Unleashing the Potential of Large Language Models for Text-to-Image
  Generation through Autoregressive Representation Alignment
Authors: name: Xing Xie, name: Jiawei Liu, name: Ziyue Lin, name: Huijie Fan, name: Zhi Han, name: Yandong Tang, name: Liangqiong Qu

We present Autoregressive Representation Alignment (ARRA), a new training
framework that unlocks global-coherent text-to-image generation in
autoregressive LLMs without architectural changes. Unlike prior work that
requires complex architectural redesigns, ARRA aligns LLM hidden states with
visual representations from external visual foundational models via a global
visual alignment loss and a hybrid token, <HYBNEXT>. This token enforces dual
constraints: local next-token prediction and global semantic distillation,
enabling LLMs to implicitly learn spatial and contextual coherence while
retaining their original autoregressive paradigm. Extensive experiments
validate ARRA's plug-and-play versatility. When training from
text-generation-only LLMs or random initialization, ARRA reduces FID by 25.5%
(MIMIC-CXR), 8.8% (DeepEyeNet), and 7.5% (ImageNet) for advanced autoregressive
LLMs like Chameleon and LlamaGen, all without framework modifications. For
domain adaption, ARRA aligns general-purpose LLMs with specialized models
(e.g., BioMedCLIP), achieving an 18.6% FID reduction over direct fine-tuning on
medical imaging (MIMIC-CXR). By demonstrating that training objective redesign
-- not just architectural innovation -- can resolve cross-modal global
coherence challenges, ARRA offers a complementary paradigm for advancing
autoregressive models. Code and models will be released to advance
autoregressive image generation.