Instructions to use kmaksatk/controlnet_80k_data_blip_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kmaksatk/controlnet_80k_data_blip_2 with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("kmaksatk/controlnet_80k_data_blip_2") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
controlnet-kmaksatk/controlnet_80k_data_blip_2
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
You can find some example images below.
prompt: Man doing a cartwheel in blue suit
prompt: Man doing a cartwheel in blue suit
prompt: Man doing a cartwheel in blue suit

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Model tree for kmaksatk/controlnet_80k_data_blip_2
Base model
runwayml/stable-diffusion-v1-5