API experiment part2 + ugly frontend

* FFmpeg with progress bar

* Fix typing

* FFmpeg with progress bar part2

* Restore streaming wording

facefusion.ini

@@ -32,8 +32,6 @@ reference_face_distance =
 reference_frame_number =
 
 [face_masker]
-face_occluder_model =
-face_parser_model =
 face_mask_types =
 face_mask_blur =
 face_mask_padding =

facefusion/_preview.py

@@ -0,0 +1,72 @@
+from typing import Optional
+
+import cv2
+import numpy
+
+from facefusion import core, state_manager
+from facefusion.audio import create_empty_audio_frame, get_audio_frame
+from facefusion.common_helper import get_first
+from facefusion.content_analyser import analyse_frame
+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 get_reference_faces
+from facefusion.filesystem import filter_audio_paths, is_image, is_video
+from facefusion.processors.core import get_processors_modules
+from facefusion.typing import AudioFrame, Face, FaceSet, VisionFrame
+from facefusion.vision import get_video_frame, read_static_image, read_static_images, resize_frame_resolution
+
+
+def process_frame(frame_number : int = 0) -> Optional[VisionFrame]:
+	core.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 = []
+
+	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()
+
+	if source_audio_path and state_manager.get_item('output_video_fps') and state_manager.get_item('reference_frame_number'):
+		reference_audio_frame_number = state_manager.get_item('reference_frame_number')
+		if state_manager.get_item('trim_frame_start'):
+			reference_audio_frame_number -= state_manager.get_item('trim_frame_start')
+		temp_audio_frame = get_audio_frame(source_audio_path, state_manager.get_item('output_video_fps'), reference_audio_frame_number)
+		if numpy.any(temp_audio_frame):
+			source_audio_frame = temp_audio_frame
+
+	if is_image(state_manager.get_item('target_path')):
+		target_vision_frame = read_static_image(state_manager.get_item('target_path'))
+		preview_vision_frame = process_preview_frame(reference_faces, source_face, source_audio_frame, target_vision_frame)
+		return preview_vision_frame
+
+	if is_video(state_manager.get_item('target_path')):
+		temp_vision_frame = get_video_frame(state_manager.get_item('target_path'), frame_number)
+		preview_vision_frame = process_preview_frame(reference_faces, source_face, source_audio_frame, temp_vision_frame)
+		return preview_vision_frame
+
+	return None
+
+
+def process_preview_frame(reference_faces : FaceSet, source_face : Face, source_audio_frame : AudioFrame, target_vision_frame : VisionFrame) -> VisionFrame:
+	target_vision_frame = resize_frame_resolution(target_vision_frame, (1024, 1024))
+	source_vision_frame = target_vision_frame.copy()
+
+	if analyse_frame(target_vision_frame):
+		return cv2.GaussianBlur(target_vision_frame, (99, 99), 0)
+
+	for processor_module in get_processors_modules(state_manager.get_item('processors')):
+		if processor_module.pre_process('preview'):
+			target_vision_frame = processor_module.process_frame(
+			{
+				'reference_faces': reference_faces,
+				'source_face': source_face,
+				'source_audio_frame': source_audio_frame,
+				'source_vision_frame': source_vision_frame,
+				'target_vision_frame': target_vision_frame
+			})
+	return target_vision_frame

facefusion/api.py

@@ -0,0 +1,121 @@
+import asyncio
+import json
+from typing import Any, List
+
+import cv2
+import uvicorn
+from litestar import Litestar, WebSocket, get as read, websocket as stream, websocket_listener
+from litestar.static_files import create_static_files_router
+
+from facefusion import _preview, choices, execution, state_manager, vision
+from facefusion.processors import choices as processors_choices
+from facefusion.state_manager import get_state
+from facefusion.typing import ExecutionDevice
+
+
+@read('/choices')
+async def read_choices() -> Any:
+	__choices__ = {}
+
+	for key in dir(choices):
+		if not key.startswith('__'):
+			value = getattr(choices, key)
+
+			if isinstance(value, (dict, list)):
+				__choices__[key] = value
+
+	return __choices__
+
+
+@read('/processors/choices')
+async def read_processors_choices() -> Any:
+	__processors_choices__ = {}
+
+	for key in dir(processors_choices):
+		if not key.startswith('__'):
+			value = getattr(processors_choices, key)
+
+			if isinstance(value, (dict, list)):
+				__processors_choices__[key] = value
+
+	return __processors_choices__
+
+
+@read('/execution/providers')
+async def read_execution_providers() -> Any:
+	return execution.get_execution_provider_set()
+
+
+@stream('/execution/devices')
+async def stream_execution_devices(socket : WebSocket[Any, Any, Any]) -> None:
+	await socket.accept()
+
+	while True:
+		await socket.send_json(execution.detect_execution_devices())
+		await asyncio.sleep(0.5)
+
+
+@read('/execution/devices')
+async def read_execution_devices() -> List[ExecutionDevice]:
+	return execution.detect_execution_devices()
+
+
+@read('/static_execution/devices')
+async def read_static_execution_devices() -> List[ExecutionDevice]:
+	return execution.detect_static_execution_devices()
+
+
+@stream('/state')
+async def stream_state(socket : WebSocket[Any, Any, Any]) -> None:
+	await socket.accept()
+
+	while True:
+		await socket.send_json(get_state())
+		await asyncio.sleep(0.5)
+
+
+@read('/preview', media_type = 'image/png', mode = "binary")
+async def read_preview(frame_number : int) -> bytes:
+	_, preview_vision_frame = cv2.imencode('.png', _preview.process_frame(frame_number)) #type:ignore
+	return preview_vision_frame.tobytes()
+
+
+@websocket_listener("/preview", send_mode = "binary")
+async def stream_preview(data : str) -> bytes:
+	frame_number = int(json.loads(data).get('frame_number'))
+	_, preview_vision_frame = cv2.imencode('.png', _preview.process_frame(frame_number)) #type:ignore
+	return preview_vision_frame.tobytes()
+
+
+@read('/ui/preview_slider')
+async def read_ui_preview_slider() -> Any:
+	target_path = state_manager.get_item('target_path')
+	video_frame_total = vision.count_video_frame_total(target_path)
+
+	return\
+	{
+		'video_frame_total': video_frame_total
+	}
+
+
+api = Litestar(
+[
+	read_choices,
+	read_processors_choices,
+	stream_execution_devices,
+	read_execution_devices,
+	read_static_execution_devices,
+	stream_state,
+	read_preview,
+	read_ui_preview_slider,
+	stream_preview,
+	create_static_files_router(
+		path = '/frontend',
+		directories = [ 'facefusion/static' ],
+		html_mode = True,
+    )
+])
+
+
+def run() -> None:
+	uvicorn.run(api)

facefusion/args.py

@@ -71,8 +71,6 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
 	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')))
@@ -107,7 +105,7 @@ 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 = [ file.get('name') for file in list_directory('facefusion/processors/modules') ]
+	available_processors = 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)

facefusion/choices.py

@@ -2,7 +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, DownloadProvider, DownloadProviderSet, DownloadScope, ExecutionProvider, ExecutionProviderSet, FaceDetectorModel, FaceDetectorSet, FaceLandmarkerModel, FaceMaskRegion, FaceMaskRegionSet, FaceMaskType, FaceOccluderModel, FaceParserModel, FaceSelectorMode, FaceSelectorOrder, Gender, JobStatus, LogLevel, LogLevelSet, OutputAudioEncoder, OutputVideoEncoder, OutputVideoPreset, Race, Score, TempFrameFormat, UiWorkflow, VideoMemoryStrategy
+from facefusion.typing import Angle, DownloadProviderSet, DownloadScope, ExecutionProviderSet, FaceDetectorSet, FaceLandmarkerModel, FaceMaskRegion, FaceMaskType, FaceSelectorMode, FaceSelectorOrder, Gender, JobStatus, LogLevelSet, OutputAudioEncoder, OutputVideoEncoder, OutputVideoPreset, Race, Score, TempFrameFormat, UiWorkflow, VideoMemoryStrategy
 
 face_detector_set : FaceDetectorSet =\
 {
@@ -11,29 +11,13 @@ 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_occluder_models : List[FaceOccluderModel] = [ 'xseg_1', 'xseg_2' ]
-face_parser_models : List[FaceParserModel] = [ 'bisenet_resnet_18', 'bisenet_resnet_34' ]
+face_selector_genders : List[Gender] = ['female', 'male']
+face_selector_races : List[Race] = ['white', 'black', 'latino', 'asian', 'indian', 'arabic']
 face_mask_types : List[FaceMaskType] = [ 'box', 'occlusion', 'region' ]
-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())
+face_mask_regions : List[FaceMaskRegion] = [ 'skin', 'left-eyebrow', 'right-eyebrow', 'left-eye', 'right-eye', 'glasses', 'nose', 'mouth', 'upper-lip', 'lower-lip' ]
 temp_frame_formats : List[TempFrameFormat] = [ 'bmp', 'jpg', 'png' ]
 output_audio_encoders : List[OutputAudioEncoder] = [ 'aac', 'libmp3lame', 'libopus', 'libvorbis' ]
 output_video_encoders : List[OutputVideoEncoder] = [ 'libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc', 'h264_amf', 'hevc_amf', 'h264_qsv', 'hevc_qsv', 'h264_videotoolbox', 'hevc_videotoolbox' ]
@@ -52,21 +36,11 @@ 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}'
-	}
+	'github': 'https://github.com/facefusion/facefusion-assets/releases/download/{base_name}/{file_name}',
+	'huggingface': 'https://huggingface.co/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' ]
@@ -78,7 +52,6 @@ log_level_set : LogLevelSet =\
 	'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' ]

facefusion/content_analyser.py

@@ -9,7 +9,7 @@ from facefusion.download import conditional_download_hashes, conditional_downloa
 from facefusion.filesystem import resolve_relative_path
 from facefusion.thread_helper import conditional_thread_semaphore
 from facefusion.typing import DownloadScope, Fps, InferencePool, ModelOptions, ModelSet, VisionFrame
-from facefusion.vision import detect_video_fps, get_video_frame, read_image
+from facefusion.vision import count_video_frame_total, detect_video_fps, get_video_frame, read_image
 
 PROBABILITY_LIMIT = 0.80
 RATE_LIMIT = 10
@@ -108,9 +108,10 @@ def analyse_image(image_path : str) -> bool:
 
