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。

神经流动扩散模型：可学习的前向过程用于改进扩散建模

Neural Flow Diffusion Models: Learnable Forward Process for Improved  Diffusion Modelling

Probabilistic diffusion models excel at sampling new images from learned
distributions. Originally motivated by drift-diffusion concepts from physics,
they apply image perturbations such as noise and blur in a forward process that
results in a tractable probability distribution. A corresponding learned
reverse process generates images and can be conditioned on side information,
which leads to a wide variety of practical applications. Most of the research
focus currently lies on practice-oriented extensions. In contrast, the
theoretical background remains largely unexplored, in particular the relations
to drift-diffusion. In order to shed light on these connections to classical
image filtering, we propose a generalised scale-space theory for probabilistic
diffusion models. Moreover, we show conceptual and empirical connections to
diffusion and osmosis filters.

通过提出概率扩散模型的广义尺度空间理论，我们揭示了这些模型与经典图像滤波的关联，并展示了与扩散和渗透滤波的概念和经验上的联系。

广义概率扩散尺度空间

Generalised Probabilistic Diffusion Scale-Spaces

Diffusion models are generative models, which gradually add and remove noise
to learn the underlying distribution of training data for data generation. The
components of diffusion models have gained significant attention with many
design choices proposed. Existing reviews have primarily focused on
higher-level solutions, thereby covering less on the design fundamentals of
components. This study seeks to address this gap by providing a comprehensive
and coherent review on component-wise design choices in diffusion models.
Specifically, we organize this review according to their three key components,
namely the forward process, the reverse process, and the sampling procedure.
This allows us to provide a fine-grained perspective of diffusion models,
benefiting future studies in the analysis of individual components, the
applicability of design choices, and the implementation of diffusion models.

该研究综述了扩散模型中的三个关键部分：前向过程、反向过程和采样过程的组成部分，旨在提供对扩散模型组件设计选择的全面和连贯的评估。

扩散模型的设计基础：一项调查

On the Design Fundamentals of Diffusion Models: A Survey

Large language models can perform various reasoning tasks by using
chain-of-thought prompting, which guides them to find answers through
step-by-step demonstrations. However, the quality of the prompts depends on the
demonstrations given to the models, and creating many of them by hand is
costly. We introduce Synthetic prompting, a method that leverages a few
handcrafted examples to prompt the model to generate more examples by itself,
and selects effective demonstrations to elicit better reasoning. Our method
alternates between a backward and forward process to generate new examples. The
backward process generates a question that match a sampled reasoning chain, so
that the question is solvable and clear. The forward process produces a more
detailed reasoning chain for the question, improving the quality of the
example. We evaluate our method on numerical, symbolic, and algorithmic
reasoning tasks, and show that it outperforms existing prompting techniques.

本文介绍了 Synthetic prompting 方法，该方法利用少量手工示例来提示模型自行生成更多示例，并选择有效的演示文稿以引出更好的推理。在数字、符号和算法推理任务上评估了该方法，并表明其优于现有提示技术。