Introduce execution device id

README.md

@@ -42,6 +42,7 @@ misc:
   --log-level {error,warn,info,debug}                                                                                                                                                   adjust the message severity displayed in the terminal
 
 execution:
+  --execution-device-id EXECUTION_DEVICE_ID                                                                                                                                             specify the device used for processing
   --execution-providers EXECUTION_PROVIDERS [EXECUTION_PROVIDERS ...]                                                                                                                   accelerate the model inference using different providers (choices: cpu, ...)
   --execution-thread-count [1-128]                                                                                                                                                      specify the amount of parallel threads while processing
   --execution-queue-count [1-32]                                                                                                                                                        specify the amount of frames each thread is processing

facefusion.ini

@@ -10,6 +10,7 @@ headless =
 log_level =
 
 [execution]
+execution_device_id =
 execution_providers =
 execution_thread_count =
 execution_queue_count =

facefusion/content_analyser.py

@@ -37,7 +37,7 @@ def get_content_analyser() -> Any:
 			sleep(0.5)
 		if CONTENT_ANALYSER is None:
 			model_path = MODELS.get('open_nsfw').get('path')
-			CONTENT_ANALYSER = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_providers))
+			CONTENT_ANALYSER = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_device_id, facefusion.globals.execution_providers))
 	return CONTENT_ANALYSER
 
 

facefusion/core.py

@@ -51,6 +51,7 @@ def cli() -> None:
 	# execution
 	execution_providers = encode_execution_providers(onnxruntime.get_available_providers())
 	group_execution = program.add_argument_group('execution')
+	group_execution.add_argument('--execution-device-id', help = wording.get('help.execution_device_id'), type = int, default = config.get_int_value('execution.face_detector_size', '0'))
 	group_execution.add_argument('--execution-providers', help = wording.get('help.execution_providers').format(choices = ', '.join(execution_providers)), default = config.get_str_list('execution.execution_providers', 'cpu'), choices = execution_providers, nargs = '+', metavar = 'EXECUTION_PROVIDERS')
 	group_execution.add_argument('--execution-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_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_metavar(facefusion.choices.execution_queue_count_range))
@@ -141,6 +142,7 @@ def apply_args(program : ArgumentParser) -> None:
 	facefusion.globals.headless = args.headless
 	facefusion.globals.log_level = args.log_level
 	# execution
+	facefusion.globals.execution_device_id = args.execution_device_id
 	facefusion.globals.execution_providers = decode_execution_providers(args.execution_providers)
 	facefusion.globals.execution_thread_count = args.execution_thread_count
 	facefusion.globals.execution_queue_count = args.execution_queue_count

facefusion/execution.py

@@ -4,6 +4,7 @@
 import xml.etree.ElementTree as ElementTree
 import onnxruntime
 
+import facefusion.globals
 from facefusion.typing import ExecutionDevice, ValueAndUnit
 
 
@@ -18,15 +19,21 @@ def decode_execution_providers(execution_providers : List[str]) -> List[str]:
 	return [ execution_provider for execution_provider, encoded_execution_provider in zip(available_execution_providers, encoded_execution_providers) if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers) ]
 
 
-def apply_execution_provider_options(execution_providers : List[str]) -> List[Any]:
+def apply_execution_provider_options(execution_device_id : int, execution_providers : List[str]) -> List[Any]:
 	execution_providers_with_options : List[Any] = []
 
 	for execution_provider in execution_providers:
 		if execution_provider == 'CUDAExecutionProvider':
 			execution_providers_with_options.append((execution_provider,
 			{
+				'device_id': execution_device_id,
 				'cudnn_conv_algo_search': 'EXHAUSTIVE' if use_exhaustive() else 'DEFAULT'
 			}))
+		elif execution_provider in [ 'DmlExecutionProvider', 'OpenVINOExecutionProvider', 'ROCMExecutionProvider' ]:
+			execution_providers_with_options.append((execution_provider,
+			{
+				'device_id': execution_device_id,
+			}))
 		else:
 			execution_providers_with_options.append(execution_provider)
 	return execution_providers_with_options

