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:
- 461ab4ace4836fe7110048c55b89e9f3f93734ebe8fe6562ac5a95550ffe9031
- Size of remote file:
- 307 MB
- SHA256:
- 6956a0f5dc654718e2668bc319a2f1c034b5c320a936b8fcb53b8c33944d481a
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