15:I[981,["658","static/chunks/658-a698693f56d0116a.js","183","static/chunks/183-6beeeb801c274aaa.js","474","static/chunks/app/%5Btype%5D/layout-631c4730b83c8223.js"],"Posts"] 16:T780,Hi HN

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

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