[OSDEnhancer] Taming Real-World Space-Time Video Super-Resolution with One-Step Diffusion (arXiv 2026)

Authors: Shuoyan Wei¹, Feng Li^2,*, Chen Zhou¹, Runmin Cong³, Yao Zhao¹, Huihui Bai¹

¹Beijing Jiaotong University, ²Hefei University of Technology, ³Shandong University

^*Corresponding Author

This repository contains the reference code for the paper "Taming Real-World Space-Time Video Super-Resolution with One-Step Diffusion".

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In this paper, we propose OSDEnhancer, the first framework that achieves real-world STVSR in one-step diffusion. Given a low-resolution and low-frame-rate video as input, OSDEnhancer generates a high-resolution and high-frame-rate video.

OSDEnhancer begins with a linear initialization to establish essential spatiotemporal structures and adapt the model for one-step reconstruction. It then applies a divide-and-conquer strategy, introducing the temporal coherence (TC) and texture enrichment (TE) LoRAs that progressively specialize in inter-frame dynamics modeling and fine-grained texture recovery, respectively, while collaborating during inference for enhanced overall performance. A bidirectional VAE decoder employs deformable recurrent blocks to leverage the multi-scale structure of the vanilla VAE, enhancing latent-to-pixel reconstruction through joint multi-scale deformable aggregation and inter-frame feature propagation.

🔈News

• ✅ [May 2026] The inference code and pretrained checkpoints are now available 👉
• ✅ [Jan 2026] The arXiv version of our paper has been released 👉

📚 Installation

git clone https://github.com/W-Shuoyan/OSDEnhancer.git
cd OSDEnhancer
conda create -n OSDEnhancer python=3.10
conda activate OSDEnhancer
pip install torch==2.8.0+cu128 torchvision==0.23.0+cu128 --index-url https://download.pytorch.org/whl/cu128
pip install -r requirements.txt

🚀 Usage

Pretrained Checkpoints

The pretrained checkpoint is available below.

Model Name	Base Model	Download Link 🔗
OSDEnhancer-v1.0	CogVideoX1.5-5B	🤗 Hugging Face

By default, the inference script automatically loads the checkpoint from Hugging Face. For local checkpoint loading, the checkpoint directory should be organized as follows:

ckpt/
├── transformer/
│   ├── config.json
│   ├── diffusion_pytorch_model-00001-of-00002.safetensors
│   ├── diffusion_pytorch_model-00002-of-00002.safetensors
│   └── diffusion_pytorch_model.safetensors.index.json
├── vae/
│   ├── config.json
│   └── diffusion_pytorch_model.safetensors
├── scheduler/
│   └── scheduler_config.json
└── prompt_embeddings/
    └── empty.safetensors

Inference

Run OSDEnhancer on an input video:

python inference.py \
  --input demo/input.mp4 \
  --output demo/output.mp4 \
  --spatial_scale 4 \
  --temporal_scale 2

For stable inference, we recommend using a GPU with not less than 80GB of VRAM. We recommend setting spatial_scale = 4 and temporal_scale = 2. To use a local checkpoint, specify --ckpt_path. For long videos or high-resolution inputs, enable chunk-based inference by additionally setting --chunk_length and --overlap, where --chunk_length should satisfy the form of 8N+1.

📧 Contact

If you meet any problems, please feel free to contact us via email: shuoyan.wei@bjtu.edu.cn

💡 Cite

If you find this work useful for your research, please consider citing our paper 😊

@article{wei2026osdenhancer,
  title={Taming Real-World Space-Time Video Super-Resolution with One-Step Diffusion},
  author={Wei, Shuoyan and Li, Feng and Zhou, Chen and Cong, Runmin and Zhao, Yao and Bai, Huihui},
  journal={arXiv preprint arXiv:2601.20308},
  year={2026}
}

📕 License & Acknowledgement

This project is released under the Apache License 2.0. OSDEnhancer is built upon CogVideoX. We also sincerely thank the authors of DOVE, EvEnhancer, and RealBasicVSR for their excellent open-source implementations, which provided valuable references for this project.