RRepoGEO

REPOGEO REPORT · LITE

openai/improved-diffusion

Default branch main · commit 1bc7bbbd · scanned 6/27/2026, 8:53:24 AM

GitHub: 3,831 stars · 550 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
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 openai/improved-diffusion, 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 relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    diffusion-models, generative-ai, image-synthesis, deep-learning, pytorch, denoising
  • mediumreadme#2
    Refine the README's opening paragraph to highlight key differentiators

    Why:

    CURRENT
    # improved-diffusion

This is the codebase for Improved Denoising Diffusion Probabilistic Models.
    COPY-PASTE FIX
    # improved-diffusion

This repository provides the official codebase for **Improved Denoising Diffusion Probabilistic Models**, offering significant advancements in generating high-quality images and achieving better log-likelihood compared to prior diffusion methods. It is designed for researchers and developers working on generative AI and image synthesis.
  • lowreadme#3
    Add a 'Key Differentiators' section to the README

    Why:

    COPY-PASTE FIX
    ## Key Differentiators

Improved Denoising Diffusion Probabilistic Models offer significant advancements over earlier diffusion methods, primarily achieving superior sample quality and better log-likelihood. This codebase implements techniques such as a refined noise schedule and a more robust network architecture to enhance image generation efficiency and fidelity.

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/improved-diffusion
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Diffusers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Diffusers · recommended 1×
  2. KerasCV · recommended 1×
  3. PyTorch-Diffusion · recommended 1×
  4. DALL-E 2 API · recommended 1×
  5. Imagen · recommended 1×
  • CATEGORY QUERY
    Seeking a Python library for generating high-quality images using advanced diffusion techniques.
    you: not recommended
    AI recommended (in order):
    1. Diffusers
    2. KerasCV
    3. PyTorch-Diffusion
    4. DALL-E 2 API
    5. Imagen

    AI recommended 5 alternatives but never named openai/improved-diffusion. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to train a custom deep learning model for image synthesis and denoising?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. torchvision
    3. diffusers
    4. kornia
    5. TensorFlow
    6. TensorFlow Hub
    7. JAX
    8. Flax
    9. Haiku
    10. Keras
    11. Fast.ai

    AI recommended 11 alternatives but never named openai/improved-diffusion. 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 openai/improved-diffusion?
    pass
    AI named openai/improved-diffusion explicitly

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

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

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

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openai/improved-diffusion — 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