duchesneaumathieu / pyperlin

GPU accelerated Perlin Noise in python

pyperlin

GPU accelerated Perlin Noise in python

Limitation: resolutions needs to divide shape

Using pytoch as the array backend makes the GPU acceleration trivial. Also it uses batch sampling to better use parallelization.

By playing with the parameters of Perlin noise, it is possible to create different textures. (code at the bottom)

Installation

pip install pyperlin

Usage

import torch
from pyperlin import FractalPerlin2D

shape = (32,256,256) #for batch size = 32 and noises' shape = (256,256)
resolutions = [(2**i,2**i) for i in range(1,7)] #for lacunarity = 2.0
factors = [.5**i for i in range(6)] #for persistence = 0.5
g_cuda = torch.Generator(device='cuda') #for GPU acceleration
fp = FractalPerlin2D(shape, resolutions, factors, generator=g_cuda)
noise = fp() #sampling

Benchmarks

CPU: Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz

GPU: Nvidia Titan XP

Algorithms	(1,256,256);1octaves	(1,256,256);6octaves	(32,256,256);6octaves	(32,1024,1024);8octaves
noise.pnoise2	73.8 ms	86.3 ms	2.85 s	48 s
pyperlin.FractalPerlin2D (cpu)	1.15 ms	11.5 ms	377 ms	8.49 s
pyperlin.FractalPerlin2D (gpu)	481 µs	2.84 ms	16.8 ms	121 ms

More Examples

import torch
from pyperlin import FractalPerlin2D
import matplotlib.pyplot as plt

shape = (1,1024,1024) #for batch size = 1 and noises' shape = (1024,1024)
factors = [.5**i for i in range(8)] #for persistence = 0.5
g_cuda = torch.Generator(device='cuda') #for GPU acceleration

clouds_resolutions = [(2**i,2**i) for i in range(1,7)] #for lacunarity = 2.0
clouds_factors = [.5**i for i in range(6)] #for persistence = 0.5
clouds = FractalPerlin2D(shape, clouds_resolutions, clouds_factors, generator=g_cuda)().cpu().numpy()[0]

fire_resolutions = [(2**i,4**i) for i in range(1,4)] #for lacunarity = 2.0 and 4.0
fire_factors = [.5**i for i in range(3)] #for persistence = 0.5
fire = FractalPerlin2D(shape, fire_resolutions, fire_factors, generator=g_cuda)().cpu().numpy()[0]

fig = plt.figure(figsize=(10,5))

ax1 = fig.add_subplot(121)
ax1.set_axis_off()
ax1.set_title('Clouds')
ax1.imshow(clouds, vmax=1.2, cmap=plt.get_cmap('Blues'))

ax2 = fig.add_subplot(122)
ax2.set_axis_off()
ax2.set_title('Fire')
ax2.imshow(fire, vmax=.3, cmap=plt.get_cmap('YlOrBr'))

fig.show()