RRepoGEO

REPOGEO REPORT · LITE

CompVis/taming-transformers

Default branch master · commit 3ba01b24 · scanned 5/26/2026, 12:32:58 AM

GitHub: 6,492 stars · 1,223 forks

AI VISIBILITY SCORE
22 /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
1 / 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 CompVis/taming-transformers, 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.

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 CompVis/taming-transformers
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. Imagen · recommended 2×
  3. DALL-E 3 · recommended 1×
  4. Midjourney · recommended 1×
  5. VQGAN + CLIP · recommended 1×
  • CATEGORY QUERY
    Exploring techniques for image generation combining convolutional efficiency with transformer expressivity.
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion
    2. DALL-E 3
    3. Midjourney
    4. VQGAN + CLIP
    5. Parti
    6. Imagen

    AI recommended 6 alternatives but never named CompVis/taming-transformers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I generate high-resolution images using a learned codebook of visual components?
    you: not recommended
    AI recommended (in order):
    1. VQGAN
    2. diffusers
    3. Stable Diffusion
    4. Latent Diffusion Models
    5. Automatic1111's web UI
    6. DALL-E 2
    7. Imagen
    8. OpenAI's DALL-E 2 API
    9. Google's Imagen API
    10. DALLE-mini (Craiyon)
    11. DALL-E-pytorch
    12. VQ-VAE-2
    13. ESRGAN
    14. SRGAN
    15. StyleGAN
    16. StyleGAN2
    17. StyleGAN3
    18. NVIDIA's official StyleGAN implementations

    AI recommended 18 alternatives but never named CompVis/taming-transformers. 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 CompVis/taming-transformers?
    pass
    AI did not name CompVis/taming-transformers — 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 CompVis/taming-transformers in production, what risks or prerequisites should they evaluate first?
    pass
    AI named CompVis/taming-transformers 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 CompVis/taming-transformers solve, and who is the primary audience?
    pass
    AI did not name CompVis/taming-transformers — 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?

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CompVis/taming-transformers — 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