Image Stack Channels

Documentation

• Class name: JWImageStackChannels
• Category: jamesWalker55
• Output node: False

This node is designed to stack two image tensors along their channel dimension, effectively combining them into a single tensor that retains the information from both input images.

Input types

Required

• image_a
  • The first image tensor to be stacked. It plays a crucial role in the stacking operation as it is combined with the second image tensor to form a single output tensor.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• image_b
  • The second image tensor to be stacked alongside the first. Its combination with the first image tensor results in a new tensor that encapsulates the data from both images.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

Output types

• image
  • Comfy dtype: IMAGE
  • The output is a single image tensor that results from stacking the two input image tensors along their channel dimension.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

@register_node("JWImageStackChannels", "Image Stack Channels")
class _:
    CATEGORY = "jamesWalker55"
    INPUT_TYPES = lambda: {
        "required": {
            "image_a": ("IMAGE",),
            "image_b": ("IMAGE",),
        }
    }
    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "execute"

    def execute(self, image_a: torch.Tensor, image_b: torch.Tensor):
        assert isinstance(image_a, torch.Tensor)
        assert isinstance(image_b, torch.Tensor)

        stacked = torch.cat((image_a, image_b), dim=3)

        return (stacked,)