Update README.md

README.md

@@ -21,17 +21,20 @@ whose training is described [here](https://www.reddit.com/r/StableDiffusion/comm
 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:
 * Manually compiled lists will inevitably be incomplete. 
 * Models might not always understand the tags well due to a dearth of training images labeled with these tags.
-* 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.
+* 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.
 <br>
 
-To address these problems, boring_e621 employs textual inversion directly on a set of 100 of the most boring images on the furry image site e621.net.  
-These images were automatically collected by querying e621 for images that no user has ever Favorited or Up-Voted. 
-Almost all images on the site have at least one up vote or favorite, so the images that no one favorited are some of the very most uninspiring images on the website. 
-Thus, you should be able to use the resulting embedding as a negative prompt to make your generated images Not Boring.
+To address these problems, boring_e621 employs textual inversion on a set of images automatically extracted from the art site 
+e621.net, a rich resource of millions of hand-labeled artworks, each of which is both hand-labeled topically and rated 
+according to its quality.  E621.net allows users to express their approval of an artwork by either up-voting it, or marking it as a favorite.  
+Boring_e621 was specifically trained artworks automatically selected from the site according to the criteria 
+that no user has ever Favorited or Up-Voted them.  boring_e621 thus learned to produce low-quality images, so when it is 
+used in the negative prompt of a stable diffusion image generator, the model avoids making mistakes that would make the generation more boring.
 <br>
 
 # Bias, Risks, and Limitations
-* Using this as a negative embedding often sacrifices some fidelity to the prompt.  For example, characters in the image may disappear or change eye/skin/fur color.
+* Using this as a negative embedding often sacrifices some fidelity to the prompt.  For example, characters in the image may disappear or change eye/skin color.
 * Using this as a negative embedding may introduce unexpected or undesired content into the image to make it look less boring.  
 * Unlike other negative embeddings, this is not intended to fix problems like extra limbs or deformed hands.  It can be used alongside other negative embeddings to fix deformities.
 <br>
@@ -62,10 +65,11 @@ Finally, I qualitatively evaluated the attractiveness and interestingness of the
 
 ## Other Models
 
-Boring_e621 has been reported to work well with SD 1.4 or 1.5 furry models such as:
+Boring_e621 has been reported to work well with SD 1.4 or 1.5 models such as:
+* https://civitai.com/models/18208?modelVersionId=68551
 * https://civitai.com/models/12979/lawlass-yiffymix-20-furry-model
 * https://civitai.com/models/4698/lawlass-yiff-mix
 * https://civitai.com/models/15503/kavka-mix
 * https://civitai.com/models/17649/bb95-furry-mix
-* https://huggingface.co/Doubleyobro/yiffy-e18
+* https://huggingface.co/Doubleyobro/yiffy-e18 .  This was the fine-tuned model used to train boring_e621.