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Denoising Diffusion models from first principle in Julia

adgjlsfhk1 4y ago
This is really cool, and I think possibly the best demonstration I've seen in a while of the power of Julia for tasks outside of science. The ability to use a high level fast and flexible language instead of being forced to compromise on a slow high level language that wraps fast but rigid libraries allows you to do some really cool stuff.
KRAKRISMOTT 4y ago
Are there any hosted Julia ML services like Grid.ai for Python?
- huitzitziltzin 4y ago
  Julia Computing offers Julia Hub. Not sure if that’s what you’re looking for but maybe.
astrange 4y ago
This claims to explain diffusion models from first principles, but the issue with explaining how they work is we don't know how they work. The explanation in the original paper turns out not to be true; you can get rid of most of their assumptions and it still works: https://arxiv.org/abs/2208.09392 https://arxiv.org/abs/2208.09392
- mlajtos 4y ago
  Huh, that is quite fascinating paper – we can learn to invert any image degradation and use it as generative model? Hm. Is there any research of using some U-net as the degradation function?
- throwitfaar 4y ago
  Thanks for the link, cold diffusion is a great idea. Only 2 comments on HN about it four months ago.
- espadrine 4y ago
  > The explanation in the original paper turns out not to be true; you can get rid of most of their assumptions and it still works I’ll admit it is amusing that some assumptions on why it works were incorrect. The core idea of a Markov chain[0] where each state change leads to higher likelihood, is bound to work, even if the rest doesn’t. In my mind, the Muse paper[1] gets closer to why it works: ultimately, the denoiser tries to match the latent space for an implicit encoder. The Muse system does this more directly and more effectively, by using cross-entropy loss on latent tokens instead. In a way, the whole problem is no different from a language translation task. The only difference is that the output needs to be decoded into pixels instead of BPE tokens. [0]: https://arxiv.org/abs/1503.03585 https://arxiv.org/abs/1503.03585 [1]: https://arxiv.org/abs/2301.00704 https://arxiv.org/abs/2301.00704
IlyaOrson 4y ago
Awesome post!
radarsat1 4y ago
I've been trying out developing my own diffusion models from scratch lately, to understand this approach better and to compare against similar trials I previously did with GAN. My impression from reading posts like these was that it would be relatively easy once you understand it.. with the advantage that you get a nice normal supervised MSE target to train against, instead of having to deal with the instabilities of GANs. I have found in practice that they do not deliver on this front. The loss curve you get is often just a big thick noisy straight line, completely devoid of information about whether it's converging. And the convergence seems to be greatly dependent on model choices and the beta schedule you choose. It's not clear to me at all how to choose those things in a principled manner. Until you train for a long time, you just basically get noise, so it's hard to know when to restart an experiment or keep going. Do I need 10 steps, 100, 1000? I found that training even longer, and longer, and longer, it does get better and better, very slowly, even though this is not displayed in the loss curve, and there seems to be no indication of when the model has "converged" in any meaningful sense. My understanding of why this is the case is that due to the integrative nature of the sampling process, even tiny errors in approximating the noise add up to large divergences. I've also tried making it conditional on vector quantization codes, and it seems to fail to use them nearly as well as VQGAN does. At least I haven't had much success doing it directly in the diffusion model. After reading more into it, I found that most diffusion-based models actually use a conditional GAN to develop a latent space and a decoder, and the diffusion model is used to generate samples in the latent space. This strikes me that the diffusion model then can never actually do better than the associated GAN's decoder, which surprised me to realize since it's usually proposed as an alternative to GAN. So, overall I'm failing to grasp the advantages this approach really has over just using a GAN. Obviously it works fantastically for these large scale generative projects, but I don't understand why it's better, to be honest, despite having read every article out there telling me again and again the same things about how it works. E.g. DALLE-1 used VQGAN, not diffusion, and people were pretty wowed by it. I'm not sure why DALLE-2's improvements can be attributed to their change to a diffusion process, if they are still using a GAN to decode the output. Looking for some intuition, if anyone can offer some. I understand that the nature of how it iteratively improves the image allows it to deduce large-scale and small-scale features progressively, but it seems to me that the many upscaling layers of a large GAN can do the same thing.
adammarples 4y ago
I get the error trying to run MNIST(:train) that modules are not callable. When I search for the error I see a link to the repo issues raising this issue but the issue itself is missing. Was it deleted rather than closed? Weird.
mk_stjames 4y ago
Just in case the author is reading: in the introduction, you use the term "virtual RAM" to describe the GPU VRAM needed for running stable diffusion, but what that VRAM actually stands for is 'Video RAM'.
- the_origami_fox 4y ago
  Auuthor here. Will update.

