RRepoGEO

REPOGEO REPORT · LITE

ChunmingHe/awesome-diffusion-models-in-low-level-vision

Default branch main · commit 56dbaaba · scanned 6/17/2026, 8:27:58 AM

GitHub: 555 stars · 12 forks

AI VISIBILITY SCORE
22 /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
1 / 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 ChunmingHe/awesome-diffusion-models-in-low-level-vision, 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
    Clarify repository type in the About description

    Why:

    CURRENT
    A Repository for Diffusion-Model-related Papers in Low-level Vision
    COPY-PASTE FIX
    A curated list and comprehensive survey of Diffusion Model papers and resources specifically for low-level vision tasks.
  • highlicense#2
    Add a LICENSE file

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the root of the repository with your chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0).
  • mediumhomepage#3
    Add a homepage URL to the About section

    Why:

    COPY-PASTE FIX
    Set the homepage URL in the repository settings to `https://github.com/ChunmingHe/awesome-diffusion-models-in-low-level-vision` (or a dedicated project page if one exists).

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 ChunmingHe/awesome-diffusion-models-in-low-level-vision
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. rwightman/pytorch-image-models · recommended 1×
  3. keras-team/keras-cv · recommended 1×
  4. xinntao/ESRGAN · recommended 1×
  5. Stability-AI/StableDiffusion · recommended 1×
  • CATEGORY QUERY
    How can diffusion models be applied to enhance image quality in low-level vision tasks?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Diffusers (huggingface/diffusers)
    2. PyTorch Image Models (timm) (rwightman/pytorch-image-models)
    3. Keras-CV (keras-team/keras-cv)
    4. ESRGAN (Enhanced Super-Resolution Generative Adversarial Networks) (xinntao/ESRGAN)
    5. Stable Diffusion (Stability-AI/StableDiffusion)
    6. ControlNet (lllyasviel/ControlNet)
    7. LaMa (Large Mask Inpainting) (saic-mdc/lama)
    8. SwinIR (JingyunLiang/SwinIR)
    9. DeOldify (jantic/DeOldify)

    AI recommended 9 alternatives but never named ChunmingHe/awesome-diffusion-models-in-low-level-vision. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a comprehensive survey of diffusion models for image restoration problems?
    you: not recommended
    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 ChunmingHe/awesome-diffusion-models-in-low-level-vision?
    pass
    AI did not name ChunmingHe/awesome-diffusion-models-in-low-level-vision — 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 ChunmingHe/awesome-diffusion-models-in-low-level-vision in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ChunmingHe/awesome-diffusion-models-in-low-level-vision 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 ChunmingHe/awesome-diffusion-models-in-low-level-vision solve, and who is the primary audience?
    pass
    AI did not name ChunmingHe/awesome-diffusion-models-in-low-level-vision — 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?

Embed your GEO score

Drop this badge into the README of ChunmingHe/awesome-diffusion-models-in-low-level-vision. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite