ImageBatchRepeatInterleaving

Documentation

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

The ImageBatchRepeatInterleaving node is designed to expand a batch of images by repeating each image in the batch a specified number of times. This operation effectively increases the batch size by duplicating images, which can be useful for data augmentation or ensuring uniform batch processing sizes.

Input types

Required

• images
  • The 'images' parameter represents the batch of images to be repeated. It is crucial for defining the input data that will undergo the repetition process.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• repeats
  • The 'repeats' parameter specifies the number of times each image in the batch should be repeated. It directly influences the resulting batch size by determining the duplication factor for each image.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • Comfy dtype: IMAGE
  • The output is a new batch of images where each original image has been repeated according to the 'repeats' parameter, effectively enlarging the batch size.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class ImageBatchRepeatInterleaving:

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "repeat"
    CATEGORY = "KJNodes/image"
    DESCRIPTION = """
Repeats each image in a batch by the specified number of times.  
Example batch of 5 images: 0, 1 ,2, 3, 4  
with repeats 2 becomes batch of 10 images: 0, 0, 1, 1, 2, 2, 3, 3, 4, 4  
"""

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                 "images": ("IMAGE",),
                 "repeats": ("INT", {"default": 1, "min": 1, "max": 4096}),
        },
    } 

    def repeat(self, images, repeats):

        repeated_images = torch.repeat_interleave(images, repeats=repeats, dim=0)
        return (repeated_images, )