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* Add real_hatgan_x4 model

* Mark it as NEXT

* Force download to be executed and exit

* Fix frame per second interpolation

* 5 to 68 landmark (#456)

* changes

* changes

* Adjust model url

* Cleanup 5 to 68 landmark convertion

* Move everything to face analyser

* Introduce matrix only face helper

* Revert facefusion.ini

* Adjust limit due false positive analysis

* changes (#457)

* Use pixel format yuv422p to merge video

* Fix some code

* Minor cleanup

* Add gpen_bfr_1024 and gpen_bfr_2048

* Revert it back to yuv420p due compatibility issues

* Add debug back to ffmpeg

* Add debug back to ffmpeg

* Migrate to conda (#461)

* Migrate from venv to conda

* Migrate from venv to conda

* Message when conda is not activated

* Use release for every slider (#463)

* Use release event handler for every slider

* Move more sliders to release handler

* Move more sliders to release handler

* Add get_ui_components() to simplify code

* Revert some changes on frame slider

* Add the first iteration of a frame colorizer

* Support for the DDColor model

* Improve model file handling

* Improve model file handling part2

* Remove deoldify

* Remove deoldify

* Voice separator (#468)

* changes

* changes

* changes

* changes

* changes

* changes

* Rename audio extractor to voice extractor

* Cosmetic changes

* Cosmetic changes

* Fix fps lowering and boosting

* Fix fps lowering and boosting

* Fix fps lowering and boosting

* Some refactoring for audio.py and some astype() here and there (#470)

* Some refactoring for audio.py and some astype() here and there

* Fix lint

* Spacing

* Add mp3 to benchmark suite for lip syncer testing

* Improve naming

* Adjust chunk size

* Use higher quality

* Revert "Use higher quality"

This reverts commit d32f287572.

* Improve naming in ffmpeg.py

* Simplify code

* Better fps calculation

* Fix naming here and there

* Add back real esrgan x2

* Remove trailing comma

* Update wording and README

* Use semaphore to prevent frame colorizer memory issues

* Revert "Remove deoldify"

This reverts commit bd8034cbc7.

* Remove unused type from frame colorizer

* Adjust naming

* Add missing clear of model initializer

* Change nvenc preset mappping to support old FFMPEG 4

* Update onnxruntime to 1.17.1

* Fix lint

* Prepare 2.5.0

* Fix Gradio overrides

* Add Deoldify Artistic back

* Feat/audio refactoring (#476)

* Improve audio naming and variables

* Improve audio naming and variables

* Refactor voice extractor like crazy

* Refactor voice extractor like crazy

* Remove spaces

* Update the usage

---------

Co-authored-by: Harisreedhar <46858047+harisreedhar@users.noreply.github.com>
This commit is contained in:
Henry Ruhs 2024-04-09 15:40:55 +02:00 committed by GitHub
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commit 4ccf4c24c7
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45 changed files with 1007 additions and 405 deletions
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@ -30,6 +30,6 @@ jobs:
uses: actions/setup-python@v2
with:
python-version: '3.10'
- run: python install.py --onnxruntime default --skip-venv
- run: python install.py --onnxruntime default --skip-conda
- run: pip install pytest
- run: pytest

