Patch 3.5.4 (#1055 )

* remove insecure flag from curl * eleminate repating definitons * limit processors and ui layouts by choices * follow couple of v4 standards * use more secure mkstemp * dynamic cache path for execution providers * fix benchmarker, prevent path traveling via job-id * fix order in execution provider choices * resort by prioroty * introduce support for QNN * close file description for Windows to stop crying * prevent ConnectionResetError under windows * needed for nested .caches directory as onnxruntime does not create it * different approach to silent asyncio * update dependencies * simplify the name to just inference providers * switch to trt_builder_optimization_level 4
Fix list literal spacing to use [ x, y ] style (#1044 )
2026-07-28 21:08:54 +02:00 · 2026-03-08 11:00:45 +01:00 · 2026-02-18 09:18:03 +01:00
41 changed files with 263 additions and 227 deletions
@@ -7,6 +7,84 @@ from facefusion.types import ApplyStateItem, Args
 from facefusion.vision import detect_video_fps


+def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
+	apply_state_item('command', args.get('command'))
+	apply_state_item('temp_path', args.get('temp_path'))
+	apply_state_item('jobs_path', args.get('jobs_path'))
+	apply_state_item('source_paths', args.get('source_paths'))
+	apply_state_item('target_path', args.get('target_path'))
+	apply_state_item('output_path', args.get('output_path'))
+	apply_state_item('source_pattern', args.get('source_pattern'))
+	apply_state_item('target_pattern', args.get('target_pattern'))
+	apply_state_item('output_pattern', args.get('output_pattern'))
+	apply_state_item('face_detector_model', args.get('face_detector_model'))
+	apply_state_item('face_detector_size', args.get('face_detector_size'))
+	apply_state_item('face_detector_margin', normalize_space(args.get('face_detector_margin')))
+	apply_state_item('face_detector_angles', args.get('face_detector_angles'))
+	apply_state_item('face_detector_score', args.get('face_detector_score'))
+	apply_state_item('face_landmarker_model', args.get('face_landmarker_model'))
+	apply_state_item('face_landmarker_score', args.get('face_landmarker_score'))
+	apply_state_item('face_selector_mode', args.get('face_selector_mode'))
+	apply_state_item('face_selector_order', args.get('face_selector_order'))
+	apply_state_item('face_selector_age_start', args.get('face_selector_age_start'))
+	apply_state_item('face_selector_age_end', args.get('face_selector_age_end'))
+	apply_state_item('face_selector_gender', args.get('face_selector_gender'))
+	apply_state_item('face_selector_race', args.get('face_selector_race'))
+	apply_state_item('reference_face_position', args.get('reference_face_position'))
+	apply_state_item('reference_face_distance', args.get('reference_face_distance'))
+	apply_state_item('reference_frame_number', args.get('reference_frame_number'))
+	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_areas', args.get('face_mask_areas'))
+	apply_state_item('face_mask_regions', args.get('face_mask_regions'))
+	apply_state_item('face_mask_blur', args.get('face_mask_blur'))
+	apply_state_item('face_mask_padding', normalize_space(args.get('face_mask_padding')))
+	apply_state_item('voice_extractor_model', args.get('voice_extractor_model'))
+	apply_state_item('trim_frame_start', args.get('trim_frame_start'))
+	apply_state_item('trim_frame_end', args.get('trim_frame_end'))
+	apply_state_item('temp_frame_format', args.get('temp_frame_format'))
+	apply_state_item('keep_temp', args.get('keep_temp'))
+	apply_state_item('output_image_quality', args.get('output_image_quality'))
+	apply_state_item('output_image_scale', args.get('output_image_scale'))
+	apply_state_item('output_audio_encoder', args.get('output_audio_encoder'))
+	apply_state_item('output_audio_quality', args.get('output_audio_quality'))
+	apply_state_item('output_audio_volume', args.get('output_audio_volume'))
+	apply_state_item('output_video_encoder', args.get('output_video_encoder'))
+	apply_state_item('output_video_preset', args.get('output_video_preset'))
+	apply_state_item('output_video_quality', args.get('output_video_quality'))
+	apply_state_item('output_video_scale', args.get('output_video_scale'))
+
+	if args.get('output_video_fps') or is_video(args.get('target_path')):
+		output_video_fps = normalize_fps(args.get('output_video_fps')) or detect_video_fps(args.get('target_path'))
+		apply_state_item('output_video_fps', output_video_fps)
+
+	available_processors = [ get_file_name(file_path) for file_path in resolve_file_paths('facefusion/processors/modules') ]
+	apply_state_item('processors', args.get('processors'))
+
+	for processor_module in get_processors_modules(available_processors):
+		processor_module.apply_args(args, apply_state_item)
+
+	apply_state_item('open_browser', args.get('open_browser'))
+	apply_state_item('ui_layouts', args.get('ui_layouts'))
+	apply_state_item('ui_workflow', args.get('ui_workflow'))
+	apply_state_item('execution_device_ids', args.get('execution_device_ids'))
+	apply_state_item('execution_providers', args.get('execution_providers'))
+	apply_state_item('execution_thread_count', args.get('execution_thread_count'))
+	apply_state_item('download_providers', args.get('download_providers'))
+	apply_state_item('download_scope', args.get('download_scope'))
+	apply_state_item('benchmark_mode', args.get('benchmark_mode'))
+	apply_state_item('benchmark_resolutions', args.get('benchmark_resolutions'))
+	apply_state_item('benchmark_cycle_count', args.get('benchmark_cycle_count'))
+	apply_state_item('video_memory_strategy', args.get('video_memory_strategy'))
+	apply_state_item('system_memory_limit', args.get('system_memory_limit'))
+	apply_state_item('log_level', args.get('log_level'))
+	apply_state_item('halt_on_error', args.get('halt_on_error'))
+	apply_state_item('job_id', args.get('job_id'))
+	apply_state_item('job_status', args.get('job_status'))
+	apply_state_item('step_index', args.get('step_index'))
+
+
 def reduce_step_args(args : Args) -> Args:
 	step_args =\
 	{
@@ -37,95 +115,3 @@ def collect_job_args() -> Args:
 		key: state_manager.get_item(key) for key in job_store.get_job_keys() #type:ignore[arg-type]
 	}
 	return job_args
-
-
-def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
-	# general
-	apply_state_item('command', args.get('command'))
-	# paths
-	apply_state_item('temp_path', args.get('temp_path'))
-	apply_state_item('jobs_path', args.get('jobs_path'))
-	apply_state_item('source_paths', args.get('source_paths'))
-	apply_state_item('target_path', args.get('target_path'))
-	apply_state_item('output_path', args.get('output_path'))
-	# patterns
-	apply_state_item('source_pattern', args.get('source_pattern'))
-	apply_state_item('target_pattern', args.get('target_pattern'))
-	apply_state_item('output_pattern', args.get('output_pattern'))
-	# face detector
-	apply_state_item('face_detector_model', args.get('face_detector_model'))
-	apply_state_item('face_detector_size', args.get('face_detector_size'))
-	apply_state_item('face_detector_margin', normalize_space(args.get('face_detector_margin')))
-	apply_state_item('face_detector_angles', args.get('face_detector_angles'))
-	apply_state_item('face_detector_score', args.get('face_detector_score'))
-	# face landmarker
-	apply_state_item('face_landmarker_model', args.get('face_landmarker_model'))
-	apply_state_item('face_landmarker_score', args.get('face_landmarker_score'))
-	# face selector
-	apply_state_item('face_selector_mode', args.get('face_selector_mode'))
-	apply_state_item('face_selector_order', args.get('face_selector_order'))
-	apply_state_item('face_selector_age_start', args.get('face_selector_age_start'))
-	apply_state_item('face_selector_age_end', args.get('face_selector_age_end'))
-	apply_state_item('face_selector_gender', args.get('face_selector_gender'))
-	apply_state_item('face_selector_race', args.get('face_selector_race'))
-	apply_state_item('reference_face_position', args.get('reference_face_position'))
-	apply_state_item('reference_face_distance', args.get('reference_face_distance'))
-	apply_state_item('reference_frame_number', args.get('reference_frame_number'))
-	# face masker
-	apply_state_item('face_occluder_model', args.get('face_occluder_model'))
-	apply_state_item('face_parser_model', args.get('face_parser_model'))
-	apply_state_item('face_mask_types', args.get('face_mask_types'))
-	apply_state_item('face_mask_areas', args.get('face_mask_areas'))
-	apply_state_item('face_mask_regions', args.get('face_mask_regions'))
-	apply_state_item('face_mask_blur', args.get('face_mask_blur'))
-	apply_state_item('face_mask_padding', normalize_space(args.get('face_mask_padding')))
-	# voice extractor
-	apply_state_item('voice_extractor_model', args.get('voice_extractor_model'))
-	# frame extraction
-	apply_state_item('trim_frame_start', args.get('trim_frame_start'))
-	apply_state_item('trim_frame_end', args.get('trim_frame_end'))
-	apply_state_item('temp_frame_format', args.get('temp_frame_format'))
-	apply_state_item('keep_temp', args.get('keep_temp'))
-	# output creation
-	apply_state_item('output_image_quality', args.get('output_image_quality'))
-	apply_state_item('output_image_scale', args.get('output_image_scale'))
-	apply_state_item('output_audio_encoder', args.get('output_audio_encoder'))
-	apply_state_item('output_audio_quality', args.get('output_audio_quality'))
-	apply_state_item('output_audio_volume', args.get('output_audio_volume'))
-	apply_state_item('output_video_encoder', args.get('output_video_encoder'))
-	apply_state_item('output_video_preset', args.get('output_video_preset'))
-	apply_state_item('output_video_quality', args.get('output_video_quality'))
-	apply_state_item('output_video_scale', args.get('output_video_scale'))
-	if args.get('output_video_fps') or is_video(args.get('target_path')):
-		output_video_fps = normalize_fps(args.get('output_video_fps')) or detect_video_fps(args.get('target_path'))
-		apply_state_item('output_video_fps', output_video_fps)
-	# processors
-	available_processors = [ get_file_name(file_path) for file_path in resolve_file_paths('facefusion/processors/modules') ]
-	apply_state_item('processors', args.get('processors'))
-	for processor_module in get_processors_modules(available_processors):
-		processor_module.apply_args(args, apply_state_item)
-	# uis
-	apply_state_item('open_browser', args.get('open_browser'))
-	apply_state_item('ui_layouts', args.get('ui_layouts'))
-	apply_state_item('ui_workflow', args.get('ui_workflow'))
-	# execution
-	apply_state_item('execution_device_ids', args.get('execution_device_ids'))
-	apply_state_item('execution_providers', args.get('execution_providers'))
-	apply_state_item('execution_thread_count', args.get('execution_thread_count'))
-	# download
-	apply_state_item('download_providers', args.get('download_providers'))
-	apply_state_item('download_scope', args.get('download_scope'))
-	# benchmark
-	apply_state_item('benchmark_mode', args.get('benchmark_mode'))
-	apply_state_item('benchmark_resolutions', args.get('benchmark_resolutions'))
-	apply_state_item('benchmark_cycle_count', args.get('benchmark_cycle_count'))
-	# memory
-	apply_state_item('video_memory_strategy', args.get('video_memory_strategy'))
-	apply_state_item('system_memory_limit', args.get('system_memory_limit'))
-	# misc
-	apply_state_item('log_level', args.get('log_level'))
-	apply_state_item('halt_on_error', args.get('halt_on_error'))
-	# jobs
-	apply_state_item('job_id', args.get('job_id'))
-	apply_state_item('job_status', args.get('job_status'))
-	apply_state_item('step_index', args.get('step_index'))
@@ -89,7 +89,7 @@ def cycle(cycle_count : int) -> BenchmarkCycleSet:

