There are two entirely different questions people bring to image generation. The first is how do I get better pictures out of this thing, and the second is what is this thing doing. This course answers the second one, and it does it well enough that I recommend it despite its age. Unit one builds up diffusion from the ground, so you implement sampling, watch noise schedules behave, and train a small model on a small dataset.
Unit two moves into fine tuning and guidance, which is the most interesting part of the whole thing, because writing your own guidance function is the moment the mathematics stops being abstract and becomes a dial you can turn. Unit three covers Stable Diffusion properly, including how the latent space and text conditioning fit together. Unit four gathers faster sampling and other extensions. The pedagogy is the usual Hugging Face pattern of a theory page paired with a notebook, and it works, because you are always a few minutes away from running the thing you just read about.
The honest problem is timing. This material was written when Stable Diffusion 1.x was the reference model, and the field has moved several generations since, through SDXL, flow matching approaches, and the current crop of much stronger commercial and open models. The core ideas survive that transition, which is why the course still has value. The specifics do not always survive.
Some model references, defaults and library patterns are dated, and you will occasionally be learning the 2022 way of doing something that has a cleaner 2026 equivalent. Sparse updates make this worse. It is fair to say the course now works better as a foundations text than as a current practitioner guide. The prerequisite bar deserves a warning too.
The introduction is welcoming, and the actual requirement is that you can read and modify PyTorch training loops without friction. If you cannot, unit one will feel like watching someone else code. Anyone in that position should build the PyTorch background first, since there is plenty of free material for it, and then come back. Scope is the other limitation.
Four units is not a lot for a subject this large, and there are real gaps, particularly around video diffusion, controllability techniques that practitioners rely on heavily, and the practicalities of running these models at any scale. The lack of a certificate does not bother me at all, though it is worth knowing if you were hoping for something to show an employer. My 3.9 is a rating of good, free, technically honest material that has not kept pace with its own subject. If you want to understand why a denoising process can produce a photograph, this is one of the clearest free routes to that understanding, and you should treat the specific models and library calls as historical detail rather than current practice.