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
- Draw Things
- DiffusionBee
controlnet- jax-diffusers-event/canny-coyo1m
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images in the following.
prompt: car, a detailed high-quality professional image
prompt: A house on the water with a small yacht out front, a detailed high-quality professional image
prompt: man with polo shirt, a detailed high-quality professional image
prompt: sneaker, a detailed high-quality professional image

- Downloads last month
- 4
Model tree for jax-diffusers-event/canny-coyo1m
Base model
runwayml/stable-diffusion-v1-5