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+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU Affero General Public License is a free, copyleft license for
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+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published
+    by the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU Affero General Public License for more details.
+
+    You should have received a copy of the GNU Affero General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If your software can interact with users remotely through a computer
+network, you should also make sure that it provides a way for users to
+get its source.  For example, if your program is a web application, its
+interface could display a "Source" link that leads users to an archive
+of the code.  There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.

app.py

@@ -0,0 +1,90 @@
+# -* coding:UTF-8 -*
+# !/usr/bin/env python
+import numpy as np
+import gradio as gr
+import roop.globals
+from roop.core import (
+    start,
+    decode_execution_providers,
+    suggest_max_memory,
+    suggest_execution_threads,
+)
+from roop.processors.frame.core import get_frame_processors_modules
+from roop.utilities import normalize_output_path
+import os
+from PIL import Image
+
+def swap_face(source_file, target_file, doFaceEnhancer):
+    source_path = "input.jpg"
+    target_path = "target.mp4"
+
+    # Save source image
+    source_image = Image.fromarray(source_file)
+    source_image.save(source_path)
+
+    # Copy the target video to the working directory
+    os.rename(target_file, target_path)
+
+    print("source_path: ", source_path)
+    print("target_path: ", target_path)
+
+    roop.globals.source_path = source_path
+    roop.globals.target_path = target_path
+    output_path = "output.mp4"
+    roop.globals.output_path = normalize_output_path(
+        roop.globals.source_path, roop.globals.target_path, output_path
+    )
+    if doFaceEnhancer:
+        roop.globals.frame_processors = ["face_swapper", "face_enhancer"]
+    else:
+        roop.globals.frame_processors = ["face_swapper"]
+    roop.globals.headless = True
+    roop.globals.keep_fps = True
+    roop.globals.keep_audio = True
+    roop.globals.keep_frames = False
+    roop.globals.many_faces = False
+    roop.globals.video_encoder = "libx264"
+    roop.globals.video_quality = 10
+    roop.globals.max_memory = suggest_max_memory()
+    roop.globals.execution_providers = decode_execution_providers(["cuda"])
+    roop.globals.execution_threads = suggest_execution_threads()
+
+    print(
+        "start process",
+        roop.globals.source_path,
+        roop.globals.target_path,
+        roop.globals.output_path,
+    )
+
+    for frame_processor in get_frame_processors_modules(
+        roop.globals.frame_processors
+    ):
+        if not frame_processor.pre_check():
+            return
+
+    start()
+    return output_path
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # Face Swap with Face Enhancer
+
+        Upload a source image and a target video, select whether to enable face enhancer, and click 'Submit' to swap faces in the video.
+        """
+    )
+
+    with gr.Row():
+        with gr.Column():
+            source_image = gr.Image(label="Source Image", type="numpy")
+            target_video = gr.Video(label="Target Video")
+            face_enhancer = gr.Checkbox(label="Enable Face Enhancer")
+            submit = gr.Button("Submit")
+
+        with gr.Column():
+            output_video = gr.Video(label="Output Video")
+
+    submit.click(swap_face, inputs=[source_image, target_video, face_enhancer], outputs=output_video)
+
+
+demo.launch()

mypy.ini

@@ -0,0 +1,7 @@
+[mypy]
+check_untyped_defs = True
+disallow_any_generics = True
+disallow_untyped_calls = True
+disallow_untyped_defs = True
+ignore_missing_imports = True
+strict_optional = False

requirements.txt

@@ -0,0 +1,23 @@
+--extra-index-url https://download.pytorch.org/whl/cu118
+
+numpy==1.23.5
+opencv-python==4.7.0.72
+onnx==1.14.0
+insightface==0.7.3
+psutil==5.9.5
+tk==0.1.0
+customtkinter==5.1.3
+pillow==9.5.0
+torch==2.0.1+cu118; sys_platform != 'darwin'
+torch==2.0.1; sys_platform == 'darwin'
+torchvision==0.15.2+cu118; sys_platform != 'darwin'
+torchvision==0.15.2; sys_platform == 'darwin'
+onnxruntime==1.15.0; sys_platform == 'darwin' and platform_machine != 'arm64'
+onnxruntime-silicon==1.13.1; sys_platform == 'darwin' and platform_machine == 'arm64'
+onnxruntime-gpu==1.15.0; sys_platform != 'darwin'
+tensorflow==2.13.0rc1; sys_platform == 'darwin'
+tensorflow==2.12.0; sys_platform != 'darwin'
+opennsfw2==0.10.2
+protobuf==4.23.2
+tqdm==4.65.0
+gfpgan==1.3.8

roop/capturer.py

@@ -0,0 +1,20 @@
+from typing import Any
+import cv2
+
+
+def get_video_frame(video_path: str, frame_number: int = 0) -> Any:
+    capture = cv2.VideoCapture(video_path)
+    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 get_video_frame_total(video_path: str) -> int:
+    capture = cv2.VideoCapture(video_path)
+    video_frame_total = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
+    capture.release()
+    return video_frame_total

