Publications
カンファレンス (国際) Which LoRA Should Be Merged Next? Retrieving an Additional LoRA from a Target Image
Daichi Sugita (Shizuoka University), Huu-Long Pham (University of Hyogo), Makoto P. Kato (University of Tsukuba), Hiroaki Ohshima (University of Hyogo), Sumio Fujita, Yoshiyuki Shoji (Shizuoka University)
The 16th ACM International Conference on Multimedia Retrieval (ICMR 2026)
2026.6.15
This paper proposes a retrieval algorithm that ranks LoRA adapters for additional merging, given a target image and the LoRA adapter currently used for image generation. In recent image generation workflows, multiple LoRA adapters are often applied simultaneously. However, selecting appropriate adapters to merge still relies on manual inspection of generated samples and associated metadata. We trained a Transformer-based classification model with token embedding to determine whether a given image is generated by a specific pair of LoRA adapters. This trained model can receive a target image, a fixed LoRA adapter in use, and candidate LoRA adapters, and then output a score reflecting the probability that two adapters, when merged, contribute to generating the target image. By ranking candidates based on this score, the proposed method can retrieve the LoRA adapter to be additionally merged. We implemented a retrieval system supporting 100 LoRA adapters, and both automatic evaluation and a user experiment demonstrate the effectiveness of the proposed approach.
Paper :
Which LoRA Should Be Merged Next? Retrieving an Additional LoRA from a Target Image
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