AlignedGen: Aligning Style Across Generated Images
Jiexuan Zhang, Yiheng Du, Qian Wang, Weiqi Li, Yu Gu, Jian Zhang
Diffusion-based generative models struggle to maintain high style consistency across generated images via text description. Although several style-aligned image generation methods have been proposed to address this issue, they exhibit suboptimal performance and are primarily built upon the U-Net architecture, limiting their compatibility with MM-DiT diffusion models like Flux that has emerged as a predominant model in the field of image generation. To address these limitations, we propose $\textit{\textbf{AlignedGen}}$, a novel training-free style-aligned image generation method for Flux to significantly enhance style consistency across generated images. Specifically, AlignedGen incorporates two key components to achieve this: Shifted Position Embedding (ShiftPE) and Selective Shared Attention (SSA) layer. ShiftPE alleviates the text controllability degradation observed in prior methods when applied to Flux through its non-overlapping position indices design, while SSA further enhances style consistency across images. In addition, our method can be seamlessly integrated with various controllable generation technologies (e.g., subject-driven generation, depth control), demonstrating broad applicability across diverse scenarios. Extensive experimental results validate that our method effectively enhances style consistency across generated images while maintaining favorable text controllability.