An Empirical Study and Analysis of Text-to-Image Generation Using Large Language Model-Powered Textual Representation

Understand

One critical prerequisite for faithful text-to-image generation is the accurate understanding of text inputs.

• Existing methods leverage the text encoder of the CLIP model to represent input prompts.
• However, the pre-trained CLIP model can merely encode English with a maximum token length of 77.
• Moreover, the model capacity of the text encoder from CLIP is relatively limited compared to Large Language Models (LLMs), which offer multilingual input, accommodate longer context, and achieve superior text representation.