Instructions to use WALIDALI/osamliby with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WALIDALI/osamliby with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("WALIDALI/osamliby", 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:
- b1fdfaa14d5ba32977d516bdf488f0f8773da4ae6ae30363a5f55ffef16c23c6
- Size of remote file:
- 246 MB
- SHA256:
- 0725ff7d90e9681bf1c234cd574e0105891e37e3fd086f0debf68efc0ed34e00
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.