Image Grid Composite 2x2

Documentation

• Class name: ImageGridComposite2x2
• Category: KJNodes/image
• Output node: False

This node is designed to concatenate four input images into a 2x2 grid, effectively creating a composite image that showcases all inputs in a structured layout. It serves the purpose of visually aggregating multiple images into a single, cohesive unit for easier comparison, presentation, or further processing.

Input types

Required

• image1
  • The first image to be placed in the top-left corner of the 2x2 grid. It plays a crucial role in the overall composition by starting the visual sequence.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• image2
  • The second image to be placed in the top-right corner of the 2x2 grid. It complements the first image, continuing the visual narrative.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• image3
  • The third image to be placed in the bottom-left corner of the 2x2 grid. It adds to the visual diversity and complexity of the composite image.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• image4
  • The fourth image to be placed in the bottom-right corner of the 2x2 grid. It completes the visual arrangement, providing a balanced and cohesive look.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

Output types

• image
  • Comfy dtype: IMAGE
  • The composite image resulting from concatenating the four input images into a 2x2 grid. This output is useful for visual analysis, presentation, or as input to further image processing tasks.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class ImageGridComposite2x2:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
            "image1": ("IMAGE",),
            "image2": ("IMAGE",),
            "image3": ("IMAGE",),
            "image4": ("IMAGE",),   
        }}

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "compositegrid"
    CATEGORY = "KJNodes/image"
    DESCRIPTION = """
Concatenates the 4 input images into a 2x2 grid. 
"""

    def compositegrid(self, image1, image2, image3, image4):
        top_row = torch.cat((image1, image2), dim=2)
        bottom_row = torch.cat((image3, image4), dim=2)
        grid = torch.cat((top_row, bottom_row), dim=1)
        return (grid,)