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fix: Vignette node resolution handling and batch processing #20

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fix: Vignette node resolution handling and batch processing #20

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23 changes: 15 additions & 8 deletions post_processing_nodes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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")
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