roop/core.py

@@ -0,0 +1,217 @@
+#!/usr/bin/env python3
+
+import os
+import sys
+# single thread doubles cuda performance - needs to be set before torch import
+if any(arg.startswith('--execution-provider') for arg in sys.argv):
+    os.environ['OMP_NUM_THREADS'] = '1'
+# reduce tensorflow log level
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+import warnings
+from typing import List
+import platform
+import signal
+import shutil
+import argparse
+import torch
+import onnxruntime
+import tensorflow
+
+import roop.globals
+import roop.metadata
+import roop.ui as ui
+from roop.predicter import predict_image, predict_video
+from roop.processors.frame.core import get_frame_processors_modules
+from roop.utilities import has_image_extension, is_image, is_video, detect_fps, create_video, extract_frames, get_temp_frame_paths, restore_audio, create_temp, move_temp, clean_temp, normalize_output_path
+
+if 'ROCMExecutionProvider' in roop.globals.execution_providers:
+    del torch
+
+warnings.filterwarnings('ignore', category=FutureWarning, module='insightface')
+warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')
+
+
+def parse_args() -> None:
+    signal.signal(signal.SIGINT, lambda signal_number, frame: destroy())
+    program = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=100))
+    program.add_argument('-s', '--source', help='select an source image', dest='source_path')
+    program.add_argument('-t', '--target', help='select an target image or video', dest='target_path')
+    program.add_argument('-o', '--output', help='select output file or directory', dest='output_path')
+    program.add_argument('--frame-processor', help='frame processors (choices: face_swapper, face_enhancer, ...)', dest='frame_processor', default=['face_swapper'], nargs='+')
+    program.add_argument('--keep-fps', help='keep original fps', dest='keep_fps', action='store_true', default=False)
+    program.add_argument('--keep-audio', help='keep original audio', dest='keep_audio', action='store_true', default=True)
+    program.add_argument('--keep-frames', help='keep temporary frames', dest='keep_frames', action='store_true', default=False)
+    program.add_argument('--many-faces', help='process every face', dest='many_faces', action='store_true', default=False)
+    program.add_argument('--video-encoder', help='adjust output video encoder', dest='video_encoder', default='libx264', choices=['libx264', 'libx265', 'libvpx-vp9'])
+    program.add_argument('--video-quality', help='adjust output video quality', dest='video_quality', type=int, default=18, choices=range(52), metavar='[0-51]')
+    program.add_argument('--max-memory', help='maximum amount of RAM in GB', dest='max_memory', type=int, default=suggest_max_memory())
+    program.add_argument('--execution-provider', help='available execution provider (choices: cpu, ...)', dest='execution_provider', default=['cpu'], choices=suggest_execution_providers(), nargs='+')
+    program.add_argument('--execution-threads', help='number of execution threads', dest='execution_threads', type=int, default=suggest_execution_threads())
+    program.add_argument('-v', '--version', action='version', version=f'{roop.metadata.name} {roop.metadata.version}')
+
+    args = program.parse_args()
+
+    roop.globals.source_path = args.source_path
+    roop.globals.target_path = args.target_path
+    roop.globals.output_path = normalize_output_path(roop.globals.source_path, roop.globals.target_path, args.output_path)
+    roop.globals.frame_processors = args.frame_processor
+    roop.globals.headless = args.source_path or args.target_path or args.output_path
+    roop.globals.keep_fps = args.keep_fps
+    roop.globals.keep_audio = args.keep_audio
+    roop.globals.keep_frames = args.keep_frames
+    roop.globals.many_faces = args.many_faces
+    roop.globals.video_encoder = args.video_encoder
+    roop.globals.video_quality = args.video_quality
+    roop.globals.max_memory = args.max_memory
+    roop.globals.execution_providers = decode_execution_providers(args.execution_provider)
+    roop.globals.execution_threads = args.execution_threads
+
+
+def encode_execution_providers(execution_providers: List[str]) -> List[str]:
+    return [execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers]
+
+
+def decode_execution_providers(execution_providers: List[str]) -> List[str]:
+    return [provider for provider, encoded_execution_provider in zip(onnxruntime.get_available_providers(), encode_execution_providers(onnxruntime.get_available_providers()))
+            if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers)]
+
+
+def suggest_max_memory() -> int:
+    if platform.system().lower() == 'darwin':
+        return 4
+    return 16
+
+
+def suggest_execution_providers() -> List[str]:
+    return encode_execution_providers(onnxruntime.get_available_providers())
+
+
+def suggest_execution_threads() -> int:
+    if 'DmlExecutionProvider' in roop.globals.execution_providers:
+        return 1
+    if 'ROCMExecutionProvider' in roop.globals.execution_providers:
+        return 1
+    return 8
+
+
+def limit_resources() -> None:
+    # prevent tensorflow memory leak
+    gpus = tensorflow.config.experimental.list_physical_devices('GPU')
+    for gpu in gpus:
+        tensorflow.config.experimental.set_virtual_device_configuration(gpu, [
+            tensorflow.config.experimental.VirtualDeviceConfiguration(memory_limit=1024)
+        ])
+    # limit memory usage
+    if roop.globals.max_memory:
+        memory = roop.globals.max_memory * 1024 ** 3
+        if platform.system().lower() == 'darwin':
+            memory = roop.globals.max_memory * 1024 ** 6
+        if platform.system().lower() == 'windows':
+            import ctypes
+            kernel32 = ctypes.windll.kernel32
+            kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(memory), ctypes.c_size_t(memory))
+        else:
+            import resource
+            resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))
+
+
+def release_resources() -> None:
+    if 'CUDAExecutionProvider' in roop.globals.execution_providers:
+        torch.cuda.empty_cache()
+
+
+def pre_check() -> bool:
+    if sys.version_info < (3, 9):
+        update_status('Python version is not supported - please upgrade to 3.9 or higher.')
+        return False
+    if not shutil.which('ffmpeg'):
+        update_status('ffmpeg is not installed.')
+        return False
+    return True
+
+
+def update_status(message: str, scope: str = 'ROOP.CORE') -> None:
+    print(f'[{scope}] {message}')
+    if not roop.globals.headless:
+        ui.update_status(message)
+
+
+def start() -> None:
+    for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
+        if not frame_processor.pre_start():
+            return
+    # process image to image
+    if has_image_extension(roop.globals.target_path):
+        # if predict_image(roop.globals.target_path):
+        #     destroy()
+        shutil.copy2(roop.globals.target_path, roop.globals.output_path)
+        for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
+            for frame_processor_name in roop.globals.frame_processors:
+                if frame_processor_name == frame_processor.frame_name:
+                    update_status('Progressing...', frame_processor.NAME)
+                    frame_processor.process_image(roop.globals.source_path, roop.globals.output_path, roop.globals.output_path)
+                    frame_processor.post_process()
+                    release_resources()
+        if is_image(roop.globals.target_path):
+            update_status('Processing to image succeed!')
+        else:
+            update_status('Processing to image failed!')
+        return
+    # process image to videos
+    # if predict_video(roop.globals.target_path):
+    #     destroy()
+    update_status('Creating temp resources...')
+    create_temp(roop.globals.target_path)
+    update_status('Extracting frames...')
+    extract_frames(roop.globals.target_path)
+    temp_frame_paths = get_temp_frame_paths(roop.globals.target_path)
+    for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
+        update_status('Progressing...', frame_processor.NAME)
+        frame_processor.process_video(roop.globals.source_path, temp_frame_paths)
+        frame_processor.post_process()
+        release_resources()
+    # handles fps
+    if roop.globals.keep_fps:
+        update_status('Detecting fps...')
+        fps = detect_fps(roop.globals.target_path)
+        update_status(f'Creating video with {fps} fps...')
+        create_video(roop.globals.target_path, fps)
+    else:
+        update_status('Creating video with 30.0 fps...')
+        create_video(roop.globals.target_path)
+    # handle audio
+    if roop.globals.keep_audio:
+        if roop.globals.keep_fps:
+            update_status('Restoring audio...')
+        else:
+            update_status('Restoring audio might cause issues as fps are not kept...')
+        restore_audio(roop.globals.target_path, roop.globals.output_path)
+    else:
+        move_temp(roop.globals.target_path, roop.globals.output_path)
+    # clean and validate
+    clean_temp(roop.globals.target_path)
+    if is_video(roop.globals.target_path):
+        update_status('Processing to video succeed!')
+    else:
+        update_status('Processing to video failed!')
+
+
+def destroy() -> None:
+    if roop.globals.target_path:
+        clean_temp(roop.globals.target_path)
+    quit()
+
+
+def run() -> None:
+    parse_args()
+    if not pre_check():
+        return
+    for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
+        if not frame_processor.pre_check():
+            return
+    limit_resources()
+    if roop.globals.headless:
+        start()
+    else:
+        window = ui.init(start, destroy)
+        window.mainloop()

