OnnxStream: Stable Diffusion XL 1.0 Base on a Raspberry Pi Zero 2

It looks like the reason it was submitted today is a new feature added "Added support for Stable Diffusion XL Turbo 1.0! (thanks to @AeroX2)", from the news section of the README.

Surely the CPU usage is what will cripple the pi?

I was just using that as a reference. Stable diffusion will run well with almost any relatively modern gpu.

You don't have to use a 4090, you'll still get double digit performance with a 3060 or whatnot.

> for people who can only otherwise afford Raspberry Pis ;)

You can rent a 4090 for 0.7USD/1hr, or get an A100 for 1.1USD/hr. And if your project is a display + raspberry pi then those costs will dwarf the rental cost.

> I didn't say any of that

I'm sorry, then I don't know what you're saying. Because what I read was 2 claims. 1) GAN quality is less than diffusion. 2) GANs can't do T2I. I think I adequately showed that both these assumptions were wrong. I'm not sure what else your comment meant as that contains all of its words...

> there is simply no open source GAN model that can compete with the open source diffusion models we have today

Again, I provided citations. Do you want the github links? Here's GigaGAN's: https://github.com/mingukkang/GigaGAN (checkpoints under the evaluation folder).

What do you mean by compete? Because the metric scores are quite comparable. I think that's a reasonable interpretation of the word "competitive" but I'm a generative researcher so we might be using the terms to mean different things. (I even like diffusion more fwiw, but I'm particularly more interested in tractable density models)

Do you mean "the open community has rallied around Stable Diffusion and sunk in more time to tuning this model and producing textual inversions and LoRAs which far out surpass that of any other model, even including other diffusion models and is a phenomena nearly exclusive to Stable Diffusion and has been mostly accomplished in the last year"? Because if so, yeah, I agree.

But that's not a really good argument for saying Diffusion is better than GANs. It's a completely different argument. That's just to say __Stable Diffusion__ (not the class of diffusion models, or even more specifically latent diffusion models (the two are different)) has better community support. That's a reasonable argument, but a different one. Because it really just shows what you can do to __any__ model. These techniques and efforts are not architecture dependent nor are they even mode dependent. We see similar community effort around OpenLLaMa and GPT but less so around Bard/Gemini or Claude. So what? That's not really relevant to the conversation nor to the specific context that we're discussing which is very likely not going to include many of those TIs or LoRAs (or if it would you'd probably be training a custom one, so the point is again moot since LoRAs are not unique to diffusion models). I'm really happy a lot of people have entered the community and are effectively doing research, but it'd be quite naive to say that such a thing isn't possible around other architectures. Evangelism is quite useful, but not if it turns into religious beliefs. Hell, we can argue that Linux is better than Windows because Linux has a bigger hacker community but I'm not sure that's a great or even meaningful argument because it's void of context in what better actually means. The better OS is clearly situationally dependent and makes a lot of the OS holy wars silly.

> and the fact that these models can be distilled

Literally any model can be distilled. I'm not understanding your argument. Are you just arguing that such efforts have already taken place? Sure, I'll agree to that. But it's worth noting that the distilled diffusion models are still quite large and much larger than some of the aforementioned works.

> 1/2/4 steps makes GANs less attractive.

I even addressed the one step process. Which, single step diffusion generation represents a significant decrease in quality, so this seems to run counter to your prior argument. This is why I'm a bit confused. If we're going with a low step diffusion model then the case for the GAN becomes clearer because even your non-distilled GAN's inference is still higher and it's quality is definitely superior.

So really, I am confused. I'm not sure what you're arguing.

We can get into the weeds and discuss diversity, recall, memorization, density estimation, and all manner of things but these are quite open questions and frankly understudied. Plus we'll have to be extra nuanced because the metrics are proxies and incorporate different biases that different architectures are going to suffer from, making it difficult to compare in a more fair sense. But that's okay.

Let's also be clear: GAN != StyleGAN{1,2,2-ada,3,XL,T,*} and diffusion != StableDiffusion. There are other GANs and other diffusions and even other image synthesis models, the vast majority of them being open source.

Again, I'll assert, there are no universal models that are best for all situations. There are only models that are best at specific situations. You can translate this to "AGI doesn't exist but we have narrow AI" if that is clearer. But my claim is a bit broader still because if that AGI took a warehouse to run it still wouldn't be contextually appropriate.

I'm not going to argue that there isn't a difference when going from 0 -> 1 and 1 -> 10, or in this case, from 1.5b (Whisper-large) -> 1.7t parameters (gpt-4). But it's not like we don't know how to do it, so it won't take 10 years to get there.