Instructions to use Ching2602/rimming with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Ching2602/rimming with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Wan-AI/Wan2.2-I2V-A14B", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Ching2602/rimming") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from Ching2602/rimming: direct link, hf CLI and curl.
- Browser
- Download file 426 Bytes
-
https://huggingface.co/Ching2602/rimming/resolve/main/README.md
- Command line
-
hf download hf://Ching2602/rimming/README.md
-
curl -L -o README.md https://huggingface.co/Ching2602/rimming/resolve/main/README.md
426 Bytes
metadata
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/368ac433-73fc-45be-86a5-bb42dbf4700c.png
text: '-'
base_model: Wan-AI/Wan2.2-I2V-A14B
instance_prompt: null
rimming

- Prompt
- -
Model description
A woman performing rimming on a man, licking his anus.
Download model
Download them in the Files & versions tab.