🔧 Images Batch Multiple

Documentation

• Class name: ImageBatchMultiple+
• Category: essentials/image batch
• Output node: False

The node 'ImageBatchMultiple+' is designed to handle multiple images simultaneously, applying operations that involve combining, transforming, or otherwise processing these images in a batch-oriented manner. It abstracts the complexity of dealing with image dimensions and formats, providing a streamlined approach to batch image manipulation.

Input types

Required

• image_i
  • Specifies the input images to be processed. It is central to the node's operation, as it determines the images that will undergo manipulation.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• method
  • Specifies the approach for resizing or manipulating the images, such as stretching or cropping, which affects the final appearance.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str

Optional

Output types

• image
  • Comfy dtype: IMAGE
  • The manipulated images after processing according to the specified parameters.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class ImageBatchMultiple:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image_1": ("IMAGE",),
                "method": (["nearest-exact", "bilinear", "area", "bicubic", "lanczos"], { "default": "lanczos" }),
            }, "optional": {
                "image_2": ("IMAGE",),
                "image_3": ("IMAGE",),
                "image_4": ("IMAGE",),
                "image_5": ("IMAGE",),
            },
        }
    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "execute"
    CATEGORY = "essentials/image batch"

    def execute(self, image_1, method, image_2=None, image_3=None, image_4=None, image_5=None):
        out = image_1

        if image_2 is not None:
            if image_1.shape[1:] != image_2.shape[1:]:
                image_2 = comfy.utils.common_upscale(image_2.movedim(-1,1), image_1.shape[2], image_1.shape[1], method, "center").movedim(1,-1)
            out = torch.cat((image_1, image_2), dim=0)
        if image_3 is not None:
            if image_1.shape[1:] != image_3.shape[1:]:
                image_3 = comfy.utils.common_upscale(image_3.movedim(-1,1), image_1.shape[2], image_1.shape[1], method, "center").movedim(1,-1)
            out = torch.cat((out, image_3), dim=0)
        if image_4 is not None:
            if image_1.shape[1:] != image_4.shape[1:]:
                image_4 = comfy.utils.common_upscale(image_4.movedim(-1,1), image_1.shape[2], image_1.shape[1], method, "center").movedim(1,-1)
            out = torch.cat((out, image_4), dim=0)
        if image_5 is not None:
            if image_1.shape[1:] != image_5.shape[1:]:
                image_5 = comfy.utils.common_upscale(image_5.movedim(-1,1), image_1.shape[2], image_1.shape[1], method, "center").movedim(1,-1)
            out = torch.cat((out, image_5), dim=0)

        return (out,)