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PyTorch implementation of VSR-Transformer
| Date | Stars |
|---|---|
| 2026-07-24 | 264 |
| 2026-07-25 | 264 |
| 2026-07-28 | 264 |
| 2026-07-30 | 264 |
| 2026-08-06 | 264 |
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# VSR-Transformer
By [Jiezhang Cao](https://github.com/caojiezhang/), [Yawei Li](https://github.com/ofsoundof), [Kai Zhang](http://github.com/cszn), [Luc Van Gool](https://scholar.google.com/citations?user=TwMib_QAAAAJ&hl=en)
This paper proposes a new Transformer for video super-resolution (called VSR-Transformer). Our VSR-Transformer block contains a spatial-temporal convolutional self-attention layer and a bidirectionaloptical flow-based feed-forward layer. Our VSR-Transformer is able to improve the performance of VSR. This repository is the official implementation of ["Video Super-Resolution Transformer"](https://arxiv.org/abs/2106.06847).
<p align="center"><img width="100%" src="figs/framework.png" /></p>
<p align="center"><img width="100%" src="figs/attention.png" /></p>
<p align="center"><img width="100%" src="figs/feedforward.png" /></p>
## Dependencies and Installation
- Python >= 3.7 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html))
- [PyTorch >= 1.3](https://pytorch.org/)
- NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
1. Clone repository
```bash
git clone https://github.com/caojiezhang/VSR-Transformer.git
```
2. Install dependent packages
```bash
cd VSR-Transformer
pip install -r requirements.txt
```
3. Compile environment
```bash
python setup.py develop
```
## Dataset Preparation
- Please refer to **[DatasetPreparation.md](docs/DatasetPreparation.md)** for more details.
- The descriptions of currently supported datasets (`torch.utils.data.Dataset` classes) are in [Datasets.md](docs/Datasets.md).
## Training
- Please refer to **[configuration of training](options/train/)** for more details and [pretrained models](https://drive.google.com/drive/folders/1HFZbuYq54U9mz_ngAqfW3pRMcry7XWx3?usp=sharing).
```bash
# Train on REDS
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/train.py -opt options/train/train_vsrTransformer_x4_REDS.yml --launcher pytorch
# Train on Vimeo-90K
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/train.py -opt options/train/train_vsrTransformer_x4_Vimeo.yml --launcher pytorch
```
## Testing
- Please refer to **[configuration of testing](options/test/)** for more details.
```bash
# Test on REDS
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/test.py -opt options/test/test_vsrTransformer_x4_REDS.yml --launcher pytorch
# Test on Vimeo-90K
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/test.py -opt options/test/test_vsrTransformer_x4_Vimeo.yml --launcher pytorch
# Test on Vid4
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/test.py -opt options/test/test_vsrTransformer_x4_Vid4.yml --launcher pytorch
```
## Citation
If you use this code of our paper please cite:
```
@article{cao2021vsrt,
title={Video Super-Resolution Transformer},
author={Cao, Jiezhang and Li, Yawei and Zhang, Kai and Van Gool, Luc},
journal={arXiv},
year={2021}
}
```
## Acknowledgments
This repository is implemented based on [BasicSR](https://github.com/xinntao/BasicSR). If you use the repository, please consider citing BasicSR.Excerpt of 3,566 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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