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Stable Diffusion XL 1.0

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Re: Stable Diffusion XL 1.0

#61
I always wondered why the vision models don't seem to be following the whole "scale up as much as possible" mantra that has defined the language models of the past few years (to the same extent). Even 3.5 billion parameters is absolutely nothing compared to the likes of GPT-3, 3.5, 4, or even the larger open-source language models (e.g. LLaMA-65B). Is it just an engineering challenge that no one has stepped up for yet? Is it a matter of finding enough training data for the scaling up to make sense?

Re: Stable Diffusion XL 1.0

#62
post #40

I'm out of date on the image-generating side of AI, but I'd like to check things out. What's the best tool for image generation that's available on a website right now? Ie, not a model that I have to run locally.

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Re: Stable Diffusion XL 1.0

#65

This explosion of AI-generated imagery will result in an explosion of millions of fake images, obivously. Perhaps in the short-term this is fun, but in the long-term, we will lose a bit more scarcity, which is not that great in my opinion. Isn't the best part of a meal eating after you've not had anything to eat for a while? The best part about a kiss that you've quenched the pain of missing your partner? The best pa…

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Re: Stable Diffusion XL 1.0

#66

Still can't draw hands correctly it looks like.

I can't say for 1.0 but in 0.9 hands get fairly often rendered perfectly. It's not always right but it's way better than any other earlier release (where it's usually consistently wrong).

Re: Stable Diffusion XL 1.0

#67

This explosion of AI-generated imagery will result in an explosion of millions of fake images, obivously. Perhaps in the short-term this is fun, but in the long-term, we will lose a bit more scarcity, which is not that great in my opinion. Isn't the best part of a meal eating after you've not had anything to eat for a while? The best part about a kiss that you've quenched the pain of missing your partner? The best pa…

The same could have been said when photoshop or CGI tools like blender replaced hand sculpting and hand painting but I think it hasn't been a net negative across the board (I think rather the opposite).

Re: Stable Diffusion XL 1.0

#68
post #61

I always wondered why the vision models don't seem to be following the whole "scale up as much as possible" mantra that has defined the language models of the past few years (to the same extent). Even 3.5 billion parameters is absolutely nothing compared to the likes of GPT-3, 3.5, 4, or even the larger open-source language models (e.g. LLaMA-65B). Is it just an engineering challenge that no one has stepped up for ye…

They often reference this paper as the motivation for that https://arxiv.org/pdf/2203.15556.pdf I.e. training with 10x data and 10x longer can yield as good models as a gpt-3 model but with fewer weights (according to the paper) and the same principle applies in vision.

Re: Stable Diffusion XL 1.0

#70
post #61

I always wondered why the vision models don't seem to be following the whole "scale up as much as possible" mantra that has defined the language models of the past few years (to the same extent). Even 3.5 billion parameters is absolutely nothing compared to the likes of GPT-3, 3.5, 4, or even the larger open-source language models (e.g. LLaMA-65B). Is it just an engineering challenge that no one has stepped up for ye…

Diffusion is relatively compute intensive compared to transformers llms, and (in current implementation) doesn't quantize as well.

A 70B parameter model would be very slow and vram hungry, hence very expensive to run.

Also, image generation is more reliant on tooling surrounding the models than pure text prompting. I dont think even a 300B model would get things quite right through text prompting alone.

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