RRepoGEO

REPOGEO REPORT · LITE

Zheng-Chong/CatVTON

Default branch edited · commit 7818397f · scanned 5/30/2026, 6:27:05 AM

GitHub: 1,719 stars · 219 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 Zheng-Chong/CatVTON, 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
    Clarify target subject (human vs. cat) in README

    Why:

    CURRENT
    The README title includes a cat emoji and the name "CatVTON", but the text does not explicitly state the target subject for virtual try-on.
    COPY-PASTE FIX
    Add the sentence "CatVTON focuses on human virtual try-on, enabling realistic clothing application to human images." to the README's introductory section.
  • highhomepage#2
    Add homepage URL to repo's About section

    Why:

    COPY-PASTE FIX
    https://zheng-chong.github.io/CatVTON/
  • mediumlicense#3
    Clarify existing license in README

    Why:

    CURRENT
    License is "NOASSERTION" with a file present, but no explicit statement in README.
    COPY-PASTE FIX
    Add a section to the README: "## License\nThis project is released under the terms specified in the [LICENCE](LICENCE) file."

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 Zheng-Chong/CatVTON
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Stable Diffusion (SDXL)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Stable Diffusion (SDXL) · recommended 1×
  2. ControlNet · recommended 1×
  3. GLIDE · recommended 1×
  4. DALL-E 2/3 · recommended 1×
  5. Hugging Face Diffusers library · recommended 1×
  • CATEGORY QUERY
    Looking for an efficient diffusion model for virtual fashion try-on applications.
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion (SDXL)
    2. ControlNet
    3. GLIDE
    4. DALL-E 2/3
    5. Hugging Face Diffusers library
    6. StyleGAN-XL

    AI recommended 6 alternatives but never named Zheng-Chong/CatVTON. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best lightweight virtual try-on solutions for low VRAM GPUs?
    you: not recommended
    AI recommended (in order):
    1. DeepMotion Animate 3D
    2. ViSenze
    3. Snap Camera
    4. Lens Studio
    5. AR.js
    6. A-Frame
    7. OpenCV
    8. MediaPipe

    AI recommended 8 alternatives but never named Zheng-Chong/CatVTON. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 Zheng-Chong/CatVTON?
    pass
    AI named Zheng-Chong/CatVTON explicitly

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

  • If a team adopts Zheng-Chong/CatVTON in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Zheng-Chong/CatVTON 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 Zheng-Chong/CatVTON solve, and who is the primary audience?
    pass
    AI named Zheng-Chong/CatVTON explicitly

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

Embed your GEO score

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MARKDOWN (README)
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HTML
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Zheng-Chong/CatVTON — 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