Image Blend

Documentation

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

The Image Blend node is designed to seamlessly blend two images together based on a specified blend percentage. This node allows for the creation of composite images by adjusting the degree of visibility of each input image in the final output.

Input types

Required

• image_a
  • The first image to be blended. It serves as one of the primary components in the blending process.
  • Comfy dtype: IMAGE
  • Python dtype: PIL.Image
• image_b
  • The second image to be blended with the first. It contributes to the final composite image alongside 'image_a'.
  • Comfy dtype: IMAGE
  • Python dtype: PIL.Image
• blend_percentage
  • A float value that determines the blending ratio between 'image_a' and 'image_b'. A higher percentage increases the visibility of 'image_b' in the output.
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

• image
  • Comfy dtype: IMAGE
  • The resulting image after blending 'image_a' and 'image_b' according to the specified blend percentage.
  • Python dtype: PIL.Image

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class WAS_Image_Blend:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "image_a": ("IMAGE",),
                "image_b": ("IMAGE",),
                "blend_percentage": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
            },
        }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("image",)
    FUNCTION = "image_blend"

    CATEGORY = "WAS Suite/Image"

    def image_blend(self, image_a, image_b, blend_percentage):

        # Convert images to PIL
        img_a = tensor2pil(image_a)
        img_b = tensor2pil(image_b)

        # Blend image
        blend_mask = Image.new(mode="L", size=img_a.size,
                               color=(round(blend_percentage * 255)))
        blend_mask = ImageOps.invert(blend_mask)
        img_result = Image.composite(img_a, img_b, blend_mask)

        del img_a, img_b, blend_mask

        return (pil2tensor(img_result), )