  As deep learning technology continues to advance, image generation models,
especially models like Stable Diffusion, are finding increasingly widespread
application in visual arts creation. However, these models often face
challenges such as overfitting, lack of stability in generated results, and
difficulties in accurately capturing the features desired by creators during
the fine-tuning process. In response to these challenges, we propose an
innovative method that integrates Singular Value Decomposition (SVD) into the
Low-Rank Adaptation (LoRA) parameter update strategy, aimed at enhancing the
fine-tuning efficiency and output quality of image generation models. By
incorporating SVD within the LoRA framework, our method not only effectively
reduces the risk of overfitting but also enhances the stability of model
outputs, and captures subtle, creator-desired feature adjustments more
accurately. We evaluated our method on multiple datasets, and the results show
that, compared to traditional fine-tuning methods, our approach significantly
improves the model's generalization ability and creative flexibility while
maintaining the quality of generation. Moreover, this method maintains LoRA's
excellent performance under resource-constrained conditions, allowing for
significant improvements in image generation quality without sacrificing the
original efficiency and resource advantages.
