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EllangoKEllangoK
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Converts Sepia Node to generic ColorTint
You can tint any color by modifying sepia scale weights
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4 files changed

+129
-93
lines changed

4 files changed

+129
-93
lines changed

‎README.md‎

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@@ -24,6 +24,7 @@ Both images have the workflow attached, and are included with the repo. Feel fre
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- CannyEdgeDetection: Applies Canny edge detection to the input image
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- Chromatic Aberration: Shifts the color channels in an image, creating a glitch aesthetic
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- $\color{#00A7B5}\textbf{ColorCorrect:}$ Adjusts the color balance, temperature, hue, brightness, contrast, saturation, and gamma of an image
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- $\color{#00A7B5}\textbf{ColorTint:}$ Applies a customizable tint to the input image, with various color modes such as sepia, RGB, CMY and several composite colors
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- Dissolve: Creates a grainy blend of two images using random pixels based on a dissolve factor.
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- DodgeAndBurn: Adjusts image brightness using dodge and burn effects based on a mask and intensity.
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- FilmGrain: Adds a film grain effect to the image, along with options to control the temperature, and vignetting
@@ -34,7 +35,6 @@ Both images have the workflow attached, and are included with the repo. Feel fre
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- $\color{#00A7B5}\textbf{PixelSort:}$ Rearranges the pixels in the input image based on their values, and input mask. Creates a cool glitch like effect.
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- Pixelize: Applies a pixelization effect, simulating the reducing of resolution
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- $\color{#00A7B5}\textbf{Quantize:}$ Set and dither the amount of colors in an image from 0-256, reducing color information
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- Sepia: Applies a mellow tone mapping, yielding an archival or vintage appearance
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- Sharpen: Enhances the details in an image by applying a sharpening filter
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- $\color{#00A7B5}\textbf{Solarize:}$ Inverts image colors based on a threshold for a striking, high-contrast effect
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- Vignette: Applies a vignette effect, putting the corners of the image in shadow

‎post_processing/color_tint.py‎

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import torch
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class ColorTint:
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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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"strength": ("FLOAT", {
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"default": 1.0,
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"min": 0.1,
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"max": 1.0,
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"step": 0.1
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}),
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"mode": (["sepia", "red", "green", "blue", "cyan", "magenta", "yellow", "purple", "orange", "warm", "cool", "lime", "navy", "vintage", "rose", "teal", "maroon", "peach", "lavender", "olive"],),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "color_tint"
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CATEGORY = "postprocessing/Color Adjustments"
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def color_tint(self, image: torch.Tensor, strength: float, mode: str = "sepia"):
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if strength == 0:
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return (image,)
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sepia_weights = torch.tensor([0.2989, 0.5870, 0.1140]).view(1, 1, 1, 3).to(image.device)
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mode_filters = {
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"sepia": torch.tensor([1.0, 0.8, 0.6]),
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"red": torch.tensor([1.0, 0.6, 0.6]),
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"green": torch.tensor([0.6, 1.0, 0.6]),
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"blue": torch.tensor([0.6, 0.8, 1.0]),
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"cyan": torch.tensor([0.6, 1.0, 1.0]),
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"magenta": torch.tensor([1.0, 0.6, 1.0]),
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"yellow": torch.tensor([1.0, 1.0, 0.6]),
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"purple": torch.tensor([0.8, 0.6, 1.0]),
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"orange": torch.tensor([1.0, 0.7, 0.3]),
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"warm": torch.tensor([1.0, 0.9, 0.7]),
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"cool": torch.tensor([0.7, 0.9, 1.0]),
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"lime": torch.tensor([0.7, 1.0, 0.3]),
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"navy": torch.tensor([0.3, 0.4, 0.7]),
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"vintage": torch.tensor([0.9, 0.85, 0.7]),
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"rose": torch.tensor([1.0, 0.8, 0.9]),
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"teal": torch.tensor([0.3, 0.8, 0.8]),
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"maroon": torch.tensor([0.7, 0.3, 0.5]),
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"peach": torch.tensor([1.0, 0.8, 0.6]),
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"lavender": torch.tensor([0.8, 0.6, 1.0]),
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"olive": torch.tensor([0.6, 0.7, 0.4]),
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}
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scale_filter = mode_filters[mode].view(1, 1, 1, 3).to(image.device)
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grayscale = torch.sum(image * sepia_weights, dim=-1, keepdim=True)
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tinted = grayscale * scale_filter
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result = tinted * strength + image * (1 - strength)
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return (result,)
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NODE_CLASS_MAPPINGS = {
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"ColorTint": ColorTint
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}

‎post_processing/sepia.py‎

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This file was deleted.

