ComfyUI Node: Load RetinaFace

Load RetinaFace:

The Load RetinaFace node is designed to facilitate the detection of faces within an image using the RetinaFace algorithm. This node is particularly useful for AI artists who need to identify and locate faces in their artwork or photographs, enabling further processing such as face swapping, enhancement, or analysis. The RetinaFace algorithm is known for its high accuracy and efficiency in detecting faces, even in challenging conditions such as varying lighting, occlusions, and different facial expressions. By leveraging this node, you can seamlessly integrate face detection capabilities into your AI art projects, ensuring precise and reliable results.

Load RetinaFace Input Parameters:

image

The image parameter is the input image in which faces need to be detected. This parameter accepts an image tensor that is processed by the RetinaFace algorithm to identify and locate faces. The quality and resolution of the input image can impact the accuracy of face detection, so it is recommended to use clear and high-resolution images for optimal results.

conf_threshold

The conf_threshold parameter sets the confidence threshold for detecting faces. Faces with detection scores below this threshold will be ignored. This parameter helps in filtering out false positives and ensuring that only faces with high confidence scores are considered. The value ranges from 0 to 1, with a default value typically set around 0.5.

nms_threshold

The nms_threshold parameter is used for non-maximum suppression (NMS) to eliminate redundant overlapping bounding boxes for the same face. This helps in refining the detection results by keeping only the most accurate bounding box for each face. The value ranges from 0 to 1, with a default value typically set around 0.3.

Load RetinaFace Output Parameters:

bounding_boxes

The bounding_boxes parameter provides the coordinates of the detected faces in the input image. Each bounding box is represented by a set of coordinates that define the rectangular area enclosing a face. This output is essential for further processing tasks such as cropping, face swapping, or applying filters to the detected faces.

landmarks

The landmarks parameter provides the coordinates of key facial landmarks for each detected face. These landmarks typically include points such as the eyes, nose, and mouth, which are crucial for tasks that require precise facial alignment and manipulation. The landmarks output enhances the ability to perform detailed facial analysis and transformations.

Load RetinaFace Usage Tips:

• Ensure that the input image is of high quality and resolution to improve the accuracy of face detection.
• Adjust the conf_threshold parameter to balance between detecting all possible faces and minimizing false positives.
• Use the nms_threshold parameter to refine the detection results by eliminating redundant bounding boxes.

Load RetinaFace Common Errors and Solutions:

"Input image is not a valid tensor"

• Explanation: This error occurs when the input image is not provided in the correct tensor format required by the node.
• Solution: Ensure that the input image is converted to a tensor format before passing it to the node.

"No faces detected"

• Explanation: This error indicates that the algorithm did not detect any faces in the input image, possibly due to low confidence scores or poor image quality.
• Solution: Check the quality of the input image and consider lowering the conf_threshold parameter to detect more faces.

"Invalid threshold values"

• Explanation: This error occurs when the conf_threshold or nms_threshold parameters are set to values outside their valid range (0 to 1).
• Solution: Ensure that both threshold parameters are set within the range of 0 to 1. Adjust the values as needed to achieve the desired detection performance.