RRepoGEO

REPOGEO REPORT · LITE

showlab/Show-o

Default branch main · commit 45a5a2de · scanned 5/26/2026, 2:18:46 AM

GitHub: 1,933 stars · 91 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface showlab/Show-o, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition and emphasize the core purpose statement at the top of the README

    Why:

    CURRENT
    The README currently starts with a centered H3 ("One Single Transformer to Unify Multimodal Understanding and Generation") and author list, making the core message less prominent.
    COPY-PASTE FIX
    ## Show-o: A Single Transformer for Unified Multimodal Understanding and Generation

This repository presents the official implementation for the Show-o series, a cutting-edge research framework designed to unify diverse multimodal tasks. It aims to provide a single architecture for both multimodal understanding and generation, as detailed in our ICLR & NeurIPS 2025 papers.
  • mediumcomparison#2
    Add a "Comparison to State-of-the-Art" section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to State-of-the-Art

Show-o differentiates itself from existing multimodal models like LLaVA, InstructBLIP, Flamingo, and CoCa by offering a truly unified single transformer architecture for both understanding and generation across various modalities, aiming for greater efficiency and coherence.
  • lowabout#3
    Slightly expand the repository description for clarity

    Why:

    CURRENT
    [ICLR & NeurIPS 2025] Repository for Show-o series, One Single Transformer to Unify Multimodal Understanding and Generation.
    COPY-PASTE FIX
    [ICLR & NeurIPS 2025] Official research repository for the Show-o series: a single transformer architecture designed to unify multimodal understanding and generation tasks.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface showlab/Show-o
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
salesforce/LAVIS
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. salesforce/LAVIS · recommended 2×
  2. Flamingo · recommended 2×
  3. CoCa · recommended 2×
  4. huggingface/transformers · recommended 1×
  5. huggingface/diffusers · recommended 1×
  • CATEGORY QUERY
    How can I build a single model for both multimodal understanding and generation tasks?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. 🤗 Diffusers (huggingface/diffusers)
    3. PEFT (huggingface/peft)
    4. LLaVA (haotian-liu/LLaVA)
    5. InstructBLIP (salesforce/LAVIS)
    6. BLIP-2 (salesforce/LAVIS)
    7. Stable Diffusion (stability-ai/stablediffusion)
    8. DALLE-2
    9. PyTorch Lightning (Lightning-AI/lightning)
    10. TorchVision (pytorch/vision)
    11. TorchText (pytorch/text)
    12. Flamingo
    13. CoCa
    14. JAX (google/jax)
    15. Flax (google/flax)
    16. PaLM-E
    17. Gato
    18. TensorFlow (tensorflow/tensorflow)
    19. Keras (keras-team/keras)
    20. TensorFlow Hub (tensorflow/hub)
    21. TF-Agents (tensorflow/agents)
    22. ViLT (dandelin/vilt)
    23. UNITER (microsoft/uniter)
    24. OpenAI API
    25. GPT-4V (Vision)
    26. DALL-E 3

    AI recommended 26 alternatives but never named showlab/Show-o. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best unified transformer architectures for combining LLMs and diffusion models?
    you: not recommended
    AI recommended (in order):
    1. U-ViT
    2. Parti
    3. NUWA-Infinity
    4. Flamingo
    5. CoCa
    6. Stable Diffusion
    7. CLIP
    8. OpenCLIP
    9. DALL-E 2
    10. Imagen
    11. T5-XXL

    AI recommended 11 alternatives but never named showlab/Show-o. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of showlab/Show-o?
    pass
    AI named showlab/Show-o explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts showlab/Show-o in production, what risks or prerequisites should they evaluate first?
    pass
    AI named showlab/Show-o explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo showlab/Show-o solve, and who is the primary audience?
    pass
    AI named showlab/Show-o explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)
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HTML
<a href="https://repogeo.com/en/r/showlab/Show-o"><img src="https://repogeo.com/badge/showlab/Show-o.svg" alt="RepoGEO" /></a>
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showlab/Show-o — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite