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3.2.0

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3.2.0
This commit is contained in:
Henry Ruhs
2025-04-29 11:33:12 +02:00
committed by GitHub
parent d4fba8421f 0b7349bde0
commit a6fb48780c
124 changed files with 2325 additions and 1499 deletions
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2
.flake8
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@@ -1,5 +1,5 @@
[flake8]
select = E3, E4, F, I1, I2
select = E22, E23, E24, E27, E3, E4, E7, F, I1, I2
per-file-ignores = facefusion.py:E402, install.py:E402
plugins = flake8-import-order
application_import_names = facefusion

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LICENSE.md
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@@ -1,3 +1,3 @@
MIT license
OpenRAIL-AS license
Copyright (c) 2024 Henry Ruhs
Copyright (c) 2025 Henry Ruhs

2
README.md
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@@ -5,7 +5,7 @@ FaceFusion
[![Build Status](https://img.shields.io/github/actions/workflow/status/facefusion/facefusion/ci.yml.svg?branch=master)](https://github.com/facefusion/facefusion/actions?query=workflow:ci)
[![Coverage Status](https://img.shields.io/coveralls/facefusion/facefusion.svg)](https://coveralls.io/r/facefusion/facefusion)
![License](https://img.shields.io/badge/license-MIT-green)
![License](https://img.shields.io/badge/license-OpenRAIL--AS-green)
Preview

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facefusion.ico
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4
facefusion.ini
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@@ -49,12 +49,13 @@ keep_temp =
output_image_quality =
output_image_resolution =
output_audio_encoder =
output_audio_quality =
output_audio_volume =
output_video_encoder =
output_video_preset =
output_video_quality =
output_video_resolution =
output_video_fps =
skip_audio =
[processors]
processors =
@@ -113,3 +114,4 @@ system_memory_limit =
[misc]
log_level =
halt_on_error =

2
facefusion/app_context.py
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@@ -1,7 +1,7 @@
import os
import sys
from facefusion.typing import AppContext
from facefusion.types import AppContext
def detect_app_context() -> AppContext:

10
facefusion/args.py
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@@ -1,9 +1,9 @@
from facefusion import state_manager
from facefusion.filesystem import is_image, is_video, list_directory
from facefusion.filesystem import get_file_name, is_image, is_video, resolve_file_paths
from facefusion.jobs import job_store
from facefusion.normalizer import normalize_fps, normalize_padding
from facefusion.processors.core import get_processors_modules
from facefusion.typing import ApplyStateItem, Args
from facefusion.types import ApplyStateItem, Args
from facefusion.vision import create_image_resolutions, create_video_resolutions, detect_image_resolution, detect_video_fps, detect_video_resolution, pack_resolution
@@ -92,6 +92,8 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
else:
apply_state_item('output_image_resolution', pack_resolution(output_image_resolution))
apply_state_item('output_audio_encoder', args.get('output_audio_encoder'))
apply_state_item('output_audio_quality', args.get('output_audio_quality'))
apply_state_item('output_audio_volume', args.get('output_audio_volume'))
apply_state_item('output_video_encoder', args.get('output_video_encoder'))
apply_state_item('output_video_preset', args.get('output_video_preset'))
apply_state_item('output_video_quality', args.get('output_video_quality'))
@@ -105,9 +107,8 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
if args.get('output_video_fps') or is_video(args.get('target_path')):
output_video_fps = normalize_fps(args.get('output_video_fps')) or detect_video_fps(args.get('target_path'))
apply_state_item('output_video_fps', output_video_fps)
apply_state_item('skip_audio', args.get('skip_audio'))
# processors
available_processors = [ file.get('name') for file in list_directory('facefusion/processors/modules') ]
available_processors = [ get_file_name(file_path) for file_path in resolve_file_paths('facefusion/processors/modules') ]
apply_state_item('processors', args.get('processors'))
for processor_module in get_processors_modules(available_processors):
processor_module.apply_args(args, apply_state_item)
@@ -128,6 +129,7 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
apply_state_item('system_memory_limit', args.get('system_memory_limit'))
# misc
apply_state_item('log_level', args.get('log_level'))
apply_state_item('halt_on_error', args.get('halt_on_error'))
# jobs
apply_state_item('job_id', args.get('job_id'))
apply_state_item('job_status', args.get('job_status'))

78
facefusion/audio.py
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@@ -3,25 +3,26 @@ from typing import Any, List, Optional
import numpy
import scipy
from numpy._typing import NDArray
from numpy.typing import NDArray
from facefusion.ffmpeg import read_audio_buffer
from facefusion.filesystem import is_audio
from facefusion.typing import Audio, AudioFrame, Fps, Mel, MelFilterBank, Spectrogram
from facefusion.types import Audio, AudioFrame, Fps, Mel, MelFilterBank, Spectrogram
from facefusion.voice_extractor import batch_extract_voice
@lru_cache(maxsize = 128)
@lru_cache()
def read_static_audio(audio_path : str, fps : Fps) -> Optional[List[AudioFrame]]:
return read_audio(audio_path, fps)
def read_audio(audio_path : str, fps : Fps) -> Optional[List[AudioFrame]]:
sample_rate = 48000
channel_total = 2
audio_sample_rate = 48000
audio_sample_size = 16
audio_channel_total = 2
if is_audio(audio_path):
audio_buffer = read_audio_buffer(audio_path, sample_rate, channel_total)
audio_buffer = read_audio_buffer(audio_path, audio_sample_rate, audio_sample_size, audio_channel_total)
audio = numpy.frombuffer(audio_buffer, dtype = numpy.int16).reshape(-1, 2)
audio = prepare_audio(audio)
spectrogram = create_spectrogram(audio)
@@ -30,21 +31,22 @@ def read_audio(audio_path : str, fps : Fps) -> Optional[List[AudioFrame]]:
return None
@lru_cache(maxsize = 128)
@lru_cache()
def read_static_voice(audio_path : str, fps : Fps) -> Optional[List[AudioFrame]]:
return read_voice(audio_path, fps)
def read_voice(audio_path : str, fps : Fps) -> Optional[List[AudioFrame]]:
sample_rate = 48000
channel_total = 2
chunk_size = 240 * 1024
step_size = 180 * 1024
voice_sample_rate = 48000
voice_sample_size = 16
voice_channel_total = 2
voice_chunk_size = 240 * 1024
voice_step_size = 180 * 1024
if is_audio(audio_path):
audio_buffer = read_audio_buffer(audio_path, sample_rate, channel_total)
audio_buffer = read_audio_buffer(audio_path, voice_sample_rate, voice_sample_size, voice_channel_total)
audio = numpy.frombuffer(audio_buffer, dtype = numpy.int16).reshape(-1, 2)
audio = batch_extract_voice(audio, chunk_size, step_size)
audio = batch_extract_voice(audio, voice_chunk_size, voice_step_size)
audio = prepare_voice(audio)
spectrogram = create_spectrogram(audio)
audio_frames = extract_audio_frames(spectrogram, fps)
@@ -60,6 +62,20 @@ def get_audio_frame(audio_path : str, fps : Fps, frame_number : int = 0) -> Opti
return None
def extract_audio_frames(spectrogram: Spectrogram, fps: Fps) -> List[AudioFrame]:
audio_frames = []
mel_filter_total = 80
audio_step_size = 16
indices = numpy.arange(0, spectrogram.shape[1], mel_filter_total / fps).astype(numpy.int16)
indices = indices[indices >= audio_step_size]
for index in indices:
start = max(0, index - audio_step_size)
audio_frames.append(spectrogram[:, start:index])
return audio_frames
def get_voice_frame(audio_path : str, fps : Fps, frame_number : int = 0) -> Optional[AudioFrame]:
if is_audio(audio_path):
voice_frames = read_static_voice(audio_path, fps)
@@ -70,8 +86,8 @@ def get_voice_frame(audio_path : str, fps : Fps, frame_number : int = 0) -> Opti
def create_empty_audio_frame() -> AudioFrame:
mel_filter_total = 80
step_size = 16
audio_frame = numpy.zeros((mel_filter_total, step_size)).astype(numpy.int16)
audio_step_size = 16
audio_frame = numpy.zeros((mel_filter_total, audio_step_size)).astype(numpy.int16)
return audio_frame
@@ -84,10 +100,10 @@ def prepare_audio(audio : Audio) -> Audio:
def prepare_voice(audio : Audio) -> Audio:
sample_rate = 48000
resample_rate = 16000
audio = scipy.signal.resample(audio, int(len(audio) * resample_rate / sample_rate))
audio_sample_rate = 48000
audio_resample_rate = 16000
audio_resample_factor = round(len(audio) * audio_resample_rate / audio_sample_rate)
audio = scipy.signal.resample(audio, audio_resample_factor)
audio = prepare_audio(audio)
return audio
@@ -101,19 +117,20 @@ def convert_mel_to_hertz(mel : Mel) -> NDArray[Any]:
def create_mel_filter_bank() -> MelFilterBank:
audio_sample_rate = 16000
audio_min_frequency = 55.0
audio_max_frequency = 7600.0
mel_filter_total = 80
mel_bin_total = 800
sample_rate = 16000
min_frequency = 55.0
max_frequency = 7600.0
mel_filter_bank = numpy.zeros((mel_filter_total, mel_bin_total // 2 + 1))
mel_frequency_range = numpy.linspace(convert_hertz_to_mel(min_frequency), convert_hertz_to_mel(max_frequency), mel_filter_total + 2)
indices = numpy.floor((mel_bin_total + 1) * convert_mel_to_hertz(mel_frequency_range) / sample_rate).astype(numpy.int16)
mel_frequency_range = numpy.linspace(convert_hertz_to_mel(audio_min_frequency), convert_hertz_to_mel(audio_max_frequency), mel_filter_total + 2)
indices = numpy.floor((mel_bin_total + 1) * convert_mel_to_hertz(mel_frequency_range) / audio_sample_rate).astype(numpy.int16)
for index in range(mel_filter_total):
start = indices[index]
end = indices[index + 1]
mel_filter_bank[index, start:end] = scipy.signal.windows.triang(end - start)
return mel_filter_bank
@@ -124,16 +141,3 @@ def create_spectrogram(audio : Audio) -> Spectrogram:
spectrogram = scipy.signal.stft(audio, nperseg = mel_bin_total, nfft = mel_bin_total, noverlap = mel_bin_overlap)[2]
spectrogram = numpy.dot(mel_filter_bank, numpy.abs(spectrogram))
return spectrogram
def extract_audio_frames(spectrogram : Spectrogram, fps : Fps) -> List[AudioFrame]:
mel_filter_total = 80
step_size = 16
audio_frames = []
indices = numpy.arange(0, spectrogram.shape[1], mel_filter_total / fps).astype(numpy.int16)
indices = indices[indices >= step_size]
for index in indices:
start = max(0, index - step_size)
audio_frames.append(spectrogram[:, start:index])
return audio_frames

76
facefusion/choices.py
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@@ -2,14 +2,14 @@ import logging
from typing import List, Sequence
from facefusion.common_helper import create_float_range, create_int_range
from facefusion.typing import Angle, DownloadProvider, DownloadProviderSet, DownloadScope, ExecutionProvider, ExecutionProviderSet, FaceDetectorModel, FaceDetectorSet, FaceLandmarkerModel, FaceMaskRegion, FaceMaskRegionSet, FaceMaskType, FaceOccluderModel, FaceParserModel, FaceSelectorMode, FaceSelectorOrder, Gender, JobStatus, LogLevel, LogLevelSet, OutputAudioEncoder, OutputVideoEncoder, OutputVideoPreset, Race, Score, TempFrameFormat, UiWorkflow, VideoMemoryStrategy
from facefusion.types import Angle, AudioEncoder, AudioFormat, AudioTypeSet, DownloadProvider, DownloadProviderSet, DownloadScope, EncoderSet, ExecutionProvider, ExecutionProviderSet, FaceDetectorModel, FaceDetectorSet, FaceLandmarkerModel, FaceMaskRegion, FaceMaskRegionSet, FaceMaskType, FaceOccluderModel, FaceParserModel, FaceSelectorMode, FaceSelectorOrder, Gender, ImageFormat, ImageTypeSet, JobStatus, LogLevel, LogLevelSet, Race, Score, TempFrameFormat, UiWorkflow, VideoEncoder, VideoFormat, VideoMemoryStrategy, VideoPreset, VideoTypeSet, WebcamMode
face_detector_set : FaceDetectorSet =\
{
'many': [ '640x640' ],
'retinaface': [ '160x160', '320x320', '480x480', '512x512', '640x640' ],
'scrfd': [ '160x160', '320x320', '480x480', '512x512', '640x640' ],
'yoloface': [ '640x640' ]
'yolo_face': [ '640x640' ]
}
face_detector_models : List[FaceDetectorModel] = list(face_detector_set.keys())
face_landmarker_models : List[FaceLandmarkerModel] = [ 'many', '2dfan4', 'peppa_wutz' ]
@@ -17,7 +17,7 @@ face_selector_modes : List[FaceSelectorMode] = [ 'many', 'one', 'reference' ]
face_selector_orders : List[FaceSelectorOrder] = [ 'left-right', 'right-left', 'top-bottom', 'bottom-top', 'small-large', 'large-small', 'best-worst', 'worst-best' ]
face_selector_genders : List[Gender] = [ 'female', 'male' ]
face_selector_races : List[Race] = [ 'white', 'black', 'latino', 'asian', 'indian', 'arabic' ]
face_occluder_models : List[FaceOccluderModel] = [ 'xseg_1', 'xseg_2' ]
face_occluder_models : List[FaceOccluderModel] = [ 'xseg_1', 'xseg_2', 'xseg_3' ]
face_parser_models : List[FaceParserModel] = [ 'bisenet_resnet_18', 'bisenet_resnet_34' ]
face_mask_types : List[FaceMaskType] = [ 'box', 'occlusion', 'region' ]
face_mask_region_set : FaceMaskRegionSet =\
@@ -34,35 +34,81 @@ face_mask_region_set : FaceMaskRegionSet =\
'lower-lip': 13
}
face_mask_regions : List[FaceMaskRegion] = list(face_mask_region_set.keys())
temp_frame_formats : List[TempFrameFormat] = [ 'bmp', 'jpg', 'png' ]
output_audio_encoders : List[OutputAudioEncoder] = [ 'aac', 'libmp3lame', 'libopus', 'libvorbis' ]
output_video_encoders : List[OutputVideoEncoder] = [ 'libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc', 'h264_amf', 'hevc_amf', 'h264_qsv', 'hevc_qsv', 'h264_videotoolbox', 'hevc_videotoolbox' ]
output_video_presets : List[OutputVideoPreset] = [ 'ultrafast', 'superfast', 'veryfast', 'faster', 'fast', 'medium', 'slow', 'slower', 'veryslow' ]
audio_type_set : AudioTypeSet =\
{
'flac': 'audio/flac',
'm4a': 'audio/mp4',
'mp3': 'audio/mpeg',
'ogg': 'audio/ogg',
'opus': 'audio/opus',
'wav': 'audio/x-wav'
}
image_type_set : ImageTypeSet =\
{
'bmp': 'image/bmp',
'jpeg': 'image/jpeg',
'png': 'image/png',
'tiff': 'image/tiff',
'webp': 'image/webp'
}
video_type_set : VideoTypeSet =\
{
'avi': 'video/x-msvideo',
'm4v': 'video/mp4',
'mkv': 'video/x-matroska',
'mp4': 'video/mp4',
'mov': 'video/quicktime',
'webm': 'video/webm'
}
audio_formats : List[AudioFormat] = list(audio_type_set.keys())
image_formats : List[ImageFormat] = list(image_type_set.keys())
video_formats : List[VideoFormat] = list(video_type_set.keys())
temp_frame_formats : List[TempFrameFormat] = [ 'bmp', 'jpeg', 'png', 'tiff' ]
output_encoder_set : EncoderSet =\
{
'audio': [ 'flac', 'aac', 'libmp3lame', 'libopus', 'libvorbis', 'pcm_s16le', 'pcm_s32le' ],
'video': [ 'libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc', 'h264_amf', 'hevc_amf', 'h264_qsv', 'hevc_qsv', 'h264_videotoolbox', 'hevc_videotoolbox', 'rawvideo' ]
}
output_audio_encoders : List[AudioEncoder] = output_encoder_set.get('audio')
output_video_encoders : List[VideoEncoder] = output_encoder_set.get('video')
output_video_presets : List[VideoPreset] = [ 'ultrafast', 'superfast', 'veryfast', 'faster', 'fast', 'medium', 'slow', 'slower', 'veryslow' ]
image_template_sizes : List[float] = [ 0.25, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 3.5, 4 ]
video_template_sizes : List[int] = [ 240, 360, 480, 540, 720, 1080, 1440, 2160, 4320 ]
webcam_modes : List[WebcamMode] = [ 'inline', 'udp', 'v4l2' ]
webcam_resolutions : List[str] = [ '320x240', '640x480', '800x600', '1024x768', '1280x720', '1280x960', '1920x1080', '2560x1440', '3840x2160' ]
execution_provider_set : ExecutionProviderSet =\
{
'cpu': 'CPUExecutionProvider',
'coreml': 'CoreMLExecutionProvider',
'cuda': 'CUDAExecutionProvider',
'tensorrt': 'TensorrtExecutionProvider',
'directml': 'DmlExecutionProvider',
'openvino': 'OpenVINOExecutionProvider',
'rocm': 'ROCMExecutionProvider',
'tensorrt': 'TensorrtExecutionProvider'
'openvino': 'OpenVINOExecutionProvider',
'coreml': 'CoreMLExecutionProvider',
'cpu': 'CPUExecutionProvider'
}
execution_providers : List[ExecutionProvider] = list(execution_provider_set.keys())
download_provider_set : DownloadProviderSet =\
{
'github':
{
'url': 'https://github.com',
'urls':
[
'https://github.com'
],
'path': '/facefusion/facefusion-assets/releases/download/{base_name}/{file_name}'
},
'huggingface':
{
'url': 'https://huggingface.co',
'urls':
[
'https://huggingface.co',
'https://hf-mirror.com'
],
'path': '/facefusion/{base_name}/resolve/main/{file_name}'
}
}
@@ -92,6 +138,8 @@ face_landmarker_score_range : Sequence[Score] = create_float_range(0.0, 1.0, 0.0
face_mask_blur_range : Sequence[float] = create_float_range(0.0, 1.0, 0.05)
face_mask_padding_range : Sequence[int] = create_int_range(0, 100, 1)
face_selector_age_range : Sequence[int] = create_int_range(0, 100, 1)
reference_face_distance_range : Sequence[float] = create_float_range(0.0, 1.5, 0.05)
reference_face_distance_range : Sequence[float] = create_float_range(0.0, 1.0, 0.05)
output_image_quality_range : Sequence[int] = create_int_range(0, 100, 1)
output_audio_quality_range : Sequence[int] = create_int_range(0, 100, 1)
output_audio_volume_range : Sequence[int] = create_int_range(0, 100, 1)
output_video_quality_range : Sequence[int] = create_int_range(0, 100, 1)

35
facefusion/cli_helper.py Normal file
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@@ -0,0 +1,35 @@
from typing import Tuple
from facefusion.logger import get_package_logger
from facefusion.types import TableContents, TableHeaders
def render_table(headers : TableHeaders, contents : TableContents) -> None:
package_logger = get_package_logger()
table_column, table_separator = create_table_parts(headers, contents)
package_logger.info(table_separator)
package_logger.info(table_column.format(*headers))
package_logger.info(table_separator)
for content in contents:
content = [ value if value else '' for value in content ]
package_logger.info(table_column.format(*content))
package_logger.info(table_separator)
def create_table_parts(headers : TableHeaders, contents : TableContents) -> Tuple[str, str]:
column_parts = []
separator_parts = []
widths = [ len(header) for header in headers ]
for content in contents:
for index, value in enumerate(content):
widths[index] = max(widths[index], len(str(value)))
for width in widths:
column_parts.append('{:<' + str(width) + '}')
separator_parts.append('-' * width)
return '| ' + ' | '.join(column_parts) + ' |', '+-' + '-+-'.join(separator_parts) + '-+'

22
facefusion/common_helper.py
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@@ -1,5 +1,5 @@
import platform
from typing import Any, Optional, Sequence
from typing import Any, Iterable, Optional, Reversible, Sequence
def is_linux() -> bool:
@@ -50,23 +50,35 @@ def calc_float_step(float_range : Sequence[float]) -> float:
return round(float_range[1] - float_range[0], 2)
def cast_int(value : Any) -> Optional[Any]:
def cast_int(value : Any) -> Optional[int]:
try:
return int(value)
except (ValueError, TypeError):
return None
def cast_float(value : Any) -> Optional[Any]:
def cast_float(value : Any) -> Optional[float]:
try:
return float(value)
except (ValueError, TypeError):
return None
def cast_bool(value : Any) -> Optional[bool]:
if value == 'True':
return True
if value == 'False':
return False
return None
def get_first(__list__ : Any) -> Any:
return next(iter(__list__), None)
if isinstance(__list__, Iterable):
return next(iter(__list__), None)
return None
def get_last(__list__ : Any) -> Any:
return next(reversed(__list__), None)
if isinstance(__list__, Reversible):
return next(reversed(__list__), None)
return None

130
facefusion/config.py
View File

@@ -1,92 +1,74 @@
from configparser import ConfigParser
from typing import Any, List, Optional
from typing import List, Optional
from facefusion import state_manager
from facefusion.common_helper import cast_float, cast_int
from facefusion.common_helper import cast_bool, cast_float, cast_int
CONFIG = None
CONFIG_PARSER = None
def get_config() -> ConfigParser:
global CONFIG
def get_config_parser() -> ConfigParser:
global CONFIG_PARSER
if CONFIG is None:
CONFIG = ConfigParser()
CONFIG.read(state_manager.get_item('config_path'), encoding = 'utf-8')
return CONFIG
if CONFIG_PARSER is None:
CONFIG_PARSER = ConfigParser()
CONFIG_PARSER.read(state_manager.get_item('config_path'), encoding = 'utf-8')
return CONFIG_PARSER
def clear_config() -> None:
global CONFIG
def clear_config_parser() -> None:
global CONFIG_PARSER
CONFIG = None
CONFIG_PARSER = None
def get_str_value(key : str, fallback : Optional[str] = None) -> Optional[str]:
value = get_value_by_notation(key)
def get_str_value(section : str, option : str, fallback : Optional[str] = None) -> Optional[str]:
config_parser = get_config_parser()
if value or fallback:
return str(value or fallback)
if config_parser.has_option(section, option) and config_parser.get(section, option).strip():
return config_parser.get(section, option)
return fallback
def get_int_value(section : str, option : str, fallback : Optional[str] = None) -> Optional[int]:
config_parser = get_config_parser()
if config_parser.has_option(section, option) and config_parser.get(section, option).strip():
return config_parser.getint(section, option)
return cast_int(fallback)
def get_float_value(section : str, option : str, fallback : Optional[str] = None) -> Optional[float]:
config_parser = get_config_parser()
if config_parser.has_option(section, option) and config_parser.get(section, option).strip():
return config_parser.getfloat(section, option)
return cast_float(fallback)
def get_bool_value(section : str, option : str, fallback : Optional[str] = None) -> Optional[bool]:
config_parser = get_config_parser()
if config_parser.has_option(section, option) and config_parser.get(section, option).strip():
return config_parser.getboolean(section, option)
return cast_bool(fallback)
def get_str_list(section : str, option : str, fallback : Optional[str] = None) -> Optional[List[str]]:
config_parser = get_config_parser()
if config_parser.has_option(section, option) and config_parser.get(section, option).strip():
return config_parser.get(section, option).split()
if fallback:
return fallback.split()
return None
def get_int_value(key : str, fallback : Optional[str] = None) -> Optional[int]:
value = get_value_by_notation(key)
def get_int_list(section : str, option : str, fallback : Optional[str] = None) -> Optional[List[int]]:
config_parser = get_config_parser()
if value or fallback:
return cast_int(value or fallback)
return None
def get_float_value(key : str, fallback : Optional[str] = None) -> Optional[float]:
value = get_value_by_notation(key)
if value or fallback:
return cast_float(value or fallback)
return None
def get_bool_value(key : str, fallback : Optional[str] = None) -> Optional[bool]:
value = get_value_by_notation(key)
if value == 'True' or fallback == 'True':
return True
if value == 'False' or fallback == 'False':
return False
return None
def get_str_list(key : str, fallback : Optional[str] = None) -> Optional[List[str]]:
value = get_value_by_notation(key)
if value or fallback:
return [ str(value) for value in (value or fallback).split(' ') ]
return None
def get_int_list(key : str, fallback : Optional[str] = None) -> Optional[List[int]]:
value = get_value_by_notation(key)
if value or fallback:
return [ cast_int(value) for value in (value or fallback).split(' ') ]
return None
def get_float_list(key : str, fallback : Optional[str] = None) -> Optional[List[float]]:
value = get_value_by_notation(key)
if value or fallback:
return [ cast_float(value) for value in (value or fallback).split(' ') ]
return None
def get_value_by_notation(key : str) -> Optional[Any]:
config = get_config()
if '.' in key:
section, name = key.split('.')
if section in config and name in config[section]:
return config[section][name]
if key in config:
return config[key]
if config_parser.has_option(section, option) and config_parser.get(section, option).strip():
return list(map(int, config_parser.get(section, option).split()))
if fallback:
return list(map(int, fallback.split()))
return None

112
facefusion/content_analyser.py
View File

@@ -1,6 +1,6 @@
from functools import lru_cache
from typing import List
import cv2
import numpy
from tqdm import tqdm
@@ -8,11 +8,9 @@ from facefusion import inference_manager, state_manager, wording
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import DownloadScope, Fps, InferencePool, ModelOptions, ModelSet, VisionFrame
from facefusion.vision import detect_video_fps, get_video_frame, read_image
from facefusion.types import Detection, DownloadScope, Fps, InferencePool, ModelOptions, ModelSet, Score, VisionFrame
from facefusion.vision import detect_video_fps, fit_frame, read_image, read_video_frame
PROBABILITY_LIMIT = 0.80
RATE_LIMIT = 10
STREAM_COUNTER = 0
@@ -20,48 +18,50 @@ STREAM_COUNTER = 0
def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
return\
{
'open_nsfw':
'yolo_nsfw':
{
'hashes':
{
'content_analyser':
{
'url': resolve_download_url('models-3.0.0', 'open_nsfw.hash'),
'path': resolve_relative_path('../.assets/models/open_nsfw.hash')
'url': resolve_download_url('models-3.2.0', 'yolo_11m_nsfw.hash'),
'path': resolve_relative_path('../.assets/models/yolo_11m_nsfw.hash')
}
},
'sources':
{
'content_analyser':
{
'url': resolve_download_url('models-3.0.0', 'open_nsfw.onnx'),
'path': resolve_relative_path('../.assets/models/open_nsfw.onnx')
'url': resolve_download_url('models-3.2.0', 'yolo_11m_nsfw.onnx'),
'path': resolve_relative_path('../.assets/models/yolo_11m_nsfw.onnx')
}
},
'size': (224, 224),
'mean': [ 104, 117, 123 ]
'size': (640, 640)
}
}
def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_sources)
model_names = [ 'yolo_nsfw' ]
model_source_set = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_names, model_source_set)
def clear_inference_pool() -> None:
inference_manager.clear_inference_pool(__name__)
model_names = [ 'yolo_nsfw' ]
inference_manager.clear_inference_pool(__name__, model_names)
def get_model_options() -> ModelOptions:
return create_static_model_set('full').get('open_nsfw')
return create_static_model_set('full').get('yolo_nsfw')
def pre_check() -> bool:
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
model_hash_set = get_model_options().get('hashes')
model_source_set = get_model_options().get('sources')
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
def analyse_stream(vision_frame : VisionFrame, video_fps : Fps) -> bool:
@@ -74,31 +74,9 @@ def analyse_stream(vision_frame : VisionFrame, video_fps : Fps) -> bool:
def analyse_frame(vision_frame : VisionFrame) -> bool:
vision_frame = prepare_frame(vision_frame)
probability = forward(vision_frame)
nsfw_scores = detect_nsfw(vision_frame)
return probability > PROBABILITY_LIMIT
def forward(vision_frame : VisionFrame) -> float:
content_analyser = get_inference_pool().get('content_analyser')
with conditional_thread_semaphore():
probability = content_analyser.run(None,
{
'input': vision_frame
})[0][0][1]
return probability
def prepare_frame(vision_frame : VisionFrame) -> VisionFrame:
model_size = get_model_options().get('size')
model_mean = get_model_options().get('mean')
vision_frame = cv2.resize(vision_frame, model_size).astype(numpy.float32)
vision_frame -= numpy.array(model_mean).astype(numpy.float32)
vision_frame = numpy.expand_dims(vision_frame, axis = 0)
return vision_frame
return len(nsfw_scores) > 0
@lru_cache(maxsize = None)
@@ -112,15 +90,55 @@ def analyse_video(video_path : str, trim_frame_start : int, trim_frame_end : int
video_fps = detect_video_fps(video_path)
frame_range = range(trim_frame_start, trim_frame_end)
rate = 0.0
total = 0
counter = 0
with tqdm(total = len(frame_range), desc = wording.get('analysing'), unit = 'frame', ascii = ' =', disable = state_manager.get_item('log_level') in [ 'warn', 'error' ]) as progress:
for frame_number in frame_range:
if frame_number % int(video_fps) == 0:
vision_frame = get_video_frame(video_path, frame_number)
vision_frame = read_video_frame(video_path, frame_number)
total += 1
if analyse_frame(vision_frame):
counter += 1
rate = counter * int(video_fps) / len(frame_range) * 100
progress.update()
if counter > 0 and total > 0:
rate = counter / total * 100
progress.set_postfix(rate = rate)
return rate > RATE_LIMIT
progress.update()
return rate > 10.0
def detect_nsfw(vision_frame : VisionFrame) -> List[Score]:
nsfw_scores = []
model_size = get_model_options().get('size')
temp_vision_frame = fit_frame(vision_frame, model_size)
detect_vision_frame = prepare_detect_frame(temp_vision_frame)
detection = forward(detect_vision_frame)
detection = numpy.squeeze(detection).T
nsfw_scores_raw = numpy.amax(detection[:, 4:], axis = 1)
keep_indices = numpy.where(nsfw_scores_raw > 0.2)[0]
if numpy.any(keep_indices):
nsfw_scores_raw = nsfw_scores_raw[keep_indices]
nsfw_scores = nsfw_scores_raw.ravel().tolist()
return nsfw_scores
def forward(vision_frame : VisionFrame) -> Detection:
content_analyser = get_inference_pool().get('content_analyser')
with conditional_thread_semaphore():
detection = content_analyser.run(None,
{
'input': vision_frame
})
return detection
def prepare_detect_frame(temp_vision_frame : VisionFrame) -> VisionFrame:
detect_vision_frame = temp_vision_frame / 255.0
detect_vision_frame = numpy.expand_dims(detect_vision_frame.transpose(2, 0, 1), axis = 0).astype(numpy.float32)
return detect_vision_frame

124
facefusion/core.py
View File

@@ -6,17 +6,17 @@ from time import time
import numpy
from facefusion import content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, logger, process_manager, state_manager, voice_extractor, wording
from facefusion import cli_helper, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, logger, process_manager, state_manager, voice_extractor, wording
from facefusion.args import apply_args, collect_job_args, reduce_job_args, reduce_step_args
from facefusion.common_helper import get_first
from facefusion.content_analyser import analyse_image, analyse_video
from facefusion.download import conditional_download_hashes, conditional_download_sources
from facefusion.exit_helper import conditional_exit, graceful_exit, hard_exit
from facefusion.exit_helper import graceful_exit, hard_exit
from facefusion.face_analyser import get_average_face, get_many_faces, get_one_face
from facefusion.face_selector import sort_and_filter_faces
from facefusion.face_store import append_reference_face, clear_reference_faces, get_reference_faces
from facefusion.ffmpeg import copy_image, extract_frames, finalize_image, merge_video, replace_audio, restore_audio
from facefusion.filesystem import filter_audio_paths, is_image, is_video, list_directory, resolve_file_pattern
from facefusion.filesystem import filter_audio_paths, get_file_name, is_image, is_video, resolve_file_paths, resolve_file_pattern
from facefusion.jobs import job_helper, job_manager, job_runner
from facefusion.jobs.job_list import compose_job_list
from facefusion.memory import limit_system_memory
@@ -24,62 +24,70 @@ from facefusion.processors.core import get_processors_modules
from facefusion.program import create_program
from facefusion.program_helper import validate_args
from facefusion.statistics import conditional_log_statistics
from facefusion.temp_helper import clear_temp_directory, create_temp_directory, get_temp_file_path, get_temp_frame_paths, move_temp_file
from facefusion.typing import Args, ErrorCode
from facefusion.vision import get_video_frame, pack_resolution, read_image, read_static_images, restrict_image_resolution, restrict_trim_frame, restrict_video_fps, restrict_video_resolution, unpack_resolution
from facefusion.temp_helper import clear_temp_directory, create_temp_directory, get_temp_file_path, move_temp_file, resolve_temp_frame_paths
from facefusion.types import Args, ErrorCode
from facefusion.vision import pack_resolution, read_image, read_static_images, read_video_frame, restrict_image_resolution, restrict_trim_frame, restrict_video_fps, restrict_video_resolution, unpack_resolution
def cli() -> None:
signal.signal(signal.SIGINT, lambda signal_number, frame: graceful_exit(0))
program = create_program()
if pre_check():
signal.signal(signal.SIGINT, lambda signal_number, frame: graceful_exit(0))
program = create_program()
if validate_args(program):
args = vars(program.parse_args())
apply_args(args, state_manager.init_item)
if validate_args(program):
args = vars(program.parse_args())
apply_args(args, state_manager.init_item)
if state_manager.get_item('command'):
logger.init(state_manager.get_item('log_level'))
route(args)
if state_manager.get_item('command'):
logger.init(state_manager.get_item('log_level'))
route(args)
else:
program.print_help()
else:
program.print_help()
hard_exit(2)
else:
hard_exit(2)
def route(args : Args) -> None:
system_memory_limit = state_manager.get_item('system_memory_limit')
if system_memory_limit and system_memory_limit > 0:
limit_system_memory(system_memory_limit)
if state_manager.get_item('command') == 'force-download':
error_code = force_download()
return conditional_exit(error_code)
return hard_exit(error_code)
if state_manager.get_item('command') in [ 'job-list', 'job-create', 'job-submit', 'job-submit-all', 'job-delete', 'job-delete-all', 'job-add-step', 'job-remix-step', 'job-insert-step', 'job-remove-step' ]:
if not job_manager.init_jobs(state_manager.get_item('jobs_path')):
hard_exit(1)
error_code = route_job_manager(args)
hard_exit(error_code)
if not pre_check():
return conditional_exit(2)
if state_manager.get_item('command') == 'run':
import facefusion.uis.core as ui
if not common_pre_check() or not processors_pre_check():
return conditional_exit(2)
return hard_exit(2)
for ui_layout in ui.get_ui_layouts_modules(state_manager.get_item('ui_layouts')):
if not ui_layout.pre_check():
return conditional_exit(2)
return hard_exit(2)
ui.init()
ui.launch()
if state_manager.get_item('command') == 'headless-run':
if not job_manager.init_jobs(state_manager.get_item('jobs_path')):
hard_exit(1)
error_core = process_headless(args)
hard_exit(error_core)
if state_manager.get_item('command') == 'batch-run':
if not job_manager.init_jobs(state_manager.get_item('jobs_path')):
hard_exit(1)
error_core = process_batch(args)
hard_exit(error_core)
if state_manager.get_item('command') in [ 'job-run', 'job-run-all', 'job-retry', 'job-retry-all' ]:
if not job_manager.init_jobs(state_manager.get_item('jobs_path')):
hard_exit(1)
@@ -91,9 +99,11 @@ def pre_check() -> bool:
if sys.version_info < (3, 10):
logger.error(wording.get('python_not_supported').format(version = '3.10'), __name__)
return False
if not shutil.which('curl'):
logger.error(wording.get('curl_not_installed'), __name__)
return False
if not shutil.which('ffmpeg'):
logger.error(wording.get('ffmpeg_not_installed'), __name__)
return False
@@ -133,17 +143,17 @@ def force_download() -> ErrorCode:
face_recognizer,
voice_extractor
]
available_processors = [ file.get('name') for file in list_directory('facefusion/processors/modules') ]
available_processors = [ get_file_name(file_path) for file_path in resolve_file_paths('facefusion/processors/modules') ]
processor_modules = get_processors_modules(available_processors)
for module in common_modules + processor_modules:
if hasattr(module, 'create_static_model_set'):
for model in module.create_static_model_set(state_manager.get_item('download_scope')).values():
model_hashes = model.get('hashes')
model_sources = model.get('sources')
model_hash_set = model.get('hashes')
model_source_set = model.get('sources')
if model_hashes and model_sources:
if not conditional_download_hashes(model_hashes) or not conditional_download_sources(model_sources):
if model_hash_set and model_source_set:
if not conditional_download_hashes(model_hash_set) or not conditional_download_sources(model_source_set):
return 1
return 0
@@ -154,39 +164,45 @@ def route_job_manager(args : Args) -> ErrorCode:
job_headers, job_contents = compose_job_list(state_manager.get_item('job_status'))
if job_contents:
logger.table(job_headers, job_contents)
cli_helper.render_table(job_headers, job_contents)
return 0
return 1
if state_manager.get_item('command') == 'job-create':
if job_manager.create_job(state_manager.get_item('job_id')):
logger.info(wording.get('job_created').format(job_id = state_manager.get_item('job_id')), __name__)
return 0
logger.error(wording.get('job_not_created').format(job_id = state_manager.get_item('job_id')), __name__)
return 1
if state_manager.get_item('command') == 'job-submit':
if job_manager.submit_job(state_manager.get_item('job_id')):
logger.info(wording.get('job_submitted').format(job_id = state_manager.get_item('job_id')), __name__)
return 0
logger.error(wording.get('job_not_submitted').format(job_id = state_manager.get_item('job_id')), __name__)
return 1
if state_manager.get_item('command') == 'job-submit-all':
if job_manager.submit_jobs():
if job_manager.submit_jobs(state_manager.get_item('halt_on_error')):
logger.info(wording.get('job_all_submitted'), __name__)
return 0
logger.error(wording.get('job_all_not_submitted'), __name__)
return 1
if state_manager.get_item('command') == 'job-delete':
if job_manager.delete_job(state_manager.get_item('job_id')):
logger.info(wording.get('job_deleted').format(job_id = state_manager.get_item('job_id')), __name__)
return 0
logger.error(wording.get('job_not_deleted').format(job_id = state_manager.get_item('job_id')), __name__)
return 1
if state_manager.get_item('command') == 'job-delete-all':
if job_manager.delete_jobs():
if job_manager.delete_jobs(state_manager.get_item('halt_on_error')):
logger.info(wording.get('job_all_deleted'), __name__)
return 0
logger.error(wording.get('job_all_not_deleted'), __name__)
return 1
if state_manager.get_item('command') == 'job-add-step':
step_args = reduce_step_args(args)
@@ -195,6 +211,7 @@ def route_job_manager(args : Args) -> ErrorCode:
return 0
logger.error(wording.get('job_step_not_added').format(job_id = state_manager.get_item('job_id')), __name__)
return 1
if state_manager.get_item('command') == 'job-remix-step':
step_args = reduce_step_args(args)
@@ -203,6 +220,7 @@ def route_job_manager(args : Args) -> ErrorCode:
return 0
logger.error(wording.get('job_remix_step_not_added').format(job_id = state_manager.get_item('job_id'), step_index = state_manager.get_item('step_index')), __name__)
return 1
if state_manager.get_item('command') == 'job-insert-step':
step_args = reduce_step_args(args)
@@ -211,6 +229,7 @@ def route_job_manager(args : Args) -> ErrorCode:
return 0
logger.error(wording.get('job_step_not_inserted').format(job_id = state_manager.get_item('job_id'), step_index = state_manager.get_item('step_index')), __name__)
return 1
if state_manager.get_item('command') == 'job-remove-step':
if job_manager.remove_step(state_manager.get_item('job_id'), state_manager.get_item('step_index')):
logger.info(wording.get('job_step_removed').format(job_id = state_manager.get_item('job_id'), step_index = state_manager.get_item('step_index')), __name__)
@@ -228,13 +247,15 @@ def route_job_runner() -> ErrorCode:
return 0
logger.info(wording.get('processing_job_failed').format(job_id = state_manager.get_item('job_id')), __name__)
return 1
if state_manager.get_item('command') == 'job-run-all':
logger.info(wording.get('running_jobs'), __name__)
if job_runner.run_jobs(process_step):
if job_runner.run_jobs(process_step, state_manager.get_item('halt_on_error')):
logger.info(wording.get('processing_jobs_succeed'), __name__)
return 0
logger.info(wording.get('processing_jobs_failed'), __name__)
return 1
if state_manager.get_item('command') == 'job-retry':
logger.info(wording.get('retrying_job').format(job_id = state_manager.get_item('job_id')), __name__)
if job_runner.retry_job(state_manager.get_item('job_id'), process_step):
@@ -242,9 +263,10 @@ def route_job_runner() -> ErrorCode:
return 0
logger.info(wording.get('processing_job_failed').format(job_id = state_manager.get_item('job_id')), __name__)
return 1
if state_manager.get_item('command') == 'job-retry-all':
logger.info(wording.get('retrying_jobs'), __name__)
if job_runner.retry_jobs(process_step):
if job_runner.retry_jobs(process_step, state_manager.get_item('halt_on_error')):
logger.info(wording.get('processing_jobs_succeed'), __name__)
return 0
logger.info(wording.get('processing_jobs_failed'), __name__)
@@ -305,14 +327,18 @@ def process_step(job_id : str, step_index : int, step_args : Args) -> bool:
def conditional_process() -> ErrorCode:
start_time = time()
for processor_module in get_processors_modules(state_manager.get_item('processors')):
if not processor_module.pre_process('output'):
return 2
conditional_append_reference_faces()
if is_image(state_manager.get_item('target_path')):
return process_image(start_time)
if is_video(state_manager.get_item('target_path')):
return process_video(start_time)
return 0
@@ -322,7 +348,7 @@ def conditional_append_reference_faces() -> None:
source_faces = get_many_faces(source_frames)
source_face = get_average_face(source_faces)
if is_video(state_manager.get_item('target_path')):
reference_frame = get_video_frame(state_manager.get_item('target_path'), state_manager.get_item('reference_frame_number'))
reference_frame = read_video_frame(state_manager.get_item('target_path'), state_manager.get_item('reference_frame_number'))
else:
reference_frame = read_image(state_manager.get_item('target_path'))
reference_faces = sort_and_filter_faces(get_many_faces([ reference_frame ]))
@@ -341,13 +367,12 @@ def conditional_append_reference_faces() -> None:
def process_image(start_time : float) -> ErrorCode:
if analyse_image(state_manager.get_item('target_path')):
return 3
# clear temp
logger.debug(wording.get('clearing_temp'), __name__)
clear_temp_directory(state_manager.get_item('target_path'))
# create temp
logger.debug(wording.get('creating_temp'), __name__)
create_temp_directory(state_manager.get_item('target_path'))
# copy image
process_manager.start()
temp_image_resolution = pack_resolution(restrict_image_resolution(state_manager.get_item('target_path'), unpack_resolution(state_manager.get_item('output_image_resolution'))))
logger.info(wording.get('copying_image').format(resolution = temp_image_resolution), __name__)
@@ -357,7 +382,7 @@ def process_image(start_time : float) -> ErrorCode:
logger.error(wording.get('copying_image_failed'), __name__)
process_manager.end()
return 1
# process image
temp_file_path = get_temp_file_path(state_manager.get_item('target_path'))
for processor_module in get_processors_modules(state_manager.get_item('processors')):
logger.info(wording.get('processing'), processor_module.__name__)
@@ -366,16 +391,16 @@ def process_image(start_time : float) -> ErrorCode:
if is_process_stopping():
process_manager.end()
return 4
# finalize image
logger.info(wording.get('finalizing_image').format(resolution = state_manager.get_item('output_image_resolution')), __name__)
if finalize_image(state_manager.get_item('target_path'), state_manager.get_item('output_path'), state_manager.get_item('output_image_resolution')):
logger.debug(wording.get('finalizing_image_succeed'), __name__)
else:
logger.warn(wording.get('finalizing_image_skipped'), __name__)
# clear temp
logger.debug(wording.get('clearing_temp'), __name__)
clear_temp_directory(state_manager.get_item('target_path'))
# validate image
if is_image(state_manager.get_item('output_path')):
seconds = '{:.2f}'.format((time() - start_time) % 60)
logger.info(wording.get('processing_image_succeed').format(seconds = seconds), __name__)
@@ -392,13 +417,12 @@ def process_video(start_time : float) -> ErrorCode:
trim_frame_start, trim_frame_end = restrict_trim_frame(state_manager.get_item('target_path'), state_manager.get_item('trim_frame_start'), state_manager.get_item('trim_frame_end'))
if analyse_video(state_manager.get_item('target_path'), trim_frame_start, trim_frame_end):
return 3
# clear temp
logger.debug(wording.get('clearing_temp'), __name__)
clear_temp_directory(state_manager.get_item('target_path'))
# create temp
logger.debug(wording.get('creating_temp'), __name__)
create_temp_directory(state_manager.get_item('target_path'))
# extract frames
process_manager.start()
temp_video_resolution = pack_resolution(restrict_video_resolution(state_manager.get_item('target_path'), unpack_resolution(state_manager.get_item('output_video_resolution'))))
temp_video_fps = restrict_video_fps(state_manager.get_item('target_path'), state_manager.get_item('output_video_fps'))
@@ -412,8 +436,8 @@ def process_video(start_time : float) -> ErrorCode:
logger.error(wording.get('extracting_frames_failed'), __name__)
process_manager.end()
return 1
# process frames
temp_frame_paths = get_temp_frame_paths(state_manager.get_item('target_path'))
temp_frame_paths = resolve_temp_frame_paths(state_manager.get_item('target_path'))
if temp_frame_paths:
for processor_module in get_processors_modules(state_manager.get_item('processors')):
logger.info(wording.get('processing'), processor_module.__name__)
@@ -425,9 +449,9 @@ def process_video(start_time : float) -> ErrorCode:
logger.error(wording.get('temp_frames_not_found'), __name__)
process_manager.end()
return 1
# merge video
logger.info(wording.get('merging_video').format(resolution = state_manager.get_item('output_video_resolution'), fps = state_manager.get_item('output_video_fps')), __name__)
if merge_video(state_manager.get_item('target_path'), state_manager.get_item('output_video_resolution'), state_manager.get_item('output_video_fps')):
if merge_video(state_manager.get_item('target_path'), temp_video_fps, state_manager.get_item('output_video_resolution'), state_manager.get_item('output_video_fps'), trim_frame_start, trim_frame_end):
logger.debug(wording.get('merging_video_succeed'), __name__)
else:
if is_process_stopping():
@@ -436,8 +460,8 @@ def process_video(start_time : float) -> ErrorCode:
logger.error(wording.get('merging_video_failed'), __name__)
process_manager.end()
return 1
# handle audio
if state_manager.get_item('skip_audio'):
if state_manager.get_item('output_audio_volume') == 0:
logger.info(wording.get('skipping_audio'), __name__)
move_temp_file(state_manager.get_item('target_path'), state_manager.get_item('output_path'))
else:
@@ -452,7 +476,7 @@ def process_video(start_time : float) -> ErrorCode:
logger.warn(wording.get('replacing_audio_skipped'), __name__)
move_temp_file(state_manager.get_item('target_path'), state_manager.get_item('output_path'))
else:
if restore_audio(state_manager.get_item('target_path'), state_manager.get_item('output_path'), state_manager.get_item('output_video_fps'), trim_frame_start, trim_frame_end):
if restore_audio(state_manager.get_item('target_path'), state_manager.get_item('output_path'), trim_frame_start, trim_frame_end):
logger.debug(wording.get('restoring_audio_succeed'), __name__)
else:
if is_process_stopping():
@@ -460,10 +484,10 @@ def process_video(start_time : float) -> ErrorCode:
return 4
logger.warn(wording.get('restoring_audio_skipped'), __name__)
move_temp_file(state_manager.get_item('target_path'), state_manager.get_item('output_path'))
# clear temp
logger.debug(wording.get('clearing_temp'), __name__)
clear_temp_directory(state_manager.get_item('target_path'))
# validate video
if is_video(state_manager.get_item('output_path')):
seconds = '{:.2f}'.format((time() - start_time))
logger.info(wording.get('processing_video_succeed').format(seconds = seconds), __name__)

