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.

传统扩散模型通常依赖于固定的前向过程，本文引入神经流扩散模型（NFDM），通过支持更广泛的前向过程以及提出一种新颖的参数化技术来增强扩散模型，实现了无需模拟的端到端优化目标，从而有效地最小化负对数似然的变分上界。实验证据显示 NFDM 具有出色的性能，表现为最先进的似然估计。此外，我们还研究了 NFDM 在学习具有特定特征的生成动态，如确定性直线轨迹方面的潜力，这一探索彰显了 NFDM 的多样性和广泛应用潜力。