I wanted to highlight PBDL’s brand-new sections on diffusion models with code and derivations! Great work by Benjamin Holzschuh, with neat Jupyter notebooks 👍 It covers all the “generative AI” basics, from normalizing flow basics over score matching to denoising & flow matching.
Here are a few examples to check out:
- Normalizing flows: https://colab.research.google.com/github/tum-pbs/pbdl-book/blob/master/probmodels-normflow.ipynb
- Flow matching: https://colab.research.google.com/github/tum-pbs/pbdl-book/blob/master/probmodels-flowmatching.ipynb
Feel free to give it a try 😀 , and let us know whether it’s understandable / works / where to improve…
