SoL-Refiner: Speed-of-Light One-Step Refinement for High-Resolution Video

arXiv:2609.37969 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Comments: 15 pages

Project page: https://nvlabs.github.io/Sana/Sol-Engine/H3-Super-Acceleration

License: http://creativecommons.org/licenses/by-nc-nd/4.0/

The gist: High-resolution video generation is expensive, as its cost grows rapidly with the number of spatiotemporal tokens.

Terminology

Abstract

High-resolution video generation is expensive, as its cost grows rapidly with the number of spatiotemporal tokens. A practical alternative first generates a lower-resolution video and then applies a refiner, but conventional multi-step refinement introduces a second sampling bottleneck. We present SoL-Refiner, a one-step video refiner that transforms low-resolution model outputs into 4K videos with a single denoising step. Our three-stage recipe combines high-resolution continual training, reinforcement learning (RL) post-training, and a final one-step distillation. We introduce Refiner-Bench, a video refinement benchmark constructed from the outputs of different video generators, and use a shared-input protocol to compare refiners at approximately 2K output resolution. At 2K, the one-step SoL-Refiner outperforms all external refiners on the VBench and UniPercept averages, while at 3840! times!2176 it improves both metrics over the three-step LTX-2.3 Refiner. With the complete acceleration stack, SoL-Refiner achieves an 8.91 times speedup in refinement latency over the same baseline in our 2K latency setting.

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