ImageContainerInheritanceMax

Documentation

• Class name: ImageContainerInheritanceMax
• Category: image/container
• Output node: False

The ImageContainerInheritanceMax node is designed to manage image data by applying a 'maximum' operation across multiple images. This node abstracts the complexity of handling image dimensions and pixel values, facilitating the creation of composite images where the maximum value of each pixel across all images is considered.

Input types

Required

• images_a
  • The 'images_a' input type accepts a collection of images to be processed, serving as one of the sets of images for the operation.
  • Comfy dtype: IMAGE
  • Python dtype: List[torch.Tensor]
• images_b
  • The 'images_b' input type accepts a second collection of images, working in conjunction with 'images_a' to determine the operation's scope.
  • Comfy dtype: IMAGE
  • Python dtype: List[torch.Tensor]
• red
  • Specifies the red color component for the operation's background or effect.
  • Comfy dtype: INT
  • Python dtype: int
• green
  • Specifies the green color component for the operation's background or effect.
  • Comfy dtype: INT
  • Python dtype: int
• blue
  • Specifies the blue color component for the operation's background or effect.
  • Comfy dtype: INT
  • Python dtype: int
• alpha
  • Defines the alpha (transparency) level for the operation's background or effect.
  • Comfy dtype: FLOAT
  • Python dtype: float
• method
  • Determines the method of applying the 'maximum' operation across the images. The 'single' option applies the operation to create a single composite image, 'for_each_pair' generates a new image for each pair of input images, and 'for_each_matrix' creates a composite image for each possible combination of images from 'images_a' and 'images_b', significantly affecting the node's execution and results.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str

Output types

• image
  • Comfy dtype: IMAGE
  • The output is a single image that represents the composite result of applying the 'maximum' operation across all input images. It encapsulates the highest pixel values from the input set for each pixel position.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class ImageContainerInheritanceMax:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "images_a": ("IMAGE",),
                "images_b": ("IMAGE",),
                "red": ("INT", {
                    "default": 255,
                    "max": 255,
                    "step": 1
                }),
                "green": ("INT", {
                    "default": 255,
                    "max": 255,
                    "step": 1
                }),
                "blue": ("INT", {
                    "default": 255,
                    "max": 255,
                    "step": 1
                }),
                "alpha": ("FLOAT", {
                    "default": 0.0,
                    "max": 1.0,
                    "step": 0.01
                }),
                "method": (["single", "for_each_pair", "for_each_matrix"],),
            },
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "node"
    CATEGORY = "image/container"

    def node(self, images_a, images_b, red, green, blue, alpha, method):
        img_a_height, img_a_width = images_a[0, :, :, 0].shape
        img_b_height, img_b_width = images_b[0, :, :, 0].shape

        width = max(img_a_width, img_b_width)
        height = max(img_a_height, img_b_height)

        image = create_rgba_image(width, height, (red, green, blue, int(alpha * 255))).image_to_tensor()

        if method == "single":
            return (image.unsqueeze(0),)
        elif method == "for_each_pair":
            length = len(images_a)
            images = torch.zeros(length, height, width, 4)
        else:
            length = len(images_a) * len(images_b)
            images = torch.zeros(length, height, width, 4)

        images[:, :, :] = image
        return (images,)