# One-Step Diffusion for Detail-Rich and Temporally Consistent Video Super-Resolution arXiv: https://arxiv.org/abs/2506.15591 ยท HTML: https://mlsanity.com/paper/2506.15591/ ## Key facts - Cited by 15 papers in the map (data through 14 Oct 2026). - Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. - Builds on 45 of its 54 references that are in the map. - First posted on arXiv 18 Jun 2025. ## Placed in - Diffusion models / [Inverse problems and image restoration](https://mlsanity.com/diffusion/inverse-problems-and-image-restoration/) ## Builds on (most cited first, 30 of 45) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021) - [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018) - [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023) - [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023) - [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024) - [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016) - [MUSIQ: Multi-scale Image Quality Transformer](https://mlsanity.com/paper/2108.05997/) (2021) - [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022) - [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021) - [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020) - [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017) - [MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2204.08958/) (2022) - [EDVR: Video Restoration with Enhanced Deformable Convolutional Networks](https://mlsanity.com/paper/1905.02716/) (2019) - [Exploiting Diffusion Prior for Real-World Image Super-Resolution](https://mlsanity.com/paper/2305.07015/) (2023) - [Optical Flow Estimation using a Spatial Pyramid Network](https://mlsanity.com/paper/1611.00850/) (2016) - [PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models](https://mlsanity.com/paper/2003.03808/) (2020) - [BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment](https://mlsanity.com/paper/2104.13371/) (2021) - [Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives](https://mlsanity.com/paper/2211.04894/) (2022) - [SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution](https://mlsanity.com/paper/2311.16518/) (2023) - [BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond](https://mlsanity.com/paper/2012.02181/) (2020) - [Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild](https://mlsanity.com/paper/2401.13627/) (2024) - [One-Step Effective Diffusion Network for Real-World Image Super-Resolution](https://mlsanity.com/paper/2406.08177/) (2024) - [SinSR: Diffusion-Based Image Super-Resolution in a Single Step](https://mlsanity.com/paper/2311.14760/) (2023) - [Detail-revealing Deep Video Super-resolution](https://mlsanity.com/paper/1704.02738/) (2017) - [Investigating Tradeoffs in Real-World Video Super-Resolution](https://mlsanity.com/paper/2111.12704/) (2021) - [Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution](https://mlsanity.com/paper/2312.06640/) (2023) - [Video Super-Resolution Transformer](https://mlsanity.com/paper/2106.06847/) (2021) - [Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approach](https://mlsanity.com/paper/2412.03017/) (2024) - [To learn image super-resolution, use a GAN to learn how to do image degradation first](https://mlsanity.com/paper/1807.11458/) (2018) - [MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution](https://mlsanity.com/paper/2007.11803/) (2020) ## Built on by (most cited first, 15 of 15) - [Single-step Diffusion-based Video Coding with Semantic-Temporal Guidance](https://mlsanity.com/paper/2512.07480/) (2025) - [InfVSR: Toward Consistency-Driven Streaming Generative Video Super-Resolution](https://mlsanity.com/paper/2510.00948/) (2025) - [PiD: Fast and High-Resolution Latent Decoding with Pixel Diffusion](https://mlsanity.com/paper/2605.23902/) (2026) - [Improved Adversarial Diffusion Compression for Real-World Video Super-Resolution](https://mlsanity.com/paper/2603.00458/) (2026) - [DUO-VSR: Dual-Stream Distillation for One-Step Video Super-Resolution](https://mlsanity.com/paper/2603.22271/) (2026) - [Asymmetric VAE for One-Step Video Super-Resolution Acceleration](https://mlsanity.com/paper/2509.24142/) (2025) - [Mirage: One-Step Video Diffusion for Photorealistic and Coherent Asset Editing in Driving Scenes](https://mlsanity.com/paper/2512.24227/) (2025) - [Towards Redundancy Reduction in Diffusion Models for Efficient Video Super-Resolution](https://mlsanity.com/paper/2509.23980/) (2025) - [Taming Real-World Space-Time Video Super-Resolution with One-Step Diffusion](https://mlsanity.com/paper/2601.20308/) (2026) - [TRaM-VSR: Importance-Aware Token Routing and Merging for One-Step Diffusion Video Super-Resolution](https://mlsanity.com/paper/2607.22231/) (2026) - [Tiled Prompts: Overcoming Prompt Misguidance in Image and Video Super-Resolution](https://mlsanity.com/paper/2602.03342/) (2026) - [GaussianZoom: Progressive Zoom-in Generative 3D Gaussian Splatting with Geometric and Semantic Guidance](https://mlsanity.com/paper/2605.18252/) (2026) - [VDFP: Video Deflickering with Flicker-banding Priors](https://mlsanity.com/paper/2605.21079/) (2026) - [RelayVSR: Large-Small Model Collaboration for Efficient Real-World Video Super-Resolution](https://mlsanity.com/paper/2609.37850/) (2026) - [High-Throughput Generative Video Super-Resolution on a Single Consumer GPU](https://mlsanity.com/paper/2610.13179/) (2026) ## Abstract (arXiv) 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.