Instructions to use Taimoor-R/model_out with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Taimoor-R/model_out with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("Taimoor-R/model_out") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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 | |
| inference: true | |
| # controlnet-Taimoor-R/model_out | |
| These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. | |
| You can find some example images below. | |
| prompt: She is young, and smiling and has high cheekbones. | |
|  | |
| prompt: The woman is wearing heavy makeup. She has arched eyebrows, and wavy hair. | |
|  | |
| prompt: Portrait of man that has black hair and blue eyes. | |
|  | |