RRepoGEO

REPOGEO REPORT · LITE

modelscope/facechain

Default branch main · commit 7bc7119c · scanned 5/13/2026, 11:12:21 AM

GitHub: 9,491 stars · 886 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 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

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

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    digital-twin, avatar-generation, ai-portrait, personalized-ai, stable-diffusion, deep-learning, face-generation, image-generation
  • highreadme#2
    Clarify README introduction for AI categorization

    Why:

    CURRENT
    FaceChain is a novel framework for generating identity-preserved human portraits. In the newest FaceChain FACT (Face Adapter with deCoupled Training) version, with only 1 photo and 10 seconds, you can generate personal portraits in different settings (multiple styles now supported!).
    COPY-PASTE FIX
    FaceChain is a powerful deep-learning toolchain for generating personalized digital twin avatars and high-quality AI portraits from a single photo. Unlike general image generation models, FaceChain excels at preserving identity across diverse styles and settings, offering a fast and seamless way to create your unique digital self.
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://modelscope.cn/models/modelscope/facechain/summary

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 modelscope/facechain
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Ready Player Me
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Ready Player Me · recommended 1×
  2. Luma AI · recommended 1×
  3. Avatar SDK · recommended 1×
  4. DeepMotion · recommended 1×
  5. Character Creator 4 · recommended 1×
  • CATEGORY QUERY
    How can I generate realistic digital twin avatars from a single image?
    you: not recommended
    AI recommended (in order):
    1. Ready Player Me
    2. Luma AI
    3. Avatar SDK
    4. DeepMotion
    5. Character Creator 4
    6. Blender
    7. FaceBuilder for Blender

    AI recommended 7 alternatives but never named modelscope/facechain. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help create personalized AI portraits with custom styles quickly?
    you: not recommended
    AI recommended (in order):
    1. Midjourney
    2. Stable Diffusion
    3. Automatic1111
    4. ComfyUI
    5. DALL-E 3
    6. ChatGPT Plus
    7. Copilot Pro
    8. Leonardo.Ai
    9. DreamStudio (Stability AI)
    10. Artbreeder

    AI recommended 10 alternatives but never named modelscope/facechain. 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 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?

Embed your GEO score

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