Instructions to use Wusul/fat-shoebill with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Wusul/fat-shoebill with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Wusul/fat-shoebill", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
Download text_encoder/pytorch_model.bin from Wusul/fat-shoebill: direct link, hf CLI and curl.
- Browser
- Download file 681 MB
-
https://huggingface.co/Wusul/fat-shoebill/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://Wusul/fat-shoebill/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Wusul/fat-shoebill/resolve/main/text_encoder/pytorch_model.bin
681 MB
- Xet hash:
- 136a04fedeaf8388d4e67b61974c53d897773179a9aebe89e6080df1fabce6dc
- Size of remote file:
- 681 MB
- SHA256:
- 828cf5b7df0dcabe67fc276acceb0c1cbc05b4043142a48380e940d74c22cdfd
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