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3.1.0 (#839)

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* Replace audio whenever set via source

* add H264_qsv&HEVC_qsv (#768)

* Update ffmpeg.py

* Update choices.py

* Update typing.py

* Fix spaces and newlines

* Fix return type

* Introduce hififace swapper

* Disable stream for expression restorer

* Webcam polishing part1 (#796)

* Cosmetics on ignore comments

* Testing for replace audio

* Testing for restore audio

* Testing for restore audio

* Fix replace_audio()

* Remove shortest and use fixed video duration

* Remove shortest and use fixed video duration

* Prevent duplicate entries to local PATH

* Do hard exit on invalid args

* Need for Python 3.10

* Fix state of face selector

* Fix OpenVINO by aliasing GPU.0 to GPU

* Fix OpenVINO by aliasing GPU.0 to GPU

* Fix/age modifier styleganex 512 (#798)

* fix

* styleganex template

* changes

* changes

* fix occlusion mask

* add age modifier scale

* change

* change

* hardcode

* Cleanup

* Use model_sizes and model_templates variables

* No need for prepare when just 2 lines of code

* Someone used spaces over tabs

* Revert back [0][0]

---------

Co-authored-by: harisreedhar <h4harisreedhar.s.s@gmail.com>

* Feat/update gradio5 (#799)

* Update to Gradio 5

* Remove overrides for Gradio

* Fix dark mode for Gradio

* Polish errors

* More styles for tabs and co

* Make slider inputs and reset like a unit

* Make slider inputs and reset like a unit

* Adjust naming

* Improved color matching (#800)

* aura fix

* fix import

* move to vision.py

* changes

* changes

* changes

* changes

* further reduction

* add test

* better test

* change name

* Minor cleanup

* Minor cleanup

* Minor cleanup

* changes (#801)

* Switch to official assets repo

* Add __pycache__ to gitignore

* Gradio pinned python-multipart to 0.0.12

* Update dependencies

* Feat/temp path second try (#802)

* Terminate base directory from temp helper

* Partial adjust program codebase

* Move arguments around

* Make `-j` absolete

* Resolve args

* Fix job register keys

* Adjust date test

* Finalize temp path

* Update onnxruntime

* Update dependencies

* Adjust color for checkboxes

* Revert due terrible performance

* Fix/enforce vp9 for webm (#805)

* Simple fix to enforce vp9 for webm

* Remove suggest methods from program helper

* Cleanup ffmpeg.py a bit

* Update onnxruntime (second try)

* Update onnxruntime (second try)

* Remove cudnn_conv_algo_search tweaks

* Remove cudnn_conv_algo_search tweaks

* changes

* add both mask instead of multiply

* adaptive color correction

* changes

* remove model size requirement

* changes

* add to facefusion.ini

* changes

* changes

* changes

* Add namespace for dfm creators

* Release five frame enhancer models

* Remove vendor from model name

* Remove vendor from model name

* changes

* changes

* changes

* changes

* Feat/download providers (#809)

* Introduce download providers

* update processors download method

* add ui

* Fix CI

* Adjust UI component order, Use download resolver for benchmark

* Remove is_download_done()

* Introduce download provider set, Remove choices method from execution, cast all dict keys() via list()

* Fix spacing

---------

Co-authored-by: harisreedhar <h4harisreedhar.s.s@gmail.com>

* Fix model paths for 3.1.0

* Introduce bulk-run (#810)

* Introduce bulk-run

* Make bulk run bullet proof

* Integration test for bulk-run

* new alignment

* Add safer global named resolve_file_pattern() (#811)

* Allow bulk runner with target pattern only

* changes

* changes

* Update Python to 3.12 for CI (#813)

* changes

* Improve NVIDIA device lookups

* Rename template key to deepfacelive

* Fix name

* Improve resolve download

* Rename bulk-run to batch-run

* Make deep swapper inputs universal

* Add more deepfacelive models

* Use different morph value

* Feat/simplify hashes sources download (#814)

* Extract download directory path from assets path

* Fix lint

* Fix force-download command, Fix urls in frame enhancer

* changes

* fix warp_face_by_bounding_box dtype error

* DFM Morph (#816)

* changes

* Improve wording, Replace [None], SideQuest: clean forward() of age modifier

* SideQuest: clean forward() of face enhancer

---------

Co-authored-by: henryruhs <info@henryruhs.com>

* Fix preview refresh after slide

* Add more deepfacelive models (#817)

* Add more deepfacelive models

* Add more deepfacelive models

* Fix deep swapper sizes

* Kill accent colors, Number input styles for Chrome

* Simplify thumbnail-item looks

* Fix first black screen

* Introduce model helper

* ci.yml: Add macOS on ARM64 to the testing (#818)

* ci.yml: Add macOS on ARM64 to the testing

* ci.yml: uses: AnimMouse/setup-ffmpeg@v1

* ci.yml: strategy: matrix: os: macos-latest,

* - name: Set up FFmpeg

* Update .github/workflows/ci.yml

* Update ci.yml

---------

Co-authored-by: Henry Ruhs <info@henryruhs.com>

* Show/hide morph slider for deep swapper (#822)

* remove dfl_head and update dfl_whole_face template

* Add deep swapper models by Mats

* Add deep swapper models by Druuzil

* Add deep swapper models by Rumateus

* Implement face enhancer weight for codeformer, Side Quest: has proces… (#823)

* Implement face enhancer weight for codeformer, Side Quest: has processor checks

* Fix typo

* Fix face enhancer blend in UI

* Use static model set creation

* Add deep swapper models by Jen

* Introduce create_static_model_set() everywhere (#824)

* Move clear over to the UI (#825)

* Fix model key

* Undo restore_audio()

* Switch to latest XSeg

* Switch to latest XSeg

* Switch to latest XSeg

* Use resolve_download_url() everywhere, Vanish --skip-download flag

* Fix resolve_download_url

* Fix space

* Kill resolve_execution_provider_keys() and move fallbacks where they belong

* Kill resolve_execution_provider_keys() and move fallbacks where they belong

* Remove as this does not work

* Change TempFrameFormat order

* Fix CoreML partially

* Remove duplicates (Rumateus is the creator)

* Add deep swapper models by Edel

* Introduce download scopes (#826)

* Introduce download scopes

* Limit download scopes to force-download command

* Change source-paths behaviour

* Fix space

* Update README

* Rename create_log_level_program to create_misc_program

* Fix wording

* Fix wording

* Update dependencies

* Use tolerant for video_memory_strategy in benchmark

* Feat/ffmpeg with progress (#827)

* FFmpeg with progress bar

* Fix typing

* FFmpeg with progress bar part2

* Restore streaming wording

* Change order in choices and typing

* Introduce File using list_directory() (#830)

* Feat/local deep swapper models (#832)

* Local model support for deep swapper

* Local model support for deep swapper part2

* Local model support for deep swapper part3

* Update yet another dfm by Druuzil

* Refactor/choices and naming (#833)

* Refactor choices, imports and naming

* Refactor choices, imports and naming

* Fix styles for tabs, Restore toast

* Update yet another dfm by Druuzil

* Feat/face masker models (#834)

* Introduce face masker models

* Introduce face masker models

* Introduce face masker models

* Register needed step keys

* Provide different XSeg models

* Simplify model context

* Fix out of range for trim frame, Fix ffmpeg extraction count (#836)

* Fix out of range for trim frame, Fix ffmpeg extraction count

* Move restrict of trim frame to the core, Make sure all values are within the range

* Fix and merge testing

* Fix typing

* Add region mask for deep swapper

* Adjust wording

* Move FACE_MASK_REGIONS to choices

* Update dependencies

* Feat/download provider fallback (#837)

* Introduce download providers fallback, Use CURL everywhre

* Fix CI

* Use readlines() over readline() to avoid while

* Use readlines() over readline() to avoid while

* Use readlines() over readline() to avoid while

* Use communicate() over wait()

* Minor updates for testing

* Stop webcam on source image change

* Feat/webcam improvements (#838)

* Detect available webcams

* Fix CI, Move webcam id dropdown to the sidebar, Disable warnings

* Fix CI

* Remove signal on hard_exit() to prevent exceptions

* Fix border color in toast timer

* Prepare release

* Update preview

* Update preview

* Hotfix progress bar

---------

Co-authored-by: DDXDB <38449595+DDXDB@users.noreply.github.com>
Co-authored-by: harisreedhar <h4harisreedhar.s.s@gmail.com>
Co-authored-by: Harisreedhar <46858047+harisreedhar@users.noreply.github.com>
Co-authored-by: Christian Clauss <cclauss@me.com>
This commit is contained in:
Henry Ruhs 2024-12-24 12:46:56 +01:00 committed by GitHub
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commit 7a09479fb5
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102 changed files with 3699 additions and 2112 deletions
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16
.github/workflows/ci.yml vendored
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@ -8,10 +8,10 @@ jobs:
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Set up Python 3.10
- name: Set up Python 3.12
uses: actions/setup-python@v5
with:
python-version: '3.10'
python-version: '3.12'
- run: pip install flake8
- run: pip install flake8-import-order
- run: pip install mypy
@ -22,17 +22,17 @@ jobs:
test:
strategy:
matrix:
os: [ macos-13, ubuntu-latest, windows-latest ]
os: [ macos-latest, ubuntu-latest, windows-latest ]
runs-on: ${{ matrix.os }}
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Set up FFmpeg
uses: FedericoCarboni/setup-ffmpeg@v3
- name: Set up Python 3.10
uses: AnimMouse/setup-ffmpeg@v1
- name: Set up Python 3.12
uses: actions/setup-python@v5
with:
python-version: '3.10'
python-version: '3.12'
- run: python install.py --onnxruntime default --skip-conda
- run: pip install pytest
- run: pytest
@ -44,10 +44,10 @@ jobs:
uses: actions/checkout@v4
- name: Set up FFmpeg
uses: FedericoCarboni/setup-ffmpeg@v3
- name: Set up Python 3.10
- name: Set up Python 3.12
uses: actions/setup-python@v5
with:
python-version: '3.10'
python-version: '3.12'
- run: python install.py --onnxruntime default --skip-conda
- run: pip install coveralls
- run: pip install pytest

1
.gitignore vendored
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@ -1,3 +1,4 @@
__pycache__
.assets
.caches
.jobs

1
README.md
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@ -35,6 +35,7 @@ options:
commands:
run run the program
headless-run run the program in headless mode
batch-run run the program in batch mode
force-download force automate downloads and exit
job-list list jobs by status
job-create create a drafted job

16
facefusion.ini
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@ -1,9 +1,15 @@
[paths]
temp_path =
jobs_path =
source_paths =
target_path =
output_path =
[patterns]
source_pattern =
target_pattern =
output_pattern =
[face_detector]
face_detector_model =
face_detector_size =
@ -26,6 +32,8 @@ reference_face_distance =
reference_frame_number =
[face_masker]
face_occluder_model =
face_parser_model =
face_mask_types =
face_mask_blur =
face_mask_padding =
@ -52,6 +60,8 @@ skip_audio =
processors =
age_modifier_model =
age_modifier_direction =
deep_swapper_model =
deep_swapper_morph =
expression_restorer_model =
expression_restorer_factor =
face_debugger_items =
@ -72,6 +82,7 @@ face_editor_head_yaw =
face_editor_head_roll =
face_enhancer_model =
face_enhancer_blend =
face_enhancer_weight =
face_swapper_model =
face_swapper_pixel_boost =
frame_colorizer_model =
@ -92,10 +103,13 @@ execution_providers =
execution_thread_count =
execution_queue_count =
[download]
download_providers =
download_scope =
[memory]
video_memory_strategy =
system_memory_limit =
[misc]
skip_download =
log_level =

39
facefusion/args.py
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@ -15,6 +15,14 @@ def reduce_step_args(args : Args) -> Args:
return step_args
def reduce_job_args(args : Args) -> Args:
job_args =\
{
key: args[key] for key in args if key in job_store.get_job_keys()
}
return job_args
def collect_step_args() -> Args:
step_args =\
{
@ -35,10 +43,15 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
# general
apply_state_item('command', args.get('command'))
# paths
apply_state_item('temp_path', args.get('temp_path'))
apply_state_item('jobs_path', args.get('jobs_path'))
apply_state_item('source_paths', args.get('source_paths'))
apply_state_item('target_path', args.get('target_path'))
apply_state_item('output_path', args.get('output_path'))
# patterns
apply_state_item('source_pattern', args.get('source_pattern'))
apply_state_item('target_pattern', args.get('target_pattern'))
apply_state_item('output_pattern', args.get('output_pattern'))
# face detector
apply_state_item('face_detector_model', args.get('face_detector_model'))
apply_state_item('face_detector_size', args.get('face_detector_size'))
@ -48,16 +61,18 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
apply_state_item('face_landmarker_model', args.get('face_landmarker_model'))
apply_state_item('face_landmarker_score', args.get('face_landmarker_score'))
# face selector
state_manager.init_item('face_selector_mode', args.get('face_selector_mode'))
state_manager.init_item('face_selector_order', args.get('face_selector_order'))
state_manager.init_item('face_selector_age_start', args.get('face_selector_age_start'))
state_manager.init_item('face_selector_age_end', args.get('face_selector_age_end'))
state_manager.init_item('face_selector_gender', args.get('face_selector_gender'))
state_manager.init_item('face_selector_race', args.get('face_selector_race'))
state_manager.init_item('reference_face_position', args.get('reference_face_position'))
state_manager.init_item('reference_face_distance', args.get('reference_face_distance'))
state_manager.init_item('reference_frame_number', args.get('reference_frame_number'))
apply_state_item('face_selector_mode', args.get('face_selector_mode'))
apply_state_item('face_selector_order', args.get('face_selector_order'))
apply_state_item('face_selector_age_start', args.get('face_selector_age_start'))
apply_state_item('face_selector_age_end', args.get('face_selector_age_end'))
apply_state_item('face_selector_gender', args.get('face_selector_gender'))
apply_state_item('face_selector_race', args.get('face_selector_race'))
apply_state_item('reference_face_position', args.get('reference_face_position'))
apply_state_item('reference_face_distance', args.get('reference_face_distance'))
apply_state_item('reference_frame_number', args.get('reference_frame_number'))
# face masker
apply_state_item('face_occluder_model', args.get('face_occluder_model'))
apply_state_item('face_parser_model', args.get('face_parser_model'))
apply_state_item('face_mask_types', args.get('face_mask_types'))
apply_state_item('face_mask_blur', args.get('face_mask_blur'))
apply_state_item('face_mask_padding', normalize_padding(args.get('face_mask_padding')))
@ -92,7 +107,7 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
apply_state_item('output_video_fps', output_video_fps)
apply_state_item('skip_audio', args.get('skip_audio'))
# processors
available_processors = list_directory('facefusion/processors/modules')
available_processors = [ file.get('name') for file in list_directory('facefusion/processors/modules') ]
apply_state_item('processors', args.get('processors'))
for processor_module in get_processors_modules(available_processors):
processor_module.apply_args(args, apply_state_item)
@ -105,11 +120,13 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
apply_state_item('execution_providers', args.get('execution_providers'))
apply_state_item('execution_thread_count', args.get('execution_thread_count'))
apply_state_item('execution_queue_count', args.get('execution_queue_count'))
# download
apply_state_item('download_providers', args.get('download_providers'))
apply_state_item('download_scope', args.get('download_scope'))
# memory
apply_state_item('video_memory_strategy', args.get('video_memory_strategy'))
apply_state_item('system_memory_limit', args.get('system_memory_limit'))
# misc
apply_state_item('skip_download', args.get('skip_download'))
apply_state_item('log_level', args.get('log_level'))
# jobs
apply_state_item('job_id', args.get('job_id'))

63
facefusion/choices.py
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@ -2,9 +2,7 @@ import logging
from typing import List, Sequence
from facefusion.common_helper import create_float_range, create_int_range
from facefusion.typing import Angle, ExecutionProviderSet, FaceDetectorSet, FaceLandmarkerModel, FaceMaskRegion, FaceMaskType, FaceSelectorMode, FaceSelectorOrder, Gender, JobStatus, LogLevelSet, OutputAudioEncoder, OutputVideoEncoder, OutputVideoPreset, Race, Score, TempFrameFormat, UiWorkflow, VideoMemoryStrategy
video_memory_strategies : List[VideoMemoryStrategy] = [ 'strict', 'moderate', 'tolerant' ]
from facefusion.typing import Angle, DownloadProvider, DownloadProviderSet, DownloadScope, ExecutionProvider, ExecutionProviderSet, FaceDetectorModel, FaceDetectorSet, FaceLandmarkerModel, FaceMaskRegion, FaceMaskRegionSet, FaceMaskType, FaceOccluderModel, FaceParserModel, FaceSelectorMode, FaceSelectorOrder, Gender, JobStatus, LogLevel, LogLevelSet, OutputAudioEncoder, OutputVideoEncoder, OutputVideoPreset, Race, Score, TempFrameFormat, UiWorkflow, VideoMemoryStrategy
face_detector_set : FaceDetectorSet =\
{
@ -13,29 +11,37 @@ face_detector_set : FaceDetectorSet =\
'scrfd': [ '160x160', '320x320', '480x480', '512x512', '640x640' ],
'yoloface': [ '640x640' ]
}
face_detector_models : List[FaceDetectorModel] = list(face_detector_set.keys())
face_landmarker_models : List[FaceLandmarkerModel] = [ 'many', '2dfan4', 'peppa_wutz' ]
face_selector_modes : List[FaceSelectorMode] = [ 'many', 'one', 'reference' ]
face_selector_orders : List[FaceSelectorOrder] = [ 'left-right', 'right-left', 'top-bottom', 'bottom-top', 'small-large', 'large-small', 'best-worst', 'worst-best' ]
face_selector_genders : List[Gender] = ['female', 'male']
face_selector_races : List[Race] = ['white', 'black', 'latino', 'asian', 'indian', 'arabic']
face_selector_genders : List[Gender] = [ 'female', 'male' ]
face_selector_races : List[Race] = [ 'white', 'black', 'latino', 'asian', 'indian', 'arabic' ]
face_occluder_models : List[FaceOccluderModel] = [ 'xseg_1', 'xseg_2' ]
face_parser_models : List[FaceParserModel] = [ 'bisenet_resnet_18', 'bisenet_resnet_34' ]
face_mask_types : List[FaceMaskType] = [ 'box', 'occlusion', 'region' ]
face_mask_regions : List[FaceMaskRegion] = [ 'skin', 'left-eyebrow', 'right-eyebrow', 'left-eye', 'right-eye', 'glasses', 'nose', 'mouth', 'upper-lip', 'lower-lip' ]
face_mask_region_set : FaceMaskRegionSet =\
{
'skin': 1,
'left-eyebrow': 2,
'right-eyebrow': 3,
'left-eye': 4,
'right-eye': 5,
'glasses': 6,
'nose': 10,
'mouth': 11,
'upper-lip': 12,
'lower-lip': 13
}
face_mask_regions : List[FaceMaskRegion] = list(face_mask_region_set.keys())
temp_frame_formats : List[TempFrameFormat] = [ 'bmp', 'jpg', 'png' ]
output_audio_encoders : List[OutputAudioEncoder] = [ 'aac', 'libmp3lame', 'libopus', 'libvorbis' ]
output_video_encoders : List[OutputVideoEncoder] = [ 'libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc', 'h264_amf', 'hevc_amf', 'h264_videotoolbox', 'hevc_videotoolbox' ]
output_video_encoders : List[OutputVideoEncoder] = [ 'libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc', 'h264_amf', 'hevc_amf', 'h264_qsv', 'hevc_qsv', 'h264_videotoolbox', 'hevc_videotoolbox' ]
output_video_presets : List[OutputVideoPreset] = [ 'ultrafast', 'superfast', 'veryfast', 'faster', 'fast', 'medium', 'slow', 'slower', 'veryslow' ]
image_template_sizes : List[float] = [ 0.25, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 3.5, 4 ]
video_template_sizes : List[int] = [ 240, 360, 480, 540, 720, 1080, 1440, 2160, 4320 ]
log_level_set : LogLevelSet =\
{
'error': logging.ERROR,
'warn': logging.WARNING,
'info': logging.INFO,
'debug': logging.DEBUG
}
execution_provider_set : ExecutionProviderSet =\
{
'cpu': 'CPUExecutionProvider',
@ -46,6 +52,33 @@ execution_provider_set : ExecutionProviderSet =\
'rocm': 'ROCMExecutionProvider',
'tensorrt': 'TensorrtExecutionProvider'
}
execution_providers : List[ExecutionProvider] = list(execution_provider_set.keys())
download_provider_set : DownloadProviderSet =\
{
'github':
{
'url': 'https://github.com',
'path': '/facefusion/facefusion-assets/releases/download/{base_name}/{file_name}'
},
'huggingface':
{
'url': 'https://huggingface.co',
'path': '/facefusion/{base_name}/resolve/main/{file_name}'
}
}
download_providers : List[DownloadProvider] = list(download_provider_set.keys())
download_scopes : List[DownloadScope] = [ 'lite', 'full' ]
video_memory_strategies : List[VideoMemoryStrategy] = [ 'strict', 'moderate', 'tolerant' ]
log_level_set : LogLevelSet =\
{
'error': logging.ERROR,
'warn': logging.WARNING,
'info': logging.INFO,
'debug': logging.DEBUG
}
log_levels : List[LogLevel] = list(log_level_set.keys())
ui_workflows : List[UiWorkflow] = [ 'instant_runner', 'job_runner', 'job_manager' ]
job_statuses : List[JobStatus] = [ 'drafted', 'queued', 'completed', 'failed' ]

44
facefusion/content_analyser.py
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@ -5,21 +5,28 @@ import numpy
from tqdm import tqdm
from facefusion import inference_manager, state_manager, wording
from facefusion.download import conditional_download_hashes, conditional_download_sources
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import Fps, InferencePool, ModelOptions, ModelSet, VisionFrame
from facefusion.vision import count_video_frame_total, detect_video_fps, get_video_frame, read_image
from facefusion.typing import DownloadScope, Fps, InferencePool, ModelOptions, ModelSet, VisionFrame
from facefusion.vision import detect_video_fps, get_video_frame, read_image
MODEL_SET : ModelSet =\
{
PROBABILITY_LIMIT = 0.80
RATE_LIMIT = 10
STREAM_COUNTER = 0
@lru_cache(maxsize = None)
def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
return\
{
'open_nsfw':
{
'hashes':
{
'content_analyser':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/open_nsfw.hash',
'url': resolve_download_url('models-3.0.0', 'open_nsfw.hash'),
'path': resolve_relative_path('../.assets/models/open_nsfw.hash')
}
},
@ -27,17 +34,14 @@ MODEL_SET : ModelSet =\
{
'content_analyser':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/open_nsfw.onnx',
'url': resolve_download_url('models-3.0.0', 'open_nsfw.onnx'),
'path': resolve_relative_path('../.assets/models/open_nsfw.onnx')
}
},
'size': (224, 224),
'mean': [ 104, 117, 123 ]
}
}
PROBABILITY_LIMIT = 0.80
RATE_LIMIT = 10
STREAM_COUNTER = 0
}
def get_inference_pool() -> InferencePool:
@ -50,15 +54,14 @@ def clear_inference_pool() -> None:
def get_model_options() -> ModelOptions:
return MODEL_SET.get('open_nsfw')
return create_static_model_set('full').get('open_nsfw')
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
return conditional_download_hashes(download_directory_path, model_hashes) and conditional_download_sources(download_directory_path, model_sources)
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
def analyse_stream(vision_frame : VisionFrame, video_fps : Fps) -> bool:
@ -100,23 +103,22 @@ def prepare_frame(vision_frame : VisionFrame) -> VisionFrame:
@lru_cache(maxsize = None)
def analyse_image(image_path : str) -> bool:
frame = read_image(image_path)
return analyse_frame(frame)
vision_frame = read_image(image_path)
return analyse_frame(vision_frame)
@lru_cache(maxsize = None)
def analyse_video(video_path : str, start_frame : int, end_frame : int) -> bool:
video_frame_total = count_video_frame_total(video_path)
def analyse_video(video_path : str, trim_frame_start : int, trim_frame_end : int) -> bool:
video_fps = detect_video_fps(video_path)
frame_range = range(start_frame or 0, end_frame or video_frame_total)
frame_range = range(trim_frame_start, trim_frame_end)
rate = 0.0
counter = 0
with tqdm(total = len(frame_range), desc = wording.get('analysing'), unit = 'frame', ascii = ' =', disable = state_manager.get_item('log_level') in [ 'warn', 'error' ]) as progress:
for frame_number in frame_range:
if frame_number % int(video_fps) == 0:
frame = get_video_frame(video_path, frame_number)
if analyse_frame(frame):
vision_frame = get_video_frame(video_path, frame_number)
if analyse_frame(vision_frame):
counter += 1
rate = counter * int(video_fps) / len(frame_range) * 100
progress.update()

152
facefusion/core.py
View File

@ -1,3 +1,4 @@
import itertools
import shutil
import signal
import sys
@ -6,7 +7,7 @@ from time import time
import numpy
from facefusion import content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, logger, process_manager, state_manager, voice_extractor, wording
from facefusion.args import apply_args, collect_job_args, reduce_step_args
from facefusion.args import apply_args, collect_job_args, reduce_job_args, reduce_step_args
from facefusion.common_helper import get_first
from facefusion.content_analyser import analyse_image, analyse_video
from facefusion.download import conditional_download_hashes, conditional_download_sources
@ -15,7 +16,7 @@ from facefusion.face_analyser import get_average_face, get_many_faces, get_one_f
from facefusion.face_selector import sort_and_filter_faces
from facefusion.face_store import append_reference_face, clear_reference_faces, get_reference_faces
from facefusion.ffmpeg import copy_image, extract_frames, finalize_image, merge_video, replace_audio, restore_audio
from facefusion.filesystem import filter_audio_paths, is_image, is_video, list_directory, resolve_relative_path
from facefusion.filesystem import filter_audio_paths, is_image, is_video, list_directory, resolve_file_pattern
from facefusion.jobs import job_helper, job_manager, job_runner
from facefusion.jobs.job_list import compose_job_list
from facefusion.memory import limit_system_memory
@ -25,7 +26,7 @@ from facefusion.program_helper import validate_args
from facefusion.statistics import conditional_log_statistics
from facefusion.temp_helper import clear_temp_directory, create_temp_directory, get_temp_file_path, get_temp_frame_paths, move_temp_file
from facefusion.typing import Args, ErrorCode
from facefusion.vision import get_video_frame, pack_resolution, read_image, read_static_images, restrict_image_resolution, restrict_video_fps, restrict_video_resolution, unpack_resolution
from facefusion.vision import get_video_frame, pack_resolution, read_image, read_static_images, restrict_image_resolution, restrict_trim_frame, restrict_video_fps, restrict_video_resolution, unpack_resolution
def cli() -> None:
@ -41,6 +42,8 @@ def cli() -> None:
route(args)
else:
program.print_help()
else:
hard_exit(2)
def route(args : Args) -> None:
@ -65,12 +68,18 @@ def route(args : Args) -> None:
for ui_layout in ui.get_ui_layouts_modules(state_manager.get_item('ui_layouts')):
if not ui_layout.pre_check():
return conditional_exit(2)
ui.init()
ui.launch()
if state_manager.get_item('command') == 'headless-run':
if not job_manager.init_jobs(state_manager.get_item('jobs_path')):
hard_exit(1)
error_core = process_headless(args)
hard_exit(error_core)
if state_manager.get_item('command') == 'batch-run':
if not job_manager.init_jobs(state_manager.get_item('jobs_path')):
hard_exit(1)
error_core = process_batch(args)
hard_exit(error_core)
if state_manager.get_item('command') in [ 'job-run', 'job-run-all', 'job-retry', 'job-retry-all' ]:
if not job_manager.init_jobs(state_manager.get_item('jobs_path')):
hard_exit(1)
@ -79,8 +88,8 @@ def route(args : Args) -> None:
def pre_check() -> bool:
if sys.version_info < (3, 9):
logger.error(wording.get('python_not_supported').format(version = '3.9'), __name__)
if sys.version_info < (3, 10):
logger.error(wording.get('python_not_supported').format(version = '3.10'), __name__)
return False
if not shutil.which('curl'):
logger.error(wording.get('curl_not_installed'), __name__)
@ -92,7 +101,7 @@ def pre_check() -> bool:
def common_pre_check() -> bool:
modules =\
common_modules =\
[
content_analyser,
face_classifier,
@ -103,7 +112,7 @@ def common_pre_check() -> bool:
voice_extractor
]
return all(module.pre_check() for module in modules)
return all(module.pre_check() for module in common_modules)
def processors_pre_check() -> bool:
@ -113,64 +122,28 @@ def processors_pre_check() -> bool:
return True
def conditional_process() -> ErrorCode:
start_time = time()
for processor_module in get_processors_modules(state_manager.get_item('processors')):
if not processor_module.pre_process('output'):
return 2
conditional_append_reference_faces()
if is_image(state_manager.get_item('target_path')):
return process_image(start_time)
if is_video(state_manager.get_item('target_path')):
return process_video(start_time)
return 0
def conditional_append_reference_faces() -> None:
if 'reference' in state_manager.get_item('face_selector_mode') and not get_reference_faces():
source_frames = read_static_images(state_manager.get_item('source_paths'))
source_faces = get_many_faces(source_frames)
source_face = get_average_face(source_faces)
if is_video(state_manager.get_item('target_path')):
reference_frame = get_video_frame(state_manager.get_item('target_path'), state_manager.get_item('reference_frame_number'))
else:
reference_frame = read_image(state_manager.get_item('target_path'))
reference_faces = sort_and_filter_faces(get_many_faces([ reference_frame ]))
reference_face = get_one_face(reference_faces, state_manager.get_item('reference_face_position'))
append_reference_face('origin', reference_face)
if source_face and reference_face:
for processor_module in get_processors_modules(state_manager.get_item('processors')):
abstract_reference_frame = processor_module.get_reference_frame(source_face, reference_face, reference_frame)
if numpy.any(abstract_reference_frame):
abstract_reference_faces = sort_and_filter_faces(get_many_faces([ abstract_reference_frame ]))
abstract_reference_face = get_one_face(abstract_reference_faces, state_manager.get_item('reference_face_position'))
append_reference_face(processor_module.__name__, abstract_reference_face)
def force_download() -> ErrorCode:
download_directory_path = resolve_relative_path('../.assets/models')
available_processors = list_directory('facefusion/processors/modules')
common_modules =\
[
content_analyser,
face_classifier,
face_detector,
face_landmarker,
face_recognizer,
face_masker,
face_recognizer,
voice_extractor
]
available_processors = [ file.get('name') for file in list_directory('facefusion/processors/modules') ]
processor_modules = get_processors_modules(available_processors)
for module in common_modules + processor_modules:
if hasattr(module, 'MODEL_SET'):
for model in module.MODEL_SET.values():
if hasattr(module, 'create_static_model_set'):
for model in module.create_static_model_set(state_manager.get_item('download_scope')).values():
model_hashes = model.get('hashes')
model_sources = model.get('sources')
if model_hashes and model_sources:
if not conditional_download_hashes(download_directory_path, model_hashes) or not conditional_download_sources(download_directory_path, model_sources):
if not conditional_download_hashes(model_hashes) or not conditional_download_sources(model_sources):
return 1
return 0
@ -279,6 +252,44 @@ def route_job_runner() -> ErrorCode:
return 2
def process_headless(args : Args) -> ErrorCode:
job_id = job_helper.suggest_job_id('headless')
step_args = reduce_step_args(args)
if job_manager.create_job(job_id) and job_manager.add_step(job_id, step_args) and job_manager.submit_job(job_id) and job_runner.run_job(job_id, process_step):
return 0
return 1
def process_batch(args : Args) -> ErrorCode:
job_id = job_helper.suggest_job_id('batch')
step_args = reduce_step_args(args)
job_args = reduce_job_args(args)
source_paths = resolve_file_pattern(job_args.get('source_pattern'))
target_paths = resolve_file_pattern(job_args.get('target_pattern'))
if job_manager.create_job(job_id):
if source_paths and target_paths:
for index, (source_path, target_path) in enumerate(itertools.product(source_paths, target_paths)):
step_args['source_paths'] = [ source_path ]
step_args['target_path'] = target_path
step_args['output_path'] = job_args.get('output_pattern').format(index = index)
if not job_manager.add_step(job_id, step_args):
return 1
if job_manager.submit_job(job_id) and job_runner.run_job(job_id, process_step):
return 0
if not source_paths and target_paths:
for index, target_path in enumerate(target_paths):
step_args['target_path'] = target_path
step_args['output_path'] = job_args.get('output_pattern').format(index = index)
if not job_manager.add_step(job_id, step_args):
return 1
if job_manager.submit_job(job_id) and job_runner.run_job(job_id, process_step):
return 0
return 1
def process_step(job_id : str, step_index : int, step_args : Args) -> bool:
clear_reference_faces()
step_total = job_manager.count_step_total(job_id)
@ -292,13 +303,39 @@ def process_step(job_id : str, step_index : int, step_args : Args) -> bool:
return False
def process_headless(args : Args) -> ErrorCode:
job_id = job_helper.suggest_job_id('headless')
step_args = reduce_step_args(args)
if job_manager.create_job(job_id) and job_manager.add_step(job_id, step_args) and job_manager.submit_job(job_id) and job_runner.run_job(job_id, process_step):
def conditional_process() -> ErrorCode:
start_time = time()
for processor_module in get_processors_modules(state_manager.get_item('processors')):
if not processor_module.pre_process('output'):
return 2
conditional_append_reference_faces()
if is_image(state_manager.get_item('target_path')):
return process_image(start_time)
if is_video(state_manager.get_item('target_path')):
return process_video(start_time)
return 0
return 1
def conditional_append_reference_faces() -> None:
if 'reference' in state_manager.get_item('face_selector_mode') and not get_reference_faces():
source_frames = read_static_images(state_manager.get_item('source_paths'))
source_faces = get_many_faces(source_frames)
source_face = get_average_face(source_faces)
if is_video(state_manager.get_item('target_path')):
reference_frame = get_video_frame(state_manager.get_item('target_path'), state_manager.get_item('reference_frame_number'))
else:
reference_frame = read_image(state_manager.get_item('target_path'))
reference_faces = sort_and_filter_faces(get_many_faces([ reference_frame ]))
reference_face = get_one_face(reference_faces, state_manager.get_item('reference_face_position'))
append_reference_face('origin', reference_face)
if source_face and reference_face:
for processor_module in get_processors_modules(state_manager.get_item('processors')):
abstract_reference_frame = processor_module.get_reference_frame(source_face, reference_face, reference_frame)
if numpy.any(abstract_reference_frame):
abstract_reference_faces = sort_and_filter_faces(get_many_faces([ abstract_reference_frame ]))
abstract_reference_face = get_one_face(abstract_reference_faces, state_manager.get_item('reference_face_position'))
append_reference_face(processor_module.__name__, abstract_reference_face)
def process_image(start_time : float) -> ErrorCode:
@ -352,7 +389,8 @@ def process_image(start_time : float) -> ErrorCode:
def process_video(start_time : float) -> ErrorCode:
if analyse_video(state_manager.get_item('target_path'), state_manager.get_item('trim_frame_start'), state_manager.get_item('trim_frame_end')):
trim_frame_start, trim_frame_end = restrict_trim_frame(state_manager.get_item('target_path'), state_manager.get_item('trim_frame_start'), state_manager.get_item('trim_frame_end'))
if analyse_video(state_manager.get_item('target_path'), trim_frame_start, trim_frame_end):
return 3
# clear temp
logger.debug(wording.get('clearing_temp'), __name__)
@ -365,7 +403,7 @@ def process_video(start_time : float) -> ErrorCode:
temp_video_resolution = pack_resolution(restrict_video_resolution(state_manager.get_item('target_path'), unpack_resolution(state_manager.get_item('output_video_resolution'))))
temp_video_fps = restrict_video_fps(state_manager.get_item('target_path'), state_manager.get_item('output_video_fps'))
logger.info(wording.get('extracting_frames').format(resolution = temp_video_resolution, fps = temp_video_fps), __name__)
if extract_frames(state_manager.get_item('target_path'), temp_video_resolution, temp_video_fps):
if extract_frames(state_manager.get_item('target_path'), temp_video_resolution, temp_video_fps, trim_frame_start, trim_frame_end):
logger.debug(wording.get('extracting_frames_succeed'), __name__)
else:
if is_process_stopping():
@ -414,7 +452,7 @@ def process_video(start_time : float) -> ErrorCode:
logger.warn(wording.get('replacing_audio_skipped'), __name__)
move_temp_file(state_manager.get_item('target_path'), state_manager.get_item('output_path'))
else:
if restore_audio(state_manager.get_item('target_path'), state_manager.get_item('output_path'), state_manager.get_item('output_video_fps')):
if restore_audio(state_manager.get_item('target_path'), state_manager.get_item('output_path'), state_manager.get_item('output_video_fps'), trim_frame_start, trim_frame_end):
logger.debug(wording.get('restoring_audio_succeed'), __name__)
else:
if is_process_stopping():

76
facefusion/download.py
View File

@ -1,22 +1,23 @@
import os
import shutil
import ssl
import subprocess
import urllib.request
from functools import lru_cache
from typing import List, Tuple
from typing import List, Optional, Tuple
from urllib.parse import urlparse
from tqdm import tqdm
import facefusion.choices
from facefusion import logger, process_manager, state_manager, wording
from facefusion.common_helper import is_macos
from facefusion.filesystem import get_file_size, is_file, remove_file
from facefusion.hash_helper import validate_hash
from facefusion.typing import DownloadSet
from facefusion.typing import DownloadProvider, DownloadSet
if is_macos():
ssl._create_default_https_context = ssl._create_unverified_context
def open_curl(args : List[str]) -> subprocess.Popen[bytes]:
commands = [ shutil.which('curl'), '--silent', '--insecure', '--location' ]
commands.extend(args)
return subprocess.Popen(commands, stdin = subprocess.PIPE, stdout = subprocess.PIPE)
def conditional_download(download_directory_path : str, urls : List[str]) -> None:
@ -24,14 +25,15 @@ def conditional_download(download_directory_path : str, urls : List[str]) -> Non
download_file_name = os.path.basename(urlparse(url).path)
download_file_path = os.path.join(download_directory_path, download_file_name)
initial_size = get_file_size(download_file_path)
download_size = get_download_size(url)
download_size = get_static_download_size(url)
if initial_size < download_size:
with tqdm(total = download_size, initial = initial_size, desc = wording.get('downloading'), unit = 'B', unit_scale = True, unit_divisor = 1024, ascii = ' =', disable = state_manager.get_item('log_level') in [ 'warn', 'error' ]) as progress:
subprocess.Popen([ shutil.which('curl'), '--create-dirs', '--silent', '--insecure', '--location', '--continue-at', '-', '--output', download_file_path, url ])
commands = [ '--create-dirs', '--continue-at', '-', '--output', download_file_path, url ]
open_curl(commands)
current_size = initial_size
progress.set_postfix(download_providers = state_manager.get_item('download_providers'), file_name = download_file_name)
progress.set_postfix(file = download_file_name)
while current_size < download_size:
if is_file(download_file_path):
current_size = get_file_size(download_file_path)
@ -39,34 +41,42 @@ def conditional_download(download_directory_path : str, urls : List[str]) -> Non
@lru_cache(maxsize = None)
def get_download_size(url : str) -> int:
try:
response = urllib.request.urlopen(url, timeout = 10)
content_length = response.headers.get('Content-Length')
def get_static_download_size(url : str) -> int:
commands = [ '-I', url ]
process = open_curl(commands)
lines = reversed(process.stdout.readlines())
for line in lines:
__line__ = line.decode().lower()
if 'content-length:' in __line__:
_, content_length = __line__.split('content-length:')
return int(content_length)
except (OSError, TypeError, ValueError):
return 0
def is_download_done(url : str, file_path : str) -> bool:
if is_file(file_path):
return get_download_size(url) == get_file_size(file_path)
return False
@lru_cache(maxsize = None)
def ping_static_url(url : str) -> bool:
commands = [ '-I', url ]
process = open_curl(commands)
process.communicate()
return process.returncode == 0
def conditional_download_hashes(download_directory_path : str, hashes : DownloadSet) -> bool:
def conditional_download_hashes(hashes : DownloadSet) -> bool:
hash_paths = [ hashes.get(hash_key).get('path') for hash_key in hashes.keys() ]
process_manager.check()
if not state_manager.get_item('skip_download'):
_, invalid_hash_paths = validate_hash_paths(hash_paths)
if invalid_hash_paths:
for index in hashes:
if hashes.get(index).get('path') in invalid_hash_paths:
invalid_hash_url = hashes.get(index).get('url')
download_directory_path = os.path.dirname(hashes.get(index).get('path'))
conditional_download(download_directory_path, [ invalid_hash_url ])
valid_hash_paths, invalid_hash_paths = validate_hash_paths(hash_paths)
for valid_hash_path in valid_hash_paths:
valid_hash_file_name, _ = os.path.splitext(os.path.basename(valid_hash_path))
logger.debug(wording.get('validating_hash_succeed').format(hash_file_name = valid_hash_file_name), __name__)
@ -79,19 +89,20 @@ def conditional_download_hashes(download_directory_path : str, hashes : Download
return not invalid_hash_paths
def conditional_download_sources(download_directory_path : str, sources : DownloadSet) -> bool:
def conditional_download_sources(sources : DownloadSet) -> bool:
source_paths = [ sources.get(source_key).get('path') for source_key in sources.keys() ]
process_manager.check()
if not state_manager.get_item('skip_download'):
_, invalid_source_paths = validate_source_paths(source_paths)
if invalid_source_paths:
for index in sources:
if sources.get(index).get('path') in invalid_source_paths:
invalid_source_url = sources.get(index).get('url')
download_directory_path = os.path.dirname(sources.get(index).get('path'))
conditional_download(download_directory_path, [ invalid_source_url ])
valid_source_paths, invalid_source_paths = validate_source_paths(source_paths)
for valid_source_path in valid_source_paths:
valid_source_file_name, _ = os.path.splitext(os.path.basename(valid_source_path))
logger.debug(wording.get('validating_source_succeed').format(source_file_name = valid_source_file_name), __name__)
@ -129,3 +140,22 @@ def validate_source_paths(source_paths : List[str]) -> Tuple[List[str], List[str
else:
invalid_source_paths.append(source_path)
return valid_source_paths, invalid_source_paths
def resolve_download_url(base_name : str, file_name : str) -> Optional[str]:
download_providers = state_manager.get_item('download_providers')
for download_provider in download_providers:
if ping_download_provider(download_provider):
return resolve_download_url_by_provider(download_provider, base_name, file_name)
return None
def ping_download_provider(download_provider : DownloadProvider) -> bool:
download_provider_value = facefusion.choices.download_provider_set.get(download_provider)
return ping_static_url(download_provider_value.get('url'))
def resolve_download_url_by_provider(download_provider : DownloadProvider, base_name : str, file_name : str) -> Optional[str]:
download_provider_value = facefusion.choices.download_provider_set.get(download_provider)
return download_provider_value.get('url') + download_provider_value.get('path').format(base_name = base_name, file_name = file_name)

