  The ability to fine-tune generative models for text-to-image generation tasks
is crucial, particularly facing the complexity involved in accurately
interpreting and visualizing textual inputs. While LoRA is efficient for
language model adaptation, it often falls short in text-to-image tasks due to
the intricate demands of image generation, such as accommodating a broad
spectrum of styles and nuances. To bridge this gap, we introduce StyleInject, a
specialized fine-tuning approach tailored for text-to-image models. StyleInject
comprises multiple parallel low-rank parameter matrices, maintaining the
diversity of visual features. It dynamically adapts to varying styles by
adjusting the variance of visual features based on the characteristics of the
input signal. This approach significantly minimizes the impact on the original
model's text-image alignment capabilities while adeptly adapting to various
styles in transfer learning. StyleInject proves particularly effective in
learning from and enhancing a range of advanced, community-fine-tuned
generative models. Our comprehensive experiments, including both small-sample
and large-scale data fine-tuning as well as base model distillation, show that
StyleInject surpasses traditional LoRA in both text-image semantic consistency
and human preference evaluation, all while ensuring greater parameter
efficiency.
