Next (#107)

* Allow passing the onnxruntime to install.py

* Fix CI

* Disallow none execution providers in the UI

* Use CV2 to detect fps

* Respect trim on videos with audio

* Respect trim on videos with audio (finally)

* Implement caching to speed up preview and webcam

* Define webcam UI and webcam performance

* Remove layout from components

* Add primary buttons

* Extract benchmark and webcam settings

* Introduce compact UI settings

* Caching for IO and **** prediction

* Caching for IO and **** prediction

* Introduce face analyser caching

* Fix some typing

* Improve setup for benchmark

* Clear image cache via post process

* Fix settings in UI, Simplify restore_audio() using shortest

* Select resolution and fps via webcam ui

* Introduce read_static_image() to stop caching temp images

* Use DirectShow under Windows

* Multi-threading for webcam

* Fix typing

* Refactor frame processor

* Refactor webcam processing

* Avoid warnings due capture.isOpened()

* Resume downloads (#110)

* Introduce resumable downloads

* Fix CURL commands

* Break execution_settings into pieces

* Cosmetic changes on cv2 webcam

* Update Gradio

* Move face cache to own file

* Uniform namings for threading

* Fix sorting of get_temp_frame_paths(), extend get_temp_frames_pattern()

* Minor changes from the review

* Looks stable to tme

* Update the disclaimer

* Update the disclaimer

* Update the disclaimer

README.md

@@ -24,7 +24,7 @@ Read the [installation](https://docs.facefusion.io/installation) now.
 Usage
 -----
 
