🔧 Mask From List

Documentation

• Class name: MaskFromList+
• Category: essentials/mask
• Output node: False

The MaskFromList+ node generates a mask based on a list of values, allowing for the creation of custom masks with specified dimensions and values. It supports both numerical and string inputs for mask values, providing flexibility in defining the mask's content.

Input types

Required

• width
  • Specifies the width of the generated mask. It determines the horizontal dimension of the mask.
  • Comfy dtype: INT
  • Python dtype: int
• height
  • Specifies the height of the generated mask. It determines the vertical dimension of the mask.
  • Comfy dtype: INT
  • Python dtype: int

Optional

• values
  • A list of numerical values (integers or floats) or a single numerical value used to populate the mask. This input allows for the customization of the mask's intensity or transparency.
  • Comfy dtype: INT,FLOAT
  • Python dtype: List[Union[int, float]] | Union[int, float]
• str_values
  • A string representation of numerical values, separated by commas, used to populate the mask. This provides an alternative method for defining the mask's content.
  • Comfy dtype: STRING
  • Python dtype: str

Output types

• mask
  • Comfy dtype: MASK
  • The output is a mask created from the provided list of values, with the specified dimensions and value intensities.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class MaskFromList:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "width": ("INT", { "default": 32, "min": 0, "max": MAX_RESOLUTION, "step": 8, }),
                "height": ("INT", { "default": 32, "min": 0, "max": MAX_RESOLUTION, "step": 8, }),
            }, "optional": {
                "values": ("INT,FLOAT", { "default": 0.0, "min": 0.0, "max": 1.0, }),
                "str_values": ("STRING", { "default": "", "multiline": True, "placeholder": "0.0, 0.5, 1.0",}),
            }
        }

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

    def execute(self, width, height, values=None, str_values=""):
        out = []

        if values is not None:
            if not isinstance(values, list):
                out = [values]
            else:
                out.extend(values)

        if str_values != "":
            str_values = [float(v) for v in str_values.split(",")]
            out.extend(str_values)

        if out == []:
            raise ValueError("No values provided")

        out = torch.tensor(out).float().clamp(0.0, 1.0)
        out = out.view(-1, 1, 1).expand(-1, height, width)

        values = None
        str_values = ""

        return (out, )