🔧 Image From Batch

Documentation

• Class name: ImageFromBatch+
• Category: essentials
• Output node: False

The ImageFromBatch node extracts a specific range of images from a batch based on the provided start index and length, allowing for selective processing or analysis of batched image data.

Input types

Required

• image
  • The batched image input from which a subset will be extracted. This parameter is crucial for specifying the source of the images to be processed.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• start
  • Specifies the starting index within the batch from which images will be extracted. This parameter determines the beginning of the subset to be processed.
  • Comfy dtype: INT
  • Python dtype: int
• length
  • Defines the number of images to extract from the specified starting index. This parameter controls the size of the subset to be processed.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • Comfy dtype: IMAGE
  • The extracted subset of images from the original batch, based on the specified start index and length.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class ImageFromBatch:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": ("IMAGE", ),
                "start": ("INT", { "default": 0, "min": 0, "step": 1, }),
                "length": ("INT", { "default": -1, "min": -1, "step": 1, }),
            }
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "execute"
    CATEGORY = "essentials"

    def execute(self, image, start, length):
        if length<0:
            length = image.shape[0]
        start = min(start, image.shape[0]-1)
        length = min(image.shape[0]-start, length)
        return (image[start:start + length], )