Instructions to use betyjx/vuss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use betyjx/vuss with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Qwen/Qwen-Image", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("betyjx/vuss") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
innie and hussy ano

- Prompt
- -
Model description
My first model trained on around 150 Pony AI generated images of close up and full body photos of female genitalia. 10,000 Steps, epoch: 36. Decided to release my v2 model!
Non-AI dataset for more realistic shots can be found here https://civitai.com/models/2299174?modelVersionId=2587089
No upscaling or adetailer used on sample images. Seems to do well anywhere from 0.65 to 1, try different strengths - all sample images were generated with 1.0 strength. If you get deformed bodies, you might need to lower the strength a bit.
Trained using ai-toolkit with the base qwen 2512 model.
a naked woman vagina close up
Download model
Download them in the Files & versions tab.
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Base model
Qwen/Qwen-Image