有条件GAN和扩散模型的高效空间稀疏推理
IEEE transactions on pattern analysis and machine intelligence
|September 15, 2023
概括
空间稀疏推理 (SSI) 通过选择性计算仅编辑区域来加速图像编辑. 这种技术作为Sparse增量生成引擎 (SIGE) 实现,可显著降低生成模型的延迟.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度生成模型经常重新合成整个图像,在图像编辑过程中浪费在未经编辑的区域上的计算.
- 小编辑需要大量的计算资源,因为整个重合成过程.
研究的目的:
- 引入空间稀疏推理 (SSI),这是加速生成模型的一般技术.
- 为了减少深度生成模型的图像编辑任务中的计算浪费和延迟.
主要方法:
- 开发了空间稀疏推理 (SSI) 以选择性计算仅编辑的图像区域.
- 实现SSI作为用于硬件加速的稀疏增量生成引擎 (SIGE).
- 从原始图像中缓存和重复使用的特征地图,用于未经编辑的区域.
主要成果:
- 在各种GPU上,SIGE将DDPM加速3.0×-4.6×,稳定扩散7.2×,GauGAN加速5.2×-5.6×.
- 通过最小的编辑区域 (约. 1%).一个百分点).
- 增强SIGE以支持注意层和果M1 Pro GPU.
结论:
- 空间稀疏推理 (SSI) 和SIGE提供了一种使用深度生成模型进行图像编辑的计算效率高的方法.
- 该方法有效地减少了延迟,而不会影响扩散和GAN等各种模型的输出质量.
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