texturize 0.5.0

🤖🖌️ Automatically generate new textures similar to a source photograph.

neural-texturize

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.

1. Examples & Usage

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=cpu

For details about the command-line options, see the tool itself:

texturize --help

Here are the command-line options currently available:

Usage:
    texturize SOURCE... [--size=WxH] [--output=FILE] [--seed=SEED] [--device=DEVICE]
                        [--octaves=O] [--precision=P] [--iterations=I]
    texturize --help


Options:
    SOURCE                  Path to source image to use as texture.
    -s WxH, --size=WxH      Output resolution as WIDTHxHEIGHT. [default: 640x480]
    --seed=SEED             Configure the random number generation.
    --device=DEVICE         Hardware to use, either "cpu" or "cuda".
    --octaves=O             Number of octaves to process. [default: 5]
    --precision=P           Set the quality for the optimization. [default: 1e-4]
    --iterations=I          Maximum number of iterations each octave. [default: 99]
    -o FILE, --output=FILE  Filename for saving the result. [default: {source}_gen.png]
    -h --help               Show this message.

2. Installation

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.git

Then, 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.yml

Once the virtual environment is created, you can activate it and finish the setup of neural-texturize with these commands:

conda activate myenv
poetry install

Finally, you can check if everything worked by calling the script:

texturize

You can use conda env remove -n myenv to delete the virtual environment once you are done.