Merge pull request #1713 from TeachDian/fix-1705-wsl-onnxruntime-gpu

320 was over optimized, put back to 640

.gitignore

@@ -25,3 +25,5 @@ models/DMDNet.pth
 faceswap/
 .vscode/
 switch_states.json
+/models
+install.bat

README.md

@@ -1,4 +1,4 @@
-<h1 align="center">Deep-Live-Cam</h1>
+<h1 align="center">Deep-Live-Cam 2.1</h1>
 
 <p align="center">
   Real-time face swap and video deepfake with a single click and only a single image.
@@ -30,11 +30,11 @@ By using this software, you agree to these terms and commit to using it in a man
 
 Users are expected to use this software responsibly and legally. If using a real person's face, obtain their consent and clearly label any output as a deepfake when sharing online. We are not responsible for end-user actions.
 
-## Exclusive v2.1 Quick Start - Pre-built (Windows/Mac Silicon)
+## Exclusive v2.7 beta Quick Start - Pre-built (Windows/Mac Silicon/CPU)
 
   <a href="https://deeplivecam.net/index.php/quickstart"> <img src="media/Download.png" width="285" height="77" />
 
-##### This is the fastest build you can get if you have a discrete NVIDIA or AMD GPU or Mac Silicon, And you'll receive special priority support.
+##### This is the fastest build you can get if you have a discrete NVIDIA or AMD GPU, CPU or Mac Silicon, And you'll receive special priority support. 2.7 beta is the best you can have with 30+ extra features than the open source version.
  
 ###### These Pre-builts are perfect for non-technical users or those who don't have time to, or can't manually install all the requirements. Just a heads-up: this is an open-source project, so you can also install it manually. 
 
@@ -124,7 +124,7 @@ cd Deep-Live-Cam
 
 **3. Download the Models**
 
-1. [GFPGANv1.4](https://huggingface.co/hacksider/deep-live-cam/resolve/main/GFPGANv1.4.pth)
+1. [GFPGANv1.4](https://huggingface.co/hacksider/deep-live-cam/resolve/main/GFPGANv1.4.onnx)
 2. [inswapper\_128\_fp16.onnx](https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128_fp16.onnx)
 
 Place these files in the "**models**" folder.
@@ -179,6 +179,11 @@ source venv/bin/activate
 
 # install the dependencies again
 pip install -r requirements.txt
+
+# gfpgan and basicsrs issue fix
+pip install git+https://github.com/xinntao/BasicSR.git@master
+pip uninstall gfpgan -y
+pip install git+https://github.com/TencentARC/GFPGAN.git@master
 ```
 
 **Run:** If you don't have a GPU, you can run Deep-Live-Cam using `python run.py`. Note that initial execution will download models (~300MB).
@@ -304,6 +309,9 @@ python run.py --execution-provider openvino
 -   Use a screen capture tool like OBS to stream.
 -   To change the face, select a new source image.
 
+## Download all models in this huggingface link
+- [**Download models here**](https://huggingface.co/hacksider/deep-live-cam/tree/main)
+
 ## Command Line Arguments (Unmaintained)
 
 ```
@@ -333,26 +341,22 @@ Looking for a CLI mode? Using the -s/--source argument will make the run program
 
 ## Press
 
-**We are always open to criticism and are ready to improve, that's why we didn't cherry-pick anything.**
+ - [**Ars Technica**](https://arstechnica.com/information-technology/2024/08/new-ai-tool-enables-real-time-face-swapping-on-webcams-raising-fraud-concerns/) - *"Deep-Live-Cam goes viral, allowing anyone to become a digital doppelganger"*
-
+ - [**Yahoo!**](https://www.yahoo.com/tech/ok-viral-ai-live-stream-080041056.html) - *"OK, this viral AI live stream software is truly terrifying"*
- - [*"Deep-Live-Cam goes viral, allowing anyone to become a digital doppelganger"*](https://arstechnica.com/information-technology/2024/08/new-ai-tool-enables-real-time-face-swapping-on-webcams-raising-fraud-concerns/) - Ars Technica
+ - [**CNN Brasil**](https://www.cnnbrasil.com.br/tecnologia/ia-consegue-clonar-rostos-na-webcam-entenda-funcionamento/) - *"AI can clone faces on webcam; understand how it works"*
- - [*"Thanks Deep Live Cam, shapeshifters are among us now"*](https://dataconomy.com/2024/08/15/what-is-deep-live-cam-github-deepfake/) - Dataconomy
+ - [**Bloomberg Technoz**](https://www.bloombergtechnoz.com/detail-news/71032/kenalan-dengan-teknologi-deep-live-cam-bisa-jadi-alat-menipu) - *"Get to know Deep Live Cam technology, it can be used as a tool for deception."*
- - [*"This free AI tool lets you become anyone during video-calls"*](https://www.newsbytesapp.com/news/science/deep-live-cam-ai-impersonation-tool-goes-viral/story) - NewsBytes
+ - [**TrendMicro**](https://www.trendmicro.com/vinfo/gb/security/news/cyber-attacks/ai-vs-ai-deepfakes-and-ekyc) - *"AI vs AI: DeepFakes and eKYC"*
- - [*"OK, this viral AI live stream software is truly terrifying"*](https://www.creativebloq.com/ai/ok-this-viral-ai-live-stream-software-is-truly-terrifying) - Creative Bloq
+ - [**PetaPixel**](https://petapixel.com/2024/08/14/deep-live-cam-deepfake-ai-tool-lets-you-become-anyone-in-a-video-call-with-single-photo-mark-zuckerberg-jd-vance-elon-musk/) - *"Deepfake AI Tool Lets You Become Anyone in a Video Call With Single Photo"*
- - [*"Deepfake AI Tool Lets You Become Anyone in a Video Call With Single Photo"*](https://petapixel.com/2024/08/14/deep-live-cam-deepfake-ai-tool-lets-you-become-anyone-in-a-video-call-with-single-photo-mark-zuckerberg-jd-vance-elon-musk/) - PetaPixel
+ - [**SomeOrdinaryGamers**](https://www.youtube.com/watch?time_continue=1074&v=py4Tc-Y8BcY) - *"That's Crazy, Oh God. That's Fucking Freaky Dude... That's So Wild Dude"*
- - [*"Deep-Live-Cam Uses AI to Transform Your Face in Real-Time, Celebrities Included"*](https://www.techeblog.com/deep-live-cam-ai-transform-face/) - TechEBlog
+ - [**IShowSpeed**](https://www.youtube.com/live/mFsCe7AIxq8?feature=shared&t=2686) - *"Alright look look look, now look chat, we can do any face we want to look like chat"*
- - [*"An AI tool that "makes you look like anyone" during a video call is going viral online"*](https://telegrafi.com/en/a-tool-that-makes-you-look-like-anyone-during-a-video-call-is-going-viral-on-the-Internet/) - Telegrafi
+ - [**TechLinked (Linus Tech Tips)**](https://www.youtube.com/watch?v=wnCghLjqv3s&t=551s) - *"They do a pretty good job matching poses, expression and even the lighting"*
- - [*"This Deepfake Tool Turning Images Into Livestreams is Topping the GitHub Charts"*](https://decrypt.co/244565/this-deepfake-tool-turning-images-into-livestreams-is-topping-the-github-charts) - Emerge
+ - [**IShowSpeed**](https://youtu.be/JbUPRmXRUtE?t=3964) - *"What the F***! Why do I look like Vinny Jr? I look exactly like Vinny Jr!? No, this shit is crazy! Bro This is F*** Crazy!"*
- - [*"New Real-Time Face-Swapping AI Allows Anyone to Mimic Famous Faces"*](https://www.digitalmusicnews.com/2024/08/15/face-swapping-ai-real-time-mimic/) - Digital Music News
- - [*"This real-time webcam deepfake tool raises alarms about the future of identity theft"*](https://www.diyphotography.net/this-real-time-webcam-deepfake-tool-raises-alarms-about-the-future-of-identity-theft/) - DIYPhotography
- - [*"That's Crazy, Oh God. That's Fucking Freaky Dude... That's So Wild Dude"*](https://www.youtube.com/watch?time_continue=1074&v=py4Tc-Y8BcY) - SomeOrdinaryGamers
- - [*"Alright look look look, now look chat, we can do any face we want to look like chat"*](https://www.youtube.com/live/mFsCe7AIxq8?feature=shared&t=2686) - IShowSpeed
- - [*"They do a pretty good job matching poses, expression and even the lighting"*](https://www.youtube.com/watch?v=wnCghLjqv3s&t=551s) - TechLinked (LTT)
 
 
 ## Credits
 
 -   [ffmpeg](https://ffmpeg.org/): for making video-related operations easy
+-   [Henry](https://github.com/henryruhs): One of the major contributor in this repo
 -   [deepinsight](https://github.com/deepinsight): for their [insightface](https://github.com/deepinsight/insightface) project which provided a well-made library and models. Please be reminded that the [use of the model is for non-commercial research purposes only](https://github.com/deepinsight/insightface?tab=readme-ov-file#license).
 -   [havok2-htwo](https://github.com/havok2-htwo): for sharing the code for webcam
 -   [GosuDRM](https://github.com/GosuDRM): for the open version of roop
@@ -360,6 +364,7 @@ Looking for a CLI mode? Using the -s/--source argument will make the run program
 -   [vic4key](https://github.com/vic4key): For supporting/contributing to this project
 -   [kier007](https://github.com/kier007): for improving the user experience
 -   [qitianai](https://github.com/qitianai): for multi-lingual support
+-   [laurigates](https://github.com/laurigates): Decoupling stuffs to make everything faster!
 -   and [all developers](https://github.com/hacksider/Deep-Live-Cam/graphs/contributors) behind libraries used in this project.
 -   Footnote: Please be informed that the base author of the code is [s0md3v](https://github.com/s0md3v/roop)
 -   All the wonderful users who helped make this project go viral by starring the repo ❤️

