Merge branch 'nodes-post-processing' of https://github.com/EllangoK/ComfyUI
This commit is contained in:
comfyanonymous
commit
cadef9ff61
|
|
@ -0,0 +1,210 @@
|
|||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from PIL import Image
|
||||
|
||||
import comfy.utils
|
||||
|
||||
|
||||
class Blend:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"image1": ("IMAGE",),
|
||||
"image2": ("IMAGE",),
|
||||
"blend_factor": ("FLOAT", {
|
||||
"default": 0.5,
|
||||
"min": 0.0,
|
||||
"max": 1.0,
|
||||
"step": 0.01
|
||||
}),
|
||||
"blend_mode": (["normal", "multiply", "screen", "overlay", "soft_light"],),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
FUNCTION = "blend_images"
|
||||
|
||||
CATEGORY = "postprocessing"
|
||||
|
||||
def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str):
|
||||
if image1.shape != image2.shape:
|
||||
image2 = image2.permute(0, 3, 1, 2)
|
||||
image2 = comfy.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center')
|
||||
image2 = image2.permute(0, 2, 3, 1)
|
||||
|
||||
blended_image = self.blend_mode(image1, image2, blend_mode)
|
||||
blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor
|
||||
blended_image = torch.clamp(blended_image, 0, 1)
|
||||
return (blended_image,)
|
||||
|
||||
def blend_mode(self, img1, img2, mode):
|
||||
if mode == "normal":
|
||||
return img2
|
||||
elif mode == "multiply":
|
||||
return img1 * img2
|
||||
elif mode == "screen":
|
||||
return 1 - (1 - img1) * (1 - img2)
|
||||
elif mode == "overlay":
|
||||
return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2))
|
||||
elif mode == "soft_light":
|
||||
return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (self.g(img1) - img1))
|
||||
else:
|
||||
raise ValueError(f"Unsupported blend mode: {mode}")
|
||||
|
||||
def g(self, x):
|
||||
return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x))
|
||||
|
||||
class Blur:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"image": ("IMAGE",),
|
||||
"blur_radius": ("INT", {
|
||||
"default": 1,
|
||||
"min": 1,
|
||||
"max": 31,
|
||||
"step": 1
|
||||
}),
|
||||
"sigma": ("FLOAT", {
|
||||
"default": 1.0,
|
||||
"min": 0.1,
|
||||
"max": 10.0,
|
||||
"step": 0.1
|
||||
}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
FUNCTION = "blur"
|
||||
|
||||
CATEGORY = "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,)
|
||||
|
||||
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)
|
||||
blurred = F.conv2d(image, kernel, padding=kernel_size // 2, groups=channels)
|
||||
blurred = blurred.permute(0, 2, 3, 1)
|
||||
|
||||
return (blurred,)
|
||||
|
||||
class Quantize:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"image": ("IMAGE",),
|
||||
"colors": ("INT", {
|
||||
"default": 256,
|
||||
"min": 1,
|
||||
"max": 256,
|
||||
"step": 1
|
||||
}),
|
||||
"dither": (["none", "floyd-steinberg"],),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
FUNCTION = "quantize"
|
||||
|
||||
CATEGORY = "postprocessing"
|
||||
|
||||
def quantize(self, image: torch.Tensor, colors: int = 256, dither: str = "FLOYDSTEINBERG"):
|
||||
batch_size, height, width, _ = image.shape
|
||||
result = torch.zeros_like(image)
|
||||
|
||||
dither_option = Image.Dither.FLOYDSTEINBERG if dither == "floyd-steinberg" else Image.Dither.NONE
|
||||
|
||||
for b in range(batch_size):
|
||||
tensor_image = image[b]
|
||||
img = (tensor_image * 255).to(torch.uint8).numpy()
|
||||
pil_image = Image.fromarray(img, mode='RGB')
|
||||
|
||||
palette = pil_image.quantize(colors=colors) # Required as described in https://github.com/python-pillow/Pillow/issues/5836
|
||||
quantized_image = pil_image.quantize(colors=colors, palette=palette, dither=dither_option)
|
||||
|
||||
quantized_array = torch.tensor(np.array(quantized_image.convert("RGB"))).float() / 255
|
||||
result[b] = quantized_array
|
||||
|
||||
return (result,)
|
||||
|
||||
class Sharpen:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"image": ("IMAGE",),
|
||||
"sharpen_radius": ("INT", {
|
||||
"default": 1,
|
||||
"min": 1,
|
||||
"max": 31,
|
||||
"step": 1
|
||||
}),
|
||||
"alpha": ("FLOAT", {
|
||||
"default": 1.0,
|
||||
"min": 0.1,
|
||||
"max": 5.0,
|
||||
"step": 0.1
|
||||
}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
FUNCTION = "sharpen"
|
||||
|
||||
CATEGORY = "postprocessing"
|
||||
|
||||
def sharpen(self, image: torch.Tensor, sharpen_radius: int, 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
|
||||
center = kernel_size // 2
|
||||
kernel[center, center] = kernel_size**2
|
||||
kernel *= alpha
|
||||
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)
|
||||
sharpened = sharpened.permute(0, 2, 3, 1)
|
||||
|
||||
result = torch.clamp(sharpened, 0, 1)
|
||||
|
||||
return (result,)
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"Blend": Blend,
|
||||
"Blur": Blur,
|
||||
"Quantize": Quantize,
|
||||
"Sharpen": Sharpen,
|
||||
}
|
||||
3
nodes.py
3
nodes.py
|
|
@ -1113,4 +1113,5 @@ def load_custom_nodes():
|
|||
|
||||
def init_custom_nodes():
|
||||
load_custom_nodes()
|
||||
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_upscale_model.py"))
|
||||
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_upscale_model.py"))
|
||||
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_post_processing.py"))
|
||||
|
|
|
|||
Loading…
Reference in New Issue