ImageBatchGet

Documentation

• Class name: ImageBatchGet
• Category: image/batch
• Output node: False

The ImageBatchGet node is designed for extracting a specific image from a batch of images based on a given index. It simplifies the process of handling image batches by allowing selective retrieval of images, which can be particularly useful in scenarios where only a subset of the batch is needed for further processing or analysis.

Input types

Required

• images
  • The images parameter represents the batch of images from which a specific image is to be retrieved. It plays a crucial role in determining the source of the image extraction.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• index
  • The index parameter specifies the position of the image to be extracted from the batch. It is essential for pinpointing the exact image within the batch that is required for further operations.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • Comfy dtype: IMAGE
  • This output is the extracted image from the specified index within the batch. It enables focused manipulation or analysis of individual images from a larger collection.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class ImageBatchGet:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "images": ("IMAGE",),
                "index": ("INT", {
                    "default": 1,
                    "min": 1,
                    "step": 1
                }),
            },
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "node"
    CATEGORY = "image/batch"

    def node(self, images, index):
        batch = images.shape[0]
        index = min(batch, index) - 1

        return (images[index].unsqueeze(0),)