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Author here, when I tried to understand diffusion models I realized that the code and math can be greatly simplified, which led to me writing this blog post and diffusion library. Happy to answer any questions.
All machine learning models are convolutions, mark my words.
I think you posted this a few times, maybe you want to elaborate on this point? I have trouble seeing reinforcement learning as convolution, for example.
oh this has code! great stuff. diffusion papers are famous for a lot of equations ( https://twitter.com/cto_junior/status/1766518604395155830 ) but code is much more legible (and precise?) for the rest of us. all theory papers should come with reference impl code. i'd love an extension of this for the diffusion transformer, which drives Sora and other videogen models. maybe combine this post with https://jaykmody.com…
They're famous for lots of equations, but truth be told, most diffusion researchers I know have the exact same response. A lot of people repeat the same exact equations and they only are there for review purposes. On the other hand, if you want to really dig in, I'd suggest reading into works by Kingma, Gao, Ricky Tian Qi Chen, and honestly, any of Max Welling's students (Tomczak (was post doc), Hoogeboom, etc), and…
It would take quite a long time but I could see this slowly happening for analogous reasons to how the printing press favored alphabetic languages over pictorial ones. Computers teach us to read, write, and express linguistically and are now thanks to LLMs capable of working much more fluently with language. Squiggles and lots of Greek letters seem like an anachronism.
Author here, when I tried to understand diffusion models I realized that the code and math can be greatly simplified, which led to me writing this blog post and diffusion library. Happy to answer any questions.
As a researcher, there's a lot of diffusion blogs that I do not like. But I actually really do like this one! It does a great job at getting to the meat of things, showing some of the complexities (often missing) but without getting lost or distracted. I especially like the discussion of trajectories as this is motivating to many things I think a lot of people struggle with (e.g. with schedulers). That can be hard to…
What? Do you mean a coworker or something as compared to a friend?
Earlier quoted context omitted.
As a researcher, there's a lot of diffusion blogs that I do not like. But I actually really do like this one! It does a great job at getting to the meat of things, showing some of the complexities (often missing) but without getting lost or distracted. I especially like the discussion of trajectories as this is motivating to many things I think a lot of people struggle with (e.g. with schedulers). That can be hard to…
> FWIW, a friend of mine (and not "a friend of mine") What? Do you mean a coworker or something as compared to a friend?
oh this has code! great stuff. diffusion papers are famous for a lot of equations ( https://twitter.com/cto_junior/status/1766518604395155830 ) but code is much more legible (and precise?) for the rest of us. all theory papers should come with reference impl code. i'd love an extension of this for the diffusion transformer, which drives Sora and other videogen models. maybe combine this post with https://jaykmody.com…
They're famous for lots of equations, but truth be told, most diffusion researchers I know have the exact same response. A lot of people repeat the same exact equations and they only are there for review purposes. On the other hand, if you want to really dig in, I'd suggest reading into works by Kingma, Gao, Ricky Tian Qi Chen, and honestly, any of Max Welling's students (Tomczak (was post doc), Hoogeboom, etc), and…
Earlier quoted context omitted.
I think you posted this a few times, maybe you want to elaborate on this point? I have trouble seeing reinforcement learning as convolution, for example.
It's a Fredholm equation which in turn is a convolutional equation.
Earlier quoted context omitted.
As a researcher, there's a lot of diffusion blogs that I do not like. But I actually really do like this one! It does a great job at getting to the meat of things, showing some of the complexities (often missing) but without getting lost or distracted. I especially like the discussion of trajectories as this is motivating to many things I think a lot of people struggle with (e.g. with schedulers). That can be hard to…
> FWIW, a friend of mine (and not "a friend of mine") What? Do you mean a coworker or something as compared to a friend?
Author here, when I tried to understand diffusion models I realized that the code and math can be greatly simplified, which led to me writing this blog post and diffusion library. Happy to answer any questions.