# Tune-A-Video

This repository is the official implementation of [Tune-A-Video](https://arxiv.org/abs/2212.11565).

**[Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation](https://arxiv.org/abs/2212.11565)**
<br/>
[Jay Zhangjie Wu](https://zhangjiewu.github.io/), 
[Yixiao Ge](https://geyixiao.com/), 
[Xintao Wang](https://xinntao.github.io/), 
[Stan Weixian Lei](), 
[Yuchao Gu](https://ycgu.site/), 
[Yufei Shi](),
[Wynne Hsu](https://www.comp.nus.edu.sg/~whsu/), 
[Ying Shan](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en), 
[Xiaohu Qie](https://scholar.google.com/citations?user=mk-F69UAAAAJ&hl=en), 
[Mike Zheng Shou](https://sites.google.com/view/showlab)
<br/>

[![Project Website](https://img.shields.io/badge/Project-Website-orange)](https://tuneavideo.github.io/)
[![arXiv](https://img.shields.io/badge/arXiv-2212.11565-b31b1b.svg)](https://arxiv.org/abs/2212.11565)
[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Tune-A-Video-library/Tune-A-Video-Training-UI)
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/showlab/Tune-A-Video/blob/main/notebooks/Tune-A-Video.ipynb)


<p align="center">
<img src="https://tuneavideo.github.io/assets/overview.png" width="800px"/>  
<br>
<em>Given a video-text pair as input, our method, Tune-A-Video, fine-tunes a pre-trained text-to-image diffusion model for text-to-video generation.</em>
</p>

## News
- [02/22/2023] Improved consistency using DDIM inversion.
- [02/08/2023] [Colab demo](https://colab.research.google.com/github/showlab/Tune-A-Video/blob/main/notebooks/Tune-A-Video.ipynb) released!
- [02/03/2023] Pre-trained Tune-A-Video models are available on [Hugging Face Library](https://huggingface.co/Tune-A-Video-library)!
- [01/28/2023] New Feature: tune a video on personalized [DreamBooth](https://dreambooth.github.io/) models.
- [01/28/2023] Code released!

## Setup

### Requirements

```shell
pip install -r requirements.txt
```

Installing [xformers](https://github.com/facebookresearch/xformers) is highly recommended for more efficiency and speed on GPUs. 
To enable xformers, set `enable_xformers_memory_efficient_attention=True` (default).

### Weights

**[Stable Diffusion]** [Stable Diffusion](https://arxiv.org/abs/2112.10752) is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. The pre-trained Stable Diffusion models can be downloaded from Hugging Face (e.g., [Stable Diffusion v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4), [v2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1)). You can also use fine-tuned Stable Diffusion models trained on different styles (e.g, [Modern Disney](https://huggingface.co/nitrosocke/mo-di-diffusion), [Redshift](https://huggingface.co/nitrosocke/redshift-diffusion), etc.).

**[DreamBooth]** [DreamBooth](https://dreambooth.github.io/) is a method to personalize text-to-image models like Stable Diffusion given just a few images (3~5 images) of a subject. Tuning a video on DreamBooth models allows personalized text-to-video generation of a specific subject. There are some public DreamBooth models available on [Hugging Face](https://huggingface.co/sd-dreambooth-library) (e.g., [mr-potato-head](https://huggingface.co/sd-dreambooth-library/mr-potato-head)). You can also train your own DreamBooth model following [this training example](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth). 


## Usage

### Training

To fine-tune the text-to-image diffusion models for text-to-video generation, run this command:

```bash
accelerate launch train_tuneavideo.py --config="configs/man-skiing.yaml"
```

Note: Tuning a 24-frame video usually takes `300~500` steps, about `10~15` minutes using one A100 GPU. 
Reduce `n_sample_frames` if your GPU memory is limited.

