Instructions to use omniousai/BootComp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use omniousai/BootComp with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("omniousai/BootComp", 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
- Xet hash:
- 51d73bb868a3ba12dd75ceb142c2781f2b62d97591dfd05968480bef4dd66977
- Size of remote file:
- 10.3 GB
- SHA256:
- ee7e36c5a4f61993617d08a4349a4bd80298fec905d99aeba25edb8bff7a4c14
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