Instructions to use kmaksatk/controlnet_80k_data_blip_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kmaksatk/controlnet_80k_data_blip_1 with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("kmaksatk/controlnet_80k_data_blip_1") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- e4ab9c31f4903ed4d7ef3fef16cf3b1adcdaa6685e7fb3762762295d67ce1b69
- Size of remote file:
- 725 MB
- SHA256:
- 66bf577154a2b5bb80714567fadb5aba23faca3a23bd5e562b14825d545ff3ea
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