ComfyUI Node: CropFace

CropFace:

The CropFace node is designed to accurately detect and crop faces from an input image, facilitating further processing such as face restoration or enhancement. This node leverages advanced facial detection algorithms to identify facial landmarks and align the face for consistent cropping. The primary benefit of using CropFace is its ability to isolate faces from an image, ensuring that subsequent operations are performed on the face region alone, which can significantly improve the quality and accuracy of face-related tasks. This node is particularly useful for AI artists who need to preprocess images for face restoration, enhancement, or other creative modifications.

CropFace Input Parameters:

image

The image parameter represents the input image from which faces will be detected and cropped. This parameter is crucial as it provides the raw data that the node will process. The quality and resolution of the input image can impact the accuracy of face detection and the quality of the cropped faces.

facedetection

The facedetection parameter is used to specify the method or model for detecting faces within the input image. This parameter influences how accurately faces are identified and can affect the overall performance of the node. Different face detection models may offer varying levels of precision and speed.

CropFace Output Parameters:

cropped_faces

The cropped_faces output parameter contains the cropped face images extracted from the input image. Each cropped face is aligned and normalized, making it ready for further processing such as restoration or enhancement. This output is essential for tasks that require isolated face regions, ensuring that subsequent operations are focused on the relevant parts of the image.

CropFace Usage Tips:

• Ensure that the input image is of high quality and resolution to improve the accuracy of face detection and the quality of the cropped faces.
• Experiment with different face detection models to find the one that best suits your needs in terms of accuracy and performance.
• Use the cropped faces as input for face restoration or enhancement nodes to achieve better results in your AI art projects.

CropFace Common Errors and Solutions:

Failed inference for CodeFormer: <error>

• Explanation: This error occurs when the face restoration model fails to process the cropped face image.
• Solution: Ensure that the input image is correctly preprocessed and that the face detection model is accurately identifying faces. Check for any issues with the face restoration model and try using different model parameters.

FaceWarpException: reference_pts.shape must be (K,2) or (2,K) and K>2

• Explanation: This error indicates that the reference facial points provided for alignment do not have the correct shape.
• Solution: Verify that the reference points are correctly formatted and match the expected shape. Ensure that the facial points used for alignment are accurate and properly structured.

FaceWarpException: facial_pts and reference_pts must have the same shape

• Explanation: This error occurs when the facial points and reference points used for alignment do not have matching shapes.
• Solution: Ensure that both the facial points and reference points are correctly formatted and have the same shape. Double-check the input data for any discrepancies.

FaceWarpException: Must have (output_size - outer_padding) = some_scale * (crop_size * (1.0 + inner_padding_factor)

• Explanation: This error indicates a mismatch between the output size, outer padding, and crop size parameters.
• Solution: Adjust the output size, outer padding, and crop size parameters to ensure they are compatible. Follow the guidelines for setting these parameters to avoid conflicts.