Recent Advances in Controllable Image Generation with Deep Learning Models

Abstract

With the wide application of diffusion models in the field of image generation, how to improve the controllability of results while ensuring the quality of generation has gradually become one of the research hotspots. Compared with the traditional generation model, the diffusion model has obvious advantages in generation stability and detailed performance, but there are still certain challenges in control accuracy and result consistency. This article reviews the development status of controllable image generation technology, and first analyzes the evolutionary background of image generation technology under the framework of diffusion model. On this basis, this paper systematically sorts out two representative controllable generation methods: the explicit structural constraint method based on additional conditions and the implicit semantic guidance method based on visual examples. The former realizes precise control of image spatial layout and geometric structure by introducing edge diagrams and attitude diagrams; the latter uses a small number of reference images to extract semantic features to achieve personalized generation. Finally, this paper summarizes the advantages and limitations of different methods in terms of control accuracy, generation flexibility and calculation cost, and briefly looks forward to the future research direction from the perspective of multi-conditional integration, control stability and evaluation standards.