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:
- e328bf34e59c1ec35d379314eb955482ecb1d0cbe4ced0e2ccd7773b43ffd8ea
- Size of remote file:
- 261 MB
- SHA256:
- ec639a0429b4819130d1405a2d3b38beaa4cc4a6c5bd9cf48b94fdf65461de83
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