Mask Resize

Documentation

• Class name: JWMaskResize
• Category: jamesWalker55
• Output node: False

The JWMaskResize node is designed for resizing masks to specified dimensions, offering various interpolation modes to best suit the resizing needs.

Input types

Required

• mask
  • The input mask to be resized. This parameter is crucial as it determines the content that will be resized.
  • Comfy dtype: MASK
  • Python dtype: torch.Tensor
• height
  • The desired height for the resized mask. This parameter directly influences the dimensions of the output mask.
  • Comfy dtype: INT
  • Python dtype: int
• width
  • The desired width for the resized mask. This parameter directly influences the dimensions of the output mask.
  • Comfy dtype: INT
  • Python dtype: int
• interpolation_mode
  • Specifies the method of interpolation to be used during resizing, allowing for flexibility in how the resizing is performed.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str

Output types

• mask
  • Comfy dtype: MASK
  • The resized mask, adjusted to the specified dimensions and using the chosen interpolation method.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

@register_node("JWImageLoadRGB", "Image Load RGB")
class _:
    CATEGORY = "jamesWalker55"
    INPUT_TYPES = lambda: {
        "required": {
            "path": ("STRING", {"default": "./image.png"}),
        }
    }
    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "execute"

    def execute(self, path: str):
        assert isinstance(path, str)

        img = load_image(path)
        return (img,)