Instructions to use laidawang/test_flux_controlnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use laidawang/test_flux_controlnet with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("laidawang/test_flux_controlnet") pipe = StableDiffusionControlNetPipeline.from_pretrained( "black-forest-labs/FLUX.1-dev", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee

- Xet hash:
- 282eb90c5f69cb569e2550c9ca61be987d75f0e7e35f275ee11f40847b1efa74
- Size of remote file:
- 2.45 MB
- SHA256:
- 86661523e98fcdb7643edf15972656af4ad49641f7f40a7b6198dce6b25f0231
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.