RRepoGEO

REPOGEO REPORT · LITE

csslc/CCSR

Default branch CCSR-v2.0 · commit 878f5adf · scanned 6/15/2026, 4:42:43 PM

GitHub: 605 stars · 43 forks

AI VISIBILITY SCORE
28 /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
2 / 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 csslc/CCSR, 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
    Add a clear, concise introductory sentence to the README

    Why:

    CURRENT
    The README currently starts with a centered H2: "Improving the Stability and Efficiency of Diffusion Models for Content Consistent Super-Resolution".
    COPY-PASTE FIX
    Add the following sentence *before* the existing H2: "This repository provides the official code for **Content Consistent Super-Resolution (CCSR) using Diffusion Models**, specifically CCSRv2 and CCSRv1."
  • mediumtopics#2
    Expand repository topics to improve category visibility

    Why:

    CURRENT
    arbitrary-steps-image-super-resolution, ccsr, diffusion
    COPY-PASTE FIX
    image-super-resolution, diffusion-models, generative-ai, computer-vision, deep-learning, image-enhancement, stable-diffusion, upscaling, content-consistent-super-resolution, arbitrary-steps-image-super-resolution, ccsr
  • lowhomepage#3
    Set the project's academic paper URL as the repository homepage

    Why:

    COPY-PASTE FIX
    https://arxiv.org/pdf/2401.00877

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 csslc/CCSR
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Stable Diffusion XL (SDXL)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Stable Diffusion XL (SDXL) · recommended 1×
  2. SDXL Refiner · recommended 1×
  3. Latent Consistency Models (LCM) · recommended 1×
  4. Turbo Models · recommended 1×
  5. SwinIR · recommended 1×
  • CATEGORY QUERY
    How to efficiently upscale images using diffusion models with minimal inference steps?
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion XL (SDXL)
    2. SDXL Refiner
    3. Latent Consistency Models (LCM)
    4. Turbo Models
    5. SwinIR
    6. Real-ESRGAN
    7. ControlNet
    8. Upscale Diffusion
    9. Hugging Face Diffusers
    10. DPM-Solver++ (SDE Karras)
    11. UniPC
    12. Euler A
    13. LoRA (Low-Rank Adaptation)
    14. NVIDIA
    15. CUDA
    16. PyTorch
    17. torch.compile
    18. xformers

    AI recommended 18 alternatives but never named csslc/CCSR. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a super-resolution model offering flexible diffusion steps without requiring retraining.
    you: not recommended
    AI recommended (in order):
    1. SDEdit
    2. DDIM
    3. Palette
    4. Latent Diffusion Models (LDMs) / Stable Diffusion
    5. Score-based Generative Models (SGM) / NCSN++

    AI recommended 5 alternatives but never named csslc/CCSR. 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 csslc/CCSR?
    pass
    AI named csslc/CCSR explicitly

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

  • If a team adopts csslc/CCSR in production, what risks or prerequisites should they evaluate first?
    pass
    AI named csslc/CCSR 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 csslc/CCSR solve, and who is the primary audience?
    pass
    AI did not name csslc/CCSR — 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 csslc/CCSR. 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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csslc/CCSR — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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