Upload folder using huggingface_hub (#1)

- Upload folder using huggingface_hub (c9c78b1c42de77555cb4594ad223c1abe764b71e)

.flake8

@@ -0,0 +1,3 @@
+[flake8]
+select = E3, E4, F
+per-file-ignores = roop/core.py:E402,F401

.gitattributes

@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+.github/examples/snapshot.mp4 filter=lfs diff=lfs merge=lfs -text
+.github/preview/output.gif filter=lfs diff=lfs merge=lfs -text
+.github/preview/target.gif filter=lfs diff=lfs merge=lfs -text
+uploads/1685074910001_vtqikl_2_0.jpg filter=lfs diff=lfs merge=lfs -text
+uploads/20230518_174347_0000.png filter=lfs diff=lfs merge=lfs -text

.github/FUNDING.yml

@@ -0,0 +1,2 @@
+github: [s0md3v, henryruhs]
+custom: [https://paypal.me/s0md3v, https://paypal.me/henryruhs]

.github/ISSUE_TEMPLATE/bug.md

@@ -0,0 +1,47 @@
+---
+name: Bug
+about: Report a bug
+labels: 'bug'
+
+---
+
+## Description
+
+A concise description of the bug and how to reproduce it.
+
+## Error
+
+Paste the error or exception from your console:
+
+```
+
+```
+
+## Details
+
+What operating system are you using?
+
+- [ ] Windows 
+- [ ] MacOS (Apple Silicon)
+- [ ] MacOS (Apple Legacy)
+- [ ] Linux
+- [ ] Linux in WSL
+
+What execution provider are you using?
+
+- [ ] CPU
+- [ ] CUDA
+- [ ] CoreML
+- [ ] DirectML
+- [ ] OpenVINO
+- [ ] Other
+
+What version of Roop are you using?
+
+- [ ] 1.0.0
+- [ ] 1.1.0
+- [ ] 1.2.0
+- [ ] 1.3.0
+- [ ] 1.3.1
+- [ ] 1.3.2
+- [ ] next

.github/ISSUE_TEMPLATE/installation.md

@@ -0,0 +1,8 @@
+---
+name: Installation
+about: Platform and installation issues
+labels: 'installation'
+
+---
+
+Please **DO NOT OPEN** platform and installation issues!

.github/examples/snapshot.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6021bee403a32b33b9d0b12916fcc40fee520681910a3faaef4b1f7e66ee386e
+size 2435482

.github/workflows/ci.yml

@@ -0,0 +1,41 @@
+name: ci
+
+on: [ push, pull_request ]
+
+jobs:
+ lint:
+  runs-on: ubuntu-latest
+  steps:
+  - name: Checkout
+    uses: actions/checkout@v2
+  - name: Set up Python 3.9
+    uses: actions/setup-python@v2
+    with:
+     python-version: 3.9
+  - run: pip install flake8
+  - run: pip install mypy
+  - run: flake8 run.py roop
+  - run: mypy run.py roop
+ test:
+  strategy:
+   matrix:
+    os: [macos-latest, ubuntu-latest, windows-latest]
+  runs-on: ${{ matrix.os }}
+  steps:
+  - name: Checkout
+    uses: actions/checkout@v2
+  - name: Set up ffmpeg
+    uses: FedericoCarboni/setup-ffmpeg@v2
+  - name: Set up Python 3.9
+    uses: actions/setup-python@v2
+    with:
+     python-version: 3.9
+  - run: pip install -r requirements-headless.txt
+  - run: python run.py -s .github/examples/source.jpg -t .github/examples/target.mp4 -o .github/examples/output.mp4
+    if: matrix.os != 'windows-latest'
+  - run: python run.py -s .github\examples\source.jpg -t .github\examples\target.mp4 -o .github\examples\output.mp4
+    if: matrix.os == 'windows-latest'
+  - run: ffmpeg -i .github/examples/snapshot.mp4 -i .github/examples/output.mp4 -filter_complex psnr -f null -
+    if: matrix.os != 'windows-latest'
+  - run: ffmpeg -i .github\examples\snapshot.mp4 -i .github\examples\output.mp4 -filter_complex psnr -f null -
+    if: matrix.os == 'windows-latest'

