Summary of Lumina-next: Making Lumina-t2x Stronger and Faster with Next-dit, by Le Zhuo et al.
Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiT
by Le Zhuo, Ruoyi Du, Han Xiao, Yangguang Li, Dongyang Liu, Rongjie Huang, Wenze Liu, Lirui Zhao, Fu-Yun Wang, Zhanyu Ma, Xu Luo, Zehan Wang, Kaipeng Zhang, Xiangyang Zhu, Si Liu, Xiangyu Yue, Dingning Liu, Wanli Ouyang, Ziwei Liu, Yu Qiao, Hongsheng Li, Peng Gao
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 Medium Difficulty Summary: Lumina-T2X, a family of Flow-based Large Diffusion Transformers, transforms noise into images and videos conditioned on text instructions. Despite its promising capabilities, it encounters training instability, slow inference, and extrapolation artifacts. This paper presents Lumina-Next, an improved version with stronger generation performance and increased efficiency. The authors analyze the Flag-DiT architecture, identifying suboptimal components addressed by introducing the Next-DiT architecture. They compare different context extrapolation methods for text-to-image generation with 3D RoPE and propose Frequency- and Time-Aware Scaled RoPE for diffusion transformers. Additionally, they introduce a sigmoid time discretization schedule and Context Drop method to reduce sampling steps and merge redundant visual tokens. The improved Lumina-Next demonstrates superior resolution extrapolation capabilities and multilingual generation using decoder-based LLMs as the text encoder in a zero-shot manner. It is applied to diverse tasks including visual recognition, multi-view, audio, music, and point cloud generation, showcasing strong performance across these domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This paper improves Lumina-T2X, a computer program that turns noise into images and videos based on text instructions. The old version had some problems, like taking too long to train or produce results. The new version, called Lumina-Next, is better at making these transformations and can do it faster too! Scientists analyzed the old code and fixed some parts to make it work better. They also tested different ways to make the program generate higher-quality images and videos. By releasing all the codes and models, they hope to help other scientists develop even better AI that can do many things. |
Keywords
* Artificial intelligence * Decoder * Diffusion * Encoder * Image generation * Inference * Sigmoid * Zero shot
