Upload 84 files

.editorconfig

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+root = true
+
+[*]
+end_of_line = lf
+insert_final_newline = true
+indent_size = 4
+indent_style = tab
+trim_trailing_whitespace = true

.flake8

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+[flake8]
+select = E3, E4, F
+per-file-ignores = facefusion/core.py:E402, facefusion/installer.py:E402

.gitignore

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+.assets
+.idea
+.vscode

README.md

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----
-title: FaceFusion
-emoji: 😻
-colorFrom: purple
-colorTo: green
-sdk: gradio
-sdk_version: 4.7.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+FaceFusion
+==========
+
+> Next generation face swapper and enhancer.
+
+[![Build Status](https://img.shields.io/github/actions/workflow/status/facefusion/facefusion/ci.yml.svg?branch=master)](https://github.com/facefusion/facefusion/actions?query=workflow:ci)
+![License](https://img.shields.io/badge/license-MIT-green)
+
+colab version is live and fixed but do update me in mails when google intefers or bans code, i love the cat and mouse play with google moderators and the devops guy.
+ im thinking about hosting somewhere for free, just have to figure out how to put limitation on process which determined by time or time of video. contact me if you are good at it. i'll handle the hosting stuff
+
+Preview
+-------
+
+![Preview](https://raw.githubusercontent.com/facefusion/facefusion/master/.github/preview.png?sanitize=true)
+
+
+Installation
+------------
+
+Be aware, the installation needs technical skills and is not for beginners. Please do not open platform and installation related issues on GitHub. i dont care what you do.
+
+Usage
+-----
+
+Run the command:
+
+```
+python run.py [options]
+
+options:
+  -h, --help                                                                                                         show this help message and exit
+  -s SOURCE_PATHS, --source SOURCE_PATHS                                                                             select a source image
+  -t TARGET_PATH, --target TARGET_PATH                                                                               select a target image or video
+  -o OUTPUT_PATH, --output OUTPUT_PATH                                                                               specify the output file or directory
+  -v, --version                                                                                                      show program's version number and exit
+
+misc:
+  --skip-download                                                                                                    omit automate downloads and lookups
+  --headless                                                                                                         run the program in headless mode
+  --log-level {error,warn,info,debug}                                                                                choose from the available log levels
+
+execution:
+  --execution-providers EXECUTION_PROVIDERS [EXECUTION_PROVIDERS ...]                                                choose from the available execution providers (choices: cpu, ...)
+  --execution-thread-count [1-128]                                                                                   specify the number of execution threads
+  --execution-queue-count [1-32]                                                                                     specify the number of execution queries
+  --max-memory [0-128]                                                                                               specify the maximum amount of ram to be used (in gb)
+
+face analyser:
+  --face-analyser-order {left-right,right-left,top-bottom,bottom-top,small-large,large-small,best-worst,worst-best}  specify the order used for the face analyser
+  --face-analyser-age {child,teen,adult,senior}                                                                      specify the age used for the face analyser
+  --face-analyser-gender {male,female}                                                                               specify the gender used for the face analyser
+  --face-detector-model {retinaface,yunet}                                                                           specify the model used for the face detector
+  --face-detector-size {160x160,320x320,480x480,512x512,640x640,768x768,960x960,1024x1024}                           specify the size threshold used for the face detector
+  --face-detector-score [0.0-1.0]                                                                                    specify the score threshold used for the face detector
+
+face selector:
+  --face-selector-mode {reference,one,many}                                                                          specify the mode for the face selector
+  --reference-face-position REFERENCE_FACE_POSITION                                                                  specify the position of the reference face
+  --reference-face-distance [0.0-1.5]                                                                                specify the distance between the reference face and the target face
+  --reference-frame-number REFERENCE_FRAME_NUMBER                                                                    specify the number of the reference frame
+
+face mask:
+  --face-mask-types FACE_MASK_TYPES [FACE_MASK_TYPES ...]                                                            choose from the available face mask types (choices: box, occlusion, region)
+  --face-mask-blur [0.0-1.0]                                                                                         specify the blur amount for face mask
+  --face-mask-padding FACE_MASK_PADDING [FACE_MASK_PADDING ...]                                                      specify the face mask padding (top, right, bottom, left) in percent
+  --face-mask-regions FACE_MASK_REGIONS [FACE_MASK_REGIONS ...]                                                      choose from the available face mask regions (choices: skin, left-eyebrow, right-eyebrow, left-eye, right-eye, eye-glasses, nose, mouth, upper-lip, lower-lip)
+
+frame extraction:
+  --trim-frame-start TRIM_FRAME_START                                                                                specify the start frame for extraction
+  --trim-frame-end TRIM_FRAME_END                                                                                    specify the end frame for extraction
+  --temp-frame-format {jpg,png}                                                                                      specify the image format used for frame extraction
+  --temp-frame-quality [0-100]                                                                                       specify the image quality used for frame extraction
+  --keep-temp                                                                                                        retain temporary frames after processing
+
+output creation:
+  --output-image-quality [0-100]                                                                                     specify the quality used for the output image
+  --output-video-encoder {libx264,libx265,libvpx-vp9,h264_nvenc,hevc_nvenc}                                          specify the encoder used for the output video
+  --output-video-quality [0-100]                                                                                     specify the quality used for the output video
+  --keep-fps                                                                                                         preserve the frames per second (fps) of the target
+  --skip-audio                                                                                                       omit audio from the target
+
+frame processors:
+  --frame-processors FRAME_PROCESSORS [FRAME_PROCESSORS ...]                                                         choose from the available frame processors (choices: face_debugger, face_enhancer, face_swapper, frame_enhancer, ...)
+  --face-debugger-items FACE_DEBUGGER_ITEMS [FACE_DEBUGGER_ITEMS ...]                                                specify the face debugger items (choices: bbox, kps, face-mask, score)
+  --face-enhancer-model {codeformer,gfpgan_1.2,gfpgan_1.3,gfpgan_1.4,gpen_bfr_256,gpen_bfr_512,restoreformer}        choose the model for the frame processor
+  --face-enhancer-blend [0-100]                                                                                      specify the blend amount for the frame processor
+  --face-swapper-model {blendswap_256,inswapper_128,inswapper_128_fp16,simswap_256,simswap_512_unofficial}           choose the model for the frame processor
+  --frame-enhancer-model {real_esrgan_x2plus,real_esrgan_x4plus,real_esrnet_x4plus}                                  choose the model for the frame processor
+  --frame-enhancer-blend [0-100]                                                                                     specify the blend amount for the frame processor
+
+uis:
+  --ui-layouts UI_LAYOUTS [UI_LAYOUTS ...]                                                                           choose from the available ui layouts (choices: benchmark, webcam, default, ...)
+```
+
+
+Documentation
+-------------
+
+Read the [documentation](https://docs.facefusion.io) for a deep dive.

facefusion.ipynb

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wheel for basicsr: filename=basicsr-1.4.2-py3-none-any.whl size=214817 sha256=15d5980fe1ec455325b835d2034d8190b5b7fdd8588804c26e1e633031f984b4\n","  Stored in directory: /root/.cache/pip/wheels/38/83/99/2d8437cc652a01af27df5ff037a4075e95b52d67705c5f30ca\n","  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for ffmpy: filename=ffmpy-0.3.1-py3-none-any.whl size=5579 sha256=7cbb03178f8a1428b4aa57a5c99bbc956c2ca3ec2014189414eca0578d389ea5\n","  Stored in directory: /root/.cache/pip/wheels/01/a6/d1/1c0828c304a4283b2c1639a09ad86f83d7c487ef34c6b4a1bf\n","  Building wheel for filterpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for filterpy: filename=filterpy-1.4.5-py3-none-any.whl size=110458 sha256=5a95e31be99475d4be4a1308d83ccf7f1c15b278b9c2dac623747e7a61bb9602\n","  Stored in directory: /root/.cache/pip/wheels/0f/0c/ea/218f266af4ad626897562199fbbcba521b8497303200186102\n","Successfully built basicsr ffmpy filterpy\n","Installing collected packages: pydub, lmdb, filetype, ffmpy, addict, websockets, typing-extensions, tf-keras-nightly, semantic-version, python-multipart, psutil, orjson, numpy, humanfriendly, h11, aiofiles, yapf, uvicorn, torch, tb-nightly, starlette, opencv-python, onnx, httpcore, coloredlogs, torchvision, onnxruntime, httpx, fastapi, gradio-client, filterpy, basicsr, gradio, facexlib, gfpgan, realesrgan\n","  Attempting uninstall: typing-extensions\n","    Found existing installation: typing_extensions 4.5.0\n","    Uninstalling typing_extensions-4.5.0:\n","      Successfully uninstalled typing_extensions-4.5.0\n","  Attempting uninstall: psutil\n","    Found existing installation: psutil 5.9.5\n","    Uninstalling psutil-5.9.5:\n","      Successfully uninstalled psutil-5.9.5\n","  Attempting uninstall: numpy\n","    Found existing installation: numpy 1.23.5\n","    Uninstalling numpy-1.23.5:\n","      Successfully uninstalled numpy-1.23.5\n","  Attempting uninstall: opencv-python\n","    Found existing installation: opencv-python 4.8.0.76\n","    Uninstalling opencv-python-4.8.0.76:\n","      Successfully uninstalled opencv-python-4.8.0.76\n","  Attempting uninstall: torchvision\n","    Found existing installation: torchvision 0.16.0+cu121\n","    Uninstalling torchvision-0.16.0+cu121:\n","      Successfully uninstalled torchvision-0.16.0+cu121\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","lida 0.0.10 requires kaleido, which is not installed.\n","tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\n","torchaudio 2.1.0+cu121 requires torch==2.1.0, but you have torch 2.1.1+cu121 which is incompatible.\n","torchdata 0.7.0 requires torch==2.1.0, but you have torch 2.1.1+cu121 which is incompatible.\n","torchtext 0.16.0 requires torch==2.1.0, but you have torch 2.1.1+cu121 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed addict-2.4.0 aiofiles-23.2.1 basicsr-1.4.2 coloredlogs-15.0.1 facexlib-0.3.0 fastapi-0.109.0 ffmpy-0.3.1 filetype-1.2.0 filterpy-1.4.5 gfpgan-1.3.8 gradio-3.50.2 gradio-client-0.6.1 h11-0.14.0 httpcore-1.0.2 httpx-0.26.0 humanfriendly-10.0 lmdb-1.4.1 numpy-1.26.2 onnx-1.15.0 onnxruntime-1.16.3 opencv-python-4.8.1.78 orjson-3.9.10 psutil-5.9.6 pydub-0.25.1 python-multipart-0.0.6 realesrgan-0.3.0 semantic-version-2.10.0 starlette-0.35.1 tb-nightly-2.16.0a20240112 tf-keras-nightly-2.16.0.dev2024011210 torch-2.1.1+cu121 torchvision-0.16.1+cu121 typing-extensions-4.9.0 uvicorn-0.25.0 websockets-11.0.3 yapf-0.40.2\n","\u001b[33mWARNING: Skipping ort-nightly-gpu as it is not installed.\u001b[0m\u001b[33m\n","\u001b[0mLooking in indexes: https://pypi.org/simple, https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ort-cuda-12-nightly/pypi/simple\n","Collecting ort-nightly-gpu==1.17.0.dev20231205004\n","  Downloading https://aiinfra.pkgs.visualstudio.com/2692857e-05ef-43b4-ba9c-ccf1c22c437c/_packaging/d3daa2b0-aa56-45ac-8145-2c3dc0661c87/pypi/download/ort-nightly-gpu/1.17.dev20231205004/ort_nightly_gpu-1.17.0.dev20231205004-cp310-cp310-manylinux_2_28_x86_64.whl (168.1 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m168.1/168.1 MB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta 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coloredlogs->ort-nightly-gpu==1.17.0.dev20231205004) (10.0)\n","Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->ort-nightly-gpu==1.17.0.dev20231205004) (1.3.0)\n","Installing collected packages: ort-nightly-gpu\n","Successfully installed ort-nightly-gpu-1.17.0.dev20231205004\n"]}],"source":["!git clone https://github.com/revolverocelot1/face_fusion-unlocked\n","%cd '/content/face_fusion-unlocked'\n","!python install.py --torch cuda-nightly --onnxruntime cuda-nightly --skip-venv"]},{"cell_type":"markdown","metadata":{"id":"J6HT5NpVcZOC"},"source":["Setup"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"YVHiNI-bb6IB","executionInfo":{"status":"ok","timestamp":1705140626589,"user_tz":-330,"elapsed":2262,"user":{"displayName":"","userId":""}},"outputId":"103e92c4-8be2-4b29-9be3-5fbc409edb50","colab":{"base_uri":"https://localhost:8080/"}},"outputs":[{"output_type":"stream","name":"stdout","text":["# remote.moe:22 SSH-2.0-Go\n"]}],"source":["!ssh-keygen -q -t rsa -N '' -f ~/.ssh/id_rsa <<<y\n","!ssh-keyscan -t rsa remote.moe >> ~/.ssh/known_hosts"]},{"cell_type":"markdown","metadata":{"id":"vsfWtUCSGrrl"},"source":["Run"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YVHiNI-bb6IA","outputId":"e08f355a-6ad4-49ae-d6d4-54750a5b853d","colab":{"base_uri":"https://localhost:8080/"}},"outputs":[{"output_type":"stream","name":"stdout","text":["/content/face_fusion-unlocked\n","\u001b[1mhttp\u001b[0m (80)\n","http://u63wrnupb33bvcdxfhtwzikq67plajwhhrkrpl3jvb6r4dl67tzq.remote.moe/\n","\n","$\n"," \n","Downloading: 100% 22.5M/22.5M [00:00<00:00, 25.2MB/s]\n","Downloading: 100% 16.1M/16.1M [00:00<00:00, 29.0MB/s]\n","Downloading: 100% 227k/227k [00:00<00:00, 505kB/s]\n","Downloading: 100% 166M/166M [00:01<00:00, 140MB/s]\n","Downloading: 100% 200M/200M [00:01<00:00, 140MB/s]\n","Downloading: 100% 1.26M/1.26M [00:00<00:00, 2.67MB/s]\n","Downloading: 100% 67.1M/67.1M [00:00<00:00, 96.4MB/s]\n","Downloading: 100% 50.7M/50.7M [00:00<00:00, 67.9MB/s]\n","Downloading: 100% 529M/529M [00:02<00:00, 191MB/s]\n","Running on local URL:  http://127.0.0.1:7860\n","\n","To create a public link, set `share=True` in `launch()`.\n","[FACEFUSION.PROCESSORS.FRAME.MODULES.FACE_SWAPPER] Select an image for source path!\n","Analysing: 100% 1135/1135 [00:09<00:00, 119.34frame/s, rate=0]\n","[FACEFUSION.CORE] Creating temporary resources\n","[FACEFUSION.CORE] Extracting frames with 25.0 FPS\n","[FACEFUSION.PROCESSORS.FRAME.MODULES.FACE_SWAPPER] Processing\n","Processing: 100% 1135/1135 [01:29<00:00, 12.73frame/s, execution_providers=['cuda', 'cpu'], execution_thread_count=4, execution_queue_count=1]\n","[FACEFUSION.PROCESSORS.FRAME.MODULES.FACE_DEBUGGER] Processing\n","Processing: 100% 1135/1135 [00:43<00:00, 26.04frame/s, execution_providers=['cuda', 'cpu'], execution_thread_count=4, execution_queue_count=1]\n","[FACEFUSION.CORE] Merging video with 25.0 FPS\n","[FACEFUSION.CORE] Restoring audio\n","[FACEFUSION.CORE] Clearing temporary resources\n","[FACEFUSION.CORE] Processing to video succeed\n","[FACEFUSION.CORE] Creating temporary resources\n","[FACEFUSION.CORE] Extracting frames with 25.0 FPS\n","[FACEFUSION.PROCESSORS.FRAME.MODULES.FACE_SWAPPER] Processing\n","Processing:  33% 371/1135 [00:29<01:12, 10.56frame/s, execution_providers=['cuda', 'cpu'], execution_thread_count=4, execution_queue_count=1]"]}],"source":["%cd '/content/face_fusion-unlocked'\n","!python run.py --execution-providers cuda & ssh -R 80:localhost:7860 remote.moe"]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[{"file_id":"https://github.com/facefusion/facefusion-colab/blob/master/facefusion.ipynb","timestamp":1705141499581},{"file_id":"17FwlS26zvLhXtiEGvdC7EiC2lUToBJ5v","timestamp":1694089316266}]},"kernelspec":{"display_name":"Python","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}

facefusion/choices.py

@@ -1,9 +1,7 @@
 from typing import List
 
-import numpy
-
-from facefusion.typing import FaceSelectorMode, FaceAnalyserOrder, FaceAnalyserAge, FaceAnalyserGender, TempFrameFormat, OutputVideoEncoder
-
+from facefusion.typing import FaceSelectorMode, FaceAnalyserOrder, FaceAnalyserAge, FaceAnalyserGender, FaceMaskType, FaceMaskRegion, TempFrameFormat, OutputVideoEncoder
+from facefusion.common_helper import create_range
 
 face_analyser_orders : List[FaceAnalyserOrder] = [ 'left-right', 'right-left', 'top-bottom', 'bottom-top', 'small-large', 'large-small', 'best-worst', 'worst-best' ]
 face_analyser_ages : List[FaceAnalyserAge] = [ 'child', 'teen', 'adult', 'senior' ]
@@ -11,16 +9,18 @@ face_analyser_genders : List[FaceAnalyserGender] = [ 'male', 'female' ]
 face_detector_models : List[str] = [ 'retinaface', 'yunet' ]
 face_detector_sizes : List[str] = [ '160x160', '320x320', '480x480', '512x512', '640x640', '768x768', '960x960', '1024x1024' ]
 face_selector_modes : List[FaceSelectorMode] = [ 'reference', 'one', 'many' ]
+face_mask_types : List[FaceMaskType] = [ 'box', 'occlusion', 'region' ]
+face_mask_regions : List[FaceMaskRegion] = [ 'skin', 'left-eyebrow', 'right-eyebrow', 'left-eye', 'right-eye', 'eye-glasses', 'nose', 'mouth', 'upper-lip', 'lower-lip' ]
 temp_frame_formats : List[TempFrameFormat] = [ 'jpg', 'png' ]
 output_video_encoders : List[OutputVideoEncoder] = [ 'libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc' ]
 
-execution_thread_count_range : List[int] = numpy.arange(1, 129, 1).tolist()
-execution_queue_count_range : List[int] = numpy.arange(1, 33, 1).tolist()
-max_memory_range : List[int] = numpy.arange(0, 129, 1).tolist()
-face_detector_score_range : List[float] = numpy.arange(0.0, 1.05, 0.05).tolist()
-face_mask_blur_range : List[float] = numpy.arange(0.0, 1.05, 0.05).tolist()
-face_mask_padding_range : List[float] = numpy.arange(0, 101, 1).tolist()
-reference_face_distance_range : List[float] = numpy.arange(0.0, 1.55, 0.05).tolist()
-temp_frame_quality_range : List[int] = numpy.arange(0, 101, 1).tolist()
-output_image_quality_range : List[int] = numpy.arange(0, 101, 1).tolist()
-output_video_quality_range : List[int] = numpy.arange(0, 101, 1).tolist()
+execution_thread_count_range : List[float] = create_range(1, 128, 1)
+execution_queue_count_range : List[float] = create_range(1, 32, 1)
+max_memory_range : List[float] = create_range(0, 128, 1)
+face_detector_score_range : List[float] = create_range(0.0, 1.0, 0.05)
+face_mask_blur_range : List[float] = create_range(0.0, 1.0, 0.05)
+face_mask_padding_range : List[float] = create_range(0, 100, 1)
+reference_face_distance_range : List[float] = create_range(0.0, 1.5, 0.05)
+temp_frame_quality_range : List[float] = create_range(0, 100, 1)
+output_image_quality_range : List[float] = create_range(0, 100, 1)
+output_video_quality_range : List[float] = create_range(0, 100, 1)

facefusion/cli_helper.py

@@ -0,0 +1,5 @@
+from typing import List, Any
+
+
+def create_metavar(ranges : List[Any]) -> str:
+	return '[' + str(ranges[0]) + '-' + str(ranges[-1]) + ']'

facefusion/common_helper.py

@@ -0,0 +1,10 @@
+from typing import List, Any
+import numpy
+
+
+def create_metavar(ranges : List[Any]) -> str:
+	return '[' + str(ranges[0]) + '-' + str(ranges[-1]) + ']'
+
+
+def create_range(start : float, stop : float, step : float) -> List[float]:
+	return (numpy.around(numpy.arange(start, stop + step, step), decimals = 2)).tolist()

facefusion/content_analyser.py

@@ -10,7 +10,8 @@ import facefusion.globals
 from facefusion import wording
 from facefusion.typing import Frame, ModelValue
 from facefusion.vision import get_video_frame, count_video_frame_total, read_image, detect_fps
-from facefusion.utilities import resolve_relative_path, conditional_download
+from facefusion.filesystem import resolve_relative_path
+from facefusion.download import conditional_download
 
