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
metadata
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
