  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
