# Asymmetric VAE for One-Step Video Super-Resolution Acceleration

arXiv: https://arxiv.org/abs/2509.24142 · HTML: https://mlsanity.com/paper/2509.24142/

## Key facts

- Cited by 3 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 29 of its 36 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Placed in

- Diffusion models / [Inverse problems and image restoration](https://mlsanity.com/diffusion/inverse-problems-and-image-restoration/)

## Builds on (most cited first, 29 of 29)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [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)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (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)
- [I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models](https://mlsanity.com/paper/2311.04145/) (2023)
- [Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives](https://mlsanity.com/paper/2211.04894/) (2022)
- [Learning Blind Video Temporal Consistency](https://mlsanity.com/paper/1808.00449/) (2018)
- [VRT: A Video Restoration Transformer](https://mlsanity.com/paper/2201.12288/) (2022)
- [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)
- [Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2503.18352/) (2025)
- [VEnhancer: Generative Space-Time Enhancement for Video Generation](https://mlsanity.com/paper/2407.07667/) (2024)
- [SeedVR: Seeding Infinity in Diffusion Transformer Towards Generic Video Restoration](https://mlsanity.com/paper/2501.01320/) (2025)
- [Motion-Guided Latent Diffusion for Temporally Consistent Real-world Video Super-resolution](https://mlsanity.com/paper/2312.00853/) (2023)
- [STAR: Spatial-Temporal Augmentation with Text-to-Video Models for Real-World Video Super-Resolution](https://mlsanity.com/paper/2501.02976/) (2025)
- [DOVE: Efficient One-Step Diffusion Model for Real-World Video Super-Resolution](https://mlsanity.com/paper/2505.16239/) (2025)
- [Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution](https://mlsanity.com/paper/1806.05764/) (2018)
- [Learned Image Compression with Dictionary-based Entropy Model](https://mlsanity.com/paper/2504.00496/) (2025)
- [Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-Resolution](https://mlsanity.com/paper/2212.05342/) (2022)
- [One-Step Diffusion for Detail-Rich and Temporally Consistent Video Super-Resolution](https://mlsanity.com/paper/2506.15591/) (2025)
- [Ultra-High-Resolution Image Synthesis: Data, Method and Evaluation](https://mlsanity.com/paper/2506.01331/) (2025)

## Built on by (most cited first, 3 of 3)

- [Efficient Video Diffusion Models: Advancements and Challenges](https://mlsanity.com/paper/2604.15911/) (2026)
- [LCUDiff: Latent Capacity Upgrade Diffusion for Faithful Human Body Restoration](https://mlsanity.com/paper/2602.04406/) (2026)
- [DiffST: Spatiotemporal-Aware Diffusion for Real-World Space-Time Video Super-Resolution](https://mlsanity.com/paper/2605.13182/) (2026)

## Abstract (arXiv)

Diffusion models have significant advantages in the field of real-world video super-resolution and have demonstrated strong performance in past research. In recent diffusion-based video super-resolution (VSR) models, the number of sampling steps has been reduced to just one, yet there remains significant room for further optimization in inference efficiency. In this paper, we propose FastVSR, which achieves substantial reductions in computational cost by implementing a high compression VAE (spatial compression ratio of 16, denoted as f16). We design the structure of the f16 VAE and introduce a stable training framework. We employ pixel shuffle and channel replication to achieve additional upsampling. Furthermore, we propose a lower-bound-guided training strategy, which introduces a simpler training objective as a lower bound for the VAE's performance. It makes the training process more stable and easier to converge. Experimental results show that FastVSR achieves speedups of 111.9 times compared to multi-step models and 3.92 times compared to existing one-step models. We will release code and models at https://github.com/JianzeLi-114/FastVSR.