Image Flip

Documentation

• Class name: Image Flip
• Category: WAS Suite/Image/Transform
• Output node: False

The Image Flip node provides functionality to flip images either horizontally or vertically, allowing for simple yet effective transformations of image data.

Input types

Required

• images
  • A batch of images to be flipped. This parameter is crucial for determining which images undergo the flipping process.
  • Comfy dtype: IMAGE
  • Python dtype: List[torch.Tensor]
• mode
  • Specifies the direction of the flip, either 'horizontal' or 'vertical'. This affects how the images are transformed.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str

Output types

• images
  • Comfy dtype: IMAGE
  • The batch of images after being flipped according to the specified mode.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class WAS_Image_Flip:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "images": ("IMAGE",),
                "mode": (["horizontal", "vertical",],),
            },
        }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("images",)
    FUNCTION = "image_flip"

    CATEGORY = "WAS Suite/Image/Transform"

    def image_flip(self, images, mode):

        batch_tensor = []
        for image in images:
            image = tensor2pil(image)
            if mode == 'horizontal':
                image = image.transpose(0)
            if mode == 'vertical':
                image = image.transpose(1)
            batch_tensor.append(pil2tensor(image))
        batch_tensor = torch.cat(batch_tensor, dim=0)

        return (batch_tensor, )