# gnobitab/InstaFlow

:zap: InstaFlow! One-Step Stable Diffusion with Rectified Flow (ICLR 2024)

Repository: https://github.com/gnobitab/InstaFlow
Canonical: https://ross.abutalabs.com/products/instaflow
Language: Python
License: MIT
License Family: permissive
Last push: 2024-06-07T12:25:11+00:00

## Health v2 (maintenance only)
Score: 28/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 78
- inputs: {"age_days": 1101, "days_push": 817, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1409, forks 47 (observed 2026-08-28T04:04:38.717181+00:00)

## What it is
InstaFlow is a one-step text-to-image generation model based on Rectified Flow, enabling ultra-fast Stable Diffusion inference without iterative solvers. It provides pre-trained models, inference code, and demos (Hugging Face, Colab, Gradio) with LoRA and ControlNet compatibility.

## Use cases
- generate images from text prompts in a single step
- speed up stable diffusion inference
- run fast text-to-image generation on limited compute
- use one-step diffusion with LoRA models
- use one-step diffusion with ControlNet
- research rectified flow generative models

## When to choose
- you need fast, low-latency text-to-image generation
- you want one-step diffusion inference instead of multi-step sampling
- you want to experiment with rectified flow research models

## When to avoid
- you need the highest possible image fidelity from multi-step diffusion
- you need a production-grade image generation service out of the box
- you work outside Python/PyTorch environments

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, image-processing, llm-inference
- domain: artificial-intelligence, deep-learning, image-processing, machine-learning
- platform: python, cross-platform
- tags: text-to-image, stable-diffusion, rectified-flow, one-step-generation, diffusion-models, generative-ai, iclr-2024, gpu

## Member repositories
- gnobitab/InstaFlow (main) score 28

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:38.717181+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 2026-08-30T04:38:32.235097+00:00, confidence not recorded.
  - readme: https://github.com/gnobitab/InstaFlow (fetched 2026-08-28T04:04:38.717181+00:00, sha 55b2e6b2b27b)
- Data as of 2026-08-30T08:39:29.467469+00:00.