🔧 Mask Batch

Documentation

• Class name: MaskBatch+
• Category: essentials
• Output node: False

The MaskBatch node is designed to combine two mask tensors into a single batched tensor. It ensures that the masks are compatible in size, potentially resizing one to match the other, before concatenating them along the batch dimension.

Input types

Required

• mask1
  • The first mask tensor to be batched. It is one of the inputs that will be combined with another mask tensor to form a batched tensor.
  • Comfy dtype: MASK
  • Python dtype: torch.Tensor
• mask2
  • The second mask tensor to be batched alongside the first mask. This tensor may be resized to ensure compatibility with the first mask before they are concatenated.
  • Comfy dtype: MASK
  • Python dtype: torch.Tensor

Output types

• mask
  • Comfy dtype: MASK
  • The output is a batched tensor combining the input masks, potentially after resizing one to match the other's dimensions.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class MaskBatch:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "mask1": ("MASK",),
                "mask2": ("MASK",),
            }
        }

    RETURN_TYPES = ("MASK",)
    FUNCTION = "execute"
    CATEGORY = "essentials"

    def execute(self, mask1, mask2):
        if mask1.shape[1:] != mask2.shape[1:]:
            mask2 = F.interpolate(mask2.unsqueeze(1), size=(mask1.shape[1], mask1.shape[2]), mode="bicubic").squeeze(1)

        out = torch.cat((mask1, mask2), dim=0)
        return (out,)