facebookresearch/flow_matching
Flow Matching
This project is a PyTorch-based generative model framework designed to transform noise into complex data distributions by learning vector fields and probability paths. It serves as a multimodal generative toolkit for producing synthetic text and images through learned probability flows.
The library distinguishes itself by supporting continuous, discrete, and Riemannian manifold integrations. This allows the framework to handle a variety of data types, including categorical data via discrete-state flow matching and non-Euclidean spaces through Riemannian manifold integration.
The toolkit covers the full generative pipeline, including probability path definition, vector-field regression, and the use of differential equation solvers for data sampling. These capabilities enable the training and inference of generative models capable of synthetic content generation across multiple modalities.
Features
- Deep Generative Modeling Frameworks - Provides a comprehensive toolkit for constructing deep generative models based on probability paths and vector fields.
- Generative Model Training Tools - Provides a framework for training generative models using flow matching to transform noise into data distributions.
- Discrete-State Flow Matching - Provides specialized flow matching implementations for categorical and discrete-state data.
- General Purpose Flow Matching - Provides a PyTorch-based library for implementing continuous and discrete flow matching algorithms to train generative models.
- Probabilistic Generative Sampling - Generates new data samples by integrating probability flows from a noise distribution.
- ODE-Based Trajectory Sampling - Uses ODE integration to sample new data points from learned vector fields.
- Multimodal AI Toolkits - Implements a framework capable of producing synthetic data across multiple modalities, specifically text and images.
- Probability Path Definitions - Allows users to specify the geometric and probabilistic transformations between noise and data.
- Probability Path Modeling - Defines the geometric probability paths used to guide flow matching training.
- Vector-Field Regression - Trains neural networks to regress the velocity vectors required to transform noise into data.
- Flow Model Training - Implements training pipelines for flow matching models across multiple geometric spaces.
- Probability Path Learning - Implements the learning process for probability paths across various data modalities.
- Differential Equation Solvers - Employs numerical differential equation solvers to compute sampling trajectories from learned vector fields.
- Continuous-Time Density Transformations - Implements continuous-time density transformations to guide the training of generative flow models.
- Flow Matching Algorithms - Implements flow matching algorithms for both continuous and discrete data transformations.
- Multimodal Frameworks - Supports the development of generative models that can process both text and image modalities.
- Synthetic Content Generators - Generates synthetic images and text by applying trained probability flow transformations.
- Noise-to-Image Generation - Generates synthetic images by integrating learned vector fields to transform noise into visual data.
- Multimodal Model Trainers - Provides capabilities to train and optimize generative models that handle both text and image data.
- Text Generation - Produces synthetic text samples using discrete-state flow matching techniques.
- Riemannian Manifold Integration - Enables flow matching in non-Euclidean spaces via integration along curved geometric manifolds.
Star history
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