Diffusion models have demonstrated remarkable performance in the domain of
text-to-image generation. However, most widely used models still employ CLIP as
their text encoder, which constrains their ability to comprehend dense prompts,
encompassing multiple objects, detailed attributes, complex relationships,
long-text alignment, etc. In this paper, we introduce an Efficient Large
Language Model Adapter, termed ELLA, which equips text-to-image diffusion
models with powerful Large Language Models (LLM) to enhance text alignment
without training of either U-Net or LLM. To seamlessly bridge two pre-trained
models, we investigate a range of semantic alignment connector designs and
propose a novel module, the Timestep-Aware Semantic Connector (TSC), which
dynamically extracts timestep-dependent conditions from LLM. Our approach
adapts semantic features at different stages of the denoising process,
assisting diffusion models in interpreting lengthy and intricate prompts over
sampling timesteps. Additionally, ELLA can be readily incorporated with
community models and tools to improve their prompt-following capabilities. To
assess text-to-image models in dense prompt following, we introduce Dense
Prompt Graph Benchmark (DPG-Bench), a challenging benchmark consisting of 1K
dense prompts. Extensive experiments demonstrate the superiority of ELLA in
dense prompt following compared to state-of-the-art methods, particularly in
multiple object compositions involving diverse attributes and relationships.

该研究提出了一种有效的大型语言模型适配器（ELLA），将文本到图像扩散模型与强大的大型语言模型相结合，以实现文本对齐，并且无需对 U-Net 或 LLM 进行训练。通过动态提取 LLM 中的时步相关条件，ELLA 在不同阶段适应语义特征，帮助扩散模型解释复杂的长文本提示。该方法在稠密提示跟随中表现出优越性，尤其是涉及多个对象组成和多样属性关系的情况。