DiffusionMat is a novel image matting framework that uses a diffusion model to transform rough to fine alpha matting. Unlike traditional methods, our approach treats image matting as a gradual learning process, starting from adding noise to the trimmed map and iteratively denoising through a pre-trained diffusion model, gradually guiding the prediction towards a clean alpha matting. A key innovation in our framework is a correction module, which adjusts the output in each denoising step to ensure that the final result aligns with the structure of the input image. We also introduce Alpha Reliability Propagation, a novel technique aimed at maximizing the utility of available guidance by selectively enhancing the alpha information in the trimmed map regions with confidence, thus simplifying the correction task. To train the correction module, we have designed a specific loss function to target the accuracy of alpha matting edges and the consistency of opaque and transparent areas. We have evaluated our model on several image matting benchmarks, and the results show that DiffusionMat always outperforms existing methods.