AIAIART Lesson #7 - Diffusion Models 

Thank you for the kind words!

Best indeed

I'm so glad to hear it :) Thanks for the kind words!

This is a bad habit of mine! I introduced the convention at the start of the course where the training loop was only a few lines and it was easy to see the link, but you're totally right that it's nicer to have them together. Changed it in the notebook.

You are so welcome!

So I came across nn.labml.ai/diffusion/ddpm/index.html - specifically their section on the loss function and their 'simplified loss'. Re-reading now I'm not sure I quite follow some of their steps, but the conclusion seems to be that with a few (possibly sketchy) assumptions we can simplify the loss term to just the mean squared error between the predicted noise and the actual noise. There is some extra scaling we *should* technically include but leaving it out actually seems to help by increasing the weight given to higher ts. I'll take a look at some actual implementations and see what the people who actually know how this all works are doing and then get back to you :)

I found a great video that goes through the maths in detail: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-HoKDTa5jHvg.html Shows *HOW* we end up with the relatively simple loss shown here.

Palette uses a Unet model, specifically I think they follow the implementation used in (Prafulla Dhariwal and Alex Nichol. 2021. Diffusion models beat gans on image synthesis. arXiv preprint arXiv:2105.05233 (2021).) but with the input image as extra conditioning (so the input to the unet is 6 channels rather than 3). github.com/Janspiry/Palette-Image-to-Image-Diffusion-Models seems like a good place to start if you want to try a model like this yourself.

@@datasciencecastnet thanks!!!!