RRepoGEO

REPOGEO REPORT · LITE

bytetriper/RAE

Default branch main · commit a4d18c4d · scanned 6/22/2026, 9:03:27 AM

GitHub: 1,940 stars · 86 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)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 bytetriper/RAE, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Refine the 'About' description for clearer domain context

    Why:

    CURRENT
    Official PyTorch Implementation of "Diffusion Transformers with Representation Autoencoders"
    COPY-PASTE FIX
    Official PyTorch Implementation of "Diffusion Transformers with Representation Autoencoders" (RAE) for high-fidelity image synthesis.
  • mediumhomepage#2
    Set the repository homepage URL

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    Add the URL for the project page mentioned in the README (e.g., 'https://bytetriper.github.io/RAE/').

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 bytetriper/RAE
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Stable Diffusion XL
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Stable Diffusion XL · recommended 1×
  2. stabilityai/stable-diffusion-xl-base-1.0 · recommended 1×
  3. stabilityai/stable-diffusion-xl-refiner-1.0 · recommended 1×
  4. runwayml/stable-diffusion-v1-5 · recommended 1×
  5. stabilityai/stable-diffusion-2-1 · recommended 1×
  • CATEGORY QUERY
    How to achieve high-fidelity image generation using a two-stage diffusion model pipeline?
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion XL
    2. stabilityai/stable-diffusion-xl-base-1.0
    3. stabilityai/stable-diffusion-xl-refiner-1.0
    4. runwayml/stable-diffusion-v1-5
    5. stabilityai/stable-diffusion-2-1
    6. SwinIR
    7. sberbank-ai/SwinIR-L-x4-JPEG
    8. sberbank-ai/SwinIR-L-x4-RealSR
    9. ESRGAN
    10. Real-ESRGAN
    11. DeepFloyd IF
    12. DeepFloyd/IF-I-XL-v1.0
    13. DeepFloyd/IF-II-L-v1.0
    14. DeepFloyd/IF-III-L-v1.0
    15. Imagen
    16. ControlNet
    17. lllyasviel/control_v11f1e_sd15_tile

    AI recommended 17 alternatives but never named bytetriper/RAE. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective methods for combining representation autoencoders with diffusion transformers in PyTorch?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Diffusers
    2. CompVis latent-diffusion
    3. OpenAI's DALL-E 2
    4. PyTorch Lightning
    5. timm (PyTorch Image Models)
    6. Transformers (Hugging Face)
    7. lucidrains/vit-pytorch (lucidrains/vit-pytorch)

    AI recommended 7 alternatives but never named bytetriper/RAE. 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 bytetriper/RAE?
    pass
    AI named bytetriper/RAE explicitly

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

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

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

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bytetriper/RAE — 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