RRepoGEO

REPOGEO REPORT · LITE

OpenImagingLab/FlashVSR

Default branch main · commit b527c6f2 · scanned 5/12/2026, 9:13:49 PM

GitHub: 1,591 stars · 130 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 OpenImagingLab/FlashVSR, 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 concise tagline to the README's opening

    Why:

    CURRENT
    The README starts with `# ⚡ FlashVSR` followed by the paper title and abstract.
    COPY-PASTE FIX
    Add a line immediately after the H1: `The first diffusion-based model for real-time streaming video super-resolution.`
  • mediumtopics#2
    Add more specific topics for real-time and streaming VSR

    Why:

    CURRENT
    diffusion-models, video-restoration, video-super-resolution
    COPY-PASTE FIX
    diffusion-models, video-restoration, video-super-resolution, real-time-vsr, streaming-vsr, diffusion-vsr
  • lowreadme#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section:
```
## 💡 Comparison with Alternatives

FlashVSR is a novel diffusion-based model architecture for real-time streaming video super-resolution, not a generic inference engine or SDK. Unlike tools such as NVIDIA TensorRT or ONNX Runtime, FlashVSR provides the core model innovation, offering a unique approach compared to other VSR models like ESRGAN-Lite by leveraging efficient one-step diffusion.
```

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 OpenImagingLab/FlashVSR
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ONNX Runtime
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ONNX Runtime · recommended 2×
  2. TensorFlow Lite · recommended 2×
  3. TensorFlow Serving · recommended 2×
  4. NVIDIA TensorRT · recommended 1×
  5. NVIDIA Broadcast Engine SDK · recommended 1×
  • CATEGORY QUERY
    How to achieve real-time video super-resolution for streaming applications efficiently?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. NVIDIA Broadcast Engine SDK
    3. OpenVINO Toolkit
    4. ONNX Runtime
    5. ESRGAN-Lite
    6. Real-ESRGAN
    7. Fast-SRGAN
    8. TensorFlow Lite
    9. TensorFlow Serving
    10. PyTorch JIT (TorchScript)
    11. LibTorch
    12. MediaPipe
    13. FFmpeg

    AI recommended 13 alternatives but never named OpenImagingLab/FlashVSR. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an efficient diffusion model framework for high-quality video restoration with low latency.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Broadcast SDK
    2. Diffusers
    3. ONNX Runtime
    4. TensorRT
    5. PyTorch
    6. OpenVINO
    7. TensorFlow
    8. KerasCV
    9. TensorFlow Lite
    10. TensorFlow Serving

    AI recommended 10 alternatives but never named OpenImagingLab/FlashVSR. 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 OpenImagingLab/FlashVSR?
    pass
    AI named OpenImagingLab/FlashVSR explicitly

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

  • If a team adopts OpenImagingLab/FlashVSR in production, what risks or prerequisites should they evaluate first?
    pass
    AI named OpenImagingLab/FlashVSR 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 OpenImagingLab/FlashVSR solve, and who is the primary audience?
    pass
    AI named OpenImagingLab/FlashVSR 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 OpenImagingLab/FlashVSR. 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/OpenImagingLab/FlashVSR.svg)](https://repogeo.com/en/r/OpenImagingLab/FlashVSR)
HTML
<a href="https://repogeo.com/en/r/OpenImagingLab/FlashVSR"><img src="https://repogeo.com/badge/OpenImagingLab/FlashVSR.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

OpenImagingLab/FlashVSR — 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