.gitignore

@@ -0,0 +1,4 @@
+.idea
+models
+temp
+__pycache__

CONTRIBUTING.md

@@ -0,0 +1,25 @@
+## Pull Requests
+
+Before submitting a pull request, please ensure to align with us as we need to establish both technical and business requirements.
+
+
+### Do
+
+- ...consider to fix bugs over adding features
+- ...one pull request for one feature or improvement 
+- ...consult us about implementation details
+- ...proper testing before you submit your code
+- ...resolve failed CI pipelines
+
+
+### Don't
+
+- ...introduce fundamental changes in terms of software architecture
+- ...introduce OOP - we accept functional programming only
+- ...ignore given requirements or try to work around them
+- ...submit code to a development branch without consulting us
+- ...submit massive amount of code changes
+- ...submit a proof of concept
+- ...submit code that is using undocumented and private APIs
+- ...solve third party issues in our project
+- ...comment what your code does - use proper naming instead

LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
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+ Everyone is permitted to copy and distribute verbatim copies
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+license to downstream recipients.  "Knowingly relying" means you have
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+
+  A patent license is "discriminatory" if it does not include within
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+conditioned on the non-exercise of one or more of the rights that are
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+or that patent license was granted, prior to 28 March 2007.
+
+  Nothing in this License shall be construed as excluding or limiting
+any implied license or other defenses to infringement that may
+otherwise be available to you under applicable patent law.
+
+  12. No Surrender of Others' Freedom.
+
+  If conditions are imposed on you (whether by court order, agreement or
+otherwise) that contradict the conditions of this License, they do not
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+
+  13. Use with the GNU Affero General Public License.
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+  14. Revised Versions of this License.
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+
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+  Later license versions may give you additional or different
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+  15. Disclaimer of Warranty.
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+  17. Interpretation of Sections 15 and 16.
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+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
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+    Copyright (C) <year>  <name of author>
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+    (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 General Public License for more details.
+
+    You should have received a copy of the GNU 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 the program does terminal interaction, make it output a short
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+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  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 GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

app.py

@@ -0,0 +1,55 @@
+from flask import Flask, render_template, request, redirect, url_for
+import subprocess
+import os
+from werkzeug.utils import secure_filename
+
+app = Flask(__name__)
+
+# Configuration for file uploads
+UPLOAD_FOLDER = 'uploads'
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+OUTPUT_FOLDER = 'output_files'
+app.config['OUTPUT_FOLDER'] = OUTPUT_FOLDER
+
+@app.route('/', methods=['GET', 'POST'])
+def index():
+    if request.method == 'POST':
+        # Handle file uploads
+        source_file = request.files['source']
+        target_file = request.files['target']
+        frame_processor = request.form.getlist('frame_processor')
+        
+        # Save uploaded files
+        source_path = os.path.join(app.config['UPLOAD_FOLDER'], secure_filename(source_file.filename))
+        target_path = os.path.join(app.config['UPLOAD_FOLDER'], secure_filename(target_file.filename))
+        source_file.save(source_path)
+        target_file.save(target_path)
+        
+        # Determine output file name
+        output_filename = secure_filename('output.jpg')  # Default output format is JPEG
+        
+        # Build the command based on the provided options
+        command = ['python', 'run.py', '-s', source_path, '-t', target_path]
+        command.extend(['-o', os.path.join(app.config['OUTPUT_FOLDER'], output_filename)])
+        command.extend(['--frame-processor', *frame_processor])
+        
+        # Execute the command
+        try:
+            subprocess.run(command, check=True)
+            return redirect(url_for('output', filename=output_filename))
+        except subprocess.CalledProcessError:
+            return render_template('error.html')
+    
+    return render_template('index.html')
+
+@app.route('/output/<filename>')
+def output(filename):
+    return render_template('output.html', filename=filename)
+@app.route('/output_image/<filename>')
+def output_image(filename):
+    return app.send_static_file(os.path.join(app.config['OUTPUT_FOLDER'], filename))
+
+if __name__ == '__main__':
+    os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
+    os.makedirs(app.config['OUTPUT_FOLDER'], exist_ok=True)
+    app.run(debug=True)

