Specific Style Image Generation Based on LoRA Fine-Tuning Technology

DOI

10.2991/978-94-6463-986-5_60How to use a DOI?

Keywords

Low-Rank Adaptation (LoRA); Ukiyo-E Style; Parameter-Efficient Fine-tuning

Abstract

This paper aims to improve the performance of general Text-to-Image models for image generation in a specific art style. While foundational models generate diverse imagery, they often fail to capture the unique nuances required for specialized artistic genres and instead produce generic results. To address the high computational cost of full fine-tuning, an efficient method based on Low-Rank Adaptation is proposed. This study enables the pre-trained Stable Diffusion v1.5 model to learn and reproduce the Japanese Ukiyo-e style, characterized by its distinct flat colors and bold outlines. A small, high-quality dataset of Ukiyo-e artworks was constructed and meticulously annotated, followed by parameter-efficient fine-tuning using low-rank adaptation (LoRA). Both qualitative and quantitative evaluation schemes were implemented to validate the method. Experimental results show that the method consistently generates images in the target art style while maintaining semantic consistency with the input text. This approach provides a practical reference for the application of AIGC to personalized and professional image generation.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Kunyu Liu
PY  - 2026
DA  - 2026/02/18
TI  - Specific Style Image Generation Based on LoRA Fine-Tuning Technology
BT  - Proceedings of the 2025 International Conference on Electronics, Electrical and Grid Technology (ICEEGT 2025)
PB  - Atlantis Press
SP  - 587
EP  - 595
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-986-5_60
DO  - 10.2991/978-94-6463-986-5_60
ID  - Liu2026
ER  -