The limited generalization and dataset scarcity of existing generative facial image detection methods present significant challenges. To address these issues, this study proposes a high-quality facial image detection model based on noise variation and the diffusion model. The proposed method employs an inversion algorithm using the denoising diffusion implicit model (DDIM) to generate inverted images with text-based guidance. By comparing the noise distribution differences between real and generated images after inversion, the method optimizes a residual network to identify image authenticity, and enhances both accuracy and generalization. Additionally, a dataset of 10000 high-quality, multi-category facial images is constructed to address the shortage of available facial data. Experimental results demonstrate that the proposed algorithm achieves 98.7% accuracy in detecting generated facial images and outperforms existing methods, enabling effective detection across diverse facial images.