adds PencilSketch effect, mimics a drawn sketch

also extracts out gaussian_kernel func

post_processing/glow.py

@@ -36,17 +36,11 @@ class Glow:
         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)
+        sigma = (kernel_size - 1) / 6
+        kernel = gaussian_kernel(kernel_size, sigma).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)
@@ -60,3 +54,9 @@ class Glow:
 NODE_CLASS_MAPPINGS = {
     "Glow": Glow,
 }
+
+def gaussian_kernel(kernel_size: int, sigma: float):
+    x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size), torch.linspace(-1, 1, kernel_size), indexing="ij")
+    d = torch.sqrt(x * x + y * y)
+    g = torch.exp(-(d * d) / (2.0 * sigma * sigma))
+    return g / g.sum()