Prompt Optimizer of Text-to-Image Diffusion Models for Abstract Concept Understanding

• URL: http://arxiv.org/abs/2404.11589v1
• Date: Wed, 17 Apr 2024 17:38:56 GMT
• Title: Prompt Optimizer of Text-to-Image Diffusion Models for Abstract Concept Understanding
• Authors: Zezhong Fan, Xiaohan Li, Chenhao Fang, Topojoy Biswas, Kaushiki Nag, Jianpeng Xu, Kannan Achan,
• Abstract summary: This paper introduces Prompt for Abstract Concepts (POAC) to enhance the performance of text-to-image diffusion models. We propose a Prompt Language Model (PLM), which is curated from a pre-trained language model, and then fine-tuned with a dataset of abstract concept prompts. Our framework employs a Reinforcement Learning (RL)-based optimization strategy, focusing on the alignment between the generated images by a stable diffusion model and optimized prompts.
• Score: 9.787025432074978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid evolution of text-to-image diffusion models has opened the door of generative AI, enabling the translation of textual descriptions into visually compelling images with remarkable quality. However, a persistent challenge within this domain is the optimization of prompts to effectively convey abstract concepts into concrete objects. For example, text encoders can hardly express "peace", while can easily illustrate olive branches and white doves. This paper introduces a novel approach named Prompt Optimizer for Abstract Concepts (POAC) specifically designed to enhance the performance of text-to-image diffusion models in interpreting and generating images from abstract concepts. We propose a Prompt Language Model (PLM), which is initialized from a pre-trained language model, and then fine-tuned with a curated dataset of abstract concept prompts. The dataset is created with GPT-4 to extend the abstract concept to a scene and concrete objects. Our framework employs a Reinforcement Learning (RL)-based optimization strategy, focusing on the alignment between the generated images by a stable diffusion model and optimized prompts. Through extensive experiments, we demonstrate that our proposed POAC significantly improves the accuracy and aesthetic quality of generated images, particularly in the description of abstract concepts and alignment with optimized prompts. We also present a comprehensive analysis of our model's performance across diffusion models under different settings, showcasing its versatility and effectiveness in enhancing abstract concept representation.

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