Duplicate Image Batch 🎥🅥🅗🅢

Documentation

• Class name: VHS_DuplicateImages
• Category: Video Helper Suite 🎥🅥🅗🅢/image
• Output node: False

The VHS_DuplicateImages node is designed to create multiple copies of a given image batch, effectively duplicating the images a specified number of times. This functionality is crucial for operations requiring augmented data or increased dataset size without new data generation.

Input types

Required

• images
  • Specifies the batch of images to be duplicated. This input is central to the node's operation, determining the base data that will be replicated.
  • Comfy dtype: IMAGE
  • Python dtype: Tensor
• multiply_by
  • Determines the number of times the input images are duplicated. This parameter directly influences the output dataset size, allowing for flexible data augmentation.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• IMAGE
  • Comfy dtype: IMAGE
  • The duplicated batch of images, expanded according to the multiply_by parameter.
  • Python dtype: Tensor
• count
  • Comfy dtype: INT
  • The total number of images in the duplicated batch, providing a straightforward count of the output dataset size.
  • Python dtype: int

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class DuplicateImages:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "images": ("IMAGE",),
                "multiply_by": ("INT", {"default": 1, "min": 1, "max": BIGMAX, "step": 1})
            }
        }

    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/image"

    RETURN_TYPES = ("IMAGE", "INT",)
    RETURN_NAMES = ("IMAGE", "count",)
    FUNCTION = "duplicate_input"

    def duplicate_input(self, images: Tensor, multiply_by: int):
        full_images = []
        for n in range(0, multiply_by):
            full_images.append(images)
        new_images = torch.cat(full_images, dim=0)
        return (new_images, new_images.size(0),)