title: K-LoRA: Unlocking Training-Free Fusion of Any Subject and Style LoRAs
summary: 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.
paper_authors._id: 67bea0cc2d6011a72335f704
paper_authors.name: Ziheng Ouyang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-26T08:25:46.941Z
paper_authors._id: 67be9daa65ae638b17e461e9
paper_authors.avatarUrl: /avatars/30ab04b8a6a4d3e1d211943c0344b95e.svg
paper_authors.fullname: Ziheng Ouyang
paper_authors.type: user
paper_authors.user: oyzh2005
paper_authors._id: 67bea0cc2d6011a72335f705
paper_authors.name: Zhen Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-26T15:37:36.061Z
paper_authors._id: 6285a9133ab6642179158944
paper_authors.avatarUrl: /avatars/6e10fa07c94141fcdbe0cab02bb731ca.svg
paper_authors.fullname: Zhen Li
paper_authors.type: user
paper_authors.user: Paper99
paper_authors._id: 67bea0cc2d6011a72335f706
paper_authors.name: Qibin Hou
paper_title: K-LoRA: Unlocking Training-Free Fusion of Any Subject and Style LoRAs
paper_summary: 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.
paper_ai_summary: K-LoRA uses LoRA embeddings in diffusion models to effectively merge learned subject and style without additional training.
paper_ai_keywords: LoRA diffusion models attention layers Top-K elements feature selection subject and style integration