LoRA Fusion: Enhancing Image Generation
Recent advancements in low-rank adaptation (LoRA) have shown its effectiveness in fine-tuning diffusion models for generating images tailored to new downstream tasks. Research on integrating multiple LoRA modules to accommodate new tasks has also gained traction. One emerging approach constructs sev...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/12/22/3474 |
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| Summary: | Recent advancements in low-rank adaptation (LoRA) have shown its effectiveness in fine-tuning diffusion models for generating images tailored to new downstream tasks. Research on integrating multiple LoRA modules to accommodate new tasks has also gained traction. One emerging approach constructs several LoRA modules, but more than three typically decrease the generation performance of pre-trained models. The mixture-of-experts model solves the performance issue, but LoRA modules are not combined using text prompts; hence, generating images by combining LoRA modules does not dynamically reflect the user’s desired requirements. This paper proposes a LoRA fusion method that applies an attention mechanism to effectively capture the user’s text-prompting intent. This method computes the cosine similarity between predefined keys and queries and uses the weighted sum of the corresponding values to generate task-specific LoRA modules without the need for retraining. This method ensures stability when merging multiple LoRA modules and performs comparably to fully retrained LoRA models. The technique offers a more efficient and scalable solution for domain adaptation in large language models, effectively maintaining stability and performance as it adapts to new tasks. In the experiments, the proposed method outperformed existing methods in text–image alignment and image similarity. Specifically, the proposed method achieved a text–image alignment score of 0.744, surpassing an SVDiff score of 0.724, and a normalized linear arithmetic composition score of 0.698. Moreover, the proposed method generates superior semantically accurate and visually coherent images. |
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| ISSN: | 2227-7390 |