RRepoGEO

REPOGEO REPORT · LITE

yu-takagi/StableDiffusionReconstruction

Default branch main · commit e187d4b3 · scanned 6/27/2026, 7:37:24 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

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

OVERALL DIRECTION
  • highabout#1
    Expand the 'About' description to clarify the project's purpose

    Why:

    CURRENT
    Takagi and Nishimoto, CVPR 2023
    COPY-PASTE FIX
    Official repository for high-resolution image reconstruction from human brain activity (fMRI) using latent diffusion models (Stable Diffusion), as presented in Takagi and Nishimoto, CVPR 2023.
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    fmri, brain-computer-interface, stable-diffusion, image-reconstruction, neuroscience, deep-learning, computer-vision, cvpr2023
  • mediumreadme#3
    Clarify the opening paragraph of the README to emphasize the unique input

    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 high-resolution visual experience reconstruction *directly from human brain activity* using Stable Diffusion. It details the method presented in Takagi and Nishimoto, CVPR 2023, focusing on decoding fMRI signals into vivid images.

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
PyTorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 2×
  2. TensorFlow · recommended 2×
  3. Stable Diffusion · recommended 1×
  4. CLIP · recommended 1×
  5. StyleGAN · recommended 1×
  • CATEGORY QUERY
    How to reconstruct visual experiences from fMRI data using generative AI models?
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion
    2. CLIP
    3. StyleGAN
    4. DALL-E mini
    5. Craiyon
    6. PyTorch
    7. TensorFlow
    8. BigGAN
    9. DCGAN
    10. Instant-NGP
    11. Mip-NeRF

    AI recommended 11 alternatives but never named yu-takagi/StableDiffusionReconstruction. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools for generating images directly from brain signals or neural patterns?
    you: not recommended
    AI recommended (in order):
    1. DeepMind's Perceiver IO
    2. Google Brain's Imagen
    3. OpenAI's DALL-E 2
    4. PyTorch
    5. TensorFlow
    6. Hugging Face Transformers
    7. Diffusers
    8. MNE-Python
    9. BrainVoyager
    10. FreeSurfer
    11. BCI2000

    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