Next (#477)
* 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 commitd32f287572. * 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 commitbd8034cbc7. * 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
GitHub
parent
6e67d7bff6
commit
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2
.github/workflows/ci.yml
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.github/workflows/ci.yml
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@ -30,6 +30,6 @@ jobs:
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uses: actions/setup-python@v2
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with:
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python-version: '3.10'
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- run: python install.py --onnxruntime default --skip-venv
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- run: python install.py --onnxruntime default --skip-conda
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- run: pip install pytest
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- run: pytest
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17
README.md
17
README.md
@ -37,6 +37,7 @@ options:
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-v, --version show program's version number and exit
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misc:
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--force-download force automate downloads and exit
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--skip-download omit automate downloads and remote lookups
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--headless run the program without a user interface
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--log-level {error,warn,info,debug} adjust the message severity displayed in the terminal
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@ -47,11 +48,11 @@ execution:
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--execution-queue-count [1-32] specify the amount of frames each thread is processing
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memory:
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--video-memory-strategy {strict,moderate,tolerant} balance fast frame processing and low vram usage
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--system-memory-limit [0-128] limit the available ram that can be used while processing
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--video-memory-strategy {strict,moderate,tolerant} balance fast frame processing and low VRAM usage
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--system-memory-limit [0-128] limit the available RAM that can be used while processing
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face analyser:
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--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.
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--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
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--face-analyser-age {child,teen,adult,senior} filter the detected faces based on their age
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--face-analyser-gender {female,male} filter the detected faces based on their gender
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--face-detector-model {many,retinaface,scrfd,yoloface,yunet} choose the model responsible for detecting the face
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@ -88,12 +89,14 @@ output creation:
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--skip-audio omit the audio from the target video
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frame processors:
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--frame-processors FRAME_PROCESSORS [FRAME_PROCESSORS ...] load a single or multiple frame processors. (choices: face_debugger, face_enhancer, face_swapper, frame_enhancer, lip_syncer, ...)
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--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)
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--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
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--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, ...)
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--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)
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--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
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--face-enhancer-blend [0-100] blend the enhanced into the previous face
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--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
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--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
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--frame-colorizer-model {ddcolor,ddcolor_artistic,deoldify_artistic} choose the model responsible for colorizing the frame
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--frame-colorizer-blend [0-100] blend the colorized into the previous frame
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--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
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--frame-enhancer-blend [0-100] blend the enhanced into the previous frame
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--lip-syncer-model {wav2lip_gan} choose the model responsible for syncing the lips
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@ -4,6 +4,7 @@ target_path =
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output_path =
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[misc]
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force_download =
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skip_download =
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headless =
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log_level =
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@ -60,6 +61,8 @@ face_debugger_items =
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face_enhancer_model =
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face_enhancer_blend =
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face_swapper_model =
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frame_colorizer_model =
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frame_colorizer_blend =
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frame_enhancer_model =
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frame_enhancer_blend =
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lip_syncer_model =
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@ -5,7 +5,49 @@ import scipy