101
README.md
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@ -30,75 +30,78 @@ Run the command:
python run.py [options]
options:
-h, --help show this help message and exit
-s SOURCE_PATHS, --source SOURCE_PATHS choose single or multiple source images or audios
-t TARGET_PATH, --target TARGET_PATH choose single target image or video
-o OUTPUT_PATH, --output OUTPUT_PATH specify the output file or directory
-v, --version show program's version number and exit
-h, --help show this help message and exit
-s SOURCE_PATHS, --source SOURCE_PATHS choose single or multiple source images or audios
-t TARGET_PATH, --target TARGET_PATH choose single target image or video
-o OUTPUT_PATH, --output OUTPUT_PATH specify the output file or directory
-v, --version show program's version number and exit
misc:
--skip-download omit automate downloads and remote lookups
--headless run the program without a user interface
--log-level {error,warn,info,debug} adjust the message severity displayed in the terminal
--force-download force automate downloads and exit
--skip-download omit automate downloads and remote lookups
--headless run the program without a user interface
--log-level {error,warn,info,debug} adjust the message severity displayed in the terminal
execution:
--execution-providers EXECUTION_PROVIDERS [EXECUTION_PROVIDERS ...] accelerate the model inference using different providers (choices: cpu, ...)
--execution-thread-count [1-128] specify the amount of parallel threads while processing
--execution-queue-count [1-32] specify the amount of frames each thread is processing
--execution-providers EXECUTION_PROVIDERS [EXECUTION_PROVIDERS ...] accelerate the model inference using different providers (choices: cpu, ...)
--execution-thread-count [1-128] specify the amount of parallel threads while processing
--execution-queue-count [1-32] specify the amount of frames each thread is processing
memory:
--video-memory-strategy {strict,moderate,tolerant} balance fast frame processing and low vram usage
--system-memory-limit [0-128] limit the available ram that can be used while processing
--video-memory-strategy {strict,moderate,tolerant} balance fast frame processing and low VRAM usage
--system-memory-limit [0-128] limit the available RAM that can be used while processing
face analyser:
--face-analyser-order {left-right,right-left,top-bottom,bottom-top,small-large,large-small,best-worst,worst-best} specify the order in which the face analyser detects faces.
--face-analyser-age {child,teen,adult,senior} filter the detected faces based on their age
--face-analyser-gender {female,male} filter the detected faces based on their gender
--face-detector-model {many,retinaface,scrfd,yoloface,yunet} choose the model responsible for detecting the face
--face-detector-size FACE_DETECTOR_SIZE specify the size of the frame provided to the face detector
--face-detector-score [0.0-1.0] filter the detected faces base on the confidence score
--face-landmarker-score [0.0-1.0] filter the detected landmarks base on the confidence score
--face-analyser-order {left-right,right-left,top-bottom,bottom-top,small-large,large-small,best-worst,worst-best} specify the order in which the face analyser detects faces
--face-analyser-age {child,teen,adult,senior} filter the detected faces based on their age
--face-analyser-gender {female,male} filter the detected faces based on their gender
--face-detector-model {many,retinaface,scrfd,yoloface,yunet} choose the model responsible for detecting the face
--face-detector-size FACE_DETECTOR_SIZE specify the size of the frame provided to the face detector
--face-detector-score [0.0-1.0] filter the detected faces base on the confidence score
--face-landmarker-score [0.0-1.0] filter the detected landmarks base on the confidence score
face selector:
--face-selector-mode {many,one,reference} use reference based tracking or simple matching
--reference-face-position REFERENCE_FACE_POSITION specify the position used to create the reference face
--reference-face-distance [0.0-1.5] specify the desired similarity between the reference face and target face
--reference-frame-number REFERENCE_FRAME_NUMBER specify the frame used to create the reference face
--face-selector-mode {many,one,reference} use reference based tracking or simple matching
--reference-face-position REFERENCE_FACE_POSITION specify the position used to create the reference face
--reference-face-distance [0.0-1.5] specify the desired similarity between the reference face and target face
--reference-frame-number REFERENCE_FRAME_NUMBER specify the frame used to create the reference face
face mask:
--face-mask-types FACE_MASK_TYPES [FACE_MASK_TYPES ...] mix and match different face mask types (choices: box, occlusion, region)
--face-mask-blur [0.0-1.0] specify the degree of blur applied the box mask
--face-mask-padding FACE_MASK_PADDING [FACE_MASK_PADDING ...] apply top, right, bottom and left padding to the box mask
--face-mask-regions FACE_MASK_REGIONS [FACE_MASK_REGIONS ...] choose the facial features used for the region mask (choices: skin, left-eyebrow, right-eyebrow, left-eye, right-eye, eye-glasses, nose, mouth, upper-lip, lower-lip)
--face-mask-types FACE_MASK_TYPES [FACE_MASK_TYPES ...] mix and match different face mask types (choices: box, occlusion, region)
--face-mask-blur [0.0-1.0] specify the degree of blur applied the box mask
--face-mask-padding FACE_MASK_PADDING [FACE_MASK_PADDING ...] apply top, right, bottom and left padding to the box mask
--face-mask-regions FACE_MASK_REGIONS [FACE_MASK_REGIONS ...] choose the facial features used for the region mask (choices: skin, left-eyebrow, right-eyebrow, left-eye, right-eye, eye-glasses, nose, mouth, upper-lip, lower-lip)
frame extraction:
--trim-frame-start TRIM_FRAME_START specify the the start frame of the target video
--trim-frame-end TRIM_FRAME_END specify the the end frame of the target video
--temp-frame-format {bmp,jpg,png} specify the temporary resources format
--keep-temp keep the temporary resources after processing
--trim-frame-start TRIM_FRAME_START specify the the start frame of the target video
--trim-frame-end TRIM_FRAME_END specify the the end frame of the target video
--temp-frame-format {bmp,jpg,png} specify the temporary resources format
--keep-temp keep the temporary resources after processing
output creation:
--output-image-quality [0-100] specify the image quality which translates to the compression factor
--output-image-resolution OUTPUT_IMAGE_RESOLUTION specify the image output resolution based on the target image
--output-video-encoder {libx264,libx265,libvpx-vp9,h264_nvenc,hevc_nvenc,h264_amf,hevc_amf} specify the encoder use for the video compression
--output-video-preset {ultrafast,superfast,veryfast,faster,fast,medium,slow,slower,veryslow} balance fast video processing and video file size
--output-video-quality [0-100] specify the video quality which translates to the compression factor
--output-video-resolution OUTPUT_VIDEO_RESOLUTION specify the video output resolution based on the target video
--output-video-fps OUTPUT_VIDEO_FPS specify the video output fps based on the target video
--skip-audio omit the audio from the target video
--output-image-quality [0-100] specify the image quality which translates to the compression factor
--output-image-resolution OUTPUT_IMAGE_RESOLUTION specify the image output resolution based on the target image
--output-video-encoder {libx264,libx265,libvpx-vp9,h264_nvenc,hevc_nvenc,h264_amf,hevc_amf} specify the encoder use for the video compression
--output-video-preset {ultrafast,superfast,veryfast,faster,fast,medium,slow,slower,veryslow} balance fast video processing and video file size
--output-video-quality [0-100] specify the video quality which translates to the compression factor
--output-video-resolution OUTPUT_VIDEO_RESOLUTION specify the video output resolution based on the target video
--output-video-fps OUTPUT_VIDEO_FPS specify the video output fps based on the target video
--skip-audio omit the audio from the target video
frame processors:
--frame-processors FRAME_PROCESSORS [FRAME_PROCESSORS ...] load a single or multiple frame processors. (choices: face_debugger, face_enhancer, face_swapper, frame_enhancer, lip_syncer, ...)
--face-debugger-items FACE_DEBUGGER_ITEMS [FACE_DEBUGGER_ITEMS ...] load a single or multiple frame processors (choices: bounding-box, face-landmark-5, face-landmark-5/68, face-landmark-68, face-mask, face-detector-score, face-landmarker-score, age, gender)
--face-enhancer-model {codeformer,gfpgan_1.2,gfpgan_1.3,gfpgan_1.4,gpen_bfr_256,gpen_bfr_512,restoreformer_plus_plus} choose the model responsible for enhancing the face
--face-enhancer-blend [0-100] blend the enhanced into the previous face
--face-swapper-model {blendswap_256,inswapper_128,inswapper_128_fp16,simswap_256,simswap_512_unofficial,uniface_256} choose the model responsible for swapping the face
--frame-enhancer-model {lsdir_x4,nomos8k_sc_x4,real_esrgan_x4,real_esrgan_x4_fp16,span_kendata_x4} choose the model responsible for enhancing the frame
--frame-enhancer-blend [0-100] blend the enhanced into the previous frame
--lip-syncer-model {wav2lip_gan} choose the model responsible for syncing the lips
--frame-processors FRAME_PROCESSORS [FRAME_PROCESSORS ...] load a single or multiple frame processors. (choices: face_debugger, face_enhancer, face_swapper, frame_colorizer, frame_enhancer, lip_syncer, ...)
--face-debugger-items FACE_DEBUGGER_ITEMS [FACE_DEBUGGER_ITEMS ...] load a single or multiple frame processors (choices: 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)
--face-enhancer-model {codeformer,gfpgan_1.2,gfpgan_1.3,gfpgan_1.4,gpen_bfr_256,gpen_bfr_512,gpen_bfr_1024,gpen_bfr_2048,restoreformer_plus_plus} choose the model responsible for enhancing the face
--face-enhancer-blend [0-100] blend the enhanced into the previous face
--face-swapper-model {blendswap_256,inswapper_128,inswapper_128_fp16,simswap_256,simswap_512_unofficial,uniface_256} choose the model responsible for swapping the face
--frame-colorizer-model {ddcolor,ddcolor_artistic,deoldify_artistic} choose the model responsible for colorizing the frame
--frame-colorizer-blend [0-100] blend the colorized into the previous frame
--frame-enhancer-model {lsdir_x4,nomos8k_sc_x4,real_esrgan_x2,real_esrgan_x2_fp16,real_esrgan_x4,real_esrgan_x4_fp16,real_hatgan_x4,span_kendata_x4} choose the model responsible for enhancing the frame
--frame-enhancer-blend [0-100] blend the enhanced into the previous frame
--lip-syncer-model {wav2lip_gan} choose the model responsible for syncing the lips
uis:
--ui-layouts UI_LAYOUTS [UI_LAYOUTS ...] launch a single or multiple UI layouts (choices: benchmark, default, webcam, ...)
--ui-layouts UI_LAYOUTS [UI_LAYOUTS ...] launch a single or multiple UI layouts (choices: benchmark, default, webcam, ...)
```

3
facefusion.ini
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@ -4,6 +4,7 @@ target_path =
output_path =
[misc]
force_download =
skip_download =
headless =
log_level =
@ -60,6 +61,8 @@ face_debugger_items =
face_enhancer_model =
face_enhancer_blend =
face_swapper_model =
frame_colorizer_model =
frame_colorizer_blend =
frame_enhancer_model =
frame_enhancer_blend =
lip_syncer_model =

123
facefusion/audio.py
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@ -5,7 +5,49 @@ import scipy
from facefusion.filesystem import is_audio
from facefusion.ffmpeg import read_audio_buffer
from facefusion.typing import Fps, Audio, Spectrogram, AudioFrame
from facefusion.typing import Fps, Audio, AudioFrame, Spectrogram, MelFilterBank
from facefusion.voice_extractor import batch_extract_voice
@lru_cache(maxsize = 128)
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 = 16000
channel_total = 2
if is_audio(audio_path):
audio_buffer = read_audio_buffer(audio_path, sample_rate, channel_total)
audio = numpy.frombuffer(audio_buffer, dtype = numpy.int16).reshape(-1, 2)
audio = prepare_audio(audio)
spectrogram = create_spectrogram(audio)
audio_frames = extract_audio_frames(spectrogram, fps)
return audio_frames
return None
@lru_cache(maxsize = 128)
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 = 16000
channel_total = 2
chunk_size = 1024 ** 3
step_size = chunk_size // 4
if is_audio(audio_path):
audio_buffer = read_audio_buffer(audio_path, sample_rate, channel_total)
audio = numpy.frombuffer(audio_buffer, dtype = numpy.int16).reshape(-1, 2)
audio = batch_extract_voice(audio, chunk_size, step_size)
audio = prepare_audio(audio)
spectrogram = create_spectrogram(audio)
audio_frames = extract_audio_frames(spectrogram, fps)
return audio_frames
return None
def get_audio_frame(audio_path : str, fps : Fps, frame_number : int = 0) -> Optional[AudioFrame]:
@ -16,33 +58,26 @@ def get_audio_frame(audio_path : str, fps : Fps, frame_number : int = 0) -> Opti
return None
def create_empty_audio_frame() -> AudioFrame:
audio_frame = numpy.zeros((80, 16), dtype = numpy.int16)
return audio_frame
@lru_cache(maxsize = None)
def read_static_audio(audio_path : str, fps : Fps) -> Optional[List[AudioFrame]]:
def get_voice_frame(audio_path : str, fps : Fps, frame_number : int = 0) -> Optional[AudioFrame]:
if is_audio(audio_path):
audio_buffer = read_audio_buffer(audio_path, 16000, 2)
audio = numpy.frombuffer(audio_buffer, dtype = numpy.int16).reshape(-1, 2)
audio = normalize_audio(audio)
audio = filter_audio(audio, -0.97)
spectrogram = create_spectrogram(audio, 16000, 80, 800, 55.0, 7600.0)
audio_frames = extract_audio_frames(spectrogram, 80, 16, fps)
return audio_frames
voice_frames = read_static_voice(audio_path, fps)
if frame_number in range(len(voice_frames)):
return voice_frames[frame_number]
return None
def normalize_audio(audio : numpy.ndarray[Any, Any]) -> Audio:
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)
return audio_frame
def prepare_audio(audio : numpy.ndarray[Any, Any]) -> Audio:
if audio.ndim > 1:
audio = numpy.mean(audio, axis = 1)
audio = audio / numpy.max(numpy.abs(audio), axis = 0)
return audio
def filter_audio(audio : Audio, filter_coefficient : float) -> Audio:
audio = scipy.signal.lfilter([ 1.0, filter_coefficient ], [1.0], audio)
audio = scipy.signal.lfilter([ 1.0, -0.97 ], [ 1.0 ], audio)
return audio
@ -54,28 +89,40 @@ def convert_mel_to_hertz(mel : numpy.ndarray[Any, Any]) -> numpy.ndarray[Any, An
return 700 * (10 ** (mel / 2595) - 1)
@lru_cache(maxsize = None)
def create_static_mel_filter(sample_rate : int, filter_total : int, filter_size : int, frequency_minimum : float, frequency_maximum : float) -> numpy.ndarray[Any, Any]:
frequency_maximum = min(sample_rate / 2, frequency_maximum)
mel_filter = numpy.zeros((filter_total, filter_size // 2 + 1))
mel_bins = numpy.linspace(convert_hertz_to_mel(frequency_minimum), convert_hertz_to_mel(frequency_maximum), filter_total + 2)
indices = numpy.floor((filter_size + 1) * convert_mel_to_hertz(mel_bins) / sample_rate).astype(numpy.int16)
for index in range(filter_total):
mel_filter[index, indices[index]: indices[index + 1]] = scipy.signal.windows.triang(indices[index + 1] - indices[index])
return mel_filter
def create_mel_filter_bank() -> MelFilterBank:
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)
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
def create_spectrogram(audio : Audio, sample_rate : int, filter_total : int, filter_size : int, frequency_minimum : float, frequency_maximum : float) -> Spectrogram:
mel_filter = create_static_mel_filter(sample_rate, filter_total, filter_size, frequency_minimum, frequency_maximum)
spectrogram = scipy.signal.stft(audio, nperseg = filter_size, noverlap = 600, nfft = filter_size)[2]
spectrogram = numpy.dot(mel_filter, numpy.abs(spectrogram))
def create_spectrogram(audio : Audio) -> Spectrogram:
mel_bin_total = 800
mel_bin_overlap = 600
mel_filter_bank = create_mel_filter_bank()
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, filter_total : int, audio_frame_step : int, fps : Fps) -> List[AudioFrame]:
indices = numpy.arange(0, spectrogram.shape[1], filter_total / fps).astype(numpy.int16)
indices = indices[indices >= audio_frame_step]
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:
audio_frames.append(spectrogram[:, max(0, index - audio_frame_step) : index])
start = max(0, index - step_size)
audio_frames.append(spectrogram[:, start:index])
return audio_frames

11
facefusion/common_helper.py
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@ -1,4 +1,4 @@
from typing import List, Any, Tuple
from typing import List, Any
import numpy
@ -16,12 +16,3 @@ def create_float_range(start : float, stop : float, step : float) -> List[float]
def get_first(__list__ : Any) -> Any:
return next(iter(__list__), None)
def extract_major_version(version : str) -> Tuple[int, int]:
versions = version.split('.')
if len(versions) > 1:
return int(versions[0]), int(versions[1])
if len(versions) == 1:
return int(versions[0]), 0
return 0, 0

18
facefusion/content_analyser.py
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@ -1,4 +1,4 @@
from typing import Any, Dict
from typing import Any
from functools import lru_cache
from time import sleep
import threading
@ -9,15 +9,15 @@ from tqdm import tqdm
import facefusion.globals
from facefusion import process_manager, wording
from facefusion.typing import VisionFrame, ModelValue, Fps
from facefusion.typing import VisionFrame, ModelSet, Fps
from facefusion.execution import apply_execution_provider_options
from facefusion.vision import get_video_frame, count_video_frame_total, read_image, detect_video_fps
from facefusion.filesystem import resolve_relative_path
from facefusion.filesystem import resolve_relative_path, is_file
from facefusion.download import conditional_download
CONTENT_ANALYSER = None
THREAD_LOCK : threading.Lock = threading.Lock()
MODELS : Dict[str, ModelValue] =\
MODELS : ModelSet =\
