This repository contains a pruned and partially reorganized version of AniPortrait, with some new features.
@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}
}
Added Features
CPU Offloading
In order to maximize VRAM, CPU offloading is made available and enabled by default when using the command-line.
- In python, use
pipeline.enable_model_cpu_offload(gpu_id: int=0)to enable it. - In the command-line, pass
--no-offloador-noto disable it.
Video Reference
In addition to using an image as reference, this repository permits using a video as a reference image using a modified temporally-aware reference U-Net.
Automatic Masked Composition
When using a video reference, you can pass paste_back=True or --paste-back/-pb to automatically re-composite the generated face onto the reference video.
Automatic Face Cropping
In addition to the above, you can pass crop_to_face=True or --crop-to-face/-ctf to automatically crop the input video to the detected face region prior to inference, then un-crop and compose the results. This means you can directly use this feature on HD video without needing to pre-process any of the inputs.
Examples
The following examples were produced using the above-referenced procedure, then interpolated and post-processed using enfugue.
They were made using the source videos at Full HD, using the following command-line format:
aniportrait video.mp4 --video video.mp4 --audio audio.mp3 --crop-to-face --num-inference-steps 50 -cfg 4.5
All videos sourced from Pexels, voices are AI-generated reading Attention is All You Need (ARXIV:1706.03762)
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 utility aniportrait is installed with the package.
Usage: aniportrait [OPTIONS] INPUT_IMAGE_OR_VIDEO
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.
-no, --no-offload Do not offload to the CPU to preserve GPU
memory.
-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.
-cd, --cache-dir DIRECTORY Cache directory to download to. Default uses
the huggingface cache.
-o, --output FILE Output file. [default: output.mp4]
-pb, --paste-back Paste the original background back in.
-pbcf, --paste-back-color-fix [adain|wavelet]
Color fix method to use when pasting back.
[default: wavelet]
-ctf, --crop-to-face Crop the input to the face prior to
execution, then merge the cropped result
with the uncropped image. Implies --paste-
back.
-pop, --pose-output FILE When passed, save the pose image(s) to this
file.
-mop, --mask-output FILE When passed, save the mask image(s) to this
file.
-cop, --combined-output FILE When passed, save the combined image(s) to
this file.
-mb, --mask-blur INTEGER Amount of blur to apply to the mask when
using cropping or pasting. [default: 15]
-md, --mask-dilate INTEGER Amount of dilation to apply to the mask when
using cropping or pasting. [default: 31]
-ms, --mask-slow Use a slower, more accurate mask generation
method.
-lss, --leading-seconds-silence FLOAT
Seconds of silence to add to the beginning
of the audio. [default: 0.0]
-tss, --trailing-seconds-silence FLOAT
Seconds of silence to add to the end of the
audio. [default: 0.0]
--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_imageto designate the pose ofreference_image. When not passed, the pose ofreference_imageis 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 = falseto disable using the long video pipeline. - Optionally pass
reference_pose_imageto designate the pose ofreference_image. When not passed, the pose ofreference_imageis automatically detected. - Optionally pass
video_lengthto 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,
pose_filename: Optional[str]=None,
leading_seconds_silence: float=0.0,
trailing_seconds_silence: float=0.0,
**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 = falseto disable using the long video pipeline. - Optionally pass
reference_pose_imageto designate the pose ofreference_image. When not passed, the pose ofreference_imageis automatically detected. - Optionally pass
video_lengthto use this many frames. Default is the same as the length of the pose reference images or the length of the audio frames (when translated to the proper FPS) - whichever is shorter. - Optionally pass
leading_seconds_silenceand/ortrailing_seconds_silenceto add silent frame(s) to the beginning and/or end of the audio. This will be adjusted for your passed or detected frame rate. - Optionally pass
pose_filename,mask_filenameand/orcombined_filenameto save the pose, mask and/or combined frames to this video file for debugging.
audiovid2vid
pipeline.audiovid2vid(
audio: str,
reference_image: List[Image.Image],
num_inference_steps: int=25,
guidance_scale: float=3.5,
fps: int=30,
eta: float=0.0,
reference_pose_image: Optional[Image.Image]=None,
pose_reference_images: Optional[List[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,
context_schedule: str="uniform",
context_frames: int=16,
context_overlap: int=4,
context_batch_size: int=1,
interpolation_factor: int=1,
width: Optional[int]=None,
height: Optional[int]=None,
video_length: Optional[int]=None,
use_long_video: bool=True,
paste_back: bool=True,
paste_back_color_fix: Optional[Literal["wavelet", "adain"]]="wavelet",
crop_to_face: bool=False,
crop_to_face_target_size: Optional[int]=512,
crop_to_face_padding: Optional[int]=64,
mask_filename: Optional[str]=None,
pose_filename: Optional[str]=None,
combined_filename: Optional[str]=None,
mask_dilate: Optional[int]=31,
mask_gaussian_kernel_size: Optional[int]=15,
mask_first_frame: bool=True,
leading_seconds_silence: float=0.0,
trailing_seconds_silence: float=0.0,
**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 using a video as a reference.
- Optionally pass width/height to modify the size. Defaults to reference image size.
- Optionally pass
use_long_video = falseto disable using the long video pipeline. - Optionally pass
video_lengthto use this many frames. Default is the same as the length of the pose reference images or the length of the audio frames (when translated to the proper FPS) - whichever is shorter. - Optionally pass
paste_back = trueto re-composite the output onto the input. - When using
paste_back, the face is color-fixed when re-pasting in order to reduce visible difference. The default method iswavelet, passadainorNonefor other options. - Optionally pass
crop_to_face = trueto crop all images to the face region (with padding) prior to diffusion. This impliespaste_back = true. - When using
crop_to_face, we must first identify where the faces are in a potentially large image - to do this we perform a slow tiled face detection across the whole image. In order to reduce time to generate additional masks, the default behavior is to only do this once, then use the faces from the first frame to know where to detect in subsequent frames. Set this tofalseto perform tiled faced detection on every frame of the input - this is slower but will allow for more variability in the frame. - Optionally pass
leading_seconds_silenceand/ortrailing_seconds_silenceto add silent frame(s) to the beginning and/or end of the audio. This will be adjusted for your passed or detected frame rate. - Optionally pass
pose_filename,mask_filenameand/orcombined_filenameto save the pose, mask and/or combined frames to this video file for debugging.
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_imageto designate the pose ofreference_image. When not passed, the pose ofreference_imageis 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_imageto designate the pose ofreference_image. When not passed, the pose ofreference_imageis automatically detected. - Optionally pass
video_lengthto 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_imageto designate the pose ofreference_image. When not passed, the pose ofreference_imageis automatically detected. - Optionally pass
video_lengthto use this many frames. Default is the same as the length of the pose images.
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