27
facefusion/curl_builder.py Normal file
View File

@@ -0,0 +1,27 @@
import itertools
import shutil
from facefusion import metadata
from facefusion.types import Commands
def run(commands : Commands) -> Commands:
user_agent = metadata.get('name') + '/' + metadata.get('version')
return [ shutil.which('curl'), '--user-agent', user_agent, '--insecure', '--location', '--silent' ] + commands
def chain(*commands : Commands) -> Commands:
return list(itertools.chain(*commands))
def head(url : str) -> Commands:
return [ '-I', url ]
def download(url : str, download_file_path : str) -> Commands:
return [ '--create-dirs', '--continue-at', '-', '--output', download_file_path, url ]
def set_timeout(timeout : int) -> Commands:
return [ '--connect-timeout', str(timeout) ]

79
facefusion/download.py
View File

@@ -1,5 +1,4 @@
import os
import shutil
import subprocess
from functools import lru_cache
from typing import List, Optional, Tuple
@@ -8,15 +7,14 @@ from urllib.parse import urlparse
from tqdm import tqdm
import facefusion.choices
from facefusion import logger, process_manager, state_manager, wording
from facefusion.filesystem import get_file_size, is_file, remove_file
from facefusion import curl_builder, logger, process_manager, state_manager, wording
from facefusion.filesystem import get_file_name, get_file_size, is_file, remove_file
from facefusion.hash_helper import validate_hash
from facefusion.typing import DownloadProvider, DownloadSet
from facefusion.types import Commands, DownloadProvider, DownloadSet
def open_curl(args : List[str]) -> subprocess.Popen[bytes]:
commands = [ shutil.which('curl'), '--silent', '--insecure', '--location' ]
commands.extend(args)
def open_curl(commands : Commands) -> subprocess.Popen[bytes]:
commands = curl_builder.run(commands)
return subprocess.Popen(commands, stdin = subprocess.PIPE, stdout = subprocess.PIPE)
@@ -29,7 +27,10 @@ def conditional_download(download_directory_path : str, urls : List[str]) -> Non
if initial_size < download_size:
with tqdm(total = download_size, initial = initial_size, desc = wording.get('downloading'), unit = 'B', unit_scale = True, unit_divisor = 1024, ascii = ' =', disable = state_manager.get_item('log_level') in [ 'warn', 'error' ]) as progress:
commands = [ '--create-dirs', '--continue-at', '-', '--output', download_file_path, url ]
commands = curl_builder.chain(
curl_builder.download(url, download_file_path),
curl_builder.set_timeout(10)
)
open_curl(commands)
current_size = initial_size
progress.set_postfix(download_providers = state_manager.get_item('download_providers'), file_name = download_file_name)
@@ -42,7 +43,10 @@ def conditional_download(download_directory_path : str, urls : List[str]) -> Non
@lru_cache(maxsize = None)
def get_static_download_size(url : str) -> int:
commands = [ '-I', url ]
commands = curl_builder.chain(
curl_builder.head(url),
curl_builder.set_timeout(5)
)
process = open_curl(commands)
lines = reversed(process.stdout.readlines())
@@ -57,32 +61,35 @@ def get_static_download_size(url : str) -> int:
@lru_cache(maxsize = None)
def ping_static_url(url : str) -> bool:
commands = [ '-I', url ]
commands = curl_builder.chain(
curl_builder.head(url),
curl_builder.set_timeout(5)
)
process = open_curl(commands)
process.communicate()
return process.returncode == 0
def conditional_download_hashes(hashes : DownloadSet) -> bool:
hash_paths = [ hashes.get(hash_key).get('path') for hash_key in hashes.keys() ]
def conditional_download_hashes(hash_set : DownloadSet) -> bool:
hash_paths = [ hash_set.get(hash_key).get('path') for hash_key in hash_set.keys() ]
process_manager.check()
_, invalid_hash_paths = validate_hash_paths(hash_paths)
if invalid_hash_paths:
for index in hashes:
if hashes.get(index).get('path') in invalid_hash_paths:
invalid_hash_url = hashes.get(index).get('url')
for index in hash_set:
if hash_set.get(index).get('path') in invalid_hash_paths:
invalid_hash_url = hash_set.get(index).get('url')
if invalid_hash_url:
download_directory_path = os.path.dirname(hashes.get(index).get('path'))
download_directory_path = os.path.dirname(hash_set.get(index).get('path'))
conditional_download(download_directory_path, [ invalid_hash_url ])
valid_hash_paths, invalid_hash_paths = validate_hash_paths(hash_paths)
for valid_hash_path in valid_hash_paths:
valid_hash_file_name, _ = os.path.splitext(os.path.basename(valid_hash_path))
valid_hash_file_name = get_file_name(valid_hash_path)
logger.debug(wording.get('validating_hash_succeed').format(hash_file_name = valid_hash_file_name), __name__)
for invalid_hash_path in invalid_hash_paths:
invalid_hash_file_name, _ = os.path.splitext(os.path.basename(invalid_hash_path))
invalid_hash_file_name = get_file_name(invalid_hash_path)
logger.error(wording.get('validating_hash_failed').format(hash_file_name = invalid_hash_file_name), __name__)
if not invalid_hash_paths:
@@ -90,26 +97,26 @@ def conditional_download_hashes(hashes : DownloadSet) -> bool:
return not invalid_hash_paths
def conditional_download_sources(sources : DownloadSet) -> bool:
source_paths = [ sources.get(source_key).get('path') for source_key in sources.keys() ]
def conditional_download_sources(source_set : DownloadSet) -> bool:
source_paths = [ source_set.get(source_key).get('path') for source_key in source_set.keys() ]
process_manager.check()
_, invalid_source_paths = validate_source_paths(source_paths)
if invalid_source_paths:
for index in sources:
if sources.get(index).get('path') in invalid_source_paths:
invalid_source_url = sources.get(index).get('url')
for index in source_set:
if source_set.get(index).get('path') in invalid_source_paths:
invalid_source_url = source_set.get(index).get('url')
if invalid_source_url:
download_directory_path = os.path.dirname(sources.get(index).get('path'))
download_directory_path = os.path.dirname(source_set.get(index).get('path'))
conditional_download(download_directory_path, [ invalid_source_url ])
valid_source_paths, invalid_source_paths = validate_source_paths(source_paths)
for valid_source_path in valid_source_paths:
valid_source_file_name, _ = os.path.splitext(os.path.basename(valid_source_path))
valid_source_file_name = get_file_name(valid_source_path)
logger.debug(wording.get('validating_source_succeed').format(source_file_name = valid_source_file_name), __name__)
for invalid_source_path in invalid_source_paths:
invalid_source_file_name, _ = os.path.splitext(os.path.basename(invalid_source_path))
invalid_source_file_name = get_file_name(invalid_source_path)
logger.error(wording.get('validating_source_failed').format(source_file_name = invalid_source_file_name), __name__)
if remove_file(invalid_source_path):
@@ -129,6 +136,7 @@ def validate_hash_paths(hash_paths : List[str]) -> Tuple[List[str], List[str]]:
valid_hash_paths.append(hash_path)
else:
invalid_hash_paths.append(hash_path)
return valid_hash_paths, invalid_hash_paths
@@ -141,6 +149,7 @@ def validate_source_paths(source_paths : List[str]) -> Tuple[List[str], List[str
valid_source_paths.append(source_path)
else:
invalid_source_paths.append(source_path)
return valid_source_paths, invalid_source_paths
@@ -148,16 +157,18 @@ def resolve_download_url(base_name : str, file_name : str) -> Optional[str]:
download_providers = state_manager.get_item('download_providers')
for download_provider in download_providers:
if ping_download_provider(download_provider):
return resolve_download_url_by_provider(download_provider, base_name, file_name)
download_url = resolve_download_url_by_provider(download_provider, base_name, file_name)
if download_url:
return download_url
return None
def ping_download_provider(download_provider : DownloadProvider) -> bool:
download_provider_value = facefusion.choices.download_provider_set.get(download_provider)
return ping_static_url(download_provider_value.get('url'))
def resolve_download_url_by_provider(download_provider : DownloadProvider, base_name : str, file_name : str) -> Optional[str]:
download_provider_value = facefusion.choices.download_provider_set.get(download_provider)
return download_provider_value.get('url') + download_provider_value.get('path').format(base_name = base_name, file_name = file_name)
for download_provider_url in download_provider_value.get('urls'):
if ping_static_url(download_provider_url):
return download_provider_url + download_provider_value.get('path').format(base_name = base_name, file_name = file_name)
return None

65
facefusion/execution.py
View File

@@ -2,12 +2,12 @@ import shutil
import subprocess
import xml.etree.ElementTree as ElementTree
from functools import lru_cache
from typing import Any, List, Optional
from typing import List, Optional
from onnxruntime import get_available_providers, set_default_logger_severity
import facefusion.choices
from facefusion.typing import ExecutionDevice, ExecutionProvider, ValueAndUnit
from facefusion.types import ExecutionDevice, ExecutionProvider, InferenceSessionProvider, ValueAndUnit
set_default_logger_severity(3)
@@ -17,28 +17,29 @@ def has_execution_provider(execution_provider : ExecutionProvider) -> bool:
def get_available_execution_providers() -> List[ExecutionProvider]:
inference_execution_providers = get_available_providers()
available_execution_providers = []
inference_session_providers = get_available_providers()
available_execution_providers : List[ExecutionProvider] = []
for execution_provider, execution_provider_value in facefusion.choices.execution_provider_set.items():
if execution_provider_value in inference_execution_providers:
available_execution_providers.append(execution_provider)
if execution_provider_value in inference_session_providers:
index = facefusion.choices.execution_providers.index(execution_provider)
available_execution_providers.insert(index, execution_provider)
return available_execution_providers
def create_inference_execution_providers(execution_device_id : str, execution_providers : List[ExecutionProvider]) -> List[Any]:
inference_execution_providers : List[Any] = []
def create_inference_session_providers(execution_device_id : str, execution_providers : List[ExecutionProvider]) -> List[InferenceSessionProvider]:
inference_session_providers : List[InferenceSessionProvider] = []
for execution_provider in execution_providers:
if execution_provider == 'cuda':
inference_execution_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
{
'device_id': execution_device_id,
'cudnn_conv_algo_search': 'DEFAULT' if is_geforce_16_series() else 'EXHAUSTIVE'
'cudnn_conv_algo_search': resolve_cudnn_conv_algo_search()
}))
if execution_provider == 'tensorrt':
inference_execution_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
{
'device_id': execution_device_id,
'trt_engine_cache_enable': True,
@@ -47,31 +48,47 @@ def create_inference_execution_providers(execution_device_id : str, execution_pr
'trt_timing_cache_path': '.caches',
'trt_builder_optimization_level': 5
}))
if execution_provider == 'openvino':
inference_execution_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
{
'device_type': 'GPU' if execution_device_id == '0' else 'GPU.' + execution_device_id,
'precision': 'FP32'
}))
if execution_provider in [ 'directml', 'rocm' ]:
inference_execution_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
{
'device_id': execution_device_id
}))
if execution_provider == 'openvino':
inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
{
'device_type': resolve_openvino_device_type(execution_device_id),
'precision': 'FP32'
}))
if execution_provider == 'coreml':
inference_execution_providers.append(facefusion.choices.execution_provider_set.get(execution_provider))
inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
{
'SpecializationStrategy': 'FastPrediction',
'ModelCacheDirectory': '.caches'
}))
if 'cpu' in execution_providers:
inference_execution_providers.append(facefusion.choices.execution_provider_set.get('cpu'))
inference_session_providers.append(facefusion.choices.execution_provider_set.get('cpu'))
return inference_execution_providers
return inference_session_providers
def is_geforce_16_series() -> bool:
def resolve_cudnn_conv_algo_search() -> str:
execution_devices = detect_static_execution_devices()
product_names = ('GeForce GTX 1630', 'GeForce GTX 1650', 'GeForce GTX 1660')
return any(execution_device.get('product').get('name').startswith(product_names) for execution_device in execution_devices)
for execution_device in execution_devices:
if execution_device.get('product').get('name').startswith(product_names):
return 'DEFAULT'
return 'EXHAUSTIVE'
def resolve_openvino_device_type(execution_device_id : str) -> str:
if execution_device_id == '0':
return 'GPU'
if execution_device_id == '∞':
return 'MULTI:GPU'
return 'GPU.' + execution_device_id
def run_nvidia_smi() -> subprocess.Popen[bytes]:
@@ -129,7 +146,7 @@ def detect_execution_devices() -> List[ExecutionDevice]:
def create_value_and_unit(text : str) -> Optional[ValueAndUnit]:
if ' ' in text:
value, unit = text.split(' ')
value, unit = text.split()
return\
{

7
facefusion/exit_helper.py
View File

@@ -4,7 +4,7 @@ from time import sleep
from facefusion import process_manager, state_manager
from facefusion.temp_helper import clear_temp_directory
from facefusion.typing import ErrorCode
from facefusion.types import ErrorCode
def hard_exit(error_code : ErrorCode) -> None:
@@ -12,11 +12,6 @@ def hard_exit(error_code : ErrorCode) -> None:
sys.exit(error_code)
def conditional_exit(error_code : ErrorCode) -> None:
if state_manager.get_item('command') == 'headless-run':
hard_exit(error_code)
def graceful_exit(error_code : ErrorCode) -> None:
process_manager.stop()
while process_manager.is_processing():

6
facefusion/face_analyser.py
View File

@@ -7,10 +7,10 @@ from facefusion.common_helper import get_first
from facefusion.face_classifier import classify_face
from facefusion.face_detector import detect_faces, detect_rotated_faces
from facefusion.face_helper import apply_nms, convert_to_face_landmark_5, estimate_face_angle, get_nms_threshold
from facefusion.face_landmarker import detect_face_landmarks, estimate_face_landmark_68_5
from facefusion.face_landmarker import detect_face_landmark, estimate_face_landmark_68_5
from facefusion.face_recognizer import calc_embedding
from facefusion.face_store import get_static_faces, set_static_faces
from facefusion.typing import BoundingBox, Face, FaceLandmark5, FaceLandmarkSet, FaceScoreSet, Score, VisionFrame
from facefusion.types import BoundingBox, Face, FaceLandmark5, FaceLandmarkSet, FaceScoreSet, Score, VisionFrame
def create_faces(vision_frame : VisionFrame, bounding_boxes : List[BoundingBox], face_scores : List[Score], face_landmarks_5 : List[FaceLandmark5]) -> List[Face]:
@@ -29,7 +29,7 @@ def create_faces(vision_frame : VisionFrame, bounding_boxes : List[BoundingBox],
face_angle = estimate_face_angle(face_landmark_68_5)
if state_manager.get_item('face_landmarker_score') > 0:
face_landmark_68, face_landmark_score_68 = detect_face_landmarks(vision_frame, bounding_box, face_angle)
face_landmark_68, face_landmark_score_68 = detect_face_landmark(vision_frame, bounding_box, face_angle)
if face_landmark_score_68 > state_manager.get_item('face_landmarker_score'):
face_landmark_5_68 = convert_to_face_landmark_5(face_landmark_68)

19
facefusion/face_classifier.py
View File

@@ -8,7 +8,7 @@ from facefusion.download import conditional_download_hashes, conditional_downloa
from facefusion.face_helper import warp_face_by_face_landmark_5
from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import Age, DownloadScope, FaceLandmark5, Gender, InferencePool, ModelOptions, ModelSet, Race, VisionFrame
from facefusion.types import Age, DownloadScope, FaceLandmark5, Gender, InferencePool, ModelOptions, ModelSet, Race, VisionFrame
@lru_cache(maxsize = None)
@@ -42,12 +42,15 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_sources)
model_names = [ 'fairface' ]
model_source_set = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_names, model_source_set)
def clear_inference_pool() -> None:
inference_manager.clear_inference_pool(__name__)
model_names = [ 'fairface' ]
inference_manager.clear_inference_pool(__name__, model_names)
def get_model_options() -> ModelOptions:
@@ -55,10 +58,10 @@ def get_model_options() -> ModelOptions:
def pre_check() -> bool:
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
model_hash_set = get_model_options().get('hashes')
model_source_set = get_model_options().get('sources')
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
def classify_face(temp_vision_frame : VisionFrame, face_landmark_5 : FaceLandmark5) -> Tuple[Gender, Age, Race]:
@@ -67,7 +70,7 @@ def classify_face(temp_vision_frame : VisionFrame, face_landmark_5 : FaceLandmar
model_mean = get_model_options().get('mean')
model_standard_deviation = get_model_options().get('standard_deviation')
crop_vision_frame, _ = warp_face_by_face_landmark_5(temp_vision_frame, face_landmark_5, model_template, model_size)
crop_vision_frame = crop_vision_frame.astype(numpy.float32)[:, :, ::-1] / 255
crop_vision_frame = crop_vision_frame.astype(numpy.float32)[:, :, ::-1] / 255.0
crop_vision_frame -= model_mean
crop_vision_frame /= model_standard_deviation
crop_vision_frame = crop_vision_frame.transpose(2, 0, 1)

155
facefusion/face_detector.py
View File

@@ -1,16 +1,16 @@
from typing import List, Tuple
from functools import lru_cache
from typing import List, Sequence, Tuple
import cv2
import numpy
from charset_normalizer.md import lru_cache
from facefusion import inference_manager, state_manager
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
from facefusion.face_helper import create_rotated_matrix_and_size, create_static_anchors, distance_to_bounding_box, distance_to_face_landmark_5, normalize_bounding_box, transform_bounding_box, transform_points
from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import thread_semaphore
from facefusion.typing import Angle, BoundingBox, Detection, DownloadScope, DownloadSet, FaceLandmark5, InferencePool, ModelSet, Score, VisionFrame
from facefusion.vision import resize_frame_resolution, unpack_resolution
from facefusion.types import Angle, BoundingBox, Detection, DownloadScope, DownloadSet, FaceLandmark5, InferencePool, ModelSet, Score, VisionFrame
from facefusion.vision import restrict_frame, unpack_resolution
@lru_cache(maxsize = None)
@@ -55,11 +55,11 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
}
}
},
'yoloface':
'yolo_face':
{
'hashes':
{
'yoloface':
'yolo_face':
{
'url': resolve_download_url('models-3.0.0', 'yoloface_8n.hash'),
'path': resolve_relative_path('../.assets/models/yoloface_8n.hash')
@@ -67,7 +67,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
},
'sources':
{
'yoloface':
'yolo_face':
{
'url': resolve_download_url('models-3.0.0', 'yoloface_8n.onnx'),
'path': resolve_relative_path('../.assets/models/yoloface_8n.onnx')
@@ -78,38 +78,34 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
def get_inference_pool() -> InferencePool:
_, model_sources = collect_model_downloads()
return inference_manager.get_inference_pool(__name__, model_sources)
model_names = [ state_manager.get_item('face_detector_model') ]
_, model_source_set = collect_model_downloads()
return inference_manager.get_inference_pool(__name__, model_names, model_source_set)
def clear_inference_pool() -> None:
inference_manager.clear_inference_pool(__name__)
model_names = [ state_manager.get_item('face_detector_model') ]
inference_manager.clear_inference_pool(__name__, model_names)
def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]:
model_hashes = {}
model_sources = {}
model_set = create_static_model_set('full')
model_hash_set = {}
model_source_set = {}
if state_manager.get_item('face_detector_model') in [ 'many', 'retinaface' ]:
model_hashes['retinaface'] = model_set.get('retinaface').get('hashes').get('retinaface')
model_sources['retinaface'] = model_set.get('retinaface').get('sources').get('retinaface')
for face_detector_model in [ 'retinaface', 'scrfd', 'yolo_face' ]:
if state_manager.get_item('face_detector_model') in [ 'many', face_detector_model ]:
model_hash_set[face_detector_model] = model_set.get(face_detector_model).get('hashes').get(face_detector_model)
model_source_set[face_detector_model] = model_set.get(face_detector_model).get('sources').get(face_detector_model)
if state_manager.get_item('face_detector_model') in [ 'many', 'scrfd' ]:
model_hashes['scrfd'] = model_set.get('scrfd').get('hashes').get('scrfd')
model_sources['scrfd'] = model_set.get('scrfd').get('sources').get('scrfd')
if state_manager.get_item('face_detector_model') in [ 'many', 'yoloface' ]:
model_hashes['yoloface'] = model_set.get('yoloface').get('hashes').get('yoloface')
model_sources['yoloface'] = model_set.get('yoloface').get('sources').get('yoloface')
return model_hashes, model_sources
return model_hash_set, model_source_set
def pre_check() -> bool:
model_hashes, model_sources = collect_model_downloads()
model_hash_set, model_source_set = collect_model_downloads()
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
def detect_faces(vision_frame : VisionFrame) -> Tuple[List[BoundingBox], List[Score], List[FaceLandmark5]]:
@@ -129,8 +125,8 @@ def detect_faces(vision_frame : VisionFrame) -> Tuple[List[BoundingBox], List[Sc
all_face_scores.extend(face_scores)
all_face_landmarks_5.extend(face_landmarks_5)
if state_manager.get_item('face_detector_model') in [ 'many', 'yoloface' ]:
bounding_boxes, face_scores, face_landmarks_5 = detect_with_yoloface(vision_frame, state_manager.get_item('face_detector_size'))
if state_manager.get_item('face_detector_model') in [ 'many', 'yolo_face' ]:
bounding_boxes, face_scores, face_landmarks_5 = detect_with_yolo_face(vision_frame, state_manager.get_item('face_detector_size'))
all_bounding_boxes.extend(bounding_boxes)
all_face_scores.extend(face_scores)
all_face_landmarks_5.extend(face_landmarks_5)
@@ -156,37 +152,39 @@ def detect_with_retinaface(vision_frame : VisionFrame, face_detector_size : str)
feature_strides = [ 8, 16, 32 ]
feature_map_channel = 3
anchor_total = 2
face_detector_score = state_manager.get_item('face_detector_score')
face_detector_width, face_detector_height = unpack_resolution(face_detector_size)
temp_vision_frame = resize_frame_resolution(vision_frame, (face_detector_width, face_detector_height))
temp_vision_frame = restrict_frame(vision_frame, (face_detector_width, face_detector_height))
ratio_height = vision_frame.shape[0] / temp_vision_frame.shape[0]
ratio_width = vision_frame.shape[1] / temp_vision_frame.shape[1]
detect_vision_frame = prepare_detect_frame(temp_vision_frame, face_detector_size)
detect_vision_frame = normalize_detect_frame(detect_vision_frame, [ -1, 1 ])
detection = forward_with_retinaface(detect_vision_frame)
for index, feature_stride in enumerate(feature_strides):
keep_indices = numpy.where(detection[index] >= state_manager.get_item('face_detector_score'))[0]
keep_indices = numpy.where(detection[index] >= face_detector_score)[0]
if numpy.any(keep_indices):
stride_height = face_detector_height // feature_stride
stride_width = face_detector_width // feature_stride
anchors = create_static_anchors(feature_stride, anchor_total, stride_height, stride_width)
bounding_box_raw = detection[index + feature_map_channel] * feature_stride
face_landmark_5_raw = detection[index + feature_map_channel * 2] * feature_stride
bounding_boxes_raw = detection[index + feature_map_channel] * feature_stride
face_landmarks_5_raw = detection[index + feature_map_channel * 2] * feature_stride
for bounding_box in distance_to_bounding_box(anchors, bounding_box_raw)[keep_indices]:
for bounding_box_raw in distance_to_bounding_box(anchors, bounding_boxes_raw)[keep_indices]:
bounding_boxes.append(numpy.array(
[
bounding_box[0] * ratio_width,
bounding_box[1] * ratio_height,
bounding_box[2] * ratio_width,
bounding_box[3] * ratio_height,
bounding_box_raw[0] * ratio_width,
bounding_box_raw[1] * ratio_height,
bounding_box_raw[2] * ratio_width,
bounding_box_raw[3] * ratio_height
]))
for score in detection[index][keep_indices]:
face_scores.append(score[0])
for face_score_raw in detection[index][keep_indices]:
face_scores.append(face_score_raw[0])
for face_landmark_5 in distance_to_face_landmark_5(anchors, face_landmark_5_raw)[keep_indices]:
face_landmarks_5.append(face_landmark_5 * [ ratio_width, ratio_height ])
for face_landmark_raw_5 in distance_to_face_landmark_5(anchors, face_landmarks_5_raw)[keep_indices]:
face_landmarks_5.append(face_landmark_raw_5 * [ ratio_width, ratio_height ])
return bounding_boxes, face_scores, face_landmarks_5
@@ -198,73 +196,77 @@ def detect_with_scrfd(vision_frame : VisionFrame, face_detector_size : str) -> T
feature_strides = [ 8, 16, 32 ]
feature_map_channel = 3
anchor_total = 2
face_detector_score = state_manager.get_item('face_detector_score')
face_detector_width, face_detector_height = unpack_resolution(face_detector_size)
temp_vision_frame = resize_frame_resolution(vision_frame, (face_detector_width, face_detector_height))
temp_vision_frame = restrict_frame(vision_frame, (face_detector_width, face_detector_height))
ratio_height = vision_frame.shape[0] / temp_vision_frame.shape[0]
ratio_width = vision_frame.shape[1] / temp_vision_frame.shape[1]
detect_vision_frame = prepare_detect_frame(temp_vision_frame, face_detector_size)
detect_vision_frame = normalize_detect_frame(detect_vision_frame, [ -1, 1 ])
detection = forward_with_scrfd(detect_vision_frame)
for index, feature_stride in enumerate(feature_strides):
keep_indices = numpy.where(detection[index] >= state_manager.get_item('face_detector_score'))[0]
keep_indices = numpy.where(detection[index] >= face_detector_score)[0]
if numpy.any(keep_indices):
stride_height = face_detector_height // feature_stride
stride_width = face_detector_width // feature_stride
anchors = create_static_anchors(feature_stride, anchor_total, stride_height, stride_width)
bounding_box_raw = detection[index + feature_map_channel] * feature_stride
face_landmark_5_raw = detection[index + feature_map_channel * 2] * feature_stride
bounding_boxes_raw = detection[index + feature_map_channel] * feature_stride
face_landmarks_5_raw = detection[index + feature_map_channel * 2] * feature_stride
for bounding_box in distance_to_bounding_box(anchors, bounding_box_raw)[keep_indices]:
for bounding_box_raw in distance_to_bounding_box(anchors, bounding_boxes_raw)[keep_indices]:
bounding_boxes.append(numpy.array(
[
bounding_box[0] * ratio_width,
bounding_box[1] * ratio_height,
bounding_box[2] * ratio_width,
bounding_box[3] * ratio_height,
bounding_box_raw[0] * ratio_width,
bounding_box_raw[1] * ratio_height,
bounding_box_raw[2] * ratio_width,
bounding_box_raw[3] * ratio_height
]))
for score in detection[index][keep_indices]:
face_scores.append(score[0])
for face_score_raw in detection[index][keep_indices]:
face_scores.append(face_score_raw[0])
for face_landmark_5 in distance_to_face_landmark_5(anchors, face_landmark_5_raw)[keep_indices]:
face_landmarks_5.append(face_landmark_5 * [ ratio_width, ratio_height ])
for face_landmark_raw_5 in distance_to_face_landmark_5(anchors, face_landmarks_5_raw)[keep_indices]:
face_landmarks_5.append(face_landmark_raw_5 * [ ratio_width, ratio_height ])
return bounding_boxes, face_scores, face_landmarks_5
def detect_with_yoloface(vision_frame : VisionFrame, face_detector_size : str) -> Tuple[List[BoundingBox], List[Score], List[FaceLandmark5]]:
def detect_with_yolo_face(vision_frame : VisionFrame, face_detector_size : str) -> Tuple[List[BoundingBox], List[Score], List[FaceLandmark5]]:
bounding_boxes = []
face_scores = []
face_landmarks_5 = []
face_detector_score = state_manager.get_item('face_detector_score')
face_detector_width, face_detector_height = unpack_resolution(face_detector_size)
temp_vision_frame = resize_frame_resolution(vision_frame, (face_detector_width, face_detector_height))
temp_vision_frame = restrict_frame(vision_frame, (face_detector_width, face_detector_height))
ratio_height = vision_frame.shape[0] / temp_vision_frame.shape[0]
ratio_width = vision_frame.shape[1] / temp_vision_frame.shape[1]
detect_vision_frame = prepare_detect_frame(temp_vision_frame, face_detector_size)
detection = forward_with_yoloface(detect_vision_frame)
detect_vision_frame = normalize_detect_frame(detect_vision_frame, [ 0, 1 ])
detection = forward_with_yolo_face(detect_vision_frame)
detection = numpy.squeeze(detection).T
bounding_box_raw, score_raw, face_landmark_5_raw = numpy.split(detection, [ 4, 5 ], axis = 1)
keep_indices = numpy.where(score_raw > state_manager.get_item('face_detector_score'))[0]
bounding_boxes_raw, face_scores_raw, face_landmarks_5_raw = numpy.split(detection, [ 4, 5 ], axis = 1)
keep_indices = numpy.where(face_scores_raw > face_detector_score)[0]
if numpy.any(keep_indices):
bounding_box_raw, face_landmark_5_raw, score_raw = bounding_box_raw[keep_indices], face_landmark_5_raw[keep_indices], score_raw[keep_indices]
bounding_boxes_raw, face_scores_raw, face_landmarks_5_raw = bounding_boxes_raw[keep_indices], face_scores_raw[keep_indices], face_landmarks_5_raw[keep_indices]
for bounding_box in bounding_box_raw:
for bounding_box_raw in bounding_boxes_raw:
bounding_boxes.append(numpy.array(
[
(bounding_box[0] - bounding_box[2] / 2) * ratio_width,
(bounding_box[1] - bounding_box[3] / 2) * ratio_height,
(bounding_box[0] + bounding_box[2] / 2) * ratio_width,
(bounding_box[1] + bounding_box[3] / 2) * ratio_height,
(bounding_box_raw[0] - bounding_box_raw[2] / 2) * ratio_width,
(bounding_box_raw[1] - bounding_box_raw[3] / 2) * ratio_height,
(bounding_box_raw[0] + bounding_box_raw[2] / 2) * ratio_width,
(bounding_box_raw[1] + bounding_box_raw[3] / 2) * ratio_height
]))
face_scores = score_raw.ravel().tolist()
face_landmark_5_raw[:, 0::3] = (face_landmark_5_raw[:, 0::3]) * ratio_width
face_landmark_5_raw[:, 1::3] = (face_landmark_5_raw[:, 1::3]) * ratio_height
face_scores = face_scores_raw.ravel().tolist()
face_landmarks_5_raw[:, 0::3] = (face_landmarks_5_raw[:, 0::3]) * ratio_width
face_landmarks_5_raw[:, 1::3] = (face_landmarks_5_raw[:, 1::3]) * ratio_height
for face_landmark_5 in face_landmark_5_raw:
face_landmarks_5.append(numpy.array(face_landmark_5.reshape(-1, 3)[:, :2]))
for face_landmark_raw_5 in face_landmarks_5_raw:
face_landmarks_5.append(numpy.array(face_landmark_raw_5.reshape(-1, 3)[:, :2]))
return bounding_boxes, face_scores, face_landmarks_5
@@ -293,8 +295,8 @@ def forward_with_scrfd(detect_vision_frame : VisionFrame) -> Detection:
return detection
def forward_with_yoloface(detect_vision_frame : VisionFrame) -> Detection:
face_detector = get_inference_pool().get('yoloface')
def forward_with_yolo_face(detect_vision_frame : VisionFrame) -> Detection:
face_detector = get_inference_pool().get('yolo_face')
with thread_semaphore():
detection = face_detector.run(None,
@@ -309,6 +311,13 @@ def prepare_detect_frame(temp_vision_frame : VisionFrame, face_detector_size : s
face_detector_width, face_detector_height = unpack_resolution(face_detector_size)
detect_vision_frame = numpy.zeros((face_detector_height, face_detector_width, 3))
detect_vision_frame[:temp_vision_frame.shape[0], :temp_vision_frame.shape[1], :] = temp_vision_frame
detect_vision_frame = (detect_vision_frame - 127.5) / 128.0
detect_vision_frame = numpy.expand_dims(detect_vision_frame.transpose(2, 0, 1), axis = 0).astype(numpy.float32)
return detect_vision_frame
def normalize_detect_frame(detect_vision_frame : VisionFrame, normalize_range : Sequence[int]) -> VisionFrame:
if normalize_range == [ -1, 1 ]:
return (detect_vision_frame - 127.5) / 128.0
if normalize_range == [ 0, 1 ]:
return detect_vision_frame / 255.0
return detect_vision_frame

