Transfer between Modalities with MetaQueries

• URL: http://arxiv.org/abs/2504.06256v1
• Date: Tue, 08 Apr 2025 17:58:47 GMT
• Title: Transfer between Modalities with MetaQueries
• Authors: Xichen Pan, Satya Narayan Shukla, Aashu Singh, Zhuokai Zhao, Shlok Kumar Mishra, Jialiang Wang, Zhiyang Xu, Jiuhai Chen, Kunpeng Li, Felix Juefei-Xu, Ji Hou, Saining Xie,
• Abstract summary: We introduce MetaQueries, a set of learnable queries that act as an efficient interface between autoregressive multimodal LLMs and diffusion models.<n>Our method simplifies training, requiring only paired image-caption data and standard diffusion objectives.<n>Our method is flexible and can be easily instruction-tuned for advanced applications such as image editing and subject-driven generation.
• Score: 44.57406292414526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unified multimodal models aim to integrate understanding (text output) and generation (pixel output), but aligning these different modalities within a single architecture often demands complex training recipes and careful data balancing. We introduce MetaQueries, a set of learnable queries that act as an efficient interface between autoregressive multimodal LLMs (MLLMs) and diffusion models. MetaQueries connects the MLLM's latents to the diffusion decoder, enabling knowledge-augmented image generation by leveraging the MLLM's deep understanding and reasoning capabilities. Our method simplifies training, requiring only paired image-caption data and standard diffusion objectives. Notably, this transfer is effective even when the MLLM backbone remains frozen, thereby preserving its state-of-the-art multimodal understanding capabilities while achieving strong generative performance. Additionally, our method is flexible and can be easily instruction-tuned for advanced applications such as image editing and subject-driven generation.

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