Instructions to use profpeng/wanhjbj with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use profpeng/wanhjbj 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/wanhjbj") prompt = "A sexy woman is giving a man a blowjob and a handjob. The man is out of frame. She is in front of the man. She is giving him a blowjob, while simultaneously giving a handjob. She is performing fellatio while stroking the shaft of his penis with one hand." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 6e2acea25d5bb3dbd9cfa00d5d9bd747f6ee9b506ad403141f96e1b29b1703fe
- Size of remote file:
- 614 MB
- SHA256:
- 0794f26ab43a5bbb479ff21bd6ae1466ff4a3d6a0c21676ab34b832b82b875e0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.