 def suggest_output_path(target_path : str) -> str:
 	target_file_extension = get_file_extension(target_path)
-	return os.path.join(tempfile.gettempdir(), hashlib.sha1().hexdigest()[:8] + target_file_extension)
+	return os.path.join(tempfile.gettempdir(), hashlib.sha1(target_path.encode()).hexdigest() + target_file_extension)


 def render() -> None:
@@ -1,5 +1,5 @@
 import logging
-from typing import List, Sequence
+from typing import List, Sequence, get_args

 from facefusion.common_helper import create_float_range, create_int_range
 from facefusion.types import Angle, AudioEncoder, AudioFormat, AudioTypeSet, BenchmarkMode, BenchmarkResolution, BenchmarkSet, DownloadProvider, DownloadProviderSet, DownloadScope, EncoderSet, ExecutionProvider, ExecutionProviderSet, FaceDetectorModel, FaceDetectorSet, FaceLandmarkerModel, FaceMaskArea, FaceMaskAreaSet, FaceMaskRegion, FaceMaskRegionSet, FaceMaskType, FaceOccluderModel, FaceParserModel, FaceSelectorMode, FaceSelectorOrder, Gender, ImageFormat, ImageTypeSet, JobStatus, LogLevel, LogLevelSet, Race, Score, TempFrameFormat, UiWorkflow, VideoEncoder, VideoFormat, VideoMemoryStrategy, VideoPreset, VideoTypeSet, VoiceExtractorModel
@@ -12,15 +12,15 @@ face_detector_set : FaceDetectorSet =\
 	'yolo_face': [ '640x640' ],
 	'yunet': [ '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] = [ 'many', 'xseg_1', 'xseg_2', 'xseg_3' ]
-face_parser_models : List[FaceParserModel] = [ 'bisenet_resnet_18', 'bisenet_resnet_34' ]
-face_mask_types : List[FaceMaskType] = [ 'box', 'occlusion', 'area', 'region' ]
+face_detector_models : List[FaceDetectorModel] = list(get_args(FaceDetectorModel))
+face_landmarker_models : List[FaceLandmarkerModel] = list(get_args(FaceLandmarkerModel))
+face_selector_modes : List[FaceSelectorMode] = list(get_args(FaceSelectorMode))
+face_selector_orders : List[FaceSelectorOrder] = list(get_args(FaceSelectorOrder))
+face_selector_genders : List[Gender] = list(get_args(Gender))
+face_selector_races : List[Race] = list(get_args(Race))
+face_occluder_models : List[FaceOccluderModel] = list(get_args(FaceOccluderModel))
+face_parser_models : List[FaceParserModel] = list(get_args(FaceParserModel))
+face_mask_types : List[FaceMaskType] = list(get_args(FaceMaskType))
 face_mask_area_set : FaceMaskAreaSet =\
 {
 	'upper-face': [ 0, 1, 2, 31, 32, 33, 34, 35, 14, 15, 16, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17 ],
@@ -40,10 +40,10 @@ face_mask_region_set : FaceMaskRegionSet =\
 	'upper-lip': 12,
 	'lower-lip': 13
 }
-face_mask_areas : List[FaceMaskArea] = list(face_mask_area_set.keys())
-face_mask_regions : List[FaceMaskRegion] = list(face_mask_region_set.keys())
+face_mask_areas : List[FaceMaskArea] = list(get_args(FaceMaskArea))
+face_mask_regions : List[FaceMaskRegion] = list(get_args(FaceMaskRegion))

