LightBagel: A Light-weighted, Double Fusion Framework for Unified
  Multimodal Understanding and Generation
Authors: name: Zeyu Wang, name: Zilong Chen, name: Chenhui Gou, name: Feng Li, name: Chaorui Deng, name: Deyao Zhu, name: Kunchang Li, name: Weihao Yu, name: Haoqin Tu, name: Haoqi Fan, name: Cihang Xie

Unified multimodal models have recently shown remarkable gains in both
capability and versatility, yet most leading systems are still trained from
scratch and require substantial computational resources. In this paper, we show
that competitive performance can be obtained far more efficiently by
strategically fusing publicly available models specialized for either
generation or understanding. Our key design is to retain the original blocks
while additionally interleaving multimodal self-attention blocks throughout the
networks. This double fusion mechanism (1) effectively enables rich multi-modal
fusion while largely preserving the original strengths of the base models, and
(2) catalyzes synergistic fusion of high-level semantic representations from
the understanding encoder with low-level spatial signals from the generation
encoder. By training with only ~ 35B tokens, this approach achieves strong
results across multiple benchmarks: 0.91 on GenEval for compositional
text-to-image generation, 82.16 on DPG-Bench for complex text-to-image
generation, 6.06 on GEditBench, and 3.77 on ImgEdit-Bench for image editing. By
fully releasing the entire suite of code, model weights, and datasets, we hope
to support future research on unified multimodal modeling.