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---
license: apache-2.0
---
This repository contains a pruned and partially reorganized version of [AniPortrait](https://fudan-generative-vision.github.io/champ/#/).
```
@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.
```sh
pip install git+https://github.com/painebenjamin/aniportrait.git
```
Now, you can create the pipeline, automatically pulling the weights from this repository, either as individual models:
```py
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:
```py
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
```py
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
```py
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
```py
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
```py
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
```py
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
```py
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
```py
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
```py
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
```py
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.
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