# showlab/Show-o

[ICLR & NeurIPS 2025] Repository for Show-o series, One Single Transformer to Unify Multimodal Understanding and Generation.

Repository: https://github.com/showlab/Show-o
Canonical: https://ross.abutalabs.com/products/show-o
Homepage: https://arxiv.org/abs/2408.12528
Language: Python
License: Apache-2.0
License Family: permissive
Topics: diffusion-models, large-language-models, multimodal
Last push: 2026-01-08T16:30:37+00:00

## Health v2 (maintenance only)
Score: 50/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 61, release rhythm 35, longevity 53
- inputs: {"age_days": 754, "days_push": 237, "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 1973, forks 93 (observed 2026-08-28T04:06:01.160115+00:00)

## What it is
Show-o is a research repository implementing a unified transformer model that combines autoregressive and discrete diffusion modeling for multimodal understanding and generation. It supports vision-language tasks such as visual question answering, text-to-image generation, text-guided inpainting, and mixed-modality generation in a single model.

## Use cases
- generate images from text prompts
- run visual question answering with a single model
- unify multimodal understanding and generation in one transformer
- experiment with discrete diffusion and autoregressive modeling
- do text-guided image inpainting and extrapolation
- reproduce ICLR/NeurIPS unified multimodal model research

## When to choose
- you want one model handling both image understanding and generation
- you're researching unified multimodal architectures or diffusion-language hybrids
- you need a research baseline for text-to-image plus VQA in a single transformer

## When to avoid
- you need a production-ready, optimized inference stack
- you only need state-of-the-art text-to-image quality from dedicated diffusion models
- you lack GPU resources for large transformer models

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, image-processing, nlp, llm-inference
- domain: artificial-intelligence, machine-learning, computer-vision, large-language-models
- platform: python
- tags: multimodal, diffusion-models, transformer, text-to-image, visual-question-answering, research-code, unified-model, natural-language-processing, gpu, linux

## Member repositories
- showlab/Show-o (main) score 50

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:01.160115+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 2026-08-30T03:04:59.916586+00:00, confidence not recorded.
  - readme: https://github.com/showlab/Show-o (fetched 2026-08-28T04:06:01.160115+00:00, sha e47f45a8cf1f)
  - homepage: https://arxiv.org/abs/2408.12528 (fetched 2026-08-29T10:44:20.346892+00:00, sha c9085d9df27b)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T10:44:20.356903+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T10:44:20.362177+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T10:44:20.364274+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T10:44:20.360013+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.