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README.md
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@@ -45,7 +45,7 @@ hand-labeled artworks which allow users to express their approval of an artwork
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The Boring embeddings were specifically trained on artworks automatically selected from these sites according to the criteria
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that no user has ever favorited them, and they have 0 or only a very small number of up or down votes. The Boring embeddings
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thus learned to produce uninteresting low-quality images, so when they are used in the negative prompt of a stable diffusion image generator,
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the model avoids making mistakes that would make the generation more boring.
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Each training sample consisted of an image paired with a comma-separated list of all the tags associated with it on the site it was sourced from, with the embedding’s name prepended to the list.
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This ensures that the model learns to associate the embedding with the characteristics of uninteresting images in a way that aligns with the dataset’s tagging system.
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<br>
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The Boring embeddings were specifically trained on artworks automatically selected from these sites according to the criteria
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| 46 |
that no user has ever favorited them, and they have 0 or only a very small number of up or down votes. The Boring embeddings
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| 47 |
thus learned to produce uninteresting low-quality images, so when they are used in the negative prompt of a stable diffusion image generator,
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| 48 |
+
the model avoids making mistakes that would make the generation more boring.
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| 49 |
Each training sample consisted of an image paired with a comma-separated list of all the tags associated with it on the site it was sourced from, with the embedding’s name prepended to the list.
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| 50 |
This ensures that the model learns to associate the embedding with the characteristics of uninteresting images in a way that aligns with the dataset’s tagging system.
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| 51 |
<br>
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