Initial commit

facefusion/uis/components/about.py

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+from typing import Optional
+import gradio
+
+from facefusion import metadata
+
+ABOUT_HTML : Optional[gradio.HTML] = None
+
+
+def render() -> None:
+	global ABOUT_HTML
+
+	with gradio.Box():
+		ABOUT_HTML = gradio.HTML('<center><a href="' + metadata.get('url') + '">' + metadata.get('name') + ' ' + metadata.get('version') + '</a></center>')

facefusion/uis/components/benchmark.py

@@ -0,0 +1,104 @@
+from typing import Any, Optional, List
+import time
+import tempfile
+import statistics
+import gradio
+
+import facefusion.globals
+from facefusion import wording
+from facefusion.capturer import get_video_frame_total
+from facefusion.core import conditional_process
+from facefusion.uis.typing import Update
+from facefusion.utilities import normalize_output_path
+
+BENCHMARK_RESULT_DATAFRAME : Optional[gradio.Dataframe] = None
+BENCHMARK_CYCLES_SLIDER : Optional[gradio.Button] = None
+BENCHMARK_START_BUTTON : Optional[gradio.Button] = None
+
+
+def render() -> None:
+	global BENCHMARK_RESULT_DATAFRAME
+	global BENCHMARK_CYCLES_SLIDER
+	global BENCHMARK_START_BUTTON
+
+	with gradio.Box():
+		BENCHMARK_RESULT_DATAFRAME = gradio.Dataframe(
+			label = wording.get('benchmark_result_dataframe_label'),
+			headers =
+			[
+				'target_path',
+				'cycles',
+				'average_run',
+				'fastest_run',
+				'slowest_run',
+				'relative_fps'
+			],
+			col_count = (6, 'fixed'),
+			row_count = (6, 'fixed'),
+			datatype =
+			[
+				'str',
+				'number',
+				'number',
+				'number',
+				'number',
+				'number'
+			]
+		)
+	BENCHMARK_CYCLES_SLIDER = gradio.Slider(
+		label = wording.get('benchmark_cycles_slider_label'),
+		minimum = 1,
+		step = 1,
+		value = 3,
+		maximum = 10
+	)
+	BENCHMARK_START_BUTTON = gradio.Button(wording.get('start_button_label'))
+
+
+def listen() -> None:
+	BENCHMARK_START_BUTTON.click(update, inputs = BENCHMARK_CYCLES_SLIDER, outputs = BENCHMARK_RESULT_DATAFRAME)
+
+
+def update(benchmark_cycles : int) -> Update:
+	facefusion.globals.source_path = '.assets/examples/source.jpg'
+	target_paths =\
+	[
+		'.assets/examples/target-240p.mp4',
+		'.assets/examples/target-360p.mp4',
+		'.assets/examples/target-540p.mp4',
+		'.assets/examples/target-720p.mp4',
+		'.assets/examples/target-1440p.mp4',
+		'.assets/examples/target-2160p.mp4'
+	]
+	value = [ benchmark(target_path, benchmark_cycles) for target_path in target_paths ]
+	return gradio.update(value = value)
+
+
+def benchmark(target_path : str, cycles : int) -> List[Any]:
+	process_times = []
+	total_fps = 0.0
+	for i in range(cycles + 1):
+		facefusion.globals.target_path = target_path
+		facefusion.globals.output_path = normalize_output_path(facefusion.globals.source_path, facefusion.globals.target_path, tempfile.gettempdir())
+		video_frame_total = get_video_frame_total(facefusion.globals.target_path)
+		start_time = time.perf_counter()
+		conditional_process()
+		end_time = time.perf_counter()
+		process_time = end_time - start_time
+		fps = video_frame_total / process_time
+		if i > 0:
+			process_times.append(process_time)
+			total_fps += fps
+	average_process_time = round(statistics.mean(process_times), 2)
+	fastest_process_time = round(min(process_times), 2)
+	slowest_process_time = round(max(process_times), 2)
+	average_fps = round(total_fps / cycles, 2)
+	return\
+	[
+		facefusion.globals.target_path,
+		cycles,
+		average_process_time,
+		fastest_process_time,
+		slowest_process_time,
+		average_fps
+	]

facefusion/uis/components/execution.py

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+from typing import List, Optional
+import gradio
+import onnxruntime
+
+import facefusion.globals
+from facefusion import wording
+from facefusion.face_analyser import clear_face_analyser
+from facefusion.processors.frame.core import clear_frame_processors_modules
+from facefusion.uis.typing import Update
+from facefusion.utilities import encode_execution_providers, decode_execution_providers
+
+EXECUTION_PROVIDERS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
+EXECUTION_THREAD_COUNT_SLIDER : Optional[gradio.Slider] = None
+EXECUTION_QUEUE_COUNT_SLIDER : Optional[gradio.Slider] = None
+
+
+def render() -> None:
+	global EXECUTION_PROVIDERS_CHECKBOX_GROUP
+	global EXECUTION_THREAD_COUNT_SLIDER
+	global EXECUTION_QUEUE_COUNT_SLIDER
+
+	with gradio.Box():
+		EXECUTION_PROVIDERS_CHECKBOX_GROUP = gradio.CheckboxGroup(
+			label = wording.get('execution_providers_checkbox_group_label'),
+			choices = encode_execution_providers(onnxruntime.get_available_providers()),
+			value = encode_execution_providers(facefusion.globals.execution_providers)
+		)
+		EXECUTION_THREAD_COUNT_SLIDER = gradio.Slider(
+			label = wording.get('execution_thread_count_slider_label'),
+			value = facefusion.globals.execution_thread_count,
+			step = 1,
+			minimum = 1,
+			maximum = 128
+		)
+		EXECUTION_QUEUE_COUNT_SLIDER = gradio.Slider(
+			label = wording.get('execution_queue_count_slider_label'),
+			value = facefusion.globals.execution_queue_count,
+			step = 1,
+			minimum = 1,
+			maximum = 16
+		)
+
+
+def listen() -> None:
+	EXECUTION_PROVIDERS_CHECKBOX_GROUP.change(update_execution_providers, inputs = EXECUTION_PROVIDERS_CHECKBOX_GROUP, outputs = EXECUTION_PROVIDERS_CHECKBOX_GROUP)
+	EXECUTION_THREAD_COUNT_SLIDER.change(update_execution_thread_count, inputs = EXECUTION_THREAD_COUNT_SLIDER, outputs = EXECUTION_THREAD_COUNT_SLIDER)
+	EXECUTION_QUEUE_COUNT_SLIDER.change(update_execution_queue_count, inputs = EXECUTION_QUEUE_COUNT_SLIDER, outputs = EXECUTION_QUEUE_COUNT_SLIDER)
+
+
+def update_execution_providers(execution_providers : List[str]) -> Update:
+	clear_face_analyser()
+	clear_frame_processors_modules()
+	facefusion.globals.execution_providers = decode_execution_providers(execution_providers)
+	return gradio.update(value = execution_providers)
+
+
+def update_execution_thread_count(execution_thread_count : int = 1) -> Update:
+	facefusion.globals.execution_thread_count = execution_thread_count
+	return gradio.update(value = execution_thread_count)
+
+
+def update_execution_queue_count(execution_queue_count : int = 1) -> Update:
+	facefusion.globals.execution_queue_count = execution_queue_count
+	return gradio.update(value = execution_queue_count)