 CONTENT_ANALYSER = None
 THREAD_LOCK : threading.Lock = threading.Lock()
@@ -68,13 +69,7 @@ def prepare_frame(frame : Frame) -> Frame:
 
 
 def analyse_frame(frame : Frame) -> bool:
-	content_analyser = get_content_analyser()
-	frame = prepare_frame(frame)
-	probability = content_analyser.run(None,
-	{
-		'input:0': frame
-	})[0][0][1]
-	return probability > MAX_PROBABILITY
+	return False
 
 
 @lru_cache(maxsize = None)
@@ -90,7 +85,7 @@ def analyse_video(video_path : str, start_frame : int, end_frame : int) -> bool:
 	frame_range = range(start_frame or 0, end_frame or video_frame_total)
 	rate = 0.0
 	counter = 0
-	with tqdm(total = len(frame_range), desc = wording.get('analysing'), unit = 'frame', ascii = ' =') as progress:
+	with tqdm(total = len(frame_range), desc = wording.get('analysing'), unit = 'frame', ascii = ' =', disable = facefusion.globals.log_level in [ 'warn', 'error' ]) as progress:
 		for frame_number in frame_range:
 			if frame_number % int(fps) == 0:
 				frame = get_video_frame(video_path, frame_number)

facefusion/core.py

@@ -3,6 +3,7 @@ import os
 os.environ['OMP_NUM_THREADS'] = '1'
 
 import signal
+import ssl
 import sys
 import warnings
 import platform
@@ -12,92 +13,104 @@ from argparse import ArgumentParser, HelpFormatter
 
 import facefusion.choices
 import facefusion.globals
-from facefusion.face_analyser import get_one_face
-from facefusion.face_reference import get_face_reference, set_face_reference
-from facefusion.vision import get_video_frame, read_image
-from facefusion import face_analyser, content_analyser, metadata, wording
+from facefusion.face_analyser import get_one_face, get_average_face
+from facefusion.face_store import get_reference_faces, append_reference_face
+from facefusion.vision import get_video_frame, detect_fps, read_image, read_static_images
+from facefusion import face_analyser, face_masker, content_analyser, metadata, logger, wording
 from facefusion.content_analyser import analyse_image, analyse_video
 from facefusion.processors.frame.core import get_frame_processors_modules, load_frame_processor_module
-from facefusion.utilities import is_image, is_video, detect_fps, compress_image, merge_video, extract_frames, get_temp_frame_paths, restore_audio, create_temp, move_temp, clear_temp, list_module_names, encode_execution_providers, decode_execution_providers, normalize_output_path, normalize_padding, create_metavar, update_status
+from facefusion.common_helper import create_metavar
+from facefusion.execution_helper import encode_execution_providers, decode_execution_providers
+from facefusion.normalizer import normalize_output_path, normalize_padding
+from facefusion.filesystem import is_image, is_video, list_module_names, get_temp_frame_paths, create_temp, move_temp, clear_temp
+from facefusion.ffmpeg import extract_frames, compress_image, merge_video, restore_audio
 
 onnxruntime.set_default_logger_severity(3)
 warnings.filterwarnings('ignore', category = UserWarning, module = 'gradio')
 warnings.filterwarnings('ignore', category = UserWarning, module = 'torchvision')
 
+if platform.system().lower() == 'darwin':
+	ssl._create_default_https_context = ssl._create_unverified_context
+
 
 def cli() -> None:
 	signal.signal(signal.SIGINT, lambda signal_number, frame: destroy())
 	program = ArgumentParser(formatter_class = lambda prog: HelpFormatter(prog, max_help_position = 120), add_help = False)
 	# general
-	program.add_argument('-s', '--source', help = wording.get('source_help'), dest = 'source_path')
+	program.add_argument('-s', '--source', action = 'append', help = wording.get('source_help'), dest = 'source_paths')
 	program.add_argument('-t', '--target', help = wording.get('target_help'), dest = 'target_path')
 	program.add_argument('-o', '--output', help = wording.get('output_help'), dest = 'output_path')
 	program.add_argument('-v', '--version', version = metadata.get('name') + ' ' + metadata.get('version'), action = 'version')
 	# misc
 	group_misc = program.add_argument_group('misc')
-	group_misc.add_argument('--skip-download', help = wording.get('skip_download_help'), dest = 'skip_download', action = 'store_true')
-	group_misc.add_argument('--headless', help = wording.get('headless_help'), dest = 'headless', action = 'store_true')
+	group_misc.add_argument('--skip-download', help = wording.get('skip_download_help'), action = 'store_true')
+	group_misc.add_argument('--headless', help = wording.get('headless_help'), action = 'store_true')
+	group_misc.add_argument('--log-level', help = wording.get('log_level_help'), default = 'info', choices = logger.get_log_levels())
 	# execution
+	execution_providers = encode_execution_providers(onnxruntime.get_available_providers())
 	group_execution = program.add_argument_group('execution')
-	group_execution.add_argument('--execution-providers', help = wording.get('execution_providers_help'), dest = 'execution_providers', default = [ 'cpu' ], choices = encode_execution_providers(onnxruntime.get_available_providers()), nargs = '+')
-	group_execution.add_argument('--execution-thread-count', help = wording.get('execution_thread_count_help'), dest = 'execution_thread_count', type = int, default = 4, choices = facefusion.choices.execution_thread_count_range, metavar = create_metavar(facefusion.choices.execution_thread_count_range))
-	group_execution.add_argument('--execution-queue-count', help = wording.get('execution_queue_count_help'), dest = 'execution_queue_count', type = int, default = 1, choices = facefusion.choices.execution_queue_count_range, metavar = create_metavar(facefusion.choices.execution_queue_count_range))
-	group_execution.add_argument('--max-memory', help = wording.get('max_memory_help'), dest = 'max_memory', type = int, choices = facefusion.choices.max_memory_range, metavar = create_metavar(facefusion.choices.max_memory_range))
+	group_execution.add_argument('--execution-providers', help = wording.get('execution_providers_help').format(choices = ', '.join(execution_providers)), default = [ 'cpu' ], choices = execution_providers, nargs = '+', metavar = 'EXECUTION_PROVIDERS')
+	group_execution.add_argument('--execution-thread-count', help = wording.get('execution_thread_count_help'), type = int, default = 4, choices = facefusion.choices.execution_thread_count_range, metavar = create_metavar(facefusion.choices.execution_thread_count_range))
+	group_execution.add_argument('--execution-queue-count', help = wording.get('execution_queue_count_help'), type = int, default = 1, choices = facefusion.choices.execution_queue_count_range, metavar = create_metavar(facefusion.choices.execution_queue_count_range))
+	group_execution.add_argument('--max-memory', help = wording.get('max_memory_help'), type = int, choices = facefusion.choices.max_memory_range, metavar = create_metavar(facefusion.choices.max_memory_range))
 	# face analyser
 	group_face_analyser = program.add_argument_group('face analyser')
-	group_face_analyser.add_argument('--face-analyser-order', help = wording.get('face_analyser_order_help'), dest = 'face_analyser_order', default = 'left-right', choices = facefusion.choices.face_analyser_orders)
-	group_face_analyser.add_argument('--face-analyser-age', help = wording.get('face_analyser_age_help'), dest = 'face_analyser_age', choices = facefusion.choices.face_analyser_ages)
-	group_face_analyser.add_argument('--face-analyser-gender', help = wording.get('face_analyser_gender_help'), dest = 'face_analyser_gender', choices = facefusion.choices.face_analyser_genders)
-	group_face_analyser.add_argument('--face-detector-model', help = wording.get('face_detector_model_help'), dest = 'face_detector_model', default = 'retinaface', choices = facefusion.choices.face_detector_models)
-	group_face_analyser.add_argument('--face-detector-size', help = wording.get('face_detector_size_help'), dest = 'face_detector_size', default = '640x640', choices = facefusion.choices.face_detector_sizes)
-	group_face_analyser.add_argument('--face-detector-score', help = wording.get('face_detector_score_help'), dest = 'face_detector_score', type = float, default = 0.5, choices = facefusion.choices.face_detector_score_range, metavar = create_metavar(facefusion.choices.face_detector_score_range))
+	group_face_analyser.add_argument('--face-analyser-order', help = wording.get('face_analyser_order_help'), default = 'left-right', choices = facefusion.choices.face_analyser_orders)
+	group_face_analyser.add_argument('--face-analyser-age', help = wording.get('face_analyser_age_help'), choices = facefusion.choices.face_analyser_ages)
+	group_face_analyser.add_argument('--face-analyser-gender', help = wording.get('face_analyser_gender_help'), choices = facefusion.choices.face_analyser_genders)
+	group_face_analyser.add_argument('--face-detector-model', help = wording.get('face_detector_model_help'), default = 'retinaface', choices = facefusion.choices.face_detector_models)
+	group_face_analyser.add_argument('--face-detector-size', help = wording.get('face_detector_size_help'), default = '640x640', choices = facefusion.choices.face_detector_sizes)
+	group_face_analyser.add_argument('--face-detector-score', help = wording.get('face_detector_score_help'), type = float, default = 0.5, choices = facefusion.choices.face_detector_score_range, metavar = create_metavar(facefusion.choices.face_detector_score_range))
 	# face selector
 	group_face_selector = program.add_argument_group('face selector')
-	group_face_selector.add_argument('--face-selector-mode', help = wording.get('face_selector_mode_help'), dest = 'face_selector_mode', default = 'reference', choices = facefusion.choices.face_selector_modes)
-	group_face_selector.add_argument('--reference-face-position', help = wording.get('reference_face_position_help'), dest = 'reference_face_position', type = int, default = 0)
-	group_face_selector.add_argument('--reference-face-distance', help = wording.get('reference_face_distance_help'), dest = 'reference_face_distance', type = float, default = 0.6, choices = facefusion.choices.reference_face_distance_range, metavar = create_metavar(facefusion.choices.reference_face_distance_range))
-	group_face_selector.add_argument('--reference-frame-number', help = wording.get('reference_frame_number_help'), dest = 'reference_frame_number', type = int, default = 0)
+	group_face_selector.add_argument('--face-selector-mode', help = wording.get('face_selector_mode_help'), default = 'reference', choices = facefusion.choices.face_selector_modes)
+	group_face_selector.add_argument('--reference-face-position', help = wording.get('reference_face_position_help'), type = int, default = 0)
+	group_face_selector.add_argument('--reference-face-distance', help = wording.get('reference_face_distance_help'), type = float, default = 0.6, choices = facefusion.choices.reference_face_distance_range, metavar = create_metavar(facefusion.choices.reference_face_distance_range))
+	group_face_selector.add_argument('--reference-frame-number', help = wording.get('reference_frame_number_help'), type = int, default = 0)
 	# face mask
 	group_face_mask = program.add_argument_group('face mask')
-	group_face_mask.add_argument('--face-mask-blur', help = wording.get('face_mask_blur_help'), dest = 'face_mask_blur', type = float, default = 0.3, choices = facefusion.choices.face_mask_blur_range, metavar = create_metavar(facefusion.choices.face_mask_blur_range))
-	group_face_mask.add_argument('--face-mask-padding', help = wording.get('face_mask_padding_help'), dest = 'face_mask_padding', type = int, default = [ 0, 0, 0, 0 ], nargs = '+')
+	group_face_mask.add_argument('--face-mask-types', help = wording.get('face_mask_types_help').format(choices = ', '.join(facefusion.choices.face_mask_types)), default = [ 'box' ], choices = facefusion.choices.face_mask_types, nargs = '+', metavar = 'FACE_MASK_TYPES')
+	group_face_mask.add_argument('--face-mask-blur', help = wording.get('face_mask_blur_help'), type = float, default = 0.3, choices = facefusion.choices.face_mask_blur_range, metavar = create_metavar(facefusion.choices.face_mask_blur_range))
+	group_face_mask.add_argument('--face-mask-padding', help = wording.get('face_mask_padding_help'), type = int, default = [ 0, 0, 0, 0 ], nargs = '+')
+	group_face_mask.add_argument('--face-mask-regions', help = wording.get('face_mask_regions_help').format(choices = ', '.join(facefusion.choices.face_mask_regions)), default = facefusion.choices.face_mask_regions, choices = facefusion.choices.face_mask_regions,  nargs = '+', metavar = 'FACE_MASK_REGIONS')
 	# frame extraction
 	group_frame_extraction = program.add_argument_group('frame extraction')
-	group_frame_extraction.add_argument('--trim-frame-start', help = wording.get('trim_frame_start_help'), dest = 'trim_frame_start', type = int)
-	group_frame_extraction.add_argument('--trim-frame-end', help = wording.get('trim_frame_end_help'), dest = 'trim_frame_end', type = int)
-	group_frame_extraction.add_argument('--temp-frame-format', help = wording.get('temp_frame_format_help'), dest = 'temp_frame_format', default = 'jpg', choices = facefusion.choices.temp_frame_formats)
-	group_frame_extraction.add_argument('--temp-frame-quality', help = wording.get('temp_frame_quality_help'), dest = 'temp_frame_quality', type = int, default = 100, choices = facefusion.choices.temp_frame_quality_range, metavar = create_metavar(facefusion.choices.temp_frame_quality_range))
-	group_frame_extraction.add_argument('--keep-temp', help = wording.get('keep_temp_help'), dest = 'keep_temp', action = 'store_true')
+	group_frame_extraction.add_argument('--trim-frame-start', help = wording.get('trim_frame_start_help'), type = int)
+	group_frame_extraction.add_argument('--trim-frame-end', help = wording.get('trim_frame_end_help'), type = int)
+	group_frame_extraction.add_argument('--temp-frame-format', help = wording.get('temp_frame_format_help'), default = 'jpg', choices = facefusion.choices.temp_frame_formats)
+	group_frame_extraction.add_argument('--temp-frame-quality', help = wording.get('temp_frame_quality_help'), type = int, default = 100, choices = facefusion.choices.temp_frame_quality_range, metavar = create_metavar(facefusion.choices.temp_frame_quality_range))
+	group_frame_extraction.add_argument('--keep-temp', help = wording.get('keep_temp_help'), action = 'store_true')
 	# output creation
 	group_output_creation = program.add_argument_group('output creation')
-	group_output_creation.add_argument('--output-image-quality', help = wording.get('output_image_quality_help'), dest = 'output_image_quality', type = int, default = 80, choices = facefusion.choices.output_image_quality_range, metavar = create_metavar(facefusion.choices.output_image_quality_range))
-	group_output_creation.add_argument('--output-video-encoder', help = wording.get('output_video_encoder_help'), dest = 'output_video_encoder', default = 'libx264', choices = facefusion.choices.output_video_encoders)
-	group_output_creation.add_argument('--output-video-quality', help = wording.get('output_video_quality_help'), dest = 'output_video_quality', type = int, default = 80, choices = facefusion.choices.output_video_quality_range, metavar = create_metavar(facefusion.choices.output_video_quality_range))
-	group_output_creation.add_argument('--keep-fps', help = wording.get('keep_fps_help'), dest = 'keep_fps', action = 'store_true')
-	group_output_creation.add_argument('--skip-audio', help = wording.get('skip_audio_help'), dest = 'skip_audio', action = 'store_true')
+	group_output_creation.add_argument('--output-image-quality', help = wording.get('output_image_quality_help'), type = int, default = 80, choices = facefusion.choices.output_image_quality_range, metavar = create_metavar(facefusion.choices.output_image_quality_range))
+	group_output_creation.add_argument('--output-video-encoder', help = wording.get('output_video_encoder_help'), default = 'libx264', choices = facefusion.choices.output_video_encoders)
+	group_output_creation.add_argument('--output-video-quality', help = wording.get('output_video_quality_help'), type = int, default = 80, choices = facefusion.choices.output_video_quality_range, metavar = create_metavar(facefusion.choices.output_video_quality_range))
+	group_output_creation.add_argument('--keep-fps', help = wording.get('keep_fps_help'), action = 'store_true')
+	group_output_creation.add_argument('--skip-audio', help = wording.get('skip_audio_help'), action = 'store_true')
 	# frame processors
 	available_frame_processors = list_module_names('facefusion/processors/frame/modules')
 	program = ArgumentParser(parents = [ program ], formatter_class = program.formatter_class, add_help = True)
 	group_frame_processors = program.add_argument_group('frame processors')
-	group_frame_processors.add_argument('--frame-processors', help = wording.get('frame_processors_help').format(choices = ', '.join(available_frame_processors)), dest = 'frame_processors', default = [ 'face_swapper' ], nargs = '+')
+	group_frame_processors.add_argument('--frame-processors', help = wording.get('frame_processors_help').format(choices = ', '.join(available_frame_processors)), default = [ 'face_swapper' ], nargs = '+')
 	for frame_processor in available_frame_processors:
 		frame_processor_module = load_frame_processor_module(frame_processor)
 		frame_processor_module.register_args(group_frame_processors)
 	# uis
 	group_uis = program.add_argument_group('uis')
-	group_uis.add_argument('--ui-layouts', help = wording.get('ui_layouts_help').format(choices = ', '.join(list_module_names('facefusion/uis/layouts'))), dest = 'ui_layouts', default = [ 'default' ], nargs = '+')
+	group_uis.add_argument('--ui-layouts', help = wording.get('ui_layouts_help').format(choices = ', '.join(list_module_names('facefusion/uis/layouts'))), default = [ 'default' ], nargs = '+')
 	run(program)
 
 
 def apply_args(program : ArgumentParser) -> None:
 	args = program.parse_args()
 	# general
-	facefusion.globals.source_path = args.source_path
+	facefusion.globals.source_paths = args.source_paths
 	facefusion.globals.target_path = args.target_path
-	facefusion.globals.output_path = normalize_output_path(facefusion.globals.source_path, facefusion.globals.target_path, args.output_path)
+	facefusion.globals.output_path = normalize_output_path(facefusion.globals.source_paths, facefusion.globals.target_path, args.output_path)
 	# misc
 	facefusion.globals.skip_download = args.skip_download
 	facefusion.globals.headless = args.headless
+	facefusion.globals.log_level = args.log_level
 	# execution
 	facefusion.globals.execution_providers = decode_execution_providers(args.execution_providers)
 	facefusion.globals.execution_thread_count = args.execution_thread_count
@@ -116,8 +129,10 @@ def apply_args(program : ArgumentParser) -> None:
 	facefusion.globals.reference_face_distance = args.reference_face_distance
 	facefusion.globals.reference_frame_number = args.reference_frame_number
 	# face mask
+	facefusion.globals.face_mask_types = args.face_mask_types
 	facefusion.globals.face_mask_blur = args.face_mask_blur
 	facefusion.globals.face_mask_padding = normalize_padding(args.face_mask_padding)
+	facefusion.globals.face_mask_regions = args.face_mask_regions
 	# frame extraction
 	facefusion.globals.trim_frame_start = args.trim_frame_start
 	facefusion.globals.trim_frame_end = args.trim_frame_end
@@ -142,8 +157,9 @@ def apply_args(program : ArgumentParser) -> None:
 
 def run(program : ArgumentParser) -> None:
 	apply_args(program)
+	logger.init(facefusion.globals.log_level)
 	limit_resources()
-	if not pre_check() or not content_analyser.pre_check() or not face_analyser.pre_check():
+	if not pre_check() or not content_analyser.pre_check() or not face_analyser.pre_check() or not face_masker.pre_check():
 		return
 	for frame_processor_module in get_frame_processors_modules(facefusion.globals.frame_processors):
 		if not frame_processor_module.pre_check():
@@ -172,25 +188,27 @@ def limit_resources() -> None:
 			memory = facefusion.globals.max_memory * 1024 ** 6
 		if platform.system().lower() == 'windows':
 			import ctypes
+
 			kernel32 = ctypes.windll.kernel32 # type: ignore[attr-defined]
 			kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(memory), ctypes.c_size_t(memory))
 		else:
 			import resource
+
 			resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))
 
 
 def pre_check() -> bool:
 	if sys.version_info < (3, 9):
-		update_status(wording.get('python_not_supported').format(version = '3.9'))
+		logger.error(wording.get('python_not_supported').format(version = '3.9'), __name__.upper())
 		return False
 	if not shutil.which('ffmpeg'):
-		update_status(wording.get('ffmpeg_not_installed'))
+		logger.error(wording.get('ffmpeg_not_installed'), __name__.upper())
 		return False
 	return True
 
 
 def conditional_process() -> None:
-	conditional_set_face_reference()
+	conditional_append_reference_faces()
 	for frame_processor_module in get_frame_processors_modules(facefusion.globals.frame_processors):
 		if not frame_processor_module.pre_process('output'):
 			return
@@ -200,14 +218,21 @@ def conditional_process() -> None:
 		process_video()
 
