Instructions to use lilpotat/sa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lilpotat/sa with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lilpotat/sa", 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 lilpotat/sa: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/lilpotat/sa/resolve/refs%2Fpr%2F4/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://lilpotat/sa@refs/pr/4/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/lilpotat/sa/resolve/refs%2Fpr%2F4/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- 9451ff53ef7100cd93d2f903eac304b3c75a3f181cde756e51beb61b102a6f5d
- Size of remote file:
- 492 MB
- SHA256:
- 53445d687fc9b2d7ec99e5ebf94d960bed402b9ce361f567a2827f1ca74322da
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