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from facefusion.filesystem import is_audio
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from facefusion.ffmpeg import read_audio_buffer
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from facefusion.typing import Fps, Audio, Spectrogram, AudioFrame
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from facefusion.typing import Fps, Audio, AudioFrame, Spectrogram, MelFilterBank
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from facefusion.voice_extractor import batch_extract_voice
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@lru_cache(maxsize = 128)
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def read_static_audio(audio_path : str, fps : Fps) -> Optional[List[AudioFrame]]:
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return read_audio(audio_path, fps)
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def read_audio(audio_path : str, fps : Fps) -> Optional[List[AudioFrame]]:
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sample_rate = 16000
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channel_total = 2
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if is_audio(audio_path):
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audio_buffer = read_audio_buffer(audio_path, sample_rate, channel_total)
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audio = numpy.frombuffer(audio_buffer, dtype = numpy.int16).reshape(-1, 2)
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audio = prepare_audio(audio)
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spectrogram = create_spectrogram(audio)
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audio_frames = extract_audio_frames(spectrogram, fps)
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return audio_frames
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return None
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@lru_cache(maxsize = 128)
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def read_static_voice(audio_path : str, fps : Fps) -> Optional[List[AudioFrame]]:
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return read_voice(audio_path, fps)
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def read_voice(audio_path : str, fps : Fps) -> Optional[List[AudioFrame]]:
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sample_rate = 16000
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channel_total = 2
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chunk_size = 1024 ** 3
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step_size = chunk_size // 4
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if is_audio(audio_path):
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audio_buffer = read_audio_buffer(audio_path, sample_rate, channel_total)
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audio = numpy.frombuffer(audio_buffer, dtype = numpy.int16).reshape(-1, 2)
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audio = batch_extract_voice(audio, chunk_size, step_size)
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audio = prepare_audio(audio)
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spectrogram = create_spectrogram(audio)
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audio_frames = extract_audio_frames(spectrogram, fps)
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return audio_frames
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return None
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def get_audio_frame(audio_path : str, fps : Fps, frame_number : int = 0) -> Optional[AudioFrame]:
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@ -16,33 +58,26 @@ def get_audio_frame(audio_path : str, fps : Fps, frame_number : int = 0) -> Opti
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return None
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def create_empty_audio_frame() -> AudioFrame:
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audio_frame = numpy.zeros((80, 16), dtype = numpy.int16)
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return audio_frame
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@lru_cache(maxsize = None)
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def read_static_audio(audio_path : str, fps : Fps) -> Optional[List[AudioFrame]]:
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def get_voice_frame(audio_path : str, fps : Fps, frame_number : int = 0) -> Optional[AudioFrame]:
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if is_audio(audio_path):
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audio_buffer = read_audio_buffer(audio_path, 16000, 2)
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audio = numpy.frombuffer(audio_buffer, dtype = numpy.int16).reshape(-1, 2)
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audio = normalize_audio(audio)
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audio = filter_audio(audio, -0.97)
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spectrogram = create_spectrogram(audio, 16000, 80, 800, 55.0, 7600.0)
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audio_frames = extract_audio_frames(spectrogram, 80, 16, fps)
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return audio_frames
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voice_frames = read_static_voice(audio_path, fps)
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if frame_number in range(len(voice_frames)):
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return voice_frames[frame_number]
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return None
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def normalize_audio(audio : numpy.ndarray[Any, Any]) -> Audio:
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def create_empty_audio_frame() -> AudioFrame:
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mel_filter_total = 80
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step_size = 16
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audio_frame = numpy.zeros((mel_filter_total, step_size)).astype(numpy.int16)
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return audio_frame
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def prepare_audio(audio : numpy.ndarray[Any, Any]) -> Audio:
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if audio.ndim > 1:
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audio = numpy.mean(audio, axis = 1)
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audio = audio / numpy.max(numpy.abs(audio), axis = 0)
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return audio
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def filter_audio(audio : Audio, filter_coefficient : float) -> Audio:
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audio = scipy.signal.lfilter([ 1.0, filter_coefficient ], [1.0], audio)
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audio = scipy.signal.lfilter([ 1.0, -0.97 ], [ 1.0 ], audio)
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return audio
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@ -54,28 +89,40 @@ def convert_mel_to_hertz(mel : numpy.ndarray[Any, Any]) -> numpy.ndarray[Any, An
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return 700 * (10 ** (mel / 2595) - 1)
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@lru_cache(maxsize = None)
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def create_static_mel_filter(sample_rate : int, filter_total : int, filter_size : int, frequency_minimum : float, frequency_maximum : float) -> numpy.ndarray[Any, Any]:
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frequency_maximum = min(sample_rate / 2, frequency_maximum)
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mel_filter = numpy.zeros((filter_total, filter_size // 2 + 1))
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mel_bins = numpy.linspace(convert_hertz_to_mel(frequency_minimum), convert_hertz_to_mel(frequency_maximum), filter_total + 2)
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indices = numpy.floor((filter_size + 1) * convert_mel_to_hertz(mel_bins) / sample_rate).astype(numpy.int16)
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for index in range(filter_total):
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mel_filter[index, indices[index]: indices[index + 1]] = scipy.signal.windows.triang(indices[index + 1] - indices[index])
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return mel_filter
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def create_mel_filter_bank() -> MelFilterBank:
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mel_filter_total = 80
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mel_bin_total = 800
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sample_rate = 16000
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min_frequency = 55.0
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max_frequency = 7600.0
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mel_filter_bank = numpy.zeros((mel_filter_total, mel_bin_total // 2 + 1))
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mel_frequency_range = numpy.linspace(convert_hertz_to_mel(min_frequency), convert_hertz_to_mel(max_frequency), mel_filter_total + 2)
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indices = numpy.floor((mel_bin_total + 1) * convert_mel_to_hertz(mel_frequency_range) / sample_rate).astype(numpy.int16)
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for index in range(mel_filter_total):
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start = indices[index]
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end = indices[index + 1]
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mel_filter_bank[index, start:end] = scipy.signal.windows.triang(end - start)
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return mel_filter_bank
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def create_spectrogram(audio : Audio, sample_rate : int, filter_total : int, filter_size : int, frequency_minimum : float, frequency_maximum : float) -> Spectrogram:
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mel_filter = create_static_mel_filter(sample_rate, filter_total, filter_size, frequency_minimum, frequency_maximum)
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spectrogram = scipy.signal.stft(audio, nperseg = filter_size, noverlap = 600, nfft = filter_size)[2]
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spectrogram = numpy.dot(mel_filter, numpy.abs(spectrogram))
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def create_spectrogram(audio : Audio) -> Spectrogram:
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mel_bin_total = 800
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mel_bin_overlap = 600
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mel_filter_bank = create_mel_filter_bank()
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spectrogram = scipy.signal.stft(audio, nperseg = mel_bin_total, nfft = mel_bin_total, noverlap = mel_bin_overlap)[2]
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spectrogram = numpy.dot(mel_filter_bank, numpy.abs(spectrogram))
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return spectrogram
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def extract_audio_frames(spectrogram : Spectrogram, filter_total : int, audio_frame_step : int, fps : Fps) -> List[AudioFrame]:
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indices = numpy.arange(0, spectrogram.shape[1], filter_total / fps).astype(numpy.int16)
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indices = indices[indices >= audio_frame_step]
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def extract_audio_frames(spectrogram : Spectrogram, fps : Fps) -> List[AudioFrame]:
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mel_filter_total = 80
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step_size = 16
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audio_frames = []
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indices = numpy.arange(0, spectrogram.shape[1], mel_filter_total / fps).astype(numpy.int16)
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indices = indices[indices >= step_size]
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for index in indices:
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audio_frames.append(spectrogram[:, max(0, index - audio_frame_step) : index])
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start = max(0, index - step_size)
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audio_frames.append(spectrogram[:, start:index])
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return audio_frames
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@ -1,4 +1,4 @@
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from typing import List, Any, Tuple
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from typing import List, Any
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import numpy
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@ -16,12 +16,3 @@ def create_float_range(start : float, stop : float, step : float) -> List[float]
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def get_first(__list__ : Any) -> Any:
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return next(iter(__list__), None)
|
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def extract_major_version(version : str) -> Tuple[int, int]:
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versions = version.split('.')