109
facefusion/execution.py
View File

@ -1,46 +1,43 @@
import shutil
import subprocess
import xml.etree.ElementTree as ElementTree
from functools import lru_cache
from typing import Any, List
from typing import Any, List, Optional
from onnxruntime import get_available_providers, set_default_logger_severity
from facefusion.choices import execution_provider_set
from facefusion.typing import ExecutionDevice, ExecutionProviderKey, ExecutionProviderSet, ValueAndUnit
import facefusion.choices
from facefusion.typing import ExecutionDevice, ExecutionProvider, ValueAndUnit
set_default_logger_severity(3)
def get_execution_provider_choices() -> List[ExecutionProviderKey]:
return list(get_available_execution_provider_set().keys())
def has_execution_provider(execution_provider : ExecutionProvider) -> bool:
return execution_provider in get_available_execution_providers()
def has_execution_provider(execution_provider_key : ExecutionProviderKey) -> bool:
return execution_provider_key in get_execution_provider_choices()
def get_available_execution_providers() -> List[ExecutionProvider]:
inference_execution_providers = get_available_providers()
available_execution_providers = []
for execution_provider, execution_provider_value in facefusion.choices.execution_provider_set.items():
if execution_provider_value in inference_execution_providers:
available_execution_providers.append(execution_provider)
return available_execution_providers
def get_available_execution_provider_set() -> ExecutionProviderSet:
available_execution_providers = get_available_providers()
available_execution_provider_set : ExecutionProviderSet = {}
def create_inference_execution_providers(execution_device_id : str, execution_providers : List[ExecutionProvider]) -> List[Any]:
inference_execution_providers : List[Any] = []
for execution_provider_key, execution_provider_value in execution_provider_set.items():
if execution_provider_value in available_execution_providers:
available_execution_provider_set[execution_provider_key] = execution_provider_value
return available_execution_provider_set
def create_execution_providers(execution_device_id : str, execution_provider_keys : List[ExecutionProviderKey]) -> List[Any]:
execution_providers : List[Any] = []
for execution_provider_key in execution_provider_keys:
if execution_provider_key == 'cuda':
execution_providers.append((execution_provider_set.get(execution_provider_key),
for execution_provider in execution_providers:
if execution_provider == 'cuda':
inference_execution_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
{
'device_id': execution_device_id,
'cudnn_conv_algo_search': 'EXHAUSTIVE' if use_exhaustive() else 'DEFAULT'
'device_id': execution_device_id
}))
if execution_provider_key == 'tensorrt':
execution_providers.append((execution_provider_set.get(execution_provider_key),
if execution_provider == 'tensorrt':
inference_execution_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
{
'device_id': execution_device_id,
'trt_engine_cache_enable': True,
@ -49,35 +46,28 @@ def create_execution_providers(execution_device_id : str, execution_provider_key
'trt_timing_cache_path': '.caches',
'trt_builder_optimization_level': 5
}))
if execution_provider_key == 'openvino':
execution_providers.append((execution_provider_set.get(execution_provider_key),
if execution_provider == 'openvino':
inference_execution_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
{
'device_type': 'GPU.' + execution_device_id,
'device_type': 'GPU' if execution_device_id == '0' else 'GPU.' + execution_device_id,
'precision': 'FP32'
}))
if execution_provider_key in [ 'directml', 'rocm' ]:
execution_providers.append((execution_provider_set.get(execution_provider_key),
if execution_provider in [ 'directml', 'rocm' ]:
inference_execution_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
{
'device_id': execution_device_id
}))
if execution_provider_key == 'coreml':
execution_providers.append(execution_provider_set.get(execution_provider_key))
if execution_provider == 'coreml':
inference_execution_providers.append(facefusion.choices.execution_provider_set.get(execution_provider))
if 'cpu' in execution_provider_keys:
execution_providers.append(execution_provider_set.get('cpu'))
if 'cpu' in execution_providers:
inference_execution_providers.append(facefusion.choices.execution_provider_set.get('cpu'))
return execution_providers
def use_exhaustive() -> bool:
execution_devices = detect_static_execution_devices()
product_names = ('GeForce GTX 1630', 'GeForce GTX 1650', 'GeForce GTX 1660')
return any(execution_device.get('product').get('name').startswith(product_names) for execution_device in execution_devices)
return inference_execution_providers
def run_nvidia_smi() -> subprocess.Popen[bytes]:
commands = [ 'nvidia-smi', '--query', '--xml-format' ]
commands = [ shutil.which('nvidia-smi'), '--query', '--xml-format' ]
return subprocess.Popen(commands, stdout = subprocess.PIPE)
@ -98,37 +88,44 @@ def detect_execution_devices() -> List[ExecutionDevice]:
for gpu_element in root_element.findall('gpu'):
execution_devices.append(
{
'driver_version': root_element.find('driver_version').text,
'driver_version': root_element.findtext('driver_version'),
'framework':
{
'name': 'CUDA',
'version': root_element.find('cuda_version').text
'version': root_element.findtext('cuda_version')
},
'product':
{
'vendor': 'NVIDIA',
'name': gpu_element.find('product_name').text.replace('NVIDIA ', '')
'name': gpu_element.findtext('product_name').replace('NVIDIA', '').strip()
},
'video_memory':
{
'total': create_value_and_unit(gpu_element.find('fb_memory_usage/total').text),
'free': create_value_and_unit(gpu_element.find('fb_memory_usage/free').text)
'total': create_value_and_unit(gpu_element.findtext('fb_memory_usage/total')),
'free': create_value_and_unit(gpu_element.findtext('fb_memory_usage/free'))
},
'temperature':
{
'gpu': create_value_and_unit(gpu_element.findtext('temperature/gpu_temp')),
'memory': create_value_and_unit(gpu_element.findtext('temperature/memory_temp'))
},
'utilization':
{
'gpu': create_value_and_unit(gpu_element.find('utilization/gpu_util').text),
'memory': create_value_and_unit(gpu_element.find('utilization/memory_util').text)
'gpu': create_value_and_unit(gpu_element.findtext('utilization/gpu_util')),
'memory': create_value_and_unit(gpu_element.findtext('utilization/memory_util'))
}
})
return execution_devices
def create_value_and_unit(text : str) -> ValueAndUnit:
value, unit = text.split()
value_and_unit : ValueAndUnit =\
def create_value_and_unit(text : str) -> Optional[ValueAndUnit]:
if ' ' in text:
value, unit = text.split(' ')
return\
{
'value': int(value),
'unit': str(unit)
}
return value_and_unit
return None

2
facefusion/exit_helper.py
View File

@ -1,3 +1,4 @@
import signal
import sys
from time import sleep
@ -7,6 +8,7 @@ from facefusion.typing import ErrorCode
def hard_exit(error_code : ErrorCode) -> None:
signal.signal(signal.SIGINT, signal.SIG_IGN)
sys.exit(error_code)

23
facefusion/face_classifier.py
View File

@ -1,23 +1,27 @@
from functools import lru_cache
from typing import List, Tuple
import numpy
from facefusion import inference_manager
from facefusion.download import conditional_download_hashes, conditional_download_sources
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
from facefusion.face_helper import warp_face_by_face_landmark_5
from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import Age, FaceLandmark5, Gender, InferencePool, ModelOptions, ModelSet, Race, VisionFrame
from facefusion.typing import Age, DownloadScope, FaceLandmark5, Gender, InferencePool, ModelOptions, ModelSet, Race, VisionFrame
MODEL_SET : ModelSet =\
{
@lru_cache(maxsize = None)
def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
return\
{
'fairface':
{
'hashes':
{
'face_classifier':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/fairface.hash',
'url': resolve_download_url('models-3.0.0', 'fairface.hash'),
'path': resolve_relative_path('../.assets/models/fairface.hash')
}
},
@ -25,7 +29,7 @@ MODEL_SET : ModelSet =\
{
'face_classifier':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/fairface.onnx',
'url': resolve_download_url('models-3.0.0', 'fairface.onnx'),
'path': resolve_relative_path('../.assets/models/fairface.onnx')
}
},
@ -34,7 +38,7 @@ MODEL_SET : ModelSet =\
'mean': [ 0.485, 0.456, 0.406 ],
'standard_deviation': [ 0.229, 0.224, 0.225 ]
}
}
}
def get_inference_pool() -> InferencePool:
@ -47,15 +51,14 @@ def clear_inference_pool() -> None:
def get_model_options() -> ModelOptions:
return MODEL_SET.get('fairface')
return create_static_model_set('full').get('fairface')
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
return conditional_download_hashes(download_directory_path, model_hashes) and conditional_download_sources(download_directory_path, model_sources)
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
def classify_face(temp_vision_frame : VisionFrame, face_landmark_5 : FaceLandmark5) -> Tuple[Gender, Age, Race]:

51
facefusion/face_detector.py
View File

@ -2,24 +2,28 @@ from typing import List, Tuple
import cv2
import numpy
from charset_normalizer.md import lru_cache
from facefusion import inference_manager, state_manager
from facefusion.download import conditional_download_hashes, conditional_download_sources
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
from facefusion.face_helper import create_rotated_matrix_and_size, create_static_anchors, distance_to_bounding_box, distance_to_face_landmark_5, normalize_bounding_box, transform_bounding_box, transform_points
from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import thread_semaphore
from facefusion.typing import Angle, BoundingBox, Detection, DownloadSet, FaceLandmark5, InferencePool, ModelSet, Score, VisionFrame
from facefusion.typing import Angle, BoundingBox, Detection, DownloadScope, DownloadSet, FaceLandmark5, InferencePool, ModelSet, Score, VisionFrame
from facefusion.vision import resize_frame_resolution, unpack_resolution
MODEL_SET : ModelSet =\
{
@lru_cache(maxsize = None)
def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
return\
{
'retinaface':
{
'hashes':
{
'retinaface':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/retinaface_10g.hash',
'url': resolve_download_url('models-3.0.0', 'retinaface_10g.hash'),
'path': resolve_relative_path('../.assets/models/retinaface_10g.hash')
}
},
@ -27,7 +31,7 @@ MODEL_SET : ModelSet =\
{
'retinaface':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/retinaface_10g.onnx',
'url': resolve_download_url('models-3.0.0', 'retinaface_10g.onnx'),
'path': resolve_relative_path('../.assets/models/retinaface_10g.onnx')
}
}
@ -38,7 +42,7 @@ MODEL_SET : ModelSet =\
{
'scrfd':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/scrfd_2.5g.hash',
'url': resolve_download_url('models-3.0.0', 'scrfd_2.5g.hash'),
'path': resolve_relative_path('../.assets/models/scrfd_2.5g.hash')
}
},
@ -46,7 +50,7 @@ MODEL_SET : ModelSet =\
{
'scrfd':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/scrfd_2.5g.onnx',
'url': resolve_download_url('models-3.0.0', 'scrfd_2.5g.onnx'),
'path': resolve_relative_path('../.assets/models/scrfd_2.5g.onnx')
}
}
@ -57,7 +61,7 @@ MODEL_SET : ModelSet =\
{
'yoloface':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/yoloface_8n.hash',
'url': resolve_download_url('models-3.0.0', 'yoloface_8n.hash'),
'path': resolve_relative_path('../.assets/models/yoloface_8n.hash')
}
},
@ -65,46 +69,47 @@ MODEL_SET : ModelSet =\
{
'yoloface':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/yoloface_8n.onnx',
'url': resolve_download_url('models-3.0.0', 'yoloface_8n.onnx'),
'path': resolve_relative_path('../.assets/models/yoloface_8n.onnx')
}
}
}
}
}
def get_inference_pool() -> InferencePool:
_, model_sources = collect_model_downloads()
model_context = __name__ + '.' + state_manager.get_item('face_detector_model')
return inference_manager.get_inference_pool(model_context, model_sources)
return inference_manager.get_inference_pool(__name__, model_sources)
def clear_inference_pool() -> None:
model_context = __name__ + '.' + state_manager.get_item('face_detector_model')
inference_manager.clear_inference_pool(model_context)
inference_manager.clear_inference_pool(__name__)
def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]:
model_hashes = {}
model_sources = {}
model_set = create_static_model_set('full')
if state_manager.get_item('face_detector_model') in [ 'many', 'retinaface' ]:
model_hashes['retinaface'] = MODEL_SET.get('retinaface').get('hashes').get('retinaface')
model_sources['retinaface'] = MODEL_SET.get('retinaface').get('sources').get('retinaface')
model_hashes['retinaface'] = model_set.get('retinaface').get('hashes').get('retinaface')
model_sources['retinaface'] = model_set.get('retinaface').get('sources').get('retinaface')
if state_manager.get_item('face_detector_model') in [ 'many', 'scrfd' ]:
model_hashes['scrfd'] = MODEL_SET.get('scrfd').get('hashes').get('scrfd')
model_sources['scrfd'] = MODEL_SET.get('scrfd').get('sources').get('scrfd')
model_hashes['scrfd'] = model_set.get('scrfd').get('hashes').get('scrfd')
model_sources['scrfd'] = model_set.get('scrfd').get('sources').get('scrfd')
if state_manager.get_item('face_detector_model') in [ 'many', 'yoloface' ]:
model_hashes['yoloface'] = MODEL_SET.get('yoloface').get('hashes').get('yoloface')
model_sources['yoloface'] = MODEL_SET.get('yoloface').get('sources').get('yoloface')
model_hashes['yoloface'] = model_set.get('yoloface').get('hashes').get('yoloface')
model_sources['yoloface'] = model_set.get('yoloface').get('sources').get('yoloface')
return model_hashes, model_sources
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_hashes, model_sources = collect_model_downloads()
return conditional_download_hashes(download_directory_path, model_hashes) and conditional_download_sources(download_directory_path, model_sources)
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
def detect_faces(vision_frame : VisionFrame) -> Tuple[List[BoundingBox], List[Score], List[FaceLandmark5]]:

24
facefusion/face_helper.py
View File

@ -33,6 +33,14 @@ WARP_TEMPLATES : WarpTemplateSet =\
[ 0.38710391, 0.72160547 ],
[ 0.61507734, 0.72034453 ]
]),
'dfl_whole_face': numpy.array(
[
[ 0.35342266, 0.39285716 ],
[ 0.62797622, 0.39285716 ],
[ 0.48660713, 0.54017860 ],
[ 0.38839287, 0.68750011 ],
[ 0.59821427, 0.68750011 ]
]),
'ffhq_512': numpy.array(
[
[ 0.37691676, 0.46864664 ],
@ -40,6 +48,22 @@ WARP_TEMPLATES : WarpTemplateSet =\
[ 0.50123859, 0.61331904 ],
[ 0.39308822, 0.72541100 ],
[ 0.61150205, 0.72490465 ]
]),
'mtcnn_512': numpy.array(
[
[ 0.36562865, 0.46733799 ],
[ 0.63305391, 0.46585885 ],
[ 0.50019127, 0.61942959 ],
[ 0.39032951, 0.77598822 ],
[ 0.61178945, 0.77476328 ]
]),
'styleganex_384': numpy.array(
[
[ 0.42353745, 0.52289879 ],
[ 0.57725008, 0.52319972 ],
[ 0.50123859, 0.61331904 ],
[ 0.43364461, 0.68337652 ],
[ 0.57015325, 0.68306005 ]
])
}

57
facefusion/face_landmarker.py
View File

@ -1,24 +1,28 @@
from functools import lru_cache
from typing import Tuple
import cv2
import numpy
from facefusion import inference_manager, state_manager
from facefusion.download import conditional_download_hashes, conditional_download_sources
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
from facefusion.face_helper import create_rotated_matrix_and_size, estimate_matrix_by_face_landmark_5, transform_points, warp_face_by_translation
from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import Angle, BoundingBox, DownloadSet, FaceLandmark5, FaceLandmark68, InferencePool, ModelSet, Prediction, Score, VisionFrame
from facefusion.typing import Angle, BoundingBox, DownloadScope, DownloadSet, FaceLandmark5, FaceLandmark68, InferencePool, ModelSet, Prediction, Score, VisionFrame
MODEL_SET : ModelSet =\
{
@lru_cache(maxsize = None)
def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
return\
{
'2dfan4':
{
'hashes':
{
'2dfan4':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/2dfan4.hash',
'url': resolve_download_url('models-3.0.0', '2dfan4.hash'),
'path': resolve_relative_path('../.assets/models/2dfan4.hash')
}
},
@ -26,7 +30,7 @@ MODEL_SET : ModelSet =\
{
'2dfan4':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/2dfan4.onnx',
'url': resolve_download_url('models-3.0.0', '2dfan4.onnx'),
'path': resolve_relative_path('../.assets/models/2dfan4.onnx')
}
},
@ -38,7 +42,7 @@ MODEL_SET : ModelSet =\
{
'peppa_wutz':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/peppa_wutz.hash',
'url': resolve_download_url('models-3.0.0', 'peppa_wutz.hash'),
'path': resolve_relative_path('../.assets/models/peppa_wutz.hash')
}
},
@ -46,7 +50,7 @@ MODEL_SET : ModelSet =\
{
'peppa_wutz':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/peppa_wutz.onnx',
'url': resolve_download_url('models-3.0.0', 'peppa_wutz.onnx'),
'path': resolve_relative_path('../.assets/models/peppa_wutz.onnx')
}
},
@ -58,7 +62,7 @@ MODEL_SET : ModelSet =\
{
'fan_68_5':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/fan_68_5.hash',
'url': resolve_download_url('models-3.0.0', 'fan_68_5.hash'),
'path': resolve_relative_path('../.assets/models/fan_68_5.hash')
}
},
@ -66,49 +70,49 @@ MODEL_SET : ModelSet =\
{
'fan_68_5':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/fan_68_5.onnx',
'url': resolve_download_url('models-3.0.0', 'fan_68_5.onnx'),
'path': resolve_relative_path('../.assets/models/fan_68_5.onnx')
}
}
}
}
}
def get_inference_pool() -> InferencePool:
_, model_sources = collect_model_downloads()
model_context = __name__ + '.' + state_manager.get_item('face_landmarker_model')
return inference_manager.get_inference_pool(model_context, model_sources)
return inference_manager.get_inference_pool(__name__, model_sources)
def clear_inference_pool() -> None:
model_context = __name__ + '.' + state_manager.get_item('face_landmarker_model')
inference_manager.clear_inference_pool(model_context)
inference_manager.clear_inference_pool(__name__)
def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]:
model_set = create_static_model_set('full')
model_hashes =\
{
'fan_68_5': MODEL_SET.get('fan_68_5').get('hashes').get('fan_68_5')
'fan_68_5': model_set.get('fan_68_5').get('hashes').get('fan_68_5')
}
model_sources =\
{
'fan_68_5': MODEL_SET.get('fan_68_5').get('sources').get('fan_68_5')
'fan_68_5': model_set.get('fan_68_5').get('sources').get('fan_68_5')
}
if state_manager.get_item('face_landmarker_model') in [ 'many', '2dfan4' ]:
model_hashes['2dfan4'] = MODEL_SET.get('2dfan4').get('hashes').get('2dfan4')
model_sources['2dfan4'] = MODEL_SET.get('2dfan4').get('sources').get('2dfan4')
model_hashes['2dfan4'] = model_set.get('2dfan4').get('hashes').get('2dfan4')
model_sources['2dfan4'] = model_set.get('2dfan4').get('sources').get('2dfan4')
if state_manager.get_item('face_landmarker_model') in [ 'many', 'peppa_wutz' ]:
model_hashes['peppa_wutz'] = MODEL_SET.get('peppa_wutz').get('hashes').get('peppa_wutz')
model_sources['peppa_wutz'] = MODEL_SET.get('peppa_wutz').get('sources').get('peppa_wutz')
model_hashes['peppa_wutz'] = model_set.get('peppa_wutz').get('hashes').get('peppa_wutz')
model_sources['peppa_wutz'] = model_set.get('peppa_wutz').get('sources').get('peppa_wutz')
return model_hashes, model_sources
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_hashes, model_sources = collect_model_downloads()
return conditional_download_hashes(download_directory_path, model_hashes) and conditional_download_sources(download_directory_path, model_sources)
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
def detect_face_landmarks(vision_frame : VisionFrame, bounding_box : BoundingBox, face_angle : Angle) -> Tuple[FaceLandmark68, Score]:
@ -119,6 +123,7 @@ def detect_face_landmarks(vision_frame : VisionFrame, bounding_box : BoundingBox
if state_manager.get_item('face_landmarker_model') in [ 'many', '2dfan4' ]:
face_landmark_2dfan4, face_landmark_score_2dfan4 = detect_with_2dfan4(vision_frame, bounding_box, face_angle)
if state_manager.get_item('face_landmarker_model') in [ 'many', 'peppa_wutz' ]:
face_landmark_peppa_wutz, face_landmark_score_peppa_wutz = detect_with_peppa_wutz(vision_frame, bounding_box, face_angle)
@ -128,7 +133,7 @@ def detect_face_landmarks(vision_frame : VisionFrame, bounding_box : BoundingBox
def detect_with_2dfan4(temp_vision_frame: VisionFrame, bounding_box: BoundingBox, face_angle: Angle) -> Tuple[FaceLandmark68, Score]:
model_size = MODEL_SET.get('2dfan4').get('size')
model_size = create_static_model_set('full').get('2dfan4').get('size')
scale = 195 / numpy.subtract(bounding_box[2:], bounding_box[:2]).max().clip(1, None)
translation = (model_size[0] - numpy.add(bounding_box[2:], bounding_box[:2]) * scale) * 0.5
rotated_matrix, rotated_size = create_rotated_matrix_and_size(face_angle, model_size)
@ -147,7 +152,7 @@ def detect_with_2dfan4(temp_vision_frame: VisionFrame, bounding_box: BoundingBox
def detect_with_peppa_wutz(temp_vision_frame : VisionFrame, bounding_box : BoundingBox, face_angle : Angle) -> Tuple[FaceLandmark68, Score]:
model_size = MODEL_SET.get('peppa_wutz').get('size')
model_size = create_static_model_set('full').get('peppa_wutz').get('size')
scale = 195 / numpy.subtract(bounding_box[2:], bounding_box[:2]).max().clip(1, None)
translation = (model_size[0] - numpy.add(bounding_box[2:], bounding_box[:2]) * scale) * 0.5
rotated_matrix, rotated_size = create_rotated_matrix_and_size(face_angle, model_size)
@ -167,7 +172,7 @@ def detect_with_peppa_wutz(temp_vision_frame : VisionFrame, bounding_box : Bound
def conditional_optimize_contrast(crop_vision_frame : VisionFrame) -> VisionFrame:
crop_vision_frame = cv2.cvtColor(crop_vision_frame, cv2.COLOR_RGB2Lab)
if numpy.mean(crop_vision_frame[:, :, 0]) < 30: # type:ignore[arg-type]
if numpy.mean(crop_vision_frame[:, :, 0]) < 30: #type:ignore[arg-type]
crop_vision_frame[:, :, 0] = cv2.createCLAHE(clipLimit = 2).apply(crop_vision_frame[:, :, 0])
crop_vision_frame = cv2.cvtColor(crop_vision_frame, cv2.COLOR_Lab2RGB)
return crop_vision_frame

134
facefusion/face_masker.py
View File

@ -1,45 +1,89 @@
from functools import lru_cache
from typing import Dict, List, Tuple
from typing import List, Tuple
import cv2
import numpy
from cv2.typing import Size
from facefusion import inference_manager
from facefusion.download import conditional_download_hashes, conditional_download_sources
import facefusion.choices
from facefusion import inference_manager, state_manager
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import DownloadSet, FaceLandmark68, FaceMaskRegion, InferencePool, Mask, ModelSet, Padding, VisionFrame
from facefusion.typing import DownloadScope, DownloadSet, FaceLandmark68, FaceMaskRegion, InferencePool, Mask, ModelSet, Padding, VisionFrame
MODEL_SET : ModelSet =\
{
'face_occluder':
@lru_cache(maxsize = None)
def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
return\
{
'xseg_1':
{
'hashes':
{
'face_occluder':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/dfl_xseg.hash',
'path': resolve_relative_path('../.assets/models/dfl_xseg.hash')
'url': resolve_download_url('models-3.1.0', 'xseg_1.hash'),
'path': resolve_relative_path('../.assets/models/xseg_1.hash')
}
},
'sources':
{
'face_occluder':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/dfl_xseg.onnx',
'path': resolve_relative_path('../.assets/models/dfl_xseg.onnx')
'url': resolve_download_url('models-3.1.0', 'xseg_1.onnx'),
'path': resolve_relative_path('../.assets/models/xseg_1.onnx')
}
},
'size': (256, 256)
},
'face_parser':
'xseg_2':
{
'hashes':
{
'face_occluder':
{
'url': resolve_download_url('models-3.1.0', 'xseg_2.hash'),
'path': resolve_relative_path('../.assets/models/xseg_2.hash')
}
},
'sources':
{
'face_occluder':
{
'url': resolve_download_url('models-3.1.0', 'xseg_2.onnx'),
'path': resolve_relative_path('../.assets/models/xseg_2.onnx')
}
},
'size': (256, 256)
},
'bisenet_resnet_18':
{
'hashes':
{
'face_parser':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/bisenet_resnet_34.hash',
'url': resolve_download_url('models-3.1.0', 'bisenet_resnet_18.hash'),
'path': resolve_relative_path('../.assets/models/bisenet_resnet_18.hash')
}
},
'sources':
{
'face_parser':
{
'url': resolve_download_url('models-3.1.0', 'bisenet_resnet_18.onnx'),
'path': resolve_relative_path('../.assets/models/bisenet_resnet_18.onnx')
}
},
'size': (512, 512)
},
'bisenet_resnet_34':
{
'hashes':
{
'face_parser':
{
'url': resolve_download_url('models-3.0.0', 'bisenet_resnet_34.hash'),
'path': resolve_relative_path('../.assets/models/bisenet_resnet_34.hash')
}
},
@ -47,26 +91,13 @@ MODEL_SET : ModelSet =\
{
'face_parser':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/bisenet_resnet_34.onnx',
'url': resolve_download_url('models-3.0.0', 'bisenet_resnet_34.onnx'),
'path': resolve_relative_path('../.assets/models/bisenet_resnet_34.onnx')
}
},
'size': (512, 512)
}
}
FACE_MASK_REGIONS : Dict[FaceMaskRegion, int] =\
{
'skin': 1,
'left-eyebrow': 2,
'right-eyebrow': 3,
'left-eye': 4,
'right-eye': 5,
'glasses': 6,
'nose': 10,
'mouth': 11,
'upper-lip': 12,
'lower-lip': 13
}
}
def get_inference_pool() -> InferencePool:
@ -79,24 +110,33 @@ def clear_inference_pool() -> None:
def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]:
model_hashes =\
{
'face_occluder': MODEL_SET.get('face_occluder').get('hashes').get('face_occluder'),
'face_parser': MODEL_SET.get('face_parser').get('hashes').get('face_parser')
}
model_sources =\
{
'face_occluder': MODEL_SET.get('face_occluder').get('sources').get('face_occluder'),
'face_parser': MODEL_SET.get('face_parser').get('sources').get('face_parser')
}
model_hashes = {}
model_sources = {}
model_set = create_static_model_set('full')
if state_manager.get_item('face_occluder_model') == 'xseg_1':
model_hashes['xseg_1'] = model_set.get('xseg_1').get('hashes').get('face_occluder')
model_sources['xseg_1'] = model_set.get('xseg_1').get('sources').get('face_occluder')
if state_manager.get_item('face_occluder_model') == 'xseg_2':
model_hashes['xseg_2'] = model_set.get('xseg_2').get('hashes').get('face_occluder')
model_sources['xseg_2'] = model_set.get('xseg_2').get('sources').get('face_occluder')
if state_manager.get_item('face_parser_model') == 'bisenet_resnet_18':
model_hashes['bisenet_resnet_18'] = model_set.get('bisenet_resnet_18').get('hashes').get('face_parser')
model_sources['bisenet_resnet_18'] = model_set.get('bisenet_resnet_18').get('sources').get('face_parser')
if state_manager.get_item('face_parser_model') == 'bisenet_resnet_34':
model_hashes['bisenet_resnet_34'] = model_set.get('bisenet_resnet_34').get('hashes').get('face_parser')
model_sources['bisenet_resnet_34'] = model_set.get('bisenet_resnet_34').get('sources').get('face_parser')
return model_hashes, model_sources
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_hashes, model_sources = collect_model_downloads()
return conditional_download_hashes(download_directory_path, model_hashes) and conditional_download_sources(download_directory_path, model_sources)
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
@lru_cache(maxsize = None)
@ -114,7 +154,8 @@ def create_static_box_mask(crop_size : Size, face_mask_blur : float, face_mask_p
def create_occlusion_mask(crop_vision_frame : VisionFrame) -> Mask:
model_size = MODEL_SET.get('face_occluder').get('size')
face_occluder_model = state_manager.get_item('face_occluder_model')
model_size = create_static_model_set('full').get(face_occluder_model).get('size')
prepare_vision_frame = cv2.resize(crop_vision_frame, model_size)
prepare_vision_frame = numpy.expand_dims(prepare_vision_frame, axis = 0).astype(numpy.float32) / 255
prepare_vision_frame = prepare_vision_frame.transpose(0, 1, 2, 3)
@ -126,7 +167,8 @@ def create_occlusion_mask(crop_vision_frame : VisionFrame) -> Mask:
def create_region_mask(crop_vision_frame : VisionFrame, face_mask_regions : List[FaceMaskRegion]) -> Mask:
model_size = MODEL_SET.get('face_parser').get('size')
face_parser_model = state_manager.get_item('face_parser_model')
model_size = create_static_model_set('full').get(face_parser_model).get('size')
prepare_vision_frame = cv2.resize(crop_vision_frame, model_size)
prepare_vision_frame = prepare_vision_frame[:, :, ::-1].astype(numpy.float32) / 255
prepare_vision_frame = numpy.subtract(prepare_vision_frame, numpy.array([ 0.485, 0.456, 0.406 ]).astype(numpy.float32))
@ -134,7 +176,7 @@ def create_region_mask(crop_vision_frame : VisionFrame, face_mask_regions : List
prepare_vision_frame = numpy.expand_dims(prepare_vision_frame, axis = 0)
prepare_vision_frame = prepare_vision_frame.transpose(0, 3, 1, 2)
region_mask = forward_parse_face(prepare_vision_frame)
region_mask = numpy.isin(region_mask.argmax(0), [ FACE_MASK_REGIONS[region] for region in face_mask_regions ])
region_mask = numpy.isin(region_mask.argmax(0), [ facefusion.choices.face_mask_region_set.get(face_mask_region) for face_mask_region in face_mask_regions ])
region_mask = cv2.resize(region_mask.astype(numpy.float32), crop_vision_frame.shape[:2][::-1])
region_mask = (cv2.GaussianBlur(region_mask.clip(0, 1), (0, 0), 5).clip(0.5, 1) - 0.5) * 2
return region_mask
@ -150,7 +192,8 @@ def create_mouth_mask(face_landmark_68 : FaceLandmark68) -> Mask:
def forward_occlude_face(prepare_vision_frame : VisionFrame) -> Mask:
face_occluder = get_inference_pool().get('face_occluder')
face_occluder_model = state_manager.get_item('face_occluder_model')
face_occluder = get_inference_pool().get(face_occluder_model)
with conditional_thread_semaphore():
occlusion_mask : Mask = face_occluder.run(None,
@ -162,7 +205,8 @@ def forward_occlude_face(prepare_vision_frame : VisionFrame) -> Mask:
def forward_parse_face(prepare_vision_frame : VisionFrame) -> Mask:
face_parser = get_inference_pool().get('face_parser')
face_parser_model = state_manager.get_item('face_parser_model')
face_parser = get_inference_pool().get(face_parser_model)
with conditional_thread_semaphore():
region_mask : Mask = face_parser.run(None,

23
facefusion/face_recognizer.py
View File

@ -1,23 +1,27 @@
from functools import lru_cache
from typing import Tuple
import numpy
from facefusion import inference_manager
from facefusion.download import conditional_download_hashes, conditional_download_sources
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
from facefusion.face_helper import warp_face_by_face_landmark_5
from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import Embedding, FaceLandmark5, InferencePool, ModelOptions, ModelSet, VisionFrame
from facefusion.typing import DownloadScope, Embedding, FaceLandmark5, InferencePool, ModelOptions, ModelSet, VisionFrame
MODEL_SET : ModelSet =\
{
@lru_cache(maxsize = None)
def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
return\
{
'arcface':
{
'hashes':
{
'face_recognizer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/arcface_w600k_r50.hash',
'url': resolve_download_url('models-3.0.0', 'arcface_w600k_r50.hash'),
'path': resolve_relative_path('../.assets/models/arcface_w600k_r50.hash')
}
},
@ -25,14 +29,14 @@ MODEL_SET : ModelSet =\
{
'face_recognizer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/arcface_w600k_r50.onnx',
'url': resolve_download_url('models-3.0.0', 'arcface_w600k_r50.onnx'),
'path': resolve_relative_path('../.assets/models/arcface_w600k_r50.onnx')
}
},
'template': 'arcface_112_v2',
'size': (112, 112)
}
}
}
def get_inference_pool() -> InferencePool:
@ -45,15 +49,14 @@ def clear_inference_pool() -> None:
def get_model_options() -> ModelOptions:
return MODEL_SET.get('arcface')
return create_static_model_set('full').get('arcface')
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
return conditional_download_hashes(download_directory_path, model_hashes) and conditional_download_sources(download_directory_path, model_sources)
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
def calc_embedding(temp_vision_frame : VisionFrame, face_landmark_5 : FaceLandmark5) -> Tuple[Embedding, Embedding]:

16
facefusion/face_selector.py
View File

@ -33,17 +33,17 @@ def calc_face_distance(face : Face, reference_face : Face) -> float:
def sort_and_filter_faces(faces : List[Face]) -> List[Face]:
if faces:
if state_manager.get_item('face_selector_order'):
faces = sort_by_order(faces, state_manager.get_item('face_selector_order'))
faces = sort_faces_by_order(faces, state_manager.get_item('face_selector_order'))
if state_manager.get_item('face_selector_gender'):
faces = filter_by_gender(faces, state_manager.get_item('face_selector_gender'))
faces = filter_faces_by_gender(faces, state_manager.get_item('face_selector_gender'))
if state_manager.get_item('face_selector_race'):
faces = filter_by_race(faces, state_manager.get_item('face_selector_race'))
faces = filter_faces_by_race(faces, state_manager.get_item('face_selector_race'))
if state_manager.get_item('face_selector_age_start') or state_manager.get_item('face_selector_age_end'):
faces = filter_by_age(faces, state_manager.get_item('face_selector_age_start'), state_manager.get_item('face_selector_age_end'))
faces = filter_faces_by_age(faces, state_manager.get_item('face_selector_age_start'), state_manager.get_item('face_selector_age_end'))
return faces
def sort_by_order(faces : List[Face], order : FaceSelectorOrder) -> List[Face]:
def sort_faces_by_order(faces : List[Face], order : FaceSelectorOrder) -> List[Face]:
if order == 'left-right':
return sorted(faces, key = lambda face: face.bounding_box[0])
if order == 'right-left':
@ -63,7 +63,7 @@ def sort_by_order(faces : List[Face], order : FaceSelectorOrder) -> List[Face]:
return faces
def filter_by_gender(faces : List[Face], gender : Gender) -> List[Face]:
def filter_faces_by_gender(faces : List[Face], gender : Gender) -> List[Face]:
filter_faces = []
for face in faces:
@ -72,7 +72,7 @@ def filter_by_gender(faces : List[Face], gender : Gender) -> List[Face]:
return filter_faces
def filter_by_age(faces : List[Face], face_selector_age_start : int, face_selector_age_end : int) -> List[Face]:
def filter_faces_by_age(faces : List[Face], face_selector_age_start : int, face_selector_age_end : int) -> List[Face]:
filter_faces = []
age = range(face_selector_age_start, face_selector_age_end)
@ -82,7 +82,7 @@ def filter_by_age(faces : List[Face], face_selector_age_start : int, face_select
return filter_faces
def filter_by_race(faces : List[Face], race : Race) -> List[Face]:
def filter_faces_by_race(faces : List[Face], race : Race) -> List[Face]:
filter_faces = []
for face in faces:

124
facefusion/ffmpeg.py
View File

@ -5,22 +5,50 @@ import tempfile
from typing import List, Optional
import filetype
from tqdm import tqdm
from facefusion import logger, process_manager, state_manager
from facefusion import logger, process_manager, state_manager, wording
from facefusion.filesystem import remove_file
from facefusion.temp_helper import get_temp_file_path, get_temp_frames_pattern
from facefusion.typing import AudioBuffer, Fps, OutputVideoPreset
from facefusion.vision import restrict_video_fps
from facefusion.temp_helper import get_temp_file_path, get_temp_frame_paths, get_temp_frames_pattern
from facefusion.typing import AudioBuffer, Fps, OutputVideoPreset, UpdateProgress
from facefusion.vision import count_trim_frame_total, detect_video_duration, restrict_video_fps
def run_ffmpeg(args : List[str]) -> subprocess.Popen[bytes]:
commands = [ shutil.which('ffmpeg'), '-hide_banner', '-loglevel', 'error' ]
def run_ffmpeg_with_progress(args: List[str], update_progress : UpdateProgress) -> subprocess.Popen[bytes]:
log_level = state_manager.get_item('log_level')
commands = [ shutil.which('ffmpeg'), '-hide_banner', '-nostats', '-loglevel', 'error', '-progress', '-' ]
commands.extend(args)
process = subprocess.Popen(commands, stderr = subprocess.PIPE, stdout = subprocess.PIPE)
while process_manager.is_processing():
try:
if state_manager.get_item('log_level') == 'debug':
while __line__ := process.stdout.readline().decode().lower():
if 'frame=' in __line__:
_, frame_number = __line__.split('frame=')
update_progress(int(frame_number))
if log_level == 'debug':
log_debug(process)
process.wait(timeout = 0.5)
except subprocess.TimeoutExpired:
continue
return process
if process_manager.is_stopping():
process.terminate()
return process
def run_ffmpeg(args : List[str]) -> subprocess.Popen[bytes]:
log_level = state_manager.get_item('log_level')
commands = [ shutil.which('ffmpeg'), '-hide_banner', '-nostats', '-loglevel', 'error' ]
commands.extend(args)
process = subprocess.Popen(commands, stderr = subprocess.PIPE, stdout = subprocess.PIPE)
while process_manager.is_processing():
try:
if log_level == 'debug':
log_debug(process)
process.wait(timeout = 0.5)
except subprocess.TimeoutExpired:
@ -33,7 +61,7 @@ def run_ffmpeg(args : List[str]) -> subprocess.Popen[bytes]:
def open_ffmpeg(args : List[str]) -> subprocess.Popen[bytes]:
commands = [ shutil.which('ffmpeg'), '-hide_banner', '-loglevel', 'quiet' ]
commands = [ shutil.which('ffmpeg'), '-loglevel', 'quiet' ]
commands.extend(args)
return subprocess.Popen(commands, stdin = subprocess.PIPE, stdout = subprocess.PIPE)
@ -47,9 +75,8 @@ def log_debug(process : subprocess.Popen[bytes]) -> None:
logger.debug(error.strip(), __name__)
def extract_frames(target_path : str, temp_video_resolution : str, temp_video_fps : Fps) -> bool:
trim_frame_start = state_manager.get_item('trim_frame_start')
trim_frame_end = state_manager.get_item('trim_frame_end')
def extract_frames(target_path : str, temp_video_resolution : str, temp_video_fps : Fps, trim_frame_start : int, trim_frame_end : int) -> bool:
extract_frame_total = count_trim_frame_total(target_path, trim_frame_start, trim_frame_end)
temp_frames_pattern = get_temp_frames_pattern(target_path, '%08d')
commands = [ '-i', target_path, '-s', str(temp_video_resolution), '-q:v', '0' ]
@ -62,34 +89,48 @@ def extract_frames(target_path : str, temp_video_resolution : str, temp_video_fp
else:
commands.extend([ '-vf', 'fps=' + str(temp_video_fps) ])
commands.extend([ '-vsync', '0', temp_frames_pattern ])
return run_ffmpeg(commands).returncode == 0
with tqdm(total = extract_frame_total, desc = wording.get('extracting'), unit = 'frame', ascii = ' =', disable = state_manager.get_item('log_level') in [ 'warn', 'error' ]) as progress:
process = run_ffmpeg_with_progress(commands, lambda frame_number: progress.update(frame_number - progress.n))
return process.returncode == 0
def merge_video(target_path : str, output_video_resolution : str, output_video_fps : Fps) -> bool:
def merge_video(target_path : str, output_video_resolution : str, output_video_fps: Fps) -> bool:
output_video_encoder = state_manager.get_item('output_video_encoder')
output_video_quality = state_manager.get_item('output_video_quality')
output_video_preset = state_manager.get_item('output_video_preset')
merge_frame_total = len(get_temp_frame_paths(target_path))
temp_video_fps = restrict_video_fps(target_path, output_video_fps)
temp_file_path = get_temp_file_path(target_path)
temp_frames_pattern = get_temp_frames_pattern(target_path, '%08d')
commands = [ '-r', str(temp_video_fps), '-i', temp_frames_pattern, '-s', str(output_video_resolution), '-c:v', state_manager.get_item('output_video_encoder') ]
is_webm = filetype.guess_mime(target_path) == 'video/webm'
if state_manager.get_item('output_video_encoder') in [ 'libx264', 'libx265' ]:
output_video_compression = round(51 - (state_manager.get_item('output_video_quality') * 0.51))
commands.extend([ '-crf', str(output_video_compression), '-preset', state_manager.get_item('output_video_preset') ])
if state_manager.get_item('output_video_encoder') in [ 'libvpx-vp9' ]:
output_video_compression = round(63 - (state_manager.get_item('output_video_quality') * 0.63))
if is_webm:
output_video_encoder = 'libvpx-vp9'
commands = [ '-r', str(temp_video_fps), '-i', temp_frames_pattern, '-s', str(output_video_resolution), '-c:v', output_video_encoder ]
if output_video_encoder in [ 'libx264', 'libx265' ]:
output_video_compression = round(51 - (output_video_quality * 0.51))
commands.extend([ '-crf', str(output_video_compression), '-preset', output_video_preset ])
if output_video_encoder in [ 'libvpx-vp9' ]:
output_video_compression = round(63 - (output_video_quality * 0.63))
commands.extend([ '-crf', str(output_video_compression) ])
if state_manager.get_item('output_video_encoder') in [ 'h264_nvenc', 'hevc_nvenc' ]:
output_video_compression = round(51 - (state_manager.get_item('output_video_quality') * 0.51))
commands.extend([ '-cq', str(output_video_compression), '-preset', map_nvenc_preset(state_manager.get_item('output_video_preset')) ])
if state_manager.get_item('output_video_encoder') in [ 'h264_amf', 'hevc_amf' ]:
output_video_compression = round(51 - (state_manager.get_item('output_video_quality') * 0.51))
commands.extend([ '-qp_i', str(output_video_compression), '-qp_p', str(output_video_compression), '-quality', map_amf_preset(state_manager.get_item('output_video_preset')) ])
if state_manager.get_item('output_video_encoder') in [ 'h264_videotoolbox', 'hevc_videotoolbox' ]:
commands.extend([ '-q:v', str(state_manager.get_item('output_video_quality')) ])
if output_video_encoder in [ 'h264_nvenc', 'hevc_nvenc' ]:
output_video_compression = round(51 - (output_video_quality * 0.51))
commands.extend([ '-cq', str(output_video_compression), '-preset', map_nvenc_preset(output_video_preset) ])
if output_video_encoder in [ 'h264_amf', 'hevc_amf' ]:
output_video_compression = round(51 - (output_video_quality * 0.51))
commands.extend([ '-qp_i', str(output_video_compression), '-qp_p', str(output_video_compression), '-quality', map_amf_preset(output_video_preset) ])
if output_video_encoder in [ 'h264_videotoolbox', 'hevc_videotoolbox' ]:
commands.extend([ '-q:v', str(output_video_quality) ])
commands.extend([ '-vf', 'framerate=fps=' + str(output_video_fps), '-pix_fmt', 'yuv420p', '-colorspace', 'bt709', '-y', temp_file_path ])
return run_ffmpeg(commands).returncode == 0
with tqdm(total = merge_frame_total, desc = wording.get('merging'), unit = 'frame', ascii = ' =', disable = state_manager.get_item('log_level') in [ 'warn', 'error' ]) as progress:
process = run_ffmpeg_with_progress(commands, lambda frame_number: progress.update(frame_number - progress.n))
return process.returncode == 0
def concat_video(output_path : str, temp_output_paths : List[str]) -> bool:
output_audio_encoder = state_manager.get_item('output_audio_encoder')
concat_video_path = tempfile.mktemp()
with open(concat_video_path, 'w') as concat_video_file:
@ -97,7 +138,7 @@ def concat_video(output_path : str, temp_output_paths : List[str]) -> bool:
concat_video_file.write('file \'' + os.path.abspath(temp_output_path) + '\'' + os.linesep)
concat_video_file.flush()
concat_video_file.close()
commands = [ '-f', 'concat', '-safe', '0', '-i', concat_video_file.name, '-c:v', 'copy', '-c:a', state_manager.get_item('output_audio_encoder'), '-y', os.path.abspath(output_path) ]
commands = [ '-f', 'concat', '-safe', '0', '-i', concat_video_file.name, '-c:v', 'copy', '-c:a', output_audio_encoder, '-y', os.path.abspath(output_path) ]
process = run_ffmpeg(commands)
process.communicate()
remove_file(concat_video_path)
@ -112,8 +153,9 @@ def copy_image(target_path : str, temp_image_resolution : str) -> bool:
def finalize_image(target_path : str, output_path : str, output_image_resolution : str) -> bool:
output_image_quality = state_manager.get_item('output_image_quality')
temp_file_path = get_temp_file_path(target_path)
output_image_compression = calc_image_compression(target_path, state_manager.get_item('output_image_quality'))
output_image_compression = calc_image_compression(target_path, output_image_quality)
commands = [ '-i', temp_file_path, '-s', str(output_image_resolution), '-q:v', str(output_image_compression), '-y', output_path ]
return run_ffmpeg(commands).returncode == 0
@ -134,10 +176,10 @@ def read_audio_buffer(target_path : str, sample_rate : int, channel_total : int)
return None
def restore_audio(target_path : str, output_path : str, output_video_fps : Fps) -> bool:
trim_frame_start = state_manager.get_item('trim_frame_start')
trim_frame_end = state_manager.get_item('trim_frame_end')
def restore_audio(target_path : str, output_path : str, output_video_fps : Fps, trim_frame_start : int, trim_frame_end : int) -> bool:
output_audio_encoder = state_manager.get_item('output_audio_encoder')
temp_file_path = get_temp_file_path(target_path)
temp_video_duration = detect_video_duration(temp_file_path)
commands = [ '-i', temp_file_path ]
if isinstance(trim_frame_start, int):
@ -146,13 +188,15 @@ def restore_audio(target_path : str, output_path : str, output_video_fps : Fps)
if isinstance(trim_frame_end, int):
end_time = trim_frame_end / output_video_fps
commands.extend([ '-to', str(end_time) ])
commands.extend([ '-i', target_path, '-c:v', 'copy', '-c:a', state_manager.get_item('output_audio_encoder'), '-map', '0:v:0', '-map', '1:a:0', '-shortest', '-y', output_path ])
commands.extend([ '-i', target_path, '-c:v', 'copy', '-c:a', output_audio_encoder, '-map', '0:v:0', '-map', '1:a:0', '-t', str(temp_video_duration), '-y', output_path ])
return run_ffmpeg(commands).returncode == 0
def replace_audio(target_path : str, audio_path : str, output_path : str) -> bool:
output_audio_encoder = state_manager.get_item('output_audio_encoder')
temp_file_path = get_temp_file_path(target_path)
commands = [ '-i', temp_file_path, '-i', audio_path, '-c:a', state_manager.get_item('output_audio_encoder'), '-af', 'apad', '-shortest', '-y', output_path ]
temp_video_duration = detect_video_duration(temp_file_path)
commands = [ '-i', temp_file_path, '-i', audio_path, '-c:v', 'copy', '-c:a', output_audio_encoder, '-t', str(temp_video_duration), '-y', output_path ]
return run_ffmpeg(commands).returncode == 0
@ -174,3 +218,13 @@ def map_amf_preset(output_video_preset : OutputVideoPreset) -> Optional[str]:
if output_video_preset in [ 'slow', 'slower', 'veryslow' ]:
return 'quality'
return None
def map_qsv_preset(output_video_preset : OutputVideoPreset) -> Optional[str]:
if output_video_preset in [ 'ultrafast', 'superfast', 'veryfast', 'faster', 'fast' ]:
return 'fast'
if output_video_preset == 'medium':
return 'medium'
if output_video_preset in [ 'slow', 'slower', 'veryslow' ]:
return 'slow'
return None

