58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
import torch
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class ChromaticAberration:
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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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"red_shift": ("INT", {
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"default": 0,
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"min": -20,
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"max": 20,
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"step": 1
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}),
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"red_direction": (["horizontal", "vertical"],),
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"green_shift": ("INT", {
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"default": 0,
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"min": -20,
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"max": 20,
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"step": 1
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}),
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"green_direction": (["horizontal", "vertical"],),
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"blue_shift": ("INT", {
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"default": 0,
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"min": -20,
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"max": 20,
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"step": 1
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}),
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"blue_direction": (["horizontal", "vertical"],),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "chromatic_aberration"
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CATEGORY = "postprocessing/Effects"
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def chromatic_aberration(self, image: torch.Tensor, red_shift: int, green_shift: int, blue_shift: int, red_direction: str, green_direction: str, blue_direction: str):
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def get_shift(direction, shift):
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shift = -shift if direction == 'vertical' else shift # invert vertical shift as otherwise positive actually shifts down
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return (shift, 0) if direction == 'vertical' else (0, shift)
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x = image.permute(0, 3, 1, 2)
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shifts = [get_shift(direction, shift) for direction, shift in zip([red_direction, green_direction, blue_direction], [red_shift, green_shift, blue_shift])]
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channels = [torch.roll(x[:, i, :, :], shifts=shifts[i], dims=(1, 2)) for i in range(3)]
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output = torch.stack(channels, dim=1)
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output = output.permute(0, 2, 3, 1)
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return (output,)
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NODE_CLASS_MAPPINGS = {
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"ChromaticAberration": ChromaticAberration
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}
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