-voice_extractor_models : List[VoiceExtractorModel] = [ 'kim_vocal_1', 'kim_vocal_2', 'uvr_mdxnet' ]
+voice_extractor_models : List[VoiceExtractorModel] = list(get_args(VoiceExtractorModel))

 audio_type_set : AudioTypeSet =\
 {
@@ -74,21 +74,21 @@ video_type_set : VideoTypeSet =\
 	'webm': 'video/webm',
 	'wmv': 'video/x-ms-wmv'
 }
-audio_formats : List[AudioFormat] = list(audio_type_set.keys())
-image_formats : List[ImageFormat] = list(image_type_set.keys())
-video_formats : List[VideoFormat] = list(video_type_set.keys())
-temp_frame_formats : List[TempFrameFormat] = [ 'bmp', 'jpeg', 'png', 'tiff' ]
+audio_formats : List[AudioFormat] = list(get_args(AudioFormat))
+image_formats : List[ImageFormat] = list(get_args(ImageFormat))
+video_formats : List[VideoFormat] = list(get_args(VideoFormat))
+temp_frame_formats : List[TempFrameFormat] = list(get_args(TempFrameFormat))

+output_audio_encoders : List[AudioEncoder] = list(get_args(AudioEncoder))
+output_video_encoders : List[VideoEncoder] = list(get_args(VideoEncoder))
 output_encoder_set : EncoderSet =\
 {
-	'audio': [ 'flac', 'aac', 'libmp3lame', 'libopus', 'libvorbis', 'pcm_s16le', 'pcm_s32le' ],
-	'video': [ 'libx264', 'libx264rgb', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc', 'h264_amf', 'hevc_amf', 'h264_qsv', 'hevc_qsv', 'h264_videotoolbox', 'hevc_videotoolbox', 'rawvideo' ]
+	'audio': output_audio_encoders,
+	'video': output_video_encoders
 }
-output_audio_encoders : List[AudioEncoder] = output_encoder_set.get('audio')
-output_video_encoders : List[VideoEncoder] = output_encoder_set.get('video')
-output_video_presets : List[VideoPreset] = [ 'ultrafast', 'superfast', 'veryfast', 'faster', 'fast', 'medium', 'slow', 'slower', 'veryslow' ]
+output_video_presets : List[VideoPreset] = list(get_args(VideoPreset))

-benchmark_modes : List[BenchmarkMode] = [ 'warm', 'cold' ]
+benchmark_modes : List[BenchmarkMode] = list(get_args(BenchmarkMode))
 benchmark_set : BenchmarkSet =\
 {
 	'240p': '.assets/examples/target-240p.mp4',
@@ -99,20 +99,21 @@ benchmark_set : BenchmarkSet =\
 	'1440p': '.assets/examples/target-1440p.mp4',
 	'2160p': '.assets/examples/target-2160p.mp4'
 }
-benchmark_resolutions : List[BenchmarkResolution] = list(benchmark_set.keys())
+benchmark_resolutions : List[BenchmarkResolution] = list(get_args(BenchmarkResolution))

 execution_provider_set : ExecutionProviderSet =\
 {
 	'cuda': 'CUDAExecutionProvider',
 	'tensorrt': 'TensorrtExecutionProvider',
-	'directml': 'DmlExecutionProvider',
 	'rocm': 'ROCMExecutionProvider',
 	'migraphx': 'MIGraphXExecutionProvider',
-	'openvino': 'OpenVINOExecutionProvider',
 	'coreml': 'CoreMLExecutionProvider',
+	'openvino': 'OpenVINOExecutionProvider',
+	'qnn': 'QNNExecutionProvider',
+	'directml': 'DmlExecutionProvider',
 	'cpu': 'CPUExecutionProvider'
 }
-execution_providers : List[ExecutionProvider] = list(execution_provider_set.keys())
+execution_providers : List[ExecutionProvider] = list(get_args(ExecutionProvider))
 download_provider_set : DownloadProviderSet =\
 {
 	'github':
@@ -133,10 +134,10 @@ download_provider_set : DownloadProviderSet =\
 		'path': '/facefusion/{base_name}/resolve/main/{file_name}'
 	}
 }
-download_providers : List[DownloadProvider] = list(download_provider_set.keys())
-download_scopes : List[DownloadScope] = [ 'lite', 'full' ]
+download_providers : List[DownloadProvider] = list(get_args(DownloadProvider))
+download_scopes : List[DownloadScope] = list(get_args(DownloadScope))

-video_memory_strategies : List[VideoMemoryStrategy] = [ 'strict', 'moderate', 'tolerant' ]
+video_memory_strategies : List[VideoMemoryStrategy] = list(get_args(VideoMemoryStrategy))

 log_level_set : LogLevelSet =\
 {
@@ -145,10 +146,10 @@ log_level_set : LogLevelSet =\
 	'info': logging.INFO,
 	'debug': logging.DEBUG
 }
-log_levels : List[LogLevel] = list(log_level_set.keys())
+log_levels : List[LogLevel] = list(get_args(LogLevel))

-ui_workflows : List[UiWorkflow] = [ 'instant_runner', 'job_runner', 'job_manager' ]
-job_statuses : List[JobStatus] = [ 'drafted', 'queued', 'completed', 'failed' ]
+ui_workflows : List[UiWorkflow] = list(get_args(UiWorkflow))
+job_statuses : List[JobStatus] = list(get_args(JobStatus))

 benchmark_cycle_count_range : Sequence[int] = create_int_range(1, 10, 1)
 execution_thread_count_range : Sequence[int] = create_int_range(1, 32, 1)
@@ -9,7 +9,7 @@ from facefusion.types import Command
 def run(commands : List[Command]) -> List[Command]:
 	user_agent = metadata.get('name') + '/' + metadata.get('version')

-	return [ shutil.which('curl'), '--user-agent', user_agent, '--insecure', '--location', '--silent' ] + commands
+	return [ shutil.which('curl'), '--user-agent', user_agent, '--location', '--silent' ] + commands


 def chain(*commands : List[Command]) -> List[Command]:
@@ -1,15 +1,17 @@
+import os
 import shutil
 import subprocess
 import xml.etree.ElementTree as ElementTree
 from functools import lru_cache
 from typing import List, Optional

-from onnxruntime import get_available_providers, set_default_logger_severity
+import onnxruntime

 import facefusion.choices
-from facefusion.types import ExecutionDevice, ExecutionProvider, InferenceSessionProvider, ValueAndUnit
+from facefusion.filesystem import create_directory, is_directory
+from facefusion.types import ExecutionDevice, ExecutionProvider, InferenceOptionSet, InferenceProvider, ValueAndUnit