12
facefusion/face_helper.py
View File

@@ -5,9 +5,9 @@ import cv2
import numpy
from cv2.typing import Size
from facefusion.typing import Anchors, Angle, BoundingBox, Distance, FaceDetectorModel, FaceLandmark5, FaceLandmark68, Mask, Matrix, Points, Scale, Score, Translation, VisionFrame, WarpTemplate, WarpTemplateSet
from facefusion.types import Anchors, Angle, BoundingBox, Distance, FaceDetectorModel, FaceLandmark5, FaceLandmark68, Mask, Matrix, Points, Scale, Score, Translation, VisionFrame, WarpTemplate, WarpTemplateSet
WARP_TEMPLATES : WarpTemplateSet =\
WARP_TEMPLATE_SET : WarpTemplateSet =\
{
'arcface_112_v1': numpy.array(
[
@@ -25,7 +25,7 @@ WARP_TEMPLATES : WarpTemplateSet =\
[ 0.37097589, 0.82469196 ],
[ 0.63151696, 0.82325089 ]
]),
'arcface_128_v2': numpy.array(
'arcface_128': numpy.array(
[
[ 0.36167656, 0.40387734 ],
[ 0.63696719, 0.40235469 ],
@@ -69,7 +69,7 @@ WARP_TEMPLATES : WarpTemplateSet =\
def estimate_matrix_by_face_landmark_5(face_landmark_5 : FaceLandmark5, warp_template : WarpTemplate, crop_size : Size) -> Matrix:
normed_warp_template = WARP_TEMPLATES.get(warp_template) * crop_size
normed_warp_template = WARP_TEMPLATE_SET.get(warp_template) * crop_size
affine_matrix = cv2.estimateAffinePartial2D(face_landmark_5, normed_warp_template, method = cv2.RANSAC, ransacReprojThreshold = 100)[0]
return affine_matrix
@@ -208,9 +208,9 @@ def estimate_face_angle(face_landmark_68 : FaceLandmark68) -> Angle:
return face_angle
def apply_nms(bounding_boxes : List[BoundingBox], face_scores : List[Score], score_threshold : float, nms_threshold : float) -> Sequence[int]:
def apply_nms(bounding_boxes : List[BoundingBox], scores : List[Score], score_threshold : float, nms_threshold : float) -> Sequence[int]:
normed_bounding_boxes = [ (x1, y1, x2 - x1, y2 - y1) for (x1, y1, x2, y2) in bounding_boxes ]
keep_indices = cv2.dnn.NMSBoxes(normed_bounding_boxes, face_scores, score_threshold = score_threshold, nms_threshold = nms_threshold)
keep_indices = cv2.dnn.NMSBoxes(normed_bounding_boxes, scores, score_threshold = score_threshold, nms_threshold = nms_threshold)
return keep_indices

34
facefusion/face_landmarker.py
View File

@@ -9,7 +9,7 @@ from facefusion.download import conditional_download_hashes, conditional_downloa
from facefusion.face_helper import create_rotated_matrix_and_size, estimate_matrix_by_face_landmark_5, transform_points, warp_face_by_translation
from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import Angle, BoundingBox, DownloadScope, DownloadSet, FaceLandmark5, FaceLandmark68, InferencePool, ModelSet, Prediction, Score, VisionFrame
from facefusion.types import Angle, BoundingBox, DownloadScope, DownloadSet, FaceLandmark5, FaceLandmark68, InferencePool, ModelSet, Prediction, Score, VisionFrame
@lru_cache(maxsize = None)
@@ -79,43 +79,43 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
def get_inference_pool() -> InferencePool:
_, model_sources = collect_model_downloads()
return inference_manager.get_inference_pool(__name__, model_sources)
model_names = [ state_manager.get_item('face_landmarker_model'), 'fan_68_5' ]
_, model_source_set = collect_model_downloads()
return inference_manager.get_inference_pool(__name__, model_names, model_source_set)
def clear_inference_pool() -> None:
inference_manager.clear_inference_pool(__name__)
model_names = [ state_manager.get_item('face_landmarker_model'), 'fan_68_5' ]
inference_manager.clear_inference_pool(__name__, model_names)
def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]:
model_set = create_static_model_set('full')
model_hashes =\
model_hash_set =\
{
'fan_68_5': model_set.get('fan_68_5').get('hashes').get('fan_68_5')
}
model_sources =\
model_source_set =\
{
'fan_68_5': model_set.get('fan_68_5').get('sources').get('fan_68_5')
}
if state_manager.get_item('face_landmarker_model') in [ 'many', '2dfan4' ]:
model_hashes['2dfan4'] = model_set.get('2dfan4').get('hashes').get('2dfan4')
model_sources['2dfan4'] = model_set.get('2dfan4').get('sources').get('2dfan4')
for face_landmarker_model in [ '2dfan4', 'peppa_wutz' ]:
if state_manager.get_item('face_landmarker_model') in [ 'many', face_landmarker_model ]:
model_hash_set[face_landmarker_model] = model_set.get(face_landmarker_model).get('hashes').get(face_landmarker_model)
model_source_set[face_landmarker_model] = model_set.get(face_landmarker_model).get('sources').get(face_landmarker_model)
if state_manager.get_item('face_landmarker_model') in [ 'many', 'peppa_wutz' ]:
model_hashes['peppa_wutz'] = model_set.get('peppa_wutz').get('hashes').get('peppa_wutz')
model_sources['peppa_wutz'] = model_set.get('peppa_wutz').get('sources').get('peppa_wutz')
return model_hashes, model_sources
return model_hash_set, model_source_set
def pre_check() -> bool:
model_hashes, model_sources = collect_model_downloads()
model_hash_set, model_source_set = collect_model_downloads()
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
def detect_face_landmarks(vision_frame : VisionFrame, bounding_box : BoundingBox, face_angle : Angle) -> Tuple[FaceLandmark68, Score]:
def detect_face_landmark(vision_frame : VisionFrame, bounding_box : BoundingBox, face_angle : Angle) -> Tuple[FaceLandmark68, Score]:
face_landmark_2dfan4 = None
face_landmark_peppa_wutz = None
face_landmark_score_2dfan4 = 0.0

83
facefusion/face_masker.py
View File

@@ -10,7 +10,7 @@ from facefusion import inference_manager, state_manager
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import DownloadScope, DownloadSet, FaceLandmark68, FaceMaskRegion, InferencePool, Mask, ModelSet, Padding, VisionFrame
from facefusion.types import DownloadScope, DownloadSet, FaceLandmark68, FaceMaskRegion, InferencePool, Mask, ModelSet, Padding, VisionFrame
@lru_cache(maxsize = None)
@@ -57,6 +57,26 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
},
'size': (256, 256)
},
'xseg_3':
{
'hashes':
{
'face_occluder':
{
'url': resolve_download_url('models-3.2.0', 'xseg_3.hash'),
'path': resolve_relative_path('../.assets/models/xseg_3.hash')
}
},
'sources':
{
'face_occluder':
{
'url': resolve_download_url('models-3.2.0', 'xseg_3.onnx'),
'path': resolve_relative_path('../.assets/models/xseg_3.onnx')
}
},
'size': (256, 256)
},
'bisenet_resnet_18':
{
'hashes':
@@ -101,42 +121,39 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
def get_inference_pool() -> InferencePool:
_, model_sources = collect_model_downloads()
return inference_manager.get_inference_pool(__name__, model_sources)
model_names = [state_manager.get_item('face_occluder_model'), state_manager.get_item('face_parser_model')]
_, model_source_set = collect_model_downloads()
return inference_manager.get_inference_pool(__name__, model_names, model_source_set)
def clear_inference_pool() -> None:
inference_manager.clear_inference_pool(__name__)
model_names = [ state_manager.get_item('face_occluder_model'), state_manager.get_item('face_parser_model') ]
inference_manager.clear_inference_pool(__name__, model_names)
def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]:
model_hashes = {}
model_sources = {}
model_set = create_static_model_set('full')
model_hash_set = {}
model_source_set = {}
if state_manager.get_item('face_occluder_model') == 'xseg_1':
model_hashes['xseg_1'] = model_set.get('xseg_1').get('hashes').get('face_occluder')
model_sources['xseg_1'] = model_set.get('xseg_1').get('sources').get('face_occluder')
for face_occluder_model in [ 'xseg_1', 'xseg_2', 'xseg_3' ]:
if state_manager.get_item('face_occluder_model') == face_occluder_model:
model_hash_set[face_occluder_model] = model_set.get(face_occluder_model).get('hashes').get('face_occluder')
model_source_set[face_occluder_model] = model_set.get(face_occluder_model).get('sources').get('face_occluder')
if state_manager.get_item('face_occluder_model') == 'xseg_2':
model_hashes['xseg_2'] = model_set.get('xseg_2').get('hashes').get('face_occluder')
model_sources['xseg_2'] = model_set.get('xseg_2').get('sources').get('face_occluder')
for face_parser_model in [ 'bisenet_resnet_18', 'bisenet_resnet_34' ]:
if state_manager.get_item('face_parser_model') == face_parser_model:
model_hash_set[face_parser_model] = model_set.get(face_parser_model).get('hashes').get('face_parser')
model_source_set[face_parser_model] = model_set.get(face_parser_model).get('sources').get('face_parser')
if state_manager.get_item('face_parser_model') == 'bisenet_resnet_18':
model_hashes['bisenet_resnet_18'] = model_set.get('bisenet_resnet_18').get('hashes').get('face_parser')
model_sources['bisenet_resnet_18'] = model_set.get('bisenet_resnet_18').get('sources').get('face_parser')
if state_manager.get_item('face_parser_model') == 'bisenet_resnet_34':
model_hashes['bisenet_resnet_34'] = model_set.get('bisenet_resnet_34').get('hashes').get('face_parser')
model_sources['bisenet_resnet_34'] = model_set.get('bisenet_resnet_34').get('sources').get('face_parser')
return model_hashes, model_sources
return model_hash_set, model_source_set
def pre_check() -> bool:
model_hashes, model_sources = collect_model_downloads()
model_hash_set, model_source_set = collect_model_downloads()
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
@lru_cache(maxsize = None)
@@ -154,10 +171,10 @@ def create_static_box_mask(crop_size : Size, face_mask_blur : float, face_mask_p
def create_occlusion_mask(crop_vision_frame : VisionFrame) -> Mask:
face_occluder_model = state_manager.get_item('face_occluder_model')
model_size = create_static_model_set('full').get(face_occluder_model).get('size')
model_name = state_manager.get_item('face_occluder_model')
model_size = create_static_model_set('full').get(model_name).get('size')
prepare_vision_frame = cv2.resize(crop_vision_frame, model_size)
prepare_vision_frame = numpy.expand_dims(prepare_vision_frame, axis = 0).astype(numpy.float32) / 255
prepare_vision_frame = numpy.expand_dims(prepare_vision_frame, axis = 0).astype(numpy.float32) / 255.0
prepare_vision_frame = prepare_vision_frame.transpose(0, 1, 2, 3)
occlusion_mask = forward_occlude_face(prepare_vision_frame)
occlusion_mask = occlusion_mask.transpose(0, 1, 2).clip(0, 1).astype(numpy.float32)
@@ -167,10 +184,10 @@ def create_occlusion_mask(crop_vision_frame : VisionFrame) -> Mask:
def create_region_mask(crop_vision_frame : VisionFrame, face_mask_regions : List[FaceMaskRegion]) -> Mask:
face_parser_model = state_manager.get_item('face_parser_model')
model_size = create_static_model_set('full').get(face_parser_model).get('size')
model_name = state_manager.get_item('face_parser_model')
model_size = create_static_model_set('full').get(model_name).get('size')
prepare_vision_frame = cv2.resize(crop_vision_frame, model_size)
prepare_vision_frame = prepare_vision_frame[:, :, ::-1].astype(numpy.float32) / 255
prepare_vision_frame = prepare_vision_frame[:, :, ::-1].astype(numpy.float32) / 255.0
prepare_vision_frame = numpy.subtract(prepare_vision_frame, numpy.array([ 0.485, 0.456, 0.406 ]).astype(numpy.float32))
prepare_vision_frame = numpy.divide(prepare_vision_frame, numpy.array([ 0.229, 0.224, 0.225 ]).astype(numpy.float32))
prepare_vision_frame = numpy.expand_dims(prepare_vision_frame, axis = 0)
@@ -192,8 +209,8 @@ def create_mouth_mask(face_landmark_68 : FaceLandmark68) -> Mask:
def forward_occlude_face(prepare_vision_frame : VisionFrame) -> Mask:
face_occluder_model = state_manager.get_item('face_occluder_model')
face_occluder = get_inference_pool().get(face_occluder_model)
model_name = state_manager.get_item('face_occluder_model')
face_occluder = get_inference_pool().get(model_name)
with conditional_thread_semaphore():
occlusion_mask : Mask = face_occluder.run(None,
@@ -205,8 +222,8 @@ def forward_occlude_face(prepare_vision_frame : VisionFrame) -> Mask:
def forward_parse_face(prepare_vision_frame : VisionFrame) -> Mask:
face_parser_model = state_manager.get_item('face_parser_model')
face_parser = get_inference_pool().get(face_parser_model)
model_name = state_manager.get_item('face_parser_model')
face_parser = get_inference_pool().get(model_name)
with conditional_thread_semaphore():
region_mask : Mask = face_parser.run(None,

17
facefusion/face_recognizer.py
View File

@@ -8,7 +8,7 @@ from facefusion.download import conditional_download_hashes, conditional_downloa
from facefusion.face_helper import warp_face_by_face_landmark_5
from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import DownloadScope, Embedding, FaceLandmark5, InferencePool, ModelOptions, ModelSet, VisionFrame
from facefusion.types import DownloadScope, Embedding, FaceLandmark5, InferencePool, ModelOptions, ModelSet, VisionFrame
@lru_cache(maxsize = None)
@@ -40,12 +40,15 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_sources)
model_names = [ 'arcface' ]
model_source_set = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_names, model_source_set)
def clear_inference_pool() -> None:
inference_manager.clear_inference_pool(__name__)
model_names = [ 'arcface' ]
inference_manager.clear_inference_pool(__name__, model_names)
def get_model_options() -> ModelOptions:
@@ -53,10 +56,10 @@ def get_model_options() -> ModelOptions:
def pre_check() -> bool:
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
model_hash_set = get_model_options().get('hashes')
model_source_set = get_model_options().get('sources')
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
def calc_embedding(temp_vision_frame : VisionFrame, face_landmark_5 : FaceLandmark5) -> Tuple[Embedding, Embedding]:

3
facefusion/face_selector.py
View File

@@ -3,7 +3,7 @@ from typing import List
import numpy
from facefusion import state_manager
from facefusion.typing import Face, FaceSelectorOrder, FaceSet, Gender, Race
from facefusion.types import Face, FaceSelectorOrder, FaceSet, Gender, Race
def find_similar_faces(faces : List[Face], reference_faces : FaceSet, face_distance : float) -> List[Face]:
@@ -21,6 +21,7 @@ def find_similar_faces(faces : List[Face], reference_faces : FaceSet, face_dista
def compare_faces(face : Face, reference_face : Face, face_distance : float) -> bool:
current_face_distance = calc_face_distance(face, reference_face)
current_face_distance = float(numpy.interp(current_face_distance, [ 0, 2 ], [ 0, 1 ]))
return current_face_distance < face_distance

7
facefusion/face_store.py
View File

@@ -3,7 +3,7 @@ from typing import List, Optional
import numpy
from facefusion.typing import Face, FaceSet, FaceStore, VisionFrame
from facefusion.types import Face, FaceSet, FaceStore, VisionFrame
FACE_STORE : FaceStore =\
{
@@ -34,7 +34,10 @@ def clear_static_faces() -> None:
def create_frame_hash(vision_frame : VisionFrame) -> Optional[str]:
return hashlib.sha1(vision_frame.tobytes()).hexdigest() if numpy.any(vision_frame) else None
if numpy.any(vision_frame):
frame_hash = hashlib.blake2b(vision_frame.tobytes(), digest_size = 16).hexdigest()
return frame_hash
return None
def get_reference_faces() -> Optional[FaceSet]:

274
facefusion/ffmpeg.py
View File

@@ -1,23 +1,23 @@
import os
import shutil
import subprocess
import tempfile
from typing import List, Optional
import filetype
from tqdm import tqdm
from facefusion import logger, process_manager, state_manager, wording
from facefusion.filesystem import remove_file
from facefusion.temp_helper import get_temp_file_path, get_temp_frame_paths, get_temp_frames_pattern
from facefusion.typing import AudioBuffer, Fps, OutputVideoPreset, UpdateProgress
from facefusion.vision import count_trim_frame_total, detect_video_duration, restrict_video_fps
import facefusion.choices
from facefusion import ffmpeg_builder, logger, process_manager, state_manager, wording
from facefusion.filesystem import get_file_format, remove_file
from facefusion.temp_helper import get_temp_file_path, get_temp_frames_pattern
from facefusion.types import AudioBuffer, Commands, EncoderSet, Fps, UpdateProgress
from facefusion.vision import detect_video_duration, detect_video_fps, predict_video_frame_total
def run_ffmpeg_with_progress(args: List[str], update_progress : UpdateProgress) -> subprocess.Popen[bytes]:
def run_ffmpeg_with_progress(commands : Commands, update_progress : UpdateProgress) -> subprocess.Popen[bytes]:
log_level = state_manager.get_item('log_level')
commands = [ shutil.which('ffmpeg'), '-hide_banner', '-nostats', '-loglevel', 'error', '-progress', '-' ]
commands.extend(args)
commands.extend(ffmpeg_builder.set_progress())
commands.extend(ffmpeg_builder.cast_stream())
commands = ffmpeg_builder.run(commands)
process = subprocess.Popen(commands, stderr = subprocess.PIPE, stdout = subprocess.PIPE)
while process_manager.is_processing():
@@ -40,10 +40,9 @@ def run_ffmpeg_with_progress(args: List[str], update_progress : UpdateProgress)
return process
def run_ffmpeg(args : List[str]) -> subprocess.Popen[bytes]:
def run_ffmpeg(commands : Commands) -> subprocess.Popen[bytes]:
log_level = state_manager.get_item('log_level')
commands = [ shutil.which('ffmpeg'), '-hide_banner', '-nostats', '-loglevel', 'error' ]
commands.extend(args)
commands = ffmpeg_builder.run(commands)
process = subprocess.Popen(commands, stderr = subprocess.PIPE, stdout = subprocess.PIPE)
while process_manager.is_processing():
@@ -60,9 +59,8 @@ def run_ffmpeg(args : List[str]) -> subprocess.Popen[bytes]:
return process
def open_ffmpeg(args : List[str]) -> subprocess.Popen[bytes]:
commands = [ shutil.which('ffmpeg'), '-loglevel', 'quiet' ]
commands.extend(args)
def open_ffmpeg(commands : Commands) -> subprocess.Popen[bytes]:
commands = ffmpeg_builder.run(commands)
return subprocess.Popen(commands, stdin = subprocess.PIPE, stdout = subprocess.PIPE)
@@ -75,100 +73,84 @@ def log_debug(process : subprocess.Popen[bytes]) -> None:
logger.debug(error.strip(), __name__)
def extract_frames(target_path : str, temp_video_resolution : str, temp_video_fps : Fps, trim_frame_start : int, trim_frame_end : int) -> bool:
extract_frame_total = count_trim_frame_total(target_path, trim_frame_start, trim_frame_end)
temp_frames_pattern = get_temp_frames_pattern(target_path, '%08d')
commands = [ '-i', target_path, '-s', str(temp_video_resolution), '-q:v', '0' ]
def get_available_encoder_set() -> EncoderSet:
available_encoder_set : EncoderSet =\
{
'audio': [],
'video': []
}
commands = ffmpeg_builder.chain(
ffmpeg_builder.get_encoders()
)
process = run_ffmpeg(commands)
if isinstance(trim_frame_start, int) and isinstance(trim_frame_end, int):
commands.extend([ '-vf', 'trim=start_frame=' + str(trim_frame_start) + ':end_frame=' + str(trim_frame_end) + ',fps=' + str(temp_video_fps) ])
elif isinstance(trim_frame_start, int):
commands.extend([ '-vf', 'trim=start_frame=' + str(trim_frame_start) + ',fps=' + str(temp_video_fps) ])
elif isinstance(trim_frame_end, int):
commands.extend([ '-vf', 'trim=end_frame=' + str(trim_frame_end) + ',fps=' + str(temp_video_fps) ])
else:
commands.extend([ '-vf', 'fps=' + str(temp_video_fps) ])
commands.extend([ '-vsync', '0', temp_frames_pattern ])
while line := process.stdout.readline().decode().lower():
if line.startswith(' a'):
audio_encoder = line.split()[1]
if audio_encoder in facefusion.choices.output_audio_encoders:
index = facefusion.choices.output_audio_encoders.index(audio_encoder) #type:ignore[arg-type]
available_encoder_set['audio'].insert(index, audio_encoder) #type:ignore[arg-type]
if line.startswith(' v'):
video_encoder = line.split()[1]
if video_encoder in facefusion.choices.output_video_encoders:
index = facefusion.choices.output_video_encoders.index(video_encoder) #type:ignore[arg-type]
available_encoder_set['video'].insert(index, video_encoder) #type:ignore[arg-type]
return available_encoder_set
def extract_frames(target_path : str, temp_video_resolution : str, temp_video_fps : Fps, trim_frame_start : int, trim_frame_end : int) -> bool:
extract_frame_total = predict_video_frame_total(target_path, temp_video_fps, trim_frame_start, trim_frame_end)
temp_frames_pattern = get_temp_frames_pattern(target_path, '%08d')
commands = ffmpeg_builder.chain(
ffmpeg_builder.set_input(target_path),
ffmpeg_builder.set_media_resolution(temp_video_resolution),
ffmpeg_builder.set_frame_quality(0),
ffmpeg_builder.select_frame_range(trim_frame_start, trim_frame_end, temp_video_fps),
ffmpeg_builder.prevent_frame_drop(),
ffmpeg_builder.set_output(temp_frames_pattern)
)
with tqdm(total = extract_frame_total, desc = wording.get('extracting'), unit = 'frame', ascii = ' =', disable = state_manager.get_item('log_level') in [ 'warn', 'error' ]) as progress:
process = run_ffmpeg_with_progress(commands, lambda frame_number: progress.update(frame_number - progress.n))
return process.returncode == 0
def merge_video(target_path : str, output_video_resolution : str, output_video_fps: Fps) -> bool:
output_video_encoder = state_manager.get_item('output_video_encoder')
output_video_quality = state_manager.get_item('output_video_quality')
output_video_preset = state_manager.get_item('output_video_preset')
merge_frame_total = len(get_temp_frame_paths(target_path))
temp_video_fps = restrict_video_fps(target_path, output_video_fps)
temp_file_path = get_temp_file_path(target_path)
temp_frames_pattern = get_temp_frames_pattern(target_path, '%08d')
is_webm = filetype.guess_mime(target_path) == 'video/webm'
if is_webm:
output_video_encoder = 'libvpx-vp9'
commands = [ '-r', str(temp_video_fps), '-i', temp_frames_pattern, '-s', str(output_video_resolution), '-c:v', output_video_encoder ]
if output_video_encoder in [ 'libx264', 'libx265' ]:
output_video_compression = round(51 - (output_video_quality * 0.51))
commands.extend([ '-crf', str(output_video_compression), '-preset', output_video_preset ])
if output_video_encoder in [ 'libvpx-vp9' ]:
output_video_compression = round(63 - (output_video_quality * 0.63))
commands.extend([ '-crf', str(output_video_compression) ])
if output_video_encoder in [ 'h264_nvenc', 'hevc_nvenc' ]:
output_video_compression = round(51 - (output_video_quality * 0.51))
commands.extend([ '-cq', str(output_video_compression), '-preset', map_nvenc_preset(output_video_preset) ])
if output_video_encoder in [ 'h264_amf', 'hevc_amf' ]:
output_video_compression = round(51 - (output_video_quality * 0.51))
commands.extend([ '-qp_i', str(output_video_compression), '-qp_p', str(output_video_compression), '-quality', map_amf_preset(output_video_preset) ])
if output_video_encoder in [ 'h264_videotoolbox', 'hevc_videotoolbox' ]:
commands.extend([ '-q:v', str(output_video_quality) ])
commands.extend([ '-vf', 'framerate=fps=' + str(output_video_fps), '-pix_fmt', 'yuv420p', '-colorspace', 'bt709', '-y', temp_file_path ])
with tqdm(total = merge_frame_total, desc = wording.get('merging'), unit = 'frame', ascii = ' =', disable = state_manager.get_item('log_level') in [ 'warn', 'error' ]) as progress:
process = run_ffmpeg_with_progress(commands, lambda frame_number: progress.update(frame_number - progress.n))
return process.returncode == 0
def concat_video(output_path : str, temp_output_paths : List[str]) -> bool:
output_audio_encoder = state_manager.get_item('output_audio_encoder')
concat_video_path = tempfile.mktemp()
with open(concat_video_path, 'w') as concat_video_file:
for temp_output_path in temp_output_paths:
concat_video_file.write('file \'' + os.path.abspath(temp_output_path) + '\'' + os.linesep)
concat_video_file.flush()
concat_video_file.close()
commands = [ '-f', 'concat', '-safe', '0', '-i', concat_video_file.name, '-c:v', 'copy', '-c:a', output_audio_encoder, '-y', os.path.abspath(output_path) ]
process = run_ffmpeg(commands)
process.communicate()
remove_file(concat_video_path)
return process.returncode == 0
def copy_image(target_path : str, temp_image_resolution : str) -> bool:
temp_file_path = get_temp_file_path(target_path)
temp_image_compression = calc_image_compression(target_path, 100)
commands = [ '-i', target_path, '-s', str(temp_image_resolution), '-q:v', str(temp_image_compression), '-y', temp_file_path ]
commands = ffmpeg_builder.chain(
ffmpeg_builder.set_input(target_path),
ffmpeg_builder.set_media_resolution(temp_image_resolution),
ffmpeg_builder.set_image_quality(target_path, 100),
ffmpeg_builder.force_output(temp_file_path)
)
return run_ffmpeg(commands).returncode == 0
def finalize_image(target_path : str, output_path : str, output_image_resolution : str) -> bool:
output_image_quality = state_manager.get_item('output_image_quality')
temp_file_path = get_temp_file_path(target_path)
output_image_compression = calc_image_compression(target_path, output_image_quality)
commands = [ '-i', temp_file_path, '-s', str(output_image_resolution), '-q:v', str(output_image_compression), '-y', output_path ]
commands = ffmpeg_builder.chain(
ffmpeg_builder.set_input(temp_file_path),
ffmpeg_builder.set_media_resolution(output_image_resolution),
ffmpeg_builder.set_image_quality(target_path, output_image_quality),
ffmpeg_builder.force_output(output_path)
)
return run_ffmpeg(commands).returncode == 0
def calc_image_compression(image_path : str, image_quality : int) -> int:
is_webp = filetype.guess_mime(image_path) == 'image/webp'
if is_webp:
image_quality = 100 - image_quality
return round(31 - (image_quality * 0.31))
def read_audio_buffer(target_path : str, audio_sample_rate : int, audio_sample_size : int, audio_channel_total : int) -> Optional[AudioBuffer]:
commands = ffmpeg_builder.chain(
ffmpeg_builder.set_input(target_path),
ffmpeg_builder.ignore_video_stream(),
ffmpeg_builder.set_audio_sample_rate(audio_sample_rate),
ffmpeg_builder.set_audio_sample_size(audio_sample_size),
ffmpeg_builder.set_audio_channel_total(audio_channel_total),
ffmpeg_builder.cast_stream()
)
def read_audio_buffer(target_path : str, sample_rate : int, channel_total : int) -> Optional[AudioBuffer]:
commands = [ '-i', target_path, '-vn', '-f', 's16le', '-acodec', 'pcm_s16le', '-ar', str(sample_rate), '-ac', str(channel_total), '-' ]
process = open_ffmpeg(commands)
audio_buffer, _ = process.communicate()
if process.returncode == 0:
@@ -176,55 +158,97 @@ def read_audio_buffer(target_path : str, sample_rate : int, channel_total : int)
return None
def restore_audio(target_path : str, output_path : str, output_video_fps : Fps, trim_frame_start : int, trim_frame_end : int) -> bool:
def restore_audio(target_path : str, output_path : str, trim_frame_start : int, trim_frame_end : int) -> bool:
output_audio_encoder = state_manager.get_item('output_audio_encoder')
output_audio_quality = state_manager.get_item('output_audio_quality')
output_audio_volume = state_manager.get_item('output_audio_volume')
target_video_fps = detect_video_fps(target_path)
temp_file_path = get_temp_file_path(target_path)
temp_video_duration = detect_video_duration(temp_file_path)
commands = [ '-i', temp_file_path ]
if isinstance(trim_frame_start, int):
start_time = trim_frame_start / output_video_fps
commands.extend([ '-ss', str(start_time) ])
if isinstance(trim_frame_end, int):
end_time = trim_frame_end / output_video_fps
commands.extend([ '-to', str(end_time) ])
commands.extend([ '-i', target_path, '-c:v', 'copy', '-c:a', output_audio_encoder, '-map', '0:v:0', '-map', '1:a:0', '-t', str(temp_video_duration), '-y', output_path ])
commands = ffmpeg_builder.chain(
ffmpeg_builder.set_input(temp_file_path),
ffmpeg_builder.select_media_range(trim_frame_start, trim_frame_end, target_video_fps),
ffmpeg_builder.set_input(target_path),
ffmpeg_builder.copy_video_encoder(),
ffmpeg_builder.set_audio_encoder(output_audio_encoder),
ffmpeg_builder.set_audio_quality(output_audio_encoder, output_audio_quality),
ffmpeg_builder.set_audio_volume(output_audio_volume),
ffmpeg_builder.select_media_stream('0:v:0'),
ffmpeg_builder.select_media_stream('1:a:0'),
ffmpeg_builder.set_video_duration(temp_video_duration),
ffmpeg_builder.force_output(output_path)
)
return run_ffmpeg(commands).returncode == 0
def replace_audio(target_path : str, audio_path : str, output_path : str) -> bool:
output_audio_encoder = state_manager.get_item('output_audio_encoder')
output_audio_quality = state_manager.get_item('output_audio_quality')
output_audio_volume = state_manager.get_item('output_audio_volume')
temp_file_path = get_temp_file_path(target_path)
temp_video_duration = detect_video_duration(temp_file_path)
commands = [ '-i', temp_file_path, '-i', audio_path, '-c:v', 'copy', '-c:a', output_audio_encoder, '-t', str(temp_video_duration), '-y', output_path ]
commands = ffmpeg_builder.chain(
ffmpeg_builder.set_input(temp_file_path),
ffmpeg_builder.set_input(audio_path),
ffmpeg_builder.copy_video_encoder(),
ffmpeg_builder.set_audio_encoder(output_audio_encoder),
ffmpeg_builder.set_audio_quality(output_audio_encoder, output_audio_quality),
ffmpeg_builder.set_audio_volume(output_audio_volume),
ffmpeg_builder.set_video_duration(temp_video_duration),
ffmpeg_builder.force_output(output_path)
)
return run_ffmpeg(commands).returncode == 0
def map_nvenc_preset(output_video_preset : OutputVideoPreset) -> Optional[str]:
if output_video_preset in [ 'ultrafast', 'superfast', 'veryfast', 'faster', 'fast' ]:
return 'fast'
if output_video_preset == 'medium':
return 'medium'
if output_video_preset in [ 'slow', 'slower', 'veryslow' ]:
return 'slow'
return None
def merge_video(target_path : str, temp_video_fps : Fps, output_video_resolution : str, output_video_fps : Fps, trim_frame_start : int, trim_frame_end : int) -> bool:
output_video_encoder = state_manager.get_item('output_video_encoder')
output_video_quality = state_manager.get_item('output_video_quality')
output_video_preset = state_manager.get_item('output_video_preset')
merge_frame_total = predict_video_frame_total(target_path, output_video_fps, trim_frame_start, trim_frame_end)
temp_file_path = get_temp_file_path(target_path)
temp_frames_pattern = get_temp_frames_pattern(target_path, '%08d')
if get_file_format(target_path) == 'webm':
output_video_encoder = 'libvpx-vp9'
commands = ffmpeg_builder.chain(
ffmpeg_builder.set_conditional_fps(temp_video_fps),
ffmpeg_builder.set_input(temp_frames_pattern),
ffmpeg_builder.set_media_resolution(output_video_resolution),
ffmpeg_builder.set_video_encoder(output_video_encoder),
ffmpeg_builder.set_video_quality(output_video_encoder, output_video_quality),
ffmpeg_builder.set_video_preset(output_video_encoder, output_video_preset),
ffmpeg_builder.set_video_fps(output_video_fps),
ffmpeg_builder.set_pixel_format(output_video_encoder),
ffmpeg_builder.set_video_colorspace('bt709'),
ffmpeg_builder.force_output(temp_file_path)
)
with tqdm(total = merge_frame_total, desc = wording.get('merging'), unit = 'frame', ascii = ' =', disable = state_manager.get_item('log_level') in [ 'warn', 'error' ]) as progress:
process = run_ffmpeg_with_progress(commands, lambda frame_number: progress.update(frame_number - progress.n))
return process.returncode == 0
def map_amf_preset(output_video_preset : OutputVideoPreset) -> Optional[str]:
if output_video_preset in [ 'ultrafast', 'superfast', 'veryfast' ]:
return 'speed'
if output_video_preset in [ 'faster', 'fast', 'medium' ]:
return 'balanced'
if output_video_preset in [ 'slow', 'slower', 'veryslow' ]:
return 'quality'
return None
def concat_video(output_path : str, temp_output_paths : List[str]) -> bool:
concat_video_path = tempfile.mktemp()
with open(concat_video_path, 'w') as concat_video_file:
for temp_output_path in temp_output_paths:
concat_video_file.write('file \'' + os.path.abspath(temp_output_path) + '\'' + os.linesep)
concat_video_file.flush()
concat_video_file.close()
def map_qsv_preset(output_video_preset : OutputVideoPreset) -> Optional[str]:
if output_video_preset in [ 'ultrafast', 'superfast', 'veryfast', 'faster', 'fast' ]:
return 'fast'
if output_video_preset == 'medium':
return 'medium'
if output_video_preset in [ 'slow', 'slower', 'veryslow' ]:
return 'slow'
return None
output_path = os.path.abspath(output_path)
commands = ffmpeg_builder.chain(
ffmpeg_builder.unsafe_concat(),
ffmpeg_builder.set_input(concat_video_file.name),
ffmpeg_builder.copy_video_encoder(),
ffmpeg_builder.copy_audio_encoder(),
ffmpeg_builder.force_output(output_path)
)
process = run_ffmpeg(commands)
process.communicate()
remove_file(concat_video_path)
return process.returncode == 0

240
facefusion/ffmpeg_builder.py Normal file
View File

@@ -0,0 +1,240 @@
import itertools
import shutil
from typing import Optional
import numpy
from facefusion.filesystem import get_file_format
from facefusion.types import AudioEncoder, Commands, Duration, Fps, StreamMode, VideoEncoder, VideoPreset
def run(commands : Commands) -> Commands:
return [ shutil.which('ffmpeg'), '-loglevel', 'error' ] + commands
def chain(*commands : Commands) -> Commands:
return list(itertools.chain(*commands))
def get_encoders() -> Commands:
return [ '-encoders' ]
def set_progress() -> Commands:
return [ '-progress' ]
def set_input(input_path : str) -> Commands:
return [ '-i', input_path ]
def set_conditional_fps(conditional_fps : Fps) -> Commands:
return [ '-r', str(conditional_fps) ]
def set_output(output_path : str) -> Commands:
return [ output_path ]
def force_output(output_path : str) -> Commands:
return [ '-y', output_path ]
def cast_stream() -> Commands:
return [ '-' ]
def set_stream_mode(stream_mode : StreamMode) -> Commands:
if stream_mode == 'udp':
return [ '-f', 'mpegts' ]
if stream_mode == 'v4l2':
return [ '-f', 'v4l2' ]
return []
def unsafe_concat() -> Commands:
return [ '-f', 'concat', '-safe', '0' ]
def set_pixel_format(video_encoder : VideoEncoder) -> Commands:
if video_encoder == 'rawvideo':
return [ '-pix_fmt', 'rgb24' ]
return [ '-pix_fmt', 'yuv420p' ]
def set_frame_quality(frame_quality : int) -> Commands:
return [ '-q:v', str(frame_quality) ]
def select_frame_range(frame_start : int, frame_end : int, video_fps : Fps) -> Commands:
if isinstance(frame_start, int) and isinstance(frame_end, int):
return [ '-vf', 'trim=start_frame=' + str(frame_start) + ':end_frame=' + str(frame_end) + ',fps=' + str(video_fps) ]
if isinstance(frame_start, int):
return [ '-vf', 'trim=start_frame=' + str(frame_start) + ',fps=' + str(video_fps) ]
if isinstance(frame_end, int):
return [ '-vf', 'trim=end_frame=' + str(frame_end) + ',fps=' + str(video_fps) ]
return [ '-vf', 'fps=' + str(video_fps) ]
def prevent_frame_drop() -> Commands:
return [ '-vsync', '0' ]
def select_media_range(frame_start : int, frame_end : int, media_fps : Fps) -> Commands:
commands = []
if isinstance(frame_start, int):
commands.extend([ '-ss', str(frame_start / media_fps) ])
if isinstance(frame_end, int):
commands.extend([ '-to', str(frame_end / media_fps) ])
return commands
def select_media_stream(media_stream : str) -> Commands:
return [ '-map', media_stream ]
def set_media_resolution(video_resolution : str) -> Commands:
return [ '-s', video_resolution ]
def set_image_quality(image_path : str, image_quality : int) -> Commands:
if get_file_format(image_path) == 'webp':
image_compression = image_quality
else:
image_compression = round(31 - (image_quality * 0.31))
return [ '-q:v', str(image_compression) ]
def set_audio_encoder(audio_codec : str) -> Commands:
return [ '-c:a', audio_codec ]
def copy_audio_encoder() -> Commands:
return set_audio_encoder('copy')
def set_audio_sample_rate(audio_sample_rate : int) -> Commands:
return [ '-ar', str(audio_sample_rate) ]
def set_audio_sample_size(audio_sample_size : int) -> Commands:
if audio_sample_size == 16:
return [ '-f', 's16le' ]
if audio_sample_size == 32:
return [ '-f', 's32le' ]
return []
def set_audio_channel_total(audio_channel_total : int) -> Commands:
return [ '-ac', str(audio_channel_total) ]
def set_audio_quality(audio_encoder : AudioEncoder, audio_quality : int) -> Commands:
if audio_encoder == 'aac':
audio_compression = round(numpy.interp(audio_quality, [ 0, 100 ], [ 0.1, 2.0 ]), 1)
return [ '-q:a', str(audio_compression) ]
if audio_encoder == 'libmp3lame':
audio_compression = round(numpy.interp(audio_quality, [ 0, 100 ], [ 9, 0 ]))
return [ '-q:a', str(audio_compression) ]
if audio_encoder == 'libopus':
audio_bit_rate = round(numpy.interp(audio_quality, [ 0, 100 ], [ 64, 320 ]))
return [ '-b:a', str(audio_bit_rate) + 'k' ]
if audio_encoder == 'libvorbis':
audio_compression = round(numpy.interp(audio_quality, [ 0, 100 ], [ -1, 10 ]), 1)
return [ '-q:a', str(audio_compression) ]
return []
def set_audio_volume(audio_volume : int) -> Commands:
return [ '-filter:a', 'volume=' + str(audio_volume / 100) ]
def set_video_encoder(video_encoder : str) -> Commands:
return [ '-c:v', video_encoder ]
def copy_video_encoder() -> Commands:
return set_video_encoder('copy')
def set_video_quality(video_encoder : VideoEncoder, video_quality : int) -> Commands:
if video_encoder in [ 'libx264', 'libx265' ]:
video_compression = round(numpy.interp(video_quality, [ 0, 100 ], [ 51, 0 ]))
return [ '-crf', str(video_compression) ]
if video_encoder == 'libvpx-vp9':
video_compression = round(numpy.interp(video_quality, [ 0, 100 ], [ 63, 0 ]))
return [ '-crf', str(video_compression) ]
if video_encoder in [ 'h264_nvenc', 'hevc_nvenc' ]:
video_compression = round(numpy.interp(video_quality, [ 0, 100 ], [ 51, 0 ]))
return [ '-cq', str(video_compression) ]
if video_encoder in [ 'h264_amf', 'hevc_amf' ]:
video_compression = round(numpy.interp(video_quality, [ 0, 100 ], [ 51, 0 ]))
return [ '-qp_i', str(video_compression), '-qp_p', str(video_compression), '-qp_b', str(video_compression) ]
if video_encoder in [ 'h264_qsv', 'hevc_qsv' ]:
video_compression = round(numpy.interp(video_quality, [ 0, 100 ], [ 51, 0 ]))
return [ '-qp', str(video_compression) ]
if video_encoder in [ 'h264_videotoolbox', 'hevc_videotoolbox' ]:
video_bit_rate = round(numpy.interp(video_quality, [ 0, 100 ], [ 1024, 50512 ]))
return [ '-b:v', str(video_bit_rate) + 'k' ]
return []
def set_video_preset(video_encoder : VideoEncoder, video_preset : VideoPreset) -> Commands:
if video_encoder in [ 'libx264', 'libx265' ]:
return [ '-preset', video_preset ]
if video_encoder in [ 'h264_nvenc', 'hevc_nvenc' ]:
return [ '-preset', map_nvenc_preset(video_preset) ]
if video_encoder in [ 'h264_amf', 'hevc_amf' ]:
return [ '-quality', map_amf_preset(video_preset) ]
if video_encoder in [ 'h264_qsv', 'hevc_qsv' ]:
return [ '-preset', map_qsv_preset(video_preset) ]
return []
def set_video_colorspace(video_colorspace : str) -> Commands:
return [ '-colorspace', video_colorspace ]
def set_video_fps(video_fps : Fps) -> Commands:
return [ '-vf', 'framerate=fps=' + str(video_fps) ]
def set_video_duration(video_duration : Duration) -> Commands:
return [ '-t', str(video_duration) ]
def capture_video() -> Commands:
return [ '-f', 'rawvideo' ]
def ignore_video_stream() -> Commands:
return [ '-vn' ]
def map_nvenc_preset(video_preset : VideoPreset) -> Optional[str]:
if video_preset in [ 'ultrafast', 'superfast', 'veryfast', 'faster', 'fast' ]:
return 'fast'
if video_preset == 'medium':
return 'medium'
if video_preset in [ 'slow', 'slower', 'veryslow' ]:
return 'slow'
return None
def map_amf_preset(video_preset : VideoPreset) -> Optional[str]:
if video_preset in [ 'ultrafast', 'superfast', 'veryfast' ]:
return 'speed'
if video_preset in [ 'faster', 'fast', 'medium' ]:
return 'balanced'
if video_preset in [ 'slow', 'slower', 'veryslow' ]:
return 'quality'
return None
def map_qsv_preset(video_preset : VideoPreset) -> Optional[str]:
if video_preset in [ 'ultrafast', 'superfast', 'veryfast' ]:
return 'veryfast'
if video_preset in [ 'faster', 'fast', 'medium', 'slow', 'slower', 'veryslow' ]:
return video_preset
return None

