a) it assumes solid PyTorch and deep-learning prerequisites, (b) the content is frozen at early-2023 and the notebooks ship known dependency/import errors that you must fix yourself, and (c) there is no certificate. Take it if you can debug environment issues and want the practical skills; otherwise pick a more actively maintained option.

How we reviewed this course

This is an independent editorial assessment by Cursarium, based on Hugging Face's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.

Sources

Free
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