Instructions to use MnLgt/densepose with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MnLgt/densepose with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("MnLgt/densepose") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- cf4e47ec3b99e0bb8c7ac03fdee237cbbc531ea8436e97a6386495155c3d9b5c
- Size of remote file:
- 1.45 GB
- SHA256:
- 002af8f6bf85ae01cb57d408ac3473b97407fb12e4efb53faf2ce09371d3b58c
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