28
facefusion/filesystem.py
View File

@ -1,3 +1,4 @@
import glob
import os
import shutil
from pathlib import Path
@ -6,6 +7,7 @@ from typing import List, Optional
import filetype
from facefusion.common_helper import is_windows
from facefusion.typing import File
if is_windows():
import ctypes
@ -125,14 +127,32 @@ def create_directory(directory_path : str) -> bool:
return False
def list_directory(directory_path : str) -> Optional[List[str]]:
def list_directory(directory_path : str) -> Optional[List[File]]:
if is_directory(directory_path):
files = os.listdir(directory_path)
files = [ Path(file).stem for file in files if not Path(file).stem.startswith(('.', '__')) ]
return sorted(files)
file_paths = sorted(os.listdir(directory_path))
files: List[File] = []
for file_path in file_paths:
file_name, file_extension = os.path.splitext(file_path)
if not file_name.startswith(('.', '__')):
files.append(
{
'name': file_name,
'extension': file_extension,
'path': os.path.join(directory_path, file_path)
})
return files
return None
def resolve_file_pattern(file_pattern : str) -> List[str]:
if in_directory(file_pattern):
return sorted(glob.glob(file_pattern))
return []
def remove_directory(directory_path : str) -> bool:
if is_directory(directory_path):
shutil.rmtree(directory_path, ignore_errors = True)

38
facefusion/inference_manager.py
View File

@ -1,20 +1,18 @@
from functools import lru_cache
from time import sleep
from typing import List
import onnx
from onnxruntime import InferenceSession
from facefusion import process_manager, state_manager
from facefusion.app_context import detect_app_context
from facefusion.execution import create_execution_providers, has_execution_provider
from facefusion.execution import create_inference_execution_providers
from facefusion.thread_helper import thread_lock
from facefusion.typing import DownloadSet, ExecutionProviderKey, InferencePool, InferencePoolSet, ModelInitializer
from facefusion.typing import DownloadSet, ExecutionProvider, InferencePool, InferencePoolSet
INFERENCE_POOLS : InferencePoolSet =\
{
'cli': {}, # type:ignore[typeddict-item]
'ui': {} # type:ignore[typeddict-item]
'cli': {}, #type:ignore[typeddict-item]
'ui': {} #type:ignore[typeddict-item]
}
@ -32,17 +30,16 @@ def get_inference_pool(model_context : str, model_sources : DownloadSet) -> Infe
if app_context == 'ui' and INFERENCE_POOLS.get('cli').get(inference_context):
INFERENCE_POOLS['ui'][inference_context] = INFERENCE_POOLS.get('cli').get(inference_context)
if not INFERENCE_POOLS.get(app_context).get(inference_context):
execution_provider_keys = resolve_execution_provider_keys(model_context)
INFERENCE_POOLS[app_context][inference_context] = create_inference_pool(model_sources, state_manager.get_item('execution_device_id'), execution_provider_keys)
INFERENCE_POOLS[app_context][inference_context] = create_inference_pool(model_sources, state_manager.get_item('execution_device_id'), state_manager.get_item('execution_providers'))
return INFERENCE_POOLS.get(app_context).get(inference_context)
def create_inference_pool(model_sources : DownloadSet, execution_device_id : str, execution_provider_keys : List[ExecutionProviderKey]) -> InferencePool:
def create_inference_pool(model_sources : DownloadSet, execution_device_id : str, execution_providers : List[ExecutionProvider]) -> InferencePool:
inference_pool : InferencePool = {}
for model_name in model_sources.keys():
inference_pool[model_name] = create_inference_session(model_sources.get(model_name).get('path'), execution_device_id, execution_provider_keys)
inference_pool[model_name] = create_inference_session(model_sources.get(model_name).get('path'), execution_device_id, execution_providers)
return inference_pool
@ -56,24 +53,11 @@ def clear_inference_pool(model_context : str) -> None:
del INFERENCE_POOLS[app_context][inference_context]
def create_inference_session(model_path : str, execution_device_id : str, execution_provider_keys : List[ExecutionProviderKey]) -> InferenceSession:
execution_providers = create_execution_providers(execution_device_id, execution_provider_keys)
return InferenceSession(model_path, providers = execution_providers)
@lru_cache(maxsize = None)
def get_static_model_initializer(model_path : str) -> ModelInitializer:
model = onnx.load(model_path)
return onnx.numpy_helper.to_array(model.graph.initializer[-1])
def resolve_execution_provider_keys(model_context : str) -> List[ExecutionProviderKey]:
if has_execution_provider('coreml') and (model_context.startswith('facefusion.processors.modules.age_modifier') or model_context.startswith('facefusion.processors.modules.frame_colorizer')):
return [ 'cpu' ]
return state_manager.get_item('execution_providers')
def create_inference_session(model_path : str, execution_device_id : str, execution_providers : List[ExecutionProvider]) -> InferenceSession:
inference_execution_providers = create_inference_execution_providers(execution_device_id, execution_providers)
return InferenceSession(model_path, providers = inference_execution_providers)
def get_inference_context(model_context : str) -> str:
execution_provider_keys = resolve_execution_provider_keys(model_context)
inference_context = model_context + '.' + '_'.join(execution_provider_keys)
inference_context = model_context + '.' + '_'.join(state_manager.get_item('execution_providers'))
return inference_context

13
facefusion/installer.py
View File

@ -13,11 +13,11 @@ from facefusion.common_helper import is_linux, is_macos, is_windows
ONNXRUNTIMES : Dict[str, Tuple[str, str]] = {}
if is_macos():
ONNXRUNTIMES['default'] = ('onnxruntime', '1.19.2')
ONNXRUNTIMES['default'] = ('onnxruntime', '1.20.1')
else:
ONNXRUNTIMES['default'] = ('onnxruntime', '1.19.2')
ONNXRUNTIMES['cuda'] = ('onnxruntime-gpu', '1.19.2')
ONNXRUNTIMES['openvino'] = ('onnxruntime-openvino', '1.19.0')
ONNXRUNTIMES['default'] = ('onnxruntime', '1.20.1')
ONNXRUNTIMES['cuda'] = ('onnxruntime-gpu', '1.20.1')
ONNXRUNTIMES['openvino'] = ('onnxruntime-openvino', '1.20.0')
if is_linux():
ONNXRUNTIMES['rocm'] = ('onnxruntime-rocm', '1.18.0')
if is_windows():
@ -72,7 +72,7 @@ def run(program : ArgumentParser) -> None:
os.path.join(os.getenv('CONDA_PREFIX'), 'lib'),
os.path.join(os.getenv('CONDA_PREFIX'), 'lib', python_id, 'site-packages', 'tensorrt_libs')
])
library_paths = [ library_path for library_path in library_paths if os.path.exists(library_path) ]
library_paths = list(dict.fromkeys([ library_path for library_path in library_paths if os.path.exists(library_path) ]))
subprocess.call([ shutil.which('conda'), 'env', 'config', 'vars', 'set', 'LD_LIBRARY_PATH=' + os.pathsep.join(library_paths) ])
@ -85,10 +85,9 @@ def run(program : ArgumentParser) -> None:
os.path.join(os.getenv('CONDA_PREFIX'), 'Lib'),
os.path.join(os.getenv('CONDA_PREFIX'), 'Lib', 'site-packages', 'tensorrt_libs')
])
library_paths = [ library_path for library_path in library_paths if os.path.exists(library_path) ]
library_paths = list(dict.fromkeys([ library_path for library_path in library_paths if os.path.exists(library_path) ]))
subprocess.call([ shutil.which('conda'), 'env', 'config', 'vars', 'set', 'PATH=' + os.pathsep.join(library_paths) ])
if onnxruntime_version < '1.19.0':
subprocess.call([ shutil.which('pip'), 'install', 'numpy==1.26.4', '--force-reinstall' ])
subprocess.call([ shutil.which('pip'), 'install', 'python-multipart==0.0.12', '--force-reinstall' ])

21
facefusion/jobs/job_manager.py
View File

@ -1,14 +1,12 @@
import glob
import os
from copy import copy
from typing import List, Optional
from facefusion.choices import job_statuses
import facefusion.choices
from facefusion.date_helper import get_current_date_time
from facefusion.filesystem import create_directory, is_directory, is_file, move_file, remove_directory, remove_file
from facefusion.filesystem import create_directory, is_directory, is_file, move_file, remove_directory, remove_file, resolve_file_pattern
from facefusion.jobs.job_helper import get_step_output_path
from facefusion.json import read_json, write_json
from facefusion.temp_helper import create_base_directory
from facefusion.typing import Args, Job, JobSet, JobStatus, JobStep, JobStepStatus
JOBS_PATH : Optional[str] = None
@ -18,9 +16,8 @@ def init_jobs(jobs_path : str) -> bool:
global JOBS_PATH
JOBS_PATH = jobs_path
job_status_paths = [ os.path.join(JOBS_PATH, job_status) for job_status in job_statuses ]
job_status_paths = [ os.path.join(JOBS_PATH, job_status) for job_status in facefusion.choices.job_statuses ]
create_base_directory()
for job_status_path in job_status_paths:
create_directory(job_status_path)
return all(is_directory(status_path) for status_path in job_status_paths)
@ -88,12 +85,12 @@ def find_jobs(job_status : JobStatus) -> JobSet:
def find_job_ids(job_status : JobStatus) -> List[str]:
job_pattern = os.path.join(JOBS_PATH, job_status, '*.json')
job_files = glob.glob(job_pattern)
job_files.sort(key = os.path.getmtime)
job_paths = resolve_file_pattern(job_pattern)
job_paths.sort(key = os.path.getmtime)
job_ids = []
for job_file in job_files:
job_id, _ = os.path.splitext(os.path.basename(job_file))
for job_path in job_paths:
job_id, _ = os.path.splitext(os.path.basename(job_path))
job_ids.append(job_id)
return job_ids
@ -248,9 +245,9 @@ def find_job_path(job_id : str) -> Optional[str]:
job_file_name = get_job_file_name(job_id)
if job_file_name:
for job_status in job_statuses:
for job_status in facefusion.choices.job_statuses:
job_pattern = os.path.join(JOBS_PATH, job_status, job_file_name)
job_paths = glob.glob(job_pattern)
job_paths = resolve_file_pattern(job_pattern)
for job_path in job_paths:
return job_path

4
facefusion/logger.py
View File

@ -1,14 +1,14 @@
from logging import Logger, basicConfig, getLogger
from typing import Tuple
from facefusion.choices import log_level_set
import facefusion.choices
from facefusion.common_helper import get_first, get_last
from facefusion.typing import LogLevel, TableContents, TableHeaders
def init(log_level : LogLevel) -> None:
basicConfig(format = '%(message)s')
get_package_logger().setLevel(log_level_set.get(log_level))
get_package_logger().setLevel(facefusion.choices.log_level_set.get(log_level))
def get_package_logger() -> Logger:

2
facefusion/metadata.py
View File

@ -4,7 +4,7 @@ METADATA =\
{
'name': 'FaceFusion',
'description': 'Industry leading face manipulation platform',
'version': '3.0.1',
'version': '3.1.0',
'license': 'MIT',
'author': 'Henry Ruhs',
'url': 'https://facefusion.io'

11
facefusion/model_helper.py Normal file
View File

@ -0,0 +1,11 @@
from functools import lru_cache
import onnx
from facefusion.typing import ModelInitializer
@lru_cache(maxsize = None)
def get_static_model_initializer(model_path : str) -> ModelInitializer:
model = onnx.load(model_path)
return onnx.numpy_helper.to_array(model.graph.initializer[-1])

169
facefusion/processors/choices.py
View File

@ -1,9 +1,170 @@
from typing import List, Sequence
from facefusion.common_helper import create_float_range, create_int_range
from facefusion.processors.typing import AgeModifierModel, ExpressionRestorerModel, FaceDebuggerItem, FaceEditorModel, FaceEnhancerModel, FaceSwapperSet, FrameColorizerModel, FrameEnhancerModel, LipSyncerModel
from facefusion.filesystem import list_directory, resolve_relative_path
from facefusion.processors.typing import AgeModifierModel, DeepSwapperModel, ExpressionRestorerModel, FaceDebuggerItem, FaceEditorModel, FaceEnhancerModel, FaceSwapperModel, FaceSwapperSet, FrameColorizerModel, FrameEnhancerModel, LipSyncerModel
age_modifier_models : List[AgeModifierModel] = [ 'styleganex_age' ]
deep_swapper_models : List[DeepSwapperModel] =\
[
'druuzil/adrianne_palicki_384',
'druuzil/agnetha_falskog_224',
'druuzil/alan_ritchson_320',
'druuzil/alicia_vikander_320',
'druuzil/amber_midthunder_320',
'druuzil/andras_arato_384',
'druuzil/andrew_tate_320',
'druuzil/anne_hathaway_320',
'druuzil/anya_chalotra_320',
'druuzil/arnold_schwarzenegger_320',
'druuzil/benjamin_affleck_320',
'druuzil/benjamin_stiller_384',
'druuzil/bradley_pitt_224',
'druuzil/brie_larson_384',
'druuzil/bryan_cranston_320',
'druuzil/catherine_blanchett_352',
'druuzil/christian_bale_320',
'druuzil/christopher_hemsworth_320',
'druuzil/christoph_waltz_384',
'druuzil/cillian_murphy_320',
'druuzil/cobie_smulders_256',
'druuzil/dwayne_johnson_384',
'druuzil/edward_norton_320',
'druuzil/elisabeth_shue_320',
'druuzil/elizabeth_olsen_384',
'druuzil/elon_musk_320',
'druuzil/emily_blunt_320',
'druuzil/emma_stone_384',
'druuzil/emma_watson_320',
'druuzil/erin_moriarty_384',
'druuzil/eva_green_320',
'druuzil/ewan_mcgregor_320',
'druuzil/florence_pugh_320',
'druuzil/freya_allan_320',
'druuzil/gary_cole_224',
'druuzil/gigi_hadid_224',
'druuzil/harrison_ford_384',
'druuzil/hayden_christensen_320',
'druuzil/heath_ledger_320',
'druuzil/henry_cavill_448',
'druuzil/hugh_jackman_384',
'druuzil/idris_elba_320',
'druuzil/jack_nicholson_320',
'druuzil/james_mcavoy_320',
'druuzil/james_varney_320',
'druuzil/jason_momoa_320',
'druuzil/jason_statham_320',
'druuzil/jennifer_connelly_384',
'druuzil/jimmy_donaldson_320',
'druuzil/jordan_peterson_384',
'druuzil/karl_urban_224',
'druuzil/kate_beckinsale_384',
'druuzil/laurence_fishburne_384',
'druuzil/lili_reinhart_320',
'druuzil/mads_mikkelsen_384',
'druuzil/mary_winstead_320',
'druuzil/margaret_qualley_384',
'druuzil/melina_juergens_320',
'druuzil/michael_fassbender_320',
'druuzil/michael_fox_320',
'druuzil/millie_bobby_brown_320',
'druuzil/morgan_freeman_320',
'druuzil/patrick_stewart_320',
'druuzil/rebecca_ferguson_320',
'druuzil/scarlett_johansson_320',
'druuzil/seth_macfarlane_384',
'druuzil/thomas_cruise_320',
'druuzil/thomas_hanks_384',
'edel/emma_roberts_224',
'edel/ivanka_trump_224',
'edel/lize_dzjabrailova_224',
'edel/sidney_sweeney_224',
'edel/winona_ryder_224',
'iperov/alexandra_daddario_224',
'iperov/alexei_navalny_224',
'iperov/amber_heard_224',
'iperov/dilraba_dilmurat_224',
'iperov/elon_musk_224',
'iperov/emilia_clarke_224',
'iperov/emma_watson_224',
'iperov/erin_moriarty_224',
'iperov/jackie_chan_224',
'iperov/james_carrey_224',
'iperov/jason_statham_320',
'iperov/keanu_reeves_320',
'iperov/margot_robbie_224',
'iperov/natalie_dormer_224',
'iperov/nicolas_coppola_224',
'iperov/robert_downey_224',
'iperov/rowan_atkinson_224',
'iperov/ryan_reynolds_224',
'iperov/scarlett_johansson_224',
'iperov/sylvester_stallone_224',
'iperov/thomas_cruise_224',
'iperov/thomas_holland_224',
'iperov/vin_diesel_224',
'iperov/vladimir_putin_224',
'jen/angelica_trae_288',
'jen/ella_freya_224',
'jen/emma_myers_320',
'jen/evie_pickerill_224',
'jen/kang_hyewon_320',
'jen/maddie_mead_224',
'jen/nicole_turnbull_288',
'mats/alica_schmidt_320',
'mats/ashley_alexiss_224',
'mats/billie_eilish_224',
'mats/brie_larson_224',
'mats/cara_delevingne_224',
'mats/carolin_kebekus_224',
'mats/chelsea_clinton_224',
'mats/claire_boucher_224',
'mats/corinna_kopf_224',
'mats/florence_pugh_224',
'mats/hillary_clinton_224',
'mats/jenna_fischer_224',
'mats/kim_jisoo_320',
'mats/mica_suarez_320',
'mats/shailene_woodley_224',
'mats/shraddha_kapoor_320',
'mats/yu_jimin_352',
'rumateus/alison_brie_224',
'rumateus/amber_heard_224',
'rumateus/angelina_jolie_224',
'rumateus/aubrey_plaza_224',
'rumateus/bridget_regan_224',
'rumateus/cobie_smulders_224',
'rumateus/deborah_woll_224',
'rumateus/dua_lipa_224',
'rumateus/emma_stone_224',
'rumateus/hailee_steinfeld_224',
'rumateus/hilary_duff_224',
'rumateus/jessica_alba_224',
'rumateus/jessica_biel_224',
'rumateus/john_cena_224',
'rumateus/kim_kardashian_224',
'rumateus/kristen_bell_224',
'rumateus/lucy_liu_224',
'rumateus/margot_robbie_224',
'rumateus/megan_fox_224',
'rumateus/meghan_markle_224',
'rumateus/millie_bobby_brown_224',
'rumateus/natalie_portman_224',
'rumateus/nicki_minaj_224',
'rumateus/olivia_wilde_224',
'rumateus/shay_mitchell_224',
'rumateus/sophie_turner_224',
'rumateus/taylor_swift_224'
]
custom_model_files = list_directory(resolve_relative_path('../.assets/models/custom'))
if custom_model_files:
for model_file in custom_model_files:
model_id = '/'.join([ 'custom', model_file.get('name') ])
deep_swapper_models.append(model_id)
expression_restorer_models : List[ExpressionRestorerModel] = [ 'live_portrait' ]
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', 'race' ]
face_editor_models : List[FaceEditorModel] = [ 'live_portrait' ]
@ -14,18 +175,21 @@ face_swapper_set : FaceSwapperSet =\
'ghost_1_256': [ '256x256', '512x512', '768x768', '1024x1024' ],
'ghost_2_256': [ '256x256', '512x512', '768x768', '1024x1024' ],
'ghost_3_256': [ '256x256', '512x512', '768x768', '1024x1024' ],
'hififace_unofficial_256': [ '256x256', '512x512', '768x768', '1024x1024' ],
'inswapper_128': [ '128x128', '256x256', '384x384', '512x512', '768x768', '1024x1024' ],
'inswapper_128_fp16': [ '128x128', '256x256', '384x384', '512x512', '768x768', '1024x1024' ],
'simswap_256': [ '256x256', '512x512', '768x768', '1024x1024' ],
'simswap_unofficial_512': [ '512x512', '768x768', '1024x1024' ],
'uniface_256': [ '256x256', '512x512', '768x768', '1024x1024' ]
}
face_swapper_models : List[FaceSwapperModel] = list(face_swapper_set.keys())
frame_colorizer_models : List[FrameColorizerModel] = [ 'ddcolor', 'ddcolor_artistic', 'deoldify', 'deoldify_artistic', 'deoldify_stable' ]
frame_colorizer_sizes : List[str] = [ '192x192', '256x256', '384x384', '512x512' ]
frame_enhancer_models : List[FrameEnhancerModel] = [ 'clear_reality_x4', 'lsdir_x4', 'nomos8k_sc_x4', 'real_esrgan_x2', 'real_esrgan_x2_fp16', 'real_esrgan_x4', 'real_esrgan_x4_fp16', 'real_esrgan_x8', 'real_esrgan_x8_fp16', 'real_hatgan_x4', 'span_kendata_x4', 'ultra_sharp_x4' ]
frame_enhancer_models : List[FrameEnhancerModel] = [ 'clear_reality_x4', 'lsdir_x4', 'nomos8k_sc_x4', 'real_esrgan_x2', 'real_esrgan_x2_fp16', 'real_esrgan_x4', 'real_esrgan_x4_fp16', 'real_esrgan_x8', 'real_esrgan_x8_fp16', 'real_hatgan_x4', 'real_web_photo_x4', 'realistic_rescaler_x4', 'remacri_x4', 'siax_x4', 'span_kendata_x4', 'swin2_sr_x4', 'ultra_sharp_x4' ]
lip_syncer_models : List[LipSyncerModel] = [ 'wav2lip_96', 'wav2lip_gan_96' ]
age_modifier_direction_range : Sequence[int] = create_int_range(-100, 100, 1)
deep_swapper_morph_range : Sequence[int] = create_int_range(0, 100, 1)
expression_restorer_factor_range : Sequence[int] = create_int_range(0, 100, 1)
face_editor_eyebrow_direction_range : Sequence[float] = create_float_range(-1.0, 1.0, 0.05)
face_editor_eye_gaze_horizontal_range : Sequence[float] = create_float_range(-1.0, 1.0, 0.05)
@ -42,5 +206,6 @@ face_editor_head_pitch_range : Sequence[float] = create_float_range(-1.0, 1.0, 0
face_editor_head_yaw_range : Sequence[float] = create_float_range(-1.0, 1.0, 0.05)
face_editor_head_roll_range : Sequence[float] = create_float_range(-1.0, 1.0, 0.05)
face_enhancer_blend_range : Sequence[int] = create_int_range(0, 100, 1)
face_enhancer_weight_range : Sequence[float] = create_float_range(0.0, 1.0, 0.05)
frame_colorizer_blend_range : Sequence[int] = create_int_range(0, 100, 1)
frame_enhancer_blend_range : Sequence[int] = create_int_range(0, 100, 1)

13
facefusion/processors/core.py
View File

@ -53,21 +53,10 @@ def get_processors_modules(processors : List[str]) -> List[ModuleType]:
return processor_modules
def clear_processors_modules(processors : List[str]) -> None:
for processor in processors:
processor_module = load_processor_module(processor)
processor_module.clear_inference_pool()
def multi_process_frames(source_paths : List[str], temp_frame_paths : List[str], process_frames : ProcessFrames) -> None:
queue_payloads = create_queue_payloads(temp_frame_paths)
with tqdm(total = len(queue_payloads), desc = wording.get('processing'), unit = 'frame', ascii = ' =', disable = state_manager.get_item('log_level') in [ 'warn', 'error' ]) as progress:
progress.set_postfix(
{
'execution_providers': state_manager.get_item('execution_providers'),
'execution_thread_count': state_manager.get_item('execution_thread_count'),
'execution_queue_count': state_manager.get_item('execution_queue_count')
})
progress.set_postfix(execution_providers = state_manager.get_item('execution_providers'))
with ThreadPoolExecutor(max_workers = state_manager.get_item('execution_thread_count')) as executor:
futures = []
queue : Queue[QueuePayload] = create_queue(queue_payloads)

120
facefusion/processors/modules/age_modifier.py
View File

@ -1,17 +1,18 @@
from argparse import ArgumentParser
from typing import Any, List
from functools import lru_cache
from typing import List
import cv2
import numpy
from cv2.typing import Size
from numpy.typing import NDArray
import facefusion.choices
import facefusion.jobs.job_manager
import facefusion.jobs.job_store
import facefusion.processors.core as processors
from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, inference_manager, logger, process_manager, state_manager, wording
from facefusion.common_helper import create_int_metavar
from facefusion.download import conditional_download_hashes, conditional_download_sources
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
from facefusion.execution import has_execution_provider
from facefusion.face_analyser import get_many_faces, get_one_face
from facefusion.face_helper import merge_matrix, paste_back, scale_face_landmark_5, warp_face_by_face_landmark_5
from facefusion.face_masker import create_occlusion_mask, create_static_box_mask
@ -19,21 +20,24 @@ from facefusion.face_selector import find_similar_faces, sort_and_filter_faces
from facefusion.face_store import get_reference_faces
from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path, same_file_extension
from facefusion.processors import choices as processors_choices
from facefusion.processors.typing import AgeModifierInputs
from facefusion.processors.typing import AgeModifierDirection, AgeModifierInputs
from facefusion.program_helper import find_argument_group
from facefusion.thread_helper import thread_semaphore
from facefusion.typing import ApplyStateItem, Args, Face, InferencePool, Mask, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.vision import read_image, read_static_image, write_image
from facefusion.typing import ApplyStateItem, Args, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.vision import match_frame_color, read_image, read_static_image, write_image
MODEL_SET : ModelSet =\
{
@lru_cache(maxsize = None)
def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
return\
{
'styleganex_age':
{
'hashes':
{
'age_modifier':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/styleganex_age.hash',
'url': resolve_download_url('models-3.1.0', 'styleganex_age.hash'),
'path': resolve_relative_path('../.assets/models/styleganex_age.hash')
}
},
@ -41,30 +45,36 @@ MODEL_SET : ModelSet =\
{
'age_modifier':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/styleganex_age.onnx',
'url': resolve_download_url('models-3.1.0', 'styleganex_age.onnx'),
'path': resolve_relative_path('../.assets/models/styleganex_age.onnx')
}
},
'template': 'ffhq_512',
'size': (512, 512)
'templates':
{
'target': 'ffhq_512',
'target_with_background': 'styleganex_384'
},
'sizes':
{
'target': (256, 256),
'target_with_background': (384, 384)
}
}
}
}
def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources')
model_context = __name__ + '.' + state_manager.get_item('age_modifier_model')
return inference_manager.get_inference_pool(model_context, model_sources)
return inference_manager.get_inference_pool(__name__, model_sources)
def clear_inference_pool() -> None:
model_context = __name__ + '.' + state_manager.get_item('age_modifier_model')
inference_manager.clear_inference_pool(model_context)
inference_manager.clear_inference_pool(__name__)
def get_model_options() -> ModelOptions:
age_modifier_model = state_manager.get_item('age_modifier_model')
return MODEL_SET.get(age_modifier_model)
return create_static_model_set('full').get(age_modifier_model)
def register_args(program : ArgumentParser) -> None:
@ -81,11 +91,10 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
return conditional_download_hashes(download_directory_path, model_hashes) and conditional_download_sources(download_directory_path, model_sources)
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
def pre_process(mode : ProcessMode) -> bool:
@ -115,15 +124,14 @@ def post_process() -> None:
def modify_age(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
model_template = get_model_options().get('template')
model_size = get_model_options().get('size')
crop_size = (model_size[0] // 2, model_size[1] // 2)
model_templates = get_model_options().get('templates')
model_sizes = get_model_options().get('sizes')
face_landmark_5 = target_face.landmark_set.get('5/68').copy()
extend_face_landmark_5 = scale_face_landmark_5(face_landmark_5, 2.0)
crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, face_landmark_5, model_template, crop_size)
extend_vision_frame, extend_affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, extend_face_landmark_5, model_template, model_size)
crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, face_landmark_5, model_templates.get('target'), model_sizes.get('target'))
extend_face_landmark_5 = scale_face_landmark_5(face_landmark_5, 0.875)
extend_vision_frame, extend_affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, extend_face_landmark_5, model_templates.get('target_with_background'), model_sizes.get('target_with_background'))
extend_vision_frame_raw = extend_vision_frame.copy()
box_mask = create_static_box_mask(model_size, state_manager.get_item('face_mask_blur'), (0, 0, 0, 0))
box_mask = create_static_box_mask(model_sizes.get('target_with_background'), state_manager.get_item('face_mask_blur'), (0, 0, 0, 0))
crop_masks =\
[
box_mask
@ -132,31 +140,36 @@ def modify_age(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFra
if 'occlusion' in state_manager.get_item('face_mask_types'):
occlusion_mask = create_occlusion_mask(crop_vision_frame)
combined_matrix = merge_matrix([ extend_affine_matrix, cv2.invertAffineTransform(affine_matrix) ])
occlusion_mask = cv2.warpAffine(occlusion_mask, combined_matrix, model_size)
occlusion_mask = cv2.warpAffine(occlusion_mask, combined_matrix, model_sizes.get('target_with_background'))
crop_masks.append(occlusion_mask)
crop_vision_frame = prepare_vision_frame(crop_vision_frame)
extend_vision_frame = prepare_vision_frame(extend_vision_frame)
extend_vision_frame = forward(crop_vision_frame, extend_vision_frame)
age_modifier_direction = numpy.array(numpy.interp(state_manager.get_item('age_modifier_direction'), [-100, 100], [2.5, -2.5])).astype(numpy.float32)
extend_vision_frame = forward(crop_vision_frame, extend_vision_frame, age_modifier_direction)
extend_vision_frame = normalize_extend_frame(extend_vision_frame)
extend_vision_frame = fix_color(extend_vision_frame_raw, extend_vision_frame)
extend_crop_mask = cv2.pyrUp(numpy.minimum.reduce(crop_masks).clip(0, 1))
extend_affine_matrix *= extend_vision_frame.shape[0] / 512
paste_vision_frame = paste_back(temp_vision_frame, extend_vision_frame, extend_crop_mask, extend_affine_matrix)
extend_vision_frame = match_frame_color(extend_vision_frame_raw, extend_vision_frame)
extend_affine_matrix *= (model_sizes.get('target')[0] * 4) / model_sizes.get('target_with_background')[0]
crop_mask = numpy.minimum.reduce(crop_masks).clip(0, 1)
crop_mask = cv2.resize(crop_mask, (model_sizes.get('target')[0] * 4, model_sizes.get('target')[1] * 4))
paste_vision_frame = paste_back(temp_vision_frame, extend_vision_frame, crop_mask, extend_affine_matrix)
return paste_vision_frame
def forward(crop_vision_frame : VisionFrame, extend_vision_frame : VisionFrame) -> VisionFrame:
def forward(crop_vision_frame : VisionFrame, extend_vision_frame : VisionFrame, age_modifier_direction : AgeModifierDirection) -> VisionFrame:
age_modifier = get_inference_pool().get('age_modifier')
age_modifier_inputs = {}
if has_execution_provider('coreml'):
age_modifier.set_providers([ facefusion.choices.execution_provider_set.get('cpu') ])
for age_modifier_input in age_modifier.get_inputs():
if age_modifier_input.name == 'target':
age_modifier_inputs[age_modifier_input.name] = crop_vision_frame
if age_modifier_input.name == 'target_with_background':
age_modifier_inputs[age_modifier_input.name] = extend_vision_frame
if age_modifier_input.name == 'direction':
age_modifier_inputs[age_modifier_input.name] = prepare_direction(state_manager.get_item('age_modifier_direction'))
age_modifier_inputs[age_modifier_input.name] = age_modifier_direction
with thread_semaphore():
crop_vision_frame = age_modifier.run(None, age_modifier_inputs)[0][0]
@ -164,38 +177,6 @@ def forward(crop_vision_frame : VisionFrame, extend_vision_frame : VisionFrame)
return crop_vision_frame
def fix_color(extend_vision_frame_raw : VisionFrame, extend_vision_frame : VisionFrame) -> VisionFrame:
color_difference = compute_color_difference(extend_vision_frame_raw, extend_vision_frame, (48, 48))
color_difference_mask = create_static_box_mask(extend_vision_frame.shape[:2][::-1], 1.0, (0, 0, 0, 0))
color_difference_mask = numpy.stack((color_difference_mask, ) * 3, axis = -1)
extend_vision_frame = normalize_color_difference(color_difference, color_difference_mask, extend_vision_frame)
return extend_vision_frame
def compute_color_difference(extend_vision_frame_raw : VisionFrame, extend_vision_frame : VisionFrame, size : Size) -> VisionFrame:
extend_vision_frame_raw = extend_vision_frame_raw.astype(numpy.float32) / 255
extend_vision_frame_raw = cv2.resize(extend_vision_frame_raw, size, interpolation = cv2.INTER_AREA)
extend_vision_frame = extend_vision_frame.astype(numpy.float32) / 255
extend_vision_frame = cv2.resize(extend_vision_frame, size, interpolation = cv2.INTER_AREA)
color_difference = extend_vision_frame_raw - extend_vision_frame
return color_difference
def normalize_color_difference(color_difference : VisionFrame, color_difference_mask : Mask, extend_vision_frame : VisionFrame) -> VisionFrame:
color_difference = cv2.resize(color_difference, extend_vision_frame.shape[:2][::-1], interpolation = cv2.INTER_CUBIC)
color_difference_mask = 1 - color_difference_mask.clip(0, 0.75)
extend_vision_frame = extend_vision_frame.astype(numpy.float32) / 255
extend_vision_frame += color_difference * color_difference_mask
extend_vision_frame = extend_vision_frame.clip(0, 1)
extend_vision_frame = numpy.multiply(extend_vision_frame, 255).astype(numpy.uint8)
return extend_vision_frame
def prepare_direction(direction : int) -> NDArray[Any]:
direction = numpy.interp(float(direction), [ -100, 100 ], [ 2.5, -2.5 ]) #type:ignore[assignment]
return numpy.array(direction).astype(numpy.float32)
def prepare_vision_frame(vision_frame : VisionFrame) -> VisionFrame:
vision_frame = vision_frame[:, :, ::-1] / 255.0
vision_frame = (vision_frame - 0.5) / 0.5
@ -204,12 +185,13 @@ def prepare_vision_frame(vision_frame : VisionFrame) -> VisionFrame:
def normalize_extend_frame(extend_vision_frame : VisionFrame) -> VisionFrame:
model_sizes = get_model_options().get('sizes')
extend_vision_frame = numpy.clip(extend_vision_frame, -1, 1)
extend_vision_frame = (extend_vision_frame + 1) / 2
extend_vision_frame = extend_vision_frame.transpose(1, 2, 0).clip(0, 255)
extend_vision_frame = (extend_vision_frame * 255.0)
extend_vision_frame = extend_vision_frame.astype(numpy.uint8)[:, :, ::-1]
extend_vision_frame = cv2.pyrDown(extend_vision_frame)
extend_vision_frame = cv2.resize(extend_vision_frame, (model_sizes.get('target')[0] * 4, model_sizes.get('target')[1] * 4), interpolation = cv2.INTER_AREA)
return extend_vision_frame

444
facefusion/processors/modules/deep_swapper.py Executable file
View File

@ -0,0 +1,444 @@
from argparse import ArgumentParser
from functools import lru_cache
from typing import List, Tuple
import cv2
import numpy
from cv2.typing import Size
import facefusion.jobs.job_manager
import facefusion.jobs.job_store
import facefusion.processors.core as processors
from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, inference_manager, logger, process_manager, state_manager, wording
from facefusion.common_helper import create_int_metavar
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url_by_provider
from facefusion.face_analyser import get_many_faces, get_one_face
from facefusion.face_helper import paste_back, warp_face_by_face_landmark_5
from facefusion.face_masker import create_occlusion_mask, create_region_mask, create_static_box_mask
from facefusion.face_selector import find_similar_faces, sort_and_filter_faces
from facefusion.face_store import get_reference_faces
from facefusion.filesystem import in_directory, is_image, is_video, list_directory, resolve_relative_path, same_file_extension
from facefusion.processors import choices as processors_choices
from facefusion.processors.typing import DeepSwapperInputs, DeepSwapperMorph
from facefusion.program_helper import find_argument_group
from facefusion.thread_helper import thread_semaphore
from facefusion.typing import ApplyStateItem, Args, DownloadScope, Face, InferencePool, Mask, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.vision import conditional_match_frame_color, read_image, read_static_image, write_image
@lru_cache(maxsize = None)
def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
model_config = []
if download_scope == 'full':
model_config.extend(
[
('druuzil', 'adrianne_palicki_384'),
('druuzil', 'agnetha_falskog_224'),
('druuzil', 'alan_ritchson_320'),
('druuzil', 'alicia_vikander_320'),
('druuzil', 'amber_midthunder_320'),
('druuzil', 'andras_arato_384'),
('druuzil', 'andrew_tate_320'),
('druuzil', 'anne_hathaway_320'),
('druuzil', 'anya_chalotra_320'),
('druuzil', 'arnold_schwarzenegger_320'),
('druuzil', 'benjamin_affleck_320'),
('druuzil', 'benjamin_stiller_384'),
('druuzil', 'bradley_pitt_224'),
('druuzil', 'brie_larson_384'),
('druuzil', 'bryan_cranston_320'),
('druuzil', 'catherine_blanchett_352'),
('druuzil', 'christian_bale_320'),
('druuzil', 'christopher_hemsworth_320'),
('druuzil', 'christoph_waltz_384'),
('druuzil', 'cillian_murphy_320'),
('druuzil', 'cobie_smulders_256'),
('druuzil', 'dwayne_johnson_384'),
('druuzil', 'edward_norton_320'),
('druuzil', 'elisabeth_shue_320'),
('druuzil', 'elizabeth_olsen_384'),
('druuzil', 'elon_musk_320'),
('druuzil', 'emily_blunt_320'),
('druuzil', 'emma_stone_384'),
('druuzil', 'emma_watson_320'),
('druuzil', 'erin_moriarty_384'),
('druuzil', 'eva_green_320'),
('druuzil', 'ewan_mcgregor_320'),
('druuzil', 'florence_pugh_320'),
('druuzil', 'freya_allan_320'),
('druuzil', 'gary_cole_224'),
('druuzil', 'gigi_hadid_224'),
('druuzil', 'harrison_ford_384'),
('druuzil', 'hayden_christensen_320'),
('druuzil', 'heath_ledger_320'),
('druuzil', 'henry_cavill_448'),
('druuzil', 'hugh_jackman_384'),
('druuzil', 'idris_elba_320'),
('druuzil', 'jack_nicholson_320'),
('druuzil', 'james_mcavoy_320'),
('druuzil', 'james_varney_320'),
('druuzil', 'jason_momoa_320'),
('druuzil', 'jason_statham_320'),
('druuzil', 'jennifer_connelly_384'),
('druuzil', 'jimmy_donaldson_320'),
('druuzil', 'jordan_peterson_384'),
('druuzil', 'karl_urban_224'),
('druuzil', 'kate_beckinsale_384'),
('druuzil', 'laurence_fishburne_384'),
('druuzil', 'lili_reinhart_320'),
('druuzil', 'mads_mikkelsen_384'),
('druuzil', 'mary_winstead_320'),
('druuzil', 'margaret_qualley_384'),
('druuzil', 'melina_juergens_320'),
('druuzil', 'michael_fassbender_320'),
('druuzil', 'michael_fox_320'),
('druuzil', 'millie_bobby_brown_320'),
('druuzil', 'morgan_freeman_320'),
('druuzil', 'patrick_stewart_320'),
('druuzil', 'rebecca_ferguson_320'),
('druuzil', 'scarlett_johansson_320'),
('druuzil', 'seth_macfarlane_384'),
('druuzil', 'thomas_cruise_320'),
('druuzil', 'thomas_hanks_384'),
('edel', 'emma_roberts_224'),
('edel', 'ivanka_trump_224'),
('edel', 'lize_dzjabrailova_224'),
('edel', 'sidney_sweeney_224'),
('edel', 'winona_ryder_224')
])
if download_scope in [ 'lite', 'full' ]:
model_config.extend(
[
('iperov', 'alexandra_daddario_224'),
('iperov', 'alexei_navalny_224'),
('iperov', 'amber_heard_224'),
('iperov', 'dilraba_dilmurat_224'),
('iperov', 'elon_musk_224'),
('iperov', 'emilia_clarke_224'),
('iperov', 'emma_watson_224'),
('iperov', 'erin_moriarty_224'),
('iperov', 'jackie_chan_224'),
('iperov', 'james_carrey_224'),
('iperov', 'jason_statham_320'),
('iperov', 'keanu_reeves_320'),
('iperov', 'margot_robbie_224'),
('iperov', 'natalie_dormer_224'),
('iperov', 'nicolas_coppola_224'),
('iperov', 'robert_downey_224'),
('iperov', 'rowan_atkinson_224'),
('iperov', 'ryan_reynolds_224'),
('iperov', 'scarlett_johansson_224'),
('iperov', 'sylvester_stallone_224'),
('iperov', 'thomas_cruise_224'),
('iperov', 'thomas_holland_224'),
('iperov', 'vin_diesel_224'),
('iperov', 'vladimir_putin_224')
])
if download_scope == 'full':
model_config.extend(
[
('jen', 'angelica_trae_288'),
('jen', 'ella_freya_224'),
('jen', 'emma_myers_320'),
('jen', 'evie_pickerill_224'),
('jen', 'kang_hyewon_320'),
('jen', 'maddie_mead_224'),
('jen', 'nicole_turnbull_288'),
('mats', 'alica_schmidt_320'),
('mats', 'ashley_alexiss_224'),
('mats', 'billie_eilish_224'),
('mats', 'brie_larson_224'),
('mats', 'cara_delevingne_224'),
('mats', 'carolin_kebekus_224'),
('mats', 'chelsea_clinton_224'),
('mats', 'claire_boucher_224'),
('mats', 'corinna_kopf_224'),
('mats', 'florence_pugh_224'),
('mats', 'hillary_clinton_224'),
('mats', 'jenna_fischer_224'),
('mats', 'kim_jisoo_320'),
('mats', 'mica_suarez_320'),
('mats', 'shailene_woodley_224'),
('mats', 'shraddha_kapoor_320'),
('mats', 'yu_jimin_352'),
('rumateus', 'alison_brie_224'),
('rumateus', 'amber_heard_224'),
('rumateus', 'angelina_jolie_224'),
('rumateus', 'aubrey_plaza_224'),
('rumateus', 'bridget_regan_224'),
('rumateus', 'cobie_smulders_224'),
('rumateus', 'deborah_woll_224'),
('rumateus', 'dua_lipa_224'),
('rumateus', 'emma_stone_224'),
('rumateus', 'hailee_steinfeld_224'),
('rumateus', 'hilary_duff_224'),
('rumateus', 'jessica_alba_224'),
('rumateus', 'jessica_biel_224'),
('rumateus', 'john_cena_224'),
('rumateus', 'kim_kardashian_224'),
('rumateus', 'kristen_bell_224'),
('rumateus', 'lucy_liu_224'),
('rumateus', 'margot_robbie_224'),
('rumateus', 'megan_fox_224'),
('rumateus', 'meghan_markle_224'),
('rumateus', 'millie_bobby_brown_224'),
('rumateus', 'natalie_portman_224'),
('rumateus', 'nicki_minaj_224'),
('rumateus', 'olivia_wilde_224'),
('rumateus', 'shay_mitchell_224'),
('rumateus', 'sophie_turner_224'),
('rumateus', 'taylor_swift_224')
])
model_set : ModelSet = {}
for model_scope, model_name in model_config:
model_id = '/'.join([ model_scope, model_name ])
model_set[model_id] =\
{
'hashes':
{
'deep_swapper':
{
'url': resolve_download_url_by_provider('huggingface', 'deepfacelive-models-' + model_scope, model_name + '.hash'),
'path': resolve_relative_path('../.assets/models/' + model_scope + '/' + model_name + '.hash')
}
},
'sources':
{
'deep_swapper':
{
'url': resolve_download_url_by_provider('huggingface', 'deepfacelive-models-' + model_scope, model_name + '.dfm'),
'path': resolve_relative_path('../.assets/models/' + model_scope + '/' + model_name + '.dfm')
}
},
'template': 'dfl_whole_face'
}
custom_model_files = list_directory(resolve_relative_path('../.assets/models/custom'))
if custom_model_files:
for model_file in custom_model_files:
model_id = '/'.join([ 'custom', model_file.get('name') ])
model_set[model_id] =\
{
'sources':
{
'deep_swapper':
{
'path': resolve_relative_path(model_file.get('path'))
}
},
'template': 'dfl_whole_face'
}
return model_set
def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_sources)
def clear_inference_pool() -> None:
inference_manager.clear_inference_pool(__name__)
def get_model_options() -> ModelOptions:
deep_swapper_model = state_manager.get_item('deep_swapper_model')
return create_static_model_set('full').get(deep_swapper_model)
def get_model_size() -> Size:
deep_swapper = get_inference_pool().get('deep_swapper')
deep_swapper_outputs = deep_swapper.get_outputs()
for deep_swapper_output in deep_swapper_outputs:
return deep_swapper_output.shape[1:3]
return 0, 0
def register_args(program : ArgumentParser) -> None:
group_processors = find_argument_group(program, 'processors')
if group_processors:
group_processors.add_argument('--deep-swapper-model', help = wording.get('help.deep_swapper_model'), default = config.get_str_value('processors.deep_swapper_model', 'iperov/elon_musk_224'), choices = processors_choices.deep_swapper_models)
group_processors.add_argument('--deep-swapper-morph', help = wording.get('help.deep_swapper_morph'), type = int, default = config.get_int_value('processors.deep_swapper_morph', '80'), choices = processors_choices.deep_swapper_morph_range, metavar = create_int_metavar(processors_choices.deep_swapper_morph_range))
facefusion.jobs.job_store.register_step_keys([ 'deep_swapper_model', 'deep_swapper_morph' ])
def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
apply_state_item('deep_swapper_model', args.get('deep_swapper_model'))
apply_state_item('deep_swapper_morph', args.get('deep_swapper_morph'))
def pre_check() -> bool:
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
if model_hashes and model_sources:
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
return True
def pre_process(mode : ProcessMode) -> bool:
if mode in [ 'output', 'preview' ] and not is_image(state_manager.get_item('target_path')) and not is_video(state_manager.get_item('target_path')):
logger.error(wording.get('choose_image_or_video_target') + wording.get('exclamation_mark'), __name__)
return False
if mode == 'output' and not in_directory(state_manager.get_item('output_path')):
logger.error(wording.get('specify_image_or_video_output') + wording.get('exclamation_mark'), __name__)
return False
if mode == 'output' and not same_file_extension([ state_manager.get_item('target_path'), state_manager.get_item('output_path') ]):
logger.error(wording.get('match_target_and_output_extension') + wording.get('exclamation_mark'), __name__)
return False
return True
def post_process() -> None:
read_static_image.cache_clear()
if state_manager.get_item('video_memory_strategy') in [ 'strict', 'moderate' ]:
clear_inference_pool()
if state_manager.get_item('video_memory_strategy') == 'strict':
content_analyser.clear_inference_pool()
face_classifier.clear_inference_pool()
face_detector.clear_inference_pool()
face_landmarker.clear_inference_pool()
face_masker.clear_inference_pool()
face_recognizer.clear_inference_pool()
def swap_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
model_template = get_model_options().get('template')
model_size = get_model_size()
crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, target_face.landmark_set.get('5/68'), model_template, model_size)
crop_vision_frame_raw = crop_vision_frame.copy()
box_mask = create_static_box_mask(crop_vision_frame.shape[:2][::-1], state_manager.get_item('face_mask_blur'), state_manager.get_item('face_mask_padding'))
crop_masks =\
[
box_mask
]
if 'occlusion' in state_manager.get_item('face_mask_types'):
occlusion_mask = create_occlusion_mask(crop_vision_frame)
crop_masks.append(occlusion_mask)
crop_vision_frame = prepare_crop_frame(crop_vision_frame)
deep_swapper_morph = numpy.array([ numpy.interp(state_manager.get_item('deep_swapper_morph'), [ 0, 100 ], [ 0, 1 ]) ]).astype(numpy.float32)
crop_vision_frame, crop_source_mask, crop_target_mask = forward(crop_vision_frame, deep_swapper_morph)
crop_vision_frame = normalize_crop_frame(crop_vision_frame)
crop_vision_frame = conditional_match_frame_color(crop_vision_frame_raw, crop_vision_frame)
crop_masks.append(prepare_crop_mask(crop_source_mask, crop_target_mask))
if 'region' in state_manager.get_item('face_mask_types'):
region_mask = create_region_mask(crop_vision_frame, state_manager.get_item('face_mask_regions'))
crop_masks.append(region_mask)
crop_mask = numpy.minimum.reduce(crop_masks).clip(0, 1)
paste_vision_frame = paste_back(temp_vision_frame, crop_vision_frame, crop_mask, affine_matrix)
return paste_vision_frame
def forward(crop_vision_frame : VisionFrame, deep_swapper_morph : DeepSwapperMorph) -> Tuple[VisionFrame, Mask, Mask]:
deep_swapper = get_inference_pool().get('deep_swapper')
deep_swapper_inputs = {}
for deep_swapper_input in deep_swapper.get_inputs():
if deep_swapper_input.name == 'in_face:0':
deep_swapper_inputs[deep_swapper_input.name] = crop_vision_frame
if deep_swapper_input.name == 'morph_value:0':
deep_swapper_inputs[deep_swapper_input.name] = deep_swapper_morph
with thread_semaphore():
crop_target_mask, crop_vision_frame, crop_source_mask = deep_swapper.run(None, deep_swapper_inputs)
return crop_vision_frame[0], crop_source_mask[0], crop_target_mask[0]
def has_morph_input() -> bool:
deep_swapper = get_inference_pool().get('deep_swapper')
for deep_swapper_input in deep_swapper.get_inputs():
if deep_swapper_input.name == 'morph_value:0':
return True
return False
def prepare_crop_frame(crop_vision_frame : VisionFrame) -> VisionFrame:
crop_vision_frame = cv2.addWeighted(crop_vision_frame, 1.75, cv2.GaussianBlur(crop_vision_frame, (0, 0), 2), -0.75, 0)
crop_vision_frame = crop_vision_frame / 255.0
crop_vision_frame = numpy.expand_dims(crop_vision_frame, axis = 0).astype(numpy.float32)
return crop_vision_frame
def normalize_crop_frame(crop_vision_frame : VisionFrame) -> VisionFrame:
crop_vision_frame = (crop_vision_frame * 255.0).clip(0, 255)
crop_vision_frame = crop_vision_frame.astype(numpy.uint8)
return crop_vision_frame
def prepare_crop_mask(crop_source_mask : Mask, crop_target_mask : Mask) -> Mask:
model_size = get_model_size()
blur_size = 6.25
kernel_size = 3
crop_mask = numpy.minimum.reduce([ crop_source_mask, crop_target_mask ])
crop_mask = crop_mask.reshape(model_size).clip(0, 1)
crop_mask = cv2.erode(crop_mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_size, kernel_size)), iterations = 2)
crop_mask = cv2.GaussianBlur(crop_mask, (0, 0), blur_size)
return crop_mask
def get_reference_frame(source_face : Face, target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
return swap_face(target_face, temp_vision_frame)
def process_frame(inputs : DeepSwapperInputs) -> VisionFrame:
reference_faces = inputs.get('reference_faces')
target_vision_frame = inputs.get('target_vision_frame')
many_faces = sort_and_filter_faces(get_many_faces([ target_vision_frame ]))
if state_manager.get_item('face_selector_mode') == 'many':
if many_faces:
for target_face in many_faces:
target_vision_frame = swap_face(target_face, target_vision_frame)
if state_manager.get_item('face_selector_mode') == 'one':
target_face = get_one_face(many_faces)
if target_face:
target_vision_frame = swap_face(target_face, target_vision_frame)
if state_manager.get_item('face_selector_mode') == 'reference':
similar_faces = find_similar_faces(many_faces, reference_faces, state_manager.get_item('reference_face_distance'))
if similar_faces:
for similar_face in similar_faces:
target_vision_frame = swap_face(similar_face, target_vision_frame)
return target_vision_frame
def process_frames(source_path : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProgress) -> None:
reference_faces = get_reference_faces() if 'reference' in state_manager.get_item('face_selector_mode') else 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(
{
'reference_faces': reference_faces,
'target_vision_frame': target_vision_frame
})
write_image(target_vision_path, output_vision_frame)
update_progress(1)
def process_image(source_path : str, target_path : str, output_path : str) -> None:
reference_faces = get_reference_faces() if 'reference' in state_manager.get_item('face_selector_mode') else None
target_vision_frame = read_static_image(target_path)
output_vision_frame = process_frame(
{
'reference_faces': reference_faces,
'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:
processors.multi_process_frames(None, temp_frame_paths, process_frames)