168
facefusion/filesystem.py
View File

@@ -1,16 +1,9 @@
import glob
import os
import shutil
from pathlib import Path
from typing import List, Optional
import filetype
from facefusion.common_helper import is_windows
from facefusion.typing import File
if is_windows():
import ctypes
import facefusion.choices
def get_file_size(file_path : str) -> int:
@@ -19,54 +12,95 @@ def get_file_size(file_path : str) -> int:
return 0
def same_file_extension(file_paths : List[str]) -> bool:
file_extensions : List[str] = []
def get_file_name(file_path : str) -> Optional[str]:
file_name, _ = os.path.splitext(os.path.basename(file_path))
for file_path in file_paths:
_, file_extension = os.path.splitext(file_path.lower())
if file_name:
return file_name
return None
if file_extensions and file_extension not in file_extensions:
return False
file_extensions.append(file_extension)
return True
def get_file_extension(file_path : str) -> Optional[str]:
_, file_extension = os.path.splitext(file_path)
if file_extension:
return file_extension.lower()
return None
def get_file_format(file_path : str) -> Optional[str]:
file_extension = get_file_extension(file_path)
if file_extension:
if file_extension == '.jpg':
return 'jpeg'
if file_extension == '.tif':
return 'tiff'
return file_extension.lstrip('.')
return None
def same_file_extension(first_file_path : str, second_file_path : str) -> bool:
first_file_extension = get_file_extension(first_file_path)
second_file_extension = get_file_extension(second_file_path)
if first_file_extension and second_file_extension:
return get_file_extension(first_file_path) == get_file_extension(second_file_path)
return False
def is_file(file_path : str) -> bool:
return bool(file_path and os.path.isfile(file_path))
def is_directory(directory_path : str) -> bool:
return bool(directory_path and os.path.isdir(directory_path))
def in_directory(file_path : str) -> bool:
if file_path and not is_directory(file_path):
return is_directory(os.path.dirname(file_path))
if file_path:
return os.path.isfile(file_path)
return False
def is_audio(audio_path : str) -> bool:
return is_file(audio_path) and filetype.helpers.is_audio(audio_path)
return is_file(audio_path) and get_file_format(audio_path) in facefusion.choices.audio_formats
def has_audio(audio_paths : List[str]) -> bool:
if audio_paths:
return any(is_audio(audio_path) for audio_path in audio_paths)
return any(map(is_audio, audio_paths))
return False
def are_audios(audio_paths : List[str]) -> bool:
if audio_paths:
return all(map(is_audio, audio_paths))
return False
def is_image(image_path : str) -> bool:
return is_file(image_path) and filetype.helpers.is_image(image_path)
return is_file(image_path) and get_file_format(image_path) in facefusion.choices.image_formats
def has_image(image_paths: List[str]) -> bool:
def has_image(image_paths : List[str]) -> bool:
if image_paths:
return any(is_image(image_path) for image_path in image_paths)
return False
def are_images(image_paths : List[str]) -> bool:
if image_paths:
return all(map(is_image, image_paths))
return False
def is_video(video_path : str) -> bool:
return is_file(video_path) and filetype.helpers.is_video(video_path)
return is_file(video_path) and get_file_format(video_path) in facefusion.choices.video_formats
def has_video(video_paths : List[str]) -> bool:
if video_paths:
return any(map(is_video, video_paths))
return False
def are_videos(video_paths : List[str]) -> bool:
if video_paths:
return any(map(is_video, video_paths))
return False
def filter_audio_paths(paths : List[str]) -> List[str]:
@@ -81,24 +115,6 @@ def filter_image_paths(paths : List[str]) -> List[str]:
return []
def resolve_relative_path(path : str) -> str:
return os.path.abspath(os.path.join(os.path.dirname(__file__), path))
def sanitize_path_for_windows(full_path : str) -> Optional[str]:
buffer_size = 0
while True:
unicode_buffer = ctypes.create_unicode_buffer(buffer_size)
buffer_limit = ctypes.windll.kernel32.GetShortPathNameW(full_path, unicode_buffer, buffer_size) #type:ignore[attr-defined]
if buffer_size > buffer_limit:
return unicode_buffer.value
if buffer_limit == 0:
return None
buffer_size = buffer_limit
def copy_file(file_path : str, move_path : str) -> bool:
if is_file(file_path):
shutil.copy(file_path, move_path)
@@ -120,31 +136,18 @@ def remove_file(file_path : str) -> bool:
return False
def create_directory(directory_path : str) -> bool:
if directory_path and not is_file(directory_path):
Path(directory_path).mkdir(parents = True, exist_ok = True)
return is_directory(directory_path)
return False
def resolve_file_paths(directory_path : str) -> List[str]:
file_paths : List[str] = []
def list_directory(directory_path : str) -> Optional[List[File]]:
if is_directory(directory_path):
file_paths = sorted(os.listdir(directory_path))
files: List[File] = []
file_names_and_extensions = sorted(os.listdir(directory_path))
for file_path in file_paths:
file_name, file_extension = os.path.splitext(file_path)
for file_name_and_extension in file_names_and_extensions:
if not file_name_and_extension.startswith(('.', '__')):
file_path = os.path.join(directory_path, file_name_and_extension)
file_paths.append(file_path)
if not file_name.startswith(('.', '__')):
files.append(
{
'name': file_name,
'extension': file_extension,
'path': os.path.join(directory_path, file_path)
})
return files
return None
return file_paths
def resolve_file_pattern(file_pattern : str) -> List[str]:
@@ -153,8 +156,33 @@ def resolve_file_pattern(file_pattern : str) -> List[str]:
return []
def is_directory(directory_path : str) -> bool:
if directory_path:
return os.path.isdir(directory_path)
return False
def in_directory(file_path : str) -> bool:
if file_path:
directory_path = os.path.dirname(file_path)
if directory_path:
return not is_directory(file_path) and is_directory(directory_path)
return False
def create_directory(directory_path : str) -> bool:
if directory_path and not is_file(directory_path):
os.makedirs(directory_path, exist_ok = True)
return is_directory(directory_path)
return False
def remove_directory(directory_path : str) -> bool:
if is_directory(directory_path):
shutil.rmtree(directory_path, ignore_errors = True)
return not is_directory(directory_path)
return False
def resolve_relative_path(path : str) -> str:
return os.path.abspath(os.path.join(os.path.dirname(__file__), path))

10
facefusion/hash_helper.py
View File

@@ -2,7 +2,7 @@ import os
import zlib
from typing import Optional
from facefusion.filesystem import is_file
from facefusion.filesystem import get_file_name, is_file
def create_hash(content : bytes) -> str:
@@ -13,8 +13,8 @@ def validate_hash(validate_path : str) -> bool:
hash_path = get_hash_path(validate_path)
if is_file(hash_path):
with open(hash_path, 'r') as hash_file:
hash_content = hash_file.read().strip()
with open(hash_path) as hash_file:
hash_content = hash_file.read()
with open(validate_path, 'rb') as validate_file:
validate_content = validate_file.read()
@@ -25,8 +25,8 @@ def validate_hash(validate_path : str) -> bool:
def get_hash_path(validate_path : str) -> Optional[str]:
if is_file(validate_path):
validate_directory_path, _ = os.path.split(validate_path)
validate_file_name, _ = os.path.splitext(_)
validate_directory_path, file_name_and_extension = os.path.split(validate_path)
validate_file_name = get_file_name(file_name_and_extension)
return os.path.join(validate_directory_path, validate_file_name + '.hash')
return None

79
facefusion/inference_manager.py
View File

@@ -1,3 +1,4 @@
import importlib
from time import sleep
from typing import List
@@ -5,59 +6,69 @@ from onnxruntime import InferenceSession
from facefusion import process_manager, state_manager
from facefusion.app_context import detect_app_context
from facefusion.execution import create_inference_execution_providers
from facefusion.thread_helper import thread_lock
from facefusion.typing import DownloadSet, ExecutionProvider, InferencePool, InferencePoolSet
from facefusion.execution import create_inference_session_providers
from facefusion.filesystem import is_file
from facefusion.types import DownloadSet, ExecutionProvider, InferencePool, InferencePoolSet
INFERENCE_POOLS : InferencePoolSet =\
INFERENCE_POOL_SET : InferencePoolSet =\
{
'cli': {}, #type:ignore[typeddict-item]
'ui': {} #type:ignore[typeddict-item]
'cli': {},
'ui': {}
}
def get_inference_pool(model_context : str, model_sources : DownloadSet) -> InferencePool:
global INFERENCE_POOLS
def get_inference_pool(module_name : str, model_names : List[str], model_source_set : DownloadSet) -> InferencePool:
while process_manager.is_checking():
sleep(0.5)
execution_device_id = state_manager.get_item('execution_device_id')
execution_providers = resolve_execution_providers(module_name)
app_context = detect_app_context()
inference_context = get_inference_context(module_name, model_names, execution_device_id, execution_providers)
with thread_lock():
while process_manager.is_checking():
sleep(0.5)
app_context = detect_app_context()
inference_context = get_inference_context(model_context)
if app_context == 'cli' and INFERENCE_POOL_SET.get('ui').get(inference_context):
INFERENCE_POOL_SET['cli'][inference_context] = INFERENCE_POOL_SET.get('ui').get(inference_context)
if app_context == 'ui' and INFERENCE_POOL_SET.get('cli').get(inference_context):
INFERENCE_POOL_SET['ui'][inference_context] = INFERENCE_POOL_SET.get('cli').get(inference_context)
if not INFERENCE_POOL_SET.get(app_context).get(inference_context):
INFERENCE_POOL_SET[app_context][inference_context] = create_inference_pool(model_source_set, execution_device_id, execution_providers)
if app_context == 'cli' and INFERENCE_POOLS.get('ui').get(inference_context):
INFERENCE_POOLS['cli'][inference_context] = INFERENCE_POOLS.get('ui').get(inference_context)
if app_context == 'ui' and INFERENCE_POOLS.get('cli').get(inference_context):
INFERENCE_POOLS['ui'][inference_context] = INFERENCE_POOLS.get('cli').get(inference_context)
if not INFERENCE_POOLS.get(app_context).get(inference_context):
INFERENCE_POOLS[app_context][inference_context] = create_inference_pool(model_sources, state_manager.get_item('execution_device_id'), state_manager.get_item('execution_providers'))
return INFERENCE_POOLS.get(app_context).get(inference_context)
return INFERENCE_POOL_SET.get(app_context).get(inference_context)
def create_inference_pool(model_sources : DownloadSet, execution_device_id : str, execution_providers : List[ExecutionProvider]) -> InferencePool:
def create_inference_pool(model_source_set : DownloadSet, execution_device_id : str, execution_providers : List[ExecutionProvider]) -> InferencePool:
inference_pool : InferencePool = {}
for model_name in model_sources.keys():
inference_pool[model_name] = create_inference_session(model_sources.get(model_name).get('path'), execution_device_id, execution_providers)
for model_name in model_source_set.keys():
model_path = model_source_set.get(model_name).get('path')
if is_file(model_path):
inference_pool[model_name] = create_inference_session(model_path, execution_device_id, execution_providers)
return inference_pool
def clear_inference_pool(model_context : str) -> None:
global INFERENCE_POOLS
def clear_inference_pool(module_name : str, model_names : List[str]) -> None:
execution_device_id = state_manager.get_item('execution_device_id')
execution_providers = resolve_execution_providers(module_name)
app_context = detect_app_context()
inference_context = get_inference_context(model_context)
inference_context = get_inference_context(module_name, model_names, execution_device_id, execution_providers)
if INFERENCE_POOLS.get(app_context).get(inference_context):
del INFERENCE_POOLS[app_context][inference_context]
if INFERENCE_POOL_SET.get(app_context).get(inference_context):
del INFERENCE_POOL_SET[app_context][inference_context]
def create_inference_session(model_path : str, execution_device_id : str, execution_providers : List[ExecutionProvider]) -> InferenceSession:
inference_execution_providers = create_inference_execution_providers(execution_device_id, execution_providers)
return InferenceSession(model_path, providers = inference_execution_providers)
inference_session_providers = create_inference_session_providers(execution_device_id, execution_providers)
return InferenceSession(model_path, providers = inference_session_providers)
def get_inference_context(model_context : str) -> str:
inference_context = model_context + '.' + '_'.join(state_manager.get_item('execution_providers'))
def get_inference_context(module_name : str, model_names : List[str], execution_device_id : str, execution_providers : List[ExecutionProvider]) -> str:
inference_context = '.'.join([ module_name ] + model_names + [ execution_device_id ] + list(execution_providers))
return inference_context
def resolve_execution_providers(module_name : str) -> List[ExecutionProvider]:
module = importlib.import_module(module_name)
if hasattr(module, 'resolve_execution_providers'):
return getattr(module, 'resolve_execution_providers')()
return state_manager.get_item('execution_providers')

47
facefusion/installer.py
View File

@@ -3,31 +3,28 @@ import shutil
import signal
import subprocess
import sys
import tempfile
from argparse import ArgumentParser, HelpFormatter
from typing import Dict, Tuple
from facefusion import metadata, wording
from facefusion.common_helper import is_linux, is_macos, is_windows
from facefusion.common_helper import is_linux, is_windows
ONNXRUNTIMES : Dict[str, Tuple[str, str]] = {}
if is_macos():
ONNXRUNTIMES['default'] = ('onnxruntime', '1.20.1')
else:
ONNXRUNTIMES['default'] = ('onnxruntime', '1.20.1')
ONNXRUNTIMES['cuda'] = ('onnxruntime-gpu', '1.20.1')
ONNXRUNTIMES['openvino'] = ('onnxruntime-openvino', '1.20.0')
if is_linux():
ONNXRUNTIMES['rocm'] = ('onnxruntime-rocm', '1.19.0')
ONNXRUNTIME_SET =\
{
'default': ('onnxruntime', '1.21.1')
}
if is_windows() or is_linux():
ONNXRUNTIME_SET['cuda'] = ('onnxruntime-gpu', '1.21.1')
ONNXRUNTIME_SET['openvino'] = ('onnxruntime-openvino', '1.21.0')
if is_windows():
ONNXRUNTIMES['directml'] = ('onnxruntime-directml', '1.17.3')
ONNXRUNTIME_SET['directml'] = ('onnxruntime-directml', '1.17.3')
if is_linux():
ONNXRUNTIME_SET['rocm'] = ('onnxruntime-rocm', '1.21.0')
def cli() -> None:
signal.signal(signal.SIGINT, lambda signal_number, frame: sys.exit(0))
program = ArgumentParser(formatter_class = lambda prog: HelpFormatter(prog, max_help_position = 50))
program.add_argument('--onnxruntime', help = wording.get('help.install_dependency').format(dependency = 'onnxruntime'), choices = ONNXRUNTIMES.keys(), required = True)
program.add_argument('--onnxruntime', help = wording.get('help.install_dependency').format(dependency = 'onnxruntime'), choices = ONNXRUNTIME_SET.keys(), required = True)
program.add_argument('--skip-conda', help = wording.get('help.skip_conda'), action = 'store_true')
program.add_argument('-v', '--version', version = metadata.get('name') + ' ' + metadata.get('version'), action = 'version')
run(program)
@@ -36,27 +33,27 @@ def cli() -> None:
def run(program : ArgumentParser) -> None:
args = program.parse_args()
has_conda = 'CONDA_PREFIX' in os.environ
onnxruntime_name, onnxruntime_version = ONNXRUNTIMES.get(args.onnxruntime)
onnxruntime_name, onnxruntime_version = ONNXRUNTIME_SET.get(args.onnxruntime)
if not args.skip_conda and not has_conda:
sys.stdout.write(wording.get('conda_not_activated') + os.linesep)
sys.exit(1)
subprocess.call([ shutil.which('pip'), 'install', '-r', 'requirements.txt', '--force-reinstall' ])
with open('requirements.txt') as file:
for line in file.readlines():
__line__ = line.strip()
if not __line__.startswith('onnxruntime'):
subprocess.call([ shutil.which('pip'), 'install', line, '--force-reinstall' ])
if args.onnxruntime == 'rocm':
python_id = 'cp' + str(sys.version_info.major) + str(sys.version_info.minor)
if python_id in [ 'cp310', 'cp312' ]:
wheel_name = 'onnxruntime_rocm-' + onnxruntime_version + '-' + python_id + '-' + python_id + '-linux_x86_64.whl'
wheel_path = os.path.join(tempfile.gettempdir(), wheel_name)
wheel_url = 'https://repo.radeon.com/rocm/manylinux/rocm-rel-6.3.1/' + wheel_name
subprocess.call([ shutil.which('curl'), '--silent', '--location', '--continue-at', '-', '--output', wheel_path, wheel_url ])
subprocess.call([ shutil.which('pip'), 'uninstall', 'onnxruntime', wheel_path, '-y', '-q' ])
subprocess.call([ shutil.which('pip'), 'install', wheel_path, '--force-reinstall' ])
os.remove(wheel_path)
wheel_url = 'https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4/' + wheel_name
subprocess.call([ shutil.which('pip'), 'install', wheel_url, '--force-reinstall' ])
else:
subprocess.call([ shutil.which('pip'), 'uninstall', 'onnxruntime', onnxruntime_name, '-y', '-q' ])
subprocess.call([ shutil.which('pip'), 'install', onnxruntime_name + '==' + onnxruntime_version, '--force-reinstall' ])
if args.onnxruntime == 'cuda' and has_conda:
@@ -89,5 +86,5 @@ def run(program : ArgumentParser) -> None:
subprocess.call([ shutil.which('conda'), 'env', 'config', 'vars', 'set', 'PATH=' + os.pathsep.join(library_paths) ])
if args.onnxruntime in [ 'directml', 'rocm' ]:
if args.onnxruntime == 'directml':
subprocess.call([ shutil.which('pip'), 'install', 'numpy==1.26.4', '--force-reinstall' ])

5
facefusion/jobs/job_helper.py
View File

@@ -2,11 +2,14 @@ import os
from datetime import datetime
from typing import Optional
from facefusion.filesystem import get_file_extension, get_file_name
def get_step_output_path(job_id : str, step_index : int, output_path : str) -> Optional[str]:
if output_path:
output_directory_path, _ = os.path.split(output_path)
output_file_name, output_file_extension = os.path.splitext(_)
output_file_name = get_file_name(_)
output_file_extension = get_file_extension(_)
return os.path.join(output_directory_path, output_file_name + '-' + job_id + '-' + str(step_index) + output_file_extension)
return None

2
facefusion/jobs/job_list.py
View File

@@ -3,7 +3,7 @@ from typing import Optional, Tuple
from facefusion.date_helper import describe_time_ago
from facefusion.jobs import job_manager
from facefusion.typing import JobStatus, TableContents, TableHeaders
from facefusion.types import JobStatus, TableContents, TableHeaders
def compose_job_list(job_status : JobStatus) -> Tuple[TableHeaders, TableContents]:

31
facefusion/jobs/job_manager.py
View File

@@ -4,10 +4,10 @@ from typing import List, Optional
import facefusion.choices
from facefusion.date_helper import get_current_date_time
from facefusion.filesystem import create_directory, is_directory, is_file, move_file, remove_directory, remove_file, resolve_file_pattern
from facefusion.filesystem import create_directory, get_file_name, is_directory, is_file, move_file, remove_directory, remove_file, resolve_file_pattern
from facefusion.jobs.job_helper import get_step_output_path
from facefusion.json import read_json, write_json
from facefusion.typing import Args, Job, JobSet, JobStatus, JobStep, JobStepStatus
from facefusion.types import Args, Job, JobSet, JobStatus, JobStep, JobStepStatus
JOBS_PATH : Optional[str] = None
@@ -48,14 +48,17 @@ def submit_job(job_id : str) -> bool:
return False
def submit_jobs() -> bool:
def submit_jobs(halt_on_error : bool) -> bool:
drafted_job_ids = find_job_ids('drafted')
has_error = False
if drafted_job_ids:
for job_id in drafted_job_ids:
if not submit_job(job_id):
return False
return True
has_error = True
if halt_on_error:
return False
return not has_error
return False
@@ -63,24 +66,27 @@ def delete_job(job_id : str) -> bool:
return delete_job_file(job_id)
def delete_jobs() -> bool:
def delete_jobs(halt_on_error : bool) -> bool:
job_ids = find_job_ids('drafted') + find_job_ids('queued') + find_job_ids('failed') + find_job_ids('completed')
has_error = False
if job_ids:
for job_id in job_ids:
if not delete_job(job_id):
return False
return True
has_error = True
if halt_on_error:
return False
return not has_error
return False
def find_jobs(job_status : JobStatus) -> JobSet:
job_ids = find_job_ids(job_status)
jobs : JobSet = {}
job_set : JobSet = {}
for job_id in job_ids:
jobs[job_id] = read_job_file(job_id)
return jobs
job_set[job_id] = read_job_file(job_id)
return job_set
def find_job_ids(job_status : JobStatus) -> List[str]:
@@ -90,7 +96,7 @@ def find_job_ids(job_status : JobStatus) -> List[str]:
job_ids = []
for job_path in job_paths:
job_id, _ = os.path.splitext(os.path.basename(job_path))
job_id = get_file_name(job_path)
job_ids.append(job_id)
return job_ids
@@ -182,7 +188,6 @@ def set_step_status(job_id : str, step_index : int, step_status : JobStepStatus)
if job:
steps = job.get('steps')
if has_step(job_id, step_index):
steps[step_index]['status'] = step_status
return update_job_file(job_id, job)

32
facefusion/jobs/job_runner.py
View File

@@ -1,7 +1,7 @@
from facefusion.ffmpeg import concat_video
from facefusion.filesystem import is_image, is_video, move_file, remove_file
from facefusion.filesystem import are_images, are_videos, move_file, remove_file
from facefusion.jobs import job_helper, job_manager
from facefusion.typing import JobOutputSet, JobStep, ProcessStep
from facefusion.types import JobOutputSet, JobStep, ProcessStep
def run_job(job_id : str, process_step : ProcessStep) -> bool:
@@ -16,14 +16,17 @@ def run_job(job_id : str, process_step : ProcessStep) -> bool:
return False
def run_jobs(process_step : ProcessStep) -> bool:
def run_jobs(process_step : ProcessStep, halt_on_error : bool) -> bool:
queued_job_ids = job_manager.find_job_ids('queued')
has_error = False
if queued_job_ids:
for job_id in queued_job_ids:
if not run_job(job_id, process_step):
return False
return True
has_error = True
if halt_on_error:
return False
return not has_error
return False
@@ -35,14 +38,17 @@ def retry_job(job_id : str, process_step : ProcessStep) -> bool:
return False
def retry_jobs(process_step : ProcessStep) -> bool:
def retry_jobs(process_step : ProcessStep, halt_on_error : bool) -> bool:
failed_job_ids = job_manager.find_job_ids('failed')
has_error = False
if failed_job_ids:
for job_id in failed_job_ids:
if not retry_job(job_id, process_step):
return False
return True
has_error = True
if halt_on_error:
return False
return not has_error
return False
@@ -73,10 +79,10 @@ def finalize_steps(job_id : str) -> bool:
output_set = collect_output_set(job_id)
for output_path, temp_output_paths in output_set.items():
if all(map(is_video, temp_output_paths)):
if are_videos(temp_output_paths):
if not concat_video(output_path, temp_output_paths):
return False
if any(map(is_image, temp_output_paths)):
if are_images(temp_output_paths):
for temp_output_path in temp_output_paths:
if not move_file(temp_output_path, output_path):
return False
@@ -95,12 +101,12 @@ def clean_steps(job_id: str) -> bool:
def collect_output_set(job_id : str) -> JobOutputSet:
steps = job_manager.get_steps(job_id)
output_set : JobOutputSet = {}
job_output_set : JobOutputSet = {}
for index, step in enumerate(steps):
output_path = step.get('args').get('output_path')
if output_path:
step_output_path = job_manager.get_step_output_path(job_id, index, output_path)
output_set.setdefault(output_path, []).append(step_output_path)
return output_set
job_output_set.setdefault(output_path, []).append(step_output_path)
return job_output_set

2
facefusion/jobs/job_store.py
View File

@@ -1,6 +1,6 @@
from typing import List
from facefusion.typing import JobStore
from facefusion.types import JobStore
JOB_STORE : JobStore =\
{

4
facefusion/json.py
View File

@@ -3,13 +3,13 @@ from json import JSONDecodeError
from typing import Optional
from facefusion.filesystem import is_file
from facefusion.typing import Content
from facefusion.types import Content
def read_json(json_path : str) -> Optional[Content]:
if is_file(json_path):
try:
with open(json_path, 'r') as json_file:
with open(json_path) as json_file:
return json.load(json_file)
except JSONDecodeError:
pass

44
facefusion/logger.py
View File

@@ -1,9 +1,8 @@
from logging import Logger, basicConfig, getLogger
from typing import Tuple
import facefusion.choices
from facefusion.common_helper import get_first, get_last
from facefusion.typing import LogLevel, TableContents, TableHeaders
from facefusion.types import LogLevel
def init(log_level : LogLevel) -> None:
@@ -32,46 +31,15 @@ def error(message : str, module_name : str) -> None:
def create_message(message : str, module_name : str) -> str:
scopes = module_name.split('.')
first_scope = get_first(scopes)
last_scope = get_last(scopes)
module_names = module_name.split('.')
first_module_name = get_first(module_names)
last_module_name = get_last(module_names)
if first_scope and last_scope:
return '[' + first_scope.upper() + '.' + last_scope.upper() + '] ' + message
if first_module_name and last_module_name:
return '[' + first_module_name.upper() + '.' + last_module_name.upper() + '] ' + message
return message
def table(headers : TableHeaders, contents : TableContents) -> None:
package_logger = get_package_logger()
table_column, table_separator = create_table_parts(headers, contents)
package_logger.info(table_separator)
package_logger.info(table_column.format(*headers))
package_logger.info(table_separator)
for content in contents:
content = [ value if value else '' for value in content ]
package_logger.info(table_column.format(*content))
package_logger.info(table_separator)
def create_table_parts(headers : TableHeaders, contents : TableContents) -> Tuple[str, str]:
column_parts = []
separator_parts = []
widths = [ len(header) for header in headers ]
for content in contents:
for index, value in enumerate(content):
widths[index] = max(widths[index], len(str(value)))
for width in widths:
column_parts.append('{:<' + str(width) + '}')
separator_parts.append('-' * width)
return '| ' + ' | '.join(column_parts) + ' |', '+-' + '-+-'.join(separator_parts) + '-+'
def enable() -> None:
get_package_logger().disabled = False

4
facefusion/metadata.py
View File

@@ -4,8 +4,8 @@ METADATA =\
{
'name': 'FaceFusion',
'description': 'Industry leading face manipulation platform',
'version': '3.1.2',
'license': 'MIT',
'version': '3.2.0',
'license': 'OpenRAIL-AS',
'author': 'Henry Ruhs',
'url': 'https://facefusion.io'
}

2
facefusion/model_helper.py
View File

@@ -2,7 +2,7 @@ from functools import lru_cache
import onnx
from facefusion.typing import ModelInitializer
from facefusion.types import ModelInitializer
@lru_cache(maxsize = None)

2
facefusion/normalizer.py
View File

@@ -1,6 +1,6 @@
from typing import List, Optional
from facefusion.typing import Fps, Padding
from facefusion.types import Fps, Padding
def normalize_padding(padding : Optional[List[int]]) -> Optional[Padding]:

2
facefusion/process_manager.py
View File

@@ -1,6 +1,6 @@
from typing import Generator, List
from facefusion.typing import ProcessState, QueuePayload
from facefusion.types import ProcessState, QueuePayload
PROCESS_STATE : ProcessState = 'pending'

13
facefusion/processors/choices.py
View File

@@ -1,8 +1,8 @@
from typing import List, Sequence
from facefusion.common_helper import create_float_range, create_int_range
from facefusion.filesystem import list_directory, resolve_relative_path
from facefusion.processors.typing import AgeModifierModel, DeepSwapperModel, ExpressionRestorerModel, FaceDebuggerItem, FaceEditorModel, FaceEnhancerModel, FaceSwapperModel, FaceSwapperSet, FrameColorizerModel, FrameEnhancerModel, LipSyncerModel
from facefusion.filesystem import get_file_name, resolve_file_paths, resolve_relative_path
from facefusion.processors.types import AgeModifierModel, DeepSwapperModel, ExpressionRestorerModel, FaceDebuggerItem, FaceEditorModel, FaceEnhancerModel, FaceSwapperModel, FaceSwapperSet, FrameColorizerModel, FrameEnhancerModel, LipSyncerModel
age_modifier_models : List[AgeModifierModel] = [ 'styleganex_age' ]
deep_swapper_models : List[DeepSwapperModel] =\
@@ -75,6 +75,7 @@ deep_swapper_models : List[DeepSwapperModel] =\
'druuzil/seth_macfarlane_384',
'druuzil/thomas_cruise_320',
'druuzil/thomas_hanks_384',
'druuzil/william_murray_384',
'edel/emma_roberts_224',
'edel/ivanka_trump_224',
'edel/lize_dzjabrailova_224',
@@ -157,12 +158,12 @@ deep_swapper_models : List[DeepSwapperModel] =\
'rumateus/taylor_swift_224'
]
custom_model_files = list_directory(resolve_relative_path('../.assets/models/custom'))
custom_model_file_paths = resolve_file_paths(resolve_relative_path('../.assets/models/custom'))
if custom_model_files:
if custom_model_file_paths:
for model_file in custom_model_files:
model_id = '/'.join([ 'custom', model_file.get('name') ])
for model_file_path in custom_model_file_paths:
model_id = '/'.join([ 'custom', get_file_name(model_file_path) ])
deep_swapper_models.append(model_id)
expression_restorer_models : List[ExpressionRestorerModel] = [ 'live_portrait' ]

2
facefusion/processors/core.py
View File

@@ -9,7 +9,7 @@ from tqdm import tqdm
from facefusion import logger, state_manager, wording
from facefusion.exit_helper import hard_exit
from facefusion.typing import ProcessFrames, QueuePayload
from facefusion.types import ProcessFrames, QueuePayload
PROCESSORS_METHODS =\
[

2
facefusion/processors/live_portrait.py
View File

@@ -3,7 +3,7 @@ from typing import Tuple
import numpy
import scipy
from facefusion.processors.typing import LivePortraitExpression, LivePortraitPitch, LivePortraitRoll, LivePortraitRotation, LivePortraitYaw
from facefusion.processors.types import LivePortraitExpression, LivePortraitPitch, LivePortraitRoll, LivePortraitRotation, LivePortraitYaw
EXPRESSION_MIN = numpy.array(
[

31
facefusion/processors/modules/age_modifier.py
View File

@@ -20,10 +20,10 @@ from facefusion.face_selector import find_similar_faces, sort_and_filter_faces
from facefusion.face_store import get_reference_faces
from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path, same_file_extension
from facefusion.processors import choices as processors_choices
from facefusion.processors.typing import AgeModifierDirection, AgeModifierInputs
from facefusion.processors.types import AgeModifierDirection, AgeModifierInputs
from facefusion.program_helper import find_argument_group
from facefusion.thread_helper import thread_semaphore
from facefusion.typing import ApplyStateItem, Args, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.types import ApplyStateItem, Args, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.vision import match_frame_color, read_image, read_static_image, write_image
@@ -64,24 +64,27 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_sources)
model_names = [ state_manager.get_item('age_modifier_model') ]
model_source_set = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_names, model_source_set)
def clear_inference_pool() -> None:
inference_manager.clear_inference_pool(__name__)
model_names = [ state_manager.get_item('age_modifier_model') ]
inference_manager.clear_inference_pool(__name__, model_names)
def get_model_options() -> ModelOptions:
age_modifier_model = state_manager.get_item('age_modifier_model')
return create_static_model_set('full').get(age_modifier_model)
model_name = state_manager.get_item('age_modifier_model')
return create_static_model_set('full').get(model_name)
def register_args(program : ArgumentParser) -> None:
group_processors = find_argument_group(program, 'processors')
if group_processors:
group_processors.add_argument('--age-modifier-model', help = wording.get('help.age_modifier_model'), default = config.get_str_value('processors.age_modifier_model', 'styleganex_age'), choices = processors_choices.age_modifier_models)
group_processors.add_argument('--age-modifier-direction', help = wording.get('help.age_modifier_direction'), type = int, default = config.get_int_value('processors.age_modifier_direction', '0'), choices = processors_choices.age_modifier_direction_range, metavar = create_int_metavar(processors_choices.age_modifier_direction_range))
group_processors.add_argument('--age-modifier-model', help = wording.get('help.age_modifier_model'), default = config.get_str_value('processors', 'age_modifier_model', 'styleganex_age'), choices = processors_choices.age_modifier_models)
group_processors.add_argument('--age-modifier-direction', help = wording.get('help.age_modifier_direction'), type = int, default = config.get_int_value('processors', 'age_modifier_direction', '0'), choices = processors_choices.age_modifier_direction_range, metavar = create_int_metavar(processors_choices.age_modifier_direction_range))
facefusion.jobs.job_store.register_step_keys([ 'age_modifier_model', 'age_modifier_direction' ])
@@ -91,10 +94,10 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
def pre_check() -> bool:
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
model_hash_set = get_model_options().get('hashes')
model_source_set = get_model_options().get('sources')
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
def pre_process(mode : ProcessMode) -> bool:
@@ -104,7 +107,7 @@ def pre_process(mode : ProcessMode) -> bool:
if mode == 'output' and not in_directory(state_manager.get_item('output_path')):
logger.error(wording.get('specify_image_or_video_output') + wording.get('exclamation_mark'), __name__)
return False
if mode == 'output' and not same_file_extension([ state_manager.get_item('target_path'), state_manager.get_item('output_path') ]):
if mode == 'output' and not same_file_extension(state_manager.get_item('target_path'), state_manager.get_item('output_path')):
logger.error(wording.get('match_target_and_output_extension') + wording.get('exclamation_mark'), __name__)
return False
return True
@@ -145,7 +148,7 @@ def modify_age(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFra
crop_vision_frame = prepare_vision_frame(crop_vision_frame)
extend_vision_frame = prepare_vision_frame(extend_vision_frame)
age_modifier_direction = numpy.array(numpy.interp(state_manager.get_item('age_modifier_direction'), [-100, 100], [2.5, -2.5])).astype(numpy.float32)
age_modifier_direction = numpy.array(numpy.interp(state_manager.get_item('age_modifier_direction'), [ -100, 100 ], [ 2.5, -2.5 ])).astype(numpy.float32)
extend_vision_frame = forward(crop_vision_frame, extend_vision_frame, age_modifier_direction)
extend_vision_frame = normalize_extend_frame(extend_vision_frame)
extend_vision_frame = match_frame_color(extend_vision_frame_raw, extend_vision_frame)

52
facefusion/processors/modules/deep_swapper.py
View File

@@ -17,12 +17,12 @@ from facefusion.face_helper import paste_back, warp_face_by_face_landmark_5
from facefusion.face_masker import create_occlusion_mask, create_region_mask, create_static_box_mask
from facefusion.face_selector import find_similar_faces, sort_and_filter_faces
from facefusion.face_store import get_reference_faces
from facefusion.filesystem import in_directory, is_image, is_video, list_directory, resolve_relative_path, same_file_extension
from facefusion.filesystem import get_file_name, in_directory, is_image, is_video, resolve_file_paths, resolve_relative_path, same_file_extension
from facefusion.processors import choices as processors_choices
from facefusion.processors.typing import DeepSwapperInputs, DeepSwapperMorph
from facefusion.processors.types import DeepSwapperInputs, DeepSwapperMorph
from facefusion.program_helper import find_argument_group
from facefusion.thread_helper import thread_semaphore
from facefusion.typing import ApplyStateItem, Args, DownloadScope, Face, InferencePool, Mask, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.types import ApplyStateItem, Args, DownloadScope, Face, InferencePool, Mask, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.vision import conditional_match_frame_color, read_image, read_static_image, write_image
@@ -101,6 +101,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
('druuzil', 'seth_macfarlane_384'),
('druuzil', 'thomas_cruise_320'),
('druuzil', 'thomas_hanks_384'),
('druuzil', 'william_murray_384'),
('edel', 'emma_roberts_224'),
('edel', 'ivanka_trump_224'),
('edel', 'lize_dzjabrailova_224'),
@@ -216,12 +217,12 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
'template': 'dfl_whole_face'
}
custom_model_files = list_directory(resolve_relative_path('../.assets/models/custom'))
custom_model_file_paths = resolve_file_paths(resolve_relative_path('../.assets/models/custom'))
if custom_model_files:
if custom_model_file_paths:
for model_file in custom_model_files:
model_id = '/'.join([ 'custom', model_file.get('name') ])
for model_file_path in custom_model_file_paths:
model_id = '/'.join([ 'custom', get_file_name(model_file_path) ])
model_set[model_id] =\
{
@@ -229,7 +230,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
{
'deep_swapper':
{
'path': resolve_relative_path(model_file.get('path'))
'path': resolve_relative_path(model_file_path)
}
},
'template': 'dfl_whole_face'
@@ -239,33 +240,37 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_sources)
model_names = [ state_manager.get_item('deep_swapper_model') ]
model_source_set = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_names, model_source_set)
def clear_inference_pool() -> None:
inference_manager.clear_inference_pool(__name__)
model_names = [ state_manager.get_item('deep_swapper_model') ]
inference_manager.clear_inference_pool(__name__, model_names)
def get_model_options() -> ModelOptions:
deep_swapper_model = state_manager.get_item('deep_swapper_model')
return create_static_model_set('full').get(deep_swapper_model)
model_name = state_manager.get_item('deep_swapper_model')
return create_static_model_set('full').get(model_name)
def get_model_size() -> Size:
deep_swapper = get_inference_pool().get('deep_swapper')
deep_swapper_outputs = deep_swapper.get_outputs()
for deep_swapper_output in deep_swapper_outputs:
return deep_swapper_output.shape[1:3]
for deep_swapper_input in deep_swapper.get_inputs():
if deep_swapper_input.name == 'in_face:0':
return deep_swapper_input.shape[1:3]
return 0, 0
def register_args(program : ArgumentParser) -> None:
group_processors = find_argument_group(program, 'processors')
if group_processors:
group_processors.add_argument('--deep-swapper-model', help = wording.get('help.deep_swapper_model'), default = config.get_str_value('processors.deep_swapper_model', 'iperov/elon_musk_224'), choices = processors_choices.deep_swapper_models)
group_processors.add_argument('--deep-swapper-morph', help = wording.get('help.deep_swapper_morph'), type = int, default = config.get_int_value('processors.deep_swapper_morph', '80'), choices = processors_choices.deep_swapper_morph_range, metavar = create_int_metavar(processors_choices.deep_swapper_morph_range))
group_processors.add_argument('--deep-swapper-model', help = wording.get('help.deep_swapper_model'), default = config.get_str_value('processors', 'deep_swapper_model', 'iperov/elon_musk_224'), choices = processors_choices.deep_swapper_models)
group_processors.add_argument('--deep-swapper-morph', help = wording.get('help.deep_swapper_morph'), type = int, default = config.get_int_value('processors', 'deep_swapper_morph', '100'), choices = processors_choices.deep_swapper_morph_range, metavar = create_int_metavar(processors_choices.deep_swapper_morph_range))
facefusion.jobs.job_store.register_step_keys([ 'deep_swapper_model', 'deep_swapper_morph' ])
@@ -275,11 +280,11 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
def pre_check() -> bool:
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
model_hash_set = get_model_options().get('hashes')
model_source_set = get_model_options().get('sources')
if model_hashes and model_sources:
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
if model_hash_set and model_source_set:
return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
return True
@@ -290,7 +295,7 @@ def pre_process(mode : ProcessMode) -> bool:
if mode == 'output' and not in_directory(state_manager.get_item('output_path')):
logger.error(wording.get('specify_image_or_video_output') + wording.get('exclamation_mark'), __name__)
return False
if mode == 'output' and not same_file_extension([ state_manager.get_item('target_path'), state_manager.get_item('output_path') ]):
if mode == 'output' and not same_file_extension(state_manager.get_item('target_path'), state_manager.get_item('output_path')):
logger.error(wording.get('match_target_and_output_extension') + wording.get('exclamation_mark'), __name__)
return False
return True
@@ -362,6 +367,7 @@ def has_morph_input() -> bool:
for deep_swapper_input in deep_swapper.get_inputs():
if deep_swapper_input.name == 'morph_value:0':
return True
return False