roop/face_analyser.py

@@ -0,0 +1,34 @@
+import threading
+from typing import Any
+import insightface
+
+import roop.globals
+from roop.typing import Frame
+
+FACE_ANALYSER = None
+THREAD_LOCK = threading.Lock()
+
+
+def get_face_analyser() -> Any:
+    global FACE_ANALYSER
+
+    with THREAD_LOCK:
+        if FACE_ANALYSER is None:
+            FACE_ANALYSER = insightface.app.FaceAnalysis(name='buffalo_l', providers=roop.globals.execution_providers)
+            FACE_ANALYSER.prepare(ctx_id=0, det_size=(640, 640))
+    return FACE_ANALYSER
+
+
+def get_one_face(frame: Frame) -> Any:
+    face = get_face_analyser().get(frame)
+    try:
+        return min(face, key=lambda x: x.bbox[0])
+    except ValueError:
+        return None
+
+
+def get_many_faces(frame: Frame) -> Any:
+    try:
+        return get_face_analyser().get(frame)
+    except IndexError:
+        return None

roop/globals.py

@@ -0,0 +1,17 @@
+from typing import List
+
+source_path = None
+target_path = None
+output_path = None
+frame_processors: List[str] = []
+keep_fps = None
+keep_audio = None
+keep_frames = None
+many_faces = None
+video_encoder = None
+video_quality = None
+max_memory = None
+execution_providers: List[str] = []
+execution_threads = None
+headless = None
+log_level = 'error'

roop/metadata.py

@@ -0,0 +1,2 @@
+name = 'roop'
+version = '1.1.0'

roop/predicter.py

@@ -0,0 +1,24 @@
+import numpy
+import opennsfw2
+from PIL import Image
+
+from roop.typing import Frame
+
+MAX_PROBABILITY = 0
+
+def predict_frame(target_frame: Frame) -> bool:
+    image = Image.fromarray(target_frame)
+    image = opennsfw2.preprocess_image(image, opennsfw2.Preprocessing.YAHOO)
+    model = opennsfw2.make_open_nsfw_model()
+    views = numpy.expand_dims(image, axis=0)
+    _, probability = model.predict(views)[0]
+    return probability > MAX_PROBABILITY
+
+
+def predict_image(target_path: str) -> bool:
+    return opennsfw2.predict_image(target_path) > MAX_PROBABILITY
+
+
+def predict_video(target_path: str) -> bool:
+    _, probabilities = opennsfw2.predict_video_frames(video_path=target_path, frame_interval=100)
+    return any(probability == MAX_PROBABILITY for probability in probabilities)

roop/processors/frame/core.py

@@ -0,0 +1,95 @@
+import os
+import importlib
+import psutil
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from queue import Queue
+from types import ModuleType
+from typing import Any, List, Callable
+from tqdm import tqdm
+
+import roop
+
+FRAME_PROCESSORS_MODULES: List[ModuleType] = []
+FRAME_PROCESSORS_INTERFACE = [
+    'pre_check',
+    'pre_start',
+    'process_frame',
+    'process_frames',
+    'process_image',
+    'process_video',
+    'post_process'
+]
+
+
+def load_frame_processor_module(frame_processor: str) -> Any:
+    try:
+        frame_processor_module = importlib.import_module(f'roop.processors.frame.{frame_processor}')
+        for method_name in FRAME_PROCESSORS_INTERFACE:
+            if not hasattr(frame_processor_module, method_name):
+                raise NotImplementedError
+    except (ImportError, NotImplementedError):
+        quit(f'Frame processor {frame_processor} crashed.')
+    return frame_processor_module
+
+
+def get_frame_processors_modules(frame_processors: List[str]) -> List[ModuleType]:
+    global FRAME_PROCESSORS_MODULES
+
+    for frame_processor in frame_processors:
+        found = False
+        for frame_processor_module in FRAME_PROCESSORS_MODULES:
+            if frame_processor_module.frame_name == frame_processor:
+                found = True
+                break
+        if not found:
+            frame_processor_module = load_frame_processor_module(frame_processor)
+            FRAME_PROCESSORS_MODULES.append(frame_processor_module)
+    # if not FRAME_PROCESSORS_MODULES:
+        
+    return 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=roop.globals.execution_threads) as executor:
+        futures = []
+        queue = create_queue(temp_frame_paths)
+        queue_per_future = len(temp_frame_paths) // roop.globals.execution_threads
+        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 create_queue(temp_frame_paths: List[str]) -> Queue[str]:
+    queue: Queue[str] = Queue()
+    for frame_path in temp_frame_paths:
+        queue.put(frame_path)
+    return queue
+
+
+def pick_queue(queue: Queue[str], queue_per_future: int) -> List[str]:
+    queues = []
+    for _ in range(queue_per_future):
+        if not queue.empty():
+            queues.append(queue.get())
+    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='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
+    progress.set_postfix({
+        'memory_usage': '{:.2f}'.format(memory_usage).zfill(5) + 'GB',
+        'execution_providers': roop.globals.execution_providers,
+        'execution_threads': roop.globals.execution_threads
+    })
+    progress.refresh()
+    progress.update(1)

