Object-centric learning methods aim to parse and model scenes in a compositional way while extracting representations of objects within those scenes. Early object-centric approaches typically employ simple pixel-mixing decoders for scene modeling. However, these methods often perform poorly when handling complex synthetic datasets and real-world datasets. In contrast, recent object-centric learning methods have begun experimenting with more complex decoders, such as autoregressive Transformers and diffusion models, to extract object representations and model scenes more effectively. Despite the improved performance of these newer methods over earlier ones, their non-compositional modeling approaches contradict human intuition and fail to generate corresponding object images given object representations. To address this issue, the proposed object-centric diffusion (OCD) model employs an improved diffusion model as a decoder. OCD generates the appearance and masks of objects separately during the scene reconstruction process, achieving true compositional modeling while maintaining model performance. Extensive experiments demonstrate that OCD excels in image segmentation and generation tasks across various datasets, including two synthetic and two real-world datasets, proving its versatility and effectiveness.