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：尽管生成模型不断涌现，但在保证采样多样性和质量的前提下实现快速推理仍具挑战。现有模型如去噪扩散概率模型（DDPM）虽能生成高质量多样化样本，却因迭代步数过多导致速度受限。去噪扩散生成对抗网络（DDGAN）尝试通过引入GAN模型实现扩散过程中的大步长来规避此局限，但应用于大规模数据集时遭遇可扩展性瓶颈。针对这些问题，我们提出一种通过匹配隐式与显式因子来解决该问题的新方法。具体而言，我们采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，通过这种组合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约束，允许推理时采用大步长；与DDGAN不同但类似DDPM，我们充分利用扩散过程的精确解析形式。实验表明，所提方法可获得与基于扩散的模型相当的生成性能，并在采样步数较少时显著优于现有模型。