LinGen: Towards High-Resolution Minute-Length Text-to-Video Generation
  with Linear Computational Complexity
Authors: name: Hongjie Wang; name: Chih-Yao Ma; name: Yen-Cheng Liu; name: Ji Hou; name: Tao Xu; name: Jialiang Wang; name: Felix Juefei-Xu; name: Yaqiao Luo; name: Peizhao Zhang; name: Tingbo Hou; name: Peter Vajda; name: Niraj K. Jha; name: Xiaoliang Dai
Published: 2024-12-13 04:55:10

Abstract: Text-to-video generation enhances content creation but is highly
computationally intensive: The computational cost of Diffusion Transformers
(DiTs) scales quadratically in the number of pixels. This makes minute-length
video generation extremely expensive, limiting most existing models to
generating videos of only 10-20 seconds length. We propose a Linear-complexity
text-to-video Generation (LinGen) framework whose cost scales linearly in the
number of pixels. For the first time, LinGen enables high-resolution
minute-length video generation on a single GPU without compromising quality. It
replaces the computationally-dominant and quadratic-complexity block,
self-attention, with a linear-complexity block called MATE, which consists of
an MA-branch and a TE-branch. The MA-branch targets short-to-long-range
correlations, combining a bidirectional Mamba2 block with our token
rearrangement method, Rotary Major Scan, and our review tokens developed for
long video generation. The TE-branch is a novel TEmporal Swin Attention block
that focuses on temporal correlations between adjacent tokens and medium-range
tokens. The MATE block addresses the adjacency preservation issue of Mamba and
improves the consistency of generated videos significantly. Experimental
results show that LinGen outperforms DiT (with a 75.6% win rate) in video
quality with up to 15times (11.5times) FLOPs (latency) reduction.
Furthermore, both automatic metrics and human evaluation demonstrate our
LinGen-4B yields comparable video quality to state-of-the-art models (with a
50.5%, 52.1%, 49.1% win rate with respect to Gen-3, LumaLabs, and Kling,
respectively). This paves the way to hour-length movie generation and real-time
interactive video generation. We provide 68s video generation results and more
examples in our project website: https://lineargen.github.io/.