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@@ -9,8 +9,8 @@ license: apache-2.0
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  # boring_e621
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  This embedding attempts to capture what it means for an image to be uninteresting. It was trained as a negative embedding using e621 style tags as prompts during training.
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- If you're using the [Automatic1111 Stable Diffusion WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui), place the .pt file in
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- stable-diffusion-webui\embeddings and add "by boring_e621" to your negative prompt for more interesting outputs.
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  <br>
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  ## Model Description
@@ -21,8 +21,7 @@ whose training is described [here](https://www.reddit.com/r/StableDiffusion/comm
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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,
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- 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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  # boring_e621
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  This embedding attempts to capture what it means for an image to be uninteresting. It was trained as a negative embedding using e621 style tags as prompts during training.
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+ If you're using the [Automatic1111 Stable Diffusion WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui), place the boring_e621_v4.pt file in
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+ stable-diffusion-webui\embeddings and add "boring_e621_v4" to your negative prompt for more interesting outputs.
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  <br>
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  ## Model Description
 
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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