Hey, I made this, thanks for posting!

It’s purposefully high level and non-technical for a general audience - my theory was that most people who aren’t into tech/AI don’t care too much about training, or how the system got to be the way that it is.

But they do have some interest in how it actually operates once you’ve typed in a prompt.

Happy to answer any questions or take on board feedback

This is awesome. If you made a book or video-course that takes this level of high level explanation and translate it to the technical and then mathematical level, I would buy it in an heartbeat.

This is what I think is missing in most AI (broad sense) learning resources. They focus too much on the math that I miss the intuitive process behind it.

Pretty cool, playing with the guidance scale slider here taught me more than re-reading the DDPM paper did.

Thanks for sharing!!

Thanks for this article, this is the best explanation and visualization I have seen for explaining this flow. Great work!

Scroll to visualise steps is such a great idea! Great writeup.

Oh I particularly loved that you made the prompts themselves interchangeable. Very well done!

Amazing explanations!! I absolutely love this. In 10 minutes it’s given me a huge boost in my intuition on diffusion, which I’ve been missing for years.

If the prompt is the compass, and represents a point in space, why walk there? Why not just go to that point in image space directly, what would be there? When does the random seed matter if you're aiming at the same point anyway, don't you end up there? Does the prompt vector not exist in the image manifold, or is there some local sampling done to pick images which are more represented in the training data?

Scrolling through pics on mobile is difficult. Wanted to see all 29 steps but couldnt scroll it reliably.