 
 @lru_cache(maxsize = None)
-def analyse_video(video_path : str, trim_frame_start : int, trim_frame_end : int) -> bool:
+def analyse_video(video_path : str, start_frame : int, end_frame : int) -> bool:
+	video_frame_total = count_video_frame_total(video_path)
 	video_fps = detect_video_fps(video_path)
-	frame_range = range(trim_frame_start, trim_frame_end)
+	frame_range = range(start_frame or 0, end_frame or video_frame_total)
 	rate = 0.0
 	counter = 0
 

facefusion/core.py

@@ -2,11 +2,12 @@ import itertools
 import shutil
 import signal
 import sys
+import webbrowser
 from time import time
 
 import numpy
 
-from facefusion import content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, logger, process_manager, state_manager, voice_extractor, wording
+from facefusion import api, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, logger, process_manager, state_manager, voice_extractor, wording
 from facefusion.args import apply_args, collect_job_args, reduce_job_args, reduce_step_args
 from facefusion.common_helper import get_first
 from facefusion.content_analyser import analyse_image, analyse_video
@@ -26,7 +27,7 @@ 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_trim_frame, 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_video_fps, restrict_video_resolution, unpack_resolution
 
 
 def cli() -> None:
@@ -61,15 +62,10 @@ def route(args : Args) -> None:
 	if not pre_check():
 		return conditional_exit(2)
 	if state_manager.get_item('command') == 'run':
-		import facefusion.uis.core as ui
-
-		if not common_pre_check() or not processors_pre_check():
-			return conditional_exit(2)
-		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('open_browser'):
+			webbrowser.open('http://127.0.0.1:8000/frontend')
+		logger.info('http://127.0.0.1:8000/frontend', __name__)
+		api.run()
 	if state_manager.get_item('command') == 'headless-run':
 		if not job_manager.init_jobs(state_manager.get_item('jobs_path')):
 			hard_exit(1)
@@ -133,7 +129,7 @@ def force_download() -> ErrorCode:
 		face_recognizer,
 		voice_extractor
 	]
-	available_processors = [ file.get('name') for file in list_directory('facefusion/processors/modules') ]
+	available_processors = list_directory('facefusion/processors/modules')
 	processor_modules = get_processors_modules(available_processors)
 
 	for module in common_modules + processor_modules:
@@ -389,8 +385,7 @@ def process_image(start_time : float) -> ErrorCode:
 
 
 def process_video(start_time : float) -> ErrorCode:
-	trim_frame_start, trim_frame_end = restrict_trim_frame(state_manager.get_item('target_path'), state_manager.get_item('trim_frame_start'), state_manager.get_item('trim_frame_end'))
-	if analyse_video(state_manager.get_item('target_path'), trim_frame_start, trim_frame_end):
+	if analyse_video(state_manager.get_item('target_path'), state_manager.get_item('trim_frame_start'), state_manager.get_item('trim_frame_end')):
 		return 3
 	# clear temp
 	logger.debug(wording.get('clearing_temp'), __name__)
@@ -403,7 +398,7 @@ 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, trim_frame_start, trim_frame_end):
+	if extract_frames(state_manager.get_item('target_path'), temp_video_resolution, temp_video_fps):
 		logger.debug(wording.get('extracting_frames_succeed'), __name__)
 	else:
 		if is_process_stopping():
@@ -452,7 +447,7 @@ def process_video(start_time : float) -> ErrorCode:
 				logger.warn(wording.get('replacing_audio_skipped'), __name__)
 				move_temp_file(state_manager.get_item('target_path'), state_manager.get_item('output_path'))
 		else:
-			if restore_audio(state_manager.get_item('target_path'), state_manager.get_item('output_path'), state_manager.get_item('output_video_fps'), trim_frame_start, trim_frame_end):
+			if restore_audio(state_manager.get_item('target_path'), state_manager.get_item('output_path'), state_manager.get_item('output_video_fps')):
 				logger.debug(wording.get('restoring_audio_succeed'), __name__)
 			else:
 				if is_process_stopping():

facefusion/download.py

@@ -1,23 +1,23 @@
 import os
 import shutil
+import ssl
 import subprocess
+import urllib.request
 from functools import lru_cache
 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.choices import download_provider_set
+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 DownloadProvider, DownloadSet
+from facefusion.typing import DownloadProviderKey, DownloadSet
 
-
-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)
+if is_macos():
+	ssl._create_default_https_context = ssl._create_unverified_context
 
 
 def conditional_download(download_directory_path : str, urls : List[str]) -> None:
@@ -25,15 +25,13 @@ 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_static_download_size(url)
+		download_size = get_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:
-				commands = [ '--create-dirs', '--continue-at', '-', '--output', download_file_path, url ]
-				open_curl(commands)
+				subprocess.Popen([ shutil.which('curl'), '--create-dirs', '--silent', '--insecure', '--location', '--continue-at', '-', '--output', download_file_path, url ])
 				current_size = initial_size
 				progress.set_postfix(download_providers = state_manager.get_item('download_providers'), file_name = download_file_name)
-
 				while current_size < download_size:
 					if is_file(download_file_path):
 						current_size = get_file_size(download_file_path)
@@ -41,26 +39,13 @@ def conditional_download(download_directory_path : str, urls : List[str]) -> Non
 
 
 @lru_cache(maxsize = None)
-def get_static_download_size(url : str) -> int:
-	commands = [ '-I', url ]
-	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)
-
-	return 0
-
-
-@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 get_download_size(url : str) -> int:
+	try:
+		response = urllib.request.urlopen(url, timeout = 10)
+		content_length = response.headers.get('Content-Length')
+		return int(content_length)
+	except (OSError, TypeError, ValueError):
+		return 0
 
 
 def conditional_download_hashes(hashes : DownloadSet) -> bool:
@@ -72,12 +57,10 @@ def conditional_download_hashes(hashes : DownloadSet) -> bool:
 		for index in hashes:
 			if hashes.get(index).get('path') in invalid_hash_paths:
 				invalid_hash_url = hashes.get(index).get('url')
-				if invalid_hash_url:
-					download_directory_path = os.path.dirname(hashes.get(index).get('path'))
-					conditional_download(download_directory_path, [ invalid_hash_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__)
@@ -99,12 +82,10 @@ def conditional_download_sources(sources : DownloadSet) -> bool:
 		for index in sources:
 			if sources.get(index).get('path') in invalid_source_paths:
 				invalid_source_url = sources.get(index).get('url')
-				if invalid_source_url:
-					download_directory_path = os.path.dirname(sources.get(index).get('path'))
-					conditional_download(download_directory_path, [ invalid_source_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__)
@@ -147,17 +128,11 @@ def validate_source_paths(source_paths : List[str]) -> Tuple[List[str], List[str
 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):
+	for download_provider in download_provider_set:
+		if download_provider in download_providers:
 			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)
+def resolve_download_url_by_provider(download_provider : DownloadProviderKey, base_name : str, file_name : str) -> Optional[str]:
+	return download_provider_set.get(download_provider).format(base_name = base_name, file_name = file_name)

facefusion/execution.py

@@ -6,38 +6,37 @@ from typing import Any, List, Optional
 
 from onnxruntime import get_available_providers, set_default_logger_severity
 
-import facefusion.choices
-from facefusion.typing import ExecutionDevice, ExecutionProvider, ValueAndUnit
+from facefusion.choices import execution_provider_set
+from facefusion.typing import ExecutionDevice, ExecutionProviderKey, ExecutionProviderSet, ValueAndUnit
 
 set_default_logger_severity(3)
 
 
-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_set().keys()
 
 
-def get_available_execution_providers() -> List[ExecutionProvider]:
-	inference_execution_providers = get_available_providers()
-	available_execution_providers = []
+def get_execution_provider_set() -> ExecutionProviderSet:
+	available_execution_providers = get_available_providers()
+	available_execution_provider_set : ExecutionProviderSet = {}
 
-	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
+	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_inference_execution_providers(execution_device_id : str, execution_providers : List[ExecutionProvider]) -> List[Any]:
-	inference_execution_providers : List[Any] = []
+def create_execution_providers(execution_device_id : str, execution_provider_keys : List[ExecutionProviderKey]) -> List[Any]:
+	execution_providers : List[Any] = []
 
-	for execution_provider in execution_providers:
-		if execution_provider == 'cuda':
-			inference_execution_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
+	for execution_provider_key in execution_provider_keys:
+		if execution_provider_key == 'cuda':
+			execution_providers.append((execution_provider_set.get(execution_provider_key),
 			{
 				'device_id': execution_device_id
 			}))
-		if execution_provider == 'tensorrt':
-			inference_execution_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
+		if execution_provider_key == 'tensorrt':
+			execution_providers.append((execution_provider_set.get(execution_provider_key),
 			{
 				'device_id': execution_device_id,
 				'trt_engine_cache_enable': True,
@@ -46,24 +45,24 @@ def create_inference_execution_providers(execution_device_id : str, execution_pr
 				'trt_timing_cache_path': '.caches',
 				'trt_builder_optimization_level': 5
 			}))
-		if execution_provider == 'openvino':
-			inference_execution_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
+		if execution_provider_key == 'openvino':
+			execution_providers.append((execution_provider_set.get(execution_provider_key),
 			{
 				'device_type': 'GPU' if execution_device_id == '0' else 'GPU.' + execution_device_id,
 				'precision': 'FP32'
 			}))
-		if execution_provider in [ 'directml', 'rocm' ]:
-			inference_execution_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
+		if execution_provider_key in [ 'directml', 'rocm' ]:
+			execution_providers.append((execution_provider_set.get(execution_provider_key),
 			{
 				'device_id': execution_device_id
 			}))
-		if execution_provider == 'coreml':
-			inference_execution_providers.append(facefusion.choices.execution_provider_set.get(execution_provider))
+		if execution_provider_key == 'coreml':
+			execution_providers.append(execution_provider_set.get(execution_provider_key))
 
-	if 'cpu' in execution_providers:
-		inference_execution_providers.append(facefusion.choices.execution_provider_set.get('cpu'))
+	if 'cpu' in execution_provider_keys:
+		execution_providers.append(execution_provider_set.get('cpu'))
 
-	return inference_execution_providers
+	return execution_providers
 
 
 def run_nvidia_smi() -> subprocess.Popen[bytes]:

facefusion/exit_helper.py

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

facefusion/face_detector.py

@@ -79,11 +79,13 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
 
 def get_inference_pool() -> InferencePool:
 	_, model_sources = collect_model_downloads()
-	return inference_manager.get_inference_pool(__name__, model_sources)
+	model_context = __name__ + '.' + state_manager.get_item('face_detector_model')
+	return inference_manager.get_inference_pool(model_context, model_sources)
 
 
 def clear_inference_pool() -> None:
-	inference_manager.clear_inference_pool(__name__)
+	model_context = __name__ + '.' + state_manager.get_item('face_detector_model')
+	inference_manager.clear_inference_pool(model_context)
 
 
 def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]:
@@ -94,15 +96,12 @@ def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]:
 	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')
-
 	if state_manager.get_item('face_detector_model') in [ 'many', 'scrfd' ]:
 		model_hashes['scrfd'] = model_set.get('scrfd').get('hashes').get('scrfd')
 		model_sources['scrfd'] = model_set.get('scrfd').get('sources').get('scrfd')
-
 	if state_manager.get_item('face_detector_model') in [ 'many', 'yoloface' ]:
 		model_hashes['yoloface'] = model_set.get('yoloface').get('hashes').get('yoloface')
 		model_sources['yoloface'] = model_set.get('yoloface').get('sources').get('yoloface')
-
 	return model_hashes, model_sources
 
 

facefusion/face_landmarker.py

@@ -80,11 +80,13 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
 
 def get_inference_pool() -> InferencePool:
 	_, model_sources = collect_model_downloads()
-	return inference_manager.get_inference_pool(__name__, model_sources)
+	model_context = __name__ + '.' + state_manager.get_item('face_landmarker_model')
+	return inference_manager.get_inference_pool(model_context, model_sources)
 
 
 def clear_inference_pool() -> None:
-	inference_manager.clear_inference_pool(__name__)
+	model_context = __name__ + '.' + state_manager.get_item('face_landmarker_model')
+	inference_manager.clear_inference_pool(model_context)
 
 
 def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]:
@@ -101,11 +103,9 @@ def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]:
 	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')
-
 	if state_manager.get_item('face_landmarker_model') in [ 'many', 'peppa_wutz' ]:
 		model_hashes['peppa_wutz'] = model_set.get('peppa_wutz').get('hashes').get('peppa_wutz')
 		model_sources['peppa_wutz'] = model_set.get('peppa_wutz').get('sources').get('peppa_wutz')
-
 	return model_hashes, model_sources
 
