Digital rock analysis is a promising approach for visualizing geological microstructures and understanding transport mechanisms for underground geo-energy resources exploitation. Accurate image reconstruction methods are vital for capturing the diverse features and variability in digital rock samples. Stable diffusion, a cutting-edge artificial intelligence model, has revolutionized computer vision by creating realistic images. However, its application in digital rock analysis is still emerging. This study explores the applications of stable diffusion in digital rock analysis, including enhancing image resolution, improving quality with denoising and deblurring, segmenting images, filling missing sections, extending images with outpainting, and reconstructing three-dimensional rocks from two-dimensional images. The powerful image generation capability of diffusion models shed light on digital rock analysis, showing potential in filling missing parts of rock images, lithologic discrimination, and generating network parameters. In addition, limitations in existing stable diffusion models are also discussed, including the lack of real digital rock images, and not being able to fully understand the mechanisms behind physical processes. Therefore, it is suggested to develop new models tailored to digital rock images for further progress. In sum, the integration of stable diffusion into digital core analysis presents immense research opportunities and holds the potential to transform the field, ushering in groundbreaking advances.