38
facefusion/processors/modules/expression_restorer.py
View File

@@ -19,12 +19,11 @@ from facefusion.face_store import get_reference_faces
from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path, same_file_extension
from facefusion.processors import choices as processors_choices
from facefusion.processors.live_portrait import create_rotation, limit_expression
from facefusion.processors.typing import ExpressionRestorerInputs
from facefusion.processors.typing import LivePortraitExpression, LivePortraitFeatureVolume, LivePortraitMotionPoints, LivePortraitPitch, LivePortraitRoll, LivePortraitScale, LivePortraitTranslation, LivePortraitYaw
from facefusion.processors.types import ExpressionRestorerInputs, LivePortraitExpression, LivePortraitFeatureVolume, LivePortraitMotionPoints, LivePortraitPitch, LivePortraitRoll, LivePortraitScale, LivePortraitTranslation, LivePortraitYaw
from facefusion.program_helper import find_argument_group
from facefusion.thread_helper import conditional_thread_semaphore, thread_semaphore
from facefusion.typing import ApplyStateItem, Args, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.vision import get_video_frame, read_image, read_static_image, write_image
from facefusion.types import ApplyStateItem, Args, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.vision import read_image, read_static_image, read_video_frame, write_image
@lru_cache(maxsize = None)
@@ -69,32 +68,35 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
'path': resolve_relative_path('../.assets/models/live_portrait_generator.onnx')
}
},
'template': 'arcface_128_v2',
'template': 'arcface_128',
'size': (512, 512)
}
}
def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_sources)
model_names = [ state_manager.get_item('expression_restorer_model') ]
model_source_set = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_names, model_source_set)
def clear_inference_pool() -> None:
inference_manager.clear_inference_pool(__name__)
model_names = [ state_manager.get_item('expression_restorer_model') ]
inference_manager.clear_inference_pool(__name__, model_names)
def get_model_options() -> ModelOptions:
expression_restorer_model = state_manager.get_item('expression_restorer_model')
return create_static_model_set('full').get(expression_restorer_model)
model_name = state_manager.get_item('expression_restorer_model')
return create_static_model_set('full').get(model_name)
def register_args(program : ArgumentParser) -> None:
group_processors = find_argument_group(program, 'processors')
if group_processors:
group_processors.add_argument('--expression-restorer-model', help = wording.get('help.expression_restorer_model'), default = config.get_str_value('processors.expression_restorer_model', 'live_portrait'), choices = processors_choices.expression_restorer_models)
group_processors.add_argument('--expression-restorer-factor', help = wording.get('help.expression_restorer_factor'), type = int, default = config.get_int_value('processors.expression_restorer_factor', '80'), choices = processors_choices.expression_restorer_factor_range, metavar = create_int_metavar(processors_choices.expression_restorer_factor_range))
facefusion.jobs.job_store.register_step_keys([ 'expression_restorer_model','expression_restorer_factor' ])
group_processors.add_argument('--expression-restorer-model', help = wording.get('help.expression_restorer_model'), default = config.get_str_value('processors', 'expression_restorer_model', 'live_portrait'), choices = processors_choices.expression_restorer_models)
group_processors.add_argument('--expression-restorer-factor', help = wording.get('help.expression_restorer_factor'), type = int, default = config.get_int_value('processors', 'expression_restorer_factor', '80'), choices = processors_choices.expression_restorer_factor_range, metavar = create_int_metavar(processors_choices.expression_restorer_factor_range))
facefusion.jobs.job_store.register_step_keys([ 'expression_restorer_model', 'expression_restorer_factor' ])
def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
@@ -103,10 +105,10 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
def pre_check() -> bool:
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
model_hash_set = get_model_options().get('hashes')
model_source_set = get_model_options().get('sources')
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
def pre_process(mode : ProcessMode) -> bool:
@@ -119,7 +121,7 @@ def pre_process(mode : ProcessMode) -> bool:
if mode == 'output' and not in_directory(state_manager.get_item('output_path')):
logger.error(wording.get('specify_image_or_video_output') + wording.get('exclamation_mark'), __name__)
return False
if mode == 'output' and not same_file_extension([ state_manager.get_item('target_path'), state_manager.get_item('output_path') ]):
if mode == 'output' and not same_file_extension(state_manager.get_item('target_path'), state_manager.get_item('output_path')):
logger.error(wording.get('match_target_and_output_extension') + wording.get('exclamation_mark'), __name__)
return False
return True
@@ -265,7 +267,7 @@ def process_frames(source_path : List[str], queue_payloads : List[QueuePayload],
frame_number = queue_payload.get('frame_number')
if state_manager.get_item('trim_frame_start'):
frame_number += state_manager.get_item('trim_frame_start')
source_vision_frame = get_video_frame(state_manager.get_item('target_path'), frame_number)
source_vision_frame = read_video_frame(state_manager.get_item('target_path'), frame_number)
target_vision_path = queue_payload.get('frame_path')
target_vision_frame = read_image(target_vision_path)
output_vision_frame = process_frame(

10
facefusion/processors/modules/face_debugger.py
View File

@@ -15,9 +15,9 @@ from facefusion.face_selector import find_similar_faces, sort_and_filter_faces
from facefusion.face_store import get_reference_faces
from facefusion.filesystem import in_directory, same_file_extension
from facefusion.processors import choices as processors_choices
from facefusion.processors.typing import FaceDebuggerInputs
from facefusion.processors.types import FaceDebuggerInputs
from facefusion.program_helper import find_argument_group
from facefusion.typing import ApplyStateItem, Args, Face, InferencePool, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.types import ApplyStateItem, Args, Face, InferencePool, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.vision import read_image, read_static_image, write_image
@@ -32,7 +32,7 @@ def clear_inference_pool() -> None:
def register_args(program : ArgumentParser) -> None:
group_processors = find_argument_group(program, 'processors')
if group_processors:
group_processors.add_argument('--face-debugger-items', help = wording.get('help.face_debugger_items').format(choices = ', '.join(processors_choices.face_debugger_items)), default = config.get_str_list('processors.face_debugger_items', 'face-landmark-5/68 face-mask'), choices = processors_choices.face_debugger_items, nargs = '+', metavar = 'FACE_DEBUGGER_ITEMS')
group_processors.add_argument('--face-debugger-items', help = wording.get('help.face_debugger_items').format(choices = ', '.join(processors_choices.face_debugger_items)), default = config.get_str_list('processors', 'face_debugger_items', 'face-landmark-5/68 face-mask'), choices = processors_choices.face_debugger_items, nargs = '+', metavar = 'FACE_DEBUGGER_ITEMS')
facefusion.jobs.job_store.register_step_keys([ 'face_debugger_items' ])
@@ -48,7 +48,7 @@ def pre_process(mode : ProcessMode) -> bool:
if mode == 'output' and not in_directory(state_manager.get_item('output_path')):
logger.error(wording.get('specify_image_or_video_output') + wording.get('exclamation_mark'), __name__)
return False
if mode == 'output' and not same_file_extension([ state_manager.get_item('target_path'), state_manager.get_item('output_path') ]):
if mode == 'output' and not same_file_extension(state_manager.get_item('target_path'), state_manager.get_item('output_path')):
logger.error(wording.get('match_target_and_output_extension') + wording.get('exclamation_mark'), __name__)
return False
return True
@@ -90,7 +90,7 @@ def debug_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFra
cv2.line(temp_vision_frame, (x1, y1), (x1, y2), primary_light_color, 3)
if 'face-mask' in face_debugger_items:
crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, target_face.landmark_set.get('5/68'), 'arcface_128_v2', (512, 512))
crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, target_face.landmark_set.get('5/68'), 'arcface_128', (512, 512))
inverse_matrix = cv2.invertAffineTransform(affine_matrix)
temp_size = temp_vision_frame.shape[:2][::-1]
crop_masks = []

57
facefusion/processors/modules/face_editor.py
View File

@@ -19,10 +19,10 @@ from facefusion.face_store import get_reference_faces
from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path, same_file_extension
from facefusion.processors import choices as processors_choices
from facefusion.processors.live_portrait import create_rotation, limit_euler_angles, limit_expression
from facefusion.processors.typing import FaceEditorInputs, LivePortraitExpression, LivePortraitFeatureVolume, LivePortraitMotionPoints, LivePortraitPitch, LivePortraitRoll, LivePortraitRotation, LivePortraitScale, LivePortraitTranslation, LivePortraitYaw
from facefusion.processors.types import FaceEditorInputs, LivePortraitExpression, LivePortraitFeatureVolume, LivePortraitMotionPoints, LivePortraitPitch, LivePortraitRoll, LivePortraitRotation, LivePortraitScale, LivePortraitTranslation, LivePortraitYaw
from facefusion.program_helper import find_argument_group
from facefusion.thread_helper import conditional_thread_semaphore, thread_semaphore
from facefusion.typing import ApplyStateItem, Args, DownloadScope, Face, FaceLandmark68, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.types import ApplyStateItem, Args, DownloadScope, Face, FaceLandmark68, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.vision import read_image, read_static_image, write_image
@@ -105,37 +105,40 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_sources)
model_names = [ state_manager.get_item('face_editor_model') ]
model_source_set = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_names, model_source_set)
def clear_inference_pool() -> None:
inference_manager.clear_inference_pool(__name__)
model_names = [ state_manager.get_item('face_editor_model') ]
inference_manager.clear_inference_pool(__name__, model_names)
def get_model_options() -> ModelOptions:
face_editor_model = state_manager.get_item('face_editor_model')
return create_static_model_set('full').get(face_editor_model)
model_name = state_manager.get_item('face_editor_model')
return create_static_model_set('full').get(model_name)
def register_args(program : ArgumentParser) -> None:
group_processors = find_argument_group(program, 'processors')
if group_processors:
group_processors.add_argument('--face-editor-model', help = wording.get('help.face_editor_model'), default = config.get_str_value('processors.face_editor_model', 'live_portrait'), choices = processors_choices.face_editor_models)
group_processors.add_argument('--face-editor-eyebrow-direction', help = wording.get('help.face_editor_eyebrow_direction'), type = float, default = config.get_float_value('processors.face_editor_eyebrow_direction', '0'), choices = processors_choices.face_editor_eyebrow_direction_range, metavar = create_float_metavar(processors_choices.face_editor_eyebrow_direction_range))
group_processors.add_argument('--face-editor-eye-gaze-horizontal', help = wording.get('help.face_editor_eye_gaze_horizontal'), type = float, default = config.get_float_value('processors.face_editor_eye_gaze_horizontal', '0'), choices = processors_choices.face_editor_eye_gaze_horizontal_range, metavar = create_float_metavar(processors_choices.face_editor_eye_gaze_horizontal_range))
group_processors.add_argument('--face-editor-eye-gaze-vertical', help = wording.get('help.face_editor_eye_gaze_vertical'), type = float, default = config.get_float_value('processors.face_editor_eye_gaze_vertical', '0'), choices = processors_choices.face_editor_eye_gaze_vertical_range, metavar = create_float_metavar(processors_choices.face_editor_eye_gaze_vertical_range))
group_processors.add_argument('--face-editor-eye-open-ratio', help = wording.get('help.face_editor_eye_open_ratio'), type = float, default = config.get_float_value('processors.face_editor_eye_open_ratio', '0'), choices = processors_choices.face_editor_eye_open_ratio_range, metavar = create_float_metavar(processors_choices.face_editor_eye_open_ratio_range))
group_processors.add_argument('--face-editor-lip-open-ratio', help = wording.get('help.face_editor_lip_open_ratio'), type = float, default = config.get_float_value('processors.face_editor_lip_open_ratio', '0'), choices = processors_choices.face_editor_lip_open_ratio_range, metavar = create_float_metavar(processors_choices.face_editor_lip_open_ratio_range))
group_processors.add_argument('--face-editor-mouth-grim', help = wording.get('help.face_editor_mouth_grim'), type = float, default = config.get_float_value('processors.face_editor_mouth_grim', '0'), choices = processors_choices.face_editor_mouth_grim_range, metavar = create_float_metavar(processors_choices.face_editor_mouth_grim_range))
group_processors.add_argument('--face-editor-mouth-pout', help = wording.get('help.face_editor_mouth_pout'), type = float, default = config.get_float_value('processors.face_editor_mouth_pout', '0'), choices = processors_choices.face_editor_mouth_pout_range, metavar = create_float_metavar(processors_choices.face_editor_mouth_pout_range))
group_processors.add_argument('--face-editor-mouth-purse', help = wording.get('help.face_editor_mouth_purse'), type = float, default = config.get_float_value('processors.face_editor_mouth_purse', '0'), choices = processors_choices.face_editor_mouth_purse_range, metavar = create_float_metavar(processors_choices.face_editor_mouth_purse_range))
group_processors.add_argument('--face-editor-mouth-smile', help = wording.get('help.face_editor_mouth_smile'), type = float, default = config.get_float_value('processors.face_editor_mouth_smile', '0'), choices = processors_choices.face_editor_mouth_smile_range, metavar = create_float_metavar(processors_choices.face_editor_mouth_smile_range))
group_processors.add_argument('--face-editor-mouth-position-horizontal', help = wording.get('help.face_editor_mouth_position_horizontal'), type = float, default = config.get_float_value('processors.face_editor_mouth_position_horizontal', '0'), choices = processors_choices.face_editor_mouth_position_horizontal_range, metavar = create_float_metavar(processors_choices.face_editor_mouth_position_horizontal_range))
group_processors.add_argument('--face-editor-mouth-position-vertical', help = wording.get('help.face_editor_mouth_position_vertical'), type = float, default = config.get_float_value('processors.face_editor_mouth_position_vertical', '0'), choices = processors_choices.face_editor_mouth_position_vertical_range, metavar = create_float_metavar(processors_choices.face_editor_mouth_position_vertical_range))
group_processors.add_argument('--face-editor-head-pitch', help = wording.get('help.face_editor_head_pitch'), type = float, default = config.get_float_value('processors.face_editor_head_pitch', '0'), choices = processors_choices.face_editor_head_pitch_range, metavar = create_float_metavar(processors_choices.face_editor_head_pitch_range))
group_processors.add_argument('--face-editor-head-yaw', help = wording.get('help.face_editor_head_yaw'), type = float, default = config.get_float_value('processors.face_editor_head_yaw', '0'), choices = processors_choices.face_editor_head_yaw_range, metavar = create_float_metavar(processors_choices.face_editor_head_yaw_range))
group_processors.add_argument('--face-editor-head-roll', help = wording.get('help.face_editor_head_roll'), type = float, default = config.get_float_value('processors.face_editor_head_roll', '0'), choices = processors_choices.face_editor_head_roll_range, metavar = create_float_metavar(processors_choices.face_editor_head_roll_range))
group_processors.add_argument('--face-editor-model', help = wording.get('help.face_editor_model'), default = config.get_str_value('processors', 'face_editor_model', 'live_portrait'), choices = processors_choices.face_editor_models)
group_processors.add_argument('--face-editor-eyebrow-direction', help = wording.get('help.face_editor_eyebrow_direction'), type = float, default = config.get_float_value('processors', 'face_editor_eyebrow_direction', '0'), choices = processors_choices.face_editor_eyebrow_direction_range, metavar = create_float_metavar(processors_choices.face_editor_eyebrow_direction_range))
group_processors.add_argument('--face-editor-eye-gaze-horizontal', help = wording.get('help.face_editor_eye_gaze_horizontal'), type = float, default = config.get_float_value('processors', 'face_editor_eye_gaze_horizontal', '0'), choices = processors_choices.face_editor_eye_gaze_horizontal_range, metavar = create_float_metavar(processors_choices.face_editor_eye_gaze_horizontal_range))
group_processors.add_argument('--face-editor-eye-gaze-vertical', help = wording.get('help.face_editor_eye_gaze_vertical'), type = float, default = config.get_float_value('processors', 'face_editor_eye_gaze_vertical', '0'), choices = processors_choices.face_editor_eye_gaze_vertical_range, metavar = create_float_metavar(processors_choices.face_editor_eye_gaze_vertical_range))
group_processors.add_argument('--face-editor-eye-open-ratio', help = wording.get('help.face_editor_eye_open_ratio'), type = float, default = config.get_float_value('processors', 'face_editor_eye_open_ratio', '0'), choices = processors_choices.face_editor_eye_open_ratio_range, metavar = create_float_metavar(processors_choices.face_editor_eye_open_ratio_range))
group_processors.add_argument('--face-editor-lip-open-ratio', help = wording.get('help.face_editor_lip_open_ratio'), type = float, default = config.get_float_value('processors', 'face_editor_lip_open_ratio', '0'), choices = processors_choices.face_editor_lip_open_ratio_range, metavar = create_float_metavar(processors_choices.face_editor_lip_open_ratio_range))
group_processors.add_argument('--face-editor-mouth-grim', help = wording.get('help.face_editor_mouth_grim'), type = float, default = config.get_float_value('processors', 'face_editor_mouth_grim', '0'), choices = processors_choices.face_editor_mouth_grim_range, metavar = create_float_metavar(processors_choices.face_editor_mouth_grim_range))
group_processors.add_argument('--face-editor-mouth-pout', help = wording.get('help.face_editor_mouth_pout'), type = float, default = config.get_float_value('processors', 'face_editor_mouth_pout', '0'), choices = processors_choices.face_editor_mouth_pout_range, metavar = create_float_metavar(processors_choices.face_editor_mouth_pout_range))
group_processors.add_argument('--face-editor-mouth-purse', help = wording.get('help.face_editor_mouth_purse'), type = float, default = config.get_float_value('processors', 'face_editor_mouth_purse', '0'), choices = processors_choices.face_editor_mouth_purse_range, metavar = create_float_metavar(processors_choices.face_editor_mouth_purse_range))
group_processors.add_argument('--face-editor-mouth-smile', help = wording.get('help.face_editor_mouth_smile'), type = float, default = config.get_float_value('processors', 'face_editor_mouth_smile', '0'), choices = processors_choices.face_editor_mouth_smile_range, metavar = create_float_metavar(processors_choices.face_editor_mouth_smile_range))
group_processors.add_argument('--face-editor-mouth-position-horizontal', help = wording.get('help.face_editor_mouth_position_horizontal'), type = float, default = config.get_float_value('processors', 'face_editor_mouth_position_horizontal', '0'), choices = processors_choices.face_editor_mouth_position_horizontal_range, metavar = create_float_metavar(processors_choices.face_editor_mouth_position_horizontal_range))
group_processors.add_argument('--face-editor-mouth-position-vertical', help = wording.get('help.face_editor_mouth_position_vertical'), type = float, default = config.get_float_value('processors', 'face_editor_mouth_position_vertical', '0'), choices = processors_choices.face_editor_mouth_position_vertical_range, metavar = create_float_metavar(processors_choices.face_editor_mouth_position_vertical_range))
group_processors.add_argument('--face-editor-head-pitch', help = wording.get('help.face_editor_head_pitch'), type = float, default = config.get_float_value('processors', 'face_editor_head_pitch', '0'), choices = processors_choices.face_editor_head_pitch_range, metavar = create_float_metavar(processors_choices.face_editor_head_pitch_range))
group_processors.add_argument('--face-editor-head-yaw', help = wording.get('help.face_editor_head_yaw'), type = float, default = config.get_float_value('processors', 'face_editor_head_yaw', '0'), choices = processors_choices.face_editor_head_yaw_range, metavar = create_float_metavar(processors_choices.face_editor_head_yaw_range))
group_processors.add_argument('--face-editor-head-roll', help = wording.get('help.face_editor_head_roll'), type = float, default = config.get_float_value('processors', 'face_editor_head_roll', '0'), choices = processors_choices.face_editor_head_roll_range, metavar = create_float_metavar(processors_choices.face_editor_head_roll_range))
facefusion.jobs.job_store.register_step_keys([ 'face_editor_model', 'face_editor_eyebrow_direction', 'face_editor_eye_gaze_horizontal', 'face_editor_eye_gaze_vertical', 'face_editor_eye_open_ratio', 'face_editor_lip_open_ratio', 'face_editor_mouth_grim', 'face_editor_mouth_pout', 'face_editor_mouth_purse', 'face_editor_mouth_smile', 'face_editor_mouth_position_horizontal', 'face_editor_mouth_position_vertical', 'face_editor_head_pitch', 'face_editor_head_yaw', 'face_editor_head_roll' ])
@@ -158,10 +161,10 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
def pre_check() -> bool:
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
model_hash_set = get_model_options().get('hashes')
model_source_set = get_model_options().get('sources')
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
def pre_process(mode : ProcessMode) -> bool:
@@ -171,7 +174,7 @@ def pre_process(mode : ProcessMode) -> bool:
if mode == 'output' and not in_directory(state_manager.get_item('output_path')):
logger.error(wording.get('specify_image_or_video_output') + wording.get('exclamation_mark'), __name__)
return False
if mode == 'output' and not same_file_extension([ state_manager.get_item('target_path'), state_manager.get_item('output_path') ]):
if mode == 'output' and not same_file_extension(state_manager.get_item('target_path'), state_manager.get_item('output_path')):
logger.error(wording.get('match_target_and_output_extension') + wording.get('exclamation_mark'), __name__)
return False
return True
@@ -360,7 +363,7 @@ def edit_lip_open(motion_points : LivePortraitMotionPoints, face_landmark_68 : F
else:
lip_motion_points = numpy.concatenate([ motion_points.ravel(), [ lip_ratio, 1.0 ] ])
lip_motion_points = lip_motion_points.reshape(1, -1).astype(numpy.float32)
lip_motion_points = forward_retarget_lip(lip_motion_points) * numpy.abs(face_editor_lip_open_ratio)
lip_motion_points = forward_retarget_lip(lip_motion_points) * numpy.abs(face_editor_lip_open_ratio)
lip_motion_points = lip_motion_points.reshape(-1, 21, 3)
return lip_motion_points

34
facefusion/processors/modules/face_enhancer.py
View File

@@ -18,10 +18,10 @@ from facefusion.face_selector import find_similar_faces, sort_and_filter_faces
from facefusion.face_store import get_reference_faces
from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path, same_file_extension
from facefusion.processors import choices as processors_choices
from facefusion.processors.typing import FaceEnhancerInputs, FaceEnhancerWeight
from facefusion.processors.types import FaceEnhancerInputs, FaceEnhancerWeight
from facefusion.program_helper import find_argument_group
from facefusion.thread_helper import thread_semaphore
from facefusion.typing import ApplyStateItem, Args, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.types import ApplyStateItem, Args, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.vision import read_image, read_static_image, write_image
@@ -131,7 +131,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
'path': resolve_relative_path('../.assets/models/gpen_bfr_256.onnx')
}
},
'template': 'arcface_128_v2',
'template': 'arcface_128',
'size': (256, 256)
},
'gpen_bfr_512':
@@ -222,25 +222,28 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_sources)
model_names = [ state_manager.get_item('face_enhancer_model') ]
model_source_set = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_names, model_source_set)
def clear_inference_pool() -> None:
inference_manager.clear_inference_pool(__name__)
model_names = [ state_manager.get_item('face_enhancer_model') ]
inference_manager.clear_inference_pool(__name__, model_names)
def get_model_options() -> ModelOptions:
face_enhancer_model = state_manager.get_item('face_enhancer_model')
return create_static_model_set('full').get(face_enhancer_model)
model_name = state_manager.get_item('face_enhancer_model')
return create_static_model_set('full').get(model_name)
def register_args(program : ArgumentParser) -> None:
group_processors = find_argument_group(program, 'processors')
if group_processors:
group_processors.add_argument('--face-enhancer-model', help = wording.get('help.face_enhancer_model'), default = config.get_str_value('processors.face_enhancer_model', 'gfpgan_1.4'), choices = processors_choices.face_enhancer_models)
group_processors.add_argument('--face-enhancer-blend', help = wording.get('help.face_enhancer_blend'), type = int, default = config.get_int_value('processors.face_enhancer_blend', '80'), choices = processors_choices.face_enhancer_blend_range, metavar = create_int_metavar(processors_choices.face_enhancer_blend_range))
group_processors.add_argument('--face-enhancer-weight', help = wording.get('help.face_enhancer_weight'), type = float, default = config.get_float_value('processors.face_enhancer_weight', '1.0'), choices = processors_choices.face_enhancer_weight_range, metavar = create_float_metavar(processors_choices.face_enhancer_weight_range))
group_processors.add_argument('--face-enhancer-model', help = wording.get('help.face_enhancer_model'), default = config.get_str_value('processors', 'face_enhancer_model', 'gfpgan_1.4'), choices = processors_choices.face_enhancer_models)
group_processors.add_argument('--face-enhancer-blend', help = wording.get('help.face_enhancer_blend'), type = int, default = config.get_int_value('processors', 'face_enhancer_blend', '80'), choices = processors_choices.face_enhancer_blend_range, metavar = create_int_metavar(processors_choices.face_enhancer_blend_range))
group_processors.add_argument('--face-enhancer-weight', help = wording.get('help.face_enhancer_weight'), type = float, default = config.get_float_value('processors', 'face_enhancer_weight', '1.0'), choices = processors_choices.face_enhancer_weight_range, metavar = create_float_metavar(processors_choices.face_enhancer_weight_range))
facefusion.jobs.job_store.register_step_keys([ 'face_enhancer_model', 'face_enhancer_blend', 'face_enhancer_weight' ])
@@ -251,10 +254,10 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
def pre_check() -> bool:
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
model_hash_set = get_model_options().get('hashes')
model_source_set = get_model_options().get('sources')
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
def pre_process(mode : ProcessMode) -> bool:
@@ -264,7 +267,7 @@ def pre_process(mode : ProcessMode) -> bool:
if mode == 'output' and not in_directory(state_manager.get_item('output_path')):
logger.error(wording.get('specify_image_or_video_output') + wording.get('exclamation_mark'), __name__)
return False
if mode == 'output' and not same_file_extension([ state_manager.get_item('target_path'), state_manager.get_item('output_path') ]):
if mode == 'output' and not same_file_extension(state_manager.get_item('target_path'), state_manager.get_item('output_path')):
logger.error(wording.get('match_target_and_output_extension') + wording.get('exclamation_mark'), __name__)
return False
return True
@@ -329,6 +332,7 @@ def has_weight_input() -> bool:
for deep_swapper_input in face_enhancer.get_inputs():
if deep_swapper_input.name == 'weight':
return True
return False

42
facefusion/processors/modules/face_swapper.py
View File

@@ -21,10 +21,10 @@ from facefusion.filesystem import filter_image_paths, has_image, in_directory, i
from facefusion.model_helper import get_static_model_initializer
from facefusion.processors import choices as processors_choices
from facefusion.processors.pixel_boost import explode_pixel_boost, implode_pixel_boost
from facefusion.processors.typing import FaceSwapperInputs
from facefusion.processors.types import FaceSwapperInputs
from facefusion.program_helper import find_argument_group
from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import ApplyStateItem, Args, DownloadScope, Embedding, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.types import ApplyStateItem, Args, DownloadScope, Embedding, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.vision import read_image, read_static_image, read_static_images, unpack_resolution, write_image
@@ -211,7 +211,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
}
},
'type': 'inswapper',
'template': 'arcface_128_v2',
'template': 'arcface_128',
'size': (128, 128),
'mean': [ 0.0, 0.0, 0.0 ],
'standard_deviation': [ 1.0, 1.0, 1.0 ]
@@ -235,7 +235,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
}
},
'type': 'inswapper',
'template': 'arcface_128_v2',
'template': 'arcface_128',
'size': (128, 128),
'mean': [ 0.0, 0.0, 0.0 ],
'standard_deviation': [ 1.0, 1.0, 1.0 ]
@@ -336,29 +336,37 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_sources)
model_names = [ get_model_name() ]
model_source_set = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_names, model_source_set)
def clear_inference_pool() -> None:
inference_manager.clear_inference_pool(__name__)
model_names = [ get_model_name() ]
inference_manager.clear_inference_pool(__name__, model_names)
def get_model_options() -> ModelOptions:
face_swapper_model = state_manager.get_item('face_swapper_model')
model_name = get_model_name()
return create_static_model_set('full').get(model_name)
if has_execution_provider('coreml') and face_swapper_model == 'inswapper_128_fp16':
return create_static_model_set('full').get('inswapper_128')
return create_static_model_set('full').get(face_swapper_model)
def get_model_name() -> str:
model_name = state_manager.get_item('face_swapper_model')
if has_execution_provider('coreml') and model_name == 'inswapper_128_fp16':
return 'inswapper_128'
return model_name
def register_args(program : ArgumentParser) -> None:
group_processors = find_argument_group(program, 'processors')
if group_processors:
group_processors.add_argument('--face-swapper-model', help = wording.get('help.face_swapper_model'), default = config.get_str_value('processors.face_swapper_model', 'inswapper_128_fp16'), choices = processors_choices.face_swapper_models)
group_processors.add_argument('--face-swapper-model', help = wording.get('help.face_swapper_model'), default = config.get_str_value('processors', 'face_swapper_model', 'inswapper_128_fp16'), choices = processors_choices.face_swapper_models)
known_args, _ = program.parse_known_args()
face_swapper_pixel_boost_choices = processors_choices.face_swapper_set.get(known_args.face_swapper_model)
group_processors.add_argument('--face-swapper-pixel-boost', help = wording.get('help.face_swapper_pixel_boost'), default = config.get_str_value('processors.face_swapper_pixel_boost', get_first(face_swapper_pixel_boost_choices)), choices = face_swapper_pixel_boost_choices)
group_processors.add_argument('--face-swapper-pixel-boost', help = wording.get('help.face_swapper_pixel_boost'), default = config.get_str_value('processors', 'face_swapper_pixel_boost', get_first(face_swapper_pixel_boost_choices)), choices = face_swapper_pixel_boost_choices)
facefusion.jobs.job_store.register_step_keys([ 'face_swapper_model', 'face_swapper_pixel_boost' ])
@@ -368,10 +376,10 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
def pre_check() -> bool:
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
model_hash_set = get_model_options().get('hashes')
model_source_set = get_model_options().get('sources')
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
def pre_process(mode : ProcessMode) -> bool:
@@ -390,7 +398,7 @@ def pre_process(mode : ProcessMode) -> bool:
if mode == 'output' and not in_directory(state_manager.get_item('output_path')):
logger.error(wording.get('specify_image_or_video_output') + wording.get('exclamation_mark'), __name__)
return False
if mode == 'output' and not same_file_extension([ state_manager.get_item('target_path'), state_manager.get_item('output_path') ]):
if mode == 'output' and not same_file_extension(state_manager.get_item('target_path'), state_manager.get_item('output_path')):
logger.error(wording.get('match_target_and_output_extension') + wording.get('exclamation_mark'), __name__)
return False
return True

38
facefusion/processors/modules/frame_colorizer.py
View File

@@ -11,12 +11,13 @@ import facefusion.processors.core as processors
from facefusion import config, content_analyser, inference_manager, logger, process_manager, state_manager, wording
from facefusion.common_helper import create_int_metavar
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
from facefusion.execution import has_execution_provider
from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path, same_file_extension
from facefusion.processors import choices as processors_choices
from facefusion.processors.typing import FrameColorizerInputs
from facefusion.processors.types import FrameColorizerInputs
from facefusion.program_helper import find_argument_group
from facefusion.thread_helper import thread_semaphore
from facefusion.typing import ApplyStateItem, Args, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.types import ApplyStateItem, Args, DownloadScope, ExecutionProvider, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.vision import read_image, read_static_image, unpack_resolution, write_image
@@ -128,25 +129,34 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_sources)
model_names = [ state_manager.get_item('frame_colorizer_model') ]
model_source_set = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_names, model_source_set)
def clear_inference_pool() -> None:
inference_manager.clear_inference_pool(__name__)
model_names = [ state_manager.get_item('frame_colorizer_model') ]
inference_manager.clear_inference_pool(__name__, model_names)
def resolve_execution_providers() -> List[ExecutionProvider]:
if has_execution_provider('coreml'):
return [ 'cpu' ]
return state_manager.get_item('execution_providers')
def get_model_options() -> ModelOptions:
frame_colorizer_model = state_manager.get_item('frame_colorizer_model')
return create_static_model_set('full').get(frame_colorizer_model)
model_name = state_manager.get_item('frame_colorizer_model')
return create_static_model_set('full').get(model_name)
def register_args(program : ArgumentParser) -> None:
group_processors = find_argument_group(program, 'processors')
if group_processors:
group_processors.add_argument('--frame-colorizer-model', help = wording.get('help.frame_colorizer_model'), default = config.get_str_value('processors.frame_colorizer_model', 'ddcolor'), choices = processors_choices.frame_colorizer_models)
group_processors.add_argument('--frame-colorizer-size', help = wording.get('help.frame_colorizer_size'), type = str, default = config.get_str_value('processors.frame_colorizer_size', '256x256'), choices = processors_choices.frame_colorizer_sizes)
group_processors.add_argument('--frame-colorizer-blend', help = wording.get('help.frame_colorizer_blend'), type = int, default = config.get_int_value('processors.frame_colorizer_blend', '100'), choices = processors_choices.frame_colorizer_blend_range, metavar = create_int_metavar(processors_choices.frame_colorizer_blend_range))
group_processors.add_argument('--frame-colorizer-model', help = wording.get('help.frame_colorizer_model'), default = config.get_str_value('processors', 'frame_colorizer_model', 'ddcolor'), choices = processors_choices.frame_colorizer_models)
group_processors.add_argument('--frame-colorizer-size', help = wording.get('help.frame_colorizer_size'), type = str, default = config.get_str_value('processors', 'frame_colorizer_size', '256x256'), choices = processors_choices.frame_colorizer_sizes)
group_processors.add_argument('--frame-colorizer-blend', help = wording.get('help.frame_colorizer_blend'), type = int, default = config.get_int_value('processors', 'frame_colorizer_blend', '100'), choices = processors_choices.frame_colorizer_blend_range, metavar = create_int_metavar(processors_choices.frame_colorizer_blend_range))
facefusion.jobs.job_store.register_step_keys([ 'frame_colorizer_model', 'frame_colorizer_blend', 'frame_colorizer_size' ])
@@ -157,10 +167,10 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
def pre_check() -> bool:
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
model_hash_set = get_model_options().get('hashes')
model_source_set = get_model_options().get('sources')
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
def pre_process(mode : ProcessMode) -> bool:
@@ -170,7 +180,7 @@ def pre_process(mode : ProcessMode) -> bool:
if mode == 'output' and not in_directory(state_manager.get_item('output_path')):
logger.error(wording.get('specify_image_or_video_output') + wording.get('exclamation_mark'), __name__)
return False
if mode == 'output' and not same_file_extension([ state_manager.get_item('target_path'), state_manager.get_item('output_path') ]):
if mode == 'output' and not same_file_extension(state_manager.get_item('target_path'), state_manager.get_item('output_path')):
logger.error(wording.get('match_target_and_output_extension') + wording.get('exclamation_mark'), __name__)
return False
return True

40
facefusion/processors/modules/frame_enhancer.py
View File

@@ -14,10 +14,10 @@ from facefusion.download import conditional_download_hashes, conditional_downloa
from facefusion.execution import has_execution_provider
from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path, same_file_extension
from facefusion.processors import choices as processors_choices
from facefusion.processors.typing import FrameEnhancerInputs
from facefusion.processors.types import FrameEnhancerInputs
from facefusion.program_helper import find_argument_group
from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import ApplyStateItem, Args, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.types import ApplyStateItem, Args, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.vision import create_tile_frames, merge_tile_frames, read_image, read_static_image, write_image
@@ -386,32 +386,40 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_sources)
model_names = [ get_frame_enhancer_model() ]
model_source_set = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_names, model_source_set)
def clear_inference_pool() -> None:
inference_manager.clear_inference_pool(__name__)
model_names = [ get_frame_enhancer_model() ]
inference_manager.clear_inference_pool(__name__, model_names)
def get_model_options() -> ModelOptions:
model_name = get_frame_enhancer_model()
return create_static_model_set('full').get(model_name)
def get_frame_enhancer_model() -> str:
frame_enhancer_model = state_manager.get_item('frame_enhancer_model')
if has_execution_provider('coreml'):
if frame_enhancer_model == 'real_esrgan_x2_fp16':
return create_static_model_set('full').get('real_esrgan_x2')
return 'real_esrgan_x2'
if frame_enhancer_model == 'real_esrgan_x4_fp16':
return create_static_model_set('full').get('real_esrgan_x4')
return 'real_esrgan_x4'
if frame_enhancer_model == 'real_esrgan_x8_fp16':
return create_static_model_set('full').get('real_esrgan_x8')
return create_static_model_set('full').get(frame_enhancer_model)
return 'real_esrgan_x8'
return frame_enhancer_model
def register_args(program : ArgumentParser) -> None:
group_processors = find_argument_group(program, 'processors')
if group_processors:
group_processors.add_argument('--frame-enhancer-model', help = wording.get('help.frame_enhancer_model'), default = config.get_str_value('processors.frame_enhancer_model', 'span_kendata_x4'), choices = processors_choices.frame_enhancer_models)
group_processors.add_argument('--frame-enhancer-blend', help = wording.get('help.frame_enhancer_blend'), type = int, default = config.get_int_value('processors.frame_enhancer_blend', '80'), choices = processors_choices.frame_enhancer_blend_range, metavar = create_int_metavar(processors_choices.frame_enhancer_blend_range))
group_processors.add_argument('--frame-enhancer-model', help = wording.get('help.frame_enhancer_model'), default = config.get_str_value('processors', 'frame_enhancer_model', 'span_kendata_x4'), choices = processors_choices.frame_enhancer_models)
group_processors.add_argument('--frame-enhancer-blend', help = wording.get('help.frame_enhancer_blend'), type = int, default = config.get_int_value('processors', 'frame_enhancer_blend', '80'), choices = processors_choices.frame_enhancer_blend_range, metavar = create_int_metavar(processors_choices.frame_enhancer_blend_range))
facefusion.jobs.job_store.register_step_keys([ 'frame_enhancer_model', 'frame_enhancer_blend' ])
@@ -421,10 +429,10 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
def pre_check() -> bool:
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
model_hash_set = get_model_options().get('hashes')
model_source_set = get_model_options().get('sources')
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
def pre_process(mode : ProcessMode) -> bool:
@@ -434,7 +442,7 @@ def pre_process(mode : ProcessMode) -> bool:
if mode == 'output' and not in_directory(state_manager.get_item('output_path')):
logger.error(wording.get('specify_image_or_video_output') + wording.get('exclamation_mark'), __name__)
return False
if mode == 'output' and not same_file_extension([ state_manager.get_item('target_path'), state_manager.get_item('output_path') ]):
if mode == 'output' and not same_file_extension(state_manager.get_item('target_path'), state_manager.get_item('output_path')):
logger.error(wording.get('match_target_and_output_extension') + wording.get('exclamation_mark'), __name__)
return False
return True
@@ -479,7 +487,7 @@ def forward(tile_vision_frame : VisionFrame) -> VisionFrame:
def prepare_tile_frame(vision_tile_frame : VisionFrame) -> VisionFrame:
vision_tile_frame = numpy.expand_dims(vision_tile_frame[:, :, ::-1], axis = 0)
vision_tile_frame = vision_tile_frame.transpose(0, 3, 1, 2)
vision_tile_frame = vision_tile_frame.astype(numpy.float32) / 255
vision_tile_frame = vision_tile_frame.astype(numpy.float32) / 255.0
return vision_tile_frame