roop/processors/frame/face_enhancer.py

@@ -0,0 +1,89 @@
+from typing import Any, List, Callable
+import cv2
+import threading
+import gfpgan
+
+import roop.globals
+import roop.processors.frame.core
+from roop.core import update_status
+from roop.face_analyser import get_one_face
+from roop.typing import Frame, Face
+from roop.utilities import conditional_download, resolve_relative_path, is_image, is_video
+import torch
+
+FACE_ENHANCER = None
+THREAD_SEMAPHORE = threading.Semaphore()
+THREAD_LOCK = threading.Lock()
+NAME = 'ROOP.FACE-ENHANCER'
+frame_name = 'face_enhancer'
+
+if torch.cuda.is_available():
+    device='cuda'
+else:
+    device='cpu'
+
+
+def get_face_enhancer() -> Any:
+    global FACE_ENHANCER
+
+    with THREAD_LOCK:
+        if FACE_ENHANCER is None:
+            model_path = resolve_relative_path('../models/GFPGANv1.4.pth')
+            # todo: set models path https://github.com/TencentARC/GFPGAN/issues/399
+            FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1,device=device) # type: ignore[attr-defined]
+    return FACE_ENHANCER
+
+
+def pre_check() -> bool:
+    download_directory_path = resolve_relative_path('../models')
+    # conditional_download(download_directory_path, ['https://huggingface.co/henryruhs/roop/resolve/main/GFPGANv1.4.pth'])
+    conditional_download(download_directory_path, ['https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth'])
+    return True
+
+
+def pre_start() -> bool:
+    if not is_image(roop.globals.target_path) and not is_video(roop.globals.target_path):
+        update_status('Select an image or video for target path.', NAME)
+        return False
+    return True
+
+
+def post_process() -> None:
+    global FACE_ENHANCER
+
+    FACE_ENHANCER = None
+
+
+def enhance_face(temp_frame: Frame) -> Frame:
+    with THREAD_SEMAPHORE:
+        _, _, temp_frame = get_face_enhancer().enhance(
+            temp_frame,
+            paste_back=True
+        )
+    return temp_frame
+
+
+def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
+    target_face = get_one_face(temp_frame)
+    if target_face:
+        temp_frame = enhance_face(temp_frame)
+    return temp_frame
+
+
+def process_frames(source_path: str, temp_frame_paths: List[str], update: Callable[[], None]) -> None:
+    for temp_frame_path in temp_frame_paths:
+        temp_frame = cv2.imread(temp_frame_path)
+        result = process_frame(None, temp_frame)
+        cv2.imwrite(temp_frame_path, result)
+        if update:
+            update()
+
+
+def process_image(source_path: str, target_path: str, output_path: str) -> None:
+    target_frame = cv2.imread(target_path)
+    result = process_frame(None, target_frame)
+    cv2.imwrite(output_path, result)
+
+
+def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
+    roop.processors.frame.core.process_video(None, temp_frame_paths, process_frames)

roop/processors/frame/face_swapper.py

@@ -0,0 +1,89 @@
+from typing import Any, List, Callable
+import cv2
+import insightface
+import threading
+
+import roop.globals
+import roop.processors.frame.core
+from roop.core import update_status
+from roop.face_analyser import get_one_face, get_many_faces
+from roop.typing import Face, Frame
+from roop.utilities import conditional_download, resolve_relative_path, is_image, is_video
+
+FACE_SWAPPER = None
+THREAD_LOCK = threading.Lock()
+NAME = 'ROOP.FACE-SWAPPER'
+frame_name = 'face_swapper'
+
+def get_face_swapper() -> Any:
+    global FACE_SWAPPER
+
+    with THREAD_LOCK:
+        if FACE_SWAPPER is None:
+            model_path = resolve_relative_path('../models/inswapper_128.onnx')
+            FACE_SWAPPER = insightface.model_zoo.get_model(model_path, providers=roop.globals.execution_providers)
+    return FACE_SWAPPER
+
+
+def pre_check() -> bool:
+    download_directory_path = resolve_relative_path('../models')
+    # conditional_download(download_directory_path, ['https://huggingface.co/henryruhs/roop/resolve/main/inswapper_128.onnx'])
+    conditional_download(download_directory_path, ['https://huggingface.co/thebiglaskowski/inswapper_128.onnx/resolve/main/inswapper_128.onnx'])
+    return True
+
+
+def pre_start() -> bool:
+    if not is_image(roop.globals.source_path):
+        update_status('Select an image for source path.', NAME)
+        return False
+    elif not get_one_face(cv2.imread(roop.globals.source_path)):
+        update_status('No face in source path detected.', NAME)
+        return False
+    if not is_image(roop.globals.target_path) and not is_video(roop.globals.target_path):
+        update_status('Select an image or video for target path.', NAME)
+        return False
+    return True
+
+
+def post_process() -> None:
+    global FACE_SWAPPER
+
+    FACE_SWAPPER = None
+
+
+def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
+    return get_face_swapper().get(temp_frame, target_face, source_face, paste_back=True)
+
+
+def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
+    if roop.globals.many_faces:
+        many_faces = get_many_faces(temp_frame)
+        if many_faces:
+            for target_face in many_faces:
+                temp_frame = swap_face(source_face, target_face, temp_frame)
+    else:
+        target_face = get_one_face(temp_frame)
+        if target_face:
+            temp_frame = swap_face(source_face, target_face, temp_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))
+    for temp_frame_path in temp_frame_paths:
+        temp_frame = cv2.imread(temp_frame_path)
+        result = process_frame(source_face, temp_frame)
+        cv2.imwrite(temp_frame_path, result)
+        if update:
+            update()
+
+
+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)
+    result = process_frame(source_face, target_frame)
+    cv2.imwrite(output_path, result)
+
+
+def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
+    roop.processors.frame.core.process_video(source_path, temp_frame_paths, process_frames)

