Large language models based on decoder-only transformers have demonstrated superior text understanding capabilities compared to CLIP and T5-series models.However, the paradigm for utilizing current advanced LLMs in text-to-image diffusion models remains to be explored.We observed an unusual phenomenon: directly using a large language model as the prompt encoder significantly degrades the prompt-following ability in image generation.We identified two main obstacles behind this issue.One is the misalignment between the next token prediction training in LLM and the requirement for discriminative prompt features in diffusion models.The other is the intrinsic positional bias introduced by the decoder-only architecture.To deal with this issue, we propose a novel framework to fully harness the capabilities of LLMs.Through the carefully designed usage guidance, we effectively enhance the text representation capability of the LLM for prompt encoding and eliminate its inherent positional bias.This allows us to flexibly integrate state-of-the-art LLMs into the text-to-image generation model.Furthermore, we also provide an effective manner to fuse multiple LLMs into our framework.Considering the excellent performance and scaling capabilities demonstrated by the transformer architecture, we further design an LLM-Infused Diffusion Transformer (LI-DIT)based on the framework.We conduct extensive experiments to validate LI-DIT across model size and data size.Benefiting from the inherent ability of the LLMs and our innovative designs, the prompt understanding performance of LI-DIT easily surpasses state-of-the-art open-source models as well as mainstream closed-source commercial models including Stable Diffusion 3, DALL-E 3, and Midjourney V6.