Enhance-A-Video: Better Generated Video for Free
Authors: name: Yang Luo, name: Xuanlei Zhao, name: Mengzhao Chen, name: Kaipeng Zhang, name: Wenqi Shao, name: Kai Wang, name: Zhangyang Wang, name: Yang You

DiT-based video generation has achieved remarkable results, but research into
enhancing existing models remains relatively unexplored. In this work, we
introduce a training-free approach to enhance the coherence and quality of
DiT-based generated videos, named Enhance-A-Video. The core idea is enhancing
the cross-frame correlations based on non-diagonal temporal attention
distributions. Thanks to its simple design, our approach can be easily applied
to most DiT-based video generation frameworks without any retraining or
fine-tuning. Across various DiT-based video generation models, our approach
demonstrates promising improvements in both temporal consistency and visual
quality. We hope this research can inspire future explorations in video
generation enhancement.