RRepoGEO

REPOGEO REPORT · LITE

wyhuai/DDNM

Default branch main · commit 00b58eac · scanned 5/23/2026, 11:38:39 AM

GitHub: 1,343 stars · 105 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 wyhuai/DDNM, 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
    Strengthen the README's opening sentence to highlight zero-shot, no-training capability

    Why:

    CURRENT
    This repository contains the code release for *Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model*. **DDNM** can solve various image restoration tasks **without any optimization or training! Yes, in a zero-shot manner**.
    COPY-PASTE FIX
    This repository provides the code for **DDNM**, a Zero-Shot Image Restoration method that solves various tasks like denoising, inpainting, and super-resolution **without any optimization or training** using a Denoising Diffusion Null-Space Model.
  • mediumtopics#2
    Expand repository topics to include related learning paradigms

    Why:

    CURRENT
    diffusion-models, iclr, iclr2023, image-restoration, zero-shot
    COPY-PASTE FIX
    diffusion-models, iclr, iclr2023, image-restoration, zero-shot, unsupervised-learning, multi-task-learning
  • lowhomepage#3
    Add the Colab demo link to the repository homepage field

    Why:

    COPY-PASTE FIX
    https://colab.research.google.com/drive/1SRSD6GXGqU0eO2CoTNY-2WykB9qRZHJv?usp=sharing

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 wyhuai/DDNM
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. ControlNet · recommended 1×
  3. GFPGAN · recommended 1×
  4. CodeFormer · recommended 1×
  5. ESRGAN · recommended 1×
  • CATEGORY QUERY
    How to perform image restoration tasks like denoising or inpainting without needing to train a model?
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion
    2. ControlNet
    3. GFPGAN
    4. CodeFormer
    5. ESRGAN
    6. Real-ESRGAN
    7. OpenCV
    8. ImageJ

    AI recommended 8 alternatives but never named wyhuai/DDNM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a diffusion model library to restore old photos and enhance image quality.
    you: not recommended
    AI recommended (in order):
    1. GFPGAN (TencentARC/GFPGAN)
    2. CodeFormer (TencentARC/CodeFormer)
    3. Stable Diffusion (Stability-AI/stablediffusion)
    4. SwinIR (JingyunLiang/SwinIR)
    5. Real-ESRGAN (xinntao/Real-ESRGAN)

    AI recommended 5 alternatives but never named wyhuai/DDNM. 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 wyhuai/DDNM?
    pass
    AI named wyhuai/DDNM explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of wyhuai/DDNM. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/wyhuai/DDNM.svg)](https://repogeo.com/en/r/wyhuai/DDNM)
HTML
<a href="https://repogeo.com/en/r/wyhuai/DDNM"><img src="https://repogeo.com/badge/wyhuai/DDNM.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

wyhuai/DDNM — 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