RRepoGEO

REPOGEO REPORT · LITE

bghira/SimpleTuner

Default branch main · commit 929f4e6e · scanned 5/17/2026, 3:12:12 PM

GitHub: 2,831 stars · 280 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
28 /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
2 / 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 bghira/SimpleTuner, 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
    Reposition the README's opening paragraph to clearly state its purpose for diffusion models

    Why:

    CURRENT
    **SimpleTuner** is geared towards simplicity, with a focus on making the code easily understood. This codebase serves as a shared academic exercise, and contributions are welcome.
    COPY-PASTE FIX
    **SimpleTuner** is a comprehensive fine-tuning kit specifically designed for image, video, and audio diffusion models. It prioritizes simplicity, making the code easily understood, and serves as a shared academic exercise where contributions are welcome.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Add a URL to the repository's homepage field in the About section (e.g., a documentation site, project website, or community hub like Discord).
  • lowreadme#3
    Add a 'Comparison' or 'Why SimpleTuner?' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., 'Why SimpleTuner?' or 'Comparison with Alternatives,' that explicitly highlights SimpleTuner's unique advantages and use cases compared to other fine-tuning tools for diffusion models.

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 bghira/SimpleTuner
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Diffusers Library
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Diffusers Library · recommended 1×
  2. PyTorch Lightning · recommended 1×
  3. Keras · recommended 1×
  4. OpenMMLab's MMDetection/MMGeneration · recommended 1×
  5. DeepSpeed · recommended 1×
  • CATEGORY QUERY
    How can I easily fine-tune image and video diffusion models for custom datasets?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Diffusers Library
    2. PyTorch Lightning
    3. Keras
    4. OpenMMLab's MMDetection/MMGeneration
    5. DeepSpeed
    6. Accelerate

    AI recommended 6 alternatives but never named bghira/SimpleTuner. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best tools for simplifying the fine-tuning process of stable diffusion models?
    you: not recommended
    AI recommended (in order):
    1. Kohya's GUI
    2. A1111 Web UI (Automatic1111/stable-diffusion-webui)
    3. Diffusers Library
    4. RunDiffusion / ThinkDiffusion
    5. Civitai's On-Site Trainer
    6. Dreambooth Extension for A1111 Web UI

    AI recommended 6 alternatives but never named bghira/SimpleTuner. 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 bghira/SimpleTuner?
    pass
    AI did not name bghira/SimpleTuner — 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 bghira/SimpleTuner in production, what risks or prerequisites should they evaluate first?
    pass
    AI named bghira/SimpleTuner 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 bghira/SimpleTuner solve, and who is the primary audience?
    pass
    AI named bghira/SimpleTuner 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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bghira/SimpleTuner — 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