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processors/modules/face_enhancer/choices.py

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+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] = list(get_args(FaceEnhancerModel))
+
+face_enhancer_blend_range : Sequence[int] = create_int_range(0, 100, 1)
+
+face_enhancer_weight_range : Sequence[float] = create_float_range(0.0, 1.0, 0.05)

processors/modules/face_enhancer/core.py

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+from argparse import ArgumentParser
+from functools import lru_cache
+from types import ModuleType
+from typing import List
+
+import numpy
+
+import facefusion.jobs.job_manager
+import facefusion.jobs.job_store
+from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, inference_manager, logger, state_manager, translator, video_manager
+from facefusion.common_helper import create_float_metavar, create_int_metavar, get_middle
+from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
+from facefusion.face_creator import scale_face
+from facefusion.face_helper import paste_back, warp_face_by_face_landmark_5
+from facefusion.face_masker import create_box_mask, create_occlusion_mask
+from facefusion.face_selector import select_faces
+from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path, same_file_extension
+from facefusion.processors.modules.face_enhancer import choices as face_enhancer_choices
+from facefusion.processors.modules.face_enhancer.types import FaceEnhancerInputs, FaceEnhancerWeight
+from facefusion.processors.types import ProcessorOutputs
+from facefusion.program_helper import find_argument_group
+from facefusion.thread_helper import thread_semaphore
+from facefusion.types import ApplyStateItem, Args, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, VisionFrame
+from facefusion.vision import blend_frame, read_static_image, read_static_video_frame
+
+
+@lru_cache()
+def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
+	return\
+	{
+		'codeformer':
+		{
+			'__metadata__':
+			{
+				'vendor': 'sczhou',
+				'license': 'S-Lab-1.0',
+				'year': 2022
+			},
+			'hashes':
+			{
+				'face_enhancer':
+				{
+					'url': resolve_download_url('models-3.0.0', 'codeformer.hash'),
+					'path': resolve_relative_path('../.assets/models/codeformer.hash')
+				}
+			},
+			'sources':
+			{
+				'face_enhancer':
+				{
+					'url': resolve_download_url('models-3.0.0', 'codeformer.onnx'),
+					'path': resolve_relative_path('../.assets/models/codeformer.onnx')
+				}
+			},
+			'template': 'ffhq_512',
+			'size': (512, 512)
+		},
+		'gfpgan_1.2':
+		{
+			'__metadata__':
+			{
+				'vendor': 'TencentARC',
+				'license': 'Apache-2.0',
+				'year': 2022
+			},
+			'hashes':
+			{
+				'face_enhancer':
+				{
+					'url': resolve_download_url('models-3.0.0', 'gfpgan_1.2.hash'),
+					'path': resolve_relative_path('../.assets/models/gfpgan_1.2.hash')
+				}
+			},
+			'sources':
+			{
+				'face_enhancer':
+				{
+					'url': resolve_download_url('models-3.0.0', 'gfpgan_1.2.onnx'),
+					'path': resolve_relative_path('../.assets/models/gfpgan_1.2.onnx')
+				}
+			},
+			'template': 'ffhq_512',
+			'size': (512, 512)
+		},
+		'gfpgan_1.3':
+		{
+			'__metadata__':
+			{
+				'vendor': 'TencentARC',
+				'license': 'Apache-2.0',
+				'year': 2022
+			},
+			'hashes':
+			{
+				'face_enhancer':
+				{
+					'url': resolve_download_url('models-3.0.0', 'gfpgan_1.3.hash'),
+					'path': resolve_relative_path('../.assets/models/gfpgan_1.3.hash')
+				}
+			},
+			'sources':
+			{
+				'face_enhancer':
+				{
+					'url': resolve_download_url('models-3.0.0', 'gfpgan_1.3.onnx'),
+					'path': resolve_relative_path('../.assets/models/gfpgan_1.3.onnx')
+				}
+			},
+			'template': 'ffhq_512',
+			'size': (512, 512)
+		},
+		'gfpgan_1.4':
+		{
+			'__metadata__':
+			{
+				'vendor': 'TencentARC',
+				'license': 'Apache-2.0',
+				'year': 2022
+			},
+			'hashes':
+			{
+				'face_enhancer':
+				{
+					'url': resolve_download_url('models-3.0.0', 'gfpgan_1.4.hash'),
+					'path': resolve_relative_path('../.assets/models/gfpgan_1.4.hash')
+				}
+			},
+			'sources':
+			{
+				'face_enhancer':
+				{
+					'url': resolve_download_url('models-3.0.0', 'gfpgan_1.4.onnx'),
+					'path': resolve_relative_path('../.assets/models/gfpgan_1.4.onnx')
+				}
+			},
+			'template': 'ffhq_512',
+			'size': (512, 512)
+		},
+		'gpen_bfr_256':
+		{
+			'__metadata__':
+			{
+				'vendor': 'yangxy',
+				'license': 'Non-Commercial',
+				'year': 2021
+			},
+			'hashes':
+			{
+				'face_enhancer':
+				{
+					'url': resolve_download_url('models-3.0.0', 'gpen_bfr_256.hash'),
+					'path': resolve_relative_path('../.assets/models/gpen_bfr_256.hash')
+				}
+			},
+			'sources':
+			{
+				'face_enhancer':
+				{
+					'url': resolve_download_url('models-3.0.0', 'gpen_bfr_256.onnx'),
+					'path': resolve_relative_path('../.assets/models/gpen_bfr_256.onnx')
+				}
+			},
+			'template': 'arcface_128',
+			'size': (256, 256)