locales/id.json

@@ -0,0 +1,45 @@
+{
+    "Source x Target Mapper": "Pemetaan Sumber x Target",
+    "select a source image": "Pilih gambar sumber",
+    "Preview": "Pratinjau",
+    "select a target image or video": "Pilih gambar atau video target",
+    "save image output file": "Simpan file keluaran gambar",
+    "save video output file": "Simpan file keluaran video",
+    "select a target image": "Pilih gambar target",
+    "source": "Sumber",
+    "Select a target": "Pilih target",
+    "Select a face": "Pilih wajah",
+    "Keep audio": "Pertahankan audio",
+    "Face Enhancer": "Peningkat wajah",
+    "Many faces": "Banyak wajah",
+    "Show FPS": "Tampilkan FPS",
+    "Keep fps": "Pertahankan FPS",
+    "Keep frames": "Pertahankan frame",
+    "Fix Blueish Cam": "Perbaiki kamera kebiruan",
+    "Mouth Mask": "Masker mulut",
+    "Show Mouth Mask Box": "Tampilkan kotak masker mulut",
+    "Start": "Mulai",
+    "Live": "Langsung",
+    "Destroy": "Hentikan",
+    "Map faces": "Petakan wajah",
+    "Processing...": "Sedang memproses...",
+    "Processing succeed!": "Pemrosesan berhasil!",
+    "Processing ignored!": "Pemrosesan diabaikan!",
+    "Failed to start camera": "Gagal memulai kamera",
+    "Please complete pop-up or close it.": "Harap selesaikan atau tutup pop-up.",
+    "Getting unique faces": "Mengambil wajah unik",
+    "Please select a source image first": "Silakan pilih gambar sumber terlebih dahulu",
+    "No faces found in target": "Tidak ada wajah ditemukan pada target",
+    "Add": "Tambah",
+    "Clear": "Bersihkan",
+    "Submit": "Kirim",
+    "Select source image": "Pilih gambar sumber",
+    "Select target image": "Pilih gambar target",
+    "Please provide mapping!": "Harap tentukan pemetaan!",
+    "At least 1 source with target is required!": "Minimal 1 sumber dengan target diperlukan!",
+    "Face could not be detected in last upload!": "Wajah tidak dapat terdeteksi pada unggahan terakhir!",
+    "Select Camera:": "Pilih Kamera:",
+    "All mappings cleared!": "Semua pemetaan telah dibersihkan!",
+    "Mappings successfully submitted!": "Pemetaan berhasil dikirim!",
+    "Source x Target Mapper is already open.": "Pemetaan Sumber x Target sudah terbuka."
+}

modules/capturer.py

@@ -1,6 +1,7 @@
 from typing import Any
 import cv2
 import modules.globals  # Import the globals to check the color correction toggle
+from modules.gpu_processing import gpu_cvt_color
 
 
 def get_video_frame(video_path: str, frame_number: int = 0) -> Any:
@@ -19,7 +20,7 @@ def get_video_frame(video_path: str, frame_number: int = 0) -> Any:
 
     if has_frame and modules.globals.color_correction:
         # Convert the frame color if necessary
-        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        frame = gpu_cvt_color(frame, cv2.COLOR_BGR2RGB)
 
     capture.release()
     return frame if has_frame else None

modules/core.py

@@ -11,7 +11,11 @@ import platform
 import signal
 import shutil
 import argparse
-import torch
+try:
+    import torch
+    HAS_TORCH = True
+except ImportError:
+    HAS_TORCH = False
 import onnxruntime
 import tensorflow
 
@@ -21,11 +25,12 @@ import modules.ui as ui
 from modules.processors.frame.core import get_frame_processors_modules
 from modules.utilities import has_image_extension, is_image, is_video, detect_fps, create_video, extract_frames, get_temp_frame_paths, restore_audio, create_temp, move_temp, clean_temp, normalize_output_path
 
-if 'ROCMExecutionProvider' in modules.globals.execution_providers:
+if HAS_TORCH and 'ROCMExecutionProvider' in modules.globals.execution_providers:
     del torch
 
 warnings.filterwarnings('ignore', category=FutureWarning, module='insightface')
-warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')
+if HAS_TORCH:
+    warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')
 
 
 def parse_args() -> None:
@@ -34,7 +39,7 @@ def parse_args() -> None:
     program.add_argument('-s', '--source', help='select an source image', dest='source_path')
     program.add_argument('-t', '--target', help='select an target image or video', dest='target_path')
     program.add_argument('-o', '--output', help='select output file or directory', dest='output_path')
-    program.add_argument('--frame-processor', help='pipeline of frame processors', dest='frame_processor', default=['face_swapper'], choices=['face_swapper', 'face_enhancer'], nargs='+')
+    program.add_argument('--frame-processor', help='pipeline of frame processors', dest='frame_processor', default=['face_swapper'], choices=['face_swapper', 'face_enhancer', 'face_enhancer_gpen256', 'face_enhancer_gpen512'], nargs='+')
     program.add_argument('--keep-fps', help='keep original fps', dest='keep_fps', action='store_true', default=False)
     program.add_argument('--keep-audio', help='keep original audio', dest='keep_audio', action='store_true', default=True)
     program.add_argument('--keep-frames', help='keep temporary frames', dest='keep_frames', action='store_true', default=False)
@@ -81,11 +86,9 @@ def parse_args() -> None:
     modules.globals.execution_threads = args.execution_threads
     modules.globals.lang = args.lang
 
-    #for ENHANCER tumbler:
+    #for ENHANCER tumblers:
-    if 'face_enhancer' in args.frame_processor:
+    for enhancer_key in ('face_enhancer', 'face_enhancer_gpen256', 'face_enhancer_gpen512'):
-        modules.globals.fp_ui['face_enhancer'] = True
+        modules.globals.fp_ui[enhancer_key] = enhancer_key in args.frame_processor
-    else:
-        modules.globals.fp_ui['face_enhancer'] = False
 
     # translate deprecated args
     if args.source_path_deprecated:
@@ -129,11 +132,22 @@ def suggest_execution_providers() -> List[str]:
 
 
 def suggest_execution_threads() -> int:
+    """Suggest optimal thread count based on hardware and execution provider."""
+    import os
+    
+    # Get CPU count
+    cpu_count = os.cpu_count() or 4
+    
     if 'DmlExecutionProvider' in modules.globals.execution_providers:
         return 1
     if 'ROCMExecutionProvider' in modules.globals.execution_providers:
         return 1
-    return 8
+    if 'CUDAExecutionProvider' in modules.globals.execution_providers:
+        # For CUDA, use more threads for parallel frame processing
+        return min(cpu_count, 16)
+    
+    # For CPU execution, use most cores but leave some for system
+    return max(4, min(cpu_count - 2, 16))
 
 
 def limit_resources() -> None:
@@ -156,7 +170,7 @@ def limit_resources() -> None:
 
 
 def release_resources() -> None:
-    if 'CUDAExecutionProvider' in modules.globals.execution_providers:
+    if 'CUDAExecutionProvider' in modules.globals.execution_providers and HAS_TORCH:
         torch.cuda.empty_cache()
 
 
@@ -176,10 +190,16 @@ def update_status(message: str, scope: str = 'DLC.CORE') -> None:
         ui.update_status(message)
 
 def start() -> None:
+    """Start processing with performance monitoring."""
+    import time
+    
+    start_time = time.time()
+    
     for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
         if not frame_processor.pre_start():
             return
     update_status('Processing...')
+    
     # process image to image
     if has_image_extension(modules.globals.target_path):
         if modules.globals.nsfw_filter and ui.check_and_ignore_nsfw(modules.globals.target_path, destroy):
@@ -193,26 +213,40 @@ def start() -> None:
             frame_processor.process_image(modules.globals.source_path, modules.globals.output_path, modules.globals.output_path)
             release_resources()
         if is_image(modules.globals.target_path):
-            update_status('Processing to image succeed!')
+            elapsed = time.time() - start_time
+            update_status(f'Processing to image succeed! (Time: {elapsed:.2f}s)')
         else:
             update_status('Processing to image failed!')
         return
+    
     # process image to videos
     if modules.globals.nsfw_filter and ui.check_and_ignore_nsfw(modules.globals.target_path, destroy):
         return
 
+    extraction_start = time.time()
     if not modules.globals.map_faces:
         update_status('Creating temp resources...')
         create_temp(modules.globals.target_path)
         update_status('Extracting frames...')
         extract_frames(modules.globals.target_path)
+    extraction_time = time.time() - extraction_start
+    update_status(f'Frame extraction completed in {extraction_time:.2f}s')
 
     temp_frame_paths = get_temp_frame_paths(modules.globals.target_path)
+    total_frames = len(temp_frame_paths)
+    update_status(f'Processing {total_frames} frames with {modules.globals.execution_threads} threads...')
+    
+    processing_start = time.time()
     for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
         update_status('Progressing...', frame_processor.NAME)
         frame_processor.process_video(modules.globals.source_path, temp_frame_paths)
         release_resources()
+    processing_time = time.time() - processing_start
+    fps_processing = total_frames / processing_time if processing_time > 0 else 0
+    update_status(f'Frame processing completed in {processing_time:.2f}s ({fps_processing:.2f} fps)')
+    
     # handles fps
+    encoding_start = time.time()
     if modules.globals.keep_fps:
         update_status('Detecting fps...')
         fps = detect_fps(modules.globals.target_path)
@@ -221,6 +255,9 @@ def start() -> None:
     else:
         update_status('Creating video with 30.0 fps...')
         create_video(modules.globals.target_path)
+    encoding_time = time.time() - encoding_start
+    update_status(f'Video encoding completed in {encoding_time:.2f}s')
+    
     # handle audio
     if modules.globals.keep_audio:
         if modules.globals.keep_fps:
@@ -230,10 +267,13 @@ def start() -> None:
         restore_audio(modules.globals.target_path, modules.globals.output_path)
     else:
         move_temp(modules.globals.target_path, modules.globals.output_path)
+    
     # clean and validate
     clean_temp(modules.globals.target_path)
+    
+    total_time = time.time() - start_time
     if is_video(modules.globals.target_path):
-        update_status('Processing to video succeed!')
+        update_status(f'Processing to video succeed! Total time: {total_time:.2f}s')
     else:
         update_status('Processing to video failed!')
 

modules/custom_types.py

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

modules/face_analyser.py

@@ -2,6 +2,7 @@ import os
 import shutil
 from typing import Any
 import insightface
+import threading
 
 import cv2
 import numpy as np
@@ -13,14 +14,23 @@ from modules.utilities import get_temp_directory_path, create_temp, extract_fram
 from pathlib import Path
 
 FACE_ANALYSER = None
+FACE_ANALYSER_LOCK = threading.Lock()
 
 
 def get_face_analyser() -> Any:
+    """Get face analyser with thread-safe initialization."""
     global FACE_ANALYSER
 
     if FACE_ANALYSER is None:
-        FACE_ANALYSER = insightface.app.FaceAnalysis(name='buffalo_l', providers=modules.globals.execution_providers)
+        with FACE_ANALYSER_LOCK:
-        FACE_ANALYSER.prepare(ctx_id=0, det_size=(640, 640))
+            # Double-check after acquiring lock
+            if FACE_ANALYSER is None:
+                FACE_ANALYSER = insightface.app.FaceAnalysis(
+                    name='buffalo_l',
+                    providers=modules.globals.execution_providers,
+                    allowed_modules=['detection', 'recognition', 'landmark_2d_106']
+                )
+                FACE_ANALYSER.prepare(ctx_id=0, det_size=(640, 640))
     return FACE_ANALYSER
 
