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Add CommonArt β link
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README.md
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@@ -72,4 +72,9 @@ Based on this random sample, I would estimate the following dataset statistics:
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For the parts of the visual world that are well-represented in Megalith-10m, definitely!
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Projects like [CommonCanvas](https://arxiv.org/abs/2310.16825), [Mitsua Diffusion](https://huggingface.co/Mitsua/mitsua-diffusion-one), and [Matroyshka Diffusion](https://arxiv.org/abs/2310.15111)
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have shown that you can train useable generative models on similarly-sized image datasets.
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Of course, many parts of the world aren't well-represented in Megalith-10m, so you'd need additional data to learn about those.
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For the parts of the visual world that are well-represented in Megalith-10m, definitely!
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Projects like [CommonCanvas](https://arxiv.org/abs/2310.16825), [Mitsua Diffusion](https://huggingface.co/Mitsua/mitsua-diffusion-one), and [Matroyshka Diffusion](https://arxiv.org/abs/2310.15111)
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have shown that you can train useable generative models on similarly-sized image datasets.
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Of course, many parts of the world aren't well-represented in Megalith-10m, so you'd need additional data to learn about those.
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### What have people done with Megalith-10m?
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1. AI Picasso have successfully trained a full text-to-image model [CommonArt β](https://huggingface.co/aipicasso/commonart-beta) on Megalith-10m (and other open datasets).
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2. I've successfully trained small [text-to-image models](https://x.com/madebyollin/status/1788282620981497981) on Megalith-10m for my own education.
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