roop/typing.py

@@ -0,0 +1,7 @@
+from typing import Any
+
+from insightface.app.common import Face
+import numpy
+
+Face = Face
+Frame = numpy.ndarray[Any, Any]

roop/ui.json

@@ -0,0 +1,158 @@
+{
+  "CTk": {
+    "fg_color": ["gray95", "gray10"]
+  },
+  "CTkToplevel": {
+    "fg_color": ["gray95", "gray10"]
+  },
+  "CTkFrame": {
+    "corner_radius": 6,
+    "border_width": 0,
+    "fg_color": ["gray90", "gray13"],
+    "top_fg_color": ["gray85", "gray16"],
+    "border_color": ["gray65", "gray28"]
+  },
+  "CTkButton": {
+    "corner_radius": 6,
+    "border_width": 0,
+    "fg_color": ["#3a7ebf", "#1f538d"],
+    "hover_color": ["#325882", "#14375e"],
+    "border_color": ["#3E454A", "#949A9F"],
+    "text_color": ["#DCE4EE", "#DCE4EE"],
+    "text_color_disabled": ["gray74", "gray60"]
+  },
+  "CTkLabel": {
+    "corner_radius": 0,
+    "fg_color": "transparent",
+    "text_color": ["gray14", "gray84"]
+  },
+  "CTkEntry": {
+    "corner_radius": 6,
+    "border_width": 2,
+    "fg_color": ["#F9F9FA", "#343638"],
+    "border_color": ["#979DA2", "#565B5E"],
+    "text_color": ["gray14", "gray84"],
+    "placeholder_text_color": ["gray52", "gray62"]
+  },
+  "CTkCheckbox": {
+    "corner_radius": 6,
+    "border_width": 3,
+    "fg_color": ["#3a7ebf", "#1f538d"],
+    "border_color": ["#3E454A", "#949A9F"],
+    "hover_color": ["#325882", "#14375e"],
+    "checkmark_color": ["#DCE4EE", "gray90"],
+    "text_color": ["gray14", "gray84"],
+    "text_color_disabled": ["gray60", "gray45"]
+  },
+  "CTkSwitch": {
+    "corner_radius": 1000,
+    "border_width": 3,
+    "button_length": 0,
+    "fg_color": ["#939BA2", "#4A4D50"],
+    "progress_color": ["#3a7ebf", "#1f538d"],
+    "button_color": ["gray36", "#D5D9DE"],
+    "button_hover_color": ["gray20", "gray100"],
+    "text_color": ["gray14", "gray84"],
+    "text_color_disabled": ["gray60", "gray45"]
+  },
+  "CTkRadiobutton": {
+    "corner_radius": 1000,
+    "border_width_checked": 6,
+    "border_width_unchecked": 3,
+    "fg_color": ["#3a7ebf", "#1f538d"],
+    "border_color": ["#3E454A", "#949A9F"],
+    "hover_color": ["#325882", "#14375e"],
+    "text_color": ["gray14", "gray84"],
+    "text_color_disabled": ["gray60", "gray45"]
+  },
+  "CTkProgressBar": {
+    "corner_radius": 1000,
+    "border_width": 0,
+    "fg_color": ["#939BA2", "#4A4D50"],
+    "progress_color": ["#3a7ebf", "#1f538d"],
+    "border_color": ["gray", "gray"]
+  },
+  "CTkSlider": {
+    "corner_radius": 1000,
+    "button_corner_radius": 1000,
+    "border_width": 6,
+    "button_length": 0,
+    "fg_color": ["#939BA2", "#4A4D50"],
+    "progress_color": ["gray40", "#AAB0B5"],
+    "button_color": ["#3a7ebf", "#1f538d"],
+    "button_hover_color": ["#325882", "#14375e"]
+  },
+  "CTkOptionMenu": {
+    "corner_radius": 6,
+    "fg_color": ["#3a7ebf", "#1f538d"],
+    "button_color": ["#325882", "#14375e"],
+    "button_hover_color": ["#234567", "#1e2c40"],
+    "text_color": ["#DCE4EE", "#DCE4EE"],
+    "text_color_disabled": ["gray74", "gray60"]
+  },
+  "CTkComboBox": {
+    "corner_radius": 6,
+    "border_width": 2,
+    "fg_color": ["#F9F9FA", "#343638"],
+    "border_color": ["#979DA2", "#565B5E"],
+    "button_color": ["#979DA2", "#565B5E"],
+    "button_hover_color": ["#6E7174", "#7A848D"],
+    "text_color": ["gray14", "gray84"],
+    "text_color_disabled": ["gray50", "gray45"]
+  },
+  "CTkScrollbar": {
+    "corner_radius": 1000,
+    "border_spacing": 4,
+    "fg_color": "transparent",
+    "button_color": ["gray55", "gray41"],
+    "button_hover_color": ["gray40", "gray53"]
+  },
+  "CTkSegmentedButton": {
+    "corner_radius": 6,
+    "border_width": 2,
+    "fg_color": ["#979DA2", "gray29"],
+    "selected_color": ["#3a7ebf", "#1f538d"],
+    "selected_hover_color": ["#325882", "#14375e"],
+    "unselected_color": ["#979DA2", "gray29"],
+    "unselected_hover_color": ["gray70", "gray41"],
+    "text_color": ["#DCE4EE", "#DCE4EE"],
+    "text_color_disabled": ["gray74", "gray60"]
+  },
+  "CTkTextbox": {
+    "corner_radius": 6,
+    "border_width": 0,
+    "fg_color": ["gray100", "gray20"],
+    "border_color": ["#979DA2", "#565B5E"],
+    "text_color": ["gray14", "gray84"],
+    "scrollbar_button_color": ["gray55", "gray41"],
+    "scrollbar_button_hover_color": ["gray40", "gray53"]
+  },
+  "CTkScrollableFrame": {
+    "label_fg_color": ["gray80", "gray21"]
+  },
+  "DropdownMenu": {
+    "fg_color": ["gray90", "gray20"],
+    "hover_color": ["gray75", "gray28"],
+    "text_color": ["gray14", "gray84"]
+  },
+  "CTkFont": {
+    "macOS": {
+      "family": "Avenir",
+      "size": 12,
+      "weight": "normal"
+    },
+    "Windows": {
+      "family": "Corbel",
+      "size": 12,
+      "weight": "normal"
+    },
+    "Linux": {
+      "family": "Montserrat",
+      "size": 12,
+      "weight": "normal"
+    }
+  },
+  "RoopDonate": {
+    "text_color": ["#3a7ebf", "gray60"]
+  }
+}

