Instructions to use ParityError/ControlNet-Shadows with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ParityError/ControlNet-Shadows with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("ParityError/ControlNet-Shadows") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 51a8931c52554d5a0b1fdeac637231f5b2d7d895c70665d09fceec24aeb9a801
- Size of remote file:
- 1.45 GB
- SHA256:
- 5fe76eed4e58442ca1fb9e3cb8ce5f64714eb4f9f171582d7be1a8a5601abbf9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.