Personalized Art Image Generation Model for Smart Art Education

With the rise of the wave of smart education, art education is gradually moving towards the transformation stage of personalization and intelligence. Thanks to the rapid development of artificial intelligence technology, the field of education has ushered in unprecedented technological empowerment, opening up new development paths for art education. Personalized art image generation technology can generate customized art images according to students’ learning situation and interest preferences, thus improving the teaching effect and learning experience. However, the existing mainstream technology has problems such as high demand for computing resources, style distortion, and lack of detail expression, so there is still room for improvement in artistic expression. Aiming at the above problems, this paper proposes a personalized art image generation method based on diffusion model. The method uses a stabilized diffusion model as the backbone network, and the model is fine-tuned using LoRA and Dreambooth, aiming to improve the artistic style recognition and detail richness of the generated images. Experimental results show that the method performs well on the task of personalized art image generation, with excellent performance in terms of realism and diversity.