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https://github.com/sd-webui/stable-diffusion-webui.git
synced 2024-12-13 18:02:31 +03:00
53 lines
1.5 KiB
Python
53 lines
1.5 KiB
Python
import numpy as np
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def perlin(x, y, seed=0):
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# permutation table
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np.random.seed(seed)
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p = np.arange(256, dtype=int)
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np.random.shuffle(p)
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p = np.stack([p, p]).flatten()
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# coordinates of the top-left
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xi, yi = x.astype(int), y.astype(int)
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# internal coordinates
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xf, yf = x - xi, y - yi
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# fade factors
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u, v = fade(xf), fade(yf)
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# noise components
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n00 = gradient(p[p[xi] + yi], xf, yf)
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n01 = gradient(p[p[xi] + yi + 1], xf, yf - 1)
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n11 = gradient(p[p[xi + 1] + yi + 1], xf - 1, yf - 1)
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n10 = gradient(p[p[xi + 1] + yi], xf - 1, yf)
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# combine noises
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x1 = lerp(n00, n10, u)
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x2 = lerp(n01, n11, u) # FIX1: I was using n10 instead of n01
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return lerp(x1, x2, v) # FIX2: I also had to reverse x1 and x2 here
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def lerp(a, b, x):
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"linear interpolation"
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return a + x * (b - a)
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def fade(t):
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"6t^5 - 15t^4 + 10t^3"
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return 6 * t**5 - 15 * t**4 + 10 * t**3
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def gradient(h, x, y):
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"grad converts h to the right gradient vector and return the dot product with (x,y)"
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vectors = np.array([[0, 1], [0, -1], [1, 0], [-1, 0]])
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g = vectors[h % 4]
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return g[:, :, 0] * x + g[:, :, 1] * y
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lin = np.linspace(0, 5, 100, endpoint=False)
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x, y = np.meshgrid(lin, lin)
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def perlinNoise(height, width, octavesx=5, octavesy=5, seed=None):
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linx = np.linspace(0, octavesx, width, endpoint=False)
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liny = np.linspace(0, octavesy, height, endpoint=False)
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x, y = np.meshgrid(linx, liny)
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return perlin(x, y, seed=seed)
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