 

modules/globals.py

@@ -1,3 +1,5 @@
+# --- START OF FILE globals.py ---
+
 import os
 from typing import List, Dict, Any
 
@@ -9,35 +11,63 @@ file_types = [
     ("Video", ("*.mp4", "*.mkv")),
 ]
 
-source_target_map = []
+# Face Mapping Data
-simple_map = {}
+source_target_map: List[Dict[str, Any]] = [] # Stores detailed map for image/video processing
+simple_map: Dict[str, Any] = {}             # Stores simplified map (embeddings/faces) for live/simple mode
 
-source_path = None
+# Paths
-target_path = None
+source_path: str | None = None
-output_path = None
+target_path: str | None = None
+output_path: str | None = None
+
+# Processing Options
 frame_processors: List[str] = []
-keep_fps = True
+keep_fps: bool = True
-keep_audio = True
+keep_audio: bool = True
-keep_frames = False
+keep_frames: bool = False
-many_faces = False
+many_faces: bool = False         # Process all detected faces with default source
-map_faces = False
+map_faces: bool = False          # Use source_target_map or simple_map for specific swaps
-color_correction = False  # New global variable for color correction toggle
+poisson_blend: bool = False      # Enable Poisson Blending for smoother face swaps
-nsfw_filter = False
+color_correction: bool = False   # Enable color correction (implementation specific)
-video_encoder = None
+nsfw_filter: bool = False
-video_quality = None
+
-live_mirror = False
+# Video Output Options
-live_resizable = True
+video_encoder: str | None = None
-max_memory = None
+video_quality: int | None = None # Typically a CRF value or bitrate
-execution_providers: List[str] = []
+
-execution_threads = None
+# Live Mode Options
-headless = None
+live_mirror: bool = False
-log_level = "error"
+live_resizable: bool = True
-fp_ui: Dict[str, bool] = {"face_enhancer": False}
+camera_input_combobox: Any | None = None # Placeholder for UI element if needed
-camera_input_combobox = None
+webcam_preview_running: bool = False
-webcam_preview_running = False
+show_fps: bool = False
-show_fps = False
+
-mouth_mask = False
+# System Configuration
-show_mouth_mask_box = False
+max_memory: int | None = None        # Memory limit in GB? (Needs clarification)
-mask_feather_ratio = 8
+execution_providers: List[str] = []  # e.g., ['CUDAExecutionProvider', 'CPUExecutionProvider']
-mask_down_size = 0.50
+execution_threads: int | None = None # Number of threads for CPU execution
-mask_size = 1
+headless: bool | None = None         # Run without UI?
+log_level: str = "error"             # Logging level (e.g., 'debug', 'info', 'warning', 'error')
+
+# Face Processor UI Toggles (Example)
+fp_ui: Dict[str, bool] = {"face_enhancer": False, "face_enhancer_gpen256": False, "face_enhancer_gpen512": False}
+
+# Face Swapper Specific Options
+face_swapper_enabled: bool = True # General toggle for the swapper processor
+opacity: float = 1.0              # Blend factor for the swapped face (0.0-1.0)
+sharpness: float = 0.0            # Sharpness enhancement for swapped face (0.0-1.0+)
+
+# Mouth Mask Options
+mouth_mask: bool = False           # Enable mouth area masking/pasting
+show_mouth_mask_box: bool = False  # Visualize the mouth mask area (for debugging)
+mask_feather_ratio: int = 12       # Denominator for feathering calculation (higher = smaller feather)
+mask_down_size: float = 0.1        # Expansion factor for lower lip mask (relative)
+mask_size: float = 1.0             # Expansion factor for upper lip mask (relative)
+mouth_mask_size: float = 0.0       # Mouth mask size (0-100; 0=off, 100=mouth to chin)
+
+# --- START: Added for Frame Interpolation ---
+enable_interpolation: bool = True # Toggle temporal smoothing
+interpolation_weight: float = 0  # Blend weight for current frame (0.0-1.0). Lower=smoother.
+# --- END: Added for Frame Interpolation ---
+
+# --- END OF FILE globals.py ---

modules/gpu_processing.py

@@ -0,0 +1,286 @@
+# --- START OF FILE gpu_processing.py ---
+"""
+GPU-accelerated image processing using OpenCV CUDA (cv2.cuda.GpuMat).
+
+Provides drop-in replacements for common cv2 functions.  When OpenCV is built
+with CUDA support the functions transparently upload → process → download via
+GpuMat; otherwise they fall back to the regular CPU path so the rest of the
+codebase never has to care whether CUDA is available.
+
+Usage
+-----
+    from modules.gpu_processing import (
+        gpu_gaussian_blur, gpu_sharpen, gpu_add_weighted,
+        gpu_resize, gpu_cvt_color, gpu_flip,
+        is_gpu_accelerated,
+    )
+"""
+
+from __future__ import annotations
+
+import cv2
+import numpy as np
+from typing import Tuple, Optional
+
+# ---------------------------------------------------------------------------
+# CUDA availability detection (evaluated once at import time)
+# ---------------------------------------------------------------------------
+CUDA_AVAILABLE: bool = False
+
+try:
+    # cv2.cuda.GpuMat is only present when OpenCV is compiled with CUDA
+    _test_mat = cv2.cuda.GpuMat()
+    # Verify we have the required filter / image-processing functions
+    _has_gauss = hasattr(cv2.cuda, "createGaussianFilter")
+    _has_resize = hasattr(cv2.cuda, "resize")
+    _has_cvt = hasattr(cv2.cuda, "cvtColor")
+    if _has_gauss and _has_resize and _has_cvt:
+        CUDA_AVAILABLE = True
+        print("[gpu_processing] OpenCV CUDA support detected – GPU-accelerated processing enabled.")
+    else:
+        missing = []
+        if not _has_gauss:
+            missing.append("createGaussianFilter")
+        if not _has_resize:
+            missing.append("resize")
+        if not _has_cvt:
+            missing.append("cvtColor")
+        print(f"[gpu_processing] cv2.cuda.GpuMat exists but missing: {', '.join(missing)} – falling back to CPU.")
+except Exception:
+    print("[gpu_processing] OpenCV CUDA not available – using CPU fallback for all operations.")
+
+
+# ---------------------------------------------------------------------------
+# Internal helpers
+# ---------------------------------------------------------------------------
+
+def _ensure_uint8(img: np.ndarray) -> np.ndarray:
+    """Clip and convert to uint8 if necessary."""
+    if img.dtype != np.uint8:
+        return np.clip(img, 0, 255).astype(np.uint8)
+    return img
+
+
+def _ksize_odd(ksize: Tuple[int, int]) -> Tuple[int, int]:
+    """Ensure kernel dimensions are positive and odd (required by GaussianBlur)."""
+    kw = max(1, ksize[0] // 2 * 2 + 1) if ksize[0] > 0 else 0
+    kh = max(1, ksize[1] // 2 * 2 + 1) if ksize[1] > 0 else 0
+    return (kw, kh)
+
+
+def _cv_type_for(img: np.ndarray) -> int:
+    """Return the OpenCV type constant matching *img* (uint8 only)."""
+    channels = 1 if img.ndim == 2 else img.shape[2]
+    if channels == 1:
+        return cv2.CV_8UC1
+    elif channels == 3:
+        return cv2.CV_8UC3
+    elif channels == 4:
+        return cv2.CV_8UC4
+    return cv2.CV_8UC3  # fallback
+
+
+# ---------------------------------------------------------------------------
+# Public API – Gaussian Blur
+# ---------------------------------------------------------------------------
+
+def gpu_gaussian_blur(
+    src: np.ndarray,
+    ksize: Tuple[int, int],
+    sigma_x: float,
+    sigma_y: float = 0,
+) -> np.ndarray:
+    """Drop-in replacement for ``cv2.GaussianBlur`` with CUDA acceleration.
+
+    Parameters match ``cv2.GaussianBlur(src, ksize, sigmaX, sigmaY)``.
+    When *ksize* is ``(0, 0)`` OpenCV computes the kernel size from *sigma_x*.
+    """
+    if CUDA_AVAILABLE:
+        try:
+            src_u8 = _ensure_uint8(src)
+            cv_type = _cv_type_for(src_u8)
+            ks = _ksize_odd(ksize) if ksize != (0, 0) else ksize
+
+            gauss = cv2.cuda.createGaussianFilter(cv_type, cv_type, ks, sigma_x, sigma_y)
+            gpu_src = cv2.cuda.GpuMat()
+            gpu_src.upload(src_u8)
+            gpu_dst = gauss.apply(gpu_src)
+            return gpu_dst.download()
+        except cv2.error:
+            pass
+
+    return cv2.GaussianBlur(src, ksize, sigma_x, sigmaY=sigma_y)
+
+
+# ---------------------------------------------------------------------------
+# Public API – addWeighted
+# ---------------------------------------------------------------------------
+
+def gpu_add_weighted(
+    src1: np.ndarray,
+    alpha: float,
+    src2: np.ndarray,
+    beta: float,
+    gamma: float,
+) -> np.ndarray:
+    """Drop-in replacement for ``cv2.addWeighted`` with CUDA acceleration."""
+    if CUDA_AVAILABLE:
+        try:
+            s1 = _ensure_uint8(src1)
+            s2 = _ensure_uint8(src2)
+            g1 = cv2.cuda.GpuMat()
+            g2 = cv2.cuda.GpuMat()
+            g1.upload(s1)
+            g2.upload(s2)
+            gpu_dst = cv2.cuda.addWeighted(g1, alpha, g2, beta, gamma)
+            return gpu_dst.download()
+        except cv2.error:
+            pass
+
+    return cv2.addWeighted(src1, alpha, src2, beta, gamma)
+
+
+# ---------------------------------------------------------------------------
+# Public API – Unsharp-mask sharpening
+# ---------------------------------------------------------------------------
+
+def gpu_sharpen(
+    src: np.ndarray,
+    strength: float,
+    sigma: float = 3,
+) -> np.ndarray:
+    """Unsharp-mask sharpening, optionally GPU-accelerated.
+
+    Equivalent to::
+
+        blurred = GaussianBlur(src, (0,0), sigma)
+        result  = addWeighted(src, 1+strength, blurred, -strength, 0)
+    """
+    if strength <= 0:
+        return src
+
+    if CUDA_AVAILABLE:
+        try:
+            src_u8 = _ensure_uint8(src)
+            cv_type = _cv_type_for(src_u8)
+
+            gauss = cv2.cuda.createGaussianFilter(cv_type, cv_type, (0, 0), sigma)
+            gpu_src = cv2.cuda.GpuMat()
+            gpu_src.upload(src_u8)
+            gpu_blurred = gauss.apply(gpu_src)
+            gpu_sharp = cv2.cuda.addWeighted(gpu_src, 1.0 + strength, gpu_blurred, -strength, 0)
+            result = gpu_sharp.download()
+            return np.clip(result, 0, 255).astype(np.uint8)
+        except cv2.error:
+            pass
+
+    blurred = cv2.GaussianBlur(src, (0, 0), sigma)
+    sharpened = cv2.addWeighted(src, 1.0 + strength, blurred, -strength, 0)
+    return np.clip(sharpened, 0, 255).astype(np.uint8)
+
+
+# ---------------------------------------------------------------------------
+# Public API – Resize
+# ---------------------------------------------------------------------------
+
+# Map common cv2 interpolation flags to their CUDA equivalents
+_INTERP_MAP = {
+    cv2.INTER_NEAREST: cv2.INTER_NEAREST,
+    cv2.INTER_LINEAR: cv2.INTER_LINEAR,
+    cv2.INTER_CUBIC: cv2.INTER_CUBIC,
+    cv2.INTER_AREA: cv2.INTER_AREA,
+    cv2.INTER_LANCZOS4: cv2.INTER_LANCZOS4,
+}
+
+
+def gpu_resize(
+    src: np.ndarray,
+    dsize: Tuple[int, int],
+    fx: float = 0,
+    fy: float = 0,
+    interpolation: int = cv2.INTER_LINEAR,
+) -> np.ndarray:
+    """Drop-in replacement for ``cv2.resize`` with CUDA acceleration.
+
+    Parameters match ``cv2.resize(src, dsize, fx=fx, fy=fy, interpolation=...)``.
+    """
+    if CUDA_AVAILABLE:
+        try:
+            src_u8 = _ensure_uint8(src)
+            gpu_src = cv2.cuda.GpuMat()
+            gpu_src.upload(src_u8)
+
+            interp = _INTERP_MAP.get(interpolation, cv2.INTER_LINEAR)
+
+            if dsize and dsize[0] > 0 and dsize[1] > 0:
+                gpu_dst = cv2.cuda.resize(gpu_src, dsize, interpolation=interp)
+            else:
+                gpu_dst = cv2.cuda.resize(gpu_src, (0, 0), fx=fx, fy=fy, interpolation=interp)
+
+            return gpu_dst.download()
+        except cv2.error:
+            pass
+
+    return cv2.resize(src, dsize, fx=fx, fy=fy, interpolation=interpolation)
+
+
+# ---------------------------------------------------------------------------
+# Public API – Color conversion
+# ---------------------------------------------------------------------------
+
+def gpu_cvt_color(
+    src: np.ndarray,
+    code: int,
+) -> np.ndarray:
+    """Drop-in replacement for ``cv2.cvtColor`` with CUDA acceleration.
+
+    Parameters match ``cv2.cvtColor(src, code)``.
+    """
+    if CUDA_AVAILABLE:
+        try:
+            src_u8 = _ensure_uint8(src)
+            gpu_src = cv2.cuda.GpuMat()
+            gpu_src.upload(src_u8)
+            gpu_dst = cv2.cuda.cvtColor(gpu_src, code)
+            return gpu_dst.download()
+        except cv2.error:
+            pass
+
+    return cv2.cvtColor(src, code)
+
+
+# ---------------------------------------------------------------------------
+# Public API – Flip
+# ---------------------------------------------------------------------------
+
+def gpu_flip(
+    src: np.ndarray,
+    flip_code: int,
+) -> np.ndarray:
+    """Drop-in replacement for ``cv2.flip`` with CUDA acceleration.
+
+    Parameters match ``cv2.flip(src, flipCode)``.
+    *flip_code*: 0 = vertical, 1 = horizontal, -1 = both.
+    """
+    if CUDA_AVAILABLE:
+        try:
+            src_u8 = _ensure_uint8(src)
+            gpu_src = cv2.cuda.GpuMat()
+            gpu_src.upload(src_u8)
+            gpu_dst = cv2.cuda.flip(gpu_src, flip_code)
+            return gpu_dst.download()
+        except cv2.error:
+            pass
+
+    return cv2.flip(src, flip_code)
+
+
+# ---------------------------------------------------------------------------
+# Convenience: check at runtime whether GPU path is active
+# ---------------------------------------------------------------------------
+
+def is_gpu_accelerated() -> bool:
+    """Return ``True`` when the CUDA path will be used."""
+    return CUDA_AVAILABLE
+
+# --- END OF FILE gpu_processing.py ---

