RRepoGEO

REPOGEO REPORT · LITE

madebyollin/taesd

Default branch main · commit e87efbcf · scanned 6/4/2026, 4:02:53 AM

GitHub: 940 stars · 53 forks

AI VISIBILITY SCORE
35 /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
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 madebyollin/taesd, 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 for VAE, diffusion, and image generation

    Why:

    COPY-PASTE FIX
    vae, autoencoder, stable-diffusion, diffusion-models, image-generation, real-time-preview, inference-optimization, tiny-models
  • highabout#2
    Expand the 'About' description to highlight speed and efficiency

    Why:

    CURRENT
    Tiny AutoEncoder for Stable Diffusion (and other image models)
    COPY-PASTE FIX
    A tiny, highly efficient autoencoder (TAESD) for rapid VAE decoding and real-time latent previewing in Stable Diffusion and other image generation models, significantly reducing inference time and memory usage.
  • mediumhomepage#3
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    https://github.com/madebyollin/taesd

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 madebyollin/taesd
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TinyVAE
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. TinyVAE · recommended 1×
  2. Diffusers · recommended 1×
  3. ONNX Runtime · recommended 1×
  4. NVIDIA TensorRT · recommended 1×
  5. OpenVINO · recommended 1×
  • CATEGORY QUERY
    How to speed up VAE decoding for diffusion models efficiently?
    you: not recommended
    AI recommended (in order):
    1. TinyVAE
    2. Diffusers
    3. ONNX Runtime
    4. NVIDIA TensorRT
    5. OpenVINO
    6. DeepSpeed
    7. Hugging Face Accelerate
    8. FlashAttention
    9. xFormers
    10. PyTorch 2.0 `torch.compile`
    11. PyTorch's `torch.quantization`

    AI recommended 11 alternatives but never named madebyollin/taesd. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a lightweight autoencoder to quickly preview generated image latents.
    you: not recommended
    AI recommended (in order):
    1. VQGAN
    2. Variational Autoencoder (VAE)
    3. TensorFlow
    4. PyTorch
    5. Tiny Autoencoder (TAE)
    6. SqueezeNet
    7. MobileNet
    8. MobileNetV2
    9. MobileNetV3

    AI recommended 9 alternatives but never named madebyollin/taesd. 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 madebyollin/taesd?
    pass
    AI named madebyollin/taesd explicitly

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

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

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

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madebyollin/taesd — 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