Muddit: Liberating Generation Beyond Text-to-Image with a Unified
  Discrete Diffusion Model
Authors: name: Qingyu Shi, name: Jinbin Bai, name: Zhuoran Zhao, name: Wenhao Chai, name: Kaidong Yu, name: Jianzong Wu, name: Shuangyong Song, name: Yunhai Tong, name: Xiangtai Li, name: Xuelong Li, name: Shuicheng Yan

Unified generation models aim to handle diverse tasks across modalities --
such as text generation, image generation, and vision-language reasoning --
within a single architecture and decoding paradigm. Autoregressive unified
models suffer from slow inference due to sequential decoding, and
non-autoregressive unified models suffer from weak generalization due to
limited pretrained backbones. We introduce Muddit, a unified discrete diffusion
transformer that enables fast and parallel generation across both text and
image modalities. Unlike prior unified diffusion models trained from scratch,
Muddit integrates strong visual priors from a pretrained text-to-image backbone
with a lightweight text decoder, enabling flexible and high-quality multimodal
generation under a unified architecture. Empirical results show that Muddit
achieves competitive or superior performance compared to significantly larger
autoregressive models in both quality and efficiency. The work highlights the
potential of purely discrete diffusion, when equipped with strong visual
priors, as a scalable and effective backbone for unified generation.