facefusion/uis/components/face_analyser.py

@@ -0,0 +1,54 @@
+from typing import Optional
+
+import gradio
+
+import facefusion.choices
+import facefusion.globals
+from facefusion import wording
+from facefusion.uis import core as ui
+from facefusion.uis.typing import Update
+
+FACE_ANALYSER_DIRECTION_DROPDOWN : Optional[gradio.Dropdown] = None
+FACE_ANALYSER_AGE_DROPDOWN : Optional[gradio.Dropdown] = None
+FACE_ANALYSER_GENDER_DROPDOWN : Optional[gradio.Dropdown] = None
+
+
+def render() -> None:
+	global FACE_ANALYSER_DIRECTION_DROPDOWN
+	global FACE_ANALYSER_AGE_DROPDOWN
+	global FACE_ANALYSER_GENDER_DROPDOWN
+
+	with gradio.Box():
+		with gradio.Row():
+			FACE_ANALYSER_DIRECTION_DROPDOWN = gradio.Dropdown(
+				label = wording.get('face_analyser_direction_dropdown_label'),
+				choices = facefusion.choices.face_analyser_direction,
+				value = facefusion.globals.face_analyser_direction
+			)
+			FACE_ANALYSER_AGE_DROPDOWN = gradio.Dropdown(
+				label = wording.get('face_analyser_age_dropdown_label'),
+				choices = ['none'] + facefusion.choices.face_analyser_age,
+				value = facefusion.globals.face_analyser_age or 'none'
+			)
+			FACE_ANALYSER_GENDER_DROPDOWN = gradio.Dropdown(
+				label = wording.get('face_analyser_gender_dropdown_label'),
+				choices = ['none'] + facefusion.choices.face_analyser_gender,
+				value = facefusion.globals.face_analyser_gender or 'none'
+			)
+		ui.register_component('face_analyser_direction_dropdown', FACE_ANALYSER_DIRECTION_DROPDOWN)
+		ui.register_component('face_analyser_age_dropdown', FACE_ANALYSER_AGE_DROPDOWN)
+		ui.register_component('face_analyser_gender_dropdown', FACE_ANALYSER_GENDER_DROPDOWN)
+
+
+def listen() -> None:
+	FACE_ANALYSER_DIRECTION_DROPDOWN.select(lambda value: update_dropdown('face_analyser_direction', value), inputs = FACE_ANALYSER_DIRECTION_DROPDOWN, outputs = FACE_ANALYSER_DIRECTION_DROPDOWN)
+	FACE_ANALYSER_AGE_DROPDOWN.select(lambda value: update_dropdown('face_analyser_age', value), inputs = FACE_ANALYSER_AGE_DROPDOWN, outputs = FACE_ANALYSER_AGE_DROPDOWN)
+	FACE_ANALYSER_GENDER_DROPDOWN.select(lambda value: update_dropdown('face_analyser_gender', value), inputs = FACE_ANALYSER_GENDER_DROPDOWN, outputs = FACE_ANALYSER_GENDER_DROPDOWN)
+
+
+def update_dropdown(name : str, value : str) -> Update:
+	if value == 'none':
+		setattr(facefusion.globals, name, None)
+	else:
+		setattr(facefusion.globals, name, value)
+	return gradio.update(value = value)