roop/ui.py

@@ -0,0 +1,231 @@
+import os
+import webbrowser
+import customtkinter as ctk
+from typing import Callable, Tuple
+import cv2
+from PIL import Image, ImageOps
+
+import roop.globals
+import roop.metadata
+from roop.face_analyser import get_one_face
+from roop.capturer import get_video_frame, get_video_frame_total
+from roop.predicter import predict_frame
+from roop.processors.frame.core import get_frame_processors_modules
+from roop.utilities import is_image, is_video, resolve_relative_path
+
+ROOT = None
+ROOT_HEIGHT = 700
+ROOT_WIDTH = 600
+
+PREVIEW = None
+PREVIEW_MAX_HEIGHT = 700
+PREVIEW_MAX_WIDTH = 1200
+
+RECENT_DIRECTORY_SOURCE = None
+RECENT_DIRECTORY_TARGET = None
+RECENT_DIRECTORY_OUTPUT = None
+
+preview_label = None
+preview_slider = None
+source_label = None
+target_label = None
+status_label = None
+
+
+def init(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.CTk:
+    global ROOT, PREVIEW
+
+    ROOT = create_root(start, destroy)
+    PREVIEW = create_preview(ROOT)
+
+    return ROOT
+
+
+def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.CTk:
+    global source_label, target_label, status_label
+
+    ctk.deactivate_automatic_dpi_awareness()
+    ctk.set_appearance_mode('system')
+    ctk.set_default_color_theme(resolve_relative_path('ui.json'))
+
+    root = ctk.CTk()
+    root.minsize(ROOT_WIDTH, ROOT_HEIGHT)
+    root.title(f'{roop.metadata.name} {roop.metadata.version}')
+    root.configure()
+    root.protocol('WM_DELETE_WINDOW', lambda: destroy())
+
+    source_label = ctk.CTkLabel(root, text=None)
+    source_label.place(relx=0.1, rely=0.1, relwidth=0.3, relheight=0.25)
+
+    target_label = ctk.CTkLabel(root, text=None)
+    target_label.place(relx=0.6, rely=0.1, relwidth=0.3, relheight=0.25)
+
+    source_button = ctk.CTkButton(root, text='Select a face', cursor='hand2', command=lambda: select_source_path())
+    source_button.place(relx=0.1, rely=0.4, relwidth=0.3, relheight=0.1)
+
+    target_button = ctk.CTkButton(root, text='Select a target', cursor='hand2', command=lambda: select_target_path())
+    target_button.place(relx=0.6, rely=0.4, relwidth=0.3, relheight=0.1)
+
+    keep_fps_value = ctk.BooleanVar(value=roop.globals.keep_fps)
+    keep_fps_checkbox = ctk.CTkSwitch(root, text='Keep fps', variable=keep_fps_value, cursor='hand2', command=lambda: setattr(roop.globals, 'keep_fps', not roop.globals.keep_fps))
+    keep_fps_checkbox.place(relx=0.1, rely=0.6)
+
+    keep_frames_value = ctk.BooleanVar(value=roop.globals.keep_frames)
+    keep_frames_switch = ctk.CTkSwitch(root, text='Keep frames', variable=keep_frames_value, cursor='hand2', command=lambda: setattr(roop.globals, 'keep_frames', keep_frames_value.get()))
+    keep_frames_switch.place(relx=0.1, rely=0.65)
+
+    keep_audio_value = ctk.BooleanVar(value=roop.globals.keep_audio)
+    keep_audio_switch = ctk.CTkSwitch(root, text='Keep audio', variable=keep_audio_value, cursor='hand2', command=lambda: setattr(roop.globals, 'keep_audio', keep_audio_value.get()))
+    keep_audio_switch.place(relx=0.6, rely=0.6)
+
+    many_faces_value = ctk.BooleanVar(value=roop.globals.many_faces)
+    many_faces_switch = ctk.CTkSwitch(root, text='Many faces', variable=many_faces_value, cursor='hand2', command=lambda: setattr(roop.globals, 'many_faces', many_faces_value.get()))
+    many_faces_switch.place(relx=0.6, rely=0.65)
+
+    start_button = ctk.CTkButton(root, text='Start', cursor='hand2', command=lambda: select_output_path(start))
+    start_button.place(relx=0.15, rely=0.75, relwidth=0.2, relheight=0.05)
+
+    stop_button = ctk.CTkButton(root, text='Destroy', cursor='hand2', command=lambda: destroy())
+    stop_button.place(relx=0.4, rely=0.75, relwidth=0.2, relheight=0.05)
+
+    preview_button = ctk.CTkButton(root, text='Preview', cursor='hand2', command=lambda: toggle_preview())
+    preview_button.place(relx=0.65, rely=0.75, relwidth=0.2, relheight=0.05)
+
+    status_label = ctk.CTkLabel(root, text=None, justify='center')
+    status_label.place(relx=0.1, rely=0.9, relwidth=0.8)
+
+    donate_label = ctk.CTkLabel(root, text='^_^ Donate to project ^_^', justify='center', cursor='hand2')
+    donate_label.place(relx=0.1, rely=0.95, relwidth=0.8)
+    donate_label.configure(text_color=ctk.ThemeManager.theme.get('RoopDonate').get('text_color'))
+    donate_label.bind('<Button>', lambda event: webbrowser.open('https://github.com/sponsors/s0md3v'))
+
+    return root
+
+
+def create_preview(parent: ctk.CTkToplevel) -> ctk.CTkToplevel:
+    global preview_label, preview_slider
+
+    preview = ctk.CTkToplevel(parent)
+    preview.withdraw()
+    preview.title('Preview')
+    preview.configure()
+    preview.protocol('WM_DELETE_WINDOW', lambda: toggle_preview())
