A Comprehensive Study of Decoder-Only LLMs for Text-to-Image Generation

Both text-to-image generation and large language models (LLMs) have made significant advancements. However, many text-to-image models still employ the somewhat outdated T5 and CLIP as their text encoders. In this work, we investigate the effectiveness of using modern decoder-only LLMs as text encoders for text-to-image diffusion models. We build a standardized training and evaluation pipeline that allows us to isolate and evaluate the effect of different text embeddings. We train a total of 27 text-to-image models with 12 different text encoders to analyze the critical aspects of LLMs that could impact text-to-image generation, including the approaches to extract embeddings, different LLMs variants, and model sizes. Our experiments reveal that the de facto way of using last-layer embeddings as conditioning leads to inferior performance. Instead, we explore embeddings from various layers and find that using layer-normalized averaging across all layers significantly improves alignment with complex prompts. Most LLMs with this conditioning outperform the baseline T5 model, showing enhanced performance in advanced visio-linguistic reasoning skills.

@article{wang2025_2506.08210,
  title={ A Comprehensive Study of Decoder-Only LLMs for Text-to-Image Generation },
  author={ Andrew Z. Wang and Songwei Ge and Tero Karras and Ming-Yu Liu and Yogesh Balaji },
  journal={arXiv preprint arXiv:2506.08210},
  year={ 2025 }
}