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
Download checkpoint-2000/optimizer.bin from Cournane/WHITEPolynomial: direct link, hf CLI and curl.
- Browser
- Download file 6.88 GB
-
https://huggingface.co/Cournane/WHITEPolynomial/resolve/main/checkpoint-2000/optimizer.bin
- Command line
-
hf download hf://Cournane/WHITEPolynomial/checkpoint-2000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/Cournane/WHITEPolynomial/resolve/main/checkpoint-2000/optimizer.bin
6.88 GB
- Xet hash:
- 52db063a2dc6ce39aeef841a945e64de6bc794820e43b0268d1b244b5088691e
- Size of remote file:
- 6.88 GB
- SHA256:
- bada56a19f10d82782e8d6ea0cb3dfdf1e1bdf82ad6d247a643899d64a2b1442
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