ImageFilterGaussianBlurAdvanced

Documentation

• Class name: ImageFilterGaussianBlurAdvanced
• Category: image/filter
• Output node: False

This node applies an advanced Gaussian blur filter to images, allowing for separate horizontal and vertical blur sizes and standard deviations. It enhances image processing capabilities by providing more control over the blurring effect.

Input types

Required

• images
  • The images to be processed. This parameter is crucial for defining the input on which the Gaussian blur will be applied.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• size_x
  • Specifies the horizontal size of the Gaussian kernel. It influences the extent of blurring along the x-axis.
  • Comfy dtype: INT
  • Python dtype: int
• size_y
  • Specifies the vertical size of the Gaussian kernel. It influences the extent of blurring along the y-axis.
  • Comfy dtype: INT
  • Python dtype: int
• sigma_x
  • Determines the horizontal standard deviation of the Gaussian kernel. It affects the spread of the blur along the x-axis.
  • Comfy dtype: INT
  • Python dtype: int
• sigma_y
  • Determines the vertical standard deviation of the Gaussian kernel. It affects the spread of the blur along the y-axis.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • Comfy dtype: IMAGE
  • The output is the blurred image, processed using the specified parameters for the Gaussian blur.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class ImageFilterGaussianBlurAdvanced:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "images": ("IMAGE",),
                "size_x": ("INT", {
                    "default": 10,
                    "min": 2,
                    "step": 2
                }),
                "size_y": ("INT", {
                    "default": 10,
                    "min": 2,
                    "step": 2
                }),
                "sigma_x": ("INT", {
                    "default": 0,
                }),
                "sigma_y": ("INT", {
                    "default": 0,
                }),
            },
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "node"
    CATEGORY = "image/filter"

    def node(self, images, size_x, size_y, sigma_x, sigma_y):
        size_x -= 1
        size_y -= 1

        return (cv2_layer(images, lambda x: cv2.GaussianBlur(x, (size_x, size_y), sigma_x, sigma_y)),)