## ___***DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors***___
<!-- ![](./assets/logo_long.png#gh-light-mode-only){: width="50%"} -->
<!-- ![](./assets/logo_long_dark.png#gh-dark-mode-only=100x20) -->
<div align="center">
<img src='assets/logo_long.png' style="height:100px"></img>




 <a href='https://arxiv.org/abs/2310.12190'><img src='https://img.shields.io/badge/arXiv-2310.12190-b31b1b.svg'></a> &nbsp;
 <a href='https://doubiiu.github.io/projects/DynamiCrafter/'><img src='https://img.shields.io/badge/Project-Page-Green'></a> &nbsp;
<a href='https://huggingface.co/spaces/Doubiiu/DynamiCrafter'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-blue'></a> &nbsp;
<a href='https://youtu.be/0NfmIsNAg-g'><img src='https://img.shields.io/badge/Youtube-Video-b31b1b.svg'></a><br>
[![Open in OpenXLab](https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg)](https://openxlab.org.cn/apps/detail/JinboXING/DynamiCrafter)&nbsp;&nbsp;
<a href='https://replicate.com/camenduru/dynami-crafter-576x1024'><img src='https://img.shields.io/badge/replicate-Demo-blue'></a>&nbsp;&nbsp;
<a href='https://github.com/camenduru/DynamiCrafter-colab'><img src='https://img.shields.io/badge/Colab-Demo-Green'></a>&nbsp;<a href='https://huggingface.co/papers/2310.12190'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Page-blue'></a>

_**[Jinbo Xing](https://doubiiu.github.io/), [Menghan Xia*](https://menghanxia.github.io), [Yong Zhang](https://yzhang2016.github.io), [Haoxin Chen](), [Wangbo Yu](), <br>[Hanyuan Liu](https://github.com/hyliu), [Xintao Wang](https://xinntao.github.io/), [Tien-Tsin Wong*](https://www.cse.cuhk.edu.hk/~ttwong/myself.html), [Ying Shan](https://scholar.google.com/citations?hl=en&user=4oXBp9UAAAAJ&view_op=list_works&sortby=pubdate)**_
<br><br>
(* corresponding authors)

From CUHK and Tencent AI Lab.

</div>
 
## 🔆 Introduction

### 🔥🔥 New Update Rolls Out for DynamiCrafter! Better Dynamic, Higher Resolution, and Stronger Coherence! <br>
🤗 DynamiCrafter can animate open-domain still images based on <strong>text prompt</strong> by leveraging the pre-trained video diffusion priors. Please check our project page and paper for more information. <br>
😀 We will continue to improve the model's performance.

👀 Seeking comparisons with [Stable Video Diffusion](https://stability.ai/news/stable-video-diffusion-open-ai-video-model) and [PikaLabs](https://pika.art/)? Click the image below.
[![](https://img.youtube.com/vi/0NfmIsNAg-g/0.jpg)](https://www.youtube.com/watch?v=0NfmIsNAg-g)


### 1.1. Showcases (576x1024)
<table class="center">
  <!-- <tr>
    <td colspan="1">"fireworks display"</td>
    <td colspan="1">"a robot is walking through a destroyed city"</td>
  </tr> -->
  <tr>
  <td>
    <img src=assets/showcase/firework03.gif width="340">
  </td>
  <td>
    <img src=assets/showcase/robot01.gif width="340">
  </td>
  </tr>

  <!-- <tr>
    <td colspan="1">"riding a bike under a bridge"</td>
    <td colspan="1">""</td>
  </tr> -->
  <tr>
  <td>
    <img src=assets/showcase/bike_chineseink.gif width="340">
  </td>
  <td>
    <img src=assets/showcase/girl07.gif width="340">
  </td>
  </tr>
</table>


### 1.2. Showcases (320x512)
<table class="center">
  <!-- <tr>
    <td colspan="1">"fireworks display"</td>
    <td colspan="1">"a robot is walking through a destroyed city"</td>
  </tr> -->
  <tr>
  <td>
    <img src=assets/showcase/bloom2.gif width="340">
  </td>
  <td>
    <img src=assets/showcase/train_anime02.gif width="340">
  </td>
  </tr>

  <!-- <tr>
    <td colspan="1">"riding a bike under a bridge"</td>
    <td colspan="1">""</td>
  </tr> -->
  <tr>
  <td>
    <img src=assets/showcase/pour_honey.gif width="340">
  </td>
  <td>
    <img src=assets/showcase/lighthouse.gif width="340">
  </td>
  </tr>
</table>