{
'open_nsfw':
{
@ -26,7 +26,7 @@ MODELS : Dict[str, ModelValue] =\
}
}
PROBABILITY_LIMIT = 0.80
RATE_LIMIT = 5
RATE_LIMIT = 10
STREAM_COUNTER = 0
@ -49,13 +49,15 @@ def clear_content_analyser() -> None:
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_url = MODELS.get('open_nsfw').get('url')
model_path = MODELS.get('open_nsfw').get('path')
if not facefusion.globals.skip_download:
download_directory_path = resolve_relative_path('../.assets/models')
model_url = MODELS.get('open_nsfw').get('url')
process_manager.check()
conditional_download(download_directory_path, [ model_url ])
process_manager.end()
return True
return is_file(model_path)
def analyse_stream(vision_frame : VisionFrame, video_fps : Fps) -> bool:

35
facefusion/core.py
View File

@ -15,7 +15,7 @@ import facefusion.choices
import facefusion.globals
from facefusion.face_analyser import get_one_face, get_average_face
from facefusion.face_store import get_reference_faces, append_reference_face
from facefusion import face_analyser, face_masker, content_analyser, config, process_manager, metadata, logger, wording
from facefusion import face_analyser, face_masker, content_analyser, config, process_manager, metadata, logger, wording, voice_extractor
from facefusion.content_analyser import analyse_image, analyse_video
from facefusion.processors.frame.core import get_frame_processors_modules, load_frame_processor_module
from facefusion.common_helper import create_metavar, get_first
@ -23,7 +23,8 @@ from facefusion.execution import encode_execution_providers, decode_execution_pr
from facefusion.normalizer import normalize_output_path, normalize_padding, normalize_fps
from facefusion.memory import limit_system_memory
from facefusion.statistics import conditional_log_statistics
from facefusion.filesystem import list_directory, get_temp_frame_paths, create_temp, move_temp, clear_temp, is_image, is_video, filter_audio_paths
from facefusion.download import conditional_download
from facefusion.filesystem import list_directory, get_temp_frame_paths, create_temp, move_temp, clear_temp, is_image, is_video, filter_audio_paths, resolve_relative_path
from facefusion.ffmpeg import extract_frames, merge_video, copy_image, finalize_image, restore_audio, replace_audio
from facefusion.vision import read_image, read_static_images, detect_image_resolution, restrict_video_fps, create_image_resolutions, get_video_frame, detect_video_resolution, detect_video_fps, restrict_video_resolution, restrict_image_resolution, create_video_resolutions, pack_resolution, unpack_resolution
@ -33,7 +34,7 @@ warnings.filterwarnings('ignore', category = UserWarning, module = 'gradio')
def cli() -> None:
signal.signal(signal.SIGINT, lambda signal_number, frame: destroy())
program = ArgumentParser(formatter_class = lambda prog: HelpFormatter(prog, max_help_position = 130), add_help = False)
program = ArgumentParser(formatter_class = lambda prog: HelpFormatter(prog, max_help_position = 160), add_help = False)
# general
program.add_argument('-s', '--source', help = wording.get('help.source'), action = 'append', dest = 'source_paths', default = config.get_str_list('general.source_paths'))
program.add_argument('-t', '--target', help = wording.get('help.target'), dest = 'target_path', default = config.get_str_value('general.target_path'))
@ -41,6 +42,7 @@ def cli() -> None:
program.add_argument('-v', '--version', version = metadata.get('name') + ' ' + metadata.get('version'), action = 'version')
# misc
group_misc = program.add_argument_group('misc')
group_misc.add_argument('--force-download', help = wording.get('help.force_download'), action = 'store_true', default = config.get_bool_value('misc.force_download'))
group_misc.add_argument('--skip-download', help = wording.get('help.skip_download'), action = 'store_true', default = config.get_bool_value('misc.skip_download'))
group_misc.add_argument('--headless', help = wording.get('help.headless'), action = 'store_true', default = config.get_bool_value('misc.headless'))
group_misc.add_argument('--log-level', help = wording.get('help.log_level'), default = config.get_str_value('misc.log_level', 'info'), choices = logger.get_log_levels())
@ -89,7 +91,7 @@ def cli() -> None:
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_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)
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'))
# frame processors
available_frame_processors = list_directory('facefusion/processors/frame/modules')
@ -113,6 +115,7 @@ def apply_args(program : ArgumentParser) -> None:
facefusion.globals.target_path = args.target_path
facefusion.globals.output_path = args.output_path
# misc
facefusion.globals.force_download = args.force_download
facefusion.globals.skip_download = args.skip_download
facefusion.globals.headless = args.headless
facefusion.globals.log_level = args.log_level
@ -184,9 +187,13 @@ def apply_args(program : ArgumentParser) -> None:
def run(program : ArgumentParser) -> None:
apply_args(program)
logger.init(facefusion.globals.log_level)
if facefusion.globals.system_memory_limit > 0:
limit_system_memory(facefusion.globals.system_memory_limit)
if not pre_check() or not content_analyser.pre_check() or not face_analyser.pre_check() or not face_masker.pre_check():
if facefusion.globals.force_download:
force_download()
return
if not pre_check() or not content_analyser.pre_check() or not face_analyser.pre_check() or not face_masker.pre_check() or not voice_extractor.pre_check():
return
for frame_processor_module in get_frame_processors_modules(facefusion.globals.frame_processors):
if not frame_processor_module.pre_check():
@ -256,6 +263,24 @@ def conditional_append_reference_faces() -> None:
append_reference_face(frame_processor_module.__name__, reference_face)
def force_download() -> None:
download_directory_path = resolve_relative_path('../.assets/models')
available_frame_processors = list_directory('facefusion/processors/frame/modules')
model_list =\
[
content_analyser.MODELS,
face_analyser.MODELS,
face_masker.MODELS,
voice_extractor.MODELS
]
for frame_processor_module in get_frame_processors_modules(available_frame_processors):
if hasattr(frame_processor_module, 'MODELS'):
model_list.append(frame_processor_module.MODELS)
model_urls = [ models[model].get('url') for models in model_list for model in models ]
conditional_download(download_directory_path, model_urls)
def process_image(start_time : float) -> None:
normed_output_path = normalize_output_path(facefusion.globals.target_path, facefusion.globals.output_path)
if analyse_image(facefusion.globals.target_path):

114
facefusion/face_analyser.py
View File

@ -8,11 +8,11 @@ import onnxruntime
import facefusion.globals
from facefusion import process_manager
from facefusion.common_helper import get_first
from facefusion.face_helper import warp_face_by_face_landmark_5, warp_face_by_translation, create_static_anchors, distance_to_face_landmark_5, distance_to_bounding_box, convert_face_landmark_68_to_5, apply_nms, categorize_age, categorize_gender
from facefusion.face_helper import estimate_matrix_by_face_landmark_5, warp_face_by_face_landmark_5, warp_face_by_translation, create_static_anchors, distance_to_face_landmark_5, distance_to_bounding_box, convert_face_landmark_68_to_5, apply_nms, categorize_age, categorize_gender
from facefusion.face_store import get_static_faces, set_static_faces
from facefusion.execution import apply_execution_provider_options
from facefusion.download import conditional_download
from facefusion.filesystem import resolve_relative_path
from facefusion.filesystem import resolve_relative_path, is_file
from facefusion.typing import VisionFrame, Face, FaceSet, FaceAnalyserOrder, FaceAnalyserAge, FaceAnalyserGender, ModelSet, BoundingBox, FaceLandmarkSet, FaceLandmark5, FaceLandmark68, Score, FaceScoreSet, Embedding
from facefusion.vision import resize_frame_resolution, unpack_resolution
@ -61,11 +61,16 @@ MODELS : ModelSet =\
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/arcface_w600k_r50.onnx',
'path': resolve_relative_path('../.assets/models/arcface_w600k_r50.onnx')
},
'face_landmarker':
'face_landmarker_68':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/2dfan4.onnx',
'path': resolve_relative_path('../.assets/models/2dfan4.onnx')
},
'face_landmarker_68_5':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/face_landmarker_68_5.onnx',
'path': resolve_relative_path('../.assets/models/face_landmarker_68_5.onnx')
},
'gender_age':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gender_age.onnx',
@ -78,22 +83,20 @@ def get_face_analyser() -> Any:
global FACE_ANALYSER
face_detectors = {}
face_landmarkers = {}
with THREAD_LOCK:
while process_manager.is_checking():
sleep(0.5)
if FACE_ANALYSER is None:
if facefusion.globals.face_detector_model in [ 'many', 'retinaface' ]:
face_detector = onnxruntime.InferenceSession(MODELS.get('face_detector_retinaface').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
face_detectors['retinaface'] = face_detector
face_detectors['retinaface'] = onnxruntime.InferenceSession(MODELS.get('face_detector_retinaface').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
if facefusion.globals.face_detector_model in [ 'many', 'scrfd' ]:
face_detector = onnxruntime.InferenceSession(MODELS.get('face_detector_scrfd').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
face_detectors['scrfd'] = face_detector
face_detectors['scrfd'] = onnxruntime.InferenceSession(MODELS.get('face_detector_scrfd').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
if facefusion.globals.face_detector_model in [ 'many', 'yoloface' ]:
face_detector = onnxruntime.InferenceSession(MODELS.get('face_detector_yoloface').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
face_detectors['yoloface'] = face_detector
face_detectors['yoloface'] = onnxruntime.InferenceSession(MODELS.get('face_detector_yoloface').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
if facefusion.globals.face_detector_model in [ 'yunet' ]:
face_detector = cv2.FaceDetectorYN.create(MODELS.get('face_detector_yunet').get('path'), '', (0, 0))
face_detectors['yunet'] = face_detector
face_detectors['yunet'] = cv2.FaceDetectorYN.create(MODELS.get('face_detector_yunet').get('path'), '', (0, 0))
if facefusion.globals.face_recognizer_model == 'arcface_blendswap':
face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_blendswap').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
if facefusion.globals.face_recognizer_model == 'arcface_inswapper':
@ -102,13 +105,14 @@ def get_face_analyser() -> Any:
face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_simswap').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
if facefusion.globals.face_recognizer_model == 'arcface_uniface':
face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_uniface').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
face_landmarker = onnxruntime.InferenceSession(MODELS.get('face_landmarker').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
face_landmarkers['68'] = onnxruntime.InferenceSession(MODELS.get('face_landmarker_68').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
face_landmarkers['68_5'] = onnxruntime.InferenceSession(MODELS.get('face_landmarker_68_5').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
gender_age = onnxruntime.InferenceSession(MODELS.get('gender_age').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
FACE_ANALYSER =\
{
'face_detectors': face_detectors,
'face_recognizer': face_recognizer,
'face_landmarker': face_landmarker,
'face_landmarkers': face_landmarkers,
'gender_age': gender_age
}
return FACE_ANALYSER
@ -121,34 +125,50 @@ def clear_face_analyser() -> Any:
def pre_check() -> bool:
if not facefusion.globals.skip_download:
download_directory_path = resolve_relative_path('../.assets/models')
model_urls =\
[
MODELS.get('face_landmarker').get('url'),
MODELS.get('gender_age').get('url')
]
download_directory_path = resolve_relative_path('../.assets/models')
model_urls =\
[
MODELS.get('face_landmarker_68').get('url'),
MODELS.get('face_landmarker_68_5').get('url'),
MODELS.get('gender_age').get('url')
]
model_paths =\
[
MODELS.get('face_landmarker_68').get('path'),
MODELS.get('face_landmarker_68_5').get('path'),
MODELS.get('gender_age').get('path')
]
if facefusion.globals.face_detector_model in [ 'many', 'retinaface' ]:
model_urls.append(MODELS.get('face_detector_retinaface').get('url'))
if facefusion.globals.face_detector_model in [ 'many', 'scrfd' ]:
model_urls.append(MODELS.get('face_detector_scrfd').get('url'))
if facefusion.globals.face_detector_model in [ 'many', 'yoloface' ]:
model_urls.append(MODELS.get('face_detector_yoloface').get('url'))
if facefusion.globals.face_detector_model in [ 'yunet' ]:
model_urls.append(MODELS.get('face_detector_yunet').get('url'))
if facefusion.globals.face_recognizer_model == 'arcface_blendswap':
model_urls.append(MODELS.get('face_recognizer_arcface_blendswap').get('url'))
if facefusion.globals.face_recognizer_model == 'arcface_inswapper':
model_urls.append(MODELS.get('face_recognizer_arcface_inswapper').get('url'))
if facefusion.globals.face_recognizer_model == 'arcface_simswap':
model_urls.append(MODELS.get('face_recognizer_arcface_simswap').get('url'))
if facefusion.globals.face_recognizer_model == 'arcface_uniface':
model_urls.append(MODELS.get('face_recognizer_arcface_uniface').get('url'))
if facefusion.globals.face_detector_model in [ 'many', 'retinaface' ]:
model_urls.append(MODELS.get('face_detector_retinaface').get('url'))
model_paths.append(MODELS.get('face_detector_retinaface').get('path'))
if facefusion.globals.face_detector_model in [ 'many', 'scrfd' ]:
model_urls.append(MODELS.get('face_detector_scrfd').get('url'))
model_paths.append(MODELS.get('face_detector_scrfd').get('path'))
if facefusion.globals.face_detector_model in [ 'many', 'yoloface' ]:
model_urls.append(MODELS.get('face_detector_yoloface').get('url'))
model_paths.append(MODELS.get('face_detector_yoloface').get('path'))
if facefusion.globals.face_detector_model in [ 'yunet' ]:
model_urls.append(MODELS.get('face_detector_yunet').get('url'))
model_paths.append(MODELS.get('face_detector_yunet').get('path'))
if facefusion.globals.face_recognizer_model == 'arcface_blendswap':
model_urls.append(MODELS.get('face_recognizer_arcface_blendswap').get('url'))
model_paths.append(MODELS.get('face_recognizer_arcface_blendswap').get('path'))
if facefusion.globals.face_recognizer_model == 'arcface_inswapper':
model_urls.append(MODELS.get('face_recognizer_arcface_inswapper').get('url'))
model_paths.append(MODELS.get('face_recognizer_arcface_inswapper').get('path'))
if facefusion.globals.face_recognizer_model == 'arcface_simswap':
model_urls.append(MODELS.get('face_recognizer_arcface_simswap').get('url'))
model_paths.append(MODELS.get('face_recognizer_arcface_simswap').get('path'))
if facefusion.globals.face_recognizer_model == 'arcface_uniface':
model_urls.append(MODELS.get('face_recognizer_arcface_uniface').get('url'))
model_paths.append(MODELS.get('face_recognizer_arcface_uniface').get('path'))
if not facefusion.globals.skip_download:
process_manager.check()
conditional_download(download_directory_path, model_urls)
process_manager.end()
return True
return all(is_file(model_path) for model_path in model_paths)
def detect_with_retinaface(vision_frame : VisionFrame, face_detector_size : str) -> Tuple[List[BoundingBox], List[FaceLandmark5], List[Score]]:
@ -321,7 +341,8 @@ def create_faces(vision_frame : VisionFrame, bounding_box_list : List[BoundingBo
for index in keep_indices:
bounding_box = bounding_box_list[index]
face_landmark_5_68 = face_landmark_5_list[index]
face_landmark_68 = None
face_landmark_68_5 = expand_face_landmark_68_from_5(face_landmark_5_68)
face_landmark_68 = face_landmark_68_5
face_landmark_68_score = 0.0
if facefusion.globals.face_landmarker_score > 0:
face_landmark_68, face_landmark_68_score = detect_face_landmark_68(vision_frame, bounding_box)
@ -331,7 +352,8 @@ def create_faces(vision_frame : VisionFrame, bounding_box_list : List[BoundingBo
{
'5': face_landmark_5_list[index],
'5/68': face_landmark_5_68,
'68': face_landmark_68
'68': face_landmark_68,
'68/5': face_landmark_68_5
}
scores : FaceScoreSet = \
{
@ -368,7 +390,7 @@ def calc_embedding(temp_vision_frame : VisionFrame, face_landmark_5 : FaceLandma
def detect_face_landmark_68(temp_vision_frame : VisionFrame, bounding_box : BoundingBox) -> Tuple[FaceLandmark68, Score]:
face_landmarker = get_face_analyser().get('face_landmarker')
face_landmarker = get_face_analyser().get('face_landmarkers').get('68')
scale = 195 / numpy.subtract(bounding_box[2:], bounding_box[:2]).max()
translation = (256 - numpy.add(bounding_box[2:], bounding_box[:2]) * scale) * 0.5
crop_vision_frame, affine_matrix = warp_face_by_translation(temp_vision_frame, translation, scale, (256, 256))
@ -390,6 +412,18 @@ def detect_face_landmark_68(temp_vision_frame : VisionFrame, bounding_box : Boun
return face_landmark_68, face_landmark_68_score
def expand_face_landmark_68_from_5(face_landmark_5 : FaceLandmark5) -> FaceLandmark68:
face_landmarker = get_face_analyser().get('face_landmarkers').get('68_5')
affine_matrix = estimate_matrix_by_face_landmark_5(face_landmark_5, 'ffhq_512', (1, 1))
face_landmark_5 = cv2.transform(face_landmark_5.reshape(1, -1, 2), affine_matrix).reshape(-1, 2)
face_landmark_68_5 = face_landmarker.run(None,
{
face_landmarker.get_inputs()[0].name: [ face_landmark_5 ]
})[0][0]
face_landmark_68_5 = cv2.transform(face_landmark_68_5.reshape(1, -1, 2), cv2.invertAffineTransform(affine_matrix)).reshape(-1, 2)
return face_landmark_68_5
def detect_gender_age(temp_vision_frame : VisionFrame, bounding_box : BoundingBox) -> Tuple[int, int]:
gender_age = get_face_analyser().get('gender_age')
bounding_box = bounding_box.reshape(2, -1)

23
facefusion/face_helper.py
