One-Step Diffusion for Detail-Rich and Temporally Consistent Video Super-Resolution

TL;DR

This paper introduces a novel one-step diffusion approach with dual LoRA learning for producing detail-rich, temporally consistent videos in super-resolution tasks, improving quality and efficiency.

Contribution

It proposes a dual LoRA learning paradigm with cross-frame retrieval and separate detail enhancement modules for the first time in real-world video super-resolution.

Findings

Achieves high-quality, temporally consistent video super-resolution in a single diffusion step.

Outperforms existing methods in accuracy and speed.

Demonstrates effective extraction of temporal priors from degraded videos.

Abstract

It is a challenging problem to reproduce rich spatial details while maintaining temporal consistency in real-world video super-resolution (Real-VSR), especially when we leverage pre-trained generative models such as stable diffusion (SD) for realistic details synthesis. Existing SD-based Real-VSR methods often compromise spatial details for temporal coherence, resulting in suboptimal visual quality. We argue that the key lies in how to effectively extract the degradation-robust temporal consistency priors from the low-quality (LQ) input video and enhance the video details while maintaining the extracted consistency priors. To achieve this, we propose a Dual LoRA Learning (DLoRAL) paradigm to train an effective SD-based one-step diffusion model, achieving realistic frame details and temporal consistency simultaneously. Specifically, we introduce a Cross-Frame Retrieval (CFR) module to aggregate complementary information across frames, and train a Consistency-LoRA (C-LoRA) to learn robust temporal representations from degraded inputs. After consistency learning, we fix the CFR and C-LoRA modules and train a Detail-LoRA (D-LoRA) to enhance spatial details while aligning with the temporal space defined by C-LoRA to keep temporal coherence. The two phases alternate iteratively for optimization, collaboratively delivering consistent and detail-rich outputs. During inference, the two LoRA branches are merged into the SD model, allowing efficient and high-quality video restoration in a single diffusion step. Experiments show that DLoRAL achieves strong performance in both accuracy and speed. Code and models are available at https://github.com/yjsunnn/DLoRAL.