FlashVSR

Real-time video quality enhancement for high-resolution content.

Playground
APIPricing
~163.06s

This model is no longer available on Segmind

Please use our selection of newer models.

Loading outputExample output

FlashVSR: Real-Time Video Super-Resolution Model

Edited by Segmind Team on October 24, 2025.

What is FlashVSR?

FlashVSR is an advanced AI model for real-time video quality enhancement. It utilizes sophisticated diffusion approaches to seamlessly boost video resolution even when it is being streamed live. FlashVSR with its integrated one-step diffusion and locality-constrained sparse attention that enables it to handle high-resolution videos at 17 frames per second on a single GPU, making it eons ahead of other models. The model is capable of rendering high-quality results due to its robust training, based on the vast VSR-120K dataset of 120,000 videos and 180,000 images, making it ideal for various platforms that need high-resolution videos in real-time.

Key Features of FlashVSR

  • It can execute real-time processing at 17 FPS on a single GPU
  • It is up to 12x faster than traditional diffusion-based VSR models
  • It has flexible upscaling options (1x to 4x resolution)
  • It is integrated with an intelligent color correction system
  • It includes an adjustable frame attention range for temporal consistency
  • It comes with two operational modes: full quality and tiny (speed-optimized)
  • It is trained on a diverse VSR-120K dataset for superior results

Best Use Cases

  • Content enhancement for live streaming videos
  • Online video platform quality improvement
  • Professional video post-production
  • Security footage enhancement
  • Gaming content upscaling
  • Legacy video restoration
  • Real-time broadcast upscaling

Prompt Tips and Output Quality

For optimal results, follow these prompt tips:

  • Choose 'full' mode for the highest quality output, 'tiny' for faster processing
  • Set scale factor based on your needs: 4x for maximum detail and 2x for balanced performance
  • Enable color_fix for realistic results, especially in varied lighting conditions
  • Adjust local_range (5-15) based on video motion; higher values for complex movement
  • Use consistent seed values for reproducible results across multiple runs