### Inference

Once the training is done, run inference:

```python
from tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline
from tuneavideo.models.unet import UNet3DConditionModel
from tuneavideo.util import save_videos_grid
import torch

pretrained_model_path = "./checkpoints/stable-diffusion-v1-4"
my_model_path = "./outputs/man-skiing"
unet = UNet3DConditionModel.from_pretrained(my_model_path, subfolder='unet', torch_dtype=torch.float16).to('cuda')
pipe = TuneAVideoPipeline.from_pretrained(pretrained_model_path, unet=unet, torch_dtype=torch.float16).to("cuda")
pipe.enable_xformers_memory_efficient_attention()
pipe.enable_vae_slicing()

prompt = "spider man is skiing"
ddim_inv_latent = torch.load(f"{my_model_path}/inv_latents/ddim_latent-500.pt").to(torch.float16)
video = pipe(prompt, latents=ddim_inv_latent, video_length=24, height=512, width=512, num_inference_steps=50, guidance_scale=12.5).videos

save_videos_grid(video, f"./{prompt}.gif")
```

## Results

### Pretrained T2I (Stable Diffusion)
<table class="center">
<tr>
  <td style="text-align:center;"><b>Input Video</b></td>
  <td style="text-align:center;" colspan="3"><b>Output Video</b></td>
</tr>
<tr>
  <td><img src="https://tuneavideo.github.io/assets/data/man-skiing.gif"></td>
  <td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/man-skiing/spiderman-beach.gif"></td>
  <td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/man-skiing/wonder-woman.gif"></td>              
  <td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/man-skiing/pink-sunset.gif"></td>
</tr>
<tr>
  <td width=25% style="text-align:center;color:gray;">"A man is skiing"</td>
  <td width=25% style="text-align:center;">"Spider Man is skiing on the beach, cartoon style”</td>
  <td width=25% style="text-align:center;">"Wonder Woman, wearing a cowboy hat, is skiing"</td>
  <td width=25% style="text-align:center;">"A man, wearing pink clothes, is skiing at sunset"</td>
</tr>

<tr>
  <td><img src="https://tuneavideo.github.io/assets/data/rabbit-watermelon.gif"></td>
  <td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/rabbit-watermelon/rabbit.gif"></td>
  <td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/rabbit-watermelon/cat.gif"></td>              
  <td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/rabbit-watermelon/puppy.gif"></td>
</tr>
<tr>
  <td width=25% style="text-align:center;color:gray;">"A rabbit is eating a watermelon"</td>
  <td width=25% style="text-align:center;">"A rabbit is <del>eating a watermelon</del> on the table"</td>
  <td width=25% style="text-align:center;">"A cat with sunglasses is eating a watermelon on the beach"</td>
  <td width=25% style="text-align:center;">"A puppy is eating a cheeseburger on the table, comic style"</td>
</tr>

<tr>
  <td><img src="https://tuneavideo.github.io/assets/data/car-turn.gif"></td>
  <td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/car-turn/porsche-beach.gif"></td>
  <td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/car-turn/car-cartoon.gif"></td>              
  <td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/car-turn/car-snow.gif"></td>
</tr>
<tr>
  <td width=25% style="text-align:center;color:gray;">"A jeep car is moving on the road"</td>
  <td width=25% style="text-align:center;">"A Porsche car is moving on the beach"</td>
  <td width=25% style="text-align:center;">"A car is moving on the road, cartoon style"</td>
  <td width=25% style="text-align:center;">"A car is moving on the snow"</td>
</tr>

<tr>
  <td><img src="https://tuneavideo.github.io/assets/data/man-basketball.gif"></td>
  <td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/man-basketball/trump.gif"></td>
  <td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/man-basketball/astronaut.gif"></td>              
  <td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/man-basketball/lego.gif"></td>
</tr>
<tr>
  <td width=25% style="text-align:center;color:gray;">"A man is dribbling a basketball"</td>
  <td width=25% style="text-align:center;">"Trump is dribbling a basketball"</td>
  <td width=25% style="text-align:center;">"An astronaut is dribbling a basketball, cartoon style"</td>
  <td width=25% style="text-align:center;">"A lego man in a black suit is dribbling a basketball"</td>
</tr>

