TIPO: Text to Image with Text Presampling for Prompt Optimization

2024-11-12

arXiv.org

AI Summary

To address the manual dependency, high computational cost, and poor scalability of prompt engineering in text-to-image generation, this paper proposes a lightweight, distribution-aware prompt optimization framework. Methodologically, it abandons large language models and reinforcement learning, introducing the novel “prompt pre-sampling” paradigm: modeling the statistical distribution of training-set prompts and performing differentiable reparameterized sampling and guided optimization in the text embedding space. The approach incurs negligible overhead during inference while enabling end-to-end prompt enhancement under semantic fidelity constraints. Experiments demonstrate substantial improvements: +12.3% in aesthetic score, −31.7% in distortion rate, and enhanced alignment between generated images and target data distributions. The method achieves superior efficiency, scalability, and generalization across diverse prompts and models, without requiring architectural modifications or additional training data.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Optimizes text prompts for text-to-image generation.
• Enhances visual quality and detail in generated images.
• Provides efficient, scalable prompt refinement without heavy resources.

Innovation

• Lightweight pre-trained model for prompt expansion
• Targeted sub-distribution sampling for refined prompts
• Computational efficiency and scalability in T2I tasks