Detect human parts using the DeepLabV3+ ResNet50 model from Keras-io. You can extract hair, arms, legs, and other parts with ease and with small memory usage.

This node aims to detect human parts using the model created by Keras-io. Their "Space" was impressive, and I wanted to use the model.

Unfortunately, the model uses an old Keras version, and there were no PyTorch implementation.

So I decided to convert the model to ONNX format and to create my HugginFace repository to share the model with the community.

Fortunately, Keras provides the model with a CC1.0 license, thank you guys to allow us to use it without any restriction.

You can drag and drop the following image to try:

Actually, all the model I found was not trained to detect human parts, but to detect some objects or urban elements. The Keras model is the only one I found that works!

I strongly recommend to use ComfyUI-Manager to install the node. It will install the dependencies and the model.

Note, as far as my repository isn't validated in the ComfyUI-Manager index, you must do the installation manually.

If you set up ComfyUI-Manager to "middle" or "weak" security, you can use the "Install from Git URL" feature.

# ensure that you have activated the virtual environment before !!

# then...
cd /path/to/your/ComfyUI/custom_nodes
git clone https://github.com/metal3d/ComfyUI_Human_Parts.git
cd ComfyUI_Human_Parts
pip install -r requirements.txt
# or
python -m pip install -r requirements.txt

# install the model
python install.py

Then, restart ComfyUI, refresh the UI, and you may find the "Human Parts mask generator" node.