  Lumina-T2X is a nascent family of Flow-based Large Diffusion Transformers
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 introduced a sigmoid time discretization
schedule to reduce sampling steps in solving the Flow ODE and the Context Drop
method to merge redundant visual tokens for faster network evaluation,
effectively boosting the overall sampling speed. Thanks to these improvements,
Lumina-Next not only improves the quality and efficiency of basic text-to-image
generation but also demonstrates superior resolution extrapolation capabilities
and 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-view, audio, music, and point cloud generation, showcasing
strong performance across these domains. By releasing all codes and model
weights, we aim to advance the development of next-generation generative AI
capable of universal modeling.
