EPIG: Emotion-Based Prompting for Personalised Image Generation

2026-06-11

AI Summary

Current text-to-image generation models exhibit limitations in conveying nuanced emotional intent. This work proposes a lightweight, training-free emotion-aware prompting framework that, without modifying or fine-tuning the underlying diffusion model, integrates psychological valence-arousal emotion representation with character-aware mechanisms for the first time. By leveraging structured prompt templates and semantic expansion, the approach enhances emotional expressiveness while preserving semantic fidelity. Evaluated across ten diverse prompt benchmarks, the method significantly outperforms strong baselines, reducing average arousal prediction error by 12%–17% while maintaining alignment in valence and overall semantic consistency, thereby enabling more emotionally coherent personalized image generation.

Abstract

Text-to-image diffusion models have achieved impressive results in synthesizing high-quality images from natural language prompts. However, commonly used prompting strategies remain relatively generic, limiting the model's ability to accurately express emotional intent and nuanced affective attributes. This work proposes EPIG, a method that enhances emotional expressiveness at the prompt level prior to image generation. Grounded in psychologically informed emotion representations (valence-arousal) and leveraging structured, role-aware prompt enrichment, EPIG enriches emotion-related components of prompts without modifying or retraining the image generation backbone. The resulting emotion-aware prompts guide the generative process toward more emotionally coherent visual outputs, with particular effectiveness in controlling arousal. EPIG is lightweight, training-free, and well suited for resource-constrained and personalized image generation scenarios. Experimental results on a benchmark of 10 diverse prompts show that EPIG reduces mean arousal error compared to strong baselines, including naive insertion and LLM-based prompt expansion, with reductions of 14% and 12%, respectively. These improvements are statistically significant. EPIG also preserves valence alignment and semantic consistency, as measured by CLIPScore and supported by ablation studies. The effect is more pronounced on prompts containing explicit subjects such as humans, children, or animals, where the reduction reaches 17%, highlighting the subject-sensitive behavior of the proposed method.

Problem

• emotion-based prompting
• personalised image generation
• text-to-image diffusion models
• emotional expressiveness
• affective attributes

Innovation

• emotion-aware prompting
• text-to-image generation
• valence-arousal model
• prompt engineering
• diffusion models