facefusion/uis/components/face_selector.py

@@ -0,0 +1,133 @@
+from typing import List, Optional, Tuple, Any, Dict
+from time import sleep
+
+import cv2
+import gradio
+
+import facefusion.choices
+import facefusion.globals
+from facefusion import wording
+from facefusion.capturer import get_video_frame
+from facefusion.face_analyser import get_many_faces
+from facefusion.face_reference import clear_face_reference
+from facefusion.typing import Frame, FaceRecognition
+from facefusion.uis import core as ui
+from facefusion.uis.typing import ComponentName, Update
+from facefusion.utilities import is_image, is_video
+
+FACE_RECOGNITION_DROPDOWN : Optional[gradio.Dropdown] = None
+REFERENCE_FACE_POSITION_GALLERY : Optional[gradio.Gallery] = None
+REFERENCE_FACE_DISTANCE_SLIDER : Optional[gradio.Slider] = None
+
+
+def render() -> None:
+	global FACE_RECOGNITION_DROPDOWN
+	global REFERENCE_FACE_POSITION_GALLERY
+	global REFERENCE_FACE_DISTANCE_SLIDER
+
+	with gradio.Box():
+		reference_face_gallery_args: Dict[str, Any] = {
+			'label': wording.get('reference_face_gallery_label'),
+			'height': 120,
+			'object_fit': 'cover',
+			'columns': 10,
+			'allow_preview': False,
+			'visible': 'reference' in facefusion.globals.face_recognition
+		}
+		if is_image(facefusion.globals.target_path):
+			reference_frame = cv2.imread(facefusion.globals.target_path)
+			reference_face_gallery_args['value'] = extract_gallery_frames(reference_frame)
+		if is_video(facefusion.globals.target_path):
+			reference_frame = get_video_frame(facefusion.globals.target_path, facefusion.globals.reference_frame_number)
+			reference_face_gallery_args['value'] = extract_gallery_frames(reference_frame)
+		FACE_RECOGNITION_DROPDOWN = gradio.Dropdown(
+			label = wording.get('face_recognition_dropdown_label'),
+			choices = facefusion.choices.face_recognition,
+			value = facefusion.globals.face_recognition
+		)
+		REFERENCE_FACE_POSITION_GALLERY = gradio.Gallery(**reference_face_gallery_args)
+		REFERENCE_FACE_DISTANCE_SLIDER = gradio.Slider(
+			label = wording.get('reference_face_distance_slider_label'),
+			value = facefusion.globals.reference_face_distance,
+			maximum = 3,
+			step = 0.05,
+			visible = 'reference' in facefusion.globals.face_recognition
+		)
+		ui.register_component('face_recognition_dropdown', FACE_RECOGNITION_DROPDOWN)
+		ui.register_component('reference_face_position_gallery', REFERENCE_FACE_POSITION_GALLERY)
+		ui.register_component('reference_face_distance_slider', REFERENCE_FACE_DISTANCE_SLIDER)
+
+
+def listen() -> None:
+	FACE_RECOGNITION_DROPDOWN.select(update_face_recognition, inputs = FACE_RECOGNITION_DROPDOWN, outputs = [ REFERENCE_FACE_POSITION_GALLERY, REFERENCE_FACE_DISTANCE_SLIDER ])
+	REFERENCE_FACE_POSITION_GALLERY.select(clear_and_update_face_reference_position)
+	REFERENCE_FACE_DISTANCE_SLIDER.change(update_reference_face_distance, inputs = REFERENCE_FACE_DISTANCE_SLIDER)
+	update_component_names : List[ComponentName] =\
+	[
+		'target_file',
+		'preview_frame_slider'
+	]
+	for component_name in update_component_names:
+		component = ui.get_component(component_name)
+		if component:
+			component.change(update_face_reference_position, outputs = REFERENCE_FACE_POSITION_GALLERY)
+	select_component_names : List[ComponentName] =\
+	[
+		'face_analyser_direction_dropdown',
+		'face_analyser_age_dropdown',
+		'face_analyser_gender_dropdown'
+	]
+	for component_name in select_component_names:
+		component = ui.get_component(component_name)
+		if component:
+			component.select(update_face_reference_position, outputs = REFERENCE_FACE_POSITION_GALLERY)
+
+
+def update_face_recognition(face_recognition : FaceRecognition) -> Tuple[Update, Update]:
+	if face_recognition == 'reference':
+		facefusion.globals.face_recognition = face_recognition
+		return gradio.update(visible = True), gradio.update(visible = True)
+	if face_recognition == 'many':
+		facefusion.globals.face_recognition = face_recognition
+		return gradio.update(visible = False), gradio.update(visible = False)
+
+
+def clear_and_update_face_reference_position(event: gradio.SelectData) -> Update:
+	clear_face_reference()
+	return update_face_reference_position(event.index)
+
+
+def update_face_reference_position(reference_face_position : int = 0) -> Update:
+	sleep(0.2)
+	gallery_frames = []
+	facefusion.globals.reference_face_position = reference_face_position
+	if is_image(facefusion.globals.target_path):
+		reference_frame = cv2.imread(facefusion.globals.target_path)
+		gallery_frames = extract_gallery_frames(reference_frame)
+	if is_video(facefusion.globals.target_path):
+		reference_frame = get_video_frame(facefusion.globals.target_path, facefusion.globals.reference_frame_number)
+		gallery_frames = extract_gallery_frames(reference_frame)
+	if gallery_frames:
+		return gradio.update(value = gallery_frames)
+	return gradio.update(value = None)
+
+
+def update_reference_face_distance(reference_face_distance : float) -> Update:
+	facefusion.globals.reference_face_distance = reference_face_distance
+	return gradio.update(value = reference_face_distance)
+
+
+def extract_gallery_frames(reference_frame : Frame) -> List[Frame]:
+	crop_frames = []
+	faces = get_many_faces(reference_frame)
+	for face in faces:
+		start_x, start_y, end_x, end_y = map(int, face['bbox'])
+		padding_x = int((end_x - start_x) * 0.25)
+		padding_y = int((end_y - start_y) * 0.25)
+		start_x = max(0, start_x - padding_x)
+		start_y = max(0, start_y - padding_y)
+		end_x = max(0, end_x + padding_x)
+		end_y = max(0, end_y + padding_y)
+		crop_frame = reference_frame[start_y:end_y, start_x:end_x]
+		crop_frames.append(ui.normalize_frame(crop_frame))
+	return crop_frames

