Instructions to use Kurudaz/hyper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Kurudaz/hyper with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Kurudaz/hyper", 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
- Xet hash:
- f08a1a79c7b7f47aa7f342ab77a94168d4fc480b341430423712941ae967d8d5
- Size of remote file:
- 167 MB
- SHA256:
- b921a15c9833d884465b5faca30a1a2fdd57cf7fc60c33a47d87be4dc3806afc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.