diff --git a/post_processing_nodes.py b/post_processing_nodes.py
index dfd9322..48a8452 100644
--- a/post_processing_nodes.py
+++ b/post_processing_nodes.py
@@ -1332,20 +1332,27 @@ def INPUT_TYPES(s):
     def apply_vignette(self, image: torch.Tensor, vignette: float):
         if vignette == 0:
             return (image,)
-        height, width, _ = image.shape[-3:]
+
+        batch_size, height, width, _ = image.shape
+
+        # Build the distance grid — use indexing="xy" so shape is (height, width)
         x = torch.linspace(-1, 1, width, device=image.device)
         y = torch.linspace(-1, 1, height, device=image.device)
-        X, Y = torch.meshgrid(x, y, indexing="ij")
+        X, Y = torch.meshgrid(x, y, indexing="xy")
         radius = torch.sqrt(X ** 2 + Y ** 2)
 
-        radius = radius / torch.amax(radius, dim=(0, 1), keepdim=True)
-        opacity = torch.tensor(vignette, device=image.device)
-        opacity = torch.clamp(opacity, 0.0, 1.0)
-        vignette = 1 - radius.unsqueeze(0).unsqueeze(-1) * opacity
+        # Normalize to [0, 1]
+        radius = radius / radius.amax()
 
-        vignette_image = torch.clamp(image * vignette, 0, 1)
+        # Scale by strength — allow full input range instead of clamping to 1
+        strength = min(vignette, 10.0)
+        vignette_mask = torch.clamp(1.0 - radius * strength, 0.0, 1.0)
 
-        return (vignette_image,)
+        # Broadcast over batch and channels: (1, H, W, 1)
+        vignette_mask = vignette_mask.unsqueeze(0).unsqueeze(-1)
+
+        result = torch.clamp(image * vignette_mask, 0, 1)
+        return (result,)
 
 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")