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:
- 7c86a140b0516b3122e35cdd7cd765d316611daae2e0dde6d28e52694dd7f54b
- Size of remote file:
- 10.3 GB
- SHA256:
- 147539ce17296abc05cb9344f31cc576d4001edaeb1fb732a419dd87176a392a
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