View File

@ -43,16 +43,21 @@ WARP_TEMPLATES : WarpTemplateSet =\
}
def warp_face_by_face_landmark_5(temp_vision_frame : VisionFrame, face_landmark_5 : FaceLandmark5, warp_template : WarpTemplate, crop_size : Size) -> Tuple[VisionFrame, Matrix]:
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
affine_matrix = cv2.estimateAffinePartial2D(face_landmark_5, normed_warp_template, method = cv2.RANSAC, ransacReprojThreshold = 100)[0]
return affine_matrix
def warp_face_by_face_landmark_5(temp_vision_frame : VisionFrame, face_landmark_5 : FaceLandmark5, warp_template : WarpTemplate, crop_size : Size) -> Tuple[VisionFrame, Matrix]:
affine_matrix = estimate_matrix_by_face_landmark_5(face_landmark_5, warp_template, crop_size)
crop_vision_frame = cv2.warpAffine(temp_vision_frame, affine_matrix, crop_size, borderMode = cv2.BORDER_REPLICATE, flags = cv2.INTER_AREA)
return crop_vision_frame, affine_matrix
def warp_face_by_bounding_box(temp_vision_frame : VisionFrame, bounding_box : BoundingBox, crop_size : Size) -> Tuple[VisionFrame, Matrix]:
source_points = numpy.array([ [ bounding_box[0], bounding_box[1] ], [bounding_box[2], bounding_box[1] ], [ bounding_box[0], bounding_box[3] ] ], dtype = numpy.float32)
target_points = numpy.array([ [ 0, 0 ], [ crop_size[0], 0 ], [ 0, crop_size[1] ] ], dtype = numpy.float32)
source_points = numpy.array([ [ bounding_box[0], bounding_box[1] ], [bounding_box[2], bounding_box[1] ], [ bounding_box[0], bounding_box[3] ] ]).astype(numpy.float32)
target_points = numpy.array([ [ 0, 0 ], [ crop_size[0], 0 ], [ 0, crop_size[1] ] ]).astype(numpy.float32)
affine_matrix = cv2.getAffineTransform(source_points, target_points)
if bounding_box[2] - bounding_box[0] > crop_size[0] or bounding_box[3] - bounding_box[1] > crop_size[1]:
interpolation_method = cv2.INTER_AREA
@ -112,14 +117,14 @@ def distance_to_face_landmark_5(points : numpy.ndarray[Any, Any], distance : num
return face_landmark_5
def convert_face_landmark_68_to_5(landmark_68 : FaceLandmark68) -> FaceLandmark5:
def convert_face_landmark_68_to_5(face_landmark_68 : FaceLandmark68) -> FaceLandmark5:
face_landmark_5 = numpy.array(
[
numpy.mean(landmark_68[36:42], axis = 0),
numpy.mean(landmark_68[42:48], axis = 0),
landmark_68[30],
landmark_68[48],
landmark_68[54]
numpy.mean(face_landmark_68[36:42], axis = 0),
numpy.mean(face_landmark_68[42:48], axis = 0),
face_landmark_68[30],
face_landmark_68[48],
face_landmark_68[54]
])
return face_landmark_5

26
facefusion/face_masker.py
View File

@ -11,7 +11,7 @@ import facefusion.globals
from facefusion import process_manager
from facefusion.typing import FaceLandmark68, VisionFrame, Mask, Padding, FaceMaskRegion, ModelSet
from facefusion.execution import apply_execution_provider_options
from facefusion.filesystem import resolve_relative_path
from facefusion.filesystem import resolve_relative_path, is_file
from facefusion.download import conditional_download
FACE_OCCLUDER = None
@ -49,6 +49,8 @@ def get_face_occluder() -> Any:
global FACE_OCCLUDER
with THREAD_LOCK:
while process_manager.is_checking():
sleep(0.5)
if FACE_OCCLUDER is None:
model_path = MODELS.get('face_occluder').get('path')
FACE_OCCLUDER = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_providers))
@ -80,17 +82,23 @@ def clear_face_parser() -> None:
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_urls =\
[
MODELS.get('face_occluder').get('url'),
MODELS.get('face_parser').get('url')
]
model_paths =\
[
MODELS.get('face_occluder').get('path'),
MODELS.get('face_parser').get('path')
]
if not facefusion.globals.skip_download:
download_directory_path = resolve_relative_path('../.assets/models')
model_urls =\
[
MODELS.get('face_occluder').get('url'),
MODELS.get('face_parser').get('url'),
]
process_manager.check()
conditional_download(download_directory_path, model_urls)
process_manager.end()
return True
return all(is_file(model_path) for model_path in model_paths)
@lru_cache(maxsize = None)
@ -139,7 +147,7 @@ def create_region_mask(crop_vision_frame : VisionFrame, face_mask_regions : List
def create_mouth_mask(face_landmark_68 : FaceLandmark68) -> Mask:
convex_hull = cv2.convexHull(face_landmark_68[numpy.r_[3:14, 31:36]].astype(numpy.int32))
mouth_mask : Mask = numpy.zeros((512, 512), dtype = numpy.float32)
mouth_mask : Mask = numpy.zeros((512, 512)).astype(numpy.float32)
mouth_mask = cv2.fillConvexPoly(mouth_mask, convex_hull, 1.0)
mouth_mask = cv2.erode(mouth_mask.clip(0, 1), numpy.ones((21, 3)))
mouth_mask = cv2.GaussianBlur(mouth_mask, (0, 0), sigmaX = 1, sigmaY = 15)

48
facefusion/ffmpeg.py
View File

@ -1,20 +1,24 @@
from typing import List, Optional
import os
import subprocess
import filetype
import facefusion.globals
from facefusion import process_manager
from facefusion import logger, process_manager
from facefusion.typing import OutputVideoPreset, Fps, AudioBuffer
from facefusion.filesystem import get_temp_frames_pattern, get_temp_output_video_path
from facefusion.vision import restrict_video_fps
def run_ffmpeg(args : List[str]) -> bool:
commands = [ 'ffmpeg', '-hide_banner', '-loglevel', 'quiet' ]
commands = [ 'ffmpeg', '-hide_banner', '-loglevel', 'error' ]
commands.extend(args)
process = subprocess.Popen(commands, stdout = subprocess.PIPE)
process = subprocess.Popen(commands, stderr = subprocess.PIPE, stdout = subprocess.PIPE)
while process_manager.is_processing():
try:
if facefusion.globals.log_level == 'debug':
log_debug(process)
return process.wait(timeout = 0.5) == 0
except subprocess.TimeoutExpired:
continue
@ -27,6 +31,15 @@ def open_ffmpeg(args : List[str]) -> subprocess.Popen[bytes]:
return subprocess.Popen(commands, stdin = subprocess.PIPE, stdout = subprocess.PIPE)
def log_debug(process : subprocess.Popen[bytes]) -> None:
_, stderr = process.communicate()
errors = stderr.decode().split(os.linesep)
for error in errors:
if error.strip():
logger.debug(error.strip(), __name__.upper())
def extract_frames(target_path : str, temp_video_resolution : str, temp_video_fps : Fps) -> bool:
trim_frame_start = facefusion.globals.trim_frame_start
trim_frame_end = facefusion.globals.trim_frame_end
@ -46,9 +59,10 @@ def extract_frames(target_path : str, temp_video_resolution : str, temp_video_fp
def merge_video(target_path : str, output_video_resolution : str, output_video_fps : Fps) -> bool:
temp_video_fps = restrict_video_fps(target_path, output_video_fps)
temp_output_video_path = get_temp_output_video_path(target_path)
temp_frames_pattern = get_temp_frames_pattern(target_path, '%04d')
commands = [ '-hwaccel', 'auto', '-s', str(output_video_resolution), '-r', str(output_video_fps), '-i', temp_frames_pattern, '-c:v', facefusion.globals.output_video_encoder ]
commands = [ '-hwaccel', 'auto', '-s', str(output_video_resolution), '-r', str(temp_video_fps), '-i', temp_frames_pattern, '-c:v', facefusion.globals.output_video_encoder ]
if facefusion.globals.output_video_encoder in [ 'libx264', 'libx265' ]:
output_video_compression = round(51 - (facefusion.globals.output_video_quality * 0.51))
@ -62,7 +76,7 @@ def merge_video(target_path : str, output_video_resolution : str, output_video_f
if facefusion.globals.output_video_encoder in [ 'h264_amf', 'hevc_amf' ]:
output_video_compression = round(51 - (facefusion.globals.output_video_quality * 0.51))
commands.extend([ '-qp_i', str(output_video_compression), '-qp_p', str(output_video_compression), '-quality', map_amf_preset(facefusion.globals.output_video_preset) ])
commands.extend([ '-pix_fmt', 'yuv420p', '-colorspace', 'bt709', '-y', temp_output_video_path ])
commands.extend([ '-vf', 'framerate=fps=' + str(output_video_fps), '-pix_fmt', 'yuv420p', '-colorspace', 'bt709', '-y', temp_output_video_path ])
return run_ffmpeg(commands)
@ -79,8 +93,8 @@ def finalize_image(output_path : str, output_image_resolution : str) -> bool:
return run_ffmpeg(commands)
def read_audio_buffer(target_path : str, sample_rate : int, total_channel : int) -> Optional[AudioBuffer]:
commands = [ '-i', target_path, '-vn', '-f', 's16le', '-acodec', 'pcm_s16le', '-ar', str(sample_rate), '-ac', str(total_channel), '-' ]
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:
@ -106,25 +120,17 @@ def restore_audio(target_path : str, output_path : str, output_video_fps : Fps)
def replace_audio(target_path : str, audio_path : str, output_path : str) -> bool:
temp_output_path = get_temp_output_video_path(target_path)
commands = [ '-hwaccel', 'auto', '-i', temp_output_path, '-i', audio_path, '-c:v', 'copy', '-af', 'apad', '-shortest', '-map', '0:v:0', '-map', '1:a:0', '-y', output_path ]
commands = [ '-hwaccel', 'auto', '-i', temp_output_path, '-i', audio_path, '-c:v', 'copy', '-af', 'apad', '-map', '0:v:0', '-map', '1:a:0', '-shortest', '-y', output_path ]
return run_ffmpeg(commands)
def map_nvenc_preset(output_video_preset : OutputVideoPreset) -> Optional[str]:
if output_video_preset in [ 'ultrafast', 'superfast', 'veryfast' ]:
return 'p1'
if output_video_preset == 'faster':
return 'p2'
if output_video_preset == 'fast':
return 'p3'
if output_video_preset in [ 'ultrafast', 'superfast', 'veryfast', 'faster', 'fast' ]:
return 'fast'
if output_video_preset == 'medium':
return 'p4'
if output_video_preset == 'slow':
return 'p5'
if output_video_preset == 'slower':
return 'p6'
if output_video_preset == 'veryslow':
return 'p7'
return 'medium'
if output_video_preset in [ 'slow', 'slower', 'veryslow' ]:
return 'slow'
return None

1
facefusion/globals.py
View File

@ -7,6 +7,7 @@ source_paths : Optional[List[str]] = None
target_path : Optional[str] = None
output_path : Optional[str] = None
# misc
force_download : Optional[bool] = None
skip_download : Optional[bool] = None
headless : Optional[bool] = None
log_level : Optional[LogLevel] = None

20
facefusion/installer.py
View File

@ -9,26 +9,29 @@ from argparse import ArgumentParser, HelpFormatter
from facefusion import metadata, wording
if platform.system().lower() == 'darwin':
os.environ['SYSTEM_VERSION_COMPAT'] = '0'
ONNXRUNTIMES : Dict[str, Tuple[str, str]] = {}
if platform.system().lower() == 'darwin':
ONNXRUNTIMES['default'] = ('onnxruntime', '1.17.1')
else:
ONNXRUNTIMES['default'] = ('onnxruntime', '1.16.3')
ONNXRUNTIMES['default'] = ('onnxruntime', '1.17.1')
ONNXRUNTIMES['cuda-12.2'] = ('onnxruntime-gpu', '1.17.1')
ONNXRUNTIMES['cuda-11.8'] = ('onnxruntime-gpu', '1.16.3')
ONNXRUNTIMES['openvino'] = ('onnxruntime-openvino', '1.16.0')
ONNXRUNTIMES['cuda-11.8'] = ('onnxruntime-gpu', '1.17.1')
ONNXRUNTIMES['openvino'] = ('onnxruntime-openvino', '1.17.1')
if platform.system().lower() == 'linux':
ONNXRUNTIMES['rocm-5.4.2'] = ('onnxruntime-rocm', '1.16.3')
ONNXRUNTIMES['rocm-5.6'] = ('onnxruntime-rocm', '1.16.3')
if platform.system().lower() == 'windows':
ONNXRUNTIMES['directml'] = ('onnxruntime-directml', '1.16.0')
ONNXRUNTIMES['directml'] = ('onnxruntime-directml', '1.17.1')
def cli() -> None:
program = ArgumentParser(formatter_class = lambda prog: HelpFormatter(prog, max_help_position = 130))
program.add_argument('--onnxruntime', help = wording.get('help.install_dependency').format(dependency = 'onnxruntime'), choices = ONNXRUNTIMES.keys())
program.add_argument('--skip-venv', help = wording.get('help.skip_venv'), action = 'store_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)
@ -37,10 +40,9 @@ def run(program : ArgumentParser) -> None:
args = program.parse_args()
python_id = 'cp' + str(sys.version_info.major) + str(sys.version_info.minor)
if platform.system().lower() == 'darwin':
os.environ['SYSTEM_VERSION_COMPAT'] = '0'
if not args.skip_venv:
os.environ['PIP_REQUIRE_VIRTUALENV'] = '1'
if not args.skip_conda and 'CONDA_PREFIX' not in os.environ:
sys.stdout.write(wording.get('conda_not_activated') + os.linesep)
sys.exit(1)
if args.onnxruntime:
answers =\
{

2
facefusion/metadata.py
View File

@ -2,7 +2,7 @@ METADATA =\
{
'name': 'FaceFusion',
'description': 'Next generation face swapper and enhancer',
'version': '2.4.1',
'version': '2.5.0',
'license': 'MIT',
'author': 'Henry Ruhs',
'url': 'https://facefusion.io'

10
facefusion/processors/frame/choices.py
View File

@ -1,13 +1,15 @@
from typing import List
from facefusion.common_helper import create_int_range
from facefusion.processors.frame.typings import FaceDebuggerItem, FaceEnhancerModel, FaceSwapperModel, FrameEnhancerModel, LipSyncerModel
from facefusion.processors.frame.typings import FaceDebuggerItem, FaceEnhancerModel, FaceSwapperModel, FrameColorizerModel, FrameEnhancerModel, LipSyncerModel
face_debugger_items : List[FaceDebuggerItem] = [ 'bounding-box', 'face-landmark-5', 'face-landmark-5/68', 'face-landmark-68', 'face-mask', 'face-detector-score', 'face-landmarker-score', 'age', 'gender' ]
face_enhancer_models : List[FaceEnhancerModel] = [ 'codeformer', 'gfpgan_1.2', 'gfpgan_1.3', 'gfpgan_1.4', 'gpen_bfr_256', 'gpen_bfr_512', 'restoreformer_plus_plus' ]
face_debugger_items : List[FaceDebuggerItem] = [ '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' ]
face_enhancer_models : List[FaceEnhancerModel] = [ 'codeformer', 'gfpgan_1.2', 'gfpgan_1.3', 'gfpgan_1.4', 'gpen_bfr_256', 'gpen_bfr_512', 'gpen_bfr_1024', 'gpen_bfr_2048', 'restoreformer_plus_plus' ]
face_swapper_models : List[FaceSwapperModel] = [ 'blendswap_256', 'inswapper_128', 'inswapper_128_fp16', 'simswap_256', 'simswap_512_unofficial', 'uniface_256' ]
frame_enhancer_models : List[FrameEnhancerModel] = [ 'lsdir_x4', 'nomos8k_sc_x4', 'real_esrgan_x4', 'real_esrgan_x4_fp16', 'span_kendata_x4' ]
frame_colorizer_models : List[FrameColorizerModel] = [ 'ddcolor', 'ddcolor_artistic', 'deoldify_artistic' ]
frame_enhancer_models : List[FrameEnhancerModel] = [ 'lsdir_x4', 'nomos8k_sc_x4', 'real_esrgan_x2', 'real_esrgan_x2_fp16', 'real_esrgan_x4', 'real_esrgan_x4_fp16', 'real_hatgan_x4', 'span_kendata_x4' ]
lip_syncer_models : List[LipSyncerModel] = [ 'wav2lip_gan' ]
face_enhancer_blend_range : List[int] = create_int_range(0, 100, 1)
frame_colorizer_blend_range : List[int] = create_int_range(0, 100, 1)
frame_enhancer_blend_range : List[int] = create_int_range(0, 100, 1)

4
facefusion/processors/frame/globals.py
View File

@ -1,11 +1,13 @@
from typing import List, Optional
from facefusion.processors.frame.typings import FaceDebuggerItem, FaceEnhancerModel, FaceSwapperModel, FrameEnhancerModel, LipSyncerModel
from facefusion.processors.frame.typings import FaceDebuggerItem, FaceEnhancerModel, FaceSwapperModel, FrameColorizerModel, FrameEnhancerModel, LipSyncerModel
face_debugger_items : Optional[List[FaceDebuggerItem]] = None
face_enhancer_model : Optional[FaceEnhancerModel] = None
face_enhancer_blend : Optional[int] = None
face_swapper_model : Optional[FaceSwapperModel] = None
frame_colorizer_model : Optional[FrameColorizerModel] = None
frame_colorizer_blend : Optional[int] = None
frame_enhancer_model : Optional[FrameEnhancerModel] = None
frame_enhancer_blend : Optional[int] = None
lip_syncer_model : Optional[LipSyncerModel] = None

13
facefusion/processors/frame/modules/face_debugger.py
View File

@ -11,7 +11,7 @@ from facefusion.face_masker import create_static_box_mask, create_occlusion_mask
from facefusion.face_helper import warp_face_by_face_landmark_5, categorize_age, categorize_gender
from facefusion.face_store import get_reference_faces
from facefusion.content_analyser import clear_content_analyser
from facefusion.typing import Face, VisionFrame, UpdateProcess, ProcessMode, QueuePayload
from facefusion.typing import Face, VisionFrame, UpdateProgress, ProcessMode, QueuePayload
from facefusion.vision import read_image, read_static_image, write_image
from facefusion.processors.frame.typings import FaceDebuggerInputs
from facefusion.processors.frame import globals as frame_processors_globals, choices as frame_processors_choices
@ -74,6 +74,7 @@ def debug_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFra
bounding_box = target_face.bounding_box.astype(numpy.int32)
temp_vision_frame = temp_vision_frame.copy()
has_face_landmark_5_fallback = numpy.array_equal(target_face.landmarks.get('5'), target_face.landmarks.get('5/68'))
has_face_landmark_68_fallback = numpy.array_equal(target_face.landmarks.get('68'), target_face.landmarks.get('68/5'))
if 'bounding-box' in frame_processors_globals.face_debugger_items:
cv2.rectangle(temp_vision_frame, (bounding_box[0], bounding_box[1]), (bounding_box[2], bounding_box[3]), primary_color, 2)
@ -109,7 +110,11 @@ def debug_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFra
if 'face-landmark-68' in frame_processors_globals.face_debugger_items and numpy.any(target_face.landmarks.get('68')):
face_landmark_68 = target_face.landmarks.get('68').astype(numpy.int32)
for index in range(face_landmark_68.shape[0]):
cv2.circle(temp_vision_frame, (face_landmark_68[index][0], face_landmark_68[index][1]), 3, secondary_color, -1)
cv2.circle(temp_vision_frame, (face_landmark_68[index][0], face_landmark_68[index][1]), 3, tertiary_color if has_face_landmark_68_fallback else secondary_color, -1)
if 'face-landmark-68/5' in frame_processors_globals.face_debugger_items and numpy.any(target_face.landmarks.get('68')):
face_landmark_68 = target_face.landmarks.get('68/5').astype(numpy.int32)
for index in range(face_landmark_68.shape[0]):
cv2.circle(temp_vision_frame, (face_landmark_68[index][0], face_landmark_68[index][1]), 3, primary_color, -1)
if bounding_box[3] - bounding_box[1] > 50 and bounding_box[2] - bounding_box[0] > 50:
top = bounding_box[1]
left = bounding_box[0] - 20
@ -157,7 +162,7 @@ def process_frame(inputs : FaceDebuggerInputs) -> VisionFrame:
return target_vision_frame
def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProcess) -> None:
def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProgress) -> None:
reference_faces = get_reference_faces() if 'reference' in facefusion.globals.face_selector_mode else None
for queue_payload in process_manager.manage(queue_payloads):