+    preview.resizable(width=False, height=False)
+
+    preview_label = ctk.CTkLabel(preview, text=None)
+    preview_label.pack(fill='both', expand=True)
+
+    preview_slider = ctk.CTkSlider(preview, from_=0, to=0, command=lambda frame_value: update_preview(frame_value))
+
+    return preview
+
+
+def update_status(text: str) -> None:
+    status_label.configure(text=text)
+    ROOT.update()
+
+
+def select_source_path() -> None:
+    global RECENT_DIRECTORY_SOURCE
+
+    PREVIEW.withdraw()
+    source_path = ctk.filedialog.askopenfilename(title='select an source image', initialdir=RECENT_DIRECTORY_SOURCE)
+    if is_image(source_path):
+        roop.globals.source_path = source_path
+        RECENT_DIRECTORY_SOURCE = os.path.dirname(roop.globals.source_path)
+        image = render_image_preview(roop.globals.source_path, (200, 200))
+        source_label.configure(image=image)
+    else:
+        roop.globals.source_path = None
+        source_label.configure(image=None)
+
+
+def select_target_path() -> None:
+    global RECENT_DIRECTORY_TARGET
+
+    PREVIEW.withdraw()
+    target_path = ctk.filedialog.askopenfilename(title='select an target image or video', initialdir=RECENT_DIRECTORY_TARGET)
+    if is_image(target_path):
+        roop.globals.target_path = target_path
+        RECENT_DIRECTORY_TARGET = os.path.dirname(roop.globals.target_path)
+        image = render_image_preview(roop.globals.target_path, (200, 200))
+        target_label.configure(image=image)
+    elif is_video(target_path):
+        roop.globals.target_path = target_path
+        RECENT_DIRECTORY_TARGET = os.path.dirname(roop.globals.target_path)
+        video_frame = render_video_preview(target_path, (200, 200))
+        target_label.configure(image=video_frame)
+    else:
+        roop.globals.target_path = None
+        target_label.configure(image=None)
+
+
+def select_output_path(start: Callable[[], None]) -> None:
+    global RECENT_DIRECTORY_OUTPUT
+
+    if is_image(roop.globals.target_path):
+        output_path = ctk.filedialog.asksaveasfilename(title='save image output file', defaultextension='.png', initialfile='output.png', initialdir=RECENT_DIRECTORY_OUTPUT)
+    elif is_video(roop.globals.target_path):
+        output_path = ctk.filedialog.asksaveasfilename(title='save video output file', defaultextension='.mp4', initialfile='output.mp4', initialdir=RECENT_DIRECTORY_OUTPUT)
+    else:
+        output_path = None
+    if output_path:
+        roop.globals.output_path = output_path
+        RECENT_DIRECTORY_OUTPUT = os.path.dirname(roop.globals.output_path)
+        start()
+
+
+def render_image_preview(image_path: str, size: Tuple[int, int]) -> ctk.CTkImage:
+    image = Image.open(image_path)
+    if size:
+        image = ImageOps.fit(image, size, Image.LANCZOS)
+    return ctk.CTkImage(image, size=image.size)
+
+
+def render_video_preview(video_path: str, size: Tuple[int, int], frame_number: int = 0) -> ctk.CTkImage:
+    capture = cv2.VideoCapture(video_path)
+    if frame_number:
+        capture.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
+    has_frame, frame = capture.read()
+    if has_frame:
+        image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+        if size:
+            image = ImageOps.fit(image, size, Image.LANCZOS)
+        return ctk.CTkImage(image, size=image.size)
+    capture.release()
+    cv2.destroyAllWindows()
+
+
+def toggle_preview() -> None:
+    if PREVIEW.state() == 'normal':
+        PREVIEW.withdraw()
+    elif roop.globals.source_path and roop.globals.target_path:
+        init_preview()
+        update_preview()
+        PREVIEW.deiconify()
+
+
+def init_preview() -> None:
+    if is_image(roop.globals.target_path):
+        preview_slider.pack_forget()
+    if is_video(roop.globals.target_path):
+        video_frame_total = get_video_frame_total(roop.globals.target_path)
+        preview_slider.configure(to=video_frame_total)
+        preview_slider.pack(fill='x')
+        preview_slider.set(0)
+
+
+def update_preview(frame_number: int = 0) -> None:
+    if roop.globals.source_path and roop.globals.target_path:
+        temp_frame = get_video_frame(roop.globals.target_path, frame_number)
+        if predict_frame(temp_frame):
+            quit()
+        for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
+            temp_frame = frame_processor.process_frame(
+                get_one_face(cv2.imread(roop.globals.source_path)),
+                temp_frame
+            )
+        image = Image.fromarray(cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB))
+        image = ImageOps.contain(image, (PREVIEW_MAX_WIDTH, PREVIEW_MAX_HEIGHT), Image.LANCZOS)
+        image = ctk.CTkImage(image, size=image.size)
+        preview_label.configure(image=image)

