RRepoGEO

REPOGEO REPORT · LITE

bahjat-kawar/ddrm

Default branch master · commit 32b6b3cc · scanned 6/15/2026, 12:37:51 AM

GitHub: 667 stars · 68 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 bahjat-kawar/ddrm, 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
  • highreadme#1
    Clarify README's opening to emphasize research framework

    Why:

    CURRENT
    DDRM uses pre-trained DDPMs for solving general linear inverse problems. It does so efficiently and without problem-specific supervised training.
    COPY-PASTE FIX
    DDRM is a research framework that leverages pre-trained Denoising Diffusion Probabilistic Models (DDPMs) to efficiently solve general linear inverse problems in image restoration, without requiring problem-specific supervised training.
  • mediumhomepage#2
    Add project website to repository homepage field

    Why:

    COPY-PASTE FIX
    Locate the 'Project Website' URL from the README and add it to the repository's homepage field.
  • mediumtopics#3
    Expand repository topics with relevant research terms

    Why:

    CURRENT
    deblurring, diffusion, diffusion-models, inpainting, inverse-problems, score-based, super-resolution, variational-inference
    COPY-PASTE FIX
    deblurring, deep-learning, diffusion, diffusion-models, generative-models, image-inpainting, image-restoration, inverse-problems, score-based-models, super-resolution, variational-inference

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 bahjat-kawar/ddrm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Diffusion Posterior Sampling (DPS)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Diffusion Posterior Sampling (DPS) · recommended 1×
  2. Score-based Generative Models (SGM) with Plug-and-Play (PnP) Priors · recommended 1×
  3. Diffusion Models as Implicit Priors (DIP-like approaches) · recommended 1×
  4. Null-space Diffusion (NuSD) · recommended 1×
  5. Diffusion Autoencoders (DAE) · recommended 1×
  • CATEGORY QUERY
    How to efficiently solve image inverse problems using pre-trained diffusion models without supervised training?
    you: not recommended
    AI recommended (in order):
    1. Diffusion Posterior Sampling (DPS)
    2. Score-based Generative Models (SGM) with Plug-and-Play (PnP) Priors
    3. Diffusion Models as Implicit Priors (DIP-like approaches)
    4. Null-space Diffusion (NuSD)
    5. Diffusion Autoencoders (DAE)
    6. Pre-trained Diffusion Models with External Optimization/Sampling

    AI recommended 6 alternatives but never named bahjat-kawar/ddrm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective methods for image restoration like deblurring and inpainting using score-based generative models?
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion
    2. DALL-E 2
    3. Midjourney
    4. Imagen
    5. ControlNet
    6. Score-SDE
    7. NCSNv3
    8. StyleGAN
    9. Latent Diffusion Models

    AI recommended 9 alternatives but never named bahjat-kawar/ddrm. 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 bahjat-kawar/ddrm?
    pass
    AI named bahjat-kawar/ddrm explicitly

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

  • If a team adopts bahjat-kawar/ddrm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named bahjat-kawar/ddrm 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 bahjat-kawar/ddrm solve, and who is the primary audience?
    pass
    AI named bahjat-kawar/ddrm 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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MARKDOWN (README)
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bahjat-kawar/ddrm — 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