K-LoRA: Unlocking Training-Free Fusion of Any Subject and Style LoRAs
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.
67bea0cc2d6011a72335f704
Ziheng Ouyang
claimed_verified
2025-02-26T08:25:46.941Z
67be9daa65ae638b17e461e9
/avatars/30ab04b8a6a4d3e1d211943c0344b95e.svg
Ziheng Ouyang
user
oyzh2005
67bea0cc2d6011a72335f705
Zhen Li
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2025-02-26T15:37:36.061Z
6285a9133ab6642179158944
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Zhen Li
user
Paper99
67bea0cc2d6011a72335f706
Qibin Hou
K-LoRA: Unlocking Training-Free Fusion of Any Subject and Style LoRAs
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.
K-LoRA uses LoRA embeddings in diffusion models to effectively merge learned subject and style without additional training.
LoRA diffusion models attention layers Top-K elements feature selection subject and style integration