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if len(versions) > 1:
|
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return int(versions[0]), int(versions[1])
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if len(versions) == 1:
|
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return int(versions[0]), 0
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return 0, 0
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@ -1,4 +1,4 @@
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from typing import Any, Dict
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from typing import Any
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from functools import lru_cache
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from time import sleep
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import threading
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@ -9,15 +9,15 @@ from tqdm import tqdm
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|
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import facefusion.globals
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from facefusion import process_manager, wording
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from facefusion.typing import VisionFrame, ModelValue, Fps
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from facefusion.typing import VisionFrame, ModelSet, Fps
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from facefusion.execution import apply_execution_provider_options
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from facefusion.vision import get_video_frame, count_video_frame_total, read_image, detect_video_fps
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from facefusion.filesystem import resolve_relative_path
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from facefusion.filesystem import resolve_relative_path, is_file
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from facefusion.download import conditional_download
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CONTENT_ANALYSER = None
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THREAD_LOCK : threading.Lock = threading.Lock()
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MODELS : Dict[str, ModelValue] =\
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MODELS : ModelSet =\
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{
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'open_nsfw':
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||||
{
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||||
@ -26,7 +26,7 @@ MODELS : Dict[str, ModelValue] =\
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||||
}
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}
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PROBABILITY_LIMIT = 0.80
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RATE_LIMIT = 5
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RATE_LIMIT = 10
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STREAM_COUNTER = 0
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|
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|
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@ -49,13 +49,15 @@ def clear_content_analyser() -> None:
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||||
|
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|
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def pre_check() -> bool:
|
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if not facefusion.globals.skip_download:
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download_directory_path = resolve_relative_path('../.assets/models')
|
||||
model_url = MODELS.get('open_nsfw').get('url')
|
||||
model_path = MODELS.get('open_nsfw').get('path')
|
||||
|
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if not facefusion.globals.skip_download:
|
||||
process_manager.check()
|
||||
conditional_download(download_directory_path, [ model_url ])
|
||||
process_manager.end()
|
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return True
|
||||
return is_file(model_path)
|
||||
|
||||
|
||||
def analyse_stream(vision_frame : VisionFrame, video_fps : Fps) -> bool:
|
||||
|
||||
@ -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):
|
||||
|
||||
@ -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('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'))
|
||||
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)
|
||||
|
||||
@ -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
|
||||
|
||||
|
||||
@ -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:
|
||||
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'),
|
||||
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:
|
||||
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)
|
||||
|
||||
@ -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
|
||||
|
||||
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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 =\
|
||||
{
|
||||
|
||||
@ -2,7 +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'
|
||||
|
||||
@ -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)
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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:
|
||||
|
||||
@ -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:
|
||||
if not facefusion.globals.skip_download:
|
||||
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 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:
|
||||
|
||||
@ -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:
|
||||
if not facefusion.globals.skip_download:
|
||||
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 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
232
facefusion/processors/frame/modules/frame_colorizer.py
Normal 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)
|
||||
@ -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:
|
||||
if not facefusion.globals.skip_download:
|
||||
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 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
|
||||
@ -184,10 +208,10 @@ def normalize_tile_frame(vision_tile_frame : VisionFrame) -> VisionFrame:
|
||||
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:
|
||||
|
||||
@ -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:
|
||||
if not facefusion.globals.skip_download:
|
||||
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 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
|
||||
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:
|
||||
|
||||
@ -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
|
||||
|
||||
@ -9,7 +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]]
|
||||
|
||||
@ -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,
|
||||
|
||||
@ -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:
|
||||
|
||||
@ -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:
|
||||
|
||||
@ -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]:
|
||||
|
||||
@ -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] =\
|
||||
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)
|
||||
|
||||
@ -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')
|
||||
|
||||
@ -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:
|
||||
|
||||
@ -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 ])
|
||||
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]:
|
||||
|
||||
@ -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] =\
|
||||
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] =\
|
||||
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:
|
||||
|
||||
@ -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' ]:
|
||||
|
||||
@ -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]:
|
||||
|
||||
@ -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:
|
||||
|
||||
@ -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',
|
||||
|
||||
@ -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:
|
||||
|
||||
@ -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
132
facefusion/voice_extractor.py
Normal 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
|
||||
@ -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',
|
||||
|
||||
@ -1,7 +1,9 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
import os
|
||||
import subprocess
|
||||
|
||||
os.environ['PIP_BREAK_SYSTEM_PACKAGES'] = '1'
|
||||
subprocess.call([ 'pip', 'install', 'inquirer', '-q' ])
|
||||
|
||||
from facefusion import installer
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user