Instructions to use jise/controlnet-X-ray with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jise/controlnet-X-ray with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("jise/controlnet-X-ray") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-2000/optimizer.bin from jise/controlnet-X-ray: direct link, hf CLI and curl.
- Browser
- Download file 725 MB
-
https://huggingface.co/jise/controlnet-X-ray/resolve/main/checkpoint-2000/optimizer.bin
- Command line
-
hf download hf://jise/controlnet-X-ray/checkpoint-2000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/jise/controlnet-X-ray/resolve/main/checkpoint-2000/optimizer.bin
725 MB
- Xet hash:
- d3d9055420553bf220dc22c866bce31c439b3795f82d9c8af9b20c234ff29ea8
- Size of remote file:
- 725 MB
- SHA256:
- a10f34213ce39d3474de8b1ac9856312c24d96d05b8397b2aa4d47ff507fe5be
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