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SimSwap/.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

SimSwap/.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+docs/ppt/
+checkpoints/
+*.tar
+*.patch
+*.zip
+*.avi
+*.pdf
+*.pptx
+
+*.pth
+*.onnx
+wandb/
+temp_results/
+output/*.*
+/cr

SimSwap/LICENSE

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+     independent of the terms and conditions of this Public License.
+
+Section 8 -- Interpretation.
+
+  a. For the avoidance of doubt, this Public License does not, and
+     shall not be interpreted to, reduce, limit, restrict, or impose
+     conditions on any use of the Licensed Material that could lawfully
+     be made without permission under this Public License.
+
+  b. To the extent possible, if any provision of this Public License is
+     deemed unenforceable, it shall be automatically reformed to the
+     minimum extent necessary to make it enforceable. If the provision
+     cannot be reformed, it shall be severed from this Public License
+     without affecting the enforceability of the remaining terms and
+     conditions.
+
+  c. No term or condition of this Public License will be waived and no
+     failure to comply consented to unless expressly agreed to by the
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+
+  d. Nothing in this Public License constitutes or may be interpreted
+     as a limitation upon, or waiver of, any privileges and immunities
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+Creative Commons may be contacted at creativecommons.org.

SimSwap/MultiSpecific.ipynb

@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"MultiSpecific.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyNw8SfPWhG77cf/e7YZd178"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"7_gtFoV8BuRx"},"source":["This is an example of SimSwap on processing video with multiple faces with designated sources.\n","\n","Code path: https://github.com/neuralchen/SimSwap\n","Paper path: https://arxiv.org/pdf/2106.06340v1.pdf."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0Y1RfpzsCAl9","executionInfo":{"status":"ok","timestamp":1625380781426,"user_tz":-480,"elapsed":586,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}},"outputId":"2a897b34-72f1-4515-ac6f-2f0e2d4ea4f7"},"source":["## make sure you are using a runtime with GPU\n","## you can check at Runtime/Change runtime type in the top bar.\n","!nvidia-smi"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Sun Jul  4 06:39:39 2021       \n","+-----------------------------------------------------------------------------+\n","| NVIDIA-SMI 465.27       Driver Version: 460.32.03    CUDA Version: 11.2     |\n","|-------------------------------+----------------------+----------------------+\n","| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n","| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n","|                               |                      |               MIG M. |\n","|===============================+======================+======================|\n","|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n","| N/A   45C    P8     9W /  70W |      0MiB / 15109MiB |      0%      Default |\n","|                               |                      |                  N/A |\n","+-------------------------------+----------------------+----------------------+\n","                                                                               \n","+-----------------------------------------------------------------------------+\n","| Processes:                                                                  |\n","|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n","|        ID   ID                                                   Usage      |\n","|=============================================================================|\n","|  No running processes found                                                 |\n","+-----------------------------------------------------------------------------+\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"0Qzzx2UpDkqw"},"source":["All file changes make by this notebook are temporary. \n","You can try to mount your own google drive to store files if you wang.\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VA_4CeWZCHLP","executionInfo":{"status":"ok","timestamp":1625380786661,"user_tz":-480,"elapsed":4693,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}},"outputId":"d0665552-be92-45b0-aab2-f84c619a51fb"},"source":["!git clone https://github.com/neuralchen/SimSwap\n","!cd SimSwap && git pull"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Cloning into 'SimSwap'...\n","remote: Enumerating objects: 667, done.\u001b[K\n","remote: Counting objects: 100% (48/48), done.\u001b[K\n","remote: Compressing objects: 100% (35/35), done.\u001b[K\n","remote: Total 667 (delta 19), reused 28 (delta 13), pack-reused 619\u001b[K\n","Receiving objects: 100% (667/667), 132.14 MiB | 44.44 MiB/s, done.\n","Resolving deltas: 100% (292/292), done.\n","Already up to date.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Y5K4au_UCkKn","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625380797906,"user_tz":-480,"elapsed":11253,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}},"outputId":"7429f153-bc6d-48c2-eb3c-21f1f02fede9"},"source":["!pip install insightface==0.2.1 onnxruntime moviepy\n","!pip install googledrivedownloader\n","!pip install imageio==2.4.1"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Collecting insightface==0.2.1\n","  Downloading https://files.pythonhosted.org/packages/ee/1e/6395bbe0db665f187c8e49266cda54fcf661f182192370d409423e4943e4/insightface-0.2.1-py2.py3-none-any.whl\n","Collecting onnxruntime\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f9/76/3d0f8bb2776961c7335693df06eccf8d099e48fa6fb552c7546867192603/onnxruntime-1.8.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5MB)\n","\u001b[K     |████████████████████████████████| 4.5MB 37.5MB/s \n","\u001b[?25hRequirement already satisfied: moviepy in /usr/local/lib/python3.7/dist-packages (0.2.3.5)\n","Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (0.22.2.post1)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (2.23.0)\n","Collecting onnx\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3f/9b/54c950d3256e27f970a83cd0504efb183a24312702deed0179453316dbd0/onnx-1.9.0-cp37-cp37m-manylinux2010_x86_64.whl (12.2MB)\n","\u001b[K     |████████████████████████████████| 12.2MB 32.2MB/s \n","\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (3.2.2)\n","Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (7.1.2)\n","Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (0.16.2)\n","Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (4.1.2.30)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (4.41.1)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (1.4.1)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (1.19.5)\n","Requirement already satisfied: easydict in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (1.9)\n","Requirement already satisfied: flatbuffers in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (1.12)\n","Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (3.12.4)\n","Requirement already satisfied: imageio<3.0,>=2.1.2 in /usr/local/lib/python3.7/dist-packages (from moviepy) (2.4.1)\n","Requirement already satisfied: decorator<5.0,>=4.0.2 in /usr/local/lib/python3.7/dist-packages (from moviepy) (4.4.2)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->insightface==0.2.1) (1.0.1)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->insightface==0.2.1) (3.0.4)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->insightface==0.2.1) (1.24.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->insightface==0.2.1) (2021.5.30)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->insightface==0.2.1) (2.10)\n","Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.7/dist-packages (from onnx->insightface==0.2.1) (3.7.4.3)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from onnx->insightface==0.2.1) (1.15.0)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->insightface==0.2.1) (2.8.