Instructions to use Overtronic/bear with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Overtronic/bear with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Overtronic/bear", 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:
- 2a486418d607a07b416610caa887920da54e82d295de235b24354e08b503d763
- Size of remote file:
- 1.72 GB
- SHA256:
- 358d49c4d4fd878239c8d5959290d1c9fa8bf7bc6f8a2bac93c55fb5808f6c79
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.