Batch Average Image

Documentation

• Class name: BatchAverageImage
• Category: image/filters
• Output node: False

The BatchAverageImage node applies statistical averaging operations, such as mean or median, across a batch of images. This process is used to create a single representative image from a collection, either by calculating the mean or median pixel values across all images in the batch.

Input types

Required

• images
  • The collection of images to be processed. This parameter is crucial for determining the input data over which the averaging operation will be performed.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• operation
  • Specifies the type of averaging operation to apply on the batch of images, such as 'mean' or 'median'. This choice directly influences the resulting output image.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str

Output types

• image
  • Comfy dtype: IMAGE
  • The output image after applying the specified averaging operation across the input batch of images. It represents a statistical summary of the input batch.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class BatchAverageImage:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "images": ("IMAGE",),
                "operation": (["mean", "median"],),
            },
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "apply"

    CATEGORY = "image/filters"

    def apply(self, images, operation):
        t = images.detach().clone()
        if operation == "mean":
            return (torch.mean(t, dim=0, keepdim=True),)
        elif operation == "median":
            return (torch.median(t, dim=0, keepdim=True)[0],)
        return(t,)