MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid
  Vision Tokenizer
Authors: name: Yanghao Li; name: Rui Qian; name: Bowen Pan; name: Haotian Zhang; name: Haoshuo Huang; name: Bowen Zhang; name: Jialing Tong; name: Haoxuan You; name: Xianzhi Du; name: Zhe Gan; name: Hyunjik Kim; name: Chao Jia; name: Zhenbang Wang; name: Yinfei Yang; name: Mingfei Gao; name: Zi-Yi Dou; name: Wenze Hu; name: Chang Gao; name: Dongxu Li; name: Philipp Dufter; name: Zirui Wang; name: Guoli Yin; name: Zhengdong Zhang; name: Chen Chen; name: Yang Zhao; name: Ruoming Pang; name: Zhifeng Chen
Published: 2025-09-19 17:58:00

Abstract: Unified multimodal Large Language Models (LLMs) that can both understand and
generate visual content hold immense potential. However, existing open-source
models often suffer from a performance trade-off between these capabilities. We
present Manzano, a simple and scalable unified framework that substantially
reduces this tension by coupling a hybrid image tokenizer with a well-curated
training recipe. A single shared vision encoder feeds two lightweight adapters
that produce continuous embeddings for image-to-text understanding and discrete
tokens for text-to-image generation within a common semantic space. A unified
autoregressive LLM predicts high-level semantics in the form of text and image
tokens, with an auxiliary diffusion decoder subsequently translating the image
tokens into pixels. The architecture, together with a unified training recipe
over understanding and generation data, enables scalable joint learning of both
capabilities. Manzano achieves state-of-the-art results among unified models,
and is competitive with specialist models, particularly on text-rich
evaluation. Our studies show minimal task conflicts and consistent gains from
scaling model size, validating our design choice of a hybrid tokenizer.