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->insightface==0.2.1) (0.10.0)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->insightface==0.2.1) (2.4.7)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->insightface==0.2.1) (1.3.1)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->insightface==0.2.1) (2.5.1)\n","Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->insightface==0.2.1) (1.1.1)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf->onnxruntime) (57.0.0)\n","Installing collected packages: onnx, insightface, onnxruntime\n","Successfully installed insightface-0.2.1 onnx-1.9.0 onnxruntime-1.8.0\n","Requirement already satisfied: googledrivedownloader in /usr/local/lib/python3.7/dist-packages (0.4)\n","Requirement already satisfied: imageio==2.4.1 in /usr/local/lib/python3.7/dist-packages (2.4.1)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from imageio==2.4.1) (1.19.5)\n","Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from imageio==2.4.1) (7.1.2)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gQ7ZoIbLFCye","executionInfo":{"status":"ok","timestamp":1625380798405,"user_tz":-480,"elapsed":533,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}},"outputId":"8448a0a3-a19e-44c2-a044-f4d3f9152e91"},"source":["import os\n","os.chdir(\"SimSwap\")\n","!ls"],"execution_count":4,"outputs":[{"output_type":"stream","text":[" crop_224\t        simswaplogo\n"," data\t\t        test_one_image.py\n"," demo_file\t        test_video_swapmulti.py\n"," docs\t\t        test_video_swap_multispecific.py\n"," insightface_func       test_video_swapsingle.py\n"," LICENSE\t        test_video_swapspecific.py\n"," models\t\t        test_wholeimage_swapmulti.py\n"," options\t        test_wholeimage_swap_multispecific.py\n"," output\t\t        test_wholeimage_swapsingle.py\n"," README.md\t        test_wholeimage_swapspecific.py\n","'SimSwap colab.ipynb'   util\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"ZvGp-p0nOmKE","executionInfo":{"status":"ok","timestamp":1625380798407,"user_tz":-480,"elapsed":17,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}}},"source":["## You can upload filed manually\n","# from google.colab import drive\n","# drive.mount('/content/gdrive')"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gLti1J0pEFjJ","executionInfo":{"status":"ok","timestamp":1625380813268,"user_tz":-480,"elapsed":14876,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}},"outputId":"99dc9306-9b9a-475d-cc7d-a3f423bd1e81"},"source":["from google_drive_downloader import GoogleDriveDownloader\n","\n","### it seems that google drive link may not be permenant, you can find this ID from our open url.\n","# GoogleDriveDownloader.download_file_from_google_drive(file_id='1TLNdIufzwesDbyr_nVTR7Zrx9oRHLM_N',\n","#                                     dest_path='./arcface_model/arcface_checkpoint.tar')\n","# GoogleDriveDownloader.download_file_from_google_drive(file_id='1PXkRiBUYbu1xWpQyDEJvGKeqqUFthJcI',\n","#                                     dest_path='./checkpoints.zip')\n","\n","!wget -P ./arcface_model https://github.com/neuralchen/SimSwap/releases/download/1.0/arcface_checkpoint.tar\n","!wget https://github.com/neuralchen/SimSwap/releases/download/1.0/checkpoints.zip\n","!unzip ./checkpoints.zip  -d ./checkpoints\n","!wget -P ./parsing_model/checkpoint https://github.com/neuralchen/SimSwap/releases/download/1.0/79999_iter.pth"],"execution_count":6,"outputs":[{"output_type":"stream","text":["--2021-07-04 06:39:56--  https://github.com/neuralchen/SimSwap/releases/download/1.0/arcface_checkpoint.tar\n","Resolving github.com (github.com)... 140.82.114.3\n","Connecting to github.com (github.com)|140.82.114.3|:443... connected.\n","HTTP request sent, awaiting response... 302 Found\n","Location: https://github-releases.githubusercontent.com/374891081/e17b9d00-dcb8-11eb-8c4f-1412bcea78a6?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20210704%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210704T063956Z&X-Amz-Expires=300&X-Amz-Signature=b6d431c65405e894ddc994061c5fe8fe87db4e71e702513aec01f398a1004825&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=374891081&response-content-disposition=attachment%3B%20filename%3Darcface_checkpoint.tar&response-content-type=application%2Foctet-stream [following]\n","--2021-07-04 06:39:56--  https://github-releases.githubusercontent.com/374891081/e17b9d00-dcb8-11eb-8c4f-1412bcea78a6?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20210704%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210704T063956Z&X-Amz-Expires=300&X-Amz-Signature=b6d431c65405e894ddc994061c5fe8fe87db4e71e702513aec01f398a1004825&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=374891081&response-content-disposition=attachment%3B%20filename%3Darcface_checkpoint.tar&response-content-type=application%2Foctet-stream\n","Resolving github-releases.githubusercontent.com (github-releases.githubusercontent.com)... 185.199.108.154, 185.199.109.154, 185.199.110.154, ...\n","Connecting to github-releases.githubusercontent.com (github-releases.githubusercontent.com)|185.199.108.154|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 766871429 (731M) [application/octet-stream]\n","Saving to: ‘./arcface_model/arcface_checkpoint.tar’\n","\n","arcface_checkpoint. 100%[===================>] 731.34M  64.4MB/s    in 11s     \n","\n","2021-07-04 06:40:07 (68.4 MB/s) - ‘./arcface_model/arcface_checkpoint.tar’ saved [766871429/766871429]\n","\n","--2021-07-04 06:40:07--  https://github.com/neuralchen/SimSwap/releases/download/1.0/checkpoints.zip\n","Resolving github.com (github.com)... 140.82.113.3\n","Connecting to github.com (github.com)|140.82.113.3|:443... connected.\n","HTTP request sent, awaiting response... 302 Found\n","Location: https://github-releases.githubusercontent.com/374891081/a8dac400-dcb6-11eb-933f-977cd7f5f554?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20210704%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210704T063831Z&X-Amz-Expires=300&X-Amz-Signature=3fd2850d03abb9301bf5ba5969d82eb73cb0b940b85e45de2e1e34f1ba2eaf09&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=374891081&response-content-disposition=attachment%3B%20filename%3Dcheckpoints.zip&response-content-type=application%2Foctet-stream [following]\n","--2021-07-04 06:40:07--  https://github-releases.githubusercontent.com/374891081/a8dac400-dcb6-11eb-933f-977cd7f5f554?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20210704%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210704T063831Z&X-Amz-Expires=300&X-Amz-Signature=3fd2850d03abb9301bf5ba5969d82eb73cb0b940b85e45de2e1e34f1ba2eaf09&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=374891081&response-content-disposition=attachment%3B%20filename%3Dcheckpoints.zip&response-content-type=application%2Foctet-stream\n","Resolving github-releases.githubusercontent.com (github-releases.githubusercontent.com)... 185.199.108.154, 185.199.109.154, 185.199.110.154, ...\n","Connecting to github-releases.githubusercontent.com (github-releases.githubusercontent.com)|185.199.108.154|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 256461775 (245M) [application/octet-stream]\n","Saving to: ‘checkpoints.zip’\n","\n","checkpoints.zip     100%[===================>] 244.58M   219MB/s    in 1.1s    \n","\n","2021-07-04 06:40:08 (219 MB/s) - ‘checkpoints.zip’ saved [256461775/256461775]\n","\n","Archive:  ./checkpoints.zip\n","   creating: ./checkpoints/people/\n","  inflating: ./checkpoints/people/iter.txt  \n","  inflating: ./checkpoints/people/latest_net_D1.pth  \n","  inflating: ./checkpoints/people/latest_net_D2.pth  \n","  inflating: ./checkpoints/people/latest_net_G.pth  \n","  inflating: ./checkpoints/people/loss_log.txt  \n","  inflating: ./checkpoints/people/opt.txt  \n","   creating: ./checkpoints/people/web/\n","   creating: ./checkpoints/people/web/images/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fJ9DYRrCPIUL","executionInfo":{"status":"ok","timestamp":1625380821122,"user_tz":-480,"elapsed":7869,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}},"outputId":"a3d1d841-440c-4244-8045-cb0ce3cc81fd"},"source":["!wget --no-check-certificate \"https://sh23tw.dm.files.1drv.com/y4mmGiIkNVigkSwOKDcV3nwMJulRGhbtHdkheehR5TArc52UjudUYNXAEvKCii2O5LAmzGCGK6IfleocxuDeoKxDZkNzDRSt4ZUlEt8GlSOpCXAFEkBwaZimtWGDRbpIGpb_pz9Nq5jATBQpezBS6G_UtspWTkgrXHHxhviV2nWy8APPx134zOZrUIbkSF6xnsqzs3uZ_SEX_m9Rey0ykpx9w\" -O antelope.zip\n","!unzip ./antelope.zip -d ./insightface_func/models/"],"execution_count":7,"outputs":[{"output_type":"stream","text":["--2021-07-04 06:40:11--  https://sh23tw.dm.files.1drv.com/y4mmGiIkNVigkSwOKDcV3nwMJulRGhbtHdkheehR5TArc52UjudUYNXAEvKCii2O5LAmzGCGK6IfleocxuDeoKxDZkNzDRSt4ZUlEt8GlSOpCXAFEkBwaZimtWGDRbpIGpb_pz9Nq5jATBQpezBS6G_UtspWTkgrXHHxhviV2nWy8APPx134zOZrUIbkSF6xnsqzs3uZ_SEX_m9Rey0ykpx9w\n","Resolving sh23tw.dm.files.1drv.com (sh23tw.dm.files.1drv.com)... 13.107.42.12\n","Connecting to sh23tw.dm.files.1drv.com (sh23tw.dm.files.1drv.com)|13.107.42.12|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 248024513 (237M) [application/zip]\n","Saving to: ‘antelope.zip’\n","\n","antelope.zip        100%[===================>] 236.53M  52.4MB/s    in 4.7s    \n","\n","2021-07-04 06:40:16 (49.9 MB/s) - ‘antelope.zip’ saved [248024513/248024513]\n","\n","Archive:  ./antelope.zip\n","   creating: ./insightface_func/models/antelope/\n","  inflating: ./insightface_func/models/antelope/glintr100.onnx  \n","  inflating: ./insightface_func/models/antelope/scrfd_10g_bnkps.onnx  \n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"PfSsND36EMvn","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625380827902,"user_tz":-480,"elapsed":6811,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}},"outputId":"8130e97d-b4a0-4988-85fb-e2bc3e755259"},"source":["import cv2\n","import torch\n","import fractions\n","import numpy as np\n","from PIL import Image\n","import torch.nn.functional as F\n","from torchvision import transforms\n","from models.models import create_model\n","from options.test_options import TestOptions\n","from insightface_func.face_detect_crop_multi import Face_detect_crop\n","from util.videoswap_multispecific import video_swap\n","import os\n","import glob"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Imageio: 'ffmpeg-linux64-v3.3.1' was not found on your computer; downloading it now.