facefusion/face_analyser.py

@@ -88,24 +88,24 @@ def get_face_analyser() -> Any:
 			sleep(0.5)
 		if FACE_ANALYSER is None:
 			if facefusion.globals.face_detector_model in [ 'many', 'retinaface' ]:
-				face_detectors['retinaface'] = onnxruntime.InferenceSession(MODELS.get('face_detector_retinaface').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
+				face_detectors['retinaface'] = onnxruntime.InferenceSession(MODELS.get('face_detector_retinaface').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_device_id, facefusion.globals.execution_providers))
 			if facefusion.globals.face_detector_model in [ 'many', 'scrfd' ]:
-				face_detectors['scrfd'] = onnxruntime.InferenceSession(MODELS.get('face_detector_scrfd').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
+				face_detectors['scrfd'] = onnxruntime.InferenceSession(MODELS.get('face_detector_scrfd').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_device_id, facefusion.globals.execution_providers))
 			if facefusion.globals.face_detector_model in [ 'many', 'yoloface' ]:
-				face_detectors['yoloface'] = onnxruntime.InferenceSession(MODELS.get('face_detector_yoloface').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
+				face_detectors['yoloface'] = onnxruntime.InferenceSession(MODELS.get('face_detector_yoloface').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_device_id, facefusion.globals.execution_providers))
 			if facefusion.globals.face_detector_model in [ 'yunet' ]:
 				face_detectors['yunet'] = cv2.FaceDetectorYN.create(MODELS.get('face_detector_yunet').get('path'), '', (0, 0))
 			if facefusion.globals.face_recognizer_model == 'arcface_blendswap':
-				face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_blendswap').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
+				face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_blendswap').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_device_id, facefusion.globals.execution_providers))
 			if facefusion.globals.face_recognizer_model == 'arcface_inswapper':
-				face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_inswapper').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
+				face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_inswapper').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_device_id, facefusion.globals.execution_providers))
 			if facefusion.globals.face_recognizer_model == 'arcface_simswap':
-				face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_simswap').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
+				face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_simswap').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_device_id, facefusion.globals.execution_providers))
 			if facefusion.globals.face_recognizer_model == 'arcface_uniface':
-				face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_uniface').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
-			face_landmarkers['68'] = onnxruntime.InferenceSession(MODELS.get('face_landmarker_68').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
-			face_landmarkers['68_5'] = onnxruntime.InferenceSession(MODELS.get('face_landmarker_68_5').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
-			gender_age = onnxruntime.InferenceSession(MODELS.get('gender_age').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
+				face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_uniface').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_device_id, facefusion.globals.execution_providers))
+			face_landmarkers['68'] = onnxruntime.InferenceSession(MODELS.get('face_landmarker_68').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_device_id, facefusion.globals.execution_providers))
+			face_landmarkers['68_5'] = onnxruntime.InferenceSession(MODELS.get('face_landmarker_68_5').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_device_id, facefusion.globals.execution_providers))
+			gender_age = onnxruntime.InferenceSession(MODELS.get('gender_age').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_device_id, facefusion.globals.execution_providers))
 			FACE_ANALYSER =\
 			{
 				'face_detectors': face_detectors,

facefusion/face_masker.py

@@ -52,7 +52,7 @@ def get_face_occluder() -> Any:
 			sleep(0.5)
 		if FACE_OCCLUDER is None:
 			model_path = MODELS.get('face_occluder').get('path')
-			FACE_OCCLUDER = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_providers))
+			FACE_OCCLUDER = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_device_id, facefusion.globals.execution_providers))
 	return FACE_OCCLUDER
 
 
@@ -64,7 +64,7 @@ def get_face_parser() -> Any:
 			sleep(0.5)
 		if FACE_PARSER is None:
 			model_path = MODELS.get('face_parser').get('path')
-			FACE_PARSER = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_providers))
+			FACE_PARSER = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_device_id, facefusion.globals.execution_providers))
 	return FACE_PARSER
 
