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https://github.com/open-mmlab/pia

[CVPR 2024] PIA, your Personalized Image Animator. Animate your images by text prompt, combing with Dreambooth, achieving stunning videos. PIA，你的个性化图像动画生成器，利用文本提示将图像变为奇妙的动画
https://github.com/open-mmlab/pia

aigc animation diffusion-models image-to-video image-to-video-generation personalized-generation stable-diffusion

Last synced: 5 days ago
JSON representation

Host: GitHub
URL: https://github.com/open-mmlab/pia
Owner: open-mmlab
License: apache-2.0
Created: 2023-12-21T03:29:34.000Z (about 1 year ago)
Default Branch: main
Last Pushed: 2024-08-05T09:11:48.000Z (7 months ago)
Last Synced: 2025-02-07T22:07:25.294Z (12 days ago)
Topics: aigc, animation, diffusion-models, image-to-video, image-to-video-generation, personalized-generation, stable-diffusion
Language: Python
Homepage: https://pi-animator.github.io/
Size: 79 MB
Stars: 937
Watchers: 24
Forks: 75
Open Issues: 7
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # CVPR 2024 | PIA：Personalized Image Animator

[**PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Models**](https://arxiv.org/abs/2312.13964)

[Yiming Zhang*](https://github.com/ymzhang0319), [Zhening Xing*](https://github.com/LeoXing1996/), [Yanhong Zeng†](https://zengyh1900.github.io/), [Youqing Fang](https://github.com/FangYouqing), [Kai Chen†](https://chenkai.site/)

(*equal contribution, †corresponding Author)

[![arXiv](https://img.shields.io/badge/arXiv-2312.13964-b31b1b.svg)](https://arxiv.org/abs/2312.13964)

[![Project Page](https://img.shields.io/badge/PIA-Website-green)](https://pi-animator.github.io)

[![Open in OpenXLab](https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg)](https://openxlab.org.cn/apps/detail/zhangyiming/PiaPia)

[![Third Party Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/camenduru/PIA-colab/blob/main/PIA_colab.ipynb)

[![HuggingFace Model](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue)](https://huggingface.co/Leoxing/PIA)



  



[![Replicate](https://replicate.com/cjwbw/pia/badge)](https://replicate.com/cjwbw/pia)

PIA is a personalized image animation method which can generate videos with **high motion controllability** and **strong text and image alignment**.

If you find our project helpful, please give it a star :star: or [cite](#bibtex) it, we would be very grateful :sparkling_heart: .



## What's New

- [x] `2024/01/03` [Replicate Demo & API](https://replicate.com/cjwbw/pia) support!

- [x] `2024/01/03` [Colab](https://github.com/camenduru/PIA-colab) support from [camenduru](https://github.com/camenduru)!

- [x] `2023/12/28` Support `scaled_dot_product_attention` for 1024x1024 images with just 16GB of GPU memory.

- [x] `2023/12/25` HuggingFace demo is available now! [🤗 Hub](https://huggingface.co/spaces/Leoxing/PIA/)

- [x] `2023/12/22` Release the demo of PIA on [OpenXLab](https://openxlab.org.cn/apps/detail/zhangyiming/PiaPia) and checkpoints on [Google Drive](https://drive.google.com/file/d/1RL3Fp0Q6pMD8PbGPULYUnvjqyRQXGHwN/view?usp=drive_link) or [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/zhangyiming/PIA)

## Setup

### Prepare Environment

Use the following command to install a conda environment for PIA from scratch:

```

conda env create -f pia.yml

conda activate pia

```

You may also want to install it based on an existing environment, then you can use `environment-pt2.yaml` for Pytorch==2.0.0. If you want to use lower version of Pytorch (e.g. 1.13.1), you can use the following command:

```

conda env create -f environment.yaml

conda activate pia

```

We strongly recommend you to use Pytorch==2.0.0 which supports `scaled_dot_product_attention` for memory-efficient image animation.

### Download checkpoints

Download the Stable Diffusion v1-5


```

conda install git-lfs

git lfs install

git clone https://huggingface.co/runwayml/stable-diffusion-v1-5 models/StableDiffusion/

```

Download PIA


```

git clone https://huggingface.co/Leoxing/PIA models/PIA/

```

Download Personalized Models


```

bash download_bashscripts/1-RealisticVision.sh

bash download_bashscripts/2-RcnzCartoon.sh

bash download_bashscripts/3-MajicMix.sh

```

You can also download *pia.ckpt* manually through link on [Google Drive](https://drive.google.com/file/d/1RL3Fp0Q6pMD8PbGPULYUnvjqyRQXGHwN/view?usp=drive_link)

or [HuggingFace](https://huggingface.co/Leoxing/PIA).