<!-- <tr>
  <td><img src="https://tuneavideo.github.io/assets/data/lion-roaring.gif"></td>
  <td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/lion-roaring/tiger-roar.gif"></td>
  <td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/lion-roaring/lion-vangogh.gif"></td>              
  <td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/lion-roaring/wolf-nyc.gif"></td>
</tr>
<tr>
  <td width=25% style="text-align:center;color:gray;">"A lion is roaring"</td>
  <td width=25% style="text-align:center;">"A tiger is roaring"</td>
  <td width=25% style="text-align:center;">"A lion is roaring, Van Gogh style"</td>
  <td width=25% style="text-align:center;">"A wolf is roaring in New York City"</td>
</tr> -->

</table>

### Pretrained T2I (personalized DreamBooth)

<img src="https://tuneavideo.github.io/assets/results/tuneavideo/modern-disney/modern-disney.png" width="240px"/>  

<table class="center">
<tr>
  <td style="text-align:center;"><b>Input Video</b></td>
  <td style="text-align:center;" colspan="3"><b>Output Video</b></td>
</tr>
<tr>
  <td><img src="https://tuneavideo.github.io/assets/data/bear-guitar.gif"></td>
  <td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/modern-disney/bear-guitar/rabbit.gif"></td>      
  <td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/modern-disney/bear-guitar/prince.gif"></td>
  <td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/modern-disney/bear-guitar/princess.gif"></td>
</tr>
<tr>
  <td width=25% style="text-align:center;color:gray;">"A bear is playing guitar"</td>
  <td width=25% style="text-align:center;">"A rabbit is playing guitar, modern disney style"</td>
  <td width=25% style="text-align:center;">"A handsome prince is playing guitar, modern disney style"</td>
  <td width=25% style="text-align:center;">"A magic princess with sunglasses is playing guitar on the stage, modern disney style"</td>
</tr>
</table>

<img src="https://tuneavideo.github.io/assets/results/tuneavideo/mr-potato-head/mr-potato-head.png" width="240px"/>  

<table class="center">
<tr>
  <td style="text-align:center;"><b>Input Video</b></td>
  <td style="text-align:center;" colspan="3"><b>Output Video</b></td>
</tr>
<tr>
  <td><img src="https://tuneavideo.github.io/assets/data/bear-guitar.gif"></td>
  <td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/mr-potato-head/bear-guitar/lego-snow.gif"></td>
  <td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/mr-potato-head/bear-guitar/sunglasses-beach.gif"></td>      
  <td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/mr-potato-head/bear-guitar/van-gogh.gif"></td>
</tr>
<tr>
  <td width=25% style="text-align:center;color:gray;">"A bear is playing guitar"</td>
  <td width=25% style="text-align:center;">"Mr Potato Head, made of lego, is playing guitar on the snow"</td>
  <td width=25% style="text-align:center;">"Mr Potato Head, wearing sunglasses, is playing guitar on the beach"</td>
  <td width=25% style="text-align:center;">"Mr Potato Head is playing guitar in the starry night, Van Gogh style"</td>
</tr>
</table>


## Citation
If you make use of our work, please cite our paper.
```bibtex
@article{wu2022tuneavideo,
    title={Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation},
    author={Wu, Jay Zhangjie and Ge, Yixiao and Wang, Xintao and Lei, Stan Weixian and Gu, Yuchao and Hsu, Wynne and Shan, Ying and Qie, Xiaohu and Shou, Mike Zheng},
    journal={arXiv preprint arXiv:2212.11565},
    year={2022}
}
```

## Shoutouts

- This code builds on [diffusers](https://github.com/huggingface/diffusers). Thanks for open-sourcing!
- Thanks [hysts](https://github.com/hysts) for the awesome [gradio demo](https://huggingface.co/spaces/Tune-A-Video-library/Tune-A-Video-Training-UI).