Repeat Into Grid (image)

Documentation

• Class name: Repeat Into Grid (image)
• Category: Bmad/image
• Output node: False

This node tiles the input image samples into a grid of configurable dimensions, effectively repeating the image across a specified number of rows and columns to create a larger, grid-like composite image.

Input types

Required

• image
  • The input image to be tiled across the grid. It determines the base image that will be repeated across the specified grid dimensions.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• columns
  • Specifies the number of columns in the grid. It determines how many times the input image is repeated horizontally.
  • Comfy dtype: INT
  • Python dtype: int
• rows
  • Specifies the number of rows in the grid. It determines how many times the input image is repeated vertically.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • Comfy dtype: IMAGE
  • The output is a single image composed of the input image tiled according to the specified rows and columns, forming a grid.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class RepeatIntoGridImage:
    """
    Tiles the input samples into a grid of configurable dimensions.
    """

    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"image": ("IMAGE",),
                             "columns": grid_len_INPUT,
                             "rows": grid_len_INPUT,
                             }}

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "repeat_into_grid"
    CATEGORY = "Bmad/image"

    def repeat_into_grid(self, image, columns, rows):
        samples = image.movedim(-1, 1)
        samples = samples.repeat(1, 1, rows, columns)
        samples = samples.movedim(1, -1)
        return (samples,)