Put checkpoints as follows:

```

└── models

    ├── DreamBooth_LoRA

    │   ├── ...

    ├── PIA

    │   ├── pia.ckpt

    └── StableDiffusion

        ├── vae

        ├── unet

        └── ...

```

## Inference

### Image Animation

Image to Video result can be obtained by:

```

python inference.py --config=example/config/lighthouse.yaml

python inference.py --config=example/config/harry.yaml

python inference.py --config=example/config/majic_girl.yaml

```

Run the command above, then you can find the results in example/result:

    

      
Input Image

      lightning, lighthouse

      sun rising, lighthouse

      fireworks, lighthouse

    

    

      

      

      

      

    

    

      Input Image

      1boy smiling

      1boy playing the magic fire

      1boy is waving hands

    

    

      

      

      

      

    

    

      Input Image

      1girl is smiling

      1girl is crying

      1girl, snowing 

    

    

      

      

      

      

    

### Motion Magnitude

You can control the motion magnitude through the parameter **magnitude**:

```sh

python inference.py --config=example/config/xxx.yaml --magnitude=0 # Small Motion

python inference.py --config=example/config/xxx.yaml --magnitude=1 # Moderate Motion

python inference.py --config=example/config/xxx.yaml --magnitude=2 # Large Motion

```

Examples:

```sh

python inference.py --config=example/config/labrador.yaml

python inference.py --config=example/config/bear.yaml

python inference.py --config=example/config/genshin.yaml

```

    

      
Input Image
& Prompt

      Small Motion

      Moderate Motion

      Large Motion

    

    

    a golden labrador is running

     

      

      

    

    

    1bear is walking, ...

     

      

      

    

    

    cherry blossom, ...

     

      

      

    

### Style Transfer

To achieve style transfer, you can run the command(*Please don't forget set the base model in xxx.yaml*):

Examples:

```sh

python inference.py --config example/config/concert.yaml --style_transfer

python inference.py --config example/config/anya.yaml --style_transfer

```

    

      
Input Image
 & Base Model

      1man is smiling

      1man is crying

      1man is singing

    

    

      Realistic Vision

      

      

      

    

    

      RCNZ Cartoon 3d

      

      

      

    

    

      

      1girl smiling

      1girl open mouth

      1girl is crying, pout

    

    

      RCNZ Cartoon 3d

      

      

      

    

### Loop Video

You can generate loop by using the parameter --loop

```sh

python inference.py --config=example/config/xxx.yaml --loop

```

Examples:

```sh

python inference.py --config=example/config/lighthouse.yaml --loop

python inference.py --config=example/config/labrador.yaml --loop

```

  

    
Input Image

    lightning, lighthouse

    sun rising, lighthouse

    fireworks, lighthouse

  

  

    

    

    

    

  

  

    Input Image

    labrador jumping

    labrador walking

    labrador running

  

  

    

    

    

    

  

## Training

We provide [training script]("train.py") for PIA. It borrows from [AnimateDiff](https://github.com/guoyww/AnimateDiff/tree/main) heavily, so please prepare the dataset and configuration files according to the [guideline](https://github.com/guoyww/AnimateDiff/blob/main/__assets__/docs/animatediff.md#steps-for-training).

After preparation, you can train the model by running the following command using torchrun:

```shell

torchrun --nnodes=1 --nproc_per_node=1 train.py --config example/config/train.yaml

```

or by slurm,

```shell

srun --quotatype=reserved --job-name=pia --gres=gpu:8 --ntasks-per-node=8 --ntasks=8  --cpus-per-task=4 --kill-on-bad-exit=1 python train.py --config example/config/train.yaml

```

## AnimateBench

We have open-sourced AnimateBench on [HuggingFace](https://huggingface.co/datasets/ymzhang319/AnimateBench) which includes images, prompts and configs to evaluate PIA and other image animation methods.

## BibTex

```

@inproceedings{zhang2024pia,

  title={Pia: Your personalized image animator via plug-and-play modules in text-to-image models},

  author={Zhang, Yiming and Xing, Zhening and Zeng, Yanhong and Fang, Youqing and Chen, Kai},

  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},

  pages={7747--7756},

  year={2024}

}

```

## Contact Us

**Yiming Zhang**: [email protected]

**Zhening Xing**: [email protected]

**Yanhong Zeng**: [email protected]

## Acknowledgements

The code is built upon [AnimateDiff](https://github.com/guoyww/AnimateDiff), [Tune-a-Video](https://github.com/showlab/Tune-A-Video) and [PySceneDetect](https://github.com/Breakthrough/PySceneDetect)

You may also want to try other project from our team: