ImageFilterGaussianBlur

Documentation

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

This node applies a Gaussian blur filter to images, smoothing out image noise and details by using a Gaussian function. It's designed to process images by blurring them in a way that mimics the effect of viewing the image through a translucent screen, effectively reducing image noise and detail.

Input types

Required

• images
  • The images to be processed. This parameter is crucial as it specifies the target images for the Gaussian blur effect.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• size_x
  • Specifies the horizontal size of the Gaussian kernel. This affects the extent of the blurring effect horizontally.
  • Comfy dtype: INT
  • Python dtype: int
• size_y
  • Specifies the vertical size of the Gaussian kernel. This affects the extent of the blurring effect vertically.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • Comfy dtype: IMAGE
  • The blurred images after applying the Gaussian blur filter.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class ImageFilterGaussianBlur:
    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
                }),
            },
        }

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

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

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