nodes/EllangoK-ComfyUI-post-processing-nodes
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updates to use radius instead of kernel size

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forces odd kernel size
This commit is contained in:
EllangoK
2023-04-02 18:15:33 -04:00
parent 8e15354512
commit d38160eb73
4 changed files with 33 additions and 17 deletions
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README.md
+1 -1
View File
@@ -30,4 +30,4 @@ or just run
python combine_files.py -h
for more help
for more help
post_processing/gaussian_blur.py
+8 -4
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@@ -10,10 +10,10 @@ class GaussianBlur:
return {
"required": {
"image": ("IMAGE",),
"kernel_size": ("INT", {
"default": 5,
"blur_radius": ("INT", {
"default": 1,
"min": 1,
"max": 31,
"max": 15,
"step": 1
}),
"sigma": ("FLOAT", {
@@ -36,9 +36,13 @@ class GaussianBlur:
g = torch.exp(-(d * d) / (2.0 * sigma * sigma))
return g / g.sum()
def blur(self, image: torch.Tensor, kernel_size: int, sigma: float):
def blur(self, image: torch.Tensor, blur_radius: int, sigma: float):
if blur_radius == 0:
return (image,)
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)
image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
post_processing/sharpen.py
+8 -4
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@@ -11,10 +11,10 @@ class Sharpen:
return {
"required": {
"image": ("IMAGE",),
"kernel_size": ("INT", {
"default": 5,
"sharpen_radius": ("INT", {
"default": 1,
"min": 1,
"max": 31,
"max": 15,
"step": 1
}),
"alpha": ("FLOAT", {
@@ -31,9 +31,13 @@ class Sharpen:
CATEGORY = "postprocessing"
def sharpen(self, image: torch.Tensor, kernel_size: int, alpha: float):
def sharpen(self, image: torch.Tensor, blur_radius: int, alpha: float):
if blur_radius == 0:
return (image,)
batch_size, height, width, channels = image.shape
kernel_size = blur_radius * 2 + 1
kernel = torch.ones((kernel_size, kernel_size), dtype=torch.float32) * -1
center = kernel_size // 2
kernel[center, center] = kernel_size**2
post_processing_nodes.py
+16 -8
View File
@@ -412,10 +412,10 @@ class GaussianBlur:
return {
"required": {
"image": ("IMAGE",),
"kernel_size": ("INT", {
"default": 5,
"blur_radius": ("INT", {
"default": 1,
"min": 1,
"max": 31,
"max": 15,
"step": 1
}),
"sigma": ("FLOAT", {
@@ -438,9 +438,13 @@ class GaussianBlur:
g = torch.exp(-(d * d) / (2.0 * sigma * sigma))
return g / g.sum()
def blur(self, image: torch.Tensor, kernel_size: int, sigma: float):
def blur(self, image: torch.Tensor, blur_radius: int, sigma: float):
if blur_radius == 0:
return (image,)
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)
image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
@@ -654,10 +658,10 @@ class Sharpen:
return {
"required": {
"image": ("IMAGE",),
"kernel_size": ("INT", {
"default": 5,
"sharpen_radius": ("INT", {
"default": 1,
"min": 1,
"max": 31,
"max": 15,
"step": 1
}),
"alpha": ("FLOAT", {
@@ -674,9 +678,13 @@ class Sharpen:
CATEGORY = "postprocessing"
def sharpen(self, image: torch.Tensor, kernel_size: int, alpha: float):
def sharpen(self, image: torch.Tensor, blur_radius: int, alpha: float):
if blur_radius == 0:
return (image,)
batch_size, height, width, channels = image.shape
kernel_size = blur_radius * 2 + 1
kernel = torch.ones((kernel_size, kernel_size), dtype=torch.float32) * -1
center = kernel_size // 2
kernel[center, center] = kernel_size**2