\n","Try 1. Download from https://github.com/imageio/imageio-binaries/raw/master/ffmpeg/ffmpeg-linux64-v3.3.1 (43.8 MB)\n","Downloading: 8192/45929032 bytes (0.0%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b3432448/45929032 bytes (7.5%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b7036928/45929032 bytes (15.3%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b10641408/45929032 bytes (23.2%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b14278656/45929032 bytes (31.1%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b18104320/45929032 bytes (39.4%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b21954560/45929032 bytes (47.8%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b25780224/45929032 bytes (56.1%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b29736960/45929032 bytes (64.7%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b33488896/45929032 bytes (72.9%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b37093376/45929032 bytes (80.8%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b40689664/45929032 bytes (88.6%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b44392448/45929032 bytes (96.7%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b45929032/45929032 bytes (100.0%)\n","  Done\n","File saved as /root/.imageio/ffmpeg/ffmpeg-linux64-v3.3.1.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"rxSbZ2EDNDlf","executionInfo":{"status":"ok","timestamp":1625380827903,"user_tz":-480,"elapsed":12,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}}},"source":["def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0\n","\n","transformer = transforms.Compose([\n","        transforms.ToTensor(),\n","        #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n","    ])\n","\n","transformer_Arcface = transforms.Compose([\n","        transforms.ToTensor(),\n","        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n","    ])\n"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ye8iS0UVPMRg","executionInfo":{"status":"ok","timestamp":1625380828574,"user_tz":-480,"elapsed":680,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}},"outputId":"cb5a4b02-b1d0-4ff8-f542-5c1b3c9703d9"},"source":["!ls ./checkpoints"],"execution_count":10,"outputs":[{"output_type":"stream","text":["people\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"wwJOwR9LNKRz","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625380828576,"user_tz":-480,"elapsed":13,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}},"outputId":"0f92f785-4d9c-4130-b24d-76871b2dafba"},"source":["opt = TestOptions()\n","opt.initialize()\n","opt.parser.add_argument('-f') ## dummy arg to avoid bug\n","opt = opt.parse()\n","opt.multisepcific_dir = './demo_file/multispecific' ## or replace it with folder from your own google drive\n","                           ## and remember to follow the dir structure in usage.md\n","opt.video_path = './demo_file/multi_people_1080p.mp4' ## or replace it with video from your own google drive\n","opt.output_path = './output/multi_test_multispecific.mp4'\n","opt.temp_path = './tmp'\n","opt.Arc_path = './arcface_model/arcface_checkpoint.tar'\n","opt.name = 'people'\n","opt.isTrain = False\n","opt.use_mask = True  ## new feature up-to-date\n","\n","crop_size = opt.crop_size\n"],"execution_count":11,"outputs":[{"output_type":"stream","text":["------------ Options -------------\n","Arc_path: models/BEST_checkpoint.tar\n","aspect_ratio: 1.0\n","batchSize: 8\n","checkpoints_dir: ./checkpoints\n","cluster_path: features_clustered_010.npy\n","data_type: 32\n","dataroot: ./datasets/cityscapes/\n","display_winsize: 512\n","engine: None\n","export_onnx: None\n","f: /root/.local/share/jupyter/runtime/kernel-19937219-895d-4d02-9a72-5cfa0e889adf.json\n","feat_num: 3\n","fineSize: 512\n","fp16: False\n","gpu_ids: [0]\n","how_many: 50\n","id_thres: 0.03\n","image_size: 224\n","input_nc: 3\n","instance_feat: False\n","isTrain: False\n","label_feat: False\n","label_nc: 0\n","latent_size: 512\n","loadSize: 1024\n","load_features: False\n","local_rank: 0\n","max_dataset_size: inf\n","model: pix2pixHD\n","multisepcific_dir: ./demo_file/multispecific\n","nThreads: 2\n","n_blocks_global: 6\n","n_blocks_local: 3\n","n_clusters: 10\n","n_downsample_E: 4\n","n_downsample_global: 3\n","n_local_enhancers: 1\n","name: people\n","nef: 16\n","netG: global\n","ngf: 64\n","niter_fix_global: 0\n","no_flip: False\n","no_instance: False\n","no_simswaplogo: False\n","norm: batch\n","norm_G: spectralspadesyncbatch3x3\n","ntest: inf\n","onnx: None\n","output_nc: 3\n","output_path: ./output/\n","phase: test\n","pic_a_path: ./crop_224/gdg.jpg\n","pic_b_path: ./crop_224/zrf.jpg\n","pic_specific_path: ./crop_224/zrf.jpg\n","resize_or_crop: scale_width\n","results_dir: ./results/\n","semantic_nc: 3\n","serial_batches: False\n","temp_path: ./temp_results\n","tf_log: False\n","use_dropout: False\n","use_encoded_image: False\n","verbose: False\n","video_path: ./demo_file/multi_people_1080p.mp4\n","which_epoch: latest\n","-------------- End ----------------\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UFt8zQrAMq9F","executionInfo":{"status":"ok","timestamp":1625381428564,"user_tz":-480,"elapsed":599996,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}},"outputId":"46188013-3f6d-4174-9959-d1fd203dcc0d"},"source":["pic_specific = opt.pic_specific_path\n","crop_size = opt.crop_size\n","multisepcific_dir = opt.multisepcific_dir\n","\n","torch.nn.Module.dump_patches = True\n","model = create_model(opt)\n","model.eval()\n","\n","app = Face_detect_crop(name='antelope', root='./insightface_func/models')\n","app.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640))\n","# The specific person to be swapped(source)\n","source_specific_id_nonorm_list = []\n","source_path = os.path.join(multisepcific_dir,'SRC_*')\n","source_specific_images_path = sorted(glob.glob(source_path))\n","\n","with torch.no_grad():\n","    for source_specific_image_path in source_specific_images_path:\n","        specific_person_whole = cv2.imread(source_specific_image_path)\n","        specific_person_align_crop, _ = app.get(specific_person_whole,crop_size)\n","        specific_person_align_crop_pil = Image.fromarray(cv2.cvtColor(specific_person_align_crop[0],cv2.COLOR_BGR2RGB)) \n","        specific_person = transformer_Arcface(specific_person_align_crop_pil)\n","        specific_person = specific_person.view(-1, specific_person.shape[0], specific_person.shape[1], specific_person.shape    [2])\n","        # convert numpy to tensor\n","        specific_person = specific_person.cuda()\n","        #create latent id\n","        specific_person_downsample = F.interpolate(specific_person, size=(112,112))\n","        specific_person_id_nonorm = model.netArc(specific_person_downsample)\n","        source_specific_id_nonorm_list.append(specific_person_id_nonorm.clone())\n","\n","    # The person who provides id information (list)\n","    target_id_norm_list = []\n","    target_path = os.path.join(multisepcific_dir,'DST_*')\n","    target_images_path = sorted(glob.glob(target_path))\n","\n","    for target_image_path in target_images_path:\n","        img_a_whole = cv2.imread(target_image_path)\n","        img_a_align_crop, _ = app.get(img_a_whole,crop_size)\n","        img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0],cv2.COLOR_BGR2RGB)) \n","        img_a = transformer_Arcface(img_a_align_crop_pil)\n","        img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])\n","        # convert numpy to tensor\n","        img_id = img_id.cuda()\n","        #create latent id\n","        img_id_downsample = F.interpolate(img_id, size=(112,112))\n","        latend_id = model.netArc(img_id_downsample)\n","        latend_id = F.normalize(latend_id, p=2, dim=1)\n","        target_id_norm_list.append(latend_id.clone())\n","        \n","    assert len(target_id_norm_list) == len(source_specific_id_nonorm_list), \"The number of images in source and target  directory must be same !!!\"\n","    video_swap(opt.video_path, target_id_norm_list,source_specific_id_nonorm_list, opt.id_thres, \\\n","        model, app, opt.output_path,temp_results_dir=opt.temp_path,no_simswaplogo=opt.no_simswaplogo,use_mask=opt.use_mask)"],"execution_count":12,"outputs":[{"output_type":"stream","text":["input mean and std: 127.5 127.5\n","find model: ./insightface_func/models/antelope/glintr100.onnx recognition\n","find model: ./insightface_func/models/antelope/scrfd_10g_bnkps.onnx detection\n","set det-size: (640, 640)\n"],"name":"stdout"},{"output_type":"stream","text":["\r  0%|          | 0/594 [00:00<?, ?it/s]"],"name":"stderr"},{"output_type":"stream","text":["(142, 366, 4)\n"],"name":"stdout"},{"output_type":"stream","text":["100%|██████████| 594/594 [08:28<00:00,  1.17it/s]\n"],"name":"stderr"},{"output_type":"stream","text":["[MoviePy] >>>> Building video ./output/multi_test_multispecific.mp4\n","[MoviePy] Writing audio in multi_test_multispecificTEMP_MPY_wvf_snd.mp3\n"],"name":"stdout"},{"output_type":"stream","text":["100%|██████████| 438/438 [00:00<00:00, 832.16it/s]"],"name":"stderr"},{"output_type":"stream","text":["[MoviePy] Done.\n","[MoviePy] Writing video ./output/multi_test_multispecific.mp4\n"],"name":"stdout"},{"output_type":"stream","text":["\n","100%|██████████| 595/595 [00:53<00:00, 11.11it/s]\n"],"name":"stderr"},{"output_type":"stream","text":["[MoviePy] Done.\n","[MoviePy] >>>> Video ready: ./output/multi_test_multispecific.mp4 \n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Rty2GsyZZrI6"},"source":[],"execution_count":null,"outputs":[]}]}

