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ComfyUI
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improve sharpen and blur nodes

This commit is contained in:
BlenderNeko 2023-05-20 15:23:28 +02:00
parent b9daf4e30f
commit 36af98d755
1 changed files with 21 additions and 14 deletions
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35
comfy_extras/nodes_post_processing.py
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@ -59,6 +59,12 @@ class Blend:
def g(self, x):
return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x))
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()
class Blur:
def __init__(self):
pass
@ -88,12 +94,6 @@ class Blur:
CATEGORY = "image/postprocessing"
def gaussian_kernel(self, 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()
def blur(self, image: torch.Tensor, blur_radius: int, sigma: float):
if blur_radius == 0:
return (image,)
@ -101,10 +101,11 @@ class Blur:
batch_size, height, width, channels = image.shape
kernel_size = blur_radius * 2 + 1
kernel = self.gaussian_kernel(kernel_size, sigma).repeat(channels, 1, 1).unsqueeze(1)
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)
padded_image = F.pad(image, (blur_radius,blur_radius,blur_radius,blur_radius), 'reflect')
blurred = F.conv2d(image, kernel, padding=kernel_size // 2, groups=channels)[:,:,blur_radius:-blur_radius, blur_radius:-blur_radius]
blurred = blurred.permute(0, 2, 3, 1)
return (blurred,)
@ -167,9 +168,15 @@ class Sharpen:
"max": 31,
"step": 1
}),
"alpha": ("FLOAT", {
"sigma": ("FLOAT", {
"default": 1.0,
"min": 0.1,
"max": 10.0,
"step": 0.1
}),
"alpha": ("FLOAT", {
"default": 1.0,
"min": 0.0,
"max": 5.0,
"step": 0.1
}),
@ -181,21 +188,21 @@ class Sharpen:
CATEGORY = "image/postprocessing"
def sharpen(self, image: torch.Tensor, sharpen_radius: int, alpha: float):
def sharpen(self, image: torch.Tensor, sharpen_radius: int, sigma:float, alpha: float):
if sharpen_radius == 0:
return (image,)
batch_size, height, width, channels = image.shape
kernel_size = sharpen_radius * 2 + 1
kernel = torch.ones((kernel_size, kernel_size), dtype=torch.float32) * -1
kernel = gaussian_kernel(kernel_size, sigma) * -(alpha*10)
center = kernel_size // 2
kernel[center, center] = kernel_size**2
kernel *= alpha
kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0
kernel = kernel.repeat(channels, 1, 1).unsqueeze(1)
tensor_image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
sharpened = F.conv2d(tensor_image, kernel, padding=center, groups=channels)
tensor_image = F.pad(tensor_image, (sharpen_radius,sharpen_radius,sharpen_radius,sharpen_radius), 'reflect')
sharpened = F.conv2d(tensor_image, kernel, padding=center, groups=channels)[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius]
sharpened = sharpened.permute(0, 2, 3, 1)
result = torch.clamp(sharpened, 0, 1)