Face Detection & Landmark Extraction: Uses MediaPipe Face Mesh to detect and extract 468 facial landmarks
Face Alignment: Automatic face alignment based on eye positions
Face Transformation:
- Warping between source and target face landmarks
- Scale and translation controls for face shape adjustment
- CPU and CUDA-accelerated processing options
Debug Visualization:
- Visual landmark overlay with customizable parameters
- Support for both detected and target landmark visualization
- Optional landmark labels

Installation

Clone this repository into your ComfyUI's custom_nodes directory:

cd ComfyUI/custom_nodes
git clone [repository-url] face_processor

Install required dependencies:

pip install mediapipe opencv-python numpy pandas pillow torch

For CUDA acceleration:

Install CUDA Toolkit
Install CuPy:

pip install cupy-cuda11x  # Replace with your CUDA version

Nodes

FaceWrapper

Main node for face detection and transformation operations.

Inputs:

image: Input image (ComfyUI IMAGE type)
mode: Operating mode
- Debug: Visualization of detected landmarks
- Un-Wrap: Transform face to normalized position
- Wrap: Transform normalized face back to original position
device: Processing device (CPU or CUDA)
show_detection: Toggle detected landmarks visualization
show_target: Toggle target landmarks visualization
landmark_size: Size of landmark points in visualization
show_labels: Toggle landmark index labels
x_scale: Horizontal scaling factor (0.5 to 1.0)
y_transform: Vertical translation (-0.5 to 0.5)
processor_settings: Optional settings dictionary

Outputs:

image: Processed image
processor_settings: Updated settings dictionary

FaceFitAndRestore

Node for face cropping and restoration operations.

Inputs:

mode: Operating mode
- Fit: Crop and align face
- Restore: Place processed face back in original image
image: Input image
padding_percent: Additional padding around face (0.0 to 1.0)
bbox_size: Output size for cropped face (512, 1024, or 2048)
processor_settings: Required for Restore mode

Outputs:

image: Processed image
processor_settings: Updated settings dictionary
mask: Mask indicating face region

Technical Details

Core Components

Face Detection

Uses MediaPipe Face Mesh for robust face detection and landmark extraction
Provides 468 facial landmarks with 3D coordinates
Supports various input formats (PIL Image, numpy array, torch tensor)

Image Processing

Automatic face rotation based on eye positions
Aspect ratio-preserving resizing
Support for square cropping with configurable padding
Boundary triangulation for complete face warping

Face Warping

Triangle-based warping using predefined mesh topology from MediaPipe Face Mesh
CPU implementation using pure Python/NumPy
CUDA-accelerated GPU implementation using CuPy
Handles both forward and inverse warping

Performance Considerations

GPU acceleration requires CUDA toolkit and CuPy
CPU fallback available for all operations
Progressive feedback during long operations
Memory-efficient processing for large images

Example Usage

Basic face detection and visualization:

face_wrapper = FaceWrapper()
result_image, settings = face_wrapper.detect_face(
    image=input_image,
    mode="Debug",
    device="CPU",
    show_detection=True,
    show_target=False,
    landmark_size=4,
    show_labels=True,
    x_scale=1.0,
    y_transform=0.0
)

Face normalization workflow:

Detect and normalize face:

# Unwrap face to normalized position
normalized_face, settings = face_wrapper.detect_face(
    image=input_image,
    mode="Un-Wrap",
    device="CUDA",
    x_scale=1.0,
    y_transform=0.0
)

Process normalized face with your preferred method
Restore face to original position:

# Wrap processed face back
final_image, _ = face_wrapper.detect_face(
    image=processed_face,
    mode="Wrap",
    device="CUDA",
    processor_settings=settings
)

License

[Your License Here]

Acknowledgments

MediaPipe Face Mesh for facial landmark detection
ComfyUI project for the node system framework