The objective of personalization and stylization in text-to-image is to
instruct a pre-trained diffusion model to analyze new concepts introduced by
users and incorporate them into expected styles. Recently, parameter-efficient
fine-tuning (PEFT) approaches have been widely adopted to address this task and
have greatly propelled the development of this field. Despite their popularity,
existing efficient fine-tuning methods still struggle to achieve effective
personalization and stylization in T2I generation. To address this issue, we
propose block-wise Low-Rank Adaptation (LoRA) to perform fine-grained
fine-tuning for different blocks of SD, which can generate images faithful to
input prompts and target identity and also with desired style. Extensive
experiments demonstrate the effectiveness of the proposed method.

个性化和风格化在文字到图像的任务中都是指导预训练扩散模型分析用户引入的新概念，并将其融入预期的风格。我们提出了基于块级低秩适应（LoRA）的精细调整方法，用于不同块的 SD 进行细粒度的调整，能够生成忠实于输入提示和目标身份且具有所需风格的图像。广泛实验证明了所提出方法的有效性。