Instructions to use georgefen/Face-Landmark-ControlNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use georgefen/Face-Landmark-ControlNet with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("georgefen/Face-Landmark-ControlNet") pipe = StableDiffusionControlNetPipeline.from_pretrained( "fill-in-base-model", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
Download annotator/uniformer/mmcv/visualization/__init__.py from georgefen/Face-Landmark-ControlNet: direct link, hf CLI and curl.
- Browser
- Download file 338 Bytes
-
https://huggingface.co/georgefen/Face-Landmark-ControlNet/resolve/main/annotator/uniformer/mmcv/visualization/__init__.py
- Command line
-
hf download hf://georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/visualization/__init__.py
-
curl -L -o __init__.py https://huggingface.co/georgefen/Face-Landmark-ControlNet/resolve/main/annotator/uniformer/mmcv/visualization/__init__.py
338 Bytes
| # Copyright (c) OpenMMLab. All rights reserved. | |
| from .color import Color, color_val | |
| from .image import imshow, imshow_bboxes, imshow_det_bboxes | |
| from .optflow import flow2rgb, flowshow, make_color_wheel | |
| __all__ = [ | |
| 'Color', 'color_val', 'imshow', 'imshow_bboxes', 'imshow_det_bboxes', | |
| 'flowshow', 'flow2rgb', 'make_color_wheel' | |
| ] | |