title: Enhance-A-Video: Better Generated Video for Free
summary: 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.
paper_authors._id: 67ac2006a6b5a26040fc94f7
paper_authors.name: Yang Luo
paper_authors._id: 67ac2006a6b5a26040fc94f8
paper_authors.name: Xuanlei Zhao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-18T09:34:22.430Z
paper_authors._id: 64f5937db6d7050b19c68fec
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64f5937db6d7050b19c68fec/4lWYw6-VxZsbl-1Rd9FSy.jpeg
paper_authors.fullname: Xuanlei Zhao
paper_authors.type: user
paper_authors.user: oahzxl
paper_authors._id: 67ac2006a6b5a26040fc94f9
paper_authors.name: Mengzhao Chen
paper_authors._id: 67ac2006a6b5a26040fc94fa
paper_authors.name: Kaipeng Zhang
paper_authors._id: 67ac2006a6b5a26040fc94fb
paper_authors.name: Wenqi Shao
paper_authors._id: 67ac2006a6b5a26040fc94fc
paper_authors.name: Kai Wang
paper_authors._id: 67ac2006a6b5a26040fc94fd
paper_authors.name: Zhangyang Wang
paper_authors._id: 67ac2006a6b5a26040fc94fe
paper_authors.name: Yang You
paper_title: Enhance-A-Video: Better Generated Video for Free
paper_summary: 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.
paper_ai_keywords: DiT-based video generation cross-frame correlations non-diagonal temporal attention distributions Enhance-A-Video temporal temporal consistency visual quality