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.

翻译：传统扩散模型通常依赖于固定的前向过程，这隐式定义了潜变量上的复杂边际分布，往往使逆向过程学习生成轨迹的任务复杂化，并导致扩散模型推理成本高昂。为解决这些局限，我们提出神经流扩散模型（NFDM），这一新型框架通过支持超出固定线性高斯过程的更广泛前向过程来增强扩散模型。我们还提出了一种参数化技术用于学习前向过程。该框架提供了端到端、无模拟的优化目标，有效最小化了负对数似然的变分上界。实验结果表明，NFDM展现了强大的性能，达到了最优的似然估计水平。此外，我们探究了NFDM学习具有特定动力学特性（如确定性直线轨迹）的生成能力，这凸显了NFDM的通用性及其广阔的应用潜力。