-Run the program as needed.
+Run the command:
 
 ```
 python run.py [options]
@@ -64,11 +64,11 @@ python run.py [options]
 Disclaimer
 ----------
 
-This software is meant to be a productive contribution to the rapidly growing AI-generated media industry. It will help artists with tasks such as animating a custom character or using the character as a model for clothing etc.
+We acknowledge the unethical potential of FaceFusion and are resolutely dedicated to establishing safeguards against such misuse. This program has been engineered to abstain from processing inappropriate content such as nudity, graphic content and sensitive material.
 
-The developers of this software are aware of its possible unethical applications and are committed to take preventative measures against them. It has a built-in check which prevents the program from working on inappropriate media including but not limited to nudity, graphic content, sensitive material such as war footage etc. We will continue to develop this project in the positive direction while adhering to law and ethics. This project may be shut down or include watermarks on the output if requested by law.
+It is important to note that we maintain a strong stance against any type of pornographic nature and do not collaborate with any websites promoting the unauthorized use of our software.
 
-Users of this software are expected to use this software responsibly while abiding the local law. If face of a real person is being used, users are suggested to get consent from the concerned person and clearly mention that it is a deepfake when posting content online. Developers of this software will not be responsible for actions of end-users.
+Users who seek to engage in such activities will face consequences, including being banned from our community. We reserve the right to report developers on GitHub who distribute unlocked forks of our software at any time.
 
 
 Documentation

facefusion/choices.py

@@ -8,3 +8,4 @@ face_analyser_age : List[FaceAnalyserAge] = [ 'child', 'teen', 'adult', 'senior'
 face_analyser_gender : List[FaceAnalyserGender] = [ 'male', 'female' ]
 temp_frame_format : List[TempFrameFormat] = [ 'jpg', 'png' ]
 output_video_encoder : List[OutputVideoEncoder] = [ 'libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc' ]
+

facefusion/face_analyser.py

@@ -1,13 +1,14 @@
-import threading
 from typing import Any, Optional, List
+import threading
 import insightface
 import numpy
 
 import facefusion.globals
+from facefusion.face_cache import get_faces_cache, set_faces_cache
 from facefusion.typing import Frame, Face, FaceAnalyserDirection, FaceAnalyserAge, FaceAnalyserGender
 
 FACE_ANALYSER = None
-THREAD_LOCK = threading.Lock()
+THREAD_LOCK : threading.Lock = threading.Lock()
 
 
 def get_face_analyser() -> Any:
@@ -38,7 +39,12 @@ def get_one_face(frame : Frame, position : int = 0) -> Optional[Face]:
 
 def get_many_faces(frame : Frame) -> List[Face]:
 	try:
-		faces = get_face_analyser().get(frame)
+		faces_cache = get_faces_cache(frame)
+		if faces_cache:
+			faces = faces_cache
+		else:
+			faces = get_face_analyser().get(frame)
+			set_faces_cache(frame, faces)
 		if facefusion.globals.face_analyser_direction:
 			faces = sort_by_direction(faces, facefusion.globals.face_analyser_direction)
 		if facefusion.globals.face_analyser_age:
@@ -100,7 +106,3 @@ def filter_by_gender(faces : List[Face], gender : FaceAnalyserGender) -> List[Fa
 		if face['gender'] == 0 and gender == 'female':
 			filter_faces.append(face)
 	return filter_faces
-
-
-def get_faces_total(frame : Frame) -> int:
-	return len(get_many_faces(frame))

facefusion/face_cache.py

@@ -0,0 +1,29 @@
+from typing import Optional, List, Dict
+import hashlib
+
+from facefusion.typing import Frame, Face
+
+FACES_CACHE : Dict[str, List[Face]] = {}
+
+
+def get_faces_cache(frame : Frame) -> Optional[List[Face]]:
+	frame_hash = create_frame_hash(frame)
+	if frame_hash in FACES_CACHE:
+		return FACES_CACHE[frame_hash]
+	return None
+
+
+def set_faces_cache(frame : Frame, faces : List[Face]) -> None:
+	frame_hash = create_frame_hash(frame)
+	if frame_hash:
+		FACES_CACHE[frame_hash] = faces
+
+
+def clear_faces_cache() -> None:
+	global FACES_CACHE
+
+	FACES_CACHE = {}
+
+
+def create_frame_hash(frame : Frame) -> Optional[str]:
+	return hashlib.sha256(frame.tobytes()).hexdigest() if frame is not None else None

facefusion/globals.py

@@ -1,6 +1,6 @@
 from typing import List, Optional
 
-from facefusion.typing import FaceRecognition, FaceAnalyserDirection, FaceAnalyserAge, FaceAnalyserGender, TempFrameFormat
+from facefusion.typing import FaceRecognition, FaceAnalyserDirection, FaceAnalyserAge, FaceAnalyserGender, TempFrameFormat, OutputVideoEncoder
 
 source_path : Optional[str] = None
 target_path : Optional[str] = None
@@ -23,7 +23,7 @@ trim_frame_end : Optional[int] = None
 temp_frame_format : Optional[TempFrameFormat] = None
 temp_frame_quality : Optional[int] = None
 output_image_quality : Optional[int] = None
-output_video_encoder : Optional[str] = None
+output_video_encoder : Optional[OutputVideoEncoder] = None
 output_video_quality : Optional[int] = None
 max_memory : Optional[int] = None
 execution_providers : List[str] = []

facefusion/installer.py

@@ -1,4 +1,5 @@
 from typing import Dict, Tuple
+import argparse
 import os
 import sys
 import subprocess
@@ -8,7 +9,7 @@ subprocess.call([ 'pip', 'install' , 'inquirer', '-q' ])
 
 import inquirer
 
-from facefusion import wording
+from facefusion import metadata, wording
 
 ONNXRUNTIMES : Dict[str, Tuple[str, str]] =\
 {
@@ -22,27 +23,38 @@ ONNXRUNTIMES : Dict[str, Tuple[str, str]] =\
 
 
 def run() -> None:
-	answers : Dict[str, str] = inquirer.prompt(
-	[
-		inquirer.List(
-			'onnxruntime_key',
-			message = wording.get('select_onnxruntime_install'),
-			choices = list(ONNXRUNTIMES.keys())
-		)
-	])
+	program = argparse.ArgumentParser(formatter_class = lambda prog: argparse.HelpFormatter(prog, max_help_position = 120))
+	program.add_argument('--onnxruntime', help = wording.get('onnxruntime_help'), dest = 'onnxruntime', choices = ONNXRUNTIMES.keys())
+	program.add_argument('-v', '--version', version = metadata.get('name') + ' ' + metadata.get('version'), action = 'version')
+	args = program.parse_args()
+
+	if args.onnxruntime:
+		answers =\
+		{
+			'onnxruntime': args.onnxruntime
+		}
+	else:
+		answers = inquirer.prompt(
+		[
+			inquirer.List(
+				'onnxruntime',
+				message = wording.get('onnxruntime_help'),
+				choices = list(ONNXRUNTIMES.keys())
+			)
+		])
 
 	if answers is not None:
-		onnxruntime_key = answers['onnxruntime_key']
-		onnxruntime_name, onnxruntime_version = ONNXRUNTIMES[onnxruntime_key]
+		onnxruntime = answers['onnxruntime']
+		onnxruntime_name, onnxruntime_version = ONNXRUNTIMES[onnxruntime]
 		python_id = 'cp' + str(sys.version_info.major) + str(sys.version_info.minor)
 		subprocess.call([ 'pip', 'uninstall', 'torch', '-y' ])
-		if onnxruntime_key == 'cuda':
+		if onnxruntime == 'cuda':
 			subprocess.call([ 'pip', 'install', '-r', 'requirements.txt', '--extra-index-url', 'https://download.pytorch.org/whl/cu118' ])
 		else:
 			subprocess.call([ 'pip', 'install', '-r', 'requirements.txt' ])
-		if onnxruntime_key != 'cpu':
+		if onnxruntime != 'cpu':
 			subprocess.call([ 'pip', 'uninstall', 'onnxruntime', onnxruntime_name, '-y' ])
-		if onnxruntime_key != 'coreml-silicon':
+		if onnxruntime != 'coreml-silicon':
 			subprocess.call([ 'pip', 'install', onnxruntime_name + '==' + onnxruntime_version ])
 		elif python_id in [ 'cp39', 'cp310', 'cp311' ]:
 			wheel_name = '-'.join([ 'onnxruntime_silicon', onnxruntime_version, python_id, python_id, 'macosx_12_0_arm64.whl' ])

facefusion/metadata.py

@@ -2,7 +2,7 @@ METADATA =\
 {
 	'name': 'FaceFusion',
 	'description': 'Next generation face swapper and enhancer',
-	'version': '1.1.0',
+	'version': '1.2.0',
 	'license': 'MIT',
 	'author': 'Henry Ruhs',
 	'url': 'https://facefusion.io'

facefusion/predictor.py

@@ -1,4 +1,5 @@
 import threading
+from functools import lru_cache
 import numpy
 import opennsfw2
 from PIL import Image
@@ -7,7 +8,7 @@ from keras import Model
 from facefusion.typing import Frame
 
 PREDICTOR = None
-THREAD_LOCK = threading.Lock()
+THREAD_LOCK : threading.Lock = threading.Lock()
 MAX_PROBABILITY = 0.75
 
 
@@ -34,10 +35,12 @@ def predict_frame(target_frame : Frame) -> bool:
 	return probability > MAX_PROBABILITY
 
 
-def predict_image(target_path : str) -> bool:
-	return opennsfw2.predict_image(target_path) > MAX_PROBABILITY
+@lru_cache(maxsize = None)
+def predict_image(image_path : str) -> bool:
+	return opennsfw2.predict_image(image_path) > MAX_PROBABILITY
 
 
-def predict_video(target_path : str) -> bool:
-	_, probabilities = opennsfw2.predict_video_frames(video_path = target_path, frame_interval = 100)
+@lru_cache(maxsize = None)
+def predict_video(video_path : str) -> bool:
+	_, probabilities = opennsfw2.predict_video_frames(video_path = video_path, frame_interval = 25)
 	return any(probability > MAX_PROBABILITY for probability in probabilities)

facefusion/processors/frame/core.py

@@ -57,16 +57,19 @@ def clear_frame_processors_modules() -> None:
 	FRAME_PROCESSORS_MODULES = []
 
 
-def multi_process_frame(source_path : str, temp_frame_paths : List[str], process_frames: Callable[[str, List[str], Any], None], update: Callable[[], None]) -> None:
-	with ThreadPoolExecutor(max_workers = facefusion.globals.execution_thread_count) as executor:
-		futures = []
-		queue = create_queue(temp_frame_paths)
-		queue_per_future = max(len(temp_frame_paths) // facefusion.globals.execution_thread_count * facefusion.globals.execution_queue_count, 1)
-		while not queue.empty():
-			future = executor.submit(process_frames, source_path, pick_queue(queue, queue_per_future), update)
-			futures.append(future)
-		for future in as_completed(futures):
-			future.result()
+def multi_process_frames(source_path : str, temp_frame_paths : List[str], process_frames : Callable[[str, List[str], Callable[[], None]], None]) -> None:
+	progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
+	with tqdm(total = len(temp_frame_paths), desc = wording.get('processing'), unit = 'frame', dynamic_ncols = True, bar_format = progress_bar_format) as progress:
+		with ThreadPoolExecutor(max_workers = facefusion.globals.execution_thread_count) as executor:
+			futures = []
