Decoder-Only LLMs are Better Controllers for Diffusion Models

TL;DR

This paper introduces a method to improve diffusion-based text-to-image models by integrating decoder-only large language models, resulting in better semantic understanding and higher quality image generation.

Contribution

It proposes an adapter to enable diffusion models to leverage decoder-only LLMs, enhancing their semantic understanding and generation performance.

Findings

Enhanced models outperform state-of-the-art in quality and reliability

Adapter effectively bridges diffusion models with decoder-only LLMs

Theoretical analysis supports architecture choices

Abstract

Groundbreaking advancements in text-to-image generation have recently been achieved with the emergence of diffusion models. These models exhibit a remarkable ability to generate highly artistic and intricately detailed images based on textual prompts. However, obtaining desired generation outcomes often necessitates repetitive trials of manipulating text prompts just like casting spells on a magic mirror, and the reason behind that is the limited capability of semantic understanding inherent in current image generation models. Specifically, existing diffusion models encode the text prompt input with a pre-trained encoder structure, which is usually trained on a limited number of image-caption pairs. The state-of-the-art large language models (LLMs) based on the decoder-only structure have shown a powerful semantic understanding capability as their architectures are more suitable for training on very large-scale unlabeled data. In this work, we propose to enhance text-to-image diffusion models by borrowing the strength of semantic understanding from large language models, and devise a simple yet effective adapter to allow the diffusion models to be compatible with the decoder-only structure. Meanwhile, we also provide a supporting theoretical analysis with various architectures (e.g., encoder-only, encoder-decoder, and decoder-only), and conduct extensive empirical evaluations to verify its effectiveness. The experimental results show that the enhanced models with our adapter module are superior to the stat-of-the-art models in terms of text-to-image generation quality and reliability.