Instructions to use amayprro552/customFaID with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use amayprro552/customFaID with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("amayprro552/customFaID", 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
- Draw Things
- DiffusionBee
- Xet hash:
- 6e71c8767e8dec12c8af4cb5f15cb4f2ca5370ec5a893d28a79582492d2fd892
- Size of remote file:
- 3.44 GB
- SHA256:
- 9d95f59174bb2d41cf4195e197fd2806022a7cbafa3cc7c20294e08052b18ab2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.