Emotion-Aware Image Generation from Korean Diary Text via LLM-based Prompt Translation and LoRA Fine-Tuning

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition · Depth: Expert, quick

Summary

A new emotion-aware text-to-image pipeline generates children's hand drawing style images from short Korean diary entries. This system addresses the limitation of traditional text-to-image models that often fail to capture sentiment from diverse text types, focusing instead on visual objects. The pipeline utilizes Qwen3-8B to recognize implicit sentiment from the diary text. For image generation, it employs Stable Diffusion 3.5 Medium, which is fine-tuned using LoRA on a dataset of children's drawing images augmented with emotion-based trigger words. The research also includes experiments on the impact of these emotion trigger words on generated images and discusses the shortcomings of CLIP Score as an evaluation metric for emotion-aware image generation tasks.

Key takeaway

For machine learning engineers developing text-to-image systems, this research highlights a robust approach to infuse emotional context into generated visuals. You should consider integrating a dedicated LLM for sentiment analysis, like Qwen3-8B, to process nuanced textual inputs before image generation. Furthermore, fine-tuning models such as Stable Diffusion 3.5 Medium with LoRA and emotion-specific trigger words can significantly enhance emotional expressiveness, moving beyond object-centric outputs.

Key insights

A pipeline generates emotion-aware, children's drawing style images from Korean diary text using LLM sentiment analysis and LoRA fine-tuned Stable Diffusion.

Principles

• T2I models often miss contextual emotional understanding.
• Emotion-based trigger words influence generated image sentiment.
• CLIP Score has limitations for emotion-aware image evaluation.

Method

The pipeline uses Qwen3-8B for implicit sentiment recognition from Korean diaries, then generates images with Stable Diffusion 3.5 Medium, fine-tuned via LoRA with emotion-based trigger words.

In practice

• Generate stylized images from emotional text inputs.
• Incorporate LLMs for nuanced sentiment extraction in T2I.
• Use LoRA with trigger words for emotion-specific image generation.

Topics

• Emotion-Aware Image Generation
• Text-to-Image Models
• Large Language Models
• LoRA Fine-Tuning
• Stable Diffusion
• Sentiment Analysis
• Korean Language Processing

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.