-set_default_logger_severity(3)
+onnxruntime.set_default_logger_severity(3)


 def has_execution_provider(execution_provider : ExecutionProvider) -> bool:
@@ -17,7 +19,7 @@ def has_execution_provider(execution_provider : ExecutionProvider) -> bool:


 def get_available_execution_providers() -> List[ExecutionProvider]:
-	inference_session_providers = get_available_providers()
+	inference_session_providers = onnxruntime.get_available_providers()
 	available_execution_providers : List[ExecutionProvider] = []

 	for execution_provider, execution_provider_value in facefusion.choices.execution_provider_set.items():
@@ -28,54 +30,86 @@ def get_available_execution_providers() -> List[ExecutionProvider]:
 	return available_execution_providers


-def create_inference_session_providers(execution_device_id : int, execution_providers : List[ExecutionProvider]) -> List[InferenceSessionProvider]:
-	inference_session_providers : List[InferenceSessionProvider] = []
+def create_inference_providers(execution_device_id : int, execution_providers : List[ExecutionProvider]) -> List[InferenceProvider]:
+	inference_providers : List[InferenceProvider] = []
+	cache_path = resolve_cache_path()

 	for execution_provider in execution_providers:
 		if execution_provider == 'cuda':
-			inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
+			inference_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
 			{
 				'device_id': execution_device_id,
 				'cudnn_conv_algo_search': resolve_cudnn_conv_algo_search()
 			}))
+
 		if execution_provider == 'tensorrt':
-			inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
+			inference_option_set : InferenceOptionSet =\
 			{
-				'device_id': execution_device_id,
-				'trt_engine_cache_enable': True,
-				'trt_engine_cache_path': '.caches',
-				'trt_timing_cache_enable': True,
-				'trt_timing_cache_path': '.caches',
-				'trt_builder_optimization_level': 5
-			}))
+				'device_id': execution_device_id
+			}
+			if is_directory(cache_path) or create_directory(cache_path):
+				inference_option_set.update(
+				{
+					'trt_engine_cache_enable': True,
+					'trt_engine_cache_path': cache_path,
+					'trt_timing_cache_enable': True,
+					'trt_timing_cache_path': cache_path,
+					'trt_builder_optimization_level': 4
+				})
+			inference_providers.append((facefusion.choices.execution_provider_set.get(execution_provider), inference_option_set))
+
 		if execution_provider in [ 'directml', 'rocm' ]:
-			inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
+			inference_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
 			{
 				'device_id': execution_device_id
 			}))
+
 		if execution_provider == 'migraphx':
-			inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
+			inference_option_set =\
 			{
-				'device_id': execution_device_id,
-				'migraphx_model_cache_dir': '.caches'
-			}))
+				'device_id': execution_device_id
+			}
+			if is_directory(cache_path) or create_directory(cache_path):
+				inference_option_set.update(
+				{
+					'migraphx_model_cache_dir': cache_path
+				})
+			inference_providers.append((facefusion.choices.execution_provider_set.get(execution_provider), inference_option_set))
+
+		if execution_provider == 'coreml':
+			inference_option_set =\
+			{
+				'SpecializationStrategy': 'FastPrediction'
+			}
+			if is_directory(cache_path) or create_directory(cache_path):
+				inference_option_set.update(
+				{
+					'ModelCacheDirectory': cache_path
+				})
+			inference_providers.append((facefusion.choices.execution_provider_set.get(execution_provider), inference_option_set))
+
 		if execution_provider == 'openvino':
-			inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
+			inference_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
 			{
 				'device_type': resolve_openvino_device_type(execution_device_id),
 				'precision': 'FP32'
 			}))
-		if execution_provider == 'coreml':
-			inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
+
+		if execution_provider == 'qnn':
+			inference_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
 			{
-				'SpecializationStrategy': 'FastPrediction',
-				'ModelCacheDirectory': '.caches'
+				'device_id': execution_device_id,
+				'backend_type': 'htp'
 			}))

 	if 'cpu' in execution_providers:
-		inference_session_providers.append(facefusion.choices.execution_provider_set.get('cpu'))
+		inference_providers.append(facefusion.choices.execution_provider_set.get('cpu'))

-	return inference_session_providers
+	return inference_providers
+
+
+def resolve_cache_path() -> str:
+	return os.path.join('.caches', onnxruntime.get_version_string())


 def resolve_cudnn_conv_algo_search() -> str:
@@ -381,11 +381,11 @@ def detect_with_yunet(vision_frame : VisionFrame, face_detector_size : str) -> T
 			face_scores.extend(face_scores_raw[keep_indices])
 			face_landmarks_5_raw = numpy.concatenate(
 			[
-				face_landmarks_5_raw[:, [0, 1]] * feature_stride + anchors,
-				face_landmarks_5_raw[:, [2, 3]] * feature_stride + anchors,
-				face_landmarks_5_raw[:, [4, 5]] * feature_stride + anchors,
-				face_landmarks_5_raw[:, [6, 7]] * feature_stride + anchors,
-				face_landmarks_5_raw[:, [8, 9]] * feature_stride + anchors
+				face_landmarks_5_raw[:, [ 0, 1 ]] * feature_stride + anchors,
+				face_landmarks_5_raw[:, [ 2, 3 ]] * feature_stride + anchors,
+				face_landmarks_5_raw[:, [ 4, 5 ]] * feature_stride + anchors,
+				face_landmarks_5_raw[:, [ 6, 7 ]] * feature_stride + anchors,
+				face_landmarks_5_raw[:, [ 8, 9 ]] * feature_stride + anchors
 			], axis = -1).reshape(-1, 5, 2)

 			for face_landmark_raw_5 in face_landmarks_5_raw[keep_indices]:
@@ -81,7 +81,7 @@ def warp_face_by_face_landmark_5(temp_vision_frame : VisionFrame, face_landmark_


 def warp_face_by_bounding_box(temp_vision_frame : VisionFrame, bounding_box : BoundingBox, crop_size : Size) -> Tuple[VisionFrame, Matrix]:
-	source_points = numpy.array([ [ bounding_box[0], bounding_box[1] ], [bounding_box[2], bounding_box[1] ], [ bounding_box[0], bounding_box[3] ] ]).astype(numpy.float32)
+	source_points = numpy.array([ [ bounding_box[0], bounding_box[1] ], [ bounding_box[2], bounding_box[1] ], [ bounding_box[0], bounding_box[3] ] ]).astype(numpy.float32)
 	target_points = numpy.array([ [ 0, 0 ], [ crop_size[0], 0 ], [ 0, crop_size[1] ] ]).astype(numpy.float32)
 	affine_matrix = cv2.getAffineTransform(source_points, target_points)
 	if bounding_box[2] - bounding_box[0] > crop_size[0] or bounding_box[3] - bounding_box[1] > crop_size[1]:
@@ -247,7 +247,7 @@ def get_nms_threshold(face_detector_model : FaceDetectorModel, face_detector_ang


 def merge_matrix(temp_matrices : List[Matrix]) -> Matrix:
-	matrix = numpy.vstack([temp_matrices[0], [0, 0, 1]])
+	matrix = numpy.vstack([ temp_matrices[0], [ 0, 0, 1 ] ])

 	for temp_matrix in temp_matrices[1:]:
 		temp_matrix = numpy.vstack([ temp_matrix, [ 0, 0, 1 ] ])
@@ -233,7 +233,7 @@ def create_area_mask(crop_vision_frame : VisionFrame, face_landmark_68 : FaceLan

 	convex_hull = cv2.convexHull(face_landmark_68[landmark_points].astype(numpy.int32))
 	area_mask = numpy.zeros(crop_size).astype(numpy.float32)
-	cv2.fillConvexPoly(area_mask, convex_hull, 1.0) # type: ignore[call-overload]
+	cv2.fillConvexPoly(area_mask, convex_hull, 1.0) #type:ignore[call-overload]
 	area_mask = (cv2.GaussianBlur(area_mask.clip(0, 1), (0, 0), 5).clip(0.5, 1) - 0.5) * 2
 	return area_mask

