Practical Deep Learning for Coders Part 2: Deep Learning Foundations to Stable Diffusion
by Jeremy Howard · fast.ai
Our Verdict
Worth it — with caveatsfast.ai's Part 2, "Deep Learning Foundations to Stable Diffusion," is one of the only free courses that rebuilds a generative image model end-to-end, taking you from raw matrix multiplication and hand-coded backpropagation up to a working Stable Diffusion implementation written from scratch in PyTorch. Across 30+ hours of video (Lessons 9-25, plus bonus lessons) Jeremy Howard and collaborators from Stability.ai and Hugging Face build a small training framework called miniai and implement papers like DDPM, DDIM and Karras et al. (2022) line by line. It is genuinely advanced and unapologetically code-first: the official prerequisite is being a "reasonably confident deep learning practitioner" who has finished Part 1 or is fluent in PyTorch and SGD training loops. Community sentiment (Hacker News, fast.ai forums) is strongly positive on the depth, with the common caveat that it demands a large, sustained time commitment and is aimed at people who want to understand and research model internals rather than ship products with off-the-shelf APIs. Verdict: take it if you want from-scratch mastery of diffusion and neural-net foundations; skip it if you only need to use generative AI rather than build it.
Exceptional, free, paper-faithful coverage of diffusion and deep-learning internals, but it is explicitly advanced (Lessons 9-25, requires Part 1 / solid PyTorch) and heavy on time, so it only pays off for people who want to build and research models from scratch rather than apply pre-built APIs.
Best for: Confident deep learning practitioners, ML engineers and aspiring researchers who already know PyTorch and SGD training loops and want to understand diffusion models from first principles, implement papers (DDPM, DDIM, Karras 2022) from scratch, and build their own mini training framework rather than just call a library.
Skip if: Beginners, people new to PyTorch, or product builders who just want to use Stable Diffusion / generative APIs to ship features. fast.ai itself points this audience to Part 1; the maintainers note that for shipping products, deep internals matter less than sound engineering. Anyone wanting a certificate or graded credential should also skip it (none is offered).
About This Course
Goes from foundations of neural networks to implementing stable diffusion from scratch, covering backprop, attention, and diffusion math.
What You'll Learn
Curriculum
Introduction to Stable Diffusion and how diffusion-based image generation works, using the Hugging Face Diffusers library before rebuilding it.
Deeper look at the diffusion pipeline and the components that will later be reimplemented from scratch.
Rebuilding the most fundamental neural-net operation from the ground up in PyTorch.
Clustering and tensor manipulation to build intuition and PyTorch fluency.
Hand-coding forward and backward passes and backpropagation through a multilayer perceptron.
Building autoencoders as a stepping stone toward latent representations.
Designing the miniai Learner and callback system that powers the rest of the course.
Weight initialization and normalization techniques (e.g. LayerNorm, BatchNorm) and why they matter.
Optimizers (AdamW, RMSProp) and residual network architectures.
Implementing the denoising diffusion probabilistic model (DDPM) and regularization.
Mixed-precision training for speed and memory efficiency.
Faster DDIM sampling and reproducing the Karras et al. 2022 diffusion design-space paper.
Applying the framework to a super-resolution task.
Implementing attention and transformer blocks from scratch.
Tying components together into latent diffusion, the basis of Stable Diffusion.
