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 diffusion_flax_model.msgpack from jax-diffusers-event/canny-coyo1m: direct link, hf CLI and curl.
- Browser
- Download file 1.45 GB
-
https://huggingface.co/jax-diffusers-event/canny-coyo1m/resolve/main/diffusion_flax_model.msgpack
- Command line
-
hf download hf://jax-diffusers-event/canny-coyo1m/diffusion_flax_model.msgpack
-
curl -L -o diffusion_flax_model.msgpack https://huggingface.co/jax-diffusers-event/canny-coyo1m/resolve/main/diffusion_flax_model.msgpack
1.45 GB
- Xet hash:
- 0e81494ceaad56e1892364a89d05076e3eb0ce505b181a5db8b0e8356c119b8c
- Size of remote file:
- 1.45 GB
- SHA256:
- f3cad8c3f48d9910f99e3777642c4baf6ea5a3c112f1f8d8e4485bbf702cb76b
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