K-LoRA: Unlocking Training-Free Fusion of Any Subject and Style LoRAs
Authors: name: Ziheng Ouyang, name: Zhen Li, name: Qibin Hou

Recent studies have explored combining different LoRAs to jointly generate
learned style and content. However, existing methods either fail to effectively
preserve both the original subject and style simultaneously or require
additional training. In this paper, we argue that the intrinsic properties of
LoRA can effectively guide diffusion models in merging learned subject and
style. Building on this insight, we propose K-LoRA, a simple yet effective
training-free LoRA fusion approach. In each attention layer, K-LoRA compares
the Top-K elements in each LoRA to be fused, determining which LoRA to select
for optimal fusion. This selection mechanism ensures that the most
representative features of both subject and style are retained during the
fusion process, effectively balancing their contributions. Experimental results
demonstrate that the proposed method effectively integrates the subject and
style information learned by the original LoRAs, outperforming state-of-the-art
training-based approaches in both qualitative and quantitative results.