Instructions to use jax-diffusers-event/canny-coyo1m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jax-diffusers-event/canny-coyo1m with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("jax-diffusers-event/canny-coyo1m") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download images_1.png from jax-diffusers-event/canny-coyo1m: direct link, hf CLI and curl.
- Browser
- Download file 3.7 MB
-
https://huggingface.co/jax-diffusers-event/canny-coyo1m/resolve/main/images_1.png
- Command line
-
hf download hf://jax-diffusers-event/canny-coyo1m/images_1.png
-
curl -L -o images_1.png https://huggingface.co/jax-diffusers-event/canny-coyo1m/resolve/main/images_1.png
3.7 MB

- Xet hash:
- 7acd0af3495b0273fef4786dee60a4fdd92086378345ea672a39cac192f83a1b
- Size of remote file:
- 3.7 MB
- SHA256:
- c0706ab1b679ecbac442185e2db29fe2e8b29e327f5a833a0fc2f0e1fde55551
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