/PIA

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

Primary LanguagePythonApache License 2.0Apache-2.0

PIA:Personalized Image Animator

PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Models

Yiming Zhang†, Zhening Xing†, Yanhong Zeng, Youqing Fang, Kai Chen*

(*Corresponding Author, †Equal Contribution)

arXiv Project Page Open in OpenXLab

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

What's New

[2023/12/22] Release the model and demo of PIA. Try it to make your personalized movie!

Setup

Prepare Environment

conda env create -f environment.yaml
conda activate pia

Download checkpoints

  • Download the Stable Diffusion v1-5
  • git lfs install
    git clone https://huggingface.co/runwayml/stable-diffusion-v1-5 models/StableDiffusion/
    
  • Download Personalized Models
  • bash download_bashscripts/1-RealisticVision.sh
    bash download_bashscripts/2-RcnzCartoon.sh
    bash download_bashscripts/3-MajicMix.sh
    
  • Download PIA
  • bash download_bashscripts/0-PIA.sh
    

    You can also download pia.ckpt through this link on Google Drive

    Put checkpoints as follows:

    └── models
        ├── DreamBooth_LoRA
        │   ├── ...
        ├── PIA
        │   ├── pia.ckpt
        └── StableDiffusion
            ├── vae
            ├── unet
            └── ...
    

    Usage

    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, you will get:

    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:

    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:

    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:

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

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

    Examples:

    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

    AnimateBench

    We have open-sourced AnimateBench on HuggingFace which includes images, prompts and configs to evaluate PIA and other image animation methods.

    Contact Us

    Yiming Zhang: zhangyiming@pjlab.org.cn

    Zhening Xing: xingzhening@pjlab.org.cn

    Yanhong Zeng: zengyanhong@pjlab.org.cn

    Acknowledgements

    The code is built upon AnimateDiff, Tune-a-Video and PySceneDetect