Batch Average Un-Jittered

Documentation

• Class name: BatchAverageUnJittered
• Category: image/filters/jitter
• Output node: False

This node is designed to process a batch of images by applying an averaging or median operation to reduce jittering effects. It operates on sub-batches of images, applying the specified operation to create a smoother, more stable output image.

Input types

Required

• images
  • The batch of images to be processed. This input is crucial for determining the set of images on which the averaging or median operation will be applied.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• operation
  • Specifies the operation ('mean' or 'median') to be applied across the images. This choice affects the method of jitter reduction and the final appearance of the output image.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str

Output types

• image
  • Comfy dtype: IMAGE
  • The processed batch of images after applying the specified averaging or median operation to reduce jittering effects.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

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

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

    CATEGORY = "image/filters/jitter"

    def apply(self, images, operation):
        t = images.detach().clone()

        batch = []
        for i in range(t.shape[0] // 9):
            if operation == "mean":
                batch.append(torch.mean(t[i*9:i*9+9], dim=0, keepdim=True))
            elif operation == "median":
                batch.append(torch.median(t[i*9:i*9+9], dim=0, keepdim=True)[0])

        return (torch.cat(batch, dim=0),)