LDGen: Enhancing Text-to-Image Synthesis via Large Language Model-Driven
  Language Representation
Authors: name: Pengzhi Li, name: Pengfei Yu, name: Zide Liu, name: Wei He, name: Xuhao Pan, name: Xudong Rao, name: Tao Wei, name: Wei Chen

In this paper, we introduce LDGen, a novel method for integrating large
language models (LLMs) into existing text-to-image diffusion models while
minimizing computational demands. Traditional text encoders, such as CLIP and
T5, exhibit limitations in multilingual processing, hindering image generation
across diverse languages. We address these challenges by leveraging the
advanced capabilities of LLMs. Our approach employs a language representation
strategy that applies hierarchical caption optimization and human instruction
techniques to derive precise semantic information,. Subsequently, we
incorporate a lightweight adapter and a cross-modal refiner to facilitate
efficient feature alignment and interaction between LLMs and image features.
LDGen reduces training time and enables zero-shot multilingual image
generation. Experimental results indicate that our method surpasses baseline
models in both prompt adherence and image aesthetic quality, while seamlessly
supporting multiple languages. Project page: https://zrealli.github.io/LDGen.