Instructions to use cuio/URSA-0.6B-FSQ320 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cuio/URSA-0.6B-FSQ320 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cuio/URSA-0.6B-FSQ320", 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:
- 3896fd5fb5c9f704935aa8620d5907afe18282f8bdb4d1c772ca6b54a15871ca
- Size of remote file:
- 225 MB
- SHA256:
- 433a1dd689f7f819ef42bf56f87e2b965e0c30a607edfda385fabac6fa56d43c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.