Instructions to use Bellaaazzzzz/model_Xray with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Bellaaazzzzz/model_Xray with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("Bellaaazzzzz/model_Xray") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
controlnet-Bellaaazzzzz/model_Xray
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
You can find some example images below.
Validation result of 1 round.
Validation result of 2 round.
Validation result of 3 round.
Validation result of 4 round.
Validation result of 5 round.
Validation result of 6 round.
Validation result of 7 round.
Validation result of 8 round.
Validation result of 9 round.
Validation result of 10 round.

- Downloads last month
- -
Model tree for Bellaaazzzzz/model_Xray
Base model
runwayml/stable-diffusion-v1-5