39
facefusion/processors/modules/expression_restorer.py
View File

@ -1,4 +1,5 @@
from argparse import ArgumentParser
from functools import lru_cache
from typing import List, Tuple
import cv2
@ -9,7 +10,7 @@ import facefusion.jobs.job_store
import facefusion.processors.core as processors
from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, inference_manager, logger, process_manager, state_manager, wording
from facefusion.common_helper import create_int_metavar
from facefusion.download import conditional_download_hashes, conditional_download_sources
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
from facefusion.face_analyser import get_many_faces, get_one_face
from facefusion.face_helper import paste_back, warp_face_by_face_landmark_5
from facefusion.face_masker import create_occlusion_mask, create_static_box_mask
@ -22,28 +23,31 @@ from facefusion.processors.typing import ExpressionRestorerInputs
from facefusion.processors.typing import LivePortraitExpression, LivePortraitFeatureVolume, LivePortraitMotionPoints, LivePortraitPitch, LivePortraitRoll, LivePortraitScale, LivePortraitTranslation, LivePortraitYaw
from facefusion.program_helper import find_argument_group
from facefusion.thread_helper import conditional_thread_semaphore, thread_semaphore
from facefusion.typing import ApplyStateItem, Args, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.typing import ApplyStateItem, Args, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.vision import get_video_frame, read_image, read_static_image, write_image
MODEL_SET : ModelSet =\
{
@lru_cache(maxsize = None)
def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
return\
{
'live_portrait':
{
'hashes':
{
'feature_extractor':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_feature_extractor.hash',
'url': resolve_download_url('models-3.0.0', 'live_portrait_feature_extractor.hash'),
'path': resolve_relative_path('../.assets/models/live_portrait_feature_extractor.hash')
},
'motion_extractor':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_motion_extractor.hash',
'url': resolve_download_url('models-3.0.0', 'live_portrait_motion_extractor.hash'),
'path': resolve_relative_path('../.assets/models/live_portrait_motion_extractor.hash')
},
'generator':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_generator.hash',
'url': resolve_download_url('models-3.0.0', 'live_portrait_generator.hash'),
'path': resolve_relative_path('../.assets/models/live_portrait_generator.hash')
}
},
@ -51,30 +55,29 @@ MODEL_SET : ModelSet =\
{
'feature_extractor':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_feature_extractor.onnx',
'url': resolve_download_url('models-3.0.0', 'live_portrait_feature_extractor.onnx'),
'path': resolve_relative_path('../.assets/models/live_portrait_feature_extractor.onnx')
},
'motion_extractor':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_motion_extractor.onnx',
'url': resolve_download_url('models-3.0.0', 'live_portrait_motion_extractor.onnx'),
'path': resolve_relative_path('../.assets/models/live_portrait_motion_extractor.onnx')
},
'generator':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_generator.onnx',
'url': resolve_download_url('models-3.0.0', 'live_portrait_generator.onnx'),
'path': resolve_relative_path('../.assets/models/live_portrait_generator.onnx')
}
},
'template': 'arcface_128_v2',
'size': (512, 512)
}
}
}
def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources')
model_context = __name__ + '.' + state_manager.get_item('expression_restorer_model')
return inference_manager.get_inference_pool(model_context, model_sources)
return inference_manager.get_inference_pool(__name__, model_sources)
def clear_inference_pool() -> None:
@ -83,7 +86,7 @@ def clear_inference_pool() -> None:
def get_model_options() -> ModelOptions:
expression_restorer_model = state_manager.get_item('expression_restorer_model')
return MODEL_SET.get(expression_restorer_model)
return create_static_model_set('full').get(expression_restorer_model)
def register_args(program : ArgumentParser) -> None:
@ -100,14 +103,16 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
return conditional_download_hashes(download_directory_path, model_hashes) and conditional_download_sources(download_directory_path, model_sources)
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
def pre_process(mode : ProcessMode) -> bool:
if mode == 'stream':
logger.error(wording.get('stream_not_supported') + wording.get('exclamation_mark'), __name__)
return False
if mode in [ 'output', 'preview' ] and not is_image(state_manager.get_item('target_path')) and not is_video(state_manager.get_item('target_path')):
logger.error(wording.get('choose_image_or_video_target') + wording.get('exclamation_mark'), __name__)
return False
@ -222,7 +227,7 @@ def prepare_crop_frame(crop_vision_frame : VisionFrame) -> VisionFrame:
def normalize_crop_frame(crop_vision_frame : VisionFrame) -> VisionFrame:
crop_vision_frame = crop_vision_frame.transpose(1, 2, 0).clip(0, 1)
crop_vision_frame = (crop_vision_frame * 255.0)
crop_vision_frame = crop_vision_frame * 255.0
crop_vision_frame = crop_vision_frame.astype(numpy.uint8)[:, :, ::-1]
return crop_vision_frame

4
facefusion/processors/modules/face_debugger.py
View File

@ -17,11 +17,11 @@ from facefusion.filesystem import in_directory, same_file_extension
from facefusion.processors import choices as processors_choices
from facefusion.processors.typing import FaceDebuggerInputs
from facefusion.program_helper import find_argument_group
from facefusion.typing import ApplyStateItem, Args, Face, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.typing import ApplyStateItem, Args, Face, InferencePool, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.vision import read_image, read_static_image, write_image
def get_inference_pool() -> None:
def get_inference_pool() -> InferencePool:
pass

49
facefusion/processors/modules/face_editor.py
View File

@ -1,4 +1,5 @@
from argparse import ArgumentParser
from functools import lru_cache
from typing import List, Tuple
import cv2
@ -9,7 +10,7 @@ import facefusion.jobs.job_store
import facefusion.processors.core as processors
from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, inference_manager, logger, process_manager, state_manager, wording
from facefusion.common_helper import create_float_metavar
from facefusion.download import conditional_download_hashes, conditional_download_sources
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
from facefusion.face_analyser import get_many_faces, get_one_face
from facefusion.face_helper import paste_back, scale_face_landmark_5, warp_face_by_face_landmark_5
from facefusion.face_masker import create_static_box_mask
@ -21,43 +22,46 @@ from facefusion.processors.live_portrait import create_rotation, limit_euler_ang
from facefusion.processors.typing import FaceEditorInputs, LivePortraitExpression, LivePortraitFeatureVolume, LivePortraitMotionPoints, LivePortraitPitch, LivePortraitRoll, LivePortraitRotation, LivePortraitScale, LivePortraitTranslation, LivePortraitYaw
from facefusion.program_helper import find_argument_group
from facefusion.thread_helper import conditional_thread_semaphore, thread_semaphore
from facefusion.typing import ApplyStateItem, Args, Face, FaceLandmark68, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.typing import ApplyStateItem, Args, DownloadScope, Face, FaceLandmark68, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.vision import read_image, read_static_image, write_image
MODEL_SET : ModelSet =\
{
@lru_cache(maxsize = None)
def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
return\
{
'live_portrait':
{
'hashes':
{
'feature_extractor':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_feature_extractor.hash',
'url': resolve_download_url('models-3.0.0', 'live_portrait_feature_extractor.hash'),
'path': resolve_relative_path('../.assets/models/live_portrait_feature_extractor.hash')
},
'motion_extractor':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_motion_extractor.hash',
'url': resolve_download_url('models-3.0.0', 'live_portrait_motion_extractor.hash'),
'path': resolve_relative_path('../.assets/models/live_portrait_motion_extractor.hash')
},
'eye_retargeter':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_eye_retargeter.hash',
'url': resolve_download_url('models-3.0.0', 'live_portrait_eye_retargeter.hash'),
'path': resolve_relative_path('../.assets/models/live_portrait_eye_retargeter.hash')
},
'lip_retargeter':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_lip_retargeter.hash',
'url': resolve_download_url('models-3.0.0', 'live_portrait_lip_retargeter.hash'),
'path': resolve_relative_path('../.assets/models/live_portrait_lip_retargeter.hash')
},
'stitcher':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_stitcher.hash',
'url': resolve_download_url('models-3.0.0', 'live_portrait_stitcher.hash'),
'path': resolve_relative_path('../.assets/models/live_portrait_stitcher.hash')
},
'generator':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_generator.hash',
'url': resolve_download_url('models-3.0.0', 'live_portrait_generator.hash'),
'path': resolve_relative_path('../.assets/models/live_portrait_generator.hash')
}
},
@ -65,55 +69,53 @@ MODEL_SET : ModelSet =\
{
'feature_extractor':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_feature_extractor.onnx',
'url': resolve_download_url('models-3.0.0', 'live_portrait_feature_extractor.onnx'),
'path': resolve_relative_path('../.assets/models/live_portrait_feature_extractor.onnx')
},
'motion_extractor':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_motion_extractor.onnx',
'url': resolve_download_url('models-3.0.0', 'live_portrait_motion_extractor.onnx'),
'path': resolve_relative_path('../.assets/models/live_portrait_motion_extractor.onnx')
},
'eye_retargeter':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_eye_retargeter.onnx',
'url': resolve_download_url('models-3.0.0', 'live_portrait_eye_retargeter.onnx'),
'path': resolve_relative_path('../.assets/models/live_portrait_eye_retargeter.onnx')
},
'lip_retargeter':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_lip_retargeter.onnx',
'url': resolve_download_url('models-3.0.0', 'live_portrait_lip_retargeter.onnx'),
'path': resolve_relative_path('../.assets/models/live_portrait_lip_retargeter.onnx')
},
'stitcher':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_stitcher.onnx',
'url': resolve_download_url('models-3.0.0', 'live_portrait_stitcher.onnx'),
'path': resolve_relative_path('../.assets/models/live_portrait_stitcher.onnx')
},
'generator':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_generator.onnx',
'url': resolve_download_url('models-3.0.0', 'live_portrait_generator.onnx'),
'path': resolve_relative_path('../.assets/models/live_portrait_generator.onnx')
}
},
'template': 'ffhq_512',
'size': (512, 512)
}
}
}
def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources')
model_context = __name__ + '.' + state_manager.get_item('face_editor_model')
return inference_manager.get_inference_pool(model_context, model_sources)
return inference_manager.get_inference_pool(__name__, model_sources)
def clear_inference_pool() -> None:
model_context = __name__ + '.' + state_manager.get_item('face_editor_model')
inference_manager.clear_inference_pool(model_context)
inference_manager.clear_inference_pool(__name__)
def get_model_options() -> ModelOptions:
face_editor_model = state_manager.get_item('face_editor_model')
return MODEL_SET.get(face_editor_model)
return create_static_model_set('full').get(face_editor_model)
def register_args(program : ArgumentParser) -> None:
@ -156,11 +158,10 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
return conditional_download_hashes(download_directory_path, model_hashes) and conditional_download_sources(download_directory_path, model_sources)
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
def pre_process(mode : ProcessMode) -> bool:

86
facefusion/processors/modules/face_enhancer.py
View File

@ -1,4 +1,5 @@
from argparse import ArgumentParser
from functools import lru_cache
from typing import List
import cv2
@ -8,8 +9,8 @@ import facefusion.jobs.job_manager
import facefusion.jobs.job_store
import facefusion.processors.core as processors
from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, inference_manager, logger, process_manager, state_manager, wording
from facefusion.common_helper import create_int_metavar
from facefusion.download import conditional_download_hashes, conditional_download_sources
from facefusion.common_helper import create_float_metavar, create_int_metavar
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
from facefusion.face_analyser import get_many_faces, get_one_face
from facefusion.face_helper import paste_back, warp_face_by_face_landmark_5
from facefusion.face_masker import create_occlusion_mask, create_static_box_mask
@ -17,21 +18,24 @@ from facefusion.face_selector import find_similar_faces, sort_and_filter_faces
from facefusion.face_store import get_reference_faces
from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path, same_file_extension
from facefusion.processors import choices as processors_choices
from facefusion.processors.typing import FaceEnhancerInputs
from facefusion.processors.typing import FaceEnhancerInputs, FaceEnhancerWeight
from facefusion.program_helper import find_argument_group
from facefusion.thread_helper import thread_semaphore
from facefusion.typing import ApplyStateItem, Args, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.typing import ApplyStateItem, Args, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.vision import read_image, read_static_image, write_image
MODEL_SET : ModelSet =\
{
@lru_cache(maxsize = None)
def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
return\
{
'codeformer':
{
'hashes':
{
'face_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/codeformer.hash',
'url': resolve_download_url('models-3.0.0', 'codeformer.hash'),
'path': resolve_relative_path('../.assets/models/codeformer.hash')
}
},
@ -39,7 +43,7 @@ MODEL_SET : ModelSet =\
{
'face_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/codeformer.onnx',
'url': resolve_download_url('models-3.0.0', 'codeformer.onnx'),
'path': resolve_relative_path('../.assets/models/codeformer.onnx')
}
},
@ -52,7 +56,7 @@ MODEL_SET : ModelSet =\
{
'face_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/gfpgan_1.2.hash',
'url': resolve_download_url('models-3.0.0', 'gfpgan_1.2.hash'),
'path': resolve_relative_path('../.assets/models/gfpgan_1.2.hash')
}
},
@ -60,7 +64,7 @@ MODEL_SET : ModelSet =\
{
'face_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/gfpgan_1.2.onnx',
'url': resolve_download_url('models-3.0.0', 'gfpgan_1.2.onnx'),
'path': resolve_relative_path('../.assets/models/gfpgan_1.2.onnx')
}
},
@ -73,7 +77,7 @@ MODEL_SET : ModelSet =\
{
'face_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/gfpgan_1.3.hash',
'url': resolve_download_url('models-3.0.0', 'gfpgan_1.3.hash'),
'path': resolve_relative_path('../.assets/models/gfpgan_1.3.hash')
}
},
@ -81,7 +85,7 @@ MODEL_SET : ModelSet =\
{
'face_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/gfpgan_1.3.onnx',
'url': resolve_download_url('models-3.0.0', 'gfpgan_1.3.onnx'),
'path': resolve_relative_path('../.assets/models/gfpgan_1.3.onnx')
}
},
@ -94,7 +98,7 @@ MODEL_SET : ModelSet =\
{
'face_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/gfpgan_1.4.hash',
'url': resolve_download_url('models-3.0.0', 'gfpgan_1.4.hash'),
'path': resolve_relative_path('../.assets/models/gfpgan_1.4.hash')
}
},
@ -102,7 +106,7 @@ MODEL_SET : ModelSet =\
{
'face_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/gfpgan_1.4.onnx',
'url': resolve_download_url('models-3.0.0', 'gfpgan_1.4.onnx'),
'path': resolve_relative_path('../.assets/models/gfpgan_1.4.onnx')
}
},
@ -115,7 +119,7 @@ MODEL_SET : ModelSet =\
{
'face_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/gpen_bfr_256.hash',
'url': resolve_download_url('models-3.0.0', 'gpen_bfr_256.hash'),
'path': resolve_relative_path('../.assets/models/gpen_bfr_256.hash')
}
},
@ -123,7 +127,7 @@ MODEL_SET : ModelSet =\
{
'face_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/gpen_bfr_256.onnx',
'url': resolve_download_url('models-3.0.0', 'gpen_bfr_256.onnx'),
'path': resolve_relative_path('../.assets/models/gpen_bfr_256.onnx')
}
},
@ -136,7 +140,7 @@ MODEL_SET : ModelSet =\
{
'face_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/gpen_bfr_512.hash',
'url': resolve_download_url('models-3.0.0', 'gpen_bfr_512.hash'),
'path': resolve_relative_path('../.assets/models/gpen_bfr_512.hash')
}
},
@ -144,7 +148,7 @@ MODEL_SET : ModelSet =\
{
'face_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/gpen_bfr_512.onnx',
'url': resolve_download_url('models-3.0.0', 'gpen_bfr_512.onnx'),
'path': resolve_relative_path('../.assets/models/gpen_bfr_512.onnx')
}
},
@ -157,7 +161,7 @@ MODEL_SET : ModelSet =\
{
'face_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/gpen_bfr_1024.hash',
'url': resolve_download_url('models-3.0.0', 'gpen_bfr_1024.hash'),
'path': resolve_relative_path('../.assets/models/gpen_bfr_1024.hash')
}
},
@ -165,7 +169,7 @@ MODEL_SET : ModelSet =\
{
'face_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/gpen_bfr_1024.onnx',
'url': resolve_download_url('models-3.0.0', 'gpen_bfr_1024.onnx'),
'path': resolve_relative_path('../.assets/models/gpen_bfr_1024.onnx')
}
},
@ -178,7 +182,7 @@ MODEL_SET : ModelSet =\
{
'face_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/gpen_bfr_2048.hash',
'url': resolve_download_url('models-3.0.0', 'gpen_bfr_2048.hash'),
'path': resolve_relative_path('../.assets/models/gpen_bfr_2048.hash')
}
},
@ -186,7 +190,7 @@ MODEL_SET : ModelSet =\
{
'face_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/gpen_bfr_2048.onnx',
'url': resolve_download_url('models-3.0.0', 'gpen_bfr_2048.onnx'),
'path': resolve_relative_path('../.assets/models/gpen_bfr_2048.onnx')
}
},
@ -199,7 +203,7 @@ MODEL_SET : ModelSet =\
{
'face_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/restoreformer_plus_plus.hash',
'url': resolve_download_url('models-3.0.0', 'restoreformer_plus_plus.hash'),
'path': resolve_relative_path('../.assets/models/restoreformer_plus_plus.hash')
}
},
@ -207,30 +211,28 @@ MODEL_SET : ModelSet =\
{
'face_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/restoreformer_plus_plus.onnx',
'url': resolve_download_url('models-3.0.0', 'restoreformer_plus_plus.onnx'),
'path': resolve_relative_path('../.assets/models/restoreformer_plus_plus.onnx')
}
},
'template': 'ffhq_512',
'size': (512, 512)
}
}
}
def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources')
model_context = __name__ + '.' + state_manager.get_item('face_enhancer_model')
return inference_manager.get_inference_pool(model_context, model_sources)
return inference_manager.get_inference_pool(__name__, model_sources)
def clear_inference_pool() -> None:
model_context = __name__ + '.' + state_manager.get_item('face_enhancer_model')
inference_manager.clear_inference_pool(model_context)
inference_manager.clear_inference_pool(__name__)
def get_model_options() -> ModelOptions:
face_enhancer_model = state_manager.get_item('face_enhancer_model')
return MODEL_SET.get(face_enhancer_model)
return create_static_model_set('full').get(face_enhancer_model)
def register_args(program : ArgumentParser) -> None:
@ -238,20 +240,21 @@ def register_args(program : ArgumentParser) -> None:
if group_processors:
group_processors.add_argument('--face-enhancer-model', help = wording.get('help.face_enhancer_model'), default = config.get_str_value('processors.face_enhancer_model', 'gfpgan_1.4'), choices = processors_choices.face_enhancer_models)
group_processors.add_argument('--face-enhancer-blend', help = wording.get('help.face_enhancer_blend'), type = int, default = config.get_int_value('processors.face_enhancer_blend', '80'), choices = processors_choices.face_enhancer_blend_range, metavar = create_int_metavar(processors_choices.face_enhancer_blend_range))
facefusion.jobs.job_store.register_step_keys([ 'face_enhancer_model', 'face_enhancer_blend' ])
group_processors.add_argument('--face-enhancer-weight', help = wording.get('help.face_enhancer_weight'), type = float, default = config.get_float_value('processors.face_enhancer_weight', '1.0'), choices = processors_choices.face_enhancer_weight_range, metavar = create_float_metavar(processors_choices.face_enhancer_weight_range))
facefusion.jobs.job_store.register_step_keys([ 'face_enhancer_model', 'face_enhancer_blend', 'face_enhancer_weight' ])
def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
apply_state_item('face_enhancer_model', args.get('face_enhancer_model'))
apply_state_item('face_enhancer_blend', args.get('face_enhancer_blend'))
apply_state_item('face_enhancer_weight', args.get('face_enhancer_weight'))
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
return conditional_download_hashes(download_directory_path, model_hashes) and conditional_download_sources(download_directory_path, model_sources)
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
def pre_process(mode : ProcessMode) -> bool:
@ -295,7 +298,8 @@ def enhance_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionF
crop_masks.append(occlusion_mask)
crop_vision_frame = prepare_crop_frame(crop_vision_frame)
crop_vision_frame = forward(crop_vision_frame)
face_enhancer_weight = numpy.array([ state_manager.get_item('face_enhancer_weight') ]).astype(numpy.double)
crop_vision_frame = forward(crop_vision_frame, face_enhancer_weight)
crop_vision_frame = normalize_crop_frame(crop_vision_frame)
crop_mask = numpy.minimum.reduce(crop_masks).clip(0, 1)
paste_vision_frame = paste_back(temp_vision_frame, crop_vision_frame, crop_mask, affine_matrix)
@ -303,7 +307,7 @@ def enhance_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionF
return temp_vision_frame
def forward(crop_vision_frame : VisionFrame) -> VisionFrame:
def forward(crop_vision_frame : VisionFrame, face_enhancer_weight : FaceEnhancerWeight) -> VisionFrame:
face_enhancer = get_inference_pool().get('face_enhancer')
face_enhancer_inputs = {}
@ -311,8 +315,7 @@ def forward(crop_vision_frame : VisionFrame) -> VisionFrame:
if face_enhancer_input.name == 'input':
face_enhancer_inputs[face_enhancer_input.name] = crop_vision_frame
if face_enhancer_input.name == 'weight':
weight = numpy.array([ 1 ]).astype(numpy.double)
face_enhancer_inputs[face_enhancer_input.name] = weight
face_enhancer_inputs[face_enhancer_input.name] = face_enhancer_weight
with thread_semaphore():
crop_vision_frame = face_enhancer.run(None, face_enhancer_inputs)[0][0]
@ -320,6 +323,15 @@ def forward(crop_vision_frame : VisionFrame) -> VisionFrame:
return crop_vision_frame
def has_weight_input() -> bool:
face_enhancer = get_inference_pool().get('face_enhancer')
for deep_swapper_input in face_enhancer.get_inputs():
if deep_swapper_input.name == 'weight':
return True
return False
def prepare_crop_frame(crop_vision_frame : VisionFrame) -> VisionFrame:
crop_vision_frame = crop_vision_frame[:, :, ::-1] / 255.0
crop_vision_frame = (crop_vision_frame - 0.5) / 0.5

151
facefusion/processors/modules/face_swapper.py
View File

@ -1,39 +1,44 @@
from argparse import ArgumentParser
from functools import lru_cache
from typing import List, Tuple
import numpy
import facefusion.choices
import facefusion.jobs.job_manager
import facefusion.jobs.job_store
import facefusion.processors.core as processors
from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, inference_manager, logger, process_manager, state_manager, wording
from facefusion.common_helper import get_first
from facefusion.download import conditional_download_hashes, conditional_download_sources
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
from facefusion.execution import has_execution_provider
from facefusion.face_analyser import get_average_face, get_many_faces, get_one_face
from facefusion.face_helper import paste_back, warp_face_by_face_landmark_5
from facefusion.face_masker import create_occlusion_mask, create_region_mask, create_static_box_mask
from facefusion.face_selector import find_similar_faces, sort_and_filter_faces
from facefusion.face_selector import find_similar_faces, sort_and_filter_faces, sort_faces_by_order
from facefusion.face_store import get_reference_faces
from facefusion.filesystem import filter_image_paths, has_image, in_directory, is_image, is_video, resolve_relative_path, same_file_extension
from facefusion.inference_manager import get_static_model_initializer
from facefusion.model_helper import get_static_model_initializer
from facefusion.processors import choices as processors_choices
from facefusion.processors.pixel_boost import explode_pixel_boost, implode_pixel_boost
from facefusion.processors.typing import FaceSwapperInputs
from facefusion.program_helper import find_argument_group, suggest_face_swapper_pixel_boost_choices
from facefusion.program_helper import find_argument_group
from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import ApplyStateItem, Args, Embedding, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.typing import ApplyStateItem, Args, DownloadScope, Embedding, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.vision import read_image, read_static_image, read_static_images, unpack_resolution, write_image
MODEL_SET : ModelSet =\
{
@lru_cache(maxsize = None)
def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
return\
{
'blendswap_256':
{
'hashes':
{
'face_swapper':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/blendswap_256.hash',
'url': resolve_download_url('models-3.0.0', 'blendswap_256.hash'),
'path': resolve_relative_path('../.assets/models/blendswap_256.hash')
}
},
@ -41,7 +46,7 @@ MODEL_SET : ModelSet =\
{
'face_swapper':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/blendswap_256.onnx',
'url': resolve_download_url('models-3.0.0', 'blendswap_256.onnx'),
'path': resolve_relative_path('../.assets/models/blendswap_256.onnx')
}
},
@ -57,12 +62,12 @@ MODEL_SET : ModelSet =\
{
'face_swapper':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/ghost_1_256.hash',
'url': resolve_download_url('models-3.0.0', 'ghost_1_256.hash'),
'path': resolve_relative_path('../.assets/models/ghost_1_256.hash')
},
'embedding_converter':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/arcface_converter_ghost.hash',
'url': resolve_download_url('models-3.0.0', 'arcface_converter_ghost.hash'),
'path': resolve_relative_path('../.assets/models/arcface_converter_ghost.hash')
}
},
@ -70,12 +75,12 @@ MODEL_SET : ModelSet =\
{
'face_swapper':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/ghost_1_256.onnx',
'url': resolve_download_url('models-3.0.0', 'ghost_1_256.onnx'),
'path': resolve_relative_path('../.assets/models/ghost_1_256.onnx')
},
'embedding_converter':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/arcface_converter_ghost.onnx',
'url': resolve_download_url('models-3.0.0', 'arcface_converter_ghost.onnx'),
'path': resolve_relative_path('../.assets/models/arcface_converter_ghost.onnx')
}
},
@ -91,12 +96,12 @@ MODEL_SET : ModelSet =\
{
'face_swapper':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/ghost_2_256.hash',
'url': resolve_download_url('models-3.0.0', 'ghost_2_256.hash'),
'path': resolve_relative_path('../.assets/models/ghost_2_256.hash')
},
'embedding_converter':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/arcface_converter_ghost.hash',
'url': resolve_download_url('models-3.0.0', 'arcface_converter_ghost.hash'),
'path': resolve_relative_path('../.assets/models/arcface_converter_ghost.hash')
}
},
@ -104,12 +109,12 @@ MODEL_SET : ModelSet =\
{
'face_swapper':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/ghost_2_256.onnx',
'url': resolve_download_url('models-3.0.0', 'ghost_2_256.onnx'),
'path': resolve_relative_path('../.assets/models/ghost_2_256.onnx')
},
'embedding_converter':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/arcface_converter_ghost.onnx',
'url': resolve_download_url('models-3.0.0', 'arcface_converter_ghost.onnx'),
'path': resolve_relative_path('../.assets/models/arcface_converter_ghost.onnx')
}
},
@ -125,12 +130,12 @@ MODEL_SET : ModelSet =\
{
'face_swapper':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/ghost_3_256.hash',
'url': resolve_download_url('models-3.0.0', 'ghost_3_256.hash'),
'path': resolve_relative_path('../.assets/models/ghost_3_256.hash')
},
'embedding_converter':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/arcface_converter_ghost.hash',
'url': resolve_download_url('models-3.0.0', 'arcface_converter_ghost.hash'),
'path': resolve_relative_path('../.assets/models/arcface_converter_ghost.hash')
}
},
@ -138,12 +143,12 @@ MODEL_SET : ModelSet =\
{
'face_swapper':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/ghost_3_256.onnx',
'url': resolve_download_url('models-3.0.0', 'ghost_3_256.onnx'),
'path': resolve_relative_path('../.assets/models/ghost_3_256.onnx')
},
'embedding_converter':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/arcface_converter_ghost.onnx',
'url': resolve_download_url('models-3.0.0', 'arcface_converter_ghost.onnx'),
'path': resolve_relative_path('../.assets/models/arcface_converter_ghost.onnx')
}
},
@ -153,13 +158,47 @@ MODEL_SET : ModelSet =\
'mean': [ 0.5, 0.5, 0.5 ],
'standard_deviation': [ 0.5, 0.5, 0.5 ]
},
'hififace_unofficial_256':
{
'hashes':
{
'face_swapper':
{
'url': resolve_download_url('models-3.1.0', 'hififace_unofficial_256.hash'),
'path': resolve_relative_path('../.assets/models/hififace_unofficial_256.hash')
},
'embedding_converter':
{
'url': resolve_download_url('models-3.1.0', 'arcface_converter_hififace.hash'),
'path': resolve_relative_path('../.assets/models/arcface_converter_hififace.hash')
}
},
'sources':
{
'face_swapper':
{
'url': resolve_download_url('models-3.1.0', 'hififace_unofficial_256.onnx'),
'path': resolve_relative_path('../.assets/models/hififace_unofficial_256.onnx')
},
'embedding_converter':
{
'url': resolve_download_url('models-3.1.0', 'arcface_converter_hififace.onnx'),
'path': resolve_relative_path('../.assets/models/arcface_converter_hififace.onnx')
}
},
'type': 'hififace',
'template': 'mtcnn_512',
'size': (256, 256),
'mean': [ 0.5, 0.5, 0.5 ],
'standard_deviation': [ 0.5, 0.5, 0.5 ]
},
'inswapper_128':
{
'hashes':
{
'face_swapper':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/inswapper_128.hash',
'url': resolve_download_url('models-3.0.0', 'inswapper_128.hash'),
'path': resolve_relative_path('../.assets/models/inswapper_128.hash')
}
},
@ -167,7 +206,7 @@ MODEL_SET : ModelSet =\
{
'face_swapper':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/inswapper_128.onnx',
'url': resolve_download_url('models-3.0.0', 'inswapper_128.onnx'),
'path': resolve_relative_path('../.assets/models/inswapper_128.onnx')
}
},
@ -183,7 +222,7 @@ MODEL_SET : ModelSet =\
{
'face_swapper':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/inswapper_128_fp16.hash',
'url': resolve_download_url('models-3.0.0', 'inswapper_128_fp16.hash'),
'path': resolve_relative_path('../.assets/models/inswapper_128_fp16.hash')
}
},
@ -191,7 +230,7 @@ MODEL_SET : ModelSet =\
{
'face_swapper':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/inswapper_128_fp16.onnx',
'url': resolve_download_url('models-3.0.0', 'inswapper_128_fp16.onnx'),
'path': resolve_relative_path('../.assets/models/inswapper_128_fp16.onnx')
}
},
@ -207,12 +246,12 @@ MODEL_SET : ModelSet =\
{
'face_swapper':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/simswap_256.hash',
'url': resolve_download_url('models-3.0.0', 'simswap_256.hash'),
'path': resolve_relative_path('../.assets/models/simswap_256.hash')
},
'embedding_converter':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/arcface_converter_simswap.hash',
'url': resolve_download_url('models-3.0.0', 'arcface_converter_simswap.hash'),
'path': resolve_relative_path('../.assets/models/arcface_converter_simswap.hash')
}
},
@ -220,12 +259,12 @@ MODEL_SET : ModelSet =\
{
'face_swapper':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/simswap_256.onnx',
'url': resolve_download_url('models-3.0.0', 'simswap_256.onnx'),
'path': resolve_relative_path('../.assets/models/simswap_256.onnx')
},
'embedding_converter':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/arcface_converter_simswap.onnx',
'url': resolve_download_url('models-3.0.0', 'arcface_converter_simswap.onnx'),
'path': resolve_relative_path('../.assets/models/arcface_converter_simswap.onnx')
}
},
@ -241,12 +280,12 @@ MODEL_SET : ModelSet =\
{
'face_swapper':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/simswap_unofficial_512.hash',
'url': resolve_download_url('models-3.0.0', 'simswap_unofficial_512.hash'),
'path': resolve_relative_path('../.assets/models/simswap_unofficial_512.hash')
},
'embedding_converter':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/arcface_converter_simswap.hash',
'url': resolve_download_url('models-3.0.0', 'arcface_converter_simswap.hash'),
'path': resolve_relative_path('../.assets/models/arcface_converter_simswap.hash')
}
},
@ -254,12 +293,12 @@ MODEL_SET : ModelSet =\
{
'face_swapper':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/simswap_unofficial_512.onnx',
'url': resolve_download_url('models-3.0.0', 'simswap_unofficial_512.onnx'),
'path': resolve_relative_path('../.assets/models/simswap_unofficial_512.onnx')
},
'embedding_converter':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/arcface_converter_simswap.onnx',
'url': resolve_download_url('models-3.0.0', 'arcface_converter_simswap.onnx'),
'path': resolve_relative_path('../.assets/models/arcface_converter_simswap.onnx')
}
},
@ -275,7 +314,7 @@ MODEL_SET : ModelSet =\
{
'face_swapper':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/uniface_256.hash',
'url': resolve_download_url('models-3.0.0', 'uniface_256.hash'),
'path': resolve_relative_path('../.assets/models/uniface_256.hash')
}
},
@ -283,7 +322,7 @@ MODEL_SET : ModelSet =\
{
'face_swapper':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/uniface_256.onnx',
'url': resolve_download_url('models-3.0.0', 'uniface_256.onnx'),
'path': resolve_relative_path('../.assets/models/uniface_256.onnx')
}
},
@ -293,31 +332,29 @@ MODEL_SET : ModelSet =\
'mean': [ 0.5, 0.5, 0.5 ],
'standard_deviation': [ 0.5, 0.5, 0.5 ]
}
}
}
def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources')
model_context = __name__ + '.' + state_manager.get_item('face_swapper_model')
return inference_manager.get_inference_pool(model_context, model_sources)
return inference_manager.get_inference_pool(__name__, model_sources)
def clear_inference_pool() -> None:
model_context = __name__ + '.' + state_manager.get_item('face_swapper_model')
inference_manager.clear_inference_pool(model_context)
inference_manager.clear_inference_pool(__name__)
def get_model_options() -> ModelOptions:
face_swapper_model = state_manager.get_item('face_swapper_model')
face_swapper_model = 'inswapper_128' if has_execution_provider('coreml') and face_swapper_model == 'inswapper_128_fp16' else face_swapper_model
return MODEL_SET.get(face_swapper_model)
return create_static_model_set('full').get(face_swapper_model)
def register_args(program : ArgumentParser) -> None:
group_processors = find_argument_group(program, 'processors')
if group_processors:
group_processors.add_argument('--face-swapper-model', help = wording.get('help.face_swapper_model'), default = config.get_str_value('processors.face_swapper_model', 'inswapper_128_fp16'), choices = processors_choices.face_swapper_set.keys())
face_swapper_pixel_boost_choices = suggest_face_swapper_pixel_boost_choices(program)
group_processors.add_argument('--face-swapper-model', help = wording.get('help.face_swapper_model'), default = config.get_str_value('processors.face_swapper_model', 'inswapper_128_fp16'), choices = processors_choices.face_swapper_models)
known_args, _ = program.parse_known_args()
face_swapper_pixel_boost_choices = processors_choices.face_swapper_set.get(known_args.face_swapper_model)
group_processors.add_argument('--face-swapper-pixel-boost', help = wording.get('help.face_swapper_pixel_boost'), default = config.get_str_value('processors.face_swapper_pixel_boost', get_first(face_swapper_pixel_boost_choices)), choices = face_swapper_pixel_boost_choices)
facefusion.jobs.job_store.register_step_keys([ 'face_swapper_model', 'face_swapper_pixel_boost' ])
@ -328,11 +365,10 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
return conditional_download_hashes(download_directory_path, model_hashes) and conditional_download_sources(download_directory_path, model_sources)
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
def pre_process(mode : ProcessMode) -> bool:
@ -410,9 +446,12 @@ def forward_swap_face(source_face : Face, crop_vision_frame : VisionFrame) -> Vi
model_type = get_model_options().get('type')
face_swapper_inputs = {}
if has_execution_provider('coreml') and model_type in [ 'ghost', 'uniface' ]:
face_swapper.set_providers([ facefusion.choices.execution_provider_set.get('cpu') ])
for face_swapper_input in face_swapper.get_inputs():
if face_swapper_input.name == 'source':
if model_type == 'blendswap' or model_type == 'uniface':
if model_type in [ 'blendswap', 'uniface' ]:
face_swapper_inputs[face_swapper_input.name] = prepare_source_frame(source_face)
else:
face_swapper_inputs[face_swapper_input.name] = prepare_source_embedding(source_face)
@ -493,7 +532,7 @@ def normalize_crop_frame(crop_vision_frame : VisionFrame) -> VisionFrame:
model_standard_deviation = get_model_options().get('standard_deviation')
crop_vision_frame = crop_vision_frame.transpose(1, 2, 0)
if model_type == 'ghost' or model_type == 'uniface':
if model_type in [ 'ghost', 'hififace', 'uniface' ]:
crop_vision_frame = crop_vision_frame * model_standard_deviation + model_mean
crop_vision_frame = crop_vision_frame.clip(0, 1)
crop_vision_frame = crop_vision_frame[:, :, ::-1] * 255
@ -529,7 +568,13 @@ def process_frame(inputs : FaceSwapperInputs) -> VisionFrame:
def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProgress) -> None:
reference_faces = get_reference_faces() if 'reference' in state_manager.get_item('face_selector_mode') else None
source_frames = read_static_images(source_paths)
source_faces = get_many_faces(source_frames)
source_faces = []
for source_frame in source_frames:
temp_faces = get_many_faces([ source_frame ])
temp_faces = sort_faces_by_order(temp_faces, 'large-small')
if temp_faces:
source_faces.append(get_first(temp_faces))
source_face = get_average_face(source_faces)
for queue_payload in process_manager.manage(queue_payloads):
@ -548,7 +593,13 @@ def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload]
def process_image(source_paths : List[str], target_path : str, output_path : str) -> None:
reference_faces = get_reference_faces() if 'reference' in state_manager.get_item('face_selector_mode') else None
source_frames = read_static_images(source_paths)
source_faces = get_many_faces(source_frames)
source_faces = []
for source_frame in source_frames:
temp_faces = get_many_faces([ source_frame ])
temp_faces = sort_faces_by_order(temp_faces, 'large-small')
if temp_faces:
source_faces.append(get_first(temp_faces))
source_face = get_average_face(source_faces)
target_vision_frame = read_static_image(target_path)
output_vision_frame = process_frame(

