To save and resume GAN training in PyTorch, you can use model checkpoints to store the generator, discriminator, and optimizer states.
Here are the steps given below which you can follow:
- Saving Checkpoints

- Loading Checkpoints

In the above steps, we are using the following steps:
- Save States: Use state_dict() for models and optimizers.
- Resume Training: Load states into models and optimizers and resume from the saved epoch.
- Ensure Device Compatibility: Use .to(device) if loading models on a different device.
Hence, by referring to the above, you can utilize model checkpoints in PyTorch to save and resume GAN training.