DyDiT++:具有时间步和空间动态的扩散变压器,用于高效的视觉生成
IEEE transactions on pattern analysis and machine intelligence
|January 15, 2026
概括
动态扩散变压器 (DyDiT) 通过动态调整计算来降低视觉生成中的计算成本. 这种新的方法加速了像DiT这样的扩散模型,提高了包括文本到图像和视频生成在内的任务的效率.
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:
- 扩散模型,特别是扩散变压器 (DiT),在视觉生成方面表现出色,但会产生高的计算成本.
- 当前模型中的静态推理导致跨时间阶段和空间区域的冗余计算.
研究的目的:
- 开发一个动态扩散变压器 (DyDiT),优化视觉生成中的计算效率.
- 在时间和空间两个维度上引入计算的动态调整方法.
主要方法:
- 提议的时间步骤智能动态宽度 (TDW) 根据生成时间步骤调整模型宽度.
- 引入了空间智能动态令牌 (SDT),以消除不必要的空间区域中的冗余计算.
- 开发了DyDiT++,包括流量匹配,视频生成,文本到图像生成和参数有效训练 (TD-LoRA).
主要成果:
- 通过减少冗余计算,DyDiT显著加快了生成过程.
- 通过加速流量匹配生成和增强复杂的视觉任务,DyDiT++展示了多功能性.
- 使用TD-LoRA进行参数高效的微调,可以实现显著的FLOP减少 (<3%的微调代) 和DiT XL的硬件加快 (1.73x),具有竞争力的FID分数 (2.07在ImageNet上).
结论:
- DyDiT和DyDiT++为基于扩散的视觉生成提供了大量的计算节省和加快速度.
- 动态方法在各种视觉生成任务和模型架构中提高了效率.
- 参数效率培训民主化了对先进视觉生成技术的访问.
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