27
facefusion/processors/modules/lip_syncer.py
View File

@@ -19,10 +19,10 @@ from facefusion.face_selector import find_similar_faces, sort_and_filter_faces
from facefusion.face_store import get_reference_faces
from facefusion.filesystem import filter_audio_paths, has_audio, in_directory, is_image, is_video, resolve_relative_path, same_file_extension
from facefusion.processors import choices as processors_choices
from facefusion.processors.typing import LipSyncerInputs
from facefusion.processors.types import LipSyncerInputs
from facefusion.program_helper import find_argument_group
from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import ApplyStateItem, Args, AudioFrame, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.types import ApplyStateItem, Args, AudioFrame, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.vision import read_image, read_static_image, restrict_video_fps, write_image
@@ -74,23 +74,26 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_sources)
model_names = [ state_manager.get_item('lip_syncer_model') ]
model_source_set = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_names, model_source_set)
def clear_inference_pool() -> None:
inference_manager.clear_inference_pool(__name__)
model_names = [ state_manager.get_item('lip_syncer_model') ]
inference_manager.clear_inference_pool(__name__, model_names)
def get_model_options() -> ModelOptions:
lip_syncer_model = state_manager.get_item('lip_syncer_model')
return create_static_model_set('full').get(lip_syncer_model)
model_name = state_manager.get_item('lip_syncer_model')
return create_static_model_set('full').get(model_name)
def register_args(program : ArgumentParser) -> None:
group_processors = find_argument_group(program, 'processors')
if group_processors:
group_processors.add_argument('--lip-syncer-model', help = wording.get('help.lip_syncer_model'), default = config.get_str_value('processors.lip_syncer_model', 'wav2lip_gan_96'), choices = processors_choices.lip_syncer_models)
group_processors.add_argument('--lip-syncer-model', help = wording.get('help.lip_syncer_model'), default = config.get_str_value('processors', 'lip_syncer_model', 'wav2lip_gan_96'), choices = processors_choices.lip_syncer_models)
facefusion.jobs.job_store.register_step_keys([ 'lip_syncer_model' ])
@@ -99,10 +102,10 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
def pre_check() -> bool:
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
model_hash_set = get_model_options().get('hashes')
model_source_set = get_model_options().get('sources')
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
def pre_process(mode : ProcessMode) -> bool:
@@ -115,7 +118,7 @@ def pre_process(mode : ProcessMode) -> bool:
if mode == 'output' and not in_directory(state_manager.get_item('output_path')):
logger.error(wording.get('specify_image_or_video_output') + wording.get('exclamation_mark'), __name__)
return False
if mode == 'output' and not same_file_extension([ state_manager.get_item('target_path'), state_manager.get_item('output_path') ]):
if mode == 'output' and not same_file_extension(state_manager.get_item('target_path'), state_manager.get_item('output_path')):
logger.error(wording.get('match_target_and_output_extension') + wording.get('exclamation_mark'), __name__)
return False
return True

4
facefusion/processors/pixel_boost.py
View File

@@ -3,7 +3,7 @@ from typing import List
import numpy
from cv2.typing import Size
from facefusion.typing import VisionFrame
from facefusion.types import VisionFrame
def implode_pixel_boost(crop_vision_frame : VisionFrame, pixel_boost_total : int, model_size : Size) -> VisionFrame:
@@ -13,6 +13,6 @@ def implode_pixel_boost(crop_vision_frame : VisionFrame, pixel_boost_total : int
def explode_pixel_boost(temp_vision_frames : List[VisionFrame], pixel_boost_total : int, model_size : Size, pixel_boost_size : Size) -> VisionFrame:
crop_vision_frame = numpy.stack(temp_vision_frames, axis = 0).reshape(pixel_boost_total, pixel_boost_total, model_size[0], model_size[1], 3)
crop_vision_frame = numpy.stack(temp_vision_frames).reshape(pixel_boost_total, pixel_boost_total, model_size[0], model_size[1], 3)
crop_vision_frame = crop_vision_frame.transpose(2, 0, 3, 1, 4).reshape(pixel_boost_size[0], pixel_boost_size[1], 3)
return crop_vision_frame

36
facefusion/processors/typing.py → facefusion/processors/types.py
View File

@@ -1,11 +1,11 @@
from typing import Any, Dict, List, Literal, TypedDict
from typing import Any, Dict, List, Literal, TypeAlias, TypedDict
from numpy._typing import NDArray
from numpy.typing import NDArray
from facefusion.typing import AppContext, AudioFrame, Face, FaceSet, VisionFrame
from facefusion.types import AppContext, AudioFrame, Face, FaceSet, VisionFrame
AgeModifierModel = Literal['styleganex_age']
DeepSwapperModel = str
DeepSwapperModel : TypeAlias = str
ExpressionRestorerModel = Literal['live_portrait']
FaceDebuggerItem = Literal['bounding-box', 'face-landmark-5', 'face-landmark-5/68', 'face-landmark-68', 'face-landmark-68/5', 'face-mask', 'face-detector-score', 'face-landmarker-score', 'age', 'gender', 'race']
FaceEditorModel = Literal['live_portrait']
@@ -15,7 +15,7 @@ FrameColorizerModel = Literal['ddcolor', 'ddcolor_artistic', 'deoldify', 'deoldi
FrameEnhancerModel = Literal['clear_reality_x4', 'lsdir_x4', 'nomos8k_sc_x4', 'real_esrgan_x2', 'real_esrgan_x2_fp16', 'real_esrgan_x4', 'real_esrgan_x4_fp16', 'real_esrgan_x8', 'real_esrgan_x8_fp16', 'real_hatgan_x4', 'real_web_photo_x4', 'realistic_rescaler_x4', 'remacri_x4', 'siax_x4', 'span_kendata_x4', 'swin2_sr_x4', 'ultra_sharp_x4']
LipSyncerModel = Literal['wav2lip_96', 'wav2lip_gan_96']
FaceSwapperSet = Dict[FaceSwapperModel, List[str]]
FaceSwapperSet : TypeAlias = Dict[FaceSwapperModel, List[str]]
AgeModifierInputs = TypedDict('AgeModifierInputs',
{
@@ -141,17 +141,17 @@ ProcessorState = TypedDict('ProcessorState',
'frame_enhancer_blend' : int,
'lip_syncer_model' : LipSyncerModel
})
ProcessorStateSet = Dict[AppContext, ProcessorState]
ProcessorStateSet : TypeAlias = Dict[AppContext, ProcessorState]
AgeModifierDirection = NDArray[Any]
DeepSwapperMorph = NDArray[Any]
FaceEnhancerWeight = NDArray[Any]
LivePortraitPitch = float
LivePortraitYaw = float
LivePortraitRoll = float
LivePortraitExpression = NDArray[Any]
LivePortraitFeatureVolume = NDArray[Any]
LivePortraitMotionPoints = NDArray[Any]
LivePortraitRotation = NDArray[Any]
LivePortraitScale = NDArray[Any]
LivePortraitTranslation = NDArray[Any]
AgeModifierDirection : TypeAlias = NDArray[Any]
DeepSwapperMorph : TypeAlias = NDArray[Any]
FaceEnhancerWeight : TypeAlias = NDArray[Any]
LivePortraitPitch : TypeAlias = float
LivePortraitYaw : TypeAlias = float
LivePortraitRoll : TypeAlias = float
LivePortraitExpression : TypeAlias = NDArray[Any]
LivePortraitFeatureVolume : TypeAlias = NDArray[Any]
LivePortraitMotionPoints : TypeAlias = NDArray[Any]
LivePortraitRotation : TypeAlias = NDArray[Any]
LivePortraitScale : TypeAlias = NDArray[Any]
LivePortraitTranslation : TypeAlias = NDArray[Any]

167
facefusion/program.py
View File

@@ -3,9 +3,10 @@ from argparse import ArgumentParser, HelpFormatter
import facefusion.choices
from facefusion import config, metadata, state_manager, wording
from facefusion.common_helper import create_float_metavar, create_int_metavar, get_last
from facefusion.common_helper import create_float_metavar, create_int_metavar, get_first, get_last
from facefusion.execution import get_available_execution_providers
from facefusion.filesystem import list_directory
from facefusion.ffmpeg import get_available_encoder_set
from facefusion.filesystem import get_file_name, resolve_file_paths
from facefusion.jobs import job_store
from facefusion.processors.core import get_processors_modules
@@ -30,7 +31,7 @@ def create_config_path_program() -> ArgumentParser:
def create_temp_path_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_paths = program.add_argument_group('paths')
group_paths.add_argument('--temp-path', help = wording.get('help.temp_path'), default = config.get_str_value('paths.temp_path', tempfile.gettempdir()))
group_paths.add_argument('--temp-path', help = wording.get('help.temp_path'), default = config.get_str_value('paths', 'temp_path', tempfile.gettempdir()))
job_store.register_job_keys([ 'temp_path' ])
return program
@@ -38,7 +39,7 @@ def create_temp_path_program() -> ArgumentParser:
def create_jobs_path_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_paths = program.add_argument_group('paths')
group_paths.add_argument('--jobs-path', help = wording.get('help.jobs_path'), default = config.get_str_value('paths.jobs_path', '.jobs'))
group_paths.add_argument('--jobs-path', help = wording.get('help.jobs_path'), default = config.get_str_value('paths', 'jobs_path', '.jobs'))
job_store.register_job_keys([ 'jobs_path' ])
return program
@@ -46,7 +47,7 @@ def create_jobs_path_program() -> ArgumentParser:
def create_source_paths_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_paths = program.add_argument_group('paths')
group_paths.add_argument('-s', '--source-paths', help = wording.get('help.source_paths'), default = config.get_str_list('paths.source_paths'), nargs = '+')
group_paths.add_argument('-s', '--source-paths', help = wording.get('help.source_paths'), default = config.get_str_list('paths', 'source_paths'), nargs = '+')
job_store.register_step_keys([ 'source_paths' ])
return program
@@ -54,7 +55,7 @@ def create_source_paths_program() -> ArgumentParser:
def create_target_path_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_paths = program.add_argument_group('paths')
group_paths.add_argument('-t', '--target-path', help = wording.get('help.target_path'), default = config.get_str_value('paths.target_path'))
group_paths.add_argument('-t', '--target-path', help = wording.get('help.target_path'), default = config.get_str_value('paths', 'target_path'))
job_store.register_step_keys([ 'target_path' ])
return program
@@ -62,7 +63,7 @@ def create_target_path_program() -> ArgumentParser:
def create_output_path_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_paths = program.add_argument_group('paths')
group_paths.add_argument('-o', '--output-path', help = wording.get('help.output_path'), default = config.get_str_value('paths.output_path'))
group_paths.add_argument('-o', '--output-path', help = wording.get('help.output_path'), default = config.get_str_value('paths', 'output_path'))
job_store.register_step_keys([ 'output_path' ])
return program
@@ -70,7 +71,7 @@ def create_output_path_program() -> ArgumentParser:
def create_source_pattern_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_patterns = program.add_argument_group('patterns')
group_patterns.add_argument('-s', '--source-pattern', help = wording.get('help.source_pattern'), default = config.get_str_value('patterns.source_pattern'))
group_patterns.add_argument('-s', '--source-pattern', help = wording.get('help.source_pattern'), default = config.get_str_value('patterns', 'source_pattern'))
job_store.register_job_keys([ 'source_pattern' ])
return program
@@ -78,7 +79,7 @@ def create_source_pattern_program() -> ArgumentParser:
def create_target_pattern_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_patterns = program.add_argument_group('patterns')
group_patterns.add_argument('-t', '--target-pattern', help = wording.get('help.target_pattern'), default = config.get_str_value('patterns.target_pattern'))
group_patterns.add_argument('-t', '--target-pattern', help = wording.get('help.target_pattern'), default = config.get_str_value('patterns', 'target_pattern'))
job_store.register_job_keys([ 'target_pattern' ])
return program
@@ -86,7 +87,7 @@ def create_target_pattern_program() -> ArgumentParser:
def create_output_pattern_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_patterns = program.add_argument_group('patterns')
group_patterns.add_argument('-o', '--output-pattern', help = wording.get('help.output_pattern'), default = config.get_str_value('patterns.output_pattern'))
group_patterns.add_argument('-o', '--output-pattern', help = wording.get('help.output_pattern'), default = config.get_str_value('patterns', 'output_pattern'))
job_store.register_job_keys([ 'output_pattern' ])
return program
@@ -94,12 +95,12 @@ def create_output_pattern_program() -> ArgumentParser:
def create_face_detector_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_face_detector = program.add_argument_group('face detector')
group_face_detector.add_argument('--face-detector-model', help = wording.get('help.face_detector_model'), default = config.get_str_value('face_detector.face_detector_model', 'yoloface'), choices = facefusion.choices.face_detector_models)
group_face_detector.add_argument('--face-detector-model', help = wording.get('help.face_detector_model'), default = config.get_str_value('face_detector', 'face_detector_model', 'yolo_face'), choices = facefusion.choices.face_detector_models)
known_args, _ = program.parse_known_args()
face_detector_size_choices = facefusion.choices.face_detector_set.get(known_args.face_detector_model)
group_face_detector.add_argument('--face-detector-size', help = wording.get('help.face_detector_size'), default = config.get_str_value('face_detector.face_detector_size', get_last(face_detector_size_choices)), choices = face_detector_size_choices)
group_face_detector.add_argument('--face-detector-angles', help = wording.get('help.face_detector_angles'), type = int, default = config.get_int_list('face_detector.face_detector_angles', '0'), choices = facefusion.choices.face_detector_angles, nargs = '+', metavar = 'FACE_DETECTOR_ANGLES')
group_face_detector.add_argument('--face-detector-score', help = wording.get('help.face_detector_score'), type = float, default = config.get_float_value('face_detector.face_detector_score', '0.5'), choices = facefusion.choices.face_detector_score_range, metavar = create_float_metavar(facefusion.choices.face_detector_score_range))
group_face_detector.add_argument('--face-detector-size', help = wording.get('help.face_detector_size'), default = config.get_str_value('face_detector', 'face_detector_size', get_last(face_detector_size_choices)), choices = face_detector_size_choices)
group_face_detector.add_argument('--face-detector-angles', help = wording.get('help.face_detector_angles'), type = int, default = config.get_int_list('face_detector', 'face_detector_angles', '0'), choices = facefusion.choices.face_detector_angles, nargs = '+', metavar = 'FACE_DETECTOR_ANGLES')
group_face_detector.add_argument('--face-detector-score', help = wording.get('help.face_detector_score'), type = float, default = config.get_float_value('face_detector', 'face_detector_score', '0.5'), choices = facefusion.choices.face_detector_score_range, metavar = create_float_metavar(facefusion.choices.face_detector_score_range))
job_store.register_step_keys([ 'face_detector_model', 'face_detector_angles', 'face_detector_size', 'face_detector_score' ])
return program
@@ -107,8 +108,8 @@ def create_face_detector_program() -> ArgumentParser:
def create_face_landmarker_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_face_landmarker = program.add_argument_group('face landmarker')
group_face_landmarker.add_argument('--face-landmarker-model', help = wording.get('help.face_landmarker_model'), default = config.get_str_value('face_landmarker.face_landmarker_model', '2dfan4'), choices = facefusion.choices.face_landmarker_models)
group_face_landmarker.add_argument('--face-landmarker-score', help = wording.get('help.face_landmarker_score'), type = float, default = config.get_float_value('face_landmarker.face_landmarker_score', '0.5'), choices = facefusion.choices.face_landmarker_score_range, metavar = create_float_metavar(facefusion.choices.face_landmarker_score_range))
group_face_landmarker.add_argument('--face-landmarker-model', help = wording.get('help.face_landmarker_model'), default = config.get_str_value('face_landmarker', 'face_landmarker_model', '2dfan4'), choices = facefusion.choices.face_landmarker_models)
group_face_landmarker.add_argument('--face-landmarker-score', help = wording.get('help.face_landmarker_score'), type = float, default = config.get_float_value('face_landmarker', 'face_landmarker_score', '0.5'), choices = facefusion.choices.face_landmarker_score_range, metavar = create_float_metavar(facefusion.choices.face_landmarker_score_range))
job_store.register_step_keys([ 'face_landmarker_model', 'face_landmarker_score' ])
return program
@@ -116,15 +117,15 @@ def create_face_landmarker_program() -> ArgumentParser:
def create_face_selector_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_face_selector = program.add_argument_group('face selector')
group_face_selector.add_argument('--face-selector-mode', help = wording.get('help.face_selector_mode'), default = config.get_str_value('face_selector.face_selector_mode', 'reference'), choices = facefusion.choices.face_selector_modes)
group_face_selector.add_argument('--face-selector-order', help = wording.get('help.face_selector_order'), default = config.get_str_value('face_selector.face_selector_order', 'large-small'), choices = facefusion.choices.face_selector_orders)
group_face_selector.add_argument('--face-selector-age-start', help = wording.get('help.face_selector_age_start'), type = int, default = config.get_int_value('face_selector.face_selector_age_start'), choices = facefusion.choices.face_selector_age_range, metavar = create_int_metavar(facefusion.choices.face_selector_age_range))
group_face_selector.add_argument('--face-selector-age-end', help = wording.get('help.face_selector_age_end'), type = int, default = config.get_int_value('face_selector.face_selector_age_end'), choices = facefusion.choices.face_selector_age_range, metavar = create_int_metavar(facefusion.choices.face_selector_age_range))
group_face_selector.add_argument('--face-selector-gender', help = wording.get('help.face_selector_gender'), default = config.get_str_value('face_selector.face_selector_gender'), choices = facefusion.choices.face_selector_genders)
group_face_selector.add_argument('--face-selector-race', help = wording.get('help.face_selector_race'), default = config.get_str_value('face_selector.face_selector_race'), choices = facefusion.choices.face_selector_races)
group_face_selector.add_argument('--reference-face-position', help = wording.get('help.reference_face_position'), type = int, default = config.get_int_value('face_selector.reference_face_position', '0'))
group_face_selector.add_argument('--reference-face-distance', help = wording.get('help.reference_face_distance'), type = float, default = config.get_float_value('face_selector.reference_face_distance', '0.6'), choices = facefusion.choices.reference_face_distance_range, metavar = create_float_metavar(facefusion.choices.reference_face_distance_range))
group_face_selector.add_argument('--reference-frame-number', help = wording.get('help.reference_frame_number'), type = int, default = config.get_int_value('face_selector.reference_frame_number', '0'))
group_face_selector.add_argument('--face-selector-mode', help = wording.get('help.face_selector_mode'), default = config.get_str_value('face_selector', 'face_selector_mode', 'reference'), choices = facefusion.choices.face_selector_modes)
group_face_selector.add_argument('--face-selector-order', help = wording.get('help.face_selector_order'), default = config.get_str_value('face_selector', 'face_selector_order', 'large-small'), choices = facefusion.choices.face_selector_orders)
group_face_selector.add_argument('--face-selector-age-start', help = wording.get('help.face_selector_age_start'), type = int, default = config.get_int_value('face_selector', 'face_selector_age_start'), choices = facefusion.choices.face_selector_age_range, metavar = create_int_metavar(facefusion.choices.face_selector_age_range))
group_face_selector.add_argument('--face-selector-age-end', help = wording.get('help.face_selector_age_end'), type = int, default = config.get_int_value('face_selector', 'face_selector_age_end'), choices = facefusion.choices.face_selector_age_range, metavar = create_int_metavar(facefusion.choices.face_selector_age_range))
group_face_selector.add_argument('--face-selector-gender', help = wording.get('help.face_selector_gender'), default = config.get_str_value('face_selector', 'face_selector_gender'), choices = facefusion.choices.face_selector_genders)
group_face_selector.add_argument('--face-selector-race', help = wording.get('help.face_selector_race'), default = config.get_str_value('face_selector', 'face_selector_race'), choices = facefusion.choices.face_selector_races)
group_face_selector.add_argument('--reference-face-position', help = wording.get('help.reference_face_position'), type = int, default = config.get_int_value('face_selector', 'reference_face_position', '0'))
group_face_selector.add_argument('--reference-face-distance', help = wording.get('help.reference_face_distance'), type = float, default = config.get_float_value('face_selector', 'reference_face_distance', '0.3'), choices = facefusion.choices.reference_face_distance_range, metavar = create_float_metavar(facefusion.choices.reference_face_distance_range))
group_face_selector.add_argument('--reference-frame-number', help = wording.get('help.reference_frame_number'), type = int, default = config.get_int_value('face_selector', 'reference_frame_number', '0'))
job_store.register_step_keys([ 'face_selector_mode', 'face_selector_order', 'face_selector_gender', 'face_selector_race', 'face_selector_age_start', 'face_selector_age_end', 'reference_face_position', 'reference_face_distance', 'reference_frame_number' ])
return program
@@ -132,12 +133,12 @@ def create_face_selector_program() -> ArgumentParser:
def create_face_masker_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_face_masker = program.add_argument_group('face masker')
group_face_masker.add_argument('--face-occluder-model', help = wording.get('help.face_occluder_model'), default = config.get_str_value('face_detector.face_occluder_model', 'xseg_1'), choices = facefusion.choices.face_occluder_models)
group_face_masker.add_argument('--face-parser-model', help = wording.get('help.face_parser_model'), default = config.get_str_value('face_detector.face_parser_model', 'bisenet_resnet_34'), choices = facefusion.choices.face_parser_models)
group_face_masker.add_argument('--face-mask-types', help = wording.get('help.face_mask_types').format(choices = ', '.join(facefusion.choices.face_mask_types)), default = config.get_str_list('face_masker.face_mask_types', 'box'), choices = facefusion.choices.face_mask_types, nargs = '+', metavar = 'FACE_MASK_TYPES')
group_face_masker.add_argument('--face-mask-blur', help = wording.get('help.face_mask_blur'), type = float, default = config.get_float_value('face_masker.face_mask_blur', '0.3'), choices = facefusion.choices.face_mask_blur_range, metavar = create_float_metavar(facefusion.choices.face_mask_blur_range))
group_face_masker.add_argument('--face-mask-padding', help = wording.get('help.face_mask_padding'), type = int, default = config.get_int_list('face_masker.face_mask_padding', '0 0 0 0'), nargs = '+')
group_face_masker.add_argument('--face-mask-regions', help = wording.get('help.face_mask_regions').format(choices = ', '.join(facefusion.choices.face_mask_regions)), default = config.get_str_list('face_masker.face_mask_regions', ' '.join(facefusion.choices.face_mask_regions)), choices = facefusion.choices.face_mask_regions, nargs = '+', metavar = 'FACE_MASK_REGIONS')
group_face_masker.add_argument('--face-occluder-model', help = wording.get('help.face_occluder_model'), default = config.get_str_value('face_masker', 'face_occluder_model', 'xseg_1'), choices = facefusion.choices.face_occluder_models)
group_face_masker.add_argument('--face-parser-model', help = wording.get('help.face_parser_model'), default = config.get_str_value('face_masker', 'face_parser_model', 'bisenet_resnet_34'), choices = facefusion.choices.face_parser_models)
group_face_masker.add_argument('--face-mask-types', help = wording.get('help.face_mask_types').format(choices = ', '.join(facefusion.choices.face_mask_types)), default = config.get_str_list('face_masker', 'face_mask_types', 'box'), choices = facefusion.choices.face_mask_types, nargs = '+', metavar = 'FACE_MASK_TYPES')
group_face_masker.add_argument('--face-mask-blur', help = wording.get('help.face_mask_blur'), type = float, default = config.get_float_value('face_masker', 'face_mask_blur', '0.3'), choices = facefusion.choices.face_mask_blur_range, metavar = create_float_metavar(facefusion.choices.face_mask_blur_range))
group_face_masker.add_argument('--face-mask-padding', help = wording.get('help.face_mask_padding'), type = int, default = config.get_int_list('face_masker', 'face_mask_padding', '0 0 0 0'), nargs = '+')
group_face_masker.add_argument('--face-mask-regions', help = wording.get('help.face_mask_regions').format(choices = ', '.join(facefusion.choices.face_mask_regions)), default = config.get_str_list('face_masker', 'face_mask_regions', ' '.join(facefusion.choices.face_mask_regions)), choices = facefusion.choices.face_mask_regions, nargs = '+', metavar = 'FACE_MASK_REGIONS')
job_store.register_step_keys([ 'face_occluder_model', 'face_parser_model', 'face_mask_types', 'face_mask_blur', 'face_mask_padding', 'face_mask_regions' ])
return program
@@ -145,35 +146,37 @@ def create_face_masker_program() -> ArgumentParser:
def create_frame_extraction_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_frame_extraction = program.add_argument_group('frame extraction')
group_frame_extraction.add_argument('--trim-frame-start', help = wording.get('help.trim_frame_start'), type = int, default = facefusion.config.get_int_value('frame_extraction.trim_frame_start'))
group_frame_extraction.add_argument('--trim-frame-end', help = wording.get('help.trim_frame_end'), type = int, default = facefusion.config.get_int_value('frame_extraction.trim_frame_end'))
group_frame_extraction.add_argument('--temp-frame-format', help = wording.get('help.temp_frame_format'), default = config.get_str_value('frame_extraction.temp_frame_format', 'png'), choices = facefusion.choices.temp_frame_formats)
group_frame_extraction.add_argument('--keep-temp', help = wording.get('help.keep_temp'), action = 'store_true', default = config.get_bool_value('frame_extraction.keep_temp'))
group_frame_extraction.add_argument('--trim-frame-start', help = wording.get('help.trim_frame_start'), type = int, default = facefusion.config.get_int_value('frame_extraction', 'trim_frame_start'))
group_frame_extraction.add_argument('--trim-frame-end', help = wording.get('help.trim_frame_end'), type = int, default = facefusion.config.get_int_value('frame_extraction', 'trim_frame_end'))
group_frame_extraction.add_argument('--temp-frame-format', help = wording.get('help.temp_frame_format'), default = config.get_str_value('frame_extraction', 'temp_frame_format', 'png'), choices = facefusion.choices.temp_frame_formats)
group_frame_extraction.add_argument('--keep-temp', help = wording.get('help.keep_temp'), action = 'store_true', default = config.get_bool_value('frame_extraction', 'keep_temp'))
job_store.register_step_keys([ 'trim_frame_start', 'trim_frame_end', 'temp_frame_format', 'keep_temp' ])
return program
def create_output_creation_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
available_encoder_set = get_available_encoder_set()
group_output_creation = program.add_argument_group('output creation')
group_output_creation.add_argument('--output-image-quality', help = wording.get('help.output_image_quality'), type = int, default = config.get_int_value('output_creation.output_image_quality', '80'), choices = facefusion.choices.output_image_quality_range, metavar = create_int_metavar(facefusion.choices.output_image_quality_range))
group_output_creation.add_argument('--output-image-resolution', help = wording.get('help.output_image_resolution'), default = config.get_str_value('output_creation.output_image_resolution'))
group_output_creation.add_argument('--output-audio-encoder', help = wording.get('help.output_audio_encoder'), default = config.get_str_value('output_creation.output_audio_encoder', 'aac'), choices = facefusion.choices.output_audio_encoders)
group_output_creation.add_argument('--output-video-encoder', help = wording.get('help.output_video_encoder'), default = config.get_str_value('output_creation.output_video_encoder', 'libx264'), choices = facefusion.choices.output_video_encoders)
group_output_creation.add_argument('--output-video-preset', help = wording.get('help.output_video_preset'), default = config.get_str_value('output_creation.output_video_preset', 'veryfast'), choices = facefusion.choices.output_video_presets)
group_output_creation.add_argument('--output-video-quality', help = wording.get('help.output_video_quality'), type = int, default = config.get_int_value('output_creation.output_video_quality', '80'), choices = facefusion.choices.output_video_quality_range, metavar = create_int_metavar(facefusion.choices.output_video_quality_range))
group_output_creation.add_argument('--output-video-resolution', help = wording.get('help.output_video_resolution'), default = config.get_str_value('output_creation.output_video_resolution'))
group_output_creation.add_argument('--output-video-fps', help = wording.get('help.output_video_fps'), type = float, default = config.get_str_value('output_creation.output_video_fps'))
group_output_creation.add_argument('--skip-audio', help = wording.get('help.skip_audio'), action = 'store_true', default = config.get_bool_value('output_creation.skip_audio'))
job_store.register_step_keys([ 'output_image_quality', 'output_image_resolution', 'output_audio_encoder', 'output_video_encoder', 'output_video_preset', 'output_video_quality', 'output_video_resolution', 'output_video_fps', 'skip_audio' ])
group_output_creation.add_argument('--output-image-quality', help = wording.get('help.output_image_quality'), type = int, default = config.get_int_value('output_creation', 'output_image_quality', '80'), choices = facefusion.choices.output_image_quality_range, metavar = create_int_metavar(facefusion.choices.output_image_quality_range))
group_output_creation.add_argument('--output-image-resolution', help = wording.get('help.output_image_resolution'), default = config.get_str_value('output_creation', 'output_image_resolution'))
group_output_creation.add_argument('--output-audio-encoder', help = wording.get('help.output_audio_encoder'), default = config.get_str_value('output_creation', 'output_audio_encoder', get_first(available_encoder_set.get('audio'))), choices = available_encoder_set.get('audio'))
group_output_creation.add_argument('--output-audio-quality', help = wording.get('help.output_audio_quality'), type = int, default = config.get_int_value('output_creation', 'output_audio_quality', '80'), choices = facefusion.choices.output_audio_quality_range, metavar = create_int_metavar(facefusion.choices.output_audio_quality_range))
group_output_creation.add_argument('--output-audio-volume', help = wording.get('help.output_audio_volume'), type = int, default = config.get_int_value('output_creation', 'output_audio_volume', '100'), choices = facefusion.choices.output_audio_volume_range, metavar = create_int_metavar(facefusion.choices.output_audio_volume_range))
group_output_creation.add_argument('--output-video-encoder', help = wording.get('help.output_video_encoder'), default = config.get_str_value('output_creation', 'output_video_encoder', get_first(available_encoder_set.get('video'))), choices = available_encoder_set.get('video'))
group_output_creation.add_argument('--output-video-preset', help = wording.get('help.output_video_preset'), default = config.get_str_value('output_creation', 'output_video_preset', 'veryfast'), choices = facefusion.choices.output_video_presets)
group_output_creation.add_argument('--output-video-quality', help = wording.get('help.output_video_quality'), type = int, default = config.get_int_value('output_creation', 'output_video_quality', '80'), choices = facefusion.choices.output_video_quality_range, metavar = create_int_metavar(facefusion.choices.output_video_quality_range))
group_output_creation.add_argument('--output-video-resolution', help = wording.get('help.output_video_resolution'), default = config.get_str_value('output_creation', 'output_video_resolution'))
group_output_creation.add_argument('--output-video-fps', help = wording.get('help.output_video_fps'), type = float, default = config.get_str_value('output_creation', 'output_video_fps'))
job_store.register_step_keys([ 'output_image_quality', 'output_image_resolution', 'output_audio_encoder', 'output_audio_quality', 'output_audio_volume', 'output_video_encoder', 'output_video_preset', 'output_video_quality', 'output_video_resolution', 'output_video_fps' ])
return program
def create_processors_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
available_processors = [ file.get('name') for file in list_directory('facefusion/processors/modules') ]
available_processors = [ get_file_name(file_path) for file_path in resolve_file_paths('facefusion/processors/modules') ]
group_processors = program.add_argument_group('processors')
group_processors.add_argument('--processors', help = wording.get('help.processors').format(choices = ', '.join(available_processors)), default = config.get_str_list('processors.processors', 'face_swapper'), nargs = '+')
group_processors.add_argument('--processors', help = wording.get('help.processors').format(choices = ', '.join(available_processors)), default = config.get_str_list('processors', 'processors', 'face_swapper'), nargs = '+')
job_store.register_step_keys([ 'processors' ])
for processor_module in get_processors_modules(available_processors):
processor_module.register_args(program)
@@ -182,11 +185,11 @@ def create_processors_program() -> ArgumentParser:
def create_uis_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
available_ui_layouts = [ file.get('name') for file in list_directory('facefusion/uis/layouts') ]
available_ui_layouts = [ get_file_name(file_path) for file_path in resolve_file_paths('facefusion/uis/layouts') ]
group_uis = program.add_argument_group('uis')
group_uis.add_argument('--open-browser', help = wording.get('help.open_browser'), action = 'store_true', default = config.get_bool_value('uis.open_browser'))
group_uis.add_argument('--ui-layouts', help = wording.get('help.ui_layouts').format(choices = ', '.join(available_ui_layouts)), default = config.get_str_list('uis.ui_layouts', 'default'), nargs = '+')
group_uis.add_argument('--ui-workflow', help = wording.get('help.ui_workflow'), default = config.get_str_value('uis.ui_workflow', 'instant_runner'), choices = facefusion.choices.ui_workflows)
group_uis.add_argument('--open-browser', help = wording.get('help.open_browser'), action = 'store_true', default = config.get_bool_value('uis', 'open_browser'))
group_uis.add_argument('--ui-layouts', help = wording.get('help.ui_layouts').format(choices = ', '.join(available_ui_layouts)), default = config.get_str_list('uis', 'ui_layouts', 'default'), nargs = '+')
group_uis.add_argument('--ui-workflow', help = wording.get('help.ui_workflow'), default = config.get_str_value('uis', 'ui_workflow', 'instant_runner'), choices = facefusion.choices.ui_workflows)
return program
@@ -194,19 +197,18 @@ def create_execution_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
available_execution_providers = get_available_execution_providers()
group_execution = program.add_argument_group('execution')
group_execution.add_argument('--execution-device-id', help = wording.get('help.execution_device_id'), default = config.get_str_value('execution.execution_device_id', '0'))
group_execution.add_argument('--execution-providers', help = wording.get('help.execution_providers').format(choices = ', '.join(available_execution_providers)), default = config.get_str_list('execution.execution_providers', 'cpu'), choices = available_execution_providers, nargs = '+', metavar = 'EXECUTION_PROVIDERS')
group_execution.add_argument('--execution-thread-count', help = wording.get('help.execution_thread_count'), type = int, default = config.get_int_value('execution.execution_thread_count', '4'), choices = facefusion.choices.execution_thread_count_range, metavar = create_int_metavar(facefusion.choices.execution_thread_count_range))
group_execution.add_argument('--execution-queue-count', help = wording.get('help.execution_queue_count'), type = int, default = config.get_int_value('execution.execution_queue_count', '1'), choices = facefusion.choices.execution_queue_count_range, metavar = create_int_metavar(facefusion.choices.execution_queue_count_range))
group_execution.add_argument('--execution-device-id', help = wording.get('help.execution_device_id'), default = config.get_str_value('execution', 'execution_device_id', '0'))
group_execution.add_argument('--execution-providers', help = wording.get('help.execution_providers').format(choices = ', '.join(available_execution_providers)), default = config.get_str_list('execution', 'execution_providers', get_first(available_execution_providers)), choices = available_execution_providers, nargs = '+', metavar = 'EXECUTION_PROVIDERS')
group_execution.add_argument('--execution-thread-count', help = wording.get('help.execution_thread_count'), type = int, default = config.get_int_value('execution', 'execution_thread_count', '4'), choices = facefusion.choices.execution_thread_count_range, metavar = create_int_metavar(facefusion.choices.execution_thread_count_range))
group_execution.add_argument('--execution-queue-count', help = wording.get('help.execution_queue_count'), type = int, default = config.get_int_value('execution', 'execution_queue_count', '1'), choices = facefusion.choices.execution_queue_count_range, metavar = create_int_metavar(facefusion.choices.execution_queue_count_range))
job_store.register_job_keys([ 'execution_device_id', 'execution_providers', 'execution_thread_count', 'execution_queue_count' ])
return program
def create_download_providers_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
download_providers = list(facefusion.choices.download_provider_set.keys())
group_download = program.add_argument_group('download')
group_download.add_argument('--download-providers', help = wording.get('help.download_providers').format(choices = ', '.join(download_providers)), default = config.get_str_list('download.download_providers', ' '.join(facefusion.choices.download_providers)), choices = download_providers, nargs = '+', metavar = 'DOWNLOAD_PROVIDERS')
group_download.add_argument('--download-providers', help = wording.get('help.download_providers').format(choices = ', '.join(facefusion.choices.download_providers)), default = config.get_str_list('download', 'download_providers', ' '.join(facefusion.choices.download_providers)), choices = facefusion.choices.download_providers, nargs = '+', metavar = 'DOWNLOAD_PROVIDERS')
job_store.register_job_keys([ 'download_providers' ])
return program
@@ -214,7 +216,7 @@ def create_download_providers_program() -> ArgumentParser:
def create_download_scope_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_download = program.add_argument_group('download')
group_download.add_argument('--download-scope', help = wording.get('help.download_scope'), default = config.get_str_value('download.download_scope', 'lite'), choices = facefusion.choices.download_scopes)
group_download.add_argument('--download-scope', help = wording.get('help.download_scope'), default = config.get_str_value('download', 'download_scope', 'lite'), choices = facefusion.choices.download_scopes)
job_store.register_job_keys([ 'download_scope' ])
return program
@@ -222,21 +224,28 @@ def create_download_scope_program() -> ArgumentParser:
def create_memory_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_memory = program.add_argument_group('memory')
group_memory.add_argument('--video-memory-strategy', help = wording.get('help.video_memory_strategy'), default = config.get_str_value('memory.video_memory_strategy', 'strict'), choices = facefusion.choices.video_memory_strategies)
group_memory.add_argument('--system-memory-limit', help = wording.get('help.system_memory_limit'), type = int, default = config.get_int_value('memory.system_memory_limit', '0'), choices = facefusion.choices.system_memory_limit_range, metavar = create_int_metavar(facefusion.choices.system_memory_limit_range))
group_memory.add_argument('--video-memory-strategy', help = wording.get('help.video_memory_strategy'), default = config.get_str_value('memory', 'video_memory_strategy', 'strict'), choices = facefusion.choices.video_memory_strategies)
group_memory.add_argument('--system-memory-limit', help = wording.get('help.system_memory_limit'), type = int, default = config.get_int_value('memory', 'system_memory_limit', '0'), choices = facefusion.choices.system_memory_limit_range, metavar = create_int_metavar(facefusion.choices.system_memory_limit_range))
job_store.register_job_keys([ 'video_memory_strategy', 'system_memory_limit' ])
return program
def create_misc_program() -> ArgumentParser:
def create_log_level_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
log_level_keys = list(facefusion.choices.log_level_set.keys())
group_misc = program.add_argument_group('misc')
group_misc.add_argument('--log-level', help = wording.get('help.log_level'), default = config.get_str_value('misc.log_level', 'info'), choices = log_level_keys)
group_misc.add_argument('--log-level', help = wording.get('help.log_level'), default = config.get_str_value('misc', 'log_level', 'info'), choices = facefusion.choices.log_levels)
job_store.register_job_keys([ 'log_level' ])
return program
def create_halt_on_error_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_misc = program.add_argument_group('misc')
group_misc.add_argument('--halt-on-error', help = wording.get('help.halt_on_error'), action = 'store_true', default = config.get_bool_value('misc', 'halt_on_error'))
job_store.register_job_keys([ 'halt_on_error' ])
return program
def create_job_id_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
program.add_argument('job_id', help = wording.get('help.job_id'))
@@ -257,11 +266,11 @@ def create_step_index_program() -> ArgumentParser:
def collect_step_program() -> ArgumentParser:
return ArgumentParser(parents= [ create_face_detector_program(), create_face_landmarker_program(), create_face_selector_program(), create_face_masker_program(), create_frame_extraction_program(), create_output_creation_program(), create_processors_program() ], add_help = False)
return ArgumentParser(parents = [ create_face_detector_program(), create_face_landmarker_program(), create_face_selector_program(), create_face_masker_program(), create_frame_extraction_program(), create_output_creation_program(), create_processors_program() ], add_help = False)
def collect_job_program() -> ArgumentParser:
return ArgumentParser(parents= [ create_execution_program(), create_download_providers_program(), create_memory_program(), create_misc_program() ], add_help = False)
return ArgumentParser(parents = [ create_execution_program(), create_download_providers_program(), create_memory_program(), create_log_level_program() ], add_help = False)
def create_program() -> ArgumentParser:
@@ -273,24 +282,24 @@ def create_program() -> ArgumentParser:
sub_program.add_parser('run', help = wording.get('help.run'), parents = [ create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), create_source_paths_program(), create_target_path_program(), create_output_path_program(), collect_step_program(), create_uis_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('headless-run', help = wording.get('help.headless_run'), parents = [ create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), create_source_paths_program(), create_target_path_program(), create_output_path_program(), collect_step_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('batch-run', help = wording.get('help.batch_run'), parents = [ create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), create_source_pattern_program(), create_target_pattern_program(), create_output_pattern_program(), collect_step_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('force-download', help = wording.get('help.force_download'), parents = [ create_download_providers_program(), create_download_scope_program(), create_misc_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('force-download', help = wording.get('help.force_download'), parents = [ create_download_providers_program(), create_download_scope_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
# job manager
sub_program.add_parser('job-list', help = wording.get('help.job_list'), parents = [ create_job_status_program(), create_jobs_path_program(), create_misc_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-create', help = wording.get('help.job_create'), parents = [ create_job_id_program(), create_jobs_path_program(), create_misc_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-submit', help = wording.get('help.job_submit'), parents = [ create_job_id_program(), create_jobs_path_program(), create_misc_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-submit-all', help = wording.get('help.job_submit_all'), parents = [ create_jobs_path_program(), create_misc_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-delete', help = wording.get('help.job_delete'), parents = [ create_job_id_program(), create_jobs_path_program(), create_misc_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-delete-all', help = wording.get('help.job_delete_all'), parents = [ create_jobs_path_program(), create_misc_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-add-step', help = wording.get('help.job_add_step'), parents = [ create_job_id_program(), create_config_path_program(), create_jobs_path_program(), create_source_paths_program(), create_target_path_program(), create_output_path_program(), collect_step_program(), create_misc_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-remix-step', help = wording.get('help.job_remix_step'), parents = [ create_job_id_program(), create_step_index_program(), create_config_path_program(), create_jobs_path_program(), create_source_paths_program(), create_output_path_program(), collect_step_program(), create_misc_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-insert-step', help = wording.get('help.job_insert_step'), parents = [ create_job_id_program(), create_step_index_program(), create_config_path_program(), create_jobs_path_program(), create_source_paths_program(), create_target_path_program(), create_output_path_program(), collect_step_program(), create_misc_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-remove-step', help = wording.get('help.job_remove_step'), parents = [ create_job_id_program(), create_step_index_program(), create_jobs_path_program(), create_misc_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-list', help = wording.get('help.job_list'), parents = [ create_job_status_program(), create_jobs_path_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-create', help = wording.get('help.job_create'), parents = [ create_job_id_program(), create_jobs_path_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-submit', help = wording.get('help.job_submit'), parents = [ create_job_id_program(), create_jobs_path_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-submit-all', help = wording.get('help.job_submit_all'), parents = [ create_jobs_path_program(), create_log_level_program(), create_halt_on_error_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-delete', help = wording.get('help.job_delete'), parents = [ create_job_id_program(), create_jobs_path_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-delete-all', help = wording.get('help.job_delete_all'), parents = [ create_jobs_path_program(), create_log_level_program(), create_halt_on_error_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-add-step', help = wording.get('help.job_add_step'), parents = [ create_job_id_program(), create_config_path_program(), create_jobs_path_program(), create_source_paths_program(), create_target_path_program(), create_output_path_program(), collect_step_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-remix-step', help = wording.get('help.job_remix_step'), parents = [ create_job_id_program(), create_step_index_program(), create_config_path_program(), create_jobs_path_program(), create_source_paths_program(), create_output_path_program(), collect_step_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-insert-step', help = wording.get('help.job_insert_step'), parents = [ create_job_id_program(), create_step_index_program(), create_config_path_program(), create_jobs_path_program(), create_source_paths_program(), create_target_path_program(), create_output_path_program(), collect_step_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-remove-step', help = wording.get('help.job_remove_step'), parents = [ create_job_id_program(), create_step_index_program(), create_jobs_path_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
# job runner
sub_program.add_parser('job-run', help = wording.get('help.job_run'), parents = [ create_job_id_program(), create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-run-all', help = wording.get('help.job_run_all'), parents = [ create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-run-all', help = wording.get('help.job_run_all'), parents = [ create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), collect_job_program(), create_halt_on_error_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-retry', help = wording.get('help.job_retry'), parents = [ create_job_id_program(), create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-retry-all', help = wording.get('help.job_retry_all'), parents = [ create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
return ArgumentParser(parents = [ program ], formatter_class = create_help_formatter_small, add_help = True)
sub_program.add_parser('job-retry-all', help = wording.get('help.job_retry_all'), parents = [ create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), collect_job_program(), create_halt_on_error_program() ], formatter_class = create_help_formatter_large)
return ArgumentParser(parents = [ program ], formatter_class = create_help_formatter_small)
def apply_config_path(program : ArgumentParser) -> None:

