# gnobitab/RectifiedFlow

Official Implementation of Rectified Flow (ICLR2023 Spotlight)

Repository: https://github.com/gnobitab/RectifiedFlow
Canonical: https://ross.abutalabs.com/products/rectifiedflow
Language: Python
License Family: other
Last push: 2024-07-20T21:10:52+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 97
- inputs: {"age_days": 1371, "days_push": 774, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1644, forks 102 (observed 2026-08-28T04:05:15.992682+00:00)

## What it is
Official PyTorch implementation of Rectified Flow, an ICLR 2023 Spotlight method for learning transport maps between distributions via straight-line ODE paths and iterative reflow. It supports generative image modeling (e.g., CIFAR-10) and unsupervised domain transfer, with Colab tutorials included.

## Use cases
- train a rectified flow generative model on image datasets
- generate images in one step with a reflowed ODE model
- learn transport maps between two data distributions
- perform unsupervised image-to-image domain transfer
- reproduce results from the Rectified Flow ICLR 2023 paper
- learn flow-based generative models with straight trajectories

## When to choose
- you want the reference implementation of the Rectified Flow paper
- you need fast one-step generation with better FID than fast diffusion models and more diversity than GANs
- you want to experiment with flow matching or reflow techniques
- you need ODE-based transport between distributions for domain transfer

## When to avoid
- you need a production-ready, well-maintained library with a license and long-term support
- you want a general-purpose diffusion or flow-matching framework with broad model support
- you need Stable Diffusion one-step generation specifically (use the related InstaFlow repo instead)
- you require Windows or non-CUDA environments out of the box

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing
- domain: machine-learning, deep-learning, image-processing, artificial-intelligence
- platform: python
- tags: rectified-flow, generative-modeling, diffusion-models, ode, research-code, image-generation, domain-transfer, iclr2023, linux, gpu

## Member repositories
- gnobitab/RectifiedFlow (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:15.992682+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:45:49.487331+00:00, confidence not recorded.
  - readme: https://github.com/gnobitab/RectifiedFlow (fetched 2026-08-28T04:05:15.992682+00:00, sha 55ad1ec0c952)
- Data as of 2026-08-30T08:39:29.467469+00:00.