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--- |
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title: boring_e621 |
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tags: |
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- textual inversion embeddings |
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- image-generation |
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license: apache-2.0 |
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--- |
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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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## 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, |
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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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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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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. |
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Boring_e621 was specifically trained artworks automatically selected from the site according to the criteria |
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that no user has ever Favorited or Up-Voted them. boring_e621 thus learned to produce low-quality images, so when it is |
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used in the negative prompt of a stable diffusion image generator, the model avoids making mistakes that would make the generation more boring. |
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# Bias, Risks, and Limitations |
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* 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. |
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* Using this as a negative embedding may introduce unexpected or undesired content into the image to make it look less boring. |
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* 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. |
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# Evaluation |
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To qualitatively evaluate how well boring_e621 has learned to improve image quality, we apply it to 4 simple sample prompts using the base Stable Diffusion 1.5 model. |
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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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Boring_e621 has been reported to work well with SD 1.4 or 1.5 models such as: |
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* https://civitai.com/models/18208?modelVersionId=68551 |
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* https://civitai.com/models/12979/lawlass-yiffymix-20-furry-model |
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* https://civitai.com/models/4698/lawlass-yiff-mix |
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* https://civitai.com/models/15503/kavka-mix |
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* https://civitai.com/models/17649/bb95-furry-mix |
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* https://huggingface.co./Doubleyobro/yiffy-e18 . This was the fine-tuned model used to train boring_e621. |
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