The latest advancements in text-guided diffusion models have revealed powerful image processing capabilities. However, applying these methods to real images requires inverting the images into the domain of diffusion models. The accuracy of this inversion process significantly impacts the final editing results and the preservation of the core content of the source image. Achieving faithful inversion while preserving the inherent suppression capability of diffusion models remains a challenge, especially when the image contains intricate details. Existing reconstruction methods have made strides, but they still fail to capture the precise spatial context and preserve the inherent suppression capability of diffusion models. In this paper, we introduce a more accurate inversion technique, \(w+\) , that enables faithful reconstruction of real images. Moreover, \(w+\) intuitively extends the inherent ability of diffusion models to perform suppression on real images using negative prompts—a capability not achieved by existing reconstruction methods. Compared to state-of-the-art inversion techniques, our \(w+\) inversion, based on the publicly available Stable Diffusion (SD) model, is extensively evaluated for image inversion and extends inherent suppression capability of SD to real images. Our code will be publicly released.