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madebyollin commited on Jun 3

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Add i1 shoutout

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@@ -84,4 +84,5 @@ Of course, many parts of the world aren't well-represented in Megalith-10m, so y
 6. Megalith-10m was used to train [otoro](https://huggingface.co/aihub-geniac/oboro), an open-weights image generator by AiHUB
 7. Megalith-10m was used to train [NextFlow](https://arxiv.org/abs/2601.02204), a large next-scale autoregressive generator from ByteDance
 8. Megalith-10m was used to train [IOMM](https://github.com/LINs-lab/IOMM), an efficient-to-train multimodal generative model
-9. Megalith-10m was used as part of [MONET](https://huggingface.co/datasets/jasperai/monet), a massive, open, non-redundant and enriched text-to-image dataset

 6. Megalith-10m was used to train [otoro](https://huggingface.co/aihub-geniac/oboro), an open-weights image generator by AiHUB
 7. Megalith-10m was used to train [NextFlow](https://arxiv.org/abs/2601.02204), a large next-scale autoregressive generator from ByteDance
 8. Megalith-10m was used to train [IOMM](https://github.com/LINs-lab/IOMM), an efficient-to-train multimodal generative model
+9. Megalith-10m was used as part of [MONET](https://huggingface.co/datasets/jasperai/monet), a massive, open, non-redundant and enriched text-to-image dataset
+10. Megalith-10m was used to train [i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models](https://github.com//zlab-princeton/i1) (via the combined [i1-captions](https://huggingface.co/datasets/zlab-princeton/i1-captions) dataset)