SimSwap/README.md

@@ -0,0 +1,247 @@
+# SimSwap: An Efficient Framework For High Fidelity Face Swapping
+## Proceedings of the 28th ACM International Conference on Multimedia
+**The official repository with Pytorch**
+
+**Our method can realize **arbitrary face swapping** on images and videos with **one single trained model**.**
+
+***We are recruiting full-time engineers. If you are interested, please send an [email](mailto:chen19910528@sjtu.edu.cn?subject=[GitHub]%20Source%20Han%20Sans) to my team. Please refer to the website for specific recruitment conditions: [Requirements](https://join.sjtu.edu.cn/Admin/QsPreview.aspx?qsid=44f5413a90974114b8f5e643177ef32d)***
+
+Training and test code are now available!
+[ <a href="https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/train.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>](https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/train.ipynb)
+
+We are working with our incoming paper SimSwap++, keeping expecting!
+
+The high resolution version of ***SimSwap-HQ*** is supported!
+
+[![simswaplogo](/docs/img/logo1.png)](https://github.com/neuralchen/SimSwap)
+
+Our paper can be downloaded from [[Arxiv]](https://arxiv.org/pdf/2106.06340v1.pdf) [[ACM DOI]](https://dl.acm.org/doi/10.1145/3394171.3413630)
+
+
+### This project also received support from [SocialBook](https://socialbook.io).
+<!-- [![logo](./simswaplogo/socialbook_logo.2020.357eed90add7705e54a8.svg)](https://socialbook.io) -->
+<img width=30% src="./simswaplogo/socialbook_logo.2020.357eed90add7705e54a8.svg"/>
+
+<!-- [[Google Drive]](https://drive.google.com/file/d/1fcfWOGt1mkBo7F0gXVKitf8GJMAXQxZD/view?usp=sharing) 
+[[Baidu Drive ]](https://pan.baidu.com/s/1-TKFuycRNUKut8hn4IimvA) Password: ```ummt``` -->
+
+## Attention
+***This project is for technical and academic use only. Please do not apply it to illegal and unethical scenarios.***
+
+***In the event of violation of the legal and ethical requirements of the user's country or region, this code repository is exempt from liability***
+
+***Please do not ignore the content at the end of this README!***
+
+If you find this project useful, please star it. It is the greatest appreciation of our work.
+
+## Top News <img width=8% src="./docs/img/new.gif"/>
+
+**`2023-09-26`**: We fixed bugs in colab!
+
+**`2023-04-25`**: We fixed the "AttributeError: 'SGD' object has no attribute 'defaults' now" bug. If you have already downloaded **arcface_checkpoint.tar**, please **download it again**. Also, you also need to update the scripts in ```./models/```.
+
+**`2022-04-21`**: For resource limited users, we provide the cropped VGGFace2-224 dataset [[Google Driver] VGGFace2-224 (10.8G)](https://drive.google.com/file/d/19pWvdEHS-CEG6tW3PdxdtZ5QEymVjImc/view?usp=sharing) [[Baidu Driver]](https://pan.baidu.com/s/1OiwLJHVBSYB4AY2vEcfN0A) [Password: lrod].
+
+**`2022-04-20`**: Training scripts are now available. We highly recommend that you guys train the simswap model with our released high quality dataset [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ).
+
+**`2021-11-24`**: We have trained a beta version of ***SimSwap-HQ*** on [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ) and open sourced the checkpoint of this model (if you think the Simswap 512 is cool, please star our  [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ) repo). Please don’t forget to go to [Preparation](./docs/guidance/preparation.md) and [Inference for image or video face swapping](./docs/guidance/usage.md) to check the latest set up.
+
+**`2021-11-23`**: The google drive link of [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ) is released. 
+
+**`2021-11-17`**: We released a high resolution face dataset [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ) and the method to generate this dataset. This dataset is for research purpose. 
+
+**`2021-08-30`**: Docker has been supported, please refer [here](https://replicate.ai/neuralchen/simswap-image) for details.
+
+**`2021-08-17`**: We have updated the [Preparation](./docs/guidance/preparation.md), The main change is that the gpu version of onnx is now installed by default, Now the time to process a video is greatly reduced.
+
+**`2021-07-19`**: ***Obvious border abruptness has been resolved***. We add the ability to using mask and upgrade the old algorithm for better visual effect, please go to [Inference for image or video face swapping](./docs/guidance/usage.md) for details. Please don’t forget to go to [Preparation](./docs/guidance/preparation.md) to check the latest set up. (Thanks for the help from [@woctezuma](https://github.com/woctezuma) and [@instant-high](https://github.com/instant-high))
+
+## The first open source high resolution dataset for face swapping!!!
+## High Resolution Dataset [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ)
+
+[![logo](./docs/img/vggface2_hq_compare.png)](https://github.com/NNNNAI/VGGFace2-HQ)
+
+
+
+
+## Dependencies
+- python3.6+
+- pytorch1.5+
+- torchvision
+- opencv
+- pillow
+- numpy
+- imageio
+- moviepy
+- insightface
+- ***timm==0.5.4***
+
+## Training
+
+[Preparation](./docs/guidance/preparation.md)
+
+The training script is slightly different from the original version, e.g., we replace the patch discriminator with the projected discriminator, which saves a lot of hardware overhead and achieves slightly better results.
+
+In order to ensure the normal training, the batch size must be greater than 1.
+
+Friendly reminder, due to the difference in training settings, the user-trained model will have subtle differences in visual effects from the pre-trained model we provide.
+
+- Train 224 models with VGGFace2 224*224 [[Google Driver] VGGFace2-224 (10.8G)](https://drive.google.com/file/d/19pWvdEHS-CEG6tW3PdxdtZ5QEymVjImc/view?usp=sharing) [[Baidu Driver] ](https://pan.baidu.com/s/1OiwLJHVBSYB4AY2vEcfN0A) [Password: lrod]
+
+For faster convergence and better results, a large batch size (more than 16) is recommended!
+
+***We recommend training more than 400K iterations (batch size is 16), 600K~800K will be better, more iterations will not be recommended.***
+
+
+```
+python train.py --name simswap224_test --batchSize 8  --gpu_ids 0 --dataset /path/to/VGGFace2HQ --Gdeep False
+```
+
+[Colab demo for training 224 model][ <a href="https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/train.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>](https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/train.ipynb)
+
+For faster convergence and better results, a large batch size (more than 16) is recommended!
+
+- Train 512 models with VGGFace2-HQ 512*512 [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ).
+```
+python train.py --name simswap512_test  --batchSize 16  --gpu_ids 0 --dataset /path/to/VGGFace2HQ --Gdeep True
+```
+
+
+
+## Inference with a pretrained SimSwap model
+[Preparation](./docs/guidance/preparation.md)
+
+[Inference for image or video face swapping](./docs/guidance/usage.md)
+
+[Colab demo](https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/SimSwap%20colab.ipynb)
+
+<div style="background: yellow; width:140px; font-weight:bold;font-family: sans-serif;">Stronger feature</div>
+
+[Colab for switching specific faces in multi-face videos][ <a href="https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/MultiSpecific.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>](https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/MultiSpecific.ipynb)
+
+[Image face swapping demo & Docker image on Replicate](https://replicate.ai/neuralchen/simswap-image)
+
+
+
+## Video
+<img src="./docs/img/video.webp"/>
+<div>
+<img width=24% src="./docs/img/anni.webp"/>
+<img width=24% src="./docs/img/chenglong.webp"/>
+<img width=24% src="./docs/img/zhoujielun.webp"/>
+<img width=24% src="./docs/img/zhuyin.webp"/>
+</div>
+<div>
+<img width=49% src="./docs/img/mama_mask_short.webp"/>
+<img width=49% src="./docs/img/mama_mask_wuyifan_short.webp"/>
+</div>
+
+## Results
+![Results1](/docs/img/results1.PNG)
+
+![Results2](/docs/img/total.PNG)
+
+
+<!-- ![video2](/docs/img/anni.webp)
+![video3](/docs/img/chenglong.webp)
+![video4](/docs/img/zhoujielun.webp)
+![video5](/docs/img/zhuyin.webp) -->
+
+
+**High-quality videos can be found in the link below:**
+
+[[Mama(video) 1080p]](https://drive.google.com/file/d/1mnSlwzz7f4H2O7UwApAHo64mgK4xSNyK/view?usp=sharing)
+
+[[Google Drive link for video 1]](https://drive.google.com/file/d/1hdne7Gw39d34zt3w1NYV3Ln5cT8PfCNm/view?usp=sharing)
+
+[[Google Drive link for video 2]](https://drive.google.com/file/d/1bDEg_pVeFYLnf9QLSMuG8bsjbRPk0X5_/view?usp=sharing)
+
+[[Google Drive link for video 3]](https://drive.google.com/file/d/1oftHAnLmgFis4XURcHTccGSWbWSXYKK1/view?usp=sharing)
+
+[[Baidu Drive link for video]](https://pan.baidu.com/s/1WTS6jm2TY17bYJurw57LUg ) Password: ```b26n```
+
+[[Online Video]](https://www.bilibili.com/video/BV12v411p7j5/)
+
+## User case
+If you have some interesting results after using our project and are willing to share, you can contact us by email or share directly on the issue. Later, we may make a separate section to show these results, which should be cool.
+
+At the same time, if you have suggestions for our project, please feel free to ask questions in the issue, or contact us directly via email: [email1](mailto:chenxuanhongzju@outlook.com), [email2](mailto:nicklau26@foxmail.com), [email3](mailto:ziangliu824@gmail.com). (All three can be contacted, just choose any one)
+
+## License
+For academic and non-commercial use only.The whole project is under the CC-BY-NC 4.0 license. See [LICENSE](https://github.com/neuralchen/SimSwap/blob/main/LICENSE) for additional details.
+
+
+## To cite our papers
+```
+@inproceedings{DBLP:conf/mm/ChenCNG20,
+  author    = {Renwang Chen and
+               Xuanhong Chen and
+               Bingbing Ni and
+               Yanhao Ge},
+  title     = {SimSwap: An Efficient Framework For High Fidelity Face Swapping},
+  booktitle = {{MM} '20: The 28th {ACM} International Conference on Multimedia},
+  year      = {2020}
+}
+```
+```
+@Article{simswapplusplus,
+    author  = {Xuanhong Chen and
+              Bingbing Ni and
+              Yutian Liu and
+              Naiyuan Liu and
+              Zhilin Zeng and
+              Hang Wang},
+    title   = {SimSwap++: Towards Faster and High-Quality Identity Swapping},
+    journal = {{IEEE} Trans. Pattern Anal. Mach. Intell.},
+    volume  = {46},
+    number  = {1},
+    pages   = {576--592},
+    year    = {2024}
+}
+```
+
+## Related Projects
+
+**Please visit our another ACMMM2020 high-quality style transfer project**
+
+[![logo](./docs/img/logo.png)](https://github.com/neuralchen/ASMAGAN)
+
+[![title](/docs/img/title.png)](https://github.com/neuralchen/ASMAGAN)
+
+**Please visit our AAAI2021 sketch based rendering project**
+
+[![logo](./docs/img/girl2.gif)](https://github.com/TZYSJTU/Sketch-Generation-with-Drawing-Process-Guided-by-Vector-Flow-and-Grayscale)
+[![title](/docs/img/girl2-RGB.png)](https://github.com/TZYSJTU/Sketch-Generation-with-Drawing-Process-Guided-by-Vector-Flow-and-Grayscale)
+
+**Please visit our high resolution face dataset VGGFace2-HQ**
+
+[![logo](./docs/img/vggface2_hq_compare.png)](https://github.com/NNNNAI/VGGFace2-HQ)
+
+Learn about our other projects 
+
+[[VGGFace2-HQ]](https://github.com/NNNNAI/VGGFace2-HQ);
+
+[[RainNet]](https://neuralchen.github.io/RainNet);
+
+[[Sketch Generation]](https://github.com/TZYSJTU/Sketch-Generation-with-Drawing-Process-Guided-by-Vector-Flow-and-Grayscale);
+
+[[CooGAN]](https://github.com/neuralchen/CooGAN);
+
+[[Knowledge Style Transfer]](https://github.com/AceSix/Knowledge_Transfer);
+
+[[SimSwap]](https://github.com/neuralchen/SimSwap);
+
+[[ASMA-GAN]](https://github.com/neuralchen/ASMAGAN);
+
+[[SNGAN-Projection-pytorch]](https://github.com/neuralchen/SNGAN_Projection)
+
+[[Pretrained_VGG19]](https://github.com/neuralchen/Pretrained_VGG19).
+
+## Acknowledgements
+
+<!--ts-->
+* [Deepfacelab](https://github.com/iperov/DeepFaceLab)
+* [Insightface](https://github.com/deepinsight/insightface)
+* [Face-parsing.PyTorch](https://github.com/zllrunning/face-parsing.PyTorch)
+* [BiSeNet](https://github.com/CoinCheung/BiSeNet)
+<!--te-->

