SSR-Merge: Subspace Signal Routing for Training-Free LoRA Merging in Diffusion Models

2026-06-09

AI Summary

Existing LoRA merging methods often suffer from parameter interference in shared parameter spaces, leading to degraded generation performance. This work proposes Subspace Signal Routing (SSR), which reframes LoRA fusion as a signal routing problem. SSR constructs a unified subspace by concatenating along the rank dimension, decouples mixed signals using an inverse correlation matrix, and allocates purified signals to task-specific subspaces via a direction-guided matrix. Theoretically, this approach is shown to be equivalent to the ordinary least squares solution. Leveraging the additivity of sufficient statistics, SSR further introduces a streaming algorithm that enables efficient online updates. Experimental results demonstrate that SSR significantly outperforms current LoRA merging techniques while maintaining computational efficiency, achieving notable improvements in multi-task generation quality.

Abstract

Low-Rank Adaptation (LoRA) merging can efficiently combine diverse generative capabilities from multiple trained LoRAs for a diffusion model. However, existing LoRA merging techniques often suffer from severe parameter interference, causing destructive collisions in the shared parameter space. To address this, we propose Subspace Signal Routing (SSR), which resolves interference by routing internal signals instead of performing parameter-space merge. Specifically, SSR first constructs a unified subspace by concatenating candidate LoRAs along the rank dimension. Next, SSR employs an inverse correlation matrix to decorrelate mixed signals within this space. Finally, a directional guide matrix steers these purified signals into their respective task-specific subspaces. We provide a rigorous theoretical analysis proving that SSR aligns with the Ordinary Least Squares (OLS) solution, thereby ensuring mathematical optimality. We utilize the additivity of sufficient statistics to design a streaming algorithm. This enables on-the-fly updates that significantly reduce memory overhead and computation time. Extensive experiments validate that SSR significantly outperforms state-of-the-art methods while maintaining comparable efficiency. Code is available at https://github.com/nagara214/SSR-Merge.

Problem

• LoRA merging
• parameter interference
• diffusion models
• subspace signal routing

Innovation

• Subspace Signal Routing
• LoRA Merging
• Training-Free Fusion
• Signal Decorrelation
• Streaming Algorithm