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FasterLivePortrait API Usage Guide

Building the Image

  • Decide on an image name, for example shaoguo/faster_liveportrait_api:v1.0. Replace the -t parameter in the following command with your chosen name.
  • Run docker build -t shaoguo/faster_liveportrait_api:v1.0 -f DockerfileAPI .

Running the Image

Ensure that your machine has Nvidia GPU drivers installed. CUDA version should be 12.0 or higher. Two scenarios are described below.

  • Running on a Local Machine (typically for self-testing)

    • Modify the image name according to what you defined above.
    • Confirm the service port number, default is 9871. You can define your own by changing the SERVER_PORT environment variable in the command below. Remember to also change -p 9871:9871 to map the port.
    • Set the model path environment variable CHECKPOINT_DIR. If you've previously downloaded FasterLivePortrait's onnx model and converted it to trt, I recommend mapping the model files into the container using -v, for example -v E:\my_projects\FasterLivePortrait\checkpoints:/root/FasterLivePortrait/checkpoints. This avoids re-downloading the onnx model and doing trt conversion. Otherwise, I will check if CHECKPOINT_DIR has models, and if not, I will automatically download (ensure network connectivity) and do trt conversion, which will take considerable time.
    • Run command (note: modify the following command according to your settings): 
      docker run -d --gpus=all \
--name faster_liveportrait_api \
-v E:\my_projects\FasterLivePortrait\checkpoints:/root/FasterLivePortrait/checkpoints \
-e CHECKPOINT_DIR=/root/FasterLivePortrait/checkpoints \
-e SERVER_PORT=9871 \
-p 9871:9871 \
--restart=always \
shaoguo/faster_liveportrait_api:v1.0 \
/bin/bash
    • Normal operation should display the following information(docker logs $container_id). The running logs are saved in /root/FasterLivePortrait/logs/log_run.log: 
      INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:9871 (Press CTRL+C to quit)

  • Running on Cloud GPU Cluster (production environment)

    • This needs to be configured according to different clusters, but the core is the configuration of docker image and environment variables.
    • Load balancing may need to be set up.

API Call Testing

Refer to tests/test_api.py. The default is the Animal model, but now it also supports the Human model. The return is a compressed package, by default unzipped to ./results/api_*. Confirm according to the actual printed log.

  • test_with_video_animal(), image and video driving. Set flag_pickle=False. It will additionally return the driving video's pkl file, which can be called directly next time.
  • test_with_pkl_animal(), image and pkl driving.
  • test_with_video_human(), image and video driving under the Human model, set flag_is_animal=False