gfpgan/weights/detection_Resnet50_Final.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6d1de9c2944f2ccddca5f5e010ea5ae64a39845a86311af6fdf30841b0a5a16d
+size 109497761

gfpgan/weights/parsing_parsenet.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3d558d8d0e42c20224f13cf5a29c79eba2d59913419f945545d8cf7b72920de2
+size 85331193

models/GFPGANv1.4.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e2cd4703ab14f4d01fd1383a8a8b266f9a5833dacee8e6a79d3bf21a1b6be5ad
+size 348632874

models/inswapper_128.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e4a3f08c753cb72d04e10aa0f7dbe3deebbf39567d4ead6dce08e98aa49e16af
+size 554253681

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-headless.txt

@@ -0,0 +1,13 @@
+numpy==1.24.3
+opencv-python==4.8.0.74
+onnx==1.14.0
+insightface==0.7.3
+psutil==5.9.5
+tk==0.1.0
+customtkinter==5.2.0
+tkinterdnd2==0.3.0
+onnxruntime==1.15.0
+tensorflow==2.13.0
+opennsfw2==0.10.2
+protobuf==4.23.4
+tqdm==4.65.0

requirements.txt

@@ -0,0 +1,21 @@
+--extra-index-url https://download.pytorch.org/whl/cu118
+
+numpy==1.24.3
+opencv-python==4.8.0.74
+onnx==1.14.0
+insightface==0.7.3
+psutil==5.9.5
+tk==0.1.0
+customtkinter==5.2.0
+tkinterdnd2==0.3.0; sys_platform != 'darwin' and platform_machine != 'arm64'
+tkinterdnd2-universal==1.7.3; sys_platform == 'darwin' and platform_machine == 'arm64'
+pillow==10.0.0
+onnxruntime==1.15.1; python_version != '3.9' and sys_platform == 'darwin' and platform_machine != 'arm64'
+onnxruntime-coreml==1.13.1; python_version == '3.9' and sys_platform == 'darwin' and platform_machine != 'arm64'
+onnxruntime-silicon==1.13.1; sys_platform == 'darwin' and platform_machine == 'arm64'
+onnxruntime-gpu==1.15.1; sys_platform != 'darwin'
+tensorflow==2.13.0
+opennsfw2==0.10.2
+protobuf==4.23.4
+tqdm==4.65.0
+gfpgan==1.3.8

