--- title: boring_e621 tags: - textual inversion embeddings - image-generation license: apache-2.0 --- # boring_e621 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. If you're using the [Automatic1111 Stable Diffusion WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui), place the .pt file in stable-diffusion-webui\embeddings and add "by boring_e621" to your negative prompt for more interesting outputs.
## Model Description The motivation for boring_e621 is that negative embeddings like [Bad Prompt](https://huggingface.co/datasets/Nerfgun3/bad_prompt), whose training is described [here](https://www.reddit.com/r/StableDiffusion/comments/yy2i5a/i_created_a_negative_embedding_textual_inversion/) 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.
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.
# 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 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.
# Evaluation I extracted the tags from three e621 images and used them to construct a set of test prompts. * one prompt was constructed from an image with a high number of favorites. * one prompt was constructed from an image with a moderate number of favorites. * one prompt was constructed from an image with 0 favorites.
I then generated test images from each of these prompts, each time using a different negative embedding as the negative prompt. Particularly, I tried: * [EasyNegative](https://huggingface.co/datasets/gsdf/EasyNegative) * [Bad Artist](https://huggingface.co/nick-x-hacker/bad-artist) * [Bad Prompt](https://huggingface.co/datasets/Nerfgun3/bad_prompt) * [boring_e621](this)
Finally, I qualitatively evaluated the attractiveness and interestingness of the resulting images, though I will let you draw your own conclusions from the output below.
## Results ![Negative Embedding Comparison](https://i.imgur.com/d7R4gGi.jpg) ## Other Models 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 . This was the fine-tuned model used to train boring_e621.