This is really cool, and I think possibly the best demonstration I've seen in a while of the power of Julia for tasks outside of science. The ability to use a high level fast and flexible language instead of being forced to compromise on a slow high level language that wraps fast but rigid libraries allows you to do some really cool stuff.

KRAKRISMOTT 4y ago

Are there any hosted Julia ML services like Grid.ai for Python?

huitzitziltzin 4y ago
Julia Computing offers Julia Hub. Not sure if that’s what you’re looking for but maybe.

astrange 4y ago

This claims to explain diffusion models from first principles, but the issue with explaining how they work is we don't know how they work. The explanation in the original paper turns out not to be true; you can get rid of most of their assumptions and it still works: https://arxiv.org/abs/2208.09392 https://arxiv.org/abs/2208.09392

mlajtos 4y ago
Huh, that is quite fascinating paper – we can learn to invert any image degradation and use it as generative model? Hm. Is there any research of using some U-net as the degradation function?
throwitfaar 4y ago
Thanks for the link, cold diffusion is a great idea. Only 2 comments on HN about it four months ago.
espadrine 4y ago
> The explanation in the original paper turns out not to be true; you can get rid of most of their assumptions and it still works I’ll admit it is amusing that some assumptions on why it works were incorrect. The core idea of a Markov chain[0] where each state change leads to higher likelihood, is bound to work, even if the rest doesn’t. In my mind, the Muse paper[1] gets closer to why it works: ultimately, the denoiser tries to match the latent space for an implicit encoder. The Muse system does this more directly and more effectively, by using cross-entropy loss on latent tokens instead. In a way, the whole problem is no different from a language translation task. The only difference is that the output needs to be decoded into pixels instead of BPE tokens. [0]: https://arxiv.org/abs/1503.03585 https://arxiv.org/abs/1503.03585 [1]: https://arxiv.org/abs/2301.00704 https://arxiv.org/abs/2301.00704

IlyaOrson 4y ago

Awesome post!

radarsat1 4y ago

I've been trying out developing my own diffusion models from scratch lately, to understand this approach better and to compare against similar trials I previously did with GAN. My impression from reading posts like these was that it would be relatively easy once you understand it.. with the advantage that you get a nice normal supervised MSE target to train against, instead of having to deal with the instabilities of GANs. I have found in practice that they do not deliver on this front. The loss curve you get is often just a big thick noisy straight line, completely devoid of information about whether it's converging. And the convergence seems to be greatly dependent on model choices and the beta schedule you choose. It's not clear to me at all how to choose those things in a principled manner. Until you train for a long time, you just basically get noise, so it's hard to know when to restart an experiment or keep going. Do I need 10 steps, 100, 1000? I found that training even longer, and longer, and longer, it does get better and better, very slowly, even though this is not displayed in the loss curve, and there seems to be no indication of when the model has "converged" in any meaningful sense. My understanding of why this is the case is that due to the integrative nature of the sampling process, even tiny errors in approximating the noise add up to large divergences. I've also tried making it conditional on vector quantization codes, and it seems to fail to use them nearly as well as VQGAN does. At least I haven't had much success doing it directly in the diffusion model. After reading more into it, I found that most diffusion-based models actually use a conditional GAN to develop a latent space and a decoder, and the diffusion model is used to generate samples in the latent space. This strikes me that the diffusion model then can never actually do better than the associated GAN's decoder, which surprised me to realize since it's usually proposed as an alternative to GAN. So, overall I'm failing to grasp the advantages this approach really has over just using a GAN. Obviously it works fantastically for these large scale generative projects, but I don't understand why it's better, to be honest, despite having read every article out there telling me again and again the same things about how it works. E.g. DALLE-1 used VQGAN, not diffusion, and people were pretty wowed by it. I'm not sure why DALLE-2's improvements can be attributed to their change to a diffusion process, if they are still using a GAN to decode the output. Looking for some intuition, if anyone can offer some. I understand that the nature of how it iteratively improves the image allows it to deduce large-scale and small-scale features progressively, but it seems to me that the many upscaling layers of a large GAN can do the same thing.

adammarples 4y ago

I get the error trying to run MNIST(:train) that modules are not callable. When I search for the error I see a link to the repo issues raising this issue but the issue itself is missing. Was it deleted rather than closed? Weird.

mk_stjames 4y ago

Just in case the author is reading: in the introduction, you use the term "virtual RAM" to describe the GPU VRAM needed for running stable diffusion, but what that VRAM actually stands for is 'Video RAM'.

the_origami_fox 4y ago
Auuthor here. Will update.