对比多种方法优化扩散模型推理，提升效率不降性能
Optimizing Inference in Transformer-Based Models: A Multi-Method Benchmark
- 综合测试剪枝、量化、知识蒸馏等五类加速技术
- 在fast-DiT模型上实现高精度下计算量降低50%以上
- 适合关注模型部署与推理加速的研究者参考
高效推理是深度生成建模中的关键挑战，尤其当扩散模型规模和复杂度不断增长时。虽然复杂度提升通常能改善精度，但也带来了更高的计算成本、延迟和内存需求。本文研究了剪枝、量化、知识蒸馏及简化注意力等技术，以在不影响性能的前提下降低计算开销。同时探索了专家混合（Mixture of Experts, MoE）方法进一步提升效率。实验基于当前最先进的Fast Diffusion Transformer（fast-DiT）模型展开，为优化推理提供了实用洞见。
原文摘要 · Abstract (English)
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.
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