Instructions to use Jenniferkmc/controlnet-model2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Jenniferkmc/controlnet-model2 with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("Jenniferkmc/controlnet-model2") pipe = StableDiffusionControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-2-1-base", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| license: creativeml-openrail-m | |
| base_model: stabilityai/stable-diffusion-2-1-base | |
| tags: | |
| - stable-diffusion | |
| - stable-diffusion-diffusers | |
| - text-to-image | |
| - diffusers | |
| - controlnet | |
| inference: true | |
| # controlnet-Jenniferkmc/controlnet-model2 | |
| These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning. | |
| You can find some example images below. | |
| prompt: High-quality close-up dslr photo of man wearing a hat with trees in the background | |
|  | |
| prompt: Girl smiling, professional dslr photograph, dark background, studio lights, high quality | |
|  | |
| prompt: Portrait of a clown face, oil on canvas, bittersweet expression | |
|  | |