In this course, containing over 30 hours of video content, we implement the astounding Stable Diffusion algorithm from scratch! That’s the killer app that made the internet freak out, and caused the media to say “you may never believe what you see online again”.
We’ve worked closely with experts from Stability.ai and Hugging Face (creators of the Diffusers library) to ensure we have rigorous coverage of the latest techniques. The course includes coverage of papers that were released after Stable Diffusion came out – so it actually goes well beyond even what Stable Diffusion includes! We also explain how to read research papers, and practice this skill by studying and implementing many papers throughout the course.
Stable diffusion, and diffusion methods in general, are a great learning goal for many reasons. For one thing, of course, you can create amazing stuff with these algorithms! To really take the technique to the next level, and create things that no-one has seen before, you need to really deeply understand what’s happening under the hood. With this understanding, you can craft your own loss functions, initialization methods, multi-model mixups, and more, to create totally new applications that have never been seen before. Just as important: it’s a great learning goal because nearly every key technique in modern deep learning comes together in these methods. Contrastive learning, transformer models, auto-encoders, CLIP embeddings, latent variables, u-nets, resnets, and much more are involved in creating a single image.
To get the most out of this course, you should be a reasonably confident deep learning practitioner. If you’ve finished fast.ai’s Practical Deep Learning course then you’ll be ready! If you haven’t done that course, but are comfortable with building an SGD training loop from scratch in Python, being competitive in Kaggle competitions, using modern NLP and computer vision algorithms for practical problems, and working with PyTorch and fastai, then you will be ready to start the course. (If you’re not sure, then we strongly recommend getting starting with Practical Deep Learning.)
Get started now!
Content summary
In this course, we’ll explore diffusion methods such as Denoising Diffusion Probabilistic Models (DDPM) and Denoising Diffusion Implicit Models (DDIM). We’ll get our hands dirty implementing unconditional and conditional diffusion models, experimenting with different samplers, and diving into recent tricks like textual inversion and Dreambooth.
Along the way, we’ll cover essential deep learning topics like neural network architectures, data augmentation approaches, and various loss functions. We’ll build our own models from scratch, such as Multi-Layer Perceptrons (MLPs), ResNets, and Unets, while experimenting with generative architectures like autoencoders and transformers.
Throughout the course, we’ll PyTorch to implement our models, and will create our own deep learning framework called miniai. We’ll master Python concepts like iterators, generators, and deco