roop/utilities.py

@@ -0,0 +1,141 @@
+import glob
+import mimetypes
+import os
+import platform
+import shutil
+import ssl
+import subprocess
+import urllib
+from pathlib import Path
+from typing import List, Any
+from tqdm import tqdm
+
+import roop.globals
+
+TEMP_FILE = 'temp.mp4'
+TEMP_DIRECTORY = 'temp'
+
+# monkey patch ssl for mac
+if platform.system().lower() == 'darwin':
+    ssl._create_default_https_context = ssl._create_unverified_context
+
+
+def run_ffmpeg(args: List[str]) -> bool:
+    commands = ['ffmpeg', '-hide_banner', '-hwaccel', 'auto', '-loglevel', roop.globals.log_level]
+    commands.extend(args)
+    try:
+        subprocess.check_output(commands, stderr=subprocess.STDOUT)
+        return True
+    except Exception:
+        pass
+    return False
+
+
+def detect_fps(target_path: str) -> float:
+    command = ['ffprobe', '-v', 'error', '-select_streams', 'v:0', '-show_entries', 'stream=r_frame_rate', '-of', 'default=noprint_wrappers=1:nokey=1', target_path]
+    output = subprocess.check_output(command).decode().strip().split('/')
+    try:
+        numerator, denominator = map(int, output)
+        return numerator / denominator
+    except Exception:
+        pass
+    return 30.0
+
+
+def extract_frames(target_path: str) -> None:
+    temp_directory_path = get_temp_directory_path(target_path)
+    run_ffmpeg(['-i', target_path, '-pix_fmt', 'rgb24', os.path.join(temp_directory_path, '%04d.png')])
+
+
+def create_video(target_path: str, fps: float = 30.0) -> None:
+    temp_output_path = get_temp_output_path(target_path)
+    temp_directory_path = get_temp_directory_path(target_path)
+    run_ffmpeg(['-r', str(fps), '-i', os.path.join(temp_directory_path, '%04d.png'), '-c:v', roop.globals.video_encoder, '-crf', str(roop.globals.video_quality), '-pix_fmt', 'yuv420p', '-vf', 'colorspace=bt709:iall=bt601-6-625:fast=1', '-y', temp_output_path])
+
+
+def restore_audio(target_path: str, output_path: str) -> None:
+    temp_output_path = get_temp_output_path(target_path)
+    done = run_ffmpeg(['-i', temp_output_path, '-i', target_path, '-c:v', 'copy', '-map', '0:v:0', '-map', '1:a:0', '-y', output_path])
+    if not done:
+        move_temp(target_path, output_path)
+
+
+def get_temp_frame_paths(target_path: str) -> List[str]:
+    temp_directory_path = get_temp_directory_path(target_path)
+    return glob.glob((os.path.join(glob.escape(temp_directory_path), '*.png')))
+
+
+def get_temp_directory_path(target_path: str) -> str:
+    target_name, _ = os.path.splitext(os.path.basename(target_path))
+    target_directory_path = os.path.dirname(target_path)
+    return os.path.join(target_directory_path, TEMP_DIRECTORY, target_name)
+
+
+def get_temp_output_path(target_path: str) -> str:
+    temp_directory_path = get_temp_directory_path(target_path)
+    return os.path.join(temp_directory_path, TEMP_FILE)
+
+
+def normalize_output_path(source_path: str, target_path: str, output_path: str) -> Any:
+    if source_path and target_path:
+        source_name, _ = os.path.splitext(os.path.basename(source_path))
+        target_name, target_extension = os.path.splitext(os.path.basename(target_path))
+        if os.path.isdir(output_path):
+            return os.path.join(output_path, source_name + '-' + target_name + target_extension)
+    return output_path
+
+
+def create_temp(target_path: str) -> None:
+    temp_directory_path = get_temp_directory_path(target_path)
+    Path(temp_directory_path).mkdir(parents=True, exist_ok=True)
+
+
+def move_temp(target_path: str, output_path: str) -> None:
+    temp_output_path = get_temp_output_path(target_path)
+    if os.path.isfile(temp_output_path):
+        if os.path.isfile(output_path):
+            os.remove(output_path)
+        shutil.move(temp_output_path, output_path)
+
+
+def clean_temp(target_path: str) -> None:
+    temp_directory_path = get_temp_directory_path(target_path)
+    parent_directory_path = os.path.dirname(temp_directory_path)
+    if not roop.globals.keep_frames and os.path.isdir(temp_directory_path):
+        shutil.rmtree(temp_directory_path)
+    if os.path.exists(parent_directory_path) and not os.listdir(parent_directory_path):
+        os.rmdir(parent_directory_path)
+
+
+def has_image_extension(image_path: str) -> bool:
+    return image_path.lower().endswith(('png', 'jpg', 'jpeg', 'webp'))
+
+
+def is_image(image_path: str) -> bool:
+    if image_path and os.path.isfile(image_path):
+        mimetype, _ = mimetypes.guess_type(image_path)
+        return bool(mimetype and mimetype.startswith('image/'))
+    return False
+
+
+def is_video(video_path: str) -> bool:
+    if video_path and os.path.isfile(video_path):
+        mimetype, _ = mimetypes.guess_type(video_path)
+        return bool(mimetype and mimetype.startswith('video/'))
+    return False
+
+
+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='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]
+
+
+def resolve_relative_path(path: str) -> str:
+    return os.path.abspath(os.path.join(os.path.dirname(__file__), path))