modules/metadata.py

@@ -1,3 +1,3 @@
 name = 'Deep-Live-Cam'
-version = '1.8.1'
+version = '2.1'
-edition = 'GitHub Edition'
+edition = 'GitHub Edition'

modules/paths.py

@@ -0,0 +1,6 @@
+"""Shared path constants for the Deep-Live-Cam project."""
+
+import os
+
+ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+MODELS_DIR = os.path.join(ROOT_DIR, "models")

modules/predicter.py

@@ -3,6 +3,7 @@ import opennsfw2
 from PIL import Image
 import cv2  # Add OpenCV import
 import modules.globals  # Import globals to access the color correction toggle
+from modules.gpu_processing import gpu_cvt_color
 
 from modules.typing import Frame
 
@@ -14,7 +15,7 @@ model = None
 def predict_frame(target_frame: Frame) -> bool:
     # Convert the frame to RGB before processing if color correction is enabled
     if modules.globals.color_correction:
-        target_frame = cv2.cvtColor(target_frame, cv2.COLOR_BGR2RGB)
+        target_frame = gpu_cvt_color(target_frame, cv2.COLOR_BGR2RGB)
         
     image = Image.fromarray(target_frame)
     image = opennsfw2.preprocess_image(image, opennsfw2.Preprocessing.YAHOO)

modules/processors/frame/_onnx_enhancer.py

@@ -0,0 +1,145 @@
+"""Shared ONNX-based face enhancement utilities for GPEN-BFR models.
+
+Provides session creation, pre/post processing, and the core
+enhance-face-via-ONNX pipeline.
+"""
+
+import os
+import platform
+import threading
+from typing import Any
+
+import cv2
+import numpy as np
+import onnxruntime
+
+import modules.globals
+
+IS_APPLE_SILICON = platform.system() == "Darwin" and platform.machine() == "arm64"
+
+# Limit concurrent ONNX calls to avoid VRAM exhaustion on multi-face frames
+THREAD_SEMAPHORE = threading.Semaphore(min(max(1, (os.cpu_count() or 1)), 8))
+
+
+def create_onnx_session(model_path: str) -> onnxruntime.InferenceSession:
+    """Create an ONNX Runtime session using the configured execution providers."""
+    providers = modules.globals.execution_providers
+    session = onnxruntime.InferenceSession(model_path, providers=providers)
+    return session
+
+
+def warmup_session(session: onnxruntime.InferenceSession) -> None:
+    """Run a dummy inference pass to trigger JIT / compile caching."""
+    try:
+        input_feed = {
+            inp.name: np.zeros(
+                [d if isinstance(d, int) and d > 0 else 1 for d in inp.shape],
+                dtype=np.float32,
+            )
+            for inp in session.get_inputs()
+        }
+        session.run(None, input_feed)
+    except Exception as e:
+        print(f"ONNX enhancer warmup skipped (non-fatal): {e}")
+
+
+def preprocess_face(face_img: np.ndarray, input_size: int) -> np.ndarray:
+    """Resize, normalize, and convert a BGR face crop to ONNX input blob.
+
+    GPEN-BFR expects [1, 3, H, W] float32 in RGB, normalized to [-1, 1].
+    """
+    resized = cv2.resize(face_img, (input_size, input_size), interpolation=cv2.INTER_LINEAR)
+    rgb = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB)
+    blob = rgb.astype(np.float32) / 255.0 * 2.0 - 1.0
+    blob = np.transpose(blob, (2, 0, 1))[np.newaxis, ...]
+    return blob
+
+
+def postprocess_face(output: np.ndarray) -> np.ndarray:
+    """Convert ONNX output [1, 3, H, W] float32 back to BGR uint8 image."""
+    img = output[0].transpose(1, 2, 0)
+    img = ((img + 1.0) / 2.0 * 255.0)
+    img = np.clip(img, 0, 255).astype(np.uint8)
+    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+    return img
+
+
+def _get_face_affine(face: Any, input_size: int):
+    """Compute affine transform to align a face to GPEN input space.
+
+    Returns (M, inv_M) — forward and inverse affine matrices.
+    """
+    template = np.array([
+        [0.31556875, 0.4615741],
+        [0.68262291, 0.4615741],
+        [0.50009375, 0.6405054],
+        [0.34947187, 0.8246919],
+        [0.65343645, 0.8246919],
+    ], dtype=np.float32) * input_size
+
+    landmarks = None
+    if hasattr(face, "kps") and face.kps is not None:
+        landmarks = face.kps.astype(np.float32)
+    elif hasattr(face, "landmark_2d_106") and face.landmark_2d_106 is not None:
+        lm106 = face.landmark_2d_106
+        landmarks = np.array([
+            lm106[38],  # left eye
+            lm106[88],  # right eye
+            lm106[86],  # nose tip
+            lm106[52],  # left mouth
+            lm106[61],  # right mouth
+        ], dtype=np.float32)
+
+    if landmarks is None or len(landmarks) < 5:
+        return None, None
+
+    M = cv2.estimateAffinePartial2D(landmarks, template, method=cv2.LMEDS)[0]
+    if M is None:
+        return None, None
+    inv_M = cv2.invertAffineTransform(M)
+    return M, inv_M
+
+
+def enhance_face_onnx(
+    frame: np.ndarray,
+    face: Any,
+    session: onnxruntime.InferenceSession,
+    input_size: int,
+) -> np.ndarray:
+    """Enhance a single face in the frame using an ONNX face restoration model."""
+    M, inv_M = _get_face_affine(face, input_size)
+    if M is None:
+        return frame
+
+    face_crop = cv2.warpAffine(
+        frame, M, (input_size, input_size),
+        flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REPLICATE,
+    )
+
+    blob = preprocess_face(face_crop, input_size)
+    with THREAD_SEMAPHORE:
+        output = session.run(None, {session.get_inputs()[0].name: blob})[0]
+    enhanced = postprocess_face(output)
+
+    # Create mask for blending (feathered edges)
+    mask = np.ones((input_size, input_size), dtype=np.float32)
+    border = max(1, input_size // 16)
+    mask[:border, :] = np.linspace(0, 1, border)[:, np.newaxis]
+    mask[-border:, :] = np.linspace(1, 0, border)[:, np.newaxis]
+    mask[:, :border] = np.minimum(mask[:, :border], np.linspace(0, 1, border)[np.newaxis, :])
+    mask[:, -border:] = np.minimum(mask[:, -border:], np.linspace(1, 0, border)[np.newaxis, :])
+
+    h, w = frame.shape[:2]
+    warped_enhanced = cv2.warpAffine(
+        enhanced, inv_M, (w, h),
+        flags=cv2.INTER_LINEAR, borderValue=(0, 0, 0),
+    )
+    warped_mask = cv2.warpAffine(
+        mask, inv_M, (w, h),
+        flags=cv2.INTER_LINEAR, borderValue=0,
+    )
+
+    mask_3ch = warped_mask[:, :, np.newaxis]
+    result = (warped_enhanced.astype(np.float32) * mask_3ch +
+              frame.astype(np.float32) * (1.0 - mask_3ch))
+    return np.clip(result, 0, 255).astype(np.uint8)

modules/processors/frame/core.py

@@ -17,8 +17,17 @@ FRAME_PROCESSORS_INTERFACE = [
     'process_video'
 ]
 
