1 dépôt
Awesome GitHub RepositoriesProbability Path Learning
The process of optimizing a model to learn the specific probability flow trajectory between noise and data.
Distinct from Flow Matching: Focuses on the learning objective of the path rather than the general architectural framework of flow matching.
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Awesome Probability Path Learning GitHub Repositories
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- facebookresearch/flow_matching
facebookresearch/flow_matching
4,562Voir sur GitHubThis 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 cove
Implements the learning process for probability paths across various data modalities.
Python