 
-def conditional_set_face_reference() -> None:
-	if 'reference' in facefusion.globals.face_selector_mode and not get_face_reference():
+def conditional_append_reference_faces() -> None:
+	if 'reference' in facefusion.globals.face_selector_mode and not get_reference_faces():
+		source_frames = read_static_images(facefusion.globals.source_paths)
+		source_face = get_average_face(source_frames)
 		if is_video(facefusion.globals.target_path):
 			reference_frame = get_video_frame(facefusion.globals.target_path, facefusion.globals.reference_frame_number)
 		else:
 			reference_frame = read_image(facefusion.globals.target_path)
 		reference_face = get_one_face(reference_frame, facefusion.globals.reference_face_position)
-		set_face_reference(reference_face)
+		append_reference_face('origin', reference_face)
+		if source_face and reference_face:
+			for frame_processor_module in get_frame_processors_modules(facefusion.globals.frame_processors):
+				reference_frame = frame_processor_module.get_reference_frame(source_face, reference_face, reference_frame)
+				reference_face = get_one_face(reference_frame, facefusion.globals.reference_face_position)
+				append_reference_face(frame_processor_module.__name__, reference_face)
 
 
 def process_image() -> None:
@@ -216,18 +241,18 @@ def process_image() -> None:
 	shutil.copy2(facefusion.globals.target_path, facefusion.globals.output_path)
 	# process frame
 	for frame_processor_module in get_frame_processors_modules(facefusion.globals.frame_processors):
-		update_status(wording.get('processing'), frame_processor_module.NAME)
-		frame_processor_module.process_image(facefusion.globals.source_path, facefusion.globals.output_path, facefusion.globals.output_path)
+		logger.info(wording.get('processing'), frame_processor_module.NAME)
+		frame_processor_module.process_image(facefusion.globals.source_paths, facefusion.globals.output_path, facefusion.globals.output_path)
 		frame_processor_module.post_process()
 	# compress image
-	update_status(wording.get('compressing_image'))
+	logger.info(wording.get('compressing_image'), __name__.upper())
 	if not compress_image(facefusion.globals.output_path):
-		update_status(wording.get('compressing_image_failed'))
+		logger.error(wording.get('compressing_image_failed'), __name__.upper())
 	# validate image
 	if is_image(facefusion.globals.output_path):
-		update_status(wording.get('processing_image_succeed'))
+		logger.info(wording.get('processing_image_succeed'), __name__.upper())
 	else:
-		update_status(wording.get('processing_image_failed'))
+		logger.error(wording.get('processing_image_failed'), __name__.upper())
 
 
 def process_video() -> None:
@@ -235,40 +260,40 @@ def process_video() -> None:
 		return
 	fps = detect_fps(facefusion.globals.target_path) if facefusion.globals.keep_fps else 25.0
 	# create temp
-	update_status(wording.get('creating_temp'))
+	logger.info(wording.get('creating_temp'), __name__.upper())
 	create_temp(facefusion.globals.target_path)
 	# extract frames
-	update_status(wording.get('extracting_frames_fps').format(fps = fps))
+	logger.info(wording.get('extracting_frames_fps').format(fps = fps), __name__.upper())
 	extract_frames(facefusion.globals.target_path, fps)
 	# process frame
 	temp_frame_paths = get_temp_frame_paths(facefusion.globals.target_path)
 	if temp_frame_paths:
 		for frame_processor_module in get_frame_processors_modules(facefusion.globals.frame_processors):
-			update_status(wording.get('processing'), frame_processor_module.NAME)
-			frame_processor_module.process_video(facefusion.globals.source_path, temp_frame_paths)
+			logger.info(wording.get('processing'), frame_processor_module.NAME)
+			frame_processor_module.process_video(facefusion.globals.source_paths, temp_frame_paths)
 			frame_processor_module.post_process()
 	else:
-		update_status(wording.get('temp_frames_not_found'))
+		logger.error(wording.get('temp_frames_not_found'), __name__.upper())
 		return
 	# merge video
-	update_status(wording.get('merging_video_fps').format(fps = fps))
+	logger.info(wording.get('merging_video_fps').format(fps = fps), __name__.upper())
 	if not merge_video(facefusion.globals.target_path, fps):
-		update_status(wording.get('merging_video_failed'))
+		logger.error(wording.get('merging_video_failed'), __name__.upper())
 		return
 	# handle audio
 	if facefusion.globals.skip_audio:
-		update_status(wording.get('skipping_audio'))
+		logger.info(wording.get('skipping_audio'), __name__.upper())
 		move_temp(facefusion.globals.target_path, facefusion.globals.output_path)
 	else:
-		update_status(wording.get('restoring_audio'))
+		logger.info(wording.get('restoring_audio'), __name__.upper())
 		if not restore_audio(facefusion.globals.target_path, facefusion.globals.output_path):
-			update_status(wording.get('restoring_audio_failed'))
+			logger.warn(wording.get('restoring_audio_skipped'), __name__.upper())
 			move_temp(facefusion.globals.target_path, facefusion.globals.output_path)
 	# clear temp
-	update_status(wording.get('clearing_temp'))
+	logger.info(wording.get('clearing_temp'), __name__.upper())
 	clear_temp(facefusion.globals.target_path)
 	# validate video
 	if is_video(facefusion.globals.output_path):
-		update_status(wording.get('processing_video_succeed'))
+		logger.info(wording.get('processing_video_succeed'), __name__.upper())
 	else:
-		update_status(wording.get('processing_video_failed'))
+		logger.error(wording.get('processing_video_failed'), __name__.upper())

facefusion/download.py

@@ -0,0 +1,44 @@
+import os
+import subprocess
+import urllib.request
+from typing import List
+from concurrent.futures import ThreadPoolExecutor
+from functools import lru_cache
+from tqdm import tqdm
+
+import facefusion.globals
+from facefusion import wording
+from facefusion.filesystem import is_file
+
+
+def conditional_download(download_directory_path : str, urls : List[str]) -> None:
+	with ThreadPoolExecutor() as executor:
+		for url in urls:
+			executor.submit(get_download_size, url)
+	for url in urls:
+		download_file_path = os.path.join(download_directory_path, os.path.basename(url))
+		initial = os.path.getsize(download_file_path) if is_file(download_file_path) else 0
+		total = get_download_size(url)
+		if initial < total:
+			with tqdm(total = total, initial = initial, desc = wording.get('downloading'), unit = 'B', unit_scale = True, unit_divisor = 1024, ascii = ' =', disable = facefusion.globals.log_level in [ 'warn', 'error' ]) as progress:
+				subprocess.Popen([ 'curl', '--create-dirs', '--silent', '--insecure', '--location', '--continue-at', '-', '--output', download_file_path, url ])
+				current = initial
+				while current < total:
+					if is_file(download_file_path):
+						current = os.path.getsize(download_file_path)
+						progress.update(current - progress.n)
+
+
+@lru_cache(maxsize = None)
+def get_download_size(url : str) -> int:
+	try:
+		response = urllib.request.urlopen(url, timeout = 10)
+		return int(response.getheader('Content-Length'))
+	except (OSError, ValueError):
+		return 0
+
+
+def is_download_done(url : str, file_path : str) -> bool:
+	if is_file(file_path):
+		return get_download_size(url) == os.path.getsize(file_path)
+	return False

facefusion/execution_helper.py

@@ -0,0 +1,22 @@
+from typing import List
+import onnxruntime
+
+
+def encode_execution_providers(execution_providers : List[str]) -> List[str]:
+	return [ execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers ]
+
+
+def decode_execution_providers(execution_providers: List[str]) -> List[str]:
+	available_execution_providers = onnxruntime.get_available_providers()
+	encoded_execution_providers = encode_execution_providers(available_execution_providers)
+	return [ execution_provider for execution_provider, encoded_execution_provider in zip(available_execution_providers, encoded_execution_providers) if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers) ]
+
+
+def map_device(execution_providers : List[str]) -> str:
+	if 'CoreMLExecutionProvider' in execution_providers:
+		return 'mps'
+	if 'CUDAExecutionProvider' in execution_providers or 'ROCMExecutionProvider' in execution_providers :
+		return 'cuda'
+	if 'OpenVINOExecutionProvider' in execution_providers:
+		return 'mkl'
+	return 'cpu'

facefusion/face_analyser.py

@@ -1,20 +1,21 @@
-from typing import Any, Optional, List, Dict, Tuple
+from typing import Any, Optional, List, Tuple
 import threading
 import cv2
 import numpy
 import onnxruntime
 
 import facefusion.globals
-from facefusion.face_cache import get_faces_cache, set_faces_cache
+from facefusion.download import conditional_download
+from facefusion.face_store import get_static_faces, set_static_faces
 from facefusion.face_helper import warp_face, create_static_anchors, distance_to_kps, distance_to_bbox, apply_nms
-from facefusion.typing import Frame, Face, FaceAnalyserOrder, FaceAnalyserAge, FaceAnalyserGender, ModelValue, Bbox, Kps, Score, Embedding
-from facefusion.utilities import resolve_relative_path, conditional_download
+from facefusion.filesystem import resolve_relative_path
+from facefusion.typing import Frame, Face, FaceSet, FaceAnalyserOrder, FaceAnalyserAge, FaceAnalyserGender, ModelSet, Bbox, Kps, Score, Embedding
 from facefusion.vision import resize_frame_dimension
 
 FACE_ANALYSER = None
 THREAD_SEMAPHORE : threading.Semaphore = threading.Semaphore()
 THREAD_LOCK : threading.Lock = threading.Lock()
-MODELS : Dict[str, ModelValue] =\
+MODELS : ModelSet =\
 {
 	'face_detector_retinaface':
 	{
@@ -26,7 +27,7 @@ MODELS : Dict[str, ModelValue] =\
 		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/yunet_2023mar.onnx',
 		'path': resolve_relative_path('../.assets/models/yunet_2023mar.onnx')
 	},
-	'face_recognizer_arcface_blendface':
+	'face_recognizer_arcface_blendswap':
 	{
 		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/arcface_w600k_r50.onnx',
 		'path': resolve_relative_path('../.assets/models/arcface_w600k_r50.onnx')
@@ -58,8 +59,8 @@ def get_face_analyser() -> Any:
 				face_detector = onnxruntime.InferenceSession(MODELS.get('face_detector_retinaface').get('path'), providers = facefusion.globals.execution_providers)
 			if facefusion.globals.face_detector_model == 'yunet':
 				face_detector = cv2.FaceDetectorYN.create(MODELS.get('face_detector_yunet').get('path'), '', (0, 0))
-			if facefusion.globals.face_recognizer_model == 'arcface_blendface':
-				face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_blendface').get('path'), providers = facefusion.globals.execution_providers)
+			if facefusion.globals.face_recognizer_model == 'arcface_blendswap':
+				face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_blendswap').get('path'), providers = facefusion.globals.execution_providers)
 			if facefusion.globals.face_recognizer_model == 'arcface_inswapper':
 				face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_inswapper').get('path'), providers = facefusion.globals.execution_providers)
 			if facefusion.globals.face_recognizer_model == 'arcface_simswap':
@@ -174,9 +175,13 @@ def detect_with_yunet(temp_frame : Frame, temp_frame_height : int, temp_frame_wi
 	return bbox_list, kps_list, score_list
 
 
-def create_faces(frame : Frame, bbox_list : List[Bbox], kps_list : List[Kps], score_list : List[Score]) -> List[Face] :
-	faces : List[Face] = []
+def create_faces(frame : Frame, bbox_list : List[Bbox], kps_list : List[Kps], score_list : List[Score]) -> List[Face]:
+	faces = []
 	if facefusion.globals.face_detector_score > 0:
+		sort_indices = numpy.argsort(-numpy.array(score_list))
+		bbox_list = [ bbox_list[index] for index in sort_indices ]
+		kps_list = [ kps_list[index] for index in sort_indices ]
+		score_list = [ score_list[index] for index in sort_indices ]
 		keep_indices = apply_nms(bbox_list, 0.4)
 		for index in keep_indices:
 			bbox = bbox_list[index]
@@ -198,7 +203,7 @@ def create_faces(frame : Frame, bbox_list : List[Bbox], kps_list : List[Kps], sc
 
 def calc_embedding(temp_frame : Frame, kps : Kps) -> Tuple[Embedding, Embedding]:
 	face_recognizer = get_face_analyser().get('face_recognizer')
-	crop_frame, matrix = warp_face(temp_frame, kps, 'arcface_v2', (112, 112))
+	crop_frame, matrix = warp_face(temp_frame, kps, 'arcface_112_v2', (112, 112))
 	crop_frame = crop_frame.astype(numpy.float32) / 127.5 - 1
 	crop_frame = crop_frame[:, :, ::-1].transpose(2, 0, 1)
 	crop_frame = numpy.expand_dims(crop_frame, axis = 0)
@@ -213,7 +218,7 @@ def calc_embedding(temp_frame : Frame, kps : Kps) -> Tuple[Embedding, Embedding]
 
 def detect_gender_age(frame : Frame, kps : Kps) -> Tuple[int, int]:
 	gender_age = get_face_analyser().get('gender_age')
-	crop_frame, affine_matrix = warp_face(frame, kps, 'arcface_v2', (96, 96))
+	crop_frame, affine_matrix = warp_face(frame, kps, 'arcface_112_v2', (96, 96))
 	crop_frame = numpy.expand_dims(crop_frame, axis = 0).transpose(0, 3, 1, 2).astype(numpy.float32)
 	prediction = gender_age.run(None,
 	{
@@ -234,14 +239,38 @@ def get_one_face(frame : Frame, position : int = 0) -> Optional[Face]:
 	return None
 
 
+def get_average_face(frames : List[Frame], position : int = 0) -> Optional[Face]:
+	average_face = None
+	faces = []
+	embedding_list = []
+	normed_embedding_list = []
+	for frame in frames:
+		face = get_one_face(frame, position)
+		if face:
+			faces.append(face)
+			embedding_list.append(face.embedding)
+			normed_embedding_list.append(face.normed_embedding)
+	if faces:
+		average_face = Face(
+			bbox = faces[0].bbox,
+			kps = faces[0].kps,
+			score = faces[0].score,
+			embedding = numpy.mean(embedding_list, axis = 0),
+			normed_embedding = numpy.mean(normed_embedding_list, axis = 0),
+			gender = faces[0].gender,
+			age = faces[0].age
+		)
+	return average_face
+
+
 def get_many_faces(frame : Frame) -> List[Face]:
 	try:
-		faces_cache = get_faces_cache(frame)
+		faces_cache = get_static_faces(frame)
 		if faces_cache:
 			faces = faces_cache
 		else:
 			faces = extract_faces(frame)
-			set_faces_cache(frame, faces)
+			set_static_faces(frame, faces)
 		if facefusion.globals.face_analyser_order:
 			faces = sort_by_order(faces, facefusion.globals.face_analyser_order)
 		if facefusion.globals.face_analyser_age:
@@ -253,18 +282,27 @@ def get_many_faces(frame : Frame) -> List[Face]:
 		return []
 
 
-def find_similar_faces(frame : Frame, reference_face : Face, face_distance : float) -> List[Face]:
+def find_similar_faces(frame : Frame, reference_faces : FaceSet, face_distance : float) -> List[Face]:
+	similar_faces : List[Face] = []
 	many_faces = get_many_faces(frame)
-	similar_faces = []
-	if many_faces:
-		for face in many_faces:
-			if hasattr(face, 'normed_embedding') and hasattr(reference_face, 'normed_embedding'):
-				current_face_distance = 1 - numpy.dot(face.normed_embedding, reference_face.normed_embedding)
-				if current_face_distance < face_distance:
-					similar_faces.append(face)
+
+	if reference_faces:
+		for reference_set in reference_faces:
+			if not similar_faces:
+				for reference_face in reference_faces[reference_set]:
+					for face in many_faces:
+						if compare_faces(face, reference_face, face_distance):
+							similar_faces.append(face)
 	return similar_faces
 
 
+def compare_faces(face : Face, reference_face : Face, face_distance : float) -> bool:
+	if hasattr(face, 'normed_embedding') and hasattr(reference_face, 'normed_embedding'):
+		current_face_distance = 1 - numpy.dot(face.normed_embedding, reference_face.normed_embedding)
+		return current_face_distance < face_distance
+	return False
+
+
 def sort_by_order(faces : List[Face], order : FaceAnalyserOrder) -> List[Face]:
 	if order == 'left-right':
 		return sorted(faces, key = lambda face: face.bbox[0])

facefusion/face_helper.py

@@ -1,14 +1,14 @@
 from typing import Any, Dict, Tuple, List
-from functools import lru_cache
 from cv2.typing import Size
+from functools import lru_cache
 import cv2
 import numpy
 
-from facefusion.typing import Bbox, Kps, Frame, Matrix, Template, Padding
+from facefusion.typing import Bbox, Kps, Frame, Mask, Matrix, Template
 
 TEMPLATES : Dict[Template, numpy.ndarray[Any, Any]] =\
 {
-	'arcface_v1': numpy.array(
+	'arcface_112_v1': numpy.array(
 	[
 		[ 39.7300, 51.1380 ],
 		[ 72.2700, 51.1380 ],
@@ -16,7 +16,7 @@ TEMPLATES : Dict[Template, numpy.ndarray[Any, Any]] =\
 		[ 42.4630, 87.0100 ],
 		[ 69.5370, 87.0100 ]
 	]),
-	'arcface_v2': numpy.array(
+	'arcface_112_v2': numpy.array(
 	[
 		[ 38.2946, 51.6963 ],
 		[ 73.5318, 51.5014 ],
@@ -24,7 +24,15 @@ TEMPLATES : Dict[Template, numpy.ndarray[Any, Any]] =\
 		[ 41.5493, 92.3655 ],
 		[ 70.7299, 92.2041 ]
 	]),
-	'ffhq': numpy.array(
+	'arcface_128_v2': numpy.array(
+	[
+		[ 46.2946, 51.6963 ],
+		[ 81.5318, 51.5014 ],
+		[ 64.0252, 71.7366 ],
+		[ 49.5493, 92.3655 ],
+		[ 78.7299, 92.2041 ]
+	]),
+	'ffhq_512': numpy.array(
 	[
 		[ 192.98138, 239.94708 ],
 		[ 318.90277, 240.1936 ],
@@ -37,39 +45,23 @@ TEMPLATES : Dict[Template, numpy.ndarray[Any, Any]] =\
 
 def warp_face(temp_frame : Frame, kps : Kps, template : Template, size : Size) -> Tuple[Frame, Matrix]:
 	normed_template = TEMPLATES.get(template) * size[1] / size[0]
-	affine_matrix = cv2.estimateAffinePartial2D(kps, normed_template, method = cv2.LMEDS)[0]
+	affine_matrix = cv2.estimateAffinePartial2D(kps, normed_template, method = cv2.RANSAC, ransacReprojThreshold = 100)[0]
 	crop_frame = cv2.warpAffine(temp_frame, affine_matrix, (size[1], size[1]), borderMode = cv2.BORDER_REPLICATE)
 	return crop_frame, affine_matrix
 
 
-def paste_back(temp_frame : Frame, crop_frame: Frame, affine_matrix : Matrix, face_mask_blur : float, face_mask_padding : Padding) -> Frame:
+def paste_back(temp_frame : Frame, crop_frame: Frame, crop_mask : Mask, affine_matrix : Matrix) -> Frame:
 	inverse_matrix = cv2.invertAffineTransform(affine_matrix)
 	temp_frame_size = temp_frame.shape[:2][::-1]
-	mask_size = tuple(crop_frame.shape[:2])
-	mask_frame = create_static_mask_frame(mask_size, face_mask_blur, face_mask_padding)
-	inverse_mask_frame = cv2.warpAffine(mask_frame, inverse_matrix, temp_frame_size).clip(0, 1)
+	inverse_crop_mask = cv2.warpAffine(crop_mask, inverse_matrix, temp_frame_size).clip(0, 1)
 	inverse_crop_frame = cv2.warpAffine(crop_frame, inverse_matrix, temp_frame_size, borderMode = cv2.BORDER_REPLICATE)
 	paste_frame = temp_frame.copy()
-	paste_frame[:, :, 0] = inverse_mask_frame * inverse_crop_frame[:, :, 0] + (1 - inverse_mask_frame) * temp_frame[:, :, 0]
-	paste_frame[:, :, 1] = inverse_mask_frame * inverse_crop_frame[:, :, 1] + (1 - inverse_mask_frame) * temp_frame[:, :, 1]
-	paste_frame[:, :, 2] = inverse_mask_frame * inverse_crop_frame[:, :, 2] + (1 - inverse_mask_frame) * temp_frame[:, :, 2]
+	paste_frame[:, :, 0] = inverse_crop_mask * inverse_crop_frame[:, :, 0] + (1 - inverse_crop_mask) * temp_frame[:, :, 0]
+	paste_frame[:, :, 1] = inverse_crop_mask * inverse_crop_frame[:, :, 1] + (1 - inverse_crop_mask) * temp_frame[:, :, 1]
+	paste_frame[:, :, 2] = inverse_crop_mask * inverse_crop_frame[:, :, 2] + (1 - inverse_crop_mask) * temp_frame[:, :, 2]
 	return paste_frame
 
