Split Image Batch 🎥🅥🅗🅢¶
Documentation¶
- Class name:
VHS_SplitImages
- Category:
Video Helper Suite 🎥🅥🅗🅢/image
- Output node:
False
The VHS_SplitImages node is designed to divide a batch of images into two groups based on a specified index. This functionality is essential for workflows that require the separation of image data for further processing or analysis.
Input types¶
Required¶
images
- The 'images' parameter represents the batch of images to be split. It is crucial for determining how the images are divided into two groups.
- Comfy dtype:
IMAGE
- Python dtype:
torch.Tensor
split_index
- The 'split_index' parameter specifies the index at which the batch of images is split. It plays a pivotal role in defining the boundary between the two resulting groups of images.
- Comfy dtype:
INT
- Python dtype:
int
Output types¶
IMAGE_A
- Comfy dtype:
IMAGE
- The first group of images obtained after the split.
- Python dtype:
torch.Tensor
- Comfy dtype:
A_count
- Comfy dtype:
INT
- The count of images in the first group after the split.
- Python dtype:
int
- Comfy dtype:
IMAGE_B
- Comfy dtype:
IMAGE
- The second group of images obtained after the split.
- Python dtype:
torch.Tensor
- Comfy dtype:
B_count
- Comfy dtype:
INT
- The count of images in the second group after the split.
- Python dtype:
int
- Comfy dtype:
Usage tips¶
- Infra type:
GPU
- Common nodes:
Source code¶
class SplitImages:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE",),
"split_index": ("INT", {"default": 0, "step": 1, "min": BIGMIN, "max": BIGMAX}),
},
}
CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/image"
RETURN_TYPES = ("IMAGE", "INT", "IMAGE", "INT")
RETURN_NAMES = ("IMAGE_A", "A_count", "IMAGE_B", "B_count")
FUNCTION = "split_images"
def split_images(self, images: Tensor, split_index: int):
group_a = images[:split_index]
group_b = images[split_index:]
return (group_a, group_a.size(0), group_b, group_b.size(0))