Ensuring character identity consistency across varying prompts remains a fundamental limitation in diffusion-based text-to-image generation. We propose CharCom, a modular and parameter-efficient framework that achieves character-consistent story illustration through composable LoRA adapters, enabling efficient per-character customization without retraining the base model. Built on a frozen diffusion backbone, CharCom dynamically composes adapters at inference using prompt-aware control. Experiments on multi-scene narratives demonstrate that CharCom significantly enhances character fidelity, semantic alignment, and temporal coherence. It remains robust in crowded scenes and enables scalable multi-character generation with minimal overhead, making it well-suited for real-world applications such as story illustration and animation.
@article{arxiv.2510.10135,
title = {CharCom: Composable Identity Control for Multi-Character Story Illustration},
author = {Zhongsheng Wang and Ming Lin and Zhedong Lin and Yaser Shakib and Qian Liu and Jiamou Liu},
journal= {arXiv preprint arXiv:2510.10135},
year = {2025}
}