1. Flow Matching 正像我们在上一篇文章 (CFM (2): 连续标准流 Continuous Normalizing Flow) 中提到的,CFM并不是一个模型,而是一个simulation-free的求解连续标准流 (CNF)的方法。 在说明CFM之前,我们先来看flow matching (FM). 为了不模拟ODE,FM希望通过下面的回归损失函数来估计ut(x): L(θ)=Et∼U[...
Normalizing Flow 1. VoiceBox 1.1 Model VoiceBox(以下简称VB)来自Meta,是比较早的一个将CFM应用在语音生成上的工作。 在解释VB之前,首先让我们回忆一下CFM的训练方式。 CFM中,给定t时刻的数据xt,我们希望得到一个模型来预测ut,因此模型的输入是xt和t,输出的是ut。训练得到模型之后,我们就可以利用数值积分从x0生...
# clone project git clone https://github.com/atong01/conditional-flow-matching.git cd conditional-flow-matching # [OPTIONAL] create conda environment conda create -n torchcfm python=3.10 conda activate torchcfm # install pytorch according to instructions # https://pytorch.org/get-started/ # ...
TorchCFM: a Conditional Flow Matching library. Contribute to ImahnShekhzadeh/conditional-flow-matching development by creating an account on GitHub.
(2) a DiT-based conditional flow matching (CFM) decoder that conditions on these factorized features and voice prompts. Additionally, (3) latent mixup is used to enhance in-context learning by combining various speaker features. This approach improves speaker similarity and naturalness in zero-shot...
EquiFlow: Equivariant Conditional Flow Matching with Optimal Transport for3D Molecular Conformation PredictionQingwen Tian 1,2 * , Yuxin Xu 2 * , Yixuan Yang 2 , Zhen Wang 2 , Ziqi Liu 2,3 ,Pengju Yan 2† , Xiaolin Li 2†1 Zhejiang University of Technology2 HIM, Chinese Academy of ...
We introduce Matcha-TTS, a new encoder-decoder architecture for speedy TTS acoustic modelling, trained using optimal-transport conditional flow matching (OT-CFM). This yields an ODE-based decoder capable of high output quality in fewer synthesis steps than models trained using score matching. Careful...
Conditional value-at-risk (CVaR)Bond immunization is an important topic in portfolio management. This paper presents a scenario based optimization framework for solving a cash flow matching problem. In this problem, the time horizon of the cash flow generated by the liability is longer than the ...
可以看到,CNF的训练目标和normalizing flow是一样的,都是最大化真实数据的对数似然,因此CNF同样解决了VAE的缺陷。 然而,优化这个objective function需要我们模拟求解ODE的过程,正像笔者在上一篇文章提到的,这个过程需要大量内存,因此我们需要一种更高效的, simulation-free的方法来训练CNF,这就是flow matching。 3. A...
Conditional Flow Matching(1):常微分方程ODE、欧拉方法和Neural ODE 本文未经允许禁止转载,谢谢合作。作者:Light Sea@知乎 笔者在之前的文章中介绍过4种生成模型:VAE、GAN、diffusion、normalizing flow,本文开始我将介绍近年流行的生成模型中的最后一员: conditional flow matching (CFM)。CFM和diffusion以及normalizing ...