Abstract: Personalized image generation refers to the process of learning personalized characteristics,such as those of a particular person,object,or style,from a small number of samples provided by the user,and generating new images that incorporate these characteristics.Methods for generating personalized images of single subject have achieved significant success in various application fields.However,scenarios involving the personalized generation of multiple subjects still face considerable challenges such as semantic confusion and subject disappearance.To enable better multi - concept subject personalized generation,this work improved the popular single-subject generation method,LoRA,by optimizing its training strategy and incorporating constraints to make it more suitable for multi-subject generation tasks.The enhancement endowed it with multi-subject combination capabilities that it previously lacked.To further alleviate the interference between multiple subjects,we introduced a layout control method to mitigate the mutual interference of different subjects in the attention map.Extensive experiments on multiple datasets demonstrated that our method exhibited excellent performance in multisubject personalized generation tasks.