facefusion/uis/components/output.py

@@ -0,0 +1,53 @@
+from typing import Tuple, Optional
+import gradio
+
+import facefusion.globals
+from facefusion import wording
+from facefusion.core import conditional_process
+from facefusion.uis.typing import Update
+from facefusion.utilities import is_image, is_video, normalize_output_path
+
+OUTPUT_START_BUTTON : Optional[gradio.Button] = None
+OUTPUT_CLEAR_BUTTON : Optional[gradio.Button] = None
+OUTPUT_IMAGE : Optional[gradio.Image] = None
+OUTPUT_VIDEO : Optional[gradio.Video] = None
+
+
+def render() -> None:
+	global OUTPUT_START_BUTTON
+	global OUTPUT_CLEAR_BUTTON
+	global OUTPUT_IMAGE
+	global OUTPUT_VIDEO
+
+	with gradio.Row():
+		with gradio.Box():
+			OUTPUT_IMAGE = gradio.Image(
+				label = wording.get('output_image_or_video_label'),
+				visible = False
+			)
+			OUTPUT_VIDEO = gradio.Video(
+				label = wording.get('output_image_or_video_label')
+			)
+	with gradio.Row():
+		OUTPUT_START_BUTTON = gradio.Button(wording.get('start_button_label'))
+		OUTPUT_CLEAR_BUTTON = gradio.Button(wording.get('clear_button_label'))
+
+
+def listen() -> None:
+	OUTPUT_START_BUTTON.click(update, outputs = [ OUTPUT_IMAGE, OUTPUT_VIDEO ])
+	OUTPUT_CLEAR_BUTTON.click(clear, outputs = [ OUTPUT_IMAGE, OUTPUT_VIDEO ])
+
+
+def update() -> Tuple[Update, Update]:
+	facefusion.globals.output_path = normalize_output_path(facefusion.globals.source_path, facefusion.globals.target_path, '.')
+	if facefusion.globals.output_path:
+		conditional_process()
+		if is_image(facefusion.globals.output_path):
+			return gradio.update(value = facefusion.globals.output_path, visible = True), gradio.update(value = None, visible = False)
+		if is_video(facefusion.globals.output_path):
+			return gradio.update(value = None, visible = False), gradio.update(value = facefusion.globals.output_path, visible = True)
+	return gradio.update(value = None, visible = False), gradio.update(value = None, visible = False)
+
+
+def clear() -> Tuple[Update, Update]:
+	return gradio.update(value = None), gradio.update(value = None)

facefusion/uis/components/output_settings.py

@@ -0,0 +1,43 @@
+from typing import Optional
+import gradio
+
+import facefusion.choices
+import facefusion.globals
+from facefusion import wording
+from facefusion.typing import OutputVideoEncoder
+from facefusion.uis.typing import Update
+
+OUTPUT_VIDEO_ENCODER_DROPDOWN : Optional[gradio.Dropdown] = None
+OUTPUT_VIDEO_QUALITY_SLIDER : Optional[gradio.Slider] = None
+
+
+def render() -> None:
+	global OUTPUT_VIDEO_ENCODER_DROPDOWN
+	global OUTPUT_VIDEO_QUALITY_SLIDER
+
+	with gradio.Box():
+		OUTPUT_VIDEO_ENCODER_DROPDOWN = gradio.Dropdown(
+			label = wording.get('output_video_encoder_dropdown_label'),
+			choices = facefusion.choices.output_video_encoder,
+			value = facefusion.globals.output_video_encoder
+		)
+		OUTPUT_VIDEO_QUALITY_SLIDER = gradio.Slider(
+			label = wording.get('output_video_quality_slider_label'),
+			value = facefusion.globals.output_video_quality,
+			step = 1
+		)
+
+
+def listen() -> None:
+	OUTPUT_VIDEO_ENCODER_DROPDOWN.select(update_output_video_encoder, inputs = OUTPUT_VIDEO_ENCODER_DROPDOWN, outputs = OUTPUT_VIDEO_ENCODER_DROPDOWN)
+	OUTPUT_VIDEO_QUALITY_SLIDER.change(update_output_video_quality, inputs = OUTPUT_VIDEO_QUALITY_SLIDER, outputs = OUTPUT_VIDEO_QUALITY_SLIDER)
+
+
+def update_output_video_encoder(output_video_encoder: OutputVideoEncoder) -> Update:
+	facefusion.globals.output_video_encoder = output_video_encoder
+	return gradio.update(value = output_video_encoder)
+
+
+def update_output_video_quality(output_video_quality : int) -> Update:
+	facefusion.globals.output_video_quality = output_video_quality
+	return gradio.update(value = output_video_quality)