 
@@ -123,7 +123,6 @@ 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)
 

facefusion/face_masker.py

@@ -1,83 +1,56 @@
 from functools import lru_cache
-from typing import List, Tuple
+from typing import Dict, List, Tuple
 
 import cv2
 import numpy
 from cv2.typing import Size
 
-import facefusion.choices
-from facefusion import inference_manager, state_manager
+from facefusion import inference_manager
 from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
 from facefusion.filesystem import resolve_relative_path
 from facefusion.thread_helper import conditional_thread_semaphore
 from facefusion.typing import DownloadScope, DownloadSet, FaceLandmark68, FaceMaskRegion, InferencePool, Mask, ModelSet, Padding, VisionFrame
 
+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
+}
+
 
 @lru_cache(maxsize = None)
 def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
 	return\
 	{
-		'xseg_1':
+		'face_occluder':
 		{
 			'hashes':
 			{
 				'face_occluder':
 				{
-					'url': resolve_download_url('models-3.1.0', 'xseg_1.hash'),
-					'path': resolve_relative_path('../.assets/models/xseg_1.hash')
+					'url': resolve_download_url('models-3.1.0', 'xseg_groggy_5.hash'),
+					'path': resolve_relative_path('../.assets/models/xseg_groggy_5.hash')
 				}
 			},
 			'sources':
 			{
 				'face_occluder':
 				{
-					'url': resolve_download_url('models-3.1.0', 'xseg_1.onnx'),
-					'path': resolve_relative_path('../.assets/models/xseg_1.onnx')
+					'url': resolve_download_url('models-3.1.0', 'xseg_groggy_5.onnx'),
+					'path': resolve_relative_path('../.assets/models/xseg_groggy_5.onnx')
 				}
 			},
 			'size': (256, 256)
 		},
-		'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': 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':
+		'face_parser':
 		{
 			'hashes':
 			{
@@ -110,26 +83,17 @@ def clear_inference_pool() -> None:
 
 
 def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]:
-	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')
-
+	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')
+	}
 	return model_hashes, model_sources
 
 
@@ -154,8 +118,7 @@ def create_static_box_mask(crop_size : Size, face_mask_blur : float, face_mask_p
 
 
 def create_occlusion_mask(crop_vision_frame : VisionFrame) -> Mask:
-	face_occluder_model = state_manager.get_item('face_occluder_model')
-	model_size = create_static_model_set('full').get(face_occluder_model).get('size')
+	model_size = create_static_model_set('full').get('face_occluder').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)
@@ -167,8 +130,7 @@ def create_occlusion_mask(crop_vision_frame : VisionFrame) -> Mask:
 
 
 def create_region_mask(crop_vision_frame : VisionFrame, face_mask_regions : List[FaceMaskRegion]) -> Mask:
-	face_parser_model = state_manager.get_item('face_parser_model')
-	model_size = create_static_model_set('full').get(face_parser_model).get('size')
+	model_size = create_static_model_set('full').get('face_parser').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))
@@ -176,7 +138,7 @@ 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), [ facefusion.choices.face_mask_region_set.get(face_mask_region) for face_mask_region in face_mask_regions ])
+	region_mask = numpy.isin(region_mask.argmax(0), [ FACE_MASK_REGIONS[region] for 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
@@ -192,8 +154,7 @@ def create_mouth_mask(face_landmark_68 : FaceLandmark68) -> Mask:
 
 
 def forward_occlude_face(prepare_vision_frame : VisionFrame) -> Mask:
-	face_occluder_model = state_manager.get_item('face_occluder_model')
-	face_occluder = get_inference_pool().get(face_occluder_model)
+	face_occluder = get_inference_pool().get('face_occluder')
 
 	with conditional_thread_semaphore():
 		occlusion_mask : Mask = face_occluder.run(None,
@@ -205,8 +166,7 @@ def forward_occlude_face(prepare_vision_frame : VisionFrame) -> Mask:
 
 
 def forward_parse_face(prepare_vision_frame : VisionFrame) -> Mask:
-	face_parser_model = state_manager.get_item('face_parser_model')
-	face_parser = get_inference_pool().get(face_parser_model)
+	face_parser = get_inference_pool().get('face_parser')
 
 	with conditional_thread_semaphore():
 		region_mask : Mask = face_parser.run(None,

facefusion/ffmpeg.py

@@ -11,7 +11,7 @@ from facefusion import logger, process_manager, state_manager, wording
 from facefusion.filesystem import remove_file
 from facefusion.temp_helper import get_temp_file_path, get_temp_frame_paths, get_temp_frames_pattern
 from facefusion.typing import AudioBuffer, Fps, OutputVideoPreset, UpdateProgress
-from facefusion.vision import count_trim_frame_total, detect_video_duration, restrict_video_fps
+from facefusion.vision import count_video_frame_total, detect_video_duration, restrict_video_fps
 
 
 def run_ffmpeg_with_progress(args: List[str], update_progress : UpdateProgress) -> subprocess.Popen[bytes]:
@@ -22,12 +22,10 @@ def run_ffmpeg_with_progress(args: List[str], update_progress : UpdateProgress)
 
 	while process_manager.is_processing():
 		try:
-
-			while __line__ := process.stdout.readline().decode().lower():
-				if 'frame=' in __line__:
-					_, frame_number = __line__.split('frame=')
+			while line := process.stdout.readline().decode():
+				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)
@@ -75,17 +73,22 @@ def log_debug(process : subprocess.Popen[bytes]) -> None:
 			logger.debug(error.strip(), __name__)
 
 
-def extract_frames(target_path : str, temp_video_resolution : str, temp_video_fps : Fps, trim_frame_start : int, trim_frame_end : int) -> bool:
-	extract_frame_total = count_trim_frame_total(target_path, trim_frame_start, trim_frame_end)
+def extract_frames(target_path : str, temp_video_resolution : str, temp_video_fps : Fps) -> bool:
+	extract_frame_total = count_video_frame_total(state_manager.get_item('target_path'))
+	trim_frame_start = state_manager.get_item('trim_frame_start')
+	trim_frame_end = state_manager.get_item('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' ]
 
 	if isinstance(trim_frame_start, int) and isinstance(trim_frame_end, int):
 		commands.extend([ '-vf', 'trim=start_frame=' + str(trim_frame_start) + ':end_frame=' + str(trim_frame_end) + ',fps=' + str(temp_video_fps) ])
+		extract_frame_total = trim_frame_end - trim_frame_start
 	elif isinstance(trim_frame_start, int):
 		commands.extend([ '-vf', 'trim=start_frame=' + str(trim_frame_start) + ',fps=' + str(temp_video_fps) ])
+		extract_frame_total -= trim_frame_start
 	elif isinstance(trim_frame_end, int):
 		commands.extend([ '-vf', 'trim=end_frame=' + str(trim_frame_end) + ',fps=' + str(temp_video_fps) ])
+		extract_frame_total -= trim_frame_end
 	else:
 		commands.extend([ '-vf', 'fps=' + str(temp_video_fps) ])
 	commands.extend([ '-vsync', '0', temp_frames_pattern ])
@@ -96,10 +99,10 @@ def extract_frames(target_path : str, temp_video_resolution : str, temp_video_fp
 
 
 def merge_video(target_path : str, output_video_resolution : str, output_video_fps: Fps) -> bool:
+	merge_frame_total = len(get_temp_frame_paths(target_path))
 	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')
@@ -176,7 +179,9 @@ def read_audio_buffer(target_path : str, sample_rate : int, channel_total : int)
 	return None
 
 
-def restore_audio(target_path : str, output_path : str, output_video_fps : Fps, trim_frame_start : int, trim_frame_end : int) -> bool:
+def restore_audio(target_path : str, output_path : str, 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')
 	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)

facefusion/filesystem.py

@@ -7,7 +7,6 @@ from typing import List, Optional
 import filetype
 
 from facefusion.common_helper import is_windows
-from facefusion.typing import File
 
 if is_windows():
 	import ctypes
@@ -127,23 +126,11 @@ def create_directory(directory_path : str) -> bool:
 	return False
 
 
-def list_directory(directory_path : str) -> Optional[List[File]]:
+def list_directory(directory_path : str) -> Optional[List[str]]:
 	if is_directory(directory_path):
-		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
+		file_paths = os.listdir(directory_path)
+		file_paths = [ Path(file_path).stem for file_path in file_paths if not Path(file_path).stem.startswith(('.', '__')) ]
+		return sorted(file_paths)
 	return None
 
 

facefusion/inference_manager.py

@@ -5,9 +5,9 @@ from onnxruntime import InferenceSession
 
 from facefusion import process_manager, state_manager
 from facefusion.app_context import detect_app_context
-from facefusion.execution import create_inference_execution_providers
+from facefusion.execution import create_execution_providers
 from facefusion.thread_helper import thread_lock
-from facefusion.typing import DownloadSet, ExecutionProvider, InferencePool, InferencePoolSet
+from facefusion.typing import DownloadSet, ExecutionProviderKey, InferencePool, InferencePoolSet
 
 INFERENCE_POOLS : InferencePoolSet =\
 {
@@ -35,11 +35,11 @@ def get_inference_pool(model_context : str, model_sources : DownloadSet) -> Infe
 		return INFERENCE_POOLS.get(app_context).get(inference_context)
 
 
-def create_inference_pool(model_sources : DownloadSet, execution_device_id : str, execution_providers : List[ExecutionProvider]) -> InferencePool:
+def create_inference_pool(model_sources : DownloadSet, execution_device_id : str, execution_provider_keys : List[ExecutionProviderKey]) -> InferencePool:
 	inference_pool : InferencePool = {}
 
 	for model_name in model_sources.keys():
-		inference_pool[model_name] = create_inference_session(model_sources.get(model_name).get('path'), execution_device_id, execution_providers)
+		inference_pool[model_name] = create_inference_session(model_sources.get(model_name).get('path'), execution_device_id, execution_provider_keys)
 	return inference_pool
 
 
@@ -53,9 +53,9 @@ 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_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 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)
 
 
 def get_inference_context(model_context : str) -> str:

facefusion/jobs/job_manager.py

@@ -2,7 +2,7 @@ import os
 from copy import copy
 from typing import List, Optional
 
-import facefusion.choices
+from facefusion.choices import job_statuses
 from facefusion.date_helper import get_current_date_time
 from facefusion.filesystem import create_directory, is_directory, is_file, move_file, remove_directory, remove_file, resolve_file_pattern
 from facefusion.jobs.job_helper import get_step_output_path
@@ -16,7 +16,7 @@ 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 facefusion.choices.job_statuses ]
+	job_status_paths = [ os.path.join(JOBS_PATH, job_status) for job_status in job_statuses ]
 
 	for job_status_path in job_status_paths:
 		create_directory(job_status_path)
@@ -245,7 +245,7 @@ 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 facefusion.choices.job_statuses:
+		for job_status in job_statuses:
 			job_pattern = os.path.join(JOBS_PATH, job_status, job_file_name)
 			job_paths = resolve_file_pattern(job_pattern)
 

facefusion/logger.py

@@ -1,14 +1,14 @@
 from logging import Logger, basicConfig, getLogger
 from typing import Tuple
 
