Instructions to use Cournane/WHITEPolynomial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Cournane/WHITEPolynomial with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Cournane/WHITEPolynomial", 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:
- dfca00084347a3e13d4335845a22fb351a7e3b2de5c6eb74525c4c6fa298e231
- Size of remote file:
- 6.88 GB
- SHA256:
- 978ba303dbb2b06f4fca6a577d76f1fb125670aa8f8caadb2b5d37add0e6381a
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