facefusion/uis/components/preview.py

@@ -0,0 +1,121 @@
+from time import sleep
+from typing import Any, Dict, Tuple, List, Optional
+import cv2
+import gradio
+
+import facefusion.globals
+from facefusion import wording
+from facefusion.capturer import get_video_frame, get_video_frame_total
+from facefusion.face_analyser import get_one_face
+from facefusion.face_reference import get_face_reference, set_face_reference
+from facefusion.predictor import predict_frame
+from facefusion.processors.frame.core import load_frame_processor_module
+from facefusion.typing import Frame
+from facefusion.uis import core as ui
+from facefusion.uis.typing import ComponentName, Update
+from facefusion.utilities import is_video, is_image
+
+PREVIEW_IMAGE : Optional[gradio.Image] = None
+PREVIEW_FRAME_SLIDER : Optional[gradio.Slider] = None
+
+
+def render() -> None:
+	global PREVIEW_IMAGE
+	global PREVIEW_FRAME_SLIDER
+
+	with gradio.Box():
+		preview_image_args: Dict[str, Any] = {
+			'label': wording.get('preview_image_label')
+		}
+		preview_frame_slider_args: Dict[str, Any] = {
+			'label': wording.get('preview_frame_slider_label'),
+			'step': 1,
+			'visible': False
+		}
+		if is_image(facefusion.globals.target_path):
+			target_frame = cv2.imread(facefusion.globals.target_path)
+			preview_frame = extract_preview_frame(target_frame)
+			preview_image_args['value'] = ui.normalize_frame(preview_frame)
+		if is_video(facefusion.globals.target_path):
+			temp_frame = get_video_frame(facefusion.globals.target_path, facefusion.globals.reference_frame_number)
+			preview_frame = extract_preview_frame(temp_frame)
+			preview_image_args['value'] = ui.normalize_frame(preview_frame)
+			preview_image_args['visible'] = True
+			preview_frame_slider_args['value'] = facefusion.globals.reference_frame_number
+			preview_frame_slider_args['maximum'] = get_video_frame_total(facefusion.globals.target_path)
+			preview_frame_slider_args['visible'] = True
+		PREVIEW_IMAGE = gradio.Image(**preview_image_args)
+		PREVIEW_FRAME_SLIDER = gradio.Slider(**preview_frame_slider_args)
+		ui.register_component('preview_frame_slider', PREVIEW_FRAME_SLIDER)
+
+
+def listen() -> None:
+	PREVIEW_FRAME_SLIDER.change(update, inputs = PREVIEW_FRAME_SLIDER, outputs = [ PREVIEW_IMAGE, PREVIEW_FRAME_SLIDER ])
+	update_component_names : List[ComponentName] =\
+	[
+		'source_file',
+		'target_file',
+		'face_recognition_dropdown',
+		'reference_face_distance_slider',
+		'frame_processors_checkbox_group'
+	]
+	for component_name in update_component_names:
+		component = ui.get_component(component_name)
+		if component:
+			component.change(update, inputs = PREVIEW_FRAME_SLIDER, outputs = [ PREVIEW_IMAGE, PREVIEW_FRAME_SLIDER ])
+	select_component_names : List[ComponentName] =\
+	[
+		'reference_face_position_gallery',
+		'face_analyser_direction_dropdown',
+		'face_analyser_age_dropdown',
+		'face_analyser_gender_dropdown'
+	]
+	for component_name in select_component_names:
+		component = ui.get_component(component_name)
+		if component:
+			component.select(update, inputs = PREVIEW_FRAME_SLIDER, outputs = [ PREVIEW_IMAGE, PREVIEW_FRAME_SLIDER ])
+
+
+def update(frame_number : int = 0) -> Tuple[Update, Update]:
+	sleep(0.1)
+	if is_image(facefusion.globals.target_path):
+		target_frame = cv2.imread(facefusion.globals.target_path)
+		preview_frame = extract_preview_frame(target_frame)
+		return gradio.update(value = ui.normalize_frame(preview_frame)), gradio.update(value = None, maximum = None, visible = False)
+	if is_video(facefusion.globals.target_path):
+		facefusion.globals.reference_frame_number = frame_number
+		video_frame_total = get_video_frame_total(facefusion.globals.target_path)
+		temp_frame = get_video_frame(facefusion.globals.target_path, facefusion.globals.reference_frame_number)
+		preview_frame = extract_preview_frame(temp_frame)
+		return gradio.update(value = ui.normalize_frame(preview_frame)), gradio.update(maximum = video_frame_total, visible = True)
+	return gradio.update(value = None), gradio.update(value = None, maximum = None, visible = False)
+
+
+def extract_preview_frame(temp_frame : Frame) -> Frame:
+	if predict_frame(temp_frame):
+		return cv2.GaussianBlur(temp_frame, (99, 99), 0)
+	source_face = get_one_face(cv2.imread(facefusion.globals.source_path)) if facefusion.globals.source_path else None
+	temp_frame = reduce_preview_frame(temp_frame)
+	if 'reference' in facefusion.globals.face_recognition and not get_face_reference():
+		reference_frame = get_video_frame(facefusion.globals.target_path, facefusion.globals.reference_frame_number)
+		reference_face = get_one_face(reference_frame, facefusion.globals.reference_face_position)
+		set_face_reference(reference_face)
+	reference_face = get_face_reference() if 'reference' in facefusion.globals.face_recognition else None
+	for frame_processor in facefusion.globals.frame_processors:
+		frame_processor_module = load_frame_processor_module(frame_processor)
+		if frame_processor_module.pre_process():
+			temp_frame = frame_processor_module.process_frame(
+				source_face,
+				reference_face,
+				temp_frame
+			)
+	return temp_frame
+
+
+def reduce_preview_frame(temp_frame : Frame, max_height : int = 480) -> Frame:
+	height, width = temp_frame.shape[:2]
+	if height > max_height:
+		scale = max_height / height
+		max_width = int(width * scale)
+		temp_frame = cv2.resize(temp_frame, (max_width, max_height))
+	return temp_frame

facefusion/uis/components/processors.py

@@ -0,0 +1,41 @@
+from typing import List, Optional
+import gradio
+
+import facefusion.globals
+from facefusion import wording
+from facefusion.processors.frame.core import load_frame_processor_module, clear_frame_processors_modules
+from facefusion.uis import core as ui
+from facefusion.uis.typing import Update
+from facefusion.utilities import list_module_names
+
+FRAME_PROCESSORS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
+
+
+def render() -> None:
+	global FRAME_PROCESSORS_CHECKBOX_GROUP
+
+	with gradio.Box():
+		FRAME_PROCESSORS_CHECKBOX_GROUP = gradio.CheckboxGroup(
+			label = wording.get('frame_processors_checkbox_group_label'),
+			choices = sort_frame_processors(facefusion.globals.frame_processors),
+			value = facefusion.globals.frame_processors
+		)
+		ui.register_component('frame_processors_checkbox_group', FRAME_PROCESSORS_CHECKBOX_GROUP)
+
+
+def listen() -> None:
+	FRAME_PROCESSORS_CHECKBOX_GROUP.change(update_frame_processors, inputs = FRAME_PROCESSORS_CHECKBOX_GROUP, outputs = FRAME_PROCESSORS_CHECKBOX_GROUP)
+
+
+def update_frame_processors(frame_processors : List[str]) -> Update:
+	clear_frame_processors_modules()
+	facefusion.globals.frame_processors = frame_processors
+	for frame_processor in facefusion.globals.frame_processors:
+		frame_processor_module = load_frame_processor_module(frame_processor)
+		frame_processor_module.pre_check()
+	return gradio.update(value = frame_processors, choices = sort_frame_processors(frame_processors))
+
+
+def sort_frame_processors(frame_processors : List[str]) -> list[str]:
+	frame_processors_names = list_module_names('facefusion/processors/frame/modules')
+	return sorted(frame_processors_names, key = lambda frame_processor : frame_processors.index(frame_processor) if frame_processor in frame_processors else len(frame_processors))

