Abstract
Conventional diffusion models typically relies on a fixed forward process, which implicitly defines complex marginal distributions over latent variables. This can often complicate the reverse process' task in learning generative trajectories, and results in costly inference for diffusion models. To address these limitations, we introduce Neural Flow Diffusion Models (NFDM), a novel framework that enhances diffusion models by supporting a broader range of forward processes beyond the fixed linear Gaussian. We also propose a novel parameterization technique for learning the forward process. Our framework provides an end-to-end, simulation-free optimization objective, effectively minimizing a variational upper bound on the negative log-likelihood. Experimental results demonstrate NFDM's strong performance, evidenced by state-of-the-art likelihood estimation. Furthermore, we investigate NFDM's capacity for learning generative dynamics with specific characteristics, such as deterministic straight lines trajectories. This exploration underscores NFDM's versatility and its potential for a wide range of applications.
Abstract (translated)
传统的扩散模型通常依赖于一个固定的前向过程，这暗示着在潜在变量上定义了复杂边缘分布。这通常会使得反向过程在学习生成轨迹的任务中变得复杂，并导致对于扩散模型的代价性推断。为了克服这些限制，我们引入了神经流扩散模型（NFDM），一种通过支持更广泛的正向过程来增强扩散模型的全新框架。我们还提出了一种新的参数化技术来学习前向过程。我们的框架提供了一个端到端的、无实验的优化目标，有效地最小化了负对数似然的上界。实验结果证明了NFDM的强大性能，这得益于最先进的 likelihood 估计。此外，我们研究了NFDM在学习具有特定特征的生成动态方面的能力，例如确定性直线轨迹。这种探索突显了NFDM的多样性和其在各种应用中的潜在能力。
URL
https://arxiv.org/abs/2404.12940
