Simple ComfyUI nodes to generate masks for specific body parts and clothes or fashion items. Runs on CPU and CUDA. Made with 💚 by the CozyMantis squad.

Original	ATR	LIP	Pascal

Installation

Clone this repository into your custom_nodes directory, then run pip install -r requirements.txt to install the required dependencies.
Copy the following models to the models/schp directory, depending on which parser you would like to use:
- Model based on the LIP dataset: Google Drive
- Model based on the ATR dataset: Google Drive
- Model based on the Pascal dataset: Google Drive

Examples

LIP Parser

LIP is the largest single person human parsing dataset with 50000+ images. This dataset focus more on the complicated real scenarios.
mIoU on LIP validation: 59.36 %
The LIP parser can detect the following categories:

['Background', 'Hat', 'Hair', 'Glove', 'Sunglasses' 'Upper-clothes', 'Dress', 'Coat', 'Socks', 'Pants', 'Jumpsuits', 'Scarf', 'Skirt', 'Face', 'Left-arm', 'Right-arm', 'Left-leg', 'Right-leg', 'Left-shoe', 'Right-shoe']

ATR Parser

ATR is a large single person human parsing dataset with 17000+ images. This dataset focus more on fashion AI.
mIoU on ATR test: 82.29%
The ATR parser can detect the following categories:

['Background', 'Hat', 'Hair', 'Sunglasses', 'Upper-clothes', 'Skirt', 'Pants', 'Dress', 'Belt', 'Left-shoe', 'Right-shoe', 'Face', 'Left-leg', 'Right-leg', 'Left-arm', 'Right-arm', 'Bag', 'Scarf']

Pascal Parser

ascal Person Part is a tiny single person human parsing dataset with 3000+ images. This dataset focus more on body parts segmentation.
mIoU on Pascal-Person-Part validation: 71.46 %
The Pascal parser can detect the following categories:

['Background', 'Head', 'Torso', 'Upper Arms', 'Lower Arms', 'Upper Legs', 'Lower Legs']

Acknowledgements

Based on the excellent paper "Self-Correction for Human Parsing" by Li, Peike and Xu, Yunqiu and Wei, Yunchao and Yang, Yi, and their original code that we've updated to also run on CPUs.

@article{li2020self,
  title={Self-Correction for Human Parsing}, 
  author={Li, Peike and Xu, Yunqiu and Wei, Yunchao and Yang, Yi},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  year={2020},
  doi={10.1109/TPAMI.2020.3048039}}