‎post_processing_nodes.py‎

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Original file line numberDiff line numberDiff line change
@@ -366,6 +366,67 @@ def color_correct(self, image: torch.Tensor, temperature: float, hue: float, bri
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return (result, )
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class ColorTint:
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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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"strength": ("FLOAT", {
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"default": 1.0,
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"min": 0.1,
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"max": 1.0,
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"step": 0.1
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}),
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"mode": (["sepia", "red", "green", "blue", "cyan", "magenta", "yellow", "purple", "orange", "warm", "cool", "lime", "navy", "vintage", "rose", "teal", "maroon", "peach", "lavender", "olive"],),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "color_tint"
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CATEGORY = "postprocessing/Color Adjustments"
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def color_tint(self, image: torch.Tensor, strength: float, mode: str = "sepia"):
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if strength == 0:
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return (image,)
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sepia_weights = torch.tensor([0.2989, 0.5870, 0.1140]).view(1, 1, 1, 3).to(image.device)
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mode_filters = {
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"sepia": torch.tensor([1.0, 0.8, 0.6]),
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"red": torch.tensor([1.0, 0.6, 0.6]),
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"green": torch.tensor([0.6, 1.0, 0.6]),
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"blue": torch.tensor([0.6, 0.8, 1.0]),
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"cyan": torch.tensor([0.6, 1.0, 1.0]),
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"magenta": torch.tensor([1.0, 0.6, 1.0]),
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"yellow": torch.tensor([1.0, 1.0, 0.6]),
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"purple": torch.tensor([0.8, 0.6, 1.0]),
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"orange": torch.tensor([1.0, 0.7, 0.3]),
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"warm": torch.tensor([1.0, 0.9, 0.7]),
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"cool": torch.tensor([0.7, 0.9, 1.0]),
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"lime": torch.tensor([0.7, 1.0, 0.3]),
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"navy": torch.tensor([0.3, 0.4, 0.7]),
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"vintage": torch.tensor([0.9, 0.85, 0.7]),
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"rose": torch.tensor([1.0, 0.8, 0.9]),
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"teal": torch.tensor([0.3, 0.8, 0.8]),
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"maroon": torch.tensor([0.7, 0.3, 0.5]),
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"peach": torch.tensor([1.0, 0.8, 0.6]),
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"lavender": torch.tensor([0.8, 0.6, 1.0]),
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"olive": torch.tensor([0.6, 0.7, 0.4]),
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}
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scale_filter = mode_filters[mode].view(1, 1, 1, 3).to(image.device)
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grayscale = torch.sum(image * sepia_weights, dim=-1, keepdim=True)
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tinted = grayscale * scale_filter
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result = tinted * strength + image * (1 - strength)
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return (result,)
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class Dissolve:
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def __init__(self):
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pass
@@ -984,49 +1045,6 @@ def quantize(self, image: torch.Tensor, colors: int = 256, dither: str = "FLOYDS
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return (result,)
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class Sepia:
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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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"strength": ("FLOAT", {
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"default": 1.0,
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"min": 0.1,
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"max": 1.0,
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"step": 0.1
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}),
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"mode": (["sepia", "blue-pia", "green-pia"],),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "sepia"
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CATEGORY = "postprocessing/Color Adjustments"
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def sepia(self, image: torch.Tensor, strength: float, mode: str = "sepia"):
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if strength == 0:
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return (image,)
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sepia_weights = torch.tensor([0.2989, 0.5870, 0.1140]).view(1, 1, 1, 3).to(image.device)
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if mode == "sepia":
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sepia_filter = torch.tensor([1.0, 0.8, 0.6]).view(1, 1, 1, 3).to(image.device)
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elif mode == "blue-pia":
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sepia_filter = torch.tensor([0.6, 0.8, 1.0]).view(1, 1, 1, 3).to(image.device)
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elif mode == "green-pia":
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sepia_filter = torch.tensor([0.6, 1.0, 0.6]).view(1, 1, 1, 3).to(image.device)
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grayscale = torch.sum(image * sepia_weights, dim=-1, keepdim=True)
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sepia = grayscale * sepia_filter
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result = sepia * strength + image * (1 - strength)
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return (result,)
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class Sharpen:
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def __init__(self):
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pass
@@ -1250,6 +1268,7 @@ def pixel_sort(img, mask, horizontal_sort=False, span_limit=None, sort_by='H', r
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"CannyEdgeDetection": CannyEdgeDetection,
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"ChromaticAberration": ChromaticAberration,
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"ColorCorrect": ColorCorrect,
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"ColorTint": ColorTint,
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"Dissolve": Dissolve,
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"DodgeAndBurn": DodgeAndBurn,
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"FilmGrain": FilmGrain,
@@ -1260,7 +1279,6 @@ def pixel_sort(img, mask, horizontal_sort=False, span_limit=None, sort_by='H', r
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"PixelSort": PixelSort,
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"Pixelize": Pixelize,
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"Quantize": Quantize,
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"Sepia": Sepia,
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"Sharpen": Sharpen,
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"Solarize": Solarize,
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"Vignette": Vignette,

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