Urban safety perception is crucial for quality of life, yet research on nighttime safety perception remains limited due to the lack of nighttime street view images (SVIs). This study uses generative models to translate daytime SVIs into nighttime ones (D2N), addressing this data gap. However, challenges exist in understanding how micro-level streetscape features affect nighttime perceived safety. To fill these gaps, this study collected 1,461 paired SVIs from high-density areas and 222 from low-density areas across four Chinese cities and Boston. We validated the high-density D2N (HD2N) and low-density D2N (LD2N) models using standard metrics and human-machine adversarial scoring. Our findings show that: (1) Urban density affects D2N translation accuracy, with higher densities posing challenges; (2) HD2N requires over 760 samples to converge; (3) Roads improve safety perception, while trees reduce it. These results offer insights for urban planning and safety improvements.