MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid
  Vision Tokenizer
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.
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Yanghao Li
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2025-09-22T02:56:15.401Z
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Bowen Pan
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Haotian Zhang
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Haotian Zhang
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Haoshuo Huang
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Haoshuo Huang
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Bowen Zhang
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Jialing Tong
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Jialing Tong
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Haoxuan You
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Xianzhi Du
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Hyunjik Kim
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Zi-Yi Dou
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Wenze Hu
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Chang Gao
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Chang Gao
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Dongxu Li
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Dongxu Li
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Philipp Dufter
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Guoli Yin
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Zhengdong Zhang
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Chen Chen
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Yang Zhao
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Ruoming Pang
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Ruoming Pang
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Zhifeng Chen
MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid
  Vision Tokenizer
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.
Manzano is a unified multimodal LLM framework that integrates image and text processing using a hybrid tokenizer and diffusion decoder, achieving state-of-the-art performance in both understanding and generating visual content.
multimodal Large Language Models hybrid image tokenizer vision encoder lightweight adapters continuous embeddings discrete tokens semantic space unified autoregressive LLM diffusion decoder joint learning text-rich evaluation task conflicts