22
facefusion/state_manager.py
View File

@@ -1,37 +1,37 @@
from typing import Any, Union
from facefusion.app_context import detect_app_context
from facefusion.processors.typing import ProcessorState, ProcessorStateKey
from facefusion.typing import State, StateKey, StateSet
from facefusion.processors.types import ProcessorState, ProcessorStateKey, ProcessorStateSet
from facefusion.types import State, StateKey, StateSet
STATES : Union[StateSet, ProcessorState] =\
STATE_SET : Union[StateSet, ProcessorStateSet] =\
{
'cli': {}, #type:ignore[typeddict-item]
'ui': {} #type:ignore[typeddict-item]
'cli': {}, #type:ignore[assignment]
'ui': {} #type:ignore[assignment]
}
def get_state() -> Union[State, ProcessorState]:
app_context = detect_app_context()
return STATES.get(app_context) #type:ignore
return STATE_SET.get(app_context)
def init_item(key : Union[StateKey, ProcessorStateKey], value : Any) -> None:
STATES['cli'][key] = value #type:ignore
STATES['ui'][key] = value #type:ignore
STATE_SET['cli'][key] = value #type:ignore[literal-required]
STATE_SET['ui'][key] = value #type:ignore[literal-required]
def get_item(key : Union[StateKey, ProcessorStateKey]) -> Any:
return get_state().get(key) #type:ignore
return get_state().get(key) #type:ignore[literal-required]
def set_item(key : Union[StateKey, ProcessorStateKey], value : Any) -> None:
app_context = detect_app_context()
STATES[app_context][key] = value #type:ignore
STATE_SET[app_context][key] = value #type:ignore[literal-required]
def sync_item(key : Union[StateKey, ProcessorStateKey]) -> None:
STATES['cli'][key] = STATES.get('ui').get(key) #type:ignore
STATE_SET['cli'][key] = STATE_SET.get('ui').get(key) #type:ignore[literal-required]
def clear_item(key : Union[StateKey, ProcessorStateKey]) -> None:

2
facefusion/statistics.py
View File

@@ -4,7 +4,7 @@ import numpy
from facefusion import logger, state_manager
from facefusion.face_store import get_face_store
from facefusion.typing import FaceSet
from facefusion.types import FaceSet
def create_statistics(static_faces : FaceSet) -> Dict[str, Any]:

26
facefusion/temp_helper.py
View File

@@ -2,12 +2,12 @@ import os
from typing import List
from facefusion import state_manager
from facefusion.filesystem import create_directory, move_file, remove_directory, resolve_file_pattern
from facefusion.filesystem import create_directory, get_file_extension, get_file_name, move_file, remove_directory, resolve_file_pattern
def get_temp_file_path(file_path : str) -> str:
_, temp_file_extension = os.path.splitext(os.path.basename(file_path))
temp_directory_path = get_temp_directory_path(file_path)
temp_file_extension = get_file_extension(file_path)
return os.path.join(temp_directory_path, 'temp' + temp_file_extension)
@@ -16,8 +16,18 @@ def move_temp_file(file_path : str, move_path : str) -> bool:
return move_file(temp_file_path, move_path)
def resolve_temp_frame_paths(target_path : str) -> List[str]:
temp_frames_pattern = get_temp_frames_pattern(target_path, '*')
return resolve_file_pattern(temp_frames_pattern)
def get_temp_frames_pattern(target_path : str, temp_frame_prefix : str) -> str:
temp_directory_path = get_temp_directory_path(target_path)
return os.path.join(temp_directory_path, temp_frame_prefix + '.' + state_manager.get_item('temp_frame_format'))
def get_temp_directory_path(file_path : str) -> str:
temp_file_name, _ = os.path.splitext(os.path.basename(file_path))
temp_file_name = get_file_name(file_path)
return os.path.join(state_manager.get_item('temp_path'), 'facefusion', temp_file_name)
@@ -31,13 +41,3 @@ def clear_temp_directory(file_path : str) -> bool:
temp_directory_path = get_temp_directory_path(file_path)
return remove_directory(temp_directory_path)
return True
def get_temp_frame_paths(target_path : str) -> List[str]:
temp_frames_pattern = get_temp_frames_pattern(target_path, '*')
return resolve_file_pattern(temp_frames_pattern)
def get_temp_frames_pattern(target_path : str, temp_frame_prefix : str) -> str:
temp_directory_path = get_temp_directory_path(target_path)
return os.path.join(temp_directory_path, temp_frame_prefix + '.' + state_manager.get_item('temp_frame_format'))

160
facefusion/typing.py → facefusion/types.py
View File

@@ -1,20 +1,20 @@
from collections import namedtuple
from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, TypedDict
from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, TypeAlias, TypedDict
import numpy
from numpy.typing import NDArray
from onnxruntime import InferenceSession
Scale = float
Score = float
Angle = int
Scale : TypeAlias = float
Score : TypeAlias = float
Angle : TypeAlias = int
Detection = NDArray[Any]
Prediction = NDArray[Any]
Detection : TypeAlias = NDArray[Any]
Prediction : TypeAlias = NDArray[Any]
BoundingBox = NDArray[Any]
FaceLandmark5 = NDArray[Any]
FaceLandmark68 = NDArray[Any]
BoundingBox : TypeAlias = NDArray[Any]
FaceLandmark5 : TypeAlias = NDArray[Any]
FaceLandmark68 : TypeAlias = NDArray[Any]
FaceLandmarkSet = TypedDict('FaceLandmarkSet',
{
'5' : FaceLandmark5, #type:ignore[valid-type]
@@ -27,9 +27,9 @@ FaceScoreSet = TypedDict('FaceScoreSet',
'detector' : Score,
'landmarker' : Score
})
Embedding = NDArray[numpy.float64]
Embedding : TypeAlias = NDArray[numpy.float64]
Gender = Literal['female', 'male']
Age = range
Age : TypeAlias = range
Race = Literal['white', 'black', 'latino', 'asian', 'indian', 'arabic']
Face = namedtuple('Face',
[
@@ -43,34 +43,34 @@ Face = namedtuple('Face',
'age',
'race'
])
FaceSet = Dict[str, List[Face]]
FaceSet : TypeAlias = Dict[str, List[Face]]
FaceStore = TypedDict('FaceStore',
{
'static_faces' : FaceSet,
'reference_faces' : FaceSet
})
VisionFrame = NDArray[Any]
Mask = NDArray[Any]
Points = NDArray[Any]
Distance = NDArray[Any]
Matrix = NDArray[Any]
Anchors = NDArray[Any]
Translation = NDArray[Any]
VisionFrame : TypeAlias = NDArray[Any]
Mask : TypeAlias = NDArray[Any]
Points : TypeAlias = NDArray[Any]
Distance : TypeAlias = NDArray[Any]
Matrix : TypeAlias = NDArray[Any]
Anchors : TypeAlias = NDArray[Any]
Translation : TypeAlias = NDArray[Any]
AudioBuffer = bytes
Audio = NDArray[Any]
AudioChunk = NDArray[Any]
AudioFrame = NDArray[Any]
Spectrogram = NDArray[Any]
Mel = NDArray[Any]
MelFilterBank = NDArray[Any]
AudioBuffer : TypeAlias = bytes
Audio : TypeAlias = NDArray[Any]
AudioChunk : TypeAlias = NDArray[Any]
AudioFrame : TypeAlias = NDArray[Any]
Spectrogram : TypeAlias = NDArray[Any]
Mel : TypeAlias = NDArray[Any]
MelFilterBank : TypeAlias = NDArray[Any]
Fps = float
Duration = float
Padding = Tuple[int, int, int, int]
Fps : TypeAlias = float
Duration : TypeAlias = float
Padding : TypeAlias = Tuple[int, int, int, int]
Orientation = Literal['landscape', 'portrait']
Resolution = Tuple[int, int]
Resolution : TypeAlias = Tuple[int, int]
ProcessState = Literal['checking', 'processing', 'stopping', 'pending']
QueuePayload = TypedDict('QueuePayload',
@@ -78,46 +78,65 @@ QueuePayload = TypedDict('QueuePayload',
'frame_number' : int,
'frame_path' : str
})
Args = Dict[str, Any]
UpdateProgress = Callable[[int], None]
ProcessFrames = Callable[[List[str], List[QueuePayload], UpdateProgress], None]
ProcessStep = Callable[[str, int, Args], bool]
Args : TypeAlias = Dict[str, Any]
UpdateProgress : TypeAlias = Callable[[int], None]
ProcessFrames : TypeAlias = Callable[[List[str], List[QueuePayload], UpdateProgress], None]
ProcessStep : TypeAlias = Callable[[str, int, Args], bool]
Content = Dict[str, Any]
Content : TypeAlias = Dict[str, Any]
WarpTemplate = Literal['arcface_112_v1', 'arcface_112_v2', 'arcface_128_v2', 'dfl_whole_face', 'ffhq_512', 'mtcnn_512', 'styleganex_384']
WarpTemplateSet = Dict[WarpTemplate, NDArray[Any]]
Commands : TypeAlias = List[str]
WarpTemplate = Literal['arcface_112_v1', 'arcface_112_v2', 'arcface_128', 'dfl_whole_face', 'ffhq_512', 'mtcnn_512', 'styleganex_384']
WarpTemplateSet : TypeAlias = Dict[WarpTemplate, NDArray[Any]]
ProcessMode = Literal['output', 'preview', 'stream']
ErrorCode = Literal[0, 1, 2, 3, 4]
LogLevel = Literal['error', 'warn', 'info', 'debug']
LogLevelSet = Dict[LogLevel, int]
LogLevelSet : TypeAlias = Dict[LogLevel, int]
TableHeaders = List[str]
TableContents = List[List[Any]]
FaceDetectorModel = Literal['many', 'retinaface', 'scrfd', 'yoloface']
FaceDetectorModel = Literal['many', 'retinaface', 'scrfd', 'yolo_face']
FaceLandmarkerModel = Literal['many', '2dfan4', 'peppa_wutz']
FaceDetectorSet = Dict[FaceDetectorModel, List[str]]
FaceDetectorSet : TypeAlias = Dict[FaceDetectorModel, List[str]]
FaceSelectorMode = Literal['many', 'one', 'reference']
FaceSelectorOrder = Literal['left-right', 'right-left', 'top-bottom', 'bottom-top', 'small-large', 'large-small', 'best-worst', 'worst-best']
FaceOccluderModel = Literal['xseg_1', 'xseg_2']
FaceOccluderModel = Literal['xseg_1', 'xseg_2', 'xseg_3']
FaceParserModel = Literal['bisenet_resnet_18', 'bisenet_resnet_34']
FaceMaskType = Literal['box', 'occlusion', 'region']
FaceMaskRegion = Literal['skin', 'left-eyebrow', 'right-eyebrow', 'left-eye', 'right-eye', 'glasses', 'nose', 'mouth', 'upper-lip', 'lower-lip']
FaceMaskRegionSet = Dict[FaceMaskRegion, int]
TempFrameFormat = Literal['bmp', 'jpg', 'png']
OutputAudioEncoder = Literal['aac', 'libmp3lame', 'libopus', 'libvorbis']
OutputVideoEncoder = Literal['libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc', 'h264_amf', 'hevc_amf','h264_qsv', 'hevc_qsv', 'h264_videotoolbox', 'hevc_videotoolbox']
OutputVideoPreset = Literal['ultrafast', 'superfast', 'veryfast', 'faster', 'fast', 'medium', 'slow', 'slower', 'veryslow']
FaceMaskRegionSet : TypeAlias = Dict[FaceMaskRegion, int]
ModelOptions = Dict[str, Any]
ModelSet = Dict[str, ModelOptions]
ModelInitializer = NDArray[Any]
AudioFormat = Literal['flac', 'm4a', 'mp3', 'ogg', 'opus', 'wav']
ImageFormat = Literal['bmp', 'jpeg', 'png', 'tiff', 'webp']
VideoFormat = Literal['avi', 'm4v', 'mkv', 'mov', 'mp4', 'webm']
TempFrameFormat = Literal['bmp', 'jpeg', 'png', 'tiff']
AudioTypeSet : TypeAlias = Dict[AudioFormat, str]
ImageTypeSet : TypeAlias = Dict[ImageFormat, str]
VideoTypeSet : TypeAlias = Dict[VideoFormat, str]
AudioEncoder = Literal['flac', 'aac', 'libmp3lame', 'libopus', 'libvorbis', 'pcm_s16le', 'pcm_s32le']
VideoEncoder = Literal['libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc', 'h264_amf', 'hevc_amf', 'h264_qsv', 'hevc_qsv', 'h264_videotoolbox', 'hevc_videotoolbox', 'rawvideo']
EncoderSet = TypedDict('EncoderSet',
{
'audio' : List[AudioEncoder],
'video' : List[VideoEncoder]
})
VideoPreset = Literal['ultrafast', 'superfast', 'veryfast', 'faster', 'fast', 'medium', 'slow', 'slower', 'veryslow']
WebcamMode = Literal['inline', 'udp', 'v4l2']
StreamMode = Literal['udp', 'v4l2']
ModelOptions : TypeAlias = Dict[str, Any]
ModelSet : TypeAlias = Dict[str, ModelOptions]
ModelInitializer : TypeAlias = NDArray[Any]
ExecutionProvider = Literal['cpu', 'coreml', 'cuda', 'directml', 'openvino', 'rocm', 'tensorrt']
ExecutionProviderValue = Literal['CPUExecutionProvider', 'CoreMLExecutionProvider', 'CUDAExecutionProvider', 'DmlExecutionProvider', 'OpenVINOExecutionProvider', 'ROCMExecutionProvider', 'TensorrtExecutionProvider']
ExecutionProviderSet = Dict[ExecutionProvider, ExecutionProviderValue]
ExecutionProviderSet : TypeAlias = Dict[ExecutionProvider, ExecutionProviderValue]
InferenceSessionProvider : TypeAlias = Any
ValueAndUnit = TypedDict('ValueAndUnit',
{
'value' : int,
@@ -161,31 +180,23 @@ ExecutionDevice = TypedDict('ExecutionDevice',
DownloadProvider = Literal['github', 'huggingface']
DownloadProviderValue = TypedDict('DownloadProviderValue',
{
'url' : str,
'urls' : List[str],
'path' : str
})
DownloadProviderSet = Dict[DownloadProvider, DownloadProviderValue]
DownloadProviderSet : TypeAlias = Dict[DownloadProvider, DownloadProviderValue]
DownloadScope = Literal['lite', 'full']
Download = TypedDict('Download',
{
'url' : str,
'path' : str
})
DownloadSet = Dict[str, Download]
DownloadSet : TypeAlias = Dict[str, Download]
VideoMemoryStrategy = Literal['strict', 'moderate', 'tolerant']
File = TypedDict('File',
{
'name' : str,
'extension' : str,
'path': str
})
AppContext = Literal['cli', 'ui']
InferencePool = Dict[str, InferenceSession]
InferencePoolSet = Dict[AppContext, Dict[str, InferencePool]]
InferencePool : TypeAlias = Dict[str, InferenceSession]
InferencePoolSet : TypeAlias = Dict[AppContext, Dict[str, InferencePool]]
UiWorkflow = Literal['instant_runner', 'job_runner', 'job_manager']
@@ -194,7 +205,7 @@ JobStore = TypedDict('JobStore',
'job_keys' : List[str],
'step_keys' : List[str]
})
JobOutputSet = Dict[str, List[str]]
JobOutputSet : TypeAlias = Dict[str, List[str]]
JobStatus = Literal['drafted', 'queued', 'completed', 'failed']
JobStepStatus = Literal['drafted', 'queued', 'started', 'completed', 'failed']
JobStep = TypedDict('JobStep',
@@ -209,9 +220,9 @@ Job = TypedDict('Job',
'date_updated' : Optional[str],
'steps' : List[JobStep]
})
JobSet = Dict[str, Job]
JobSet : TypeAlias = Dict[str, Job]
ApplyStateItem = Callable[[Any, Any], None]
ApplyStateItem : TypeAlias = Callable[[Any, Any], None]
StateKey = Literal\
[
'command',
@@ -252,12 +263,13 @@ StateKey = Literal\
'output_image_quality',
'output_image_resolution',
'output_audio_encoder',
'output_audio_quality',
'output_audio_volume',
'output_video_encoder',
'output_video_preset',
'output_video_quality',
'output_video_resolution',
'output_video_fps',
'skip_audio',
'processors',
'open_browser',
'ui_layouts',
@@ -271,6 +283,7 @@ StateKey = Literal\
'video_memory_strategy',
'system_memory_limit',
'log_level',
'halt_on_error',
'job_id',
'job_status',
'step_index'
@@ -314,13 +327,14 @@ State = TypedDict('State',
'keep_temp' : bool,
'output_image_quality' : int,
'output_image_resolution' : str,
'output_audio_encoder' : OutputAudioEncoder,
'output_video_encoder' : OutputVideoEncoder,
'output_video_preset' : OutputVideoPreset,
'output_audio_encoder' : AudioEncoder,
'output_audio_quality' : int,
'output_audio_volume' : int,
'output_video_encoder' : VideoEncoder,
'output_video_preset' : VideoPreset,
'output_video_quality' : int,
'output_video_resolution' : str,
'output_video_fps' : float,
'skip_audio' : bool,
'processors' : List[str],
'open_browser' : bool,
'ui_layouts' : List[str],
@@ -334,8 +348,10 @@ State = TypedDict('State',
'video_memory_strategy' : VideoMemoryStrategy,
'system_memory_limit' : int,
'log_level' : LogLevel,
'halt_on_error' : bool,
'job_id' : str,
'job_status' : JobStatus,
'step_index' : int
})
StateSet = Dict[AppContext, State]
StateSet : TypeAlias = Dict[AppContext, State]

12
facefusion/uis/assets/overrides.css
View File

@@ -1,9 +1,13 @@
:root:root:root:root .gradio-container
{
max-width: 110em;
overflow: unset;
}
:root:root:root:root main
{
max-width: 110em;
}
:root:root:root:root input[type="number"]
{
appearance: textfield;
@@ -65,6 +69,12 @@
min-height: unset;
}
:root:root:root:root .box-face-selector .empty,
:root:root:root:root .box-face-selector .gallery-container
{
min-height: 7.375rem;
}
:root:root:root:root .tab-wrapper
{
padding: 0 0.625rem;

6
facefusion/uis/choices.py
View File

@@ -1,11 +1,9 @@
from typing import List
from facefusion.uis.typing import JobManagerAction, JobRunnerAction, WebcamMode
from facefusion.uis.types import JobManagerAction, JobRunnerAction
job_manager_actions : List[JobManagerAction] = [ 'job-create', 'job-submit', 'job-delete', 'job-add-step', 'job-remix-step', 'job-insert-step', 'job-remove-step' ]
job_runner_actions : List[JobRunnerAction] = [ 'job-run', 'job-run-all', 'job-retry', 'job-retry-all' ]
common_options : List[str] = [ 'keep-temp', 'skip-audio' ]
common_options : List[str] = [ 'keep-temp' ]
webcam_modes : List[WebcamMode] = [ 'inline', 'udp', 'v4l2' ]
webcam_resolutions : List[str] = [ '320x240', '640x480', '800x600', '1024x768', '1280x720', '1280x960', '1920x1080', '2560x1440', '3840x2160' ]

2
facefusion/uis/components/age_modifier_options.py
View File

@@ -6,7 +6,7 @@ from facefusion import state_manager, wording
from facefusion.common_helper import calc_float_step
from facefusion.processors import choices as processors_choices
from facefusion.processors.core import load_processor_module
from facefusion.processors.typing import AgeModifierModel
from facefusion.processors.types import AgeModifierModel
from facefusion.uis.core import get_ui_component, register_ui_component
AGE_MODIFIER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None

10
facefusion/uis/components/benchmark.py
View File

@@ -9,14 +9,13 @@ import gradio
from facefusion import state_manager, wording
from facefusion.core import conditional_process
from facefusion.filesystem import is_video
from facefusion.filesystem import get_file_extension, is_video
from facefusion.memory import limit_system_memory
from facefusion.uis.core import get_ui_component
from facefusion.vision import count_video_frame_total, detect_video_fps, detect_video_resolution, pack_resolution
BENCHMARK_BENCHMARKS_DATAFRAME : Optional[gradio.Dataframe] = None
BENCHMARK_START_BUTTON : Optional[gradio.Button] = None
BENCHMARK_CLEAR_BUTTON : Optional[gradio.Button] = None
BENCHMARKS : Dict[str, str] =\
{
'240p': '.assets/examples/target-240p.mp4',
@@ -32,7 +31,6 @@ BENCHMARKS : Dict[str, str] =\
def render() -> None:
global BENCHMARK_BENCHMARKS_DATAFRAME
global BENCHMARK_START_BUTTON
global BENCHMARK_CLEAR_BUTTON
BENCHMARK_BENCHMARKS_DATAFRAME = gradio.Dataframe(
headers =
@@ -72,8 +70,8 @@ def listen() -> None:
def suggest_output_path(target_path : str) -> Optional[str]:
if is_video(target_path):
_, target_extension = os.path.splitext(target_path)
return os.path.join(tempfile.gettempdir(), hashlib.sha1().hexdigest()[:8] + target_extension)
target_file_extension = get_file_extension(target_path)
return os.path.join(tempfile.gettempdir(), hashlib.sha1().hexdigest()[:8] + target_file_extension)
return None
@@ -81,8 +79,8 @@ def start(benchmark_runs : List[str], benchmark_cycles : int) -> Generator[List[
state_manager.init_item('source_paths', [ '.assets/examples/source.jpg', '.assets/examples/source.mp3' ])
state_manager.init_item('face_landmarker_score', 0)
state_manager.init_item('temp_frame_format', 'bmp')
state_manager.init_item('output_audio_volume', 0)
state_manager.init_item('output_video_preset', 'ultrafast')
state_manager.init_item('skip_audio', True)
state_manager.sync_item('execution_providers')
state_manager.sync_item('execution_thread_count')
state_manager.sync_item('execution_queue_count')

4
facefusion/uis/components/common_options.py
View File

@@ -15,8 +15,6 @@ def render() -> None:
if state_manager.get_item('keep_temp'):
common_options.append('keep-temp')
if state_manager.get_item('skip_audio'):
common_options.append('skip-audio')
COMMON_OPTIONS_CHECKBOX_GROUP = gradio.Checkboxgroup(
label = wording.get('uis.common_options_checkbox_group'),
@@ -31,6 +29,4 @@ def listen() -> None:
def update(common_options : List[str]) -> None:
keep_temp = 'keep-temp' in common_options
skip_audio = 'skip-audio' in common_options
state_manager.set_item('keep_temp', keep_temp)
state_manager.set_item('skip_audio', skip_audio)

9
facefusion/uis/components/deep_swapper_options.py
View File

@@ -6,8 +6,7 @@ from facefusion import state_manager, wording
from facefusion.common_helper import calc_int_step
from facefusion.processors import choices as processors_choices
from facefusion.processors.core import load_processor_module
from facefusion.processors.modules.deep_swapper import has_morph_input
from facefusion.processors.typing import DeepSwapperModel
from facefusion.processors.types import DeepSwapperModel
from facefusion.uis.core import get_ui_component, register_ui_component
DEEP_SWAPPER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None
@@ -31,7 +30,7 @@ def render() -> None:
step = calc_int_step(processors_choices.deep_swapper_morph_range),
minimum = processors_choices.deep_swapper_morph_range[0],
maximum = processors_choices.deep_swapper_morph_range[-1],
visible = has_deep_swapper and has_morph_input()
visible = has_deep_swapper and load_processor_module('deep_swapper').get_inference_pool() and load_processor_module('deep_swapper').has_morph_input()
)
register_ui_component('deep_swapper_model_dropdown', DEEP_SWAPPER_MODEL_DROPDOWN)
register_ui_component('deep_swapper_morph_slider', DEEP_SWAPPER_MORPH_SLIDER)
@@ -48,7 +47,7 @@ def listen() -> None:
def remote_update(processors : List[str]) -> Tuple[gradio.Dropdown, gradio.Slider]:
has_deep_swapper = 'deep_swapper' in processors
return gradio.Dropdown(visible = has_deep_swapper), gradio.Slider(visible = has_deep_swapper and has_morph_input())
return gradio.Dropdown(visible = has_deep_swapper), gradio.Slider(visible = has_deep_swapper and load_processor_module('deep_swapper').get_inference_pool() and load_processor_module('deep_swapper').has_morph_input())
def update_deep_swapper_model(deep_swapper_model : DeepSwapperModel) -> Tuple[gradio.Dropdown, gradio.Slider]:
@@ -57,7 +56,7 @@ def update_deep_swapper_model(deep_swapper_model : DeepSwapperModel) -> Tuple[gr
state_manager.set_item('deep_swapper_model', deep_swapper_model)
if deep_swapper_module.pre_check():
return gradio.Dropdown(value = state_manager.get_item('deep_swapper_model')), gradio.Slider(visible = has_morph_input())
return gradio.Dropdown(value = state_manager.get_item('deep_swapper_model')), gradio.Slider(visible = deep_swapper_module.has_morph_input())
return gradio.Dropdown(), gradio.Slider()

6
facefusion/uis/components/download.py
View File

@@ -4,9 +4,9 @@ import gradio
import facefusion.choices
from facefusion import content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, state_manager, voice_extractor, wording
from facefusion.filesystem import list_directory
from facefusion.filesystem import get_file_name, resolve_file_paths
from facefusion.processors.core import get_processors_modules
from facefusion.typing import DownloadProvider
from facefusion.types import DownloadProvider
DOWNLOAD_PROVIDERS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
@@ -36,7 +36,7 @@ def update_download_providers(download_providers : List[DownloadProvider]) -> gr
face_masker,
voice_extractor
]
available_processors = [ file.get('name') for file in list_directory('facefusion/processors/modules') ]
available_processors = [ get_file_name(file_path) for file_path in resolve_file_paths('facefusion/processors/modules') ]
processor_modules = get_processors_modules(available_processors)
for module in common_modules + processor_modules:

6
facefusion/uis/components/execution.py
View File

@@ -4,9 +4,9 @@ import gradio
from facefusion import content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, state_manager, voice_extractor, wording
from facefusion.execution import get_available_execution_providers
from facefusion.filesystem import list_directory
from facefusion.filesystem import get_file_name, resolve_file_paths
from facefusion.processors.core import get_processors_modules
from facefusion.typing import ExecutionProvider
from facefusion.types import ExecutionProvider
EXECUTION_PROVIDERS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
@@ -36,7 +36,7 @@ def update_execution_providers(execution_providers : List[ExecutionProvider]) ->
face_recognizer,
voice_extractor
]
available_processors = [ file.get('name') for file in list_directory('facefusion/processors/modules') ]
available_processors = [ get_file_name(file_path) for file_path in resolve_file_paths('facefusion/processors/modules') ]
processor_modules = get_processors_modules(available_processors)
for module in common_modules + processor_modules:

2
facefusion/uis/components/expression_restorer_options.py
View File

@@ -6,7 +6,7 @@ from facefusion import state_manager, wording
from facefusion.common_helper import calc_float_step
from facefusion.processors import choices as processors_choices
from facefusion.processors.core import load_processor_module
from facefusion.processors.typing import ExpressionRestorerModel
from facefusion.processors.types import ExpressionRestorerModel
from facefusion.uis.core import get_ui_component, register_ui_component
EXPRESSION_RESTORER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None

2
facefusion/uis/components/face_debugger_options.py
View File

@@ -4,7 +4,7 @@ import gradio
from facefusion import state_manager, wording
from facefusion.processors import choices as processors_choices
from facefusion.processors.typing import FaceDebuggerItem
from facefusion.processors.types import FaceDebuggerItem
from facefusion.uis.core import get_ui_component, register_ui_component
FACE_DEBUGGER_ITEMS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None

4
facefusion/uis/components/face_detector.py
View File

@@ -5,9 +5,9 @@ import gradio
import facefusion.choices
from facefusion import face_detector, state_manager, wording
from facefusion.common_helper import calc_float_step, get_last
from facefusion.typing import Angle, FaceDetectorModel, Score
from facefusion.types import Angle, FaceDetectorModel, Score
from facefusion.uis.core import register_ui_component
from facefusion.uis.typing import ComponentOptions
from facefusion.uis.types import ComponentOptions
FACE_DETECTOR_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None
FACE_DETECTOR_SIZE_DROPDOWN : Optional[gradio.Dropdown] = None

2
facefusion/uis/components/face_editor_options.py
View File

@@ -6,7 +6,7 @@ from facefusion import state_manager, wording
from facefusion.common_helper import calc_float_step
from facefusion.processors import choices as processors_choices
from facefusion.processors.core import load_processor_module
from facefusion.processors.typing import FaceEditorModel
from facefusion.processors.types import FaceEditorModel
from facefusion.uis.core import get_ui_component, register_ui_component
FACE_EDITOR_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None

9
facefusion/uis/components/face_enhancer_options.py
View File

@@ -6,8 +6,7 @@ from facefusion import state_manager, wording
from facefusion.common_helper import calc_float_step, calc_int_step
from facefusion.processors import choices as processors_choices
from facefusion.processors.core import load_processor_module
from facefusion.processors.modules.face_enhancer import has_weight_input
from facefusion.processors.typing import FaceEnhancerModel
from facefusion.processors.types import FaceEnhancerModel
from facefusion.uis.core import get_ui_component, register_ui_component
FACE_ENHANCER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None
@@ -41,7 +40,7 @@ def render() -> None:
step = calc_float_step(processors_choices.face_enhancer_weight_range),
minimum = processors_choices.face_enhancer_weight_range[0],
maximum = processors_choices.face_enhancer_weight_range[-1],
visible = has_face_enhancer and has_weight_input()
visible = has_face_enhancer and load_processor_module('face_enhancer').get_inference_pool() and load_processor_module('face_enhancer').has_weight_input()
)
register_ui_component('face_enhancer_model_dropdown', FACE_ENHANCER_MODEL_DROPDOWN)
register_ui_component('face_enhancer_blend_slider', FACE_ENHANCER_BLEND_SLIDER)
@@ -60,7 +59,7 @@ def listen() -> None:
def remote_update(processors : List[str]) -> Tuple[gradio.Dropdown, gradio.Slider, gradio.Slider]:
has_face_enhancer = 'face_enhancer' in processors
return gradio.Dropdown(visible = has_face_enhancer), gradio.Slider(visible = has_face_enhancer), gradio.Slider(visible = has_face_enhancer and has_weight_input())
return gradio.Dropdown(visible = has_face_enhancer), gradio.Slider(visible = has_face_enhancer), gradio.Slider(visible = has_face_enhancer and load_processor_module('face_enhancer').get_inference_pool() and load_processor_module('face_enhancer').has_weight_input())
def update_face_enhancer_model(face_enhancer_model : FaceEnhancerModel) -> Tuple[gradio.Dropdown, gradio.Slider]:
@@ -69,7 +68,7 @@ def update_face_enhancer_model(face_enhancer_model : FaceEnhancerModel) -> Tuple
state_manager.set_item('face_enhancer_model', face_enhancer_model)
if face_enhancer_module.pre_check():
return gradio.Dropdown(value = state_manager.get_item('face_enhancer_model')), gradio.Slider(visible = has_weight_input())
return gradio.Dropdown(value = state_manager.get_item('face_enhancer_model')), gradio.Slider(visible = face_enhancer_module.has_weight_input())
return gradio.Dropdown(), gradio.Slider()

2
facefusion/uis/components/face_landmarker.py
View File

@@ -5,7 +5,7 @@ import gradio
import facefusion.choices
from facefusion import face_landmarker, state_manager, wording
from facefusion.common_helper import calc_float_step
from facefusion.typing import FaceLandmarkerModel, Score
from facefusion.types import FaceLandmarkerModel, Score
from facefusion.uis.core import register_ui_component
FACE_LANDMARKER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None

2
facefusion/uis/components/face_masker.py
View File

@@ -5,7 +5,7 @@ import gradio
import facefusion.choices
from facefusion import face_masker, state_manager, wording
from facefusion.common_helper import calc_float_step, calc_int_step
from facefusion.typing import FaceMaskRegion, FaceMaskType, FaceOccluderModel, FaceParserModel
from facefusion.types import FaceMaskRegion, FaceMaskType, FaceOccluderModel, FaceParserModel
from facefusion.uis.core import register_ui_component
FACE_OCCLUDER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None

20
facefusion/uis/components/face_selector.py
View File

@@ -10,11 +10,11 @@ from facefusion.face_analyser import get_many_faces
from facefusion.face_selector import sort_and_filter_faces
from facefusion.face_store import clear_reference_faces, clear_static_faces
from facefusion.filesystem import is_image, is_video
from facefusion.typing import FaceSelectorMode, FaceSelectorOrder, Gender, Race, VisionFrame
from facefusion.types import FaceSelectorMode, FaceSelectorOrder, Gender, Race, VisionFrame
from facefusion.uis.core import get_ui_component, get_ui_components, register_ui_component
from facefusion.uis.typing import ComponentOptions
from facefusion.uis.types import ComponentOptions
from facefusion.uis.ui_helper import convert_str_none
from facefusion.vision import get_video_frame, normalize_frame_color, read_static_image
from facefusion.vision import normalize_frame_color, read_static_image, read_video_frame
FACE_SELECTOR_MODE_DROPDOWN : Optional[gradio.Dropdown] = None
FACE_SELECTOR_ORDER_DROPDOWN : Optional[gradio.Dropdown] = None
@@ -38,15 +38,16 @@ def render() -> None:
{
'label': wording.get('uis.reference_face_gallery'),
'object_fit': 'cover',
'columns': 8,
'columns': 7,
'allow_preview': False,
'elem_classes': 'box-face-selector',
'visible': 'reference' in state_manager.get_item('face_selector_mode')
}
if is_image(state_manager.get_item('target_path')):
reference_frame = read_static_image(state_manager.get_item('target_path'))
reference_face_gallery_options['value'] = extract_gallery_frames(reference_frame)
if is_video(state_manager.get_item('target_path')):
reference_frame = get_video_frame(state_manager.get_item('target_path'), state_manager.get_item('reference_frame_number'))
reference_frame = read_video_frame(state_manager.get_item('target_path'), state_manager.get_item('reference_frame_number'))
reference_face_gallery_options['value'] = extract_gallery_frames(reference_frame)
FACE_SELECTOR_MODE_DROPDOWN = gradio.Dropdown(
label = wording.get('uis.face_selector_mode_dropdown'),
@@ -112,7 +113,7 @@ def listen() -> None:
'target_image',
'target_video'
]):
for method in [ 'upload', 'change', 'clear' ]:
for method in [ 'change', 'clear' ]:
getattr(ui_component, method)(update_reference_face_position)
getattr(ui_component, method)(update_reference_position_gallery, outputs = REFERENCE_FACE_POSITION_GALLERY)
@@ -130,8 +131,9 @@ def listen() -> None:
preview_frame_slider = get_ui_component('preview_frame_slider')
if preview_frame_slider:
preview_frame_slider.release(update_reference_frame_number, inputs = preview_frame_slider)
preview_frame_slider.release(update_reference_position_gallery, outputs = REFERENCE_FACE_POSITION_GALLERY)
for method in [ 'change', 'release' ]:
getattr(preview_frame_slider, method)(update_reference_frame_number, inputs = preview_frame_slider, show_progress = 'hidden')
getattr(preview_frame_slider, method)(update_reference_position_gallery, outputs = REFERENCE_FACE_POSITION_GALLERY, show_progress = 'hidden')
def update_face_selector_mode(face_selector_mode : FaceSelectorMode) -> Tuple[gradio.Gallery, gradio.Slider]:
@@ -197,7 +199,7 @@ def update_reference_position_gallery() -> gradio.Gallery:
temp_vision_frame = read_static_image(state_manager.get_item('target_path'))
gallery_vision_frames = extract_gallery_frames(temp_vision_frame)
if is_video(state_manager.get_item('target_path')):
temp_vision_frame = get_video_frame(state_manager.get_item('target_path'), state_manager.get_item('reference_frame_number'))
temp_vision_frame = read_video_frame(state_manager.get_item('target_path'), state_manager.get_item('reference_frame_number'))
gallery_vision_frames = extract_gallery_frames(temp_vision_frame)
if gallery_vision_frames:
return gradio.Gallery(value = gallery_vision_frames)

