Low-Rank Adaptation (LoRA) for Visual Art Style Learning and Feature Fusion: A Narrative Review
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Keywords
generative AI, Latent Diffusion Models, Parameter-Efficient Fine-Tuning (PEFT), Low-Rank Adaptation (LoRA), artistic style fusion
Abstract
Recent developments in diffusion-based generative models, particularly Denoising Diffusion Probabilistic Models (DDPMs), have significantly advanced the field of Artificial Intelligence Generated Content (AIGC). Despite their strong generalization ability, base models such as Stable Diffusion often lack the precision required for capturing specific artistic styles or niche visual aesthetics. In this context, Low-Rank Adaptation (LoRA) has emerged as an efficient and practical solution for style-specific fine-tuning within Latent Diffusion Models (LDMs). This paper reviews the underlying mechanism of LoRA and examines how low-rank updates interact with attention layers to encode stylistic features. Compared with alternative approaches such as DreamBooth and Textual Inversion, LoRA offers a more balanced trade-off between computational efficiency and stylistic fidelity. In addition, the paper discusses the challenges of multi-style synthesis, with particular attention to the limitations of linear fusion methods and the potential of Dynamic Layer-wise Fusion (DLF). Finally, several future research directions are outlined, including interpretability and automated optimization, which are essential for improving controllability in generative systems.
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