MANZANO
📌 核心贡献
本文提出了 MANZANO，针对MANZANO: A Simple and Scalable Unified M… 方向进行研究，提出了新颖的方法并在相关基准上取得了优异性能。
📖 摘要
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.
🔍 关键信息
| 字段 | 内容 |
|---|---|
| 机构 | |
| 发表 | 2025-09-19 |
| 分类 | cs.CV |
| 链接 | arXiv |