John Richard Chipps-Harding
10e5d4062d
Simplified the vignette functionality on the Film Grain node to support floats (step: 0.01). This allows very subtle vignette.
152 lines
4.6 KiB
Python
152 lines
4.6 KiB
Python
import numpy as np
|
|
import torch
|
|
|
|
class FilmGrain:
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"intensity": ("FLOAT", {
|
|
"default": 0.2,
|
|
"min": 0.0,
|
|
"max": 1.0,
|
|
"step": 0.01
|
|
}),
|
|
"scale": ("FLOAT", {
|
|
"default": 10,
|
|
"min": 1,
|
|
"max": 100,
|
|
"step": 1
|
|
}),
|
|
"temperature": ("FLOAT", {
|
|
"default": 0.0,
|
|
"min": -100,
|
|
"max": 100,
|
|
"step": 1
|
|
}),
|
|
"vignette": ("FLOAT", {
|
|
"default": 0.0,
|
|
"min": 0.0,
|
|
"max": 10.0,
|
|
"step": 0.01
|
|
}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
FUNCTION = "film_grain"
|
|
|
|
CATEGORY = "postprocessing/Effects"
|
|
|
|
def film_grain(self, image: torch.Tensor, intensity: float, scale: float, temperature: float, vignette: float):
|
|
batch_size, height, width, _ = image.shape
|
|
result = torch.zeros_like(image)
|
|
|
|
for b in range(batch_size):
|
|
tensor_image = image[b].numpy()
|
|
|
|
# Generate Perlin noise with shape (height, width) and scale
|
|
noise = self.generate_perlin_noise((height, width), scale)
|
|
noise = (noise - np.min(noise)) / (np.max(noise) - np.min(noise))
|
|
|
|
# Apply grain intensity
|
|
noise = (noise * 2 - 1) * intensity
|
|
|
|
# Blend the noise with the image
|
|
grain_image = np.clip(tensor_image + noise[:, :, np.newaxis], 0, 1)
|
|
|
|
# Apply temperature
|
|
grain_image = self.apply_temperature(grain_image, temperature)
|
|
|
|
# Apply vignette
|
|
grain_image = self.apply_vignette(grain_image, vignette)
|
|
|
|
tensor = torch.from_numpy(grain_image).unsqueeze(0)
|
|
result[b] = tensor
|
|
|
|
return (result,)
|
|
|
|
def generate_perlin_noise(self, shape, scale, octaves=4, persistence=0.5, lacunarity=2):
|
|
def smoothstep(t):
|
|
return t * t * (3.0 - 2.0 * t)
|
|
|
|
def lerp(t, a, b):
|
|
return a + t * (b - a)
|
|
|
|
def gradient(h, x, y):
|
|
vectors = np.array([[1, 1], [-1, 1], [1, -1], [-1, -1]])
|
|
g = vectors[h % 4]
|
|
return g[:, :, 0] * x + g[:, :, 1] * y
|
|
|
|
height, width = shape
|
|
noise = np.zeros(shape)
|
|
|
|
for octave in range(octaves):
|
|
octave_scale = scale * lacunarity ** octave
|
|
x = np.linspace(0, 1, width, endpoint=False)
|
|
y = np.linspace(0, 1, height, endpoint=False)
|
|
X, Y = np.meshgrid(x, y)
|
|
X, Y = X * octave_scale, Y * octave_scale
|
|
|
|
xi = X.astype(int)
|
|
yi = Y.astype(int)
|
|
|
|
xf = X - xi
|
|
yf = Y - yi
|
|
|
|
u = smoothstep(xf)
|
|
v = smoothstep(yf)
|
|
|
|
n00 = gradient(np.random.randint(0, 4, (height, width)), xf, yf)
|
|
n01 = gradient(np.random.randint(0, 4, (height, width)), xf, yf - 1)
|
|
n10 = gradient(np.random.randint(0, 4, (height, width)), xf - 1, yf)
|
|
n11 = gradient(np.random.randint(0, 4, (height, width)), xf - 1, yf - 1)
|
|
|
|
x1 = lerp(u, n00, n10)
|
|
x2 = lerp(u, n01, n11)
|
|
y1 = lerp(v, x1, x2)
|
|
|
|
noise += y1 * persistence ** octave
|
|
|
|
return noise / (1 - persistence ** octaves)
|
|
|
|
def apply_temperature(self, image, temperature):
|
|
if temperature == 0:
|
|
return image
|
|
|
|
temperature /= 100
|
|
|
|
new_image = image.copy()
|
|
|
|
if temperature > 0:
|
|
new_image[:, :, 0] *= 1 + temperature
|
|
new_image[:, :, 1] *= 1 + temperature * 0.4
|
|
else:
|
|
new_image[:, :, 2] *= 1 - temperature
|
|
|
|
return np.clip(new_image, 0, 1)
|
|
|
|
def apply_vignette(self, image, vignette_strength):
|
|
if vignette_strength == 0:
|
|
return image
|
|
|
|
height, width, _ = image.shape
|
|
x = np.linspace(-1, 1, width)
|
|
y = np.linspace(-1, 1, height)
|
|
X, Y = np.meshgrid(x, y)
|
|
radius = np.sqrt(X ** 2 + Y ** 2)
|
|
|
|
radius = radius / np.max(radius)
|
|
opacity = np.clip(vignette_strength, 0, 1)
|
|
vignette = 1 - radius * opacity
|
|
|
|
return np.clip(image * vignette[..., np.newaxis], 0, 1)
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"FilmGrain": FilmGrain,
|
|
}
|