@@ -244,7 +244,8 @@ def merge_video(target_path : str, temp_video_fps : Fps, output_video_resolution


 def concat_video(output_path : str, temp_output_paths : List[str]) -> bool:
-	concat_video_path = tempfile.mktemp()
+	file_descriptor, concat_video_path = tempfile.mkstemp()
+	os.close(file_descriptor)

 	with open(concat_video_path, 'w') as concat_video_file:
 		for temp_output_path in temp_output_paths:
@@ -48,7 +48,7 @@ def set_input(input_path : str) -> List[Command]:


 def set_input_fps(input_fps : Fps) -> List[Command]:
-	return [ '-r', str(input_fps)]
+	return [ '-r', str(input_fps) ]


 def set_output(output_path : str) -> List[Command]:
@@ -8,7 +8,7 @@ from onnxruntime import InferenceSession
 from facefusion import logger, process_manager, state_manager, translator
 from facefusion.app_context import detect_app_context
 from facefusion.common_helper import is_windows
-from facefusion.execution import create_inference_session_providers, has_execution_provider
+from facefusion.execution import create_inference_providers, has_execution_provider
 from facefusion.exit_helper import fatal_exit
 from facefusion.filesystem import get_file_name, is_file
 from facefusion.time_helper import calculate_end_time
@@ -72,8 +72,8 @@ def create_inference_session(model_path : str, execution_device_id : int, execut
 	start_time = time()

 	try:
-		inference_session_providers = create_inference_session_providers(execution_device_id, execution_providers)
-		inference_session = InferenceSession(model_path, providers = inference_session_providers)
+		inference_providers = create_inference_providers(execution_device_id, execution_providers)
+		inference_session = InferenceSession(model_path, providers = inference_providers)
 		logger.debug(translator.get('loading_model_succeeded').format(model_name = model_file_name, seconds = calculate_end_time(start_time)), __name__)
 		return inference_session

@@ -22,12 +22,13 @@ ONNXRUNTIME_SET =\
 	'default': ('onnxruntime', '1.24.1')
 }
 if is_windows() or is_linux():
-	ONNXRUNTIME_SET['cuda'] = ('onnxruntime-gpu', '1.24.1')
-	ONNXRUNTIME_SET['openvino'] = ('onnxruntime-openvino', '1.23.0')
+	ONNXRUNTIME_SET['cuda'] = ('onnxruntime-gpu', '1.24.3')
+	ONNXRUNTIME_SET['openvino'] = ('onnxruntime-openvino', '1.24.1')
 if is_windows():
-	ONNXRUNTIME_SET['directml'] = ('onnxruntime-directml', '1.24.1')
+	ONNXRUNTIME_SET['directml'] = ('onnxruntime-directml', '1.24.3')
+	ONNXRUNTIME_SET['qnn'] = ('onnxruntime-qnn', '1.24.3')
 if is_linux():
-	ONNXRUNTIME_SET['migraphx'] = ('onnxruntime-migraphx', '1.23.2')
+	ONNXRUNTIME_SET['migraphx'] = ('onnxruntime-migraphx', '1.24.2')
 	ONNXRUNTIME_SET['rocm'] = ('onnxruntime-rocm', '1.22.2.post1')


@@ -68,7 +69,7 @@ def run(program : ArgumentParser) -> None:
 	onnxruntime_name, onnxruntime_version = ONNXRUNTIME_SET.get(args.onnxruntime)
 	commands.append(onnxruntime_name + '==' + onnxruntime_version)

-	subprocess.call([shutil.which('pip'), 'uninstall', 'onnxruntime', onnxruntime_name, '-y', '-q' ])
+	subprocess.call([ shutil.which('pip'), 'uninstall', 'onnxruntime', onnxruntime_name, '-y', '-q' ])

 	subprocess.call(commands)

@@ -4,7 +4,7 @@ METADATA =\
 {
 	'name': 'FaceFusion',
 	'description': 'Industry leading face manipulation platform',
-	'version': '3.5.3',
+	'version': '3.5.4',
 	'license': 'OpenRAIL-AS',
 	'author': 'Henry Ruhs',
 	'url': 'https://facefusion.io'
@@ -19,9 +19,9 @@ def normalize_space(spaces : Optional[List[int]]) -> Optional[Padding]:
 	if spaces and len(spaces) == 1:
 		return tuple([spaces[0]] * 4) #type:ignore[return-value]
 	if spaces and len(spaces) == 2:
-		return tuple([spaces[0], spaces[1], spaces[0], spaces[1]]) #type:ignore[return-value]
+		return tuple([ spaces[0], spaces[1], spaces[0], spaces[1] ]) #type:ignore[return-value]
 	if spaces and len(spaces) == 3:
-		return tuple([spaces[0], spaces[1], spaces[2], spaces[1]]) #type:ignore[return-value]
+		return tuple([ spaces[0], spaces[1], spaces[2], spaces[1] ]) #type:ignore[return-value]
 	if spaces and len(spaces) == 4:
 		return tuple(spaces) #type:ignore[return-value]
 	return None
@@ -1,8 +1,8 @@
-from typing import List, Sequence
+from typing import List, Sequence, get_args

 from facefusion.common_helper import create_int_range
 from facefusion.processors.modules.age_modifier.types import AgeModifierModel

-age_modifier_models : List[AgeModifierModel] = [ 'styleganex_age' ]
+age_modifier_models : List[AgeModifierModel] = list(get_args(AgeModifierModel))

 age_modifier_direction_range : Sequence[int] = create_int_range(-100, 100, 1)
@@ -1,8 +1,8 @@
-from typing import List, Sequence
+from typing import List, Sequence, get_args

 from facefusion.common_helper import create_int_range
 from facefusion.processors.modules.background_remover.types import BackgroundRemoverModel

-background_remover_models : List[BackgroundRemoverModel] = [ 'ben_2', 'birefnet_general', 'birefnet_portrait', 'isnet_general', 'modnet', 'ormbg', 'rmbg_1.4', 'rmbg_2.0', 'silueta', 'u2net_cloth', 'u2net_general', 'u2net_human', 'u2netp' ]
+background_remover_models : List[BackgroundRemoverModel] = list(get_args(BackgroundRemoverModel))

 background_remover_color_range : Sequence[int] = create_int_range(0, 255, 1)
@@ -1,10 +1,10 @@
-from typing import List, Sequence
+from typing import List, Sequence, get_args

 from facefusion.common_helper import create_int_range
 from facefusion.processors.modules.expression_restorer.types import ExpressionRestorerArea, ExpressionRestorerModel

-expression_restorer_models : List[ExpressionRestorerModel] = [ 'live_portrait' ]
+expression_restorer_models : List[ExpressionRestorerModel] = list(get_args(ExpressionRestorerModel))

