dodge and burn, dissolve, and arithmetic blend
This commit is contained in:
EllangoK
+170
-80
@@ -1,11 +1,11 @@
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import torch
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import torch.nn.functional as F
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import cv2
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import numpy as np
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from PIL import Image, ImageEnhance
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import torch.nn.functional as F
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class Blend:
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class ArithmeticBlend:
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def __init__(self):
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pass
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@@ -15,43 +15,86 @@ class Blend:
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"required": {
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"image1": ("IMAGE",),
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"image2": ("IMAGE",),
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"blend_factor": ("FLOAT", {
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"default": 0.5,
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"min": 0.0,
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"max": 1.0,
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"step": 0.01
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}),
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"blend_mode": (["normal", "multiply", "screen", "overlay", "soft_light"],),
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"blend_mode": (["add", "subtract", "difference"],),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "blend_images"
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FUNCTION = "arithmetic_blend_images"
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CATEGORY = "postprocessing"
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def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str):
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blended_image = self.blend_mode(image1, image2, blend_mode)
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blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor
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def arithmetic_blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_mode: str):
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if blend_mode == "add":
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blended_image = self.add(image1, image2)
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elif blend_mode == "subtract":
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blended_image = self.subtract(image1, image2)
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elif blend_mode == "difference":
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blended_image = self.difference(image1, image2)
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else:
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raise ValueError(f"Unsupported arithmetic blend mode: {blend_mode}")
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blended_image = torch.clamp(blended_image, 0, 1)
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return (blended_image,)
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def blend_mode(self, img1, img2, mode):
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if mode == "normal":
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return img2
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elif mode == "multiply":
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return img1 * img2
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elif mode == "screen":
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return 1 - (1 - img1) * (1 - img2)
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elif mode == "overlay":
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return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2))
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elif mode == "soft_light":
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return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (self.g(img1) - img1))
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else:
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raise ValueError(f"Unsupported blend mode: {mode}")
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def add(self, img1, img2):
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return img1 + img2
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def g(self, x):
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return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x))
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def subtract(self, img1, img2):
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return img1 - img2
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def difference(self, img1, img2):
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return torch.abs(img1 - img2)
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class Blur:
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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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"blur_radius": ("INT", {
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"default": 1,
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"min": 1,
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"max": 15,
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"step": 1
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}),
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"sigma": ("FLOAT", {
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"default": 1.0,
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"min": 0.1,
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"max": 10.0,
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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 = "blur"
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CATEGORY = "postprocessing"
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def gaussian_kernel(self, kernel_size: int, sigma: float):
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x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size), torch.linspace(-1, 1, kernel_size))
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d = torch.sqrt(x * x + y * y)
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g = torch.exp(-(d * d) / (2.0 * sigma * sigma))
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return g / g.sum()
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def blur(self, image: torch.Tensor, blur_radius: int, sigma: float):
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if blur_radius == 0:
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return (image,)
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batch_size, height, width, channels = image.shape
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kernel_size = blur_radius * 2 + 1
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kernel = self.gaussian_kernel(kernel_size, sigma).repeat(channels, 1, 1).unsqueeze(1)
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image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
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blurred = F.conv2d(image, kernel, padding=kernel_size // 2, groups=channels)
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blurred = blurred.permute(0, 2, 3, 1)
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return (blurred,)
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class CannyEdgeDetection:
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def __init__(self):
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@@ -201,6 +244,38 @@ class ColorCorrect:
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return (result, )
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class Dissolve:
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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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"image1": ("IMAGE",),
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"image2": ("IMAGE",),
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"dissolve_factor": ("FLOAT", {
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"default": 0.5,
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"min": 0.0,
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"max": 1.0,
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"step": 0.01
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}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "dissolve_images"
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CATEGORY = "postprocessing"
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def dissolve_images(self, image1: torch.Tensor, image2: torch.Tensor, dissolve_factor: float):
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dither_pattern = torch.rand_like(image1)
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mask = (dither_pattern < dissolve_factor).float()
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dissolved_image = image1 * mask + image2 * (1 - mask)
