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arXiv · 2512.02536

WeMMU: Enhanced Bridging of Vision-Language Models and Diffusion Models via Noisy Query Tokens

Jian Yang·Dacheng Yin·Xiaoxuan He·Yong Li·Fengyun Rao·Jing Lyu·Wei Zhai·Yang Cao·Zheng-Jun Zha
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Abstract

Recent progress in multimodal large language models (MLLMs) has highlighted the challenge of efficiently bridging pre-trained Vision-Language Models (VLMs) with Diffusion Models. While methods using a fixed number of learnable query tokens offer computational efficiency, they suffer from task generalization collapse, failing to adapt to new tasks that are distant from their pre-training tasks. To overcome this, we propose Noisy Query Tokens, which learn a distributed representation space between the VLM and Diffusion Model via end-to-end optimization, enhancing continual learning. Additionally, we introduce a VAE branch with linear projection to recover fine-grained image details. Experimental results confirm our approach mitigates generalization collapse and enables stable continual learning across diverse tasks.

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Jian Yang, Dacheng Yin, Xiaoxuan He, Yong Li, Fengyun Rao, Jing Lyu, Wei Zhai, Yang Cao, Zheng-Jun Zha. 2025-12-02. WeMMU: Enhanced Bridging of Vision-Language Models and Diffusion Models via Noisy Query Tokens. https://arxiv.org/abs/2512.02536

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