# AIGCDesignGroup/ReplaceAnything

Repository: https://github.com/AIGCDesignGroup/ReplaceAnything
Canonical: https://ross.abutalabs.com/products/replaceanything
License Family: other
Last push: 2024-05-17T01:44:20+00:00

## Health v2 (maintenance only)
Score: 26/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 69
- inputs: {"age_days": 971, "days_push": 839, "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 2426, forks 91 (observed 2026-08-28T04:06:50.779926+00:00)

## What it is
ReplaceAnything is a research project from Alibaba's Institute for Intelligent Computing for ultra-high quality content replacement in images, such as swapping clothing, backgrounds, or people while strictly retaining masked regions. As of the latest update it provides demos on HuggingFace and ModelScope, with paper and code release still pending.

## Use cases
- replace clothing in a photo while keeping the person intact
- swap the background of an ID photo
- replace a person in a family photo
- change photo backgrounds with high fidelity
- try AI content replacement without installing anything

## When to choose
- you want to try state-of-the-art masked-region image replacement via the hosted demos
- you want to follow or cite the research project

## When to avoid
- you need runnable source code, which has not been released
- you need a production-ready or self-hosted pipeline
- you need a permissive license for commercial use

## Facets
- artifact type: application
- maturity: experimental
- function: image-processing, machine-learning, deep-learning
- domain: artificial-intelligence, image-processing, computer-vision
- platform: python
- tags: image-inpainting, content-replacement, aigc, diffusion, demo-only, no-code-released, web-server, gpu

## Member repositories
- AIGCDesignGroup/ReplaceAnything (main) score 26

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:50.779926+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 2026-08-30T02:31:36.754730+00:00, confidence not recorded.
  - readme: https://github.com/AIGCDesignGroup/ReplaceAnything (fetched 2026-08-28T04:06:50.779926+00:00, sha 4b5cefb049c2)
- Data as of 2026-08-30T08:39:29.467469+00:00.