Enhance-A-Video: Better Generated Video for Free
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.
67ac2006a6b5a26040fc94f7
Yang Luo
67ac2006a6b5a26040fc94f8
Xuanlei Zhao
claimed_verified
2025-02-18T09:34:22.430Z
64f5937db6d7050b19c68fec
https://cdn-avatars.huggingface.co/v1/production/uploads/64f5937db6d7050b19c68fec/4lWYw6-VxZsbl-1Rd9FSy.jpeg
Xuanlei Zhao
user
oahzxl
67ac2006a6b5a26040fc94f9
Mengzhao Chen
claimed_verified
2025-05-22T07:19:11.830Z
64aea082704210bf815e7551
/avatars/5c8dc0df57596c526b2bccea21835f53.svg
Mengzhao Chen
user
ChenMnZ
67ac2006a6b5a26040fc94fa
Kaipeng Zhang
claimed_verified
2025-06-02T07:53:37.571Z
65f1713552c38a91e0a445e8
/avatars/47ab3ada51c9b9976ac1cd0c4301c373.svg
kaipeng
user
kpzhang996
67ac2006a6b5a26040fc94fb
Wenqi Shao
67ac2006a6b5a26040fc94fc
Kai Wang
67ac2006a6b5a26040fc94fd
Zhangyang Wang
67ac2006a6b5a26040fc94fe
Yang You
Enhance-A-Video: Better Generated Video for Free
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.
An approach called Enhance-A-Video improves the coherence and quality of DiT-based generated videos by enhancing cross-frame correlations using non-diagonal temporal attention without requiring retraining or fine-tuning.
DiT-based video generation cross-frame correlations non-diagonal temporal attention Enhance-A-Video