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@@ -1,19 +1,30 @@ # 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://m1.afileditch.ch/ajjMsHrRhnikrrCiUXgY.pth). The downloaded file should be placed in `./weights/` and will be ignored when pushing as seen in `./.gitignore` +Weights can be downloaded from [here](https://m1.afileditch.ch/ajjMsHrRhnikrrCiUXgY.pth). The downloaded file should be placed in `./weights/` and any file will be ignored from there when pushing, as seen in `./.gitignore`. Update: Alternatively, if the `./weights/` directory is empty, `./neuralart.py` will automatically download the aformentioned default weights. ### 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 inputing a style and content image rather than a well curated prompt with a dozen of switches. Consider this repo as a quick *just works* solution that can run on both CPU and GPU effectively. ## Usage -The script sits comfortably in `./stylize.sh` so just run it in the project directory: +The script sits comfortably in `./stylize.sh` so run it (strictly from the project directory): ```bash ./stylize.sh path/to/style_image path/to/content_image ``` +A `./all.sh` helper script is also available that runs `./stylize.sh` for each distinct pair of images present in the `./Images/` directory. + +```bash +./all.sh +``` + +If, at any point, curious of the individual frames that comprise the generated `./content_in_style.mp4` check `./Output/`. +There's also a (redundant) `./images.npy` file that contains raw array data. `./clear_dir.sh` removes redundant files each time they're no longer needed. + ### Requirements All requirements are specified in `./requirements.txt` as per python etiquette: |