RRepoGEO

REPOGEO REPORT · LITE

modelscope/facechain

Default branch main · commit 7bc7119c · scanned 6/23/2026, 10:19:14 PM

GitHub: 9,495 stars · 881 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
60 /100
Needs work
Category recall
1 / 2
Avg rank #4.0 when recommended
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 modelscope/facechain, 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

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

OVERALL DIRECTION
  • highreadme#1
    Emphasize 'digital twin portraits' in the README introduction

    Why:

    CURRENT
    FaceChain is a novel framework for generating identity-preserved human portraits.
    COPY-PASTE FIX
    FaceChain is a novel framework for generating high-fidelity, identity-preserved **digital twin portraits** from a single input photo.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Add the official project homepage URL (e.g., a dedicated project website or the ModelScope project page).

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
1 / 2
50% of queries surface modelscope/facechain
Avg rank
#4.0
Lower is better. #1 = top recommendation.
Share of voice
6%
Of all named tools, what % are you?
Top rival
Midjourney
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Midjourney · recommended 2×
  2. Stable Diffusion · recommended 2×
  3. RunwayML · recommended 2×
  4. ControlNet · recommended 1×
  5. Dreambooth · recommended 1×
  • CATEGORY QUERY
    How to create realistic AI-generated digital twin portraits from input images?
    you: not recommended
    AI recommended (in order):
    1. Midjourney
    2. Stable Diffusion
    3. ControlNet
    4. Dreambooth
    5. LoRA
    6. FaceFusion
    7. D-ID Creative Reality Studio
    8. RunwayML
    9. StyleGAN

    AI recommended 9 alternatives but never named modelscope/facechain. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What deep learning tools exist for fast personalized AI avatar generation with custom styles?
    you: #4
    AI recommended (in order):
    1. Stable Diffusion
    2. Midjourney
    3. InstantID
    4. FaceChain ← you
    5. Pika Labs
    6. RunwayML
    7. Lensa AI
    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 modelscope/facechain?
    pass
    AI named modelscope/facechain explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite