Instructions to use PublicPrompts/All-In-One-Pixel-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use PublicPrompts/All-In-One-Pixel-Model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("PublicPrompts/All-In-One-Pixel-Model", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from PublicPrompts/All-In-One-Pixel-Model: direct link, hf CLI and curl.
- Browser
- Download file 1.73 kB
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https://huggingface.co/PublicPrompts/All-In-One-Pixel-Model/resolve/refs%2Fpr%2F13/README.md
- Command line
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hf download hf://PublicPrompts/All-In-One-Pixel-Model@refs/pr/13/README.md
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curl -L -o README.md https://huggingface.co/PublicPrompts/All-In-One-Pixel-Model/resolve/refs%2Fpr%2F13/README.md
1.73 kB
| license: creativeml-openrail-m | |
| Stable Diffusion model trained using dreambooth to create pixel art, in 2 styles | |
| the sprite art can be used with the trigger word "pixelsprite" | |
| the scene art can be used with the trigger word "16bitscene" | |
| the art is not pixel perfect, but it can be fixed with pixelating tools like https://pinetools.com/pixelate-effect-image (they also have bulk pixelation) | |
| I've tried this tool, and the algorithm works better for the pixelation process: https://mate.tools/pixelate-effect-image but both are good. | |
| some example generations | |
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