RRepoGEO

REPOGEO REPORT · LITE

KohakuBlueleaf/LyCORIS

Default branch main · commit ac63d7fa · scanned 5/24/2026, 7:48:05 AM

GitHub: 2,500 stars · 176 forks

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 KohakuBlueleaf/LyCORIS, 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
    Strengthen README's opening sentence to emphasize LyCORIS as a comprehensive framework

    Why:

    CURRENT
    A project that implements different parameter-efficient fine-tuning algorithms for Stable Diffusion.
    COPY-PASTE FIX
    LyCORIS is a comprehensive framework that implements a diverse collection of advanced parameter-efficient fine-tuning (PEFT) algorithms for Stable Diffusion, extending beyond conventional LoRA methods.
  • hightopics#2
    Add more specific topics for parameter-efficient fine-tuning

    Why:

    CURRENT
    finetune, stable-diffusion
    COPY-PASTE FIX
    finetune, stable-diffusion, parameter-efficient-fine-tuning, peft, low-rank-adaptation, lora-variants, stable-diffusion-finetuning
  • mediumhomepage#3
    Add the paper's arXiv link as the repository homepage

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2401.08910

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 KohakuBlueleaf/LyCORIS
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/diffusers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/diffusers · recommended 1×
  2. Kohya's LoRA GUI/Scripts · recommended 1×
  3. TimDettmers/bitsandbytes · recommended 1×
  4. QLoRA · recommended 1×
  5. LoRA+ · recommended 1×
  • CATEGORY QUERY
    What are the best methods for efficiently fine-tuning Stable Diffusion models?
    you: not recommended
    AI recommended (in order):
    1. diffusers (huggingface/diffusers)
    2. Kohya's LoRA GUI/Scripts
    3. bitsandbytes (TimDettmers/bitsandbytes)

    AI recommended 3 alternatives but never named KohakuBlueleaf/LyCORIS. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for advanced parameter-efficient fine-tuning techniques beyond standard LoRA for image generation.
    you: not recommended
    AI recommended (in order):
    1. QLoRA
    2. LoRA+
    3. DoRA
    4. AdaLoRA
    5. LongLoRA
    6. IA3
    7. Prompt Tuning
    8. Prefix Tuning

    AI recommended 8 alternatives but never named KohakuBlueleaf/LyCORIS. 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 KohakuBlueleaf/LyCORIS?
    pass
    AI named KohakuBlueleaf/LyCORIS explicitly

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

  • If a team adopts KohakuBlueleaf/LyCORIS in production, what risks or prerequisites should they evaluate first?
    pass
    AI named KohakuBlueleaf/LyCORIS 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 KohakuBlueleaf/LyCORIS solve, and who is the primary audience?
    pass
    AI named KohakuBlueleaf/LyCORIS 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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MARKDOWN (README)
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KohakuBlueleaf/LyCORIS — 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