RRepoGEO

REPOGEO REPORT · LITE

Linaqruf/kohya-trainer

Default branch main · commit c2a9dc89 · scanned 5/21/2026, 2:21:51 AM

GitHub: 1,909 stars · 325 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
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 Linaqruf/kohya-trainer, 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
    Strengthen the README's opening statement and description

    Why:

    CURRENT
    Github Repository for kohya-ss/sd-scripts colab notebook implementation
    COPY-PASTE FIX
    This repository provides a collection of Google Colab notebooks for easily training and fine-tuning Stable Diffusion models using methods like LoRA and Dreambooth, adapted from kohya-ss/sd-scripts. It simplifies the process for creating custom image generation models directly in the cloud.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/Linaqruf/kohya-trainer/blob/main/kohya-LoRA-dreambooth.ipynb

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 Linaqruf/kohya-trainer
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
https://github.com/kohya-ss/sd-scripts
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. https://github.com/kohya-ss/sd-scripts · recommended 1×
  2. https://github.com/huggingface/diffusers · recommended 1×
  3. Dreambooth LoRA Colab Notebooks · recommended 1×
  4. https://github.com/AUTOMATIC1111/stable-diffusion-webui · recommended 1×
  5. DreamBooth · recommended 1×
  • CATEGORY QUERY
    How can I train custom image generation models using LoRA on Google Colab?
    you: not recommended
    AI recommended (in order):
    1. Kohya's LoRA GUI (https://github.com/kohya-ss/sd-scripts)
    2. Diffusers Library (https://github.com/huggingface/diffusers)
    3. Dreambooth LoRA Colab Notebooks
    4. A1111 Web UI (https://github.com/AUTOMATIC1111/stable-diffusion-webui)

    AI recommended 4 alternatives but never named Linaqruf/kohya-trainer. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a simple method to personalize diffusion models with custom datasets?
    you: not recommended
    AI recommended (in order):
    1. DreamBooth
    2. LoRA
    3. Textual Inversion
    4. ControlNet
    5. Kohya's GUI
    6. SD-WebUI (Automatic1111)

    AI recommended 6 alternatives but never named Linaqruf/kohya-trainer. 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 Linaqruf/kohya-trainer?
    pass
    AI named Linaqruf/kohya-trainer explicitly

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

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

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

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Linaqruf/kohya-trainer — 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