This one-day works shop is an introduction to diffusion models and how to apply them in a practical way. We talk through the diffusion algorithm as it was introduced in the DDPM paper, and then try to implement it in PyTorch. We then move on to the Diffusers library, which is a high-level library that abstracts out the details of the diffusion algorithm, and allows you to focus on the important parts of your project.
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Table of Contents
Life will be easier if you already have some experience with the following:
- Probability distributions
- PyTorch
- PyTorch Datasets and Dataloaders
- Convolutional Neural Networks
- Other neural network components (normalization, activation functions, etc.)
- Hugging Face Libraries (optional)
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under a GNU GPL-3.0 License. See LICENSE for more information.