Learning the Integral of a Diffusion Model

It is a good post, but is missing the connection to continuous normalizing flows. Diffusion models, flow matching, consistency models are biased approximations of continuous normalizing flows (which themselves have some slight biases, but less). Adversarial losses can somewhat help with bias (e.g. RL, GANs), but training those has issues.

Does anyone have good resources into a more practical approach toward building diffusion models? I found the book by Rashka for Building an LLM from Scratch really helpful in understanding a lot of concepts behind LLMs, and I am looking for a similar resource for diffusion models

Haven't finished this but for me it's so refreshing to read some science on deep learning and not just weird predictions.

- just a headsup

- your links to the slides for deeplearning you did here https://sander.ai/2014/05/29/slides-meetup.html are broken

This is way outside of my expertise, can anyone given a TL;DR or ai;dr?