+ALLOWED_PROCESSORS = {
+    'face_swapper',
+    'face_enhancer',
+    'face_enhancer_gpen256',
+    'face_enhancer_gpen512'
+}
 
 def load_frame_processor_module(frame_processor: str) -> Any:
+    if frame_processor not in ALLOWED_PROCESSORS:
+        print(f"Frame processor {frame_processor} is not allowed")
+        sys.exit()
     try:
         frame_processor_module = importlib.import_module(f'modules.processors.frame.{frame_processor}')
         for method_name in FRAME_PROCESSORS_INTERFACE:
@@ -67,13 +76,29 @@ def set_frame_processors_modules_from_ui(frame_processors: List[str]) -> None:
                  print(f"Warning: Error removing frame processor {frame_processor}: {e}")
 
 def multi_process_frame(source_path: str, temp_frame_paths: List[str], process_frames: Callable[[str, List[str], Any], None], progress: Any = None) -> None:
-    with ThreadPoolExecutor(max_workers=modules.globals.execution_threads) as executor:
+    """Process frames in parallel with optimized batching and memory management."""
-        futures = []
+    max_workers = modules.globals.execution_threads
-        for path in temp_frame_paths:
+    
-            future = executor.submit(process_frames, source_path, [path], progress)
+    # Determine optimal batch size based on available memory and thread count
-            futures.append(future)
+    # Process frames in batches to avoid memory overflow
-        for future in futures:
+    batch_size = max(1, min(32, len(temp_frame_paths) // max(1, max_workers)))
-            future.result()
+    
+    with ThreadPoolExecutor(max_workers=max_workers) as executor:
+        # Process in batches to manage memory better
+        for i in range(0, len(temp_frame_paths), batch_size):
+            batch = temp_frame_paths[i:i + batch_size]
+            futures = []
+            
+            for path in batch:
+                future = executor.submit(process_frames, source_path, [path], progress)
+                futures.append(future)
+            
+            # Wait for batch to complete before starting next batch
+            for future in futures:
+                try:
+                    future.result()
+                except Exception as e:
+                    print(f"Error processing frame: {e}")
 
 
 def process_video(source_path: str, frame_paths: list[str], process_frames: Callable[[str, List[str], Any], None]) -> None:

modules/processors/frame/face_enhancer.py

@@ -1,18 +1,20 @@
+# --- START OF FILE face_enhancer.py ---
+# Uses ONNX Runtime for GFPGAN face enhancement (no torch/gfpgan dependency)
+
 from typing import Any, List
 import cv2
 import threading
-import gfpgan
+import numpy as np
 import os
 
+import onnxruntime
+
 import modules.globals
 import modules.processors.frame.core
 from modules.core import update_status
-from modules.face_analyser import get_one_face
+from modules.face_analyser import get_one_face, get_many_faces
 from modules.typing import Frame, Face
-import platform
-import torch
 from modules.utilities import (
-    conditional_download,
     is_image,
     is_video,
 )
@@ -27,15 +29,29 @@ models_dir = os.path.join(
     os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
 )
 
+# Standard FFHQ 5-point face template for 512x512 resolution
+# Points: left_eye, right_eye, nose, left_mouth, right_mouth
+FFHQ_TEMPLATE_512 = np.array(
+    [
+        [192.98138, 239.94708],
+        [318.90277, 240.19366],
+        [256.63416, 314.01935],
+        [201.26117, 371.41043],
+        [313.08905, 371.15118],
+    ],
+    dtype=np.float32,
+)
+
 
 def pre_check() -> bool:
-    download_directory_path = models_dir
+    model_path = os.path.join(models_dir, "gfpgan-1024.onnx")
-    conditional_download(
+    if not os.path.exists(model_path):
-        download_directory_path,
+        update_status(
-        [
+            f"GFPGAN ONNX model not found at {model_path}. "
-            "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth"
+            "Please place gfpgan-1024.onnx in the models folder.",
-        ],
+            NAME,
-    )
+        )
+        return False
     return True
 
 
@@ -48,83 +64,309 @@ def pre_start() -> bool:
     return True
 
 
-TENSORRT_AVAILABLE = False
+def get_face_enhancer() -> onnxruntime.InferenceSession:
-try:
+    """
-    import torch_tensorrt
+    Initializes and returns the GFPGAN ONNX Runtime inference session,
-    TENSORRT_AVAILABLE = True
+    using the execution providers configured in modules.globals.
-except ImportError as im:
+    """
-    print(f"TensorRT is not available: {im}")
-    pass
-except Exception as e:
-    print(f"TensorRT is not available: {e}")
-    pass
-
-def get_face_enhancer() -> Any:
     global FACE_ENHANCER
 
     with THREAD_LOCK:
         if FACE_ENHANCER is None:
-            model_path = os.path.join(models_dir, "GFPGANv1.4.pth")
+            model_path = os.path.join(models_dir, "gfpgan-1024.onnx")
-            
-            selected_device = None
-            device_priority = []
 
-            if TENSORRT_AVAILABLE and torch.cuda.is_available():
+            if not os.path.exists(model_path):
-                selected_device = torch.device("cuda")
+                raise FileNotFoundError(
-                device_priority.append("TensorRT+CUDA")
+                    f"{NAME}: Model not found at {model_path}"
-            elif torch.cuda.is_available():
+                )
-                selected_device = torch.device("cuda")
+
-                device_priority.append("CUDA")
+            try:
-            elif torch.backends.mps.is_available() and platform.system() == "Darwin":
+                providers = modules.globals.execution_providers
-                selected_device = torch.device("mps")
+
-                device_priority.append("MPS")
+                session_options = onnxruntime.SessionOptions()
-            elif not torch.cuda.is_available():
+                session_options.graph_optimization_level = (
-                selected_device = torch.device("cpu")
+                    onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
-                device_priority.append("CPU")
+                )
-            
+
-            FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1, device=selected_device)
+                FACE_ENHANCER = onnxruntime.InferenceSession(
+                    model_path,
+                    sess_options=session_options,
+                    providers=providers,
+                )
+
+                input_info = FACE_ENHANCER.get_inputs()[0]
+                output_info = FACE_ENHANCER.get_outputs()[0]
+                active_providers = FACE_ENHANCER.get_providers()
+                print(
+                    f"{NAME}: GFPGAN ONNX model loaded successfully."
+                )
+                print(
+                    f"{NAME}: Input: {input_info.name}, "
+                    f"shape: {input_info.shape}, type: {input_info.type}"
+                )
+                print(
+                    f"{NAME}: Output: {output_info.name}, "
+                    f"shape: {output_info.shape}, type: {output_info.type}"
+                )
+                print(f"{NAME}: Active providers: {active_providers}")
+
+            except Exception as e:
+                print(f"{NAME}: Error loading GFPGAN ONNX model: {e}")
+                FACE_ENHANCER = None
+                raise RuntimeError(
+                    f"{NAME}: Failed to load GFPGAN ONNX model: {e}"
+                )
+
+    if FACE_ENHANCER is None:
+        raise RuntimeError(
+            f"{NAME}: Failed to initialize GFPGAN ONNX session. Check logs."
+        )
 
-            # for debug:
-            print(f"Selected device: {selected_device} and device priority: {device_priority}")
     return FACE_ENHANCER
 