### 1.3. Showcases (256x256)

<table class="center">
  <tr>
    <td colspan="2">"bear playing guitar happily, snowing"</td>
    <td colspan="2">"boy walking on the street"</td>
  </tr>
  <tr>
  <td>
    <img src=assets/showcase/guitar0.jpeg_00.png width="170">
  </td>
  <td>
    <img src=assets/showcase/guitar0.gif width="170">
  </td>
  <td>
    <img src=assets/showcase/walk0.png_00.png width="170">
  </td>
  <td>
    <img src=assets/showcase/walk0.gif width="170">
  </td>
  </tr>


  <!-- <tr>
    <td colspan="2">"two people dancing"</td>
    <td colspan="2">"girl talking and blinking"</td>
  </tr>
  <tr>
  <td>
    <img src=assets/showcase/dance1.jpeg_00.png width="170">
  </td>
  <td>
    <img src=assets/showcase/dance1.gif width="170">
  </td>

  <td>
    <img src=assets/showcase/girl3.jpeg_00.png width="170">
  </td>
  <td>
    <img src=assets/showcase/girl3.gif width="170">
  </td>
  </tr> -->


  <!-- <tr>
    <td colspan="2">"zoom-in, a landscape, springtime"</td>
    <td colspan="2">"A blonde woman rides on top of a moving <br>washing machine into the sunset."</td>
  </tr>
  <tr>
  <td>
    <img src=assets/showcase/Upscaled_Aime_Tribolet_springtime_landscape_golden_hour_morning_pale_yel_e6946f8d-37c1-4ce8-bf62-6ba90d23bd93.mp4_00.png width="170">
  </td>
  <td>
    <img src=assets/showcase/Upscaled_Aime_Tribolet_springtime_landscape_golden_hour_morning_pale_yel_e6946f8d-37c1-4ce8-bf62-6ba90d23bd93.gif width="170">
  </td>

  <td>
    <img src=assets/showcase/Upscaled_Alex__State_Blonde_woman_riding_on_top_of_a_moving_washing_mach_c31acaa3-dd30-459f-a109-2d2eb4c00fe2.mp4_00.png width="170">
  </td>
  <td>
    <img src=assets/showcase/Upscaled_Alex__State_Blonde_woman_riding_on_top_of_a_moving_washing_mach_c31acaa3-dd30-459f-a109-2d2eb4c00fe2.gif width="170">
  </td>
  </tr>

  <tr>
    <td colspan="2">"explode colorful smoke coming out"</td>
    <td colspan="2">"a bird on the tree branch"</td>
  </tr>
  <tr>
  <td>
    <img src=assets/showcase/explode0.jpeg_00.png width="170">
  </td>
  <td>
    <img src=assets/showcase/explode0.gif width="170">
  </td>

  <td>
    <img src=assets/showcase/bird000.jpeg width="170">
  </td>
  <td>
    <img src=assets/showcase/bird000.gif width="170">
  </td>
  </tr> -->
</table >

### 2. Applications
#### 2.1 Storytelling video generation (see project page for more details)
<table class="center">
    <!-- <tr style="font-weight: bolder;text-align:center;">
        <td>Input</td>
        <td>Output</td>
        <td>Input</td>
        <td>Output</td>
    </tr> -->
  <tr>
    <td colspan="4"><img src=assets/application/storytellingvideo.gif width="250"></td>
  </tr>
</table >

#### 2.2 Looping video generation
<table class="center">

  <tr>
  <td>
    <img src=assets/application/60.gif width="300">
  </td>
  <td>
    <img src=assets/application/35.gif width="300">
  </td>
  <td>
    <img src=assets/application/36.gif width="300">
  </td>
  </tr>
  <!-- <tr>
  <td>
    <img src=assets/application/05.gif width="300">
  </td>
  <td>
    <img src=assets/application/25.gif width="300">
  </td>
  <td>
    <img src=assets/application/34.gif width="300">
  </td>
  </tr> -->
</table >

#### 2.3 Generative frame interpolation

<table class="center">
    <tr style="font-weight: bolder;text-align:center;">
        <td>Input starting frame</td>
        <td>Input ending frame</td>
        <td>Generated video</td>
    </tr>
  <tr>
  <td>
    <img src=assets/application/gkxX0kb8mE8_input_start.png width="250">
  </td>
  <td>
    <img src=assets/application/gkxX0kb8mE8_input_end.png width="250">
  </td>
  <td>
    <img src=assets/application/gkxX0kb8mE8.gif width="250">
  </td>
  </tr>

  <!-- <tr>
  <td>
    <img src=assets/application/YwHJYWvv_dM_input_start.png width="250">
  </td>
  <td>
    <img src=assets/application/YwHJYWvv_dM_input_end.png width="250">
  </td>
  <td>
    <img src=assets/application/YwHJYWvv_dM.gif width="250">
  </td>
  </tr>

