RRepoGEO

REPOGEO REPORT · LITE

bytedance/UNO

Default branch main · commit a563432d · scanned 5/22/2026, 7:28:23 PM

GitHub: 1,354 stars · 77 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
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 bytedance/UNO, 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's opening to clearly state UNO's purpose for image customization

    Why:

    CURRENT
    <h3 align="center">
    
    Less-to-More Generalization: </br>Unlocking More Controllability by In-Context Generation
</h3>
    COPY-PASTE FIX
    Add the following sentence at the very top of the README, before any headings: 'UNO is a universal customization method for both single and multi-subject conditioning in image generation, presented at ICCV 2025.'
  • mediumreadme#2
    Integrate specific keywords from failing queries into the README

    Why:

    COPY-PASTE FIX
    Integrate phrases like 'universal diffusion model,' 'multi-subject conditioning,' and 'in-context learning for subject-driven image generation' into the README's introduction or features section.
  • lowreadme#3
    Clarify the relationship between UNO, UMO, and OmniGen2 in the README

    Why:

    CURRENT
    2025.09.12** 🔥 UMO is here! It can freely combine one-to-many identity with any subjects in any scenarios, delivering outputs with high subject/identity consistency. You now can experience a more powerful UNO or OmniGen2 here! You can also visit our <a href="https://bytedance.github.io/UMO/" target="_blank">project page</a> for more examples. 🔥
    COPY-PASTE FIX
    Add a clarifying sentence or short paragraph in the README, perhaps near the 'News' section, explaining the relationship between UNO, UMO, and OmniGen2. For example: 'UMO and OmniGen2 are advanced iterations or related projects that build upon the UNO framework, offering enhanced capabilities. This repository focuses on the core UNO method.'

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 bytedance/UNO
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DreamBooth
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DreamBooth · recommended 2×
  2. LoRA · recommended 2×
  3. Stable Diffusion XL · recommended 1×
  4. lllyasviel/ControlNet · recommended 1×
  5. Reference-Only · recommended 1×
  • CATEGORY QUERY
    How to generate images with multiple custom subjects using a universal diffusion model?
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion XL
    2. ControlNet (lllyasviel/ControlNet)
    3. Reference-Only
    4. IP-Adapter
    5. Automatic1111 WebUI (AUTOMATIC1111/stable-diffusion-webui)
    6. ComfyUI (comfyanonymous/ComfyUI)
    7. InvokeAI (invoke-ai/InvokeAI)
    8. Midjourney
    9. DreamBooth
    10. LoRA
    11. Kohya_ss GUI (bmaltais/kohya_ss)
    12. Adobe Firefly
    13. Photoshop
    14. DALL-E 3
    15. ChatGPT Plus
    16. Microsoft Copilot

    AI recommended 16 alternatives but never named bytedance/UNO. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What methods allow in-context learning for highly controllable subject-driven image generation?
    you: not recommended
    AI recommended (in order):
    1. DreamBooth
    2. LoRA
    3. Textual Inversion
    4. Custom Diffusion
    5. ControlNet

    AI recommended 5 alternatives but never named bytedance/UNO. 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 bytedance/UNO?
    pass
    AI named bytedance/UNO explicitly

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

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

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

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bytedance/UNO — 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