+			queue_temp_frame_paths : Queue[str] = create_queue(temp_frame_paths)
+			queue_per_future = max(len(temp_frame_paths) // facefusion.globals.execution_thread_count * facefusion.globals.execution_queue_count, 1)
+			while not queue_temp_frame_paths.empty():
+				payload_temp_frame_paths = pick_queue(queue_temp_frame_paths, queue_per_future)
+				future = executor.submit(process_frames, source_path, payload_temp_frame_paths, lambda: update_progress(progress))
+				futures.append(future)
+			for future_done in as_completed(futures):
+				future_done.result()
 
 
 def create_queue(temp_frame_paths : List[str]) -> Queue[str]:
@@ -84,13 +87,6 @@ def pick_queue(queue : Queue[str], queue_per_future : int) -> List[str]:
 	return queues
 
 
-def process_video(source_path : str, frame_paths : List[str], process_frames : Callable[[str, List[str], Any], None]) -> None:
-	progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
-	total = len(frame_paths)
-	with tqdm(total = total, desc = wording.get('processing'), unit = 'frame', dynamic_ncols = True, bar_format = progress_bar_format) as progress:
-		multi_process_frame(source_path, frame_paths, process_frames, lambda: update_progress(progress))
-
-
 def update_progress(progress : Any = None) -> None:
 	process = psutil.Process(os.getpid())
 	memory_usage = process.memory_info().rss / 1024 / 1024 / 1024

facefusion/processors/frame/modules/face_enhancer.py

@@ -1,5 +1,4 @@
 from typing import Any, List, Callable
-import cv2
 import threading
 from gfpgan.utils import GFPGANer
 
@@ -9,10 +8,11 @@ from facefusion.core import update_status
 from facefusion.face_analyser import get_many_faces
 from facefusion.typing import Frame, Face, ProcessMode
 from facefusion.utilities import conditional_download, resolve_relative_path, is_image, is_video
+from facefusion.vision import read_image, read_static_image, write_image
 
 FRAME_PROCESSOR = None
-THREAD_SEMAPHORE = threading.Semaphore()
-THREAD_LOCK = threading.Lock()
+THREAD_SEMAPHORE : threading.Semaphore = threading.Semaphore()
+THREAD_LOCK : threading.Lock = threading.Lock()
 NAME = 'FACEFUSION.FRAME_PROCESSOR.FACE_ENHANCER'
 
 
@@ -54,6 +54,7 @@ def pre_process(mode : ProcessMode) -> bool:
 
 def post_process() -> None:
 	clear_frame_processor()
+	read_static_image.cache_clear()
 
 
 def enhance_face(target_face : Face, temp_frame : Frame) -> Frame:
@@ -83,20 +84,19 @@ def process_frame(source_face : Face, reference_face : Face, temp_frame : Frame)
 	return temp_frame
 
 
-def process_frames(source_path : str, temp_frame_paths : List[str], update: Callable[[], None]) -> None:
+def process_frames(source_path : str, temp_frame_paths : List[str], update_progress: Callable[[], None]) -> None:
 	for temp_frame_path in temp_frame_paths:
-		temp_frame = cv2.imread(temp_frame_path)
+		temp_frame = read_image(temp_frame_path)
 		result_frame = process_frame(None, None, temp_frame)
-		cv2.imwrite(temp_frame_path, result_frame)
-		if update:
-			update()
+		write_image(temp_frame_path, result_frame)
+		update_progress()
 
 
 def process_image(source_path : str, target_path : str, output_path : str) -> None:
-	target_frame = cv2.imread(target_path)
+	target_frame = read_static_image(target_path)
 	result_frame = process_frame(None, None, target_frame)
-	cv2.imwrite(output_path, result_frame)
+	write_image(output_path, result_frame)
 
 
 def process_video(source_path : str, temp_frame_paths : List[str]) -> None:
-	facefusion.processors.frame.core.process_video(None, temp_frame_paths, process_frames)
+	facefusion.processors.frame.core.multi_process_frames(None, temp_frame_paths, process_frames)

facefusion/processors/frame/modules/face_swapper.py

@@ -1,5 +1,4 @@
 from typing import Any, List, Callable
-import cv2
 import insightface
 import threading
 
@@ -11,9 +10,10 @@ from facefusion.face_analyser import get_one_face, get_many_faces, find_similar_
 from facefusion.face_reference import get_face_reference, set_face_reference
 from facefusion.typing import Face, Frame, ProcessMode
 from facefusion.utilities import conditional_download, resolve_relative_path, is_image, is_video
+from facefusion.vision import read_image, read_static_image, write_image
 
 FRAME_PROCESSOR = None
-THREAD_LOCK = threading.Lock()
+THREAD_LOCK : threading.Lock = threading.Lock()
 NAME = 'FACEFUSION.FRAME_PROCESSOR.FACE_SWAPPER'
 
 
@@ -43,7 +43,7 @@ def pre_process(mode : ProcessMode) -> bool:
 	if not is_image(facefusion.globals.source_path):
 		update_status(wording.get('select_image_source') + wording.get('exclamation_mark'), NAME)
 		return False
-	elif not get_one_face(cv2.imread(facefusion.globals.source_path)):
+	elif not get_one_face(read_static_image(facefusion.globals.source_path)):
 		update_status(wording.get('no_source_face_detected') + wording.get('exclamation_mark'), NAME)
 		return False
 	if mode in [ 'output', 'preview' ] and not is_image(facefusion.globals.target_path) and not is_video(facefusion.globals.target_path):
@@ -56,6 +56,7 @@ def pre_process(mode : ProcessMode) -> bool:
 
 def post_process() -> None:
 	clear_frame_processor()
+	read_static_image.cache_clear()
 
 
 def swap_face(source_face : Face, target_face : Face, temp_frame : Frame) -> Frame:
@@ -76,32 +77,31 @@ def process_frame(source_face : Face, reference_face : Face, temp_frame : Frame)
 	return temp_frame
 
 
-def process_frames(source_path : str, temp_frame_paths : List[str], update: Callable[[], None]) -> None:
-	source_face = get_one_face(cv2.imread(source_path))
+def process_frames(source_path : str, temp_frame_paths : List[str], update_progress: Callable[[], None]) -> None:
+	source_face = get_one_face(read_static_image(source_path))
 	reference_face = get_face_reference() if 'reference' in facefusion.globals.face_recognition else None
 	for temp_frame_path in temp_frame_paths:
-		temp_frame = cv2.imread(temp_frame_path)
+		temp_frame = read_image(temp_frame_path)
 		result_frame = process_frame(source_face, reference_face, temp_frame)
-		cv2.imwrite(temp_frame_path, result_frame)
-		if update:
-			update()
+		write_image(temp_frame_path, result_frame)
+		update_progress()
 
 
 def process_image(source_path : str, target_path : str, output_path : str) -> None:
-	source_face = get_one_face(cv2.imread(source_path))
-	target_frame = cv2.imread(target_path)
+	source_face = get_one_face(read_static_image(source_path))
+	target_frame = read_static_image(target_path)
 	reference_face = get_one_face(target_frame, facefusion.globals.reference_face_position) if 'reference' in facefusion.globals.face_recognition else None
 	result_frame = process_frame(source_face, reference_face, target_frame)
-	cv2.imwrite(output_path, result_frame)
+	write_image(output_path, result_frame)
 
 
 def process_video(source_path : str, temp_frame_paths : List[str]) -> None:
 	conditional_set_face_reference(temp_frame_paths)
-	frame_processors.process_video(source_path, temp_frame_paths, process_frames)
+	frame_processors.multi_process_frames(source_path, temp_frame_paths, process_frames)
 
 
 def conditional_set_face_reference(temp_frame_paths : List[str]) -> None:
 	if 'reference' in facefusion.globals.face_recognition and not get_face_reference():
-		reference_frame = cv2.imread(temp_frame_paths[facefusion.globals.reference_frame_number])
+		reference_frame = read_static_image(temp_frame_paths[facefusion.globals.reference_frame_number])
 		reference_face = get_one_face(reference_frame, facefusion.globals.reference_face_position)
 		set_face_reference(reference_face)

facefusion/processors/frame/modules/frame_enhancer.py

@@ -1,5 +1,4 @@
 from typing import Any, List, Callable
-import cv2
 import threading
 from basicsr.archs.rrdbnet_arch import RRDBNet
 from realesrgan import RealESRGANer
@@ -10,10 +9,11 @@ from facefusion import wording, utilities
 from facefusion.core import update_status
 from facefusion.typing import Frame, Face, ProcessMode
 from facefusion.utilities import conditional_download, resolve_relative_path
+from facefusion.vision import read_image, read_static_image, write_image
 
 FRAME_PROCESSOR = None
-THREAD_SEMAPHORE = threading.Semaphore()
-THREAD_LOCK = threading.Lock()
+THREAD_SEMAPHORE : threading.Semaphore = threading.Semaphore()
+THREAD_LOCK : threading.Lock = threading.Lock()
 NAME = 'FACEFUSION.FRAME_PROCESSOR.FRAME_ENHANCER'
 
 
@@ -63,6 +63,7 @@ def pre_process(mode : ProcessMode) -> bool:
 
 def post_process() -> None:
 	clear_frame_processor()
+	read_static_image.cache_clear()
 
 
 def enhance_frame(temp_frame : Frame) -> Frame:
@@ -75,20 +76,19 @@ def process_frame(source_face : Face, reference_face : Face, temp_frame : Frame)
 	return enhance_frame(temp_frame)
 
 
-def process_frames(source_path : str, temp_frame_paths : List[str], update: Callable[[], None]) -> None:
+def process_frames(source_path : str, temp_frame_paths : List[str], update_progress: Callable[[], None]) -> None:
 	for temp_frame_path in temp_frame_paths:
-		temp_frame = cv2.imread(temp_frame_path)
+		temp_frame = read_image(temp_frame_path)
 		result_frame = process_frame(None, None, temp_frame)
-		cv2.imwrite(temp_frame_path, result_frame)
-		if update:
-			update()
+		write_image(temp_frame_path, result_frame)
+		update_progress()
 
 
 def process_image(source_path : str, target_path : str, output_path : str) -> None:
-	target_frame = cv2.imread(target_path)
+	target_frame = read_static_image(target_path)
 	result = process_frame(None, None, target_frame)
-	cv2.imwrite(output_path, result)
+	write_image(output_path, result)
 
 
 def process_video(source_path : str, temp_frame_paths : List[str]) -> None:
-	frame_processors.process_video(None, temp_frame_paths, process_frames)
+	frame_processors.multi_process_frames(None, temp_frame_paths, process_frames)

facefusion/uis/choices.py

@@ -0,0 +1,7 @@
+from typing import List
+
+from facefusion.uis.typing import WebcamMode
+
+settings : List[str] = [ 'keep-fps', 'keep-temp', 'skip-audio' ]
+webcam_mode : List[WebcamMode] = [ 'inline', 'stream_udp', 'stream_v4l2' ]
+webcam_resolution : List[str] = [ '320x240', '640x480', '1280x720', '1920x1080', '2560x1440', '3840x2160' ]

facefusion/uis/components/about.py

@@ -9,5 +9,4 @@ 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>')
+	ABOUT_HTML = gradio.HTML('<center><a href="' + metadata.get('url') + '">' + metadata.get('name') + ' ' + metadata.get('version') + '</a></center>')

