Instructions to use Moon-jack/Democratize-CSG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Moon-jack/Democratize-CSG with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Moon-jack/Democratize-CSG", 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 checkpoints/Animation/face_encoder.pth from Moon-jack/Democratize-CSG: direct link, hf CLI and curl.
- Browser
- Download file 114 MB
-
https://huggingface.co/Moon-jack/Democratize-CSG/resolve/main/checkpoints/Animation/face_encoder.pth
- Command line
-
hf download hf://Moon-jack/Democratize-CSG/checkpoints/Animation/face_encoder.pth
-
curl -L -o face_encoder.pth https://huggingface.co/Moon-jack/Democratize-CSG/resolve/main/checkpoints/Animation/face_encoder.pth
114 MB
- Xet hash:
- 86df1960585c5f35fa190f7af8d79013b8e1500c7a7b465c4528b5d0b1419b9c
- Size of remote file:
- 114 MB
- SHA256:
- 98cf8c635aa5b63bd8a07135208e884cb50d5721dd4b81ed4de5071ccf7ecf2a
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