Prerequisites
- Completion of fast.ai Practical Deep Learning Part 1, or equivalent status as a "reasonably confident deep learning practitioner"
- Comfort writing and debugging SGD/training loops in PyTorch
- Familiarity with modern NLP and computer-vision model basics (e.g. via Kaggle or prior projects)
- High-school-level calculus and probability (deeper math is introduced in context, not assumed up front)
Instructor
Jeremy Howard
Instructor · fast.ai
Pros & Cons
Pros
- Completely free with no paywall, and the full notebooks (the miniai framework) are open-source on GitHub (fastai/course22p2, 500+ stars, ~290 forks)
- Rare from-scratch depth: you actually implement Stable Diffusion and diffusion math rather than just calling an API, which learners say leaves them able to read and reproduce arbitrary research papers
- Built with practitioners from Stability.ai and Hugging Face (Jeremy Howard, Tanishq Abraham, Jonathan Whitaker, Pedro Cuenca, Kat Crowson), so it reflects real research practice
- Goes beyond the original Stable Diffusion paper to cover follow-up research (e.g. Karras et al. 2022), keeping the techniques modern
- Teaches transferable internals - backprop, initialization, normalization, attention, optimizers - not just one model, so the knowledge generalizes
Cons
- Steep difficulty and large time commitment: community reports suggest roughly 10+ hours per lesson and months for full mastery, far heavier than typical online courses
- Not for beginners or product builders - it requires Part 1 / solid PyTorch, and fast.ai itself steers app developers to Part 1 instead
- No certificate, grades, or formal credential, which matters for learners who need proof of completion
- Content is anchored to the 2022/23 cohort and the then-current Stable Diffusion era, so some tooling and SOTA references have moved on since (course material lastUpdated around early 2024)
Alternatives To Consider
Frequently Asked Questions
Is Practical Deep Learning for Coders Part 2: Deep Learning Foundations to Stable Diffusion free?
Yes — Practical Deep Learning for Coders Part 2: Deep Learning Foundations to Stable Diffusion is free to access. Free to audit the full video course and access all notebooks on GitHub; no paid tier and no certificate. The optional companion path uses free/open tools (PyTorch, Hugging Face Diffusers, miniai); only third-party compute (e.g. cloud GPUs) may incur cost if you don't have a local GPU.
Who is Practical Deep Learning for Coders Part 2: Deep Learning Foundations to Stable Diffusion for?
Confident deep learning practitioners, ML engineers and aspiring researchers who already know PyTorch and SGD training loops and want to understand diffusion models from first principles, implement papers (DDPM, DDIM, Karras 2022) from scratch, and build their own mini training framework rather than just call a library.
What will you learn in Practical Deep Learning for Coders Part 2: Deep Learning Foundations to Stable Diffusion?
Implement Stable Diffusion and diffusion models (DDPM and DDIM) from scratch in PyTorch, including unconditional and conditional generation and custom samplers; Build core neural-net machinery by hand: matrix multiplication, forward/backward passes, and backpropagation through an MLP before relying on autograd; Construct a small deep-learning training framework (miniai) with a flexible Learner/callback system using nbdev; Apply and reason about initialization, normalization (LayerNorm/BatchNorm), accelerated optimizers (AdamW, RMSProp), mixed-precision training, and ResNet/U-Net architectures.
What are the prerequisites for Practical Deep Learning for Coders Part 2: Deep Learning Foundations to Stable Diffusion?
Completion of fast.ai Practical Deep Learning Part 1, or equivalent status as a "reasonably confident deep learning practitioner"; Comfort writing and debugging SGD/training loops in PyTorch; Familiarity with modern NLP and computer-vision model basics (e.g. via Kaggle or prior projects); High-school-level calculus and probability (deeper math is introduced in context, not assumed up front).
Is Practical Deep Learning for Coders Part 2: Deep Learning Foundations to Stable Diffusion worth it?
Exceptional, free, paper-faithful coverage of diffusion and deep-learning internals, but it is explicitly advanced (Lessons 9-25, requires Part 1 / solid PyTorch) and heavy on time, so it only pays off for people who want to build and research models from scratch rather than apply pre-built APIs.
How we reviewed this course
This is an independent editorial assessment by Cursarium, based on fast.ai'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
- Official course overview - Practical Deep Learning Part 2 (Lessons 9-25 syllabus)
- fast.ai announcement - From Deep Learning Foundations to Stable Diffusion (instructors, 30+ hours, prerequisites, papers)
- Hacker News discussion - community sentiment on difficulty, time commitment, and who it's for
- Official course code repository (miniai) - fastai/course22p2 (500+ stars, ~290 forks)