Exploring the Role of Large Language Models in Prompt Encoding for Diffusion Models

TL;DR

This paper introduces a novel framework that effectively integrates large language models into diffusion-based text-to-image generation, overcoming previous limitations and significantly improving prompt understanding and image quality.

Contribution

The paper proposes a new method to harness LLMs for prompt encoding in diffusion models, including a design to eliminate positional bias and fuse multiple LLMs, leading to superior performance.

Findings

LI-DiT surpasses state-of-the-art models like Stable Diffusion 3 and DALL-E 3.

The framework effectively fuses multiple LLMs for enhanced prompt understanding.

Experimental results validate the scalability and robustness of LI-DiT.

Abstract

Large language models (LLMs) 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 for prompt encoding and eliminate its inherent positional bias. This allows us to integrate state-of-the-art LLMs into the text-to-image generation model flexibly. 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. The LLM-Infused Diffuser framework is also one of the core technologies powering SenseMirage, a highly advanced text-to-image model.