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The Diffusion of Passive Tracers in Laminar Shear Flow
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基于梯度流量的代修剪,以获得高效和高质量的轻量扩散模型
Ben Wan1, Tianyi Zheng1, Yuxiao Wang1
1Shanghai Jiao Tong University, Shanghai, Minhang Distinct, China.
概括
本研究介绍了扩散模型 (DM) 的代修剪方法,以提高效率并保持发电质量. 梯度流方法通过仔细删除参数来优化轻量化DM,优于一次性修剪技术.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 扩散模型 (DMs) 是强大的生成模型,但由于推断速度缓慢和计算成本高,它们受到影响.
- 现有的轻量级DM方法使用一次性修剪通常会降低发电质量并删除关键参数.
研究的目的:
- 开发一种高效有效的方法来创建轻量级的扩散模型.
- 在保持发电质量和参数完整性方面克服一次性修剪的局限性.
主要方法:
- 提出了一种以梯度流为指导的代修剪方法.
- 集成了一个梯度流修剪标准,以识别和删除最小影响损失函数梯度规范的参数.
- 采用渐进的软修剪策略,以保持面罩的连续性,并设计了一个粗的修剪框架,以提高效率.
主要成果:
- 梯度流修剪方法减轻了突然的信息丢失,比香草代修剪更好地优化了面具.
- 实现了卓越的性能,提高了效率,并与原始预训练模型保持了更大的一致性.
- 粗粒度修剪框架显示了可比的结果,显著减少了时间消耗.
结论:
- 提出的代梯度流量修剪方法有效地创建轻量级的扩散模型,具有高生成质量和效率.
- 这种方法为生成AI中的模型压缩提供了比一次性修剪更强大的替代方案.
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