Make sure to customize the following parameters in the code according to your requirements:

n_epochs: Number of training epochs crit_cycles: Number of critic training cycles per generator training step z_dim: Dimensionality of the noise vector wandbact: Flag indicating whether to use Weights & Biases for logging (set to 1 for active, 0 for inactive) Other relevant parameters for your specific use case Dependencies Python 3.x Pytorch tqdm Weights & Biases (optional, set wandbact accordingly) Matplotlib (for visualization) Training Process The training loop iterates through epochs, training the critic and generator alternatively. Critic training involves calculating the Wasserstein distance, and generator training aims to minimize the critic's ability to distinguish real from fake samples.

Visualization The code provides visualizations of generated and real samples during training. Additionally, losses are logged using Weights & Biases for easy monitoring.

License This project is licensed under the MIT License.

Feel free to customize and integrate this training loop into your GAN projects!