@ -169,7 +174,7 @@ def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload]
'target_vision_frame': target_vision_frame
})
write_image(target_vision_path, output_vision_frame)
update_progress()
update_progress(1)
def process_image(source_paths : List[str], target_path : str, output_path : str) -> None:

33
facefusion/processors/frame/modules/face_enhancer.py
View File

@ -16,7 +16,7 @@ from facefusion.execution import apply_execution_provider_options
from facefusion.content_analyser import clear_content_analyser
from facefusion.face_store import get_reference_faces
from facefusion.normalizer import normalize_output_path
from facefusion.typing import Face, VisionFrame, UpdateProcess, ProcessMode, ModelSet, OptionsWithModel, QueuePayload
from facefusion.typing import Face, VisionFrame, UpdateProgress, ProcessMode, ModelSet, OptionsWithModel, QueuePayload
from facefusion.common_helper import create_metavar
from facefusion.filesystem import is_file, is_image, is_video, resolve_relative_path
from facefusion.download import conditional_download, is_download_done
@ -73,6 +73,20 @@ MODELS : ModelSet =\
'template': 'ffhq_512',
'size': (512, 512)
},
'gpen_bfr_1024':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gpen_bfr_1024.onnx',
'path': resolve_relative_path('../.assets/models/gpen_bfr_1024.onnx'),
'template': 'ffhq_512',
'size': (1024, 1024)
},
'gpen_bfr_2048':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gpen_bfr_2048.onnx',
'path': resolve_relative_path('../.assets/models/gpen_bfr_2048.onnx'),
'template': 'ffhq_512',
'size': (2048, 2048)
},
'restoreformer_plus_plus':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/restoreformer_plus_plus.onnx',
@ -131,22 +145,25 @@ def apply_args(program : ArgumentParser) -> None:
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_url = get_options('model').get('url')
model_path = get_options('model').get('path')
if not facefusion.globals.skip_download:
download_directory_path = resolve_relative_path('../.assets/models')
model_url = get_options('model').get('url')
process_manager.check()
conditional_download(download_directory_path, [ model_url ])
process_manager.end()
return True
return is_file(model_path)
def post_check() -> bool:
model_url = get_options('model').get('url')
model_path = get_options('model').get('path')
if not facefusion.globals.skip_download and not is_download_done(model_url, model_path):
logger.error(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME)
return False
elif not is_file(model_path):
if not is_file(model_path):
logger.error(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
return False
return True
@ -202,7 +219,7 @@ def apply_enhance(crop_vision_frame : VisionFrame) -> VisionFrame:
if frame_processor_input.name == 'input':
frame_processor_inputs[frame_processor_input.name] = crop_vision_frame
if frame_processor_input.name == 'weight':
weight = numpy.array([ 1 ], dtype = numpy.double)
weight = numpy.array([ 1 ]).astype(numpy.double)
frame_processor_inputs[frame_processor_input.name] = weight
with THREAD_SEMAPHORE:
crop_vision_frame = frame_processor.run(None, frame_processor_inputs)[0][0]
@ -256,7 +273,7 @@ def process_frame(inputs : FaceEnhancerInputs) -> VisionFrame:
return target_vision_frame
def process_frames(source_path : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProcess) -> None:
def process_frames(source_path : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProgress) -> None:
reference_faces = get_reference_faces() if 'reference' in facefusion.globals.face_selector_mode else None
for queue_payload in process_manager.manage(queue_payloads):
@ -268,7 +285,7 @@ def process_frames(source_path : List[str], queue_payloads : List[QueuePayload],
'target_vision_frame': target_vision_frame
})
write_image(target_vision_path, output_vision_frame)
update_progress()
update_progress(1)
def process_image(source_path : str, target_path : str, output_path : str) -> None:

46
facefusion/processors/frame/modules/face_swapper.py
View File

@ -1,6 +1,7 @@
from typing import Any, List, Literal, Optional
from argparse import ArgumentParser
from time import sleep
import platform
import threading
import numpy
import onnx
@ -15,10 +16,9 @@ from facefusion.face_analyser import get_one_face, get_average_face, get_many_fa
from facefusion.face_masker import create_static_box_mask, create_occlusion_mask, create_region_mask, clear_face_occluder, clear_face_parser
from facefusion.face_helper import warp_face_by_face_landmark_5, paste_back
from facefusion.face_store import get_reference_faces
from facefusion.common_helper import extract_major_version
from facefusion.content_analyser import clear_content_analyser
from facefusion.normalizer import normalize_output_path
from facefusion.typing import Face, Embedding, VisionFrame, UpdateProcess, ProcessMode, ModelSet, OptionsWithModel, QueuePayload
from facefusion.typing import Face, Embedding, VisionFrame, UpdateProgress, ProcessMode, ModelSet, OptionsWithModel, QueuePayload
from facefusion.filesystem import is_file, is_image, has_image, is_video, filter_image_paths, resolve_relative_path
from facefusion.download import conditional_download, is_download_done
from facefusion.vision import read_image, read_static_image, read_static_images, write_image
@ -27,7 +27,7 @@ from facefusion.processors.frame import globals as frame_processors_globals
from facefusion.processors.frame import choices as frame_processors_choices
FRAME_PROCESSOR = None
MODEL_MATRIX = None
MODEL_INITIALIZER = None
THREAD_LOCK : threading.Lock = threading.Lock()
NAME = __name__.upper()
MODELS : ModelSet =\
@ -114,23 +114,23 @@ def clear_frame_processor() -> None:
FRAME_PROCESSOR = None
def get_model_matrix() -> Any:
global MODEL_MATRIX
def get_model_initializer() -> Any:
global MODEL_INITIALIZER
with THREAD_LOCK:
while process_manager.is_checking():
sleep(0.5)
if MODEL_MATRIX is None:
if MODEL_INITIALIZER is None:
model_path = get_options('model').get('path')
model = onnx.load(model_path)
MODEL_MATRIX = numpy_helper.to_array(model.graph.initializer[-1])
return MODEL_MATRIX
MODEL_INITIALIZER = numpy_helper.to_array(model.graph.initializer[-1])
return MODEL_INITIALIZER
def clear_model_matrix() -> None:
global MODEL_MATRIX
def clear_model_initializer() -> None:
global MODEL_INITIALIZER
MODEL_MATRIX = None
MODEL_INITIALIZER = None
def get_options(key : Literal['model']) -> Any:
@ -151,8 +151,7 @@ def set_options(key : Literal['model'], value : Any) -> None:
def register_args(program : ArgumentParser) -> None:
onnxruntime_version = extract_major_version(onnxruntime.__version__)
if onnxruntime_version > (1, 16):
if platform.system().lower() == 'darwin':
face_swapper_model_fallback = 'inswapper_128'
else:
face_swapper_model_fallback = 'inswapper_128_fp16'
@ -173,22 +172,25 @@ def apply_args(program : ArgumentParser) -> None:
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_url = get_options('model').get('url')
model_path = get_options('model').get('path')
if not facefusion.globals.skip_download:
download_directory_path = resolve_relative_path('../.assets/models')
model_url = get_options('model').get('url')
process_manager.check()
conditional_download(download_directory_path, [ model_url ])
process_manager.end()
return True
return is_file(model_path)
def post_check() -> bool:
model_url = get_options('model').get('url')
model_path = get_options('model').get('path')
if not facefusion.globals.skip_download and not is_download_done(model_url, model_path):
logger.error(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME)
return False
elif not is_file(model_path):
if not is_file(model_path):
logger.error(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
return False
return True
@ -216,8 +218,8 @@ def pre_process(mode : ProcessMode) -> bool:
def post_process() -> None:
read_static_image.cache_clear()
if facefusion.globals.video_memory_strategy == 'strict' or facefusion.globals.video_memory_strategy == 'moderate':
clear_model_initializer()
clear_frame_processor()
clear_model_matrix()
if facefusion.globals.video_memory_strategy == 'strict':
clear_face_analyser()
clear_content_analyser()
@ -281,9 +283,9 @@ def prepare_source_frame(source_face : Face) -> VisionFrame:
def prepare_source_embedding(source_face : Face) -> Embedding:
model_type = get_options('model').get('type')
if model_type == 'inswapper':
model_matrix = get_model_matrix()
model_initializer = get_model_initializer()
source_embedding = source_face.embedding.reshape((1, -1))
source_embedding = numpy.dot(source_embedding, model_matrix) / numpy.linalg.norm(source_embedding)
source_embedding = numpy.dot(source_embedding, model_initializer) / numpy.linalg.norm(source_embedding)
else:
source_embedding = source_face.normed_embedding.reshape(1, -1)
return source_embedding
@ -332,7 +334,7 @@ def process_frame(inputs : FaceSwapperInputs) -> VisionFrame:
return target_vision_frame
def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProcess) -> None:
def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProgress) -> None:
reference_faces = get_reference_faces() if 'reference' in facefusion.globals.face_selector_mode else None
source_frames = read_static_images(source_paths)
source_face = get_average_face(source_frames)
@ -347,7 +349,7 @@ def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload]
'target_vision_frame': target_vision_frame
})
write_image(target_vision_path, output_vision_frame)
update_progress()
update_progress(1)
def process_image(source_paths : List[str], target_path : str, output_path : str) -> None:

232
facefusion/processors/frame/modules/frame_colorizer.py Normal file
View File

@ -0,0 +1,232 @@
from typing import Any, List, Literal, Optional
from argparse import ArgumentParser
from time import sleep
import threading
import cv2
import numpy
import onnxruntime
import facefusion.globals
import facefusion.processors.frame.core as frame_processors
from facefusion import config, process_manager, logger, wording
from facefusion.face_analyser import clear_face_analyser
from facefusion.content_analyser import clear_content_analyser
from facefusion.execution import apply_execution_provider_options
from facefusion.normalizer import normalize_output_path
from facefusion.typing import Face, VisionFrame, UpdateProgress, ProcessMode, ModelSet, OptionsWithModel, QueuePayload
from facefusion.common_helper import create_metavar
from facefusion.filesystem import is_file, resolve_relative_path, is_image, is_video
from facefusion.download import conditional_download, is_download_done
from facefusion.vision import read_image, read_static_image, write_image
from facefusion.processors.frame.typings import FrameColorizerInputs
from facefusion.processors.frame import globals as frame_processors_globals
from facefusion.processors.frame import choices as frame_processors_choices
FRAME_PROCESSOR = None
THREAD_LOCK : threading.Lock = threading.Lock()
THREAD_SEMAPHORE : threading.Semaphore = threading.Semaphore()
NAME = __name__.upper()
MODELS : ModelSet =\
{
'ddcolor':
{
'type': 'ddcolor',
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/ddcolor.onnx',
'path': resolve_relative_path('../.assets/models/ddcolor.onnx'),
'size': (512, 512)
},
'ddcolor_artistic':
{
'type': 'ddcolor',
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/ddcolor_artistic.onnx',
'path': resolve_relative_path('../.assets/models/ddcolor_artistic.onnx'),
'size': (512, 512)
},
'deoldify_artistic':
{
'type': 'deoldify',
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/deoldify_artistic.onnx',
'path': resolve_relative_path('../.assets/models/deoldify_artistic.onnx'),
'size': (512, 512)
}
}
OPTIONS : Optional[OptionsWithModel] = None
def get_frame_processor() -> Any:
global FRAME_PROCESSOR
with THREAD_LOCK:
while process_manager.is_checking():
sleep(0.5)
if FRAME_PROCESSOR is None:
model_path = get_options('model').get('path')
FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_providers))
return FRAME_PROCESSOR
def clear_frame_processor() -> None:
global FRAME_PROCESSOR
FRAME_PROCESSOR = None
def get_options(key : Literal['model']) -> Any:
global OPTIONS
if OPTIONS is None:
OPTIONS =\
{
'model': MODELS[frame_processors_globals.frame_colorizer_model]
}
return OPTIONS.get(key)
def set_options(key : Literal['model'], value : Any) -> None:
global OPTIONS
OPTIONS[key] = value
def register_args(program : ArgumentParser) -> None:
program.add_argument('--frame-colorizer-model', help = wording.get('help.frame_colorizer_model'), default = config.get_str_value('frame_processors.frame_colorizer_model', 'ddcolor'), choices = frame_processors_choices.frame_colorizer_models)
program.add_argument('--frame-colorizer-blend', help = wording.get('help.frame_colorizer_blend'), type = int, default = config.get_int_value('frame_processors.frame_colorizer_blend', '100'), choices = frame_processors_choices.frame_colorizer_blend_range, metavar = create_metavar(frame_processors_choices.frame_colorizer_blend_range))
def apply_args(program : ArgumentParser) -> None:
args = program.parse_args()
frame_processors_globals.frame_colorizer_model = args.frame_colorizer_model
frame_processors_globals.frame_colorizer_blend = args.frame_colorizer_blend
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_url = get_options('model').get('url')
model_path = get_options('model').get('path')
if not facefusion.globals.skip_download:
process_manager.check()
conditional_download(download_directory_path, [ model_url ])
process_manager.end()
return is_file(model_path)
def post_check() -> bool:
model_url = get_options('model').get('url')
model_path = get_options('model').get('path')
if not facefusion.globals.skip_download and not is_download_done(model_url, model_path):
logger.error(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME)
return False
if not is_file(model_path):
logger.error(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
return False
return True
def pre_process(mode : ProcessMode) -> bool:
if mode in [ 'output', 'preview' ] and not is_image(facefusion.globals.target_path) and not is_video(facefusion.globals.target_path):
logger.error(wording.get('select_image_or_video_target') + wording.get('exclamation_mark'), NAME)
return False
if mode == 'output' and not normalize_output_path(facefusion.globals.target_path, facefusion.globals.output_path):
logger.error(wording.get('select_file_or_directory_output') + wording.get('exclamation_mark'), NAME)
return False
return True
def post_process() -> None:
read_static_image.cache_clear()
if facefusion.globals.video_memory_strategy == 'strict' or facefusion.globals.video_memory_strategy == 'moderate':
clear_frame_processor()
if facefusion.globals.video_memory_strategy == 'strict':
clear_face_analyser()
clear_content_analyser()
def colorize_frame(temp_vision_frame : VisionFrame) -> VisionFrame:
frame_processor = get_frame_processor()
prepare_vision_frame = prepare_temp_frame(temp_vision_frame)
with THREAD_SEMAPHORE:
color_vision_frame = frame_processor.run(None,
{
frame_processor.get_inputs()[0].name: prepare_vision_frame
})[0][0]
color_vision_frame = merge_color_frame(temp_vision_frame, color_vision_frame)
color_vision_frame = blend_frame(temp_vision_frame, color_vision_frame)
return color_vision_frame
def prepare_temp_frame(temp_vision_frame : VisionFrame) -> VisionFrame:
model_size = get_options('model').get('size')
model_type = get_options('model').get('type')
temp_vision_frame = cv2.cvtColor(temp_vision_frame, cv2.COLOR_BGR2GRAY)
temp_vision_frame = cv2.cvtColor(temp_vision_frame, cv2.COLOR_GRAY2RGB)
if model_type == 'ddcolor':
temp_vision_frame = (temp_vision_frame / 255.0).astype(numpy.float32)
temp_vision_frame = cv2.cvtColor(temp_vision_frame, cv2.COLOR_RGB2LAB)[:, :, :1]
temp_vision_frame = numpy.concatenate((temp_vision_frame, numpy.zeros_like(temp_vision_frame), numpy.zeros_like(temp_vision_frame)), axis = -1)
temp_vision_frame = cv2.cvtColor(temp_vision_frame, cv2.COLOR_LAB2RGB)
temp_vision_frame = cv2.resize(temp_vision_frame, model_size)
temp_vision_frame = temp_vision_frame.transpose((2, 0, 1))
temp_vision_frame = numpy.expand_dims(temp_vision_frame, axis = 0).astype(numpy.float32)
return temp_vision_frame
def merge_color_frame(temp_vision_frame : VisionFrame, color_vision_frame : VisionFrame) -> VisionFrame:
model_type = get_options('model').get('type')
color_vision_frame = color_vision_frame.transpose(1, 2, 0)
color_vision_frame = cv2.resize(color_vision_frame, (temp_vision_frame.shape[1], temp_vision_frame.shape[0]))
if model_type == 'ddcolor':
temp_vision_frame = (temp_vision_frame / 255.0).astype(numpy.float32)
temp_vision_frame = cv2.cvtColor(temp_vision_frame, cv2.COLOR_BGR2LAB)[:, :, :1]
color_vision_frame = numpy.concatenate((temp_vision_frame, color_vision_frame), axis = -1)
color_vision_frame = cv2.cvtColor(color_vision_frame, cv2.COLOR_LAB2BGR)
color_vision_frame = (color_vision_frame * 255.0).round().astype(numpy.uint8)
if model_type == 'deoldify':
temp_blue_channel, _, _ = cv2.split(temp_vision_frame)
color_vision_frame = cv2.cvtColor(color_vision_frame, cv2.COLOR_BGR2RGB).astype(numpy.uint8)
color_vision_frame = cv2.cvtColor(color_vision_frame, cv2.COLOR_BGR2LAB)
_, color_green_channel, color_red_channel = cv2.split(color_vision_frame)
color_vision_frame = cv2.merge((temp_blue_channel, color_green_channel, color_red_channel))
color_vision_frame = cv2.cvtColor(color_vision_frame, cv2.COLOR_LAB2BGR)
return color_vision_frame
def blend_frame(temp_vision_frame : VisionFrame, paste_vision_frame : VisionFrame) -> VisionFrame:
frame_colorizer_blend = 1 - (frame_processors_globals.frame_colorizer_blend / 100)
temp_vision_frame = cv2.addWeighted(temp_vision_frame, frame_colorizer_blend, paste_vision_frame, 1 - frame_colorizer_blend, 0)
return temp_vision_frame
def get_reference_frame(source_face : Face, target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