 
+def _align_face(
+    frame: Frame, landmarks_5: np.ndarray, output_size: int
+) -> tuple:
+    """
+    Align and crop a face from the frame using 5-point landmarks and the
+    standard FFHQ template.
+
+    Returns:
+        (aligned_face, affine_matrix) or (None, None) on failure.
+    """
+    # Scale the 512-base template to the desired output size
+    scale = output_size / 512.0
+    template = FFHQ_TEMPLATE_512 * scale
+
+    # Estimate a similarity transform (4 DOF: rotation, scale, tx, ty)
+    affine_matrix, _ = cv2.estimateAffinePartial2D(
+        landmarks_5, template, method=cv2.LMEDS
+    )
+    if affine_matrix is None:
+        return None, None
+
+    # Warp the face to the aligned position
+    aligned_face = cv2.warpAffine(
+        frame,
+        affine_matrix,
+        (output_size, output_size),
+        borderMode=cv2.BORDER_CONSTANT,
+        borderValue=(135, 133, 132),
+    )
+
+    return aligned_face, affine_matrix
+
+
+def _paste_back(
+    frame: Frame,
+    enhanced_face: np.ndarray,
+    affine_matrix: np.ndarray,
+    output_size: int,
+) -> Frame:
+    """
+    Paste an enhanced (aligned) face back onto the original frame using the
+    inverse affine transform with feathered-edge blending.
+    """
+    h, w = frame.shape[:2]
+
+    # Inverse the affine warp
+    inv_matrix = cv2.invertAffineTransform(affine_matrix)
+    inv_restored = cv2.warpAffine(
+        enhanced_face,
+        inv_matrix,
+        (w, h),
+        borderMode=cv2.BORDER_CONSTANT,
+        borderValue=(0, 0, 0),
+    )
+
+    # Build a soft feathered mask in aligned space for edge blending
+    face_mask = np.ones((output_size, output_size), dtype=np.float32)
+
+    # Feather the border (5 % of the size on each edge)
+    border = max(1, int(output_size * 0.05))
+    ramp_up = np.linspace(0.0, 1.0, border, dtype=np.float32)
+    ramp_down = np.linspace(1.0, 0.0, border, dtype=np.float32)
+
+    # Top / bottom rows
+    face_mask[:border, :] *= ramp_up[:, None]
+    face_mask[-border:, :] *= ramp_down[:, None]
+    # Left / right columns
+    face_mask[:, :border] *= ramp_up[None, :]
+    face_mask[:, -border:] *= ramp_down[None, :]
+
+    # Expand to 3-channel
+    face_mask_3c = np.stack([face_mask] * 3, axis=-1)
+
+    # Warp mask back to original frame space
+    inv_mask = cv2.warpAffine(
+        face_mask_3c,
+        inv_matrix,
+        (w, h),
+        borderMode=cv2.BORDER_CONSTANT,
+        borderValue=(0, 0, 0),
+    )
+    inv_mask = np.clip(inv_mask, 0.0, 1.0)
+
+    # Alpha-blend
+    result = (
+        frame.astype(np.float32) * (1.0 - inv_mask)
+        + inv_restored.astype(np.float32) * inv_mask
+    )
+    return np.clip(result, 0, 255).astype(np.uint8)
+
+
+def _preprocess_face(aligned_face: np.ndarray) -> np.ndarray:
+    """
+    Convert an aligned BGR uint8 face image to the ONNX model input tensor.
+    Format: NCHW float32, normalised to [-1, 1].
+    """
+    # BGR -> RGB
+    rgb = cv2.cvtColor(aligned_face, cv2.COLOR_BGR2RGB).astype(np.float32)
+    # [0, 255] -> [0, 1] -> [-1, 1]
+    rgb = rgb / 255.0
+    rgb = (rgb - 0.5) / 0.5
+    # HWC -> CHW, add batch dim
+    chw = np.transpose(rgb, (2, 0, 1))
+    return np.expand_dims(chw, axis=0)  # shape: (1, 3, H, W)
+
+
+def _postprocess_face(output: np.ndarray) -> np.ndarray:
+    """
+    Convert the ONNX model output tensor back to a BGR uint8 image.
+    Expects input in NCHW format with values in [-1, 1].
+    """
+    face = np.squeeze(output)  # remove batch dim -> (3, H, W)
+    face = np.transpose(face, (1, 2, 0))  # CHW -> HWC
+    # [-1, 1] -> [0, 1] -> [0, 255]
+    face = (face + 1.0) / 2.0
+    face = np.clip(face * 255.0, 0, 255).astype(np.uint8)
+    # RGB -> BGR
+    return cv2.cvtColor(face, cv2.COLOR_RGB2BGR)
+
+
 def enhance_face(temp_frame: Frame) -> Frame:
-    with THREAD_SEMAPHORE:
+    """Enhances all faces in a frame using the GFPGAN ONNX model."""
-        _, _, temp_frame = get_face_enhancer().enhance(temp_frame, paste_back=True)
+    session = get_face_enhancer()
-    return temp_frame
+
+    # Determine model input resolution from the session metadata
+    input_info = session.get_inputs()[0]
+    input_name = input_info.name
+    input_shape = input_info.shape  # e.g. [1, 3, 512, 512]
+    # Safely extract input size (handle dynamic / symbolic dimensions)
+    try:
+        align_size = int(input_shape[2])
+        if align_size <= 0:
+            align_size = 512
+    except (ValueError, TypeError, IndexError):
+        align_size = 512
+
+    # Detect faces using InsightFace (already a project dependency)
+    faces = get_many_faces(temp_frame)
+    if not faces:
+        return temp_frame
+
+    result_frame = temp_frame.copy()
+
+    for face in faces:
+        # Need the 5-point key-points for alignment
+        if not hasattr(face, "kps") or face.kps is None:
+            continue
+
+        landmarks_5 = face.kps.astype(np.float32)
+        if landmarks_5.shape[0] < 5:
+            continue
+
+        # Align / crop the face at the model's INPUT resolution
+        aligned_face, affine_matrix = _align_face(
+            temp_frame, landmarks_5, output_size=align_size
+        )
+        if aligned_face is None or affine_matrix is None:
+            continue
+
+        try:
+            with THREAD_SEMAPHORE:
+                input_tensor = _preprocess_face(aligned_face)
+                output_tensor = session.run(None, {input_name: input_tensor})[0]
+                enhanced_bgr = _postprocess_face(output_tensor)
+
+            # The model may output at a different resolution than its input
+            # (e.g. input 512x512 → output 1024x1024).  Resize the enhanced
+            # face back to the alignment size so the inverse affine maps
+            # correctly.
+            eh, ew = enhanced_bgr.shape[:2]
+            if eh != align_size or ew != align_size:
+                enhanced_bgr = cv2.resize(
+                    enhanced_bgr,
+                    (align_size, align_size),
+                    interpolation=cv2.INTER_LANCZOS4,
+                )
+
+            # Paste enhanced face back onto the frame
+            result_frame = _paste_back(
+                result_frame, enhanced_bgr, affine_matrix, output_size=align_size
+            )
+        except Exception as e:
+            print(f"{NAME}: Error enhancing a face: {e}")
+            continue
+
+    return result_frame
 
 
-def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
+def process_frame(source_face: Face | None, temp_frame: Frame) -> Frame:
-    target_face = get_one_face(temp_frame)
+    """Processes a frame: enhances face if detected."""
-    if target_face:
+    temp_frame = enhance_face(temp_frame)
-        temp_frame = enhance_face(temp_frame)
     return temp_frame
 
 
 def process_frames(
-    source_path: str, temp_frame_paths: List[str], progress: Any = None
+    source_path: str | None, temp_frame_paths: List[str], progress: Any = None
 ) -> None:
+    """Processes multiple frames from file paths."""
     for temp_frame_path in temp_frame_paths:
+        if not os.path.exists(temp_frame_path):
+            print(
+                f"{NAME}: Warning: Frame path not found {temp_frame_path}, skipping."
+            )
+            if progress:
+                progress.update(1)
+            continue
+
         temp_frame = cv2.imread(temp_frame_path)
-        result = process_frame(None, temp_frame)
+        if temp_frame is None:
-        cv2.imwrite(temp_frame_path, result)
+            print(
+                f"{NAME}: Warning: Failed to read frame {temp_frame_path}, skipping."
+            )
+            if progress:
+                progress.update(1)
+            continue
+
+        result_frame = process_frame(None, temp_frame)
+        cv2.imwrite(temp_frame_path, result_frame)
         if progress:
             progress.update(1)
 
 
-def process_image(source_path: str, target_path: str, output_path: str) -> None:
+def process_image(
+    source_path: str | None, target_path: str, output_path: str
+) -> None:
+    """Processes a single image file."""
     target_frame = cv2.imread(target_path)
-    result = process_frame(None, target_frame)
+    if target_frame is None:
-    cv2.imwrite(output_path, result)
+        print(f"{NAME}: Error: Failed to read target image {target_path}")
+        return
+    result_frame = process_frame(None, target_frame)
+    cv2.imwrite(output_path, result_frame)
+    print(f"{NAME}: Enhanced image saved to {output_path}")
 
 
-def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
+def process_video(
-    modules.processors.frame.core.process_video(None, temp_frame_paths, process_frames)
+    source_path: str | None, temp_frame_paths: List[str]
+) -> None:
+    """Processes video frames using the frame processor core."""
+    modules.processors.frame.core.process_video(
+        source_path, temp_frame_paths, process_frames
+    )
 
 
-def process_frame_v2(temp_frame: Frame) -> Frame:
+# --- END OF FILE face_enhancer.py ---
-    target_face = get_one_face(temp_frame)
-    if target_face:
-        temp_frame = enhance_face(temp_frame)
-    return temp_frame

modules/processors/frame/face_enhancer_gpen256.py

@@ -0,0 +1,125 @@
+"""GPEN-BFR-256 face enhancer — ONNX-based face restoration at 256x256."""
+
+from typing import Any, List
+import os
+import threading
+
+import cv2
+import numpy as np
+
+import modules.globals
+import modules.processors.frame.core
+from modules.core import update_status
+from modules.face_analyser import get_one_face
+from modules.typing import Frame, Face
+from modules.utilities import (
+    is_image,
+    is_video,
+)
+from modules.processors.frame._onnx_enhancer import (
+    create_onnx_session,
+    warmup_session,
+    enhance_face_onnx,
+)
+
+NAME = "DLC.FACE-ENHANCER-GPEN256"
+INPUT_SIZE = 256
+MODEL_URL = "https://github.com/harisreedhar/Face-Upscalers-ONNX/releases/download/GPEN-BFR/GPEN-BFR-256.onnx"
+MODEL_FILE = "GPEN-BFR-256.onnx"
+
+ENHANCER = None
+THREAD_LOCK = threading.Lock()
+
+abs_dir = os.path.dirname(os.path.abspath(__file__))
+models_dir = os.path.join(
+    os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
+)
+
+
+def pre_check() -> bool:
+    model_path = os.path.join(models_dir, MODEL_FILE)
+    if not os.path.exists(model_path):
+        update_status(f"Downloading {MODEL_FILE}...", NAME)
+        from modules.utilities import conditional_download
+        conditional_download(models_dir, [MODEL_URL])
+    return True
+
+
+def pre_start() -> bool:
+    if not is_image(modules.globals.target_path) and not is_video(modules.globals.target_path):
+        update_status("Select an image or video for target path.", NAME)
+        return False
+    return True
+
+
+def get_enhancer() -> Any:
+    global ENHANCER
+    with THREAD_LOCK:
+        if ENHANCER is None:
+            model_path = os.path.join(models_dir, MODEL_FILE)
+            if not os.path.exists(model_path):
+                from modules.utilities import conditional_download
+                conditional_download(models_dir, [MODEL_URL])
+            if not os.path.exists(model_path):
+                raise FileNotFoundError(f"Model file not found: {model_path}")
+            print(f"{NAME}: Loading ONNX model from {model_path}")
+            ENHANCER = create_onnx_session(model_path)
+            warmup_session(ENHANCER)
+            print(f"{NAME}: Model loaded successfully.")
+    return ENHANCER
+
+
+def enhance_face(temp_frame: Frame, face: Face) -> Frame:
+    try:
+        session = get_enhancer()
+    except Exception as e:
+        print(f"{NAME}: {e}")
+        return temp_frame
+    try:
+        return enhance_face_onnx(temp_frame, face, session, INPUT_SIZE)
+    except Exception as e:
+        print(f"{NAME}: Error during face enhancement: {e}")
+        return temp_frame
+
+
+def process_frame(source_face: Face | None, temp_frame: Frame) -> Frame:
+    target_face = get_one_face(temp_frame)
+    if target_face is None:
+        return temp_frame
+    return enhance_face(temp_frame, target_face)
+
+
+def process_frame_v2(temp_frame: Frame) -> Frame:
+    target_face = get_one_face(temp_frame)
+    if target_face:
+        temp_frame = enhance_face(temp_frame, target_face)
+    return temp_frame
+
+
+def process_frames(
+    source_path: str | None, temp_frame_paths: List[str], progress: Any = None
+) -> None:
+    for temp_frame_path in temp_frame_paths:
+        temp_frame = cv2.imread(temp_frame_path)
+        if temp_frame is None:
+            if progress:
+                progress.update(1)
+            continue
+        result = process_frame(None, temp_frame)
+        cv2.imwrite(temp_frame_path, result)
+        if progress:
+            progress.update(1)
+
+
+def process_image(source_path: str | None, target_path: str, output_path: str) -> None:
+    target_frame = cv2.imread(target_path)
+    if target_frame is None:
+        print(f"{NAME}: Error: Failed to read target image {target_path}")
+        return
+    result_frame = process_frame(None, target_frame)
+    cv2.imwrite(output_path, result_frame)
+    print(f"{NAME}: Enhanced image saved to {output_path}")
+
+
+def process_video(source_path: str | None, temp_frame_paths: List[str]) -> None:
+    modules.processors.frame.core.process_video(source_path, temp_frame_paths, process_frames)

