Instructions to use profpeng/facefuckv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use profpeng/facefuckv2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("profpeng/facefuckv2") prompt = "-" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 2789916d82b3dab3d9ef44ea2878b1739a8131c1bd109891993185c6f4fa5d6b
- Size of remote file:
- 307 MB
- SHA256:
- f3fec4670cb96fbd9cc1f51c6d33b3f14866b5cb39daa25d6afeef01b53b4151
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.