Instructions to use JCTN/Arc2Face with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JCTN/Arc2Face with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("JCTN/Arc2Face", 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
- Xet hash:
- d5b048b25b09e31423a94c812b47ffee553fa86d3b450837a8271ea98c510f06
- Size of remote file:
- 492 MB
- SHA256:
- e2d364df774b7d3975f85de42bda73c0c0cdb952273dd5f138511b6cf65424aa
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