Instructions to use killah-t-cell/model_out with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use killah-t-cell/model_out with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("killah-t-cell/model_out") pipe = StableDiffusionControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-2-1-base", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
controlnet-killah-t-cell/model_out
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: Crowd of men wearing hats with trees in the background
prompt: Girl smiling, professional dslr photograph, dark background, studio lights, high quality
prompt: Group photo of clowns, oil on canvas, bittersweet expression

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Model tree for killah-t-cell/model_out
Base model
stabilityai/stable-diffusion-2-1-base