# DuoLoRA : Cycle-consistent and Rank-disentangled Content-Style Personalization

arXiv: https://arxiv.org/abs/2504.13206 · HTML: https://mlsanity.com/paper/2504.13206/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2025.

## Built on by (most cited first, 10 of 10)

- [MultLFG: Training-free Multi-LoRA composition using Frequency-domain Guidance](https://mlsanity.com/paper/2505.20525/) (2025)
- [SplitFlux: Learning to Decouple Content and Style from a Single Image](https://mlsanity.com/paper/2511.15258/) (2025)
- [Not All Layers Are Created Equal: Adaptive LoRA Ranks for Personalized Image Generation](https://mlsanity.com/paper/2603.21884/) (2026)
- [SSR-Merge: Subspace Signal Routing for Training-Free LoRA Merging in Diffusion Models](https://mlsanity.com/paper/2606.10617/) (2026)
- [Expanding the Content-Style Frontier: a Balanced Subspace Blending Approach for Content-Style LoRA Fusion](https://mlsanity.com/paper/2511.01355/) (2025)
- [NP-LoRA: Null Space Projection for Subject-Style LoRA Fusion](https://mlsanity.com/paper/2511.11051/) (2025)
- [Zero-Shot Personalization of Objects via Textual Inversion](https://mlsanity.com/paper/2603.23010/) (2026)
- [Disco-LoRA: Disentangled Composition of Content, Style, and Motion for Multi-concept Video Customization](https://mlsanity.com/paper/2606.26668/) (2026)
- [Beyond Emotion Prompts: Fine-Grained Text-to-Image Generation Driven by Valence-Arousal-Dominance](https://mlsanity.com/paper/2609.24215/) (2026)
- [CLeaR: A Unified Framework for Resolving the Leakage-Degradation Dilemma in Style Transfer](https://mlsanity.com/paper/2609.38136/) (2026)

## Abstract (arXiv)

We tackle the challenge of jointly personalizing content and style from a few examples. A promising approach is to train separate Low-Rank Adapters (LoRA) and merge them effectively, preserving both content and style. Existing methods, such as ZipLoRA, treat content and style as independent entities, merging them by learning masks in LoRA's output dimensions. However, content and style are intertwined, not independent. To address this, we propose DuoLoRA, a content-style personalization framework featuring three key components: (i) rank-dimension mask learning, (ii) effective merging via layer priors, and (iii) Constyle loss, which leverages cycle-consistency in the merging process. First, we introduce ZipRank, which performs content-style merging within the rank dimension, offering adaptive rank flexibility and significantly reducing the number of learnable parameters. Additionally, we incorporate SDXL layer priors to apply implicit rank constraints informed by each layer's content-style bias and adaptive merger initialization, enhancing the integration of content and style. To further refine the merging process, we introduce Constyle loss, which leverages the cycle-consistency between content and style. Our experimental results demonstrate that DuoLoRA outperforms state-of-the-art content-style merging methods across multiple benchmarks.