facefusion/uis/components/settings.py

@@ -0,0 +1,41 @@
+from typing import Optional
+import gradio
+
+import facefusion.globals
+from facefusion import wording
+from facefusion.uis.typing import Update
+
+KEEP_FPS_CHECKBOX : Optional[gradio.Checkbox] = None
+KEEP_TEMP_CHECKBOX : Optional[gradio.Checkbox] = None
+SKIP_AUDIO_CHECKBOX : Optional[gradio.Checkbox] = None
+
+
+def render() -> None:
+	global KEEP_FPS_CHECKBOX
+	global KEEP_TEMP_CHECKBOX
+	global SKIP_AUDIO_CHECKBOX
+
+	with gradio.Box():
+		KEEP_FPS_CHECKBOX = gradio.Checkbox(
+			label = wording.get('keep_fps_checkbox_label'),
+			value = facefusion.globals.keep_fps
+		)
+		KEEP_TEMP_CHECKBOX = gradio.Checkbox(
+			label = wording.get('keep_temp_checkbox_label'),
+			value = facefusion.globals.keep_temp
+		)
+		SKIP_AUDIO_CHECKBOX = gradio.Checkbox(
+			label = wording.get('skip_audio_checkbox_label'),
+			value = facefusion.globals.skip_audio
+		)
+
+
+def listen() -> None:
+	KEEP_FPS_CHECKBOX.change(lambda value: update_checkbox('keep_fps', value), inputs = KEEP_FPS_CHECKBOX, outputs = KEEP_FPS_CHECKBOX)
+	KEEP_TEMP_CHECKBOX.change(lambda value: update_checkbox('keep_temp', value), inputs = KEEP_TEMP_CHECKBOX, outputs = KEEP_TEMP_CHECKBOX)
+	SKIP_AUDIO_CHECKBOX.change(lambda value: update_checkbox('skip_audio', value), inputs = SKIP_AUDIO_CHECKBOX, outputs = SKIP_AUDIO_CHECKBOX)
+
+
+def update_checkbox(name : str, value: bool) -> Update:
+	setattr(facefusion.globals, name, value)
+	return gradio.update(value = value)

facefusion/uis/components/source.py

@@ -0,0 +1,48 @@
+from typing import Any, IO, Optional
+import gradio
+
+import facefusion.globals
+from facefusion import wording
+from facefusion.uis import core as ui
+from facefusion.uis.typing import Update
+from facefusion.utilities import is_image
+
+SOURCE_FILE : Optional[gradio.File] = None
+SOURCE_IMAGE : Optional[gradio.Image] = None
+
+
+def render() -> None:
+	global SOURCE_FILE
+	global SOURCE_IMAGE
+
+	with gradio.Box():
+		is_source_image = is_image(facefusion.globals.source_path)
+		SOURCE_FILE = gradio.File(
+			file_count = 'single',
+			file_types=
+			[
+				'.png',
+				'.jpg',
+				'.webp'
+			],
+			label = wording.get('source_file_label'),
+			value = facefusion.globals.source_path if is_source_image else None
+		)
+		ui.register_component('source_file', SOURCE_FILE)
+		SOURCE_IMAGE = gradio.Image(
+			value = SOURCE_FILE.value['name'] if is_source_image else None,
+			visible = is_source_image,
+			show_label = False
+		)
+
+
+def listen() -> None:
+	SOURCE_FILE.change(update, inputs = SOURCE_FILE, outputs = SOURCE_IMAGE)
+
+
+def update(file: IO[Any]) -> Update:
+	if file and is_image(file.name):
+		facefusion.globals.source_path = file.name
+		return gradio.update(value = file.name, visible = True)
+	facefusion.globals.source_path = None
+	return gradio.update(value = None, visible = False)

facefusion/uis/components/target.py

@@ -0,0 +1,62 @@
+from typing import Any, IO, Tuple, Optional
+import gradio
+
+import facefusion.globals
+from facefusion import wording
+from facefusion.face_reference import clear_face_reference
+from facefusion.uis import core as ui
+from facefusion.uis.typing import Update
+from facefusion.utilities import is_image, is_video
+
+TARGET_FILE : Optional[gradio.File] = None
+TARGET_IMAGE : Optional[gradio.Image] = None
+TARGET_VIDEO : Optional[gradio.Video] = None
+
+
+def render() -> None:
+	global TARGET_FILE
+	global TARGET_IMAGE
+	global TARGET_VIDEO
+
+	with gradio.Box():
+		is_target_image = is_image(facefusion.globals.target_path)
+		is_target_video = is_video(facefusion.globals.target_path)
+		TARGET_FILE = gradio.File(
+			label = wording.get('target_file_label'),
+			file_count = 'single',
+			file_types =
+			[
+				'.png',
+				'.jpg',
+				'.webp',
+				'.mp4'
+			],
+			value = facefusion.globals.target_path if is_target_image or is_target_video else None
+		)
+		TARGET_IMAGE = gradio.Image(
+			value = TARGET_FILE.value['name'] if is_target_image else None,
+			visible = is_target_image,
+			show_label = False
+		)
+		TARGET_VIDEO = gradio.Video(
+			value = TARGET_FILE.value['name'] if is_target_video else None,
+			visible = is_target_video,
+			show_label = False
+		)
+		ui.register_component('target_file', TARGET_FILE)
+
+
+def listen() -> None:
+	TARGET_FILE.change(update, inputs = TARGET_FILE, outputs = [ TARGET_IMAGE, TARGET_VIDEO ])
+
+
+def update(file : IO[Any]) -> Tuple[Update, Update]:
+	clear_face_reference()
+	if file and is_image(file.name):
+		facefusion.globals.target_path = file.name
+		return gradio.update(value = file.name, visible = True), gradio.update(value = None, visible = False)
+	if file and is_video(file.name):
+		facefusion.globals.target_path = file.name
+		return gradio.update(value = None, visible = False), gradio.update(value = file.name, visible = True)
+	facefusion.globals.target_path = None
+	return gradio.update(value = None, visible = False), gradio.update(value = None, visible = False)