-expression_restorer_areas : List[ExpressionRestorerArea] = [ 'upper-face', 'lower-face' ]
+expression_restorer_areas : List[ExpressionRestorerArea] = list(get_args(ExpressionRestorerArea))

 expression_restorer_factor_range : Sequence[int] = create_int_range(0, 100, 1)
@@ -192,12 +192,12 @@ def restrict_expression_areas(temp_expression : LivePortraitExpression, target_e
 	expression_restorer_areas = state_manager.get_item('expression_restorer_areas')

 	if 'upper-face' not in expression_restorer_areas:
-		target_expression[:, [1, 2, 6, 10, 11, 12, 13, 15, 16]] = temp_expression[:, [1, 2, 6, 10, 11, 12, 13, 15, 16]]
+		target_expression[:, [ 1, 2, 6, 10, 11, 12, 13, 15, 16 ]] = temp_expression[:, [ 1, 2, 6, 10, 11, 12, 13, 15, 16 ]]

 	if 'lower-face' not in expression_restorer_areas:
-		target_expression[:, [3, 7, 14, 17, 18, 19, 20]] = temp_expression[:, [3, 7, 14, 17, 18, 19, 20]]
+		target_expression[:, [ 3, 7, 14, 17, 18, 19, 20 ]] = temp_expression[:, [ 3, 7, 14, 17, 18, 19, 20 ]]

-	target_expression[:, [0, 4, 5, 8, 9]] = temp_expression[:, [0, 4, 5, 8, 9]]
+	target_expression[:, [ 0, 4, 5, 8, 9 ]] = temp_expression[:, [ 0, 4, 5, 8, 9 ]]
 	return target_expression


@@ -1,5 +1,5 @@
-from typing import List
+from typing import List, get_args

 from facefusion.processors.modules.face_debugger.types import FaceDebuggerItem

-face_debugger_items : List[FaceDebuggerItem] = [ 'bounding-box', 'face-landmark-5', 'face-landmark-5/68', 'face-landmark-68', 'face-landmark-68/5', 'face-mask' ]
+face_debugger_items : List[FaceDebuggerItem] = list(get_args(FaceDebuggerItem))
@@ -1,9 +1,9 @@
-from typing import List, Sequence
+from typing import List, Sequence, get_args

 from facefusion.common_helper import create_float_range
 from facefusion.processors.modules.face_editor.types import FaceEditorModel

-face_editor_models : List[FaceEditorModel] = [ 'live_portrait' ]
+face_editor_models : List[FaceEditorModel] = list(get_args(FaceEditorModel))

 face_editor_eyebrow_direction_range : Sequence[float] = create_float_range(-1.0, 1.0, 0.05)
 face_editor_eye_gaze_horizontal_range : Sequence[float] = create_float_range(-1.0, 1.0, 0.05)
@@ -1,9 +1,9 @@
-from typing import List, Sequence
+from typing import List, Sequence, get_args

 from facefusion.common_helper import create_float_range, create_int_range
 from facefusion.processors.modules.face_enhancer.types import FaceEnhancerModel

-face_enhancer_models : List[FaceEnhancerModel] = [ 'codeformer', 'gfpgan_1.2', 'gfpgan_1.3', 'gfpgan_1.4', 'gpen_bfr_256', 'gpen_bfr_512', 'gpen_bfr_1024', 'gpen_bfr_2048', 'restoreformer_plus_plus' ]
+face_enhancer_models : List[FaceEnhancerModel] = list(get_args(FaceEnhancerModel))

 face_enhancer_blend_range : Sequence[int] = create_int_range(0, 100, 1)

@@ -1,8 +1,9 @@
-from typing import List, Sequence
+from typing import List, Sequence, get_args

 from facefusion.common_helper import create_float_range
 from facefusion.processors.modules.face_swapper.types import FaceSwapperModel, FaceSwapperSet, FaceSwapperWeight

+
 face_swapper_set : FaceSwapperSet =\
 {
 	'blendswap_256': [ '256x256', '384x384', '512x512', '768x768', '1024x1024' ],
@@ -20,6 +21,6 @@ face_swapper_set : FaceSwapperSet =\
 	'uniface_256': [ '256x256', '512x512', '768x768', '1024x1024' ]
 }

-face_swapper_models : List[FaceSwapperModel] = list(face_swapper_set.keys())
+face_swapper_models : List[FaceSwapperModel] = list(get_args(FaceSwapperModel))

 face_swapper_weight_range : Sequence[FaceSwapperWeight] = create_float_range(0.0, 1.0, 0.05)
@@ -1,9 +1,9 @@
-from typing import List, Sequence
+from typing import List, Sequence, get_args

 from facefusion.common_helper import create_int_range
 from facefusion.processors.modules.frame_colorizer.types import FrameColorizerModel

-frame_colorizer_models : List[FrameColorizerModel] = [ 'ddcolor', 'ddcolor_artistic', 'deoldify', 'deoldify_artistic', 'deoldify_stable' ]
+frame_colorizer_models : List[FrameColorizerModel] = list(get_args(FrameColorizerModel))

 frame_colorizer_sizes : List[str] = [ '192x192', '256x256', '384x384', '512x512' ]

@@ -1,8 +1,8 @@
-from typing import List, Sequence
+from typing import List, Sequence, get_args

 from facefusion.common_helper import create_int_range
 from facefusion.processors.modules.frame_enhancer.types import FrameEnhancerModel

-frame_enhancer_models : List[FrameEnhancerModel] = [ 'clear_reality_x4', 'face_dat_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', 'tghq_face_x8', 'ultra_sharp_x4', 'ultra_sharp_2_x4' ]
+frame_enhancer_models : List[FrameEnhancerModel] = list(get_args(FrameEnhancerModel))

 frame_enhancer_blend_range : Sequence[int] = create_int_range(0, 100, 1)
@@ -1,8 +1,8 @@
-from typing import List, Sequence
+from typing import List, Sequence, get_args

 from facefusion.common_helper import create_float_range
 from facefusion.processors.modules.lip_syncer.types import LipSyncerModel

-lip_syncer_models : List[LipSyncerModel] = [ 'edtalk_256', 'wav2lip_96', 'wav2lip_gan_96' ]
+lip_syncer_models : List[LipSyncerModel] = list(get_args(LipSyncerModel))

 lip_syncer_weight_range : Sequence[float] = create_float_range(0.0, 1.0, 0.05)
@@ -10,7 +10,7 @@ from facefusion.ffmpeg import get_available_encoder_set
 from facefusion.filesystem import get_file_name, resolve_file_paths
 from facefusion.jobs import job_store
 from facefusion.processors.core import get_processors_modules
-from facefusion.sanitizer import sanitize_int_range
+from facefusion.sanitizer import sanitize_int_range, sanitize_job_id


 def create_help_formatter_small(prog : str) -> HelpFormatter:
@@ -188,7 +188,7 @@ def create_processors_program() -> ArgumentParser:
 	program = ArgumentParser(add_help = False)
 	available_processors = [ get_file_name(file_path) for file_path in resolve_file_paths('facefusion/processors/modules') ]
 	group_processors = program.add_argument_group('processors')
-	group_processors.add_argument('--processors', help = translator.get('help.processors').format(choices = ', '.join(available_processors)), default = config.get_str_list('processors', 'processors', 'face_swapper'), nargs = '+')
+	group_processors.add_argument('--processors', help = translator.get('help.processors').format(choices = ', '.join(available_processors)), default = config.get_str_list('processors', 'processors', 'face_swapper'), choices = available_processors, nargs = '+', metavar = 'PROCESSORS')
 	job_store.register_step_keys([ 'processors' ])
 	for processor_module in get_processors_modules(available_processors):
 		processor_module.register_args(program)
@@ -200,7 +200,7 @@ def create_uis_program() -> ArgumentParser:
 	available_ui_layouts = [ get_file_name(file_path) for file_path in resolve_file_paths('facefusion/uis/layouts') ]
 	group_uis = program.add_argument_group('uis')
 	group_uis.add_argument('--open-browser', help = translator.get('help.open_browser'), action = 'store_true', default = config.get_bool_value('uis', 'open_browser'))
-	group_uis.add_argument('--ui-layouts', help = translator.get('help.ui_layouts').format(choices = ', '.join(available_ui_layouts)), default = config.get_str_list('uis', 'ui_layouts', 'default'), nargs = '+')
+	group_uis.add_argument('--ui-layouts', help = translator.get('help.ui_layouts').format(choices = ', '.join(available_ui_layouts)), default = config.get_str_list('uis', 'ui_layouts', 'default'), choices = available_ui_layouts, nargs = '+', metavar = 'UI_LAYOUTS')
 	group_uis.add_argument('--ui-workflow', help = translator.get('help.ui_workflow'), default = config.get_str_value('uis', 'ui_workflow', 'instant_runner'), choices = facefusion.choices.ui_workflows)
 	return program