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dissolved_image = torch.clamp(dissolved_image, 0, 1)
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return (dissolved_image,)
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class Dither:
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def __init__(self):
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pass
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@@ -258,6 +333,69 @@ class Dither:
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return (result,)
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class DodgeAndBurn:
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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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"mask": ("IMAGE",),
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"intensity": ("FLOAT", {
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"default": 0.5,
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"min": 0.0,
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"max": 1.0,
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"step": 0.01
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}),
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"mode": (["dodge", "burn", "dodge_and_burn", "burn_and_dodge", "color_dodge", "color_burn", "linear_dodge", "linear_burn"],),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "dodge_and_burn"
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CATEGORY = "postprocessing"
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def dodge_and_burn(self, image: torch.Tensor, mask: torch.Tensor, intensity: float, mode: str):
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if mode in ["dodge", "color_dodge", "linear_dodge"]:
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dodged_image = self.dodge(image, mask, intensity, mode)
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return (dodged_image,)
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elif mode in ["burn", "color_burn", "linear_burn"]:
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burned_image = self.burn(image, mask, intensity, mode)
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return (burned_image,)
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elif mode == "dodge_and_burn":
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dodged_image = self.dodge(image, mask, intensity, "dodge")
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burned_image = self.burn(dodged_image, mask, intensity, "burn")
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return (burned_image,)
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elif mode == "burn_and_dodge":
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burned_image = self.burn(image, mask, intensity, "burn")
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dodged_image = self.dodge(burned_image, mask, intensity, "dodge")
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return (dodged_image,)
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else:
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raise ValueError(f"Unsupported dodge and burn mode: {mode}")
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def dodge(self, img, mask, intensity, mode):
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if mode == "dodge":
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return img / (1 - mask * intensity + 1e-7)
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elif mode == "color_dodge":
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return torch.where(mask < 1, img / (1 - mask * intensity), img)
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elif mode == "linear_dodge":
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return torch.clamp(img + mask * intensity, 0, 1)
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else:
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raise ValueError(f"Unsupported dodge mode: {mode}")
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def burn(self, img, mask, intensity, mode):
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if mode == "burn":
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return 1 - (1 - img) / (mask * intensity + 1e-7)
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elif mode == "color_burn":
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return torch.where(mask > 0, 1 - (1 - img) / (mask * intensity), img)
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elif mode == "linear_burn":
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return torch.clamp(img - mask * intensity, 0, 1)
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else:
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raise ValueError(f"Unsupported burn mode: {mode}")
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class FilmGrain:
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def __init__(self):
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pass
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@@ -403,56 +541,6 @@ class FilmGrain:
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return np.clip(image * vignette[..., np.newaxis], 0, 1)
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class GaussianBlur:
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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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"blur_radius": ("INT", {
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"default": 1,
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"min": 1,
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"max": 15,
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"step": 1
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}),
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"sigma": ("FLOAT", {
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"default": 1.0,
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"min": 0.1,
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"max": 10.0,
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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 = "blur"
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CATEGORY = "postprocessing"
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def gaussian_kernel(self, kernel_size: int, sigma: float):
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x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size), torch.linspace(-1, 1, kernel_size))
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d = torch.sqrt(x * x + y * y)
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g = torch.exp(-(d * d) / (2.0 * sigma * sigma))
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return g / g.sum()
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def blur(self, image: torch.Tensor, blur_radius: int, sigma: float):
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if blur_radius == 0:
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return (image,)
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batch_size, height, width, channels = image.shape
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kernel_size = blur_radius * 2 + 1
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kernel = self.gaussian_kernel(kernel_size, sigma).repeat(channels, 1, 1).unsqueeze(1)
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image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
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blurred = F.conv2d(image, kernel, padding=kernel_size // 2, groups=channels)
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blurred = blurred.permute(0, 2, 3, 1)
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return (blurred,)
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class Glow:
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def __init__(self):
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pass
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@@ -791,12 +879,14 @@ def pixel_sort(img, mask, horizontal_sort=False, span_limit=None, sort_by='H', r
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return sorted_image
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NODE_CLASS_MAPPINGS = {
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"Blend": Blend,
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"ArithmeticBlend": ArithmeticBlend,
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"Blur": Blur,
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"CannyEdgeDetection": CannyEdgeDetection,
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"ColorCorrect": ColorCorrect,
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"Dissolve": Dissolve,
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"Dither": Dither,
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"DodgeAndBurn": DodgeAndBurn,
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"FilmGrain": FilmGrain,
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"GaussianBlur": GaussianBlur,
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"Glow": Glow,
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"KMeansQuantize": KMeansQuantize,
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"PixelSort": PixelSort,
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