Dynamiqs is a high-performance Python library for simulating quantum systems, built on JAX for GPU acceleration and automatic differentiation. It provides solvers for the Schrödinger equation, Lindblad master equation, stochastic master equations (diffusive and jump), and Floquet integration, with support for time-dependent Hamiltonians, batching over parameters, and gradient computation (including Hessians). The architecture is modular: an abstract integrator hierarchy (with concrete solvers using Diffrax, matrix-exponential, low-rank, Rouchon, and Monte Carlo methods), custom quantum array types (dense and sparse diagonal DIA layout), a clean `TimeQArray` abstraction for time-dependent operators, and utilities for state/operator construction, plotting, and quantum information metrics. The library is designed for quantum optimal control and parameter estimation, and is used in production at Alice & Bob. It features extensive automated tests (including numerical gradient/Hessian validation) and is actively maintained with a growing community.
This repository is the official PyTorch library for Flow Matching generative models, developed by Meta FAIR. It provides a modular and extensible framework for implementing continuous, discrete, and Riemannian flow matching algorithms. The core library consists of four main components: probability paths (defining how data transforms from noise to samples), schedulers (noise/time schedules), solvers (ODE or CTMC integration for sampling), and loss functions (e.g., mixture path generalized KL divergence). It also includes utilities for common operations like model wrapping, manifold geometry (Euclidean, sphere, flat torus), and categorical sampling. The library is designed to be flexible, allowing researchers to easily combine different paths, schedulers, and solvers. Beyond the core library, the repository contains extensive examples for image generation (pixel-space continuous and discrete flow matching on CIFAR-10 and ImageNet) and text generation (discrete flow matching on FineWeb-Edu and Wikitext-103). These examples include full training pipelines, evaluation scripts, and model architectures (UNet, DiT-like transformer). The code is research-grade but well-structured, with clear separation of concerns and thorough unit tests for core components. While the examples are somewhat tied to specific datasets and hardware configurations, the library itself is a reusable foundation for any flow matching project. The repository is a hybrid of a research codebase and a production-ready library. The core modules are abstracted and documented, making them suitable for integration into other projects. However, the examples contain research artifacts like hardcoded paths and experimental features (torch.compile, mixed precision) that may require adaptation. Overall, this is a high-quality, actively maintained resource for anyone working on generative modeling with flow matching.
RectifiedFlow is a unified PyTorch library for implementing Rectified Flow models, a type of generative model that learns ordinary differential equations to transform data between distributions. The library provides a modular framework for training various architectures (MLP, UNet, DiT, LightningDiT) on different datasets, with support for multiple sampling algorithms and interpolation schemes. Key features include a symbolic solver for affine interpolation equations, utilities for converting between different interpolation schemes while preserving rectified flow properties, and a flexible sampler base class for implementing custom integration methods. The codebase demonstrates strong software engineering practices with clear separation of components, comprehensive documentation through examples, and active maintenance.