pass
def process_frame(inputs : FrameColorizerInputs) -> VisionFrame:
target_vision_frame = inputs.get('target_vision_frame')
return colorize_frame(target_vision_frame)
def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProgress) -> None:
for queue_payload in process_manager.manage(queue_payloads):
target_vision_path = queue_payload['frame_path']
target_vision_frame = read_image(target_vision_path)
output_vision_frame = process_frame(
{
'target_vision_frame': target_vision_frame
})
write_image(target_vision_path, output_vision_frame)
update_progress(1)
def process_image(source_paths : List[str], target_path : str, output_path : str) -> None:
target_vision_frame = read_static_image(target_path)
output_vision_frame = process_frame(
{
'target_vision_frame': target_vision_frame
})
write_image(output_path, output_vision_frame)
def process_video(source_paths : List[str], temp_frame_paths : List[str]) -> None:
frame_processors.multi_process_frames(None, temp_frame_paths, process_frames)

48
facefusion/processors/frame/modules/frame_enhancer.py
View File

@ -13,7 +13,7 @@ from facefusion.face_analyser import clear_face_analyser
from facefusion.content_analyser import clear_content_analyser
from facefusion.execution import apply_execution_provider_options
from facefusion.normalizer import normalize_output_path
from facefusion.typing import Face, VisionFrame, UpdateProcess, ProcessMode, ModelSet, OptionsWithModel, QueuePayload
from facefusion.typing import Face, VisionFrame, UpdateProgress, ProcessMode, ModelSet, OptionsWithModel, QueuePayload
from facefusion.common_helper import create_metavar
from facefusion.filesystem import is_file, resolve_relative_path, is_image, is_video
from facefusion.download import conditional_download, is_download_done
@ -41,6 +41,20 @@ MODELS : ModelSet =\
'size': (128, 8, 2),
'scale': 4
},
'real_esrgan_x2':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/real_esrgan_x2.onnx',
'path': resolve_relative_path('../.assets/models/real_esrgan_x2.onnx'),
'size': (128, 8, 2),
'scale': 2
},
'real_esrgan_x2_fp16':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/real_esrgan_x2_fp16.onnx',
'path': resolve_relative_path('../.assets/models/real_esrgan_x2_fp16.onnx'),
'size': (128, 8, 2),
'scale': 2
},
'real_esrgan_x4':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/real_esrgan_x4.onnx',
@ -55,6 +69,13 @@ MODELS : ModelSet =\
'size': (128, 8, 2),
'scale': 4
},
'real_hatgan_x4':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/real_hatgan_x4.onnx',
'path': resolve_relative_path('../.assets/models/real_hatgan_x4.onnx'),
'size': (256, 8, 2),
'scale': 4
},
'span_kendata_x4':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/span_kendata_x4.onnx',
@ -113,22 +134,25 @@ def apply_args(program : ArgumentParser) -> None:
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_url = get_options('model').get('url')
model_path = get_options('model').get('path')
if not facefusion.globals.skip_download:
download_directory_path = resolve_relative_path('../.assets/models')
model_url = get_options('model').get('url')
process_manager.check()
conditional_download(download_directory_path, [ model_url ])
process_manager.end()
return True
return is_file(model_path)
def post_check() -> bool:
model_url = get_options('model').get('url')
model_path = get_options('model').get('path')
if not facefusion.globals.skip_download and not is_download_done(model_url, model_path):
logger.error(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME)
return False
elif not is_file(model_path):
if not is_file(model_path):
logger.error(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
return False
return True
@ -172,7 +196,7 @@ def enhance_frame(temp_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 = 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
return vision_tile_frame
@ -180,14 +204,14 @@ def prepare_tile_frame(vision_tile_frame : VisionFrame) -> VisionFrame:
def normalize_tile_frame(vision_tile_frame : VisionFrame) -> VisionFrame:
vision_tile_frame = vision_tile_frame.transpose(0, 2, 3, 1).squeeze(0) * 255
vision_tile_frame = vision_tile_frame.clip(0, 255).astype(numpy.uint8)[:,:,::-1]
vision_tile_frame = vision_tile_frame.clip(0, 255).astype(numpy.uint8)[:, :, ::-1]
return vision_tile_frame
def blend_frame(temp_vision_frame : VisionFrame, paste_vision_frame : VisionFrame) -> VisionFrame:
def blend_frame(temp_vision_frame : VisionFrame, merge_vision_frame : VisionFrame) -> VisionFrame:
frame_enhancer_blend = 1 - (frame_processors_globals.frame_enhancer_blend / 100)
temp_vision_frame = cv2.resize(temp_vision_frame, (paste_vision_frame.shape[1], paste_vision_frame.shape[0]))
temp_vision_frame = cv2.addWeighted(temp_vision_frame, frame_enhancer_blend, paste_vision_frame, 1 - frame_enhancer_blend, 0)
temp_vision_frame = cv2.resize(temp_vision_frame, (merge_vision_frame.shape[1], merge_vision_frame.shape[0]))
temp_vision_frame = cv2.addWeighted(temp_vision_frame, frame_enhancer_blend, merge_vision_frame, 1 - frame_enhancer_blend, 0)
return temp_vision_frame
@ -200,7 +224,7 @@ def process_frame(inputs : FrameEnhancerInputs) -> VisionFrame:
return enhance_frame(target_vision_frame)
def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProcess) -> None:
def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProgress) -> None:
for queue_payload in process_manager.manage(queue_payloads):
target_vision_path = queue_payload['frame_path']
target_vision_frame = read_image(target_vision_path)
@ -209,7 +233,7 @@ def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload]
'target_vision_frame': target_vision_frame
})
write_image(target_vision_path, output_vision_frame)
update_progress()
update_progress(1)
def process_image(source_paths : List[str], target_path : str, output_path : str) -> None:

44
facefusion/processors/frame/modules/lip_syncer.py
View File

@ -16,19 +16,19 @@ from facefusion.face_helper import warp_face_by_face_landmark_5, warp_face_by_bo
from facefusion.face_store import get_reference_faces
from facefusion.content_analyser import clear_content_analyser
from facefusion.normalizer import normalize_output_path
from facefusion.typing import Face, VisionFrame, UpdateProcess, ProcessMode, ModelSet, OptionsWithModel, AudioFrame, QueuePayload
from facefusion.typing import Face, VisionFrame, UpdateProgress, ProcessMode, ModelSet, OptionsWithModel, AudioFrame, QueuePayload
from facefusion.filesystem import is_file, has_audio, resolve_relative_path
from facefusion.download import conditional_download, is_download_done
from facefusion.audio import read_static_audio, get_audio_frame, create_empty_audio_frame
from facefusion.audio import read_static_voice, get_voice_frame, create_empty_audio_frame
from facefusion.filesystem import is_image, is_video, filter_audio_paths
from facefusion.common_helper import get_first
from facefusion.vision import read_image, write_image, read_static_image
from facefusion.vision import read_image, read_static_image, write_image, restrict_video_fps
from facefusion.processors.frame.typings import LipSyncerInputs
from facefusion.voice_extractor import clear_voice_extractor
from facefusion.processors.frame import globals as frame_processors_globals
from facefusion.processors.frame import choices as frame_processors_choices
FRAME_PROCESSOR = None
MODEL_MATRIX = None
THREAD_LOCK : threading.Lock = threading.Lock()
NAME = __name__.upper()
MODELS : ModelSet =\
@ -36,7 +36,7 @@ MODELS : ModelSet =\
'wav2lip_gan':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/wav2lip_gan.onnx',
'path': resolve_relative_path('../.assets/models/wav2lip_gan.onnx'),
'path': resolve_relative_path('../.assets/models/wav2lip_gan.onnx')
}
}
OPTIONS : Optional[OptionsWithModel] = None
@ -87,22 +87,25 @@ def apply_args(program : ArgumentParser) -> None:
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_url = get_options('model').get('url')
model_path = get_options('model').get('path')
if not facefusion.globals.skip_download:
download_directory_path = resolve_relative_path('../.assets/models')
model_url = get_options('model').get('url')
process_manager.check()
conditional_download(download_directory_path, [ model_url ])
process_manager.end()
return True
return is_file(model_path)
def post_check() -> bool:
model_url = get_options('model').get('url')
model_path = get_options('model').get('path')
if not facefusion.globals.skip_download and not is_download_done(model_url, model_path):
logger.error(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME)
return False
elif not is_file(model_path):
if not is_file(model_path):
logger.error(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
return False
return True
@ -123,7 +126,7 @@ def pre_process(mode : ProcessMode) -> bool:
def post_process() -> None:
read_static_image.cache_clear()
read_static_audio.cache_clear()
read_static_voice.cache_clear()
if facefusion.globals.video_memory_strategy == 'strict' or facefusion.globals.video_memory_strategy == 'moderate':
clear_frame_processor()
if facefusion.globals.video_memory_strategy == 'strict':
@ -131,6 +134,7 @@ def post_process() -> None:
clear_content_analyser()
clear_face_occluder()
clear_face_parser()
clear_voice_extractor()
def sync_lip(target_face : Face, temp_audio_frame : AudioFrame, temp_vision_frame : VisionFrame) -> VisionFrame:
@ -138,14 +142,11 @@ def sync_lip(target_face : Face, temp_audio_frame : AudioFrame, temp_vision_fram
crop_mask_list = []
temp_audio_frame = prepare_audio_frame(temp_audio_frame)
crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, target_face.landmarks.get('5/68'), 'ffhq_512', (512, 512))
if numpy.any(target_face.landmarks.get('68')):
face_landmark_68 = cv2.transform(target_face.landmarks.get('68').reshape(1, -1, 2), affine_matrix).reshape(-1, 2)
bounding_box = create_bounding_box_from_face_landmark_68(face_landmark_68)
bounding_box[1] -= numpy.abs(bounding_box[3] - bounding_box[1]) * 0.125
mouth_mask = create_mouth_mask(face_landmark_68)
crop_mask_list.append(mouth_mask)
else:
bounding_box = target_face.bounding_box
face_landmark_68 = cv2.transform(target_face.landmarks.get('68').reshape(1, -1, 2), affine_matrix).reshape(-1, 2)
bounding_box = create_bounding_box_from_face_landmark_68(face_landmark_68)
bounding_box[1] -= numpy.abs(bounding_box[3] - bounding_box[1]) * 0.125
mouth_mask = create_mouth_mask(face_landmark_68)
crop_mask_list.append(mouth_mask)
box_mask = create_static_box_mask(crop_vision_frame.shape[:2][::-1], facefusion.globals.face_mask_blur, facefusion.globals.face_mask_padding)
crop_mask_list.append(box_mask)
@ -216,14 +217,15 @@ def process_frame(inputs : LipSyncerInputs) -> VisionFrame:
return target_vision_frame
def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProcess) -> None:
def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProgress) -> None:
reference_faces = get_reference_faces() if 'reference' in facefusion.globals.face_selector_mode else None
source_audio_path = get_first(filter_audio_paths(source_paths))
temp_video_fps = restrict_video_fps(facefusion.globals.target_path, facefusion.globals.output_video_fps)
for queue_payload in process_manager.manage(queue_payloads):
frame_number = queue_payload['frame_number']
target_vision_path = queue_payload['frame_path']
source_audio_frame = get_audio_frame(source_audio_path, facefusion.globals.output_video_fps, frame_number)
source_audio_frame = get_voice_frame(source_audio_path, temp_video_fps, frame_number)
if not numpy.any(source_audio_frame):
source_audio_frame = create_empty_audio_frame()
target_vision_frame = read_image(target_vision_path)
@ -234,7 +236,7 @@ def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload]
'target_vision_frame': target_vision_frame
})
write_image(target_vision_path, output_vision_frame)
update_progress()
update_progress(1)
def process_image(source_paths : List[str], target_path : str, output_path : str) -> None:

11
facefusion/processors/frame/typings.py
View File

@ -2,10 +2,11 @@ from typing import Literal, TypedDict
from facefusion.typing import Face, FaceSet, AudioFrame, VisionFrame
FaceDebuggerItem = Literal['bounding-box', 'face-landmark-5', 'face-landmark-5/68', 'face-landmark-68', 'face-mask', 'face-detector-score', 'face-landmarker-score', 'age', 'gender']
FaceEnhancerModel = Literal['codeformer', 'gfpgan_1.2', 'gfpgan_1.3', 'gfpgan_1.4', 'gpen_bfr_256', 'gpen_bfr_512', 'restoreformer_plus_plus']
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']
FaceEnhancerModel = Literal['codeformer', 'gfpgan_1.2', 'gfpgan_1.3', 'gfpgan_1.4', 'gpen_bfr_256', 'gpen_bfr_512', 'gpen_bfr_1024', 'gpen_bfr_2048', 'restoreformer_plus_plus']
FaceSwapperModel = Literal['blendswap_256', 'inswapper_128', 'inswapper_128_fp16', 'simswap_256', 'simswap_512_unofficial', 'uniface_256']
FrameEnhancerModel = Literal['lsdir_x4', 'nomos8k_sc_x4', 'real_esrgan_x4', 'real_esrgan_x4_fp16', 'span_kendata_x4']
FrameColorizerModel = Literal['ddcolor', 'ddcolor_artistic', 'deoldify_artistic']
FrameEnhancerModel = Literal['lsdir_x4', 'nomos8k_sc_x4', 'real_esrgan_x2', 'real_esrgan_x2_fp16', 'real_esrgan_x4', 'real_esrgan_x4_fp16', 'real_hatgan_x4', 'span_kendata_x4']
LipSyncerModel = Literal['wav2lip_gan']
FaceDebuggerInputs = TypedDict('FaceDebuggerInputs',
@ -24,6 +25,10 @@ FaceSwapperInputs = TypedDict('FaceSwapperInputs',
'source_face' : Face,
'target_vision_frame' : VisionFrame
})
FrameColorizerInputs = TypedDict('FrameColorizerInputs',
{
'target_vision_frame' : VisionFrame
})
FrameEnhancerInputs = TypedDict('FrameEnhancerInputs',
{
'target_vision_frame' : VisionFrame

9
facefusion/typing.py
View File

@ -9,7 +9,8 @@ FaceLandmarkSet = TypedDict('FaceLandmarkSet',
{
'5' : FaceLandmark5, # type: ignore[valid-type]
'5/68' : FaceLandmark5, # type: ignore[valid-type]
'68' : FaceLandmark68 # type: ignore[valid-type]
'68' : FaceLandmark68, # type: ignore[valid-type]
'68/5' : FaceLandmark68 # type: ignore[valid-type]
})
Score = float
FaceScoreSet = TypedDict('FaceScoreSet',
@ -42,8 +43,10 @@ Translation = numpy.ndarray[Any, Any]
AudioBuffer = bytes
Audio = numpy.ndarray[Any, Any]
AudioChunk = numpy.ndarray[Any, Any]
AudioFrame = numpy.ndarray[Any, Any]
Spectrogram = numpy.ndarray[Any, Any]
MelFilterBank = numpy.ndarray[Any, Any]
Fps = float
Padding = Tuple[int, int, int, int]
@ -55,8 +58,8 @@ QueuePayload = TypedDict('QueuePayload',
'frame_number' : int,
'frame_path' : str
})
UpdateProcess = Callable[[], None]
ProcessFrames = Callable[[List[str], List[QueuePayload], UpdateProcess], None]
UpdateProgress = Callable[[int], None]
ProcessFrames = Callable[[List[str], List[QueuePayload], UpdateProgress], None]
WarpTemplate = Literal['arcface_112_v1', 'arcface_112_v2', 'arcface_128_v2', 'ffhq_512']
WarpTemplateSet = Dict[WarpTemplate, numpy.ndarray[Any, Any]]

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

@ -76,7 +76,7 @@ def listen() -> None:
def start(benchmark_runs : List[str], benchmark_cycles : int) -> Generator[List[Any], None, None]:
facefusion.globals.source_paths = [ '.assets/examples/source.jpg' ]
facefusion.globals.source_paths = [ '.assets/examples/source.jpg', '.assets/examples/source.mp3' ]
facefusion.globals.output_path = tempfile.gettempdir()
facefusion.globals.face_landmarker_score = 0
facefusion.globals.temp_frame_format = 'bmp'
@ -87,7 +87,8 @@ def start(benchmark_runs : List[str], benchmark_cycles : int) -> Generator[List[
if target_paths:
pre_process()
for target_path in target_paths:
benchmark_results.append(benchmark(target_path, benchmark_cycles))
facefusion.globals.target_path = target_path
benchmark_results.append(benchmark(benchmark_cycles))
yield benchmark_results
post_process()
@ -103,10 +104,8 @@ def post_process() -> None:
clear_static_faces()
def benchmark(target_path : str, benchmark_cycles : int) -> List[Any]:
def benchmark(benchmark_cycles : int) -> List[Any]:
process_times = []
total_fps = 0.0
facefusion.globals.target_path = target_path
video_frame_total = count_video_frame_total(facefusion.globals.target_path)
output_video_resolution = detect_video_resolution(facefusion.globals.target_path)
facefusion.globals.output_video_resolution = pack_resolution(output_video_resolution)
@ -116,13 +115,12 @@ def benchmark(target_path : str, benchmark_cycles : int) -> List[Any]:
start_time = perf_counter()
conditional_process()
end_time = perf_counter()
process_time = end_time - start_time
total_fps += video_frame_total / process_time
process_times.append(process_time)
process_times.append(end_time - start_time)
average_run = round(statistics.mean(process_times), 2)
fastest_run = round(min(process_times), 2)
slowest_run = round(max(process_times), 2)
relative_fps = round(total_fps / benchmark_cycles, 2)
relative_fps = round(video_frame_total * benchmark_cycles / sum(process_times), 2)
return\
[
facefusion.globals.target_path,

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

@ -21,7 +21,7 @@ def render() -> None:
def listen() -> None:
EXECUTION_QUEUE_COUNT_SLIDER.change(update_execution_queue_count, inputs = EXECUTION_QUEUE_COUNT_SLIDER)
EXECUTION_QUEUE_COUNT_SLIDER.release(update_execution_queue_count, inputs = EXECUTION_QUEUE_COUNT_SLIDER)
def update_execution_queue_count(execution_queue_count : int = 1) -> None:

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

@ -21,7 +21,7 @@ def render() -> None:
def listen() -> None:
EXECUTION_THREAD_COUNT_SLIDER.change(update_execution_thread_count, inputs = EXECUTION_THREAD_COUNT_SLIDER)
EXECUTION_THREAD_COUNT_SLIDER.release(update_execution_thread_count, inputs = EXECUTION_THREAD_COUNT_SLIDER)
def update_execution_thread_count(execution_thread_count : int = 1) -> None:

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

@ -92,11 +92,11 @@ def render() -> None:
def listen() -> None:
FACE_MASK_TYPES_CHECKBOX_GROUP.change(update_face_mask_type, inputs = FACE_MASK_TYPES_CHECKBOX_GROUP, outputs = [ FACE_MASK_TYPES_CHECKBOX_GROUP, FACE_MASK_BOX_GROUP, FACE_MASK_REGION_CHECKBOX_GROUP ])
FACE_MASK_BLUR_SLIDER.change(update_face_mask_blur, inputs = FACE_MASK_BLUR_SLIDER)
FACE_MASK_BLUR_SLIDER.release(update_face_mask_blur, inputs = FACE_MASK_BLUR_SLIDER)
FACE_MASK_REGION_CHECKBOX_GROUP.change(update_face_mask_regions, inputs = FACE_MASK_REGION_CHECKBOX_GROUP, outputs = FACE_MASK_REGION_CHECKBOX_GROUP)
face_mask_padding_sliders = [ FACE_MASK_PADDING_TOP_SLIDER, FACE_MASK_PADDING_RIGHT_SLIDER, FACE_MASK_PADDING_BOTTOM_SLIDER, FACE_MASK_PADDING_LEFT_SLIDER ]
for face_mask_padding_slider in face_mask_padding_sliders:
face_mask_padding_slider.change(update_face_mask_padding, inputs = face_mask_padding_sliders)
face_mask_padding_slider.release(update_face_mask_padding, inputs = face_mask_padding_sliders)
def update_face_mask_type(face_mask_types : List[FaceMaskType]) -> Tuple[gradio.CheckboxGroup, gradio.Group, gradio.CheckboxGroup]:

47
facefusion/uis/components/face_selector.py
View File

@ -10,8 +10,7 @@ from facefusion.vision import get_video_frame, read_static_image, normalize_fram
from facefusion.filesystem import is_image, is_video
from facefusion.face_analyser import get_many_faces
from facefusion.typing import VisionFrame, FaceSelectorMode
from facefusion.uis.core import get_ui_component, register_ui_component
from facefusion.uis.typing import ComponentName
from facefusion.uis.core import get_ui_component, get_ui_components, register_ui_component
FACE_SELECTOR_MODE_DROPDOWN : Optional[gradio.Dropdown] = None
REFERENCE_FACE_POSITION_GALLERY : Optional[gradio.Gallery] = None
@ -59,39 +58,39 @@ def render() -> None:
def listen() -> None:
FACE_SELECTOR_MODE_DROPDOWN.change(update_face_selector_mode, inputs = FACE_SELECTOR_MODE_DROPDOWN, outputs = [ REFERENCE_FACE_POSITION_GALLERY, REFERENCE_FACE_DISTANCE_SLIDER ])
REFERENCE_FACE_POSITION_GALLERY.select(clear_and_update_reference_face_position)
REFERENCE_FACE_DISTANCE_SLIDER.change(update_reference_face_distance, inputs = REFERENCE_FACE_DISTANCE_SLIDER)
multi_component_names : List[ComponentName] =\
REFERENCE_FACE_DISTANCE_SLIDER.release(update_reference_face_distance, inputs = REFERENCE_FACE_DISTANCE_SLIDER)
for ui_component in get_ui_components(
[
'target_image',
'target_video'
]
for component_name in multi_component_names:
component = get_ui_component(component_name)
if component:
for method in [ 'upload', 'change', 'clear' ]:
getattr(component, method)(update_reference_face_position)
getattr(component, method)(update_reference_position_gallery, outputs = REFERENCE_FACE_POSITION_GALLERY)
change_one_component_names : List[ComponentName] =\
]):
for method in [ 'upload', 'change', 'clear' ]:
getattr(ui_component, method)(update_reference_face_position)
getattr(ui_component, method)(update_reference_position_gallery, outputs = REFERENCE_FACE_POSITION_GALLERY)
for ui_component in get_ui_components(
[
'face_analyser_order_dropdown',
'face_analyser_age_dropdown',
'face_analyser_gender_dropdown'
]
for component_name in change_one_component_names:
component = get_ui_component(component_name)
if component:
component.change(update_reference_position_gallery, outputs = REFERENCE_FACE_POSITION_GALLERY)
change_two_component_names : List[ComponentName] =\
]):
ui_component.change(update_reference_position_gallery, outputs = REFERENCE_FACE_POSITION_GALLERY)
for ui_component in get_ui_components(
[
'face_detector_model_dropdown',
'face_detector_size_dropdown',
'face_detector_size_dropdown'
]):
ui_component.change(clear_and_update_reference_position_gallery, outputs = REFERENCE_FACE_POSITION_GALLERY)
for ui_component in get_ui_components(
[
'face_detector_score_slider',
'face_landmarker_score_slider'
]
for component_name in change_two_component_names:
component = get_ui_component(component_name)
if component:
component.change(clear_and_update_reference_position_gallery, outputs = REFERENCE_FACE_POSITION_GALLERY)
]):
ui_component.release(clear_and_update_reference_position_gallery, outputs=REFERENCE_FACE_POSITION_GALLERY)
preview_frame_slider = get_ui_component('preview_frame_slider')
if preview_frame_slider:
preview_frame_slider.change(update_reference_frame_number, inputs = preview_frame_slider)

50
facefusion/uis/components/frame_processors_options.py
View File

@ -5,13 +5,15 @@ import facefusion.globals
from facefusion import face_analyser, wording
from facefusion.processors.frame.core import load_frame_processor_module
from facefusion.processors.frame import globals as frame_processors_globals, choices as frame_processors_choices
from facefusion.processors.frame.typings import FaceDebuggerItem, FaceEnhancerModel, FaceSwapperModel, FrameEnhancerModel, LipSyncerModel
from facefusion.processors.frame.typings import FaceDebuggerItem, FaceEnhancerModel, FaceSwapperModel, FrameColorizerModel, FrameEnhancerModel, LipSyncerModel
from facefusion.uis.core import get_ui_component, register_ui_component
FACE_DEBUGGER_ITEMS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
FACE_ENHANCER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None
FACE_ENHANCER_BLEND_SLIDER : Optional[gradio.Slider] = None
FACE_SWAPPER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None
FRAME_COLORIZER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None
FRAME_COLORIZER_BLEND_SLIDER : Optional[gradio.Slider] = None
FRAME_ENHANCER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None
FRAME_ENHANCER_BLEND_SLIDER : Optional[gradio.Slider] = None
LIP_SYNCER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None
@ -22,6 +24,8 @@ def render() -> None:
global FACE_ENHANCER_MODEL_DROPDOWN
global FACE_ENHANCER_BLEND_SLIDER
global FACE_SWAPPER_MODEL_DROPDOWN
global FRAME_COLORIZER_MODEL_DROPDOWN
global FRAME_COLORIZER_BLEND_SLIDER
global FRAME_ENHANCER_MODEL_DROPDOWN
global FRAME_ENHANCER_BLEND_SLIDER
global LIP_SYNCER_MODEL_DROPDOWN
@ -52,6 +56,20 @@ def render() -> None:
value = frame_processors_globals.face_swapper_model,
visible = 'face_swapper' in facefusion.globals.frame_processors
)
FRAME_COLORIZER_MODEL_DROPDOWN = gradio.Dropdown(
label = wording.get('uis.frame_colorizer_model_dropdown'),
choices = frame_processors_choices.frame_colorizer_models,
value = frame_processors_globals.frame_colorizer_model,
visible = 'frame_colorizer' in facefusion.globals.frame_processors
)
FRAME_COLORIZER_BLEND_SLIDER = gradio.Slider(
label = wording.get('uis.frame_colorizer_blend_slider'),
value = frame_processors_globals.frame_colorizer_blend,
step = frame_processors_choices.frame_colorizer_blend_range[1] - frame_processors_choices.frame_colorizer_blend_range[0],
minimum = frame_processors_choices.frame_colorizer_blend_range[0],
maximum = frame_processors_choices.frame_colorizer_blend_range[-1],
visible = 'frame_colorizer' in facefusion.globals.frame_processors
)
FRAME_ENHANCER_MODEL_DROPDOWN = gradio.Dropdown(
label = wording.get('uis.frame_enhancer_model_dropdown'),
choices = frame_processors_choices.frame_enhancer_models,
@ -76,6 +94,8 @@ def render() -> None:
register_ui_component('face_enhancer_model_dropdown', FACE_ENHANCER_MODEL_DROPDOWN)
register_ui_component('face_enhancer_blend_slider', FACE_ENHANCER_BLEND_SLIDER)
register_ui_component('face_swapper_model_dropdown', FACE_SWAPPER_MODEL_DROPDOWN)
register_ui_component('frame_colorizer_model_dropdown', FRAME_COLORIZER_MODEL_DROPDOWN)
register_ui_component('frame_colorizer_blend_slider', FRAME_COLORIZER_BLEND_SLIDER)
register_ui_component('frame_enhancer_model_dropdown', FRAME_ENHANCER_MODEL_DROPDOWN)
register_ui_component('frame_enhancer_blend_slider', FRAME_ENHANCER_BLEND_SLIDER)
register_ui_component('lip_syncer_model_dropdown', LIP_SYNCER_MODEL_DROPDOWN)
@ -84,23 +104,26 @@ def render() -> None:
def listen() -> None:
FACE_DEBUGGER_ITEMS_CHECKBOX_GROUP.change(update_face_debugger_items, inputs = FACE_DEBUGGER_ITEMS_CHECKBOX_GROUP)
FACE_ENHANCER_MODEL_DROPDOWN.change(update_face_enhancer_model, inputs = FACE_ENHANCER_MODEL_DROPDOWN, outputs = FACE_ENHANCER_MODEL_DROPDOWN)
FACE_ENHANCER_BLEND_SLIDER.change(update_face_enhancer_blend, inputs = FACE_ENHANCER_BLEND_SLIDER)
FACE_ENHANCER_BLEND_SLIDER.release(update_face_enhancer_blend, inputs = FACE_ENHANCER_BLEND_SLIDER)
FACE_SWAPPER_MODEL_DROPDOWN.change(update_face_swapper_model, inputs = FACE_SWAPPER_MODEL_DROPDOWN, outputs = FACE_SWAPPER_MODEL_DROPDOWN)
FRAME_COLORIZER_MODEL_DROPDOWN.change(update_frame_colorizer_model, inputs = FRAME_COLORIZER_MODEL_DROPDOWN, outputs = FRAME_COLORIZER_MODEL_DROPDOWN)
FRAME_COLORIZER_BLEND_SLIDER.release(update_frame_colorizer_blend, inputs = FRAME_COLORIZER_BLEND_SLIDER)
FRAME_ENHANCER_MODEL_DROPDOWN.change(update_frame_enhancer_model, inputs = FRAME_ENHANCER_MODEL_DROPDOWN, outputs = FRAME_ENHANCER_MODEL_DROPDOWN)
FRAME_ENHANCER_BLEND_SLIDER.change(update_frame_enhancer_blend, inputs = FRAME_ENHANCER_BLEND_SLIDER)
FRAME_ENHANCER_BLEND_SLIDER.release(update_frame_enhancer_blend, inputs = FRAME_ENHANCER_BLEND_SLIDER)
LIP_SYNCER_MODEL_DROPDOWN.change(update_lip_syncer_model, inputs = LIP_SYNCER_MODEL_DROPDOWN, outputs = LIP_SYNCER_MODEL_DROPDOWN)
frame_processors_checkbox_group = get_ui_component('frame_processors_checkbox_group')
if frame_processors_checkbox_group:
frame_processors_checkbox_group.change(update_frame_processors, inputs = frame_processors_checkbox_group, outputs = [ FACE_DEBUGGER_ITEMS_CHECKBOX_GROUP, FACE_ENHANCER_MODEL_DROPDOWN, FACE_ENHANCER_BLEND_SLIDER, FACE_SWAPPER_MODEL_DROPDOWN, FRAME_ENHANCER_MODEL_DROPDOWN, FRAME_ENHANCER_BLEND_SLIDER, LIP_SYNCER_MODEL_DROPDOWN ])
frame_processors_checkbox_group.change(update_frame_processors, inputs = frame_processors_checkbox_group, outputs = [ FACE_DEBUGGER_ITEMS_CHECKBOX_GROUP, FACE_ENHANCER_MODEL_DROPDOWN, FACE_ENHANCER_BLEND_SLIDER, FACE_SWAPPER_MODEL_DROPDOWN, FRAME_COLORIZER_MODEL_DROPDOWN, FRAME_COLORIZER_BLEND_SLIDER, FRAME_ENHANCER_MODEL_DROPDOWN, FRAME_ENHANCER_BLEND_SLIDER, LIP_SYNCER_MODEL_DROPDOWN ])
def update_frame_processors(frame_processors : List[str]) -> Tuple[gradio.CheckboxGroup, gradio.Dropdown, gradio.Slider, gradio.Dropdown, gradio.Dropdown, gradio.Slider, gradio.Dropdown]:
def update_frame_processors(frame_processors : List[str]) -> Tuple[gradio.CheckboxGroup, gradio.Dropdown, gradio.Slider, gradio.Dropdown, gradio.Dropdown, gradio.Slider, gradio.Dropdown, gradio.Slider, gradio.Dropdown]:
has_face_debugger = 'face_debugger' in frame_processors
has_face_enhancer = 'face_enhancer' in frame_processors
has_face_swapper = 'face_swapper' in frame_processors
has_frame_colorizer = 'frame_colorizer' in frame_processors
has_frame_enhancer = 'frame_enhancer' in frame_processors
has_lip_syncer = 'lip_syncer' in frame_processors
return gradio.CheckboxGroup(visible = has_face_debugger), gradio.Dropdown(visible = has_face_enhancer), gradio.Slider(visible = has_face_enhancer), gradio.Dropdown(visible = has_face_swapper), gradio.Dropdown(visible = has_frame_enhancer), gradio.Slider(visible = has_frame_enhancer), gradio.Dropdown(visible = has_lip_syncer)
return gradio.CheckboxGroup(visible = has_face_debugger), gradio.Dropdown(visible = has_face_enhancer), gradio.Slider(visible = has_face_enhancer), gradio.Dropdown(visible = has_face_swapper), gradio.Dropdown(visible = has_frame_colorizer), gradio.Slider(visible = has_frame_colorizer), gradio.Dropdown(visible = has_frame_enhancer), gradio.Slider(visible = has_frame_enhancer), gradio.Dropdown(visible = has_lip_syncer)
def update_face_debugger_items(face_debugger_items : List[FaceDebuggerItem]) -> None:
@ -132,6 +155,7 @@ def update_face_swapper_model(face_swapper_model : FaceSwapperModel) -> gradio.D
if face_swapper_model == 'uniface_256':
facefusion.globals.face_recognizer_model = 'arcface_uniface'
face_swapper_module = load_frame_processor_module('face_swapper')
face_swapper_module.clear_model_initializer()
face_swapper_module.clear_frame_processor()
face_swapper_module.set_options('model', face_swapper_module.MODELS[face_swapper_model])
if face_analyser.pre_check() and face_swapper_module.pre_check():
@ -139,6 +163,20 @@ def update_face_swapper_model(face_swapper_model : FaceSwapperModel) -> gradio.D
return gradio.Dropdown()
def update_frame_colorizer_model(frame_colorizer_model : FrameColorizerModel) -> gradio.Dropdown:
frame_processors_globals.frame_colorizer_model = frame_colorizer_model
frame_colorizer_module = load_frame_processor_module('frame_colorizer')
frame_colorizer_module.clear_frame_processor()
frame_colorizer_module.set_options('model', frame_colorizer_module.MODELS[frame_colorizer_model])
if frame_colorizer_module.pre_check():
return gradio.Dropdown(value = frame_processors_globals.frame_colorizer_model)
return gradio.Dropdown()
def update_frame_colorizer_blend(frame_colorizer_blend : int) -> None:
frame_processors_globals.frame_colorizer_blend = frame_colorizer_blend
def update_frame_enhancer_model(frame_enhancer_model : FrameEnhancerModel) -> gradio.Dropdown:
frame_processors_globals.frame_enhancer_model = frame_enhancer_model
frame_enhancer_module = load_frame_processor_module('frame_enhancer')

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

@ -6,15 +6,15 @@ import facefusion.choices
from facefusion.typing import VideoMemoryStrategy
from facefusion import wording
VIDEO_MEMORY_STRATEGY : Optional[gradio.Dropdown] = None
VIDEO_MEMORY_STRATEGY_DROPDOWN : Optional[gradio.Dropdown] = None
SYSTEM_MEMORY_LIMIT_SLIDER : Optional[gradio.Slider] = None
def render() -> None:
global VIDEO_MEMORY_STRATEGY
global VIDEO_MEMORY_STRATEGY_DROPDOWN
global SYSTEM_MEMORY_LIMIT_SLIDER
VIDEO_MEMORY_STRATEGY = gradio.Dropdown(
VIDEO_MEMORY_STRATEGY_DROPDOWN = gradio.Dropdown(
label = wording.get('uis.video_memory_strategy_dropdown'),
choices = facefusion.choices.video_memory_strategies,
value = facefusion.globals.video_memory_strategy
@ -29,8 +29,8 @@ def render() -> None:
def listen() -> None:
VIDEO_MEMORY_STRATEGY.change(update_video_memory_strategy, inputs = VIDEO_MEMORY_STRATEGY)
SYSTEM_MEMORY_LIMIT_SLIDER.change(update_system_memory_limit, inputs = SYSTEM_MEMORY_LIMIT_SLIDER)
VIDEO_MEMORY_STRATEGY_DROPDOWN.change(update_video_memory_strategy, inputs = VIDEO_MEMORY_STRATEGY_DROPDOWN)
SYSTEM_MEMORY_LIMIT_SLIDER.release(update_system_memory_limit, inputs = SYSTEM_MEMORY_LIMIT_SLIDER)
def update_video_memory_strategy(video_memory_strategy : VideoMemoryStrategy) -> None:

23
facefusion/uis/components/output_options.py
View File

@ -1,4 +1,4 @@
from typing import Optional, Tuple, List
from typing import Optional, Tuple
import gradio
import facefusion.globals
@ -6,8 +6,7 @@ import facefusion.choices
from facefusion import wording
from facefusion.typing import OutputVideoEncoder, OutputVideoPreset, Fps
from facefusion.filesystem import is_image, is_video
from facefusion.uis.typing import ComponentName
from facefusion.uis.core import get_ui_component, register_ui_component
from facefusion.uis.core import get_ui_components, register_ui_component
from facefusion.vision import detect_image_resolution, create_image_resolutions, detect_video_fps, detect_video_resolution, create_video_resolutions, pack_resolution
OUTPUT_PATH_TEXTBOX : Optional[gradio.Textbox] = None
@ -98,23 +97,21 @@ def render() -> None:
def listen() -> None:
OUTPUT_PATH_TEXTBOX.change(update_output_path, inputs = OUTPUT_PATH_TEXTBOX)
OUTPUT_IMAGE_QUALITY_SLIDER.change(update_output_image_quality, inputs = OUTPUT_IMAGE_QUALITY_SLIDER)
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_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.change(update_output_video_quality, inputs = OUTPUT_VIDEO_QUALITY_SLIDER)
OUTPUT_VIDEO_QUALITY_SLIDER.release(update_output_video_quality, inputs = OUTPUT_VIDEO_QUALITY_SLIDER)
OUTPUT_VIDEO_RESOLUTION_DROPDOWN.change(update_output_video_resolution, inputs = OUTPUT_VIDEO_RESOLUTION_DROPDOWN)
OUTPUT_VIDEO_FPS_SLIDER.change(update_output_video_fps, inputs = OUTPUT_VIDEO_FPS_SLIDER)
multi_component_names : List[ComponentName] =\
OUTPUT_VIDEO_FPS_SLIDER.release(update_output_video_fps, inputs = OUTPUT_VIDEO_FPS_SLIDER)
for ui_component in get_ui_components(
[
'target_image',
'target_video'
]
for component_name in multi_component_names:
component = get_ui_component(component_name)
if component:
for method in [ 'upload', 'change', 'clear' ]:
getattr(component, method)(remote_update, outputs = [ OUTPUT_IMAGE_QUALITY_SLIDER, OUTPUT_IMAGE_RESOLUTION_DROPDOWN, OUTPUT_VIDEO_ENCODER_DROPDOWN, OUTPUT_VIDEO_PRESET_DROPDOWN, OUTPUT_VIDEO_QUALITY_SLIDER, OUTPUT_VIDEO_RESOLUTION_DROPDOWN, OUTPUT_VIDEO_FPS_SLIDER ])
]):
for method in [ 'upload', 'change', 'clear' ]:
getattr(ui_component, method)(remote_update, outputs = [ OUTPUT_IMAGE_QUALITY_SLIDER, OUTPUT_IMAGE_RESOLUTION_DROPDOWN, 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.Slider, gradio.Dropdown, gradio.Slider]:

75
facefusion/uis/components/preview.py
View File

@ -1,4 +1,4 @@
from typing import Any, Dict, List, Optional
from typing import Any, Dict, Optional
from time import sleep
import cv2
import gradio
@ -16,8 +16,7 @@ from facefusion.vision import get_video_frame, count_video_frame_total, normaliz
from facefusion.filesystem import is_image, is_video, filter_audio_paths
from facefusion.content_analyser import analyse_frame
from facefusion.processors.frame.core import load_frame_processor_module
from facefusion.uis.typing import ComponentName
from facefusion.uis.core import get_ui_component, register_ui_component
from facefusion.uis.core import get_ui_component, get_ui_components, register_ui_component
PREVIEW_IMAGE : Optional[gradio.Image] = None
PREVIEW_FRAME_SLIDER : Optional[gradio.Slider] = None
@ -72,69 +71,73 @@ def listen() -> None:
reference_face_position_gallery = get_ui_component('reference_face_position_gallery')
if reference_face_position_gallery:
reference_face_position_gallery.select(update_preview_image, inputs = PREVIEW_FRAME_SLIDER, outputs = PREVIEW_IMAGE)
