RRepoGEO

REPOGEO REPORT · LITE

openai/image-gpt

Default branch master · commit c6af2ebf · scanned 6/26/2026, 3:08:15 PM

GitHub: 2,095 stars · 389 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)

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

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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/image-gpt, 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
  • highreadme#1
    Reposition the README's opening to clarify its research and archived status

    Why:

    CURRENT
    **Status:** Archive (code is provided as-is, no updates expected)

# image-gpt

Code and models from the paper "Generative Pretraining from Pixels".
    COPY-PASTE FIX
    **Status:** Archive (code is provided as-is, no updates expected)

# image-gpt: Generative Pretraining from Pixels (iGPT) Research Codebase

This repository provides the official research codebase and models from the paper "Generative Pretraining from Pixels". It demonstrates how large-scale transformer models can be applied to unconditional image generation, treating images as sequences of discrete tokens. Intended for AI researchers and machine learning engineers, this project serves as a starting point for experimenting with the iGPT model architecture.
  • highabout#2
    Add a concise description for the repository

    Why:

    COPY-PASTE FIX
    Official archived research codebase for 'Generative Pretraining from Pixels' (iGPT), demonstrating large-scale transformer models for unconditional image generation. For AI researchers and ML engineers.

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/image-gpt
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Stable Diffusion · recommended 1×
  2. DALL-E 3 · recommended 1×
  3. Midjourney · recommended 1×
  4. SDXL · recommended 1×
  5. Imagen · recommended 1×
  • CATEGORY QUERY
    How can I generate images using a large-scale generative pretraining model from pixels?
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion
    2. DALL-E 3
    3. Midjourney
    4. SDXL
    5. Imagen
    6. Kandinsky
    7. DeepFloyd IF

    AI recommended 7 alternatives but never named openai/image-gpt. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks or libraries exist for experimenting with generative image models based on transformers?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PyTorch Lightning (Lightning-AI/pytorch-lightning)
    3. Keras (keras-team/keras)
    4. TensorFlow (tensorflow/tensorflow)
    5. Diffusers (huggingface/diffusers)
    6. OpenAI's Jukebox
    7. DALL-E
    8. TensorFlow Generative Models

    AI recommended 8 alternatives but never named openai/image-gpt. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 openai/image-gpt?
    pass
    AI did not name openai/image-gpt — 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/image-gpt in production, what risks or prerequisites should they evaluate first?
    pass
    AI named openai/image-gpt 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/image-gpt solve, and who is the primary audience?
    pass
    AI named openai/image-gpt 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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