Diffusion Probabilistic Models (DPMs) have emerged as a powerful class of deep generative models, achieving remarkable performance in image synthesis tasks. However, these models face challenges in terms of widespread adoption due to their reliance on sequential denoising steps during sample generation. This dependence leads to substantial computational requirements, making them unsuitable for resource-constrained or real-time processing systems. To address these challenges, we propose a novel method that integrates denoising phases directly into the model's architecture, thereby reducing the need for resource-intensive computations. Our approach combines diffusion models with generative adversarial networks (GANs) through knowledge distillation, enabling more efficient training and evaluation. By utilizing a pre-trained diffusion model as a teacher model, we train a student model through adversarial learning, employing layerwise transformations for denoising and submodules for predicting the teacher model's output at various points in time. This integration significantly reduces the number of parameters and denoising steps required, leading to improved sampling speed at test time. We validate our method with extensive experiments, demonstrating comparable performance with reduced computational requirements compared to existing approaches. By enabling the deployment of diffusion models on resource-constrained devices, our research mitigates their computational burden and paves the way for wider accessibility and practical use across the research community and end-users. Our code is publicly available at https://github.com/kidist-amde/Adv-KD

翻译：扩散概率模型已成为一类强大的深度生成模型，在图像合成任务中取得了显著性能。然而，由于其在样本生成过程中依赖序列化去噪步骤，这些模型在广泛采用方面面临挑战。这种依赖性导致大量计算需求，使其不适用于资源受限或实时处理系统。为应对这些挑战，我们提出一种将去噪阶段直接集成至模型架构的新方法，从而减少对资源密集型计算的需求。我们的方法通过知识蒸馏将扩散模型与生成对抗网络相结合，实现了更高效的训练与评估。通过使用预训练扩散模型作为教师模型，我们借助对抗学习训练学生模型，采用分层变换进行去噪，并利用子模块预测教师模型在不同时间点的输出。这种集成显著减少了所需参数量与去噪步骤，从而提升了测试时的采样速度。我们通过大量实验验证了该方法，在保持可比性能的同时，较现有方法显著降低了计算需求。通过使扩散模型能够部署于资源受限设备，我们的研究减轻了其计算负担，为研究社区和终端用户实现更广泛的可用性与实际应用铺平了道路。代码已公开于 https://github.com/kidist-amde/Adv-KD