Combine Masks

Documentation

• Class name: Combine Masks
• Category: Masquerade Nodes
• Output node: False

The Combine Masks node is designed to perform various operations on two input masks, such as union, intersection, difference, and others, to produce a single output mask. It allows for flexible manipulation of masks, enabling the creation of complex mask shapes and patterns by combining simpler ones.

Input types

Required

• image1
  • The first input mask for the operation. It plays a crucial role in determining the base of the operation, such as the starting point for union, intersection, or difference calculations.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• image2
  • The second input mask that is combined with the first mask according to the specified operation. It contributes to the final shape and content of the output mask.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• op
  • Specifies the type of operation to perform on the input masks, such as union, intersection, difference, etc. This determines how the masks are combined to produce the output.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• clamp_result
  • Determines whether the resulting mask values should be clamped between 0 and 1, ensuring valid mask values.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• round_result
  • Indicates whether the values in the resulting mask should be rounded to the nearest integer, potentially simplifying the mask.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str

Output types

• image
  • Comfy dtype: IMAGE
  • The resulting mask after applying the specified operation on the input masks. It represents the combined effect of the inputs according to the operation.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class MaskCombineOp:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "image1": ("IMAGE",),
                "image2": ("IMAGE",),
                "op": (["union (max)", "intersection (min)", "difference", "multiply", "multiply_alpha", "add", "greater_or_equal", "greater"],),
                "clamp_result": (["yes", "no"],),
                "round_result": (["no", "yes"],),
            },
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "combine"

    CATEGORY = "Masquerade Nodes"

    def combine(self, image1, image2, op, clamp_result, round_result):
        image1, image2 = tensors2common(image1, image2)

        if op == "union (max)":
            result = torch.max(image1, image2)
        elif op == "intersection (min)":
            result = torch.min(image1, image2)
        elif op == "difference":
            result = image1 - image2
        elif op == "multiply":
            result = image1 * image2
        elif op == "multiply_alpha":
            image1 = tensor2rgba(image1)
            image2 = tensor2mask(image2)
            result = torch.cat((image1[:, :, :, :3], (image1[:, :, :, 3] * image2).unsqueeze(3)), dim=3)
        elif op == "add":
            result = image1 + image2
        elif op == "greater_or_equal":
            result = torch.where(image1 >= image2, 1., 0.)
        elif op == "greater":
            result = torch.where(image1 > image2, 1., 0.)

        if clamp_result == "yes":
            result = torch.min(torch.max(result, torch.tensor(0.)), torch.tensor(1.))
        if round_result == "yes":
            result = torch.round(result)

        return (result,)