SwiftVR: Real-Time One-Step Generative Video Restoration

Hugging Face Daily Papers Papers
video-restoration real-time generative diffusion autoencoder streaming consumer-gpu

Summary

SwiftVR is a real-time one-step generative video restoration framework that achieves high frame rates on consumer GPUs using efficient attention mechanisms and a lightweight restoration-aware autoencoder.

Real-time video restoration (VR) for live streams requires high-resolution outputs under strict per-frame latency constraints. Existing one-step diffusion-based VR models remain difficult to deploy on consumer-grade GPUs due to two main bottlenecks: quadratic spatial attention at high resolutions and the latency-memory overhead of large video autoencoders. We present SwiftVR, a streaming one-step generative VR framework that reduces both bottlenecks under a causal chunk-wise protocol. For attention, mask-free shifted-window self-attention gathers each spatial window into a dense tensor via deterministic indexing, keeping all attention calls on the dense scaled dot-product attention path without masks, cyclic shifts, padding, or hardware-specific sparse kernels. Because SwiftVR uses only standard dense SDPA calls, the trained model transfers to consumer GPUs without retraining or custom kernels. For autoencoding, a lightweight Restoration-aware Autoencoder enables fast chunk-wise decoding while preserving reconstruction quality. On a single H100, SwiftVR sustains 31~FPS at 2560x1440 and 14~FPS at 3840x2160, whereas all compared diffusion-based VR baselines exceed the memory limit at 4K. On a consumer RTX~5090, SwiftVR reaches 26~FPS at 1920x1080. To our knowledge, SwiftVR is the first generative VR model to achieve real-time 1080p streaming on a consumer-grade GPU, while attaining strong no-reference perceptual quality with lower inference cost. Project is available at https://h-oliday.github.io/SwiftVR.
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Cached at: 06/09/26, 08:41 AM

Paper page - SwiftVR: Real-Time One-Step Generative Video Restoration

Source: https://huggingface.co/papers/2606.09516

Abstract

SwiftVR enables real-time video restoration on consumer GPUs through efficient attention mechanisms and lightweight autoencoding, achieving high frame rates at 4K resolution.

Real-time video restoration(VR) for live streams requires high-resolution outputs under strict per-frame latency constraints. Existing one-stepdiffusion-based VRmodels remain difficult to deploy onconsumer-grade GPUs due to two main bottlenecks: quadratic spatial attention at high resolutions and the latency-memory overhead of large video autoencoders. We present SwiftVR, a streaming one-step generative VR framework that reduces both bottlenecks under acausal chunk-wise protocol. For attention, mask-freeshifted-window self-attentiongathers each spatial window into a dense tensor via deterministic indexing, keeping all attention calls on the dense scaled dot-product attention path without masks, cyclic shifts, padding, or hardware-specific sparse kernels. Because SwiftVR uses only standarddense SDPAcalls, the trained model transfers to consumer GPUs without retraining or custom kernels. For autoencoding, a lightweightRestoration-aware Autoencoderenables fast chunk-wise decoding while preserving reconstruction quality. On a single H100, SwiftVR sustains 31~FPS at 2560x1440 and 14~FPS at 3840x2160, whereas all compareddiffusion-based VRbaselines exceed the memory limit at 4K. On a consumer RTX~5090, SwiftVR reaches 26~FPS at 1920x1080. To our knowledge, SwiftVR is the first generative VR model to achieve real-time 1080p streaming on aconsumer-grade GPU, while attaining strongno-reference perceptual qualitywith lower inference cost. Project is available at https://h-oliday.github.io/SwiftVR.

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