Maintainer: ShadoWxShinigamI

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Last updated 5/28/2024


Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
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Model overview

The SD2-768-Papercut model is a Textual Inversion Embedding for Stable Diffusion 2.0 created by ShadoWxShinigamI. It is based on ShadoWxShinigamI's previous MJv4-Paper Cut Model, and is designed to be fully customizable with prompts. The model was trained using 106 manually captioned images at 768x768 resolution over 150 steps, with 8 Textual Inversion vectors.

Model inputs and outputs


  • Textual prompts that can be customized for a variety of subjects, including ships, dogs, lions, people, landscapes, and buildings.


  • High-quality, photorealistic images generated in the style of the training data, which includes a variety of scenes and subject matter.


The SD2-768-Papercut model is capable of generating detailed, visually striking images across a range of subject matter. The examples provided show its versatility in depicting ships, animals, people, and landscapes with a distinctive photographic quality. The model's ability to handle a variety of prompt inputs without extensive engineering suggests it could be a useful tool for content creators or designers looking to quickly generate concept art or visual references.

What can I use it for?

The SD2-768-Papercut model could be used for a variety of projects that require high-quality, customizable images. Some potential use cases include:

  • Generating visual concepts or references for game, film, or product design
  • Creating unique images for blog posts, social media, or marketing materials
  • Exploring different artistic styles or photographic techniques through prompt experimentation

Things to try

One interesting aspect of the SD2-768-Papercut model is its ability to generate visually cohesive images from diverse prompt inputs. For example, the model can depict a wide range of subjects, from natural landscapes to man-made objects, while maintaining a consistent level of detail and photorealism. This suggests the model has learned robust representations of visual elements that can be flexibly combined to create novel compositions.

Prompt engineers or artists could experiment with the model by trying to push the boundaries of its capabilities, such as by combining multiple subjects in a single image, or by introducing more abstract or fantastical elements into the prompts. The model's performance on these types of prompts could reveal interesting insights about its inner workings and potential areas for further development.

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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The vray-render model is a Textual Inversion Embedding created by ShadoWxShinigamI for the Stable Diffusion 2.0 (768) model. It is designed to generate images with a V-Ray Render style, resulting in slightly soft outputs. The model was trained on 44 images at 768x768 resolution with a batch size of 4, gradient accumulation of 11, and 500 training steps. Similar models created by ShadoWxShinigamI include the SD2-768-Papercut and Midjourney-v4-PaintArt embeddings, which focus on Papercut and Midjourney-inspired art styles respectively. Model inputs and outputs Inputs Text prompts that describe the desired output image Outputs Images generated based on the input text prompt, with a V-Ray Render style Capabilities The vray-render model can generate a variety of photorealistic images with a distinctive V-Ray Render aesthetic, including scenes like cabins, cars, lions, ships, and human portraits. What can I use it for? The vray-render model can be used to create visually striking and photorealistic images for a range of applications, such as digital art, product visualizations, or architectural renderings. Its unique style can also be useful for creative projects that require a specific look and feel. Things to try Experimenting with different prompts and prompt engineering techniques can help unlock the full potential of the vray-render model. Trying out various subjects, scene compositions, and combinations of keywords may result in unexpected and compelling outputs.

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