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[...
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 flow有很多联系,首先,CFM和diffusion一样都基于微分方程,其次,CFM其实就是一种连续空间上的normalizing flow,即continuous nor...
Conditional Flow Matching (CFM) is a fast way to train continuous normalizing flow (CNF) models. CFM is a simulation-free training objective for continuous normalizing flows that allows conditional generative modeling and speeds up training and inference. CFM's performance closes the gap between C...
TorchCFM: a Conditional Flow Matching library. Contribute to ImahnShekhzadeh/conditional-flow-matching development by creating an account on GitHub.
a robust zero-shot VC model that leverages in-context learning with voice prompts. VoicePrompter is composed of (1) a factorization method that disentangles speech components and (2) a DiT-based conditional flow matching (CFM) decoder that conditions on these factorized features and voice prompts...
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...
but slow training speeds and diff i-culties in utilizing high-degree features limit performance.WeproposeEquiFlow,anequivariantconditionalf l owmatch-ing model with optimal transport. EquiFlow uniquely ap-plies conditional f l ow matching in molecular 3D conforma-tion prediction, leveraging simulation-...
Flowchart of the conditional matching model. The fea- tures are mapped into the RKHS, and the conditional distributions of the domains are represented by their conditional covariance op- erators in RKHS. Then the conditional distribution discrepancy is estimated based on the CKB metric, and the ...
We have applied Diffusion models approach, Flow matching, usual Autoencoder (AE) and compared the results of the models and approaches. As a metric for the study, physical PyMatGen matcher was employed: we compare target structure with generated one using default tolerances. So far, our ...
These high-level contextual features are incorporated into loss function in order to further help the generator to correct predicted disparity maps. We evaluate our model on the Scene Flow dataset and an improvement is achieved compared with the most related work pix2pix....