@@ -268,7 +268,7 @@ def create_halt_on_error_program() -> ArgumentParser:

 def create_job_id_program() -> ArgumentParser:
 	program = ArgumentParser(add_help = False)
-	program.add_argument('job_id', help = translator.get('help.job_id'))
+	program.add_argument('job_id', help = translator.get('help.job_id'), type = sanitize_job_id)
 	return program


@@ -297,13 +297,11 @@ def create_program() -> ArgumentParser:
 	program._positionals.title = 'commands'
 	program.add_argument('-v', '--version', version = metadata.get('name') + ' ' + metadata.get('version'), action = 'version')
 	sub_program = program.add_subparsers(dest = 'command')
-	# general
 	sub_program.add_parser('run', help = translator.get('help.run'), parents = [ create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), create_source_paths_program(), create_target_path_program(), create_output_path_program(), collect_step_program(), create_uis_program(), create_benchmark_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
 	sub_program.add_parser('headless-run', help = translator.get('help.headless_run'), parents = [ create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), create_source_paths_program(), create_target_path_program(), create_output_path_program(), collect_step_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
 	sub_program.add_parser('batch-run', help = translator.get('help.batch_run'), parents = [ create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), create_source_pattern_program(), create_target_pattern_program(), create_output_pattern_program(), collect_step_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
 	sub_program.add_parser('force-download', help = translator.get('help.force_download'), parents = [ create_download_providers_program(), create_download_scope_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
 	sub_program.add_parser('benchmark', help = translator.get('help.benchmark'), parents = [ create_temp_path_program(), collect_step_program(), create_benchmark_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
-	# job manager
 	sub_program.add_parser('job-list', help = translator.get('help.job_list'), parents = [ create_job_status_program(), create_jobs_path_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
 	sub_program.add_parser('job-create', help = translator.get('help.job_create'), parents = [ create_job_id_program(), create_jobs_path_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
 	sub_program.add_parser('job-submit', help = translator.get('help.job_submit'), parents = [ create_job_id_program(), create_jobs_path_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
@@ -314,7 +312,6 @@ def create_program() -> ArgumentParser:
 	sub_program.add_parser('job-remix-step', help = translator.get('help.job_remix_step'), parents = [ create_job_id_program(), create_step_index_program(), create_config_path_program(), create_jobs_path_program(), create_source_paths_program(), create_output_path_program(), collect_step_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
 	sub_program.add_parser('job-insert-step', help = translator.get('help.job_insert_step'), parents = [ create_job_id_program(), create_step_index_program(), create_config_path_program(), create_jobs_path_program(), create_source_paths_program(), create_target_path_program(), create_output_path_program(), collect_step_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
 	sub_program.add_parser('job-remove-step', help = translator.get('help.job_remove_step'), parents = [ create_job_id_program(), create_step_index_program(), create_jobs_path_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
-	# job runner
 	sub_program.add_parser('job-run', help = translator.get('help.job_run'), parents = [ create_job_id_program(), create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
 	sub_program.add_parser('job-run-all', help = translator.get('help.job_run_all'), parents = [ create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), collect_job_program(), create_halt_on_error_program() ], formatter_class = create_help_formatter_large)
 	sub_program.add_parser('job-retry', help = translator.get('help.job_retry'), parents = [ create_job_id_program(), create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
@@ -1,6 +1,15 @@
+import hashlib
 from typing import Sequence


+def sanitize_job_id(job_id : str) -> str:
+	__job_id__ = job_id.replace('-', '')
+
+	if __job_id__.isalnum():
+		return job_id
+	return hashlib.sha1(job_id.encode()).hexdigest()
+
+
 def sanitize_int_range(value : int, int_range : Sequence[int]) -> int:
 	if value in int_range:
 		return value
@@ -167,10 +167,11 @@ ModelOptions : TypeAlias = Dict[str, Any]
 ModelSet : TypeAlias = Dict[str, ModelOptions]
 ModelInitializer : TypeAlias = NDArray[Any]

-ExecutionProvider = Literal['cpu', 'coreml', 'cuda', 'directml', 'openvino', 'migraphx', 'rocm', 'tensorrt']
-ExecutionProviderValue = Literal['CPUExecutionProvider', 'CoreMLExecutionProvider', 'CUDAExecutionProvider', 'DmlExecutionProvider', 'OpenVINOExecutionProvider', 'MIGraphXExecutionProvider', 'ROCMExecutionProvider', 'TensorrtExecutionProvider']
+ExecutionProvider = Literal['cuda', 'tensorrt', 'rocm', 'migraphx', 'coreml', 'openvino', 'qnn', 'directml', 'cpu']
+ExecutionProviderValue = Literal['CPUExecutionProvider', 'CoreMLExecutionProvider', 'CUDAExecutionProvider', 'DmlExecutionProvider', 'OpenVINOExecutionProvider', 'MIGraphXExecutionProvider', 'QNNExecutionProvider', 'ROCMExecutionProvider', 'TensorrtExecutionProvider']
 ExecutionProviderSet : TypeAlias = Dict[ExecutionProvider, ExecutionProviderValue]
-InferenceSessionProvider : TypeAlias = Any
+InferenceProvider : TypeAlias = Any
+InferenceOptionSet : TypeAlias = Dict[str, Any]
 ValueAndUnit = TypedDict('ValueAndUnit',
 {
 	'value' : int,
@@ -81,7 +81,7 @@ def listen() -> None:

 	processors_checkbox_group = get_ui_component('processors_checkbox_group')
 	if processors_checkbox_group:
-		processors_checkbox_group.change(remote_update, inputs = processors_checkbox_group, outputs = [BACKGROUND_REMOVER_MODEL_DROPDOWN, BACKGROUND_REMOVER_COLOR_WRAPPER])
+		processors_checkbox_group.change(remote_update, inputs = processors_checkbox_group, outputs = [ BACKGROUND_REMOVER_MODEL_DROPDOWN, BACKGROUND_REMOVER_COLOR_WRAPPER ])


