Optimizing Inference in Transformer-Based Models: A Multi-Method Benchmark
This work addresses efficiency challenges in diffusion models for AI practitioners, but appears incremental as it benchmarks existing techniques without introducing a new method.
The paper tackled the problem of high computational costs in deep generative models by investigating pruning, quantization, knowledge distillation, simplified attention, and Mixture of Experts to optimize inference for the Fast Diffusion Transformer, but did not report concrete performance numbers in the abstract.
Efficient inference is a critical challenge in deep generative modeling, particularly as diffusion models grow in capacity and complexity. While increased complexity often improves accuracy, it raises compute costs, latency, and memory requirements. This work investigates techniques such as pruning, quantization, knowledge distillation, and simplified attention to reduce computational overhead without impacting performance. The study also explores the Mixture of Experts (MoE) approach to further enhance efficiency. These experiments provide insights into optimizing inference for the state-of-the-art Fast Diffusion Transformer (fast-DiT) model.