nodes/EllangoK-ComfyUI-post-processing-nodes
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Converts Sepia Node to generic ColorTint

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You can tint any color by modifying sepia scale weights
This commit is contained in:
EllangoK
2023-05-06 19:43:46 -04:00
parent 5fdd0593ae
commit 4344b40dc7
4 changed files with 129 additions and 93 deletions
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post_processing_nodes.py
+62 -44
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@@ -366,6 +366,67 @@ class ColorCorrect:
return (result, )
class ColorTint:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"strength": ("FLOAT", {
"default": 1.0,
"min": 0.1,
"max": 1.0,
"step": 0.1
}),
"mode": (["sepia", "red", "green", "blue", "cyan", "magenta", "yellow", "purple", "orange", "warm", "cool", "lime", "navy", "vintage", "rose", "teal", "maroon", "peach", "lavender", "olive"],),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "color_tint"
CATEGORY = "postprocessing/Color Adjustments"
def color_tint(self, image: torch.Tensor, strength: float, mode: str = "sepia"):
if strength == 0:
return (image,)
sepia_weights = torch.tensor([0.2989, 0.5870, 0.1140]).view(1, 1, 1, 3).to(image.device)
mode_filters = {
"sepia": torch.tensor([1.0, 0.8, 0.6]),
"red": torch.tensor([1.0, 0.6, 0.6]),
"green": torch.tensor([0.6, 1.0, 0.6]),
"blue": torch.tensor([0.6, 0.8, 1.0]),
"cyan": torch.tensor([0.6, 1.0, 1.0]),
"magenta": torch.tensor([1.0, 0.6, 1.0]),
"yellow": torch.tensor([1.0, 1.0, 0.6]),
"purple": torch.tensor([0.8, 0.6, 1.0]),
"orange": torch.tensor([1.0, 0.7, 0.3]),
"warm": torch.tensor([1.0, 0.9, 0.7]),
"cool": torch.tensor([0.7, 0.9, 1.0]),
"lime": torch.tensor([0.7, 1.0, 0.3]),
"navy": torch.tensor([0.3, 0.4, 0.7]),
"vintage": torch.tensor([0.9, 0.85, 0.7]),
"rose": torch.tensor([1.0, 0.8, 0.9]),
"teal": torch.tensor([0.3, 0.8, 0.8]),
"maroon": torch.tensor([0.7, 0.3, 0.5]),
"peach": torch.tensor([1.0, 0.8, 0.6]),
"lavender": torch.tensor([0.8, 0.6, 1.0]),
"olive": torch.tensor([0.6, 0.7, 0.4]),
}
scale_filter = mode_filters[mode].view(1, 1, 1, 3).to(image.device)
grayscale = torch.sum(image * sepia_weights, dim=-1, keepdim=True)
tinted = grayscale * scale_filter
result = tinted * strength + image * (1 - strength)
return (result,)
class Dissolve:
def __init__(self):
pass
@@ -984,49 +1045,6 @@ class Quantize:
return (result,)
class Sepia:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"strength": ("FLOAT", {
"default": 1.0,
"min": 0.1,
"max": 1.0,
"step": 0.1
}),
"mode": (["sepia", "blue-pia", "green-pia"],),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "sepia"
CATEGORY = "postprocessing/Color Adjustments"
def sepia(self, image: torch.Tensor, strength: float, mode: str = "sepia"):
if strength == 0:
return (image,)
sepia_weights = torch.tensor([0.2989, 0.5870, 0.1140]).view(1, 1, 1, 3).to(image.device)
if mode == "sepia":
sepia_filter = torch.tensor([1.0, 0.8, 0.6]).view(1, 1, 1, 3).to(image.device)
elif mode == "blue-pia":
sepia_filter = torch.tensor([0.6, 0.8, 1.0]).view(1, 1, 1, 3).to(image.device)
elif mode == "green-pia":
sepia_filter = torch.tensor([0.6, 1.0, 0.6]).view(1, 1, 1, 3).to(image.device)
grayscale = torch.sum(image * sepia_weights, dim=-1, keepdim=True)
sepia = grayscale * sepia_filter
result = sepia * strength + image * (1 - strength)
return (result,)
class Sharpen:
def __init__(self):
pass
@@ -1250,6 +1268,7 @@ NODE_CLASS_MAPPINGS = {
"CannyEdgeDetection": CannyEdgeDetection,
"ChromaticAberration": ChromaticAberration,
"ColorCorrect": ColorCorrect,
"ColorTint": ColorTint,
"Dissolve": Dissolve,
"DodgeAndBurn": DodgeAndBurn,
"FilmGrain": FilmGrain,
@@ -1260,7 +1279,6 @@ NODE_CLASS_MAPPINGS = {
"PixelSort": PixelSort,
"Pixelize": Pixelize,
"Quantize": Quantize,
"Sepia": Sepia,
"Sharpen": Sharpen,
"Solarize": Solarize,
"Vignette": Vignette,