-import facefusion.choices
+from facefusion.choices import log_level_set
 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(facefusion.choices.log_level_set.get(log_level))
+	get_package_logger().setLevel(log_level_set.get(log_level))
 
 
 def get_package_logger() -> Logger:

facefusion/metadata.py

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

facefusion/processors/choices.py

@@ -1,8 +1,7 @@
 from typing import List, Sequence
 
 from facefusion.common_helper import create_float_range, create_int_range
-from facefusion.filesystem import list_directory, resolve_relative_path
-from facefusion.processors.typing import AgeModifierModel, DeepSwapperModel, ExpressionRestorerModel, FaceDebuggerItem, FaceEditorModel, FaceEnhancerModel, FaceSwapperModel, FaceSwapperSet, FrameColorizerModel, FrameEnhancerModel, LipSyncerModel
+from facefusion.processors.typing import AgeModifierModel, DeepSwapperModel, ExpressionRestorerModel, FaceDebuggerItem, FaceEditorModel, FaceEnhancerModel, FaceSwapperSet, FrameColorizerModel, FrameEnhancerModel, LipSyncerModel
 
 age_modifier_models : List[AgeModifierModel] = [ 'styleganex_age' ]
 deep_swapper_models : List[DeepSwapperModel] =\
@@ -20,7 +19,6 @@ deep_swapper_models : List[DeepSwapperModel] =\
 	'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',
@@ -63,7 +61,6 @@ deep_swapper_models : List[DeepSwapperModel] =\
 	'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',
@@ -156,15 +153,6 @@ deep_swapper_models : List[DeepSwapperModel] =\
 	'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' ]
@@ -182,7 +170,6 @@ face_swapper_set : FaceSwapperSet =\
 	'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', 'real_web_photo_x4', 'realistic_rescaler_x4', 'remacri_x4', 'siax_x4', 'span_kendata_x4', 'swin2_sr_x4', 'ultra_sharp_x4' ]

facefusion/processors/modules/age_modifier.py

@@ -5,11 +5,11 @@ from typing import List
 import cv2
 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.choices import execution_provider_set
 from facefusion.common_helper import create_int_metavar
 from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
 from facefusion.execution import has_execution_provider
@@ -65,11 +65,13 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
 
 def get_inference_pool() -> InferencePool:
 	model_sources = get_model_options().get('sources')
-	return inference_manager.get_inference_pool(__name__, model_sources)
+	model_context = __name__ + '.' + state_manager.get_item('age_modifier_model')
+	return inference_manager.get_inference_pool(model_context, model_sources)
 
 
 def clear_inference_pool() -> None:
-	inference_manager.clear_inference_pool(__name__)
+	model_context = __name__ + '.' + state_manager.get_item('age_modifier_model')
+	inference_manager.clear_inference_pool(model_context)
 
 
 def get_model_options() -> ModelOptions:
@@ -161,7 +163,7 @@ def forward(crop_vision_frame : VisionFrame, extend_vision_frame : VisionFrame,
 	age_modifier_inputs = {}
 
 	if has_execution_provider('coreml'):
-		age_modifier.set_providers([ facefusion.choices.execution_provider_set.get('cpu') ])
+		age_modifier.set_providers([ execution_provider_set.get('cpu') ])
 
 	for age_modifier_input in age_modifier.get_inputs():
 		if age_modifier_input.name == 'target':

facefusion/processors/modules/deep_swapper.py

@@ -4,7 +4,6 @@ from typing import List, Tuple
 
 import cv2
 import numpy
-from cv2.typing import Size
 
 import facefusion.jobs.job_manager
 import facefusion.jobs.job_store
@@ -14,10 +13,10 @@ 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_masker import create_occlusion_mask, create_static_box_mask
 from facefusion.face_selector import find_similar_faces, sort_and_filter_faces
 from facefusion.face_store import get_reference_faces
-from facefusion.filesystem import in_directory, is_image, is_video, list_directory, resolve_relative_path, same_file_extension
+from facefusion.filesystem import 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 DeepSwapperInputs, DeepSwapperMorph
 from facefusion.program_helper import find_argument_group
@@ -33,167 +32,165 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
 	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')
+			('druuzil', 'adrianne_palicki_384', (384, 384)),
+			('druuzil', 'agnetha_falskog_224', (224, 224)),
+			('druuzil', 'alan_ritchson_320', (320, 320)),
+			('druuzil', 'alicia_vikander_320', (320, 320)),
+			('druuzil', 'amber_midthunder_320', (320, 320)),
+			('druuzil', 'andras_arato_384', (384, 384)),
+			('druuzil', 'andrew_tate_320', (320, 320)),
+			('druuzil', 'anne_hathaway_320', (320, 320)),
+			('druuzil', 'anya_chalotra_320', (320, 320)),
+			('druuzil', 'arnold_schwarzenegger_320', (320, 320)),
+			('druuzil', 'benjamin_affleck_320', (320, 320)),
+			('druuzil', 'benjamin_stiller_384', (384, 384)),
+			('druuzil', 'bradley_pitt_224', (224, 224)),
+			('druuzil', 'bryan_cranston_320', (320, 320)),
+			('druuzil', 'catherine_blanchett_352', (352, 352)),
+			('druuzil', 'christian_bale_320', (320, 320)),
+			('druuzil', 'christopher_hemsworth_320', (320, 320)),
+			('druuzil', 'christoph_waltz_384', (384, 384)),
+			('druuzil', 'cillian_murphy_320', (320, 320)),
+			('druuzil', 'cobie_smulders_256', (256, 256)),
+			('druuzil', 'dwayne_johnson_384', (384, 384)),
+			('druuzil', 'edward_norton_320', (320, 320)),
+			('druuzil', 'elisabeth_shue_320', (320, 320)),
+			('druuzil', 'elizabeth_olsen_384', (384, 384)),
+			('druuzil', 'elon_musk_320', (320, 320)),
+			('druuzil', 'emily_blunt_320', (320, 320)),
+			('druuzil', 'emma_stone_384', (384, 384)),
+			('druuzil', 'emma_watson_320', (320, 320)),
+			('druuzil', 'erin_moriarty_384', (384, 384)),
+			('druuzil', 'eva_green_320', (320, 320)),
+			('druuzil', 'ewan_mcgregor_320', (320, 320)),
+			('druuzil', 'florence_pugh_320', (320, 320)),
+			('druuzil', 'freya_allan_320', (320, 320)),
+			('druuzil', 'gary_cole_224', (224, 224)),
+			('druuzil', 'gigi_hadid_224', (224, 224)),
+			('druuzil', 'harrison_ford_384', (384, 384)),
+			('druuzil', 'hayden_christensen_320', (320, 320)),
+			('druuzil', 'heath_ledger_320', (320, 320)),
+			('druuzil', 'henry_cavill_448', (448, 448)),
+			('druuzil', 'hugh_jackman_384', (384, 384)),
+			('druuzil', 'idris_elba_320', (320, 320)),
+			('druuzil', 'jack_nicholson_320', (320, 320)),
+			('druuzil', 'james_mcavoy_320', (320, 320)),
+			('druuzil', 'james_varney_320', (320, 320)),
+			('druuzil', 'jason_momoa_320', (320, 320)),
+			('druuzil', 'jason_statham_320', (320, 320)),
+			('druuzil', 'jennifer_connelly_384', (384, 384)),
+			('druuzil', 'jimmy_donaldson_320', (320, 320)),
+			('druuzil', 'jordan_peterson_384', (384, 384)),
+			('druuzil', 'karl_urban_224', (224, 224)),
+			('druuzil', 'kate_beckinsale_384', (384, 384)),
+			('druuzil', 'laurence_fishburne_384', (384, 384)),
+			('druuzil', 'lili_reinhart_320', (320, 320)),
+			('druuzil', 'mads_mikkelsen_384', (384, 384)),
+			('druuzil', 'mary_winstead_320', (320, 320)),
+			('druuzil', 'melina_juergens_320', (320, 320)),
+			('druuzil', 'michael_fassbender_320', (320, 320)),
+			('druuzil', 'michael_fox_320', (320, 320)),
+			('druuzil', 'millie_bobby_brown_320', (320, 320)),
+			('druuzil', 'morgan_freeman_320', (320, 320)),
+			('druuzil', 'patrick_stewart_320', (320, 320)),
+			('druuzil', 'rebecca_ferguson_320', (320, 320)),
+			('druuzil', 'scarlett_johansson_320', (320, 320)),
+			('druuzil', 'seth_macfarlane_384', (384, 384)),
+			('druuzil', 'thomas_cruise_320', (320, 320)),
+			('druuzil', 'thomas_hanks_384', (384, 384)),
+			('edel', 'emma_roberts_224', (224, 224)),
+			('edel', 'ivanka_trump_224', (224, 224)),
+			('edel', 'lize_dzjabrailova_224', (224, 224)),
+			('edel', 'sidney_sweeney_224', (224, 224)),
+			('edel', 'winona_ryder_224', (224, 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')
+			('iperov', 'alexandra_daddario_224', (224, 224)),
+			('iperov', 'alexei_navalny_224', (224, 224)),
+			('iperov', 'amber_heard_224', (224, 224)),
+			('iperov', 'dilraba_dilmurat_224', (224, 224)),
+			('iperov', 'elon_musk_224', (224, 224)),
+			('iperov', 'emilia_clarke_224', (224, 224)),
+			('iperov', 'emma_watson_224', (224, 224)),
+			('iperov', 'erin_moriarty_224', (224, 224)),
+			('iperov', 'jackie_chan_224', (224, 224)),
+			('iperov', 'james_carrey_224', (224, 224)),
+			('iperov', 'jason_statham_320', (320, 320)),
+			('iperov', 'keanu_reeves_320', (320, 320)),
+			('iperov', 'margot_robbie_224', (224, 224)),
+			('iperov', 'natalie_dormer_224', (224, 224)),
+			('iperov', 'nicolas_coppola_224', (224, 224)),
+			('iperov', 'robert_downey_224', (224, 224)),
+			('iperov', 'rowan_atkinson_224', (224, 224)),
+			('iperov', 'ryan_reynolds_224', (224, 224)),
+			('iperov', 'scarlett_johansson_224', (224, 224)),
+			('iperov', 'sylvester_stallone_224', (224, 224)),
+			('iperov', 'thomas_cruise_224', (224, 224)),
+			('iperov', 'thomas_holland_224', (224, 224)),
+			('iperov', 'vin_diesel_224', (224, 224)),
+			('iperov', 'vladimir_putin_224', (224, 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')
+			('jen', 'angelica_trae_288', (288, 288)),
+			('jen', 'ella_freya_224', (224, 224)),
+			('jen', 'emma_myers_320', (320, 320)),
+			('jen', 'evie_pickerill_224', (224, 224)),
+			('jen', 'kang_hyewon_320', (320, 320)),
+			('jen', 'maddie_mead_224', (224, 224)),
+			('jen', 'nicole_turnbull_288', (288, 288)),
+			('mats', 'alica_schmidt_320', (320, 320)),
+			('mats', 'ashley_alexiss_224', (224, 224)),
+			('mats', 'billie_eilish_224', (224, 224)),
+			('mats', 'brie_larson_224', (224, 224)),
+			('mats', 'cara_delevingne_224', (224, 224)),
+			('mats', 'carolin_kebekus_224', (224, 224)),
+			('mats', 'chelsea_clinton_224', (224, 224)),
+			('mats', 'claire_boucher_224', (224, 224)),
+			('mats', 'corinna_kopf_224', (224, 224)),
+			('mats', 'florence_pugh_224', (224, 224)),
+			('mats', 'hillary_clinton_224', (224, 224)),
+			('mats', 'jenna_fischer_224', (224, 224)),
+			('mats', 'kim_jisoo_320', (320, 320)),
+			('mats', 'mica_suarez_320', (320, 320)),
+			('mats', 'shailene_woodley_224', (224, 224)),
+			('mats', 'shraddha_kapoor_320', (320, 320)),
+			('mats', 'yu_jimin_352', (352, 352)),
+			('rumateus', 'alison_brie_224', (224, 224)),
+			('rumateus', 'amber_heard_224', (224, 224)),
+			('rumateus', 'angelina_jolie_224', (224, 224)),
+			('rumateus', 'aubrey_plaza_224', (224, 224)),
+			('rumateus', 'bridget_regan_224', (224, 224)),
+			('rumateus', 'cobie_smulders_224', (224, 224)),
+			('rumateus', 'deborah_woll_224', (224, 224)),
+			('rumateus', 'dua_lipa_224', (224, 224)),
+			('rumateus', 'emma_stone_224', (224, 224)),
+			('rumateus', 'hailee_steinfeld_224', (224, 224)),
+			('rumateus', 'hilary_duff_224', (224, 224)),
+			('rumateus', 'jessica_alba_224', (224, 224)),
+			('rumateus', 'jessica_biel_224', (224, 224)),
+			('rumateus', 'john_cena_224', (224, 224)),
+			('rumateus', 'kim_kardashian_224', (224, 224)),
+			('rumateus', 'kristen_bell_224', (224, 224)),
+			('rumateus', 'lucy_liu_224', (224, 224)),
+			('rumateus', 'margot_robbie_224', (224, 224)),
+			('rumateus', 'megan_fox_224', (224, 224)),
+			('rumateus', 'meghan_markle_224', (224, 224)),
+			('rumateus', 'millie_bobby_brown_224', (224, 224)),
+			('rumateus', 'natalie_portman_224', (224, 224)),
+			('rumateus', 'nicki_minaj_224', (224, 224)),
+			('rumateus', 'olivia_wilde_224', (224, 224)),
+			('rumateus', 'shay_mitchell_224', (224, 224)),
+			('rumateus', 'sophie_turner_224', (224, 224)),
+			('rumateus', 'taylor_swift_224', (224, 224))
 		])
 	model_set : ModelSet = {}
 
-	for model_scope, model_name in model_config:
-		model_id = '/'.join([ model_scope, model_name ])
+	for model_creator, model_name, model_size in model_config:
+		model_id = '/'.join([ model_creator, model_name ])
 
 		model_set[model_id] =\
 		{
@@ -201,50 +198,34 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
 			{
 				'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')
+					'url': resolve_download_url_by_provider('huggingface', 'deepfacelive-models-' + model_creator, model_name + '.hash'),
+					'path': resolve_relative_path('../.assets/models/' + model_creator + '/' + 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')
+					'url': resolve_download_url_by_provider('huggingface', 'deepfacelive-models-' + model_creator, model_name + '.dfm'),
+					'path': resolve_relative_path('../.assets/models/' + model_creator + '/' + model_name + '.dfm')
 				}
 			},
-			'template': 'dfl_whole_face'
+			'template': 'dfl_whole_face',
+			'size': model_size
 		}
 
-	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)
+	model_context = __name__ + '.' + state_manager.get_item('deep_swapper_model')
+	return inference_manager.get_inference_pool(model_context, model_sources)
 
 
 def clear_inference_pool() -> None:
-	inference_manager.clear_inference_pool(__name__)
+	model_context = __name__ + '.' + state_manager.get_item('deep_swapper_model')
+	inference_manager.clear_inference_pool(model_context)
 
 
 def get_model_options() -> ModelOptions:
@@ -252,15 +233,6 @@ def get_model_options() -> ModelOptions:
 	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:
@@ -278,9 +250,7 @@ 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
+	return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
 
 
 def pre_process(mode : ProcessMode) -> bool:
@@ -311,7 +281,7 @@ def post_process() -> None:
 
 def swap_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
 	model_template = get_model_options().get('template')
-	model_size = get_model_size()
+	model_size = get_model_options().get('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'))
@@ -330,11 +300,6 @@ def swap_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFram
 	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
@@ -379,7 +344,7 @@ def normalize_crop_frame(crop_vision_frame : VisionFrame) -> VisionFrame:
 
 
 def prepare_crop_mask(crop_source_mask : Mask, crop_target_mask : Mask) -> Mask:
-	model_size = get_model_size()
+	model_size = get_model_options().get('size')
 	blur_size = 6.25
 	kernel_size = 3
 	crop_mask = numpy.minimum.reduce([ crop_source_mask, crop_target_mask ])

facefusion/processors/modules/expression_restorer.py

@@ -77,7 +77,8 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
 
 def get_inference_pool() -> InferencePool:
 	model_sources = get_model_options().get('sources')
-	return inference_manager.get_inference_pool(__name__, model_sources)
+	model_context = __name__ + '.' + state_manager.get_item('expression_restorer_model')
+	return inference_manager.get_inference_pool(model_context, model_sources)
 
 
 def clear_inference_pool() -> None:

facefusion/processors/modules/face_editor.py

@@ -106,11 +106,13 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
 
 def get_inference_pool() -> InferencePool:
 	model_sources = get_model_options().get('sources')
-	return inference_manager.get_inference_pool(__name__, model_sources)
+	model_context = __name__ + '.' + state_manager.get_item('face_editor_model')
+	return inference_manager.get_inference_pool(model_context, model_sources)
 
 
 def clear_inference_pool() -> None:
-	inference_manager.clear_inference_pool(__name__)
+	model_context = __name__ + '.' + state_manager.get_item('face_editor_model')
+	inference_manager.clear_inference_pool(model_context)
 
 
 def get_model_options() -> ModelOptions:

facefusion/processors/modules/face_enhancer.py

@@ -223,11 +223,13 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
 
 def get_inference_pool() -> InferencePool:
 	model_sources = get_model_options().get('sources')
-	return inference_manager.get_inference_pool(__name__, model_sources)
+	model_context = __name__ + '.' + state_manager.get_item('face_enhancer_model')
+	return inference_manager.get_inference_pool(model_context, model_sources)
 
 
 def clear_inference_pool() -> None:
-	inference_manager.clear_inference_pool(__name__)
+	model_context = __name__ + '.' + state_manager.get_item('face_enhancer_model')
+	inference_manager.clear_inference_pool(model_context)
 
 
 def get_model_options() -> ModelOptions:

facefusion/processors/modules/face_swapper.py

@@ -4,11 +4,11 @@ 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.choices import execution_provider_set
 from facefusion.common_helper import get_first
 from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
 from facefusion.execution import has_execution_provider
@@ -337,11 +337,13 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
 
 def get_inference_pool() -> InferencePool:
 	model_sources = get_model_options().get('sources')
-	return inference_manager.get_inference_pool(__name__, model_sources)
+	model_context = __name__ + '.' + state_manager.get_item('face_swapper_model')
+	return inference_manager.get_inference_pool(model_context, model_sources)
 
 
 def clear_inference_pool() -> None:
-	inference_manager.clear_inference_pool(__name__)
+	model_context = __name__ + '.' + state_manager.get_item('face_swapper_model')
+	inference_manager.clear_inference_pool(model_context)
 
 
 def get_model_options() -> ModelOptions:
@@ -352,7 +354,7 @@ def get_model_options() -> ModelOptions:
 def register_args(program : ArgumentParser) -> None:
 	group_processors = find_argument_group(program, 'processors')
 	if group_processors:
-		group_processors.add_argument('--face-swapper-model', help = wording.get('help.face_swapper_model'), default = config.get_str_value('processors.face_swapper_model', 'inswapper_128_fp16'), choices = processors_choices.face_swapper_models)
+		group_processors.add_argument('--face-swapper-model', help = wording.get('help.face_swapper_model'), default = config.get_str_value('processors.face_swapper_model', 'inswapper_128_fp16'), choices = processors_choices.face_swapper_set.keys())
 		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)
@@ -447,7 +449,7 @@ def forward_swap_face(source_face : Face, crop_vision_frame : VisionFrame) -> Vi
 	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') ])
+		face_swapper.set_providers([ execution_provider_set.get('cpu') ])
 
 	for face_swapper_input in face_swapper.get_inputs():
 		if face_swapper_input.name == 'source':

facefusion/processors/modules/frame_colorizer.py

@@ -129,11 +129,13 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
 
 def get_inference_pool() -> InferencePool:
 	model_sources = get_model_options().get('sources')
-	return inference_manager.get_inference_pool(__name__, model_sources)
+	model_context = __name__ + '.' + state_manager.get_item('frame_colorizer_model')
+	return inference_manager.get_inference_pool(model_context, model_sources)
 
 
 def clear_inference_pool() -> None:
-	inference_manager.clear_inference_pool(__name__)
+	model_context = __name__ + '.' + state_manager.get_item('frame_colorizer_model')
+	inference_manager.clear_inference_pool(model_context)
 
 
 def get_model_options() -> ModelOptions:

facefusion/processors/modules/frame_enhancer.py

@@ -386,11 +386,13 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
 
 def get_inference_pool() -> InferencePool:
 	model_sources = get_model_options().get('sources')
-	return inference_manager.get_inference_pool(__name__, model_sources)
+	model_context = __name__ + '.' + state_manager.get_item('frame_enhancer_model')
+	return inference_manager.get_inference_pool(model_context, model_sources)
 
 
 def clear_inference_pool() -> None:
-	inference_manager.clear_inference_pool(__name__)
+	model_context = __name__ + '.' + state_manager.get_item('frame_enhancer_model')
+	inference_manager.clear_inference_pool(model_context)
 
 
 def get_model_options() -> ModelOptions:

facefusion/processors/modules/lip_syncer.py

@@ -75,11 +75,13 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
 
 def get_inference_pool() -> InferencePool:
 	model_sources = get_model_options().get('sources')
-	return inference_manager.get_inference_pool(__name__, model_sources)
+	model_context = __name__ + '.' + state_manager.get_item('lip_syncer_model')
+	return inference_manager.get_inference_pool(model_context, model_sources)
 
 
 def clear_inference_pool() -> None:
-	inference_manager.clear_inference_pool(__name__)
+	model_context = __name__ + '.' + state_manager.get_item('lip_syncer_model')
+	inference_manager.clear_inference_pool(model_context)
 
 
 def get_model_options() -> ModelOptions:

facefusion/processors/typing.py

@@ -5,7 +5,155 @@ from numpy._typing import NDArray
 from facefusion.typing import AppContext, AudioFrame, Face, FaceSet, VisionFrame
 
 AgeModifierModel = Literal['styleganex_age']
-DeepSwapperModel = str
+DeepSwapperModel = Literal\
+[
+	'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/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/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'
+]
 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']

facefusion/program.py

@@ -4,7 +4,7 @@ from argparse import ArgumentParser, HelpFormatter
 import facefusion.choices
 from facefusion import config, metadata, state_manager, wording
 from facefusion.common_helper import create_float_metavar, create_int_metavar, get_last
-from facefusion.execution import get_available_execution_providers
+from facefusion.execution import get_execution_provider_set
 from facefusion.filesystem import list_directory
 from facefusion.jobs import job_store
 from facefusion.processors.core import get_processors_modules
@@ -94,7 +94,7 @@ def create_output_pattern_program() -> ArgumentParser:
 def create_face_detector_program() -> ArgumentParser:
 	program = ArgumentParser(add_help = False)
 	group_face_detector = program.add_argument_group('face detector')
-	group_face_detector.add_argument('--face-detector-model', help = wording.get('help.face_detector_model'), default = config.get_str_value('face_detector.face_detector_model', 'yoloface'), choices = facefusion.choices.face_detector_models)
+	group_face_detector.add_argument('--face-detector-model', help = wording.get('help.face_detector_model'), default = config.get_str_value('face_detector.face_detector_model', 'yoloface'), choices = list(facefusion.choices.face_detector_set.keys()))
 	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)
@@ -132,13 +132,11 @@ 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_occluder_model', 'face_parser_model', 'face_mask_types', 'face_mask_blur', 'face_mask_padding', 'face_mask_regions' ])
+	job_store.register_step_keys([ 'face_mask_types', 'face_mask_blur', 'face_mask_padding', 'face_mask_regions' ])
 	return program
 
 
@@ -171,7 +169,7 @@ def create_output_creation_program() -> ArgumentParser:
 
 def create_processors_program() -> ArgumentParser:
 	program = ArgumentParser(add_help = False)
-	available_processors = [ file.get('name') for file in list_directory('facefusion/processors/modules') ]
+	available_processors = 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' ])
@@ -182,7 +180,7 @@ def create_processors_program() -> ArgumentParser:
 
 def create_uis_program() -> ArgumentParser:
 	program = ArgumentParser(add_help = False)
-	available_ui_layouts = [ file.get('name') for file in list_directory('facefusion/uis/layouts') ]
+	available_ui_layouts = 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 = '+')
@@ -192,10 +190,9 @@ def create_uis_program() -> ArgumentParser:
 
 def create_execution_program() -> ArgumentParser:
 	program = ArgumentParser(add_help = False)
-	available_execution_providers = get_available_execution_providers()
 	group_execution = program.add_argument_group('execution')
 	group_execution.add_argument('--execution-device-id', help = wording.get('help.execution_device_id'), default = config.get_str_value('execution.execution_device_id', '0'))
-	group_execution.add_argument('--execution-providers', help = wording.get('help.execution_providers').format(choices = ', '.join(available_execution_providers)), default = config.get_str_list('execution.execution_providers', 'cpu'), choices = available_execution_providers, nargs = '+', metavar = 'EXECUTION_PROVIDERS')
+	group_execution.add_argument('--execution-providers', help = wording.get('help.execution_providers').format(choices = ', '.join(list(get_execution_provider_set().keys()))), default = config.get_str_list('execution.execution_providers', 'cpu'), choices = list(get_execution_provider_set().keys()), 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' ])
@@ -204,9 +201,8 @@ def create_execution_program() -> ArgumentParser:
 
 def create_download_providers_program() -> ArgumentParser:
 	program = ArgumentParser(add_help = False)
-	download_providers = list(facefusion.choices.download_provider_set.keys())
 	group_download = program.add_argument_group('download')
-	group_download.add_argument('--download-providers', help = wording.get('help.download_providers').format(choices = ', '.join(download_providers)), default = config.get_str_list('download.download_providers', ' '.join(facefusion.choices.download_providers)), choices = download_providers, nargs = '+', metavar = 'DOWNLOAD_PROVIDERS')
+	group_download.add_argument('--download-providers', help = wording.get('help.download_providers').format(choices = ', '.join(list(facefusion.choices.download_provider_set.keys()))), default = config.get_str_list('download.download_providers', 'github'), choices = list(facefusion.choices.download_provider_set.keys()), nargs = '+', metavar = 'DOWNLOAD_PROVIDERS')
 	job_store.register_job_keys([ 'download_providers' ])
 	return program
 
@@ -214,7 +210,7 @@ def create_download_providers_program() -> ArgumentParser:
 def create_download_scope_program() -> ArgumentParser:
 	program = ArgumentParser(add_help = False)
 	group_download = program.add_argument_group('download')
-	group_download.add_argument('--download-scope', help = wording.get('help.download_scope'), default = config.get_str_value('download.download_scope', 'lite'), choices = facefusion.choices.download_scopes)
+	group_download.add_argument('--download-scope', help = wording.get('help.download_scope'), default = config.get_str_value('download.download_scope', 'lite'), choices = list(facefusion.choices.download_scopes))
 	job_store.register_job_keys([ 'download_scope' ])
 	return program
 
@@ -230,9 +226,8 @@ def create_memory_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('--log-level', help = wording.get('help.log_level'), default = config.get_str_value('misc.log_level', 'info'), choices = log_level_keys)
+	group_misc.add_argument('--log-level', help = wording.get('help.log_level'), default = config.get_str_value('misc.log_level', 'info'), choices = list(facefusion.choices.log_level_set.keys()))
 	job_store.register_job_keys([ 'log_level' ])
 	return program
 

facefusion/static/index.html

@@ -0,0 +1,242 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Demo</title>
+	<style>
+		.preview, meter {
+			width: 100%;
+		}
+		meter { border-radius: 0}
+        img, textarea {
+            width: 100%;
+        }
+        input[type="range"] {
+            width: 100%;
+            margin-top: 1em;
+        }
+	</style>
+</head>
+<body>
+	<div style="display:flex">
+		<div class="preview">
+			<h1>Preview</h1>
+			<div class="image-container">
+				<img id="image" alt="Frame Image">
+			</div>
+			<input type="range" id="slider" value="0">
+			<p>Frame: <span id="frameValue">0</span></p>
+			<button id="playBtn">Play</button>
+    		<button id="stopBtn" disabled>Stop</button>
+			<input type="checkbox" id="useWebSocket" /> Use WebSocket for Preview
+		</div>
+		<div>
+			<h1>Debug</h1>
+			<p>Video Memory <meter id="video_memory" min="0" max="100" value="0"></meter></p>
+			<p>GPU Utilization <meter id="gpu_utilization" min="0" max="100" value="0"></meter></p>
+			<textarea id="debug" rows="1" cols="80" readonly></textarea>
+			<textarea id="devices" rows="4" cols="80" readonly></textarea>
+			<textarea id="state" rows="30" cols="80" readonly></textarea>
+			<textarea id="log" rows="10" cols="80" readonly></textarea>
+			<textarea id="fps" rows="2" cols="80" readonly></textarea>
+		</div>
+	</div>
+ 	<script>
+		function createWebSocketConnection(url, debug, container) {
+			const socket = new WebSocket(url);
+
+			socket.onopen = () => {
+				debug.value = `WebSocket connection established for URL: ${url}`;
+			};
+
+			socket.onmessage = event => {
+				debug.value = `WebSocket Event: ${event.type}`;
+				container.value = event.data;
+			};
+
+			socket.onerror = error => {
+				debug.value = `WebSocket Error: ${error}`;
+			};
+
+			socket.onclose = () => {
+				debug.value = `WebSocket connection closed for URL: ${url} -> Reloading Page`;
+				setTimeout(() => location.reload(), 1000);
+			};
+
+			return socket;
+		}
+
+		devicesSocket = createWebSocketConnection('ws://127.0.0.1:8000/execution/devices', debug, devices);
+		createWebSocketConnection('ws://127.0.0.1:8000/state', debug, state);
+
+		devicesSocket.addEventListener('message', event => {
+			const data = JSON.parse(event.data)[0]
+			const freeMemory = data.video_memory.free.value;
+			const totalMemory = data.video_memory.total.value;
+			const usedMemory = totalMemory - freeMemory;
+			const usedMemoryPercentage = (usedMemory / totalMemory) * 100;
+
+			video_memory.value = usedMemoryPercentage;
+			gpu_utilization.value = data.utilization.gpu.value
+		})
+    </script>
+	<script>
+		const slider = document.getElementById('slider');
+		const image = document.getElementById('image');
+		const frameValue = document.getElementById('frameValue');
+		const playBtn = document.getElementById('playBtn');
+		const stopBtn = document.getElementById('stopBtn');
+		const logTextarea = document.getElementById('log');
+		const useWebSocketCheckbox = document.getElementById('useWebSocket');
+
+		let totalFrames = 0;
+		let currentFrame = 0;
+		let totalFps = 0;
+		let requestCount = 0;
+		let isPlaying = false;
+		let socket;
+
+		// Fetch the total frame count and set up the slider
+		async function fetchSliderTotal() {
+			try {
+				const start = performance.now();
+				const response = await fetch('http://127.0.0.1:8000/ui/preview_slider');
+				const end = performance.now();
+
+				logRequest('GET', 'http://127.0.0.1:8000/ui/preview_slider', start, end);
+
+				if (response.ok) {
+					const data = await response.json();
+					totalFrames = data.video_frame_total;
+					slider.max = totalFrames;
+					slider.value = 0;
+					frameValue.textContent = 0;
+					image.src = `http://127.0.0.1:8000/preview?frame_number=0`;
+				} else {
+					console.error('Failed to fetch total frame count');
+				}
+			} catch (error) {
+				console.error('Error fetching total frame count:', error);
+			}
+		}
+
+		// Function to log request details to the textarea
+		function logRequest(method, url, startTime, endTime) {
+			const duration = (endTime - startTime).toFixed(2);
+			const logMessage = `${method} ${url} | Duration: ${duration}ms\n`;
+
+			// Append to the log textarea
+			logTextarea.value += logMessage;
+			logTextarea.scrollTop = logTextarea.scrollHeight; // Auto scroll to the bottom
+		}
+
+		function logFps(startTime, endTime) {
+			const duration = (endTime - startTime).toFixed(2);
+			const durationInSeconds = duration / 1000;  // Convert ms to seconds
+			const fps = (1 / durationInSeconds).toFixed(2);  // FPS for this request
+
+			// Update total FPS and request count
+			totalFps += parseFloat(fps);
+			requestCount++;
+
+			// Calculate average FPS
+			const averageFps = (totalFps / requestCount).toFixed(2);
+
+			// Update the textarea with id 'fps' to show the average FPS
+			const fpsTextarea = document.getElementById('fps');
+			fpsTextarea.value = `Average FPS: ${averageFps}\n`;
+
+			// Optionally, you can append the current FPS to the textarea as well:
+			fpsTextarea.value += `Current FPS: ${fps}\n`;
+		}
+
+		// Function to update the image based on the slider's value or WebSocket message
+		function updateImage() {
+			const frameNumber = slider.value;
+
+
+			// If WebSocket is enabled, use WebSocket to fetch the image
+			if (useWebSocketCheckbox.checked) {
+				image.onload = null
+				if (!socket) {
+					socket = new WebSocket('ws://127.0.0.1:8000/preview');
+				}
+				const start = performance.now();
+				socket.send(JSON.stringify({ frame_number: frameNumber }));
+				socket.onmessage = function (event) {
+						const end = performance.now();
+						logRequest('WEBSOCKET', 'ws://127.0.0.1:8000/preview', start, end);
+						logFps(start, end)
+
+						// Create a Blob URL from the WebSocket message (assumed to be a Blob)
+						const imageUrl = URL.createObjectURL(event.data);
+
+						// Set the image source to the Blob URL
+						image.src = imageUrl;
+						frameValue.textContent = frameNumber;
+
+						// Continue if playing
+						if (isPlaying && currentFrame < totalFrames) {
+							currentFrame++;
+							slider.value = currentFrame;
+							updateImage(); // Continue to next frame
+						}
+					};
+			} else {
+				socket = null
+				// Use default fetch for the image
+				const start = performance.now();
+				image.src = `http://127.0.0.1:8000/preview?frame_number=${frameNumber}`;
+				image.onload = function () {
+					const end = performance.now();
+					logRequest('GET', image.src, start, end);
+					logFps(start, end)
+					frameValue.textContent = frameNumber;
+
+					// Continue if playing
+					if (isPlaying && currentFrame < totalFrames) {
+						currentFrame++;
+						slider.value = currentFrame;
+						updateImage(); // Continue to next frame
+					}
+				};
+			}
+		}
+
+		// Function to start the play action (without setInterval, only on image load)
+		function startPlay() {
+			playBtn.disabled = true;
+			stopBtn.disabled = false;
+			isPlaying = true;
+			currentFrame = parseInt(slider.value, 10);
+
+			// Start loading the first image
+			updateImage();
+		}
+
+		// Function to stop the play action
+		function stopPlay() {
+			isPlaying = false;
+			playBtn.disabled = false;
+			stopBtn.disabled = true;
+			if (socket) {
+				socket.close(); // Close WebSocket when stopping
+			}
+		}
+
+		// Event listeners for Play/Stop buttons
+		playBtn.addEventListener('click', startPlay);
+		stopBtn.addEventListener('click', stopPlay);
+
+		// Slider manual update
+		slider.addEventListener('change', function () {
+			currentFrame = slider.value;
+			updateImage();
+		});
+
+		// Fetch the total number of frames when the page loads
+		window.onload = fetchSliderTotal;
+	</script>
+</body>
+</html>

facefusion/typing.py

@@ -101,11 +101,8 @@ 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']
-FaceMaskRegionSet = Dict[FaceMaskRegion, int]
 TempFrameFormat = Literal['bmp', 'jpg', 'png']
 OutputAudioEncoder = Literal['aac', 'libmp3lame', 'libopus', 'libvorbis']
 OutputVideoEncoder = Literal['libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc', 'h264_amf', 'hevc_amf','h264_qsv', 'hevc_qsv', 'h264_videotoolbox', 'hevc_videotoolbox']
@@ -115,9 +112,9 @@ ModelOptions = Dict[str, Any]
 ModelSet = Dict[str, ModelOptions]
 ModelInitializer = NDArray[Any]
 