facefusion/uis/components/benchmark.py

@@ -6,17 +6,19 @@ import gradio
 
 import facefusion.globals
 from facefusion import wording
+from facefusion.face_analyser import get_face_analyser
+from facefusion.face_cache import clear_faces_cache
+from facefusion.processors.frame.core import get_frame_processors_modules
 from facefusion.vision import count_video_frame_total
 from facefusion.core import limit_resources, conditional_process
 from facefusion.uis.typing import Update
+from facefusion.uis import core as ui
 from facefusion.utilities import normalize_output_path, clear_temp
 
 BENCHMARK_RESULTS_DATAFRAME : Optional[gradio.Dataframe] = None
-BENCHMARK_RUNS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
-BENCHMARK_CYCLES_SLIDER : Optional[gradio.Button] = None
 BENCHMARK_START_BUTTON : Optional[gradio.Button] = None
 BENCHMARK_CLEAR_BUTTON : Optional[gradio.Button] = None
-BENCHMARKS : Dict[str, str] = \
+BENCHMARKS : Dict[str, str] =\
 {
 	'240p': '.assets/examples/target-240p.mp4',
 	'360p': '.assets/examples/target-360p.mp4',
@@ -30,77 +32,68 @@ BENCHMARKS : Dict[str, str] = \
 
 def render() -> None:
 	global BENCHMARK_RESULTS_DATAFRAME
-	global BENCHMARK_RUNS_CHECKBOX_GROUP
-	global BENCHMARK_CYCLES_SLIDER
 	global BENCHMARK_START_BUTTON
 	global BENCHMARK_CLEAR_BUTTON
 
-	with gradio.Box():
-		BENCHMARK_RESULTS_DATAFRAME = gradio.Dataframe(
-			label = wording.get('benchmark_results_dataframe_label'),
-			headers =
-			[
-				'target_path',
-				'benchmark_cycles',
-				'average_run',
-				'fastest_run',
-				'slowest_run',
-				'relative_fps'
-			],
-			row_count = len(BENCHMARKS),
-			datatype =
-			[
-				'str',
-				'number',
-				'number',
-				'number',
-				'number',
-				'number'
-			]
-		)
-	with gradio.Box():
-		BENCHMARK_RUNS_CHECKBOX_GROUP = gradio.CheckboxGroup(
-			label = wording.get('benchmark_runs_checkbox_group_label'),
-			value = list(BENCHMARKS.keys()),
-			choices = list(BENCHMARKS.keys())
-		)
-		BENCHMARK_CYCLES_SLIDER = gradio.Slider(
-			label = wording.get('benchmark_cycles_slider_label'),
-			minimum = 1,
-			step = 1,
-			value = 3,
-			maximum = 10
-		)
-	with gradio.Row():
-		BENCHMARK_START_BUTTON = gradio.Button(wording.get('start_button_label'))
-		BENCHMARK_CLEAR_BUTTON = gradio.Button(wording.get('clear_button_label'))
+	BENCHMARK_RESULTS_DATAFRAME = gradio.Dataframe(
+		label = wording.get('benchmark_results_dataframe_label'),
+		headers =
+		[
+			'target_path',
+			'benchmark_cycles',
+			'average_run',
+			'fastest_run',
+			'slowest_run',
+			'relative_fps'
+		],
+		datatype =
+		[
+			'str',
+			'number',
+			'number',
+			'number',
+			'number',
+			'number'
+		]
+	)
+	BENCHMARK_START_BUTTON = gradio.Button(
+		value = wording.get('start_button_label'),
+		variant = 'primary'
+	)
+	BENCHMARK_CLEAR_BUTTON = gradio.Button(
+		value = wording.get('clear_button_label')
+	)
 
 
 def listen() -> None:
-	BENCHMARK_RUNS_CHECKBOX_GROUP.change(update_benchmark_runs, inputs = BENCHMARK_RUNS_CHECKBOX_GROUP, outputs = BENCHMARK_RUNS_CHECKBOX_GROUP)
-	BENCHMARK_START_BUTTON.click(start, inputs = [ BENCHMARK_RUNS_CHECKBOX_GROUP, BENCHMARK_CYCLES_SLIDER ], outputs = BENCHMARK_RESULTS_DATAFRAME)
+	benchmark_runs_checkbox_group = ui.get_component('benchmark_runs_checkbox_group')
+	benchmark_cycles_slider = ui.get_component('benchmark_cycles_slider')
+	if benchmark_runs_checkbox_group and benchmark_cycles_slider:
+		BENCHMARK_START_BUTTON.click(start, inputs = [ benchmark_runs_checkbox_group, benchmark_cycles_slider ], outputs = BENCHMARK_RESULTS_DATAFRAME)
 	BENCHMARK_CLEAR_BUTTON.click(clear, outputs = BENCHMARK_RESULTS_DATAFRAME)
 
 
-def update_benchmark_runs(benchmark_runs : List[str]) -> Update:
-	return gradio.update(value = benchmark_runs)
-
-
 def start(benchmark_runs : List[str], benchmark_cycles : int) -> Generator[List[Any], None, None]:
 	facefusion.globals.source_path = '.assets/examples/source.jpg'
 	target_paths = [ BENCHMARKS[benchmark_run] for benchmark_run in benchmark_runs if benchmark_run in BENCHMARKS ]
 	benchmark_results = []
 	if target_paths:
-		warm_up(BENCHMARKS['240p'])
+		pre_process()
 		for target_path in target_paths:
 			benchmark_results.append(benchmark(target_path, benchmark_cycles))
 			yield benchmark_results
+		post_process()
 
 
-def warm_up(target_path : str) -> None:
-	facefusion.globals.target_path = target_path
-	facefusion.globals.output_path = normalize_output_path(facefusion.globals.source_path, facefusion.globals.target_path, tempfile.gettempdir())
-	conditional_process()
+def pre_process() -> None:
+	limit_resources()
+	get_face_analyser()
+	for frame_processor_module in get_frame_processors_modules(facefusion.globals.frame_processors):
+		frame_processor_module.get_frame_processor()
+
+
+def post_process() -> None:
+	clear_faces_cache()
 
 
 def benchmark(target_path : str, benchmark_cycles : int) -> List[Any]:
@@ -111,7 +104,6 @@ def benchmark(target_path : str, benchmark_cycles : int) -> List[Any]:
 		facefusion.globals.output_path = normalize_output_path(facefusion.globals.source_path, facefusion.globals.target_path, tempfile.gettempdir())
 		video_frame_total = count_video_frame_total(facefusion.globals.target_path)
 		start_time = time.perf_counter()
-		limit_resources()
 		conditional_process()
 		end_time = time.perf_counter()
 		process_time = end_time - start_time

facefusion/uis/components/benchmark_settings.py

@@ -0,0 +1,38 @@
+from typing import Optional, List
+import gradio
+
+from facefusion import wording
+from facefusion.uis.typing import Update
+from facefusion.uis import core as ui
+from facefusion.uis.components.benchmark import BENCHMARKS
+
+BENCHMARK_RUNS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
+BENCHMARK_CYCLES_SLIDER : Optional[gradio.Button] = None
+
+
+def render() -> None:
+	global BENCHMARK_RUNS_CHECKBOX_GROUP
+	global BENCHMARK_CYCLES_SLIDER
+
+	BENCHMARK_RUNS_CHECKBOX_GROUP = gradio.CheckboxGroup(
+		label = wording.get('benchmark_runs_checkbox_group_label'),
+		value = list(BENCHMARKS.keys()),
+		choices = list(BENCHMARKS.keys())
+	)
+	BENCHMARK_CYCLES_SLIDER = gradio.Slider(
+		label = wording.get('benchmark_cycles_slider_label'),
+		minimum = 1,
+		step = 1,
+		value = 3,
+		maximum = 10
+	)
+	ui.register_component('benchmark_runs_checkbox_group', BENCHMARK_RUNS_CHECKBOX_GROUP)
+	ui.register_component('benchmark_cycles_slider', BENCHMARK_CYCLES_SLIDER)
+
+
+def listen() -> None:
+	BENCHMARK_RUNS_CHECKBOX_GROUP.change(update_benchmark_runs, inputs = BENCHMARK_RUNS_CHECKBOX_GROUP, outputs = BENCHMARK_RUNS_CHECKBOX_GROUP)
+
+
+def update_benchmark_runs(benchmark_runs : List[str]) -> Update:
+	return gradio.update(value = benchmark_runs)

facefusion/uis/components/execution.py

@@ -10,55 +10,26 @@ 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
-		)
+	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)
+	)
 
 
 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()
+	if not execution_providers:
+		execution_providers = encode_execution_providers(onnxruntime.get_available_providers())
 	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/execution_queue_count.py

@@ -0,0 +1,29 @@
+from typing import Optional
+import gradio
+
+import facefusion.globals
+from facefusion import wording
+from facefusion.uis.typing import Update
+
+EXECUTION_QUEUE_COUNT_SLIDER : Optional[gradio.Slider] = None
+
+
+def render() -> None:
+	global EXECUTION_QUEUE_COUNT_SLIDER
+
+	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_QUEUE_COUNT_SLIDER.change(update_execution_queue_count, inputs = EXECUTION_QUEUE_COUNT_SLIDER, outputs = EXECUTION_QUEUE_COUNT_SLIDER)
+
+
+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/execution_thread_count.py

@@ -0,0 +1,29 @@
+from typing import Optional
+import gradio
+
+import facefusion.globals
+from facefusion import wording
+from facefusion.uis.typing import Update
+
+EXECUTION_THREAD_COUNT_SLIDER : Optional[gradio.Slider] = None
+
+
+def render() -> None:
+	global EXECUTION_THREAD_COUNT_SLIDER
+
+	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
+	)
+
+
+def listen() -> None:
+	EXECUTION_THREAD_COUNT_SLIDER.change(update_execution_thread_count, inputs = EXECUTION_THREAD_COUNT_SLIDER, outputs = EXECUTION_THREAD_COUNT_SLIDER)
+
+
+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)

facefusion/uis/components/face_analyser.py

@@ -18,26 +18,24 @@ def render() -> None:
 	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)
+	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:

facefusion/uis/components/face_selector.py

@@ -1,12 +1,11 @@
 from typing import List, Optional, Tuple, Any, Dict
 
-import cv2
 import gradio
 
 import facefusion.choices
 import facefusion.globals
 from facefusion import wording
-from facefusion.vision import get_video_frame, normalize_frame_color
+from facefusion.vision import get_video_frame, normalize_frame_color, read_static_image
 from facefusion.face_analyser import get_many_faces
 from facefusion.face_reference import clear_face_reference
 from facefusion.typing import Frame, FaceRecognition
@@ -24,38 +23,37 @@ def render() -> None:
 	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)
+	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 = read_static_image(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:
@@ -106,7 +104,7 @@ def update_face_reference_position(reference_face_position : int = 0) -> Update:
 	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)
+		reference_frame = read_static_image(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)

facefusion/uis/components/limit_resources.py

@@ -11,13 +11,12 @@ MAX_MEMORY_SLIDER : Optional[gradio.Slider] = None
 def render() -> None:
 	global MAX_MEMORY_SLIDER
 
-	with gradio.Box():
-		MAX_MEMORY_SLIDER = gradio.Slider(
-			label = wording.get('max_memory_slider_label'),
-			minimum = 0,
-			maximum = 128,
-			step = 1
-		)
+	MAX_MEMORY_SLIDER = gradio.Slider(
+		label = wording.get('max_memory_slider_label'),
+		minimum = 0,
+		maximum = 128,
+		step = 1
+	)
 
 
 def listen() -> None:

facefusion/uis/components/output.py

@@ -22,23 +22,25 @@ def render() -> None:
 	global OUTPUT_START_BUTTON
 	global OUTPUT_CLEAR_BUTTON
 
-	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')
-			)
-			OUTPUT_PATH_TEXTBOX = gradio.Textbox(
-				label = wording.get('output_path_textbox_label'),
-				value = facefusion.globals.output_path or tempfile.gettempdir(),