SimSwap/SimSwap colab.ipynb

@@ -0,0 +1,579 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "SimSwap colab.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7_gtFoV8BuRx"
+      },
+      "source": [
+        "This is a simple example of SimSwap on processing video with multiple faces. You can change the codes for inference based on our other scripts for image or single face swapping.\n",
+        "\n",
+        "Code path: https://github.com/neuralchen/SimSwap\n",
+        "\n",
+        "Paper path: https://arxiv.org/pdf/2106.06340v1.pdf or https://dl.acm.org/doi/10.1145/3394171.3413630"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0Y1RfpzsCAl9",
+        "outputId": "a39470a0-9689-409d-a0a4-e2afd5d3b5dd"
+      },
+      "source": [
+        "## make sure you are using a runtime with GPU\n",
+        "## you can check at Runtime/Change runtime type in the top bar.\n",
+        "!nvidia-smi"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Mon Jun 21 02:13:20 2021       \n",
+            "+-----------------------------------------------------------------------------+\n",
+            "| NVIDIA-SMI 465.27       Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
+            "|-------------------------------+----------------------+----------------------+\n",
+            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+            "|                               |                      |               MIG M. |\n",
+            "|===============================+======================+======================|\n",
+            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
+            "| N/A   45C    P8    10W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
+            "|                               |                      |                  N/A |\n",
+            "+-------------------------------+----------------------+----------------------+\n",
+            "                                                                               \n",
+            "+-----------------------------------------------------------------------------+\n",
+            "| Processes:                                                                  |\n",
+            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+            "|        ID   ID                                                   Usage      |\n",
+            "|=============================================================================|\n",
+            "|  No running processes found                                                 |\n",
+            "+-----------------------------------------------------------------------------+\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0Qzzx2UpDkqw"
+      },
+      "source": [
+        "## Installation\n",
+        "\n",
+        "All file changes made by this notebook are temporary. \n",
+        "You can try to mount your own google drive to store files if you want.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VA_4CeWZCHLP",
+        "outputId": "4b0f176f-87e7-4772-8b47-c2098d8f3bf6"
+      },
+      "source": [
+        "!git clone https://github.com/neuralchen/SimSwap\n",
+        "!cd SimSwap && git pull"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Cloning into 'SimSwap'...\n",
+            "remote: Enumerating objects: 362, done.\u001b[K\n",
+            "remote: Counting objects: 100% (362/362), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (281/281), done.\u001b[K\n",
+            "remote: Total 362 (delta 149), reused 272 (delta 67), pack-reused 0\u001b[K\n",
+            "Receiving objects: 100% (362/362), 101.31 MiB | 32.47 MiB/s, done.\n",
+            "Resolving deltas: 100% (149/149), done.\n",
+            "Already up to date.\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Y5K4au_UCkKn",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9691a7a4-192e-4ec2-c3c1-1f2c933d7b6a"
+      },
+      "source": [
+        "!pip install insightface==0.2.1 onnxruntime moviepy\n",
+        "!pip install googledrivedownloader\n",
+        "!pip install imageio==2.4.1"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Collecting insightface==0.2.1\n",
+            "  Downloading https://files.pythonhosted.org/packages/ee/1e/6395bbe0db665f187c8e49266cda54fcf661f182192370d409423e4943e4/insightface-0.2.1-py2.py3-none-any.whl\n",
+            "Collecting onnxruntime\n",
+            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f9/76/3d0f8bb2776961c7335693df06eccf8d099e48fa6fb552c7546867192603/onnxruntime-1.8.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5MB)\n",
+            "\u001b[K     |████████████████████████████████| 4.5MB 10.2MB/s \n",
+            "\u001b[?25hRequirement already satisfied: moviepy in /usr/local/lib/python3.7/dist-packages (0.2.3.5)\n",
+            "Collecting onnx\n",
+            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3f/9b/54c950d3256e27f970a83cd0504efb183a24312702deed0179453316dbd0/onnx-1.9.0-cp37-cp37m-manylinux2010_x86_64.whl (12.2MB)\n",
+            "\u001b[K     |████████████████████████████████| 12.2MB 51.4MB/s \n",
+            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (3.2.2)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (4.41.1)\n",
+            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (7.1.2)\n",
+            "Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (0.16.2)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (2.23.0)\n",
+            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (0.22.2.post1)\n",
+            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (4.1.2.30)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (1.19.5)\n",
+            "Requirement already satisfied: easydict in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (1.9)\n",
+            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (1.4.1)\n",
+            "Requirement already satisfied: flatbuffers in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (1.12)\n",
+            "Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (3.12.4)\n",
+            "Requirement already satisfied: decorator<5.0,>=4.0.2 in /usr/local/lib/python3.7/dist-packages (from moviepy) (4.4.2)\n",
+            "Requirement already satisfied: imageio<3.0,>=2.1.2 in /usr/local/lib/python3.7/dist-packages (from moviepy) (2.4.1)\n",
+            "Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.7/dist-packages (from onnx->insightface==0.2.1) (3.7.4.3)\n",
+            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from onnx->insightface==0.2.1) (1.15.0)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->insightface==0.2.1) (1.3.1)\n",
+            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->insightface==0.2.1) (2.8.1)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->insightface==0.2.1) (0.10.0)\n",
+            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->insightface==0.2.1) (2.4.7)\n",
+            "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->insightface==0.2.1) (2.5.1)\n",
+            "Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->insightface==0.2.1) (1.1.1)\n",
+            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->insightface==0.2.1) (2.10)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->insightface==0.2.1) (2021.5.30)\n",
+            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->insightface==0.2.1) (3.0.4)\n",
+            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->insightface==0.2.1) (1.24.3)\n",
+            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->insightface==0.2.1) (1.0.1)\n",
+            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf->onnxruntime) (57.0.0)\n",
+            "Installing collected packages: onnx, insightface, onnxruntime\n",
+            "Successfully installed insightface-0.2.1 onnx-1.9.0 onnxruntime-1.8.0\n",
+            "Requirement already satisfied: googledrivedownloader in /usr/local/lib/python3.7/dist-packages (0.4)\n",
+            "Requirement already satisfied: imageio==2.4.1 in /usr/local/lib/python3.7/dist-packages (2.4.1)\n",
+            "Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from imageio==2.4.1) (7.1.2)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from imageio==2.4.1) (1.19.5)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gQ7ZoIbLFCye",
+        "outputId": "bb35e7e2-14b7-4f36-d62a-499ba041cf64"
+      },
+      "source": [
+        "import os\n",
+        "os.chdir(\"SimSwap\")\n",
+        "!ls"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            " crop_224\t    models\t\t   test_one_image.py\n",
+            " data\t\t    options\t\t   test_video_swapmulti.py\n",
+            " demo_file\t    output\t\t   test_video_swapsingle.py\n",
+            " doc\t\t    README.md\t\t   test_wholeimage_swapmulti.py\n",
+            " insightface_func  'SimSwap colab.ipynb'   test_wholeimage_swapsingle.py\n",
+            " LICENSE\t    simswaplogo\t\t   util\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gLti1J0pEFjJ",
+        "outputId": "e93c3f98-01df-458e-b791-c32f7343e705"
+      },
+      "source": [
+        "from google_drive_downloader import GoogleDriveDownloader\n",
+        "\n",
+        "### it seems that google drive link may not be permenant, you can find this ID from our open url.\n",
+        "# GoogleDriveDownloader.download_file_from_google_drive(file_id='1TLNdIufzwesDbyr_nVTR7Zrx9oRHLM_N',\n",
+        "#                                     dest_path='./arcface_model/arcface_checkpoint.tar')\n",
+        "# GoogleDriveDownloader.download_file_from_google_drive(file_id='1PXkRiBUYbu1xWpQyDEJvGKeqqUFthJcI',\n",
+        "#                                     dest_path='./checkpoints.zip')\n",
+        "\n",
+        "!wget -P ./arcface_model https://github.com/neuralchen/SimSwap/releases/download/1.0/arcface_checkpoint.tar\n",
+        "!wget https://github.com/neuralchen/SimSwap/releases/download/1.0/checkpoints.zip\n",
+        "!unzip ./checkpoints.zip  -d ./checkpoints\n",
+        "!wget -P ./parsing_model/checkpoint https://github.com/neuralchen/SimSwap/releases/download/1.0/79999_iter.pth"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Downloading 1TLNdIufzwesDbyr_nVTR7Zrx9oRHLM_N into ./arcface_model/arcface_checkpoint.tar... Done.\n",
+            "Downloading 1PXkRiBUYbu1xWpQyDEJvGKeqqUFthJcI into ./checkpoints.zip... Done.\n",
+            "Archive:  ./checkpoints.zip\n",
+            "   creating: ./checkpoints/people/\n",
+            "  inflating: ./checkpoints/people/iter.txt  \n",
+            "  inflating: ./checkpoints/people/latest_net_D1.pth  \n",
+            "  inflating: ./checkpoints/people/latest_net_D2.pth  \n",
+            "  inflating: ./checkpoints/people/latest_net_G.pth  \n",
+            "  inflating: ./checkpoints/people/loss_log.txt  \n",
+            "  inflating: ./checkpoints/people/opt.txt  \n",
+            "   creating: ./checkpoints/people/web/\n",
+            "   creating: ./checkpoints/people/web/images/\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "aSRnK5V4HI-k",
+        "outputId": "e688746c-c33a-485c-808c-54a7370f0c53"
+      },
+      "source": [
+        "## You can upload filed manually\n",
