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.

翻译：文本到图像生成技术因引入文本到图像扩散模型而取得重大进展。这类模型通常由解读用户提示的语言模型和生成对应图像的视觉模型构成。随着语言与视觉模型在各自领域持续进步，探索用更先进组件替换文本到图像扩散模型中的现有组件具有巨大潜力。更广泛的研究目标因此可归结为：探究任意两个无关联的语言模型与生成式视觉模型的集成方法，以实现文本到图像生成。本文围绕这一目标展开研究，提出LaVi-Bridge流水线——该方案能够集成多种预训练语言模型与生成式视觉模型完成文本到图像生成任务。通过利用LoRA和适配器技术，LaVi-Bridge在不修改原始语言模型与视觉模型权重的前提下，提供灵活且即插即用的实现方案。该流水线兼容多种语言模型与生成式视觉模型，可适配不同网络结构。在此框架下，我们证明引入更先进的语言模型或生成式视觉模型等优质模块，能显著提升文本对齐或图像质量等性能。通过广泛实验验证了LaVi-Bridge的有效性。代码开源地址：https://github.com/ShihaoZhaoZSH/LaVi-Bridge。