roop/capturer.py

@@ -0,0 +1,22 @@
+from typing import Optional
+import cv2
+
+from roop.typing import Frame
+
+
+def get_video_frame(video_path: str, frame_number: int = 0) -> Optional[Frame]:
+    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 +1,220 @@
+#!/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 onnxruntime
+import tensorflow
+import roop.globals
+import roop.metadata
+import roop.ui as ui
+from roop.predictor 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
+
+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 target fps', dest='keep_fps', action='store_true')
+    program.add_argument('--keep-frames', help='keep temporary frames', dest='keep_frames', action='store_true')
+    program.add_argument('--skip-audio', help='skip target audio', dest='skip_audio', action='store_true')
+    program.add_argument('--many-faces', help='process every face', dest='many_faces', action='store_true')
+    program.add_argument('--reference-face-position', help='position of the reference face', dest='reference_face_position', type=int, default=0)
+    program.add_argument('--reference-frame-number', help='number of the reference frame', dest='reference_frame_number', type=int, default=0)
+    program.add_argument('--similar-face-distance', help='face distance used for recognition', dest='similar_face_distance', type=float, default=0.85)
+    program.add_argument('--temp-frame-format', help='image format used for frame extraction', dest='temp_frame_format', default='png', choices=['jpg', 'png'])
+    program.add_argument('--temp-frame-quality', help='image quality used for frame extraction', dest='temp_frame_quality', type=int, default=0, choices=range(101), metavar='[0-100]')
+    program.add_argument('--output-video-encoder', help='encoder used for the output video', dest='output_video_encoder', default='libx264', choices=['libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc'])
+    program.add_argument('--output-video-quality', help='quality used for the output video', dest='output_video_quality', type=int, default=35, choices=range(101), metavar='[0-100]')
+    program.add_argument('--max-memory', help='maximum amount of RAM in GB', dest='max_memory', type=int)
+    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.headless = roop.globals.source_path is not None and roop.globals.target_path is not None and roop.globals.output_path is not None
+    roop.globals.frame_processors = args.frame_processor
+    roop.globals.keep_fps = args.keep_fps
+    roop.globals.keep_frames = args.keep_frames
+    roop.globals.skip_audio = args.skip_audio
+    roop.globals.many_faces = args.many_faces
+    roop.globals.reference_face_position = args.reference_face_position
+    roop.globals.reference_frame_number = args.reference_frame_number
+    roop.globals.similar_face_distance = args.similar_face_distance
+    roop.globals.temp_frame_format = args.temp_frame_format
+    roop.globals.temp_frame_quality = args.temp_frame_quality
+    roop.globals.output_video_encoder = args.output_video_encoder
+    roop.globals.output_video_quality = args.output_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_execution_providers() -> List[str]:
+    return encode_execution_providers(onnxruntime.get_available_providers())
+
+
+def suggest_execution_threads() -> int:
+    if 'CUDAExecutionProvider' in onnxruntime.get_available_providers():
+        return 8
+    return 1
+
+
+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  # type: ignore[attr-defined]
+            kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(memory), ctypes.c_size_t(memory))
+        else:
+            import resource
+            resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))
+
+
+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)
+        # process frame
+        for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
+            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()
+        # validate image
+        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 temporary resources...')
+    create_temp(roop.globals.target_path)
+    # extract frames
+    if roop.globals.keep_fps:
+        fps = detect_fps(roop.globals.target_path)
+        update_status(f'Extracting frames with {fps} FPS...')
+        extract_frames(roop.globals.target_path, fps)
+    else:
+        update_status('Extracting frames with 30 FPS...')
+        extract_frames(roop.globals.target_path)
+    # process frame
+    temp_frame_paths = get_temp_frame_paths(roop.globals.target_path)
+    if temp_frame_paths:
+        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()
+    else:
+        update_status('Frames not found...')
+        return
+    # create video
+    if roop.globals.keep_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 FPS...')
+        create_video(roop.globals.target_path)
+    # handle audio
+    if roop.globals.skip_audio:
+        move_temp(roop.globals.target_path, roop.globals.output_path)
+        update_status('Skipping audio...')
+    else:
+        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)
+    # clean temp
+    update_status('Cleaning temporary resources...')
+    clean_temp(roop.globals.target_path)
+    # validate video
+    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)
+    sys.exit()
+
+
+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 +1,54 @@
+import threading
+from typing import Any, Optional, List
+import insightface
+import numpy
+
+import roop.globals
+from roop.typing import Frame, Face
+
+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)
+    return FACE_ANALYSER
+
+
+def clear_face_analyser() -> Any:
+    global FACE_ANALYSER
+
+    FACE_ANALYSER = None
+
+
+def get_one_face(frame: Frame, position: int = 0) -> Optional[Face]:
+    many_faces = get_many_faces(frame)
+    if many_faces:
+        try:
+            return many_faces[position]
+        except IndexError:
+            return many_faces[-1]
+    return None
+
+
+def get_many_faces(frame: Frame) -> Optional[List[Face]]:
+    try:
+        return get_face_analyser().get(frame)
+    except ValueError:
+        return None
+
+
+def find_similar_face(frame: Frame, reference_face: Face) -> Optional[Face]:
+    many_faces = get_many_faces(frame)
+    if many_faces:
+        for face in many_faces:
+            if hasattr(face, 'normed_embedding') and hasattr(reference_face, 'normed_embedding'):
+                distance = numpy.sum(numpy.square(face.normed_embedding - reference_face.normed_embedding))
+                if distance < roop.globals.similar_face_distance:
+                    return face
+    return None

