Instructions to use camenduru/facechain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use camenduru/facechain with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("camenduru/facechain", 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 test/pytorch_lora_weights.bin from camenduru/facechain: direct link, hf CLI and curl.
- Browser
- Download file 102 MB
-
https://huggingface.co/camenduru/facechain/resolve/main/test/pytorch_lora_weights.bin
- Command line
-
hf download hf://camenduru/facechain/test/pytorch_lora_weights.bin
-
curl -L -o pytorch_lora_weights.bin https://huggingface.co/camenduru/facechain/resolve/main/test/pytorch_lora_weights.bin
102 MB
- Xet hash:
- 02554dde1f1bfbf156631f5c63618ab68dda066e05af1e4a5ed64b7358d1112b
- Size of remote file:
- 102 MB
- SHA256:
- b47c109406650ad7e8eacfa2afd832fc966145e5155d38a7b6eeb62a370139c9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.