简体中文

ComfyUI MiVolo V2 Node

Use the advanced MiVolo V2 model directly in ComfyUI for high-precision age and gender prediction!

This project is a ComfyUI wrapper node for the iitolstykh/mivolo_v2 model. MiVolo is a Transformer-based, multi-input (face and body) model that provides reliable age and gender estimations.

🌟 Core Features

• Age Estimation: Receives an image (containing a face or body) and outputs the predicted age as a string.
• Gender Estimation: Outputs the predicted gender (e.g., Male/Female) as a string.
• Multi-Person Support: Automatically processes face and body crops to improve accuracy (based on the original model's capabilities).

🖼️ Nodes and Workflow Examples

Example Workflow:

🚀 How to Install

1. (Recommended) Use ComfyUI Manager

1. Open ComfyUI Manager.
2. Click "Install Custom Nodes".
3. Search for ComfyUI-MiVolo-V2 and install it.
4. Restart ComfyUI.

2. (Manual) Git Clone

1. Open a terminal and navigate to your ComfyUI custom_nodes directory:

   cd ComfyUI/custom_nodes/
   

2. Clone this repository:

   git clone [https://github.com/](https://github.com/)[YourUsername]/ComfyUI-MiVolo-V2.git
   

   (Please replace [YourUsername] with your actual GitHub username)
3. Install the dependencies:

   pip install -r requirements.txt
   

4. Restart ComfyUI.

📦 Model Installation

This custom node requires two types of models:

1. MiVOLO Age/Gender Model (for prediction)
2. YOLO Detector Model (for finding faces and bodies, optional)

This project supports both automatic downloading and manual placement of models.

1. MiVOLO Age/Gender Model (MiVOLOLoader)

This is the main prediction model.

• Model Name: iitolstykh/mivolo_v2
• Storage Path: ComfyUI/models/mivolo/

Method A: Automatic Download (Recommended)

1. The code is configured to handle this automatically.
2. In ComfyUI, add the "Load MiVOLO Model" node.
3. In the model_name field, keep the default "iitolstykh/mivolo_v2" selected.
4. The first time you run a workflow, the transformers library will automatically download this model from Hugging Face and cache it on your system.

Method B: Manual Download

If you want to manage models manually or use them in an offline environment:

1. Visit the Hugging Face repo: https://huggingface.co/iitolstykh/mivolo_v2
2. Download or git clone the entire repository.
3. Ensure all model files (like config.json, pytorch_model.bin, etc.) are located in a folder named after the model.
4. Place this folder inside the mivolo directory in your ComfyUI models directory.

The final path structure should be:

ComfyUI/
└── models/
    └── mivolo/
        └── iitolstykh/mivolo_v2/
            ├── config.json
            ├── configuration_mivolo.py
            ├── modeling_mivolo.py
            ├── pytorch_model.bin
            └── ... (and all other files)

Once done, the "Load MiVOLO Model" node will automatically detect it in the dropdown list.

2. YOLO Detector Model (MiVOLODetectorLoader)

This is a .pt file used to detect people and faces in an image.

• Model Name: yolov8x_person_face.pt
• Hugging Face Repo: iitolstykh/demo_yolov8_detector
• Storage Path: ComfyUI/models/yolo/

Method A: Automatic Download (Recommended)

1. In ComfyUI, add the "Load MiVOLO Detector (YOLO)" node.
2. Keep the default model_name selected: "iitolstykh/demo_yolov8_detector/yolov8x_person_face.pt".
3. The first time you run a workflow, the script will check the ComfyUI/models/yolo/ folder.
4. If the yolov8x_person_face.pt file is not found, the script will automatically download it from Hugging Face and place it in the correct yolo folder.

Method B: Manual Download

If you prefer to download it manually:

1. Visit the Hugging Face repo: https://huggingface.co/iitolstykh/demo_yolov8_detector/tree/main
2. Download the single file yolov8x_person_face.pt.
3. Place this file in the yolo directory under your ComfyUI models directory. (Create the yolo folder if it doesn't exist).

The final path structure should be:

ComfyUI/
└── models/
    └── yolo/
        └── yolov8x_person_face.pt

Once done, the "Load MiVOLO Detector (YOLO)" node will be able to load the model immediately.

💡 Usage Tips

• For best results, ensure the input image is clear and the face/body is visible.
• Supports using pre-cropped faces as input, as well as automatic detection.
• Can be used to analyze AI-generated portraits or for conditional control based on age/gender.

📜 Acknowledgments and License

This project is Adapted Material based on iitolstykh/mivolo_v2.

• Original Model: iitolstykh/mivolo_v2 (Hugging Face)
• Original Papers:
  • MiVOLO: Multi-input Transformer for Age and Gender Estimation (2023)
  • Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation (2024)
• Original License: The original project uses a custom license based on CC BY-SA 4.0. See the original repository's LICENSE file for details.

In accordance with the original license's "ShareAlike" condition, this ComfyUI node project is also open-sourced under the Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0) license.

This means you are free to use, modify, and distribute this project, provided you give appropriate attribution and share your adaptations under the same license.

🐞 Bug Reports

If you encounter any issues or have feature suggestions, please feel free to open an Issue on the "Issues" page!