Instructions to use amused/amused-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use amused/amused-256 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("amused/amused-256", 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

- Xet hash:
- 685d5ad031cd30147509628d8cb6362bfcdac5005cbd18945b531fe61a64cf5c
- Size of remote file:
- 1.5 MB
- SHA256:
- 5110bedd28f9d68eed175f8234a53c807dedee1f015d54913a1e2758c83e58c2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.