Automatically generate new textures similar to your source image. Useful if you want to make variations on a theme or expand the size of an existing texture.
The main script takes a source image as a texture, and generates a new output that captures the style of the original. Here are some examples:
texturize samples/grass.webp --size=1440x960 --output=result.png
texturize samples/gravel.png --iterations=200 --precision=1e-5
texturize samples/sand.tiff --output=tmp/{source}-{octave}.webp
texturize samples/brick.jpg --device=cpuFor details about the command-line options, see the tool itself:
texturize --helpHere are the command-line options currently available:
Usage:
texturize SOURCE... [--size=WxH] [--output=FILE] [--variations=V] [--seed=SEED]
[--mode=MODE] [--octaves=O] [--threshold=H] [--iterations=I]
[--device=DEVICE] [--precision=PRECISION] [--quiet] [--verbose]
Options:
SOURCE Path to source image to use as texture.
-s WxH, --size=WxH Output resolution as WIDTHxHEIGHT. [default: 640x480]
-o FILE, --output=FILE Filename for saving the result, includes format variables.
[default: {source}_gen{variation}.png]
--variations=V Number of images to generate at same time. [default: 1]
--seed=SEED Configure the random number generation.
--mode=MODE Either "patch" or "gram" to specify critics. [default: gram]
--octaves=O Number of octaves to process. [default: 5]
--threshold=T Quality for optimization, lower is better. [default: 1e-4]
--iterations=I Maximum number of iterations each octave. [default: 99]
--device=DEVICE Hardware to use, either "cpu" or "cuda".
--precision=PRECISION Floating-point format to use, "float16" or "float32".
--quiet Suppress any messages going to stdout.
--verbose Display more information on stdout.
-h, --help Show this message.This repository uses submodules, so you'll need to clone it recursively to ensure dependencies are available:
git clone --recursive https://github.com/photogeniq/neural-texturize.gitThen, you can create a new virtual environment called myenv by installing Miniconda and calling the following commands, depending whether you want to run on CPU or GPU (via CUDA):
cd neural-texturize
# a) Use this if you have an *Nvidia GPU only*.
conda env create -n myenv -f tasks/setup-cuda.yml
# b) Fallback if you just want to run on CPU.
conda env create -n myenv -f tasks/setup-cpu.ymlOnce the virtual environment is created, you can activate it and finish the setup of neural-texturize with these commands:
conda activate myenv
poetry installFinally, you can check if everything worked by calling the script:
texturizeYou can use conda env remove -n myenv to delete the virtual environment once you are done.