ImageFromBatch

Documentation

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

The ImageFromBatch node is designed to extract a specific segment of images from a larger batch based on a given index and length. This functionality is crucial for operations that require processing or analyzing subsets of images within a batch, enabling targeted manipulation or inspection of images.

Input types

Required

• image
  • The 'image' parameter represents the batch of images from which a subset will be extracted. It is crucial for specifying the source batch.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• batch_index
  • The 'batch_index' parameter specifies the starting index within the batch from which the extraction begins, allowing for precise selection of the subset.
  • Comfy dtype: INT
  • Python dtype: int
• length
  • The 'length' parameter determines the number of images to extract from the specified starting index, enabling control over the size of the resulting subset.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • Comfy dtype: IMAGE
  • The output is a subset of images extracted from the original batch, based on the specified 'batch_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",),
                              "batch_index": ("INT", {"default": 0, "min": 0, "max": 4095}),
                              "length": ("INT", {"default": 1, "min": 1, "max": 4096}),
                              }}
    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "frombatch"

    CATEGORY = "image/batch"

    def frombatch(self, image, batch_index, length):
        s_in = image
        batch_index = min(s_in.shape[0] - 1, batch_index)
        length = min(s_in.shape[0] - batch_index, length)
        s = s_in[batch_index:batch_index + length].clone()
        return (s,)