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@@ -15,14 +15,12 @@ stable-diffusion-webui\embeddings and add "boring_e621_v4" to your negative prom
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  ## Model Description
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  The motivation for boring_e621 is that negative embeddings like [Bad Prompt](https://huggingface.co/datasets/Nerfgun3/bad_prompt),
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  whose training is described [here](https://www.reddit.com/r/StableDiffusion/comments/yy2i5a/i_created_a_negative_embedding_textual_inversion/)
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  depend on manually curated lists of tags describing features people do not want their images to have, such as "deformed hands". Some problems with this approach are:
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  * Manually compiled lists will inevitably be incomplete.
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  * Models might not always understand the tags well due to a dearth of training images labeled with these tags.
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  * It can only capture named concepts. If there exist unnamed yet visually unappealing concepts that just make an image look wrong, but for reasons that cannot be succinctly explained, they will not be captured by a list of tags.
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- <br>
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  To address these problems, boring_e621 employs textual inversion on a set of images automatically extracted from the art site
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  e621.net, a rich resource of millions of hand-labeled artworks, each of which is both human-labeled topically and rated
@@ -45,7 +43,7 @@ To qualitatively evaluate how well boring_e621 has learned to improve image qual
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  ![boring_e621 and boring_e621_v4 Performance on Simple Prompts](tmpoqs1d_vv.png)
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  As we can see, putting these embeddings in the negative prompt yields a more delicious burger, a more vibrant and detailed landscape, a prettier pharoah, and a more 3-d-looking aquarium.
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  ## Other Models
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  ## Model Description
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  The motivation for boring_e621 is that negative embeddings like [Bad Prompt](https://huggingface.co/datasets/Nerfgun3/bad_prompt),
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  whose training is described [here](https://www.reddit.com/r/StableDiffusion/comments/yy2i5a/i_created_a_negative_embedding_textual_inversion/)
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  depend on manually curated lists of tags describing features people do not want their images to have, such as "deformed hands". Some problems with this approach are:
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  * Manually compiled lists will inevitably be incomplete.
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  * Models might not always understand the tags well due to a dearth of training images labeled with these tags.
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  * It can only capture named concepts. If there exist unnamed yet visually unappealing concepts that just make an image look wrong, but for reasons that cannot be succinctly explained, they will not be captured by a list of tags.
 
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  To address these problems, boring_e621 employs textual inversion on a set of images automatically extracted from the art site
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  e621.net, a rich resource of millions of hand-labeled artworks, each of which is both human-labeled topically and rated
 
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  ![boring_e621 and boring_e621_v4 Performance on Simple Prompts](tmpoqs1d_vv.png)
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  As we can see, putting these embeddings in the negative prompt yields a more delicious burger, a more vibrant and detailed landscape, a prettier pharoah, and a more 3-d-looking aquarium.
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+ <br>
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  ## Other Models
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