RRepoGEO

REPOGEO REPORT · LITE

wangkai930418/awesome-diffusion-categorized

Default branch master · commit 15a421b8 · scanned 5/17/2026, 12:12:58 PM

GitHub: 2,203 stars · 102 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
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 wangkai930418/awesome-diffusion-categorized, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with your chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0).
  • highreadme#2
    Reposition the README's opening to clearly state its purpose as a categorized paper list

    Why:

    CURRENT
    # Awesome Diffusion Categorized
    COPY-PASTE FIX
    # Awesome Diffusion Categorized

A curated and categorized collection of research papers on diffusion models, organized by their subareas and applications.
  • mediumtopics#3
    Add 'awesome-list' to the repository topics

    Why:

    CURRENT
    continual-learning, controlnet, detection, diffusion, diffusion-model, diffusion-models, few-shot, image-edit, inpainting, inversion, segmentation, stable-diffusion, text-guided, tracking
    COPY-PASTE FIX
    continual-learning, controlnet, detection, diffusion, diffusion-model, diffusion-models, few-shot, image-edit, inpainting, inversion, segmentation, stable-diffusion, text-guided, tracking, awesome-list

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 wangkai930418/awesome-diffusion-categorized
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Papers With Code
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Papers With Code · recommended 1×
  2. arXiv · recommended 1×
  3. Hugging Face · recommended 1×
  4. Google Scholar · recommended 1×
  5. Awesome Diffusion Models · recommended 1×
  • CATEGORY QUERY
    Where can I find categorized research papers on various diffusion model applications?
    you: not recommended
    AI recommended (in order):
    1. Papers With Code
    2. arXiv
    3. Hugging Face
    4. Google Scholar
    5. Awesome Diffusion Models
    6. Towards Data Science
    7. Medium
    8. NeurIPS
    9. ICML
    10. ICLR
    11. CVPR
    12. ICCV
    13. AAAI
    14. OpenReview
    15. CVF Open Access

    AI recommended 15 alternatives but never named wangkai930418/awesome-diffusion-categorized. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the latest diffusion model techniques for image editing and restoration tasks?
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion
    2. SDXL
    3. InstructPix2Pix
    4. ControlNet
    5. GLIDE
    6. LaMa
    7. RePaint
    8. SDEdit
    9. DreamBooth
    10. LoRA

    AI recommended 10 alternatives but never named wangkai930418/awesome-diffusion-categorized. 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 wangkai930418/awesome-diffusion-categorized?
    pass
    AI named wangkai930418/awesome-diffusion-categorized explicitly

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

  • If a team adopts wangkai930418/awesome-diffusion-categorized in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name wangkai930418/awesome-diffusion-categorized — 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?

  • In one sentence, what problem does the repo wangkai930418/awesome-diffusion-categorized solve, and who is the primary audience?
    pass
    AI did not name wangkai930418/awesome-diffusion-categorized — 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

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wangkai930418/awesome-diffusion-categorized — 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