Image List To Image Batch

Documentation

• Class name: easy imageListToImageBatch
• Category: EasyUse/Image
• Output node: False

This node is designed to transform a list of images into a single image batch, effectively consolidating multiple images into a unified tensor structure for batch processing. It is particularly useful in scenarios where operations need to be applied to multiple images simultaneously, streamlining the workflow by handling images in a batched manner.

Input types

Required

• images
  • Represents the list of images to be batched together. Each image in the list is processed and combined into a single batch, facilitating operations that require batch-level processing.
  • Comfy dtype: IMAGE
  • Python dtype: List[torch.Tensor]

Output types

• image
  • Comfy dtype: IMAGE
  • The output is a single tensor that combines all input images into a batch. This batched format is suitable for further processing or analysis that benefits from batched data handling.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

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

  INPUT_IS_LIST = True

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

  CATEGORY = "EasyUse/Image"

  def doit(self, images):
    if len(images) <= 1:
      return (images[0],)
    else:
      image1 = images[0]
      for image2 in images[1:]:
        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,)