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
- Draw Things
- DiffusionBee
metadata
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)
some example generations