-				max_lines = 1
-			)
-	with gradio.Row():
-		OUTPUT_START_BUTTON = gradio.Button(wording.get('start_button_label'))
-		OUTPUT_CLEAR_BUTTON = gradio.Button(wording.get('clear_button_label'))
+	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')
+	)
+	OUTPUT_PATH_TEXTBOX = gradio.Textbox(
+		label = wording.get('output_path_textbox_label'),
+		value = facefusion.globals.output_path or tempfile.gettempdir(),
+		max_lines = 1
+	)
+	OUTPUT_START_BUTTON = gradio.Button(
+		value = wording.get('start_button_label'),
+		variant = 'primary'
+	)
+	OUTPUT_CLEAR_BUTTON = gradio.Button(
+		value = wording.get('clear_button_label'),
+	)
 
 
 def listen() -> None:

facefusion/uis/components/output_settings.py

@@ -19,25 +19,24 @@ def render() -> None:
 	global OUTPUT_VIDEO_ENCODER_DROPDOWN
 	global OUTPUT_VIDEO_QUALITY_SLIDER
 
-	with gradio.Box():
-		OUTPUT_IMAGE_QUALITY_SLIDER = gradio.Slider(
-			label = wording.get('output_image_quality_slider_label'),
-			value = facefusion.globals.output_image_quality,
-			step = 1,
-			visible = is_image(facefusion.globals.target_path)
-		)
-		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,
-			visible = is_video(facefusion.globals.target_path)
-		)
-		OUTPUT_VIDEO_QUALITY_SLIDER = gradio.Slider(
-			label = wording.get('output_video_quality_slider_label'),
-			value = facefusion.globals.output_video_quality,
-			step = 1,
-			visible = is_video(facefusion.globals.target_path)
-		)
+	OUTPUT_IMAGE_QUALITY_SLIDER = gradio.Slider(
+		label = wording.get('output_image_quality_slider_label'),
+		value = facefusion.globals.output_image_quality,
+		step = 1,
+		visible = is_image(facefusion.globals.target_path)
+	)
+	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,
+		visible = is_video(facefusion.globals.target_path)
+	)
+	OUTPUT_VIDEO_QUALITY_SLIDER = gradio.Slider(
+		label = wording.get('output_video_quality_slider_label'),
+		value = facefusion.globals.output_video_quality,
+		step = 1,
+		visible = is_video(facefusion.globals.target_path)
+	)
 
 
 def listen() -> None:

facefusion/uis/components/preview.py

@@ -4,12 +4,12 @@ import gradio
 
 import facefusion.globals
 from facefusion import wording
-from facefusion.vision import get_video_frame, count_video_frame_total, normalize_frame_color, resize_frame_dimension
+from facefusion.vision import get_video_frame, count_video_frame_total, normalize_frame_color, resize_frame_dimension, read_static_image
 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.typing import Frame, Face
 from facefusion.uis import core as ui
 from facefusion.uis.typing import ComponentName, Update
 from facefusion.utilities import is_video, is_image
@@ -22,32 +22,34 @@ 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 = process_preview_frame(target_frame)
-			preview_image_args['value'] = normalize_frame_color(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 = process_preview_frame(temp_frame)
-			preview_image_args['value'] = normalize_frame_color(preview_frame)
-			preview_image_args['visible'] = True
-			preview_frame_slider_args['value'] = facefusion.globals.reference_frame_number
-			preview_frame_slider_args['maximum'] = count_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)
+	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
+	}
+	conditional_set_face_reference()
+	source_face = get_one_face(read_static_image(facefusion.globals.source_path))
+	reference_face = get_face_reference() if 'reference' in facefusion.globals.face_recognition else None
+	if is_image(facefusion.globals.target_path):
+		target_frame = read_static_image(facefusion.globals.target_path)
+		preview_frame = process_preview_frame(source_face, reference_face, target_frame)
+		preview_image_args['value'] = normalize_frame_color(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 = process_preview_frame(source_face, reference_face, temp_frame)
+		preview_image_args['value'] = normalize_frame_color(preview_frame)
+		preview_image_args['visible'] = True
+		preview_frame_slider_args['value'] = facefusion.globals.reference_frame_number
+		preview_frame_slider_args['maximum'] = count_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:
@@ -90,17 +92,18 @@ def listen() -> None:
 
 
 def update_preview_image(frame_number : int = 0) -> Update:
+	conditional_set_face_reference()
+	source_face = get_one_face(read_static_image(facefusion.globals.source_path))
+	reference_face = get_face_reference() if 'reference' in facefusion.globals.face_recognition else None
 	if is_image(facefusion.globals.target_path):
-		conditional_set_face_reference()
-		target_frame = cv2.imread(facefusion.globals.target_path)
-		preview_frame = process_preview_frame(target_frame)
+		target_frame = read_static_image(facefusion.globals.target_path)
+		preview_frame = process_preview_frame(source_face, reference_face, target_frame)
 		preview_frame = normalize_frame_color(preview_frame)
 		return gradio.update(value = preview_frame)
 	if is_video(facefusion.globals.target_path):
-		conditional_set_face_reference()
 		facefusion.globals.reference_frame_number = frame_number
 		temp_frame = get_video_frame(facefusion.globals.target_path, facefusion.globals.reference_frame_number)
-		preview_frame = process_preview_frame(temp_frame)
+		preview_frame = process_preview_frame(source_face, reference_face, temp_frame)
 		preview_frame = normalize_frame_color(preview_frame)
 		return gradio.update(value = preview_frame)
 	return gradio.update(value = None)
@@ -116,11 +119,9 @@ def update_preview_frame_slider(frame_number : int = 0) -> Update:
 	return gradio.update(value = None, maximum = None, visible = False)
 
 
-def process_preview_frame(temp_frame : Frame) -> Frame:
+def process_preview_frame(source_face : Face, reference_face : Face, 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
-	reference_face = get_face_reference() if 'reference' in facefusion.globals.face_recognition else None
 	temp_frame = resize_frame_dimension(temp_frame, 480)
 	for frame_processor in facefusion.globals.frame_processors:
 		frame_processor_module = load_frame_processor_module(frame_processor)

facefusion/uis/components/processors.py

@@ -14,13 +14,12 @@ 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)
+	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:

facefusion/uis/components/settings.py

@@ -1,41 +1,37 @@
-from typing import Optional
+from typing import Optional, List
 import gradio
 
 import facefusion.globals
 from facefusion import wording
+from facefusion.uis import choices
 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
+SETTINGS_CHECKBOX_GROUP : Optional[gradio.Checkboxgroup] = None
 
 
 def render() -> None:
-	global KEEP_FPS_CHECKBOX
-	global KEEP_TEMP_CHECKBOX
-	global SKIP_AUDIO_CHECKBOX
+	global SETTINGS_CHECKBOX_GROUP
 
-	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
-		)
+	value = []
+	if facefusion.globals.keep_fps:
+		value.append('keep-fps')
+	if facefusion.globals.keep_temp:
+		value.append('keep-temp')
+	if facefusion.globals.skip_audio:
+		value.append('skip-audio')
+	SETTINGS_CHECKBOX_GROUP = gradio.Checkboxgroup(
+		label = wording.get('settings_checkbox_group_label'),
+		choices = choices.settings,
+		value = value
+	)
 
 
 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)
+	SETTINGS_CHECKBOX_GROUP.change(update, inputs = SETTINGS_CHECKBOX_GROUP, outputs = SETTINGS_CHECKBOX_GROUP)
 
 
-def update_checkbox(name : str, value: bool) -> Update:
-	setattr(facefusion.globals, name, value)
-	return gradio.update(value = value)
+def update(settings : List[str]) -> Update:
+	facefusion.globals.keep_fps = 'keep-fps' in settings
+	facefusion.globals.keep_temp = 'keep-temp' in settings
+	facefusion.globals.skip_audio = 'skip-audio' in settings
+	return gradio.update(value = settings)

facefusion/uis/components/source.py

@@ -15,25 +15,24 @@ 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
-		)
-		SOURCE_IMAGE = gradio.Image(
-			value = SOURCE_FILE.value['name'] if is_source_image else None,
-			visible = is_source_image,
-			show_label = False
-		)
-		ui.register_component('source_image', SOURCE_IMAGE)
+	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
+	)
+	SOURCE_IMAGE = gradio.Image(
+		value = SOURCE_FILE.value['name'] if is_source_image else None,
+		visible = is_source_image,
+		show_label = False
+	)
+	ui.register_component('source_image', SOURCE_IMAGE)
 
 
 def listen() -> None:

facefusion/uis/components/target.py

@@ -18,33 +18,32 @@ def render() -> None:
 	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_image', TARGET_IMAGE)
-		ui.register_component('target_video', TARGET_VIDEO)
+	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_image', TARGET_IMAGE)
+	ui.register_component('target_video', TARGET_VIDEO)
 
 
 def listen() -> None:

facefusion/uis/components/temp_frame.py

@@ -17,19 +17,18 @@ 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,
-			visible = is_video(facefusion.globals.target_path)
-		)
-		TEMP_FRAME_QUALITY_SLIDER = gradio.Slider(
-			label = wording.get('temp_frame_quality_slider_label'),
-			value = facefusion.globals.temp_frame_quality,
-			step = 1,
-			visible = is_video(facefusion.globals.target_path)
-		)
+	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,
+		visible = is_video(facefusion.globals.target_path)
+	)
+	TEMP_FRAME_QUALITY_SLIDER = gradio.Slider(
+		label = wording.get('temp_frame_quality_slider_label'),
+		value = facefusion.globals.temp_frame_quality,
+		step = 1,
+		visible = is_video(facefusion.globals.target_path)
+	)
 
 
 def listen() -> None:

facefusion/uis/components/trim_frame.py

@@ -16,30 +16,28 @@ 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'),
-			'step': 1,
-			'visible': False
-		}
-		trim_frame_end_slider_args : Dict[str, Any] =\
-		{
-			'label': wording.get('trim_frame_end_slider_label'),
-			'step': 1,
-			'visible': False
-		}
-		if is_video(facefusion.globals.target_path):
-			video_frame_total = count_video_frame_total(facefusion.globals.target_path)
-			trim_frame_start_slider_args['value'] = facefusion.globals.trim_frame_start or 0
-			trim_frame_start_slider_args['maximum'] = video_frame_total
-			trim_frame_start_slider_args['visible'] = True
-			trim_frame_end_slider_args['value'] = facefusion.globals.trim_frame_end or 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)
+	trim_frame_start_slider_args : Dict[str, Any] =\
