Instructions to use Boogu/Boogu-Image-0.1-Edit-Turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Boogu/Boogu-Image-0.1-Edit-Turbo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Boogu/Boogu-Image-0.1-Edit-Turbo", 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:
- 56e7cfd8b641735553c1fa90d8a1cc5ab12ceff18e19f8a08be50520df62c5f6
- Size of remote file:
- 5.45 MB
- SHA256:
- dac4758996833e314ab1d65f89759f5382316639c79f6e578cf5f99aa4b12eed
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