Image Concatenate

Documentation

• Class name: ImageConcanate
• Category: KJNodes/image
• Output node: False

The ImageConcatenate node is designed for combining two images into a single image along a specified direction. It supports matching the sizes of the input images and adjusting them to have the same batch size if necessary, allowing for flexible image concatenation operations.

Input types

Required

• image1
  • The first image tensor to be concatenated. It plays a crucial role in determining the final image's layout and size, especially when matching image sizes or batch sizes.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• image2
  • The second image tensor to be concatenated with the first one. Its size and batch size can be adjusted to match the first image, ensuring seamless concatenation.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• direction
  • Specifies the direction ('right', 'left', 'up', 'down') in which the second image should be concatenated to the first one, influencing the final image's orientation.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• match_image_size
  • A boolean flag indicating whether the sizes of the two images should be matched before concatenation, affecting the resizing operation if true.
  • Comfy dtype: BOOLEAN
  • Python dtype: bool

Output types

• image
  • Comfy dtype: IMAGE
  • The resulting tensor after concatenating the two input images along the specified direction.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class ImageConcanate:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
            "image1": ("IMAGE",),
            "image2": ("IMAGE",),
            "direction": (
            [   'right',
                'down',
                'left',
                'up',
            ],
            {
            "default": 'right'
             }),
            "match_image_size": ("BOOLEAN", {"default": False}),
        }}

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "concanate"
    CATEGORY = "KJNodes/image"
    DESCRIPTION = """
Concatenates the image2 to image1 in the specified direction.
"""

    def concanate(self, image1, image2, direction, match_image_size, first_image_shape=None):
        # Check if the batch sizes are different
        batch_size1 = image1.size(0)
        batch_size2 = image2.size(0)

        if batch_size1 != batch_size2:
            # Calculate the number of repetitions needed
            max_batch_size = max(batch_size1, batch_size2)
            repeats1 = max_batch_size // batch_size1
            repeats2 = max_batch_size // batch_size2

            # Repeat the images to match the largest batch size
            image1 = image1.repeat(repeats1, 1, 1, 1)
            image2 = image2.repeat(repeats2, 1, 1, 1)
        if match_image_size:
            first_image_shape = first_image_shape if first_image_shape is not None else image1.shape
            image2_resized = image2.movedim(-1,1)
            image2_resized = common_upscale(image2_resized, first_image_shape[2], first_image_shape[1], "lanczos", "disabled").movedim(1,-1)
        else:
            image2_resized = image2
        if direction == 'right':
            row = torch.cat((image1, image2_resized), dim=2)
        elif direction == 'down':
            row = torch.cat((image1, image2_resized), dim=1)
        elif direction == 'left':
            row = torch.cat((image2_resized, image1), dim=2)
        elif direction == 'up':
            row = torch.cat((image2_resized, image1), dim=1)
        return (row,)