raubatz's picture
download
raw
2.06 kB
import torch
class ChromaticAberration:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"red_shift": ("INT", {
"default": 0,
"min": -20,
"max": 20,
"step": 1
}),
"red_direction": (["horizontal", "vertical"],),
"green_shift": ("INT", {
"default": 0,
"min": -20,
"max": 20,
"step": 1
}),
"green_direction": (["horizontal", "vertical"],),
"blue_shift": ("INT", {
"default": 0,
"min": -20,
"max": 20,
"step": 1
}),
"blue_direction": (["horizontal", "vertical"],),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "chromatic_aberration"
CATEGORY = "postprocessing/Effects"
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):
def get_shift(direction, shift):
shift = -shift if direction == 'vertical' else shift # invert vertical shift as otherwise positive actually shifts down
return (shift, 0) if direction == 'vertical' else (0, shift)
x = image.permute(0, 3, 1, 2)
shifts = [get_shift(direction, shift) for direction, shift in zip([red_direction, green_direction, blue_direction], [red_shift, green_shift, blue_shift])]
channels = [torch.roll(x[:, i, :, :], shifts=shifts[i], dims=(1, 2)) for i in range(3)]
output = torch.stack(channels, dim=1)
output = output.permute(0, 2, 3, 1)
return (output,)
NODE_CLASS_MAPPINGS = {
"ChromaticAberration": ChromaticAberration
}

Xet Storage Details

Size:
2.06 kB
·
Xet hash:
1c44794de99c9eeee72f7d086393a4fe2b32e11c20d5da39f599c0313d1f7380

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.