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

• URL: http://arxiv.org/abs/2506.08210v1
• Date: Mon, 09 Jun 2025 20:29:53 GMT
• Title: A Comprehensive Study of Decoder-Only LLMs for Text-to-Image Generation
• Authors: Andrew Z. Wang, Songwei Ge, Tero Karras, Ming-Yu Liu, Yogesh Balaji,
• Abstract summary: Many text-to-image models still employ the somewhat outdated T5 and CLIP as their text encoders.<n>We build a standardized training and evaluation pipeline that allows us to isolate and evaluate the effect of different text embeddings.<n>Our experiments reveal that the de facto way of using last-layer embeddings as conditioning leads to inferior performance.
• Score: 30.041283605038316
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 However, many text-to-image models still employ the somewhat outdated T5 and CLIP as their text encoders.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.

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