adds glow effect

README.md

@@ -9,6 +9,7 @@ A collection of post processing nodes for [ComfyUI](https://github.com/comfyanon
  - ColorCorrect: Adjusts the color balance, temperature, hue, brightness, contrast, saturation, and gamma of an image
  - Dither: Reduces the color information in an image by dithering, resulting in a patterned, pixelated appearance
  - FilmGrain: Adds a film grain effect to the image, along with options to control the temperature, and vignetting
+ - Glow: Applies a blur with a specified radius and then blends it with the original image. Creates a nice glowing effect.
  - GaussianBlur: Applies a Gaussian blur to the input image, softening the details
  - KMeansQuantize: Reduce the amount of colors in an image from 0-256
  - PixelSort: Rearranges the pixels in the input image based on their values, and input mask. Creates a cool glitch like effect.

post_processing/glow.py

@@ -0,0 +1,62 @@
+import torch
+import torch.nn.functional as F
+
+class Glow:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "intensity": ("FLOAT", {
+                    "default": 1.0,
+                    "min": 0.0,
+                    "max": 5.0,
+                    "step": 0.01
+                }),
+                "blur_radius": ("INT", {
+                    "default": 5,
+                    "min": 1,
+                    "max": 50,
+                    "step": 1
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "apply_glow"
+
+    CATEGORY = "postprocessing"
+
+    def apply_glow(self, image: torch.Tensor, intensity: float, blur_radius: int):
+        blurred_image = self.gaussian_blur(image, 2 * blur_radius + 1)
+        glowing_image = self.add_glow(image, blurred_image, intensity)
+        glowing_image = torch.clamp(glowing_image, 0, 1)
+        return (glowing_image,)
+
+    def gaussian_kernel(self, kernel_size: int):
+        sigma = (kernel_size - 1) / 6
+        x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size), torch.linspace(-1, 1, kernel_size))
+        d = torch.sqrt(x * x + y * y)
+        g = torch.exp(-(d * d) / (2.0 * sigma * sigma))
+        return g / g.sum()
+
+    def gaussian_blur(self, image: torch.Tensor, kernel_size: int):
+        batch_size, height, width, channels = image.shape
+
+        kernel = self.gaussian_kernel(kernel_size).repeat(channels, 1, 1).unsqueeze(1)
+
+        image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
+        blurred = F.conv2d(image, kernel, padding=kernel_size // 2, groups=channels)
+        blurred = blurred.permute(0, 2, 3, 1)
+
+        return blurred
+
+    def add_glow(self, img, blurred_img, intensity):
+        return img + blurred_img * intensity
+
+NODE_CLASS_MAPPINGS = {
+    "Glow": Glow,
+}