Lesson 19: Deep Learning Foundations to Stable Diffusion

[@giorda77]

Thank you Jeremy, Tanisqh and Jono I appreciate you guys for this lesson

[@faqeerhasnain]

Love Jeremy so much,Thank You

[@timandersen8030]

Why is the coding implementation for sampling of x_t-1 is different @1:06:25 from the algorithm 2 for sampling mentioned in the paper @53:50 ??

[@michaelmuller136]

Very interesting, thank you very much!

[@deepschoolai]

46:03 I might be wrong here, but I don't think sigma is sqrt of beta. It is the square root of beta tilde which is NOT the value that has linearly spaced values. Getting this from section 3.2 of the DDPM paper.

[@laugh_n_share_life]

you are correct, this lecture was below their normal standard

[@deepschoolai]

@@laugh_n_share_life whoa, that is certainly not what I'm saying here. Just a simple question

[@deepschoolai]

Does the course dive deeper into the architecture of the Unet model. I feel there's a lot of intricacies we are missing out on there.

[@rjScubaSki]

Greek letters for identifiers are a pointless distraction imo - the alternative isn't spelled out greek letter names, but meaningful names. "For coders" ...

[@Ayanshandseals]

I cannot find DDPM notebook in diffusion-nbs repo? Can somebody past the link for the same?

[@scottnewcomer1835]

I love the way Tanishq described the forward pass. Good on ya! kzbin.info/www/bejne/f6XcgGupaZ2tmsU