 def remote_update(processors : List[str]) -> Tuple[gradio.Dropdown, gradio.Group]:
@@ -132,7 +132,7 @@ def listen() -> None:
 		'target_video'
 	]):
 		for method in [ 'change', 'clear' ]:
-			getattr(ui_component, method)(remote_update, outputs = [OUTPUT_IMAGE_QUALITY_SLIDER, OUTPUT_IMAGE_SCALE_SLIDER, OUTPUT_AUDIO_ENCODER_DROPDOWN, OUTPUT_AUDIO_QUALITY_SLIDER, OUTPUT_AUDIO_VOLUME_SLIDER, OUTPUT_VIDEO_ENCODER_DROPDOWN, OUTPUT_VIDEO_PRESET_DROPDOWN, OUTPUT_VIDEO_QUALITY_SLIDER, OUTPUT_VIDEO_SCALE_SLIDER, OUTPUT_VIDEO_FPS_SLIDER])
+			getattr(ui_component, method)(remote_update, outputs = [ OUTPUT_IMAGE_QUALITY_SLIDER, OUTPUT_IMAGE_SCALE_SLIDER, OUTPUT_AUDIO_ENCODER_DROPDOWN, OUTPUT_AUDIO_QUALITY_SLIDER, OUTPUT_AUDIO_VOLUME_SLIDER, OUTPUT_VIDEO_ENCODER_DROPDOWN, OUTPUT_VIDEO_PRESET_DROPDOWN, OUTPUT_VIDEO_QUALITY_SLIDER, OUTPUT_VIDEO_SCALE_SLIDER, OUTPUT_VIDEO_FPS_SLIDER ])


 def remote_update() -> Tuple[gradio.Slider, gradio.Slider, gradio.Dropdown, gradio.Slider, gradio.Slider, gradio.Dropdown, gradio.Dropdown, gradio.Slider, gradio.Slider, gradio.Slider]:
@@ -90,7 +90,7 @@ def start(webcam_device_id : int, webcam_mode : WebcamMode, webcam_resolution :
 	stream = None

 	if webcam_mode in [ 'udp', 'v4l2' ]:
-		stream = open_stream(webcam_mode, webcam_resolution, webcam_fps) # type:ignore[arg-type]
+		stream = open_stream(webcam_mode, webcam_resolution, webcam_fps) #type:ignore[arg-type]
 	webcam_width, webcam_height = unpack_resolution(webcam_resolution)

 	if camera_capture and camera_capture.isOpened():
@@ -1,4 +1,5 @@
 import importlib
+import logging
 import os
 import warnings
 from types import ModuleType
@@ -72,6 +73,7 @@ def init() -> None:
 	os.environ['GRADIO_ANALYTICS_ENABLED'] = '0'
 	os.environ['GRADIO_TEMP_DIR'] = os.path.join(state_manager.get_item('temp_path'), 'gradio')

+	logging.getLogger('asyncio').setLevel(logging.CRITICAL)
 	warnings.filterwarnings('ignore', category = UserWarning, module = 'gradio')
 	gradio.processing_utils._check_allowed = uis_overrides.mock
 	gradio.processing_utils.convert_video_to_playable_mp4 = uis_overrides.convert_video_to_playable_mp4
@@ -24,7 +24,7 @@ def process(start_time : float) -> ErrorCode:
 	process_manager.start()

 	for task in tasks:
-		error_code = task() # type:ignore[operator]
+		error_code = task() #type:ignore[operator]

 		if error_code > 0:
 			process_manager.end()
@@ -31,7 +31,7 @@ def process(start_time : float) -> ErrorCode:
 	process_manager.start()

 	for task in tasks:
-		error_code = task() # type:ignore[operator]
+		error_code = task() #type:ignore[operator]

 		if error_code > 0:
 			process_manager.end()
@@ -2,7 +2,7 @@ gradio-rangeslider==0.0.8
 gradio==5.44.1
 numpy==2.2.1
 onnx==1.20.1
-onnxruntime==1.24.1
+onnxruntime==1.24.3
 opencv-python==4.13.0.92
 tqdm==4.67.3
-scipy==1.17.0
+scipy==1.17.1
@@ -15,7 +15,7 @@ def before_all() -> None:
 		'https://github.com/facefusion/facefusion-assets/releases/download/examples-3.0.0/source.jpg',
 		'https://github.com/facefusion/facefusion-assets/releases/download/examples-3.0.0/target-240p.mp4'
 	])
-	subprocess.run(['ffmpeg', '-i', get_test_example_file('target-240p.mp4'), '-vframes', '1', get_test_example_file('target-240p.jpg')])
+	subprocess.run([ 'ffmpeg', '-i', get_test_example_file('target-240p.mp4'), '-vframes', '1', get_test_example_file('target-240p.jpg') ])


@pytest.fixture(scope = 'function', autouse = True)
@@ -20,8 +20,8 @@ def test_create_float_range() -> None:


 def test_calc_int_step() -> None:
-	assert calculate_int_step([0, 1]) == 1
+	assert calculate_int_step([ 0, 1 ]) == 1


 def test_calc_float_step() -> None:
-	assert calculate_float_step([0.1, 0.2]) == 0.1
+	assert calculate_float_step([ 0.1, 0.2 ]) == 0.1
@@ -7,7 +7,7 @@ from facefusion.curl_builder import chain, ping, run, set_timeout
 def test_run() -> None:
 	user_agent = metadata.get('name') + '/' + metadata.get('version')

-	assert run([]) == [ which('curl'), '--user-agent', user_agent, '--insecure', '--location', '--silent' ]
+	assert run([]) == [ which('curl'), '--user-agent', user_agent, '--location', '--silent' ]


 def test_chain() -> None:
@@ -1,4 +1,4 @@
-from facefusion.execution import create_inference_session_providers, get_available_execution_providers, has_execution_provider
+from facefusion.execution import create_inference_providers, get_available_execution_providers, has_execution_provider


 def test_has_execution_provider() -> None:
@@ -10,8 +10,8 @@ def test_get_available_execution_providers() -> None:
 	assert 'cpu' in get_available_execution_providers()


-def test_create_inference_session_providers() -> None:
-	inference_session_providers =\
+def test_create_inference_providers() -> None:
+	inference_providers =\
 	[
 		('CUDAExecutionProvider',
 		{
@@ -21,4 +21,4 @@ def test_create_inference_session_providers() -> None:
 		'CPUExecutionProvider'
 	]

-	assert create_inference_session_providers(1, [ 'cpu', 'cuda' ]) == inference_session_providers
+	assert create_inference_providers(1, [ 'cpu', 'cuda' ]) == inference_providers
@@ -1,10 +1,12 @@
+import os
 import tempfile

 from facefusion.json import read_json, write_json


 def test_read_json() -> None:
-	_, json_path = tempfile.mkstemp(suffix = '.json')
+	file_descriptor, json_path = tempfile.mkstemp(suffix = '.json')
+	os.close(file_descriptor)

 	assert not read_json(json_path)

@@ -14,6 +16,7 @@ def test_read_json() -> None:


 def test_write_json() -> None:
-	_, json_path = tempfile.mkstemp(suffix = '.json')
+	file_descriptor, json_path = tempfile.mkstemp(suffix = '.json')
+	os.close(file_descriptor)

 	assert write_json(json_path, {})
@@ -3,6 +3,6 @@ from facefusion.sanitizer import sanitize_int_range

 def test_sanitize_int_range() -> None:
 	assert sanitize_int_range(0, [ 0, 1, 2 ]) == 0
-	assert sanitize_int_range(2, [0, 1, 2]) == 2
+	assert sanitize_int_range(2, [ 0, 1, 2 ]) == 2
 	assert sanitize_int_range(-1, [ 0, 1 ]) == 0
 	assert sanitize_int_range(3, [ 0, 1 ]) == 0