Image Mix

Documentation

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

The JWImageMix node is designed for blending two images together using specified blend modes and a blend factor. It supports operations like mixing and multiplying images, allowing for flexible image manipulation and combination.

Input types

Required

• blend_type
  • Specifies the blend mode to use for combining the images. It determines how the images are mathematically combined, affecting the visual outcome of the blend.
  • Comfy dtype: ['mix', 'multiply']
  • Python dtype: str
• factor
  • Determines the weight of the second image in the blend. A higher factor gives more prominence to the second image, while a lower factor favors the first image.
  • Comfy dtype: FLOAT
  • Python dtype: float
• image_a
  • The first image to be blended. Acts as the base layer in the blending operation.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• image_b
  • The second image to be blended with the first. Its contribution is controlled by the blend factor.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

Output types

• image
  • Comfy dtype: IMAGE
  • The result of blending the two input images according to the specified blend type and factor.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

@register_node("JWImageMix", "Image Mix")
class _:
    CATEGORY = "jamesWalker55"
    BLEND_TYPES = ("mix", "multiply")

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "blend_type": (cls.BLEND_TYPES, {"default": "mix"}),
                "factor": ("FLOAT", {"min": 0, "max": 1, "step": 0.01, "default": 0.5}),
                "image_a": ("IMAGE",),
                "image_b": ("IMAGE",),
            }
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "execute"

    def execute(
        self,
        blend_type: str,
        factor: float,
        image_a: torch.Tensor,
        image_b: torch.Tensor,
    ):
        assert blend_type in self.BLEND_TYPES
        assert isinstance(factor, float)
        assert isinstance(image_a, torch.Tensor)
        assert isinstance(image_b, torch.Tensor)

        assert image_a.shape == image_b.shape

        if blend_type == "mix":
            mixed = image_a * (1 - factor) + image_b * factor
        elif blend_type == "multiply":
            mixed = image_a * (1 - factor + image_b * factor)
        else:
            raise NotImplementedError(f"Blend type not yet implemented: {blend_type}")

        return (mixed,)