+        "# from google.colab import drive\n",
+        "# drive.mount('/content/gdrive')\n",
+        "\n",
+        "### Now onedrive file can be downloaded in Colab directly!\n",
+        "### If the link blow is not permanent, you can just download it from the \n",
+        "### open url(can be found at [our repo]/doc/guidance/preparation.md) and copy the assigned download link here.\n",
+        "### many thanks to woctezuma for this very useful help\n",
+        "!wget --no-check-certificate \"https://sh23tw.dm.files.1drv.com/y4mmGiIkNVigkSwOKDcV3nwMJulRGhbtHdkheehR5TArc52UjudUYNXAEvKCii2O5LAmzGCGK6IfleocxuDeoKxDZkNzDRSt4ZUlEt8GlSOpCXAFEkBwaZimtWGDRbpIGpb_pz9Nq5jATBQpezBS6G_UtspWTkgrXHHxhviV2nWy8APPx134zOZrUIbkSF6xnsqzs3uZ_SEX_m9Rey0ykpx9w\" -O antelope.zip\n",
+        "!unzip ./antelope.zip -d ./insightface_func/models/\n"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "--2021-06-21 02:14:17--  https://sh23tw.dm.files.1drv.com/y4mmGiIkNVigkSwOKDcV3nwMJulRGhbtHdkheehR5TArc52UjudUYNXAEvKCii2O5LAmzGCGK6IfleocxuDeoKxDZkNzDRSt4ZUlEt8GlSOpCXAFEkBwaZimtWGDRbpIGpb_pz9Nq5jATBQpezBS6G_UtspWTkgrXHHxhviV2nWy8APPx134zOZrUIbkSF6xnsqzs3uZ_SEX_m9Rey0ykpx9w\n",
+            "Resolving sh23tw.dm.files.1drv.com (sh23tw.dm.files.1drv.com)... 13.107.42.12\n",
+            "Connecting to sh23tw.dm.files.1drv.com (sh23tw.dm.files.1drv.com)|13.107.42.12|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 248024513 (237M) [application/zip]\n",
+            "Saving to: ‘antelope.zip’\n",
+            "\n",
+            "antelope.zip        100%[===================>] 236.53M  6.16MB/s    in 31s     \n",
+            "\n",
+            "2021-06-21 02:14:48 (7.66 MB/s) - ‘antelope.zip’ saved [248024513/248024513]\n",
+            "\n",
+            "Archive:  ./antelope.zip\n",
+            "   creating: ./insightface_func/models/antelope/\n",
+            "  inflating: ./insightface_func/models/antelope/glintr100.onnx  \n",
+            "  inflating: ./insightface_func/models/antelope/scrfd_10g_bnkps.onnx  \n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BsGmIMxLVxyO"
+      },
+      "source": [
+        "## Inference"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PfSsND36EMvn",
+        "outputId": "f28c98fd-4c6d-40fa-e3c7-99b606c7492a"
+      },
+      "source": [
+        "import cv2\n",
+        "import torch\n",
+        "import fractions\n",
+        "import numpy as np\n",
+        "from PIL import Image\n",
+        "import torch.nn.functional as F\n",
+        "from torchvision import transforms\n",
+        "from models.models import create_model\n",
+        "from options.test_options import TestOptions\n",
+        "from insightface_func.face_detect_crop_multi import Face_detect_crop\n",
+        "from util.videoswap import video_swap\n",
+        "from util.add_watermark import watermark_image"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Imageio: 'ffmpeg-linux64-v3.3.1' was not found on your computer; downloading it now.\n",
+            "Try 1. Download from https://github.com/imageio/imageio-binaries/raw/master/ffmpeg/ffmpeg-linux64-v3.3.1 (43.8 MB)\n",
+            "Downloading: 8192/45929032 bytes (0.0%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b1286144/45929032 bytes (2.8%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b3653632/45929032 bytes (8.0%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b7479296/45929032 bytes (16.3%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b11526144/45929032 bytes (25.1%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b15171584/45929032 bytes (33.0%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b18997248/45929032 bytes (41.4%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b22724608/45929032 bytes (49.5%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b26673152/45929032 bytes (58.1%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b30728192/45929032 bytes (66.9%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b34725888/45929032 bytes (75.6%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b38879232/45929032 bytes (84.7%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b42680320/45929032 bytes (92.9%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b45929032/45929032 bytes (100.0%)\n",
+            "  Done\n",
+            "File saved as /root/.imageio/ffmpeg/ffmpeg-linux64-v3.3.1.\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "rxSbZ2EDNDlf"
+      },
+      "source": [
+        "transformer = transforms.Compose([\n",
+        "        transforms.ToTensor(),\n",
+        "        #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
+        "    ])\n",
+        "\n",
+        "transformer_Arcface = transforms.Compose([\n",
+        "        transforms.ToTensor(),\n",
+        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
+        "    ])\n",
+        "\n",
+        "detransformer = transforms.Compose([\n",
+        "        transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]),\n",
+        "        transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1])\n",
+        "    ])"
+      ],
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wwJOwR9LNKRz",
+        "outputId": "bdc82f7b-21c4-403f-94d1-b92911698b4a"
+      },
+      "source": [
+        "opt = TestOptions()\n",
+        "opt.initialize()\n",
+        "opt.parser.add_argument('-f') ## dummy arg to avoid bug\n",
+        "opt = opt.parse()\n",
+        "opt.pic_a_path = './demo_file/Iron_man.jpg' ## or replace it with image from your own google drive\n",
+        "opt.video_path = './demo_file/multi_people_1080p.mp4' ## or replace it with video from your own google drive\n",
+        "opt.output_path = './output/demo.mp4'\n",
+        "opt.temp_path = './tmp'\n",
+        "opt.Arc_path = './arcface_model/arcface_checkpoint.tar'\n",
+        "opt.isTrain = False\n",
+        "opt.use_mask = True  ## new feature up-to-date\n",
+        "\n",
+        "crop_size = opt.crop_size\n",
+        "\n",
+        "torch.nn.Module.dump_patches = True\n",
+        "model = create_model(opt)\n",
+        "model.eval()\n",
+        "\n",
+        "app = Face_detect_crop(name='antelope', root='./insightface_func/models')\n",
+        "app.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640))\n",
+        "\n",
+        "with torch.no_grad():\n",
+        "    pic_a = opt.pic_a_path\n",
+        "    # img_a = Image.open(pic_a).convert('RGB')\n",
+        "    img_a_whole = cv2.imread(pic_a)\n",
+        "    img_a_align_crop, _ = app.get(img_a_whole,crop_size)\n",
+        "    img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0],cv2.COLOR_BGR2RGB)) \n",
+        "    img_a = transformer_Arcface(img_a_align_crop_pil)\n",
+        "    img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])\n",
+        "\n",
+        "    # convert numpy to tensor\n",
+        "    img_id = img_id.cuda()\n",
+        "\n",
+        "    #create latent id\n",
+        "    img_id_downsample = F.interpolate(img_id, size=(112,112))\n",
+        "    latend_id = model.netArc(img_id_downsample)\n",
+        "    latend_id = latend_id.detach().to('cpu')\n",
+        "    latend_id = latend_id/np.linalg.norm(latend_id,axis=1,keepdims=True)\n",
+        "    latend_id = latend_id.to('cuda')\n",
+        "\n",
+        "    video_swap(opt.video_path, latend_id, model, app, opt.output_path, temp_results_dir=opt.temp_path, use_mask=opt.use_mask)"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "------------ Options -------------\n",
+            "Arc_path: models/BEST_checkpoint.tar\n",
+            "aspect_ratio: 1.0\n",
+            "batchSize: 8\n",
+            "checkpoints_dir: ./checkpoints\n",
+            "cluster_path: features_clustered_010.npy\n",
+            "data_type: 32\n",
+            "dataroot: ./datasets/cityscapes/\n",
+            "display_winsize: 512\n",
+            "engine: None\n",
+            "export_onnx: None\n",
+            "f: /root/.local/share/jupyter/runtime/kernel-6d955151-4911-464a-824d-f0806d8071f6.json\n",
+            "feat_num: 3\n",
+            "fineSize: 512\n",
+            "fp16: False\n",
+            "gpu_ids: [0]\n",
+            "how_many: 50\n",
+            "image_size: 224\n",
+            "input_nc: 3\n",
+            "instance_feat: False\n",
+            "isTrain: False\n",
+            "label_feat: False\n",
+            "label_nc: 0\n",
+            "latent_size: 512\n",
+            "loadSize: 1024\n",
+            "load_features: False\n",
+            "local_rank: 0\n",
+            "max_dataset_size: inf\n",
+            "model: pix2pixHD\n",
+            "nThreads: 2\n",
+            "n_blocks_global: 6\n",
+            "n_blocks_local: 3\n",
+            "n_clusters: 10\n",
+            "n_downsample_E: 4\n",
+            "n_downsample_global: 3\n",
+            "n_local_enhancers: 1\n",
+            "name: people\n",
+            "nef: 16\n",
+            "netG: global\n",
+            "ngf: 64\n",
+            "niter_fix_global: 0\n",
+            "no_flip: False\n",
+            "no_instance: False\n",
+            "norm: batch\n",
+            "norm_G: spectralspadesyncbatch3x3\n",
+            "ntest: inf\n",
+            "onnx: None\n",
+            "output_nc: 3\n",
+            "output_path: ./output/\n",
+            "phase: test\n",
+            "pic_a_path: ./crop_224/gdg.jpg\n",
+            "pic_b_path: ./crop_224/zrf.jpg\n",
+            "resize_or_crop: scale_width\n",
+            "results_dir: ./results/\n",
+            "semantic_nc: 3\n",
+            "serial_batches: False\n",
+            "temp_path: ./temp_results\n",
+            "tf_log: False\n",
+            "use_dropout: False\n",
+            "use_encoded_image: False\n",
+            "verbose: False\n",
+            "video_path: ./demo_file/multi_people_1080p.mp4\n",
+            "which_epoch: latest\n",
+            "-------------- End ----------------\n",
+            "input mean and std: 127.5 127.5\n",
+            "find model: ./insightface_func/models/antelope/glintr100.onnx recognition\n",
+            "find model: ./insightface_func/models/antelope/scrfd_10g_bnkps.onnx detection\n",
+            "set det-size: (640, 640)\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "\r  0%|          | 0/594 [00:00<?, ?it/s]"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "(142, 366, 4)\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 594/594 [08:45<00:00,  1.13it/s]\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "[MoviePy] >>>> Building video ./output/demo.mp4\n",
+            "[MoviePy] Writing audio in demoTEMP_MPY_wvf_snd.mp3\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 438/438 [00:00<00:00, 877.18it/s]\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "[MoviePy] Done.\n",
+            "[MoviePy] Writing video ./output/demo.mp4\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 595/595 [00:53<00:00, 11.15it/s]\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "[MoviePy] Done.\n",
+            "[MoviePy] >>>> Video ready: ./output/demo.mp4 \n",
+            "\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Rty2GsyZZrI6"
+      },
+      "source": [],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

