Image Gaussian Blur

Documentation

• Class name: ImageGaussianBlur
• Category: Art Venture/Utils
• Output node: False

The ImageGaussianBlur node applies a Gaussian blur filter to a collection of images, effectively smoothing them by a specified radius. This operation is commonly used in image processing to reduce noise and detail, or to create a visual effect.

Input types

Required

• images
  • The collection of images to be blurred. This input is crucial for defining the set of images that will undergo the Gaussian blur transformation.
  • Comfy dtype: IMAGE
  • Python dtype: List[torch.Tensor]
• radius
  • Specifies the radius of the Gaussian blur. A larger radius results in a more pronounced blurring effect.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • Comfy dtype: IMAGE
  • The blurred images, returned as a single tensor by concatenating the individually blurred images along the batch dimension.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class UtilImageGaussianBlur:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "images": ("IMAGE",),
                "radius": ("INT", {"default": 1, "min": 1, "max": 100}),
            }
        }

    RETURN_TYPES = ("IMAGE",)
    CATEGORY = "Art Venture/Utils"
    FUNCTION = "image_gaussian_blur"

    def image_gaussian_blur(self, images, radius):
        blured_images = []
        for image in images:
            img = tensor2pil(image)
            img = img.filter(ImageFilter.GaussianBlur(radius=radius))
            blured_images.append(pil2tensor(img))

        return (torch.cat(blured_images, dim=0),)