# tyxsspa/AnyText

Official implementation code of the paper <AnyText: Multilingual Visual Text Generation And Editing>

Repository: https://github.com/tyxsspa/AnyText
Canonical: https://ross.abutalabs.com/products/anytext
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2025-03-07T11:06:07+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 10, release rhythm 35, longevity 77
- inputs: {"age_days": 1081, "days_push": 544, "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 4874, forks 304 (observed 2026-08-28T04:09:01.689586+00:00)

## What it is
AnyText is the official implementation of a diffusion-based model for multilingual visual text generation and editing in images, accepted as an ICLR 2024 Spotlight paper. It includes inference and training code, benchmark datasets (AnyText-benchmark, AnyWord-3M), and online demos on ModelScope and HuggingFace.

## Use cases
- generate images containing accurate text in multiple languages
- edit or replace text rendered inside existing images
- add stylized text to AI-generated artwork
- train a custom text-rendering diffusion model
- evaluate text rendering accuracy in generated images
- merge AnyText weights with community SD1.5 models or LoRAs

## When to choose
- you need legible, correctly spelled text inside generated images, especially for Chinese or other non-Latin scripts
- you want a research-grade, open-source text-in-image generation model with training code and datasets
- you want to integrate text rendering into Stable Diffusion 1.5 pipelines

## When to avoid
- you only need simple image captioning or OCR without generation
- you need production text layout typesetting rather than diffusion-based generation
- you cannot run GPU inference with at least 8GB memory

## Facets
- artifact type: library
- maturity: active
- function: image-processing, machine-learning, deep-learning, stable-diffusion
- domain: artificial-intelligence, image-processing, computer-vision
- platform: python, cross-platform
- tags: text-generation, text-editing, diffusion-models, multilingual, aigc, iclr-2024, research-code, natural-language-processing, gpu

## Member repositories
- tyxsspa/AnyText (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:01.689586+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 2026-08-29T18:18:18.266140+00:00, confidence not recorded.
  - readme: https://github.com/tyxsspa/AnyText (fetched 2026-08-28T04:09:01.689586+00:00, sha bfb344ac01db)
- Data as of 2026-08-30T08:39:29.467469+00:00.