ImageEffectsLensChromaticAberration¶
Documentation¶
- Class name:
ImageEffectsLensChromaticAberration - Category:
image/effects/lens - Output node:
False
This node applies a chromatic aberration effect to images, simulating the way light disperses through a lens, creating a visually distinctive color-fringing effect. It enhances visual content by adding an artistic or realistic touch of lens imperfection, often used to give images a more dynamic or vintage appearance.
Input types¶
Required¶
images- The input images to which the chromatic aberration effect will be applied. This parameter is crucial for defining the base content that will undergo the transformation.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
shift- Determines the shift in pixels to simulate the chromatic aberration effect, affecting the displacement of color channels.
- Comfy dtype:
INT - Python dtype:
int
method- Specifies the method used for handling edge pixels during the chromatic aberration effect, ensuring the effect is applied consistently across the image.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
shift_type- Defines the type of shift applied to the chromatic aberration effect, influencing the direction and nature of the color channel displacement.
- Comfy dtype:
INT - Python dtype:
int
mixing_type- Controls how the original and shifted color channels are combined, affecting the overall intensity and appearance of the chromatic aberration effect.
- Comfy dtype:
INT - Python dtype:
int
transpose- Determines if and how the image is transposed before applying the chromatic aberration effect, impacting the orientation of the effect.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
colors- Selects the color channels to be shifted, directly influencing the color composition of the chromatic aberration effect.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
lens_curvy- Adjusts the curvature of the lens effect, modifying how pronounced the chromatic aberration appears across the images.
- Comfy dtype:
FLOAT - Python dtype:
float
Output types¶
image- Comfy dtype:
IMAGE - The transformed images with the chromatic aberration effect applied, showcasing the visual enhancement or artistic touch added to the original content.
- Python dtype:
torch.Tensor
- Comfy dtype:
Usage tips¶
- Infra type:
GPU - Common nodes: unknown
Source code¶
class ImageEffectsLensChromaticAberration:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"images": ("IMAGE",),
"shift": ("INT", {
"default": 10,
"step": 1,
}),
"method": (["reflect", "edge", "constant"],),
"shift_type": ("INT", {
"default": 1,
"min": 1,
"max": 4,
"step": 1,
}),
"mixing_type": ("INT", {
"default": 1,
"min": 1,
"max": 4,
"step": 1,
}),
"transpose": (["none", "rotate", "reflect"],),
"colors": (["rb", "rg", "gb"],),
"lens_curvy": ("FLOAT", {
"default": 1.0,
"max": 15.0,
"step": 0.1,
}),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "node"
CATEGORY = "image/effects/lens"
def node(self, images, shift, method, shift_type, mixing_type, transpose, colors, lens_curvy):
# noinspection PyUnboundLocalVariable
def apply(image):
img = image.clone().detach()
if transpose == "rotate":
img = img.permute(1, 0, 2)
elif transpose == "none" or transpose == "reflect":
pass
else:
raise ValueError("Not existing reverse.")
r, g, b = img[:, :, 0:3].split(1, 2)
height, width = img[:, :, 0].shape
def get_space(size, min_val, max_val):
size += shift * 2
if min_val == max_val:
return torch.linspace(min_val, max_val, size)
else:
return radialspace_1D(size, lens_curvy, 1.0, min_val, max_val)
if shift_type == 1:
f_shifts = get_space(height, -shift, shift)
if transpose == "reflect":
t_shifts = get_space(width, -shift, shift)
elif shift_type == 2:
f_shifts = get_space(height, 0, shift)
f_shifts = torch.flip(f_shifts, dims=(0,))
if transpose == "reflect":
t_shifts = get_space(width, 0, shift)
t_shifts = torch.flip(t_shifts, dims=(0,))
elif shift_type == 3:
f_shifts = get_space(height, 0, shift)
if transpose == "reflect":
t_shifts = get_space(width, 0, shift)
elif shift_type == 4:
f_shifts = get_space(height, shift, shift)
if transpose == "reflect":
t_shifts = get_space(width, shift, shift)
else:
raise ValueError("Not existing shift_type.")
if mixing_type == 1:
f_shifts = f_shifts
s_shifts = -f_shifts
if transpose == "reflect":
t_shifts = t_shifts
c_shifts = -t_shifts
elif mixing_type == 2:
f_shifts = -f_shifts
s_shifts = f_shifts
if transpose == "reflect":
t_shifts = -t_shifts
c_shifts = t_shifts
elif mixing_type == 3:
f_shifts = f_shifts
s_shifts = f_shifts
if transpose == "reflect":
t_shifts = t_shifts
c_shifts = t_shifts
elif mixing_type == 4:
f_shifts = -f_shifts
s_shifts = -f_shifts
if transpose == "reflect":
t_shifts = -t_shifts
c_shifts = -t_shifts
else:
raise ValueError("Not existing mixing_type.")
if colors == "rb":
def cat(f_value, s_value):
return torch.cat([f_value, g, s_value], 2)
f = r
s = b
elif colors == "rg":
def cat(f_value, s_value):
return torch.cat([f_value, s_value, b], 2)
f = r
s = g
elif colors == "gb":
def cat(f_value, s_value):
return torch.cat([r, f_value, s_value], 2)
f = g
s = b
else:
raise ValueError("Not existing colors.")
f_pad = F.pad(f.squeeze(), [shift, shift], padding_mode=method).unsqueeze(2)
s_pad = F.pad(s.squeeze(), [shift, shift], padding_mode=method).unsqueeze(2)
f_shifted = torch.zeros_like(f_pad)
s_shifted = torch.zeros_like(s_pad)
for i in range(height + (shift * 2)):
f_shifted[i] = torch.roll(f_pad[i], shifts=int(f_shifts[i]), dims=0)
s_shifted[i] = torch.roll(s_pad[i], shifts=int(s_shifts[i]), dims=0)
if transpose == "reflect":
for i in range(width + (shift * 2)):
f_shifted[:, i] = torch.roll(f_shifted[:, i], shifts=int(t_shifts[i]), dims=0)
s_shifted[:, i] = torch.roll(s_shifted[:, i], shifts=int(c_shifts[i]), dims=0)
f_result = f_shifted[shift:-shift, shift:-shift, :]
s_result = s_shifted[shift:-shift, shift:-shift, :]
img[:, :, 0:3] = cat(f_result, s_result)
if transpose == "rotate":
img = img.permute(1, 0, 2)
elif transpose == "none" or transpose == "reflect":
pass
else:
raise ValueError("Not existing reverse.")
return img
return (torch.stack([
apply(images[i]) for i in range(len(images))
]),)