starstruckmon

starstruckmon t1_it8q0jm wrote

The only reason he became popular is because he was the default example in the Disco Diffusion notebook ( where all this really started ) and people started copying from that. I don't know who came up with the tagging nonsense. There's thousands of other artists who have just as much work out there and their names produce just as consistent styles as Greg.

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starstruckmon t1_it816bi wrote

If we're talking about directly using the current text to image generators, thats true. But that's not what I'm talking about.

The real fun starts when you start connecting them to language models

https://twitter.com/jaukia/status/1567914039061217287

This is an example with GPT3. But will get better in the coming years ( year? ) with even more powerful LLMs, especially the multimodal ones that can directly inagine visuals instead of just text that is passed to the text-to-image renderer ( the rendering model might still be used for the final composition but it helps to have the designer model be able to think in actual visual terms ).

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starstruckmon t1_irnsty3 wrote

Depends on your definition of it. There's definitely a bit of this

>Or maybe they thought coming up with a copy of another AI would be more irrelevant than coming up with an AI that has something different to offer?

but another reason was that it's easy to get funding ( in the form of compute in this case ) from public institutions when there's a virtue signalling angle.

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starstruckmon t1_irmt5ng wrote

It's not open to anyone. He's putting on a show by recreating examples from their paper.

It's basically a fine-tuned variation of Chinchilla ( smaller than GPT3 with just 1/3rd the parameters but performs better since it was trained adequately data-wise ) to be more aligned, like how they modded GPT3 into the current InstructGPT variation.

It's not really a jack of all trades in that sense since it was trained on a dataset simmilar to GPT3 of mostly English text.

Most of the new models we'll be seeing ( like the topic of this post ) will definitely be following this path.

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starstruckmon t1_irmlck4 wrote

While this is the current consensus ( they went too broad ) , it's still a guess. These are all black boxes so we can't say for certain.

Basically, a "jack of all trades , master of none" type issue. As we all know from the Chinchilla paper, current models are already severely undertrained data-wise. They went even further by having even less data per language , even if the total dataset was comparable to GPT3.

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starstruckmon t1_irmabdw wrote

We'd have already had a open source GPT competitor in Bloom ( same amount of parameters as GPT3 and open source / open model ) if they didn't decide to virtue signal. They trained it on too many diverse languages and sources and the AI came out an idiot ( significantly underperforms GPT3 in almost all metrics ).

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