Bridging Different Language Models and Generative Vision Models for
  Text-to-Image Generation
Shihao Zhao Shaozhe Hao Bojia Zi Huaizhe Xu Kwan-Yee K. Wong
Text-to-image generation has made significant advancements with the
introduction of text-to-image diffusion models. These models typically consist
of a language model that interprets user prompts and a vision model that
generates corresponding images. As language and vision models continue to
progress in their respective domains, there is a great potential in exploring
the replacement of components in text-to-image diffusion models with more
advanced counterparts. A broader research objective would therefore be to
investigate the integration of any two unrelated language and generative vision
models for text-to-image generation. In this paper, we explore this objective
and propose LaVi-Bridge, a pipeline that enables the integration of diverse
pre-trained language models and generative vision models for text-to-image
generation. By leveraging LoRA and adapters, LaVi-Bridge offers a flexible and
plug-and-play approach without requiring modifications to the original weights
of the language and vision models. Our pipeline is compatible with various
language models and generative vision models, accommodating different
structures. Within this framework, we demonstrate that incorporating superior
modules, such as more advanced language models or generative vision models,
results in notable improvements in capabilities like text alignment or image
quality. Extensive evaluations have been conducted to verify the effectiveness
of LaVi-Bridge. Code is available at
https://github.com/ShihaoZhaoZSH/LaVi-Bridge.