RRepoGEO

REPOGEO REPORT · LITE

Fanghua-Yu/SUPIR

Default branch master · commit bda91af2 · scanned 5/24/2026, 2:52:57 AM

GitHub: 5,544 stars · 469 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 Fanghua-Yu/SUPIR, 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
    Reposition README's opening to clarify project type and audience

    Why:

    CURRENT
    ## (CVPR2024) Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild
> [Paper] &emsp; [Project Page] &emsp; [[Online App]](https://supir.suppixel.ai/home) <br>
> Fanghua, Yu, Jinjin Gu, Zheyuan Li, Jinfan Hu, Xiangtao Kong, Xintao Wang, Jingwen He, Yu Qiao, Chao Dong <br>
> Shenzhen Institute of Advanced Technology; Shanghai AI Laboratory; University of Sydney; The Hong Kong Polytechnic University; ARC Lab, Tencent PCG; The Chinese University of Hong Kong <br>
    COPY-PASTE FIX
    SUPIR is an open-source framework for **photo-realistic image restoration and super-resolution in the wild**, designed for researchers and developers. It provides practical algorithms leveraging diffusion models to achieve state-of-the-art results, powering applications like SupPixel AI.

## (CVPR2024) Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild
> [Paper] &emsp; [Project Page] &emsp; [[Online App]](https://supir.suppixel.ai/home) <br>
> Fanghua, Yu, Jinjin Gu, Zheyuan Li, Jinfan Hu, Xiangtao Kong, Xintao Wang, Jingwen He, Yu Qiao, Chao Dong <br>
> Shenzhen Institute of Advanced Technology; Shanghai AI Laboratory; University of Sydney; The Hong Kong Polytechnic University; ARC Lab, Tencent PCG; The Chinese University of Hong Kong <br>
  • mediumtopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    deep-learning, diffusion-models, llava, pytorch, pytorch-lightning, restoration, sdxl, stable-diffusion, super-resolution
    COPY-PASTE FIX
    deep-learning, diffusion-models, llava, pytorch, pytorch-lightning, restoration, sdxl, stable-diffusion, super-resolution, image-restoration-model, generative-ai-models, photo-restoration, real-world-image-restoration, computer-vision-models
  • lowlicense#3
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    ## License 
 This project is released under the terms specified in the [LICENSE](LICENSE) file. Please review the LICENSE file for specific conditions regarding use, modification, and distribution.

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 Fanghua-Yu/SUPIR
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Stable Diffusion · recommended 2×
  2. Adobe Photoshop · recommended 1×
  3. Topaz Photo AI · recommended 1×
  4. Luminar Neo · recommended 1×
  5. Remini · recommended 1×
  • CATEGORY QUERY
    How can I achieve high-quality, photo-realistic image restoration for diverse real-world photos?
    you: not recommended
    AI recommended (in order):
    1. Adobe Photoshop
    2. Topaz Photo AI
    3. Luminar Neo
    4. Remini
    5. GFPGAN
    6. CodeFormer
    7. Stable Diffusion
    8. Midjourney

    AI recommended 8 alternatives but never named Fanghua-Yu/SUPIR. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best deep learning models for super-resolution and image enhancement using diffusion?
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion
    2. ControlNet
    3. SwinIR
    4. SR3
    5. Palette
    6. DDPM
    7. Latent Diffusion Models

    AI recommended 7 alternatives but never named Fanghua-Yu/SUPIR. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 Fanghua-Yu/SUPIR?
    pass
    AI named Fanghua-Yu/SUPIR explicitly

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

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

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite