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README.md

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+# Taming Real-World Space-Time Video Super-Resolution with One-Step Diffusion (arXiv 2026)
+
+**Authors**: [Shuoyan Wei](https://github.com/W-Shuoyan)<sup>1</sup>, [Feng Li](https://lifengcs.github.io/)<sup>2,\*</sup>, Chen Zhou<sup>1</sup>, [Runmin Cong](https://rmcong.github.io)<sup>3</sup>, [Yao Zhao](https://scholar.google.com/citations?user=474TbQYAAAAJ&hl=en&oi=ao)<sup>1</sup>, [Huihui Bai](https://scholar.google.com/citations?user=iXuCUcQAAAAJ&hl=en&oi=ao)<sup>1</sup>
+
+<sup>1</sup>*Beijing Jiaotong University*, <sup>2</sup>*Hefei University of Technology*, <sup>3</sup>*Shandong University*
+
+<small><sup>\*</sup>Corresponding Author</small>
+
+[![arXiv](https://img.shields.io/badge/arXiv-2601.20308-da282a)](https://arxiv.org/abs/2601.20308)
+[![Hugging Face](https://img.shields.io/badge/🤗-%20Hugging%20Face-yellow)](https://huggingface.co/W-Shuoyan/OSDEnhancer)
+[![GitHub Stars](https://img.shields.io/github/stars/W-Shuoyan/OSDEnhancer?style=social)](https://github.com/W-Shuoyan/OSDEnhancer)
+
+This repository contains the reference code for the paper "[**Taming Real-World Space-Time Video Super-Resolution with One-Step Diffusion**](https://arxiv.org/pdf/2601.20308)".
+
+---
+
+
+![HEAD](figures/framework.png)
+
+**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 👉 [![GitHub Stars](https://img.shields.io/github/stars/W-Shuoyan/OSDEnhancer?style=social)](https://github.com/W-Shuoyan/OSDEnhancer) [![Hugging Face](https://img.shields.io/badge/🤗%20Hugging%20Face-OSDEnhancer-yellow)](https://huggingface.co/W-Shuoyan/OSDEnhancer)
+- ✅ **[Jan 2026]** The arXiv version of our paper has been released 👉 [![arXiv](https://img.shields.io/badge/arXiv-2601.20308-da282a)](https://arxiv.org/abs/2601.20308)
+
+
+## 📚 Installation
+
+```shell
+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
+Download the pretrained checkpoint below.
+| Model Name| Base Model | Download Link 🔗|
+|---|---|---|
+| OSDEnhancer-v1.0 | [CogVideoX1.5-5B](https://huggingface.co/zai-org/CogVideoX1.5-5B) | [🤗 Hugging Face](https://huggingface.co/W-Shuoyan/OSDEnhancer) |
+
+
+The checkpoint directory should be organized as follows:
+```text
+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:
+
+```bash
+python inference.py \
+  --input demo/input.mp4 \      # Path to the input MP4 video
+  --output demo/output.mp4 \    # Path to save the enhanced MP4 video
+  --ckpt_path ckpt \            # Path to the pretrained checkpoint directory
+  --spatial_scale 4 \           # Spatial upsampling scale
+  --temporal_scale 2            # Temporal upsampling scale
+```
+We recommend setting `spatial_scale = 4` and `temporal_scale = 2`. For long videos or high-resolution inputs, enable chunk-based inference by additionally setting `--chunk_num` and `--overlap`, where `--chunk_num` 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 😊
+
+```shell
+@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}
+}
+```
+
+## 📕 Acknowledgement
+
+OSDEnhancer is built upon [CogVideoX](https://github.com/zai-org/CogVideo). We also sincerely thank the authors of [DOVE](https://github.com/zhengchen1999/DOVE), [EvEnhancer](https://github.com/W-Shuoyan/EvEnhancer), and [RealBasicVSR](https://github.com/ckkelvinchan/realbasicvsr) for their excellent open-source implementations, which provided valuable references for this project.