modules/processors/frame/face_enhancer_gpen512.py

@@ -0,0 +1,125 @@
+"""GPEN-BFR-512 face enhancer — ONNX-based face restoration at 512x512."""
+
+from typing import Any, List
+import os
+import threading
+
+import cv2
+import numpy as np
+
+import modules.globals
+import modules.processors.frame.core
+from modules.core import update_status
+from modules.face_analyser import get_one_face
+from modules.typing import Frame, Face
+from modules.utilities import (
+    is_image,
+    is_video,
+)
+from modules.processors.frame._onnx_enhancer import (
+    create_onnx_session,
+    warmup_session,
+    enhance_face_onnx,
+)
+
+NAME = "DLC.FACE-ENHANCER-GPEN512"
+INPUT_SIZE = 512
+MODEL_URL = "https://github.com/harisreedhar/Face-Upscalers-ONNX/releases/download/GPEN-BFR/GPEN-BFR-512.onnx"
+MODEL_FILE = "GPEN-BFR-512.onnx"
+
+ENHANCER = None
+THREAD_LOCK = threading.Lock()
+
+abs_dir = os.path.dirname(os.path.abspath(__file__))
+models_dir = os.path.join(
+    os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
+)
+
+
+def pre_check() -> bool:
+    model_path = os.path.join(models_dir, MODEL_FILE)
+    if not os.path.exists(model_path):
+        update_status(f"Downloading {MODEL_FILE}...", NAME)
+        from modules.utilities import conditional_download
+        conditional_download(models_dir, [MODEL_URL])
+    return True
+
+
+def pre_start() -> bool:
+    if not is_image(modules.globals.target_path) and not is_video(modules.globals.target_path):
+        update_status("Select an image or video for target path.", NAME)
+        return False
+    return True
+
+
+def get_enhancer() -> Any:
+    global ENHANCER
+    with THREAD_LOCK:
+        if ENHANCER is None:
+            model_path = os.path.join(models_dir, MODEL_FILE)
+            if not os.path.exists(model_path):
+                from modules.utilities import conditional_download
+                conditional_download(models_dir, [MODEL_URL])
+            if not os.path.exists(model_path):
+                raise FileNotFoundError(f"Model file not found: {model_path}")
+            print(f"{NAME}: Loading ONNX model from {model_path}")
+            ENHANCER = create_onnx_session(model_path)
+            warmup_session(ENHANCER)
+            print(f"{NAME}: Model loaded successfully.")
+    return ENHANCER
+
+
+def enhance_face(temp_frame: Frame, face: Face) -> Frame:
+    try:
+        session = get_enhancer()
+    except Exception as e:
+        print(f"{NAME}: {e}")
+        return temp_frame
+    try:
+        return enhance_face_onnx(temp_frame, face, session, INPUT_SIZE)
+    except Exception as e:
+        print(f"{NAME}: Error during face enhancement: {e}")
+        return temp_frame
+
+
+def process_frame(source_face: Face | None, temp_frame: Frame) -> Frame:
+    target_face = get_one_face(temp_frame)
+    if target_face is None:
+        return temp_frame
+    return enhance_face(temp_frame, target_face)
+
+
+def process_frame_v2(temp_frame: Frame) -> Frame:
+    target_face = get_one_face(temp_frame)
+    if target_face:
+        temp_frame = enhance_face(temp_frame, target_face)
+    return temp_frame
+
+
+def process_frames(
+    source_path: str | None, temp_frame_paths: List[str], progress: Any = None
+) -> None:
+    for temp_frame_path in temp_frame_paths:
+        temp_frame = cv2.imread(temp_frame_path)
+        if temp_frame is None:
+            if progress:
+                progress.update(1)
+            continue
+        result = process_frame(None, temp_frame)
+        cv2.imwrite(temp_frame_path, result)
+        if progress:
+            progress.update(1)
+
+
+def process_image(source_path: str | None, target_path: str, output_path: str) -> None:
+    target_frame = cv2.imread(target_path)
+    if target_frame is None:
+        print(f"{NAME}: Error: Failed to read target image {target_path}")
+        return
+    result_frame = process_frame(None, target_frame)
+    cv2.imwrite(output_path, result_frame)
+    print(f"{NAME}: Enhanced image saved to {output_path}")
+
+
+def process_video(source_path: str | None, temp_frame_paths: List[str]) -> None:
+    modules.processors.frame.core.process_video(source_path, temp_frame_paths, process_frames)

