How do you manage capacity constraints in deep generative models to balance between speed and quality during training

Question

With the help of proper code can you tell me How do you manage capacity constraints in deep generative models to balance between speed and quality during training?

Answer 1

Use model pruning, quantization, and dynamic batch sizing to manage capacity constraints and balance training speed and output quality.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Uses dynamic quantization to reduce model size and improve inference speed.
• Increases batch size (32) for faster throughput without memory overflow.
• Maintains mixed precision training (fp16) for computational efficiency.
• Applies gradient accumulation (steps=4) for stable training with larger effective batches.

Hence, this approach efficiently balances training speed and output quality by managing model capacity constraints.