+		},
+		'gpen_bfr_512':
+		{
+			'__metadata__':
+			{
+				'vendor': 'yangxy',
+				'license': 'Non-Commercial',
+				'year': 2021
+			},
+			'hashes':
+			{
+				'face_enhancer':
+				{
+					'url': resolve_download_url('models-3.0.0', 'gpen_bfr_512.hash'),
+					'path': resolve_relative_path('../.assets/models/gpen_bfr_512.hash')
+				}
+			},
+			'sources':
+			{
+				'face_enhancer':
+				{
+					'url': resolve_download_url('models-3.0.0', 'gpen_bfr_512.onnx'),
+					'path': resolve_relative_path('../.assets/models/gpen_bfr_512.onnx')
+				}
+			},
+			'template': 'ffhq_512',
+			'size': (512, 512)
+		},
+		'gpen_bfr_1024':
+		{
+			'__metadata__':
+			{
+				'vendor': 'yangxy',
+				'license': 'Non-Commercial',
+				'year': 2021
+			},
+			'hashes':
+			{
+				'face_enhancer':
+				{
+					'url': resolve_download_url('models-3.0.0', 'gpen_bfr_1024.hash'),
+					'path': resolve_relative_path('../.assets/models/gpen_bfr_1024.hash')
+				}
+			},
+			'sources':
+			{
+				'face_enhancer':
+				{
+					'url': resolve_download_url('models-3.0.0', 'gpen_bfr_1024.onnx'),
+					'path': resolve_relative_path('../.assets/models/gpen_bfr_1024.onnx')
+				}
+			},
+			'template': 'ffhq_512',
+			'size': (1024, 1024)
+		},
+		'gpen_bfr_2048':
+		{
+			'__metadata__':
+			{
+				'vendor': 'yangxy',
+				'license': 'Non-Commercial',
+				'year': 2021
+			},
+			'hashes':
+			{
+				'face_enhancer':
+				{
+					'url': resolve_download_url('models-3.0.0', 'gpen_bfr_2048.hash'),
+					'path': resolve_relative_path('../.assets/models/gpen_bfr_2048.hash')
+				}
+			},
+			'sources':
+			{
+				'face_enhancer':
+				{
+					'url': resolve_download_url('models-3.0.0', 'gpen_bfr_2048.onnx'),
+					'path': resolve_relative_path('../.assets/models/gpen_bfr_2048.onnx')
+				}
+			},
+			'template': 'ffhq_512',
+			'size': (2048, 2048)
+		},
+		'restoreformer_plus_plus':
+		{
+			'__metadata__':
+			{
+				'vendor': 'wzhouxiff',
+				'license': 'Apache-2.0',
+				'year': 2022
+			},
+			'hashes':
+			{
+				'face_enhancer':
+				{
+					'url': resolve_download_url('models-3.0.0', 'restoreformer_plus_plus.hash'),
+					'path': resolve_relative_path('../.assets/models/restoreformer_plus_plus.hash')
+				}
+			},
+			'sources':
+			{
+				'face_enhancer':
+				{
+					'url': resolve_download_url('models-3.0.0', 'restoreformer_plus_plus.onnx'),
+					'path': resolve_relative_path('../.assets/models/restoreformer_plus_plus.onnx')
+				}
+			},
+			'template': 'ffhq_512',
+			'size': (512, 512)
+		}
+	}
+
+
+def get_inference_pool() -> InferencePool:
+	model_names = [ state_manager.get_item('face_enhancer_model') ]
+	model_source_set = get_model_options().get('sources')
+
+	return inference_manager.get_inference_pool(__name__, model_names, model_source_set)
+
+
+def clear_inference_pool() -> None:
+	model_names = [ state_manager.get_item('face_enhancer_model') ]
+	inference_manager.clear_inference_pool(__name__, model_names)
+
+
+def get_model_options() -> ModelOptions:
+	model_name = state_manager.get_item('face_enhancer_model')
+	return create_static_model_set('full').get(model_name)
+
+
+def register_args(program : ArgumentParser) -> None:
+	group_processors = find_argument_group(program, 'processors')
+	if group_processors:
+		group_processors.add_argument('--face-enhancer-model', help = translator.get('help.model', __package__), default = config.get_str_value('processors', 'face_enhancer_model', 'gfpgan_1.4'), choices = face_enhancer_choices.face_enhancer_models)
+		group_processors.add_argument('--face-enhancer-blend', help = translator.get('help.blend', __package__), type = int, default = config.get_int_value('processors', 'face_enhancer_blend', '80'), choices = face_enhancer_choices.face_enhancer_blend_range, metavar = create_int_metavar(face_enhancer_choices.face_enhancer_blend_range))
+		group_processors.add_argument('--face-enhancer-weight', help = translator.get('help.weight', __package__), type = float, default = config.get_float_value('processors', 'face_enhancer_weight', '0.5'), choices = face_enhancer_choices.face_enhancer_weight_range, metavar = create_float_metavar(face_enhancer_choices.face_enhancer_weight_range))
+		facefusion.jobs.job_store.register_step_keys([ 'face_enhancer_model', 'face_enhancer_blend', 'face_enhancer_weight' ])
+
+
+def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
+	apply_state_item('face_enhancer_model', args.get('face_enhancer_model'))
+	apply_state_item('face_enhancer_blend', args.get('face_enhancer_blend'))
+	apply_state_item('face_enhancer_weight', args.get('face_enhancer_weight'))
+
+
+def get_common_modules() -> List[ModuleType]:
+	return [ content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer ]
+
+
+def pre_check() -> bool:
+	model_hash_set = get_model_options().get('hashes')
+	model_source_set = get_model_options().get('sources')
+
+	for common_module in get_common_modules():
+		if not common_module.pre_check():
+			return False
+
