ComfyUI Node: Masks Combine Batch

Masks Combine Batch:

The Masks Combine Batch node is designed to merge multiple mask tensors into a single combined mask. This node is particularly useful when you have several masks representing different regions or features of an image and you want to create a unified mask that encompasses all these regions. By summing the individual masks and ensuring the combined values are clamped between 0 and 1, this node effectively integrates the masks while maintaining the integrity of the mask values. This process is essential for tasks that require a comprehensive mask representation, such as image segmentation, object detection, or any application where multiple mask layers need to be consolidated into one.

Masks Combine Batch Input Parameters:

masks

This parameter accepts a list of mask tensors that you want to combine. Each mask in the list should be a tensor representing a specific region or feature of an image. The function will stack these masks along a new dimension, sum them, and then clamp the resulting values to ensure they remain between 0 and 1. This ensures that the combined mask accurately represents all the input masks without exceeding the valid value range for mask tensors.

Masks Combine Batch Output Parameters:

MASK

The output is a single combined mask tensor. This tensor is the result of summing all the input masks and clamping the values between 0 and 1. The combined mask can be used in subsequent image processing tasks where a unified mask is required. It effectively represents all the regions or features indicated by the input masks in a single tensor.

Masks Combine Batch Usage Tips:

• Ensure that all input masks are of the same dimensions to avoid dimension mismatch errors.
• Use this node when you need to create a comprehensive mask from multiple individual masks, such as combining masks for different objects in an image.
• Verify the combined mask visually to ensure that the integration of individual masks has been performed correctly.

Masks Combine Batch Common Errors and Solutions:

Dimension mismatch error

• Explanation: This error occurs when the input masks have different dimensions.
• Solution: Ensure that all input masks are resized or cropped to the same dimensions before passing them to the node.

Empty input list

• Explanation: This error occurs when no masks are provided as input.
• Solution: Provide at least one mask tensor in the input list to ensure the node can perform the combination.

Invalid mask values

• Explanation: This error occurs when the input masks contain values outside the range of 0 to 1.
• Solution: Normalize the input masks to ensure all values are within the valid range before combining them.