facefusion/uis/components/temp_frame.py

@@ -0,0 +1,44 @@
+from typing import Optional
+import gradio
+
+import facefusion.choices
+import facefusion.globals
+from facefusion import wording
+from facefusion.typing import TempFrameFormat
+
+from facefusion.uis.typing import Update
+
+TEMP_FRAME_FORMAT_DROPDOWN : Optional[gradio.Dropdown] = None
+TEMP_FRAME_QUALITY_SLIDER : Optional[gradio.Slider] = None
+
+
+def render() -> None:
+	global TEMP_FRAME_FORMAT_DROPDOWN
+	global TEMP_FRAME_QUALITY_SLIDER
+
+	with gradio.Box():
+		TEMP_FRAME_FORMAT_DROPDOWN = gradio.Dropdown(
+			label = wording.get('temp_frame_format_dropdown_label'),
+			choices = facefusion.choices.temp_frame_format,
+			value = facefusion.globals.temp_frame_format
+		)
+		TEMP_FRAME_QUALITY_SLIDER = gradio.Slider(
+			label = wording.get('temp_frame_quality_slider_label'),
+			value = facefusion.globals.temp_frame_quality,
+			step = 1
+		)
+
+
+def listen() -> None:
+	TEMP_FRAME_FORMAT_DROPDOWN.select(update_temp_frame_format, inputs = TEMP_FRAME_FORMAT_DROPDOWN, outputs = TEMP_FRAME_FORMAT_DROPDOWN)
+	TEMP_FRAME_QUALITY_SLIDER.change(update_temp_frame_quality, inputs = TEMP_FRAME_QUALITY_SLIDER, outputs = TEMP_FRAME_QUALITY_SLIDER)
+
+
+def update_temp_frame_format(temp_frame_format : TempFrameFormat) -> Update:
+	facefusion.globals.temp_frame_format = temp_frame_format
+	return gradio.update(value = temp_frame_format)
+
+
+def update_temp_frame_quality(temp_frame_quality : int) -> Update:
+	facefusion.globals.temp_frame_quality = temp_frame_quality
+	return gradio.update(value = temp_frame_quality)

facefusion/uis/components/trim_frame.py

@@ -0,0 +1,66 @@
+from time import sleep
+from typing import Any, Dict, Tuple, Optional
+
+import gradio
+
+import facefusion.globals
+from facefusion import wording
+from facefusion.capturer import get_video_frame_total
+from facefusion.uis import core as ui
+from facefusion.uis.typing import Update
+from facefusion.utilities import is_video
+
+TRIM_FRAME_START_SLIDER : Optional[gradio.Slider] = None
+TRIM_FRAME_END_SLIDER : Optional[gradio.Slider] = None
+
+
+def render() -> None:
+	global TRIM_FRAME_START_SLIDER
+	global TRIM_FRAME_END_SLIDER
+
+	with gradio.Box():
+		trim_frame_start_slider_args : Dict[str, Any] = {
+			'label': wording.get('trim_frame_start_slider_label'),
+			'value': facefusion.globals.trim_frame_start,
+			'step': 1,
+			'visible': False
+		}
+		trim_frame_end_slider_args : Dict[str, Any] = {
+			'label': wording.get('trim_frame_end_slider_label'),
+			'value': facefusion.globals.trim_frame_end,
+			'step': 1,
+			'visible': False
+		}
+		if is_video(facefusion.globals.target_path):
+			video_frame_total = get_video_frame_total(facefusion.globals.target_path)
+			trim_frame_start_slider_args['maximum'] = video_frame_total
+			trim_frame_start_slider_args['visible'] = True
+			trim_frame_end_slider_args['value'] = video_frame_total
+			trim_frame_end_slider_args['maximum'] = video_frame_total
+			trim_frame_end_slider_args['visible'] = True
+		with gradio.Row():
+			TRIM_FRAME_START_SLIDER = gradio.Slider(**trim_frame_start_slider_args)
+			TRIM_FRAME_END_SLIDER = gradio.Slider(**trim_frame_end_slider_args)
+
+
+def listen() -> None:
+	target_file = ui.get_component('target_file')
+	if target_file:
+		target_file.change(remote_update, outputs = [ TRIM_FRAME_START_SLIDER, TRIM_FRAME_END_SLIDER ])
+	TRIM_FRAME_START_SLIDER.change(lambda value : update_number('trim_frame_start', int(value)), inputs = TRIM_FRAME_START_SLIDER, outputs = TRIM_FRAME_START_SLIDER)
+	TRIM_FRAME_END_SLIDER.change(lambda value : update_number('trim_frame_end', int(value)), inputs = TRIM_FRAME_END_SLIDER, outputs = TRIM_FRAME_END_SLIDER)
+
+
+def remote_update() -> Tuple[Update, Update]:
+	sleep(0.1)
+	if is_video(facefusion.globals.target_path):
+		video_frame_total = get_video_frame_total(facefusion.globals.target_path)
+		facefusion.globals.trim_frame_start = 0
+		facefusion.globals.trim_frame_end = video_frame_total
+		return gradio.update(value = 0, maximum = video_frame_total, visible = True), gradio.update(value = video_frame_total, maximum = video_frame_total, visible = True)
+	return gradio.update(value = None, maximum = None, visible = False), gradio.update(value = None, maximum = None, visible = False)
+
+
+def update_number(name : str, value : int) -> Update:
+	setattr(facefusion.globals, name, value)
+	return gradio.update(value = value)