45
facefusion/processors/modules/frame_colorizer.py
View File

@ -1,4 +1,5 @@
from argparse import ArgumentParser
from functools import lru_cache
from typing import List
import cv2
@ -9,24 +10,27 @@ import facefusion.jobs.job_store
import facefusion.processors.core as processors
from facefusion import config, content_analyser, inference_manager, logger, process_manager, state_manager, wording
from facefusion.common_helper import create_int_metavar
from facefusion.download import conditional_download_hashes, conditional_download_sources
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path, same_file_extension
from facefusion.processors import choices as processors_choices
from facefusion.processors.typing import FrameColorizerInputs
from facefusion.program_helper import find_argument_group
from facefusion.thread_helper import thread_semaphore
from facefusion.typing import ApplyStateItem, Args, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.typing import ApplyStateItem, Args, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.vision import read_image, read_static_image, unpack_resolution, write_image
MODEL_SET : ModelSet =\
{
@lru_cache(maxsize = None)
def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
return\
{
'ddcolor':
{
'hashes':
{
'frame_colorizer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/ddcolor.hash',
'url': resolve_download_url('models-3.0.0', 'ddcolor.hash'),
'path': resolve_relative_path('../.assets/models/ddcolor.hash')
}
},
@ -34,7 +38,7 @@ MODEL_SET : ModelSet =\
{
'frame_colorizer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/ddcolor.onnx',
'url': resolve_download_url('models-3.0.0', 'ddcolor.onnx'),
'path': resolve_relative_path('../.assets/models/ddcolor.onnx')
}
},
@ -46,7 +50,7 @@ MODEL_SET : ModelSet =\
{
'frame_colorizer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/ddcolor_artistic.hash',
'url': resolve_download_url('models-3.0.0', 'ddcolor_artistic.hash'),
'path': resolve_relative_path('../.assets/models/ddcolor_artistic.hash')
}
},
@ -54,7 +58,7 @@ MODEL_SET : ModelSet =\
{
'frame_colorizer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/ddcolor_artistic.onnx',
'url': resolve_download_url('models-3.0.0', 'ddcolor_artistic.onnx'),
'path': resolve_relative_path('../.assets/models/ddcolor_artistic.onnx')
}
},
@ -66,7 +70,7 @@ MODEL_SET : ModelSet =\
{
'frame_colorizer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/deoldify.hash',
'url': resolve_download_url('models-3.0.0', 'deoldify.hash'),
'path': resolve_relative_path('../.assets/models/deoldify.hash')
}
},
@ -74,7 +78,7 @@ MODEL_SET : ModelSet =\
{
'frame_colorizer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/deoldify.onnx',
'url': resolve_download_url('models-3.0.0', 'deoldify.onnx'),
'path': resolve_relative_path('../.assets/models/deoldify.onnx')
}
},
@ -86,7 +90,7 @@ MODEL_SET : ModelSet =\
{
'frame_colorizer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/deoldify_artistic.hash',
'url': resolve_download_url('models-3.0.0', 'deoldify_artistic.hash'),
'path': resolve_relative_path('../.assets/models/deoldify_artistic.hash')
}
},
@ -94,7 +98,7 @@ MODEL_SET : ModelSet =\
{
'frame_colorizer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/deoldify_artistic.onnx',
'url': resolve_download_url('models-3.0.0', 'deoldify_artistic.onnx'),
'path': resolve_relative_path('../.assets/models/deoldify_artistic.onnx')
}
},
@ -106,7 +110,7 @@ MODEL_SET : ModelSet =\
{
'frame_colorizer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/deoldify_stable.hash',
'url': resolve_download_url('models-3.0.0', 'deoldify_stable.hash'),
'path': resolve_relative_path('../.assets/models/deoldify_stable.hash')
}
},
@ -114,29 +118,27 @@ MODEL_SET : ModelSet =\
{
'frame_colorizer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/deoldify_stable.onnx',
'url': resolve_download_url('models-3.0.0', 'deoldify_stable.onnx'),
'path': resolve_relative_path('../.assets/models/deoldify_stable.onnx')
}
},
'type': 'deoldify'
}
}
}
def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources')
model_context = __name__ + '.' + state_manager.get_item('frame_colorizer_model')
return inference_manager.get_inference_pool(model_context, model_sources)
return inference_manager.get_inference_pool(__name__, model_sources)
def clear_inference_pool() -> None:
model_context = __name__ + '.' + state_manager.get_item('frame_colorizer_model')
inference_manager.clear_inference_pool(model_context)
inference_manager.clear_inference_pool(__name__)
def get_model_options() -> ModelOptions:
frame_colorizer_model = state_manager.get_item('frame_colorizer_model')
return MODEL_SET.get(frame_colorizer_model)
return create_static_model_set('full').get(frame_colorizer_model)
def register_args(program : ArgumentParser) -> None:
@ -155,11 +157,10 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
return conditional_download_hashes(download_directory_path, model_hashes) and conditional_download_sources(download_directory_path, model_sources)
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
def pre_process(mode : ProcessMode) -> bool:

178
facefusion/processors/modules/frame_enhancer.py
View File

@ -1,4 +1,5 @@
from argparse import ArgumentParser
from functools import lru_cache
from typing import List
import cv2
@ -9,24 +10,27 @@ import facefusion.jobs.job_store
import facefusion.processors.core as processors
from facefusion import config, content_analyser, inference_manager, logger, process_manager, state_manager, wording
from facefusion.common_helper import create_int_metavar
from facefusion.download import conditional_download_hashes, conditional_download_sources
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path, same_file_extension
from facefusion.processors import choices as processors_choices
from facefusion.processors.typing import FrameEnhancerInputs
from facefusion.program_helper import find_argument_group
from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import ApplyStateItem, Args, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.typing import ApplyStateItem, Args, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.vision import create_tile_frames, merge_tile_frames, read_image, read_static_image, write_image
MODEL_SET : ModelSet =\
{
@lru_cache(maxsize = None)
def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
return\
{
'clear_reality_x4':
{
'hashes':
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/clear_reality_x4.hash',
'url': resolve_download_url('models-3.0.0', 'clear_reality_x4.hash'),
'path': resolve_relative_path('../.assets/models/clear_reality_x4.hash')
}
},
@ -34,7 +38,7 @@ MODEL_SET : ModelSet =\
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/clear_reality_x4.onnx',
'url': resolve_download_url('models-3.0.0', 'clear_reality_x4.onnx'),
'path': resolve_relative_path('../.assets/models/clear_reality_x4.onnx')
}
},
@ -47,7 +51,7 @@ MODEL_SET : ModelSet =\
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/lsdir_x4.hash',
'url': resolve_download_url('models-3.0.0', 'lsdir_x4.hash'),
'path': resolve_relative_path('../.assets/models/lsdir_x4.hash')
}
},
@ -55,7 +59,7 @@ MODEL_SET : ModelSet =\
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/lsdir_x4.onnx',
'url': resolve_download_url('models-3.0.0', 'lsdir_x4.onnx'),
'path': resolve_relative_path('../.assets/models/lsdir_x4.onnx')
}
},
@ -68,7 +72,7 @@ MODEL_SET : ModelSet =\
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/nomos8k_sc_x4.hash',
'url': resolve_download_url('models-3.0.0', 'nomos8k_sc_x4.hash'),
'path': resolve_relative_path('../.assets/models/nomos8k_sc_x4.hash')
}
},
@ -76,7 +80,7 @@ MODEL_SET : ModelSet =\
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/nomos8k_sc_x4.onnx',
'url': resolve_download_url('models-3.0.0', 'nomos8k_sc_x4.onnx'),
'path': resolve_relative_path('../.assets/models/nomos8k_sc_x4.onnx')
}
},
@ -89,7 +93,7 @@ MODEL_SET : ModelSet =\
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/real_esrgan_x2.hash',
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x2.hash'),
'path': resolve_relative_path('../.assets/models/real_esrgan_x2.hash')
}
},
@ -97,7 +101,7 @@ MODEL_SET : ModelSet =\
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/real_esrgan_x2.onnx',
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x2.onnx'),
'path': resolve_relative_path('../.assets/models/real_esrgan_x2.onnx')
}
},
@ -110,7 +114,7 @@ MODEL_SET : ModelSet =\
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/real_esrgan_x2_fp16.hash',
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x2_fp16.hash'),
'path': resolve_relative_path('../.assets/models/real_esrgan_x2_fp16.hash')
}
},
@ -118,7 +122,7 @@ MODEL_SET : ModelSet =\
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/real_esrgan_x2_fp16.onnx',
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x2_fp16.onnx'),
'path': resolve_relative_path('../.assets/models/real_esrgan_x2_fp16.onnx')
}
},
@ -131,7 +135,7 @@ MODEL_SET : ModelSet =\
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/real_esrgan_x4.hash',
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x4.hash'),
'path': resolve_relative_path('../.assets/models/real_esrgan_x4.hash')
}
},
@ -139,7 +143,7 @@ MODEL_SET : ModelSet =\
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/real_esrgan_x4.onnx',
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x4.onnx'),
'path': resolve_relative_path('../.assets/models/real_esrgan_x4.onnx')
}
},
@ -152,7 +156,7 @@ MODEL_SET : ModelSet =\
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/real_esrgan_x4_fp16.hash',
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x4_fp16.hash'),
'path': resolve_relative_path('../.assets/models/real_esrgan_x4_fp16.hash')
}
},
@ -160,7 +164,7 @@ MODEL_SET : ModelSet =\
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/real_esrgan_x4_fp16.onnx',
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x4_fp16.onnx'),
'path': resolve_relative_path('../.assets/models/real_esrgan_x4_fp16.onnx')
}
},
@ -173,7 +177,7 @@ MODEL_SET : ModelSet =\
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/real_esrgan_x8.hash',
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x8.hash'),
'path': resolve_relative_path('../.assets/models/real_esrgan_x8.hash')
}
},
@ -181,7 +185,7 @@ MODEL_SET : ModelSet =\
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/real_esrgan_x8.onnx',
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x8.onnx'),
'path': resolve_relative_path('../.assets/models/real_esrgan_x8.onnx')
}
},
@ -194,7 +198,7 @@ MODEL_SET : ModelSet =\
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/real_esrgan_x8_fp16.hash',
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x8_fp16.hash'),
'path': resolve_relative_path('../.assets/models/real_esrgan_x8_fp16.hash')
}
},
@ -202,7 +206,7 @@ MODEL_SET : ModelSet =\
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/real_esrgan_x8_fp16.onnx',
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x8_fp16.onnx'),
'path': resolve_relative_path('../.assets/models/real_esrgan_x8_fp16.onnx')
}
},
@ -215,7 +219,7 @@ MODEL_SET : ModelSet =\
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/real_hatgan_x4.hash',
'url': resolve_download_url('models-3.0.0', 'real_hatgan_x4.hash'),
'path': resolve_relative_path('../.assets/models/real_hatgan_x4.hash')
}
},
@ -223,20 +227,104 @@ MODEL_SET : ModelSet =\
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/real_hatgan_x4.onnx',
'url': resolve_download_url('models-3.0.0', 'real_hatgan_x4.onnx'),
'path': resolve_relative_path('../.assets/models/real_hatgan_x4.onnx')
}
},
'size': (256, 16, 8),
'scale': 4
},
'real_web_photo_x4':
{
'hashes':
{
'frame_enhancer':
{
'url': resolve_download_url('models-3.1.0', 'real_web_photo_x4.hash'),
'path': resolve_relative_path('../.assets/models/real_web_photo_x4.hash')
}
},
'sources':
{
'frame_enhancer':
{
'url': resolve_download_url('models-3.1.0', 'real_web_photo_x4.onnx'),
'path': resolve_relative_path('../.assets/models/real_web_photo_x4.onnx')
}
},
'size': (64, 4, 2),
'scale': 4
},
'realistic_rescaler_x4':
{
'hashes':
{
'frame_enhancer':
{
'url': resolve_download_url('models-3.1.0', 'realistic_rescaler_x4.hash'),
'path': resolve_relative_path('../.assets/models/realistic_rescaler_x4.hash')
}
},
'sources':
{
'frame_enhancer':
{
'url': resolve_download_url('models-3.1.0', 'realistic_rescaler_x4.onnx'),
'path': resolve_relative_path('../.assets/models/realistic_rescaler_x4.onnx')
}
},
'size': (128, 8, 4),
'scale': 4
},
'remacri_x4':
{
'hashes':
{
'frame_enhancer':
{
'url': resolve_download_url('models-3.1.0', 'remacri_x4.hash'),
'path': resolve_relative_path('../.assets/models/remacri_x4.hash')
}
},
'sources':
{
'frame_enhancer':
{
'url': resolve_download_url('models-3.1.0', 'remacri_x4.onnx'),
'path': resolve_relative_path('../.assets/models/remacri_x4.onnx')
}
},
'size': (128, 8, 4),
'scale': 4
},
'siax_x4':
{
'hashes':
{
'frame_enhancer':
{
'url': resolve_download_url('models-3.1.0', 'siax_x4.hash'),
'path': resolve_relative_path('../.assets/models/siax_x4.hash')
}
},
'sources':
{
'frame_enhancer':
{
'url': resolve_download_url('models-3.1.0', 'siax_x4.onnx'),
'path': resolve_relative_path('../.assets/models/siax_x4.onnx')
}
},
'size': (128, 8, 4),
'scale': 4
},
'span_kendata_x4':
{
'hashes':
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/span_kendata_x4.hash',
'url': resolve_download_url('models-3.0.0', 'span_kendata_x4.hash'),
'path': resolve_relative_path('../.assets/models/span_kendata_x4.hash')
}
},
@ -244,20 +332,41 @@ MODEL_SET : ModelSet =\
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/span_kendata_x4.onnx',
'url': resolve_download_url('models-3.0.0', 'span_kendata_x4.onnx'),
'path': resolve_relative_path('../.assets/models/span_kendata_x4.onnx')
}
},
'size': (128, 8, 4),
'scale': 4
},
'swin2_sr_x4':
{
'hashes':
{
'frame_enhancer':
{
'url': resolve_download_url('models-3.1.0', 'swin2_sr_x4.hash'),
'path': resolve_relative_path('../.assets/models/swin2_sr_x4.hash')
}
},
'sources':
{
'frame_enhancer':
{
'url': resolve_download_url('models-3.1.0', 'swin2_sr_x4.onnx'),
'path': resolve_relative_path('../.assets/models/swin2_sr_x4.onnx')
}
},
'size': (128, 8, 4),
'scale': 4
},
'ultra_sharp_x4':
{
'hashes':
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/ultra_sharp_x4.hash',
'url': resolve_download_url('models-3.0.0', 'ultra_sharp_x4.hash'),
'path': resolve_relative_path('../.assets/models/ultra_sharp_x4.hash')
}
},
@ -265,30 +374,28 @@ MODEL_SET : ModelSet =\
{
'frame_enhancer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/ultra_sharp_x4.onnx',
'url': resolve_download_url('models-3.0.0', 'ultra_sharp_x4.onnx'),
'path': resolve_relative_path('../.assets/models/ultra_sharp_x4.onnx')
}
},
'size': (128, 8, 4),
'scale': 4
}
}
}
def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources')
model_context = __name__ + '.' + state_manager.get_item('frame_enhancer_model')
return inference_manager.get_inference_pool(model_context, model_sources)
return inference_manager.get_inference_pool(__name__, model_sources)
def clear_inference_pool() -> None:
model_context = __name__ + '.' + state_manager.get_item('frame_enhancer_model')
inference_manager.clear_inference_pool(model_context)
inference_manager.clear_inference_pool(__name__)
def get_model_options() -> ModelOptions:
frame_enhancer_model = state_manager.get_item('frame_enhancer_model')
return MODEL_SET.get(frame_enhancer_model)
return create_static_model_set('full').get(frame_enhancer_model)
def register_args(program : ArgumentParser) -> None:
@ -305,11 +412,10 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
return conditional_download_hashes(download_directory_path, model_hashes) and conditional_download_sources(download_directory_path, model_sources)
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
def pre_process(mode : ProcessMode) -> bool:

33
facefusion/processors/modules/lip_syncer.py
View File

@ -1,4 +1,5 @@
from argparse import ArgumentParser
from functools import lru_cache
from typing import List
import cv2
@ -10,7 +11,7 @@ import facefusion.processors.core as processors
from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, inference_manager, logger, process_manager, state_manager, voice_extractor, wording
from facefusion.audio import create_empty_audio_frame, get_voice_frame, read_static_voice
from facefusion.common_helper import get_first
from facefusion.download import conditional_download_hashes, conditional_download_sources
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
from facefusion.face_analyser import get_many_faces, get_one_face
from facefusion.face_helper import create_bounding_box, paste_back, warp_face_by_bounding_box, warp_face_by_face_landmark_5
from facefusion.face_masker import create_mouth_mask, create_occlusion_mask, create_static_box_mask
@ -21,18 +22,21 @@ from facefusion.processors import choices as processors_choices
from facefusion.processors.typing import LipSyncerInputs
from facefusion.program_helper import find_argument_group
from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import ApplyStateItem, Args, AudioFrame, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.typing import ApplyStateItem, Args, AudioFrame, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
from facefusion.vision import read_image, read_static_image, restrict_video_fps, write_image
MODEL_SET : ModelSet =\
{
@lru_cache(maxsize = None)
def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
return\
{
'wav2lip_96':
{
'hashes':
{
'lip_syncer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/wav2lip_96.hash',
'url': resolve_download_url('models-3.0.0', 'wav2lip_96.hash'),
'path': resolve_relative_path('../.assets/models/wav2lip_96.hash')
}
},
@ -40,7 +44,7 @@ MODEL_SET : ModelSet =\
{
'lip_syncer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/wav2lip_96.onnx',
'url': resolve_download_url('models-3.0.0', 'wav2lip_96.onnx'),
'path': resolve_relative_path('../.assets/models/wav2lip_96.onnx')
}
},
@ -52,7 +56,7 @@ MODEL_SET : ModelSet =\
{
'lip_syncer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/wav2lip_gan_96.hash',
'url': resolve_download_url('models-3.0.0', 'wav2lip_gan_96.hash'),
'path': resolve_relative_path('../.assets/models/wav2lip_gan_96.hash')
}
},
@ -60,29 +64,27 @@ MODEL_SET : ModelSet =\
{
'lip_syncer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/wav2lip_gan_96.onnx',
'url': resolve_download_url('models-3.0.0', 'wav2lip_gan_96.onnx'),
'path': resolve_relative_path('../.assets/models/wav2lip_gan_96.onnx')
}
},
'size': (96, 96)
}
}
}
def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources')
model_context = __name__ + '.' + state_manager.get_item('lip_syncer_model')
return inference_manager.get_inference_pool(model_context, model_sources)
return inference_manager.get_inference_pool(__name__, model_sources)
def clear_inference_pool() -> None:
model_context = __name__ + '.' + state_manager.get_item('lip_syncer_model')
inference_manager.clear_inference_pool(model_context)
inference_manager.clear_inference_pool(__name__)
def get_model_options() -> ModelOptions:
lip_syncer_model = state_manager.get_item('lip_syncer_model')
return MODEL_SET.get(lip_syncer_model)
return create_static_model_set('full').get(lip_syncer_model)
def register_args(program : ArgumentParser) -> None:
@ -97,11 +99,10 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
return conditional_download_hashes(download_directory_path, model_hashes) and conditional_download_sources(download_directory_path, model_sources)
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
def pre_process(mode : ProcessMode) -> bool:

19
facefusion/processors/typing.py
View File

@ -5,13 +5,14 @@ from numpy._typing import NDArray
from facefusion.typing import AppContext, AudioFrame, Face, FaceSet, VisionFrame
AgeModifierModel = Literal['styleganex_age']
DeepSwapperModel = str
ExpressionRestorerModel = Literal['live_portrait']
FaceDebuggerItem = Literal['bounding-box', 'face-landmark-5', 'face-landmark-5/68', 'face-landmark-68', 'face-landmark-68/5', 'face-mask', 'face-detector-score', 'face-landmarker-score', 'age', 'gender', 'race']
FaceEditorModel = Literal['live_portrait']
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', 'ghost_1_256', 'ghost_2_256', 'ghost_3_256', 'inswapper_128', 'inswapper_128_fp16', 'simswap_256', 'simswap_unofficial_512', 'uniface_256']
FaceSwapperModel = Literal['blendswap_256', 'ghost_1_256', 'ghost_2_256', 'ghost_3_256', 'hififace_unofficial_256', 'inswapper_128', 'inswapper_128_fp16', 'simswap_256', 'simswap_unofficial_512', 'uniface_256']
FrameColorizerModel = Literal['ddcolor', 'ddcolor_artistic', 'deoldify', 'deoldify_artistic', 'deoldify_stable']
FrameEnhancerModel = Literal['clear_reality_x4', 'lsdir_x4', 'nomos8k_sc_x4', 'real_esrgan_x2', 'real_esrgan_x2_fp16', 'real_esrgan_x4', 'real_esrgan_x4_fp16', 'real_hatgan_x4', 'real_esrgan_x8', 'real_esrgan_x8_fp16', 'span_kendata_x4', 'ultra_sharp_x4']
FrameEnhancerModel = Literal['clear_reality_x4', 'lsdir_x4', 'nomos8k_sc_x4', 'real_esrgan_x2', 'real_esrgan_x2_fp16', 'real_esrgan_x4', 'real_esrgan_x4_fp16', 'real_esrgan_x8', 'real_esrgan_x8_fp16', 'real_hatgan_x4', 'real_web_photo_x4', 'realistic_rescaler_x4', 'remacri_x4', 'siax_x4', 'span_kendata_x4', 'swin2_sr_x4', 'ultra_sharp_x4']
LipSyncerModel = Literal['wav2lip_96', 'wav2lip_gan_96']
FaceSwapperSet = Dict[FaceSwapperModel, List[str]]
@ -21,6 +22,11 @@ AgeModifierInputs = TypedDict('AgeModifierInputs',
'reference_faces' : FaceSet,
'target_vision_frame' : VisionFrame
})
DeepSwapperInputs = TypedDict('DeepSwapperInputs',
{
'reference_faces' : FaceSet,
'target_vision_frame' : VisionFrame
})
ExpressionRestorerInputs = TypedDict('ExpressionRestorerInputs',
{
'reference_faces' : FaceSet,
@ -67,6 +73,8 @@ ProcessorStateKey = Literal\
[
'age_modifier_model',
'age_modifier_direction',
'deep_swapper_model',
'deep_swapper_morph',
'expression_restorer_model',
'expression_restorer_factor',
'face_debugger_items',
@ -87,6 +95,7 @@ ProcessorStateKey = Literal\
'face_editor_head_roll',
'face_enhancer_model',
'face_enhancer_blend',
'face_enhancer_weight',
'face_swapper_model',
'face_swapper_pixel_boost',
'frame_colorizer_model',
@ -100,6 +109,8 @@ ProcessorState = TypedDict('ProcessorState',
{
'age_modifier_model' : AgeModifierModel,
'age_modifier_direction' : int,
'deep_swapper_model' : DeepSwapperModel,
'deep_swapper_morph' : int,
'expression_restorer_model' : ExpressionRestorerModel,
'expression_restorer_factor' : int,
'face_debugger_items' : List[FaceDebuggerItem],
@ -120,6 +131,7 @@ ProcessorState = TypedDict('ProcessorState',
'face_editor_head_roll' : float,
'face_enhancer_model' : FaceEnhancerModel,
'face_enhancer_blend' : int,
'face_enhancer_weight' : float,
'face_swapper_model' : FaceSwapperModel,
'face_swapper_pixel_boost' : str,
'frame_colorizer_model' : FrameColorizerModel,
@ -131,6 +143,9 @@ ProcessorState = TypedDict('ProcessorState',
})
ProcessorStateSet = Dict[AppContext, ProcessorState]
AgeModifierDirection = NDArray[Any]
DeepSwapperMorph = NDArray[Any]
FaceEnhancerWeight = NDArray[Any]
LivePortraitPitch = float
LivePortraitYaw = float
LivePortraitRoll = float

153
facefusion/program.py
View File

@ -1,13 +1,13 @@
import tempfile
from argparse import ArgumentParser, HelpFormatter
import facefusion.choices
from facefusion import config, metadata, state_manager, wording
from facefusion.common_helper import create_float_metavar, create_int_metavar
from facefusion.execution import get_execution_provider_choices
from facefusion.common_helper import create_float_metavar, create_int_metavar, get_last
from facefusion.execution import get_available_execution_providers
from facefusion.filesystem import list_directory
from facefusion.jobs import job_store
from facefusion.processors.core import get_processors_modules
from facefusion.program_helper import remove_args, suggest_face_detector_choices
def create_help_formatter_small(prog : str) -> HelpFormatter:
@ -18,38 +18,86 @@ def create_help_formatter_large(prog : str) -> HelpFormatter:
return HelpFormatter(prog, max_help_position = 300)
def create_config_program() -> ArgumentParser:
def create_config_path_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_paths = program.add_argument_group('paths')
group_paths.add_argument('-c', '--config-path', help = wording.get('help.config_path'), default = 'facefusion.ini')
job_store.register_job_keys([ 'config-path' ])
group_paths.add_argument('--config-path', help = wording.get('help.config_path'), default = 'facefusion.ini')
job_store.register_job_keys([ 'config_path' ])
apply_config_path(program)
return program
def create_temp_path_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_paths = program.add_argument_group('paths')
group_paths.add_argument('--temp-path', help = wording.get('help.temp_path'), default = config.get_str_value('paths.temp_path', tempfile.gettempdir()))
job_store.register_job_keys([ 'temp_path' ])
return program
def create_jobs_path_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_paths = program.add_argument_group('paths')
group_paths.add_argument('-j', '--jobs-path', help = wording.get('help.jobs_path'), default = config.get_str_value('paths.jobs_path', '.jobs'))
group_paths.add_argument('--jobs-path', help = wording.get('help.jobs_path'), default = config.get_str_value('paths.jobs_path', '.jobs'))
job_store.register_job_keys([ 'jobs_path' ])
return program
def create_paths_program() -> ArgumentParser:
def create_source_paths_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_paths = program.add_argument_group('paths')
group_paths.add_argument('-s', '--source-paths', help = wording.get('help.source_paths'), default = config.get_str_list('paths.source_paths'), nargs = '+')
job_store.register_step_keys([ 'source_paths' ])
return program
def create_target_path_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_paths = program.add_argument_group('paths')
group_paths.add_argument('-s', '--source-paths', help = wording.get('help.source_paths'), action = 'append', default = config.get_str_list('paths.source_paths'))
group_paths.add_argument('-t', '--target-path', help = wording.get('help.target_path'), default = config.get_str_value('paths.target_path'))
job_store.register_step_keys([ 'target_path' ])
return program
def create_output_path_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_paths = program.add_argument_group('paths')
group_paths.add_argument('-o', '--output-path', help = wording.get('help.output_path'), default = config.get_str_value('paths.output_path'))
job_store.register_step_keys([ 'source_paths', 'target_path', 'output_path' ])
job_store.register_step_keys([ 'output_path' ])
return program
def create_source_pattern_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_patterns = program.add_argument_group('patterns')
group_patterns.add_argument('-s', '--source-pattern', help = wording.get('help.source_pattern'), default = config.get_str_value('patterns.source_pattern'))
job_store.register_job_keys([ 'source_pattern' ])
return program
def create_target_pattern_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_patterns = program.add_argument_group('patterns')
group_patterns.add_argument('-t', '--target-pattern', help = wording.get('help.target_pattern'), default = config.get_str_value('patterns.target_pattern'))
job_store.register_job_keys([ 'target_pattern' ])
return program
def create_output_pattern_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_patterns = program.add_argument_group('patterns')
group_patterns.add_argument('-o', '--output-pattern', help = wording.get('help.output_pattern'), default = config.get_str_value('patterns.output_pattern'))
job_store.register_job_keys([ 'output_pattern' ])
return program
def create_face_detector_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_face_detector = program.add_argument_group('face detector')
group_face_detector.add_argument('--face-detector-model', help = wording.get('help.face_detector_model'), default = config.get_str_value('face_detector.face_detector_model', 'yoloface'), choices = facefusion.choices.face_detector_set.keys())
group_face_detector.add_argument('--face-detector-size', help = wording.get('help.face_detector_size'), default = config.get_str_value('face_detector.face_detector_size', '640x640'), choices = suggest_face_detector_choices(program))
group_face_detector.add_argument('--face-detector-model', help = wording.get('help.face_detector_model'), default = config.get_str_value('face_detector.face_detector_model', 'yoloface'), choices = facefusion.choices.face_detector_models)
known_args, _ = program.parse_known_args()
face_detector_size_choices = facefusion.choices.face_detector_set.get(known_args.face_detector_model)
group_face_detector.add_argument('--face-detector-size', help = wording.get('help.face_detector_size'), default = config.get_str_value('face_detector.face_detector_size', get_last(face_detector_size_choices)), choices = face_detector_size_choices)
group_face_detector.add_argument('--face-detector-angles', help = wording.get('help.face_detector_angles'), type = int, default = config.get_int_list('face_detector.face_detector_angles', '0'), choices = facefusion.choices.face_detector_angles, nargs = '+', metavar = 'FACE_DETECTOR_ANGLES')
group_face_detector.add_argument('--face-detector-score', help = wording.get('help.face_detector_score'), type = float, default = config.get_float_value('face_detector.face_detector_score', '0.5'), choices = facefusion.choices.face_detector_score_range, metavar = create_float_metavar(facefusion.choices.face_detector_score_range))
job_store.register_step_keys([ 'face_detector_model', 'face_detector_angles', 'face_detector_size', 'face_detector_score' ])
@ -84,11 +132,13 @@ def create_face_selector_program() -> ArgumentParser:
def create_face_masker_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_face_masker = program.add_argument_group('face masker')
group_face_masker.add_argument('--face-occluder-model', help = wording.get('help.face_occluder_model'), default = config.get_str_value('face_detector.face_occluder_model', 'xseg_1'), choices = facefusion.choices.face_occluder_models)
group_face_masker.add_argument('--face-parser-model', help = wording.get('help.face_parser_model'), default = config.get_str_value('face_detector.face_parser_model', 'bisenet_resnet_34'), choices = facefusion.choices.face_parser_models)
group_face_masker.add_argument('--face-mask-types', help = wording.get('help.face_mask_types').format(choices = ', '.join(facefusion.choices.face_mask_types)), default = config.get_str_list('face_masker.face_mask_types', 'box'), choices = facefusion.choices.face_mask_types, nargs = '+', metavar = 'FACE_MASK_TYPES')
group_face_masker.add_argument('--face-mask-blur', help = wording.get('help.face_mask_blur'), type = float, default = config.get_float_value('face_masker.face_mask_blur', '0.3'), choices = facefusion.choices.face_mask_blur_range, metavar = create_float_metavar(facefusion.choices.face_mask_blur_range))
group_face_masker.add_argument('--face-mask-padding', help = wording.get('help.face_mask_padding'), type = int, default = config.get_int_list('face_masker.face_mask_padding', '0 0 0 0'), nargs = '+')
group_face_masker.add_argument('--face-mask-regions', help = wording.get('help.face_mask_regions').format(choices = ', '.join(facefusion.choices.face_mask_regions)), default = config.get_str_list('face_masker.face_mask_regions', ' '.join(facefusion.choices.face_mask_regions)), choices = facefusion.choices.face_mask_regions, nargs = '+', metavar = 'FACE_MASK_REGIONS')
job_store.register_step_keys([ 'face_mask_types', 'face_mask_blur', 'face_mask_padding', 'face_mask_regions' ])
job_store.register_step_keys([ 'face_occluder_model', 'face_parser_model', 'face_mask_types', 'face_mask_blur', 'face_mask_padding', 'face_mask_regions' ])
return program
@ -121,7 +171,7 @@ def create_output_creation_program() -> ArgumentParser:
def create_processors_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
available_processors = list_directory('facefusion/processors/modules')
available_processors = [ file.get('name') for file in list_directory('facefusion/processors/modules') ]
group_processors = program.add_argument_group('processors')
group_processors.add_argument('--processors', help = wording.get('help.processors').format(choices = ', '.join(available_processors)), default = config.get_str_list('processors.processors', 'face_swapper'), nargs = '+')
job_store.register_step_keys([ 'processors' ])
@ -132,7 +182,7 @@ def create_processors_program() -> ArgumentParser:
def create_uis_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
available_ui_layouts = list_directory('facefusion/uis/layouts')
available_ui_layouts = [ file.get('name') for file in list_directory('facefusion/uis/layouts') ]
group_uis = program.add_argument_group('uis')
group_uis.add_argument('--open-browser', help = wording.get('help.open_browser'), action = 'store_true', default = config.get_bool_value('uis.open_browser'))
group_uis.add_argument('--ui-layouts', help = wording.get('help.ui_layouts').format(choices = ', '.join(available_ui_layouts)), default = config.get_str_list('uis.ui_layouts', 'default'), nargs = '+')
@ -142,16 +192,33 @@ def create_uis_program() -> ArgumentParser:
def create_execution_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
execution_providers = get_execution_provider_choices()
available_execution_providers = get_available_execution_providers()
group_execution = program.add_argument_group('execution')
group_execution.add_argument('--execution-device-id', help = wording.get('help.execution_device_id'), default = config.get_str_value('execution.execution_device_id', '0'))
group_execution.add_argument('--execution-providers', help = wording.get('help.execution_providers').format(choices = ', '.join(execution_providers)), default = config.get_str_list('execution.execution_providers', 'cpu'), choices = execution_providers, nargs = '+', metavar = 'EXECUTION_PROVIDERS')
group_execution.add_argument('--execution-providers', help = wording.get('help.execution_providers').format(choices = ', '.join(available_execution_providers)), default = config.get_str_list('execution.execution_providers', 'cpu'), choices = available_execution_providers, nargs = '+', metavar = 'EXECUTION_PROVIDERS')
group_execution.add_argument('--execution-thread-count', help = wording.get('help.execution_thread_count'), type = int, default = config.get_int_value('execution.execution_thread_count', '4'), choices = facefusion.choices.execution_thread_count_range, metavar = create_int_metavar(facefusion.choices.execution_thread_count_range))
group_execution.add_argument('--execution-queue-count', help = wording.get('help.execution_queue_count'), type = int, default = config.get_int_value('execution.execution_queue_count', '1'), choices = facefusion.choices.execution_queue_count_range, metavar = create_int_metavar(facefusion.choices.execution_queue_count_range))
job_store.register_job_keys([ 'execution_device_id', 'execution_providers', 'execution_thread_count', 'execution_queue_count' ])
return program
def create_download_providers_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
download_providers = list(facefusion.choices.download_provider_set.keys())
group_download = program.add_argument_group('download')
group_download.add_argument('--download-providers', help = wording.get('help.download_providers').format(choices = ', '.join(download_providers)), default = config.get_str_list('download.download_providers', ' '.join(facefusion.choices.download_providers)), choices = download_providers, nargs = '+', metavar = 'DOWNLOAD_PROVIDERS')
job_store.register_job_keys([ 'download_providers' ])
return program
def create_download_scope_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_download = program.add_argument_group('download')
group_download.add_argument('--download-scope', help = wording.get('help.download_scope'), default = config.get_str_value('download.download_scope', 'lite'), choices = facefusion.choices.download_scopes)
job_store.register_job_keys([ 'download_scope' ])
return program
def create_memory_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_memory = program.add_argument_group('memory')
@ -161,18 +228,11 @@ def create_memory_program() -> ArgumentParser:
return program
def create_skip_download_program() -> ArgumentParser:
def create_misc_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
log_level_keys = list(facefusion.choices.log_level_set.keys())
group_misc = program.add_argument_group('misc')
group_misc.add_argument('--skip-download', help = wording.get('help.skip_download'), action = 'store_true', default = config.get_bool_value('misc.skip_download'))
job_store.register_job_keys([ 'skip_download' ])
return program
def create_log_level_program() -> ArgumentParser:
program = ArgumentParser(add_help = False)
group_misc = program.add_argument_group('misc')
group_misc.add_argument('--log-level', help = wording.get('help.log_level'), default = config.get_str_value('misc.log_level', 'info'), choices = facefusion.choices.log_level_set.keys())
group_misc.add_argument('--log-level', help = wording.get('help.log_level'), default = config.get_str_value('misc.log_level', 'info'), choices = log_level_keys)
job_store.register_job_keys([ 'log_level' ])
return program
@ -197,11 +257,11 @@ def create_step_index_program() -> ArgumentParser:
def collect_step_program() -> ArgumentParser:
return ArgumentParser(parents= [ create_config_program(), create_jobs_path_program(), create_paths_program(), create_face_detector_program(), create_face_landmarker_program(), create_face_selector_program(), create_face_masker_program(), create_frame_extraction_program(), create_output_creation_program(), create_processors_program() ], add_help = False)
return ArgumentParser(parents= [ create_face_detector_program(), create_face_landmarker_program(), create_face_selector_program(), create_face_masker_program(), create_frame_extraction_program(), create_output_creation_program(), create_processors_program() ], add_help = False)
def collect_job_program() -> ArgumentParser:
return ArgumentParser(parents= [ create_execution_program(), create_memory_program(), create_skip_download_program(), create_log_level_program() ], add_help = False)
return ArgumentParser(parents= [ create_execution_program(), create_download_providers_program(), create_memory_program(), create_misc_program() ], add_help = False)
def create_program() -> ArgumentParser:
@ -210,25 +270,26 @@ def create_program() -> ArgumentParser:
program.add_argument('-v', '--version', version = metadata.get('name') + ' ' + metadata.get('version'), action = 'version')
sub_program = program.add_subparsers(dest = 'command')
# general
sub_program.add_parser('run', help = wording.get('help.run'), parents = [ collect_step_program(), create_uis_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('headless-run', help = wording.get('help.headless_run'), parents = [ collect_step_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('force-download', help = wording.get('help.force_download'), parents = [ create_log_level_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('run', help = wording.get('help.run'), parents = [ create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), create_source_paths_program(), create_target_path_program(), create_output_path_program(), collect_step_program(), create_uis_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('headless-run', help = wording.get('help.headless_run'), parents = [ create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), create_source_paths_program(), create_target_path_program(), create_output_path_program(), collect_step_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('batch-run', help = wording.get('help.batch_run'), parents = [ create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), create_source_pattern_program(), create_target_pattern_program(), create_output_pattern_program(), collect_step_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('force-download', help = wording.get('help.force_download'), parents = [ create_download_providers_program(), create_download_scope_program(), create_misc_program() ], formatter_class = create_help_formatter_large)
# job manager
sub_program.add_parser('job-list', help = wording.get('help.job_list'), parents = [ create_job_status_program(), create_jobs_path_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-create', help = wording.get('help.job_create'), parents = [ create_job_id_program(), create_jobs_path_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-submit', help = wording.get('help.job_submit'), parents = [ create_job_id_program(), create_jobs_path_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-submit-all', help = wording.get('help.job_submit_all'), parents = [ create_jobs_path_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-delete', help = wording.get('help.job_delete'), parents = [ create_job_id_program(), create_jobs_path_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-delete-all', help = wording.get('help.job_delete_all'), parents = [ create_jobs_path_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-add-step', help = wording.get('help.job_add_step'), parents = [ create_job_id_program(), collect_step_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-remix-step', help = wording.get('help.job_remix_step'), parents = [ create_job_id_program(), create_step_index_program(), remove_args(collect_step_program(), [ 'target_path' ]), create_log_level_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-insert-step', help = wording.get('help.job_insert_step'), parents = [ create_job_id_program(), create_step_index_program(), collect_step_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-remove-step', help = wording.get('help.job_remove_step'), parents = [ create_job_id_program(), create_step_index_program(), create_jobs_path_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-list', help = wording.get('help.job_list'), parents = [ create_job_status_program(), create_jobs_path_program(), create_misc_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-create', help = wording.get('help.job_create'), parents = [ create_job_id_program(), create_jobs_path_program(), create_misc_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-submit', help = wording.get('help.job_submit'), parents = [ create_job_id_program(), create_jobs_path_program(), create_misc_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-submit-all', help = wording.get('help.job_submit_all'), parents = [ create_jobs_path_program(), create_misc_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-delete', help = wording.get('help.job_delete'), parents = [ create_job_id_program(), create_jobs_path_program(), create_misc_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-delete-all', help = wording.get('help.job_delete_all'), parents = [ create_jobs_path_program(), create_misc_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-add-step', help = wording.get('help.job_add_step'), parents = [ create_job_id_program(), create_config_path_program(), create_jobs_path_program(), create_source_paths_program(), create_target_path_program(), create_output_path_program(), collect_step_program(), create_misc_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-remix-step', help = wording.get('help.job_remix_step'), parents = [ create_job_id_program(), create_step_index_program(), create_config_path_program(), create_jobs_path_program(), create_source_paths_program(), create_output_path_program(), collect_step_program(), create_misc_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-insert-step', help = wording.get('help.job_insert_step'), parents = [ create_job_id_program(), create_step_index_program(), create_config_path_program(), create_jobs_path_program(), create_source_paths_program(), create_target_path_program(), create_output_path_program(), collect_step_program(), create_misc_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-remove-step', help = wording.get('help.job_remove_step'), parents = [ create_job_id_program(), create_step_index_program(), create_jobs_path_program(), create_misc_program() ], formatter_class = create_help_formatter_large)
# job runner
sub_program.add_parser('job-run', help = wording.get('help.job_run'), parents = [ create_job_id_program(), create_config_program(), create_jobs_path_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-run-all', help = wording.get('help.job_run_all'), parents = [ create_config_program(), create_jobs_path_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-retry', help = wording.get('help.job_retry'), parents = [ create_job_id_program(), create_config_program(), create_jobs_path_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-retry-all', help = wording.get('help.job_retry_all'), parents = [ create_config_program(), create_jobs_path_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-run', help = wording.get('help.job_run'), parents = [ create_job_id_program(), create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-run-all', help = wording.get('help.job_run_all'), parents = [ create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-retry', help = wording.get('help.job_retry'), parents = [ create_job_id_program(), create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
sub_program.add_parser('job-retry-all', help = wording.get('help.job_retry_all'), parents = [ create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
return ArgumentParser(parents = [ program ], formatter_class = create_help_formatter_small, add_help = True)

23
facefusion/program_helper.py
View File

@ -1,8 +1,5 @@
from argparse import ArgumentParser, _ArgumentGroup, _SubParsersAction
from typing import List, Optional
import facefusion.choices
from facefusion.processors import choices as processors_choices
from typing import Optional
def find_argument_group(program : ArgumentParser, group_name : str) -> Optional[_ArgumentGroup]:
@ -32,21 +29,3 @@ def validate_actions(program : ArgumentParser) -> bool:
elif action.default not in action.choices:
return False
return True
def remove_args(program : ArgumentParser, remove_names : List[str]) -> ArgumentParser:
actions = [ action for action in program._actions if action.dest in remove_names ]
for action in actions:
program._actions.remove(action)
return program
def suggest_face_detector_choices(program : ArgumentParser) -> List[str]:
known_args, _ = program.parse_known_args()
return facefusion.choices.face_detector_set.get(known_args.face_detector_model) #type:ignore[call-overload]
def suggest_face_swapper_pixel_boost_choices(program : ArgumentParser) -> List[str]:
known_args, _ = program.parse_known_args()
return processors_choices.face_swapper_set.get(known_args.face_swapper_model) #type:ignore[call-overload]

41
facefusion/temp_helper.py
View File

@ -1,10 +1,8 @@
import glob
import os
import tempfile
from typing import List
from facefusion import state_manager
from facefusion.filesystem import create_directory, move_file, remove_directory
from facefusion.filesystem import create_directory, move_file, remove_directory, resolve_file_pattern
def get_temp_file_path(file_path : str) -> str:
@ -18,34 +16,9 @@ def move_temp_file(file_path : str, move_path : str) -> bool:
return move_file(temp_file_path, move_path)
def get_temp_frame_paths(target_path : str) -> List[str]:
temp_frames_pattern = get_temp_frames_pattern(target_path, '*')
return sorted(glob.glob(temp_frames_pattern))
def get_temp_frames_pattern(target_path : str, temp_frame_prefix : str) -> str:
temp_directory_path = get_temp_directory_path(target_path)
return os.path.join(temp_directory_path, temp_frame_prefix + '.' + state_manager.get_item('temp_frame_format'))
def get_base_directory_path() -> str:
return os.path.join(tempfile.gettempdir(), 'facefusion')
def create_base_directory() -> bool:
base_directory_path = get_base_directory_path()
return create_directory(base_directory_path)
def clear_base_directory() -> bool:
base_directory_path = get_base_directory_path()
return remove_directory(base_directory_path)
def get_temp_directory_path(file_path : str) -> str:
temp_file_name, _ = os.path.splitext(os.path.basename(file_path))
base_directory_path = get_base_directory_path()
return os.path.join(base_directory_path, temp_file_name)
return os.path.join(state_manager.get_item('temp_path'), 'facefusion', temp_file_name)
def create_temp_directory(file_path : str) -> bool:
@ -58,3 +31,13 @@ def clear_temp_directory(file_path : str) -> bool:
temp_directory_path = get_temp_directory_path(file_path)
return remove_directory(temp_directory_path)
return True
def get_temp_frame_paths(target_path : str) -> List[str]:
temp_frames_pattern = get_temp_frames_pattern(target_path, '*')
return resolve_file_pattern(temp_frames_pattern)
def get_temp_frames_pattern(target_path : str, temp_frame_prefix : str) -> str:
temp_directory_path = get_temp_directory_path(target_path)
return os.path.join(temp_directory_path, temp_frame_prefix + '.' + state_manager.get_item('temp_frame_format'))

