This repository contains a pruned and partially reorganized version of AniPortrait.

@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}
}

Usage

Installation

First, install the AniPortrait package into your python environment. If you're creating a new environment for AniPortrait, be sure you also specify the version of torch you want with CUDA support, or else this will try to run only on CPU.

pip install git+https://github.com/painebenjamin/aniportrait.git

Command-Line

A command-line utilitiy aniportrait is installed with the package.

Usage: aniportrait [OPTIONS] INPUT_IMAGE

  Run AniPortrait on an input image with a video, and/or audio file. When only
  a video file is provided, a video-to-video (face reenactment) animation is
  performed. When only an audio file is provided, an audio-to-video (lip-sync)
  animation is performed. When both a video and audio file are provided, a
  video-to-video animation is performed with the audio as guidance for the
  face and mouth movements.

Options:
  -v, --video FILE                Video file to drive the animation.
  -a, --audio FILE                Audio file to drive the animation.
  -fps, --frame-rate INTEGER      Video FPS. Also controls the sampling rate
                                  of the audio. Will default to the video FPS
                                  if a video file is provided, or 30 if not.
  -cfg, --guidance-scale FLOAT    Guidance scale for the diffusion process.
                                  [default: 3.5]
  -ns, --num-inference-steps INTEGER
                                  Number of diffusion steps.  [default: 20]
  -cf, --context-frames INTEGER   Number of context frames to use.  [default:
                                  16]
  -co, --context-overlap INTEGER  Number of context frames to overlap.
                                  [default: 4]
  -nf, --num-frames INTEGER       An explicit number of frames to use. When
                                  not passed, use the length of the audio or
                                  video
  -s, --seed INTEGER              Random seed.
  -w, --width INTEGER             Output video width. Defaults to the input
                                  image width.
  -h, --height INTEGER            Output video height. Defaults to the input
                                  image height.
  -m, --model TEXT                HuggingFace model name.
  -nh, --no-half                  Do not use half precision.
  -g, --gpu-id INTEGER            GPU ID to use.
  -sf, --model-single-file        Download and use a single file instead of a
                                  directory.
  -cf, --config-file TEXT         Config file to use when using the model-
                                  single-file option. Accepts a path or a
                                  filename in the same directory as the single
                                  file. Will download from the repository
                                  passed in the model option if not provided.
                                  [default: config.json]
  -mf, --model-filename TEXT      The model file to download when using the
                                  model-single-file option.  [default:
                                  aniportrait.safetensors]
  -rs, --remote-subfolder TEXT    Remote subfolder to download from when using
                                  the model-single-file option.
  -c, --cache-dir DIRECTORY       Cache directory to download to. Default uses
                                  the huggingface cache.
  -o, --output FILE               Output file.  [default: output.mp4]
  --help                          Show this message and exit.

Python

You can create the pipeline, automatically pulling the weights from this repository, either as individual models:

from aniportrait import AniPortraitPipeline
pipeline = AniPortraitPipeline.from_pretrained(
  "benjamin-paine/aniportrait",
  torch_dtype=torch.float16,
  variant="fp16",
).to("cuda", dtype=torch.float16)

Or, as a single file:

from aniportrait import AniPortraitPipeline
pipeline = AniPortraitPipeline.from_single_file(
  "benjamin-paine/aniportrait",
  torch_dtype=torch.float16,
  variant="fp16",
).to("cuda", dtype=torch.float16)

The AniPortraitPipeline is a mega pipeline, capable of instantiating and executing other pipelines. It provides the following functions:

Workflows

img2img

pipeline.img2img(
    reference_image: PIL.Image.Image,
    pose_reference_image: PIL.Image.Image,
    num_inference_steps: int,
    guidance_scale: float,
    eta: float=0.0,
    reference_pose_image: Optional[Image.Image]=None,
    generation: Optional[Union[torch.Generator, List[torch.Generator]]]=None,
    output_type: Optional[str]="pil",
    return_dict: bool=True,
    callback: Optional[Callable[[int, int, torch.FloatTensor], None]]=None,
    callback_steps: Optional[int]=None,
    width: Optional[int]=None,
    height: Optional[int]=None,
    **kwargs: Any
) -> Pose2VideoPipelineOutput

Using a reference image (for structure) and a pose reference image (for pose), render an image of the former in the pose of the latter.

