RRepoGEO

REPOGEO REPORT · LITE

nv-tlabs/PiD

Default branch main · commit b5e5eaec · scanned 6/16/2026, 12:47:42 PM

GitHub: 741 stars · 36 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 nv-tlabs/PiD, 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 opening to clarify component role

    Why:

    CURRENT
    > **TL;DR** — PiD is a plug-and-play diffusion decoder that replaces VAE/RAE decoders, turning latent representations directly into super-resolved pixels in a single pass.
    COPY-PASTE FIX
    > **TL;DR** — PiD is a plug-and-play diffusion decoder that replaces VAE/RAE decoders, turning latent representations directly into super-resolved pixels in a single pass. **Unlike full generative models such as Stable Diffusion or DALL-E, PiD focuses specifically on the high-resolution decoding step, offering a faster and higher-quality alternative to traditional decoders.**
  • mediumtopics#2
    Expand repository topics for broader category matching

    Why:

    CURRENT
    diffusion-decoder, pixel-diffusion
    COPY-PASTE FIX
    diffusion-decoder, pixel-diffusion, latent-decoding, image-super-resolution, generative-ai-component, vae-alternative
  • lowreadme#3
    Clarify license terms in README

    Why:

    COPY-PASTE FIX
    ## License 
 This project is licensed under the terms specified in the [LICENSE](LICENSE) file. Please refer to the file for full details on usage and distribution.

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 nv-tlabs/PiD
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. StyleGAN3 · recommended 2×
  3. BigGAN · recommended 2×
  4. DALL-E 2 · recommended 1×
  5. Midjourney · recommended 1×
  • CATEGORY QUERY
    What are alternatives to VAE decoders for faster, higher-resolution image generation?
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion
    2. DALL-E 2
    3. Midjourney
    4. Imagen
    5. StyleGAN3
    6. BigGAN
    7. ProGAN
    8. VQ-GAN
    9. Parti
    10. Glow
    11. WaveGlow

    AI recommended 11 alternatives but never named nv-tlabs/PiD. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I achieve super-resolution image decoding directly from latent representations in one pass?
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion
    2. SDXL
    3. StyleGAN3
    4. BigGAN
    5. VQGAN

    AI recommended 5 alternatives but never named nv-tlabs/PiD. 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 nv-tlabs/PiD?
    pass
    AI named nv-tlabs/PiD explicitly

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

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

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

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nv-tlabs/PiD — 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