RRepoGEO

REPOGEO REPORT · LITE

deepgenteam/deepgen

Default branch main · commit 7261969a · scanned 6/9/2026, 9:53:15 PM

GitHub: 581 stars · 36 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 deepgenteam/deepgen, 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 specific topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    image-generation, image-editing, multimodal-model, diffusion-models, generative-ai, deep-learning, text-to-image, lightweight-ai, unified-model
  • highreadme#2
    Reposition the core value proposition to the top of the README

    Why:

    CURRENT
    The current README excerpt starts with links and 'News' before the 'Introduction' section detailing the model's capabilities.
    COPY-PASTE FIX
    Insert the following paragraph immediately after the main H1 title:

DeepGen 1.0 is a lightweight unified multimodal model with only 5B parameters (3B VLM + 2B DiT). It integrates five core capabilities: general image generation, general image editing, reasoning image generation, reasoning image editing, and text rendering—within a single model. It is competitive with or surpasses state-of-the-art unified multimodal models that are 3× to 16× larger.
  • mediumhomepage#3
    Update the repository's 'Homepage' metadata field

    Why:

    CURRENT
    https://arxiv.org/abs/2602.12205
    COPY-PASTE FIX
    https://deepgenteam.github.io/

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 deepgenteam/deepgen
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. SDXL-Turbo · recommended 1×
  3. LCM-LoRA · recommended 1×
  4. Distilled SDXL · recommended 1×
  5. PixArt-α · recommended 1×
  • CATEGORY QUERY
    What are some lightweight multimodal models for combined image generation and editing tasks?
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion
    2. SDXL-Turbo
    3. LCM-LoRA
    4. Distilled SDXL
    5. PixArt-α
    6. DeepFloyd IF
    7. ControlNet
    8. Stable Diffusion v1.5
    9. Mini-DALL-E
    10. GLIDE

    AI recommended 10 alternatives but never named deepgenteam/deepgen. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for efficient unified models capable of both text-to-image generation and editing.
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion
    2. DALL-E 3
    3. ChatGPT Plus
    4. Microsoft Copilot
    5. Midjourney
    6. Adobe Firefly
    7. Imagen

    AI recommended 7 alternatives but never named deepgenteam/deepgen. 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 deepgenteam/deepgen?
    pass
    AI named deepgenteam/deepgen explicitly

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

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

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

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deepgenteam/deepgen — 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