Customizing image generation remains a core challenge in controllable image synthesis. For single-concept generation, maintaining both identity preservation and prompt alignment is challenging. In multi-concept scenarios, relying solely on a prompt without additional conditions like layout boxes or semantic masks, often leads to identity loss and concept omission. In this paper, we introduce ShowFlow, a comprehensive framework designed to tackle these challenges. We propose ShowFlow-S for single-concept image generation, and ShowFlow-M for handling multiple concepts. ShowFlow-S introduces a KronA-WED adapter, which integrates a Kronecker adapter with weight and embedding decomposition, and employs a disentangled learning approach with a novel attention regularization objective to enhance single-concept generation. Building on this foundation, ShowFlow-M directly reuses the learned models from ShowFlow-S to support multi-concept generation without extra conditions, incorporating a Subject-Adaptive Matching Attention (SAMA) and a layout consistency strategy as the plug-and-play module. Extensive experiments and user studies validate ShowFlow’s effectiveness, highlighting its potential in real-world applications like advertising and virtual dressing.
We introduce a new adapter called Kronecker Adaptation with Weight and Embedding Decomposition (KronA-WED) and a fine-tuning strategy that combines disentangled learning with a novel attention regularization objective to achieving the balance of reconstruction and editability.
Given $N$ learned concepts using KronA-WEDs, we adopt gradient fusion to obtain the fused update weights. Using this weight for generating multi-concept images, we propose Subject-Adaptive Matching Attention (SAMA) module to preserve the identity details of each concept within the composite image, and layout consistency guidance to mitigate the concepts missing issue.
ShowFlow-S component exhibit comparable results in all metrics, thereby demonstrating its capability in balancing the trade-off of reconstruction and editability. Meanwhile, ShowFlow-M outperforms other methods in identity preservation and layout consistency.
Feel free to contact Trong-Vu Hoang or Quang-Binh Nguyen for any question. If you find this work useful, please consider citing:
@misc{hoang2025showflowrobustsingleconcept,
title={ShowFlow: From Robust Single Concept to Condition-Free Multi-Concept Generation},
author={Trong-Vu Hoang and Quang-Binh Nguyen and Thanh-Toan Do and Tam V. Nguyen and Minh-Triet Tran and Trung-Nghia Le},
year={2025},
eprint={2506.18493},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2506.18493},
}