X-Fusion: Introducing New Modality to Frozen Large Language Models

• URL: http://arxiv.org/abs/2504.20996v1
• Date: Tue, 29 Apr 2025 17:59:45 GMT
• Title: X-Fusion: Introducing New Modality to Frozen Large Language Models
• Authors: Sicheng Mo, Thao Nguyen, Xun Huang, Siddharth Srinivasan Iyer, Yijun Li, Yuchen Liu, Abhishek Tandon, Eli Shechtman, Krishna Kumar Singh, Yong Jae Lee, Bolei Zhou, Yuheng Li,
• Abstract summary: We propose X-Fusion, a framework that extends pretrained Large Language Models for multimodal tasks.<n>X-Fusion employs a dual-tower design with modality-specific weights, keeping the LLM's parameters frozen while integrating vision-specific information for both understanding and generation.<n>Our experiments demonstrate that X-Fusion consistently outperforms alternative architectures on both image-to-text and text-to-image tasks.
• Score: 82.3508830643655
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose X-Fusion, a framework that extends pretrained Large Language Models (LLMs) for multimodal tasks while preserving their language capabilities. X-Fusion employs a dual-tower design with modality-specific weights, keeping the LLM's parameters frozen while integrating vision-specific information for both understanding and generation. Our experiments demonstrate that X-Fusion consistently outperforms alternative architectures on both image-to-text and text-to-image tasks. We find that incorporating understanding-focused data improves generation quality, reducing image data noise enhances overall performance, and feature alignment accelerates convergence for smaller models but has minimal impact on larger ones. Our findings provide valuable insights into building efficient unified multimodal models.

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