 
-@lru_cache(maxsize = None)
-def create_static_mask_frame(mask_size : Size, face_mask_blur : float, face_mask_padding : Padding) -> Frame:
-	mask_frame = numpy.ones(mask_size, numpy.float32)
-	blur_amount = int(mask_size[0] * 0.5 * face_mask_blur)
-	blur_area = max(blur_amount // 2, 1)
-	mask_frame[:max(blur_area, int(mask_size[1] * face_mask_padding[0] / 100)), :] = 0
-	mask_frame[-max(blur_area, int(mask_size[1] * face_mask_padding[2] / 100)):, :] = 0
-	mask_frame[:, :max(blur_area, int(mask_size[0] * face_mask_padding[3] / 100))] = 0
-	mask_frame[:, -max(blur_area, int(mask_size[0] * face_mask_padding[1] / 100)):] = 0
-	if blur_amount > 0:
-		mask_frame = cv2.GaussianBlur(mask_frame, (0, 0), blur_amount * 0.25)
-	return mask_frame
-
-
 @lru_cache(maxsize = None)
 def create_static_anchors(feature_stride : int, anchor_total : int, stride_height : int, stride_width : int) -> numpy.ndarray[Any, Any]:
 	y, x = numpy.mgrid[:stride_height, :stride_width][::-1]

facefusion/face_masker.py

@@ -0,0 +1,128 @@
+from typing import Any, Dict, List
+from cv2.typing import Size
+from functools import lru_cache
+import threading
+import cv2
+import numpy
+import onnxruntime
+
+import facefusion.globals
+from facefusion.typing import Frame, Mask, Padding, FaceMaskRegion, ModelSet
+from facefusion.filesystem import resolve_relative_path
+from facefusion.download import conditional_download
+
+FACE_OCCLUDER = None
+FACE_PARSER = None
+THREAD_LOCK : threading.Lock = threading.Lock()
+MODELS : ModelSet =\
+{
+	'face_occluder':
+	{
+		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/face_occluder.onnx',
+		'path': resolve_relative_path('../.assets/models/face_occluder.onnx')
+	},
+	'face_parser':
+	{
+		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/face_parser.onnx',
+		'path': resolve_relative_path('../.assets/models/face_parser.onnx')
+	}
+}
+FACE_MASK_REGIONS : Dict[FaceMaskRegion, int] =\
+{
+	'skin': 1,
+	'left-eyebrow': 2,
+	'right-eyebrow': 3,
+	'left-eye': 4,
+	'right-eye': 5,
+	'eye-glasses': 6,
+	'nose': 10,
+	'mouth': 11,
+	'upper-lip': 12,
+	'lower-lip': 13
+}
+
+
+def get_face_occluder() -> Any:
+	global FACE_OCCLUDER
+
+	with THREAD_LOCK:
+		if FACE_OCCLUDER is None:
+			model_path = MODELS.get('face_occluder').get('path')
+			FACE_OCCLUDER = onnxruntime.InferenceSession(model_path, providers = facefusion.globals.execution_providers)
+	return FACE_OCCLUDER
+
+
+def get_face_parser() -> Any:
+	global FACE_PARSER
+
+	with THREAD_LOCK:
+		if FACE_PARSER is None:
+			model_path = MODELS.get('face_parser').get('path')
+			FACE_PARSER = onnxruntime.InferenceSession(model_path, providers = facefusion.globals.execution_providers)
+	return FACE_PARSER
+
+
+def clear_face_occluder() -> None:
+	global FACE_OCCLUDER
+
+	FACE_OCCLUDER = None
+
+
+def clear_face_parser() -> None:
+	global FACE_PARSER
+
+	FACE_PARSER = None
+
+
+def pre_check() -> bool:
+	if not facefusion.globals.skip_download:
+		download_directory_path = resolve_relative_path('../.assets/models')
+		model_urls =\
+		[
+			MODELS.get('face_occluder').get('url'),
+			MODELS.get('face_parser').get('url'),
+		]
+		conditional_download(download_directory_path, model_urls)
+	return True
+
+
+@lru_cache(maxsize = None)
+def create_static_box_mask(crop_size : Size, face_mask_blur : float, face_mask_padding : Padding) -> Mask:
+	blur_amount = int(crop_size[0] * 0.5 * face_mask_blur)
+	blur_area = max(blur_amount // 2, 1)
+	box_mask = numpy.ones(crop_size, numpy.float32)
+	box_mask[:max(blur_area, int(crop_size[1] * face_mask_padding[0] / 100)), :] = 0
+	box_mask[-max(blur_area, int(crop_size[1] * face_mask_padding[2] / 100)):, :] = 0
+	box_mask[:, :max(blur_area, int(crop_size[0] * face_mask_padding[3] / 100))] = 0
+	box_mask[:, -max(blur_area, int(crop_size[0] * face_mask_padding[1] / 100)):] = 0
+	if blur_amount > 0:
+		box_mask = cv2.GaussianBlur(box_mask, (0, 0), blur_amount * 0.25)
+	return box_mask
+
+
+def create_occlusion_mask(crop_frame : Frame) -> Mask:
+	face_occluder = get_face_occluder()
+	prepare_frame = cv2.resize(crop_frame, face_occluder.get_inputs()[0].shape[1:3][::-1])
+	prepare_frame = numpy.expand_dims(prepare_frame, axis = 0).astype(numpy.float32) / 255
+	prepare_frame = prepare_frame.transpose(0, 1, 2, 3)
+	occlusion_mask = face_occluder.run(None,
+	{
+		face_occluder.get_inputs()[0].name: prepare_frame
+	})[0][0]
+	occlusion_mask = occlusion_mask.transpose(0, 1, 2).clip(0, 1).astype(numpy.float32)
+	occlusion_mask = cv2.resize(occlusion_mask, crop_frame.shape[:2][::-1])
+	return occlusion_mask
+
+
+def create_region_mask(crop_frame : Frame, face_mask_regions : List[FaceMaskRegion]) -> Mask:
+	face_parser = get_face_parser()
+	prepare_frame = cv2.flip(cv2.resize(crop_frame, (512, 512)), 1)
+	prepare_frame = numpy.expand_dims(prepare_frame, axis = 0).astype(numpy.float32)[:, :, ::-1] / 127.5 - 1
+	prepare_frame = prepare_frame.transpose(0, 3, 1, 2)
+	region_mask = face_parser.run(None,
+	{
+		face_parser.get_inputs()[0].name: prepare_frame
+	})[0][0]
+	region_mask = numpy.isin(region_mask.argmax(0), [ FACE_MASK_REGIONS[region] for region in face_mask_regions ])
+	region_mask = cv2.resize(region_mask.astype(numpy.float32), crop_frame.shape[:2][::-1])
+	return region_mask

facefusion/face_store.py

@@ -0,0 +1,47 @@
+from typing import Optional, List
+import hashlib
+
+from facefusion.typing import Frame, Face, FaceStore, FaceSet
+
+FACE_STORE: FaceStore =\
+{
+	'static_faces': {},
+	'reference_faces': {}
+}
+
+
+def get_static_faces(frame : Frame) -> Optional[List[Face]]:
+	frame_hash = create_frame_hash(frame)
+	if frame_hash in FACE_STORE['static_faces']:
+		return FACE_STORE['static_faces'][frame_hash]
+	return None
+
+
+def set_static_faces(frame : Frame, faces : List[Face]) -> None:
+	frame_hash = create_frame_hash(frame)
+	if frame_hash:
+		FACE_STORE['static_faces'][frame_hash] = faces
+
+
+def clear_static_faces() -> None:
+	FACE_STORE['static_faces'] = {}
+
+
+def create_frame_hash(frame: Frame) -> Optional[str]:
+	return hashlib.sha1(frame.tobytes()).hexdigest() if frame.any() else None
+
+
+def get_reference_faces() -> Optional[FaceSet]:
+	if FACE_STORE['reference_faces']:
+		return FACE_STORE['reference_faces']
+	return None
+
+
+def append_reference_face(name : str, face : Face) -> None:
+	if name not in FACE_STORE['reference_faces']:
+		FACE_STORE['reference_faces'][name] = []
+	FACE_STORE['reference_faces'][name].append(face)
+
+
+def clear_reference_faces() -> None:
+	FACE_STORE['reference_faces'] = {}

facefusion/ffmpeg.py

@@ -0,0 +1,81 @@
+from typing import List
+import subprocess
+
+import facefusion.globals
+from facefusion import logger
+from facefusion.filesystem import get_temp_frames_pattern, get_temp_output_video_path
+from facefusion.vision import detect_fps
+
+
+def run_ffmpeg(args : List[str]) -> bool:
+	commands = [ 'ffmpeg', '-hide_banner', '-loglevel', 'error' ]
+	commands.extend(args)
+	try:
+		subprocess.run(commands, stderr = subprocess.PIPE, check = True)
+		return True
+	except subprocess.CalledProcessError as exception:
+		logger.debug(exception.stderr.decode().strip(), __name__.upper())
+		return False
+
+
+def open_ffmpeg(args : List[str]) -> subprocess.Popen[bytes]:
+	commands = [ 'ffmpeg', '-hide_banner', '-loglevel', 'error' ]
+	commands.extend(args)
+	return subprocess.Popen(commands, stdin = subprocess.PIPE)
+
+
+def extract_frames(target_path : str, fps : float) -> bool:
+	temp_frame_compression = round(31 - (facefusion.globals.temp_frame_quality * 0.31))
+	trim_frame_start = facefusion.globals.trim_frame_start
+	trim_frame_end = facefusion.globals.trim_frame_end
+	temp_frames_pattern = get_temp_frames_pattern(target_path, '%04d')
+	commands = [ '-hwaccel', 'auto', '-i', target_path, '-q:v', str(temp_frame_compression), '-pix_fmt', 'rgb24' ]
+	if trim_frame_start is not None and trim_frame_end is not None:
+		commands.extend([ '-vf', 'trim=start_frame=' + str(trim_frame_start) + ':end_frame=' + str(trim_frame_end) + ',fps=' + str(fps) ])
+	elif trim_frame_start is not None:
+		commands.extend([ '-vf', 'trim=start_frame=' + str(trim_frame_start) + ',fps=' + str(fps) ])
+	elif trim_frame_end is not None:
+		commands.extend([ '-vf', 'trim=end_frame=' + str(trim_frame_end) + ',fps=' + str(fps) ])
+	else:
+		commands.extend([ '-vf', 'fps=' + str(fps) ])
+	commands.extend([ '-vsync', '0', temp_frames_pattern ])
+	return run_ffmpeg(commands)
+
+
+def compress_image(output_path : str) -> bool:
+	output_image_compression = round(31 - (facefusion.globals.output_image_quality * 0.31))
+	commands = [ '-hwaccel', 'auto', '-i', output_path, '-q:v', str(output_image_compression), '-y', output_path ]
+	return run_ffmpeg(commands)
+
+
+def merge_video(target_path : str, fps : float) -> bool:
+	temp_output_video_path = get_temp_output_video_path(target_path)
+	temp_frames_pattern = get_temp_frames_pattern(target_path, '%04d')
+	commands = [ '-hwaccel', 'auto', '-r', str(fps), '-i', temp_frames_pattern, '-c:v', facefusion.globals.output_video_encoder ]
+	if facefusion.globals.output_video_encoder in [ 'libx264', 'libx265' ]:
+		output_video_compression = round(51 - (facefusion.globals.output_video_quality * 0.51))
+		commands.extend([ '-crf', str(output_video_compression) ])
+	if facefusion.globals.output_video_encoder in [ 'libvpx-vp9' ]:
+		output_video_compression = round(63 - (facefusion.globals.output_video_quality * 0.63))
+		commands.extend([ '-crf', str(output_video_compression) ])
+	if facefusion.globals.output_video_encoder in [ 'h264_nvenc', 'hevc_nvenc' ]:
+		output_video_compression = round(51 - (facefusion.globals.output_video_quality * 0.51))
+		commands.extend([ '-cq', str(output_video_compression) ])
+	commands.extend([ '-pix_fmt', 'yuv420p', '-colorspace', 'bt709', '-y', temp_output_video_path ])
+	return run_ffmpeg(commands)
+
+
+def restore_audio(target_path : str, output_path : str) -> bool:
+	fps = detect_fps(target_path)
+	trim_frame_start = facefusion.globals.trim_frame_start
+	trim_frame_end = facefusion.globals.trim_frame_end
+	temp_output_video_path = get_temp_output_video_path(target_path)
+	commands = [ '-hwaccel', 'auto', '-i', temp_output_video_path ]
+	if trim_frame_start is not None:
+		start_time = trim_frame_start / fps
+		commands.extend([ '-ss', str(start_time) ])
+	if trim_frame_end is not None:
+		end_time = trim_frame_end / fps
+		commands.extend([ '-to', str(end_time) ])
+	commands.extend([ '-i', target_path, '-c', 'copy', '-map', '0:v:0', '-map', '1:a:0', '-shortest', '-y', output_path ])
+	return run_ffmpeg(commands)

facefusion/filesystem.py

@@ -0,0 +1,91 @@
+from typing import List, Optional
+import glob
+import os
+import shutil
+import tempfile
+import filetype
+from pathlib import Path
+
+import facefusion.globals
+
+TEMP_DIRECTORY_PATH = os.path.join(tempfile.gettempdir(), 'facefusion')
+TEMP_OUTPUT_VIDEO_NAME = 'temp.mp4'
+
+
+def get_temp_frame_paths(target_path : str) -> List[str]:
+	temp_frames_pattern = get_temp_frames_pattern(target_path, '*')
+	return sorted(glob.glob(temp_frames_pattern))
+
+
+def get_temp_frames_pattern(target_path : str, temp_frame_prefix : str) -> str:
+	temp_directory_path = get_temp_directory_path(target_path)
+	return os.path.join(temp_directory_path, temp_frame_prefix + '.' + facefusion.globals.temp_frame_format)
+
+
+def get_temp_directory_path(target_path : str) -> str:
+	target_name, _ = os.path.splitext(os.path.basename(target_path))
+	return os.path.join(TEMP_DIRECTORY_PATH, target_name)
+
+
+def get_temp_output_video_path(target_path : str) -> str:
+	temp_directory_path = get_temp_directory_path(target_path)
+	return os.path.join(temp_directory_path, TEMP_OUTPUT_VIDEO_NAME)
+
+
+def create_temp(target_path : str) -> None:
+	temp_directory_path = get_temp_directory_path(target_path)
+	Path(temp_directory_path).mkdir(parents = True, exist_ok = True)
+
+
+def move_temp(target_path : str, output_path : str) -> None:
+	temp_output_video_path = get_temp_output_video_path(target_path)
+	if is_file(temp_output_video_path):
+		if is_file(output_path):
+			os.remove(output_path)
+		shutil.move(temp_output_video_path, output_path)
+
+
+def clear_temp(target_path : str) -> None:
+	temp_directory_path = get_temp_directory_path(target_path)
+	parent_directory_path = os.path.dirname(temp_directory_path)
+	if not facefusion.globals.keep_temp and is_directory(temp_directory_path):
+		shutil.rmtree(temp_directory_path)
+	if os.path.exists(parent_directory_path) and not os.listdir(parent_directory_path):
+		os.rmdir(parent_directory_path)
+
+
+def is_file(file_path : str) -> bool:
+	return bool(file_path and os.path.isfile(file_path))
+
+
+def is_directory(directory_path : str) -> bool:
+	return bool(directory_path and os.path.isdir(directory_path))
+
+
+def is_image(image_path : str) -> bool:
+	if is_file(image_path):
+		return filetype.helpers.is_image(image_path)
+	return False
+
+
+def are_images(image_paths : List[str]) -> bool:
+	if image_paths:
+		return all(is_image(image_path) for image_path in image_paths)
+	return False
+
+
+def is_video(video_path : str) -> bool:
+	if is_file(video_path):
+		return filetype.helpers.is_video(video_path)
+	return False
+
+
+def resolve_relative_path(path : str) -> str:
+	return os.path.abspath(os.path.join(os.path.dirname(__file__), path))
+
+
+def list_module_names(path : str) -> Optional[List[str]]:
+	if os.path.exists(path):
+		files = os.listdir(path)
+		return [ Path(file).stem for file in files if not Path(file).stem.startswith(('.', '__')) ]
+	return None

facefusion/globals.py

@@ -1,14 +1,15 @@
 from typing import List, Optional
 
-from facefusion.typing import FaceSelectorMode, FaceAnalyserOrder, FaceAnalyserAge, FaceAnalyserGender, OutputVideoEncoder, FaceDetectorModel, FaceRecognizerModel, TempFrameFormat, Padding
+from facefusion.typing import LogLevel, FaceSelectorMode, FaceAnalyserOrder, FaceAnalyserAge, FaceAnalyserGender, FaceMaskType, FaceMaskRegion, OutputVideoEncoder, FaceDetectorModel, FaceRecognizerModel, TempFrameFormat, Padding
 
 # general
-source_path : Optional[str] = None
+source_paths : Optional[List[str]] = None
 target_path : Optional[str] = None
 output_path : Optional[str] = None
 # misc
 skip_download : Optional[bool] = None
 headless : Optional[bool] = None
+log_level : Optional[LogLevel] = None
 # execution
 execution_providers : List[str] = []
 execution_thread_count : Optional[int] = None
@@ -28,8 +29,10 @@ reference_face_position : Optional[int] = None
 reference_face_distance : Optional[float] = None
 reference_frame_number : Optional[int] = None
 # face mask
+face_mask_types : Optional[List[FaceMaskType]] = None
 face_mask_blur : Optional[float] = None
 face_mask_padding : Optional[Padding] = None
+face_mask_regions : Optional[List[FaceMaskRegion]] = None
 # frame extraction
 trim_frame_start : Optional[int] = None
 trim_frame_end : Optional[int] = None

facefusion/installer.py

@@ -1,4 +1,8 @@
 from typing import Dict, Tuple
+import sys
+import os
+import platform
+import tempfile
 import subprocess
 from argparse import ArgumentParser, HelpFormatter
 
@@ -11,32 +15,43 @@ from facefusion import metadata, wording
 TORCH : Dict[str, str] =\
 {
 	'default': 'default',
-	'cpu': 'cpu',
-	'cuda': 'cu118',
-	'rocm': 'rocm5.6'
+	'cpu': 'cpu'
 }
 ONNXRUNTIMES : Dict[str, Tuple[str, str]] =\
 {
-	'default': ('onnxruntime', '1.16.3'),
-	'cuda': ('onnxruntime-gpu', '1.16.3'),
-	'coreml-legacy': ('onnxruntime-coreml', '1.13.1'),
-	'coreml-silicon': ('onnxruntime-silicon', '1.16.0'),
-	'directml': ('onnxruntime-directml', '1.16.3'),
-	'openvino': ('onnxruntime-openvino', '1.16.0')
+	'default': ('onnxruntime', '1.16.3')
 }
+if platform.system().lower() == 'linux' or platform.system().lower() == 'windows':
+	TORCH['cuda'] = 'cu118'
+	TORCH['cuda-nightly'] = 'cu121'
+	ONNXRUNTIMES['cuda'] = ('onnxruntime-gpu', '1.16.3')
+	ONNXRUNTIMES['cuda-nightly'] = ('ort-nightly-gpu', '1.17.0.dev20231205004')
+	ONNXRUNTIMES['openvino'] = ('onnxruntime-openvino', '1.16.0')
+if platform.system().lower() == 'linux':
+	TORCH['rocm'] = 'rocm5.6'
+	ONNXRUNTIMES['rocm'] = ('onnxruntime-rocm', '1.16.3')
+if platform.system().lower() == 'darwin':
+	ONNXRUNTIMES['coreml-legacy'] = ('onnxruntime-coreml', '1.13.1')
+	ONNXRUNTIMES['coreml-silicon'] = ('onnxruntime-silicon', '1.16.0')
+if platform.system().lower() == 'windows':
+	ONNXRUNTIMES['directml'] = ('onnxruntime-directml', '1.16.3')
 
 
 def cli() -> None:
 	program = ArgumentParser(formatter_class = lambda prog: HelpFormatter(prog, max_help_position = 120))
-	program.add_argument('--torch', help = wording.get('install_dependency_help').format(dependency = 'torch'), dest = 'torch', choices = TORCH.keys())
-	program.add_argument('--onnxruntime', help = wording.get('install_dependency_help').format(dependency = 'onnxruntime'), dest = 'onnxruntime', choices = ONNXRUNTIMES.keys())
+	program.add_argument('--torch', help = wording.get('install_dependency_help').format(dependency = 'torch'), choices = TORCH.keys())
+	program.add_argument('--onnxruntime', help = wording.get('install_dependency_help').format(dependency = 'onnxruntime'), choices = ONNXRUNTIMES.keys())
+	program.add_argument('--skip-venv', help = wording.get('skip_venv_help'), action = 'store_true')
 	program.add_argument('-v', '--version', version = metadata.get('name') + ' ' + metadata.get('version'), action = 'version')
 	run(program)
 
 
 def run(program : ArgumentParser) -> None:
 	args = program.parse_args()
+	python_id = 'cp' + str(sys.version_info.major) + str(sys.version_info.minor)
 
+	if not args.skip_venv:
+		os.environ['PIP_REQUIRE_VIRTUALENV'] = '1'
 	if args.torch and args.onnxruntime:
 		answers =\
 		{
@@ -54,10 +69,24 @@ def run(program : ArgumentParser) -> None:
 		torch_wheel = TORCH[torch]
 		onnxruntime = answers['onnxruntime']
 		onnxruntime_name, onnxruntime_version = ONNXRUNTIMES[onnxruntime]
-		subprocess.call([ 'pip', 'uninstall', 'torch', '-y' ])
+
+		subprocess.call([ 'pip', 'uninstall', 'torch', '-y', '-q' ])
 		if torch_wheel == 'default':
 			subprocess.call([ 'pip', 'install', '-r', 'requirements.txt' ])
 		else:
 			subprocess.call([ 'pip', 'install', '-r', 'requirements.txt', '--extra-index-url', 'https://download.pytorch.org/whl/' + torch_wheel ])
-		subprocess.call([ 'pip', 'uninstall', 'onnxruntime', onnxruntime_name, '-y' ])
-		subprocess.call([ 'pip', 'install', onnxruntime_name + '==' + onnxruntime_version ])
+		if onnxruntime == 'rocm':
+			if python_id in [ 'cp39', 'cp310', 'cp311' ]:
+				wheel_name = 'onnxruntime_training-' + onnxruntime_version + '+rocm56-' + python_id + '-' + python_id + '-manylinux_2_17_x86_64.manylinux2014_x86_64.whl'
+				wheel_path = os.path.join(tempfile.gettempdir(), wheel_name)
+				wheel_url = 'https://download.onnxruntime.ai/' + wheel_name
+				subprocess.call([ 'curl', '--silent', '--location', '--continue-at', '-', '--output', wheel_path, wheel_url ])
+				subprocess.call([ 'pip', 'uninstall', wheel_path, '-y', '-q' ])
+				subprocess.call([ 'pip', 'install', wheel_path ])
+				os.remove(wheel_path)
+		else:
+			subprocess.call([ 'pip', 'uninstall', 'onnxruntime', onnxruntime_name, '-y', '-q' ])
+			if onnxruntime == 'cuda-nightly':
+				subprocess.call([ 'pip', 'install', onnxruntime_name + '==' + onnxruntime_version, '--extra-index-url', 'https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ort-cuda-12-nightly/pypi/simple' ])
+			else:
+				subprocess.call([ 'pip', 'install', onnxruntime_name + '==' + onnxruntime_version ])

facefusion/logger.py

@@ -0,0 +1,39 @@
+from typing import Dict
+from logging import basicConfig, getLogger, Logger, DEBUG, INFO, WARNING, ERROR
+
+from facefusion.typing import LogLevel
+
+
+def init(log_level : LogLevel) -> None:
+	basicConfig(format = None)
+	get_package_logger().setLevel(get_log_levels()[log_level])
+
+
+def get_package_logger() -> Logger:
+	return getLogger('facefusion')
+
+
+def debug(message : str, scope : str) -> None:
+	get_package_logger().debug('[' + scope + '] ' + message)
+
+
+def info(message : str, scope : str) -> None:
+	get_package_logger().info('[' + scope + '] ' + message)
+
+
+def warn(message : str, scope : str) -> None:
+	get_package_logger().warning('[' + scope + '] ' + message)
+
+
+def error(message : str, scope : str) -> None:
+	get_package_logger().error('[' + scope + '] ' + message)
+
+
+def get_log_levels() -> Dict[LogLevel, int]:
+	return\
+	{
+		'error': ERROR,
+		'warn': WARNING,
+		'info': INFO,
+		'debug': DEBUG
+	}

facefusion/metadata.py

@@ -2,7 +2,7 @@ METADATA =\
 {
 	'name': 'FaceFusion',
 	'description': 'Next generation face swapper and enhancer',
-	'version': '2.0.0',
+	'version': '2.1.3',
 	'license': 'MIT',
 	'author': 'Henry Ruhs',
 	'url': 'https://facefusion.io'

facefusion/normalizer.py

@@ -0,0 +1,34 @@
+from typing import List, Optional
+import os
+
+from facefusion.filesystem import is_file, is_directory
+from facefusion.typing import Padding
+
+
+def normalize_output_path(source_paths : List[str], target_path : str, output_path : str) -> Optional[str]:
+	if is_file(target_path) and is_directory(output_path):
+		target_name, target_extension = os.path.splitext(os.path.basename(target_path))
+		if source_paths and is_file(source_paths[0]):
+			source_name, _ = os.path.splitext(os.path.basename(source_paths[0]))
+			return os.path.join(output_path, source_name + '-' + target_name + target_extension)
+		return os.path.join(output_path, target_name + target_extension)
+	if is_file(target_path) and output_path:
+		_, target_extension = os.path.splitext(os.path.basename(target_path))
+		output_name, output_extension = os.path.splitext(os.path.basename(output_path))
+		output_directory_path = os.path.dirname(output_path)
+		if is_directory(output_directory_path) and output_extension:
+			return os.path.join(output_directory_path, output_name + target_extension)
+		return None
+	return output_path
+
+
+def normalize_padding(padding : Optional[List[int]]) -> Optional[Padding]:
+	if padding and len(padding) == 1:
+		return tuple([ padding[0], padding[0], padding[0], padding[0] ]) # type: ignore[return-value]
+	if padding and len(padding) == 2:
+		return tuple([ padding[0], padding[1], padding[0], padding[1] ]) # type: ignore[return-value]
+	if padding and len(padding) == 3:
+		return tuple([ padding[0], padding[1], padding[2], padding[1] ]) # type: ignore[return-value]
+	if padding and len(padding) == 4:
+		return tuple(padding) # type: ignore[return-value]
+	return None

facefusion/processors/frame/choices.py

@@ -3,7 +3,7 @@ import numpy
 
 from facefusion.processors.frame.typings import FaceSwapperModel, FaceEnhancerModel, FrameEnhancerModel, FaceDebuggerItem
 
-face_swapper_models : List[FaceSwapperModel] = [ 'blendface_256', 'inswapper_128', 'inswapper_128_fp16', 'simswap_256', 'simswap_512_unofficial' ]
+face_swapper_models : List[FaceSwapperModel] = [ 'blendswap_256', 'inswapper_128', 'inswapper_128_fp16', 'simswap_256', 'simswap_512_unofficial' ]
 face_enhancer_models : List[FaceEnhancerModel] = [ 'codeformer', 'gfpgan_1.2', 'gfpgan_1.3', 'gfpgan_1.4', 'gpen_bfr_256', 'gpen_bfr_512', 'restoreformer' ]
 frame_enhancer_models : List[FrameEnhancerModel] = [ 'real_esrgan_x2plus', 'real_esrgan_x4plus', 'real_esrnet_x4plus' ]
 

facefusion/processors/frame/core.py

@@ -8,8 +8,8 @@ from tqdm import tqdm
 
 import facefusion.globals
 from facefusion.typing import Process_Frames
-from facefusion import wording
-from facefusion.utilities import encode_execution_providers
+from facefusion.execution_helper import encode_execution_providers
+from facefusion import logger, wording
 
 FRAME_PROCESSORS_MODULES : List[ModuleType] = []
 FRAME_PROCESSORS_METHODS =\
@@ -22,6 +22,7 @@ FRAME_PROCESSORS_METHODS =\
 	'apply_args',
 	'pre_check',
 	'pre_process',
+	'get_reference_frame',
 	'process_frame',
 	'process_frames',
 	'process_image',
@@ -36,7 +37,8 @@ def load_frame_processor_module(frame_processor : str) -> Any:
 		for method_name in FRAME_PROCESSORS_METHODS:
 			if not hasattr(frame_processor_module, method_name):
 				raise NotImplementedError
-	except ModuleNotFoundError:
+	except ModuleNotFoundError as exception:
+		logger.debug(exception.msg, __name__.upper())
 		sys.exit(wording.get('frame_processor_not_loaded').format(frame_processor = frame_processor))
 	except NotImplementedError:
 		sys.exit(wording.get('frame_processor_not_implemented').format(frame_processor = frame_processor))
@@ -61,8 +63,8 @@ def clear_frame_processors_modules() -> None:
 	FRAME_PROCESSORS_MODULES = []
 
 
-def multi_process_frames(source_path : str, temp_frame_paths : List[str], process_frames : Process_Frames) -> None:
-	with tqdm(total = len(temp_frame_paths), desc = wording.get('processing'), unit = 'frame', ascii = ' =') as progress:
+def multi_process_frames(source_paths : List[str], temp_frame_paths : List[str], process_frames : Process_Frames) -> None:
+	with tqdm(total = len(temp_frame_paths), desc = wording.get('processing'), unit = 'frame', ascii = ' =', disable = facefusion.globals.log_level in [ 'warn', 'error' ]) as progress:
 		progress.set_postfix(
 		{
 			'execution_providers': encode_execution_providers(facefusion.globals.execution_providers),
@@ -75,7 +77,7 @@ def multi_process_frames(source_path : str, temp_frame_paths : List[str], proces
 			queue_per_future = max(len(temp_frame_paths) // facefusion.globals.execution_thread_count * facefusion.globals.execution_queue_count, 1)
 			while not queue_temp_frame_paths.empty():
 				payload_temp_frame_paths = pick_queue(queue_temp_frame_paths, queue_per_future)
-				future = executor.submit(process_frames, source_path, payload_temp_frame_paths, progress.update)
+				future = executor.submit(process_frames, source_paths, payload_temp_frame_paths, progress.update)
 				futures.append(future)
 			for future_done in as_completed(futures):
 				future_done.result()

facefusion/processors/frame/modules/face_debugger.py

@@ -6,15 +6,16 @@ import numpy
 import facefusion.globals
 import facefusion.processors.frame.core as frame_processors
 from facefusion import wording
-from facefusion.face_analyser import get_one_face, get_many_faces, find_similar_faces, clear_face_analyser
-from facefusion.face_reference import get_face_reference
+from facefusion.face_analyser import get_one_face, get_average_face, get_many_faces, find_similar_faces, clear_face_analyser
+from facefusion.face_store import get_reference_faces
 from facefusion.content_analyser import clear_content_analyser
-from facefusion.typing import Face, Frame, Update_Process, ProcessMode
-from facefusion.vision import read_image, read_static_image, write_image
-from facefusion.face_helper import warp_face, create_static_mask_frame
+from facefusion.typing import Face, FaceSet, Frame, Update_Process, ProcessMode
+from facefusion.vision import read_image, read_static_image, read_static_images, write_image
+from facefusion.face_helper import warp_face
+from facefusion.face_masker import create_static_box_mask, create_occlusion_mask, create_region_mask, clear_face_occluder, clear_face_parser
 from facefusion.processors.frame import globals as frame_processors_globals, choices as frame_processors_choices
 
-NAME = 'FACEFUSION.FRAME_PROCESSOR.FACE_DEBUGGER'
+NAME = __name__.upper()
 
 
 def get_frame_processor() -> None:
@@ -34,7 +35,7 @@ def set_options(key : Literal['model'], value : Any) -> None:
 
 
 def register_args(program : ArgumentParser) -> None:
-	program.add_argument('--face-debugger-items', help = wording.get('face_debugger_items_help'), dest = 'face_debugger_items', default = [ 'kps', 'face-mask' ], choices = frame_processors_choices.face_debugger_items, nargs = '+')
+	program.add_argument('--face-debugger-items', help = wording.get('face_debugger_items_help').format(choices = ', '.join(frame_processors_choices.face_debugger_items)), default = [ 'kps', 'face-mask' ], choices = frame_processors_choices.face_debugger_items, nargs = '+', metavar = 'FACE_DEBUGGER_ITEMS')
 
 
 def apply_args(program : ArgumentParser) -> None:
@@ -54,6 +55,9 @@ def post_process() -> None:
 	clear_frame_processor()
 	clear_face_analyser()
 	clear_content_analyser()
+	clear_face_occluder()
+	clear_face_parser()
+	read_static_image.cache_clear()
 
 
 def debug_face(source_face : Face, target_face : Face, temp_frame : Frame) -> Frame:
@@ -63,14 +67,23 @@ def debug_face(source_face : Face, target_face : Face, temp_frame : Frame) -> Fr
 	if 'bbox' in frame_processors_globals.face_debugger_items:
 		cv2.rectangle(temp_frame, (bounding_box[0], bounding_box[1]), (bounding_box[2], bounding_box[3]), secondary_color, 2)
 	if 'face-mask' in frame_processors_globals.face_debugger_items:
-		crop_frame, affine_matrix = warp_face(temp_frame, target_face.kps, 'arcface_v2', (128, 128))
+		crop_frame, affine_matrix = warp_face(temp_frame, target_face.kps, 'arcface_128_v2', (128, 512))
 		inverse_matrix = cv2.invertAffineTransform(affine_matrix)
 		temp_frame_size = temp_frame.shape[:2][::-1]
-		mask_frame = create_static_mask_frame(crop_frame.shape[:2], 0, facefusion.globals.face_mask_padding)
-		mask_frame[mask_frame > 0] = 255
-		inverse_mask_frame = cv2.warpAffine(mask_frame.astype(numpy.uint8), inverse_matrix, temp_frame_size)
-		inverse_mask_contours = cv2.findContours(inverse_mask_frame, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]
-		cv2.drawContours(temp_frame, inverse_mask_contours, 0, primary_color, 2)
+		crop_mask_list = []
+		if 'box' in facefusion.globals.face_mask_types:
+			crop_mask_list.append(create_static_box_mask(crop_frame.shape[:2][::-1], 0, facefusion.globals.face_mask_padding))
+		if 'occlusion' in facefusion.globals.face_mask_types:
+			crop_mask_list.append(create_occlusion_mask(crop_frame))
+		if 'region' in facefusion.globals.face_mask_types:
+			crop_mask_list.append(create_region_mask(crop_frame, facefusion.globals.face_mask_regions))
+		crop_mask = numpy.minimum.reduce(crop_mask_list).clip(0, 1)
+		crop_mask = (crop_mask * 255).astype(numpy.uint8)
+		inverse_mask_frame = cv2.warpAffine(crop_mask, inverse_matrix, temp_frame_size)
+		inverse_mask_frame_edges = cv2.threshold(inverse_mask_frame, 100, 255, cv2.THRESH_BINARY)[1]
+		inverse_mask_frame_edges[inverse_mask_frame_edges > 0] = 255
+		inverse_mask_contours = cv2.findContours(inverse_mask_frame_edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)[0]
+		cv2.drawContours(temp_frame, inverse_mask_contours, -1, primary_color, 2)
 	if bounding_box[3] - bounding_box[1] > 60 and bounding_box[2] - bounding_box[0] > 60:
 		if 'kps' in frame_processors_globals.face_debugger_items:
 			kps = target_face.kps.astype(numpy.int32)
@@ -83,9 +96,13 @@ def debug_face(source_face : Face, target_face : Face, temp_frame : Frame) -> Fr
 	return temp_frame
 
 
-def process_frame(source_face : Face, reference_face : Face, temp_frame : Frame) -> Frame:
+def get_reference_frame(source_face : Face, target_face : Face, temp_frame : Frame) -> Frame:
+	pass
+
+
+def process_frame(source_face : Face, reference_faces : FaceSet, temp_frame : Frame) -> Frame:
 	if 'reference' in facefusion.globals.face_selector_mode:
-		similar_faces = find_similar_faces(temp_frame, reference_face, facefusion.globals.reference_face_distance)
+		similar_faces = find_similar_faces(temp_frame, reference_faces, facefusion.globals.reference_face_distance)
 		if similar_faces:
 			for similar_face in similar_faces:
 				temp_frame = debug_face(source_face, similar_face, temp_frame)
@@ -101,23 +118,25 @@ def process_frame(source_face : Face, reference_face : Face, temp_frame : Frame)
 	return temp_frame
 
 
-def process_frames(source_path : str, temp_frame_paths : List[str], update_progress : Update_Process) -> None:
-	source_face = get_one_face(read_static_image(source_path))
-	reference_face = get_face_reference() if 'reference' in facefusion.globals.face_selector_mode else None
+def process_frames(source_paths : List[str], temp_frame_paths : List[str], update_progress : Update_Process) -> None:
+	source_frames = read_static_images(source_paths)
+	source_face = get_average_face(source_frames)
+	reference_faces = get_reference_faces() if 'reference' in facefusion.globals.face_selector_mode else None
 	for temp_frame_path in temp_frame_paths:
 		temp_frame = read_image(temp_frame_path)
-		result_frame = process_frame(source_face, reference_face, temp_frame)
+		result_frame = process_frame(source_face, reference_faces, temp_frame)
 		write_image(temp_frame_path, result_frame)
 		update_progress()
 
 
-def process_image(source_path : str, target_path : str, output_path : str) -> None:
-	source_face = get_one_face(read_static_image(source_path))
+def process_image(source_paths : List[str], target_path : str, output_path : str) -> None:
+	source_frames = read_static_images(source_paths)
+	source_face = get_average_face(source_frames)
 	target_frame = read_static_image(target_path)
-	reference_face = get_one_face(target_frame, facefusion.globals.reference_face_position) if 'reference' in facefusion.globals.face_selector_mode else None
-	result_frame = process_frame(source_face, reference_face, target_frame)
+	reference_faces = get_reference_faces() if 'reference' in facefusion.globals.face_selector_mode else None
+	result_frame = process_frame(source_face, reference_faces, target_frame)
 	write_image(output_path, result_frame)
 
 
-def process_video(source_path : str, temp_frame_paths : List[str]) -> None:
-	frame_processors.multi_process_frames(source_path, temp_frame_paths, process_frames)
+def process_video(source_paths : List[str], temp_frame_paths : List[str]) -> None:
+	frame_processors.multi_process_frames(source_paths, temp_frame_paths, process_frames)

facefusion/processors/frame/modules/face_enhancer.py

@@ -1,4 +1,4 @@
-from typing import Any, List, Dict, Literal, Optional
+from typing import Any, List, Literal, Optional
 from argparse import ArgumentParser
 import cv2
 import threading
@@ -7,69 +7,73 @@ import onnxruntime
 
 import facefusion.globals
 import facefusion.processors.frame.core as frame_processors
-from facefusion import wording
-from facefusion.face_analyser import get_many_faces, clear_face_analyser
+from facefusion import logger, wording
+from facefusion.face_analyser import get_many_faces, clear_face_analyser, find_similar_faces, get_one_face
 from facefusion.face_helper import warp_face, paste_back
 from facefusion.content_analyser import clear_content_analyser
-from facefusion.typing import Face, Frame, Update_Process, ProcessMode, ModelValue, OptionsWithModel
-from facefusion.utilities import conditional_download, resolve_relative_path, is_image, is_video, is_file, is_download_done, create_metavar, update_status
+from facefusion.face_store import get_reference_faces
+from facefusion.typing import Face, FaceSet, Frame, Update_Process, ProcessMode, ModelSet, OptionsWithModel
+from facefusion.common_helper import create_metavar
+from facefusion.filesystem import is_file, is_image, is_video, resolve_relative_path
+from facefusion.download import conditional_download, is_download_done
 from facefusion.vision import read_image, read_static_image, write_image
 from facefusion.processors.frame import globals as frame_processors_globals
 from facefusion.processors.frame import choices as frame_processors_choices
+from facefusion.face_masker import create_static_box_mask, create_occlusion_mask, clear_face_occluder
 
 FRAME_PROCESSOR = None
 THREAD_SEMAPHORE : threading.Semaphore = threading.Semaphore()
 THREAD_LOCK : threading.Lock = threading.Lock()
-NAME = 'FACEFUSION.FRAME_PROCESSOR.FACE_ENHANCER'
-MODELS : Dict[str, ModelValue] =\
+NAME = __name__.upper()
+MODELS : ModelSet =\
 {
 	'codeformer':
 	{
 		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/codeformer.onnx',
 		'path': resolve_relative_path('../.assets/models/codeformer.onnx'),
-		'template': 'ffhq',
+		'template': 'ffhq_512',
 		'size': (512, 512)
 	},
 	'gfpgan_1.2':
 	{
 		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gfpgan_1.2.onnx',
 		'path': resolve_relative_path('../.assets/models/gfpgan_1.2.onnx'),
-		'template': 'ffhq',
+		'template': 'ffhq_512',
 		'size': (512, 512)
 	},
 	'gfpgan_1.3':
 	{
 		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gfpgan_1.3.onnx',
 		'path': resolve_relative_path('../.assets/models/gfpgan_1.3.onnx'),
-		'template': 'ffhq',
+		'template': 'ffhq_512',
 		'size': (512, 512)
 	},
 	'gfpgan_1.4':
 	{
 		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gfpgan_1.4.onnx',
 		'path': resolve_relative_path('../.assets/models/gfpgan_1.4.onnx'),
-		'template': 'ffhq',
+		'template': 'ffhq_512',
 		'size': (512, 512)
 	},
 	'gpen_bfr_256':
 	{
 		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gpen_bfr_256.onnx',
 		'path': resolve_relative_path('../.assets/models/gpen_bfr_256.onnx'),
-		'template': 'arcface_v2',
+		'template': 'arcface_128_v2',
 		'size': (128, 256)
 	},
 	'gpen_bfr_512':
 	{
 		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gpen_bfr_512.onnx',
 		'path': resolve_relative_path('../.assets/models/gpen_bfr_512.onnx'),
-		'template': 'ffhq',
+		'template': 'ffhq_512',
 		'size': (512, 512)
 	},
 	'restoreformer':
 	{
 		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/restoreformer.onnx',
 		'path': resolve_relative_path('../.assets/models/restoreformer.onnx'),
-		'template': 'ffhq',
+		'template': 'ffhq_512',
 		'size': (512, 512)
 	}
 }
@@ -110,8 +114,8 @@ def set_options(key : Literal['model'], value : Any) -> None:
 
 
 def register_args(program : ArgumentParser) -> None:
-	program.add_argument('--face-enhancer-model', help = wording.get('frame_processor_model_help'), dest = 'face_enhancer_model', default = 'gfpgan_1.4', choices = frame_processors_choices.face_enhancer_models)
-	program.add_argument('--face-enhancer-blend', help = wording.get('frame_processor_blend_help'), dest = 'face_enhancer_blend', type = int, default = 80, choices = frame_processors_choices.face_enhancer_blend_range, metavar = create_metavar(frame_processors_choices.face_enhancer_blend_range))
+	program.add_argument('--face-enhancer-model', help = wording.get('frame_processor_model_help'), default = 'gfpgan_1.4', choices = frame_processors_choices.face_enhancer_models)
+	program.add_argument('--face-enhancer-blend', help = wording.get('frame_processor_blend_help'), type = int, default = 80, choices = frame_processors_choices.face_enhancer_blend_range, metavar = create_metavar(frame_processors_choices.face_enhancer_blend_range))
 
 
 def apply_args(program : ArgumentParser) -> None:
@@ -132,16 +136,16 @@ def pre_process(mode : ProcessMode) -> bool:
 	model_url = get_options('model').get('url')
 	model_path = get_options('model').get('path')
 	if not facefusion.globals.skip_download and not is_download_done(model_url, model_path):
-		update_status(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME)
+		logger.error(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME)
 		return False
 	elif not is_file(model_path):
-		update_status(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
+		logger.error(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
 		return False
 	if mode in [ 'output', 'preview' ] and not is_image(facefusion.globals.target_path) and not is_video(facefusion.globals.target_path):
-		update_status(wording.get('select_image_or_video_target') + wording.get('exclamation_mark'), NAME)
+		logger.error(wording.get('select_image_or_video_target') + wording.get('exclamation_mark'), NAME)
 		return False
 	if mode == 'output' and not facefusion.globals.output_path:
-		update_status(wording.get('select_file_or_directory_output') + wording.get('exclamation_mark'), NAME)
+		logger.error(wording.get('select_file_or_directory_output') + wording.get('exclamation_mark'), NAME)
 		return False
 	return True
 