81
facefusion/typing.py
View File

@ -67,6 +67,7 @@ Mel = NDArray[Any]
MelFilterBank = NDArray[Any]
Fps = float
Duration = float
Padding = Tuple[int, int, int, int]
Orientation = Literal['landscape', 'portrait']
Resolution = Tuple[int, int]
@ -84,7 +85,7 @@ ProcessStep = Callable[[str, int, Args], bool]
Content = Dict[str, Any]
WarpTemplate = Literal['arcface_112_v1', 'arcface_112_v2', 'arcface_128_v2', 'ffhq_512']
WarpTemplate = Literal['arcface_112_v1', 'arcface_112_v2', 'arcface_128_v2', 'dfl_whole_face', 'ffhq_512', 'mtcnn_512', 'styleganex_384']
WarpTemplateSet = Dict[WarpTemplate, NDArray[Any]]
ProcessMode = Literal['output', 'preview', 'stream']
@ -95,34 +96,28 @@ LogLevelSet = Dict[LogLevel, int]
TableHeaders = List[str]
TableContents = List[List[Any]]
VideoMemoryStrategy = Literal['strict', 'moderate', 'tolerant']
FaceDetectorModel = Literal['many', 'retinaface', 'scrfd', 'yoloface']
FaceLandmarkerModel = Literal['many', '2dfan4', 'peppa_wutz']
FaceDetectorSet = Dict[FaceDetectorModel, List[str]]
FaceSelectorMode = Literal['many', 'one', 'reference']
FaceSelectorOrder = Literal['left-right', 'right-left', 'top-bottom', 'bottom-top', 'small-large', 'large-small', 'best-worst', 'worst-best']
FaceOccluderModel = Literal['xseg_1', 'xseg_2']
FaceParserModel = Literal['bisenet_resnet_18', 'bisenet_resnet_34']
FaceMaskType = Literal['box', 'occlusion', 'region']
FaceMaskRegion = Literal['skin', 'left-eyebrow', 'right-eyebrow', 'left-eye', 'right-eye', 'glasses', 'nose', 'mouth', 'upper-lip', 'lower-lip']
TempFrameFormat = Literal['jpg', 'png', 'bmp']
FaceMaskRegionSet = Dict[FaceMaskRegion, int]
TempFrameFormat = Literal['bmp', 'jpg', 'png']
OutputAudioEncoder = Literal['aac', 'libmp3lame', 'libopus', 'libvorbis']
OutputVideoEncoder = Literal['libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc', 'h264_amf', 'hevc_amf', 'h264_videotoolbox', 'hevc_videotoolbox']
OutputVideoEncoder = Literal['libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc', 'h264_amf', 'hevc_amf','h264_qsv', 'hevc_qsv', 'h264_videotoolbox', 'hevc_videotoolbox']
OutputVideoPreset = Literal['ultrafast', 'superfast', 'veryfast', 'faster', 'fast', 'medium', 'slow', 'slower', 'veryslow']
Download = TypedDict('Download',
{
'url' : str,
'path' : str
})
DownloadSet = Dict[str, Download]
ModelOptions = Dict[str, Any]
ModelSet = Dict[str, ModelOptions]
ModelInitializer = NDArray[Any]
ExecutionProviderKey = Literal['cpu', 'coreml', 'cuda', 'directml', 'openvino', 'rocm', 'tensorrt']
ExecutionProvider = Literal['cpu', 'coreml', 'cuda', 'directml', 'openvino', 'rocm', 'tensorrt']
ExecutionProviderValue = Literal['CPUExecutionProvider', 'CoreMLExecutionProvider', 'CUDAExecutionProvider', 'DmlExecutionProvider', 'OpenVINOExecutionProvider', 'ROCMExecutionProvider', 'TensorrtExecutionProvider']
ExecutionProviderSet = Dict[ExecutionProviderKey, ExecutionProviderValue]
ExecutionProviderSet = Dict[ExecutionProvider, ExecutionProviderValue]
ValueAndUnit = TypedDict('ValueAndUnit',
{
'value' : int,
@ -140,13 +135,18 @@ ExecutionDeviceProduct = TypedDict('ExecutionDeviceProduct',
})
ExecutionDeviceVideoMemory = TypedDict('ExecutionDeviceVideoMemory',
{
'total' : ValueAndUnit,
'free' : ValueAndUnit
'total' : Optional[ValueAndUnit],
'free' : Optional[ValueAndUnit]
})
ExecutionDeviceTemperature = TypedDict('ExecutionDeviceTemperature',
{
'gpu' : Optional[ValueAndUnit],
'memory' : Optional[ValueAndUnit]
})
ExecutionDeviceUtilization = TypedDict('ExecutionDeviceUtilization',
{
'gpu' : ValueAndUnit,
'memory' : ValueAndUnit
'gpu' : Optional[ValueAndUnit],
'memory' : Optional[ValueAndUnit]
})
ExecutionDevice = TypedDict('ExecutionDevice',
{
@ -154,9 +154,34 @@ ExecutionDevice = TypedDict('ExecutionDevice',
'framework' : ExecutionDeviceFramework,
'product' : ExecutionDeviceProduct,
'video_memory' : ExecutionDeviceVideoMemory,
'temperature': ExecutionDeviceTemperature,
'utilization' : ExecutionDeviceUtilization
})
DownloadProvider = Literal['github', 'huggingface']
DownloadProviderValue = TypedDict('DownloadProviderValue',
{
'url' : str,
'path' : str
})
DownloadProviderSet = Dict[DownloadProvider, DownloadProviderValue]
DownloadScope = Literal['lite', 'full']
Download = TypedDict('Download',
{
'url' : str,
'path' : str
})
DownloadSet = Dict[str, Download]
VideoMemoryStrategy = Literal['strict', 'moderate', 'tolerant']
File = TypedDict('File',
{
'name' : str,
'extension' : str,
'path': str
})
AppContext = Literal['cli', 'ui']
InferencePool = Dict[str, InferenceSession]
@ -191,10 +216,14 @@ StateKey = Literal\
[
'command',
'config_path',
'temp_path',
'jobs_path',
'source_paths',
'target_path',
'output_path',
'source_pattern',
'target_pattern',
'output_pattern',
'face_detector_model',
'face_detector_size',
'face_detector_angles',
@ -210,6 +239,8 @@ StateKey = Literal\
'reference_face_position',
'reference_face_distance',
'reference_frame_number',
'face_occluder_model',
'face_parser_model',
'face_mask_types',
'face_mask_blur',
'face_mask_padding',
@ -235,9 +266,10 @@ StateKey = Literal\
'execution_providers',
'execution_thread_count',
'execution_queue_count',
'download_providers',
'download_scope',
'video_memory_strategy',
'system_memory_limit',
'skip_download',
'log_level',
'job_id',
'job_status',
@ -247,10 +279,14 @@ State = TypedDict('State',
{
'command' : str,
'config_path' : str,
'temp_path' : str,
'jobs_path' : str,
'source_paths' : List[str],
'target_path' : str,
'output_path' : str,
'source_pattern' : str,
'target_pattern' : str,
'output_pattern' : str,
'face_detector_model' : FaceDetectorModel,
'face_detector_size' : str,
'face_detector_angles' : List[Angle],
@ -266,6 +302,8 @@ State = TypedDict('State',
'reference_face_position' : int,
'reference_face_distance' : float,
'reference_frame_number' : int,
'face_occluder_model' : FaceOccluderModel,
'face_parser_model' : FaceParserModel,
'face_mask_types' : List[FaceMaskType],
'face_mask_blur' : float,
'face_mask_padding' : Padding,
@ -288,12 +326,13 @@ State = TypedDict('State',
'ui_layouts' : List[str],
'ui_workflow' : UiWorkflow,
'execution_device_id' : str,
'execution_providers' : List[ExecutionProviderKey],
'execution_providers' : List[ExecutionProvider],
'execution_thread_count' : int,
'execution_queue_count' : int,
'download_providers' : List[DownloadProvider],
'download_scope' : DownloadScope,
'video_memory_strategy' : VideoMemoryStrategy,
'system_memory_limit' : int,
'skip_download' : bool,
'log_level' : LogLevel,
'job_id' : str,
'job_status' : JobStatus,

116
facefusion/uis/assets/overrides.css
View File

@ -6,7 +6,29 @@
:root:root:root:root input[type="number"]
{
max-width: 6rem;
appearance: textfield;
border-radius: unset;
text-align: center;
order: 1;
padding: unset
}
:root:root:root:root input[type="number"]::-webkit-inner-spin-button
{
appearance: none;
}
:root:root:root:root input[type="number"]:focus
{
outline: unset;
}
:root:root:root:root .reset-button
{
background: var(--background-fill-secondary);
border: unset;
font-size: unset;
padding: unset;
}
:root:root:root:root [type="checkbox"],
@ -17,39 +39,25 @@
width: 1.125rem;
}
:root:root:root:root input[type="range"],
:root:root:root:root .range-slider div
:root:root:root:root input[type="range"]
{
height: 0.5rem;
border-radius: 0.5rem;
background: transparent;
}
:root:root:root:root input[type="range"]::-moz-range-thumb,
:root:root:root:root input[type="range"]::-webkit-slider-thumb
{
background: var(--neutral-300);
border: unset;
box-shadow: unset;
border-radius: 50%;
height: 1.125rem;
width: 1.125rem;
}
:root:root:root:root input[type="range"]::-webkit-slider-thumb
:root:root:root:root .thumbnail-item
{
margin-top: 0.375rem;
}
:root:root:root:root .range-slider input[type="range"]::-webkit-slider-thumb
{
margin-top: 0.125rem;
}
:root:root:root:root .range-slider div,
:root:root:root:root .range-slider input[type="range"]
{
bottom: 50%;
margin-top: -0.25rem;
top: 50%;
border: unset;
box-shadow: unset;
}
:root:root:root:root .grid-wrap.fixed-height
@ -57,27 +65,61 @@
min-height: unset;
}
:root:root:root:root .generating,
:root:root:root:root .thumbnail-item
:root:root:root:root .tab-wrapper
{
border: unset;
padding: 0 0.625rem;
}
:root:root:root:root .tab-container
{
gap: 0.5em;
}
:root:root:root:root .tab-container button
{
background: unset;
border-bottom: 0.125rem solid;
}
:root:root:root:root .tab-container button.selected
{
color: var(--primary-500)
}
:root:root:root:root .toast-body
{
background: white;
color: var(--primary-500);
border: unset;
border-radius: unset;
}
:root:root:root:root .dark .toast-body
{
background: var(--neutral-900);
color: var(--primary-600);
}
:root:root:root:root .toast-icon,
:root:root:root:root .toast-title,
:root:root:root:root .toast-text,
:root:root:root:root .toast-close
{
color: unset;
}
:root:root:root:root .toast-body .timer
{
background: currentColor;
}
:root:root:root:root .slider_input_container > span,
:root:root:root:root .feather-upload,
:root:root:root:root footer
{
display: none;
}
:root:root:root:root .tab-nav > button
{
border: unset;
box-shadow: 0 0.125rem;
font-size: 1.125em;
margin: 0.5rem 0.75rem;
padding: unset;
}
:root:root:root:root .image-frame
{
width: 100%;
@ -94,3 +136,11 @@
top: 0;
z-index: 100;
}
:root:root:root:root .block .error
{
border: 0.125rem solid;
padding: 0.375rem 0.75rem;
font-size: 0.75rem;
text-transform: uppercase;
}

2
facefusion/uis/choices.py
View File

@ -5,7 +5,7 @@ from facefusion.uis.typing import JobManagerAction, JobRunnerAction, WebcamMode
job_manager_actions : List[JobManagerAction] = [ 'job-create', 'job-submit', 'job-delete', 'job-add-step', 'job-remix-step', 'job-insert-step', 'job-remove-step' ]
job_runner_actions : List[JobRunnerAction] = [ 'job-run', 'job-run-all', 'job-retry', 'job-retry-all' ]
common_options : List[str] = [ 'keep-temp', 'skip-audio', 'skip-download' ]
common_options : List[str] = [ 'keep-temp', 'skip-audio' ]
webcam_modes : List[WebcamMode] = [ 'inline', 'udp', 'v4l2' ]
webcam_resolutions : List[str] = [ '320x240', '640x480', '800x600', '1024x768', '1280x720', '1280x960', '1920x1080', '2560x1440', '3840x2160' ]

5
facefusion/uis/components/age_modifier_options.py
View File

@ -17,11 +17,12 @@ def render() -> None:
global AGE_MODIFIER_MODEL_DROPDOWN
global AGE_MODIFIER_DIRECTION_SLIDER
has_age_modifier = 'age_modifier' in state_manager.get_item('processors')
AGE_MODIFIER_MODEL_DROPDOWN = gradio.Dropdown(
label = wording.get('uis.age_modifier_model_dropdown'),
choices = processors_choices.age_modifier_models,
value = state_manager.get_item('age_modifier_model'),
visible = 'age_modifier' in state_manager.get_item('processors')
visible = has_age_modifier
)
AGE_MODIFIER_DIRECTION_SLIDER = gradio.Slider(
label = wording.get('uis.age_modifier_direction_slider'),
@ -29,7 +30,7 @@ def render() -> None:
step = calc_float_step(processors_choices.age_modifier_direction_range),
minimum = processors_choices.age_modifier_direction_range[0],
maximum = processors_choices.age_modifier_direction_range[-1],
visible = 'age_modifier' in state_manager.get_item('processors')
visible = has_age_modifier
)
register_ui_component('age_modifier_model_dropdown', AGE_MODIFIER_MODEL_DROPDOWN)
register_ui_component('age_modifier_direction_slider', AGE_MODIFIER_DIRECTION_SLIDER)

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

@ -16,8 +16,8 @@ def render() -> None:
BENCHMARK_RUNS_CHECKBOX_GROUP = gradio.CheckboxGroup(
label = wording.get('uis.benchmark_runs_checkbox_group'),
value = list(BENCHMARKS.keys()),
choices = list(BENCHMARKS.keys())
choices = list(BENCHMARKS.keys()),
value = list(BENCHMARKS.keys())
)
BENCHMARK_CYCLES_SLIDER = gradio.Slider(
label = wording.get('uis.benchmark_cycles_slider'),

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

@ -13,8 +13,6 @@ def render() -> None:
common_options = []
if state_manager.get_item('skip_download'):
common_options.append('skip-download')
if state_manager.get_item('keep_temp'):
common_options.append('keep-temp')
if state_manager.get_item('skip_audio'):
@ -32,9 +30,7 @@ def listen() -> None:
def update(common_options : List[str]) -> None:
skip_temp = 'skip-download' in common_options
keep_temp = 'keep-temp' in common_options
skip_audio = 'skip-audio' in common_options
state_manager.set_item('skip_download', skip_temp)
state_manager.set_item('keep_temp', keep_temp)
state_manager.set_item('skip_audio', skip_audio)

65
facefusion/uis/components/deep_swapper_options.py Executable file
View File

@ -0,0 +1,65 @@
from typing import List, Optional, Tuple
import gradio
from facefusion import state_manager, wording
from facefusion.common_helper import calc_int_step
from facefusion.processors import choices as processors_choices
from facefusion.processors.core import load_processor_module
from facefusion.processors.modules.deep_swapper import has_morph_input
from facefusion.processors.typing import DeepSwapperModel
from facefusion.uis.core import get_ui_component, register_ui_component
DEEP_SWAPPER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None
DEEP_SWAPPER_MORPH_SLIDER : Optional[gradio.Slider] = None
def render() -> None:
global DEEP_SWAPPER_MODEL_DROPDOWN
global DEEP_SWAPPER_MORPH_SLIDER
has_deep_swapper = 'deep_swapper' in state_manager.get_item('processors')
DEEP_SWAPPER_MODEL_DROPDOWN = gradio.Dropdown(
label = wording.get('uis.deep_swapper_model_dropdown'),
choices = processors_choices.deep_swapper_models,
value = state_manager.get_item('deep_swapper_model'),
visible = has_deep_swapper
)
DEEP_SWAPPER_MORPH_SLIDER = gradio.Slider(
label = wording.get('uis.deep_swapper_morph_slider'),
value = state_manager.get_item('deep_swapper_morph'),
step = calc_int_step(processors_choices.deep_swapper_morph_range),
minimum = processors_choices.deep_swapper_morph_range[0],
maximum = processors_choices.deep_swapper_morph_range[-1],
visible = has_deep_swapper and has_morph_input()
)
register_ui_component('deep_swapper_model_dropdown', DEEP_SWAPPER_MODEL_DROPDOWN)
register_ui_component('deep_swapper_morph_slider', DEEP_SWAPPER_MORPH_SLIDER)
def listen() -> None:
DEEP_SWAPPER_MODEL_DROPDOWN.change(update_deep_swapper_model, inputs = DEEP_SWAPPER_MODEL_DROPDOWN, outputs = [ DEEP_SWAPPER_MODEL_DROPDOWN, DEEP_SWAPPER_MORPH_SLIDER ])
DEEP_SWAPPER_MORPH_SLIDER.release(update_deep_swapper_morph, inputs = DEEP_SWAPPER_MORPH_SLIDER)
processors_checkbox_group = get_ui_component('processors_checkbox_group')
if processors_checkbox_group:
processors_checkbox_group.change(remote_update, inputs = processors_checkbox_group, outputs = [ DEEP_SWAPPER_MODEL_DROPDOWN, DEEP_SWAPPER_MORPH_SLIDER ])
def remote_update(processors : List[str]) -> Tuple[gradio.Dropdown, gradio.Slider]:
has_deep_swapper = 'deep_swapper' in processors
return gradio.Dropdown(visible = has_deep_swapper), gradio.Slider(visible = has_deep_swapper and has_morph_input())
def update_deep_swapper_model(deep_swapper_model : DeepSwapperModel) -> Tuple[gradio.Dropdown, gradio.Slider]:
deep_swapper_module = load_processor_module('deep_swapper')
deep_swapper_module.clear_inference_pool()
state_manager.set_item('deep_swapper_model', deep_swapper_model)
if deep_swapper_module.pre_check():
return gradio.Dropdown(value = state_manager.get_item('deep_swapper_model')), gradio.Slider(visible = has_morph_input())
return gradio.Dropdown(), gradio.Slider()
def update_deep_swapper_morph(deep_swapper_morph : int) -> None:
state_manager.set_item('deep_swapper_morph', deep_swapper_morph)

48
facefusion/uis/components/download.py Normal file
View File

@ -0,0 +1,48 @@
from typing import List, Optional
import gradio
import facefusion.choices
from facefusion import content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, state_manager, voice_extractor, wording
from facefusion.filesystem import list_directory
from facefusion.processors.core import get_processors_modules
from facefusion.typing import DownloadProvider
DOWNLOAD_PROVIDERS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
def render() -> None:
global DOWNLOAD_PROVIDERS_CHECKBOX_GROUP
DOWNLOAD_PROVIDERS_CHECKBOX_GROUP = gradio.CheckboxGroup(
label = wording.get('uis.download_providers_checkbox_group'),
choices = facefusion.choices.download_providers,
value = state_manager.get_item('download_providers')
)
def listen() -> None:
DOWNLOAD_PROVIDERS_CHECKBOX_GROUP.change(update_download_providers, inputs = DOWNLOAD_PROVIDERS_CHECKBOX_GROUP, outputs = DOWNLOAD_PROVIDERS_CHECKBOX_GROUP)
def update_download_providers(download_providers : List[DownloadProvider]) -> gradio.CheckboxGroup:
common_modules =\
[
content_analyser,
face_classifier,
face_detector,
face_landmarker,
face_recognizer,
face_masker,
voice_extractor
]
available_processors = [ file.get('name') for file in list_directory('facefusion/processors/modules') ]
processor_modules = get_processors_modules(available_processors)
for module in common_modules + processor_modules:
if hasattr(module, 'create_static_model_set'):
module.create_static_model_set.cache_clear()
download_providers = download_providers or facefusion.choices.download_providers
state_manager.set_item('download_providers', download_providers)
return gradio.CheckboxGroup(value = state_manager.get_item('download_providers'))

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

@ -3,9 +3,10 @@ from typing import List, Optional
import gradio
from facefusion import content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, state_manager, voice_extractor, wording
from facefusion.execution import get_execution_provider_choices
from facefusion.processors.core import clear_processors_modules
from facefusion.typing import ExecutionProviderKey
from facefusion.execution import get_available_execution_providers
from facefusion.filesystem import list_directory
from facefusion.processors.core import get_processors_modules
from facefusion.typing import ExecutionProvider
EXECUTION_PROVIDERS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
@ -15,7 +16,7 @@ def render() -> None:
EXECUTION_PROVIDERS_CHECKBOX_GROUP = gradio.CheckboxGroup(
label = wording.get('uis.execution_providers_checkbox_group'),
choices = get_execution_provider_choices(),
choices = get_available_execution_providers(),
value = state_manager.get_item('execution_providers')
)
@ -24,15 +25,24 @@ def listen() -> None:
EXECUTION_PROVIDERS_CHECKBOX_GROUP.change(update_execution_providers, inputs = EXECUTION_PROVIDERS_CHECKBOX_GROUP, outputs = EXECUTION_PROVIDERS_CHECKBOX_GROUP)
def update_execution_providers(execution_providers : List[ExecutionProviderKey]) -> gradio.CheckboxGroup:
content_analyser.clear_inference_pool()
face_classifier.clear_inference_pool()
face_detector.clear_inference_pool()
face_landmarker.clear_inference_pool()
face_masker.clear_inference_pool()
face_recognizer.clear_inference_pool()
voice_extractor.clear_inference_pool()
clear_processors_modules(state_manager.get_item('processors'))
execution_providers = execution_providers or get_execution_provider_choices()
def update_execution_providers(execution_providers : List[ExecutionProvider]) -> gradio.CheckboxGroup:
common_modules =\
[
content_analyser,
face_classifier,
face_detector,
face_landmarker,
face_masker,
face_recognizer,
voice_extractor
]
available_processors = [ file.get('name') for file in list_directory('facefusion/processors/modules') ]
processor_modules = get_processors_modules(available_processors)
for module in common_modules + processor_modules:
if hasattr(module, 'clear_inference_pool'):
module.clear_inference_pool()
execution_providers = execution_providers or get_available_execution_providers()
state_manager.set_item('execution_providers', execution_providers)
return gradio.CheckboxGroup(value = state_manager.get_item('execution_providers'))

5
facefusion/uis/components/expression_restorer_options.py
View File

@ -17,11 +17,12 @@ def render() -> None:
global EXPRESSION_RESTORER_MODEL_DROPDOWN
global EXPRESSION_RESTORER_FACTOR_SLIDER
has_expression_restorer = 'expression_restorer' in state_manager.get_item('processors')
EXPRESSION_RESTORER_MODEL_DROPDOWN = gradio.Dropdown(
label = wording.get('uis.expression_restorer_model_dropdown'),
choices = processors_choices.expression_restorer_models,
value = state_manager.get_item('expression_restorer_model'),
visible = 'expression_restorer' in state_manager.get_item('processors')
visible = has_expression_restorer
)
EXPRESSION_RESTORER_FACTOR_SLIDER = gradio.Slider(
label = wording.get('uis.expression_restorer_factor_slider'),
@ -29,7 +30,7 @@ def render() -> None:
step = calc_float_step(processors_choices.expression_restorer_factor_range),
minimum = processors_choices.expression_restorer_factor_range[0],
maximum = processors_choices.expression_restorer_factor_range[-1],
visible = 'expression_restorer' in state_manager.get_item('processors'),
visible = has_expression_restorer
)
register_ui_component('expression_restorer_model_dropdown', EXPRESSION_RESTORER_MODEL_DROPDOWN)
register_ui_component('expression_restorer_factor_slider', EXPRESSION_RESTORER_FACTOR_SLIDER)

3
facefusion/uis/components/face_debugger_options.py
View File

@ -13,11 +13,12 @@ FACE_DEBUGGER_ITEMS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
def render() -> None:
global FACE_DEBUGGER_ITEMS_CHECKBOX_GROUP
has_face_debugger = 'face_debugger' in state_manager.get_item('processors')
FACE_DEBUGGER_ITEMS_CHECKBOX_GROUP = gradio.CheckboxGroup(
label = wording.get('uis.face_debugger_items_checkbox_group'),
choices = processors_choices.face_debugger_items,
value = state_manager.get_item('face_debugger_items'),
visible = 'face_debugger' in state_manager.get_item('processors')
visible = has_face_debugger
)
register_ui_component('face_debugger_items_checkbox_group', FACE_DEBUGGER_ITEMS_CHECKBOX_GROUP)

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

@ -3,7 +3,7 @@ from typing import Optional, Sequence, Tuple
import gradio
import facefusion.choices
from facefusion import choices, face_detector, state_manager, wording
from facefusion import face_detector, state_manager, wording
from facefusion.common_helper import calc_float_step, get_last
from facefusion.typing import Angle, FaceDetectorModel, Score
from facefusion.uis.core import register_ui_component
@ -31,7 +31,7 @@ def render() -> None:
with gradio.Row():
FACE_DETECTOR_MODEL_DROPDOWN = gradio.Dropdown(
label = wording.get('uis.face_detector_model_dropdown'),
choices = facefusion.choices.face_detector_set.keys(),
choices = facefusion.choices.face_detector_models,
value = state_manager.get_item('face_detector_model')
)
FACE_DETECTOR_SIZE_DROPDOWN = gradio.Dropdown(**face_detector_size_dropdown_options)
@ -65,7 +65,7 @@ def update_face_detector_model(face_detector_model : FaceDetectorModel) -> Tuple
state_manager.set_item('face_detector_model', face_detector_model)
if face_detector.pre_check():
face_detector_size_choices = choices.face_detector_set.get(state_manager.get_item('face_detector_model'))
face_detector_size_choices = facefusion.choices.face_detector_set.get(state_manager.get_item('face_detector_model'))
state_manager.set_item('face_detector_size', get_last(face_detector_size_choices))
return gradio.Dropdown(value = state_manager.get_item('face_detector_model')), gradio.Dropdown(value = state_manager.get_item('face_detector_size'), choices = face_detector_size_choices)
return gradio.Dropdown(), gradio.Dropdown()

31
facefusion/uis/components/face_editor_options.py
View File

@ -43,11 +43,12 @@ def render() -> None:
global FACE_EDITOR_HEAD_YAW_SLIDER
global FACE_EDITOR_HEAD_ROLL_SLIDER
has_face_editor = 'face_editor' in state_manager.get_item('processors')
FACE_EDITOR_MODEL_DROPDOWN = gradio.Dropdown(
label = wording.get('uis.face_editor_model_dropdown'),
choices = processors_choices.face_editor_models,
value = state_manager.get_item('face_editor_model'),
visible = 'face_editor' in state_manager.get_item('processors')
visible = has_face_editor
)
FACE_EDITOR_EYEBROW_DIRECTION_SLIDER = gradio.Slider(
label = wording.get('uis.face_editor_eyebrow_direction_slider'),
@ -55,7 +56,7 @@ def render() -> None:
step = calc_float_step(processors_choices.face_editor_eyebrow_direction_range),
minimum = processors_choices.face_editor_eyebrow_direction_range[0],
maximum = processors_choices.face_editor_eyebrow_direction_range[-1],
visible = 'face_editor' in state_manager.get_item('processors'),
visible = has_face_editor
)
FACE_EDITOR_EYE_GAZE_HORIZONTAL_SLIDER = gradio.Slider(
label = wording.get('uis.face_editor_eye_gaze_horizontal_slider'),
@ -63,7 +64,7 @@ def render() -> None:
step = calc_float_step(processors_choices.face_editor_eye_gaze_horizontal_range),
minimum = processors_choices.face_editor_eye_gaze_horizontal_range[0],
maximum = processors_choices.face_editor_eye_gaze_horizontal_range[-1],
visible = 'face_editor' in state_manager.get_item('processors'),
visible = has_face_editor
)
FACE_EDITOR_EYE_GAZE_VERTICAL_SLIDER = gradio.Slider(
label = wording.get('uis.face_editor_eye_gaze_vertical_slider'),
@ -71,7 +72,7 @@ def render() -> None:
step = calc_float_step(processors_choices.face_editor_eye_gaze_vertical_range),
minimum = processors_choices.face_editor_eye_gaze_vertical_range[0],
maximum = processors_choices.face_editor_eye_gaze_vertical_range[-1],
visible = 'face_editor' in state_manager.get_item('processors'),
visible = has_face_editor
)
FACE_EDITOR_EYE_OPEN_RATIO_SLIDER = gradio.Slider(
label = wording.get('uis.face_editor_eye_open_ratio_slider'),
@ -79,7 +80,7 @@ def render() -> None:
step = calc_float_step(processors_choices.face_editor_eye_open_ratio_range),
minimum = processors_choices.face_editor_eye_open_ratio_range[0],
maximum = processors_choices.face_editor_eye_open_ratio_range[-1],
visible = 'face_editor' in state_manager.get_item('processors'),
visible = has_face_editor
)
FACE_EDITOR_LIP_OPEN_RATIO_SLIDER = gradio.Slider(
label = wording.get('uis.face_editor_lip_open_ratio_slider'),
@ -87,7 +88,7 @@ def render() -> None:
step = calc_float_step(processors_choices.face_editor_lip_open_ratio_range),
minimum = processors_choices.face_editor_lip_open_ratio_range[0],
maximum = processors_choices.face_editor_lip_open_ratio_range[-1],
visible = 'face_editor' in state_manager.get_item('processors'),
visible = has_face_editor
)
FACE_EDITOR_MOUTH_GRIM_SLIDER = gradio.Slider(
label = wording.get('uis.face_editor_mouth_grim_slider'),
@ -95,7 +96,7 @@ def render() -> None:
step = calc_float_step(processors_choices.face_editor_mouth_grim_range),
minimum = processors_choices.face_editor_mouth_grim_range[0],
maximum = processors_choices.face_editor_mouth_grim_range[-1],
visible = 'face_editor' in state_manager.get_item('processors'),
visible = has_face_editor
)
FACE_EDITOR_MOUTH_POUT_SLIDER = gradio.Slider(
label = wording.get('uis.face_editor_mouth_pout_slider'),
@ -103,7 +104,7 @@ def render() -> None:
step = calc_float_step(processors_choices.face_editor_mouth_pout_range),
minimum = processors_choices.face_editor_mouth_pout_range[0],
maximum = processors_choices.face_editor_mouth_pout_range[-1],
visible = 'face_editor' in state_manager.get_item('processors'),
visible = has_face_editor
)
FACE_EDITOR_MOUTH_PURSE_SLIDER = gradio.Slider(
label = wording.get('uis.face_editor_mouth_purse_slider'),
@ -111,7 +112,7 @@ def render() -> None:
step = calc_float_step(processors_choices.face_editor_mouth_purse_range),
minimum = processors_choices.face_editor_mouth_purse_range[0],
maximum = processors_choices.face_editor_mouth_purse_range[-1],
visible = 'face_editor' in state_manager.get_item('processors'),
visible = has_face_editor
)
FACE_EDITOR_MOUTH_SMILE_SLIDER = gradio.Slider(
label = wording.get('uis.face_editor_mouth_smile_slider'),
@ -119,7 +120,7 @@ def render() -> None:
step = calc_float_step(processors_choices.face_editor_mouth_smile_range),
minimum = processors_choices.face_editor_mouth_smile_range[0],
maximum = processors_choices.face_editor_mouth_smile_range[-1],
visible = 'face_editor' in state_manager.get_item('processors'),
visible = has_face_editor
)
FACE_EDITOR_MOUTH_POSITION_HORIZONTAL_SLIDER = gradio.Slider(
label = wording.get('uis.face_editor_mouth_position_horizontal_slider'),
@ -127,7 +128,7 @@ def render() -> None:
step = calc_float_step(processors_choices.face_editor_mouth_position_horizontal_range),
minimum = processors_choices.face_editor_mouth_position_horizontal_range[0],
maximum = processors_choices.face_editor_mouth_position_horizontal_range[-1],
visible = 'face_editor' in state_manager.get_item('processors'),
visible = has_face_editor
)
FACE_EDITOR_MOUTH_POSITION_VERTICAL_SLIDER = gradio.Slider(
label = wording.get('uis.face_editor_mouth_position_vertical_slider'),
@ -135,7 +136,7 @@ def render() -> None:
step = calc_float_step(processors_choices.face_editor_mouth_position_vertical_range),
minimum = processors_choices.face_editor_mouth_position_vertical_range[0],
maximum = processors_choices.face_editor_mouth_position_vertical_range[-1],
visible = 'face_editor' in state_manager.get_item('processors'),
visible = has_face_editor
)
FACE_EDITOR_HEAD_PITCH_SLIDER = gradio.Slider(
label = wording.get('uis.face_editor_head_pitch_slider'),
@ -143,7 +144,7 @@ def render() -> None:
step = calc_float_step(processors_choices.face_editor_head_pitch_range),
minimum = processors_choices.face_editor_head_pitch_range[0],
maximum = processors_choices.face_editor_head_pitch_range[-1],
visible = 'face_editor' in state_manager.get_item('processors'),
visible = has_face_editor
)
FACE_EDITOR_HEAD_YAW_SLIDER = gradio.Slider(
label = wording.get('uis.face_editor_head_yaw_slider'),
@ -151,7 +152,7 @@ def render() -> None:
step = calc_float_step(processors_choices.face_editor_head_yaw_range),
minimum = processors_choices.face_editor_head_yaw_range[0],
maximum = processors_choices.face_editor_head_yaw_range[-1],
visible = 'face_editor' in state_manager.get_item('processors'),
visible = has_face_editor
)
FACE_EDITOR_HEAD_ROLL_SLIDER = gradio.Slider(
label = wording.get('uis.face_editor_head_roll_slider'),
@ -159,7 +160,7 @@ def render() -> None:
step = calc_float_step(processors_choices.face_editor_head_roll_range),
minimum = processors_choices.face_editor_head_roll_range[0],
maximum = processors_choices.face_editor_head_roll_range[-1],
visible = 'face_editor' in state_manager.get_item('processors'),
visible = has_face_editor
)
register_ui_component('face_editor_model_dropdown', FACE_EDITOR_MODEL_DROPDOWN)
register_ui_component('face_editor_eyebrow_direction_slider', FACE_EDITOR_EYEBROW_DIRECTION_SLIDER)

39
facefusion/uis/components/face_enhancer_options.py
View File

@ -3,25 +3,29 @@ from typing import List, Optional, Tuple
import gradio
from facefusion import state_manager, wording
from facefusion.common_helper import calc_int_step
from facefusion.common_helper import calc_float_step, calc_int_step
from facefusion.processors import choices as processors_choices
from facefusion.processors.core import load_processor_module
from facefusion.processors.modules.face_enhancer import has_weight_input
from facefusion.processors.typing import FaceEnhancerModel
from facefusion.uis.core import get_ui_component, register_ui_component
FACE_ENHANCER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None
FACE_ENHANCER_BLEND_SLIDER : Optional[gradio.Slider] = None
FACE_ENHANCER_WEIGHT_SLIDER : Optional[gradio.Slider] = None
def render() -> None:
global FACE_ENHANCER_MODEL_DROPDOWN
global FACE_ENHANCER_BLEND_SLIDER
global FACE_ENHANCER_WEIGHT_SLIDER
has_face_enhancer = 'face_enhancer' in state_manager.get_item('processors')
FACE_ENHANCER_MODEL_DROPDOWN = gradio.Dropdown(
label = wording.get('uis.face_enhancer_model_dropdown'),
choices = processors_choices.face_enhancer_models,
value = state_manager.get_item('face_enhancer_model'),
visible = 'face_enhancer' in state_manager.get_item('processors')
visible = has_face_enhancer
)
FACE_ENHANCER_BLEND_SLIDER = gradio.Slider(
label = wording.get('uis.face_enhancer_blend_slider'),
@ -29,35 +33,50 @@ def render() -> None:
step = calc_int_step(processors_choices.face_enhancer_blend_range),
minimum = processors_choices.face_enhancer_blend_range[0],
maximum = processors_choices.face_enhancer_blend_range[-1],
visible = 'face_enhancer' in state_manager.get_item('processors')
visible = has_face_enhancer
)
FACE_ENHANCER_WEIGHT_SLIDER = gradio.Slider(
label = wording.get('uis.face_enhancer_weight_slider'),
value = state_manager.get_item('face_enhancer_weight'),
step = calc_float_step(processors_choices.face_enhancer_weight_range),
minimum = processors_choices.face_enhancer_weight_range[0],
maximum = processors_choices.face_enhancer_weight_range[-1],
visible = has_face_enhancer and has_weight_input()
)
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_enhancer_weight_slider', FACE_ENHANCER_WEIGHT_SLIDER)
def listen() -> None:
FACE_ENHANCER_MODEL_DROPDOWN.change(update_face_enhancer_model, inputs = FACE_ENHANCER_MODEL_DROPDOWN, outputs = FACE_ENHANCER_MODEL_DROPDOWN)
FACE_ENHANCER_MODEL_DROPDOWN.change(update_face_enhancer_model, inputs = FACE_ENHANCER_MODEL_DROPDOWN, outputs = [ FACE_ENHANCER_MODEL_DROPDOWN, FACE_ENHANCER_WEIGHT_SLIDER ])
FACE_ENHANCER_BLEND_SLIDER.release(update_face_enhancer_blend, inputs = FACE_ENHANCER_BLEND_SLIDER)
FACE_ENHANCER_WEIGHT_SLIDER.release(update_face_enhancer_weight, inputs = FACE_ENHANCER_WEIGHT_SLIDER)
processors_checkbox_group = get_ui_component('processors_checkbox_group')
if processors_checkbox_group:
processors_checkbox_group.change(remote_update, inputs = processors_checkbox_group, outputs = [ FACE_ENHANCER_MODEL_DROPDOWN, FACE_ENHANCER_BLEND_SLIDER ])
processors_checkbox_group.change(remote_update, inputs = processors_checkbox_group, outputs = [ FACE_ENHANCER_MODEL_DROPDOWN, FACE_ENHANCER_BLEND_SLIDER, FACE_ENHANCER_WEIGHT_SLIDER ])
def remote_update(processors : List[str]) -> Tuple[gradio.Dropdown, gradio.Slider]:
def remote_update(processors : List[str]) -> Tuple[gradio.Dropdown, gradio.Slider, gradio.Slider]:
has_face_enhancer = 'face_enhancer' in processors
return gradio.Dropdown(visible = has_face_enhancer), gradio.Slider(visible = has_face_enhancer)
return gradio.Dropdown(visible = has_face_enhancer), gradio.Slider(visible = has_face_enhancer), gradio.Slider(visible = has_face_enhancer and has_weight_input())
def update_face_enhancer_model(face_enhancer_model : FaceEnhancerModel) -> gradio.Dropdown:
def update_face_enhancer_model(face_enhancer_model : FaceEnhancerModel) -> Tuple[gradio.Dropdown, gradio.Slider]:
face_enhancer_module = load_processor_module('face_enhancer')
face_enhancer_module.clear_inference_pool()
state_manager.set_item('face_enhancer_model', face_enhancer_model)
if face_enhancer_module.pre_check():
return gradio.Dropdown(value = state_manager.get_item('face_enhancer_model'))
return gradio.Dropdown()
return gradio.Dropdown(value = state_manager.get_item('face_enhancer_model')), gradio.Slider(visible = has_weight_input())
return gradio.Dropdown(), gradio.Slider()
def update_face_enhancer_blend(face_enhancer_blend : float) -> None:
state_manager.set_item('face_enhancer_blend', int(face_enhancer_blend))
def update_face_enhancer_weight(face_enhancer_weight : float) -> None:
state_manager.set_item('face_enhancer_weight', face_enhancer_weight)

41
facefusion/uis/components/face_masker.py
View File

@ -3,11 +3,13 @@ from typing import List, Optional, Tuple
import gradio
import facefusion.choices
from facefusion import state_manager, wording
from facefusion import face_masker, state_manager, wording
from facefusion.common_helper import calc_float_step, calc_int_step
from facefusion.typing import FaceMaskRegion, FaceMaskType
from facefusion.typing import FaceMaskRegion, FaceMaskType, FaceOccluderModel, FaceParserModel
from facefusion.uis.core import register_ui_component
FACE_OCCLUDER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None
FACE_PARSER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None
FACE_MASK_TYPES_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
FACE_MASK_REGIONS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
FACE_MASK_BLUR_SLIDER : Optional[gradio.Slider] = None
@ -18,6 +20,8 @@ FACE_MASK_PADDING_LEFT_SLIDER : Optional[gradio.Slider] = None
def render() -> None:
global FACE_OCCLUDER_MODEL_DROPDOWN
global FACE_PARSER_MODEL_DROPDOWN
global FACE_MASK_TYPES_CHECKBOX_GROUP
global FACE_MASK_REGIONS_CHECKBOX_GROUP
global FACE_MASK_BLUR_SLIDER
@ -28,6 +32,17 @@ def render() -> None:
has_box_mask = 'box' in state_manager.get_item('face_mask_types')
has_region_mask = 'region' in state_manager.get_item('face_mask_types')
with gradio.Row():
FACE_OCCLUDER_MODEL_DROPDOWN = gradio.Dropdown(
label = wording.get('uis.face_occluder_model_dropdown'),
choices = facefusion.choices.face_occluder_models,
value = state_manager.get_item('face_occluder_model')
)
FACE_PARSER_MODEL_DROPDOWN = gradio.Dropdown(
label = wording.get('uis.face_parser_model_dropdown'),
choices = facefusion.choices.face_parser_models,
value = state_manager.get_item('face_parser_model')
)
FACE_MASK_TYPES_CHECKBOX_GROUP = gradio.CheckboxGroup(
label = wording.get('uis.face_mask_types_checkbox_group'),
choices = facefusion.choices.face_mask_types,
@ -82,6 +97,8 @@ def render() -> None:
value = state_manager.get_item('face_mask_padding')[3],
visible = has_box_mask
)
register_ui_component('face_occluder_model_dropdown', FACE_OCCLUDER_MODEL_DROPDOWN)
register_ui_component('face_parser_model_dropdown', FACE_PARSER_MODEL_DROPDOWN)
register_ui_component('face_mask_types_checkbox_group', FACE_MASK_TYPES_CHECKBOX_GROUP)
register_ui_component('face_mask_regions_checkbox_group', FACE_MASK_REGIONS_CHECKBOX_GROUP)
register_ui_component('face_mask_blur_slider', FACE_MASK_BLUR_SLIDER)
@ -92,6 +109,8 @@ def render() -> None:
def listen() -> None:
FACE_OCCLUDER_MODEL_DROPDOWN.change(update_face_occluder_model, inputs = FACE_OCCLUDER_MODEL_DROPDOWN)
FACE_PARSER_MODEL_DROPDOWN.change(update_face_parser_model, inputs = FACE_PARSER_MODEL_DROPDOWN)
FACE_MASK_TYPES_CHECKBOX_GROUP.change(update_face_mask_types, inputs = FACE_MASK_TYPES_CHECKBOX_GROUP, outputs = [ FACE_MASK_TYPES_CHECKBOX_GROUP, FACE_MASK_REGIONS_CHECKBOX_GROUP, FACE_MASK_BLUR_SLIDER, FACE_MASK_PADDING_TOP_SLIDER, FACE_MASK_PADDING_RIGHT_SLIDER, FACE_MASK_PADDING_BOTTOM_SLIDER, FACE_MASK_PADDING_LEFT_SLIDER ])
FACE_MASK_REGIONS_CHECKBOX_GROUP.change(update_face_mask_regions, inputs = FACE_MASK_REGIONS_CHECKBOX_GROUP, outputs = FACE_MASK_REGIONS_CHECKBOX_GROUP)
FACE_MASK_BLUR_SLIDER.release(update_face_mask_blur, inputs = FACE_MASK_BLUR_SLIDER)
@ -100,6 +119,24 @@ def listen() -> None:
face_mask_padding_slider.release(update_face_mask_padding, inputs = face_mask_padding_sliders)
def update_face_occluder_model(face_occluder_model : FaceOccluderModel) -> gradio.Dropdown:
face_masker.clear_inference_pool()
state_manager.set_item('face_occluder_model', face_occluder_model)
if face_masker.pre_check():
return gradio.Dropdown(value = state_manager.get_item('face_occluder_model'))
return gradio.Dropdown()
def update_face_parser_model(face_parser_model : FaceParserModel) -> gradio.Dropdown:
face_masker.clear_inference_pool()
state_manager.set_item('face_parser_model', face_parser_model)
if face_masker.pre_check():
return gradio.Dropdown(value = state_manager.get_item('face_parser_model'))
return gradio.Dropdown()
def update_face_mask_types(face_mask_types : List[FaceMaskType]) -> Tuple[gradio.CheckboxGroup, gradio.CheckboxGroup, gradio.Slider, gradio.Slider, gradio.Slider, gradio.Slider, gradio.Slider]:
face_mask_types = face_mask_types or facefusion.choices.face_mask_types
state_manager.set_item('face_mask_types', face_mask_types)

7
facefusion/uis/components/face_swapper_options.py
View File

@ -17,17 +17,18 @@ def render() -> None:
global FACE_SWAPPER_MODEL_DROPDOWN
global FACE_SWAPPER_PIXEL_BOOST_DROPDOWN
has_face_swapper = 'face_swapper' in state_manager.get_item('processors')
FACE_SWAPPER_MODEL_DROPDOWN = gradio.Dropdown(
label = wording.get('uis.face_swapper_model_dropdown'),
choices = processors_choices.face_swapper_set.keys(),
choices = processors_choices.face_swapper_models,
value = state_manager.get_item('face_swapper_model'),
visible = 'face_swapper' in state_manager.get_item('processors')
visible = has_face_swapper
)
FACE_SWAPPER_PIXEL_BOOST_DROPDOWN = gradio.Dropdown(
label = wording.get('uis.face_swapper_pixel_boost_dropdown'),
choices = processors_choices.face_swapper_set.get(state_manager.get_item('face_swapper_model')),
value = state_manager.get_item('face_swapper_pixel_boost'),
visible = 'face_swapper' in state_manager.get_item('processors')
visible = has_face_swapper
)
register_ui_component('face_swapper_model_dropdown', FACE_SWAPPER_MODEL_DROPDOWN)
register_ui_component('face_swapper_pixel_boost_dropdown', FACE_SWAPPER_PIXEL_BOOST_DROPDOWN)

