Instructions to use leonelhs/faceparser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leonelhs/faceparser with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="leonelhs/faceparser")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("leonelhs/faceparser", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| tags: | |
| - vision | |
| - image-segmentation | |
| - makeup | |
| ### This is an non official Face Parser model mirror. | |
| Reasons of this copy is because AI models can be very fragmented, and its hard to keeping point to an official source. | |
| Meanwhile this concern will be solved, the projects listed here, will be using this repo. | |
| ### [Face Shine](https://github.com/leonelhs/face-shine) | |
| Face Shine Is a backend server for photo enhancement and restoration. | |
| ### [Super Face](https://github.com/leonelhs/SuperFace/) | |
| Super Face is a Python QT frontend for Face Shine server. | |
| <img src="https://drive.google.com/uc?export=view&id=1D7hpjQSlUkzfTba-E5Ul4Rb1c8lYkFj5"/> | |
| <img src="https://drive.google.com/uc?export=view&id=1oKpJe-Ff3SeEekhGVRP1Ap3eIFqt0c8u"/> |