 

facefusion/globals.py

@@ -13,6 +13,7 @@
 headless : Optional[bool] = None
 log_level : Optional[LogLevel] = None
 # execution
+execution_device_id : Optional[int] = None
 execution_providers : List[str] = []
 execution_thread_count : Optional[int] = None
 execution_queue_count : Optional[int] = None

facefusion/processors/frame/modules/face_enhancer.py

@@ -104,7 +104,7 @@ def get_frame_processor() -> Any:
 			sleep(0.5)
 		if FRAME_PROCESSOR is None:
 			model_path = get_options('model').get('path')
-			FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_providers))
+			FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_device_id, facefusion.globals.execution_providers))
 	return FRAME_PROCESSOR
 
 

facefusion/processors/frame/modules/face_swapper.py

@@ -103,7 +103,7 @@ def get_frame_processor() -> Any:
 			sleep(0.5)
 		if FRAME_PROCESSOR is None:
 			model_path = get_options('model').get('path')
-			FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_providers))
+			FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_device_id, facefusion.globals.execution_providers))
 	return FRAME_PROCESSOR
 
 

facefusion/processors/frame/modules/frame_colorizer.py

@@ -68,7 +68,7 @@ def get_frame_processor() -> Any:
 			sleep(0.5)
 		if FRAME_PROCESSOR is None:
 			model_path = get_options('model').get('path')
-			FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_providers))
+			FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_device_id, facefusion.globals.execution_providers))
 	return FRAME_PROCESSOR
 
 

facefusion/processors/frame/modules/frame_enhancer.py

@@ -108,7 +108,7 @@ def get_frame_processor() -> Any:
 			sleep(0.5)
 		if FRAME_PROCESSOR is None:
 			model_path = get_options('model').get('path')
-			FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_providers))
+			FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_device_id, facefusion.globals.execution_providers))
 	return FRAME_PROCESSOR
 
 

facefusion/processors/frame/modules/lip_syncer.py

@@ -49,7 +49,7 @@ def get_frame_processor() -> Any:
 			sleep(0.5)
 		if FRAME_PROCESSOR is None:
 			model_path = get_options('model').get('path')
-			FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_providers))
+			FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_device_id, facefusion.globals.execution_providers))
 	return FRAME_PROCESSOR
 
 

facefusion/voice_extractor.py

@@ -31,7 +31,7 @@ def get_voice_extractor() -> Any:
 			sleep(0.5)
 		if VOICE_EXTRACTOR is None:
 			model_path = MODELS.get('voice_extractor').get('path')
-			VOICE_EXTRACTOR = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_providers))
+			VOICE_EXTRACTOR = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_device_id, facefusion.globals.execution_providers))
 	return VOICE_EXTRACTOR
 
 

facefusion/wording.py

@@ -65,6 +65,7 @@
 		'headless': 'run the program without a user interface',
 		'log_level': 'adjust the message severity displayed in the terminal',
 		# execution
+		'execution_device_id': 'specify the device used for processing',
 		'execution_providers': 'accelerate the model inference using different providers (choices: {choices}, ...)',
 		'execution_thread_count': 'specify the amount of parallel threads while processing',
 		'execution_queue_count': 'specify the amount of frames each thread is processing',

tests/test_execution.py

@@ -15,7 +15,8 @@ def test_multiple_execution_providers() -> None:
 		'CPUExecutionProvider',
 		('CUDAExecutionProvider',
 		{
+			'device_id': 1,
 			'cudnn_conv_algo_search': 'DEFAULT'
 		})
 	]
-	assert apply_execution_provider_options([ 'CPUExecutionProvider', 'CUDAExecutionProvider' ]) == execution_provider_with_options
+	assert apply_execution_provider_options(1, [ 'CPUExecutionProvider', 'CUDAExecutionProvider' ]) == execution_provider_with_options