7
facefusion/uis/components/frame_colorizer_options.py
View File

@ -19,17 +19,18 @@ def render() -> None:
global FRAME_COLORIZER_SIZE_DROPDOWN
global FRAME_COLORIZER_BLEND_SLIDER
has_frame_colorizer = 'frame_colorizer' in state_manager.get_item('processors')
FRAME_COLORIZER_MODEL_DROPDOWN = gradio.Dropdown(
label = wording.get('uis.frame_colorizer_model_dropdown'),
choices = processors_choices.frame_colorizer_models,
value = state_manager.get_item('frame_colorizer_model'),
visible = 'frame_colorizer' in state_manager.get_item('processors')
visible = has_frame_colorizer
)
FRAME_COLORIZER_SIZE_DROPDOWN = gradio.Dropdown(
label = wording.get('uis.frame_colorizer_size_dropdown'),
choices = processors_choices.frame_colorizer_sizes,
value = state_manager.get_item('frame_colorizer_size'),
visible = 'frame_colorizer' in state_manager.get_item('processors')
visible = has_frame_colorizer
)
FRAME_COLORIZER_BLEND_SLIDER = gradio.Slider(
label = wording.get('uis.frame_colorizer_blend_slider'),
@ -37,7 +38,7 @@ def render() -> None:
step = calc_int_step(processors_choices.frame_colorizer_blend_range),
minimum = processors_choices.frame_colorizer_blend_range[0],
maximum = processors_choices.frame_colorizer_blend_range[-1],
visible = 'frame_colorizer' in state_manager.get_item('processors')
visible = has_frame_colorizer
)
register_ui_component('frame_colorizer_model_dropdown', FRAME_COLORIZER_MODEL_DROPDOWN)
register_ui_component('frame_colorizer_size_dropdown', FRAME_COLORIZER_SIZE_DROPDOWN)

5
facefusion/uis/components/frame_enhancer_options.py
View File

@ -17,11 +17,12 @@ def render() -> None:
global FRAME_ENHANCER_MODEL_DROPDOWN
global FRAME_ENHANCER_BLEND_SLIDER
has_frame_enhancer = 'frame_enhancer' in state_manager.get_item('processors')
FRAME_ENHANCER_MODEL_DROPDOWN = gradio.Dropdown(
label = wording.get('uis.frame_enhancer_model_dropdown'),
choices = processors_choices.frame_enhancer_models,
value = state_manager.get_item('frame_enhancer_model'),
visible = 'frame_enhancer' in state_manager.get_item('processors')
visible = has_frame_enhancer
)
FRAME_ENHANCER_BLEND_SLIDER = gradio.Slider(
label = wording.get('uis.frame_enhancer_blend_slider'),
@ -29,7 +30,7 @@ def render() -> None:
step = calc_int_step(processors_choices.frame_enhancer_blend_range),
minimum = processors_choices.frame_enhancer_blend_range[0],
maximum = processors_choices.frame_enhancer_blend_range[-1],
visible = 'frame_enhancer' in state_manager.get_item('processors')
visible = has_frame_enhancer
)
register_ui_component('frame_enhancer_model_dropdown', FRAME_ENHANCER_MODEL_DROPDOWN)
register_ui_component('frame_enhancer_blend_slider', FRAME_ENHANCER_BLEND_SLIDER)

3
facefusion/uis/components/lip_syncer_options.py
View File

@ -14,11 +14,12 @@ LIP_SYNCER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None
def render() -> None:
global LIP_SYNCER_MODEL_DROPDOWN
has_lip_syncer = 'lip_syncer' in state_manager.get_item('processors')
LIP_SYNCER_MODEL_DROPDOWN = gradio.Dropdown(
label = wording.get('uis.lip_syncer_model_dropdown'),
choices = processors_choices.lip_syncer_models,
value = state_manager.get_item('lip_syncer_model'),
visible = 'lip_syncer' in state_manager.get_item('processors')
visible = has_lip_syncer
)
register_ui_component('lip_syncer_model_dropdown', LIP_SYNCER_MODEL_DROPDOWN)

18
facefusion/uis/components/preview.py
View File

@ -11,6 +11,7 @@ from facefusion.common_helper import get_first
from facefusion.content_analyser import analyse_frame
from facefusion.core import conditional_append_reference_faces
from facefusion.face_analyser import get_average_face, get_many_faces
from facefusion.face_selector import sort_faces_by_order
from facefusion.face_store import clear_reference_faces, clear_static_faces, get_reference_faces
from facefusion.filesystem import filter_audio_paths, is_image, is_video
from facefusion.processors.core import get_processors_modules
@ -74,7 +75,7 @@ def render() -> None:
def listen() -> None:
PREVIEW_FRAME_SLIDER.release(update_preview_image, inputs = PREVIEW_FRAME_SLIDER, outputs = PREVIEW_IMAGE, show_progress = 'hidden')
PREVIEW_FRAME_SLIDER.change(slide_preview_image, inputs = PREVIEW_FRAME_SLIDER, outputs = PREVIEW_IMAGE, show_progress = 'hidden')
PREVIEW_FRAME_SLIDER.change(slide_preview_image, inputs = PREVIEW_FRAME_SLIDER, outputs = PREVIEW_IMAGE, show_progress = 'hidden', trigger_mode = 'once')
reference_face_position_gallery = get_ui_component('reference_face_position_gallery')
if reference_face_position_gallery:
@ -110,6 +111,7 @@ def listen() -> None:
for ui_component in get_ui_components(
[
'age_modifier_direction_slider',
'deep_swapper_morph_slider',
'expression_restorer_factor_slider',
'face_editor_eyebrow_direction_slider',
'face_editor_eye_gaze_horizontal_slider',
@ -126,6 +128,7 @@ def listen() -> None:
'face_editor_head_yaw_slider',
'face_editor_head_roll_slider',
'face_enhancer_blend_slider',
'face_enhancer_weight_slider',
'frame_colorizer_blend_slider',
'frame_enhancer_blend_slider',
'reference_face_distance_slider',
@ -142,6 +145,7 @@ def listen() -> None:
for ui_component in get_ui_components(
[
'age_modifier_model_dropdown',
'deep_swapper_model_dropdown',
'expression_restorer_model_dropdown',
'processors_checkbox_group',
'face_editor_model_dropdown',
@ -158,7 +162,9 @@ def listen() -> None:
'face_detector_model_dropdown',
'face_detector_size_dropdown',
'face_detector_angles_checkbox_group',
'face_landmarker_model_dropdown'
'face_landmarker_model_dropdown',
'face_occluder_model_dropdown',
'face_parser_model_dropdown'
]):
ui_component.change(clear_and_update_preview_image, inputs = PREVIEW_FRAME_SLIDER, outputs = PREVIEW_IMAGE)
@ -190,7 +196,13 @@ def update_preview_image(frame_number : int = 0) -> gradio.Image:
conditional_append_reference_faces()
reference_faces = get_reference_faces() if 'reference' in state_manager.get_item('face_selector_mode') else None
source_frames = read_static_images(state_manager.get_item('source_paths'))
source_faces = get_many_faces(source_frames)
source_faces = []
for source_frame in source_frames:
temp_faces = get_many_faces([ source_frame ])
temp_faces = sort_faces_by_order(temp_faces, 'large-small')
if temp_faces:
source_faces.append(get_first(temp_faces))
source_face = get_average_face(source_faces)
source_audio_path = get_first(filter_audio_paths(state_manager.get_item('source_paths')))
source_audio_frame = create_empty_audio_frame()

13
facefusion/uis/components/processors.py
View File

@ -4,7 +4,7 @@ import gradio
from facefusion import state_manager, wording
from facefusion.filesystem import list_directory
from facefusion.processors.core import clear_processors_modules, get_processors_modules
from facefusion.processors.core import get_processors_modules
from facefusion.uis.core import register_ui_component
PROCESSORS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
@ -26,15 +26,18 @@ def listen() -> None:
def update_processors(processors : List[str]) -> gradio.CheckboxGroup:
clear_processors_modules(state_manager.get_item('processors'))
state_manager.set_item('processors', processors)
for processor_module in get_processors_modules(state_manager.get_item('processors')):
if hasattr(processor_module, 'clear_inference_pool'):
processor_module.clear_inference_pool()
for processor_module in get_processors_modules(processors):
if not processor_module.pre_check():
return gradio.CheckboxGroup()
state_manager.set_item('processors', processors)
return gradio.CheckboxGroup(value = state_manager.get_item('processors'), choices = sort_processors(state_manager.get_item('processors')))
def sort_processors(processors : List[str]) -> List[str]:
available_processors = list_directory('facefusion/processors/modules')
available_processors = [ file.get('name') for file in list_directory('facefusion/processors/modules') ]
return sorted(available_processors, key = lambda processor : processors.index(processor) if processor in processors else len(processors))

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

@ -7,8 +7,8 @@ from typing import Optional
import gradio
from tqdm import tqdm
import facefusion.choices
from facefusion import logger, state_manager, wording
from facefusion.choices import log_level_set
from facefusion.typing import LogLevel
LOG_LEVEL_DROPDOWN : Optional[gradio.Dropdown] = None
@ -24,7 +24,7 @@ def render() -> None:
LOG_LEVEL_DROPDOWN = gradio.Dropdown(
label = wording.get('uis.log_level_dropdown'),
choices = log_level_set.keys(),
choices = facefusion.choices.log_levels,
value = state_manager.get_item('log_level')
)
TERMINAL_TEXTBOX = gradio.Textbox(

62
facefusion/uis/components/webcam.py
View File

@ -2,7 +2,7 @@ import os
import subprocess
from collections import deque
from concurrent.futures import ThreadPoolExecutor
from typing import Deque, Generator, Optional
from typing import Deque, Generator, List, Optional
import cv2
import gradio
@ -10,7 +10,7 @@ from tqdm import tqdm
from facefusion import logger, state_manager, wording
from facefusion.audio import create_empty_audio_frame
from facefusion.common_helper import is_windows
from facefusion.common_helper import get_first, is_windows
from facefusion.content_analyser import analyse_stream
from facefusion.face_analyser import get_average_face, get_many_faces
from facefusion.ffmpeg import open_ffmpeg
@ -27,14 +27,17 @@ WEBCAM_START_BUTTON : Optional[gradio.Button] = None
WEBCAM_STOP_BUTTON : Optional[gradio.Button] = None
def get_webcam_capture() -> Optional[cv2.VideoCapture]:
def get_webcam_capture(webcam_device_id : int) -> Optional[cv2.VideoCapture]:
global WEBCAM_CAPTURE
if WEBCAM_CAPTURE is None:
cv2.setLogLevel(0)
if is_windows():
webcam_capture = cv2.VideoCapture(0, cv2.CAP_DSHOW)
webcam_capture = cv2.VideoCapture(webcam_device_id, cv2.CAP_DSHOW)
else:
webcam_capture = cv2.VideoCapture(0)
webcam_capture = cv2.VideoCapture(webcam_device_id)
cv2.setLogLevel(3)
if webcam_capture and webcam_capture.isOpened():
WEBCAM_CAPTURE = webcam_capture
return WEBCAM_CAPTURE
@ -43,7 +46,7 @@ def get_webcam_capture() -> Optional[cv2.VideoCapture]:
def clear_webcam_capture() -> None:
global WEBCAM_CAPTURE
if WEBCAM_CAPTURE:
if WEBCAM_CAPTURE and WEBCAM_CAPTURE.isOpened():
WEBCAM_CAPTURE.release()
WEBCAM_CAPTURE = None
@ -68,32 +71,42 @@ def render() -> None:
def listen() -> None:
webcam_device_id_dropdown = get_ui_component('webcam_device_id_dropdown')
webcam_mode_radio = get_ui_component('webcam_mode_radio')
webcam_resolution_dropdown = get_ui_component('webcam_resolution_dropdown')
webcam_fps_slider = get_ui_component('webcam_fps_slider')
source_image = get_ui_component('source_image')
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)
if webcam_device_id_dropdown and webcam_mode_radio and webcam_resolution_dropdown and webcam_fps_slider:
start_event = WEBCAM_START_BUTTON.click(start, inputs = [ webcam_device_id_dropdown, webcam_mode_radio, webcam_resolution_dropdown, webcam_fps_slider ], outputs = WEBCAM_IMAGE)
WEBCAM_STOP_BUTTON.click(stop, cancels = start_event, outputs = WEBCAM_IMAGE)
if source_image:
source_image.change(stop, cancels = start_event, outputs = WEBCAM_IMAGE)
def start(webcam_mode : WebcamMode, webcam_resolution : str, webcam_fps : Fps) -> Generator[VisionFrame, None, None]:
def start(webcam_device_id : int, webcam_mode : WebcamMode, webcam_resolution : str, webcam_fps : Fps) -> Generator[VisionFrame, None, None]:
state_manager.set_item('face_selector_mode', 'one')
source_image_paths = filter_image_paths(state_manager.get_item('source_paths'))
source_frames = read_static_images(source_image_paths)
source_faces = get_many_faces(source_frames)
source_face = get_average_face(source_faces)
stream = None
webcam_capture = None
if webcam_mode in [ 'udp', 'v4l2' ]:
stream = open_stream(webcam_mode, webcam_resolution, webcam_fps) #type:ignore[arg-type]
webcam_width, webcam_height = unpack_resolution(webcam_resolution)
webcam_capture = get_webcam_capture()
if isinstance(webcam_device_id, int):
webcam_capture = get_webcam_capture(webcam_device_id)
if webcam_capture and webcam_capture.isOpened():
webcam_capture.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG')) #type:ignore[attr-defined]
webcam_capture.set(cv2.CAP_PROP_FRAME_WIDTH, webcam_width)
webcam_capture.set(cv2.CAP_PROP_FRAME_HEIGHT, webcam_height)
webcam_capture.set(cv2.CAP_PROP_FPS, webcam_fps)
for capture_frame in multi_process_capture(source_face, webcam_capture, webcam_fps):
if webcam_mode == 'inline':
yield normalize_frame_color(capture_frame)
@ -107,19 +120,15 @@ def start(webcam_mode : WebcamMode, webcam_resolution : str, webcam_fps : Fps) -
def multi_process_capture(source_face : Face, webcam_capture : cv2.VideoCapture, webcam_fps : Fps) -> Generator[VisionFrame, None, None]:
deque_capture_frames: Deque[VisionFrame] = deque()
with tqdm(desc = wording.get('processing'), unit = 'frame', ascii = ' =', disable = state_manager.get_item('log_level') in [ 'warn', 'error' ]) as progress:
progress.set_postfix(
{
'execution_providers': state_manager.get_item('execution_providers'),
'execution_thread_count': state_manager.get_item('execution_thread_count')
})
with tqdm(desc = wording.get('streaming'), unit = 'frame', disable = state_manager.get_item('log_level') in [ 'warn', 'error' ]) as progress:
with ThreadPoolExecutor(max_workers = state_manager.get_item('execution_thread_count')) as executor:
futures = []
while webcam_capture and webcam_capture.isOpened():
_, capture_frame = webcam_capture.read()
if analyse_stream(capture_frame, webcam_fps):
return
yield None
future = executor.submit(process_stream_frame, source_face, capture_frame)
futures.append(future)
@ -140,6 +149,7 @@ def stop() -> gradio.Image:
def process_stream_frame(source_face : Face, target_vision_frame : VisionFrame) -> VisionFrame:
source_audio_frame = create_empty_audio_frame()
for processor_module in get_processors_modules(state_manager.get_item('processors')):
logger.disable()
if processor_module.pre_process('stream'):
@ -155,13 +165,27 @@ def process_stream_frame(source_face : Face, target_vision_frame : VisionFrame)
def open_stream(stream_mode : StreamMode, stream_resolution : str, stream_fps : Fps) -> subprocess.Popen[bytes]:
commands = [ '-f', 'rawvideo', '-pix_fmt', 'bgr24', '-s', stream_resolution, '-r', str(stream_fps), '-i', '-']
if stream_mode == 'udp':
commands.extend([ '-b:v', '2000k', '-f', 'mpegts', 'udp://localhost:27000?pkt_size=1316' ])
if stream_mode == 'v4l2':
try:
device_name = os.listdir('/sys/devices/virtual/video4linux')[0]
device_name = get_first(os.listdir('/sys/devices/virtual/video4linux'))
if device_name:
commands.extend([ '-f', 'v4l2', '/dev/' + device_name ])
except FileNotFoundError:
logger.error(wording.get('stream_not_loaded').format(stream_mode = stream_mode), __name__)
return open_ffmpeg(commands)
def get_available_webcam_ids(webcam_id_start : int, webcam_id_end : int) -> List[int]:
available_webcam_ids = []
for index in range(webcam_id_start, webcam_id_end):
webcam_capture = get_webcam_capture(index)
if webcam_capture and webcam_capture.isOpened():
available_webcam_ids.append(index)
clear_webcam_capture()
return available_webcam_ids

11
facefusion/uis/components/webcam_options.py
View File

@ -3,19 +3,29 @@ from typing import Optional
import gradio
from facefusion import wording
from facefusion.common_helper import get_first
from facefusion.uis import choices as uis_choices
from facefusion.uis.components.webcam import get_available_webcam_ids
from facefusion.uis.core import register_ui_component
WEBCAM_DEVICE_ID_DROPDOWN : Optional[gradio.Dropdown] = None
WEBCAM_MODE_RADIO : Optional[gradio.Radio] = None
WEBCAM_RESOLUTION_DROPDOWN : Optional[gradio.Dropdown] = None
WEBCAM_FPS_SLIDER : Optional[gradio.Slider] = None
def render() -> None:
global WEBCAM_DEVICE_ID_DROPDOWN
global WEBCAM_MODE_RADIO
global WEBCAM_RESOLUTION_DROPDOWN
global WEBCAM_FPS_SLIDER
available_webcam_ids = get_available_webcam_ids(0, 10) or [ 'none' ] #type:ignore[list-item]
WEBCAM_DEVICE_ID_DROPDOWN = gradio.Dropdown(
value = get_first(available_webcam_ids),
label = wording.get('uis.webcam_device_id_dropdown'),
choices = available_webcam_ids
)
WEBCAM_MODE_RADIO = gradio.Radio(
label = wording.get('uis.webcam_mode_radio'),
choices = uis_choices.webcam_modes,
@ -33,6 +43,7 @@ def render() -> None:
minimum = 1,
maximum = 60
)
register_ui_component('webcam_device_id_dropdown', WEBCAM_DEVICE_ID_DROPDOWN)
register_ui_component('webcam_mode_radio', WEBCAM_MODE_RADIO)
register_ui_component('webcam_resolution_dropdown', WEBCAM_RESOLUTION_DROPDOWN)
register_ui_component('webcam_fps_slider', WEBCAM_FPS_SLIDER)

66
facefusion/uis/core.py
View File

@ -10,16 +10,8 @@ from gradio.themes import Size
from facefusion import logger, metadata, state_manager, wording
from facefusion.exit_helper import hard_exit
from facefusion.filesystem import resolve_relative_path
from facefusion.uis import overrides
from facefusion.uis.typing import Component, ComponentName
os.environ['GRADIO_ANALYTICS_ENABLED'] = '0'
warnings.filterwarnings('ignore', category = UserWarning, module = 'gradio')
gradio.processing_utils.encode_array_to_base64 = overrides.encode_array_to_base64
gradio.processing_utils.encode_pil_to_base64 = overrides.encode_pil_to_base64
UI_COMPONENTS: Dict[ComponentName, Component] = {}
UI_LAYOUT_MODULES : List[ModuleType] = []
UI_LAYOUT_METHODS =\
@ -77,6 +69,13 @@ def register_ui_component(component_name : ComponentName, component: Component)
UI_COMPONENTS[component_name] = component
def init() -> None:
os.environ['GRADIO_ANALYTICS_ENABLED'] = '0'
os.environ['GRADIO_TEMP_DIR'] = os.path.join(state_manager.get_item('temp_path'), 'gradio')
warnings.filterwarnings('ignore', category = UserWarning, module = 'gradio')
def launch() -> None:
ui_layouts_total = len(state_manager.get_item('ui_layouts'))
with gradio.Blocks(theme = get_theme(), css = get_css(), title = metadata.get('name') + ' ' + metadata.get('version'), fill_width = True) as ui:
@ -99,7 +98,20 @@ def launch() -> None:
def get_theme() -> gradio.Theme:
return gradio.themes.Base(
primary_hue = gradio.themes.colors.red,
secondary_hue = gradio.themes.colors.neutral,
secondary_hue = gradio.themes.Color(
name = 'neutral',
c50 = '#fafafa',
c100 = '#f5f5f5',
c200 = '#e5e5e5',
c300 = '#d4d4d4',
c400 = '#a3a3a3',
c500 = '#737373',
c600 = '#525252',
c700 = '#404040',
c800 = '#262626',
c900 = '#212121',
c950 = '#171717',
),
radius_size = Size(
xxs = '0.375rem',
xs = '0.375rem',
@ -111,11 +123,18 @@ def get_theme() -> gradio.Theme:
),
font = gradio.themes.GoogleFont('Open Sans')
).set(
color_accent = 'transparent',
color_accent_soft = 'transparent',
color_accent_soft_dark = 'transparent',
background_fill_primary = '*neutral_100',
background_fill_primary_dark = '*neutral_950',
background_fill_secondary = '*neutral_50',
background_fill_secondary_dark = '*neutral_800',
block_background_fill = 'white',
block_background_fill_dark = '*neutral_900',
block_border_width = '0',
block_label_background_fill = '*neutral_100',
block_label_background_fill_dark = '*neutral_700',
block_label_background_fill_dark = '*neutral_800',
block_label_border_width = 'none',
block_label_margin = '0.5rem',
block_label_radius = '*radius_md',
@ -124,39 +143,48 @@ def get_theme() -> gradio.Theme:
block_label_text_color_dark = 'white',
block_label_text_weight = '600',
block_title_background_fill = '*neutral_100',
block_title_background_fill_dark = '*neutral_700',
block_title_background_fill_dark = '*neutral_800',
block_title_padding = '*block_label_padding',
block_title_radius = '*block_label_radius',
block_title_text_color = '*neutral_700',
block_title_text_size = '*text_sm',
block_title_text_weight = '600',
block_padding = '0.5rem',
border_color_accent = 'transparent',
border_color_accent_dark = 'transparent',
border_color_accent_subdued = 'transparent',
border_color_accent_subdued_dark = 'transparent',
border_color_primary = 'transparent',
border_color_primary_dark = 'transparent',
button_large_padding = '2rem 0.5rem',
button_large_text_weight = 'normal',
button_primary_background_fill = '*primary_500',
button_primary_background_fill_dark = '*primary_600',
button_primary_text_color = 'white',
button_secondary_background_fill = 'white',
button_secondary_border_color = 'transparent',
button_secondary_border_color_dark = 'transparent',
button_secondary_border_color_hover = 'transparent',
button_secondary_border_color_hover_dark = 'transparent',
button_secondary_background_fill_dark = '*neutral_800',
button_secondary_background_fill_hover = 'white',
button_secondary_background_fill_hover_dark = '*neutral_800',
button_secondary_text_color = '*neutral_800',
button_small_padding = '0.75rem',
button_small_text_size = '0.875rem',
checkbox_background_color = '*neutral_200',
checkbox_background_color_dark = '*neutral_900',
checkbox_background_color_selected = '*primary_600',
checkbox_background_color_selected_dark = '*primary_700',
checkbox_border_color_focus = '*primary_500',
checkbox_border_color_focus_dark = '*primary_600',
checkbox_border_color_selected = '*primary_600',
checkbox_border_color_selected_dark = '*primary_700',
checkbox_label_background_fill = '*neutral_50',
checkbox_label_background_fill_dark = '*neutral_800',
checkbox_label_background_fill_hover = '*neutral_50',
checkbox_label_background_fill_hover_dark = '*neutral_800',
checkbox_label_background_fill_selected = '*primary_500',
checkbox_label_background_fill_selected_dark = '*primary_600',
checkbox_label_text_color_selected = 'white',
error_background_fill = 'white',
error_background_fill_dark = '*neutral_900',
error_text_color = '*primary_500',
error_text_color_dark = '*primary_600',
input_background_fill = '*neutral_50',
input_background_fill_dark = '*neutral_800',
shadow_drop = 'none',
slider_color = '*primary_500',
slider_color_dark = '*primary_600'

31
facefusion/uis/layouts/benchmark.py
View File

@ -1,26 +1,24 @@
import gradio
from facefusion import state_manager
from facefusion.download import conditional_download
from facefusion.uis.components import about, age_modifier_options, benchmark, benchmark_options, execution, execution_queue_count, execution_thread_count, expression_restorer_options, face_debugger_options, face_editor_options, face_enhancer_options, face_swapper_options, frame_colorizer_options, frame_enhancer_options, lip_syncer_options, memory, processors
from facefusion.download import conditional_download, resolve_download_url
from facefusion.uis.components import about, age_modifier_options, benchmark, benchmark_options, deep_swapper_options, download, execution, execution_queue_count, execution_thread_count, expression_restorer_options, face_debugger_options, face_editor_options, face_enhancer_options, face_swapper_options, frame_colorizer_options, frame_enhancer_options, lip_syncer_options, memory, processors
def pre_check() -> bool:
if not state_manager.get_item('skip_download'):
conditional_download('.assets/examples',
[
'https://github.com/facefusion/facefusion-assets/releases/download/examples-3.0.0/source.jpg',
'https://github.com/facefusion/facefusion-assets/releases/download/examples-3.0.0/source.mp3',
'https://github.com/facefusion/facefusion-assets/releases/download/examples-3.0.0/target-240p.mp4',
'https://github.com/facefusion/facefusion-assets/releases/download/examples-3.0.0/target-360p.mp4',
'https://github.com/facefusion/facefusion-assets/releases/download/examples-3.0.0/target-540p.mp4',
'https://github.com/facefusion/facefusion-assets/releases/download/examples-3.0.0/target-720p.mp4',
'https://github.com/facefusion/facefusion-assets/releases/download/examples-3.0.0/target-1080p.mp4',
'https://github.com/facefusion/facefusion-assets/releases/download/examples-3.0.0/target-1440p.mp4',
'https://github.com/facefusion/facefusion-assets/releases/download/examples-3.0.0/target-2160p.mp4'
resolve_download_url('examples-3.0.0', 'source.jpg'),
resolve_download_url('examples-3.0.0', 'source.mp3'),
resolve_download_url('examples-3.0.0', 'target-240p.mp4'),
resolve_download_url('examples-3.0.0', 'target-360p.mp4'),
resolve_download_url('examples-3.0.0', 'target-540p.mp4'),
resolve_download_url('examples-3.0.0', 'target-720p.mp4'),
resolve_download_url('examples-3.0.0', 'target-1080p.mp4'),
resolve_download_url('examples-3.0.0', 'target-1440p.mp4'),
resolve_download_url('examples-3.0.0', 'target-2160p.mp4')
])
return True
return False
def render() -> gradio.Blocks:
@ -33,6 +31,8 @@ def render() -> gradio.Blocks:
processors.render()
with gradio.Blocks():
age_modifier_options.render()
with gradio.Blocks():
deep_swapper_options.render()
with gradio.Blocks():
expression_restorer_options.render()
with gradio.Blocks():
@ -54,6 +54,9 @@ def render() -> gradio.Blocks:
execution_thread_count.render()
execution_queue_count.render()
with gradio.Blocks():
download.render()
with gradio.Blocks():
state_manager.set_item('video_memory_strategy', 'tolerant')
memory.render()
with gradio.Blocks():
benchmark_options.render()
@ -66,7 +69,9 @@ def render() -> gradio.Blocks:
def listen() -> None:
processors.listen()
age_modifier_options.listen()
deep_swapper_options.listen()
expression_restorer_options.listen()
download.listen()
face_debugger_options.listen()
face_editor_options.listen()
face_enhancer_options.listen()

8
facefusion/uis/layouts/default.py
View File

@ -1,7 +1,7 @@
import gradio
from facefusion import state_manager
from facefusion.uis.components import about, age_modifier_options, common_options, execution, execution_queue_count, execution_thread_count, expression_restorer_options, face_debugger_options, face_detector, face_editor_options, face_enhancer_options, face_landmarker, face_masker, face_selector, face_swapper_options, frame_colorizer_options, frame_enhancer_options, instant_runner, job_manager, job_runner, lip_syncer_options, memory, output, output_options, preview, processors, source, target, temp_frame, terminal, trim_frame, ui_workflow
from facefusion.uis.components import about, age_modifier_options, common_options, deep_swapper_options, download, execution, execution_queue_count, execution_thread_count, expression_restorer_options, face_debugger_options, face_detector, face_editor_options, face_enhancer_options, face_landmarker, face_masker, face_selector, face_swapper_options, frame_colorizer_options, frame_enhancer_options, instant_runner, job_manager, job_runner, lip_syncer_options, memory, output, output_options, preview, processors, source, target, temp_frame, terminal, trim_frame, ui_workflow
def pre_check() -> bool:
@ -18,6 +18,8 @@ def render() -> gradio.Blocks:
processors.render()
with gradio.Blocks():
age_modifier_options.render()
with gradio.Blocks():
deep_swapper_options.render()
with gradio.Blocks():
expression_restorer_options.render()
with gradio.Blocks():
@ -38,6 +40,8 @@ def render() -> gradio.Blocks:
execution.render()
execution_thread_count.render()
execution_queue_count.render()
with gradio.Blocks():
download.render()
with gradio.Blocks():
memory.render()
with gradio.Blocks():
@ -79,6 +83,7 @@ def render() -> gradio.Blocks:
def listen() -> None:
processors.listen()
age_modifier_options.listen()
deep_swapper_options.listen()
expression_restorer_options.listen()
face_debugger_options.listen()
face_editor_options.listen()
@ -90,6 +95,7 @@ def listen() -> None:
execution.listen()
execution_thread_count.listen()
execution_queue_count.listen()
download.listen()
memory.listen()
temp_frame.listen()
output_options.listen()

14
facefusion/uis/layouts/webcam.py
View File

@ -1,7 +1,7 @@
import gradio
from facefusion import state_manager
from facefusion.uis.components import about, age_modifier_options, execution, execution_thread_count, face_debugger_options, face_enhancer_options, face_swapper_options, frame_colorizer_options, frame_enhancer_options, lip_syncer_options, processors, source, webcam, webcam_options
from facefusion.uis.components import about, age_modifier_options, deep_swapper_options, download, execution, execution_thread_count, expression_restorer_options, face_debugger_options, face_editor_options, face_enhancer_options, face_swapper_options, frame_colorizer_options, frame_enhancer_options, lip_syncer_options, processors, source, webcam, webcam_options
def pre_check() -> bool:
@ -18,8 +18,14 @@ def render() -> gradio.Blocks:
processors.render()
with gradio.Blocks():
age_modifier_options.render()
with gradio.Blocks():
deep_swapper_options.render()
with gradio.Blocks():
expression_restorer_options.render()
with gradio.Blocks():
face_debugger_options.render()
with gradio.Blocks():
face_editor_options.render()
with gradio.Blocks():
face_enhancer_options.render()
with gradio.Blocks():
@ -33,6 +39,8 @@ def render() -> gradio.Blocks:
with gradio.Blocks():
execution.render()
execution_thread_count.render()
with gradio.Blocks():
download.render()
with gradio.Blocks():
webcam_options.render()
with gradio.Blocks():
@ -46,7 +54,11 @@ def render() -> gradio.Blocks:
def listen() -> None:
processors.listen()
age_modifier_options.listen()
deep_swapper_options.listen()
expression_restorer_options.listen()
download.listen()
face_debugger_options.listen()
face_editor_options.listen()
face_enhancer_options.listen()
face_swapper_options.listen()
frame_colorizer_options.listen()

15
facefusion/uis/overrides.py
View File

@ -1,15 +0,0 @@
import base64
from typing import Any
import cv2
import numpy
from numpy._typing import NDArray
def encode_array_to_base64(array : NDArray[Any]) -> str:
_, buffer = cv2.imencode('.jpg', array[:, :, ::-1])
return 'data:image/jpeg;base64,' + base64.b64encode(buffer.tobytes()).decode('utf-8')
def encode_pil_to_base64(image : Any) -> str:
return encode_array_to_base64(numpy.asarray(image)[:, :, ::-1])

6
facefusion/uis/typing.py
View File

@ -9,6 +9,8 @@ ComponentName = Literal\
'age_modifier_model_dropdown',
'benchmark_cycles_slider',
'benchmark_runs_checkbox_group',
'deep_swapper_model_dropdown',
'deep_swapper_morph_slider',
'expression_restorer_factor_slider',
'expression_restorer_model_dropdown',
'face_debugger_items_checkbox_group',
@ -33,6 +35,7 @@ ComponentName = Literal\
'face_editor_mouth_smile_slider',
'face_enhancer_blend_slider',
'face_enhancer_model_dropdown',
'face_enhancer_weight_slider',
'face_landmarker_model_dropdown',
'face_landmarker_score_slider',
'face_mask_blur_slider',
@ -49,6 +52,8 @@ ComponentName = Literal\
'face_selector_race_dropdown',
'face_swapper_model_dropdown',
'face_swapper_pixel_boost_dropdown',
'face_occluder_model_dropdown',
'face_parser_model_dropdown',
'frame_colorizer_blend_slider',
'frame_colorizer_model_dropdown',
'frame_colorizer_size_dropdown',
@ -68,6 +73,7 @@ ComponentName = Literal\
'target_image',
'target_video',
'ui_workflow_dropdown',
'webcam_device_id_dropdown',
'webcam_fps_slider',
'webcam_mode_radio',
'webcam_resolution_dropdown'

2
facefusion/uis/ui_helper.py
View File

@ -21,6 +21,6 @@ def convert_str_none(value : str) -> Optional[str]:
def suggest_output_path(output_directory_path : str, target_path : str) -> Optional[str]:
if is_image(target_path) or is_video(target_path):
_, target_extension = os.path.splitext(target_path)
output_name = hashlib.sha1(str(state_manager.get_state()).encode('utf-8')).hexdigest()[:8]
output_name = hashlib.sha1(str(state_manager.get_state()).encode()).hexdigest()[:8]
return os.path.join(output_directory_path, output_name + target_extension)
return None

83
facefusion/vision.py
View File

@ -5,10 +5,10 @@ import cv2
import numpy
from cv2.typing import Size
from facefusion.choices import image_template_sizes, video_template_sizes
import facefusion.choices
from facefusion.common_helper import is_windows
from facefusion.filesystem import is_image, is_video, sanitize_path_for_windows
from facefusion.typing import Fps, Orientation, Resolution, VisionFrame
from facefusion.typing import Duration, Fps, Orientation, Resolution, VisionFrame
@lru_cache(maxsize = 128)
@ -64,8 +64,8 @@ def create_image_resolutions(resolution : Resolution) -> List[str]:
if resolution:
width, height = resolution
temp_resolutions.append(normalize_resolution(resolution))
for template_size in image_template_sizes:
temp_resolutions.append(normalize_resolution((width * template_size, height * template_size)))
for image_template_size in facefusion.choices.image_template_sizes:
temp_resolutions.append(normalize_resolution((width * image_template_size, height * image_template_size)))
temp_resolutions = sorted(set(temp_resolutions))
for temp_resolution in temp_resolutions:
resolutions.append(pack_resolution(temp_resolution))
@ -119,6 +119,39 @@ def restrict_video_fps(video_path : str, fps : Fps) -> Fps:
return fps
def detect_video_duration(video_path : str) -> Duration:
video_frame_total = count_video_frame_total(video_path)
video_fps = detect_video_fps(video_path)
if video_frame_total and video_fps:
return video_frame_total / video_fps
return 0
def count_trim_frame_total(video_path : str, trim_frame_start : Optional[int], trim_frame_end : Optional[int]) -> int:
trim_frame_start, trim_frame_end = restrict_trim_frame(video_path, trim_frame_start, trim_frame_end)
return trim_frame_end - trim_frame_start
def restrict_trim_frame(video_path : str, trim_frame_start : Optional[int], trim_frame_end : Optional[int]) -> Tuple[int, int]:
video_frame_total = count_video_frame_total(video_path)
if isinstance(trim_frame_start, int):
trim_frame_start = max(0, min(trim_frame_start, video_frame_total))
if isinstance(trim_frame_end, int):
trim_frame_end = max(0, min(trim_frame_end, video_frame_total))
if isinstance(trim_frame_start, int) and isinstance(trim_frame_end, int):
return trim_frame_start, trim_frame_end
if isinstance(trim_frame_start, int):
return trim_frame_start, video_frame_total
if isinstance(trim_frame_end, int):
return 0, trim_frame_end
return 0, video_frame_total
def detect_video_resolution(video_path : str) -> Optional[Resolution]:
if is_video(video_path):
if is_windows():
@ -147,11 +180,11 @@ def create_video_resolutions(resolution : Resolution) -> List[str]:
if resolution:
width, height = resolution
temp_resolutions.append(normalize_resolution(resolution))
for template_size in video_template_sizes:
for video_template_size in facefusion.choices.video_template_sizes:
if width > height:
temp_resolutions.append(normalize_resolution((template_size * width / height, template_size)))
temp_resolutions.append(normalize_resolution((video_template_size * width / height, video_template_size)))
else:
temp_resolutions.append(normalize_resolution((template_size, template_size * height / width)))
temp_resolutions.append(normalize_resolution((video_template_size, video_template_size * height / width)))
temp_resolutions = sorted(set(temp_resolutions))
for temp_resolution in temp_resolutions:
resolutions.append(pack_resolution(temp_resolution))
@ -202,6 +235,42 @@ def normalize_frame_color(vision_frame : VisionFrame) -> VisionFrame:
return cv2.cvtColor(vision_frame, cv2.COLOR_BGR2RGB)
def conditional_match_frame_color(source_vision_frame : VisionFrame, target_vision_frame : VisionFrame) -> VisionFrame:
histogram_factor = calc_histogram_difference(source_vision_frame, target_vision_frame)
target_vision_frame = blend_vision_frames(target_vision_frame, match_frame_color(source_vision_frame, target_vision_frame), histogram_factor)
return target_vision_frame
def match_frame_color(source_vision_frame : VisionFrame, target_vision_frame : VisionFrame) -> VisionFrame:
color_difference_sizes = numpy.linspace(16, target_vision_frame.shape[0], 3, endpoint = False)
for color_difference_size in color_difference_sizes:
source_vision_frame = equalize_frame_color(source_vision_frame, target_vision_frame, normalize_resolution((color_difference_size, color_difference_size)))
target_vision_frame = equalize_frame_color(source_vision_frame, target_vision_frame, target_vision_frame.shape[:2][::-1])
return target_vision_frame
def equalize_frame_color(source_vision_frame : VisionFrame, target_vision_frame : VisionFrame, size : Size) -> VisionFrame:
source_frame_resize = cv2.resize(source_vision_frame, size, interpolation = cv2.INTER_AREA).astype(numpy.float32)
target_frame_resize = cv2.resize(target_vision_frame, size, interpolation = cv2.INTER_AREA).astype(numpy.float32)
color_difference_vision_frame = numpy.subtract(source_frame_resize, target_frame_resize)
color_difference_vision_frame = cv2.resize(color_difference_vision_frame, target_vision_frame.shape[:2][::-1], interpolation = cv2.INTER_CUBIC)
target_vision_frame = numpy.add(target_vision_frame, color_difference_vision_frame).clip(0, 255).astype(numpy.uint8)
return target_vision_frame
def calc_histogram_difference(source_vision_frame : VisionFrame, target_vision_frame : VisionFrame) -> float:
histogram_source = cv2.calcHist([cv2.cvtColor(source_vision_frame, cv2.COLOR_BGR2HSV)], [ 0, 1 ], None, [ 50, 60 ], [ 0, 180, 0, 256 ])
histogram_target = cv2.calcHist([cv2.cvtColor(target_vision_frame, cv2.COLOR_BGR2HSV)], [ 0, 1 ], None, [ 50, 60 ], [ 0, 180, 0, 256 ])
histogram_differnce = float(numpy.interp(cv2.compareHist(histogram_source, histogram_target, cv2.HISTCMP_CORREL), [ -1, 1 ], [ 0, 1 ]))
return histogram_differnce
def blend_vision_frames(source_vision_frame : VisionFrame, target_vision_frame : VisionFrame, blend_factor : float) -> VisionFrame:
blend_vision_frame = cv2.addWeighted(source_vision_frame, 1 - blend_factor, target_vision_frame, blend_factor, 0)
return blend_vision_frame
def create_tile_frames(vision_frame : VisionFrame, size : Size) -> Tuple[List[VisionFrame], int, int]:
vision_frame = numpy.pad(vision_frame, ((size[1], size[1]), (size[1], size[1]), (0, 0)))
tile_width = size[0] - 2 * size[2]

23
facefusion/voice_extractor.py
View File

@ -1,23 +1,27 @@
from functools import lru_cache
from typing import Tuple
import numpy
import scipy
from facefusion import inference_manager
from facefusion.download import conditional_download_hashes, conditional_download_sources
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import thread_semaphore
from facefusion.typing import Audio, AudioChunk, InferencePool, ModelOptions, ModelSet
from facefusion.typing import Audio, AudioChunk, DownloadScope, InferencePool, ModelOptions, ModelSet
MODEL_SET : ModelSet =\
{
@lru_cache(maxsize = None)
def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
return\
{
'kim_vocal_2':
{
'hashes':
{
'voice_extractor':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/kim_vocal_2.hash',
'url': resolve_download_url('models-3.0.0', 'kim_vocal_2.hash'),
'path': resolve_relative_path('../.assets/models/kim_vocal_2.hash')
}
},
@ -25,12 +29,12 @@ MODEL_SET : ModelSet =\
{
'voice_extractor':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/kim_vocal_2.onnx',
'url': resolve_download_url('models-3.0.0', 'kim_vocal_2.onnx'),
'path': resolve_relative_path('../.assets/models/kim_vocal_2.onnx')
}
}
}
}
}
def get_inference_pool() -> InferencePool:
@ -43,15 +47,14 @@ def clear_inference_pool() -> None:
def get_model_options() -> ModelOptions:
return MODEL_SET.get('kim_vocal_2')
return create_static_model_set('full').get('kim_vocal_2')
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
return conditional_download_hashes(download_directory_path, model_hashes) and conditional_download_sources(download_directory_path, model_sources)
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
def batch_extract_voice(audio : Audio, chunk_size : int, step_size : int) -> Audio:

