https://github.com/renan-siqueira/my-own-wgan-gp-implementation
My own GAN implementation (WGAN-GP with Pytorch)
https://github.com/renan-siqueira/my-own-wgan-gp-implementation
gan gpu-acceleration image-generation image-generator image-processing python pytorch training video-generation video-generator video-interpolation wgan wgan-gp
Last synced: 2 months ago
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My own GAN implementation (WGAN-GP with Pytorch)
- Host: GitHub
- URL: https://github.com/renan-siqueira/my-own-wgan-gp-implementation
- Owner: renan-siqueira
- License: mit
- Created: 2023-09-08T14:14:08.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-12-06T23:29:54.000Z (over 1 year ago)
- Last Synced: 2023-12-07T01:56:11.071Z (over 1 year ago)
- Topics: gan, gpu-acceleration, image-generation, image-generator, image-processing, python, pytorch, training, video-generation, video-generator, video-interpolation, wgan, wgan-gp
- Language: Python
- Homepage:
- Size: 125 KB
- Stars: 2
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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.
---
## 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);
---
# How to Use This Project
---
## 1. Cloning the Repository:
To clone this repository, use the following command:
```bash
git clone https://github.com/renan-siqueira/my-own-WGAN-GP-implementation.git
```
---
## 2. Creating and activating the virtual environment:
__Windows:__
```bash
python -m venv virtual_environment_name
```
To activate the virtual environment:
```bash
virtual_environment_name\Scripts\activate
```
__Linux/Mac:__
```bash
python3 -m venv virtual_environment_name
```
To activate the virtual environment:
```bash
source virtual_environment_name/bin/activate
```
---
## 3. Installing the dependencies:
__Windows / Linux / Mac:__
```bash
pip install -r requirements.txt
```
---
*__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:__*
```bash
pip install torch torchvision torchaudio
```
---
# How to Use GPU:
## 1. Installing specific dependencies:
After creating and activating your virtual environment:
__Windows/Linux/Mac:__
```bash
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+.__*
---
## 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.
---
## 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.
---
## 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:
```bash
python run.py --training
```
---
## 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.
---
## 8. How to generate images after completing the training (Beta version):
To generate images after completing the training, execute:
```bash
python run.py --image
```
*You can adjust the parameters for image generation in the configuration file at `settings.PATH_IMAGE_PARAMS`.*
---
## 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:
```bash
python run.py --video
```
*Adjust the parameters for video generation in the configuration file located at settings.PATH_VIDEO_PARAMS.*
---
## 10. Upscaling:
If you want to upscale the generated __images__ or __video__, use the `--upscale` argument followed by the width value:
```bash
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.*
---
## License
This project is open-sourced and available to everyone under the [MIT License](LICENSE).
---
## 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.