# K-LoRA: Unlocking Training-Free Fusion of Any Subject and Style LoRAs

arXiv: https://arxiv.org/abs/2502.18461 · HTML: https://mlsanity.com/paper/2502.18461/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 51 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 30 of 42)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [Multi-Concept Customization of Text-to-Image Diffusion](https://mlsanity.com/paper/2212.04488/) (2022)
- [LoRA+: Efficient Low Rank Adaptation of Large Models](https://mlsanity.com/paper/2402.12354/) (2024)
- [InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning](https://mlsanity.com/paper/2304.03411/) (2023)
- [PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding](https://mlsanity.com/paper/2312.04461/) (2023)
- [FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention](https://mlsanity.com/paper/2305.10431/) (2023)
- [VeRA: Vector-based Random Matrix Adaptation](https://mlsanity.com/paper/2310.11454/) (2023)
- [Inversion-Based Style Transfer with Diffusion Models](https://mlsanity.com/paper/2211.13203/) (2022)
- [SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model](https://mlsanity.com/paper/2212.05034/) (2022)
- [Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models](https://mlsanity.com/paper/2305.18292/) (2023)
- [P+: Extended Textual Conditioning in Text-to-Image Generation](https://mlsanity.com/paper/2303.09522/) (2023)
- [Break-A-Scene: Extracting Multiple Concepts from a Single Image](https://mlsanity.com/paper/2305.16311/) (2023)
- [Diffusion Model-Based Image Editing: A Survey](https://mlsanity.com/paper/2402.17525/) (2024)
- [StyleDrop: Text-to-Image Generation in Any Style](https://mlsanity.com/paper/2306.00983/) (2023)
- [Style Injection in Diffusion: A Training-free Approach for Adapting Large-scale Diffusion Models for Style Transfer](https://mlsanity.com/paper/2312.09008/) (2023)
- [ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs](https://mlsanity.com/paper/2311.13600/) (2023)
- [Cones: Concept Neurons in Diffusion Models for Customized Generation](https://mlsanity.com/paper/2303.05125/) (2023)
- [Localizing Object-level Shape Variations with Text-to-Image Diffusion Models](https://mlsanity.com/paper/2303.11306/) (2023)
- [LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning](https://mlsanity.com/paper/2308.03303/) (2023)
- [Implicit Style-Content Separation using B-LoRA](https://mlsanity.com/paper/2403.14572/) (2024)
- [A Neural Space-Time Representation for Text-to-Image Personalization](https://mlsanity.com/paper/2305.15391/) (2023)
- [StyleBank: An Explicit Representation for Neural Image Style Transfer](https://mlsanity.com/paper/1703.09210/) (2017)
- [Multi-LoRA Composition for Image Generation](https://mlsanity.com/paper/2402.16843/) (2024)
- [CSGO: Content-Style Composition in Text-to-Image Generation](https://mlsanity.com/paper/2408.16766/) (2024)

## Built on by (most cited first, 30 of 48)

- [Decouple and Orthogonalize: A Data-Free Framework for LoRA Merging](https://mlsanity.com/paper/2505.15875/) (2025)
- [LoRA-Mixer: Coordinate Modular LoRA Experts Through Serial Attention Routing](https://mlsanity.com/paper/2507.00029/) (2025)
- [ConsisLoRA: Enhancing Content and Style Consistency for LoRA-based Style Transfer](https://mlsanity.com/paper/2503.10614/) (2025)
- [LiON-LoRA: Rethinking LoRA Fusion to Unify Controllable Spatial and Temporal Generation for Video Diffusion](https://mlsanity.com/paper/2507.05678/) (2025)
- [MultLFG: Training-free Multi-LoRA composition using Frequency-domain Guidance](https://mlsanity.com/paper/2505.20525/) (2025)
- [FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization](https://mlsanity.com/paper/2507.01792/) (2025)
- [Subject or Style: Adaptive and Training-Free Mixture of LoRAs](https://mlsanity.com/paper/2508.02165/) (2025)
- [TARA: Token-Aware LoRA for Composable Personalization in Diffusion Models](https://mlsanity.com/paper/2508.08812/) (2025)
- [SplitFlux: Learning to Decouple Content and Style from a Single Image](https://mlsanity.com/paper/2511.15258/) (2025)
- [The Consistency Critic: Correcting Inconsistencies in Generated Images via Reference-Guided Attentive Alignment](https://mlsanity.com/paper/2511.20614/) (2025)
- [ReStyle-TTS: Relative and Continuous Style Control for Zero-Shot Speech Synthesis](https://mlsanity.com/paper/2601.03632/) (2026)
- [DiffGraph: An Automated Agent-driven Model Merging Framework for In-the-Wild Text-to-Image Generation](https://mlsanity.com/paper/2603.20470/) (2026)
- [Calligrapher: Freestyle Text Image Customization](https://mlsanity.com/paper/2506.24123/) (2025)
- [FantasyStyle: Controllable Stylized Distillation for 3D Gaussian Splatting](https://mlsanity.com/paper/2508.08136/) (2025)
- [LoRAtorio: An intrinsic approach to LoRA Skill Composition](https://mlsanity.com/paper/2508.11624/) (2025)
- [AgeBooth: Controllable Facial Aging and Rejuvenation via Diffusion Models](https://mlsanity.com/paper/2510.05715/) (2025)
- [LoRA on the Go: Instance-level Dynamic LoRA Selection and Merging](https://mlsanity.com/paper/2511.07129/) (2025)
- [CleanStyle: Plug-and-Play Style Conditioning Purification for Text-to-Image Stylization](https://mlsanity.com/paper/2602.20721/) (2026)
- [OrthoFuse: Training-free Riemannian Fusion of Orthogonal Style-Concept Adapters for Diffusion Models](https://mlsanity.com/paper/2604.05183/) (2026)
- [Localized LoRA: A Structured Low-Rank Approximation for Efficient Fine-Tuning](https://mlsanity.com/paper/2506.00236/) (2025)
- [CraftGraffiti: Exploring Human Identity with Custom Graffiti Art via Facial-Preserving Diffusion Models](https://mlsanity.com/paper/2508.20640/) (2025)
- [OmniStyle2: Learning to Stylize by Learning to Destylize](https://mlsanity.com/paper/2509.05970/) (2025)
- [Mixture of Style Experts for Diverse Image Stylization](https://mlsanity.com/paper/2603.16649/) (2026)
- [SSR-Merge: Subspace Signal Routing for Training-Free LoRA Merging in Diffusion Models](https://mlsanity.com/paper/2606.10617/) (2026)
- [DreamStyle3D: Efficient 3D Stylized Asset Generation via Dual-Attention Disentanglement](https://mlsanity.com/paper/2607.24721/) (2026)
- [RichControl: Structure- and Appearance-Rich Training-Free Spatial Control for Text-to-Image Generation](https://mlsanity.com/paper/2507.02792/) (2025)
- [SPG: Style-Prompting Guidance for Style-Specific Content Creation](https://mlsanity.com/paper/2508.11476/) (2025)
- [FreeFuse: Multi-Subject LoRA Fusion via Adaptive Token-Level Routing at Test Time](https://mlsanity.com/paper/2510.23515/) (2025)
- [Expanding the Content-Style Frontier: a Balanced Subspace Blending Approach for Content-Style LoRA Fusion](https://mlsanity.com/paper/2511.01355/) (2025)
- [V-Shuffle: Zero-Shot Style Transfer via Value Shuffle](https://mlsanity.com/paper/2511.06365/) (2025)

## Abstract (arXiv)

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.