UnGridify (image)

Documentation

• Class name: UnGridify (image)
• Category: Bmad/image
• Output node: False

The UnGridify (image) node is designed to deconstruct a grid of images into its individual tiles. It takes an image composed of a grid layout and separates it into distinct image tiles based on specified row and column parameters, facilitating the manipulation or analysis of each tile independently.

Input types

Required

• image
  • The composite image arranged in a grid format. This input is crucial for determining the structure from which individual tiles will be extracted.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• columns
  • Specifies the number of columns in the grid. This parameter is essential for accurately dividing the image into the correct number of horizontal segments.
  • Comfy dtype: INT
  • Python dtype: int
• rows
  • Indicates the number of rows in the grid. It is vital for splitting the image into the appropriate number of vertical segments.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • Comfy dtype: IMAGE
  • A list of images, where each image represents a tile extracted from the original grid. This output allows for further individual processing or analysis of each tile.
  • Python dtype: List[torch.Tensor]

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

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

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "ungridify"
    CATEGORY = images_category_path
    OUTPUT_IS_LIST = (True,)

    def ungridify(self, image, columns, rows):
        tiles = []
        samples = image.movedim(-1, 1)
        _, _, height, width = samples.size()
        tile_height = height // rows
        tile_width = width // columns

        for y in range(0, rows * tile_height, tile_height):
            for x in range(0, columns * tile_width, tile_width):
                tile = samples[:, :, y:y + tile_height, x:x + tile_width]
                tile = tile.movedim(1, -1)
                tiles.append(tile)

        return (tiles,)