RRepoGEO

REPOGEO REPORT · LITE

bytetriper/RAE

Default branch main · commit a4d18c4d · scanned 5/12/2026, 2:48:21 AM

GitHub: 1,885 stars · 81 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 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

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

OVERALL DIRECTION
  • highabout#1
    Update the About description for clarity

    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.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    pytorch, diffusion-models, transformers, image-synthesis, autoencoders, deep-learning, computer-vision, generative-ai
  • mediumreadme#3
    Add a concise introductory sentence to the README

    Why:

    CURRENT
    ## Diffusion Transformers with Representation Autoencoders (RAE)<br><sub>Official PyTorch Implementation</sub>

### Paper | Project Page
    COPY-PASTE FIX
    ## Diffusion Transformers with Representation Autoencoders (RAE)<br><sub>Official PyTorch Implementation</sub>

This repository provides the official PyTorch implementation for high-fidelity image synthesis using Diffusion Transformers with Representation Autoencoders.

### Paper | Project Page

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
huggingface/diffusers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/diffusers · recommended 1×
  2. Lightning-AI/lightning · recommended 1×
  3. keras-team/keras · recommended 1×
  4. google/jax · recommended 1×
  5. google/flax · recommended 1×
  • CATEGORY QUERY
    How to implement high-fidelity image synthesis using a two-stage latent diffusion model pipeline?
    you: not recommended
    AI recommended (in order):
    1. Diffusers (huggingface/diffusers)
    2. PyTorch Lightning (Lightning-AI/lightning)
    3. Keras (keras-team/keras)
    4. JAX (google/jax)
    5. Flax (google/flax)
    6. TensorFlow (tensorflow/tensorflow)

    AI recommended 6 alternatives but never named bytetriper/RAE. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good approaches for training diffusion models with robust representation autoencoders for image generation?
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion
    2. SDXL
    3. VQ-GAN
    4. DALL-E 2
    5. ALAE
    6. StyleGAN-VAE hybrids
    7. NVAE
    8. VQ-VAE-2
    9. SimCLR
    10. CLIP
    11. DINO
    12. MAE

    AI recommended 12 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?

Embed your GEO score

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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