RRepoGEO

REPOGEO REPORT · LITE

openai/pixel-cnn

Default branch master · commit bbc15688 · scanned 5/25/2026, 7:58:19 AM

GitHub: 1,962 stars · 433 forks

AI VISIBILITY SCORE
33 /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
2 / 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 openai/pixel-cnn, 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 category recall

    Why:

    CURRENT
    ["paper"]
    COPY-PASTE FIX
    ["generative-models", "image-synthesis", "pixelcnn", "autoregressive-models", "deep-learning", "tensorflow"]
  • highreadme#2
    Reposition the README's opening to clarify model type and context

    Why:

    CURRENT
    This is a Python3 / Tensorflow implementation of PixelCNN++, as described in the following paper:
    COPY-PASTE FIX
    This repository provides a Python3 / TensorFlow 1.x implementation of PixelCNN++, an autoregressive generative model for high-fidelity image synthesis with tractable likelihood, as described in the following paper:
  • mediumreadme#3
    Add a section to the README clarifying the existing license

    Why:

    COPY-PASTE FIX
    ## License
This project is licensed under [insert specific license name(s) here, e.g., MIT License, Apache 2.0 License, or a custom license description if applicable]. Please refer to the [LICENSE](LICENSE) file for full details.

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 openai/pixel-cnn
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. NVIDIA StyleGAN · recommended 1×
  3. VQ-VAE-2 · recommended 1×
  4. NVAE · recommended 1×
  5. PixelCNN · recommended 1×
  • CATEGORY QUERY
    How to build powerful generative models for image synthesis with tractable likelihood?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA StyleGAN
    2. VQ-VAE-2
    3. NVAE
    4. PixelCNN
    5. PixelRNN
    6. Image Transformer
    7. Glow
    8. RealNVP
    9. FFJORD
    10. Denoising Diffusion Probabilistic Models
    11. Latent Diffusion Models
    12. Stable Diffusion

    AI recommended 12 alternatives but never named openai/pixel-cnn. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good deep learning models for high-quality image generation with easy sampling?
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion
    2. SDXL
    3. Midjourney
    4. DALL-E 3
    5. Adobe Firefly
    6. Kandinsky 2.2
    7. DeepFloyd IF

    AI recommended 7 alternatives but never named openai/pixel-cnn. 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 openai/pixel-cnn?
    pass
    AI did not name openai/pixel-cnn — likely talking about a different project

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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