facefusion/uis/core.py

@@ -0,0 +1,67 @@
+from typing import Dict, Optional, Any
+import importlib
+import sys
+import cv2
+import gradio
+
+import facefusion.globals
+from facefusion import metadata, wording
+from facefusion.typing import Frame
+from facefusion.uis.typing import Component, ComponentName
+
+COMPONENTS: Dict[ComponentName, Component] = {}
+UI_LAYOUT_METHODS =\
+[
+	'pre_check',
+	'render',
+	'listen'
+]
+
+
+def launch() -> None:
+	with gradio.Blocks(theme = get_theme(), title = metadata.get('name') + ' ' + metadata.get('version')) as ui:
+		for ui_layout in facefusion.globals.ui_layouts:
+			ui_layout_module = load_ui_layout_module(ui_layout)
+			ui_layout_module.pre_check()
+			ui_layout_module.render()
+			ui_layout_module.listen()
+	ui.launch(show_api = False)
+
+
+def load_ui_layout_module(ui_layout : str) -> Any:
+	try:
+		ui_layout_module = importlib.import_module('facefusion.uis.layouts.' + ui_layout)
+		for method_name in UI_LAYOUT_METHODS:
+			if not hasattr(ui_layout_module, method_name):
+				raise NotImplementedError
+	except ModuleNotFoundError:
+		sys.exit(wording.get('ui_layout_not_loaded').format(ui_layout = ui_layout))
+	except NotImplementedError:
+		sys.exit(wording.get('ui_layout_not_implemented').format(ui_layout = ui_layout))
+	return ui_layout_module
+
+
+def get_theme() -> gradio.Theme:
+	return gradio.themes.Soft(
+		primary_hue = gradio.themes.colors.red,
+		secondary_hue = gradio.themes.colors.gray,
+		font = gradio.themes.GoogleFont('Inter')
+	).set(
+		background_fill_primary = '*neutral_50',
+		block_label_text_size = '*text_sm',
+		block_title_text_size = '*text_sm'
+	)
+
+
+def get_component(name: ComponentName) -> Optional[Component]:
+	if name in COMPONENTS:
+		return COMPONENTS[name]
+	return None
+
+
+def register_component(name: ComponentName, component: Component) -> None:
+	COMPONENTS[name] = component
+
+
+def normalize_frame(frame : Frame) -> Frame:
+	return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

facefusion/uis/layouts/benchmark.py

@@ -0,0 +1,37 @@
+import gradio
+
+from facefusion.uis.components import about, processors, execution, benchmark
+from facefusion.utilities import conditional_download
+
+
+def pre_check() -> bool:
+	conditional_download('.assets/examples',
+	[
+		'https://huggingface.co/facefusion/examples/resolve/main/source.jpg',
+		'https://huggingface.co/facefusion/examples/resolve/main/target-240p.mp4',
+		'https://huggingface.co/facefusion/examples/resolve/main/target-360p.mp4',
+		'https://huggingface.co/facefusion/examples/resolve/main/target-540p.mp4',
+		'https://huggingface.co/facefusion/examples/resolve/main/target-720p.mp4',
+		'https://huggingface.co/facefusion/examples/resolve/main/target-1080p.mp4',
+		'https://huggingface.co/facefusion/examples/resolve/main/target-1440p.mp4',
+		'https://huggingface.co/facefusion/examples/resolve/main/target-2160p.mp4'
+	])
+	return True
+
+
+def render() -> gradio.Blocks:
+	with gradio.Blocks() as layout:
+		with gradio.Row():
+			with gradio.Column(scale = 2):
+				about.render()
+				processors.render()
+				execution.render()
+			with gradio.Column(scale= 5):
+				benchmark.render()
+	return layout
+
+
+def listen() -> None:
+	processors.listen()
+	execution.listen()
+	benchmark.listen()

facefusion/uis/layouts/default.py

@@ -0,0 +1,44 @@
+import gradio
+
+from facefusion.uis.components import about, processors, execution, temp_frame, settings, source, target, preview, trim_frame, face_analyser, face_selector, output_settings, output
+
+
+def pre_check() -> bool:
+	return True
+
+
+def render() -> gradio.Blocks:
+	with gradio.Blocks() as layout:
+		with gradio.Row():
+			with gradio.Column(scale = 2):
+				about.render()
+				processors.render()
+				execution.render()
+				temp_frame.render()
+				settings.render()
+			with gradio.Column(scale = 2):
+				source.render()
+				target.render()
+				output_settings.render()
+				output.render()
+			with gradio.Column(scale = 3):
+				preview.render()
+				trim_frame.render()
+				face_selector.render()
+				face_analyser.render()
+	return layout
+
+
+def listen() -> None:
+	processors.listen()
+	execution.listen()
+	settings.listen()
+	temp_frame.listen()
+	source.listen()
+	target.listen()
+	preview.listen()
+	trim_frame.listen()
+	face_selector.listen()
+	face_analyser.listen()
+	output_settings.listen()
+	output.listen()

facefusion/uis/typing.py

@@ -0,0 +1,18 @@
+from typing import Literal, Dict, Any
+import gradio
+
+Component = gradio.File or gradio.Image or gradio.Video or gradio.Slider
+ComponentName = Literal\
+[
+	'source_file',
+	'target_file',
+	'preview_frame_slider',
+	'face_recognition_dropdown',
+	'reference_face_position_gallery',
+	'reference_face_distance_slider',
+	'face_analyser_direction_dropdown',
+	'face_analyser_age_dropdown',
+	'face_analyser_gender_dropdown',
+	'frame_processors_checkbox_group'
+]
+Update = Dict[Any, Any]