Image Batch Multi

Documentation

• Class name: ImageBatchMulti
• Category: KJNodes/image
• Output node: False

Facilitates the creation of a batch of images by combining multiple input images into a single batch. This node allows for dynamic adjustment of the number of input images, supporting a flexible and scalable approach to batch image processing.

Input types

Required

• inputcount
  • Specifies the number of images to be included in the batch, allowing for dynamic adjustment of the batch size.
  • Comfy dtype: INT
  • Python dtype: int
• image_i
  • Represents any image to be included in the batch, starting from 'image_1' to 'image_{inputcount}'. Each 'image_i' is dynamically added based on the 'inputcount', contributing to the combined batch of images.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

Output types

• images
  • Comfy dtype: IMAGE
  • The combined batch of images resulting from the aggregation of individual input images.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class ImageBatchMulti:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}),
                "image_1": ("IMAGE", ),
                "image_2": ("IMAGE", ),
            },
    }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("images",)
    FUNCTION = "combine"
    CATEGORY = "KJNodes/image"
    DESCRIPTION = """
Creates an image batch from multiple images.  
You can set how many inputs the node has,  
with the **inputcount** and clicking update.
"""

    def combine(self, inputcount, **kwargs):
        from nodes import ImageBatch
        image_batch_node = ImageBatch()
        image = kwargs["image_1"]
        for c in range(1, inputcount):
            new_image = kwargs[f"image_{c + 1}"]
            image, = image_batch_node.batch(new_image, image)
        return (image,)