renan-siqueira / my-own-WGAN-GP-implementation

My own GAN implementation (WGAN-GP with Pytorch)

My Own WGAN-GP Implementation

My own GAN implementation with Pytorch. Adapted to utilize GPU if you have an NVIDIA graphics card.

The architecture chosen for this project was WGAN-GP.

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With this project you will be able to:

• Train your own GAN with the images of your choice;
• Generate as many images as you want after completing the training (Beta version);
• Produce videos through interpolation of the generated images (Beta version);

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How to Use This Project

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1. Cloning the Repository:

To clone this repository, use the following command:

git clone https://github.com/renan-siqueira/my-own-WGAN-GP-implementation.git

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2. Creating and activating the virtual environment:

Windows:

python -m venv virtual_environment_name

To activate the virtual environment:

virtual_environment_name\Scripts\activate

Linux/Mac:

python3 -m venv virtual_environment_name

To activate the virtual environment:

source virtual_environment_name/bin/activate

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3. Installing the dependencies:

Windows / Linux / Mac:

pip install -r requirements.txt

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If you have a GPU, follow the steps in the "How to Use GPU" section (below). Otherwise, if you're not using a GPU, install PyTorch with the following command:

pip install torch torchvision torchaudio

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How to Use GPU:

1. Installing specific dependencies:

After creating and activating your virtual environment:

Windows/Linux/Mac:

pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu121

Note: Make sure your hardware and operating system are compatible with CUDA 12+.

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4. Preparing the dataset:

• 1. Create a folder named dataset inside the src folder.
• 2. Inside the dataset folder, create another folder with a name of your choice for the labels.
• 3. Copy all the images you wish to use for training into this folder.

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5. Configuring training parameters:

The src/json/training_params.json file is set up with optimized parameters for this type of architecture. However, feel free to modify it according to your needs.

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6. How to use the main script:

The run.py script is now your central point for executing various operations. It has been set up to accept arguments to dictate its behavior. Here's how to use it:

Training the model:

To train the model, execute the following command:

python run.py --training

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7. Monitoring the Training:

• You can follow the progress directly in the terminal or console.
• A log file will be generated in the directory specified version training.
• At the end of each epoch, samples of generated images will be saved in the folder of version training, inside the samples folder.

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8. How to generate images after completing the training (Beta version):

To generate images after completing the training, execute:

python run.py --image

You can adjust the parameters for image generation in the configuration file at settings.PATH_IMAGE_PARAMS.

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9. How to generate a video through interpolation of the generated images (Beta version):

To generate a video through interpolation of the generated images, execute:

python run.py --video

Adjust the parameters for video generation in the configuration file located at settings.PATH_VIDEO_PARAMS.

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10. Upscaling:

If you want to upscale the generated images or video, use the --upscale argument followed by the width value:

python run.py --image --upscale 1024

Replace --image with --video if you're generating a video. The above command will upscale the images to a width of 1024 pixels. Adjust as needed.

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License

This project is open-sourced and available to everyone under the MIT License.

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Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request if you find any bugs or have suggestions for improvements.