Instructions to use Sgiuowa/hassaku with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Sgiuowa/hassaku with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Sgiuowa/hassaku", 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:
- f92a26e281b391844c239cf88fd4243a6762263635c3715efe27a9b8c57a2dd2
- Size of remote file:
- 335 MB
- SHA256:
- 0f8f1f6d6ceed3f2c5ef822dd6119fbf2c23847c0a9c098ecacdab5d7b2d71f6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.