Deep Data Consistency (DDC) offers a new solution for image inverse problems using diffusion models, addressing the common challenge of balancing data consistency and realness. This approach employs a deep learning model to update the data consistency step, enhancing the diffusion process. The variational bound training objective maximizes the conditional posterior while minimizing the impact on diffusion, ensuring high-quality image generation.

DDC utilizes a U-Net architecture for the data consistency network and is trained on the ImageNet-1k dataset. It introduces Gaussian noise during training to test robustness. The implementation includes setting inference steps to five, allowing for rapid sampling. DDC performs various inverse problem tasks, such as super-resolution, Gaussian blur, and random inpainting, demonstrating superior performance in comparison to existing methods.

The effectiveness of DDC is highlighted by its ability to generate high-quality solutions with only five inference steps in an average of 0.77 seconds. The model shows robustness across different datasets and noise levels, proving capable of solving multiple tasks with a single pre-trained model. By integrating a deep data consistency network within the diffusion framework, DDC effectively addresses the limitations of previous studies, offering a promising approach for efficiently solving inverse problems while maintaining high-quality results.