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import torch

class ColorModCompress:

def __init__(self):

pass

@classmethod

def INPUT_TYPES(s):

return {

"required": {

"image": ("IMAGE",),

"mode": (["clip", "normalize", "compress"],)

}

}

RETURN_TYPES = ("IMAGE",)

FUNCTION = "mod_compress"

CATEGORY = "ColorMod"

TITLE = "ColorMod (compress)"

def mod_compress(self, image, mode):

image = image.clone()

if mode == "clip":

out = torch.clip(image, 0.0, 1.0)

elif mode == "normalize":

out = []

for img in image:

img_min = torch.min(image)

img_max = torch.max(image)

print(f"Normalizing [{img_min:6.4f}:{img_max:6.4f}] => [0.0;1.0]")

img = (img - img_min) / (img_max - img_min)

out.append(img)

out = torch.stack(out, dim=0)

elif mode == "compress":

out = []

for img in image:

ll = torch.minimum(image, torch.zeros(image.shape))

ll = torch.clip(torch.abs(ll), 0.0, 1.0)

hh = torch.maximum(image, torch.ones(image.shape))

hh = torch.clip((torch.abs(hh)-1.0), 0.0, 1.0)

out.append(ll + hh)

out = torch.stack(out, dim=0)

else:

raise ValueError(f"Unknown mode '{mode}'")

# self.check_range(image) # debug

out = torch.clip(out, 0.0, 1.0) # sanity

return (out,)

class ColorModMove:

def __init__(self):

pass

@classmethod

def INPUT_TYPES(s):

return {

"required": {

"image": ("IMAGE",),

"move": ("FLOAT", {"default": 0.0, "min": -1.000, "max": 1.000, "step": 0.01}),

"clip": ([True, False], {"default": True}),

}

}

RETURN_TYPES = ("IMAGE",)

FUNCTION = "mod_move"

CATEGORY = "ColorMod"

TITLE = "ColorMod (move)"

def mod_move(self, image, move, clip=True):

image = image.clone()

move_map = torch.ones(image.shape) * move

out = torch.clip((image + move_map), 0.0, 1.0) if clip else (image + move_map)

return (out,)

class ColorModPivot:

def __init__(self):

pass

@classmethod

def INPUT_TYPES(s):

return {

"required": {

"image": ("IMAGE",),

"pivot": ("FLOAT", {"default": 0.5, "min": 0.001, "max": 0.999, "step": 0.01}),

"move": ("FLOAT", {"default": 0.0, "min": -2.000, "max": 2.000, "step": 0.01}),

"clip": ([True, False], {"default": True}),

}

}

RETURN_TYPES = ("IMAGE",)

FUNCTION = "mod_pivot"

CATEGORY = "ColorMod"

TITLE = "ColorMod (move pivot)"

def mod_pivot(self, image, pivot, move, clip=True):

image = image.clone()

pivot_map = torch.ones(image.shape) * pivot

image_high = torch.maximum(image, pivot_map) - pivot

image_low = torch.minimum(image, pivot_map)

image_high = image_high * (1/(1-pivot)) * (1-(pivot + move))

image_low = image_low * (1/pivot) * (pivot + move)

out = torch.clip((image_high + image_low), 0.0, 1.0) if clip else (image_high + image_low)

return (out,)

class ColorModEdges:

def __init__(self):

pass

@classmethod

def INPUT_TYPES(s):

return {

"required": {

"image": ("IMAGE",),

"low": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.01}),

"pivot": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),

"high": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.01}),

"clip": ([True, False], {"default": True})

}

}

RETURN_TYPES = ("IMAGE",)

FUNCTION = "mod_edges"

CATEGORY = "ColorMod"

TITLE = "ColorMod (edges)"

def mod_edges(self, image, low, pivot, high, clip=True):

image = image.clone()

pivot_map = torch.ones(image.shape) * pivot

image_high = torch.maximum(image, pivot_map) - pivot

image_low = torch.minimum(image, pivot_map)

image_low = image_low * low + pivot * (1-low)

image_high = image_high * high

out = torch.clip((image_high + image_low), 0.0, 1.0) if clip else (image_high + image_low)

return (out,)

NODE_CLASS_MAPPINGS = {

"ColorModCompress": ColorModCompress,

"ColorModMove" : ColorModMove,

"ColorModPivot": ColorModPivot,

"ColorModEdges": ColorModEdges,

}