The pose reference image here is an unprocessed image, from which the face pose will be extracted.
Optionally pass reference_pose_image to designate the pose of reference_image. When not passed, the pose of reference_image is automatically detected.

vid2vid

pipeline.vid2vid(
    reference_image: PIL.Image.Image,
    pose_reference_images: List[PIL.Image.Image],
    num_inference_steps: int,
    guidance_scale: float,
    eta: float=0.0,
    reference_pose_image: Optional[Image.Image]=None,
    generation: Optional[Union[torch.Generator, List[torch.Generator]]]=None,
    output_type: Optional[str]="pil",
    return_dict: bool=True,
    callback: Optional[Callable[[int, int, torch.FloatTensor], None]]=None,
    callback_steps: Optional[int]=None,
    width: Optional[int]=None,
    height: Optional[int]=None,
    video_length: Optional[int]=None,
    context_schedule: str="uniform",
    context_frames: int=16,
    context_overlap: int=4,
    context_batch_size: int=1,
    interpolation_factor: int=1,
    use_long_video: bool=True,
    **kwargs: Any
) -> Pose2VideoPipelineOutput

Using a reference image (for structure) and a sequence of pose reference images (for pose), render a video of the former in the poses of the latter, using context windowing for long-video generation when the poses are longer than 16 frames.

Optionally pass use_long_video = false to disable using the long video pipeline.
Optionally pass reference_pose_image to designate the pose of reference_image. When not passed, the pose of reference_image is automatically detected.
Optionally pass video_length to use this many frames. Default is the same as the length of the pose reference images.

audio2vid

pipeline.audio2vid(
    audio: str,
    reference_image: PIL.Image.Image,
    num_inference_steps: int,
    guidance_scale: float,
    fps: int=30,
    eta: float=0.0,
    reference_pose_image: Optional[Image.Image]=None,
    pose_reference_images: Optional[List[PIL.Image.Image]]=None,
    generation: Optional[Union[torch.Generator, List[torch.Generator]]]=None,
    output_type: Optional[str]="pil",
    return_dict: bool=True,
    callback: Optional[Callable[[int, int, torch.FloatTensor], None]]=None,
    callback_steps: Optional[int]=None,
    width: Optional[int]=None,
    height: Optional[int]=None,
    video_length: Optional[int]=None,
    context_schedule: str="uniform",
    context_frames: int=16,
    context_overlap: int=4,
    context_batch_size: int=1,
    interpolation_factor: int=1,
    use_long_video: bool=True,
    **kwargs: Any
) -> Pose2VideoPipelineOutput

Using an audio file, draw fps face pose images per second for the duration of the audio. Then, using those face pose images, render a video.

Optionally include a list of images to extract the poses from prior to merging with audio-generated poses (in essence, pass a video here to control non-speech motion). The default is a moderately active loop of head movement.
Optionally pass width/height to modify the size. Defaults to reference image size.
Optionally pass use_long_video = false to disable using the long video pipeline.
Optionally pass reference_pose_image to designate the pose of reference_image. When not passed, the pose of reference_image is automatically detected.
Optionally pass video_length to use this many frames. Default is the same as the length of the pose reference images.

Internals/Helpers

img2pose

pipeline.img2pose(
    reference_image: PIL.Image.Image,
    width: Optional[int]=None,
    height: Optional[int]=None
) -> PIL.Image.Image

Detects face landmarks in an image and draws a face pose image.

Optionally modify the original width and height.

vid2pose

pipeline.vid2pose(
    reference_image: PIL.Image.Image,
    retarget_image: Optional[PIL.Image.Image],
    width: Optional[int]=None,
    height: Optional[int]=None
) -> List[PIL.Image.Image]

Detects face landmarks in a series of images and draws pose images.

