Diffusion models are generative models that have shown significant advantages compared to other generative models in terms of higher generation quality and more stable training. However, the computational need for training diffusion models is considerably increased. In this work, we incorporate prototype learning into diffusion models to achieve high generation quality faster than the original diffusion model. Instead of randomly initialized class embeddings, we use separately learned class prototypes as the conditioning information to guide the diffusion process. We observe that our method, called ProtoDiffusion, achieves better performance in the early stages of training compared to the baseline method, signifying that using the learned prototypes shortens the training time. We demonstrate the performance of ProtoDiffusion using various datasets and experimental settings, achieving the best performance in shorter times across all settings.

翻译：扩散模型是生成模型，与其他生成模型相比，在生成质量更高、训练更稳定方面展现出显著优势。然而，训练扩散模型的计算需求大幅增加。本研究将原型学习融入扩散模型，以实现比原始扩散模型更快的高质量生成。我们使用单独学习的类别原型作为条件信息来引导扩散过程，而非随机初始化的类别嵌入。我们观察到，所提出的方法（称为ProtoDiffusion）在训练初期相比基线方法取得了更优性能，这表明使用学习得到的原型缩短了训练时间。我们通过多种数据集和实验设置展示了ProtoDiffusion的性能，在所有设置下均在更短时间内取得了最佳表现。