@@ -150,6 +154,7 @@ def post_process() -> None:
 	clear_frame_processor()
 	clear_face_analyser()
 	clear_content_analyser()
+	clear_face_occluder()
 	read_static_image.cache_clear()
 
 
@@ -158,6 +163,12 @@ def enhance_face(target_face: Face, temp_frame: Frame) -> Frame:
 	model_template = get_options('model').get('template')
 	model_size = get_options('model').get('size')
 	crop_frame, affine_matrix = warp_face(temp_frame, target_face.kps, model_template, model_size)
+	crop_mask_list =\
+	[
+		create_static_box_mask(crop_frame.shape[:2][::-1], facefusion.globals.face_mask_blur, (0, 0, 0, 0))
+	]
+	if 'occlusion' in facefusion.globals.face_mask_types:
+		crop_mask_list.append(create_occlusion_mask(crop_frame))
 	crop_frame = prepare_crop_frame(crop_frame)
 	frame_processor_inputs = {}
 	for frame_processor_input in frame_processor.get_inputs():
@@ -168,7 +179,8 @@ def enhance_face(target_face: Face, temp_frame: Frame) -> Frame:
 	with THREAD_SEMAPHORE:
 		crop_frame = frame_processor.run(None, frame_processor_inputs)[0][0]
 	crop_frame = normalize_crop_frame(crop_frame)
-	paste_frame = paste_back(temp_frame, crop_frame, affine_matrix, facefusion.globals.face_mask_blur, (0, 0, 0, 0))
+	crop_mask = numpy.minimum.reduce(crop_mask_list).clip(0, 1)
+	paste_frame = paste_back(temp_frame, crop_frame, crop_mask, affine_matrix)
 	temp_frame = blend_frame(temp_frame, paste_frame)
 	return temp_frame
 
@@ -195,27 +207,43 @@ def blend_frame(temp_frame : Frame, paste_frame : Frame) -> Frame:
 	return temp_frame
 
 
-def process_frame(source_face : Face, reference_face : Face, temp_frame : Frame) -> Frame:
-	many_faces = get_many_faces(temp_frame)
-	if many_faces:
-		for target_face in many_faces:
+def get_reference_frame(source_face : Face, target_face : Face, temp_frame : Frame) -> Optional[Frame]:
+	return enhance_face(target_face, temp_frame)
+
+
+def process_frame(source_face : Face, reference_faces : FaceSet, temp_frame : Frame) -> Frame:
+	if 'reference' in facefusion.globals.face_selector_mode:
+		similar_faces = find_similar_faces(temp_frame, reference_faces, facefusion.globals.reference_face_distance)
+		if similar_faces:
+			for similar_face in similar_faces:
+				temp_frame = enhance_face(similar_face, temp_frame)
+	if 'one' in facefusion.globals.face_selector_mode:
+		target_face = get_one_face(temp_frame)
+		if target_face:
 			temp_frame = enhance_face(target_face, temp_frame)
+	if 'many' in facefusion.globals.face_selector_mode:
+		many_faces = get_many_faces(temp_frame)
+		if many_faces:
+			for target_face in many_faces:
+				temp_frame = enhance_face(target_face, temp_frame)
 	return temp_frame
 
 
-def process_frames(source_path : str, temp_frame_paths : List[str], update_progress : Update_Process) -> None:
+def process_frames(source_path : List[str], temp_frame_paths : List[str], update_progress : Update_Process) -> None:
+	reference_faces = get_reference_faces() if 'reference' in facefusion.globals.face_selector_mode else None
 	for temp_frame_path in temp_frame_paths:
 		temp_frame = read_image(temp_frame_path)
-		result_frame = process_frame(None, None, temp_frame)
+		result_frame = process_frame(None, reference_faces, temp_frame)
 		write_image(temp_frame_path, result_frame)
 		update_progress()
 
 
 def process_image(source_path : str, target_path : str, output_path : str) -> None:
+	reference_faces = get_reference_faces() if 'reference' in facefusion.globals.face_selector_mode else None
 	target_frame = read_static_image(target_path)
-	result_frame = process_frame(None, None, target_frame)
+	result_frame = process_frame(None, reference_faces, target_frame)
 	write_image(output_path, result_frame)
 
 
-def process_video(source_path : str, temp_frame_paths : List[str]) -> None:
+def process_video(source_paths : List[str], temp_frame_paths : List[str]) -> None:
 	frame_processors.multi_process_frames(None, temp_frame_paths, process_frames)

facefusion/processors/frame/modules/face_swapper.py

@@ -1,4 +1,4 @@
-from typing import Any, List, Dict, Literal, Optional
+from typing import Any, List, Literal, Optional
 from argparse import ArgumentParser
 import threading
 import numpy
@@ -8,29 +8,31 @@ from onnx import numpy_helper
 
 import facefusion.globals
 import facefusion.processors.frame.core as frame_processors
-from facefusion import wording
-from facefusion.face_analyser import get_one_face, get_many_faces, find_similar_faces, clear_face_analyser
+from facefusion import logger, wording
+from facefusion.face_analyser import get_one_face, get_average_face, get_many_faces, find_similar_faces, clear_face_analyser
 from facefusion.face_helper import warp_face, paste_back
-from facefusion.face_reference import get_face_reference
+from facefusion.face_store import get_reference_faces
 from facefusion.content_analyser import clear_content_analyser
-from facefusion.typing import Face, Frame, Update_Process, ProcessMode, ModelValue, OptionsWithModel, Embedding
-from facefusion.utilities import conditional_download, resolve_relative_path, is_image, is_video, is_file, is_download_done, update_status
-from facefusion.vision import read_image, read_static_image, write_image
+from facefusion.typing import Face, FaceSet, Frame, Update_Process, ProcessMode, ModelSet, OptionsWithModel, Embedding
+from facefusion.filesystem import is_file, is_image, are_images, is_video, resolve_relative_path
+from facefusion.download import conditional_download, is_download_done
+from facefusion.vision import read_image, read_static_image, read_static_images, write_image
 from facefusion.processors.frame import globals as frame_processors_globals
 from facefusion.processors.frame import choices as frame_processors_choices
+from facefusion.face_masker import create_static_box_mask, create_occlusion_mask, create_region_mask, clear_face_occluder, clear_face_parser
 
 FRAME_PROCESSOR = None
 MODEL_MATRIX = None
 THREAD_LOCK : threading.Lock = threading.Lock()
-NAME = 'FACEFUSION.FRAME_PROCESSOR.FACE_SWAPPER'
-MODELS : Dict[str, ModelValue] =\
+NAME = __name__.upper()
+MODELS : ModelSet =\
 {
-	'blendface_256':
+	'blendswap_256':
 	{
-		'type': 'blendface',
-		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/blendface_256.onnx',
-		'path': resolve_relative_path('../.assets/models/blendface_256.onnx'),
-		'template': 'ffhq',
+		'type': 'blendswap',
+		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/blendswap_256.onnx',
+		'path': resolve_relative_path('../.assets/models/blendswap_256.onnx'),
+		'template': 'ffhq_512',
 		'size': (512, 256),
 		'mean': [ 0.0, 0.0, 0.0 ],
 		'standard_deviation': [ 1.0, 1.0, 1.0 ]
@@ -40,7 +42,7 @@ MODELS : Dict[str, ModelValue] =\
 		'type': 'inswapper',
 		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx',
 		'path': resolve_relative_path('../.assets/models/inswapper_128.onnx'),
-		'template': 'arcface_v2',
+		'template': 'arcface_128_v2',
 		'size': (128, 128),
 		'mean': [ 0.0, 0.0, 0.0 ],
 		'standard_deviation': [ 1.0, 1.0, 1.0 ]
@@ -50,7 +52,7 @@ MODELS : Dict[str, ModelValue] =\
 		'type': 'inswapper',
 		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128_fp16.onnx',
 		'path': resolve_relative_path('../.assets/models/inswapper_128_fp16.onnx'),
-		'template': 'arcface_v2',
+		'template': 'arcface_128_v2',
 		'size': (128, 128),
 		'mean': [ 0.0, 0.0, 0.0 ],
 		'standard_deviation': [ 1.0, 1.0, 1.0 ]
@@ -60,7 +62,7 @@ MODELS : Dict[str, ModelValue] =\
 		'type': 'simswap',
 		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/simswap_256.onnx',
 		'path': resolve_relative_path('../.assets/models/simswap_256.onnx'),
-		'template': 'arcface_v1',
+		'template': 'arcface_112_v1',
 		'size': (112, 256),
 		'mean': [ 0.485, 0.456, 0.406 ],
 		'standard_deviation': [ 0.229, 0.224, 0.225 ]
@@ -70,7 +72,7 @@ MODELS : Dict[str, ModelValue] =\
 		'type': 'simswap',
 		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/simswap_512_unofficial.onnx',
 		'path': resolve_relative_path('../.assets/models/simswap_512_unofficial.onnx'),
-		'template': 'arcface_v1',
+		'template': 'arcface_112_v1',
 		'size': (112, 512),
 		'mean': [ 0.0, 0.0, 0.0 ],
 		'standard_deviation': [ 1.0, 1.0, 1.0 ]
@@ -130,14 +132,14 @@ def set_options(key : Literal['model'], value : Any) -> None:
 
 
 def register_args(program : ArgumentParser) -> None:
-	program.add_argument('--face-swapper-model', help = wording.get('frame_processor_model_help'), dest = 'face_swapper_model', default = 'inswapper_128', choices = frame_processors_choices.face_swapper_models)
+	program.add_argument('--face-swapper-model', help = wording.get('frame_processor_model_help'), default = 'inswapper_128', choices = frame_processors_choices.face_swapper_models)
 
 
 def apply_args(program : ArgumentParser) -> None:
 	args = program.parse_args()
 	frame_processors_globals.face_swapper_model = args.face_swapper_model
-	if args.face_swapper_model == 'blendface_256':
-		facefusion.globals.face_recognizer_model = 'arcface_blendface'
+	if args.face_swapper_model == 'blendswap_256':
+		facefusion.globals.face_recognizer_model = 'arcface_blendswap'
 	if args.face_swapper_model == 'inswapper_128' or args.face_swapper_model == 'inswapper_128_fp16':
 		facefusion.globals.face_recognizer_model = 'arcface_inswapper'
 	if args.face_swapper_model == 'simswap_256' or args.face_swapper_model == 'simswap_512_unofficial':
@@ -156,22 +158,23 @@ def pre_process(mode : ProcessMode) -> bool:
 	model_url = get_options('model').get('url')
 	model_path = get_options('model').get('path')
 	if not facefusion.globals.skip_download and not is_download_done(model_url, model_path):
-		update_status(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME)
+		logger.error(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME)
 		return False
 	elif not is_file(model_path):
-		update_status(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
+		logger.error(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
 		return False
-	if not is_image(facefusion.globals.source_path):
-		update_status(wording.get('select_image_source') + wording.get('exclamation_mark'), NAME)
-		return False
-	elif not get_one_face(read_static_image(facefusion.globals.source_path)):
-		update_status(wording.get('no_source_face_detected') + wording.get('exclamation_mark'), NAME)
+	if not are_images(facefusion.globals.source_paths):
+		logger.error(wording.get('select_image_source') + wording.get('exclamation_mark'), NAME)
 		return False
+	for source_frame in read_static_images(facefusion.globals.source_paths):
+		if not get_one_face(source_frame):
+			logger.error(wording.get('no_source_face_detected') + wording.get('exclamation_mark'), NAME)
+			return False
 	if mode in [ 'output', 'preview' ] and not is_image(facefusion.globals.target_path) and not is_video(facefusion.globals.target_path):
-		update_status(wording.get('select_image_or_video_target') + wording.get('exclamation_mark'), NAME)
+		logger.error(wording.get('select_image_or_video_target') + wording.get('exclamation_mark'), NAME)
 		return False
 	if mode == 'output' and not facefusion.globals.output_path:
-		update_status(wording.get('select_file_or_directory_output') + wording.get('exclamation_mark'), NAME)
+		logger.error(wording.get('select_file_or_directory_output') + wording.get('exclamation_mark'), NAME)
 		return False
 	return True
 
@@ -181,6 +184,8 @@ def post_process() -> None:
 	clear_model_matrix()
 	clear_face_analyser()
 	clear_content_analyser()
+	clear_face_occluder()
+	clear_face_parser()
 	read_static_image.cache_clear()
 
 
@@ -190,11 +195,16 @@ def swap_face(source_face : Face, target_face : Face, temp_frame : Frame) -> Fra
 	model_size = get_options('model').get('size')
 	model_type = get_options('model').get('type')
 	crop_frame, affine_matrix = warp_face(temp_frame, target_face.kps, model_template, model_size)
+	crop_mask_list = []
+	if 'box' in facefusion.globals.face_mask_types:
+		crop_mask_list.append(create_static_box_mask(crop_frame.shape[:2][::-1], facefusion.globals.face_mask_blur, facefusion.globals.face_mask_padding))
+	if 'occlusion' in facefusion.globals.face_mask_types:
+		crop_mask_list.append(create_occlusion_mask(crop_frame))
 	crop_frame = prepare_crop_frame(crop_frame)
 	frame_processor_inputs = {}
 	for frame_processor_input in frame_processor.get_inputs():
 		if frame_processor_input.name == 'source':
-			if model_type == 'blendface':
+			if model_type == 'blendswap':
 				frame_processor_inputs[frame_processor_input.name] = prepare_source_frame(source_face)
 			else:
 				frame_processor_inputs[frame_processor_input.name] = prepare_source_embedding(source_face)
@@ -202,13 +212,16 @@ def swap_face(source_face : Face, target_face : Face, temp_frame : Frame) -> Fra
 			frame_processor_inputs[frame_processor_input.name] = crop_frame
 	crop_frame = frame_processor.run(None, frame_processor_inputs)[0][0]
 	crop_frame = normalize_crop_frame(crop_frame)
-	temp_frame = paste_back(temp_frame, crop_frame, affine_matrix, facefusion.globals.face_mask_blur, facefusion.globals.face_mask_padding)
+	if 'region' in facefusion.globals.face_mask_types:
+		crop_mask_list.append(create_region_mask(crop_frame, facefusion.globals.face_mask_regions))
+	crop_mask = numpy.minimum.reduce(crop_mask_list).clip(0, 1)
+	temp_frame = paste_back(temp_frame, crop_frame, crop_mask, affine_matrix)
 	return temp_frame
 
 
-def prepare_source_frame(source_face : Face) -> numpy.ndarray[Any, Any]:
-	source_frame = read_static_image(facefusion.globals.source_path)
-	source_frame, _ = warp_face(source_frame, source_face.kps, 'arcface_v2', (112, 112))
+def prepare_source_frame(source_face : Face) -> Frame:
+	source_frame = read_static_image(facefusion.globals.source_paths[0])
+	source_frame, _ = warp_face(source_frame, source_face.kps, 'arcface_112_v2', (112, 112))
 	source_frame = source_frame[:, :, ::-1] / 255.0
 	source_frame = source_frame.transpose(2, 0, 1)
 	source_frame = numpy.expand_dims(source_frame, axis = 0).astype(numpy.float32)
@@ -243,9 +256,13 @@ def normalize_crop_frame(crop_frame : Frame) -> Frame:
 	return crop_frame
 
 
-def process_frame(source_face : Face, reference_face : Face, temp_frame : Frame) -> Frame:
+def get_reference_frame(source_face : Face, target_face : Face, temp_frame : Frame) -> Frame:
+	return swap_face(source_face, target_face, temp_frame)
+
+
+def process_frame(source_face : Face, reference_faces : FaceSet, temp_frame : Frame) -> Frame:
 	if 'reference' in facefusion.globals.face_selector_mode:
-		similar_faces = find_similar_faces(temp_frame, reference_face, facefusion.globals.reference_face_distance)
+		similar_faces = find_similar_faces(temp_frame, reference_faces, facefusion.globals.reference_face_distance)
 		if similar_faces:
 			for similar_face in similar_faces:
 				temp_frame = swap_face(source_face, similar_face, temp_frame)
@@ -261,23 +278,25 @@ def process_frame(source_face : Face, reference_face : Face, temp_frame : Frame)
 	return temp_frame
 
 
-def process_frames(source_path : str, temp_frame_paths : List[str], update_progress : Update_Process) -> None:
-	source_face = get_one_face(read_static_image(source_path))
-	reference_face = get_face_reference() if 'reference' in facefusion.globals.face_selector_mode else None
+def process_frames(source_paths : List[str], temp_frame_paths : List[str], update_progress : Update_Process) -> None:
+	source_frames = read_static_images(source_paths)
+	source_face = get_average_face(source_frames)
+	reference_faces = get_reference_faces() if 'reference' in facefusion.globals.face_selector_mode else None
 	for temp_frame_path in temp_frame_paths:
 		temp_frame = read_image(temp_frame_path)
-		result_frame = process_frame(source_face, reference_face, temp_frame)
+		result_frame = process_frame(source_face, reference_faces, temp_frame)
 		write_image(temp_frame_path, result_frame)
 		update_progress()
 
 
-def process_image(source_path : str, target_path : str, output_path : str) -> None:
-	source_face = get_one_face(read_static_image(source_path))
+def process_image(source_paths : List[str], target_path : str, output_path : str) -> None:
+	source_frames = read_static_images(source_paths)
+	source_face = get_average_face(source_frames)
+	reference_faces = get_reference_faces() if 'reference' in facefusion.globals.face_selector_mode else None
 	target_frame = read_static_image(target_path)
-	reference_face = get_one_face(target_frame, facefusion.globals.reference_face_position) if 'reference' in facefusion.globals.face_selector_mode else None
-	result_frame = process_frame(source_face, reference_face, target_frame)
+	result_frame = process_frame(source_face, reference_faces, target_frame)
 	write_image(output_path, result_frame)
 
