Instructions to use 22Plaruno/face_generation_ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use 22Plaruno/face_generation_ with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("22Plaruno/face_generation_", 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 checkpoint-4000/optimizer.bin from 22Plaruno/face_generation_: direct link, hf CLI and curl.
- Browser
- Download file 910 MB
-
https://huggingface.co/22Plaruno/face_generation_/resolve/main/checkpoint-4000/optimizer.bin
- Command line
-
hf download hf://22Plaruno/face_generation_/checkpoint-4000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/22Plaruno/face_generation_/resolve/main/checkpoint-4000/optimizer.bin
910 MB
- Xet hash:
- 2b60568953bac092c1b7a4d73a2ffac81810ef316970ec55228fb50b5c622d73
- Size of remote file:
- 910 MB
- SHA256:
- e2f2b470b01f72fee97688357178a8164396507fc93029d0b8d3bba9fd6275c4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.