RRepoGEO

REPOGEO REPORT · LITE

Alpha-VLLM/Lumina-DiMOO

Default branch main · commit 81344e60 · scanned 6/3/2026, 10:17:58 AM

GitHub: 989 stars · 61 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 Alpha-VLLM/Lumina-DiMOO, 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 README introduction to emphasize LLM + discrete diffusion

    Why:

    CURRENT
    We introduce Lumina-DiMOO, an omni foundational model for seamless multimodal generation and understanding.
    COPY-PASTE FIX
    We introduce Lumina-DiMOO, a pioneering **Large Language Model (LLM)** built on a **unified discrete diffusion architecture** for seamless multimodal generation and understanding.
  • mediumreadme#2
    Explicitly mention dialogue-based interaction in README features

    Why:

    COPY-PASTE FIX
    Add a bullet point or sentence under 'Versatile Multimodal Capabilities' or a new 'Key Features' section: 'Specialized for dialogue-based interaction with multi-modal objects, enabling more natural and intuitive user experiences.'
  • lowtopics#3
    Add application-focused topics for broader reach

    Why:

    CURRENT
    diffusion-large-language-model, discrete-diffusion-models, unified-multimodal-understanding-and-generation
    COPY-PASTE FIX
    diffusion-large-language-model, discrete-diffusion-models, unified-multimodal-understanding-and-generation, text-to-image, image-editing, multimodal-ai, large-language-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 Alpha-VLLM/Lumina-DiMOO
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Stable Diffusion
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Stable Diffusion · recommended 2×
  2. DALL-E 2 · recommended 1×
  3. VQ-GAN · recommended 1×
  4. Parti · recommended 1×
  5. Imagen · recommended 1×
  • CATEGORY QUERY
    What open-source models unify discrete diffusion for multimodal generation and understanding?
    you: not recommended
    AI recommended (in order):
    1. DALL-E 2
    2. Stable Diffusion
    3. VQ-GAN
    4. Parti
    5. Imagen
    6. Hugging Face Diffusers Library

    AI recommended 6 alternatives but never named Alpha-VLLM/Lumina-DiMOO. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a large language model for high-resolution text-to-image generation and advanced image editing.
    you: not recommended
    AI recommended (in order):
    1. Midjourney
    2. Stable Diffusion
    3. Automatic1111
    4. ComfyUI
    5. DALL-E 3
    6. ChatGPT Plus
    7. Copilot Pro
    8. Adobe Firefly
    9. Leonardo.Ai
    10. Fooocus

    AI recommended 10 alternatives but never named Alpha-VLLM/Lumina-DiMOO. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 Alpha-VLLM/Lumina-DiMOO?
    pass
    AI named Alpha-VLLM/Lumina-DiMOO explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of Alpha-VLLM/Lumina-DiMOO. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/Alpha-VLLM/Lumina-DiMOO.svg)](https://repogeo.com/en/r/Alpha-VLLM/Lumina-DiMOO)
HTML
<a href="https://repogeo.com/en/r/Alpha-VLLM/Lumina-DiMOO"><img src="https://repogeo.com/badge/Alpha-VLLM/Lumina-DiMOO.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

Alpha-VLLM/Lumina-DiMOO — 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