multi_one_component_names : List[ComponentName] =\
for ui_component in get_ui_components(
[
'source_audio',
'source_image',
'target_image',
'target_video'
]
for component_name in multi_one_component_names:
component = get_ui_component(component_name)
if component:
for method in [ 'upload', 'change', 'clear' ]:
getattr(component, method)(update_preview_image, inputs = PREVIEW_FRAME_SLIDER, outputs = PREVIEW_IMAGE)
multi_two_component_names : List[ComponentName] =\
]):
for method in [ 'upload', 'change', 'clear' ]:
getattr(ui_component, method)(update_preview_image, inputs = PREVIEW_FRAME_SLIDER, outputs = PREVIEW_IMAGE)
for ui_component in get_ui_components(
[
'target_image',
'target_video'
]
for component_name in multi_two_component_names:
component = get_ui_component(component_name)
if component:
for method in [ 'upload', 'change', 'clear' ]:
getattr(component, method)(update_preview_frame_slider, outputs = PREVIEW_FRAME_SLIDER)
change_one_component_names : List[ComponentName] =\
]):
for method in [ 'upload', 'change', 'clear' ]:
getattr(ui_component, method)(update_preview_frame_slider, outputs = PREVIEW_FRAME_SLIDER)
for ui_component in get_ui_components(
[
'face_debugger_items_checkbox_group',
'face_selector_mode_dropdown',
'face_mask_types_checkbox_group',
'face_mask_region_checkbox_group',
'face_analyser_order_dropdown',
'face_analyser_age_dropdown',
'face_analyser_gender_dropdown'
]):
ui_component.change(update_preview_image, inputs = PREVIEW_FRAME_SLIDER, outputs = PREVIEW_IMAGE)
for ui_component in get_ui_components(
[
'face_enhancer_blend_slider',
'frame_colorizer_blend_slider',
'frame_enhancer_blend_slider',
'trim_frame_start_slider',
'trim_frame_end_slider',
'face_selector_mode_dropdown',
'reference_face_distance_slider',
'face_mask_types_checkbox_group',
'face_mask_blur_slider',
'face_mask_padding_top_slider',
'face_mask_padding_bottom_slider',
'face_mask_padding_left_slider',
'face_mask_padding_right_slider',
'face_mask_region_checkbox_group',
'face_analyser_order_dropdown',
'face_analyser_age_dropdown',
'face_analyser_gender_dropdown',
'output_video_fps_slider'
]
for component_name in change_one_component_names:
component = get_ui_component(component_name)
if component:
component.change(update_preview_image, inputs = PREVIEW_FRAME_SLIDER, outputs = PREVIEW_IMAGE)
change_two_component_names : List[ComponentName] =\
]):
ui_component.release(update_preview_image, inputs = PREVIEW_FRAME_SLIDER, outputs = PREVIEW_IMAGE)
for ui_component in get_ui_components(
[
'frame_processors_checkbox_group',
'face_enhancer_model_dropdown',
'face_swapper_model_dropdown',
'frame_colorizer_model_dropdown',
'frame_enhancer_model_dropdown',
'lip_syncer_model_dropdown',
'face_detector_model_dropdown',
'face_detector_size_dropdown',
'face_detector_size_dropdown'
]):
ui_component.change(clear_and_update_preview_image, inputs = PREVIEW_FRAME_SLIDER, outputs = PREVIEW_IMAGE)
for ui_component in get_ui_components(
[
'face_detector_score_slider',
'face_landmarker_score_slider'
]
for component_name in change_two_component_names:
component = get_ui_component(component_name)
if component:
component.change(clear_and_update_preview_image, inputs = PREVIEW_FRAME_SLIDER, outputs = PREVIEW_IMAGE)
]):
ui_component.release(clear_and_update_preview_image, inputs = PREVIEW_FRAME_SLIDER, outputs = PREVIEW_IMAGE)
def clear_and_update_preview_image(frame_number : int = 0) -> gradio.Image:

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

@ -47,8 +47,8 @@ def render() -> None:
def listen() -> None:
TRIM_FRAME_START_SLIDER.change(update_trim_frame_start, inputs = TRIM_FRAME_START_SLIDER)
TRIM_FRAME_END_SLIDER.change(update_trim_frame_end, inputs = TRIM_FRAME_END_SLIDER)
TRIM_FRAME_START_SLIDER.release(update_trim_frame_start, inputs = TRIM_FRAME_START_SLIDER)
TRIM_FRAME_END_SLIDER.release(update_trim_frame_end, inputs = TRIM_FRAME_END_SLIDER)
target_video = get_ui_component('target_video')
if target_video:
for method in [ 'upload', 'change', 'clear' ]:

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

@ -1,4 +1,4 @@
from typing import Optional, Generator, Deque, List
from typing import Optional, Generator, Deque
import os
import platform
import subprocess
@ -19,8 +19,8 @@ from facefusion.face_analyser import get_average_face
from facefusion.processors.frame.core import get_frame_processors_modules, load_frame_processor_module
from facefusion.ffmpeg import open_ffmpeg
from facefusion.vision import normalize_frame_color, read_static_images, unpack_resolution
from facefusion.uis.typing import StreamMode, WebcamMode, ComponentName
from facefusion.uis.core import get_ui_component
from facefusion.uis.typing import StreamMode, WebcamMode
from facefusion.uis.core import get_ui_component, get_ui_components
WEBCAM_CAPTURE : Optional[cv2.VideoCapture] = None
WEBCAM_IMAGE : Optional[gradio.Image] = None
@ -76,7 +76,8 @@ def listen() -> None:
if webcam_mode_radio and webcam_resolution_dropdown and webcam_fps_slider:
start_event = WEBCAM_START_BUTTON.click(start, inputs = [ webcam_mode_radio, webcam_resolution_dropdown, webcam_fps_slider ], outputs = WEBCAM_IMAGE)
WEBCAM_STOP_BUTTON.click(stop, cancels = start_event)
change_two_component_names : List[ComponentName] =\
for ui_component in get_ui_components(
[
'frame_processors_checkbox_group',
'face_swapper_model_dropdown',
@ -84,11 +85,8 @@ def listen() -> None:
'frame_enhancer_model_dropdown',
'lip_syncer_model_dropdown',
'source_image'
]
for component_name in change_two_component_names:
component = get_ui_component(component_name)
if component:
component.change(update, cancels = start_event)
]):
ui_component.change(update, cancels = start_event)
def start(webcam_mode : WebcamMode, webcam_resolution : str, webcam_fps : Fps) -> Generator[VisionFrame, None, None]:

20
facefusion/uis/core.py
View File

@ -51,14 +51,24 @@ def get_ui_layouts_modules(ui_layouts : List[str]) -> List[ModuleType]:
return UI_LAYOUT_MODULES
def get_ui_component(name : ComponentName) -> Optional[Component]:
if name in UI_COMPONENTS:
return UI_COMPONENTS[name]
def get_ui_component(component_name : ComponentName) -> Optional[Component]:
if component_name in UI_COMPONENTS:
return UI_COMPONENTS[component_name]
return None
def register_ui_component(name : ComponentName, component: Component) -> None:
UI_COMPONENTS[name] = component
def get_ui_components(component_names : List[ComponentName]) -> Optional[List[Component]]:
ui_components = []
for component_name in component_names:
component = get_ui_component(component_name)
if component:
ui_components.append(component)
return ui_components
def register_ui_component(component_name : ComponentName, component: Component) -> None:
UI_COMPONENTS[component_name] = component
def launch() -> None:

1
facefusion/uis/layouts/benchmark.py
View File

@ -11,6 +11,7 @@ def pre_check() -> bool:
conditional_download('.assets/examples',
[
'https://github.com/facefusion/facefusion-assets/releases/download/examples/source.jpg',
'https://github.com/facefusion/facefusion-assets/releases/download/examples/source.mp3',
'https://github.com/facefusion/facefusion-assets/releases/download/examples/target-240p.mp4',
'https://github.com/facefusion/facefusion-assets/releases/download/examples/target-360p.mp4',
'https://github.com/facefusion/facefusion-assets/releases/download/examples/target-540p.mp4',

2
facefusion/uis/overrides.py
View File

@ -6,7 +6,7 @@ import base64
def encode_array_to_base64(array : numpy.ndarray[Any, Any]) -> str:
buffer = cv2.imencode('.jpg', array[:, :, ::-1])[1]
return 'data:image/jpg;base64,' + base64.b64encode(buffer.tobytes()).decode('utf-8')
return 'data:image/jpeg;base64,' + base64.b64encode(buffer.tobytes()).decode('utf-8')
def encode_pil_to_base64(image : Any) -> str:

2
facefusion/uis/typing.py
View File

@ -34,6 +34,8 @@ ComponentName = Literal\
'face_enhancer_model_dropdown',
'face_enhancer_blend_slider',
'face_swapper_model_dropdown',
'frame_colorizer_model_dropdown',
'frame_colorizer_blend_slider',
'frame_enhancer_model_dropdown',
'frame_enhancer_blend_slider',
'lip_syncer_model_dropdown',

132
facefusion/voice_extractor.py Normal file
View File

@ -0,0 +1,132 @@
from typing import Any, Tuple
from time import sleep
import threading
import scipy
import numpy
import onnxruntime
import facefusion.globals
from facefusion import process_manager
from facefusion.typing import ModelSet, AudioChunk, Audio
from facefusion.execution import apply_execution_provider_options
from facefusion.filesystem import resolve_relative_path, is_file
from facefusion.download import conditional_download
VOICE_EXTRACTOR = None
THREAD_SEMAPHORE : threading.Semaphore = threading.Semaphore()
THREAD_LOCK : threading.Lock = threading.Lock()
MODELS : ModelSet =\
{
'voice_extractor':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/voice_extractor.onnx',
'path': resolve_relative_path('../.assets/models/voice_extractor.onnx')
}
}
def get_voice_extractor() -> Any:
global VOICE_EXTRACTOR
with THREAD_LOCK:
while process_manager.is_checking():
sleep(0.5)
if VOICE_EXTRACTOR is None:
model_path = MODELS.get('voice_extractor').get('path')
VOICE_EXTRACTOR = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_providers))
return VOICE_EXTRACTOR
def clear_voice_extractor() -> None:
global VOICE_EXTRACTOR
VOICE_EXTRACTOR = None
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_url = MODELS.get('voice_extractor').get('url')
model_path = MODELS.get('voice_extractor').get('path')
if not facefusion.globals.skip_download:
process_manager.check()
conditional_download(download_directory_path, [ model_url ])
process_manager.end()
return is_file(model_path)
def batch_extract_voice(audio : Audio, chunk_size : int, step_size : int) -> Audio:
temp_audio = numpy.zeros((audio.shape[0], 2)).astype(numpy.float32)
temp_chunk = numpy.zeros((audio.shape[0], 2)).astype(numpy.float32)
for start in range(0, audio.shape[0], step_size):
end = min(start + chunk_size, audio.shape[0])
temp_audio[start:end, ...] += extract_voice(audio[start:end, ...])
temp_chunk[start:end, ...] += 1
audio = temp_audio / temp_chunk
return audio
def extract_voice(temp_audio_chunk : AudioChunk) -> AudioChunk:
voice_extractor = get_voice_extractor()
chunk_size = 1024 * (voice_extractor.get_inputs()[0].shape[3] - 1)
trim_size = 3840
temp_audio_chunk, pad_size = prepare_audio_chunk(temp_audio_chunk.T, chunk_size, trim_size)
temp_audio_chunk = decompose_audio_chunk(temp_audio_chunk, trim_size)
with THREAD_SEMAPHORE:
temp_audio_chunk = voice_extractor.run(None,
{
voice_extractor.get_inputs()[0].name: temp_audio_chunk
})[0]
temp_audio_chunk = compose_audio_chunk(temp_audio_chunk, trim_size)
temp_audio_chunk = normalize_audio_chunk(temp_audio_chunk, chunk_size, trim_size, pad_size)
return temp_audio_chunk
def prepare_audio_chunk(temp_audio_chunk : AudioChunk, chunk_size : int, trim_size : int) -> Tuple[AudioChunk, int]:
step_size = chunk_size - 2 * trim_size
pad_size = step_size - temp_audio_chunk.shape[1] % step_size
audio_chunk_size = temp_audio_chunk.shape[1] + pad_size
temp_audio_chunk = temp_audio_chunk.astype(numpy.float32) / numpy.iinfo(numpy.int16).max
temp_audio_chunk = numpy.pad(temp_audio_chunk, ((0, 0), (trim_size, trim_size + pad_size)))
temp_audio_chunks = []
for index in range(0, audio_chunk_size, step_size):
temp_audio_chunk = temp_audio_chunk[:, index:index + chunk_size]
temp_audio_chunks.append(temp_audio_chunk)
temp_audio_chunk = numpy.concatenate(temp_audio_chunks, axis = 0)
temp_audio_chunk = temp_audio_chunk.reshape((-1, chunk_size))
return temp_audio_chunk, pad_size
def decompose_audio_chunk(temp_audio_chunk : AudioChunk, trim_size : int) -> AudioChunk:
frame_size = 7680
frame_overlap = 6656
voice_extractor_shape = get_voice_extractor().get_inputs()[0].shape
window = scipy.signal.windows.hann(frame_size)
temp_audio_chunk = scipy.signal.stft(temp_audio_chunk, nperseg = frame_size, noverlap = frame_overlap, window = window)[2]
temp_audio_chunk = numpy.stack((numpy.real(temp_audio_chunk), numpy.imag(temp_audio_chunk)), axis = -1).transpose((0, 3, 1, 2))
temp_audio_chunk = temp_audio_chunk.reshape(-1, 2, 2, trim_size + 1, voice_extractor_shape[3]).reshape(-1, voice_extractor_shape[1], trim_size + 1, voice_extractor_shape[3])
temp_audio_chunk = temp_audio_chunk[:, :, :voice_extractor_shape[2]]
temp_audio_chunk /= numpy.sqrt(1.0 / window.sum() ** 2)
return temp_audio_chunk
def compose_audio_chunk(temp_audio_chunk : AudioChunk, trim_size : int) -> AudioChunk:
frame_size = 7680
frame_overlap = 6656
voice_extractor_shape = get_voice_extractor().get_inputs()[0].shape
window = scipy.signal.windows.hann(frame_size)
temp_audio_chunk = numpy.pad(temp_audio_chunk, ((0, 0), (0, 0), (0, trim_size + 1 - voice_extractor_shape[2]), (0, 0)))
temp_audio_chunk = temp_audio_chunk.reshape(-1, 2, trim_size + 1, voice_extractor_shape[3]).transpose((0, 2, 3, 1))
temp_audio_chunk = temp_audio_chunk[:, :, :, 0] + 1j * temp_audio_chunk[:, :, :, 1]
temp_audio_chunk = scipy.signal.istft(temp_audio_chunk, nperseg = frame_size, noverlap = frame_overlap, window = window)[1]
temp_audio_chunk *= numpy.sqrt(1.0 / window.sum() ** 2)
return temp_audio_chunk
def normalize_audio_chunk(temp_audio_chunk : AudioChunk, chunk_size : int, trim_size : int, pad_size : int) -> AudioChunk:
temp_audio_chunk = temp_audio_chunk.reshape((-1, 2, chunk_size))
temp_audio_chunk = temp_audio_chunk[:, :, trim_size:-trim_size].transpose(1, 0, 2)
temp_audio_chunk = temp_audio_chunk.reshape(2, -1)[:, :-pad_size].T
return temp_audio_chunk

14
facefusion/wording.py
View File

@ -2,6 +2,7 @@ from typing import Any, Dict, Optional
WORDING : Dict[str, Any] =\
{
'conda_not_activated': 'Conda is not activated',
'python_not_supported': 'Python version is not supported, upgrade to {version} or higher',
'ffmpeg_not_installed': 'FFMpeg is not installed',
'creating_temp': 'Creating temporary resources',
@ -52,12 +53,13 @@ WORDING : Dict[str, Any] =\
{
# installer
'install_dependency': 'select the variant of {dependency} to install',
'skip_venv': 'skip the virtual environment check',
'skip_conda': 'skip the conda environment check',
# general
'source': 'choose single or multiple source images or audios',
'target': 'choose single target image or video',
'output': 'specify the output file or directory',
# misc
'force_download': 'force automate downloads and exit',
'skip_download': 'omit automate downloads and remote lookups',
'headless': 'run the program without a user interface',
'log_level': 'adjust the message severity displayed in the terminal',
@ -66,10 +68,10 @@ WORDING : Dict[str, Any] =\
'execution_thread_count': 'specify the amount of parallel threads while processing',
'execution_queue_count': 'specify the amount of frames each thread is processing',
# memory
'video_memory_strategy': 'balance fast frame processing and low vram usage',
'system_memory_limit': 'limit the available ram that can be used while processing',
'video_memory_strategy': 'balance fast frame processing and low VRAM usage',
'system_memory_limit': 'limit the available RAM that can be used while processing',
# face analyser
'face_analyser_order': 'specify the order in which the face analyser detects faces.',
'face_analyser_order': 'specify the order in which the face analyser detects faces',
'face_analyser_age': 'filter the detected faces based on their age',
'face_analyser_gender': 'filter the detected faces based on their gender',
'face_detector_model': 'choose the model responsible for detecting the face',
@ -106,6 +108,8 @@ WORDING : Dict[str, Any] =\
'face_enhancer_model': 'choose the model responsible for enhancing the face',
'face_enhancer_blend': 'blend the enhanced into the previous face',
'face_swapper_model': 'choose the model responsible for swapping the face',
'frame_colorizer_model': 'choose the model responsible for colorizing the frame',
'frame_colorizer_blend': 'blend the colorized into the previous frame',
'frame_enhancer_model': 'choose the model responsible for enhancing the frame',
'frame_enhancer_blend': 'blend the enhanced into the previous frame',
'lip_syncer_model': 'choose the model responsible for syncing the lips',
@ -160,6 +164,8 @@ WORDING : Dict[str, Any] =\
'face_enhancer_model_dropdown': 'FACE ENHANCER MODEL',
'face_enhancer_blend_slider': 'FACE ENHANCER BLEND',
'face_swapper_model_dropdown': 'FACE SWAPPER MODEL',
'frame_colorizer_model_dropdown': 'FRAME COLORIZER MODEL',
'frame_colorizer_blend_slider': 'FRAME COLORIZER BLEND',
'frame_enhancer_model_dropdown': 'FRAME ENHANCER MODEL',
'frame_enhancer_blend_slider': 'FRAME ENHANCER BLEND',
'lip_syncer_model_dropdown': 'LIP SYNCER MODEL',

4
install.py
View File

@ -1,8 +1,10 @@
#!/usr/bin/env python3
import os
import subprocess
subprocess.call([ 'pip', 'install' , 'inquirer', '-q' ])
os.environ['PIP_BREAK_SYSTEM_PACKAGES'] = '1'
subprocess.call([ 'pip', 'install', 'inquirer', '-q' ])
from facefusion import installer

4
requirements.txt
View File

@ -1,8 +1,8 @@
filetype==1.2.0
gradio==3.50.2
numpy==1.26.4
onnx==1.15.0
onnxruntime==1.16.3
onnx==1.16.0
onnxruntime==1.17.1
opencv-python==4.8.1.78
psutil==5.9.8
tqdm==4.66.2

7
tests/test_common_helper.py
View File

@ -1,4 +1,4 @@
from facefusion.common_helper import create_metavar, create_int_range, create_float_range, extract_major_version
from facefusion.common_helper import create_metavar, create_int_range, create_float_range
def test_create_metavar() -> None:
@ -14,8 +14,3 @@ def test_create_float_range() -> None:
assert create_float_range(0.0, 1.0, 0.5) == [ 0.0, 0.5, 1.0 ]
assert create_float_range(0.0, 0.2, 0.05) == [ 0.0, 0.05, 0.10, 0.15, 0.20 ]
def test_extract_major_version() -> None:
assert extract_major_version('1') == (1, 0)
assert extract_major_version('1.1') == (1, 1)
assert extract_major_version('1.2.0') == (1, 2)