TIPO (Text to Image with text pre-sampling for Prompt Optimization) is an innovative framework designed to enhance text-to-image (T2I) generation by language model (LM) for automatic prompt engineering. By refining and extending user-provided prompts, TIPO bridges the gap between simple inputs and the detailed prompts required for high-quality image generation. Unlike previous approaches that rely on Large Language Models (LLMs) or reinforcement learning (RL), TIPO adjusts user input prompts with the distribution of a trained prompt dataset, eliminating the need for complex runtime cost via lightweight model. This pre-sampling approach enables efficient and scalable prompt optimization, grounded in the model's training distribution. Experimental results demonstrate TIPO's effectiveness in improving aesthetic scores, reducing image corruption, and better aligning generated images with dataset distributions. These findings highlight the critical role of prompt engineering in T2I systems and open avenues for broader applications of automatic prompt refinement.

该研究旨在解决文本到图像生成中用户提示输入简单导致的图像质量差的问题。TIPO框架通过用户提示的预抽样和细化，实现高效的提示优化，而无需复杂的运行成本。实验证明，TIPO能显著提高图像的美学评分，减少图像损坏，并更好地与数据集分布对齐，表明提示工程在文本到图像系统中的重要性。

TIPO：通过文本预抽样优化提示的文本到图像生成