Instructions to use kmaksatk/controlnet_80k_data_blip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kmaksatk/controlnet_80k_data_blip with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("kmaksatk/controlnet_80k_data_blip") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
controlnet-kmaksatk/controlnet_80k_data_blip
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
You can find some example images below.
prompt: High qualityphoto of male gymnast in a blue sport outfit in the olympic game
prompt: High qualityphoto of male gymnast in a blue sport outfit in the olympic game
prompt: High qualityphoto of male gymnast in a blue sport outfit in the olympic game

- Downloads last month
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Model tree for kmaksatk/controlnet_80k_data_blip
Base model
runwayml/stable-diffusion-v1-5