RRepoGEO

REPOGEO REPORT · LITE

OpenImagingLab/FlashVSR

Default branch main · commit b527c6f2 · scanned 6/23/2026, 5:53:12 AM

GitHub: 1,680 stars · 137 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
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 introductory sentence to the README

    Why:

    CURRENT
    # ⚡ FlashVSR
    **Towards Real-Time Diffusion-Based Streaming Video Super-Resolution**
    Authors: Junhao Zhuang, Shi Guo, Xin Cai, Xiaohui Li, Yihao Liu, Chun Yuan, Tianfan Xue
    COPY-PASTE FIX
    # ⚡ FlashVSR
    **Towards Real-Time Diffusion-Based Streaming Video Super-Resolution**
    This repository provides the official PyTorch implementation of FlashVSR, an efficient one-step diffusion framework designed for real-time streaming video super-resolution.
    Authors: Junhao Zhuang, Shi Guo, Xin Cai, Xiaohui Li, Yihao Liu, Chun Yuan, Tianfan Xue
  • mediumtopics#2
    Expand repository topics with more specific keywords

    Why:

    CURRENT
    diffusion-models, video-restoration, video-super-resolution
    COPY-PASTE FIX
    diffusion-models, video-restoration, video-super-resolution, real-time, streaming, deep-learning-model, pytorch
  • mediumreadme#3
    Add a 'Key Features' section to highlight core differentiators

    Why:

    COPY-PASTE FIX
    ### ✨ Key Features
    
    *   **Real-Time Performance:** Achieves ~17 FPS for 768 × 1408 videos on a single A100 GPU, making diffusion-based VSR practical for streaming.
    *   **Efficiency:** Utilizes a train-friendly three-stage distillation pipeline and a tiny conditional decoder for accelerated reconstruction.
    *   **Scalability:** Employs locality-constrained sparse attention to handle ultra-high resolutions and bridge the train–test resolution gap.
    *   **Diffusion-Based:** Leverages the power of diffusion models for high-quality video super-resolution with a novel one-step streaming framework.

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
NVIDIA Broadcast SDK
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA Broadcast SDK · recommended 1×
  2. OpenVINO · recommended 1×
  3. TensorRT · recommended 1×
  4. ONNX Runtime · recommended 1×
  5. MediaPipe · recommended 1×
  • CATEGORY QUERY
    Need a fast video super-resolution solution for real-time streaming applications.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Broadcast SDK
    2. OpenVINO
    3. TensorRT
    4. ONNX Runtime
    5. MediaPipe
    6. FFmpeg

    AI recommended 6 alternatives but never named OpenImagingLab/FlashVSR. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an efficient diffusion model framework to enhance video resolution quickly.
    you: not recommended
    AI recommended (in order):
    1. Diffusers (huggingface/diffusers)
    2. Kandinsky (ai-forever/Kandinsky)
    3. MMDetection (open-mmlab/mmdetection)
    4. MMEditing (open-mmlab/mmediting)
    5. PyTorch Video (facebookresearch/pytorchvideo)
    6. TensorFlow Lite
    7. ONNX Runtime (microsoft/onnxruntime)

    AI recommended 7 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?

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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