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Flow Matching Python Packages

Python packages with the GitHub topic flow-matching. Sorted by relevance, with stars and monthly downloads.
lucidrains
rectified-flow-pytorch

Implementation of rectified flow and some of its followup research / improvements in Pytorch

6K 452 32
lucasnewman
f5-tts-mlx

Implementation of F5-TTS in MLX

6K 630 63
lucidrains
transfusion-pytorch

Pytorch implementation of Transfusion, "Predict the Next Token and Diffuse Images with One Multi-Modal Model", from MetaAI

4K 1K 72
lucidrains
pi-zero-pytorch

π0 in Pytorch

4K 570 27
lucidrains
gaia2-pytorch

Gaia2 - Pytorch

1K 59 2
radiradev
flowmatching-bdt

Flow Matching with BDTs

533 9 0
ilya16
symupe

SyMuPe: Affective and Controllable Symbolic Music Performance (ACM MM '25, Outstanding Paper Award)

436 14 0
JosefAlbers
e2tts-mlx

Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS in MLX

375 29 4
aurelio-amerio
gensbi-examples

Examples for the GenSBI library

291 0 0
aurelio-amerio
gensbi

GenSBI is a library for Simulation-Based Inference using generative models in JAX.

250 9 0
lucasnewman
mlx-e2-tts

Implementation of E2-TTS, "Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS", in MLX

241 21 3
Lotfollahi-lab
mintflow

Generation of disentangled microenvironment-induced and intrinsic gene expression vectors from spatial transcriptomics data

168 26 5
The-Swarm-Corporation
mixture-of-flows

This work introduces Flow Matching Mixture of Experts (FM-MoE), a framework that replaces conventional MLP experts with flow matching networks. Each expert learns a continuous transformation through an ordinary differential equation (ODE), enabling more expressive feature mappings while maintaining the computational efficiency of sparse experts

129 6 1
BioinfoMachineLearning
multicom-ligand

Comprehensive ensembling of protein-ligand structure and affinity prediction methods (CASP16)

127 8 0
Lotfollahi-lab
inigen

Generation of disentangled microenvironment-induced and intrinsic gene expression vectors from spatial transcriptomics data

80 26 5
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