+	return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
+
+
+def pre_process(mode : ProcessMode) -> bool:
+	if mode in [ 'output', 'preview' ] and not is_image(state_manager.get_item('target_path')) and not is_video(state_manager.get_item('target_path')):
+		logger.error(translator.get('choose_image_or_video_target') + translator.get('exclamation_mark'), __name__)
+		return False
+	if mode == 'output' and not in_directory(state_manager.get_item('output_path')):
+		logger.error(translator.get('specify_image_or_video_output') + translator.get('exclamation_mark'), __name__)
+		return False
+	if mode == 'output' and not same_file_extension(state_manager.get_item('target_path'), state_manager.get_item('output_path')):
+		logger.error(translator.get('match_target_and_output_extension') + translator.get('exclamation_mark'), __name__)
+		return False
+	return True
+
+
+def post_process() -> None:
+	read_static_image.cache_clear()
+	read_static_video_frame.cache_clear()
+	video_manager.clear_video_pool()
+
+	if state_manager.get_item('video_memory_strategy') in [ 'strict', 'moderate' ]:
+		clear_inference_pool()
+
+	if state_manager.get_item('video_memory_strategy') == 'strict':
+		for common_module in get_common_modules():
+			common_module.clear_inference_pool()
+
+
+def enhance_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
+	model_template = get_model_options().get('template')
+	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)
+	box_mask = create_box_mask(crop_vision_frame, state_manager.get_item('face_mask_blur'), (0, 0, 0, 0))
+	crop_masks =\
+	[
+		box_mask
+	]
+
+	if 'occlusion' in state_manager.get_item('face_mask_types'):
+		occlusion_mask = create_occlusion_mask(crop_vision_frame)
+		crop_masks.append(occlusion_mask)
+
+	crop_vision_frame = prepare_crop_frame(crop_vision_frame)
+	face_enhancer_weight = numpy.array([ state_manager.get_item('face_enhancer_weight') ]).astype(numpy.double)
+	crop_vision_frame = forward(crop_vision_frame, face_enhancer_weight)
+	crop_vision_frame = normalize_crop_frame(crop_vision_frame)
+	crop_mask = numpy.minimum.reduce(crop_masks).clip(0, 1)
+	paste_vision_frame = paste_back(temp_vision_frame, crop_vision_frame, crop_mask, affine_matrix)
+	temp_vision_frame = blend_paste_frame(temp_vision_frame, paste_vision_frame)
+	return temp_vision_frame
+
+
+def forward(crop_vision_frame : VisionFrame, face_enhancer_weight : FaceEnhancerWeight) -> VisionFrame:
+	face_enhancer = get_inference_pool().get('face_enhancer')
+	face_enhancer_inputs = {}
+
+	for face_enhancer_input in face_enhancer.get_inputs():
+		if face_enhancer_input.name == 'input':
+			face_enhancer_inputs[face_enhancer_input.name] = crop_vision_frame
+		if face_enhancer_input.name == 'weight':
+			face_enhancer_inputs[face_enhancer_input.name] = face_enhancer_weight
+
+	with thread_semaphore():
+		crop_vision_frame = face_enhancer.run(None, face_enhancer_inputs)[0][0]
+
+	return crop_vision_frame
+
+
+def has_weight_input() -> bool:
+	face_enhancer = get_inference_pool().get('face_enhancer')
+
+	for deep_swapper_input in face_enhancer.get_inputs():
+		if deep_swapper_input.name == 'weight':
+			return True
+
+	return False
+
+
+def prepare_crop_frame(crop_vision_frame : VisionFrame) -> VisionFrame:
+	crop_vision_frame = crop_vision_frame[:, :, ::-1] / 255.0
+	crop_vision_frame = (crop_vision_frame - 0.5) / 0.5
+	crop_vision_frame = numpy.expand_dims(crop_vision_frame.transpose(2, 0, 1), axis = 0).astype(numpy.float32)
+	return crop_vision_frame
+
+
+def normalize_crop_frame(crop_vision_frame : VisionFrame) -> VisionFrame:
+	crop_vision_frame = numpy.clip(crop_vision_frame, -1, 1)
+	crop_vision_frame = (crop_vision_frame + 1) / 2
+	crop_vision_frame = crop_vision_frame.transpose(1, 2, 0)
+	crop_vision_frame = (crop_vision_frame * 255.0).round()
+	crop_vision_frame = crop_vision_frame.astype(numpy.uint8)[:, :, ::-1]
+	return crop_vision_frame
+
+
+def blend_paste_frame(temp_vision_frame : VisionFrame, paste_vision_frame : VisionFrame) -> VisionFrame:
+	face_enhancer_blend = 1 - (state_manager.get_item('face_enhancer_blend') / 100)
+	temp_vision_frame = blend_frame(temp_vision_frame, paste_vision_frame, 1 - face_enhancer_blend)
+	return temp_vision_frame
+
+
+def process_frame(inputs : FaceEnhancerInputs) -> ProcessorOutputs:
+	reference_vision_frame = inputs.get('reference_vision_frame')
+	source_vision_frames = inputs.get('source_vision_frames')
+	target_vision_frames = inputs.get('target_vision_frames')
+	temp_vision_frame = inputs.get('temp_vision_frame')
+	temp_vision_mask = inputs.get('temp_vision_mask')
+
+	target_vision_frame = get_middle(target_vision_frames)
+	target_faces = select_faces(reference_vision_frame, source_vision_frames, target_vision_frames)
+
+	if target_faces:
+		for target_face in target_faces:
+			target_face = scale_face(target_face, target_vision_frame, temp_vision_frame)
+			temp_vision_frame = enhance_face(target_face, temp_vision_frame)
+
+	return temp_vision_frame, temp_vision_mask