Optionally modify the original width and height.
Optionally retarget to a different face position, useful for video-to-video tasks.

audio2pose

pipeline.audio2pose(
    audio_path: str,
    fps: int=30,
    reference_image: Optional[PIL.Image.Image]=None,
    pose_reference_images: Optional[List[PIL.Image.Image]]=None,
    width: Optional[int]=None,
    height: Optional[int]=None
) -> List[PIL.Image.Image]

Using an audio file, draw fps face pose images per second for the duration of the audio.

Optionally include a reference image to extract the face shape and initial position from. Default has a generic androgynous face shape.
Optionally include a list of images to extract the poses from prior to merging with audio-generated poses (in essence, pass a video here to control non-speech motion). The default is a moderately active loop of head movement.
Optionally pass width/height to modify the size. Defaults to reference image size, then pose image sizes, then 256.

pose2img

pipeline.pose2img(
    reference_image: PIL.Image.Image,
    pose_image: PIL.Image.Image,
    num_inference_steps: int,
    guidance_scale: float,
    eta: float=0.0,
    reference_pose_image: Optional[Image.Image]=None,
    generation: Optional[Union[torch.Generator, List[torch.Generator]]]=None,
    output_type: Optional[str]="pil",
    return_dict: bool=True,
    callback: Optional[Callable[[int, int, torch.FloatTensor], None]]=None,
    callback_steps: Optional[int]=None,
    width: Optional[int]=None,
    height: Optional[int]=None,
    **kwargs: Any
) -> Pose2VideoPipelineOutput

Using a reference image (for structure) and a pose image (for pose), render an image of the former in the pose of the latter.

The pose image here is a processed face pose. To pass a non-processed face pose, see img2img.
Optionally pass reference_pose_image to designate the pose of reference_image. When not passed, the pose of reference_image is automatically detected.

pose2vid

pipeline.pose2vid(
    reference_image: PIL.Image.Image,
    pose_images: List[PIL.Image.Image],
    num_inference_steps: int,
    guidance_scale: float,
    eta: float=0.0,
    reference_pose_image: Optional[Image.Image]=None,
    generation: Optional[Union[torch.Generator, List[torch.Generator]]]=None,
    output_type: Optional[str]="pil",
    return_dict: bool=True,
    callback: Optional[Callable[[int, int, torch.FloatTensor], None]]=None,
    callback_steps: Optional[int]=None,
    width: Optional[int]=None,
    height: Optional[int]=None,
    video_length: Optional[int]=None,
    **kwargs: Any
) -> Pose2VideoPipelineOutput

Using a reference image (for structure) and pose images (for pose), render a video of the former in the poses of the latter.

The pose images here are a processed face poses. To non-processed face poses, see vid2vid.
Optionally pass reference_pose_image to designate the pose of reference_image. When not passed, the pose of reference_image is automatically detected.
Optionally pass video_length to use this many frames. Default is the same as the length of the pose images.

pose2vid_long

pipeline.pose2vid_long(
    reference_image: PIL.Image.Image,
    pose_images: List[PIL.Image.Image],
    num_inference_steps: int,
    guidance_scale: float,
    eta: float=0.0,
    reference_pose_image: Optional[Image.Image]=None,
    generation: Optional[Union[torch.Generator, List[torch.Generator]]]=None,
    output_type: Optional[str]="pil",
    return_dict: bool=True,
    callback: Optional[Callable[[int, int, torch.FloatTensor], None]]=None,
    callback_steps: Optional[int]=None,
    width: Optional[int]=None,
    height: Optional[int]=None,
    video_length: Optional[int]=None,
    context_schedule: str="uniform",
    context_frames: int=16,
    context_overlap: int=4,
    context_batch_size: int=1,
    interpolation_factor: int=1,
    **kwargs: Any
) -> Pose2VideoPipelineOutput

Using a reference image (for structure) and pose images (for pose), render a video of the former in the poses of the latter, using context windowing for long-video generation.

The pose images here are a processed face poses. To non-processed face poses, see vid2vid.
Optionally pass reference_pose_image to designate the pose of reference_image. When not passed, the pose of reference_image is automatically detected.
Optionally pass video_length to use this many frames. Default is the same as the length of the pose images.