RRepoGEO

REPOGEO REPORT · LITE

Nerogar/OneTrainer

Default branch master · commit 26987343 · scanned 5/23/2026, 6:37:45 AM

GitHub: 2,996 stars · 292 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 Nerogar/OneTrainer, 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 H1 to highlight the GUI differentiator

    Why:

    CURRENT
    # OneTrainer

OneTrainer is a one-stop solution for all your Diffusion training needs.
    COPY-PASTE FIX
    # OneTrainer: A Unified GUI for Diffusion Model Training

OneTrainer is your comprehensive, one-stop graphical user interface (GUI) solution for all your Diffusion model training needs, including LoRA, embeddings, and full fine-tuning.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Add the official project website or documentation URL to the repository's homepage field in the 'About' section.
  • mediumtopics#3
    Refine repository topics to emphasize GUI and unified platform

    Why:

    CURRENT
    fine-tuning, image-model-training, lora, training
    COPY-PASTE FIX
    diffusion-models, stable-diffusion, lora-training, fine-tuning, gui, machine-learning-gui, image-generation, ai-training-tool, unified-platform

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 Nerogar/OneTrainer
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
kohya-ss/kohya_ss
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. kohya-ss/kohya_ss · recommended 1×
  2. huggingface/diffusers · recommended 1×
  3. AUTOMATIC1111/stable-diffusion-webui · recommended 1×
  4. comfyanonymous/ComfyUI · recommended 1×
  5. invoke-ai/InvokeAI · recommended 1×
  • CATEGORY QUERY
    What's a comprehensive tool for fine-tuning various diffusion models with LoRA and embeddings?
    you: not recommended
    AI recommended (in order):
    1. Kohya's GUI (kohya-ss/kohya_ss)
    2. Diffusers (huggingface/diffusers)
    3. A1111 Web UI (AUTOMATIC1111/stable-diffusion-webui)
    4. ComfyUI (comfyanonymous/ComfyUI)
    5. InvokeAI (invoke-ai/InvokeAI)

    AI recommended 5 alternatives but never named Nerogar/OneTrainer. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I efficiently train and augment datasets for different stable diffusion models?
    you: not recommended
    AI recommended (in order):
    1. Kohya's Stable Diffusion GUI (kohya_ss)
    2. Axel (Automatic eXperimental ELectron)
    3. Dreambooth Extension for Automatic1111's WebUI
    4. Diffusers (Hugging Face)
    5. Albumentations
    6. imgaug
    7. Dataset Wizard (Automatic1111 Extension)

    AI recommended 7 alternatives but never named Nerogar/OneTrainer. 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 Nerogar/OneTrainer?
    pass
    AI did not name Nerogar/OneTrainer — 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 Nerogar/OneTrainer in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Nerogar/OneTrainer 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 Nerogar/OneTrainer solve, and who is the primary audience?
    pass
    AI named Nerogar/OneTrainer explicitly

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

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Nerogar/OneTrainer — 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