Summary of Lingen: Towards High-resolution Minute-length Text-to-video Generation with Linear Computational Complexity, by Hongjie Wang et al.
LinGen: Towards High-Resolution Minute-Length Text-to-Video Generation with Linear Computational Complexity
by Hongjie Wang, Chih-Yao Ma, Yen-Cheng Liu, Ji Hou, Tao Xu, Jialiang Wang, Felix Juefei-Xu, Yaqiao Luo, Peizhao Zhang, Tingbo Hou, Peter Vajda, Niraj K. Jha, Xiaoliang Dai
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Linear-complexity text-to-video Generation (LinGen) framework revolutionizes the field of video generation by scaling computational complexity linearly with pixel count. This breakthrough enables high-resolution minute-length video creation on a single GPU, replacing quadratic-complexity blocks with novel components like MATE and TEmporal Swin Attention. LinGen outperforms Diffusion Transformers in terms of quality and efficiency, achieving up to 15x FLOPs reduction while yielding comparable video quality. The framework’s potential is vast, paving the way for hour-length movie generation and real-time interactive videos. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new technology called Linear-complexity text-to-video Generation (LinGen) lets computers create longer and better-looking videos. Before this, it was hard for computers to make videos that were both high-quality and long because they got too slow. LinGen solves this problem by making the computer’s calculations faster and more efficient. It replaces old technology with new parts called MATE and TEmporal Swin Attention. This makes LinGen better than before at creating videos, using fewer resources while still looking good. |
Keywords
» Artificial intelligence » Attention » Diffusion