-ExecutionProvider = Literal['cpu', 'coreml', 'cuda', 'directml', 'openvino', 'rocm', 'tensorrt']
+ExecutionProviderKey = Literal['cpu', 'coreml', 'cuda', 'directml', 'openvino', 'rocm', 'tensorrt']
 ExecutionProviderValue = Literal['CPUExecutionProvider', 'CoreMLExecutionProvider', 'CUDAExecutionProvider', 'DmlExecutionProvider', 'OpenVINOExecutionProvider', 'ROCMExecutionProvider', 'TensorrtExecutionProvider']
-ExecutionProviderSet = Dict[ExecutionProvider, ExecutionProviderValue]
+ExecutionProviderSet = Dict[ExecutionProviderKey, ExecutionProviderValue]
 ValueAndUnit = TypedDict('ValueAndUnit',
 {
 	'value' : int,
@@ -158,13 +155,8 @@ ExecutionDevice = TypedDict('ExecutionDevice',
 	'utilization' : ExecutionDeviceUtilization
 })
 
-DownloadProvider = Literal['github', 'huggingface']
-DownloadProviderValue = TypedDict('DownloadProviderValue',
-{
-	'url' : str,
-	'path' : str
-})
-DownloadProviderSet = Dict[DownloadProvider, DownloadProviderValue]
+DownloadProviderKey = Literal['github', 'huggingface']
+DownloadProviderSet = Dict[DownloadProviderKey, str]
 DownloadScope = Literal['lite', 'full']
 Download = TypedDict('Download',
 {
@@ -175,13 +167,6 @@ 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]
@@ -239,8 +224,6 @@ 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',
@@ -302,8 +285,6 @@ 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,
@@ -326,10 +307,10 @@ State = TypedDict('State',
 	'ui_layouts' : List[str],
 	'ui_workflow' : UiWorkflow,
 	'execution_device_id' : str,
-	'execution_providers' : List[ExecutionProvider],
+	'execution_providers' : List[ExecutionProviderKey],
 	'execution_thread_count' : int,
 	'execution_queue_count' : int,
-	'download_providers' : List[DownloadProvider],
+	'download_providers' : List[DownloadProviderKey],
 	'download_scope' : DownloadScope,
 	'video_memory_strategy' : VideoMemoryStrategy,
 	'system_memory_limit' : int,

facefusion/uis/assets/overrides.css

@@ -65,56 +65,35 @@
 	min-height: unset;
 }
 
-:root:root:root:root .tab-wrapper
+:root:root:root:root .tabs button:hover
 {
-	padding: 0 0.625rem;
+	background: unset;
 }
 
 :root:root:root:root .tab-container
 {
-	gap: 0.5em;
+	height: 2.5rem;
 }
 
-:root:root:root:root .tab-container button
+:root:root:root:root .tabitem
 {
-	background: unset;
-	border-bottom: 0.125rem solid;
+	padding: 0.75rem 0 0 0
 }
 
-:root:root:root:root .tab-container button.selected
+:root:root:root:root .tab-container:after,
+:root:root:root:root .tabs button:after
 {
-	color: var(--primary-500)
+	border-width: 0.125rem;
 }
 
-:root:root:root:root .toast-body
+:root:root:root:root .tab-container:after
 {
-	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;
+	border-color: var(--block-background-fill)
 }
 
 :root:root:root:root .slider_input_container > span,
 :root:root:root:root .feather-upload,
+:root:root:root:root .toast-wrap,
 :root:root:root:root footer
 {
 	display: none;

facefusion/uis/components/download.py

@@ -2,11 +2,11 @@ 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.choices import download_provider_set
 from facefusion.filesystem import list_directory
 from facefusion.processors.core import get_processors_modules
-from facefusion.typing import DownloadProvider
+from facefusion.typing import DownloadProviderKey
 
 DOWNLOAD_PROVIDERS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
 
@@ -16,7 +16,7 @@ def render() -> None:
 
 	DOWNLOAD_PROVIDERS_CHECKBOX_GROUP = gradio.CheckboxGroup(
 		label = wording.get('uis.download_providers_checkbox_group'),
-		choices = facefusion.choices.download_providers,
+		choices = list(download_provider_set.keys()),
 		value = state_manager.get_item('download_providers')
 	)
 
@@ -25,7 +25,7 @@ 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:
+def update_download_providers(download_providers : List[DownloadProviderKey]) -> gradio.CheckboxGroup:
 	common_modules =\
 	[
 		content_analyser,
@@ -36,13 +36,13 @@ def update_download_providers(download_providers : List[DownloadProvider]) -> gr
 		face_masker,
 		voice_extractor
 	]
-	available_processors = [ file.get('name') for file in list_directory('facefusion/processors/modules') ]
+	available_processors = 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
+	download_providers = download_providers or list(download_provider_set.keys())
 	state_manager.set_item('download_providers', download_providers)
 	return gradio.CheckboxGroup(value = state_manager.get_item('download_providers'))

facefusion/uis/components/execution.py

@@ -3,10 +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_available_execution_providers
+from facefusion.execution import get_execution_provider_set
 from facefusion.filesystem import list_directory
 from facefusion.processors.core import get_processors_modules
-from facefusion.typing import ExecutionProvider
+from facefusion.typing import ExecutionProviderKey
 
 EXECUTION_PROVIDERS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
 
@@ -16,7 +16,7 @@ def render() -> None:
 
 	EXECUTION_PROVIDERS_CHECKBOX_GROUP = gradio.CheckboxGroup(
 		label = wording.get('uis.execution_providers_checkbox_group'),
-		choices = get_available_execution_providers(),
+		choices = list(get_execution_provider_set().keys()),
 		value = state_manager.get_item('execution_providers')
 	)
 
@@ -25,7 +25,7 @@ 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[ExecutionProvider]) -> gradio.CheckboxGroup:
+def update_execution_providers(execution_providers : List[ExecutionProviderKey]) -> gradio.CheckboxGroup:
 	common_modules =\
 	[
 		content_analyser,
@@ -36,13 +36,13 @@ def update_execution_providers(execution_providers : List[ExecutionProvider]) ->
 		face_recognizer,
 		voice_extractor
 	]
-	available_processors = [ file.get('name') for file in list_directory('facefusion/processors/modules') ]
+	available_processors = 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()
+	execution_providers = execution_providers or list(get_execution_provider_set())
 	state_manager.set_item('execution_providers', execution_providers)
 	return gradio.CheckboxGroup(value = state_manager.get_item('execution_providers'))

facefusion/uis/components/face_detector.py

@@ -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_models,
+			choices = list(facefusion.choices.face_detector_set.keys()),
 			value = state_manager.get_item('face_detector_model')
 		)
 		FACE_DETECTOR_SIZE_DROPDOWN = gradio.Dropdown(**face_detector_size_dropdown_options)

facefusion/uis/components/face_masker.py

@@ -3,13 +3,11 @@ from typing import List, Optional, Tuple
 import gradio
 
 import facefusion.choices
-from facefusion import face_masker, state_manager, wording
+from facefusion import state_manager, wording
 from facefusion.common_helper import calc_float_step, calc_int_step
-from facefusion.typing import FaceMaskRegion, FaceMaskType, FaceOccluderModel, FaceParserModel
+from facefusion.typing import FaceMaskRegion, FaceMaskType
 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
@@ -20,8 +18,6 @@ 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
@@ -32,17 +28,6 @@ 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,
@@ -97,8 +82,6 @@ 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)
@@ -109,8 +92,6 @@ 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)
@@ -119,24 +100,6 @@ 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)

facefusion/uis/components/face_swapper_options.py

@@ -20,7 +20,7 @@ def render() -> None:
 	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_models,
+		choices = list(processors_choices.face_swapper_set.keys()),
 		value = state_manager.get_item('face_swapper_model'),
 		visible = has_face_swapper
 	)

facefusion/uis/components/preview.py

@@ -162,9 +162,7 @@ def listen() -> None:
 		'face_detector_model_dropdown',
 		'face_detector_size_dropdown',
 		'face_detector_angles_checkbox_group',
-		'face_landmarker_model_dropdown',
-		'face_occluder_model_dropdown',
-		'face_parser_model_dropdown'
+		'face_landmarker_model_dropdown'
 	]):
 		ui_component.change(clear_and_update_preview_image, inputs = PREVIEW_FRAME_SLIDER, outputs = PREVIEW_IMAGE)
 

facefusion/uis/components/processors.py

@@ -39,5 +39,5 @@ def update_processors(processors : List[str]) -> gradio.CheckboxGroup:
 
 
 def sort_processors(processors : List[str]) -> List[str]:
-	available_processors = [ file.get('name') for file in list_directory('facefusion/processors/modules') ]
+	available_processors = list_directory('facefusion/processors/modules')
 	return sorted(available_processors, key = lambda processor : processors.index(processor) if processor in processors else len(processors))

facefusion/uis/components/terminal.py

@@ -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 = facefusion.choices.log_levels,
+		choices = list(log_level_set.keys()),
 		value = state_manager.get_item('log_level')
 	)
 	TERMINAL_TEXTBOX = gradio.Textbox(

facefusion/uis/components/webcam.py

@@ -2,7 +2,7 @@ import os
 import subprocess
 from collections import deque
 from concurrent.futures import ThreadPoolExecutor
-from typing import Deque, Generator, List, Optional
+from typing import Deque, Generator, 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 get_first, is_windows
+from facefusion.common_helper import is_windows
 from facefusion.content_analyser import analyse_stream
 from facefusion.face_analyser import get_average_face, get_many_faces
 from facefusion.ffmpeg import open_ffmpeg
@@ -27,17 +27,14 @@ WEBCAM_START_BUTTON : Optional[gradio.Button] = None
 WEBCAM_STOP_BUTTON : Optional[gradio.Button] = None
 
 
-def get_webcam_capture(webcam_device_id : int) -> Optional[cv2.VideoCapture]:
+def get_webcam_capture() -> Optional[cv2.VideoCapture]:
 	global WEBCAM_CAPTURE
 
 	if WEBCAM_CAPTURE is None:
-		cv2.setLogLevel(0)
 		if is_windows():
-			webcam_capture = cv2.VideoCapture(webcam_device_id, cv2.CAP_DSHOW)
+			webcam_capture = cv2.VideoCapture(0, cv2.CAP_DSHOW)
 		else:
-			webcam_capture = cv2.VideoCapture(webcam_device_id)
-		cv2.setLogLevel(3)
-
+			webcam_capture = cv2.VideoCapture(0)
 		if webcam_capture and webcam_capture.isOpened():
 			WEBCAM_CAPTURE = webcam_capture
 	return WEBCAM_CAPTURE
@@ -71,35 +68,27 @@ 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_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)
+	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)
 
