Despite the proliferation of generative models, achieving fast sampling during inference without compromising sample diversity and quality remains challenging. Existing models such as Denoising Diffusion Probabilistic Models (DDPM) deliver high-quality, diverse samples but are slowed by an inherently high number of iterative steps. The Denoising Diffusion Generative Adversarial Networks (DDGAN) attempted to circumvent this limitation by integrating a GAN model for larger jumps in the diffusion process. However, DDGAN encountered scalability limitations when applied to large datasets. To address these limitations, we introduce a novel approach that tackles the problem by matching implicit and explicit factors. More specifically, our approach involves utilizing an implicit model to match the marginal distributions of noisy data and the explicit conditional distribution of the forward diffusion. This combination allows us to effectively match the joint denoising distributions. Unlike DDPM but similar to DDGAN, we do not enforce a parametric distribution for the reverse step, enabling us to take large steps during inference. Similar to the DDPM but unlike DDGAN, we take advantage of the exact form of the diffusion process. We demonstrate that our proposed method obtains comparable generative performance to diffusion-based models and vastly superior results to models with a small number of sampling steps.

翻译：尽管生成模型层出不穷，但在保证样本多样性和质量的同时实现推理时的快速采样仍然是一个挑战。去噪扩散概率模型（DDPM）等现有模型虽然能生成高质量、多样化的样本，但受到其固有的大量迭代步骤的限制而速度较慢。去噪扩散生成对抗网络（DDGAN）试图通过集成GAN模型以实现扩散过程中的更大跳跃来规避这一限制。然而，DDGAN在应用于大规模数据集时遇到了可扩展性限制。为解决这些局限，我们提出了一种新颖的方法，通过匹配隐式因子与显式因子来应对该问题。具体而言，我们的方法包括利用隐式模型匹配含噪数据的边际分布与前向扩散的显式条件分布。这种组合使我们能够有效匹配联合去噪分布。与DDPM不同但类似DDGAN，我们不对反向步骤施加参数化分布，从而允许在推理过程中采取大步长。与DDPM类似但不同于DDGAN，我们利用了扩散过程的精确形式。实验表明，我们提出的方法在生成性能上与基于扩散的模型相当，并在少量采样步骤下显著优于现有模型。