Instructions to use nawed/nawed-new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nawed/nawed-new with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nawed/nawed-new", 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 Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 26649a55781bac13c4e11c0955dee7c36017c52596904c749741f36455ef1763
- Size of remote file:
- 3.44 GB
- SHA256:
- 5edd52b7289dc99a1d8585c150abd4c7d51da5441b45a029916f3ee583060ea0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.