Blizaine / AniPortrait

AniPortrait with Gradio: Audio-Driven Synthesis of Photorealistic Portrait Animation

AniPortrait

AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait Animations

Huawei Wei, Zejun Yang, Zhisheng Wang

Tencent Games Zhiji, Tencent

Here we propose AniPortrait, a novel framework for generating high-quality animation driven by audio and a reference portrait image. You can also provide a video to achieve face reenacment.

Gradio UI for AniPortrait Vid2Vid

This is a fork of the original project located here: https://github.com/Zejun-Yang/AniPortrait I've added a Gradio UI for the Vid2Vid pipeline.

The changes & additions to the original project are as follows:

• Updated requirements to include Gradio install
• The Gradio UI allows for reference image upload, reference video upload, frame counting with automatic length field populating, and customizations for height, width, length, cfg, seed, and steps.
• Added app.py in the root dir, which is what you will launch for the Gradio UI
• Added a modified version of vid2vid.py, called vid2vid_gr.py in the scripts dir. I had to make a slight modification to the vid2vid pipeline, but I didn't want to break the existing one if users wanted to have access to the cmd line inference options. The primary change was how the generated videos were saved. Previously, they were saved in a unique directory with a unique file name based on date, time, size, seed, etc. This made it difficult for Gradio to display the result. So vid2vid_gr.py will save the result the same each time, which also means it will be overwritten, so download it if you want to keep it.

Installation

Build environment

We recommend a Python version >=3.10 and a Cuda version =11.7. Then, build the environment as follows:

Create a conda environment:

conda create -n AniPortrait python=3.10
conda activate AniPortrait

pip install -r requirements.txt

Download weights

All the weights should be placed under the ./pretrained_model directory. You can download weights manually as follows:

1. Download our trained models, which include four parts: denoising_unet.pth, reference_unet.pth, pose_guider.pth, motion_module.pth and audio2mesh.pt.

2. Download pretrained models of based models and other components:

   • StableDiffusion V1.5
   • sd-vae-ft-mse
   • image_encoder
   • wav2vec2-base-960h

Finally, these weights should be organized as follows:

./pretrained_model/
|-- image_encoder
|   |-- config.json
|   `-- pytorch_model.bin
|-- sd-vae-ft-mse
|   |-- config.json
|   |-- diffusion_pytorch_model.bin
|   `-- diffusion_pytorch_model.safetensors
|-- stable-diffusion-v1-5
|   |-- feature_extractor
|   |   `-- preprocessor_config.json
|   |-- model_index.json
|   |-- unet
|   |   |-- config.json
|   |   `-- diffusion_pytorch_model.bin
|   `-- v1-inference.yaml
|-- wav2vec2-base-960h
|   |-- config.json
|   |-- feature_extractor_config.json
|   |-- preprocessor_config.json
|   |-- pytorch_model.bin
|   |-- README.md
|   |-- special_tokens_map.json
|   |-- tokenizer_config.json
|   `-- vocab.json
|-- audio2mesh.pt
|-- denoising_unet.pth
|-- motion_module.pth
|-- pose_guider.pth
`-- reference_unet.pth

Note: If you have installed some of the pretrained models, such as StableDiffusion V1.5, you can specify their paths in the config file (e.g. ./config/prompts/animation.yaml).

Gradio UI Start (Vid2Vid Inference)

From AniPortrait Root Dir, enable conda env if it's not:

conda activate AniPortrait

Start Gradio UI App:

python app.py

Pipeline

Various Generated Videos

Self driven

cxk.mp4

solo.mp4

Face reenacment

Aragaki.mp4

num18.mp4

Audio driven

jijin.mp4	kara.mp4
lyl.mp4	zl.mp4

Command Line Inference (disregard if only using Gradio UI)

Here are the cli commands for running inference scripts:

Self driven

python -m scripts.pose2vid --config ./configs/prompts/animation.yaml -W 512 -H 512 -L 64

You can refer the format of animation.yaml to add your own reference images or pose videos. To convert the raw video into a pose video (keypoint sequence), you can run with the following command:

python -m scripts.vid2pose --video_path pose_video_path.mp4

Face reenacment

python -m scripts.vid2vid --config ./configs/prompts/animation_facereenac.yaml -W 512 -H 512 -L 64

Add source face videos and reference images in the animation_facereenac.yaml.

Audio driven

python -m scripts.audio2vid --config ./configs/prompts/animation_audio.yaml -W 512 -H 512 -L 64

Add audios and reference images in the animation_audio.yaml.

You can use this command to generate a pose_temp.npy for head pose control:

python -m scripts.generate_ref_pose --ref_video ./configs/inference/head_pose_temp/pose_ref_video.mp4 --save_path ./configs/inference/head_pose_temp/pose.npy

Training

Data preparation

Download VFHQ and CelebV-HQ

Extract keypoints from raw videos and write training json file (here is an example of processing VFHQ):

python -m scripts.preprocess_dataset --input_dir VFHQ_PATH --output_dir SAVE_PATH --training_json JSON_PATH

Update lines in the training config file:

data:
  json_path: JSON_PATH

Stage1

Run command:

accelerate launch train_stage_1.py --config ./configs/train/stage1.yaml

Stage2

Put the pretrained motion module weights mm_sd_v15_v2.ckpt (download link) under ./pretrained_weights.

Specify the stage1 training weights in the config file stage2.yaml, for example:

stage1_ckpt_dir: './exp_output/stage1'
stage1_ckpt_step: 30000 

Run command:

accelerate launch train_stage_2.py --config ./configs/train/stage2.yaml

Acknowledgements

We first thank the authors of EMO. Additionally, we would like to thank the contributors to the Moore-AnimateAnyone, majic-animate, animatediff and Open-AnimateAnyone repositories, for their open research and exploration.

Citation

@misc{wei2024aniportrait,
      title={AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait Animations}, 
      author={Huawei Wei and Zejun Yang and Zhisheng Wang},
      year={2024},
      eprint={2403.17694},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}