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2024

ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment

Hu, Xiwei, Wang, Rui, Fang, Yixiao et al.

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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.

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