OpenImagingLab/FlashVSR

• highreadme#1

  Add a concise introductory sentence to the README

  原因：

  当前

  # ⚡ 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

  复制粘贴的修复

  复制

  # ⚡ 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

  待办完成忽略
• mediumtopics#2

  Expand repository topics with more specific keywords

  原因：

  当前

  diffusion-models, video-restoration, video-super-resolution

  复制粘贴的修复

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  diffusion-models, video-restoration, video-super-resolution, real-time, streaming, deep-learning-model, pytorch

  待办完成忽略
• mediumreadme#3

  Add a 'Key Features' section to highlight core differentiators

  原因：

  复制粘贴的修复

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  ### ✨ 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.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  Need a fast video super-resolution solution for real-time streaming applications.

  你：未被推荐
  AI 推荐顺序：

  1. NVIDIA Broadcast SDK
  2. OpenVINO
  3. TensorRT
  4. ONNX Runtime
  5. MediaPipe
  6. FFmpeg

  AI 推荐了 6 个替代方案，却始终没点名 OpenImagingLab/FlashVSR。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  Looking for an efficient diffusion model framework to enhance video resolution quickly.

  你：未被推荐
  AI 推荐顺序：

  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 推荐了 7 个替代方案，却始终没点名 OpenImagingLab/FlashVSR。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of OpenImagingLab/FlashVSR?

  pass
  ✓AI 明确点名了 OpenImagingLab/FlashVSR

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts OpenImagingLab/FlashVSR in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 OpenImagingLab/FlashVSR

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo OpenImagingLab/FlashVSR solve, and who is the primary audience?

  pass
  ✓AI 明确点名了 OpenImagingLab/FlashVSR

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？