pixelart
Maintainer: irateas
72
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The pixelart model is a beta embedding for Stable Diffusion 2.0 that was created by the maintainer irateas to generate 2D pixel art imagery. It was trained on a small initial dataset of 70 images, with plans to expand the dataset to 128 or 256 images that have been processed through a pixelate tool to maintain consistent pixel size.
Similar models include epic-diffusion, a general-purpose Stable Diffusion 1.x model focused on high-quality outputs in a variety of styles, and PixArt-XL-2-1024-MS, a diffusion-transformer model capable of generating 1024px images directly from text prompts.
Model inputs and outputs
Inputs
- Text prompts describing the desired pixel art image
Outputs
- 2D pixel art images at 768x768 resolution
Capabilities
The pixelart model is able to generate various styles of pixel art, from more generic and readable styles to more vintage/old-school looks. The maintainer has provided several specific embedding variants - pixelart, pixelart-soft, pixelart-hard, pixelart-1, pixelart-2, and pixelizer - that can be used to achieve different aesthetic results.
What can I use it for?
The pixelart model could be useful for projects or applications that involve the generation of retro/nostalgic pixel art imagery, such as video games, digital art, or multimedia design. The maintainer has recommended using the Euler a diffuser for best results, and provided some tips on using negative prompts to refine the outputs.
Things to try
One interesting capability of the pixelart model is its ability to be used in an img2img workflow, where it can be used to "pixelate" existing images. This could be a useful tool for designers or artists looking to create pixel art versions of their work.
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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