+	{
+		'label': wording.get('trim_frame_start_slider_label'),
+		'step': 1,
+		'visible': False
+	}
+	trim_frame_end_slider_args : Dict[str, Any] =\
+	{
+		'label': wording.get('trim_frame_end_slider_label'),
+		'step': 1,
+		'visible': False
+	}
+	if is_video(facefusion.globals.target_path):
+		video_frame_total = count_video_frame_total(facefusion.globals.target_path)
+		trim_frame_start_slider_args['value'] = facefusion.globals.trim_frame_start or 0
+		trim_frame_start_slider_args['maximum'] = video_frame_total
+		trim_frame_start_slider_args['visible'] = True
+		trim_frame_end_slider_args['value'] = facefusion.globals.trim_frame_end or video_frame_total
+		trim_frame_end_slider_args['maximum'] = video_frame_total
+		trim_frame_end_slider_args['visible'] = True
+	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:

facefusion/uis/components/webcam.py

@@ -1,87 +1,114 @@
-from typing import Optional, Generator
+from typing import Optional, Generator, Deque
+from concurrent.futures import ThreadPoolExecutor
+from collections import deque
 import os
+import platform
 import subprocess
 import cv2
 import gradio
+from tqdm import tqdm
 
 import facefusion.globals
 from facefusion import wording
-from facefusion.typing import Frame
+from facefusion.typing import Frame, Face
 from facefusion.face_analyser import get_one_face
 from facefusion.processors.frame.core import load_frame_processor_module
+from facefusion.uis import core as ui
 from facefusion.uis.typing import StreamMode, WebcamMode, Update
 from facefusion.utilities import open_ffmpeg
-from facefusion.vision import normalize_frame_color
+from facefusion.vision import normalize_frame_color, read_static_image
 
 WEBCAM_IMAGE : Optional[gradio.Image] = None
-WEBCAM_MODE_RADIO : Optional[gradio.Radio] = None
 WEBCAM_START_BUTTON : Optional[gradio.Button] = None
 WEBCAM_STOP_BUTTON : Optional[gradio.Button] = None
 
 
 def render() -> None:
 	global WEBCAM_IMAGE
-	global WEBCAM_MODE_RADIO
 	global WEBCAM_START_BUTTON
 	global WEBCAM_STOP_BUTTON
 
 	WEBCAM_IMAGE = gradio.Image(
 		label = wording.get('webcam_image_label')
 	)
-	WEBCAM_MODE_RADIO = gradio.Radio(
-		label = wording.get('webcam_mode_radio_label'),
-		choices = [ 'inline', 'stream_udp', 'stream_v4l2' ],
-		value = 'inline'
+	WEBCAM_START_BUTTON = gradio.Button(
+		value = wording.get('start_button_label'),
+		variant = 'primary'
+	)
+	WEBCAM_STOP_BUTTON = gradio.Button(
+		value = wording.get('stop_button_label')
 	)
-	WEBCAM_START_BUTTON = gradio.Button(wording.get('start_button_label'))
-	WEBCAM_STOP_BUTTON = gradio.Button(wording.get('stop_button_label'))
 
 
 def listen() -> None:
-	start_event = WEBCAM_START_BUTTON.click(start, inputs = WEBCAM_MODE_RADIO, outputs = WEBCAM_IMAGE)
-	WEBCAM_MODE_RADIO.change(update, outputs = WEBCAM_IMAGE, cancels = start_event)
-	WEBCAM_STOP_BUTTON.click(None, cancels = start_event)
+	start_event = None
+	webcam_mode_radio = ui.get_component('webcam_mode_radio')
+	webcam_resolution_dropdown = ui.get_component('webcam_resolution_dropdown')
+	webcam_fps_slider = ui.get_component('webcam_fps_slider')
+	if webcam_mode_radio and webcam_resolution_dropdown and webcam_fps_slider:
+		start_event = WEBCAM_START_BUTTON.click(start, inputs = [ webcam_mode_radio, webcam_resolution_dropdown, webcam_fps_slider ], outputs = WEBCAM_IMAGE)
+		webcam_mode_radio.change(stop, outputs = WEBCAM_IMAGE, cancels = start_event)
+		webcam_resolution_dropdown.change(stop, outputs = WEBCAM_IMAGE, cancels = start_event)
+		webcam_fps_slider.change(stop, outputs = WEBCAM_IMAGE, cancels = start_event)
+	WEBCAM_STOP_BUTTON.click(stop, cancels = start_event)
+	source_image = ui.get_component('source_image')
+	if source_image:
+		for method in [ 'upload', 'change', 'clear' ]:
+			getattr(source_image, method)(stop, cancels = start_event)
 
 
-def update() -> Update:
+def start(mode: WebcamMode, resolution: str, fps: float) -> Generator[Frame, None, None]:
+	facefusion.globals.face_recognition = 'many'
+	source_face = get_one_face(read_static_image(facefusion.globals.source_path))
+	stream = None
+	if mode == 'stream_udp':
+		stream = open_stream('udp', resolution, fps)
+	if mode == 'stream_v4l2':
+		stream = open_stream('v4l2', resolution, fps)
+	capture = capture_webcam(resolution, fps)
+	if capture.isOpened():
+		for capture_frame in multi_process_capture(source_face, capture):
+			if stream is not None:
+				stream.stdin.write(capture_frame.tobytes())
+			yield normalize_frame_color(capture_frame)
+
+
+def multi_process_capture(source_face: Face, capture : cv2.VideoCapture) -> Generator[Frame, None, None]:
+	progress = tqdm(desc = wording.get('processing'), unit = 'frame', dynamic_ncols = True)
+	with ThreadPoolExecutor(max_workers = facefusion.globals.execution_thread_count) as executor:
+		futures = []
+		deque_capture_frames : Deque[Frame] = deque()
+		while True:
+			_, capture_frame = capture.read()
+			future = executor.submit(process_stream_frame, source_face, capture_frame)
+			futures.append(future)
+			for future_done in [ future for future in futures if future.done() ]:
+				capture_frame = future_done.result()
+				deque_capture_frames.append(capture_frame)
+				futures.remove(future_done)
+			while deque_capture_frames:
+				yield deque_capture_frames.popleft()
+				progress.update()
+
+
+def stop() -> Update:
 	return gradio.update(value = None)
 
 
-def start(webcam_mode : WebcamMode) -> Generator[Frame, None, None]:
-	if webcam_mode == 'inline':
-		yield from start_inline()
-	if webcam_mode == 'stream_udp':
-		yield from start_stream('udp')
-	if webcam_mode == 'stream_v4l2':
-		yield from start_stream('v4l2')
+def capture_webcam(resolution : str, fps : float) -> cv2.VideoCapture:
+	width, height = resolution.split('x')
+	if platform.system().lower() == 'windows':
+		capture = cv2.VideoCapture(0, cv2.CAP_DSHOW)
+	else:
+		capture = cv2.VideoCapture(0)
+	capture.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG')) # type: ignore[attr-defined]
+	capture.set(cv2.CAP_PROP_FRAME_WIDTH, int(width))
+	capture.set(cv2.CAP_PROP_FRAME_HEIGHT, int(height))
+	capture.set(cv2.CAP_PROP_FPS, fps)
+	return capture
 
 
-def start_inline() -> Generator[Frame, None, None]:
-	facefusion.globals.face_recognition = 'many'
-	capture = cv2.VideoCapture(0)
-	if capture.isOpened():
-		while True:
-			_, temp_frame = capture.read()
-			temp_frame = process_stream_frame(temp_frame)
-			if temp_frame is not None:
-				yield normalize_frame_color(temp_frame)
-
-
-def start_stream(mode : StreamMode) -> Generator[None, None, None]:
-	facefusion.globals.face_recognition = 'many'
-	capture = cv2.VideoCapture(0)
-	ffmpeg_process = open_stream(mode)
-	if capture.isOpened():
-		while True:
-			_, frame = capture.read()
-			temp_frame = process_stream_frame(frame)
-			if temp_frame is not None:
-				ffmpeg_process.stdin.write(temp_frame.tobytes())
-				yield normalize_frame_color(temp_frame)
-
-
-def process_stream_frame(temp_frame : Frame) -> Frame:
-	source_face = get_one_face(cv2.imread(facefusion.globals.source_path)) if facefusion.globals.source_path else None
+def process_stream_frame(source_face : Face, temp_frame : Frame) -> Frame:
 	for frame_processor in facefusion.globals.frame_processors:
 		frame_processor_module = load_frame_processor_module(frame_processor)
 		if frame_processor_module.pre_process('stream'):
@@ -93,8 +120,8 @@ def process_stream_frame(temp_frame : Frame) -> Frame:
 	return temp_frame
 
 
-def open_stream(mode : StreamMode) -> subprocess.Popen[bytes]:
-	commands = [ '-f', 'rawvideo', '-pix_fmt', 'bgr24', '-s', '640x480', '-r', '30', '-i', '-' ]
+def open_stream(mode : StreamMode, resolution : str, fps : float) -> subprocess.Popen[bytes]:
+	commands = [ '-f', 'rawvideo', '-pix_fmt', 'bgr24', '-s', resolution, '-r', str(fps), '-i', '-' ]
 	if mode == 'udp':
 		commands.extend([ '-b:v', '2000k', '-f', 'mpegts', 'udp://localhost:27000?pkt_size=1316' ])
 	if mode == 'v4l2':

facefusion/uis/components/webcam_settings.py

@@ -0,0 +1,42 @@
+from typing import Optional
+import gradio
+
+from facefusion import wording
+from facefusion.uis import choices
+from facefusion.uis import core as ui
+from facefusion.uis.typing import Update
+
+WEBCAM_MODE_RADIO : Optional[gradio.Radio] = None
+WEBCAM_RESOLUTION_DROPDOWN : Optional[gradio.Dropdown] = None
+WEBCAM_FPS_SLIDER : Optional[gradio.Slider] = None
+
+
+def render() -> None:
+	global WEBCAM_MODE_RADIO
+	global WEBCAM_RESOLUTION_DROPDOWN
+	global WEBCAM_FPS_SLIDER
+
+	WEBCAM_MODE_RADIO = gradio.Radio(
+		label = wording.get('webcam_mode_radio_label'),
+		choices = choices.webcam_mode,
+		value = 'inline'
+	)
+	WEBCAM_RESOLUTION_DROPDOWN = gradio.Dropdown(
+		label = wording.get('webcam_resolution_dropdown'),
+		choices = choices.webcam_resolution,
+		value = choices.webcam_resolution[0]
+	)
+	WEBCAM_FPS_SLIDER = gradio.Slider(
+		label = wording.get('webcam_fps_slider'),
+		minimum = 1,
+		maximum = 60,
+		step = 1,
+		value = 25
+	)
+	ui.register_component('webcam_mode_radio', WEBCAM_MODE_RADIO)
+	ui.register_component('webcam_resolution_dropdown', WEBCAM_RESOLUTION_DROPDOWN)
+	ui.register_component('webcam_fps_slider', WEBCAM_FPS_SLIDER)
+
+
+def update() -> Update:
+	return gradio.update(value = None)

facefusion/uis/layouts/benchmark.py

@@ -1,6 +1,6 @@
 import gradio
 
-from facefusion.uis.components import about, processors, execution, limit_resources, benchmark
+from facefusion.uis.components import about, processors, execution, execution_thread_count, execution_queue_count, limit_resources, benchmark_settings, benchmark
 from facefusion.utilities import conditional_download
 
 
@@ -27,19 +27,31 @@ def render() -> gradio.Blocks:
 	with gradio.Blocks() as layout:
 		with gradio.Row():
 			with gradio.Column(scale = 2):
-				about.render()
-				processors.render()
-				execution.render()
-				limit_resources.render()
+				with gradio.Box():
+					about.render()
+				with gradio.Blocks():
+					processors.render()
+				with gradio.Blocks():
+					execution.render()
+					execution_thread_count.render()
+					execution_queue_count.render()
+				with gradio.Blocks():
+					limit_resources.render()
+				with gradio.Blocks():
+					benchmark_settings.render()
 			with gradio.Column(scale= 5):
-				benchmark.render()
+				with gradio.Blocks():
+					benchmark.render()
 	return layout
 
 
 def listen() -> None:
 	processors.listen()
 	execution.listen()
+	execution_thread_count.listen()
+	execution_queue_count.listen()
 	limit_resources.listen()
+	benchmark_settings.listen()
 	benchmark.listen()
 
 

facefusion/uis/layouts/default.py

@@ -1,6 +1,6 @@
 import gradio
 
-from facefusion.uis.components import about, processors, execution, limit_resources, temp_frame, output_settings, settings, source, target, preview, trim_frame, face_analyser, face_selector, output
+from facefusion.uis.components import about, processors, execution, execution_thread_count, execution_queue_count, limit_resources, temp_frame, output_settings, settings, source, target, preview, trim_frame, face_analyser, face_selector, output
 
 
 def pre_check() -> bool:
@@ -15,28 +15,46 @@ def render() -> gradio.Blocks:
 	with gradio.Blocks() as layout:
 		with gradio.Row():
 			with gradio.Column(scale = 2):
-				about.render()
-				processors.render()
-				execution.render()
-				limit_resources.render()
-				temp_frame.render()
-				output_settings.render()
-				settings.render()
+				with gradio.Box():
+					about.render()
+				with gradio.Blocks():
+					processors.render()
+				with gradio.Blocks():
+					execution.render()
+					execution_thread_count.render()
+					execution_queue_count.render()
+				with gradio.Blocks():
+					limit_resources.render()
+				with gradio.Blocks():
+					temp_frame.render()
+				with gradio.Blocks():
+					output_settings.render()
+				with gradio.Blocks():
+					settings.render()
 			with gradio.Column(scale = 2):
-				source.render()
-				target.render()
-				output.render()
+				with gradio.Blocks():
+					source.render()
+				with gradio.Blocks():
+					target.render()
+				with gradio.Blocks():
+					output.render()
 			with gradio.Column(scale = 3):
-				preview.render()
-				trim_frame.render()
-				face_selector.render()
-				face_analyser.render()
+				with gradio.Blocks():
+					preview.render()
+				with gradio.Row():
+					trim_frame.render()
+				with gradio.Blocks():
+					face_selector.render()
+				with gradio.Row():
+					face_analyser.render()
 	return layout
 
 
 def listen() -> None:
 	processors.listen()
 	execution.listen()
+	execution_thread_count.listen()
+	execution_queue_count.listen()
 	limit_resources.listen()
 	temp_frame.listen()
 	output_settings.listen()

facefusion/uis/layouts/webcam.py

@@ -1,6 +1,6 @@
 import gradio
 
-from facefusion.uis.components import about, processors, execution, limit_resources, source, webcam
+from facefusion.uis.components import about, processors, execution, execution_thread_count, webcam_settings, source, webcam
 
 
 def pre_check() -> bool:
@@ -15,20 +15,27 @@ def render() -> gradio.Blocks:
 	with gradio.Blocks() as layout:
 		with gradio.Row():
 			with gradio.Column(scale = 2):
-				about.render()
-				processors.render()
-				execution.render()
-				limit_resources.render()
-				source.render()
+				with gradio.Box():
+					about.render()
+				with gradio.Blocks():
+					processors.render()
+				with gradio.Blocks():
+					execution.render()
+					execution_thread_count.render()
+				with gradio.Blocks():
+					webcam_settings.render()
+				with gradio.Blocks():
+					source.render()
 			with gradio.Column(scale = 5):
-				webcam.render()
+				with gradio.Blocks():
+					webcam.render()
 	return layout
 
 
 def listen() -> None:
 	processors.listen()
 	execution.listen()
-	limit_resources.listen()
+	execution_thread_count.listen()
 	source.listen()
 	webcam.listen()
 

facefusion/uis/typing.py

@@ -14,8 +14,13 @@ ComponentName = Literal\
 	'face_analyser_direction_dropdown',
 	'face_analyser_age_dropdown',
 	'face_analyser_gender_dropdown',
-	'frame_processors_checkbox_group'
+	'frame_processors_checkbox_group',
+	'benchmark_runs_checkbox_group',
+	'benchmark_cycles_slider',
+	'webcam_mode_radio',
+	'webcam_resolution_dropdown',
+	'webcam_fps_slider'
 ]
 WebcamMode = Literal[ 'inline', 'stream_udp', 'stream_v4l2' ]
-StreamMode = Literal['udp', 'v4l2']
+StreamMode = Literal[ 'udp', 'v4l2' ]
 Update = Dict[Any, Any]

facefusion/utilities.py

@@ -1,4 +1,3 @@
-import json
 from typing import List, Optional
 from pathlib import Path
 from tqdm import tqdm
@@ -15,9 +14,10 @@ import onnxruntime
 
 import facefusion.globals
 from facefusion import wording
+from facefusion.vision import detect_fps
 
 TEMP_DIRECTORY_PATH = os.path.join(tempfile.gettempdir(), 'facefusion')
-TEMP_OUTPUT_NAME = 'temp.mp4'
+TEMP_OUTPUT_VIDEO_NAME = 'temp.mp4'
 
 # monkey patch ssl
 if platform.system().lower() == 'darwin':
@@ -40,24 +40,11 @@ def open_ffmpeg(args : List[str]) -> subprocess.Popen[bytes]:
 	return subprocess.Popen(commands, stdin = subprocess.PIPE)
 
 
-def detect_fps(target_path : str) -> Optional[float]:
-	commands = [ 'ffprobe', '-v', 'error', '-select_streams', 'v:0', '-show_entries', 'stream=r_frame_rate', '-of', 'json', target_path ]
-	output = subprocess.check_output(commands).decode().strip()
-	try:
-		entries = json.loads(output)
-		for stream in entries.get('streams'):
-			numerator, denominator = map(int, stream.get('r_frame_rate').split('/'))
-			return numerator / denominator
-		return None
-	except (ValueError, ZeroDivisionError):
-		return None
-
-
 def extract_frames(target_path : str, fps : float) -> bool:
-	temp_directory_path = get_temp_directory_path(target_path)
 	temp_frame_compression = round(31 - (facefusion.globals.temp_frame_quality * 0.31))
 	trim_frame_start = facefusion.globals.trim_frame_start
 	trim_frame_end = facefusion.globals.trim_frame_end
+	temp_frames_pattern = get_temp_frames_pattern(target_path, '%04d')
 	commands = [ '-hwaccel', 'auto', '-i', target_path, '-q:v', str(temp_frame_compression), '-pix_fmt', 'rgb24' ]
 	if trim_frame_start is not None and trim_frame_end is not None:
 		commands.extend([ '-vf', 'trim=start_frame=' + str(trim_frame_start) + ':end_frame=' + str(trim_frame_end) + ',fps=' + str(fps) ])
@@ -67,7 +54,7 @@ def extract_frames(target_path : str, fps : float) -> bool:
 		commands.extend([ '-vf', 'trim=end_frame=' + str(trim_frame_end) + ',fps=' + str(fps) ])
 	else:
 		commands.extend([ '-vf', 'fps=' + str(fps) ])
-	commands.extend([os.path.join(temp_directory_path, '%04d.' + facefusion.globals.temp_frame_format)])
+	commands.extend([ '-vsync', '0', temp_frames_pattern ])
 	return run_ffmpeg(commands)
 
 
@@ -78,19 +65,19 @@ def compress_image(output_path : str) -> bool:
 
 
 def merge_video(target_path : str, fps : float) -> bool:
-	temp_output_path = get_temp_output_path(target_path)
-	temp_directory_path = get_temp_directory_path(target_path)
-	commands = [ '-hwaccel', 'auto', '-r', str(fps), '-i', os.path.join(temp_directory_path, '%04d.' + facefusion.globals.temp_frame_format), '-c:v', facefusion.globals.output_video_encoder ]
+	temp_output_video_path = get_temp_output_video_path(target_path)
+	temp_frames_pattern = get_temp_frames_pattern(target_path, '%04d')
+	commands = [ '-hwaccel', 'auto', '-r', str(fps), '-i', temp_frames_pattern, '-c:v', facefusion.globals.output_video_encoder ]
 	if facefusion.globals.output_video_encoder in [ 'libx264', 'libx265' ]:
 		output_video_compression = round(51 - (facefusion.globals.output_video_quality * 0.5))
 		commands.extend([ '-crf', str(output_video_compression) ])
-	if facefusion.globals.output_video_encoder in [ 'libvpx' ]:
+	if facefusion.globals.output_video_encoder in [ 'libvpx-vp9' ]:
 		output_video_compression = round(63 - (facefusion.globals.output_video_quality * 0.5))
 		commands.extend([ '-crf', str(output_video_compression) ])
 	if facefusion.globals.output_video_encoder in [ 'h264_nvenc', 'hevc_nvenc' ]:
 		output_video_compression = round(51 - (facefusion.globals.output_video_quality * 0.5))
 		commands.extend([ '-cq', str(output_video_compression) ])
-	commands.extend([ '-pix_fmt', 'yuv420p', '-vf', 'colorspace=bt709:iall=bt601-6-625', '-y', temp_output_path ])
+	commands.extend([ '-pix_fmt', 'yuv420p', '-colorspace', 'bt709', '-y', temp_output_video_path ])
 	return run_ffmpeg(commands)
 
 
@@ -98,27 +85,26 @@ def restore_audio(target_path : str, output_path : str) -> bool:
 	fps = detect_fps(target_path)
 	trim_frame_start = facefusion.globals.trim_frame_start
 	trim_frame_end = facefusion.globals.trim_frame_end
-	temp_output_path = get_temp_output_path(target_path)
-	commands = [ '-hwaccel', 'auto', '-i', temp_output_path, '-i', target_path ]
-	if trim_frame_start is None and trim_frame_end is None:
-		commands.extend([ '-c:a', 'copy' ])
-	else:
-		if trim_frame_start is not None:
-			start_time = trim_frame_start / fps
-			commands.extend([ '-ss', str(start_time) ])
-		else:
-			commands.extend([ '-ss', '0' ])
-		if trim_frame_end is not None:
-			end_time = trim_frame_end / fps
-			commands.extend([ '-to', str(end_time) ])
-		commands.extend([ '-c:a', 'aac' ])
-	commands.extend([ '-map', '0:v:0', '-map', '1:a:0', '-y', output_path ])
+	temp_output_video_path = get_temp_output_video_path(target_path)
+	commands = [ '-hwaccel', 'auto', '-i', temp_output_video_path ]
+	if trim_frame_start is not None:
+		start_time = trim_frame_start / fps
+		commands.extend([ '-ss', str(start_time) ])
+	if trim_frame_end is not None:
+		end_time = trim_frame_end / fps
+		commands.extend([ '-to', str(end_time) ])
+	commands.extend([ '-i', target_path, '-c',  'copy', '-map', '0:v:0', '-map', '1:a:0', '-shortest', '-y', output_path ])
 	return run_ffmpeg(commands)
 
 
 def get_temp_frame_paths(target_path : str) -> List[str]:
+	temp_frames_pattern = get_temp_frames_pattern(target_path, '*')
+	return sorted(glob.glob(temp_frames_pattern))
+
+
+def get_temp_frames_pattern(target_path : str, temp_frame_prefix : str) -> str:
 	temp_directory_path = get_temp_directory_path(target_path)
-	return glob.glob((os.path.join(glob.escape(temp_directory_path), '*.' + facefusion.globals.temp_frame_format)))
+	return os.path.join(temp_directory_path, temp_frame_prefix + '.' + facefusion.globals.temp_frame_format)
 
 
 def get_temp_directory_path(target_path : str) -> str:
@@ -126,9 +112,9 @@ def get_temp_directory_path(target_path : str) -> str:
 	return os.path.join(TEMP_DIRECTORY_PATH, target_name)
 
 
-def get_temp_output_path(target_path : str) -> str:
+def get_temp_output_video_path(target_path : str) -> str:
 	temp_directory_path = get_temp_directory_path(target_path)
-	return os.path.join(temp_directory_path, TEMP_OUTPUT_NAME)