processors/modules/face_enhancer/locales.py

@@ -0,0 +1,20 @@
+from facefusion.types import Locales
+
+LOCALES : Locales =\
+{
+	'en':
+	{
+		'help':
+		{
+			'model': 'choose the model responsible for enhancing the face',
+			'blend': 'blend the enhanced into the previous face',
+			'weight': 'specify the degree of weight applied to the face'
+		},
+		'uis':
+		{
+			'blend_slider': 'FACE ENHANCER BLEND',
+			'model_dropdown': 'FACE ENHANCER MODEL',
+			'weight_slider': 'FACE ENHANCER WEIGHT'
+		}
+	}
+}

processors/modules/face_enhancer/types.py

@@ -0,0 +1,18 @@
+from typing import Any, List, Literal, TypeAlias, TypedDict
+
+from numpy.typing import NDArray
+
+from facefusion.types import Mask, VisionFrame
+
+FaceEnhancerInputs = TypedDict('FaceEnhancerInputs',
+{
+	'reference_vision_frame' : VisionFrame,
+	'source_vision_frames' : List[VisionFrame],
+	'target_vision_frames' : List[VisionFrame],
+	'temp_vision_frame' : VisionFrame,
+	'temp_vision_mask' : Mask
+})
+
+FaceEnhancerModel = Literal['codeformer', 'gfpgan_1.2', 'gfpgan_1.3', 'gfpgan_1.4', 'gpen_bfr_256', 'gpen_bfr_512', 'gpen_bfr_1024', 'gpen_bfr_2048', 'restoreformer_plus_plus']
+
+FaceEnhancerWeight : TypeAlias = NDArray[Any]