roop/face_reference.py

@@ -0,0 +1,21 @@
+from typing import Optional
+
+from roop.typing import Face
+
+FACE_REFERENCE = None
+
+
+def get_face_reference() -> Optional[Face]:
+    return FACE_REFERENCE
+
+
+def set_face_reference(face: Face) -> None:
+    global FACE_REFERENCE
+
+    FACE_REFERENCE = face
+
+
+def clear_face_reference() -> None:
+    global FACE_REFERENCE
+
+    FACE_REFERENCE = None

roop/globals.py

@@ -0,0 +1,22 @@
+from typing import List, Optional
+
+source_path: Optional[str] = None
+target_path: Optional[str] = None
+output_path: Optional[str] = None
+headless: Optional[bool] = None
+frame_processors: List[str] = []
+keep_fps: Optional[bool] = None
+keep_frames: Optional[bool] = None
+skip_audio: Optional[bool] = None
+many_faces: Optional[bool] = None
+reference_face_position: Optional[int] = None
+reference_frame_number: Optional[int] = None
+similar_face_distance: Optional[float] = None
+temp_frame_format: Optional[str] = None
+temp_frame_quality: Optional[int] = None
+output_video_encoder: Optional[str] = None
+output_video_quality: Optional[int] = None
+max_memory: Optional[int] = None
+execution_providers: List[str] = []
+execution_threads: Optional[int] = None
+log_level: str = 'error'

roop/metadata.py

@@ -0,0 +1,2 @@
+name = 'roop'
+version = '1.3.2'

roop/predictor.py

@@ -0,0 +1,43 @@
+import threading
+import numpy
+import opennsfw2
+from PIL import Image
+from keras import Model
+
+from roop.typing import Frame
+
+PREDICTOR = None
+THREAD_LOCK = threading.Lock()
+MAX_PROBABILITY = 0.85
+
+
+def get_predictor() -> Model:
+    global PREDICTOR
+
+    with THREAD_LOCK:
+        if PREDICTOR is None:
+            PREDICTOR = opennsfw2.make_open_nsfw_model()
+    return PREDICTOR
+
+
+def clear_predictor() -> None:
+    global PREDICTOR
+
+    PREDICTOR = None
+
+
+def predict_frame(target_frame: Frame) -> bool:
+    image = Image.fromarray(target_frame)
+    image = opennsfw2.preprocess_image(image, opennsfw2.Preprocessing.YAHOO)
+    views = numpy.expand_dims(image, axis=0)
+    _, probability = get_predictor().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 +1,91 @@
+import os
+import sys
+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 ModuleNotFoundError:
+        sys.exit(f'Frame processor {frame_processor} not found.')
+    except NotImplementedError:
+        sys.exit(f'Frame processor {frame_processor} not implemented correctly.')
+    return frame_processor_module
+
+
+def get_frame_processors_modules(frame_processors: List[str]) -> List[ModuleType]:
+    global FRAME_PROCESSORS_MODULES
+
+    if not FRAME_PROCESSORS_MODULES:
+        for frame_processor in frame_processors:
+            frame_processor_module = load_frame_processor_module(frame_processor)
+            FRAME_PROCESSORS_MODULES.append(frame_processor_module)
+    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 = max(len(temp_frame_paths) // roop.globals.execution_threads, 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 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)