We’ve built SeedVR2, a one-step high-resolution video restoration model that restores clarity, texture, and motion consistency — without multi-stage diffusion or frame-by-frame passes. It’s fast enough for near real-time workflows, yet accurate enough for professional production and archival recovery.
Key Features
One-Step High-Res Restoration
Single forward pass delivers clean, sharp, temporally consistent video — no cumulative diffusion errors or quality drift.
1080p+ Adaptive Window Attention
Efficiently handles large-frame input while preserving fine detail, motion stability, and temporal alignment.
Adversarial Post-Training
Trained against real video to produce natural texture and perceptual realism, outperforming standard restoration GANs.
Feature-Matching Loss
Improves structure and sharpness while keeping GAN training stable — better than traditional perceptual loss methods.
Near Real-Time Performance
Delivers high-fidelity restoration with low latency, ideal for creators, studios, and post-production pipelines.
Use Cases
Film and archival video cleanup
Creator and social video enhancement
Product / UGC / e-commerce content
Sports and fast-motion restoration
Mobile / CCTV / surveillance footage
Still-frame photo enhancement
We’d Love Your Feedback
We’re exploring how SeedVR2 can best fit real workflows — from restoration studios to automated content pipelines.
We’d love to hear your thoughts on:
Specific video restoration needs in your domain
Benchmarks or datasets you’d like to see tested
Preferred export formats or integration options
Try it here: https://www.aiupscaler.net/seedvr2?i=d1d5k
Thanks, HN — excited to hear your feedback and ideas for making SeedVR2 even more capable