The recent success of transformer-based image generative models in object-centric learning highlights the importance of powerful image generators for handling complex scenes. However, despite the high expressiveness of diffusion models in image generation, their integration into object-centric learning remains largely unexplored in this domain. In this paper, we explore the feasibility and potential of integrating diffusion models into object-centric learning and investigate the pros and cons of this approach. We introduce Latent Slot Diffusion (LSD), a novel model that serves dual purposes: it is the first object-centric learning model to replace conventional slot decoders with a latent diffusion model conditioned on object slots, and it is also the first unsupervised compositional conditional diffusion model that operates without the need for supervised annotations like text. Through experiments on various object-centric tasks, including the first application of the FFHQ dataset in this field, we demonstrate that LSD significantly outperforms state-of-the-art transformer-based decoders, particularly in more complex scenes, and exhibits superior unsupervised compositional generation quality. In addition, we conduct a preliminary investigation into the integration of pre-trained diffusion models in LSD and demonstrate its effectiveness in real-world image segmentation and generation. Project page is available at https://latentslotdiffusion.github.io

翻译：近期，基于Transformer的图像生成模型在对象中心学习中的成功凸显了强大图像生成器处理复杂场景的重要性。然而，尽管扩散模型在图像生成领域具有高表达能力，但其在对象中心学习中的整合仍鲜有探索。本文探究了将扩散模型整合到对象中心学习中的可行性与潜力，并分析了该方法的优劣。我们提出了潜在槽扩散（LSD）这一新型模型，它兼具双重功能：作为首个用基于对象槽的条件潜在扩散模型替代传统槽解码器的对象中心学习模型，同时也是首个无需文本等监督标注即可运行的无监督组合条件扩散模型。通过在包括FFHQ数据集首次应用于该领域的多种对象中心任务上的实验，我们证明LSD显著优于最先进的基于Transformer的解码器，尤其在复杂场景中表现突出，并展现了卓越的无监督组合生成质量。此外，我们初步探究了预训练扩散模型在LSD中的整合，并验证了其在真实图像分割与生成中的有效性。项目页面请访问https://latentslotdiffusion.github.io