ComfyUI Node: Multival Scaled Mask 🎭🅐🅓

Multival Scaled Mask 🎭🅐🅓:

The ADE_MultivalScaledMask node is designed to apply scaling transformations to a given mask based on specified minimum and maximum float values. This node is particularly useful for AI artists who need to manipulate masks dynamically within their workflows, allowing for both absolute and relative scaling. By leveraging this node, you can ensure that your masks are adjusted to the desired range, enhancing the precision and flexibility of your artistic creations. The node automatically handles iterable inputs and ensures that the mask dimensions match the expected batch size, making it a robust tool for complex masking operations.

Multival Scaled Mask 🎭🅐🅓 Input Parameters:

min_float_val

This parameter sets the minimum float value for scaling the mask. It can be a single float or an iterable of floats. If an iterable is provided, the node will ensure that the mask and the iterable match in length. The minimum value is 0.0, and the maximum value is not explicitly defined, but it should be within a reasonable range for your specific use case. The default value is typically set to a low float value to ensure proper scaling.

max_float_val

This parameter sets the maximum float value for scaling the mask. Similar to min_float_val, it can be a single float or an iterable of floats. The node will adjust the mask dimensions to match the length of the iterable if provided. The minimum value is 0.0, and the maximum value is not explicitly defined, but it should be within a reasonable range for your specific use case. The default value is typically set to a high float value to ensure proper scaling.

scaling

This parameter determines the type of scaling to be applied to the mask. It accepts two options: ABSOLUTE and RELATIVE. ABSOLUTE scaling will linearly convert the mask values to the new range defined by min_float_val and max_float_val, while RELATIVE scaling will normalize the mask values to the new range. This parameter is crucial for defining how the mask values are adjusted and can significantly impact the final output.

mask

This parameter is the input mask that will be scaled. It should be a tensor representing the mask you wish to transform. The node will ensure that the mask dimensions match the expected batch size and apply the specified scaling transformation.

Multival Scaled Mask 🎭🅐🅓 Output Parameters:

mask

The output is the scaled mask tensor. This tensor will have the same dimensions as the input mask but with values adjusted according to the specified min_float_val, max_float_val, and scaling parameters. The scaled mask can then be used in subsequent nodes or processes within your workflow, providing a dynamically adjusted mask that meets your specific requirements.

Multival Scaled Mask 🎭🅐🅓 Usage Tips:

• Ensure that the min_float_val and max_float_val parameters are set appropriately for your specific use case to achieve the desired scaling effect.
• Use the scaling parameter to switch between absolute and relative scaling, depending on whether you need a linear conversion or normalization of the mask values.
• If you are working with iterable float values, make sure that the lengths of the iterables match the dimensions of the mask to avoid dimension mismatches.

Multival Scaled Mask 🎭🅐🅓 Common Errors and Solutions:

scaling '<value>' not recognized.

• Explanation: This error occurs when an unrecognized value is provided for the scaling parameter.
• Solution: Ensure that the scaling parameter is set to either ABSOLUTE or RELATIVE.

ValueError: mask and float values must match in length.

• Explanation: This error occurs when the lengths of the mask and the iterable float values do not match.
• Solution: Verify that the lengths of the iterable float values match the dimensions of the mask. Adjust the lengths accordingly to ensure they are compatible.

TypeError: Expected float or iterable of floats for min_float_val and max_float_val.

• Explanation: This error occurs when the min_float_val or max_float_val parameters are not provided as floats or iterables of floats.
• Solution: Ensure that the min_float_val and max_float_val parameters are correctly set as either single float values or iterables of floats.