In the evolving field of machine learning, video generation has witnessed
significant advancements with autoregressive-based transformer models and
diffusion models, known for synthesizing dynamic and realistic scenes. However,
these models often face challenges with prolonged inference times, even for
generating short video clips such as GIFs. This paper introduces FlashVideo, a
novel framework tailored for swift Text-to-Video generation. FlashVideo
represents the first successful adaptation of the RetNet architecture for video
generation, bringing a unique approach to the field. Leveraging the
RetNet-based architecture, FlashVideo reduces the time complexity of inference
from $\mathcal{O}(L^2)$ to $\mathcal{O}(L)$ for a sequence of length $L$,
significantly accelerating inference speed. Additionally, we adopt a
redundant-free frame interpolation method, enhancing the efficiency of frame
interpolation. Our comprehensive experiments demonstrate that FlashVideo
achieves a $\times9.17$ efficiency improvement over a traditional
autoregressive-based transformer model, and its inference speed is of the same
order of magnitude as that of BERT-based transformer models.

FlashVideo 是一种新颖框架，通过使用 RetNet 架构，将序列长度为 L 的推理时间复杂度从 O (L^2) 降低到 O (L)，从而显著加快推理速度，并且通过抛弃冗余帧插值方法来增强帧插值的效率，实现了相对传统自回归转换模型的 9.17 倍效率提升，并且推理速度与基于 BERT 的转换模型相当。