atong01/conditional-flow-matching

Health v2 · maintenance only

Adoption not part of the score

2596 stars · 224 forks observed · 2026-10-08

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

TorchCFM is a PyTorch library implementing Conditional Flow Matching (CFM), a simulation-free training objective for continuous normalizing flow generative models. It includes optimal transport variants (OT-CFM) and examples for image generation, single-cell dynamics, and tabular data.

Use cases

• train continuous normalizing flows without simulation
• train flow matching generative models in pytorch
• use optimal transport conditional flow matching
• generate images with flow matching instead of diffusion
• model single-cell dynamics with CNFs
• speed up training and inference of normalizing flows

When to choose

• you want a fast, simulation-free alternative to diffusion model training
• you need deterministic flows with efficient inference
• you want OT-based couplings for straighter, more stable flows
• you work in PyTorch on generative modeling research

When to avoid

• you need a production image-generation pipeline rather than a research library
• you prefer standard diffusion frameworks with large pretrained model ecosystems
• you don't use PyTorch

Facets

library · maturity active

machine-learning deep-learning simulation machine-learning deep-learning artificial-intelligence data-science python flow-matching continuous-normalizing-flows optimal-transport generative-models pytorch diffusion-models generative-modeling gpu

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markdown · JSON · MCP: product_card(name="atong01/conditional-flow-matching")