2
facefusion/uis/components/face_swapper_options.py
View File

@@ -6,7 +6,7 @@ from facefusion import state_manager, wording
from facefusion.common_helper import get_first
from facefusion.processors import choices as processors_choices
from facefusion.processors.core import load_processor_module
from facefusion.processors.typing import FaceSwapperModel
from facefusion.processors.types import FaceSwapperModel
from facefusion.uis.core import get_ui_component, register_ui_component
FACE_SWAPPER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None

2
facefusion/uis/components/frame_colorizer_options.py
View File

@@ -6,7 +6,7 @@ from facefusion import state_manager, wording
from facefusion.common_helper import calc_int_step
from facefusion.processors import choices as processors_choices
from facefusion.processors.core import load_processor_module
from facefusion.processors.typing import FrameColorizerModel
from facefusion.processors.types import FrameColorizerModel
from facefusion.uis.core import get_ui_component, register_ui_component
FRAME_COLORIZER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None

2
facefusion/uis/components/frame_enhancer_options.py
View File

@@ -6,7 +6,7 @@ from facefusion import state_manager, wording
from facefusion.common_helper import calc_int_step
from facefusion.processors import choices as processors_choices
from facefusion.processors.core import load_processor_module
from facefusion.processors.typing import FrameEnhancerModel
from facefusion.processors.types import FrameEnhancerModel
from facefusion.uis.core import get_ui_component, register_ui_component
FRAME_ENHANCER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None

4
facefusion/uis/components/instant_runner.py
View File

@@ -9,7 +9,7 @@ from facefusion.core import process_step
from facefusion.filesystem import is_directory, is_image, is_video
from facefusion.jobs import job_helper, job_manager, job_runner, job_store
from facefusion.temp_helper import clear_temp_directory
from facefusion.typing import Args, UiWorkflow
from facefusion.types import Args, UiWorkflow
from facefusion.uis.core import get_ui_component
from facefusion.uis.ui_helper import suggest_output_path
@@ -92,7 +92,7 @@ def create_and_run_job(step_args : Args) -> bool:
job_id = job_helper.suggest_job_id('ui')
for key in job_store.get_job_keys():
state_manager.sync_item(key) #type:ignore
state_manager.sync_item(key) #type:ignore[arg-type]
return job_manager.create_job(job_id) and job_manager.add_step(job_id, step_args) and job_manager.submit_job(job_id) and job_runner.run_job(job_id, process_step)

2
facefusion/uis/components/job_list.py
View File

@@ -6,7 +6,7 @@ import facefusion.choices
from facefusion import state_manager, wording
from facefusion.common_helper import get_first
from facefusion.jobs import job_list, job_manager
from facefusion.typing import JobStatus
from facefusion.types import JobStatus
from facefusion.uis.core import get_ui_component
JOB_LIST_JOBS_DATAFRAME : Optional[gradio.Dataframe] = None

2
facefusion/uis/components/job_list_options.py
View File

@@ -6,7 +6,7 @@ import facefusion.choices
from facefusion import state_manager, wording
from facefusion.common_helper import get_first
from facefusion.jobs import job_manager
from facefusion.typing import JobStatus
from facefusion.types import JobStatus
from facefusion.uis.core import register_ui_component
JOB_LIST_JOB_STATUS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None

14
facefusion/uis/components/job_manager.py
View File

@@ -7,10 +7,10 @@ from facefusion.args import collect_step_args
from facefusion.common_helper import get_first, get_last
from facefusion.filesystem import is_directory
from facefusion.jobs import job_manager
from facefusion.typing import UiWorkflow
from facefusion.types import UiWorkflow
from facefusion.uis import choices as uis_choices
from facefusion.uis.core import get_ui_component
from facefusion.uis.typing import JobManagerAction
from facefusion.uis.types import JobManagerAction
from facefusion.uis.ui_helper import convert_int_none, convert_str_none, suggest_output_path
JOB_MANAGER_WRAPPER : Optional[gradio.Column] = None
@@ -89,6 +89,7 @@ def apply(job_action : JobManagerAction, created_job_id : str, selected_job_id :
if is_directory(step_args.get('output_path')):
step_args['output_path'] = suggest_output_path(step_args.get('output_path'), state_manager.get_item('target_path'))
if job_action == 'job-create':
if created_job_id and job_manager.create_job(created_job_id):
updated_job_ids = job_manager.find_job_ids('drafted') or [ 'none' ]
@@ -97,6 +98,7 @@ def apply(job_action : JobManagerAction, created_job_id : str, selected_job_id :
return gradio.Dropdown(value = 'job-add-step'), gradio.Textbox(visible = False), gradio.Dropdown(value = created_job_id, choices = updated_job_ids, visible = True), gradio.Dropdown()
else:
logger.error(wording.get('job_not_created').format(job_id = created_job_id), __name__)
if job_action == 'job-submit':
if selected_job_id and job_manager.submit_job(selected_job_id):
updated_job_ids = job_manager.find_job_ids('drafted') or [ 'none' ]
@@ -105,6 +107,7 @@ def apply(job_action : JobManagerAction, created_job_id : str, selected_job_id :
return gradio.Dropdown(), gradio.Textbox(), gradio.Dropdown(value = get_last(updated_job_ids), choices = updated_job_ids, visible = True), gradio.Dropdown()
else:
logger.error(wording.get('job_not_submitted').format(job_id = selected_job_id), __name__)
if job_action == 'job-delete':
if selected_job_id and job_manager.delete_job(selected_job_id):
updated_job_ids = job_manager.find_job_ids('drafted') + job_manager.find_job_ids('queued') + job_manager.find_job_ids('failed') + job_manager.find_job_ids('completed') or [ 'none' ]
@@ -113,6 +116,7 @@ def apply(job_action : JobManagerAction, created_job_id : str, selected_job_id :
return gradio.Dropdown(), gradio.Textbox(), gradio.Dropdown(value = get_last(updated_job_ids), choices = updated_job_ids, visible = True), gradio.Dropdown()
else:
logger.error(wording.get('job_not_deleted').format(job_id = selected_job_id), __name__)
if job_action == 'job-add-step':
if selected_job_id and job_manager.add_step(selected_job_id, step_args):
state_manager.set_item('output_path', output_path)
@@ -121,6 +125,7 @@ def apply(job_action : JobManagerAction, created_job_id : str, selected_job_id :
else:
state_manager.set_item('output_path', output_path)
logger.error(wording.get('job_step_not_added').format(job_id = selected_job_id), __name__)
if job_action == 'job-remix-step':
if selected_job_id and job_manager.has_step(selected_job_id, selected_step_index) and job_manager.remix_step(selected_job_id, selected_step_index, step_args):
updated_step_choices = get_step_choices(selected_job_id) or [ 'none' ] #type:ignore[list-item]
@@ -131,6 +136,7 @@ def apply(job_action : JobManagerAction, created_job_id : str, selected_job_id :
else:
state_manager.set_item('output_path', output_path)
logger.error(wording.get('job_remix_step_not_added').format(job_id = selected_job_id, step_index = selected_step_index), __name__)
if job_action == 'job-insert-step':
if selected_job_id and job_manager.has_step(selected_job_id, selected_step_index) and job_manager.insert_step(selected_job_id, selected_step_index, step_args):
updated_step_choices = get_step_choices(selected_job_id) or [ 'none' ] #type:ignore[list-item]
@@ -141,6 +147,7 @@ def apply(job_action : JobManagerAction, created_job_id : str, selected_job_id :
else:
state_manager.set_item('output_path', output_path)
logger.error(wording.get('job_step_not_inserted').format(job_id = selected_job_id, step_index = selected_step_index), __name__)
if job_action == 'job-remove-step':
if selected_job_id and job_manager.has_step(selected_job_id, selected_step_index) and job_manager.remove_step(selected_job_id, selected_step_index):
updated_step_choices = get_step_choices(selected_job_id) or [ 'none' ] #type:ignore[list-item]
@@ -160,16 +167,19 @@ def get_step_choices(job_id : str) -> List[int]:
def update(job_action : JobManagerAction, selected_job_id : str) -> Tuple[gradio.Textbox, gradio.Dropdown, gradio.Dropdown]:
if job_action == 'job-create':
return gradio.Textbox(value = None, visible = True), gradio.Dropdown(visible = False), gradio.Dropdown(visible = False)
if job_action == 'job-delete':
updated_job_ids = job_manager.find_job_ids('drafted') + job_manager.find_job_ids('queued') + job_manager.find_job_ids('failed') + job_manager.find_job_ids('completed') or [ 'none' ]
updated_job_id = selected_job_id if selected_job_id in updated_job_ids else get_last(updated_job_ids)
return gradio.Textbox(visible = False), gradio.Dropdown(value = updated_job_id, choices = updated_job_ids, visible = True), gradio.Dropdown(visible = False)
if job_action in [ 'job-submit', 'job-add-step' ]:
updated_job_ids = job_manager.find_job_ids('drafted') or [ 'none' ]
updated_job_id = selected_job_id if selected_job_id in updated_job_ids else get_last(updated_job_ids)
return gradio.Textbox(visible = False), gradio.Dropdown(value = updated_job_id, choices = updated_job_ids, visible = True), gradio.Dropdown(visible = False)
if job_action in [ 'job-remix-step', 'job-insert-step', 'job-remove-step' ]:
updated_job_ids = job_manager.find_job_ids('drafted') or [ 'none' ]
updated_job_id = selected_job_id if selected_job_id in updated_job_ids else get_last(updated_job_ids)

16
facefusion/uis/components/job_runner.py
View File

@@ -7,10 +7,10 @@ from facefusion import logger, process_manager, state_manager, wording
from facefusion.common_helper import get_first, get_last
from facefusion.core import process_step
from facefusion.jobs import job_manager, job_runner, job_store
from facefusion.typing import UiWorkflow
from facefusion.types import UiWorkflow
from facefusion.uis import choices as uis_choices
from facefusion.uis.core import get_ui_component
from facefusion.uis.typing import JobRunnerAction
from facefusion.uis.types import JobRunnerAction
from facefusion.uis.ui_helper import convert_str_none
JOB_RUNNER_WRAPPER : Optional[gradio.Column] = None
@@ -84,7 +84,7 @@ def run(job_action : JobRunnerAction, job_id : str) -> Tuple[gradio.Button, grad
job_id = convert_str_none(job_id)
for key in job_store.get_job_keys():
state_manager.sync_item(key) #type:ignore
state_manager.sync_item(key) #type:ignore[arg-type]
if job_action == 'job-run':
logger.info(wording.get('running_job').format(job_id = job_id), __name__)
@@ -95,12 +95,15 @@ def run(job_action : JobRunnerAction, job_id : str) -> Tuple[gradio.Button, grad
updated_job_ids = job_manager.find_job_ids('queued') or [ 'none' ]
return gradio.Button(visible = True), gradio.Button(visible = False), gradio.Dropdown(value = get_last(updated_job_ids), choices = updated_job_ids)
if job_action == 'job-run-all':
logger.info(wording.get('running_jobs'), __name__)
if job_runner.run_jobs(process_step):
halt_on_error = False
if job_runner.run_jobs(process_step, halt_on_error):
logger.info(wording.get('processing_jobs_succeed'), __name__)
else:
logger.info(wording.get('processing_jobs_failed'), __name__)
if job_action == 'job-retry':
logger.info(wording.get('retrying_job').format(job_id = job_id), __name__)
if job_id and job_runner.retry_job(job_id, process_step):
@@ -110,9 +113,11 @@ def run(job_action : JobRunnerAction, job_id : str) -> Tuple[gradio.Button, grad
updated_job_ids = job_manager.find_job_ids('failed') or [ 'none' ]
return gradio.Button(visible = True), gradio.Button(visible = False), gradio.Dropdown(value = get_last(updated_job_ids), choices = updated_job_ids)
if job_action == 'job-retry-all':
logger.info(wording.get('retrying_jobs'), __name__)
if job_runner.retry_jobs(process_step):
halt_on_error = False
if job_runner.retry_jobs(process_step, halt_on_error):
logger.info(wording.get('processing_jobs_succeed'), __name__)
else:
logger.info(wording.get('processing_jobs_failed'), __name__)
@@ -129,6 +134,7 @@ def update_job_action(job_action : JobRunnerAction) -> gradio.Dropdown:
updated_job_ids = job_manager.find_job_ids('queued') or [ 'none' ]
return gradio.Dropdown(value = get_last(updated_job_ids), choices = updated_job_ids, visible = True)
if job_action == 'job-retry':
updated_job_ids = job_manager.find_job_ids('failed') or [ 'none' ]

2
facefusion/uis/components/lip_syncer_options.py
View File

@@ -5,7 +5,7 @@ import gradio
from facefusion import state_manager, wording
from facefusion.processors import choices as processors_choices
from facefusion.processors.core import load_processor_module
from facefusion.processors.typing import LipSyncerModel
from facefusion.processors.types import LipSyncerModel
from facefusion.uis.core import get_ui_component, register_ui_component
LIP_SYNCER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None

2
facefusion/uis/components/memory.py
View File

@@ -5,7 +5,7 @@ import gradio
import facefusion.choices
from facefusion import state_manager, wording
from facefusion.common_helper import calc_int_step
from facefusion.typing import VideoMemoryStrategy
from facefusion.types import VideoMemoryStrategy
VIDEO_MEMORY_STRATEGY_DROPDOWN : Optional[gradio.Dropdown] = None
SYSTEM_MEMORY_LIMIT_SLIDER : Optional[gradio.Slider] = None

56
facefusion/uis/components/output_options.py
View File

@@ -5,14 +5,17 @@ import gradio
import facefusion.choices
from facefusion import state_manager, wording
from facefusion.common_helper import calc_int_step
from facefusion.ffmpeg import get_available_encoder_set
from facefusion.filesystem import is_image, is_video
from facefusion.typing import Fps, OutputAudioEncoder, OutputVideoEncoder, OutputVideoPreset
from facefusion.types import AudioEncoder, Fps, VideoEncoder, VideoPreset
from facefusion.uis.core import get_ui_components, register_ui_component
from facefusion.vision import create_image_resolutions, create_video_resolutions, detect_image_resolution, detect_video_fps, detect_video_resolution, pack_resolution
OUTPUT_IMAGE_QUALITY_SLIDER : Optional[gradio.Slider] = None
OUTPUT_IMAGE_RESOLUTION_DROPDOWN : Optional[gradio.Dropdown] = None
OUTPUT_AUDIO_ENCODER_DROPDOWN : Optional[gradio.Dropdown] = None
OUTPUT_AUDIO_QUALITY_SLIDER : Optional[gradio.Slider] = None
OUTPUT_AUDIO_VOLUME_SLIDER : Optional[gradio.Slider] = None
OUTPUT_VIDEO_ENCODER_DROPDOWN : Optional[gradio.Dropdown] = None
OUTPUT_VIDEO_PRESET_DROPDOWN : Optional[gradio.Dropdown] = None
OUTPUT_VIDEO_RESOLUTION_DROPDOWN : Optional[gradio.Dropdown] = None
@@ -24,6 +27,8 @@ def render() -> None:
global OUTPUT_IMAGE_QUALITY_SLIDER
global OUTPUT_IMAGE_RESOLUTION_DROPDOWN
global OUTPUT_AUDIO_ENCODER_DROPDOWN
global OUTPUT_AUDIO_QUALITY_SLIDER
global OUTPUT_AUDIO_VOLUME_SLIDER
global OUTPUT_VIDEO_ENCODER_DROPDOWN
global OUTPUT_VIDEO_PRESET_DROPDOWN
global OUTPUT_VIDEO_RESOLUTION_DROPDOWN
@@ -32,6 +37,7 @@ def render() -> None:
output_image_resolutions = []
output_video_resolutions = []
available_encoder_set = get_available_encoder_set()
if is_image(state_manager.get_item('target_path')):
output_image_resolution = detect_image_resolution(state_manager.get_item('target_path'))
output_image_resolutions = create_image_resolutions(output_image_resolution)
@@ -54,13 +60,29 @@ def render() -> None:
)
OUTPUT_AUDIO_ENCODER_DROPDOWN = gradio.Dropdown(
label = wording.get('uis.output_audio_encoder_dropdown'),
choices = facefusion.choices.output_audio_encoders,
choices = available_encoder_set.get('audio'),
value = state_manager.get_item('output_audio_encoder'),
visible = is_video(state_manager.get_item('target_path'))
)
OUTPUT_AUDIO_QUALITY_SLIDER = gradio.Slider(
label = wording.get('uis.output_audio_quality_slider'),
value = state_manager.get_item('output_audio_quality'),
step = calc_int_step(facefusion.choices.output_audio_quality_range),
minimum = facefusion.choices.output_audio_quality_range[0],
maximum = facefusion.choices.output_audio_quality_range[-1],
visible = is_video(state_manager.get_item('target_path'))
)
OUTPUT_AUDIO_VOLUME_SLIDER = gradio.Slider(
label = wording.get('uis.output_audio_volume_slider'),
value = state_manager.get_item('output_audio_volume'),
step = calc_int_step(facefusion.choices.output_audio_volume_range),
minimum = facefusion.choices.output_audio_volume_range[0],
maximum = facefusion.choices.output_audio_volume_range[-1],
visible = is_video(state_manager.get_item('target_path'))
)
OUTPUT_VIDEO_ENCODER_DROPDOWN = gradio.Dropdown(
label = wording.get('uis.output_video_encoder_dropdown'),
choices = facefusion.choices.output_video_encoders,
choices = available_encoder_set.get('video'),
value = state_manager.get_item('output_video_encoder'),
visible = is_video(state_manager.get_item('target_path'))
)
@@ -99,6 +121,8 @@ def listen() -> None:
OUTPUT_IMAGE_QUALITY_SLIDER.release(update_output_image_quality, inputs = OUTPUT_IMAGE_QUALITY_SLIDER)
OUTPUT_IMAGE_RESOLUTION_DROPDOWN.change(update_output_image_resolution, inputs = OUTPUT_IMAGE_RESOLUTION_DROPDOWN)
OUTPUT_AUDIO_ENCODER_DROPDOWN.change(update_output_audio_encoder, inputs = OUTPUT_AUDIO_ENCODER_DROPDOWN)
OUTPUT_AUDIO_QUALITY_SLIDER.release(update_output_audio_quality, inputs = OUTPUT_AUDIO_QUALITY_SLIDER)
OUTPUT_AUDIO_VOLUME_SLIDER.release(update_output_audio_volume, inputs = OUTPUT_AUDIO_VOLUME_SLIDER)
OUTPUT_VIDEO_ENCODER_DROPDOWN.change(update_output_video_encoder, inputs = OUTPUT_VIDEO_ENCODER_DROPDOWN)
OUTPUT_VIDEO_PRESET_DROPDOWN.change(update_output_video_preset, inputs = OUTPUT_VIDEO_PRESET_DROPDOWN)
OUTPUT_VIDEO_QUALITY_SLIDER.release(update_output_video_quality, inputs = OUTPUT_VIDEO_QUALITY_SLIDER)
@@ -110,23 +134,23 @@ def listen() -> None:
'target_image',
'target_video'
]):
for method in [ 'upload', 'change', 'clear' ]:
getattr(ui_component, method)(remote_update, outputs = [ OUTPUT_IMAGE_QUALITY_SLIDER, OUTPUT_IMAGE_RESOLUTION_DROPDOWN, OUTPUT_AUDIO_ENCODER_DROPDOWN, OUTPUT_VIDEO_ENCODER_DROPDOWN, OUTPUT_VIDEO_PRESET_DROPDOWN, OUTPUT_VIDEO_QUALITY_SLIDER, OUTPUT_VIDEO_RESOLUTION_DROPDOWN, OUTPUT_VIDEO_FPS_SLIDER ])
for method in [ 'change', 'clear' ]:
getattr(ui_component, method)(remote_update, outputs = [ OUTPUT_IMAGE_QUALITY_SLIDER, OUTPUT_IMAGE_RESOLUTION_DROPDOWN, OUTPUT_AUDIO_ENCODER_DROPDOWN, OUTPUT_AUDIO_QUALITY_SLIDER, OUTPUT_AUDIO_VOLUME_SLIDER, OUTPUT_VIDEO_ENCODER_DROPDOWN, OUTPUT_VIDEO_PRESET_DROPDOWN, OUTPUT_VIDEO_QUALITY_SLIDER, OUTPUT_VIDEO_RESOLUTION_DROPDOWN, OUTPUT_VIDEO_FPS_SLIDER ])
def remote_update() -> Tuple[gradio.Slider, gradio.Dropdown, gradio.Dropdown, gradio.Dropdown, gradio.Dropdown, gradio.Slider, gradio.Dropdown, gradio.Slider]:
def remote_update() -> Tuple[gradio.Slider, gradio.Dropdown, gradio.Dropdown, gradio.Slider, gradio.Slider, gradio.Dropdown, gradio.Dropdown, gradio.Slider, gradio.Dropdown, gradio.Slider]:
if is_image(state_manager.get_item('target_path')):
output_image_resolution = detect_image_resolution(state_manager.get_item('target_path'))
output_image_resolutions = create_image_resolutions(output_image_resolution)
state_manager.set_item('output_image_resolution', pack_resolution(output_image_resolution))
return gradio.Slider(visible = True), gradio.Dropdown(value = state_manager.get_item('output_image_resolution'), choices = output_image_resolutions, visible = True), gradio.Dropdown(visible = False), gradio.Dropdown(visible = False), gradio.Dropdown(visible = False), gradio.Slider(visible = False), gradio.Dropdown(visible = False), gradio.Slider(visible = False)
return gradio.Slider(visible = True), gradio.Dropdown(value = state_manager.get_item('output_image_resolution'), choices = output_image_resolutions, visible = True), gradio.Dropdown(visible = False), gradio.Slider(visible = False), gradio.Slider(visible = False), gradio.Dropdown(visible = False), gradio.Dropdown(visible = False), gradio.Slider(visible = False), gradio.Dropdown(visible = False), gradio.Slider(visible = False)
if is_video(state_manager.get_item('target_path')):
output_video_resolution = detect_video_resolution(state_manager.get_item('target_path'))
output_video_resolutions = create_video_resolutions(output_video_resolution)
state_manager.set_item('output_video_resolution', pack_resolution(output_video_resolution))
state_manager.set_item('output_video_fps', detect_video_fps(state_manager.get_item('target_path')))
return gradio.Slider(visible = False), gradio.Dropdown(visible = False), gradio.Dropdown(visible = True), gradio.Dropdown(visible = True), gradio.Dropdown(visible = True), gradio.Slider(visible = True), gradio.Dropdown(value = state_manager.get_item('output_video_resolution'), choices = output_video_resolutions, visible = True), gradio.Slider(value = state_manager.get_item('output_video_fps'), visible = True)
return gradio.Slider(visible = False), gradio.Dropdown(visible = False), gradio.Dropdown(visible = False), gradio.Dropdown(visible = False), gradio.Dropdown(visible = False), gradio.Slider(visible = False), gradio.Dropdown(visible = False), gradio.Slider(visible = False)
return gradio.Slider(visible = False), gradio.Dropdown(visible = False), gradio.Dropdown(visible = True), gradio.Slider(visible = True), gradio.Slider(visible = True), gradio.Dropdown(visible = True), gradio.Dropdown(visible = True), gradio.Slider(visible = True), gradio.Dropdown(value = state_manager.get_item('output_video_resolution'), choices = output_video_resolutions, visible = True), gradio.Slider(value = state_manager.get_item('output_video_fps'), visible = True)
return gradio.Slider(visible = False), gradio.Dropdown(visible = False), gradio.Dropdown(visible = False), gradio.Slider(visible = False), gradio.Slider(visible = False), gradio.Dropdown(visible = False), gradio.Dropdown(visible = False), gradio.Slider(visible = False), gradio.Dropdown(visible = False), gradio.Slider(visible = False)
def update_output_image_quality(output_image_quality : float) -> None:
@@ -137,15 +161,23 @@ def update_output_image_resolution(output_image_resolution : str) -> None:
state_manager.set_item('output_image_resolution', output_image_resolution)
def update_output_audio_encoder(output_audio_encoder : OutputAudioEncoder) -> None:
def update_output_audio_encoder(output_audio_encoder : AudioEncoder) -> None:
state_manager.set_item('output_audio_encoder', output_audio_encoder)
def update_output_video_encoder(output_video_encoder : OutputVideoEncoder) -> None:
def update_output_audio_quality(output_audio_quality : float) -> None:
state_manager.set_item('output_audio_quality', int(output_audio_quality))
def update_output_audio_volume(output_audio_volume: float) -> None:
state_manager.set_item('output_audio_volume', int(output_audio_volume))
def update_output_video_encoder(output_video_encoder : VideoEncoder) -> None:
state_manager.set_item('output_video_encoder', output_video_encoder)
def update_output_video_preset(output_video_preset : OutputVideoPreset) -> None:
def update_output_video_preset(output_video_preset : VideoPreset) -> None:
state_manager.set_item('output_video_preset', output_video_preset)

20
facefusion/uis/components/preview.py
View File

@@ -15,10 +15,10 @@ from facefusion.face_selector import sort_faces_by_order
from facefusion.face_store import clear_reference_faces, clear_static_faces, get_reference_faces
from facefusion.filesystem import filter_audio_paths, is_image, is_video
from facefusion.processors.core import get_processors_modules
from facefusion.typing import AudioFrame, Face, FaceSet, VisionFrame
from facefusion.types import AudioFrame, Face, FaceSet, VisionFrame
from facefusion.uis.core import get_ui_component, get_ui_components, register_ui_component
from facefusion.uis.typing import ComponentOptions
from facefusion.vision import count_video_frame_total, detect_frame_orientation, get_video_frame, normalize_frame_color, read_static_image, read_static_images, resize_frame_resolution
from facefusion.uis.types import ComponentOptions
from facefusion.vision import count_video_frame_total, detect_frame_orientation, normalize_frame_color, read_static_image, read_static_images, read_video_frame, restrict_frame
PREVIEW_IMAGE : Optional[gradio.Image] = None
PREVIEW_FRAME_SLIDER : Optional[gradio.Slider] = None
@@ -60,7 +60,7 @@ def render() -> None:
preview_image_options['elem_classes'] = [ 'image-preview', 'is-' + detect_frame_orientation(preview_vision_frame) ]
if is_video(state_manager.get_item('target_path')):
temp_vision_frame = get_video_frame(state_manager.get_item('target_path'), state_manager.get_item('reference_frame_number'))
temp_vision_frame = read_video_frame(state_manager.get_item('target_path'), state_manager.get_item('reference_frame_number'))
preview_vision_frame = process_preview_frame(reference_faces, source_face, source_audio_frame, temp_vision_frame)
preview_image_options['value'] = normalize_frame_color(preview_vision_frame)
preview_image_options['elem_classes'] = [ 'image-preview', 'is-' + detect_frame_orientation(preview_vision_frame) ]
@@ -88,7 +88,7 @@ def listen() -> None:
'target_image',
'target_video'
]):
for method in [ 'upload', 'change', 'clear' ]:
for method in [ 'change', 'clear' ]:
getattr(ui_component, method)(update_preview_image, inputs = PREVIEW_FRAME_SLIDER, outputs = PREVIEW_IMAGE)
for ui_component in get_ui_components(
@@ -96,7 +96,7 @@ def listen() -> None:
'target_image',
'target_video'
]):
for method in [ 'upload', 'change', 'clear' ]:
for method in [ 'change', 'clear' ]:
getattr(ui_component, method)(update_preview_frame_slider, outputs = PREVIEW_FRAME_SLIDER)
for ui_component in get_ui_components(
@@ -184,8 +184,8 @@ def clear_and_update_preview_image(frame_number : int = 0) -> gradio.Image:
def slide_preview_image(frame_number : int = 0) -> gradio.Image:
if is_video(state_manager.get_item('target_path')):
preview_vision_frame = normalize_frame_color(get_video_frame(state_manager.get_item('target_path'), frame_number))
preview_vision_frame = resize_frame_resolution(preview_vision_frame, (1024, 1024))
preview_vision_frame = normalize_frame_color(read_video_frame(state_manager.get_item('target_path'), frame_number))
preview_vision_frame = restrict_frame(preview_vision_frame, (1024, 1024))
return gradio.Image(value = preview_vision_frame)
return gradio.Image(value = None)
@@ -222,7 +222,7 @@ def update_preview_image(frame_number : int = 0) -> gradio.Image:
return gradio.Image(value = preview_vision_frame, elem_classes = [ 'image-preview', 'is-' + detect_frame_orientation(preview_vision_frame) ])
if is_video(state_manager.get_item('target_path')):
temp_vision_frame = get_video_frame(state_manager.get_item('target_path'), frame_number)
temp_vision_frame = read_video_frame(state_manager.get_item('target_path'), frame_number)
preview_vision_frame = process_preview_frame(reference_faces, source_face, source_audio_frame, temp_vision_frame)
preview_vision_frame = normalize_frame_color(preview_vision_frame)
return gradio.Image(value = preview_vision_frame, elem_classes = [ 'image-preview', 'is-' + detect_frame_orientation(preview_vision_frame) ])
@@ -237,7 +237,7 @@ def update_preview_frame_slider() -> gradio.Slider:
def process_preview_frame(reference_faces : FaceSet, source_face : Face, source_audio_frame : AudioFrame, target_vision_frame : VisionFrame) -> VisionFrame:
target_vision_frame = resize_frame_resolution(target_vision_frame, (1024, 1024))
target_vision_frame = restrict_frame(target_vision_frame, (1024, 1024))
source_vision_frame = target_vision_frame.copy()
if analyse_frame(target_vision_frame):
return cv2.GaussianBlur(target_vision_frame, (99, 99), 0)

4
facefusion/uis/components/processors.py
View File

@@ -3,7 +3,7 @@ from typing import List, Optional
import gradio
from facefusion import state_manager, wording
from facefusion.filesystem import list_directory
from facefusion.filesystem import get_file_name, resolve_file_paths
from facefusion.processors.core import get_processors_modules
from facefusion.uis.core import register_ui_component
@@ -39,5 +39,5 @@ def update_processors(processors : List[str]) -> gradio.CheckboxGroup:
def sort_processors(processors : List[str]) -> List[str]:
available_processors = [ file.get('name') for file in list_directory('facefusion/processors/modules') ]
available_processors = [ get_file_name(file_path) for file_path in resolve_file_paths('facefusion/processors/modules') ]
return sorted(available_processors, key = lambda processor : processors.index(processor) if processor in processors else len(processors))

9
facefusion/uis/components/source.py
View File

@@ -6,7 +6,7 @@ from facefusion import state_manager, wording
from facefusion.common_helper import get_first
from facefusion.filesystem import filter_audio_paths, filter_image_paths, has_audio, has_image
from facefusion.uis.core import register_ui_component
from facefusion.uis.typing import File
from facefusion.uis.types import File
SOURCE_FILE : Optional[gradio.File] = None
SOURCE_AUDIO : Optional[gradio.Audio] = None
@@ -23,11 +23,6 @@ def render() -> None:
SOURCE_FILE = gradio.File(
label = wording.get('uis.source_file'),
file_count = 'multiple',
file_types =
[
'audio',
'image'
],
value = state_manager.get_item('source_paths') if has_source_audio or has_source_image else None
)
source_file_names = [ source_file_value.get('path') for source_file_value in SOURCE_FILE.value ] if SOURCE_FILE.value else None
@@ -55,10 +50,12 @@ def update(files : List[File]) -> Tuple[gradio.Audio, gradio.Image]:
file_names = [ file.name for file in files ] if files else None
has_source_audio = has_audio(file_names)
has_source_image = has_image(file_names)
if has_source_audio or has_source_image:
source_audio_path = get_first(filter_audio_paths(file_names))
source_image_path = get_first(filter_image_paths(file_names))
state_manager.set_item('source_paths', file_names)
return gradio.Audio(value = source_audio_path, visible = has_source_audio), gradio.Image(value = source_image_path, visible = has_source_image)
state_manager.clear_item('source_paths')
return gradio.Audio(value = None, visible = False), gradio.Image(value = None, visible = False)

28
facefusion/uis/components/target.py
View File

@@ -4,12 +4,9 @@ import gradio
from facefusion import state_manager, wording
from facefusion.face_store import clear_reference_faces, clear_static_faces
from facefusion.filesystem import get_file_size, is_image, is_video
from facefusion.filesystem import is_image, is_video
from facefusion.uis.core import register_ui_component
from facefusion.uis.typing import ComponentOptions, File
from facefusion.vision import get_video_frame, normalize_frame_color
FILE_SIZE_LIMIT = 512 * 1024 * 1024
from facefusion.uis.types import ComponentOptions, File
TARGET_FILE : Optional[gradio.File] = None
TARGET_IMAGE : Optional[gradio.Image] = None
@@ -25,12 +22,6 @@ def render() -> None:
is_target_video = is_video(state_manager.get_item('target_path'))
TARGET_FILE = gradio.File(
label = wording.get('uis.target_file'),
file_count = 'single',
file_types =
[
'image',
'video'
],
value = state_manager.get_item('target_path') if is_target_image or is_target_video else None
)
target_image_options : ComponentOptions =\
@@ -47,13 +38,8 @@ def render() -> None:
target_image_options['value'] = TARGET_FILE.value.get('path')
target_image_options['visible'] = True
if is_target_video:
if get_file_size(state_manager.get_item('target_path')) > FILE_SIZE_LIMIT:
preview_vision_frame = normalize_frame_color(get_video_frame(state_manager.get_item('target_path')))
target_image_options['value'] = preview_vision_frame
target_image_options['visible'] = True
else:
target_video_options['value'] = TARGET_FILE.value.get('path')
target_video_options['visible'] = True
target_video_options['value'] = TARGET_FILE.value.get('path')
target_video_options['visible'] = True
TARGET_IMAGE = gradio.Image(**target_image_options)
TARGET_VIDEO = gradio.Video(**target_video_options)
register_ui_component('target_image', TARGET_IMAGE)
@@ -67,14 +53,14 @@ def listen() -> None:
def update(file : File) -> Tuple[gradio.Image, gradio.Video]:
clear_reference_faces()
clear_static_faces()
if file and is_image(file.name):
state_manager.set_item('target_path', file.name)
return gradio.Image(value = file.name, visible = True), gradio.Video(value = None, visible = False)
if file and is_video(file.name):
state_manager.set_item('target_path', file.name)
if get_file_size(file.name) > FILE_SIZE_LIMIT:
preview_vision_frame = normalize_frame_color(get_video_frame(file.name))
return gradio.Image(value = preview_vision_frame, visible = True), gradio.Video(value = None, visible = False)
return gradio.Image(value = None, visible = False), gradio.Video(value = file.name, visible = True)
state_manager.clear_item('target_path')
return gradio.Image(value = None, visible = False), gradio.Video(value = None, visible = False)

4
facefusion/uis/components/temp_frame.py
View File

@@ -5,7 +5,7 @@ import gradio
import facefusion.choices
from facefusion import state_manager, wording
from facefusion.filesystem import is_video
from facefusion.typing import TempFrameFormat
from facefusion.types import TempFrameFormat
from facefusion.uis.core import get_ui_component
TEMP_FRAME_FORMAT_DROPDOWN : Optional[gradio.Dropdown] = None
@@ -27,7 +27,7 @@ def listen() -> None:
target_video = get_ui_component('target_video')
if target_video:
for method in [ 'upload', 'change', 'clear' ]:
for method in [ 'change', 'clear' ]:
getattr(target_video, method)(remote_update, outputs = TEMP_FRAME_FORMAT_DROPDOWN)

6
facefusion/uis/components/terminal.py
View File

@@ -9,7 +9,7 @@ from tqdm import tqdm
import facefusion.choices
from facefusion import logger, state_manager, wording
from facefusion.typing import LogLevel
from facefusion.types import LogLevel
LOG_LEVEL_DROPDOWN : Optional[gradio.Dropdown] = None
TERMINAL_TEXTBOX : Optional[gradio.Textbox] = None
@@ -38,8 +38,6 @@ def render() -> None:
def listen() -> None:
global LOG_LEVEL_DROPDOWN
LOG_LEVEL_DROPDOWN.change(update_log_level, inputs = LOG_LEVEL_DROPDOWN)
logger.get_package_logger().addHandler(LOG_HANDLER)
tqdm.update = tqdm_update
@@ -78,5 +76,5 @@ def create_tqdm_output(self : tqdm) -> Optional[str]:
def read_logs() -> str:
LOG_BUFFER.seek(0)
logs = LOG_BUFFER.read().rstrip()
logs = LOG_BUFFER.read().strip()
return logs

4
facefusion/uis/components/trim_frame.py
View File

@@ -6,7 +6,7 @@ from facefusion import state_manager, wording
from facefusion.face_store import clear_static_faces
from facefusion.filesystem import is_video
from facefusion.uis.core import get_ui_components
from facefusion.uis.typing import ComponentOptions
from facefusion.uis.types import ComponentOptions
from facefusion.vision import count_video_frame_total
TRIM_FRAME_RANGE_SLIDER : Optional[RangeSlider] = None
@@ -39,7 +39,7 @@ def listen() -> None:
'target_image',
'target_video'
]):
for method in [ 'upload', 'change', 'clear' ]:
for method in [ 'change', 'clear' ]:
getattr(ui_component, method)(remote_update, outputs = [ TRIM_FRAME_RANGE_SLIDER ])

38
facefusion/uis/components/webcam.py
View File

@@ -8,17 +8,16 @@ import cv2
import gradio
from tqdm import tqdm
from facefusion import logger, state_manager, wording
from facefusion import ffmpeg_builder, logger, state_manager, wording
from facefusion.audio import create_empty_audio_frame
from facefusion.common_helper import get_first, is_windows
from facefusion.common_helper import is_windows
from facefusion.content_analyser import analyse_stream
from facefusion.face_analyser import get_average_face, get_many_faces
from facefusion.ffmpeg import open_ffmpeg
from facefusion.filesystem import filter_image_paths
from facefusion.filesystem import filter_image_paths, is_directory
from facefusion.processors.core import get_processors_modules
from facefusion.typing import Face, Fps, VisionFrame
from facefusion.types import Face, Fps, StreamMode, VisionFrame, WebcamMode
from facefusion.uis.core import get_ui_component
from facefusion.uis.typing import StreamMode, WebcamMode
from facefusion.vision import normalize_frame_color, read_static_images, unpack_resolution
WEBCAM_CAPTURE : Optional[cv2.VideoCapture] = None
@@ -164,17 +163,32 @@ def process_stream_frame(source_face : Face, target_vision_frame : VisionFrame)
def open_stream(stream_mode : StreamMode, stream_resolution : str, stream_fps : Fps) -> subprocess.Popen[bytes]:
commands = [ '-f', 'rawvideo', '-pix_fmt', 'bgr24', '-s', stream_resolution, '-r', str(stream_fps), '-i', '-']
commands = ffmpeg_builder.chain(
ffmpeg_builder.capture_video(),
ffmpeg_builder.set_media_resolution(stream_resolution),
ffmpeg_builder.set_conditional_fps(stream_fps)
)
if stream_mode == 'udp':
commands.extend([ '-b:v', '2000k', '-f', 'mpegts', 'udp://localhost:27000?pkt_size=1316' ])
commands.extend(ffmpeg_builder.set_input('-'))
commands.extend(ffmpeg_builder.set_stream_mode('udp'))
commands.extend(ffmpeg_builder.set_output('udp://localhost:27000?pkt_size=1316'))
if stream_mode == 'v4l2':
try:
device_name = get_first(os.listdir('/sys/devices/virtual/video4linux'))
if device_name:
commands.extend([ '-f', 'v4l2', '/dev/' + device_name ])
except FileNotFoundError:
device_directory_path = '/sys/devices/virtual/video4linux'
commands.extend(ffmpeg_builder.set_input('-'))
commands.extend(ffmpeg_builder.set_stream_mode('v4l2'))
if is_directory(device_directory_path):
device_names = os.listdir(device_directory_path)
for device_name in device_names:
device_path = '/dev/' + device_name
commands.extend(ffmpeg_builder.set_output(device_path))
else:
logger.error(wording.get('stream_not_loaded').format(stream_mode = stream_mode), __name__)
return open_ffmpeg(commands)

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