Since these videos take an enormous amount of time (this one took about 300 hours), would you like to see, additionally, paper explanations in the style of Yannic Kilcher (www.youtube.com/@YannicKilcher) ? I could cover papers very quickly after they are released and also cover topics I wouldn’t do an animated video for. Let me know what you think :)
Sure! But I would prefer a deep dive once in a while to many simple paper explanations. There aren't many (video) resources for diffusion that go in such depth. So this is really great, thanks a lot for doing the video!
Wow! I did not expect this video to go this deep. But this is awesome! Please make more in depth explanation like this. It’s clear a lot of hard work went into it and the animation is sooo elegant
I absolutely love how you started from scratch, as in what the underlying PDF was. I'm working on a project on diffusion models and I don't know anything about it, and all the resources available are catered towards those with prerequisites I don't have yet, until this one. I haven't yet watched the whole thing, but I'm going to keep coming back to this till I understand everything in this video. Cheers mate!
Thank you for your work! I have started to learn about diffusion models and found that this is more complex idea than VAE idea and GAN idea. However, the people who try to explain these complex concepts to others are very impressive!
Amazing video, thank you. I learned most of it a year ago in university but this was a great refresher which also provided me with new insights to some of the stuff. I really liked the conclusion of the Denoising Score Matching part, very beautiful.
Regarding your pinned comment. No offense to Yannic, but your explanations are 10x better. The topics you've covered you actually understand, you explain not only what is going on, but also why. That, and you going into mathematical explanations are really appreciated. Don't worry about the quantity, it's easy to read a paper, and put surface level explanations out for more views, what you're doing is more valuable. Your videos are a treasure for amateur Deep Learning hobbyists like me who want to dig deeper into this field.
The mathematical derivation and explanation is such a lifesaver, I also never really understood the underlying meaning when reading the diffusion models but now everything clicked. Thank you so much for the videos, really enjoyed it. Please make more of such videos. Liked and subscribed : ).
What an amazing video! I did not expect the video to contain the derivations which I have personally struggled to search for. If its not too much, can you do a pytorch implementation of VP-SDE or SDE - DDPM/DDIM? Your previous video of DDPM in Pytorch was extremely useful and would appreciate it if a similar video for this is possible. Finally, love the work you put in this. This channel is a gem for AI enthusiasts.
There was another mistake with a sign, which cancels this one out. He was wrong with a sign after integrating by parts (after that it should have changed and be plus instead of minus)
32:38 To correct myself here, the paper gives explanation how to derive the sampler. I personally just find that approach much harder to understand and generally the papers don’t go into too much details for their derivations.
Thanks for your hard work! Amazing explanation! Just want to check the squared equation at 5:55. Can you explain why $\mathbb{E}[p(x)] = \int p(x) dx$? I feel like the equation has something missing...
@@swaystar1235 Unfortunately even doing Würstchen style video models is still super expensive and there are many things that you have to solve first outside the model :/
Calling ∇s stretches terminology a bit, right? Given s is a gradient vector field itself. Cool effort, thanks for going through all the manipulations. As for designing a read thread for the video, I'm not sure fully sure why you work 10 minutes for the E[s^2]+... term, but then in the explained denoising approach it's not really showing up anymore. Last note: Unlike Lagrang-ian dynamics, Langevin dynamics is not Langev-ian dynamics. But I think Langevin is still on the easier side to pronounce - don't be afraid.
i know its a short video but some of the syntax may be confusing eg the subscript on the \mathbb{E} that is p(x) in a financial context we often use things such as \mathbb{E}_t [ h(X_T) ] = the conditional probability of h(X_T) where X is a stochastic process creating a filtration such as so it is equal to \mathbb{E} [ h(X_T) | \mathcal{F}_t ] I know its a totally different domains but oftentimes notation like this can be dripping with meaning, so, what is the _meaning_ of the subscript p(x) and what is the _meaning_ of the double bar ( ||_2^2 ) in the expectation ? is that the L2 Norm? timestamp 8:17
If you want to make videos with quicker production, maybe you could use a whitescreen and write everything out, so you can still explain it intuitively but quicker.
@@outliier To ask more clearly, have you been working on the basics of score matching and diffusion models for the last year? Assuming that you are using diffusion models at Luma, you also studied advanced topics on the related subject.