36
facefusion/wording.py
View File

@ -11,7 +11,10 @@ WORDING : Dict[str, Any] =\
'extracting_frames_succeed': 'Extracting frames succeed',
'extracting_frames_failed': 'Extracting frames failed',
'analysing': 'Analysing',
'extracting': 'Extracting',
'streaming': 'Streaming',
'processing': 'Processing',
'merging': 'Merging',
'downloading': 'Downloading',
'temp_frames_not_found': 'Temporary frames not found',
'copying_image': 'Copying image with a resolution of {resolution}',
@ -46,6 +49,7 @@ WORDING : Dict[str, Any] =\
'ui_layout_not_loaded': 'UI layout {ui_layout} could not be loaded',
'ui_layout_not_implemented': 'UI layout {ui_layout} not implemented correctly',
'stream_not_loaded': 'Stream {stream_mode} could not be loaded',
'stream_not_supported': 'Stream not supported',
'job_created': 'Job {job_id} created',
'job_not_created': 'Job {job_id} not created',
'job_submitted': 'Job {job_id} submitted',
@ -94,10 +98,15 @@ WORDING : Dict[str, Any] =\
'skip_conda': 'skip the conda environment check',
# paths
'config_path': 'choose the config file to override defaults',
'temp_path': 'specify the directory for the temporary resources',
'jobs_path': 'specify the directory to store jobs',
'source_paths': 'choose single or multiple source images or audios',
'target_path': 'choose single target image or video',
'output_path': 'specify the output image or video within a directory',
'source_paths': 'choose the image or audio paths',
'target_path': 'choose the image or video path',
'output_path': 'specify the image or video within a directory',
# patterns
'source_pattern': 'choose the image or audio pattern',
'target_pattern': 'choose the image or video pattern',
'output_pattern': 'specify the image or video pattern',
# face detector
'face_detector_model': 'choose the model responsible for detecting the faces',
'face_detector_size': 'specify the frame size provided to the face detector',
@ -117,8 +126,10 @@ WORDING : Dict[str, Any] =\
'reference_face_distance': 'specify the similarity between the reference face and target face',
'reference_frame_number': 'specify the frame used to create the reference face',
# face masker
'face_occluder_model': 'choose the model responsible for the occlusion mask',
'face_parser_model': 'choose the model responsible for the region mask',
'face_mask_types': 'mix and match different face mask types (choices: {choices})',
'face_mask_blur': 'specify the degree of blur applied the box mask',
'face_mask_blur': 'specify the degree of blur applied to the box mask',
'face_mask_padding': 'apply top, right, bottom and left padding to the box mask',
'face_mask_regions': 'choose the facial features used for the region mask (choices: {choices})',
# frame extraction
@ -140,6 +151,8 @@ WORDING : Dict[str, Any] =\
'processors': 'load a single or multiple processors (choices: {choices}, ...)',
'age_modifier_model': 'choose the model responsible for aging the face',
'age_modifier_direction': 'specify the direction in which the age should be modified',
'deep_swapper_model': 'choose the model responsible for swapping the face',
'deep_swapper_morph': 'morph between source face and target faces',
'expression_restorer_model': 'choose the model responsible for restoring the expression',
'expression_restorer_factor': 'restore factor of expression from the target face',
'face_debugger_items': 'load a single or multiple processors (choices: {choices})',
@ -160,6 +173,7 @@ WORDING : Dict[str, Any] =\
'face_editor_head_roll': 'specify the head roll',
'face_enhancer_model': 'choose the model responsible for enhancing the face',
'face_enhancer_blend': 'blend the enhanced into the previous face',
'face_enhancer_weight': 'specify the degree of weight applied to the face',
'face_swapper_model': 'choose the model responsible for swapping the face',
'face_swapper_pixel_boost': 'choose the pixel boost resolution for the face swapper',
'frame_colorizer_model': 'choose the model responsible for colorizing the frame',
@ -174,18 +188,21 @@ WORDING : Dict[str, Any] =\
'ui_workflow': 'choose the ui workflow',
# execution
'execution_device_id': 'specify the device used for processing',
'execution_providers': 'accelerate the model inference using different providers (choices: {choices}, ...)',
'execution_providers': 'inference using different providers (choices: {choices}, ...)',
'execution_thread_count': 'specify the amount of parallel threads while processing',
'execution_queue_count': 'specify the amount of frames each thread is processing',
# download
'download_providers': 'download using different providers (choices: {choices}, ...)',
'download_scope': 'specify the download scope',
# memory
'video_memory_strategy': 'balance fast processing and low VRAM usage',
'system_memory_limit': 'limit the available RAM that can be used while processing',
# misc
'skip_download': 'omit downloads and remote lookups',
'log_level': 'adjust the message severity displayed in the terminal',
# run
'run': 'run the program',
'headless_run': 'run the program in headless mode',
'batch_run': 'run the program in batch mode',
'force_download': 'force automate downloads and exit',
# jobs
'job_id': 'specify the job id',
@ -223,6 +240,9 @@ WORDING : Dict[str, Any] =\
'benchmark_runs_checkbox_group': 'BENCHMARK RUNS',
'clear_button': 'CLEAR',
'common_options_checkbox_group': 'OPTIONS',
'download_providers_checkbox_group': 'DOWNLOAD PROVIDERS',
'deep_swapper_model_dropdown': 'DEEP SWAPPER MODEL',
'deep_swapper_morph_slider': 'DEEP SWAPPER MORPH',
'execution_providers_checkbox_group': 'EXECUTION PROVIDERS',
'execution_queue_count_slider': 'EXECUTION QUEUE COUNT',
'execution_thread_count_slider': 'EXECUTION THREAD COUNT',
@ -250,6 +270,7 @@ WORDING : Dict[str, Any] =\
'face_editor_mouth_smile_slider': 'FACE EDITOR MOUTH SMILE',
'face_enhancer_blend_slider': 'FACE ENHANCER BLEND',
'face_enhancer_model_dropdown': 'FACE ENHANCER MODEL',
'face_enhancer_weight_slider': 'FACE ENHANCER WEIGHT',
'face_landmarker_model_dropdown': 'FACE LANDMARKER MODEL',
'face_landmarker_score_slider': 'FACE LANDMARKER SCORE',
'face_mask_blur_slider': 'FACE MASK BLUR',
@ -266,6 +287,8 @@ WORDING : Dict[str, Any] =\
'face_selector_race_dropdown': 'FACE SELECTOR RACE',
'face_swapper_model_dropdown': 'FACE SWAPPER MODEL',
'face_swapper_pixel_boost_dropdown': 'FACE SWAPPER PIXEL BOOST',
'face_occluder_model_dropdown': 'FACE OCCLUDER MODEL',
'face_parser_model_dropdown': 'FACE PARSER MODEL',
'frame_colorizer_blend_slider': 'FRAME COLORIZER BLEND',
'frame_colorizer_model_dropdown': 'FRAME COLORIZER MODEL',
'frame_colorizer_size_dropdown': 'FRAME COLORIZER SIZE',
@ -307,6 +330,7 @@ WORDING : Dict[str, Any] =\
'video_memory_strategy_dropdown': 'VIDEO MEMORY STRATEGY',
'webcam_fps_slider': 'WEBCAM FPS',
'webcam_image': 'WEBCAM',
'webcam_device_id_dropdown': 'WEBCAM DEVICE ID',
'webcam_mode_radio': 'WEBCAM MODE',
'webcam_resolution_dropdown': 'WEBCAM RESOLUTION'
}

14
requirements.txt
View File

@ -1,10 +1,10 @@
filetype==1.2.0
gradio==4.44.0
gradio-rangeslider==0.0.6
numpy==2.1.0
onnx==1.16.1
onnxruntime==1.19.2
gradio==5.9.1
gradio-rangeslider==0.0.8
numpy==2.2.0
onnx==1.17.0
onnxruntime==1.20.1
opencv-python==4.10.0.84
psutil==6.0.0
tqdm==4.66.5
psutil==6.1.1
tqdm==4.67.1
scipy==1.14.1

8
tests/helper.py
View File

@ -1,7 +1,7 @@
import os
import tempfile
from facefusion.filesystem import create_directory, is_directory, is_file, remove_directory
from facefusion.temp_helper import get_base_directory_path
from facefusion.typing import JobStatus
@ -14,7 +14,7 @@ def get_test_job_file(file_path : str, job_status : JobStatus) -> str:
def get_test_jobs_directory() -> str:
return os.path.join(get_base_directory_path(), 'test-jobs')
return os.path.join(tempfile.gettempdir(), 'facefusion-test-jobs')
def get_test_example_file(file_path : str) -> str:
@ -22,7 +22,7 @@ def get_test_example_file(file_path : str) -> str:
def get_test_examples_directory() -> str:
return os.path.join(get_base_directory_path(), 'test-examples')
return os.path.join(tempfile.gettempdir(), 'facefusion-test-examples')
def is_test_output_file(file_path : str) -> bool:
@ -34,7 +34,7 @@ def get_test_output_file(file_path : str) -> str:
def get_test_outputs_directory() -> str:
return os.path.join(get_base_directory_path(), 'test-outputs')
return os.path.join(tempfile.gettempdir(), 'facefusion-test-outputs')
def prepare_test_output_directory() -> bool:

4
tests/test_audio.py
View File

@ -17,8 +17,8 @@ def before_all() -> None:
def test_get_audio_frame() -> None:
assert get_audio_frame(get_test_example_file('source.mp3'), 25) is not None
assert get_audio_frame(get_test_example_file('source.wav'), 25) is not None
assert hasattr(get_audio_frame(get_test_example_file('source.mp3'), 25), '__array_interface__')
assert hasattr(get_audio_frame(get_test_example_file('source.wav'), 25), '__array_interface__')
assert get_audio_frame('invalid', 25) is None

4
tests/test_cli_age_modifier.py
View File

@ -25,14 +25,14 @@ def before_each() -> None:
def test_modify_age_to_image() -> None:
commands = [ sys.executable, 'facefusion.py', 'headless-run', '-j', get_test_jobs_directory(), '--processors', 'age_modifier', '--age-modifier-direction', '100', '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-age-face-to-image.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'headless-run', '--jobs-path', get_test_jobs_directory(), '--processors', 'age_modifier', '--age-modifier-direction', '100', '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-age-face-to-image.jpg') ]
assert subprocess.run(commands).returncode == 0
assert is_test_output_file('test-age-face-to-image.jpg') is True
def test_modify_age_to_video() -> None:
commands = [ sys.executable, 'facefusion.py', 'headless-run', '-j', get_test_jobs_directory(), '--processors', 'age_modifier', '--age-modifier-direction', '100', '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test-age-face-to-video.mp4'), '--trim-frame-end', '1' ]
commands = [ sys.executable, 'facefusion.py', 'headless-run', '--jobs-path', get_test_jobs_directory(), '--processors', 'age_modifier', '--age-modifier-direction', '100', '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test-age-face-to-video.mp4'), '--trim-frame-end', '1' ]
assert subprocess.run(commands).returncode == 0
assert is_test_output_file('test-age-face-to-video.mp4') is True

45
tests/test_cli_batch_runner.py Normal file
View File

@ -0,0 +1,45 @@
import subprocess
import sys
import pytest
from facefusion.download import conditional_download
from facefusion.jobs.job_manager import clear_jobs, init_jobs
from .helper import get_test_example_file, get_test_examples_directory, get_test_jobs_directory, get_test_output_file, is_test_output_file, prepare_test_output_directory
@pytest.fixture(scope = 'module', autouse = True)
def before_all() -> None:
conditional_download(get_test_examples_directory(),
[
'https://github.com/facefusion/facefusion-assets/releases/download/examples-3.0.0/target-240p.mp4'
])
subprocess.run([ 'ffmpeg', '-i', get_test_example_file('target-240p.mp4'), '-vframes', '1', get_test_example_file('target-240p-batch-1.jpg') ])
subprocess.run([ 'ffmpeg', '-i', get_test_example_file('target-240p.mp4'), '-vframes', '2', get_test_example_file('target-240p-batch-2.jpg') ])
@pytest.fixture(scope = 'function', autouse = True)
def before_each() -> None:
clear_jobs(get_test_jobs_directory())
init_jobs(get_test_jobs_directory())
prepare_test_output_directory()
def test_batch_run_targets() -> None:
commands = [ sys.executable, 'facefusion.py', 'batch-run', '--jobs-path', get_test_jobs_directory(), '--processors', 'face_debugger', '-t', get_test_example_file('target-240p-batch-*.jpg'), '-o', get_test_output_file('test-batch-run-targets-{index}.jpg') ]
assert subprocess.run(commands).returncode == 0
assert is_test_output_file('test-batch-run-targets-0.jpg') is True
assert is_test_output_file('test-batch-run-targets-1.jpg') is True
assert is_test_output_file('test-batch-run-targets-2.jpg') is False
def test_batch_run_sources_to_targets() -> None:
commands = [ sys.executable, 'facefusion.py', 'batch-run', '--jobs-path', get_test_jobs_directory(), '-s', get_test_example_file('target-240p-batch-*.jpg'), '-t', get_test_example_file('target-240p-batch-*.jpg'), '-o', get_test_output_file('test-batch-run-sources-to-targets-{index}.jpg') ]
assert subprocess.run(commands).returncode == 0
assert is_test_output_file('test-batch-run-sources-to-targets-0.jpg') is True
assert is_test_output_file('test-batch-run-sources-to-targets-1.jpg') is True
assert is_test_output_file('test-batch-run-sources-to-targets-2.jpg') is True
assert is_test_output_file('test-batch-run-sources-to-targets-3.jpg') is True
assert is_test_output_file('test-batch-run-sources-to-targets-4.jpg') is False

4
tests/test_cli_expression_restorer.py
View File

@ -25,14 +25,14 @@ def before_each() -> None:
def test_restore_expression_to_image() -> None:
commands = [ sys.executable, 'facefusion.py', 'headless-run', '-j', get_test_jobs_directory(), '--processors', 'expression_restorer', '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-restore-expression-to-image.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'headless-run', '--jobs-path', get_test_jobs_directory(), '--processors', 'expression_restorer', '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-restore-expression-to-image.jpg') ]
assert subprocess.run(commands).returncode == 0
assert is_test_output_file('test-restore-expression-to-image.jpg') is True
def test_restore_expression_to_video() -> None:
commands = [ sys.executable, 'facefusion.py', 'headless-run', '-j', get_test_jobs_directory(), '--processors', 'expression_restorer', '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test-restore-expression-to-video.mp4'), '--trim-frame-end', '1' ]
commands = [ sys.executable, 'facefusion.py', 'headless-run', '--jobs-path', get_test_jobs_directory(), '--processors', 'expression_restorer', '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test-restore-expression-to-video.mp4'), '--trim-frame-end', '1' ]
assert subprocess.run(commands).returncode == 0
assert is_test_output_file('test-restore-expression-to-video.mp4') is True

4
tests/test_cli_face_debugger.py
View File

@ -26,14 +26,14 @@ def before_each() -> None:
def test_debug_face_to_image() -> None:
commands = [ sys.executable, 'facefusion.py', 'headless-run', '-j', get_test_jobs_directory(), '--processors', 'face_debugger', '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-debug-face-to-image.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'headless-run', '--jobs-path', get_test_jobs_directory(), '--processors', 'face_debugger', '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-debug-face-to-image.jpg') ]
assert subprocess.run(commands).returncode == 0
assert is_test_output_file('test-debug-face-to-image.jpg') is True
def test_debug_face_to_video() -> None:
commands = [ sys.executable, 'facefusion.py', 'headless-run', '-j', get_test_jobs_directory(), '--processors', 'face_debugger', '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test-debug-face-to-video.mp4'), '--trim-frame-end', '1' ]
commands = [ sys.executable, 'facefusion.py', 'headless-run', '--jobs-path', get_test_jobs_directory(), '--processors', 'face_debugger', '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test-debug-face-to-video.mp4'), '--trim-frame-end', '1' ]
assert subprocess.run(commands).returncode == 0
assert is_test_output_file('test-debug-face-to-video.mp4') is True

4
tests/test_cli_face_editor.py
View File

@ -26,14 +26,14 @@ def before_each() -> None:
def test_edit_face_to_image() -> None:
commands = [ sys.executable, 'facefusion.py', 'headless-run', '-j', get_test_jobs_directory(), '--processors', 'face_editor', '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-edit-face-to-image.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'headless-run', '--jobs-path', get_test_jobs_directory(), '--processors', 'face_editor', '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-edit-face-to-image.jpg') ]
assert subprocess.run(commands).returncode == 0
assert is_test_output_file('test-edit-face-to-image.jpg') is True
def test_edit_face_to_video() -> None:
commands = [ sys.executable, 'facefusion.py', 'headless-run', '-j', get_test_jobs_directory(), '--processors', 'face_editor', '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test-edit-face-to-video.mp4'), '--trim-frame-end', '1' ]
commands = [ sys.executable, 'facefusion.py', 'headless-run', '--jobs-path', get_test_jobs_directory(), '--processors', 'face_editor', '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test-edit-face-to-video.mp4'), '--trim-frame-end', '1' ]
assert subprocess.run(commands).returncode == 0
assert is_test_output_file('test-edit-face-to-video.mp4') is True

4
tests/test_cli_face_enhancer.py
View File

@ -26,14 +26,14 @@ def before_each() -> None:
def test_enhance_face_to_image() -> None:
commands = [ sys.executable, 'facefusion.py', 'headless-run', '-j', get_test_jobs_directory(), '--processors', 'face_enhancer', '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-enhance-face-to-image.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'headless-run', '--jobs-path', get_test_jobs_directory(), '--processors', 'face_enhancer', '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-enhance-face-to-image.jpg') ]
assert subprocess.run(commands).returncode == 0
assert is_test_output_file('test-enhance-face-to-image.jpg') is True
def test_enhance_face_to_video() -> None:
commands = [ sys.executable, 'facefusion.py', 'headless-run', '-j', get_test_jobs_directory(), '--processors', 'face_enhancer', '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test-enhance-face-to-video.mp4'), '--trim-frame-end', '1' ]
commands = [ sys.executable, 'facefusion.py', 'headless-run', '--jobs-path', get_test_jobs_directory(), '--processors', 'face_enhancer', '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test-enhance-face-to-video.mp4'), '--trim-frame-end', '1' ]
assert subprocess.run(commands).returncode == 0
assert is_test_output_file('test-enhance-face-to-video.mp4') is True

4
tests/test_cli_face_swapper.py
View File

@ -26,14 +26,14 @@ def before_each() -> None:
def test_swap_face_to_image() -> None:
commands = [ sys.executable, 'facefusion.py', 'headless-run', '-j', get_test_jobs_directory(), '--processors', 'face_swapper', '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-swap-face-to-image.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'headless-run', '--jobs-path', get_test_jobs_directory(), '--processors', 'face_swapper', '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-swap-face-to-image.jpg') ]
assert subprocess.run(commands).returncode == 0
assert is_test_output_file('test-swap-face-to-image.jpg') is True
def test_swap_face_to_video() -> None:
commands = [ sys.executable, 'facefusion.py', 'headless-run', '-j', get_test_jobs_directory(), '--processors', 'face_swapper', '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test-swap-face-to-video.mp4'), '--trim-frame-end', '1' ]
commands = [ sys.executable, 'facefusion.py', 'headless-run', '--jobs-path', get_test_jobs_directory(), '--processors', 'face_swapper', '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test-swap-face-to-video.mp4'), '--trim-frame-end', '1' ]
assert subprocess.run(commands).returncode == 0
assert is_test_output_file('test-swap-face-to-video.mp4') is True

4
tests/test_cli_frame_colorizer.py
View File

@ -27,14 +27,14 @@ def before_each() -> None:
def test_colorize_frame_to_image() -> None:
commands = [ sys.executable, 'facefusion.py', 'headless-run', '-j', get_test_jobs_directory(), '--processors', 'frame_colorizer', '-t', get_test_example_file('target-240p-0sat.jpg'), '-o', get_test_output_file('test_colorize-frame-to-image.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'headless-run', '--jobs-path', get_test_jobs_directory(), '--processors', 'frame_colorizer', '-t', get_test_example_file('target-240p-0sat.jpg'), '-o', get_test_output_file('test_colorize-frame-to-image.jpg') ]
assert subprocess.run(commands).returncode == 0
assert is_test_output_file('test_colorize-frame-to-image.jpg') is True
def test_colorize_frame_to_video() -> None:
commands = [ sys.executable, 'facefusion.py', 'headless-run', '-j', get_test_jobs_directory(), '--processors', 'frame_colorizer', '-t', get_test_example_file('target-240p-0sat.mp4'), '-o', get_test_output_file('test-colorize-frame-to-video.mp4'), '--trim-frame-end', '1' ]
commands = [ sys.executable, 'facefusion.py', 'headless-run', '--jobs-path', get_test_jobs_directory(), '--processors', 'frame_colorizer', '-t', get_test_example_file('target-240p-0sat.mp4'), '-o', get_test_output_file('test-colorize-frame-to-video.mp4'), '--trim-frame-end', '1' ]
assert subprocess.run(commands).returncode == 0
assert is_test_output_file('test-colorize-frame-to-video.mp4') is True

4
tests/test_cli_frame_enhancer.py
View File

@ -26,14 +26,14 @@ def before_each() -> None:
def test_enhance_frame_to_image() -> None:
commands = [ sys.executable, 'facefusion.py', 'headless-run', '-j', get_test_jobs_directory(), '--processors', 'frame_enhancer', '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-enhance-frame-to-image.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'headless-run', '--jobs-path', get_test_jobs_directory(), '--processors', 'frame_enhancer', '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-enhance-frame-to-image.jpg') ]
assert subprocess.run(commands).returncode == 0
assert is_test_output_file('test-enhance-frame-to-image.jpg') is True
def test_enhance_frame_to_video() -> None:
commands = [ sys.executable, 'facefusion.py', 'headless-run', '-j', get_test_jobs_directory(), '--processors', 'frame_enhancer', '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test-enhance-frame-to-video.mp4'), '--trim-frame-end', '1' ]
commands = [ sys.executable, 'facefusion.py', 'headless-run', '--jobs-path', get_test_jobs_directory(), '--processors', 'frame_enhancer', '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test-enhance-frame-to-video.mp4'), '--trim-frame-end', '1' ]
assert subprocess.run(commands).returncode == 0
assert is_test_output_file('test-enhance-frame-to-video.mp4') is True

74
tests/test_cli_job_manager.py
View File

@ -30,30 +30,30 @@ def test_job_list() -> None:
def test_job_create() -> None:
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-create', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-create', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 0
assert is_test_job_file('test-job-create.json', 'drafted') is True
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-create', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-create', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 1
def test_job_submit() -> None:
commands = [ sys.executable, 'facefusion.py', 'job-submit', 'test-job-submit', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-submit', 'test-job-submit', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 1
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-submit', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-submit', '--jobs-path', get_test_jobs_directory() ]
subprocess.run(commands)
assert subprocess.run(commands).returncode == 1
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-submit', '-j', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-submit', '--jobs-path', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-submit', 'test-job-submit', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-submit', 'test-job-submit', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 0
assert is_test_job_file('test-job-submit.json', 'queued') is True
@ -61,25 +61,25 @@ def test_job_submit() -> None:
def test_submit_all() -> None:
commands = [ sys.executable, 'facefusion.py', 'job-submit-all', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-submit-all', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 1
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-submit-all-1', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-submit-all-1', '--jobs-path', get_test_jobs_directory() ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-submit-all-2', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-submit-all-2', '--jobs-path', get_test_jobs_directory() ]
subprocess.run(commands)
assert subprocess.run(commands).returncode == 1
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-submit-all-1', '-j', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-submit-all-1', '--jobs-path', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-submit-all-2', '-j', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-submit-all-2', '--jobs-path', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-submit-all', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-submit-all', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 0
assert is_test_job_file('test-job-submit-all-1.json', 'queued') is True
@ -88,14 +88,14 @@ def test_submit_all() -> None:
def test_job_delete() -> None:
commands = [ sys.executable, 'facefusion.py', 'job-delete', 'test-job-delete', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-delete', 'test-job-delete', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 1
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-delete', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-delete', '--jobs-path', get_test_jobs_directory() ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-delete', 'test-job-delete', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-delete', 'test-job-delete', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 0
assert is_test_job_file('test-job-delete.json', 'drafted') is False
@ -103,17 +103,17 @@ def test_job_delete() -> None:
def test_job_delete_all() -> None:
commands = [ sys.executable, 'facefusion.py', 'job-delete-all', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-delete-all', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 1
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-delete-all-1', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-delete-all-1', '--jobs-path', get_test_jobs_directory() ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-delete-all-2', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-delete-all-2', '--jobs-path', get_test_jobs_directory() ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-delete-all', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-delete-all', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 0
assert is_test_job_file('test-job-delete-all-1.json', 'drafted') is False
@ -122,87 +122,87 @@ def test_job_delete_all() -> None:
def test_job_add_step() -> None:
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-add-step', '-j', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-add-step', '--jobs-path', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
assert subprocess.run(commands).returncode == 1
assert count_step_total('test-job-add-step') == 0
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-add-step', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-add-step', '--jobs-path', get_test_jobs_directory() ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-add-step', '-j', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-add-step', '--jobs-path', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
assert subprocess.run(commands).returncode == 0
assert count_step_total('test-job-add-step') == 1
def test_job_remix() -> None:
commands = [ sys.executable, 'facefusion.py', 'job-remix-step', 'test-job-remix-step', '0', '-j', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'job-remix-step', 'test-job-remix-step', '0', '--jobs-path', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
assert subprocess.run(commands).returncode == 1
assert count_step_total('test-job-remix-step') == 0
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-remix-step', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-remix-step', '--jobs-path', get_test_jobs_directory() ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-remix-step', '-j', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-remix-step', '--jobs-path', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-remix-step', 'test-job-remix-step', '0', '-j', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'job-remix-step', 'test-job-remix-step', '0', '--jobs-path', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
assert count_step_total('test-job-remix-step') == 1
assert subprocess.run(commands).returncode == 0
assert count_step_total('test-job-remix-step') == 2
commands = [ sys.executable, 'facefusion.py', 'job-remix-step', 'test-job-remix-step', '-1', '-j', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'job-remix-step', 'test-job-remix-step', '-1', '--jobs-path', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
assert subprocess.run(commands).returncode == 0
assert count_step_total('test-job-remix-step') == 3
def test_job_insert_step() -> None:
commands = [ sys.executable, 'facefusion.py', 'job-insert-step', 'test-job-insert-step', '0', '-j', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'job-insert-step', 'test-job-insert-step', '0', '--jobs-path', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
assert subprocess.run(commands).returncode == 1
assert count_step_total('test-job-insert-step') == 0
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-insert-step', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-insert-step', '--jobs-path', get_test_jobs_directory() ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-insert-step', '-j', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-insert-step', '--jobs-path', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-insert-step', 'test-job-insert-step', '0', '-j', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'job-insert-step', 'test-job-insert-step', '0', '--jobs-path', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
assert count_step_total('test-job-insert-step') == 1
assert subprocess.run(commands).returncode == 0
assert count_step_total('test-job-insert-step') == 2
commands = [ sys.executable, 'facefusion.py', 'job-insert-step', 'test-job-insert-step', '-1', '-j', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'job-insert-step', 'test-job-insert-step', '-1', '--jobs-path', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
assert subprocess.run(commands).returncode == 0
assert count_step_total('test-job-insert-step') == 3
def test_job_remove_step() -> None:
commands = [ sys.executable, 'facefusion.py', 'job-remove-step', 'test-job-remove-step', '0', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-remove-step', 'test-job-remove-step', '0', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 1
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-remove-step', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-remove-step', '--jobs-path', get_test_jobs_directory() ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-remove-step', '-j', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-remove-step', '--jobs-path', get_test_jobs_directory(), '-s', get_test_example_file('source.jpg'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-remix-step.jpg') ]
subprocess.run(commands)
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-remove-step', 'test-job-remove-step', '0', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-remove-step', 'test-job-remove-step', '0', '--jobs-path', get_test_jobs_directory() ]
assert count_step_total('test-job-remove-step') == 2
assert subprocess.run(commands).returncode == 0
assert count_step_total('test-job-remove-step') == 1
commands = [ sys.executable, 'facefusion.py', 'job-remove-step', 'test-job-remove-step', '-1', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-remove-step', 'test-job-remove-step', '-1', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 0
assert subprocess.run(commands).returncode == 1

56
tests/test_cli_job_runner.py
View File

@ -26,24 +26,24 @@ def before_each() -> None:
def test_job_run() -> None:
commands = [ sys.executable, 'facefusion.py', 'job-run', 'test-job-run', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-run', 'test-job-run', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 1
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-run', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-run', '--jobs-path', get_test_jobs_directory() ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-run', '-j', get_test_jobs_directory(), '--processors', 'face_debugger', '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-run.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-run', '--jobs-path', get_test_jobs_directory(), '--processors', 'face_debugger', '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-run.jpg') ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-run', 'test-job-run', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-run', 'test-job-run', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 1
commands = [ sys.executable, 'facefusion.py', 'job-submit', 'test-job-run', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-submit', 'test-job-run', '--jobs-path', get_test_jobs_directory() ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-run', 'test-job-run', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-run', 'test-job-run', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 0
assert subprocess.run(commands).returncode == 1
@ -51,33 +51,33 @@ def test_job_run() -> None:
def test_job_run_all() -> None:
commands = [ sys.executable, 'facefusion.py', 'job-run-all', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-run-all', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 1
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-run-all-1', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-run-all-1', '--jobs-path', get_test_jobs_directory() ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-run-all-2', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-run-all-2', '--jobs-path', get_test_jobs_directory() ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-run-all-1', '-j', get_test_jobs_directory(), '--processors', 'face_debugger', '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-run-all-1.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-run-all-1', '--jobs-path', get_test_jobs_directory(), '--processors', 'face_debugger', '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-run-all-1.jpg') ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-run-all-2', '-j', get_test_jobs_directory(), '--processors', 'face_debugger', '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test-job-run-all-2.mp4'), '--trim-frame-end', '1' ]
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-run-all-2', '--jobs-path', get_test_jobs_directory(), '--processors', 'face_debugger', '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test-job-run-all-2.mp4'), '--trim-frame-end', '1' ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-run-all-2', '-j', get_test_jobs_directory(), '--processors', 'face_debugger', '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test-job-run-all-2.mp4'), '--trim-frame-start', '0', '--trim-frame-end', '1' ]
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-run-all-2', '--jobs-path', get_test_jobs_directory(), '--processors', 'face_debugger', '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test-job-run-all-2.mp4'), '--trim-frame-start', '0', '--trim-frame-end', '1' ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-run-all', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-run-all', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 1
commands = [ sys.executable, 'facefusion.py', 'job-submit-all', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-submit-all', '--jobs-path', get_test_jobs_directory() ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-run-all', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-run-all', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 0
assert subprocess.run(commands).returncode == 1
@ -86,24 +86,24 @@ def test_job_run_all() -> None:
def test_job_retry() -> None:
commands = [ sys.executable, 'facefusion.py', 'job-retry', 'test-job-retry', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-retry', 'test-job-retry', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 1
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-retry', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-retry', '--jobs-path', get_test_jobs_directory() ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-retry', '-j', get_test_jobs_directory(), '--processors', 'face_debugger', '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-retry.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-retry', '--jobs-path', get_test_jobs_directory(), '--processors', 'face_debugger', '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-retry.jpg') ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-retry', 'test-job-retry', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-retry', 'test-job-retry', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 1
set_steps_status('test-job-retry', 'failed')
move_job_file('test-job-retry', 'failed')
commands = [ sys.executable, 'facefusion.py', 'job-retry', 'test-job-retry', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-retry', 'test-job-retry', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 0
assert subprocess.run(commands).returncode == 1
@ -111,26 +111,26 @@ def test_job_retry() -> None:
def test_job_retry_all() -> None:
commands = [ sys.executable, 'facefusion.py', 'job-retry-all', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-retry-all', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 1
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-retry-all-1', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-retry-all-1', '--jobs-path', get_test_jobs_directory() ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-retry-all-2', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-create', 'test-job-retry-all-2', '--jobs-path', get_test_jobs_directory() ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-retry-all-1', '-j', get_test_jobs_directory(), '--processors', 'face_debugger', '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-retry-all-1.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-retry-all-1', '--jobs-path', get_test_jobs_directory(), '--processors', 'face_debugger', '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test-job-retry-all-1.jpg') ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-retry-all-2', '-j', get_test_jobs_directory(), '--processors', 'face_debugger', '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test-job-retry-all-2.mp4'), '--trim-frame-end', '1' ]
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-retry-all-2', '--jobs-path', get_test_jobs_directory(), '--processors', 'face_debugger', '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test-job-retry-all-2.mp4'), '--trim-frame-end', '1' ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-retry-all-2', '-j', get_test_jobs_directory(), '--processors', 'face_debugger', '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test-job-retry-all-2.mp4'), '--trim-frame-start', '0', '--trim-frame-end', '1' ]
commands = [ sys.executable, 'facefusion.py', 'job-add-step', 'test-job-retry-all-2', '--jobs-path', get_test_jobs_directory(), '--processors', 'face_debugger', '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test-job-retry-all-2.mp4'), '--trim-frame-start', '0', '--trim-frame-end', '1' ]
subprocess.run(commands)
commands = [ sys.executable, 'facefusion.py', 'job-retry-all', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-retry-all', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 1
@ -139,7 +139,7 @@ def test_job_retry_all() -> None:
move_job_file('test-job-retry-all-1', 'failed')
move_job_file('test-job-retry-all-2', 'failed')
commands = [ sys.executable, 'facefusion.py', 'job-retry-all', '-j', get_test_jobs_directory() ]
commands = [ sys.executable, 'facefusion.py', 'job-retry-all', '--jobs-path', get_test_jobs_directory() ]
assert subprocess.run(commands).returncode == 0
assert subprocess.run(commands).returncode == 1

4
tests/test_cli_lip_syncer.py
View File

@ -27,14 +27,14 @@ def before_each() -> None:
def test_sync_lip_to_image() -> None:
commands = [ sys.executable, 'facefusion.py', 'headless-run', '-j', get_test_jobs_directory(), '--processors', 'lip_syncer', '-s', get_test_example_file('source.mp3'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test_sync_lip_to_image.jpg') ]
commands = [ sys.executable, 'facefusion.py', 'headless-run', '--jobs-path', get_test_jobs_directory(), '--processors', 'lip_syncer', '-s', get_test_example_file('source.mp3'), '-t', get_test_example_file('target-240p.jpg'), '-o', get_test_output_file('test_sync_lip_to_image.jpg') ]
assert subprocess.run(commands).returncode == 0
assert is_test_output_file('test_sync_lip_to_image.jpg') is True
def test_sync_lip_to_video() -> None:
commands = [ sys.executable, 'facefusion.py', 'headless-run', '-j', get_test_jobs_directory(), '--processors', 'lip_syncer', '-s', get_test_example_file('source.mp3'), '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test_sync_lip_to_video.mp4'), '--trim-frame-end', '1' ]
commands = [ sys.executable, 'facefusion.py', 'headless-run', '--jobs-path', get_test_jobs_directory(), '--processors', 'lip_syncer', '-s', get_test_example_file('source.mp3'), '-t', get_test_example_file('target-240p.mp4'), '-o', get_test_output_file('test_sync_lip_to_video.mp4'), '--trim-frame-end', '1' ]
assert subprocess.run(commands).returncode == 0
assert is_test_output_file('test_sync_lip_to_video.mp4') is True

4
tests/test_date_helper.py
View File

@ -10,6 +10,6 @@ def get_time_ago(days : int, hours : int, minutes : int) -> datetime:
def test_describe_time_ago() -> None:
assert describe_time_ago(get_time_ago(0, 0, 0)) == 'just now'
assert describe_time_ago(get_time_ago(0, 0, 5)) == '5 minutes ago'
assert describe_time_ago(get_time_ago(0, 0, 10)) == '10 minutes ago'
assert describe_time_ago(get_time_ago(0, 5, 10)) == '5 hours and 10 minutes ago'
assert describe_time_ago(get_time_ago(5, 10, 15)) == '5 days, 10 hours and 15 minutes ago'
assert describe_time_ago(get_time_ago(1, 5, 10)) == '1 days, 5 hours and 10 minutes ago'

30
tests/test_download.py
View File

@ -1,24 +1,18 @@
import pytest
from facefusion.download import conditional_download, get_download_size, is_download_done
from .helper import get_test_example_file, get_test_examples_directory
from facefusion.download import get_static_download_size, ping_static_url, resolve_download_url_by_provider
@pytest.fixture(scope = 'module', autouse = True)
def before_all() -> None:
conditional_download(get_test_examples_directory(),
[
'https://github.com/facefusion/facefusion-assets/releases/download/examples-3.0.0/target-240p.mp4'
])
def test_get_static_download_size() -> None:
assert get_static_download_size('https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/fairface.onnx') == 85170772
assert get_static_download_size('https://huggingface.co/facefusion/models-3.0.0/resolve/main/fairface.onnx') == 85170772
assert get_static_download_size('invalid') == 0
def test_get_download_size() -> None:
assert get_download_size('https://github.com/facefusion/facefusion-assets/releases/download/examples-3.0.0/target-240p.mp4') == 191675
assert get_download_size('https://github.com/facefusion/facefusion-assets/releases/download/examples-3.0.0/target-360p.mp4') == 370732
assert get_download_size('invalid') == 0
def test_static_ping_url() -> None:
assert ping_static_url('https://github.com') is True
assert ping_static_url('https://huggingface.co') is True
assert ping_static_url('invalid') is False
def test_is_download_done() -> None:
assert is_download_done('https://github.com/facefusion/facefusion-assets/releases/download/examples-3.0.0/target-240p.mp4', get_test_example_file('target-240p.mp4')) is True
assert is_download_done('https://github.com/facefusion/facefusion-assets/releases/download/examples-3.0.0/target-240p.mp4', 'invalid') is False
assert is_download_done('invalid', 'invalid') is False
def test_resolve_download_url_by_provider() -> None:
assert resolve_download_url_by_provider('github', 'models-3.0.0', 'fairface.onnx') == 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/fairface.onnx'
assert resolve_download_url_by_provider('huggingface', 'models-3.0.0', 'fairface.onnx') == 'https://huggingface.co/facefusion/models-3.0.0/resolve/main/fairface.onnx'

17
tests/test_execution.py
View File

@ -1,8 +1,4 @@
from facefusion.execution import create_execution_providers, get_execution_provider_choices, has_execution_provider
def test_get_execution_provider_choices() -> None:
assert 'cpu' in get_execution_provider_choices()
from facefusion.execution import create_inference_execution_providers, get_available_execution_providers, has_execution_provider
def test_has_execution_provider() -> None:
@ -10,15 +6,18 @@ def test_has_execution_provider() -> None:
assert has_execution_provider('openvino') is False
def test_multiple_execution_providers() -> None:
def test_get_available_execution_providers() -> None:
assert 'cpu' in get_available_execution_providers()
def test_create_inference_execution_providers() -> None:
execution_providers =\
[
('CUDAExecutionProvider',
{
'device_id': '1',
'cudnn_conv_algo_search': 'DEFAULT'
'device_id': '1'
}),
'CPUExecutionProvider'
]
assert create_execution_providers('1', [ 'cpu', 'cuda' ]) == execution_providers
assert create_inference_execution_providers('1', [ 'cpu', 'cuda' ]) == execution_providers

1
tests/test_face_analyser.py
View File

@ -21,6 +21,7 @@ def before_all() -> None:
subprocess.run([ 'ffmpeg', '-i', get_test_example_file('source.jpg'), '-vf', 'crop=iw*0.6:ih*0.6', get_test_example_file('source-60crop.jpg') ])
state_manager.init_item('execution_device_id', 0)
state_manager.init_item('execution_providers', [ 'cpu' ])
state_manager.init_item('download_providers', [ 'github' ])
state_manager.init_item('face_detector_angles', [ 0 ])
state_manager.init_item('face_detector_model', 'many')
state_manager.init_item('face_detector_score', 0.5)

126
tests/test_ffmpeg.py
View File

@ -1,12 +1,13 @@
import glob
import subprocess
import tempfile
import pytest
from facefusion import process_manager, state_manager
from facefusion.download import conditional_download
from facefusion.ffmpeg import concat_video, extract_frames, read_audio_buffer
from facefusion.temp_helper import clear_temp_directory, create_temp_directory, get_temp_directory_path
from facefusion.ffmpeg import concat_video, extract_frames, read_audio_buffer, replace_audio, restore_audio
from facefusion.filesystem import copy_file
from facefusion.temp_helper import clear_temp_directory, create_temp_directory, get_temp_file_path, get_temp_frame_paths
from .helper import get_test_example_file, get_test_examples_directory, get_test_output_file, prepare_test_output_directory
@ -23,89 +24,40 @@ def before_all() -> None:
subprocess.run([ 'ffmpeg', '-i', get_test_example_file('target-240p.mp4'), '-vf', 'fps=25', get_test_example_file('target-240p-25fps.mp4') ])
subprocess.run([ 'ffmpeg', '-i', get_test_example_file('target-240p.mp4'), '-vf', 'fps=30', get_test_example_file('target-240p-30fps.mp4') ])
subprocess.run([ 'ffmpeg', '-i', get_test_example_file('target-240p.mp4'), '-vf', 'fps=60', get_test_example_file('target-240p-60fps.mp4') ])
state_manager.init_item('temp_frame_format', 'jpg')
subprocess.run([ 'ffmpeg', '-i', get_test_example_file('source.mp3'), '-i', get_test_example_file('target-240p.mp4'), '-ar', '16000', get_test_example_file('target-240p-16khz.mp4') ])
subprocess.run([ 'ffmpeg', '-i', get_test_example_file('source.mp3'), '-i', get_test_example_file('target-240p.mp4'), '-ar', '48000', get_test_example_file('target-240p-48khz.mp4') ])
state_manager.init_item('temp_path', tempfile.gettempdir())
state_manager.init_item('temp_frame_format', 'png')
state_manager.init_item('output_audio_encoder', 'aac')
@pytest.fixture(scope = 'function', autouse = True)
def before_each() -> None:
state_manager.clear_item('trim_frame_start')
state_manager.clear_item('trim_frame_end')
prepare_test_output_directory()
def test_extract_frames() -> None:
target_paths =\
extract_set =\
[
get_test_example_file('target-240p-25fps.mp4'),
get_test_example_file('target-240p-30fps.mp4'),
get_test_example_file('target-240p-60fps.mp4')
(get_test_example_file('target-240p-25fps.mp4'), 0, 270, 324),
(get_test_example_file('target-240p-25fps.mp4'), 224, 270, 55),
(get_test_example_file('target-240p-25fps.mp4'), 124, 224, 120),
(get_test_example_file('target-240p-25fps.mp4'), 0, 100, 120),
(get_test_example_file('target-240p-30fps.mp4'), 0, 324, 324),
(get_test_example_file('target-240p-30fps.mp4'), 224, 324, 100),
(get_test_example_file('target-240p-30fps.mp4'), 124, 224, 100),
(get_test_example_file('target-240p-30fps.mp4'), 0, 100, 100),
(get_test_example_file('target-240p-60fps.mp4'), 0, 648, 324),
(get_test_example_file('target-240p-60fps.mp4'), 224, 648, 212),
(get_test_example_file('target-240p-60fps.mp4'), 124, 224, 50),
(get_test_example_file('target-240p-60fps.mp4'), 0, 100, 50)
]
for target_path in target_paths:
temp_directory_path = get_temp_directory_path(target_path)
for target_path, trim_frame_start, trim_frame_end, frame_total in extract_set:
create_temp_directory(target_path)
assert extract_frames(target_path, '452x240', 30.0) is True
assert len(glob.glob1(temp_directory_path, '*.jpg')) == 324
clear_temp_directory(target_path)
def test_extract_frames_with_trim_start() -> None:
state_manager.init_item('trim_frame_start', 224)
providers =\
[
(get_test_example_file('target-240p-25fps.mp4'), 55),
(get_test_example_file('target-240p-30fps.mp4'), 100),
(get_test_example_file('target-240p-60fps.mp4'), 212)
]
for target_path, frame_total in providers:
temp_directory_path = get_temp_directory_path(target_path)
create_temp_directory(target_path)
assert extract_frames(target_path, '452x240', 30.0) is True
assert len(glob.glob1(temp_directory_path, '*.jpg')) == frame_total
clear_temp_directory(target_path)
def test_extract_frames_with_trim_start_and_trim_end() -> None:
state_manager.init_item('trim_frame_start', 124)
state_manager.init_item('trim_frame_end', 224)
providers =\
[
(get_test_example_file('target-240p-25fps.mp4'), 120),
(get_test_example_file('target-240p-30fps.mp4'), 100),
(get_test_example_file('target-240p-60fps.mp4'), 50)
]
for target_path, frame_total in providers:
temp_directory_path = get_temp_directory_path(target_path)
create_temp_directory(target_path)
assert extract_frames(target_path, '452x240', 30.0) is True
assert len(glob.glob1(temp_directory_path, '*.jpg')) == frame_total
clear_temp_directory(target_path)
def test_extract_frames_with_trim_end() -> None:
state_manager.init_item('trim_frame_end', 100)
providers =\
[
(get_test_example_file('target-240p-25fps.mp4'), 120),
(get_test_example_file('target-240p-30fps.mp4'), 100),
(get_test_example_file('target-240p-60fps.mp4'), 50)
]
for target_path, frame_total in providers:
temp_directory_path = get_temp_directory_path(target_path)
create_temp_directory(target_path)
assert extract_frames(target_path, '426x240', 30.0) is True
assert len(glob.glob1(temp_directory_path, '*.jpg')) == frame_total
assert extract_frames(target_path, '452x240', 30.0, trim_frame_start, trim_frame_end) is True
assert len(get_temp_frame_paths(target_path)) == frame_total
clear_temp_directory(target_path)
@ -125,3 +77,33 @@ def test_read_audio_buffer() -> None:
assert isinstance(read_audio_buffer(get_test_example_file('source.mp3'), 1, 1), bytes)
assert isinstance(read_audio_buffer(get_test_example_file('source.wav'), 1, 1), bytes)
assert read_audio_buffer(get_test_example_file('invalid.mp3'), 1, 1) is None
def test_restore_audio() -> None:
target_paths =\
[
get_test_example_file('target-240p-16khz.mp4'),
get_test_example_file('target-240p-48khz.mp4')
]
output_path = get_test_output_file('test-restore-audio.mp4')
for target_path in target_paths:
create_temp_directory(target_path)
copy_file(target_path, get_temp_file_path(target_path))
assert restore_audio(target_path, output_path, 30, 0, 270) is True
clear_temp_directory(target_path)
def test_replace_audio() -> None:
target_path = get_test_example_file('target-240p.mp4')
output_path = get_test_output_file('test-replace-audio.mp4')
create_temp_directory(target_path)
copy_file(target_path, get_temp_file_path(target_path))
assert replace_audio(target_path, get_test_example_file('source.mp3'), output_path) is True
assert replace_audio(target_path, get_test_example_file('source.wav'), output_path) is True
clear_temp_directory(target_path)

7
tests/test_filesystem.py
View File

@ -105,8 +105,11 @@ def test_create_directory() -> None:
def test_list_directory() -> None:
assert list_directory(get_test_examples_directory())
assert list_directory(get_test_example_file('source.jpg')) is None
files = list_directory(get_test_examples_directory())
for file in files:
assert file.get('path') == get_test_example_file(file.get('name') + file.get('extension'))
assert list_directory('invalid') is None

3
tests/test_inference_pool.py
View File

@ -9,9 +9,10 @@ from facefusion.inference_manager import INFERENCE_POOLS, get_inference_pool
@pytest.fixture(scope = 'module', autouse = True)
def before_all() -> None:
content_analyser.pre_check()
state_manager.init_item('execution_device_id', 0)
state_manager.init_item('execution_providers', [ 'cpu' ])
state_manager.init_item('download_providers', [ 'github' ])
content_analyser.pre_check()
def test_get_inference_pool() -> None:

22
tests/test_program_helper.py
View File

@ -2,7 +2,7 @@ from argparse import ArgumentParser
import pytest
from facefusion.program_helper import find_argument_group, remove_args, validate_actions
from facefusion.program_helper import find_argument_group, validate_actions
def test_find_argument_group() -> None:
@ -38,23 +38,3 @@ def test_validate_actions() -> None:
action.default = args[action.dest]
assert validate_actions(program) is False
def test_remove_args() -> None:
program = ArgumentParser()
program.add_argument('--test-1')
program.add_argument('--test-2')
program.add_argument('--test-3')
actions = [ action.dest for action in program._actions ]
assert 'test_1' in actions
assert 'test_2' in actions
assert 'test_3' in actions
program = remove_args(program, [ 'test_1', 'test_2' ])
actions = [ action.dest for action in program._actions ]
assert 'test_1' not in actions
assert 'test_2' not in actions
assert 'test_3' in actions

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