RRepoGEO

REPOGEO REPORT · LITE

NVlabs/Sana

Default branch main · commit 6554c8d9 · scanned 5/19/2026, 1:50:59 PM

GitHub: 6,908 stars · 494 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)

3 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 NVlabs/Sana, 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 the project's purpose

    Why:

    CURRENT
    The README currently starts with a large block of links and badges, delaying the core message.
    COPY-PASTE FIX
    Add a concise, direct sentence at the very top of the README (before any links or badges) like: "SANA is an efficient linear diffusion transformer designed for high-resolution image and video synthesis, offering state-of-the-art performance in text-to-image and text-to-video generation."
  • mediumtopics#2
    Add more specific and common keywords to the topics list

    Why:

    CURRENT
    diffusion, dit, linear-transformer, nvfp4, pytorch, reinforcement-learning, sana, system-algorithm-deisgn, text-to-image-generation, text-to-video, transformers, video-generation
    COPY-PASTE FIX
    Add the following topics: generative-ai, diffusion-models, image-generation, video-synthesis, high-resolution-image-synthesis.
  • lowcomparison#3
    Add a "Comparison with Alternatives" section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README titled "Comparison with Alternatives" or "Why SANA?" that highlights SANA's unique benefits (e.g., efficiency, linear diffusion, high-resolution capabilities) compared to popular models like Stable Diffusion, Sora, or DiT.

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 NVlabs/Sana
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. Sora · recommended 2×
  3. RunwayML Gen-1/Gen-2 · recommended 1×
  4. Midjourney · recommended 1×
  5. Adobe Firefly · recommended 1×
  • CATEGORY QUERY
    What are efficient methods for high-resolution image and video generation?
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion
    2. RunwayML Gen-1/Gen-2
    3. Midjourney
    4. Adobe Firefly
    5. DALL-E 3
    6. ChatGPT Plus
    7. ControlNet
    8. Pika Labs
    9. Sora

    AI recommended 9 alternatives but never named NVlabs/Sana. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a linear diffusion transformer for text-to-image and video synthesis.
    you: not recommended
    AI recommended (in order):
    1. DiT
    2. U-ViT
    3. Stable Diffusion
    4. Phenaki
    5. Imagen Video
    6. Open-Sora
    7. Sora

    AI recommended 7 alternatives but never named NVlabs/Sana. 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 NVlabs/Sana?
    pass
    AI named NVlabs/Sana explicitly

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

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

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

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NVlabs/Sana — 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