RRepoGEO

REPOGEO REPORT · LITE

yu-takagi/StableDiffusionReconstruction

Default branch main · commit e187d4b3 · scanned 5/16/2026, 9:53:45 PM

GitHub: 1,128 stars · 71 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
22 /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
1 / 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 yu-takagi/StableDiffusionReconstruction, 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
  • highabout#1
    Improve the About description for clarity

    Why:

    CURRENT
    Takagi and Nishimoto, CVPR 2023
    COPY-PASTE FIX
    Code for high-resolution visual experience reconstruction from human brain activity (fMRI) using latent diffusion models like Stable Diffusion. (CVPR 2023)
  • highreadme#2
    Clarify the README's initial positioning for AI

    Why:

    CURRENT
    This is a repository for reproducing the method we presented (Takagi and Nishimoto, CVPR 2023) for visual experience reconstruction from brain activity using Stable Diffusion.
    COPY-PASTE FIX
    This repository provides the official implementation for reconstructing high-resolution visual experiences directly from human brain activity (fMRI) using Stable Diffusion, as presented in Takagi and Nishimoto, CVPR 2023.

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 yu-takagi/StableDiffusionReconstruction
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorFlow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow · recommended 2×
  2. PyTorch · recommended 2×
  3. BrainIAK · recommended 2×
  4. MNE-Python · recommended 1×
  5. Scikit-learn · recommended 1×
  • CATEGORY QUERY
    How to reconstruct visual experiences or images directly from brain activity data?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow
    2. PyTorch
    3. BrainIAK
    4. MNE-Python
    5. Scikit-learn
    6. FSL
    7. SPM

    AI recommended 7 alternatives but never named yu-takagi/StableDiffusionReconstruction. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best methods for decoding and visualizing perceived images from fMRI scans?
    you: not recommended
    AI recommended (in order):
    1. Pycortex
    2. BrainIAK
    3. MVPA-Light
    4. TensorFlow
    5. PyTorch
    6. VGG16
    7. ResNet
    8. StyleGAN
    9. PyMVPA
    10. scikit-learn
    11. CoSMoMVPA

    AI recommended 11 alternatives but never named yu-takagi/StableDiffusionReconstruction. 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 yu-takagi/StableDiffusionReconstruction?
    pass
    AI did not name yu-takagi/StableDiffusionReconstruction — likely talking about a different project

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

  • If a team adopts yu-takagi/StableDiffusionReconstruction in production, what risks or prerequisites should they evaluate first?
    pass
    AI named yu-takagi/StableDiffusionReconstruction 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 yu-takagi/StableDiffusionReconstruction solve, and who is the primary audience?
    pass
    AI did not name yu-takagi/StableDiffusionReconstruction — likely talking about a different project

    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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yu-takagi/StableDiffusionReconstruction — 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