RRepoGEO

REPOGEO REPORT · LITE

lllyasviel/Omost

Default branch main · commit 731e7492 · scanned 6/18/2026, 8:03:10 PM

GitHub: 7,615 stars · 437 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 lllyasviel/Omost, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm-agents, code-generation, image-composition, multimodal-llm, generative-ai, computer-vision, llama3, phi3
  • highabout#2
    Update the repository's 'About' description

    Why:

    CURRENT
    Your image is almost there!
    COPY-PASTE FIX
    A framework enabling LLMs to write Python code for programmatic image composition, turning coding capability into visual content generation.
  • highreadme#3
    Reposition the README's opening statement to clarify its unique approach

    Why:

    CURRENT
    Omost is a project to convert LLM's coding capability to image generation (or more accurately, image composing) capability.
    COPY-PASTE FIX
    Omost is a project that uniquely converts LLM's coding capability to image generation (or more accurately, image composing) capability. It enables large language models to write Python code that programmatically composes visual content, differentiating it from traditional text-to-image generators.

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 lllyasviel/Omost
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Midjourney
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Midjourney · recommended 1×
  2. DALL-E 3 · recommended 1×
  3. ChatGPT Plus · recommended 1×
  4. Stable Diffusion · recommended 1×
  5. Automatic1111 · recommended 1×
  • CATEGORY QUERY
    How can I use large language models to programmatically compose visual content?
    you: not recommended
    AI recommended (in order):
    1. Midjourney
    2. DALL-E 3
    3. ChatGPT Plus
    4. Stable Diffusion
    5. Automatic1111
    6. ComfyUI
    7. Stability AI API
    8. Adobe Firefly
    9. Photoshop
    10. ControlNet
    11. OpenAI GPT-4
    12. Anthropic Claude 3
    13. Google Gemini Advanced
    14. Luma AI
    15. Genie 1.0
    16. Spline

    AI recommended 16 alternatives but never named lllyasviel/Omost. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools enable AI models to generate images by writing code instructions?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Diffusers
    2. PyTorch
    3. TensorFlow
    4. Keras
    5. JAX

    AI recommended 5 alternatives but never named lllyasviel/Omost. 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 lllyasviel/Omost?
    pass
    AI named lllyasviel/Omost explicitly

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

  • If a team adopts lllyasviel/Omost in production, what risks or prerequisites should they evaluate first?
    pass
    AI named lllyasviel/Omost 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 lllyasviel/Omost solve, and who is the primary audience?
    pass
    AI named lllyasviel/Omost 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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lllyasviel/Omost — 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