|
--- |
|
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. |
