Maintainer: dranzerstar

Total Score


Last updated 5/28/2024


Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The SD-textual-inversion-embeddings-repo by maintainer dranzerstar is a repository focused on Lora networks for Stable Diffusion. It includes a variety of character and outfit embeddings that can be used to personalize Stable Diffusion outputs. The model explores techniques like textual inversion and LoRA to create custom visual styles.

Similar models in this space include the sd-nai-lora-index which indexes various LoRA works, and the LoraByTanger collection which focuses on Genshin Impact and anime-style characters.

Model inputs and outputs


  • Textual prompts: The model takes in text-based prompts that describe the desired output image, such as character names, outfits, and visual attributes.
  • LoRA embeddings: The model can utilize custom LoRA embeddings trained on specific characters or styles to personalize the output.


  • Stable Diffusion images: The model generates high-quality images based on the input prompt and LoRA embeddings.


The SD-textual-inversion-embeddings-repo showcases the versatility of Lora networks for Stable Diffusion. It demonstrates how custom embeddings can be used to create a wide range of character designs and outfits, from anime-inspired styles to more realistic depictions. The repository provides examples of generating images with specific characters and outfits by combining textual prompts and LoRA embeddings.

What can I use it for?

This model could be useful for artists, character designers, and creative professionals who want to quickly generate personalized Stable Diffusion outputs. The LoRA embeddings can be used to create custom assets for games, illustrations, or other visual projects. Additionally, the techniques showcased in this repository could be applied to generate unique content for marketing, advertising, or social media purposes.

Things to try

One interesting aspect of this model is the exploration of combining different LoRA embeddings, such as "char-

" and "outfit-
" tags, to create unique character designs. Users could experiment with blending various character and outfit styles to generate unexpected and original results. Additionally, trying different prompting techniques, like using character names or detailed visual descriptions, could lead to interesting discoveries and help unlock the full potential of this model.

This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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