54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
import torch
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import torch.nn.functional as F
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class Sharpen:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"kernel_size": ("INT", {
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"default": 5,
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"min": 1,
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"max": 31,
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"step": 1
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}),
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"alpha": ("FLOAT", {
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"default": 1.0,
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"min": 0.1,
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"max": 5.0,
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"step": 0.1
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}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "sharpen"
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CATEGORY = "postprocessing"
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def sharpen(self, image: torch.Tensor, kernel_size: int, alpha: float):
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batch_size, height, width, channels = image.shape
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kernel = torch.ones((kernel_size, kernel_size), dtype=torch.float32) * -1
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center = kernel_size // 2
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kernel[center, center] = kernel_size**2
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kernel *= alpha
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kernel = kernel.repeat(channels, 1, 1).unsqueeze(1)
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tensor_image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
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sharpened = F.conv2d(tensor_image, kernel, padding=center, groups=channels)
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sharpened = sharpened.permute(0, 2, 3, 1)
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result = torch.clamp(sharpened, 0, 1)
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return (result,)
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NODE_CLASS_MAPPINGS = {
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"Sharpen": Sharpen
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}
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