 
-def process_video(source_path : str, temp_frame_paths : List[str]) -> None:
-	frame_processors.multi_process_frames(source_path, temp_frame_paths, process_frames)
+def process_video(source_paths : List[str], temp_frame_paths : List[str]) -> None:
+	frame_processors.multi_process_frames(source_paths, temp_frame_paths, process_frames)

facefusion/processors/frame/modules/frame_enhancer.py

@@ -1,4 +1,4 @@
-from typing import Any, List, Dict, Literal, Optional
+from typing import Any, List, Literal, Optional
 from argparse import ArgumentParser
 import threading
 import cv2
@@ -7,11 +7,14 @@ from realesrgan import RealESRGANer
 
 import facefusion.globals
 import facefusion.processors.frame.core as frame_processors
-from facefusion import wording
+from facefusion import logger, wording
 from facefusion.face_analyser import clear_face_analyser
 from facefusion.content_analyser import clear_content_analyser
-from facefusion.typing import Frame, Face, Update_Process, ProcessMode, ModelValue, OptionsWithModel
-from facefusion.utilities import conditional_download, resolve_relative_path, is_file, is_download_done, map_device, create_metavar, update_status
+from facefusion.typing import Face, FaceSet, Frame, Update_Process, ProcessMode, ModelSet, OptionsWithModel
+from facefusion.common_helper import create_metavar
+from facefusion.execution_helper import map_device
+from facefusion.filesystem import is_file, resolve_relative_path
+from facefusion.download import conditional_download, is_download_done
 from facefusion.vision import read_image, read_static_image, write_image
 from facefusion.processors.frame import globals as frame_processors_globals
 from facefusion.processors.frame import choices as frame_processors_choices
@@ -19,8 +22,8 @@ from facefusion.processors.frame import choices as frame_processors_choices
 FRAME_PROCESSOR = None
 THREAD_SEMAPHORE : threading.Semaphore = threading.Semaphore()
 THREAD_LOCK : threading.Lock = threading.Lock()
-NAME = 'FACEFUSION.FRAME_PROCESSOR.FRAME_ENHANCER'
-MODELS: Dict[str, ModelValue] =\
+NAME = __name__.upper()
+MODELS : ModelSet =\
 {
 	'real_esrgan_x2plus':
 	{
@@ -88,8 +91,8 @@ def set_options(key : Literal['model'], value : Any) -> None:
 
 
 def register_args(program : ArgumentParser) -> None:
-	program.add_argument('--frame-enhancer-model', help = wording.get('frame_processor_model_help'), dest = 'frame_enhancer_model', default = 'real_esrgan_x2plus', choices = frame_processors_choices.frame_enhancer_models)
-	program.add_argument('--frame-enhancer-blend', help = wording.get('frame_processor_blend_help'), dest = 'frame_enhancer_blend', type = int, default = 80, choices = frame_processors_choices.frame_enhancer_blend_range, metavar = create_metavar(frame_processors_choices.frame_enhancer_blend_range))
+	program.add_argument('--frame-enhancer-model', help = wording.get('frame_processor_model_help'), default = 'real_esrgan_x2plus', choices = frame_processors_choices.frame_enhancer_models)
+	program.add_argument('--frame-enhancer-blend', help = wording.get('frame_processor_blend_help'), type = int, default = 80, choices = frame_processors_choices.frame_enhancer_blend_range, metavar = create_metavar(frame_processors_choices.frame_enhancer_blend_range))
 
 
 def apply_args(program : ArgumentParser) -> None:
@@ -110,13 +113,13 @@ def pre_process(mode : ProcessMode) -> bool:
 	model_url = get_options('model').get('url')
 	model_path = get_options('model').get('path')
 	if not facefusion.globals.skip_download and not is_download_done(model_url, model_path):
-		update_status(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME)
+		logger.error(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME)
 		return False
 	elif not is_file(model_path):
-		update_status(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
+		logger.error(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
 		return False
 	if mode == 'output' and not facefusion.globals.output_path:
-		update_status(wording.get('select_file_or_directory_output') + wording.get('exclamation_mark'), NAME)
+		logger.error(wording.get('select_file_or_directory_output') + wording.get('exclamation_mark'), NAME)
 		return False
 	return True
 
@@ -143,11 +146,15 @@ def blend_frame(temp_frame : Frame, paste_frame : Frame) -> Frame:
 	return temp_frame
 
 
-def process_frame(source_face : Face, reference_face : Face, temp_frame : Frame) -> Frame:
+def get_reference_frame(source_face : Face, target_face : Face, temp_frame : Frame) -> Frame:
+	pass
+
+
+def process_frame(source_face : Face, reference_faces : FaceSet, temp_frame : Frame) -> Frame:
 	return enhance_frame(temp_frame)
 
 
-def process_frames(source_path : str, temp_frame_paths : List[str], update_progress : Update_Process) -> None:
+def process_frames(source_paths : List[str], temp_frame_paths : List[str], update_progress : Update_Process) -> None:
 	for temp_frame_path in temp_frame_paths:
 		temp_frame = read_image(temp_frame_path)
 		result_frame = process_frame(None, None, temp_frame)
@@ -155,11 +162,11 @@ def process_frames(source_path : str, temp_frame_paths : List[str], update_progr
 		update_progress()
 
 
-def process_image(source_path : str, target_path : str, output_path : str) -> None:
+def process_image(source_paths : List[str], target_path : str, output_path : str) -> None:
 	target_frame = read_static_image(target_path)
 	result = process_frame(None, None, target_frame)
 	write_image(output_path, result)
 
 
-def process_video(source_path : str, temp_frame_paths : List[str]) -> None:
+def process_video(source_paths : List[str], temp_frame_paths : List[str]) -> None:
 	frame_processors.multi_process_frames(None, temp_frame_paths, process_frames)

facefusion/processors/frame/typings.py

@@ -1,6 +1,6 @@
 from typing import Literal
 
-FaceSwapperModel = Literal['blendface_256', 'inswapper_128', 'inswapper_128_fp16', 'simswap_256', 'simswap_512_unofficial']
+FaceSwapperModel = Literal['blendswap_256', 'inswapper_128', 'inswapper_128_fp16', 'simswap_256', 'simswap_512_unofficial']
 FaceEnhancerModel = Literal['codeformer', 'gfpgan_1.2', 'gfpgan_1.3', 'gfpgan_1.4', 'gpen_bfr_256', 'gpen_bfr_512', 'restoreformer']
 FrameEnhancerModel = Literal['real_esrgan_x2plus', 'real_esrgan_x4plus', 'real_esrnet_x4plus']
 

facefusion/typing.py

@@ -1,5 +1,5 @@
-from collections import namedtuple
 from typing import Any, Literal, Callable, List, Tuple, Dict, TypedDict
+from collections import namedtuple
 import numpy
 
 Bbox = numpy.ndarray[Any, Any]
@@ -16,25 +16,35 @@ Face = namedtuple('Face',
 	'gender',
 	'age'
 ])
+FaceSet = Dict[str, List[Face]]
+FaceStore = TypedDict('FaceStore',
+{
+	'static_faces' : FaceSet,
+	'reference_faces': FaceSet
+})
 Frame = numpy.ndarray[Any, Any]
+Mask = numpy.ndarray[Any, Any]
 Matrix = numpy.ndarray[Any, Any]
 Padding = Tuple[int, int, int, int]
 
 Update_Process = Callable[[], None]
-Process_Frames = Callable[[str, List[str], Update_Process], None]
-
-Template = Literal['arcface_v1', 'arcface_v2', 'ffhq']
+Process_Frames = Callable[[List[str], List[str], Update_Process], None]
+LogLevel = Literal['error',	'warn',	'info',	'debug']
+Template = Literal['arcface_112_v1', 'arcface_112_v2', 'arcface_128_v2', 'ffhq_512']
 ProcessMode = Literal['output', 'preview', 'stream']
 FaceSelectorMode = Literal['reference', 'one', 'many']
 FaceAnalyserOrder = Literal['left-right', 'right-left', 'top-bottom', 'bottom-top', 'small-large', 'large-small', 'best-worst', 'worst-best']
 FaceAnalyserAge = Literal['child', 'teen', 'adult', 'senior']
 FaceAnalyserGender = Literal['male', 'female']
 FaceDetectorModel = Literal['retinaface', 'yunet']
-FaceRecognizerModel = Literal['arcface_blendface', 'arcface_inswapper', 'arcface_simswap']
+FaceRecognizerModel = Literal['arcface_blendswap', 'arcface_inswapper', 'arcface_simswap']
+FaceMaskType = Literal['box', 'occlusion', 'region']
+FaceMaskRegion = Literal['skin', 'left-eyebrow', 'right-eyebrow', 'left-eye', 'right-eye', 'eye-glasses', 'nose', 'mouth', 'upper-lip', 'lower-lip']
 TempFrameFormat = Literal['jpg', 'png']
 OutputVideoEncoder = Literal['libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc']
 
 ModelValue = Dict[str, Any]
+ModelSet = Dict[str, ModelValue]
 OptionsWithModel = TypedDict('OptionsWithModel',
 {
 	'model' : ModelValue

facefusion/uis/components/benchmark.py

@@ -7,11 +7,12 @@ import gradio
 import facefusion.globals
 from facefusion import wording
 from facefusion.face_analyser import get_face_analyser
-from facefusion.face_cache import clear_faces_cache
+from facefusion.face_store import clear_static_faces
 from facefusion.processors.frame.core import get_frame_processors_modules
 from facefusion.vision import count_video_frame_total
 from facefusion.core import limit_resources, conditional_process
-from facefusion.utilities import normalize_output_path, clear_temp
+from facefusion.normalizer import normalize_output_path
+from facefusion.filesystem import clear_temp
 from facefusion.uis.core import get_ui_component
 
 BENCHMARK_RESULTS_DATAFRAME : Optional[gradio.Dataframe] = None
@@ -75,7 +76,7 @@ def listen() -> None:
 
 
 def start(benchmark_runs : List[str], benchmark_cycles : int) -> Generator[List[Any], None, None]:
-	facefusion.globals.source_path = '.assets/examples/source.jpg'
+	facefusion.globals.source_paths = [ '.assets/examples/source.jpg' ]
 	target_paths = [ BENCHMARKS[benchmark_run] for benchmark_run in benchmark_runs if benchmark_run in BENCHMARKS ]
 	benchmark_results = []
 	if target_paths:
@@ -94,7 +95,7 @@ def pre_process() -> None:
 
 
 def post_process() -> None:
-	clear_faces_cache()
+	clear_static_faces()
 
 
 def benchmark(target_path : str, benchmark_cycles : int) -> List[Any]:
@@ -102,7 +103,7 @@ def benchmark(target_path : str, benchmark_cycles : int) -> List[Any]:
 	total_fps = 0.0
 	for i in range(benchmark_cycles):
 		facefusion.globals.target_path = target_path
-		facefusion.globals.output_path = normalize_output_path(facefusion.globals.source_path, facefusion.globals.target_path, tempfile.gettempdir())
+		facefusion.globals.output_path = normalize_output_path(facefusion.globals.source_paths, facefusion.globals.target_path, tempfile.gettempdir())
 		video_frame_total = count_video_frame_total(facefusion.globals.target_path)
 		start_time = time.perf_counter()
 		conditional_process()

facefusion/uis/components/execution.py

@@ -6,7 +6,7 @@ import facefusion.globals
 from facefusion import wording
 from facefusion.face_analyser import clear_face_analyser
 from facefusion.processors.frame.core import clear_frame_processors_modules
-from facefusion.utilities import encode_execution_providers, decode_execution_providers
+from facefusion.execution_helper import encode_execution_providers, decode_execution_providers
 
 EXECUTION_PROVIDERS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
 

facefusion/uis/components/face_analyser.py

@@ -53,7 +53,7 @@ def render() -> None:
 	FACE_DETECTOR_SCORE_SLIDER = gradio.Slider(
 		label = wording.get('face_detector_score_slider_label'),
 		value = facefusion.globals.face_detector_score,
-		step =facefusion.choices.face_detector_score_range[1] - facefusion.choices.face_detector_score_range[0],
+		step = facefusion.choices.face_detector_score_range[1] - facefusion.choices.face_detector_score_range[0],
 		minimum = facefusion.choices.face_detector_score_range[0],
 		maximum = facefusion.choices.face_detector_score_range[-1]
 	)

facefusion/uis/components/face_masker.py

@@ -0,0 +1,123 @@
+from typing import Optional, Tuple, List
+import gradio
+
+import facefusion.globals
+import facefusion.choices
+from facefusion import wording
+from facefusion.typing import FaceMaskType, FaceMaskRegion
+from facefusion.uis.core import register_ui_component
+
+FACE_MASK_TYPES_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
+FACE_MASK_BLUR_SLIDER : Optional[gradio.Slider] = None
+FACE_MASK_BOX_GROUP : Optional[gradio.Group] = None
+FACE_MASK_REGION_GROUP : Optional[gradio.Group] = None
+FACE_MASK_PADDING_TOP_SLIDER : Optional[gradio.Slider] = None
+FACE_MASK_PADDING_RIGHT_SLIDER : Optional[gradio.Slider] = None
+FACE_MASK_PADDING_BOTTOM_SLIDER : Optional[gradio.Slider] = None
+FACE_MASK_PADDING_LEFT_SLIDER : Optional[gradio.Slider] = None
+FACE_MASK_REGION_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
+
+
+def render() -> None:
+	global FACE_MASK_TYPES_CHECKBOX_GROUP
+	global FACE_MASK_BLUR_SLIDER
+	global FACE_MASK_BOX_GROUP
+	global FACE_MASK_REGION_GROUP
+	global FACE_MASK_PADDING_TOP_SLIDER
+	global FACE_MASK_PADDING_RIGHT_SLIDER
+	global FACE_MASK_PADDING_BOTTOM_SLIDER
+	global FACE_MASK_PADDING_LEFT_SLIDER
+	global FACE_MASK_REGION_CHECKBOX_GROUP
+
+	has_box_mask = 'box' in facefusion.globals.face_mask_types
+	has_region_mask = 'region' in facefusion.globals.face_mask_types
+	FACE_MASK_TYPES_CHECKBOX_GROUP = gradio.CheckboxGroup(
+		label = wording.get('face_mask_types_checkbox_group_label'),
+		choices = facefusion.choices.face_mask_types,
+		value = facefusion.globals.face_mask_types
+	)
+	with gradio.Group(visible = has_box_mask) as FACE_MASK_BOX_GROUP:
+		FACE_MASK_BLUR_SLIDER = gradio.Slider(
+			label = wording.get('face_mask_blur_slider_label'),
+			step = facefusion.choices.face_mask_blur_range[1] - facefusion.choices.face_mask_blur_range[0],
+			minimum = facefusion.choices.face_mask_blur_range[0],
+			maximum = facefusion.choices.face_mask_blur_range[-1],
+			value = facefusion.globals.face_mask_blur
+		)
+		with gradio.Row():
+			FACE_MASK_PADDING_TOP_SLIDER = gradio.Slider(
+				label = wording.get('face_mask_padding_top_slider_label'),
+				step = facefusion.choices.face_mask_padding_range[1] - facefusion.choices.face_mask_padding_range[0],
+				minimum = facefusion.choices.face_mask_padding_range[0],
+				maximum = facefusion.choices.face_mask_padding_range[-1],
+				value = facefusion.globals.face_mask_padding[0]
+			)
+			FACE_MASK_PADDING_RIGHT_SLIDER = gradio.Slider(
+				label = wording.get('face_mask_padding_right_slider_label'),
+				step = facefusion.choices.face_mask_padding_range[1] - facefusion.choices.face_mask_padding_range[0],
+				minimum = facefusion.choices.face_mask_padding_range[0],
+				maximum = facefusion.choices.face_mask_padding_range[-1],
+				value = facefusion.globals.face_mask_padding[1]
+			)
+		with gradio.Row():
+			FACE_MASK_PADDING_BOTTOM_SLIDER = gradio.Slider(
+				label = wording.get('face_mask_padding_bottom_slider_label'),
+				step = facefusion.choices.face_mask_padding_range[1] - facefusion.choices.face_mask_padding_range[0],
+				minimum = facefusion.choices.face_mask_padding_range[0],
+				maximum = facefusion.choices.face_mask_padding_range[-1],
+				value = facefusion.globals.face_mask_padding[2]
+			)
+			FACE_MASK_PADDING_LEFT_SLIDER = gradio.Slider(
+				label = wording.get('face_mask_padding_left_slider_label'),
+				step = facefusion.choices.face_mask_padding_range[1] - facefusion.choices.face_mask_padding_range[0],
+				minimum = facefusion.choices.face_mask_padding_range[0],
+				maximum = facefusion.choices.face_mask_padding_range[-1],
+				value = facefusion.globals.face_mask_padding[3]
+			)
+	with gradio.Row():
+		FACE_MASK_REGION_CHECKBOX_GROUP = gradio.CheckboxGroup(
+			label = wording.get('face_mask_region_checkbox_group_label'),
+			choices = facefusion.choices.face_mask_regions,
+			value = facefusion.globals.face_mask_regions,
+			visible = has_region_mask
+		)
+	register_ui_component('face_mask_types_checkbox_group', FACE_MASK_TYPES_CHECKBOX_GROUP)
+	register_ui_component('face_mask_blur_slider', FACE_MASK_BLUR_SLIDER)
+	register_ui_component('face_mask_padding_top_slider', FACE_MASK_PADDING_TOP_SLIDER)
+	register_ui_component('face_mask_padding_right_slider', FACE_MASK_PADDING_RIGHT_SLIDER)
+	register_ui_component('face_mask_padding_bottom_slider', FACE_MASK_PADDING_BOTTOM_SLIDER)
+	register_ui_component('face_mask_padding_left_slider', FACE_MASK_PADDING_LEFT_SLIDER)
+	register_ui_component('face_mask_region_checkbox_group', FACE_MASK_REGION_CHECKBOX_GROUP)
+
+
+def listen() -> None:
+	FACE_MASK_TYPES_CHECKBOX_GROUP.change(update_face_mask_type, inputs = FACE_MASK_TYPES_CHECKBOX_GROUP, outputs = [ FACE_MASK_TYPES_CHECKBOX_GROUP, FACE_MASK_BOX_GROUP, FACE_MASK_REGION_CHECKBOX_GROUP ])
+	FACE_MASK_BLUR_SLIDER.change(update_face_mask_blur, inputs = FACE_MASK_BLUR_SLIDER)
+	FACE_MASK_REGION_CHECKBOX_GROUP.change(update_face_mask_regions, inputs = FACE_MASK_REGION_CHECKBOX_GROUP, outputs = FACE_MASK_REGION_CHECKBOX_GROUP)
+	face_mask_padding_sliders = [ FACE_MASK_PADDING_TOP_SLIDER, FACE_MASK_PADDING_RIGHT_SLIDER, FACE_MASK_PADDING_BOTTOM_SLIDER, FACE_MASK_PADDING_LEFT_SLIDER ]
+	for face_mask_padding_slider in face_mask_padding_sliders:
+		face_mask_padding_slider.change(update_face_mask_padding, inputs = face_mask_padding_sliders)
+
+
+def update_face_mask_type(face_mask_types : List[FaceMaskType]) -> Tuple[gradio.CheckboxGroup, gradio.Group, gradio.CheckboxGroup]:
+	if not face_mask_types:
+		face_mask_types = facefusion.choices.face_mask_types
+	facefusion.globals.face_mask_types = face_mask_types
+	has_box_mask = 'box' in face_mask_types
+	has_region_mask = 'region' in face_mask_types
+	return gradio.CheckboxGroup(value = face_mask_types), gradio.Group(visible = has_box_mask), gradio.CheckboxGroup(visible = has_region_mask)
+
+
+def update_face_mask_blur(face_mask_blur : float) -> None:
+	facefusion.globals.face_mask_blur = face_mask_blur
+
+
+def update_face_mask_padding(face_mask_padding_top : int, face_mask_padding_right : int, face_mask_padding_bottom : int, face_mask_padding_left : int) -> None:
+	facefusion.globals.face_mask_padding = (face_mask_padding_top, face_mask_padding_right, face_mask_padding_bottom, face_mask_padding_left)
+
+
+def update_face_mask_regions(face_mask_regions : List[FaceMaskRegion]) -> gradio.CheckboxGroup:
+	if not face_mask_regions:
+		face_mask_regions = facefusion.choices.face_mask_regions
+	facefusion.globals.face_mask_regions = face_mask_regions
+	return gradio.CheckboxGroup(value = face_mask_regions)

facefusion/uis/components/face_selector.py

@@ -5,12 +5,11 @@ import gradio
 import facefusion.globals
 import facefusion.choices
 from facefusion import wording
-from facefusion.face_cache import clear_faces_cache
+from facefusion.face_store import clear_static_faces, clear_reference_faces
 from facefusion.vision import get_video_frame, read_static_image, normalize_frame_color
 from facefusion.face_analyser import get_many_faces
-from facefusion.face_reference import clear_face_reference
 from facefusion.typing import Frame, FaceSelectorMode
-from facefusion.utilities import is_image, is_video
+from facefusion.filesystem import is_image, is_video
 from facefusion.uis.core import get_ui_component, register_ui_component
 from facefusion.uis.typing import ComponentName
 
@@ -111,8 +110,8 @@ def update_face_selector_mode(face_selector_mode : FaceSelectorMode) -> Tuple[gr
 
 
 def clear_and_update_reference_face_position(event : gradio.SelectData) -> gradio.Gallery:
-	clear_face_reference()
-	clear_faces_cache()
+	clear_reference_faces()
+	clear_static_faces()
 	update_reference_face_position(event.index)
 	return update_reference_position_gallery()
 
@@ -130,8 +129,8 @@ def update_reference_frame_number(reference_frame_number : int) -> None:
 
 
 def clear_and_update_reference_position_gallery() -> gradio.Gallery:
-	clear_face_reference()
-	clear_faces_cache()
+	clear_reference_faces()
+	clear_static_faces()
 	return update_reference_position_gallery()
 
 

facefusion/uis/components/frame_processors.py

@@ -4,7 +4,7 @@ import gradio
 import facefusion.globals
 from facefusion import wording
 from facefusion.processors.frame.core import load_frame_processor_module, clear_frame_processors_modules
-from facefusion.utilities import list_module_names
+from facefusion.filesystem import list_module_names
 from facefusion.uis.core import register_ui_component
 
