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# neural-art
Neural Style Transfer done from the CLI using a VGG backbone and presented as an MP4.
Weights can be downloaded from [here](https://files.catbox.moe/wcao20.pth). The downloaded file (renamed to `vgg_conv_weights.pth`) should be placed in `./weights/` and it will be ignored when pushing, as seen in `./.gitignore`. Update: Alternatively, if the `./weights/` directory is empty, `./neuralart.py` will automatically download publicly available VGG19 weights for the user.
More in depth information about Neural Style Transfer ( NST ) can be found in this great [paper](https://arxiv.org/abs/1705.04058). Make sure to check [Requirements](#requirements) and [Usage](#usage).
### Why use this in 2023 ?
Because Style Transfer hasn't changed drastically in terms of actual results in the past years. I personally find a certain beauty in inputting a style and content image rather than a well curated prompt with a dozen of switches. Consider this repo as a quick and simple ***just works*** solution that can run on both CPU and GPU effectively.
I developed this tool as a means to obtain fancy images and visuals for me and my friends. It somehow grew into something bigger that is actually usable, so much so that I got to integrate it in a workflow in conjunction with [Stable Diffusion](https://github.com/CompVis/stable-diffusion) ( see also [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui) ).
## Requirements
Clone the repository:
```bash
git clone https://github.com/xAlpharax/neural-art
```
Create a virtual environment to separate the required packages from system-wide packages:
```bash
virtualenv path/to/neural-art
source path/to/neural-art/bin/activate
```
( ! ) When you're finished with the environment:
```bash
# deactivate
```
All the required packages are listed in `./requirements.txt` as per python etiquette:
```bash
pip install -r requirements.txt
```
## Usage
The main script sits comfortably in `./stylize.sh` so run it from the project's directory:
```bash
./stylize.sh path/to/style_image path/to/content_image
```
A helper script is also available to run `./stylize.sh` for each distinct pair of images present in the `./Images/` directory:
```bash
./all.sh
```
Moreover, `./all.sh` is aware of the already rendered mp4 files and will skip stylizing the combinations that are already present.
### Output videos / images and temporary files
If, at any point, curious of the individual frames that comprise the generated `./content_in_style.mp4` check `./Output/` for PNG images with exactly that. Keep in mind that these files get removed and overwritten each time ./stylize.sh is called ( this is also why running multiple instances of `./stylize.sh` is advised against; if you need to batch/automate the process, try `./all.sh`)
The `./images.npy` file contains raw numpy array data generated by `./neuralart.py` and is manipulated by `./renderer.py` to achieve the `./Output` directory of PNG images.
Considering this workflow, `./clear_dir.sh` removes temporary files each time a new call to `./stylize.sh` is made.
## Contributing
Any sort of help, especially regarding the QoS ( Quality of Service ) of the project, is appreciated. Feel free to open an issue in the **Issues** tab and discuss the possible changes there. As of now, **neural-art** would be in great need of a clean and friendly arguments handler ( i.e. the one the `argparse` python package provides ) in order to accommodate to a cleaner interface for `./neuralart.py` and / or `./stylize.sh`.
Thank you. Happy neural-art-ing !
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