4 ms·

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 previous

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.

siraben 4y ago
What datasets were you using, how large was the model and what was the noise schedule? I’ve been contemplating implementing my own from scratch as well. I’m surprised that training with conditional labels did not help as much.
- radarsat1 4y ago
  Data is mel spectrograms. To be clear about the conditional labels, I was trying to get it to come up with a vector quantized code, so it's not conditionally labeled but rather I was using an embedding layer with a VQ layer to have it come up with its own codebook. This works well with VQGAN so I was surprised that for diffusion it just keeps setting all the codes to the same value and ignoring them, but maybe I'm doing something wrong. Still working on it. I'm just expressing here that my expectation was that this method would be less finicky than GAN because it uses an MSE loss, but unfortunately it seems to have its own difficulties. No silver bullet, I guess. The integration sampling can be quite sensitive to imperfections and diverge easily, at least in early stages of training. I decided to write this because it feels like the early days of GAN where overall there seems to be lots of these "explain diffusion from scratch" type articles out there, but not yet a lot discussing common pitfalls and how to deal with them.
  - siraben 4y ago
    I see. What types of sampling methods are you using? IIRC they are different approaches of solving the diffusion ODE and creating a sample, but I’ve only played around with them during inference.
  - lukasga 4y ago
    I'm doing my thesis right now in diffusion models (for audio) and have experienced a lot of the same things you mention. One paper which I found illuminating was this one: https://proceedings.mlr.press/v139/nichol21a.html https://proceedings.mlr.press/v139/nichol21a.html Particularly relevant to the noisy training you mentioned earlier is their alternative timestep sampling procedure they propose which seem to reduce gradient noise significantly judging from their experiments. Would love to hear or discuss if you have found any other design changes which have improved training / sample qualities :)

siraben 4y ago

What datasets were you using, how large was the model and what was the noise schedule? I’ve been contemplating implementing my own from scratch as well. I’m surprised that training with conditional labels did not help as much.

radarsat1 4y ago
Data is mel spectrograms. To be clear about the conditional labels, I was trying to get it to come up with a vector quantized code, so it's not conditionally labeled but rather I was using an embedding layer with a VQ layer to have it come up with its own codebook. This works well with VQGAN so I was surprised that for diffusion it just keeps setting all the codes to the same value and ignoring them, but maybe I'm doing something wrong. Still working on it. I'm just expressing here that my expectation was that this method would be less finicky than GAN because it uses an MSE loss, but unfortunately it seems to have its own difficulties. No silver bullet, I guess. The integration sampling can be quite sensitive to imperfections and diverge easily, at least in early stages of training. I decided to write this because it feels like the early days of GAN where overall there seems to be lots of these "explain diffusion from scratch" type articles out there, but not yet a lot discussing common pitfalls and how to deal with them.
- siraben 4y ago
  I see. What types of sampling methods are you using? IIRC they are different approaches of solving the diffusion ODE and creating a sample, but I’ve only played around with them during inference.
- lukasga 4y ago
  I'm doing my thesis right now in diffusion models (for audio) and have experienced a lot of the same things you mention. One paper which I found illuminating was this one: https://proceedings.mlr.press/v139/nichol21a.html https://proceedings.mlr.press/v139/nichol21a.html Particularly relevant to the noisy training you mentioned earlier is their alternative timestep sampling procedure they propose which seem to reduce gradient noise significantly judging from their experiments. Would love to hear or discuss if you have found any other design changes which have improved training / sample qualities :)