REPOGEO REPORT · LITE
OpenImagingLab/FlashVSR
Default branch main · commit b527c6f2 · scanned 6/23/2026, 5:53:12 AM
GitHub: 1,680 stars · 137 forks
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.
2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).
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.
- highreadme#1Add 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#2Expand repository topics with more specific keywords
Why:
CURRENTdiffusion-models, video-restoration, video-super-resolution
COPY-PASTE FIXdiffusion-models, video-restoration, video-super-resolution, real-time, streaming, deep-learning-model, pytorch
- mediumreadme#3Add 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.
- NVIDIA Broadcast SDK · recommended 1×
- OpenVINO · recommended 1×
- TensorRT · recommended 1×
- ONNX Runtime · recommended 1×
- MediaPipe · recommended 1×
- CATEGORY QUERYNeed a fast video super-resolution solution for real-time streaming applications.you: not recommendedAI recommended (in order):
- NVIDIA Broadcast SDK
- OpenVINO
- TensorRT
- ONNX Runtime
- MediaPipe
- FFmpeg
AI recommended 6 alternatives but never named OpenImagingLab/FlashVSR. This is the gap to close.
Show full AI answer
- CATEGORY QUERYLooking for an efficient diffusion model framework to enhance video resolution quickly.you: not recommendedAI recommended (in order):
- Diffusers (huggingface/diffusers)
- Kandinsky (ai-forever/Kandinsky)
- MMDetection (open-mmlab/mmdetection)
- MMEditing (open-mmlab/mmediting)
- PyTorch Video (facebookresearch/pytorchvideo)
- TensorFlow Lite
- 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 completenesspass
- README presencepass
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?passAI 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?passAI 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?passAI 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
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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