The electrical impedance tomography (EIT) is a noninvasive imaging technique, which has been widely used in the fields of industrial inspection, medical monitoring, and tactile sensing. However, due to the inherent nonlinearity and ill-conditioned nature of the EIT inverse problem, the reconstructed image is highly sensitive to the measured data, and random noise artifacts often appear in the reconstructed image, which greatly limits the application of EIT. To address this issue, a conditional diffusion model (CDM) with voltage consistency (CDMVC) is proposed in this study. The method consists of a preimaging module, a CDM for reconstruction, a forward voltage constraint network (FVCN), and a scheme of voltage consistency constraint during the sampling process. The preimaging module is employed to generate the initial reconstruction. This serves as a condition for training CDM. Finally, based on FVCN, a voltage consistency constraint is implemented in the sampling phase to incorporate forward information of EIT, thereby enhancing imaging quality. A more complete dataset, including both common and complex concave shapes, is generated. The proposed method is validated using both simulation and physical experiments. Experimental results demonstrate that our method can significantly improve the quality of reconstructed images. In addition, experimental results also demonstrate that our method has good robustness and generalization performance.