ComfyUI Node: SAMDetector (combined)

SAMDetector (combined):

The SAMDetectorCombined node is designed to facilitate the creation of masks for images using a combination of segmentation and detection techniques. This node leverages the capabilities of the SAM (Segment Anything Model) to generate precise masks based on various detection hints and parameters. It is particularly useful for AI artists who need to create detailed and accurate masks for their images, enabling more refined and controlled image manipulation. By combining segmentation and detection, this node provides a robust solution for generating masks that can be tailored to specific needs, enhancing the overall quality and precision of the artwork.

SAMDetector (combined) Input Parameters:

sam_model

This parameter specifies the SAM model to be used for mask generation. The SAM model is a pre-trained model that helps in segmenting the image based on the provided hints and parameters.

segs

This parameter represents the segments or regions of interest within the image that the SAM model will use to generate the mask. These segments guide the model in focusing on specific areas of the image.

image

This parameter is the input image for which the mask needs to be generated. The image serves as the base on which the SAM model will apply its segmentation and detection techniques.

detection_hint

This parameter provides hints to the SAM model on how to approach the detection process. Options include "center-1", "horizontal-2", "vertical-2", "rect-4", "diamond-4", "mask-area", "mask-points", "mask-point-bbox", and "none". These hints help in guiding the model to focus on specific patterns or areas within the image.

dilation

This parameter controls the dilation of the mask, which can expand or contract the mask boundaries. It accepts integer values with a default of 0, a minimum of -512, and a maximum of 512, with a step of 1. Dilation can help in refining the mask edges.

threshold

This parameter sets the confidence threshold for the mask generation. It accepts float values with a default of 0.93, a minimum of 0.0, and a maximum of 1.0, with a step of 0.01. A higher threshold results in more confident but potentially smaller masks.

bbox_expansion

This parameter controls the expansion of the bounding box around the detected segments. It accepts integer values with a default of 0, a minimum of 0, and a maximum of 1000, with a step of 1. Expanding the bounding box can help in capturing more context around the detected segments.

mask_hint_threshold

This parameter sets the threshold for using mask hints. It accepts float values with a default of 0.7, a minimum of 0.0, and a maximum of 1.0, with a step of 0.01. This threshold helps in determining the relevance of mask hints in the detection process.

mask_hint_use_negative

This parameter specifies whether to use negative hints for mask generation. Options include "False", "Small", and "Outter". Negative hints can help in excluding certain areas from the mask.

SAMDetector (combined) Output Parameters:

MASK

The output of this node is a mask generated based on the input parameters and the SAM model. The mask highlights the areas of interest within the image, providing a precise and detailed segmentation that can be used for further image manipulation or analysis.

SAMDetector (combined) Usage Tips:

• Experiment with different detection_hint options to see which one best suits your image and desired outcome.
• Adjust the threshold parameter to balance between mask confidence and coverage. A higher threshold may result in more accurate but smaller masks.
• Use the dilation parameter to refine the edges of your mask, either expanding or contracting them as needed.
• Consider using bbox_expansion to capture more context around your detected segments, especially if the initial mask is too tight.
• Utilize mask_hint_use_negative to exclude unwanted areas from your mask, improving the overall quality of the segmentation.

SAMDetector (combined) Common Errors and Solutions:

"Invalid SAM model"

• Explanation: This error occurs when the provided SAM model is not recognized or is incompatible with the node.
• Solution: Ensure that you are using a valid and compatible SAM model. Check the model's documentation for compatibility details.

"Image not provided"

• Explanation: This error occurs when the input image is missing or not correctly specified.
• Solution: Make sure to provide a valid image as input. Verify the image path and format.

"Threshold out of range"

• Explanation: This error occurs when the threshold value is outside the acceptable range.
• Solution: Adjust the threshold value to be within the range of 0.0 to 1.0. Use the default value if unsure.

"Dilation value out of range"

• Explanation: This error occurs when the dilation value is outside the acceptable range.
• Solution: Adjust the dilation value to be within the range of -512 to 512. Use the default value if unsure.

"Invalid detection hint"

• Explanation: This error occurs when an unrecognized detection hint is provided.
• Solution: Use one of the valid detection hints: "center-1", "horizontal-2", "vertical-2", "rect-4", "diamond-4", "mask-area", "mask-points", "mask-point-bbox", or "none".