Instructions to use Nahrawy/controlnet-vidit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Nahrawy/controlnet-vidit with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("Nahrawy/controlnet-vidit") 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- Nahrawy/controlnet-vidit | |
| 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 building with a stone wall and a cliff, light temperature 4500, light direction NW | |
|  | |
| prompt: a wooden table in front of a brick building, light temperature 5500, light direction SE | |
|  | |
| prompt: a stone tower on a rocky island, light temperature 2500, light direction SW | |
|  | |