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
| license: creativeml-openrail-m | |
| base_model: runwayml/stable-diffusion-v1-5 | |
| tags: | |
| - stable-diffusion | |
| - stable-diffusion-diffusers | |
| - text-to-image | |
| - diffusers | |
| - controlnet | |
| - jax-diffusers-event | |
| inference: true | |
| # controlnet- ParityError/ControlNet-Shadows | |
| 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: a hollow wooden box and ribbed green ball in the air with shadows on the ground, light direction west+32, light elevation 25 | |
|  | |
| prompt: a hollow green cube and silver planet in the sky and metallic wedge with shadows on the ground, light direction east+34, light elevation 25 | |
|  | |
| prompt: a tree house with a rope ladder, light direction nw, light elevation 25 | |
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