In order to avoid exploding gradients in large-scale generative models, especially for large models. You can refer to the code snippet below.
Here is the code showing how:
![](https://www.edureka.co/community/?qa=blob&qa_blobid=18238383368440426910)
In the above code, we are using the following key steps:
- Gradient Clipping: clip_grad_norm_ prevents gradients from exploding.
- Spectral Normalization: Regularizes the discriminator’s weights.
- Learning Rate Scheduling: Dynamically adjusts learning rates.
- Proper Optimizers: Adam optimizer with tuned betas.
- Stable Initialization: Default PyTorch initialization is robust for GANs.
Hence, these strategies collectively ensure stable training for large-scale generative models.