  <tr>
  <td>
    <img src=assets/application/ypDLB52Ykk4_input_start.png width="250">
  </td>
  <td>
    <img src=assets/application/ypDLB52Ykk4_input_end.png width="250">
  </td>
  <td>
    <img src=assets/application/ypDLB52Ykk4.gif width="250">
  </td>
  </tr> -->
</table >



## 📝 Changelog
- __[2024.02.05]__: 🔥🔥 Release high-resolution models (320x512 & 576x1024).
- __[2023.12.02]__: Launch the local Gradio demo.
- __[2023.11.29]__: Release the main model at a resolution of 256x256.
- __[2023.11.27]__: Launch the project page and update the arXiv preprint.
<br>


## 🧰 Models

|Model|Resolution|GPU Mem. & Inference Time (A100, ddim 50steps)|Checkpoint|
|:---------|:---------|:--------|:--------|
|DynamiCrafter1024|576x1024|18.3GB & 75s (`perframe_ae=True`)|[Hugging Face](https://huggingface.co/Doubiiu/DynamiCrafter_1024/blob/main/model.ckpt)|
|DynamiCrafter512|320x512|12.8GB & 20s (`perframe_ae=True`)|[Hugging Face](https://huggingface.co/Doubiiu/DynamiCrafter_512/blob/main/model.ckpt)|
|DynamiCrafter256|256x256|11.9GB  & 10s (`perframe_ae=False`)|[Hugging Face](https://huggingface.co/Doubiiu/DynamiCrafter/blob/main/model.ckpt)|

Currently, our DynamiCrafter can support generating videos of up to 16 frames with a resolution of 576x1024. The inference time can be reduced by using fewer DDIM steps.

GPU memory consumed on RTX 4090 reported by @noguchis in [Twitter](https://x.com/noguchis/status/1754488826016432341?s=20): 18.3GB (576x1024), 12.8GB (320x512), 11.9GB (256x256).
<!-- It takes approximately 10 seconds and requires a peak GPU memory of 20 GB to animate an image using a single NVIDIA A100 (40G) GPU. -->

## ⚙️ Setup

### Install Environment via Anaconda (Recommended)
```bash
conda create -n dynamicrafter python=3.8.5
conda activate dynamicrafter
pip install -r requirements.txt
```


## 💫 Inference 
### 1. Command line
1) Download pretrained models via Hugging Face, and put the `model.ckpt` with the required resolution in `checkpoints/dynamicrafter_[1024|512|256]_v1/model.ckpt`.
2) Run the commands based on your devices and needs in terminal.
```bash
  # Run on a single GPU:
  # Select the model based on required resolutions: i.e., 1024|512|320:
  sh scripts/run.sh 1024
  # Run on multiple GPUs for parallel inference:
  sh scripts/run_mp.sh 1024
```



### 2. Local Gradio demo
1. Download the pretrained models and put them in the corresponding directory according to the previous guidelines.
2. Input the following commands in terminal (choose a model based on the required resolution: 1024, 512 or 256).
```bash
  python gradio_app.py --res 1024
```

Community Extensions: [ComfyUI](https://github.com/chaojie/ComfyUI-DynamiCrafter) (Thanks to [chaojie](https://github.com/chaojie)).

## 👨‍👩‍👧‍👦 Crafter Family
[VideoCrafter1](https://github.com/AILab-CVC/VideoCrafter): Framework for high-quality video generation.

[ScaleCrafter](https://github.com/YingqingHe/ScaleCrafter): Tuning-free method for high-resolution image/video generation.

[TaleCrafter](https://github.com/AILab-CVC/TaleCrafter): An interactive story visualization tool that supports multiple characters.  

[LongerCrafter](https://github.com/arthur-qiu/LongerCrafter): Tuning-free method for longer high-quality video generation.  

[MakeYourVideo, might be a Crafter:)](https://doubiiu.github.io/projects/Make-Your-Video/): Video generation/editing with textual and structural guidance.

[StyleCrafter](https://gongyeliu.github.io/StyleCrafter.github.io/): Stylized-image-guided text-to-image and text-to-video generation.
## 😉 Citation
```bib
@article{xing2023dynamicrafter,
  title={DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors},
  author={Xing, Jinbo and Xia, Menghan and Zhang, Yong and Chen, Haoxin and Yu, Wangbo and Liu, Hanyuan and Wang, Xintao and Wong, Tien-Tsin and Shan, Ying},
  journal={arXiv preprint arXiv:2310.12190},
  year={2023}
}
```

## 🙏 Acknowledgements
We would like to thank [AK(@_akhaliq)](https://twitter.com/_akhaliq?lang=en) for the help of setting up hugging face online demo, and [camenduru](https://twitter.com/camenduru) for providing the replicate & colab online demo.

## 📢 Disclaimer
We develop this repository for RESEARCH purposes, so it can only be used for personal/research/non-commercial purposes.
****