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
- Draw Things
- DiffusionBee
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.

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Model tree for Taimoor-R/model_out
Base model
runwayml/stable-diffusion-v1-5