Flow matching is a recent framework to train generative models that exhibits
impressive empirical performance while being relatively easier to train
compared with diffusion-based models. Despite its advantageous properties,
prior methods still face the challenges of expensive computing and a large
number of function evaluations of off-the-shelf solvers in the pixel space.
Furthermore, although latent-based generative methods have shown great success
in recent years, this particular model type remains underexplored in this area.
In this work, we propose to apply flow matching in the latent spaces of
pretrained autoencoders, which offers improved computational efficiency and
scalability for high-resolution image synthesis. This enables flow-matching
training on constrained computational resources while maintaining their quality
and flexibility. Additionally, our work stands as a pioneering contribution in
the integration of various conditions into flow matching for conditional
generation tasks, including label-conditioned image generation, image
inpainting, and semantic-to-image generation. Through extensive experiments,
our approach demonstrates its effectiveness in both quantitative and
qualitative results on various datasets, such as CelebA-HQ, FFHQ, LSUN Church &
Bedroom, and ImageNet. We also provide a theoretical control of the
Wasserstein-2 distance between the reconstructed latent flow distribution and
true data distribution, showing it is upper-bounded by the latent flow matching
objective. Our code will be available at
this https URL

本文提出了一种在预训练自编码器的潜在空间中应用流匹配的方法，以实现高分辨率图像合成的计算效率和可扩展性的提高，并将各种条件集成到流匹配中进行条件式生成任务，包括标签条件下的图像生成、图像修复和语义到图像的生成。通过大量实验，本方法在各种数据集上均具有定量和定性的有效性，并提供了重构潜在流分布与真实数据分布之间 Wasserstein-2 距离的理论控制。

潜空间中的流匹配

Flow Matching in Latent Space

Neural posterior estimation methods based on discrete normalizing flows have
become established tools for simulation-based inference (SBI), but scaling them
to high-dimensional problems can be challenging. Building on recent advances in
generative modeling, we here present flow matching posterior estimation (FMPE),
a technique for SBI using continuous normalizing flows. Like diffusion models,
and in contrast to discrete flows, flow matching allows for unconstrained
architectures, providing enhanced flexibility for complex data modalities. Flow
matching, therefore, enables exact density evaluation, fast training, and
seamless scalability to large architectures--making it ideal for SBI. We show
that FMPE achieves competitive performance on an established SBI benchmark, and
then demonstrate its improved scalability on a challenging scientific problem:
for gravitational-wave inference, FMPE outperforms methods based on comparable
discrete flows, reducing training time by 30% with substantially improved
accuracy. Our work underscores the potential of FMPE to enhance performance in
challenging inference scenarios, thereby paving the way for more advanced
applications to scientific problems.

提出了流匹配后验概率估计（FMPE）方法，使用连续正则化流实现了基于模拟的推断任务，较于离散流，在保持精度的情况下，提供了更高的灵活性和可伸缩性。在模拟数据集上取得了有竞争力的性能，而在重力波推断任务中，FMPE 方法的训练速度比离散流方法快 30％，且准确度得到了大幅提高。

可扩展仿真推断的流匹配

Flow Matching for Scalable Simulation-Based Inference

We introduce a new paradigm for generative modeling built on Continuous
Normalizing Flows (CNFs), allowing us to train CNFs at unprecedented scale.
Specifically, we present the notion of Flow Matching (FM), a simulation-free
approach for training CNFs based on regressing vector fields of fixed
conditional probability paths. Flow Matching is compatible with a general
family of Gaussian probability paths for transforming between noise and data
samples -- which subsumes existing diffusion paths as specific instances.
Interestingly, we find that employing FM with diffusion paths results in a more
robust and stable alternative for training diffusion models. Furthermore, Flow
Matching opens the door to training CNFs with other, non-diffusion probability
paths. An instance of particular interest is using Optimal Transport (OT)
displacement interpolation to define the conditional probability paths. These
paths are more efficient than diffusion paths, provide faster training and
sampling, and result in better generalization. Training CNFs using Flow
Matching on ImageNet leads to consistently better performance than alternative
diffusion-based methods in terms of both likelihood and sample quality, and
allows fast and reliable sample generation using off-the-shelf numerical ODE
solvers.

基于连续归一化流的生成建模范例中，发现使用流匹配方法与扩散路径一起训练更具有鲁棒性和稳定性，并且可以开启使用优化运输插值定义的非扩散概率路径，该方法比传统扩散模型更适用于训练 ImageNet，并能快速生成可靠采样结果。