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---
license: cc-by-nc-4.0
tags:
- text-to-video
---
# show-1-sr2
Pixel-based VDMs can generate motion accurately aligned with the textual prompt but typically demand expensive computational costs in terms of time and GPU memory, especially when generating high-resolution videos. Latent-based VDMs are more resource-efficient because they work in a reduced-dimension latent space. But it is challenging for such small latent space (e.g., 64×40 for 256×160 videos) to cover rich yet necessary visual semantic details as described by the textual prompt.
To marry the strength and alleviate the weakness of pixel-based and latent-based VDMs, we introduce **Show-1**, an efficient text-to-video model that generates videos of not only decent video-text alignment but also high visual quality.
## Model Details
This is the super-resolution model of Show-1 that upscales videos from a 256x160 resolution to 576x320. The model is finetuned using diffusion timesteps 0-900 on the [WebVid-10M](https://maxbain.com/webvid-dataset/) dataset.
- **Developed by:** [Show Lab, National University of Singapore](https://sites.google.com/view/showlab/home?authuser=0)
- **Model type:** pixel- and latent-based cascaded text-to-video diffusion model
- **Cascade stage:** super-resolution (256x160->576x320)
- **Finetuned from model:** [cerspense/zeroscope_v2_576w](https://huggingface.co/cerspense/zeroscope_v2_576w)
- **License:** Creative Commons Attribution Non Commercial 4.0
- **Resources for more information:** [GitHub](https://github.com/showlab/Show-1), [Website](https://showlab.github.io/Show-1/), [arXiv](https://arxiv.org/abs/2309.15818)
## Usage
Clone the GitHub repository and install the requirements:
```bash
git clone https://github.com/showlab/Show-1.git
pip install -r requirements.txt
```
Run the following command to generate a video from a text prompt. By default, this will automatically download all the model weights from huggingface.
```bash
python run_inference.py
```
You can also download the weights manually and change the `pretrained_model_path` in `run_inference.py` to run the inference.
```bash
git lfs install
# base
git clone https://huggingface.co/showlab/show-1-base
# interp
git clone https://huggingface.co/showlab/show-1-interpolation
# sr1
git clone https://huggingface.co/showlab/show-1-sr1
# sr2
git clone https://huggingface.co/showlab/show-1-sr2
```
## Citation
If you make use of our work, please cite our paper.
```bibtex
@misc{zhang2023show1,
title={Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation},
author={David Junhao Zhang and Jay Zhangjie Wu and Jia-Wei Liu and Rui Zhao and Lingmin Ran and Yuchao Gu and Difei Gao and Mike Zheng Shou},
year={2023},
eprint={2309.15818},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## Model Card Contact
This model card is maintained by [David Junhao Zhang](https://junhaozhang98.github.io/) and [Jay Zhangjie Wu](https://jayzjwu.github.io/). For any questions, please feel free to contact us or open an issue in the repository. |