diffusers text-to-video arxiv:2309.15818 license:cc-by-nc-4.0 region:us
---
license: cc-by-nc-4.0
tags: 
  - text-to-video
---

# show-1-base

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.

![](https://showlab.github.io/Show-1/assets/images/method.png)

## Model Details

This is the base model of Show-1 that generates videos with 8 keyframes at a resolution of 64x40. The model is finetuned from [DeepFloyd/IF-I-L-v1.0](https://huggingface.co/DeepFloyd/IF-I-L-v1.0) on the [WebVid-10M](https://maxbain.com/webvid-dataset/) and [InternVid](https://huggingface.co/datasets/OpenGVLab/InternVid) 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:** base (keyframe generation)
- **Finetuned from model:** [DeepFloyd/IF-I-L-v1.0](https://huggingface.co/DeepFloyd/IF-I-L-v1.0)
- **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.