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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.23515 |
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| _version_ | 1866912858835517440 |
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| author | Liu, Yaoli Ding, Yao-Xiang Zhou, Kun |
| author_facet | Liu, Yaoli Ding, Yao-Xiang Zhou, Kun |
| contents | This paper proposes FreeFuse, a training-free framework for multi-subject text-to-image generation through automatic fusion of multiple subject LoRAs. In contrast to prior studies that focus on retraining LoRA to alleviate feature conflicts, our analysis reveals that simply spatially confining the subject LoRA's output to its target region and preventing other LoRAs from directly intruding into this area is sufficient for effective mitigation. Accordingly, we implement Adaptive Token-Level Routing during the inference phase. We introduce FreeFuseAttn, a mechanism that exploits the flow matching model's intrinsic semantic alignment to dynamically match subject-specific tokens to their corresponding spatial regions at early denoising timesteps, thereby bypassing the need for external segmentors. FreeFuse distinguishes itself through high practicality: it necessitates no additional training, model modifications, or user-defined masks spatial conditions. Users need only provide subject activation words to achieve seamless integration into standard workflows. Extensive experiments validate that FreeFuse outperforms existing approaches in both identity preservation and compositional fidelity. Our code is available at https://github.com/yaoliliu/FreeFuse. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23515 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FreeFuse: Multi-Subject LoRA Fusion via Adaptive Token-Level Routing at Test Time Liu, Yaoli Ding, Yao-Xiang Zhou, Kun Computer Vision and Pattern Recognition This paper proposes FreeFuse, a training-free framework for multi-subject text-to-image generation through automatic fusion of multiple subject LoRAs. In contrast to prior studies that focus on retraining LoRA to alleviate feature conflicts, our analysis reveals that simply spatially confining the subject LoRA's output to its target region and preventing other LoRAs from directly intruding into this area is sufficient for effective mitigation. Accordingly, we implement Adaptive Token-Level Routing during the inference phase. We introduce FreeFuseAttn, a mechanism that exploits the flow matching model's intrinsic semantic alignment to dynamically match subject-specific tokens to their corresponding spatial regions at early denoising timesteps, thereby bypassing the need for external segmentors. FreeFuse distinguishes itself through high practicality: it necessitates no additional training, model modifications, or user-defined masks spatial conditions. Users need only provide subject activation words to achieve seamless integration into standard workflows. Extensive experiments validate that FreeFuse outperforms existing approaches in both identity preservation and compositional fidelity. Our code is available at https://github.com/yaoliliu/FreeFuse. |
| title | FreeFuse: Multi-Subject LoRA Fusion via Adaptive Token-Level Routing at Test Time |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.23515 |