Existing generative adversarial network (GAN) compression methods focus more on optimizing network architecture and the spatial domain, while neglecting the impact of spectral-domain optimization on distillation effectiveness and model performance. This limitation results in discrepancies between lightweight models and teacher models in generating high-frequency image details. In addition, conventional feature extraction methods in image translation often cause detail loss. To address these issues, this study proposes a spectral knowledge distillation scheme with integrated feature enhancement (FESD-CycleGAN). In FESD-CycleGAN, by shifting certain feature channels in the feature map, the receptive field is expanded and feature diversity is enhanced, thus improving both the details and the overall quality of generated images. Moreover, since spectral-domain knowledge distillation enables the generator to capture high-frequency details of images, knowledge distillation in both the spatial and spectral domains is integrated on top of feature enhancement in the feature map. This approach enhances the model’s ability to preserve fine details in generated images. Experimental results show that on the horse2zebra, summer2winter, and edges2shoes datasets, FESD-CycleGAN reduces the FID by 2.19, 0.68, and 0.76 compared to the baseline DCD model, achieving scores of 54.98, 73.41, and 27.45, respectively. The generative performance of lightweight models is effectively improved by FESD-CycleGAN.