SimSwap/arcface_model/arcface_checkpoint.tar

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:52ea5ce4902017b77a2bb811dd9a82f57dee3883e06c18a42d288437263c7a20
+size 209280521

SimSwap/checkpoints/people/iter.txt

@@ -0,0 +1,2 @@
+519
+4062

SimSwap/checkpoints/people/latest_net_D1.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:42903e7cb7d8e250f96ad62efa00e6b8a8c2c507588d8e5e3f264a4dc4d925de
+size 27865618

SimSwap/checkpoints/people/latest_net_D2.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e17ba6e4198ea06e5428079a14b3e2106627f2786e14a55d89b0ae3c647111bc
+size 27865618

SimSwap/checkpoints/people/latest_net_G.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:24caf144e9aabd5acd1127b06f13ed3528240adb9e747d77a94ac3f33e672330
+size 220243703

SimSwap/checkpoints/people/opt.txt

@@ -0,0 +1,72 @@
+------------ Options -------------
+batchSize: 8
+beta1: 0.5
+checkpoints_dir: ./checkpoints
+continue_train: False
+data_type: 32
+dataroot: ./datasets/cityscapes/
+debug: False
+display_freq: 99
+display_winsize: 512
+feat_num: 3
+fineSize: 512
+fp16: False
+gan_mode: hinge
+gpu_ids: [0]
+image_size: 224
+input_nc: 3
+instance_feat: False
+isTrain: True
+label_feat: False
+label_nc: 0
+lambda_GP: 10.0
+lambda_feat: 10.0
+lambda_id: 20.0
+lambda_rec: 10.0
+latent_size: 512
+loadSize: 1024
+load_features: False
+load_pretrain: 
+local_rank: 0
+lr: 0.0002
+max_dataset_size: inf
+model: pix2pixHD
+nThreads: 2
+n_blocks_global: 6
+n_blocks_local: 3
+n_clusters: 10
+n_downsample_E: 4
+n_downsample_global: 3
+n_layers_D: 4
+n_local_enhancers: 1
+name: people
+ndf: 64
+nef: 16
+netG: global
+ngf: 64
+niter: 10000
+niter_decay: 10000
+niter_fix_global: 0
+no_flip: False
+no_ganFeat_loss: False
+no_html: False
+no_instance: False
+no_vgg_loss: False
+norm: batch
+norm_G: spectralspadesyncbatch3x3
+num_D: 2
+output_nc: 3
+phase: train
+pool_size: 0
+print_freq: 100
+resize_or_crop: scale_width
+save_epoch_freq: 10000
+save_latest_freq: 10000
+semantic_nc: 3
+serial_batches: False
+tf_log: False
+times_G: 1
+use_dropout: False
+verbose: False
+which_epoch: latest
+-------------- End ----------------

SimSwap/cog.yaml

@@ -0,0 +1,20 @@
+build:
+  gpu: true
+  python_version: "3.8"
+  system_packages:
+    - "libgl1-mesa-glx"
+    - "libglib2.0-0"
+  python_packages:
+    - "imageio==2.9.0"
+    - "torch==1.8.0"
+    - "torchvision==0.9.0"
+    - "numpy==1.21.1"
+    - "insightface==0.2.1"
+    - "ipython==7.21.0"
+    - "Pillow==8.3.1"
+    - "opencv-python==4.5.3.56"
+    - "Fraction==1.5.1"
+    - "onnxruntime-gpu==1.8.1"
+    - "moviepy==1.0.3"
+
+predict: "predict.py:Predictor"

SimSwap/data/data_loader_Swapping.py

@@ -0,0 +1,125 @@
+import os
+import glob
+import torch
+import random
+from PIL import Image
+from torch.utils import data
+from torchvision import transforms as T
+
+class data_prefetcher():
+    def __init__(self, loader):
+        self.loader = loader
+        self.dataiter = iter(loader)
+        self.stream = torch.cuda.Stream()
+        self.mean = torch.tensor([0.485, 0.456, 0.406]).cuda().view(1,3,1,1)
+        self.std = torch.tensor([0.229, 0.224, 0.225]).cuda().view(1,3,1,1)
+        # With Amp, it isn't necessary to manually convert data to half.
+        # if args.fp16:
+        #     self.mean = self.mean.half()
+        #     self.std = self.std.half()
+        self.num_images = len(loader)
+        self.preload()
+
+    def preload(self):
+        try:
+            self.src_image1, self.src_image2 = next(self.dataiter)
+        except StopIteration:
+            self.dataiter = iter(self.loader)
+            self.src_image1, self.src_image2 = next(self.dataiter)
+            
+        with torch.cuda.stream(self.stream):
+            self.src_image1  = self.src_image1.cuda(non_blocking=True)
+            self.src_image1  = self.src_image1.sub_(self.mean).div_(self.std)
+            self.src_image2  = self.src_image2.cuda(non_blocking=True)
+            self.src_image2  = self.src_image2.sub_(self.mean).div_(self.std)
+
+    def next(self):
+        torch.cuda.current_stream().wait_stream(self.stream)
+        src_image1  = self.src_image1
+        src_image2  = self.src_image2
+        self.preload()
+        return src_image1, src_image2
+    
+    def __len__(self):
+        """Return the number of images."""
+        return self.num_images
+
+class SwappingDataset(data.Dataset):
+    """Dataset class for the Artworks dataset and content dataset."""
+
+    def __init__(self,
+                    image_dir,
+                    img_transform,
+                    subffix='jpg',
+                    random_seed=1234):
+        """Initialize and preprocess the Swapping dataset."""
+        self.image_dir      = image_dir
+        self.img_transform  = img_transform   
+        self.subffix        = subffix
+        self.dataset        = []
+        self.random_seed    = random_seed
+        self.preprocess()
+        self.num_images = len(self.dataset)
+
+    def preprocess(self):
+        """Preprocess the Swapping dataset."""
+        print("processing Swapping dataset images...")
+
+        temp_path   = os.path.join(self.image_dir,'*/')
+        pathes      = glob.glob(temp_path)
+        self.dataset = []
+        for dir_item in pathes:
+            join_path = glob.glob(os.path.join(dir_item,'*.jpg'))
+            print("processing %s"%dir_item,end='\r')
+            temp_list = []
+            for item in join_path:
+                temp_list.append(item)
+            self.dataset.append(temp_list)
+        random.seed(self.random_seed)
+        random.shuffle(self.dataset)
+        print('Finished preprocessing the Swapping dataset, total dirs number: %d...'%len(self.dataset))
+             
+    def __getitem__(self, index):
+        """Return two src domain images and two dst domain images."""
+        dir_tmp1        = self.dataset[index]
+        dir_tmp1_len    = len(dir_tmp1)
+
+        filename1   = dir_tmp1[random.randint(0,dir_tmp1_len-1)]
+        filename2   = dir_tmp1[random.randint(0,dir_tmp1_len-1)]
+        image1      = self.img_transform(Image.open(filename1))
+        image2      = self.img_transform(Image.open(filename2))
+        return image1, image2
+    
+    def __len__(self):
+        """Return the number of images."""
+        return self.num_images
+
+def GetLoader(  dataset_roots,
+                batch_size=16,
+                dataloader_workers=8,
+                random_seed = 1234
+                ):
+    """Build and return a data loader."""
+        
+    num_workers         = dataloader_workers
+    data_root           = dataset_roots
+    random_seed         = random_seed
+    
+    c_transforms = []
+    
+    c_transforms.append(T.ToTensor())
+    c_transforms = T.Compose(c_transforms)
+
+    content_dataset = SwappingDataset(
+                            data_root, 
+                            c_transforms,
+                            "jpg",
+                            random_seed)
+    content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size,
+                    drop_last=True,shuffle=True,num_workers=num_workers,pin_memory=True)
+    prefetcher = data_prefetcher(content_data_loader)
+    return prefetcher
+
+def denorm(x):
+    out = (x + 1) / 2
+    return out.clamp_(0, 1)

SimSwap/docs/css/bootstrap-theme.min.css

@@ -0,0 +1,6 @@
+/*!
+ * Bootstrap v3.3.7 (http://getbootstrap.com)
+ * Copyright 2011-2016 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */.btn-danger,.btn-default,.btn-info,.btn-primary,.btn-success,.btn-warning{text-shadow:0 -1px 0 rgba(0,0,0,.2);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 1px rgba(0,0,0,.075)}.btn-danger.active,.btn-danger:active,.btn-default.active,.btn-default:active,.btn-info.active,.btn-info:active,.btn-primary.active,.btn-primary:active,.btn-success.active,.btn-success:active,.btn-warning.active,.btn-warning:active{-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn-danger.disabled,.btn-danger[disabled],.btn-default.disabled,.btn-default[disabled],.btn-info.disabled,.btn-info[disabled],.btn-primary.disabled,.btn-primary[disabled],.btn-success.disabled,.btn-success[disabled],.btn-warning.disabled,.btn-warning[disabled],fieldset[disabled] .btn-danger,fieldset[disabled] .btn-default,fieldset[disabled] .btn-info,fieldset[disabled] 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+/*# sourceMappingURL=bootstrap-theme.min.css.map */

SimSwap/docs/css/ie10-viewport-bug-workaround.css

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+/*!
+ * IE10 viewport hack for Surface/desktop Windows 8 bug
+ * Copyright 2014-2015 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */
+
+/*
+ * See the Getting Started docs for more information:
+ * http://getbootstrap.com/getting-started/#support-ie10-width
+ */
+@-ms-viewport     { width: device-width; }
+@-o-viewport      { width: device-width; }
+@viewport         { width: device-width; }

SimSwap/docs/css/jumbotron.css

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+/* Move down content because we have a fixed navbar that is 50px tall */
+body {
+  padding-top: 50px;
+  padding-bottom: 20px;
+}

SimSwap/docs/css/page.css

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+.which-image-container {
+    display: flex;
+    flex-wrap: wrap;
+    align-items: center;
+    flex-direction: column;
+    justify-content: space-between;
+    height: 100%;
+}
+
+.which-image-container :nth-child(2) {
+    display: flex;
+    flex-wrap: wrap;
+    align-items: center;
+    flex-direction: column;
+    justify-content: flex-end;
+}
+
+.which-image {
+    display: inline-block;
+    flex: 1;
+    flex-basis: 45%;
+    margin: 1% 1% 1% 1%;
+    width: 100%;
+    height: auto;
+
+}
+
+.which-image img {
+    float: right;
+    width: 100%;
+}
+
+.image-display2 img {
+    float: right;
+    width: 100%;
+}
+/* .image-display{
+    align-items: center;
+}
+.image-display2{
+    align-items: center;
+} */
+.select-show {
+    border-style: dashed;
+    border-width: 2px;
+    border-color: purple;
+
+    /* padding: 2px; */
+}

