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] | 28000 | hf://datasets/6DammK9/e621_2024-captions-1ktar@89929fdf1ceaeffdeaaa1736eaf4a742af223ce6/captions/0000.tar |
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] | 35000 | hf://datasets/6DammK9/e621_2024-captions-1ktar@89929fdf1ceaeffdeaaa1736eaf4a742af223ce6/captions/0000.tar |
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] | 58000 | hf://datasets/6DammK9/e621_2024-captions-1ktar@89929fdf1ceaeffdeaaa1736eaf4a742af223ce6/captions/0000.tar |
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] | 71000 | hf://datasets/6DammK9/e621_2024-captions-1ktar@89929fdf1ceaeffdeaaa1736eaf4a742af223ce6/captions/0000.tar |
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] | 72000 | hf://datasets/6DammK9/e621_2024-captions-1ktar@89929fdf1ceaeffdeaaa1736eaf4a742af223ce6/captions/0000.tar |
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] | 76000 | hf://datasets/6DammK9/e621_2024-captions-1ktar@89929fdf1ceaeffdeaaa1736eaf4a742af223ce6/captions/0000.tar |
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] | 143000 | hf://datasets/6DammK9/e621_2024-captions-1ktar@89929fdf1ceaeffdeaaa1736eaf4a742af223ce6/captions/0000.tar |
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E621 2024 captions only in 1k tar
Raw captions jointed from lodestones/e621-captions
It doesn't align to any dataset yet.
meta_cap.jsonhas been provided in compressed format if you want to train with kohyas triner. Currently I'm trying to merge this with my 2024 version.
Core logic
There is not much choice, I don't have GPU to run for 1M captions with VLM so I just "take it or leave it".
rearranged_tags = [row.regular_summary, row.brief_summary] if row.response_finish_reason == "STOP" else [row.tags]
must_exist = [tag for tag in rearranged_tags if tag]
caption = " ".join(must_exist).replace("\n","").replace("\r","")
How to build the "dataset" with speed
Refer to the tags-1ktar repo.
This repo is still in develeopment. My current task of building the dataset for "pretraining".
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