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

Zezhong Fan, Xiaohan Li, Chenhao Fang, Topojoy Biswas, Kaushiki Nag, Jianpeng Xu, Kannan Achan

TL;DR

The paper tackles the difficulty of visualizing abstract concepts with text-to-image diffusion models. It introduces POAC, a framework that combines a Prompt Language Model (PLM) trained to rewrite abstract prompts into concrete scenes and objects with a Reward Feedback Learning (ReFL) loop to align the diffusion outputs with the optimized prompts and aesthetic quality. Key contributions include building a GPT-4–driven dataset mapping abstract concepts to concrete elements, supervised fine-tuning of the PLM, and two-stage RL to fine-tune the diffusion model (SDXL) for faithful and aesthetically pleasing representations; quantitative and qualitative results show meaningful gains over baseline diffusion models. The work advances abstract-concept visualization in diffusion-based generation and demonstrates a scalable pathway for improving alignment with human intent and preferences, with potential extensions to bias-aware prompting and broader concept coverage.

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.

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

TL;DR

The paper tackles the difficulty of visualizing abstract concepts with text-to-image diffusion models. It introduces POAC, a framework that combines a Prompt Language Model (PLM) trained to rewrite abstract prompts into concrete scenes and objects with a Reward Feedback Learning (ReFL) loop to align the diffusion outputs with the optimized prompts and aesthetic quality. Key contributions include building a GPT-4–driven dataset mapping abstract concepts to concrete elements, supervised fine-tuning of the PLM, and two-stage RL to fine-tune the diffusion model (SDXL) for faithful and aesthetically pleasing representations; quantitative and qualitative results show meaningful gains over baseline diffusion models. The work advances abstract-concept visualization in diffusion-based generation and demonstrates a scalable pathway for improving alignment with human intent and preferences, with potential extensions to bias-aware prompting and broader concept coverage.

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.
Paper Structure (15 sections, 7 equations, 3 figures, 2 tables)

This paper contains 15 sections, 7 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related works
  3. Text-to-Image Generation
  4. Concept Representation in Image Generation
  5. Prompt Optimization for Diffusion Models
  6. Methodology
  7. Datasets
  8. Supervised Fine-tuning
  9. Reward Function
  10. Reward Feedback Learning
  11. Experiments
  12. Implementation Detail
  13. Qualitative Comparison
  14. Quantitative Comparison
  15. Conclusion

Figures (3)

  • Figure 1: Examples of image generation with or without prompt optimizer. (a) SDXL: only input the original prompt. (b) SDXL (with POAC): input the prompt optimized by POAC to SDXL. (c) Generate the image with the fine-tuned SDXL with ReFL and optimized prompt.
  • Figure 2: (a) The dataset construction process. We manually rewrite the abstract concept "wisdom" to a short prompt. With the help of GPT, we prompt is optimized with detailed and concrete objects (in red). The art styles are randomly selected and added to the optimized prompt. (b) The training process of Prompt Language Model (PLM) and Stable Diffusion XL (SDXL). PLM is fine-tuned with original and optimized prompts by Supervised Fine-Tune (SFT). The SDXL is fine-tuned by Reward Feedback Learning (ReFL) to align the prompts and the image.
  • Figure 3: Qualitative comparison of SDXL only, SDXL with POAC and SDXL with POAC, fine-tuned with ReFL.