+	return os.path.join(temp_directory_path, TEMP_OUTPUT_VIDEO_NAME)
 
 
 def normalize_output_path(source_path : Optional[str], target_path : Optional[str], output_path : Optional[str]) -> Optional[str]:
@@ -152,11 +138,11 @@ def create_temp(target_path : str) -> None:
 
 
 def move_temp(target_path : str, output_path : str) -> None:
-	temp_output_path = get_temp_output_path(target_path)
-	if is_file(temp_output_path):
+	temp_output_video_path = get_temp_output_video_path(target_path)
+	if is_file(temp_output_video_path):
 		if is_file(output_path):
 			os.remove(output_path)
-		shutil.move(temp_output_path, output_path)
+		shutil.move(temp_output_video_path, output_path)
 
 
 def clear_temp(target_path : str) -> None:
@@ -191,15 +177,29 @@ def is_video(video_path : str) -> bool:
 
 
 def conditional_download(download_directory_path : str, urls : List[str]) -> None:
-	if not os.path.exists(download_directory_path):
-		os.makedirs(download_directory_path)
 	for url in urls:
 		download_file_path = os.path.join(download_directory_path, os.path.basename(url))
-		if not os.path.exists(download_file_path):
-			request = urllib.request.urlopen(url) # type: ignore[attr-defined]
-			total = int(request.headers.get('Content-Length', 0))
-			with tqdm(total = total, desc = wording.get('downloading'), unit = 'B', unit_scale = True, unit_divisor = 1024) as progress:
-				urllib.request.urlretrieve(url, download_file_path, reporthook = lambda count, block_size, total_size: progress.update(block_size)) # type: ignore[attr-defined]
+		total = get_download_size(url)
+		if is_file(download_file_path):
+			initial = os.path.getsize(download_file_path)
+		else:
+			initial = 0
+		if initial < total:
+			with tqdm(total = total, initial = initial, desc = wording.get('downloading'), unit = 'B', unit_scale = True, unit_divisor = 1024) as progress:
+				subprocess.Popen([ 'curl', '--create-dirs', '--silent', '--location', '--continue-at', '-', '--output', download_file_path, url ])
+				current = initial
+				while current < total:
+					if is_file(download_file_path):
+						current = os.path.getsize(download_file_path)
+						progress.update(current - progress.n)
+
+
+def get_download_size(url : str) -> int:
+	response = urllib.request.urlopen(url) # type: ignore[attr-defined]
+	content_length = response.getheader('Content-Length')
+	if content_length:
+		return int(content_length)
+	return 0
 
 
 def resolve_relative_path(path : str) -> str:

facefusion/vision.py

@@ -1,27 +1,38 @@
 from typing import Optional
+from functools import lru_cache
 import cv2
 
 from facefusion.typing import Frame
 
 
 def get_video_frame(video_path : str, frame_number : int = 0) -> Optional[Frame]:
-	capture = cv2.VideoCapture(video_path)
-	if capture.isOpened():
-		frame_total = capture.get(cv2.CAP_PROP_FRAME_COUNT)
-		capture.set(cv2.CAP_PROP_POS_FRAMES, min(frame_total, frame_number - 1))
-		has_frame, frame = capture.read()
-		capture.release()
-		if has_frame:
-			return frame
+	if video_path:
+		capture = cv2.VideoCapture(video_path)
+		if capture.isOpened():
+			frame_total = capture.get(cv2.CAP_PROP_FRAME_COUNT)
+			capture.set(cv2.CAP_PROP_POS_FRAMES, min(frame_total, frame_number - 1))
+			has_frame, frame = capture.read()
+			capture.release()
+			if has_frame:
+				return frame
+	return None
+
+
+def detect_fps(video_path : str) -> Optional[float]:
+	if video_path:
+		capture = cv2.VideoCapture(video_path)
+		if capture.isOpened():
+			return capture.get(cv2.CAP_PROP_FPS)
 	return None
 
 
 def count_video_frame_total(video_path : str) -> int:
-	capture = cv2.VideoCapture(video_path)
-	if capture.isOpened():
-		video_frame_total = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
-		capture.release()
-		return video_frame_total
+	if video_path:
+		capture = cv2.VideoCapture(video_path)
+		if capture.isOpened():
+			video_frame_total = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
+			capture.release()
+			return video_frame_total
 	return 0
 
 
@@ -36,3 +47,20 @@ def resize_frame_dimension(frame : Frame, max_height : int) -> Frame:
 		max_width = int(width * scale)
 		frame = cv2.resize(frame, (max_width, max_height))
 	return frame
+
+
+@lru_cache(maxsize = 128)
+def read_static_image(image_path : str) -> Optional[Frame]:
+	return read_image(image_path)
+
+
+def read_image(image_path : str) -> Optional[Frame]:
+	if image_path:
+		return cv2.imread(image_path)
+	return None
+
+
+def write_image(image_path : str, frame : Frame) -> bool:
+	if image_path:
+		return cv2.imwrite(image_path, frame)
+	return False

facefusion/wording.py

@@ -1,8 +1,8 @@
 WORDING =\
 {
-	'select_onnxruntime_install': 'Select the onnxruntime to be installed',
 	'python_not_supported': 'Python version is not supported, upgrade to {version} or higher',
 	'ffmpeg_not_installed': 'FFMpeg is not installed',
+	'onnxruntime_help': 'select the onnxruntime to be installed',
 	'source_help': 'select a source image',
 	'target_help': 'select a target image or video',
 	'output_help': 'specify the output file or directory',
@@ -79,9 +79,7 @@ WORDING =\
 	'preview_image_label': 'PREVIEW',
 	'preview_frame_slider_label': 'PREVIEW FRAME',
 	'frame_processors_checkbox_group_label': 'FRAME PROCESSORS',
-	'keep_fps_checkbox_label': 'KEEP FPS',
-	'keep_temp_checkbox_label': 'KEEP TEMP',
-	'skip_audio_checkbox_label': 'SKIP AUDIO',
+	'settings_checkbox_group_label': 'SETTINGS',
 	'temp_frame_format_dropdown_label': 'TEMP FRAME FORMAT',
 	'temp_frame_quality_slider_label': 'TEMP FRAME QUALITY',
 	'trim_frame_start_slider_label': 'TRIM FRAME START',
@@ -90,6 +88,8 @@ WORDING =\
 	'target_file_label': 'TARGET',
 	'webcam_image_label': 'WEBCAM',
 	'webcam_mode_radio_label': 'WEBCAM MODE',
+	'webcam_resolution_dropdown': 'WEBCAM RESOLUTION',
+	'webcam_fps_slider': 'WEBCAM FPS',
 	'point': '.',
 	'comma': ',',
 	'colon': ':',

requirements.txt

@@ -1,5 +1,5 @@
 gfpgan==1.3.8
-gradio==3.42.0
+gradio==3.44.3
 insightface==0.7.3
 numpy==1.24.3
 onnx==1.14.1

tests/test_cli.py

@@ -1,4 +1,5 @@
 import subprocess
+import sys
 import pytest
 
 from facefusion import wording
@@ -16,7 +17,7 @@ def before_all() -> None:
 
 
 def test_image_to_image() -> None:
-	commands = [ 'python', 'run.py', '-s', '.assets/examples/source.jpg', '-t', '.assets/examples/target-1080p.jpg', '-o', '.assets/examples', '--headless' ]
+	commands = [ sys.executable, 'run.py', '-s', '.assets/examples/source.jpg', '-t', '.assets/examples/target-1080p.jpg', '-o', '.assets/examples', '--headless' ]
 	run = subprocess.run(commands, stdout = subprocess.PIPE)
 
 	assert run.returncode == 0
@@ -24,7 +25,7 @@ def test_image_to_image() -> None:
 
 
 def test_image_to_video() -> None:
-	commands = [ 'python', 'run.py', '-s', '.assets/examples/source.jpg', '-t', '.assets/examples/target-1080p.mp4', '-o', '.assets/examples', '--trim-frame-end', '10', '--headless' ]
+	commands = [ sys.executable, 'run.py', '-s', '.assets/examples/source.jpg', '-t', '.assets/examples/target-1080p.mp4', '-o', '.assets/examples', '--trim-frame-end', '10', '--headless' ]
 	run = subprocess.run(commands, stdout = subprocess.PIPE)
 
 	assert run.returncode == 0

tests/test_utilities.py

@@ -4,7 +4,7 @@ import subprocess
 import pytest
 
 import facefusion.globals
-from facefusion.utilities import conditional_download, detect_fps, extract_frames, create_temp, get_temp_directory_path, clear_temp, normalize_output_path, is_file, is_directory, is_image, is_video, encode_execution_providers, decode_execution_providers
+from facefusion.utilities import conditional_download, extract_frames, create_temp, get_temp_directory_path, clear_temp, normalize_output_path, is_file, is_directory, is_image, is_video, encode_execution_providers, decode_execution_providers
 
 
 @pytest.fixture(scope = 'module', autouse = True)
@@ -31,12 +31,6 @@ def before_each() -> None:
 	facefusion.globals.temp_frame_format = 'jpg'
 
 
-def test_detect_fps() -> None:
-	assert detect_fps('.assets/examples/target-240p-25fps.mp4') == 25.0
-	assert detect_fps('.assets/examples/target-240p-30fps.mp4') == 30.0
-	assert detect_fps('.assets/examples/target-240p-60fps.mp4') == 60.0
-
-
 def test_extract_frames() -> None:
 	target_paths =\
 	[

tests/test_vision.py

@@ -0,0 +1,49 @@
+import subprocess
+import pytest
+
+import facefusion.globals
+from facefusion.utilities import  conditional_download
+from facefusion.vision import get_video_frame, detect_fps, count_video_frame_total
+
+
+@pytest.fixture(scope = 'module', autouse = True)
+def before_all() -> None:
+	facefusion.globals.temp_frame_quality = 100
+	facefusion.globals.trim_frame_start = None
+	facefusion.globals.trim_frame_end = None
+	facefusion.globals.temp_frame_format = 'png'
+	conditional_download('.assets/examples',
+	[
+		'https://github.com/facefusion/facefusion-assets/releases/download/examples/source.jpg',
+		'https://github.com/facefusion/facefusion-assets/releases/download/examples/target-240p.mp4'
+	])
+	subprocess.run([ 'ffmpeg', '-i', '.assets/examples/target-240p.mp4', '-vf', 'fps=25', '.assets/examples/target-240p-25fps.mp4' ])
+	subprocess.run([ 'ffmpeg', '-i', '.assets/examples/target-240p.mp4', '-vf', 'fps=30', '.assets/examples/target-240p-30fps.mp4' ])
+	subprocess.run([ 'ffmpeg', '-i', '.assets/examples/target-240p.mp4', '-vf', 'fps=60', '.assets/examples/target-240p-60fps.mp4' ])
+
+
+@pytest.fixture(scope = 'function', autouse = True)
+def before_each() -> None:
+	facefusion.globals.trim_frame_start = None
+	facefusion.globals.trim_frame_end = None
+	facefusion.globals.temp_frame_quality = 90
+	facefusion.globals.temp_frame_format = 'jpg'
+
+
+def test_get_video_frame() -> None:
+	assert get_video_frame('.assets/examples/target-240p-25fps.mp4') is not None
+	assert get_video_frame('invalid') is None
+
+
+def test_detect_fps() -> None:
+	assert detect_fps('.assets/examples/target-240p-25fps.mp4') == 25.0
+	assert detect_fps('.assets/examples/target-240p-30fps.mp4') == 30.0
+	assert detect_fps('.assets/examples/target-240p-60fps.mp4') == 60.0
+	assert detect_fps('invalid') is None
+
+
+def test_count_video_frame_total() -> None:
+	assert count_video_frame_total('.assets/examples/target-240p-25fps.mp4') == 270
+	assert count_video_frame_total('.assets/examples/target-240p-30fps.mp4') == 324
+	assert count_video_frame_total('.assets/examples/target-240p-60fps.mp4') == 648
+	assert count_video_frame_total('invalid') == 0