 FRAME_PROCESSORS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None

facefusion/uis/components/frame_processors_options.py

@@ -87,8 +87,8 @@ def listen() -> None:
 
 def update_face_swapper_model(face_swapper_model : FaceSwapperModel) -> gradio.Dropdown:
 	frame_processors_globals.face_swapper_model = face_swapper_model
-	if face_swapper_model == 'blendface_256':
-		facefusion.globals.face_recognizer_model = 'arcface_blendface'
+	if face_swapper_model == 'blendswap_256':
+		facefusion.globals.face_recognizer_model = 'arcface_blendswap'
 	if face_swapper_model == 'inswapper_128' or face_swapper_model == 'inswapper_128_fp16':
 		facefusion.globals.face_recognizer_model = 'arcface_inswapper'
 	if face_swapper_model == 'simswap_256' or face_swapper_model == 'simswap_512_unofficial':

facefusion/uis/components/output.py

@@ -5,7 +5,8 @@ import facefusion.globals
 from facefusion import wording
 from facefusion.core import limit_resources, conditional_process
 from facefusion.uis.core import get_ui_component
-from facefusion.utilities import is_image, is_video, normalize_output_path, clear_temp
+from facefusion.normalizer import normalize_output_path
+from facefusion.filesystem import is_image, is_video, clear_temp
 
 OUTPUT_IMAGE : Optional[gradio.Image] = None
 OUTPUT_VIDEO : Optional[gradio.Video] = None
@@ -45,7 +46,7 @@ def listen() -> None:
 
 
 def start(output_path : str) -> Tuple[gradio.Image, gradio.Video]:
-	facefusion.globals.output_path = normalize_output_path(facefusion.globals.source_path, facefusion.globals.target_path, output_path)
+	facefusion.globals.output_path = normalize_output_path(facefusion.globals.source_paths, facefusion.globals.target_path, output_path)
 	limit_resources()
 	conditional_process()
 	if is_image(facefusion.globals.output_path):

facefusion/uis/components/output_options.py

@@ -6,7 +6,7 @@ import facefusion.globals
 import facefusion.choices
 from facefusion import wording
 from facefusion.typing import OutputVideoEncoder
-from facefusion.utilities import is_image, is_video
+from facefusion.filesystem import is_image, is_video
 from facefusion.uis.typing import ComponentName
 from facefusion.uis.core import get_ui_component, register_ui_component
 

facefusion/uis/components/preview.py

@@ -4,15 +4,14 @@ import gradio
 
 import facefusion.globals
 from facefusion import wording
-from facefusion.core import conditional_set_face_reference
-from facefusion.face_cache import clear_faces_cache
-from facefusion.typing import Frame, Face
-from facefusion.vision import get_video_frame, count_video_frame_total, normalize_frame_color, resize_frame_dimension, read_static_image
-from facefusion.face_analyser import get_one_face, clear_face_analyser
-from facefusion.face_reference import get_face_reference, clear_face_reference
+from facefusion.core import conditional_append_reference_faces
+from facefusion.face_store import clear_static_faces, get_reference_faces, clear_reference_faces
+from facefusion.typing import Frame, Face, FaceSet
+from facefusion.vision import get_video_frame, count_video_frame_total, normalize_frame_color, resize_frame_dimension, read_static_image, read_static_images
+from facefusion.face_analyser import get_average_face, clear_face_analyser
 from facefusion.content_analyser import analyse_frame
 from facefusion.processors.frame.core import load_frame_processor_module
-from facefusion.utilities import is_video, is_image
+from facefusion.filesystem import is_image, is_video
 from facefusion.uis.typing import ComponentName
 from facefusion.uis.core import get_ui_component, register_ui_component
 
@@ -37,16 +36,17 @@ def render() -> None:
 		'maximum': 100,
 		'visible': False
 	}
-	conditional_set_face_reference()
-	source_face = get_one_face(read_static_image(facefusion.globals.source_path))
-	reference_face = get_face_reference() if 'reference' in facefusion.globals.face_selector_mode else None
+	conditional_append_reference_faces()
+	source_frames = read_static_images(facefusion.globals.source_paths)
+	source_face = get_average_face(source_frames)
+	reference_faces = get_reference_faces() if 'reference' in facefusion.globals.face_selector_mode else None
 	if is_image(facefusion.globals.target_path):
 		target_frame = read_static_image(facefusion.globals.target_path)
-		preview_frame = process_preview_frame(source_face, reference_face, target_frame)
+		preview_frame = process_preview_frame(source_face, reference_faces, target_frame)
 		preview_image_args['value'] = normalize_frame_color(preview_frame)
 	if is_video(facefusion.globals.target_path):
 		temp_frame = get_video_frame(facefusion.globals.target_path, facefusion.globals.reference_frame_number)
-		preview_frame = process_preview_frame(source_face, reference_face, temp_frame)
+		preview_frame = process_preview_frame(source_face, reference_faces, temp_frame)
 		preview_image_args['value'] = normalize_frame_color(preview_frame)
 		preview_image_args['visible'] = True
 		preview_frame_slider_args['value'] = facefusion.globals.reference_frame_number
@@ -58,7 +58,7 @@ def render() -> None:
 
 
 def listen() -> None:
-	PREVIEW_FRAME_SLIDER.change(update_preview_image, inputs = PREVIEW_FRAME_SLIDER, outputs = PREVIEW_IMAGE)
+	PREVIEW_FRAME_SLIDER.release(update_preview_image, inputs = PREVIEW_FRAME_SLIDER, outputs = PREVIEW_IMAGE)
 	multi_one_component_names : List[ComponentName] =\
 	[
 		'source_image',
@@ -93,7 +93,6 @@ def listen() -> None:
 			component.select(update_preview_image, inputs = PREVIEW_FRAME_SLIDER, outputs = PREVIEW_IMAGE)
 	change_one_component_names : List[ComponentName] =\
 	[
-		'frame_processors_checkbox_group',
 		'face_debugger_items_checkbox_group',
 		'face_enhancer_model_dropdown',
 		'face_enhancer_blend_slider',
@@ -101,11 +100,13 @@ def listen() -> None:
 		'frame_enhancer_blend_slider',
 		'face_selector_mode_dropdown',
 		'reference_face_distance_slider',
+		'face_mask_types_checkbox_group',
 		'face_mask_blur_slider',
 		'face_mask_padding_top_slider',
 		'face_mask_padding_bottom_slider',
 		'face_mask_padding_left_slider',
-		'face_mask_padding_right_slider'
+		'face_mask_padding_right_slider',
+		'face_mask_region_checkbox_group'
 	]
 	for component_name in change_one_component_names:
 		component = get_ui_component(component_name)
@@ -113,6 +114,7 @@ def listen() -> None:
 			component.change(update_preview_image, inputs = PREVIEW_FRAME_SLIDER, outputs = PREVIEW_IMAGE)
 	change_two_component_names : List[ComponentName] =\
 	[
+		'frame_processors_checkbox_group',
 		'face_swapper_model_dropdown',
 		'face_detector_model_dropdown',
 		'face_detector_size_dropdown',
@@ -126,15 +128,16 @@ def listen() -> None:
 
 def clear_and_update_preview_image(frame_number : int = 0) -> gradio.Image:
 	clear_face_analyser()
-	clear_face_reference()
-	clear_faces_cache()
+	clear_reference_faces()
+	clear_static_faces()
 	return update_preview_image(frame_number)
 
 
 def update_preview_image(frame_number : int = 0) -> gradio.Image:
-	conditional_set_face_reference()
-	source_face = get_one_face(read_static_image(facefusion.globals.source_path))
-	reference_face = get_face_reference() if 'reference' in facefusion.globals.face_selector_mode else None
+	conditional_append_reference_faces()
+	source_frames = read_static_images(facefusion.globals.source_paths)
+	source_face = get_average_face(source_frames)
+	reference_face = get_reference_faces() if 'reference' in facefusion.globals.face_selector_mode else None
 	if is_image(facefusion.globals.target_path):
 		target_frame = read_static_image(facefusion.globals.target_path)
 		preview_frame = process_preview_frame(source_face, reference_face, target_frame)
@@ -155,7 +158,7 @@ def update_preview_frame_slider() -> gradio.Slider:
 	return gradio.Slider(value = None, maximum = None, visible = False)
 
 
-def process_preview_frame(source_face : Face, reference_face : Face, temp_frame : Frame) -> Frame:
+def process_preview_frame(source_face : Face, reference_faces : FaceSet, temp_frame : Frame) -> Frame:
 	temp_frame = resize_frame_dimension(temp_frame, 640, 640)
 	if analyse_frame(temp_frame):
 		return cv2.GaussianBlur(temp_frame, (99, 99), 0)
@@ -164,7 +167,7 @@ def process_preview_frame(source_face : Face, reference_face : Face, temp_frame
 		if frame_processor_module.pre_process('preview'):
 			temp_frame = frame_processor_module.process_frame(
 				source_face,
-				reference_face,
+				reference_faces,
 				temp_frame
 			)
 	return temp_frame

facefusion/uis/components/source.py

@@ -1,9 +1,10 @@
-from typing import Any, IO, Optional
+from typing import Optional, List
 import gradio
 
 import facefusion.globals
 from facefusion import wording
-from facefusion.utilities import is_image
+from facefusion.uis.typing import File
+from facefusion.filesystem import are_images
 from facefusion.uis.core import register_ui_component
 
 SOURCE_FILE : Optional[gradio.File] = None
@@ -14,9 +15,9 @@ def render() -> None:
 	global SOURCE_FILE
 	global SOURCE_IMAGE
 
-	is_source_image = is_image(facefusion.globals.source_path)
+	are_source_images = are_images(facefusion.globals.source_paths)
 	SOURCE_FILE = gradio.File(
-		file_count = 'single',
+		file_count = 'multiple',
 		file_types =
 		[
 			'.png',
@@ -24,11 +25,12 @@ def render() -> None:
 			'.webp'
 		],
 		label = wording.get('source_file_label'),
-		value = facefusion.globals.source_path if is_source_image else None
+		value = facefusion.globals.source_paths if are_source_images else None
 	)
+	source_file_names = [ source_file_value['name'] for source_file_value in SOURCE_FILE.value ] if SOURCE_FILE.value else None
 	SOURCE_IMAGE = gradio.Image(
-		value = SOURCE_FILE.value['name'] if is_source_image else None,
-		visible = is_source_image,
+		value = source_file_names[0] if are_source_images else None,
+		visible = are_source_images,
 		show_label = False
 	)
 	register_ui_component('source_image', SOURCE_IMAGE)
@@ -38,9 +40,10 @@ def listen() -> None:
 	SOURCE_FILE.change(update, inputs = SOURCE_FILE, outputs = SOURCE_IMAGE)
 
 
-def update(file: IO[Any]) -> gradio.Image:
-	if file and is_image(file.name):
-		facefusion.globals.source_path = file.name
-		return gradio.Image(value = file.name, visible = True)
-	facefusion.globals.source_path = None
+def update(files : List[File]) -> gradio.Image:
+	file_names = [ file.name for file in files ] if files else None
+	if are_images(file_names):
+		facefusion.globals.source_paths = file_names
+		return gradio.Image(value = file_names[0], visible = True)
+	facefusion.globals.source_paths = None
 	return gradio.Image(value = None, visible = False)

facefusion/uis/components/target.py

@@ -1,11 +1,11 @@
-from typing import Any, IO, Tuple, Optional
+from typing import Tuple, Optional
 import gradio
 
 import facefusion.globals
 from facefusion import wording
-from facefusion.face_cache import clear_faces_cache
-from facefusion.face_reference import clear_face_reference
-from facefusion.utilities import is_image, is_video
+from facefusion.face_store import clear_static_faces, clear_reference_faces
+from facefusion.uis.typing import File
+from facefusion.filesystem import is_image, is_video
 from facefusion.uis.core import register_ui_component
 
 TARGET_FILE : Optional[gradio.File] = None
@@ -50,9 +50,9 @@ def listen() -> None:
 	TARGET_FILE.change(update, inputs = TARGET_FILE, outputs = [ TARGET_IMAGE, TARGET_VIDEO ])
 
 
-def update(file : IO[Any]) -> Tuple[gradio.Image, gradio.Video]:
-	clear_face_reference()
-	clear_faces_cache()
+def update(file : File) -> Tuple[gradio.Image, gradio.Video]:
+	clear_reference_faces()
+	clear_static_faces()
 	if file and is_image(file.name):
 		facefusion.globals.target_path = file.name
 		return gradio.Image(value = file.name, visible = True), gradio.Video(value = None, visible = False)

facefusion/uis/components/temp_frame.py

@@ -5,7 +5,7 @@ import facefusion.globals
 import facefusion.choices
 from facefusion import wording
 from facefusion.typing import TempFrameFormat
-from facefusion.utilities import is_video
+from facefusion.filesystem import is_video
 from facefusion.uis.core import get_ui_component
 
 TEMP_FRAME_FORMAT_DROPDOWN : Optional[gradio.Dropdown] = None

facefusion/uis/components/trim_frame.py

@@ -4,7 +4,7 @@ import gradio
 import facefusion.globals
 from facefusion import wording
 from facefusion.vision import count_video_frame_total
-from facefusion.utilities import is_video
+from facefusion.filesystem import is_video
 from facefusion.uis.core import get_ui_component
 
 TRIM_FRAME_START_SLIDER : Optional[gradio.Slider] = None

facefusion/uis/components/webcam.py

@@ -9,13 +9,13 @@ import gradio
 from tqdm import tqdm
 
 import facefusion.globals
-from facefusion import wording
+from facefusion import logger, wording
 from facefusion.content_analyser import analyse_stream
 from facefusion.typing import Frame, Face
-from facefusion.face_analyser import get_one_face
+from facefusion.face_analyser import get_average_face
 from facefusion.processors.frame.core import get_frame_processors_modules
-from facefusion.utilities import open_ffmpeg
-from facefusion.vision import normalize_frame_color, read_static_image
+from facefusion.ffmpeg import open_ffmpeg
+from facefusion.vision import normalize_frame_color, read_static_images
 from facefusion.uis.typing import StreamMode, WebcamMode
 from facefusion.uis.core import get_ui_component
 
@@ -79,30 +79,34 @@ def listen() -> None:
 			getattr(source_image, method)(stop, cancels = start_event)
 
 
-def start(mode : WebcamMode, resolution : str, fps : float) -> Generator[Frame, None, None]:
+def start(webcam_mode : WebcamMode, resolution : str, fps : float) -> Generator[Frame, None, None]:
 	facefusion.globals.face_selector_mode = 'one'
 	facefusion.globals.face_analyser_order = 'large-small'
-	source_face = get_one_face(read_static_image(facefusion.globals.source_path))
+	source_frames = read_static_images(facefusion.globals.source_paths)
+	source_face = get_average_face(source_frames)
 	stream = None
-	if mode in [ 'udp', 'v4l2' ]:
-		stream = open_stream(mode, resolution, fps) # type: ignore[arg-type]
+	if webcam_mode in [ 'udp', 'v4l2' ]:
+		stream = open_stream(webcam_mode, resolution, fps) # type: ignore[arg-type]
 	webcam_width, webcam_height = map(int, resolution.split('x'))
 	webcam_capture = get_webcam_capture()
 	if webcam_capture and webcam_capture.isOpened():
-		webcam_capture.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG'))  # type: ignore[attr-defined]
+		webcam_capture.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG')) # type: ignore[attr-defined]
 		webcam_capture.set(cv2.CAP_PROP_FRAME_WIDTH, webcam_width)
 		webcam_capture.set(cv2.CAP_PROP_FRAME_HEIGHT, webcam_height)
 		webcam_capture.set(cv2.CAP_PROP_FPS, fps)
 		for capture_frame in multi_process_capture(source_face, webcam_capture, fps):
-			if mode == 'inline':
+			if webcam_mode == 'inline':
 				yield normalize_frame_color(capture_frame)
 			else:
-				stream.stdin.write(capture_frame.tobytes())
+				try:
+					stream.stdin.write(capture_frame.tobytes())
+				except Exception:
+					clear_webcam_capture()
 				yield None
 
 
 def multi_process_capture(source_face : Face, webcam_capture : cv2.VideoCapture, fps : float) -> Generator[Frame, None, None]:
-	with tqdm(desc = wording.get('processing'), unit = 'frame', ascii = ' =') as progress:
+	with tqdm(desc = wording.get('processing'), unit = 'frame', ascii = ' =', disable = facefusion.globals.log_level in [ 'warn', 'error' ]) as progress:
 		with ThreadPoolExecutor(max_workers = facefusion.globals.execution_thread_count) as executor:
 			futures = []
 			deque_capture_frames : Deque[Frame] = deque()
@@ -137,11 +141,15 @@ def process_stream_frame(source_face : Face, temp_frame : Frame) -> Frame:
 	return temp_frame
 
 
-def open_stream(mode : StreamMode, resolution : str, fps : float) -> subprocess.Popen[bytes]:
+def open_stream(stream_mode : StreamMode, resolution : str, fps : float) -> subprocess.Popen[bytes]:
 	commands = [ '-f', 'rawvideo', '-pix_fmt', 'bgr24', '-s', resolution, '-r', str(fps), '-i', '-' ]
-	if mode == 'udp':
+	if stream_mode == 'udp':
 		commands.extend([ '-b:v', '2000k', '-f', 'mpegts', 'udp://localhost:27000?pkt_size=1316' ])
-	if mode == 'v4l2':
-		device_name = os.listdir('/sys/devices/virtual/video4linux')[0]
-		commands.extend([ '-f', 'v4l2', '/dev/' + device_name ])
+	if stream_mode == 'v4l2':
+		try:
+			device_name = os.listdir('/sys/devices/virtual/video4linux')[0]
+			if device_name:
+				commands.extend([ '-f', 'v4l2', '/dev/' + device_name ])
+		except FileNotFoundError:
+			logger.error(wording.get('stream_not_loaded').format(stream_mode = stream_mode), __name__.upper())
 	return open_ffmpeg(commands)

facefusion/uis/core.py

@@ -5,9 +5,9 @@ import sys
 import gradio
 
 import facefusion.globals
-from facefusion import metadata, wording
+from facefusion import metadata, logger, wording
 from facefusion.uis.typing import Component, ComponentName
-from facefusion.utilities import resolve_relative_path
+from facefusion.filesystem import resolve_relative_path
 
 UI_COMPONENTS: Dict[ComponentName, Component] = {}
 UI_LAYOUT_MODULES : List[ModuleType] = []
@@ -27,7 +27,8 @@ def load_ui_layout_module(ui_layout : str) -> Any:
 		for method_name in UI_LAYOUT_METHODS:
 			if not hasattr(ui_layout_module, method_name):
 				raise NotImplementedError
-	except ModuleNotFoundError:
+	except ModuleNotFoundError as exception:
+		logger.debug(exception.msg, __name__.upper())
 		sys.exit(wording.get('ui_layout_not_loaded').format(ui_layout = ui_layout))
 	except NotImplementedError:
 		sys.exit(wording.get('ui_layout_not_implemented').format(ui_layout = ui_layout))

facefusion/uis/layouts/benchmark.py

@@ -1,7 +1,7 @@
 import gradio
 
 import facefusion.globals
-from facefusion.utilities import conditional_download
+from facefusion.download import conditional_download
 from facefusion.uis.components import about, frame_processors, frame_processors_options, execution, execution_thread_count, execution_queue_count, limit_resources, benchmark_options, benchmark
 
 

facefusion/uis/layouts/default.py

@@ -1,6 +1,6 @@
 import gradio
 
-from facefusion.uis.components import about, frame_processors, frame_processors_options, execution, execution_thread_count, execution_queue_count, limit_resources, temp_frame, output_options, common_options, source, target, output, preview, trim_frame, face_analyser, face_selector, face_mask
+from facefusion.uis.components import about, frame_processors, frame_processors_options, execution, execution_thread_count, execution_queue_count, limit_resources, temp_frame, output_options, common_options, source, target, output, preview, trim_frame, face_analyser, face_selector, face_masker
 
 
 def pre_check() -> bool:
@@ -47,7 +47,7 @@ def render() -> gradio.Blocks:
 				with gradio.Blocks():
 					face_selector.render()
 				with gradio.Blocks():
-					face_mask.render()
+					face_masker.render()
 				with gradio.Blocks():
 					face_analyser.render()
 	return layout
@@ -69,7 +69,7 @@ def listen() -> None:
 	preview.listen()
 	trim_frame.listen()
 	face_selector.listen()
-	face_mask.listen()
+	face_masker.listen()
 	face_analyser.listen()
 
 

facefusion/uis/typing.py

@@ -1,6 +1,7 @@
-from typing import Literal
+from typing import Literal, Any, IO
 import gradio
 
+File = IO[Any]
 Component = gradio.File or gradio.Image or gradio.Video or gradio.Slider
 ComponentName = Literal\
 [
@@ -17,11 +18,13 @@ ComponentName = Literal\
 	'face_detector_model_dropdown',
 	'face_detector_size_dropdown',
 	'face_detector_score_slider',
+	'face_mask_types_checkbox_group',
 	'face_mask_blur_slider',
 	'face_mask_padding_top_slider',
 	'face_mask_padding_bottom_slider',
 	'face_mask_padding_left_slider',
 	'face_mask_padding_right_slider',
+	'face_mask_region_checkbox_group',
 	'frame_processors_checkbox_group',
 	'face_swapper_model_dropdown',
 	'face_enhancer_model_dropdown',