Recent studies have explored the combination of multiple LoRAs to simultaneously generate user-specified subjects and styles. However, most existing approaches fuse LoRA weights using static statistical heuristics that deviate from LoRA's original purpose of learning adaptive feature adjustments and ignore the randomness of sampled inputs. To address this, we propose a dynamic training-free fusion framework that operates throughout the generation process. During the forward pass, at each LoRA-applied layer, we dynamically compute the KL divergence between the base model's original features and those produced by subject and style LoRAs, respectively, and adaptively select the most appropriate weights for fusion. In the reverse denoising stage, we further refine the generation trajectory by dynamically applying gradient-based corrections derived from objective metrics such as CLIP and DINO scores, providing continuous semantic and stylistic guidance. By integrating these two complementary mechanisms-feature-level selection and metric-guided latent adjustment-across the entire diffusion timeline, our method dynamically achieves coherent subject-style synthesis without any retraining. Extensive experiments across diverse subject-style combinations demonstrate that our approach consistently outperforms state-of-the-art LoRA fusion methods both qualitatively and quantitatively.
Figures & tables
Figure 1: (a) Existing methods directly rely on properties of LoRA weights to achieve fusion. (b) Our method integrates feature-level selection in the forward pass and latent-level refinement in the reverse process to enable dynamic training-free LoRAs fusion.
Figure 2: Overview of our method. By performing dynamic feature selection based on representation perturbation and applying metric-guided refinement throughout the denoising process, our framework enables training-free fusion of subject and style LoRAs.
Method
Style Sim ↑
CLIP Score ↑
DINO Score ↑
Direct
48.9%
66.6%
43.0%
B-LoRA [ 11 ]
58.0%
63.8%
30.6%
ZipLoRA [ 32 ]
60.4%
64.4%
35.7%
K-LoRA [ 28 ]
58.7%
69.4%
46.9%
Ours
63.0%
78.5%
43.3%
Table 1: Comparison of alignment results. Direct denotes direct arithmetic merging.
Method
User Preference
GPT-4o Feedback
Qwen2.5-VL Feedback
ZipLoRA [ 32 ]
13.80%
20.13%
3.40%
B-LoRA [ 11 ]
21.89%
11.67%
9.11%
K-LoRA [ 28 ]
11.11%
12.56%
21.82%
Ours
53.20%
55.64%
65.67%
Table 2: The Performance Comparison of user study results, GPT-4o and Qwen2.5-VL feedback.
Figure 3: Qualitative comparisons. We present images generated by our method and the compared advanced generation methods. Through nput-adaptive, representation-aware decisions throughout the generation process, our method effectively enables training-free fusion of subject and style LoRAs.
Figure 4: Robustness Analysis. We present images generated by our method and K-LoRA with random seeds to analyze the robustness.
Method
Style Sim ↑
CLIP Score ↑
DINO Score ↑
Baseline
60.3%
75.6%
40.1%
Only with FLS
59.5%
75.9%
43.7%
Only with LLR
66.1%
78.9%
40.7%
Ours(FLS+LLR)
64.0%
79.1%
43.4%
Table 3: The ablation study of different components, including Feature-Level Selection (FLS) and Latent-Level Refinement (LLR).
Divergence
Style Sim ↑
CLIP Score ↑
DINO Score ↑
KL
59.5%
75.9%
43.7%
JS
59.3%
75.3%
43.1%
Cosine Similarity
58.9%
75.4%
43.3%
Dot Product
58.4%
74.9%
43.8%
Table 4: The ablation study of selection criteria in feature-level selection, including Kullback-Leibler (KL), Jensen-Shannon (JS) divergence, cosine similarity, and dot product.
Scaling Factor m
Style Sim ↑
CLIP Score ↑
DINO Score ↑
1
62.1%
72.8%
32.4%
5
64.8%
74.1%
32.3%
10
64.0%
79.1%
43.4%
20
66.8%
76.8%
35.1%
Table 5: The ablation study of scaling factor m for Latent-Level Refinement.
Figure 5: LoRA Selection During the Generation Process. The vertical axis represents 50 diffusion steps, while the horizontal axis denotes varying LoRA layers. Dark green indicates the selection of subject LoRA, and light blue indicates the selection of style LoRA.
Method
ViT Content ↑
ViT Style ↑
BLIP-2 Content ↑
BLIP-2 Style ↑
ZipLoRA [ 32 ]
23.2%
18.0%
48.2%
46.0%
B-LoRA [ 11 ]
29.6%
19.9%
42.0%
54.3%
K-LoRA [ 28 ]
32.8%
20.7%
49.4%
51.1%
Ours
33.7%
21.8%
49.5%
51.4%
Table 1: Comparison of alignment results with additional evaluation metrics.
Figure 1: Additional results generated using FLUX. Each image corresponds to the object label indicated above and the style reference on the left. The results demonstrate the effects of applying different LoRA modules through our proposed method.
Figure 2: Additional results generated using FLUX. Each image corresponds to the object label indicated above and the style reference on the left. The results demonstrate the effects of applying different LoRA modules through our proposed method.