Make Image Batch

Documentation

• Class name: ImpactMakeImageBatch
• Category: ImpactPack/Util
• Output node: False

The ImpactMakeImageBatch node is designed to aggregate multiple images into a single batch. This process involves potentially resizing images to ensure uniform dimensions across the batch, facilitating operations that require consistent image sizes. The node serves as a utility within the Impact Pack, streamlining the handling of images for batch processing.

Input types

Required

• image1
  • The primary image to which subsequent images will be concatenated. It serves as the reference for resizing operations if other images differ in dimensions.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

Output types

• image
  • Comfy dtype: IMAGE
  • A single tensor representing a batch of images, where each image has been resized as necessary to match the dimensions of the first image in the batch.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes:
  • CR Image Grid Panel

Source code

class MakeImageBatch:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"image1": ("IMAGE",), }}

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

    CATEGORY = "ImpactPack/Util"

    def doit(self, **kwargs):
        image1 = kwargs['image1']
        del kwargs['image1']
        images = [value for value in kwargs.values()]

        if len(images) == 0:
            return (image1,)
        else:
            for image2 in images:
                if image1.shape[1:] != image2.shape[1:]:
                    image2 = comfy.utils.common_upscale(image2.movedim(-1, 1), image1.shape[2], image1.shape[1], "lanczos", "center").movedim(1, -1)
                image1 = torch.cat((image1, image2), dim=0)
            return (image1,)