 
-def start(webcam_device_id : int, webcam_mode : WebcamMode, webcam_resolution : str, webcam_fps : Fps) -> Generator[VisionFrame, None, None]:
+def start(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)
-
-	if isinstance(webcam_device_id, int):
-		webcam_capture = get_webcam_capture(webcam_device_id)
+	webcam_capture = get_webcam_capture()
 
 	if webcam_capture and webcam_capture.isOpened():
 		webcam_capture.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG')) #type:ignore[attr-defined]
@@ -170,22 +159,9 @@ def open_stream(stream_mode : StreamMode, stream_resolution : str, stream_fps :
 		commands.extend([ '-b:v', '2000k', '-f', 'mpegts', 'udp://localhost:27000?pkt_size=1316' ])
 	if stream_mode == 'v4l2':
 		try:
-			device_name = get_first(os.listdir('/sys/devices/virtual/video4linux'))
+			device_name = os.listdir('/sys/devices/virtual/video4linux')[0]
 			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

facefusion/uis/components/webcam_options.py

@@ -3,29 +3,19 @@ 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,
@@ -43,7 +33,6 @@ 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)

facefusion/uis/core.py

@@ -74,7 +74,6 @@ def init() -> None:
 	os.environ['GRADIO_TEMP_DIR'] = os.path.join(state_manager.get_item('temp_path'), 'gradio')
 
 	warnings.filterwarnings('ignore', category = UserWarning, module = 'gradio')
-	gradio.processing_utils._check_allowed = lambda path, check_in_upload_folder: None
 
 
 def launch() -> None:

facefusion/uis/typing.py

@@ -52,8 +52,6 @@ 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',
@@ -73,7 +71,6 @@ ComponentName = Literal\
 	'target_image',
 	'target_video',
 	'ui_workflow_dropdown',
-	'webcam_device_id_dropdown',
 	'webcam_fps_slider',
 	'webcam_mode_radio',
 	'webcam_resolution_dropdown'

facefusion/vision.py

@@ -5,7 +5,7 @@ import cv2
 import numpy
 from cv2.typing import Size
 
-import facefusion.choices
+from facefusion.choices import image_template_sizes, video_template_sizes
 from facefusion.common_helper import is_windows
 from facefusion.filesystem import is_image, is_video, sanitize_path_for_windows
 from facefusion.typing import Duration, Fps, Orientation, Resolution, VisionFrame
@@ -64,8 +64,8 @@ def create_image_resolutions(resolution : Resolution) -> List[str]:
 	if resolution:
 		width, height = resolution
 		temp_resolutions.append(normalize_resolution(resolution))
-		for image_template_size in facefusion.choices.image_template_sizes:
-			temp_resolutions.append(normalize_resolution((width * image_template_size, height * image_template_size)))
+		for template_size in image_template_sizes:
+			temp_resolutions.append(normalize_resolution((width * template_size, height * template_size)))
 		temp_resolutions = sorted(set(temp_resolutions))
 		for temp_resolution in temp_resolutions:
 			resolutions.append(pack_resolution(temp_resolution))
@@ -122,36 +122,11 @@ def restrict_video_fps(video_path : str, fps : Fps) -> 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():
@@ -180,11 +155,11 @@ def create_video_resolutions(resolution : Resolution) -> List[str]:
 	if resolution:
 		width, height = resolution
 		temp_resolutions.append(normalize_resolution(resolution))
-		for video_template_size in facefusion.choices.video_template_sizes:
+		for template_size in video_template_sizes:
 			if width > height:
-				temp_resolutions.append(normalize_resolution((video_template_size * width / height, video_template_size)))
+				temp_resolutions.append(normalize_resolution((template_size * width / height, template_size)))
 			else:
-				temp_resolutions.append(normalize_resolution((video_template_size, video_template_size * height / width)))
+				temp_resolutions.append(normalize_resolution((template_size, template_size * height / width)))
 		temp_resolutions = sorted(set(temp_resolutions))
 		for temp_resolution in temp_resolutions:
 			resolutions.append(pack_resolution(temp_resolution))

facefusion/wording.py

@@ -126,8 +126,6 @@ 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 to the box mask',
 		'face_mask_padding': 'apply top, right, bottom and left padding to the box mask',
@@ -287,8 +285,6 @@ 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',
@@ -330,7 +326,6 @@ 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'
 	}

requirements.txt

@@ -1,10 +1,9 @@
 filetype==1.2.0
-gradio==5.9.1
-gradio-rangeslider==0.0.8
-numpy==2.2.0
+litestar==2.13.0
+numpy==2.1.3
 onnx==1.17.0
 onnxruntime==1.20.1
 opencv-python==4.10.0.84
-psutil==6.1.1
+psutil==6.1.0
 tqdm==4.67.1
 scipy==1.14.1

tests/test_audio.py

@@ -17,8 +17,8 @@ def before_all() -> None:
 
 
 def test_get_audio_frame() -> 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(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 get_audio_frame('invalid', 25) is None
 
 

tests/test_download.py

@@ -1,18 +1,18 @@
-from facefusion.download import get_static_download_size, ping_static_url, resolve_download_url_by_provider
+import pytest
+
+from facefusion.download import conditional_download, get_download_size
+from .helper import get_test_examples_directory
 
 
-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
+@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_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_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'
+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

tests/test_execution.py

@@ -1,4 +1,8 @@
-from facefusion.execution import create_inference_execution_providers, get_available_execution_providers, has_execution_provider
+from facefusion.execution import create_execution_providers, get_execution_provider_set, has_execution_provider
+
+
+def test_get_execution_provider_set() -> None:
+	assert 'cpu' in get_execution_provider_set().keys()
 
 
 def test_has_execution_provider() -> None:
@@ -6,11 +10,7 @@ def test_has_execution_provider() -> None:
 	assert has_execution_provider('openvino') is False
 
 
-def test_get_available_execution_providers() -> None:
-	assert 'cpu' in get_available_execution_providers()
-
-
-def test_create_inference_execution_providers() -> None:
+def test_multiple_execution_providers() -> None:
 	execution_providers =\
 	[
 		('CUDAExecutionProvider',
@@ -20,4 +20,4 @@ def test_create_inference_execution_providers() -> None:
 		'CPUExecutionProvider'
 	]
 
-	assert create_inference_execution_providers('1', [ 'cpu', 'cuda' ]) == execution_providers
+	assert create_execution_providers('1', [ 'cpu', 'cuda' ]) == execution_providers

tests/test_ffmpeg.py

@@ -33,30 +33,78 @@ def before_all() -> None:
 
 @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:
-	extract_set =\
+	target_paths =\
 	[
-		(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)
+		get_test_example_file('target-240p-25fps.mp4'),
+		get_test_example_file('target-240p-30fps.mp4'),
+		get_test_example_file('target-240p-60fps.mp4')
 	]
 
-	for target_path, trim_frame_start, trim_frame_end, frame_total in extract_set:
+	for target_path in target_paths:
 		create_temp_directory(target_path)
 
-		assert extract_frames(target_path, '452x240', 30.0, trim_frame_start, trim_frame_end) is True
+		assert extract_frames(target_path, '452x240', 30.0) is True
+		assert len(get_temp_frame_paths(target_path)) == 324
+
+		clear_temp_directory(target_path)
+
+
+def test_extract_frames_with_trim_start() -> None:
+	state_manager.init_item('trim_frame_start', 224)
+	target_paths =\
+	[
+		(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 target_paths:
+		create_temp_directory(target_path)
+
+		assert extract_frames(target_path, '452x240', 30.0) is True
+		assert len(get_temp_frame_paths(target_path)) == 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)
+	target_paths =\
+	[
+		(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 target_paths:
+		create_temp_directory(target_path)
+
+		assert extract_frames(target_path, '452x240', 30.0) is True
+		assert len(get_temp_frame_paths(target_path)) == frame_total
+
+		clear_temp_directory(target_path)
+
+
+def test_extract_frames_with_trim_end() -> None:
+	state_manager.init_item('trim_frame_end', 100)
+	target_paths =\
+	[
+		(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 target_paths:
+		create_temp_directory(target_path)
+
+		assert extract_frames(target_path, '426x240', 30.0) is True
 		assert len(get_temp_frame_paths(target_path)) == frame_total
 
 		clear_temp_directory(target_path)
@@ -91,7 +139,7 @@ def test_restore_audio() -> None:
 		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
+		assert restore_audio(target_path, output_path, 30) is True
 
 		clear_temp_directory(target_path)
 

tests/test_filesystem.py

@@ -105,11 +105,8 @@ def test_create_directory() -> None:
 
 
 def test_list_directory() -> 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(get_test_examples_directory())
+	assert list_directory(get_test_example_file('source.jpg')) is None
 	assert list_directory('invalid') is None
 
 

tests/test_vision.py

@@ -3,7 +3,7 @@ import subprocess
 import pytest
 
 from facefusion.download import conditional_download
-from facefusion.vision import calc_histogram_difference, count_trim_frame_total, count_video_frame_total, create_image_resolutions, create_video_resolutions, detect_image_resolution, detect_video_duration, detect_video_fps, detect_video_resolution, get_video_frame, match_frame_color, normalize_resolution, pack_resolution, read_image, restrict_image_resolution, restrict_trim_frame, restrict_video_fps, restrict_video_resolution, unpack_resolution
+from facefusion.vision import calc_histogram_difference, count_video_frame_total, create_image_resolutions, create_video_resolutions, detect_image_resolution, detect_video_duration, detect_video_fps, detect_video_resolution, get_video_frame, match_frame_color, normalize_resolution, pack_resolution, read_image, restrict_image_resolution, restrict_video_fps, restrict_video_resolution, unpack_resolution
 from .helper import get_test_example_file, get_test_examples_directory
 
 
@@ -50,7 +50,7 @@ def test_create_image_resolutions() -> None:
 
 
 def test_get_video_frame() -> None:
-	assert hasattr(get_video_frame(get_test_example_file('target-240p-25fps.mp4')), '__array_interface__')
+	assert get_video_frame(get_test_example_file('target-240p-25fps.mp4')) is not None
 	assert get_video_frame('invalid') is None
 
 
@@ -79,26 +79,6 @@ def test_detect_video_duration() -> None:
 	assert detect_video_duration('invalid') == 0
 
 
-def test_count_trim_frame_total() -> None:
-	assert count_trim_frame_total(get_test_example_file('target-240p.mp4'), 0, 200) == 200
-	assert count_trim_frame_total(get_test_example_file('target-240p.mp4'), 70, 270) == 200
-	assert count_trim_frame_total(get_test_example_file('target-240p.mp4'), -10, None) == 270
-	assert count_trim_frame_total(get_test_example_file('target-240p.mp4'), None, -10) == 0
-	assert count_trim_frame_total(get_test_example_file('target-240p.mp4'), 280, None) == 0
-	assert count_trim_frame_total(get_test_example_file('target-240p.mp4'), None, 280) == 270
-	assert count_trim_frame_total(get_test_example_file('target-240p.mp4'), None, None) == 270
-
-
-def test_restrict_trim_frame() -> None:
-	assert restrict_trim_frame(get_test_example_file('target-240p.mp4'), 0, 200) == (0, 200)
-	assert restrict_trim_frame(get_test_example_file('target-240p.mp4'), 70, 270) == (70, 270)
-	assert restrict_trim_frame(get_test_example_file('target-240p.mp4'), -10, None) == (0, 270)
-	assert restrict_trim_frame(get_test_example_file('target-240p.mp4'), None, -10) == (0, 0)
-	assert restrict_trim_frame(get_test_example_file('target-240p.mp4'), 280, None) == (270, 270)
-	assert restrict_trim_frame(get_test_example_file('target-240p.mp4'), None, 280) == (0, 270)
-	assert restrict_trim_frame(get_test_example_file('target-240p.mp4'), None, None) == (0, 270)
-
-
 def test_detect_video_resolution() -> None:
 	assert detect_video_resolution(get_test_example_file('target-240p.mp4')) == (426, 226)
 	assert detect_video_resolution(get_test_example_file('target-240p-90deg.mp4')) == (226, 426)