SimSwap/docs/guidance/preparation.md

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+
+# Preparation
+
+### Installation
+**We highly recommand that you use Anaconda for Installation**
+```
+conda create -n simswap python=3.6
+conda activate simswap
+conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=10.2 -c pytorch
+(option): pip install --ignore-installed imageio
+pip install insightface==0.2.1 onnxruntime moviepy
+(option): pip install onnxruntime-gpu  (If you want to reduce the inference time)(It will be diffcult to install onnxruntime-gpu , the specify version of onnxruntime-gpu may depends on your machine and cuda version.)
+```
+- ***We have now updated the prepare document. The main change gpu version of onnx is supported now. If you have configured the environment before, now use pip install onnxruntime-gpu ,You can increase the computing speed.***
+- We use the face detection and alignment methods from **[insightface](https://github.com/deepinsight/insightface)** for image preprocessing. Please download the relative files and unzip them to ./insightface_func/models from [this link](https://onedrive.live.com/?authkey=%21ADJ0aAOSsc90neY&cid=4A83B6B633B029CC&id=4A83B6B633B029CC%215837&parId=4A83B6B633B029CC%215834&action=locate).
+- We use the face parsing from **[face-parsing.PyTorch](https://github.com/zllrunning/face-parsing.PyTorch)** for image postprocessing. Please download the relative file and place it in ./parsing_model/checkpoint from [this link](https://drive.google.com/file/d/154JgKpzCPW82qINcVieuPH3fZ2e0P812/view).
+- The pytorch and cuda versions above are most recommanded. They may vary.
+- Using insightface with different versions is not recommanded. Please use this specific version.
+- These settings are tested valid on both Windows and Ubuntu.
+
+### Pretrained model
+There are two archive files in the drive: **checkpoints.zip** and **arcface_checkpoint.tar**
+
+- **Copy the arcface_checkpoint.tar into ./arcface_model**
+- **Unzip checkpoints.zip, place it in the root dir ./**
+
+[[Google Drive]](https://drive.google.com/drive/folders/1jV6_0FIMPC53FZ2HzZNJZGMe55bbu17R?usp=sharing)
+[[Baidu Drive]](https://pan.baidu.com/s/1wFV11RVZMHqd-ky4YpLdcA) Password: ```jd2v```
+
+**Simswap 512 (optional)**
+
+The checkpoint of **Simswap 512 beta version** has been uploaded in [Github release](https://github.com/neuralchen/SimSwap/releases/download/512_beta/512.zip).If you want to experience Simswap 512, feel free to try.
+- **Unzip 512.zip, place it in the root dir ./checkpoints**.
+
+
+### Note
+We expect users to have GPU with at least 3G memory. For those who do not, we provide [[Colab Notebook implementation]](https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/SimSwap%20colab.ipynb).

SimSwap/docs/guidance/usage.md

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+<!--
+ * @FilePath: \SimSwap\docs\guidance\usage.md
+ * @Author: AceSix
+ * @Date: 2021-06-28 10:01:40
+ * @LastEditors: AceSix
+ * @LastEditTime: 2021-06-28 10:05:11
+ * Copyright (C) 2021 SJTU. All rights reserved.
+-->
+
+# Usage
+
+###### Before running, please make sure you have installed the environment and downloaded requested files according to the [preparation guidance](./preparation.md).
+###### The below example command lines are using mask by default.
+
+### Simple face swapping for already face-aligned images
+```
+python test_one_image.py --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path crop_224/6.jpg --pic_b_path crop_224/ds.jpg --output_path output/ 
+```
+
+### Face swapping for video
+
+- Swap only one face within the video(the one with highest confidence by face detection).
+```
+python test_video_swapsingle.py --crop_size 224 --use_mask --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --video_path ./demo_file/multi_people_1080p.mp4 --output_path ./output/multi_test_swapsingle.mp4 --temp_path ./temp_results 
+```
+- Swap all faces within the video.
+```
+python test_video_swapmulti.py --crop_size 224 --use_mask --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --video_path ./demo_file/multi_people_1080p.mp4 --output_path ./output/multi_test_swapmulti.mp4 --temp_path ./temp_results 
+```
+- Swap the ***specific*** face within the video.
+```
+python test_video_swapspecific.py --crop_size 224 --use_mask --pic_specific_path ./demo_file/specific1.png --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --video_path ./demo_file/multi_people_1080p.mp4 --output_path ./output/multi_test_specific.mp4 --temp_path ./temp_results 
+```
+When changing the specified face, you need to give a picture of the person whose face is to be changed. Then assign the picture path to the argument "***--pic_specific_path***". This picture should be a front face and show the entire head and neck, which can help accurately change the face (if you still don’t know how to choose the picture, you can refer to the specific*.png of [./demo_file/](https://github.com/neuralchen/SimSwap/tree/main/demo_file)). It would be better if this picture was taken from the video to be changed.
+
+- Swap ***multi specific*** face with **multi specific id** within the video.
+```
+python test_video_swap_multispecific.py --crop_size 224 --use_mask  --name people --Arc_path arcface_model/arcface_checkpoint.tar --video_path ./demo_file/multi_people_1080p.mp4 --output_path ./output/multi_test_multispecific.mp4 --temp_path ./temp_results --multisepcific_dir ./demo_file/multispecific 
+```
+The folder you assign to ***"--multisepcific_dir"*** should be looked like:
+```
+$Your folder name$
+
+├── DST_01.jpg(png)
+└── DST_02.jpg(png)
+└──...
+└── SRC_01.jpg(png)
+└── SRC_02.jpg(png)
+└──...
+```
+The result is that the face corresponding to SRC_01.jpg (png) in the video will be replaced by the face corresponding to DST_01.jpg (png). Then the character corresponding to SRC_02.jpg(png) will be replaced by the face of DST_02.jpg(png), and so on. Note that when using your own data and naming it, do not remove the **0** in SRC_(DST_)**0**1.jpg(png), etc.
+
+
+
+### Face swapping for Arbitrary images
+
+- Swap only one face within one image(the one with highest confidence by face detection). The result would be saved to ./output/result_whole_swapsingle.jpg
+```
+python test_wholeimage_swapsingle.py --crop_size 224 --use_mask  --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --pic_b_path ./demo_file/multi_people.jpg --output_path ./output/ 
+```
+- Swap all faces within one image. The result would be saved to ./output/result_whole_swapmulti.jpg
+```
+python test_wholeimage_swapmulti.py --crop_size 224 --use_mask  --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --pic_b_path ./demo_file/multi_people.jpg --output_path ./output/ 
+```
+- Swap **specific** face within one image. The result would be saved to ./output/result_whole_swapspecific.jpg
+```
+python test_wholeimage_swapspecific.py --crop_size 224 --use_mask  --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --pic_b_path ./demo_file/multi_people.jpg --output_path ./output/ --pic_specific_path ./demo_file/specific2.png 
+```
+- Swap **multi specific** face with **multi specific id** within one image. The result would be saved to ./output/result_whole_swap_multispecific.jpg
+```
+python test_wholeimage_swap_multispecific.py --crop_size 224 --use_mask  --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_b_path ./demo_file/multi_people.jpg --output_path ./output/ --multisepcific_dir ./demo_file/multispecific 
+```
+### About using Simswap 512 (beta version)
+We trained a beta version of Simswap 512 on [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ) and open sourced the model (if you think the Simswap 512 is cool, please star our  [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ) repo).
+
+The usage of applying Simswap 512 is to modify the value of the argument: "***--crop_size***" to 512 , take the command line of "Swap **multi specific** face with **multi specific id** within one image." as an example, the following command line can get the result without watermark:
+```
+python test_wholeimage_swap_multispecific.py --crop_size 512 --use_mask  --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_b_path ./demo_file/multi_people.jpg --output_path ./output/ --multisepcific_dir ./demo_file/multispecific 
+```
+The effect of Simswap 512 is shown below.
+<img src="../img/result_whole_swap_multispecific_512.jpg"/>
+
+### About watermark of simswap logo
+The above example command lines are to add the simswap logo as the watermark by default. After our discussion, we have added a hyper parameter to control whether to remove watermark.
+
+The usage of removing the watermark is to add an argument: "***--no_simswaplogo***" to the command line, take the command line of "Swap all faces within one image" as an example, the following command line can get the result without watermark:
+```
+python test_wholeimage_swapmulti.py --no_simswaplogo  --crop_size 224 --use_mask  --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --pic_b_path ./demo_file/multi_people.jpg --output_path ./output/
+```
+### About using mask for better result
+We provide two methods to paste the face back to the original image after changing the face: Using mask or using bounding box. At present, the effect of using mask is the best. All the above code examples are using mask. If you want to use the bounding box, you only need to remove the --use_mask in the code example.
+Difference between using mask and not using mask can be found [here](https://imgsli.com/NjE3OTA).
+
+### Difference between single face swapping and all face swapping are shown below.
+<img src="../img/multi_face_comparison.png"/>
+
+
+
+
+### Parameters
+|  Parameters   | Function  |
+|  :----  | :----  |
+| --name  | The SimSwap training logs name |
+| --pic_a_path  | Path of image with the target face |
+| --pic_b_path  | Path of image with the source face to swap |
+| --pic_specific_path  | Path of image with the specific face to be swapped |
+|--multisepcific_dir  |Path of image folder for multi specific face swapping|
+| --video_path  | Path of video with the source face to swap |
+| --temp_path  | Path to store intermediate files  |
+| --output_path  | Path of directory to store the face swapping result  |
+| --no_simswaplogo  |The hyper parameter to control whether to remove watermark |
+| --use_mask  |The hyper parameter to control whether to use face parsing for the better visual effects(I recommend to use)|
+
+### Note
+We expect users to have GPU with at least 3G memory.the For those who do not, we will provide Colab Notebook implementation in the future.