@inproceedings{
zhuo2024luminanext,
title={Lumina-Next : Making Lumina-T2X Stronger and Faster with Next-DiT},
author={Le Zhuo and Ruoyi Du and Han Xiao and Yangguang Li and Dongyang Liu and Rongjie Huang and Wenze Liu and Xiangyang Zhu and Fu-Yun Wang and Zhanyu Ma and Xu Luo and Zehan Wang and Kaipeng Zhang and Lirui Zhao and Si Liu and Xiangyu Yue and Wanli Ouyang and Yu Qiao and Hongsheng Li and Peng Gao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ieYdf9TZ2u}
}
Lumina-T2X is a nascent family of Flow-based Large Diffusion Transformers (Flag-DiT) that establishes a unified framework for transforming noise into various modalities, such as images and videos, conditioned on text instructions. Despite its promising capabilities, Lumina-T2X still encounters challenges including training instability, slow inference, and extrapolation artifacts. In this paper, we present Lumina-Next, an improved version of Lumina-T2X, showcasing stronger generation performance with increased training and inference efficiency. We begin with a comprehensive analysis of the Flag-DiT architecture and identify several suboptimal components, which we address by introducing the Next-DiT architecture with 3D RoPE and sandwich normalizations. To enable better resolution extrapolation, we thoroughly compare different context extrapolation methods applied to text-to-image generation with 3D RoPE, and propose Frequency- and Time-Aware Scaled RoPE tailored for diffusion transformers. Additionally, we introduce a sigmoid time discretization schedule for diffusion sampling, which achieves high-quality generation in 5-10 steps combined with higher-order ODE solvers. Thanks to these improvements, Lumina-Next not only improves the basic text-to-image generation but also demonstrates superior resolution extrapolation capabilities as well as multilingual generation using decoder-based LLMs as the text encoder, all in a zero-shot manner. To further validate Lumina-Next as a versatile generative framework, we instantiate it on diverse tasks including visual recognition, multi-views, audio, music, and point cloud generation, showcasing strong performance across these domains. By releasing all codes and model weights at https://github.com/Alpha-VLLM/Lumina-T2X, we aim to advance the development of next-generation generative AI capable of universal modeling.
Lumina-Next : Making Lumina-T2X Stronger and Faster with Next-DiT
Le Zhuo Ruoyi Du Han Xiao Yangguang Li Dongyang Liu Rongjie Huang Wenze Liu Xiangyang Zhu Fu-Yun Wang Zhanyu Ma Xu Luo Zehan Wang Kaipeng Zhang Lirui Zhao Si Liu Xiangyu Yue Wanli Ouyang Yu Qiao Hongsheng Li Peng Gao