Instructions to use Ching2602/wanfacesitting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Ching2602/wanfacesitting 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/wanfacesitting") prompt = "A woman places her crotch over the camera, obscuring the view. The final frame of the video is a close up of the woman's crotch" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 857 Bytes
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tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/videoframe_2863.png
text: >-
A woman places her crotch over the camera, obscuring the view. The final
frame of the video is a close up of the woman's crotch
base_model: Wan-AI/Wan2.2-I2V-A14B
instance_prompt: >-
A woman places her crotch over the camera, obscuring the view. The final frame
of the video is a close up of the woman's crotch
---
# wanfacesitting
<Gallery />
## Trigger words
You should use `A woman places her crotch over the camera` to trigger the image generation.
You should use `obscuring the view. The final frame of the video is a close up of the woman's crotch` to trigger the image generation.
## Download model
[Download](/profpeng/wanfacesitting/tree/main) them in the Files & versions tab.
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