1 रिपॉजिटरी
Awesome GitHub RepositoriesDiscrete-State Flow Matching
Flow matching techniques specifically designed for categorical and discrete-state data transformations.
Distinct from Flow-Matching Frameworks: Distinct from general Flow-Matching Frameworks by focusing on discrete transitions rather than continuous noise-to-image flows.
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Awesome Discrete-State Flow Matching GitHub Repositories
AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
- facebookresearch/flow_matching
facebookresearch/flow_matching
4,562GitHub पर देखेंThis 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
Provides specialized flow matching implementations for categorical and discrete-state data.
Python
