Existing deep learning architectures usually use a separate encoder and decoder to generate the desired simulated images, which is inefficient for feature analysis and synthesis. Aiming at the problem that the existing methods fail to fully utilize the correlation of codecs, this paper focuses on the codec-unified invertible networks to accurately guide the image deblurring process by controlling latent variables. Inspired by U-Net, a U-shaped multi-level invertible network (UML-IN) is proposed by integrating the wavelet invertible networks into a supervised U-shape architecture to establish the multi-resolution correlation between blurry and sharp image features under the guidance of hybrid loss. Further, this paper proposes to use L1 regularization constraints to obtain sparse latent variables, thereby alleviating the information dispersion problem caused by high-dimensional inference in invertible networks. Finally, we fine-tune the weights of invertible modules by calculating a similarity loss between blur-sharp variable pairs. Extensive experiments on real and synthetic blurry sets show that the proposed approach is efficient and competitive compared with the state-of-the-art methods.