modules/processors/frame/face_masking.py

@@ -0,0 +1,577 @@
+import cv2
+import numpy as np
+from modules.typing import Face, Frame
+import modules.globals
+from modules.gpu_processing import gpu_gaussian_blur, gpu_resize, gpu_cvt_color
+
+def apply_color_transfer(source, target):
+    """
+    Apply color transfer from target to source image using LAB color space.
+    Uses float32 throughout for performance (sufficient precision for 8-bit images).
+    """
+    # Convert to float32 [0,1] range for proper LAB conversion
+    source_f32 = source.astype(np.float32) / 255.0
+    target_f32 = target.astype(np.float32) / 255.0
+
+    source_lab = cv2.cvtColor(source_f32, cv2.COLOR_BGR2LAB)
+    target_lab = cv2.cvtColor(target_f32, cv2.COLOR_BGR2LAB)
+
+    source_mean, source_std = cv2.meanStdDev(source_lab)
+    target_mean, target_std = cv2.meanStdDev(target_lab)
+
+    # Reshape mean and std to be broadcastable (already float64 from meanStdDev, cast to f32)
+    source_mean = source_mean.reshape(1, 1, 3).astype(np.float32)
+    source_std = np.maximum(source_std.reshape(1, 1, 3), 1e-6).astype(np.float32)
+    target_mean = target_mean.reshape(1, 1, 3).astype(np.float32)
+    target_std = target_std.reshape(1, 1, 3).astype(np.float32)
+
+    # Perform the color transfer in LAB space
+    result_lab = (source_lab - source_mean) * (target_std / source_std) + target_mean
+
+    # Convert back to BGR and uint8
+    result_bgr = cv2.cvtColor(result_lab, cv2.COLOR_LAB2BGR)
+    return np.clip(result_bgr * 255.0, 0, 255).astype(np.uint8)
+
+def create_face_mask(face: Face, frame: Frame) -> np.ndarray:
+    mask = np.zeros(frame.shape[:2], dtype=np.uint8)
+    landmarks = face.landmark_2d_106
+    if landmarks is not None:
+        # Convert landmarks to int32
+        landmarks = landmarks.astype(np.int32)
+
+        # Extract facial features
+        right_side_face = landmarks[0:16]
+        left_side_face = landmarks[17:32]
+        right_eye = landmarks[33:42]
+        right_eye_brow = landmarks[43:51]
+        left_eye = landmarks[87:96]
+        left_eye_brow = landmarks[97:105]
+
+        # Calculate padding
+        padding = int(
+            np.linalg.norm(right_side_face[0] - left_side_face[-1]) * 0.05
+        )  # 5% of face width
+
+        # Create a slightly larger convex hull for padding
+        face_outline = landmarks[0:33]
+        hull = cv2.convexHull(face_outline)
+        # Vectorized hull padding — expand each point outward from center
+        center = np.mean(face_outline, axis=0, dtype=np.float32)
+        hull_pts = hull.reshape(-1, 2).astype(np.float32)
+        directions = hull_pts - center
+        norms = np.linalg.norm(directions, axis=1, keepdims=True)
+        norms = np.maximum(norms, 1e-6)  # avoid division by zero
+        directions /= norms
+        hull_padded = (hull_pts + directions * padding).astype(np.int32)
+
+        # Fill the padded convex hull
+        cv2.fillConvexPoly(mask, hull_padded, 255)
+
+        # Smooth the mask edges (GPU-accelerated when available)
+        mask = gpu_gaussian_blur(mask, (5, 5), 3)
+
+    return mask
+
+def create_lower_mouth_mask(
+    face: Face, frame: Frame
+) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
+    mask = np.zeros(frame.shape[:2], dtype=np.uint8)
+    mouth_cutout = None
+    lower_lip_polygon = None
+    mouth_box = (0,0,0,0)
+
+    landmarks = face.landmark_2d_106
+    if landmarks is not None:
+        # Use outer mouth landmarks (52-71) to capture the full mouth area
+        lower_lip_order = list(range(52, 72))
+        
+        if max(lower_lip_order) >= landmarks.shape[0]:
+            return mask, mouth_cutout, mouth_box, lower_lip_polygon
+
+        lower_lip_landmarks = landmarks[lower_lip_order].astype(np.float32)
+
+        # Calculate the center of the landmarks
+        center = np.mean(lower_lip_landmarks, axis=0)
+
+        # Expand the landmarks outward using the mouth_mask_size
+        mouth_mask_size = getattr(modules.globals, "mouth_mask_size", 0.0) # 0-100 slider
+        expansion_factor = 1 + (mouth_mask_size / 100.0) * 2.5
+
+        # Expand with extra downward bias toward chin
+        offsets = lower_lip_landmarks - center
+        chin_bias = 1 + (mouth_mask_size / 100.0) * 1.5
+        scale_y = np.where(offsets[:, 1] > 0, expansion_factor * chin_bias, expansion_factor)
+        expanded_landmarks = lower_lip_landmarks.copy()
+        expanded_landmarks[:, 0] = center[0] + offsets[:, 0] * expansion_factor
+        expanded_landmarks[:, 1] = center[1] + offsets[:, 1] * scale_y
+
+        # Convert back to integer coordinates
+        expanded_landmarks = expanded_landmarks.astype(np.int32)
+
+        # Calculate bounding box for the expanded lower mouth
+        min_x, min_y = np.min(expanded_landmarks, axis=0)
+        max_x, max_y = np.max(expanded_landmarks, axis=0)
+
+        # Add some padding to the bounding box
+        padding = int((max_x - min_x) * 0.1)  # 10% padding
+        min_x = max(0, min_x - padding)
+        min_y = max(0, min_y - padding)
+        max_x = min(frame.shape[1], max_x + padding)
+        max_y = min(frame.shape[0], max_y + padding)
+
+        # Ensure the bounding box dimensions are valid
+        if max_x <= min_x or max_y <= min_y:
+            if (max_x - min_x) <= 1:
+                max_x = min_x + 1
+            if (max_y - min_y) <= 1:
+                max_y = min_y + 1
+
+        # Create the mask
+        mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8)
+        # Shift polygon coordinates relative to the ROI's top-left corner
+        polygon_relative_to_roi = expanded_landmarks - [min_x, min_y]
+        cv2.fillPoly(mask_roi, [polygon_relative_to_roi], 255)
+
+        # Apply Gaussian blur to soften the mask edges (GPU-accelerated when available)
+        mask_roi = gpu_gaussian_blur(mask_roi, (15, 15), 5)
+
+        # Place the mask ROI in the full-sized mask
+        mask[min_y:max_y, min_x:max_x] = mask_roi
+
+        # Extract the masked area from the frame
+        mouth_cutout = frame[min_y:max_y, min_x:max_x].copy()
+
+        # Return the expanded lower lip polygon in original frame coordinates
+        lower_lip_polygon = expanded_landmarks
+        mouth_box = (min_x, min_y, max_x, max_y)
+
+    return mask, mouth_cutout, mouth_box, lower_lip_polygon
+
+def create_eyes_mask(face: Face, frame: Frame) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
+    mask = np.zeros(frame.shape[:2], dtype=np.uint8)
+    eyes_cutout = None
+    landmarks = face.landmark_2d_106
+    if landmarks is not None:
+        # Left eye landmarks (87-96) and right eye landmarks (33-42)
+        left_eye = landmarks[87:96]
+        right_eye = landmarks[33:42]
+        
+        # Calculate centers and dimensions for each eye
+        left_eye_center = np.mean(left_eye, axis=0).astype(np.int32)
+        right_eye_center = np.mean(right_eye, axis=0).astype(np.int32)
+        
+        # Calculate eye dimensions with size adjustment
+        def get_eye_dimensions(eye_points):
+            x_coords = eye_points[:, 0]
+            y_coords = eye_points[:, 1]
+            width = int((np.max(x_coords) - np.min(x_coords)) * (1 + modules.globals.mask_down_size * modules.globals.eyes_mask_size))
+            height = int((np.max(y_coords) - np.min(y_coords)) * (1 + modules.globals.mask_down_size * modules.globals.eyes_mask_size))
+            return width, height
+        
+        left_width, left_height = get_eye_dimensions(left_eye)
+        right_width, right_height = get_eye_dimensions(right_eye)
+        
+        # Add extra padding
+        padding = int(max(left_width, right_width) * 0.2)
+        
+        # Calculate bounding box for both eyes
+        min_x = min(left_eye_center[0] - left_width//2, right_eye_center[0] - right_width//2) - padding
+        max_x = max(left_eye_center[0] + left_width//2, right_eye_center[0] + right_width//2) + padding
+        min_y = min(left_eye_center[1] - left_height//2, right_eye_center[1] - right_height//2) - padding
+        max_y = max(left_eye_center[1] + left_height//2, right_eye_center[1] + right_height//2) + padding
+        
+        # Ensure coordinates are within frame bounds
+        min_x = max(0, min_x)
+        min_y = max(0, min_y)
+        max_x = min(frame.shape[1], max_x)
+        max_y = min(frame.shape[0], max_y)
+        
+        # Create mask for the eyes region
+        mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8)
+        
+        # Draw ellipses for both eyes
+        left_center = (left_eye_center[0] - min_x, left_eye_center[1] - min_y)
+        right_center = (right_eye_center[0] - min_x, right_eye_center[1] - min_y)
+        
+        # Calculate axes lengths (half of width and height)
+        left_axes = (left_width//2, left_height//2)
+        right_axes = (right_width//2, right_height//2)
+        
+        # Draw filled ellipses
+        cv2.ellipse(mask_roi, left_center, left_axes, 0, 0, 360, 255, -1)
+        cv2.ellipse(mask_roi, right_center, right_axes, 0, 0, 360, 255, -1)
+        
+        # Apply Gaussian blur to soften mask edges (GPU-accelerated when available)
+        mask_roi = gpu_gaussian_blur(mask_roi, (15, 15), 5)
+        
+        # Place the mask ROI in the full-sized mask
+        mask[min_y:max_y, min_x:max_x] = mask_roi
+        
+        # Extract the masked area from the frame
+        eyes_cutout = frame[min_y:max_y, min_x:max_x].copy()
+        
+        # Create polygon points for visualization
+        def create_ellipse_points(center, axes):
+            t = np.linspace(0, 2*np.pi, 32)
+            x = center[0] + axes[0] * np.cos(t)
+            y = center[1] + axes[1] * np.sin(t)
+            return np.column_stack((x, y)).astype(np.int32)
+        
+        # Generate points for both ellipses
+        left_points = create_ellipse_points((left_eye_center[0], left_eye_center[1]), (left_width//2, left_height//2))
+        right_points = create_ellipse_points((right_eye_center[0], right_eye_center[1]), (right_width//2, right_height//2))
+        
+        # Combine points for both eyes
+        eyes_polygon = np.vstack([left_points, right_points])
+        
+    return mask, eyes_cutout, (min_x, min_y, max_x, max_y), eyes_polygon
+
+def create_curved_eyebrow(points):
+    if len(points) >= 5:
+        # Sort points by x-coordinate
+        sorted_idx = np.argsort(points[:, 0])
+        sorted_points = points[sorted_idx]
+        
+        # Calculate dimensions
+        x_min, y_min = np.min(sorted_points, axis=0)
+        x_max, y_max = np.max(sorted_points, axis=0)
+        width = x_max - x_min
+        height = y_max - y_min
+        
+        # Create more points for smoother curve
+        num_points = 50
+        x = np.linspace(x_min, x_max, num_points)
+        
+        # Fit quadratic curve through points for more natural arch
+        coeffs = np.polyfit(sorted_points[:, 0], sorted_points[:, 1], 2)
+        y = np.polyval(coeffs, x)
+        
+        # Increased offsets to create more separation
+        top_offset = height * 0.5  # Increased from 0.3 to shift up more
+        bottom_offset = height * 0.2  # Increased from 0.1 to shift down more
+        
+        # Create smooth curves
+        top_curve = y - top_offset
+        bottom_curve = y + bottom_offset
+        
+        # Create curved endpoints with more pronounced taper
+        end_points = 5
+        start_x = np.linspace(x[0] - width * 0.15, x[0], end_points)  # Increased taper
+        end_x = np.linspace(x[-1], x[-1] + width * 0.15, end_points)  # Increased taper
+        
+        # Create tapered ends
+        start_curve = np.column_stack((
+            start_x,
+            np.linspace(bottom_curve[0], top_curve[0], end_points)
+        ))
+        end_curve = np.column_stack((
+            end_x,
+            np.linspace(bottom_curve[-1], top_curve[-1], end_points)
+        ))
+        
+        # Combine all points to form a smooth contour
+        contour_points = np.vstack([
+            start_curve,
+            np.column_stack((x, top_curve)),
+            end_curve,
+            np.column_stack((x[::-1], bottom_curve[::-1]))
+        ])
+        
+        # Add slight padding for better coverage
+        center = np.mean(contour_points, axis=0)
+        vectors = contour_points - center
+        padded_points = center + vectors * 1.2  # Increased padding slightly
+        
+        return padded_points
+    return points
+
+def create_eyebrows_mask(face: Face, frame: Frame) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
+    mask = np.zeros(frame.shape[:2], dtype=np.uint8)
+    eyebrows_cutout = None
+    landmarks = face.landmark_2d_106
+    if landmarks is not None:
+        # Left eyebrow landmarks (97-105) and right eyebrow landmarks (43-51)
+        left_eyebrow = landmarks[97:105].astype(np.float32)
+        right_eyebrow = landmarks[43:51].astype(np.float32)
+        
+        # Calculate centers and dimensions for each eyebrow
+        left_center = np.mean(left_eyebrow, axis=0)
+        right_center = np.mean(right_eyebrow, axis=0)
+        
+        # Calculate bounding box with padding adjusted by size
+        all_points = np.vstack([left_eyebrow, right_eyebrow])
+        padding_factor = modules.globals.eyebrows_mask_size
+        min_x = np.min(all_points[:, 0]) - 25 * padding_factor
+        max_x = np.max(all_points[:, 0]) + 25 * padding_factor
+        min_y = np.min(all_points[:, 1]) - 20 * padding_factor
+        max_y = np.max(all_points[:, 1]) + 15 * padding_factor
+        
+        # Ensure coordinates are within frame bounds
+        min_x = max(0, int(min_x))
+        min_y = max(0, int(min_y))
+        max_x = min(frame.shape[1], int(max_x))
+        max_y = min(frame.shape[0], int(max_y))
+        
+        # Create mask for the eyebrows region
+        mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8)
+        
+        try:
+            # Convert points to local coordinates
+            left_local = left_eyebrow - [min_x, min_y]
+            right_local = right_eyebrow - [min_x, min_y]
+            
+            def create_curved_eyebrow(points):
+                if len(points) >= 5:
+                    # Sort points by x-coordinate
+                    sorted_idx = np.argsort(points[:, 0])
+                    sorted_points = points[sorted_idx]
+                    
+                    # Calculate dimensions
+                    x_min, y_min = np.min(sorted_points, axis=0)
+                    x_max, y_max = np.max(sorted_points, axis=0)
+                    width = x_max - x_min
+                    height = y_max - y_min
+                    
+                    # Create more points for smoother curve
+                    num_points = 50
+                    x = np.linspace(x_min, x_max, num_points)
+                    
+                    # Fit quadratic curve through points for more natural arch
+                    coeffs = np.polyfit(sorted_points[:, 0], sorted_points[:, 1], 2)
+                    y = np.polyval(coeffs, x)
+                    
+                    # Increased offsets to create more separation
+                    top_offset = height * 0.5  # Increased from 0.3 to shift up more
+                    bottom_offset = height * 0.2  # Increased from 0.1 to shift down more
+                    
+                    # Create smooth curves
+                    top_curve = y - top_offset
+                    bottom_curve = y + bottom_offset
+                    
+                    # Create curved endpoints with more pronounced taper
+                    end_points = 5
+                    start_x = np.linspace(x[0] - width * 0.15, x[0], end_points)  # Increased taper
+                    end_x = np.linspace(x[-1], x[-1] + width * 0.15, end_points)  # Increased taper
+                    
+                    # Create tapered ends
+                    start_curve = np.column_stack((
+                        start_x,
+                        np.linspace(bottom_curve[0], top_curve[0], end_points)
+                    ))
+                    end_curve = np.column_stack((
+                        end_x,
+                        np.linspace(bottom_curve[-1], top_curve[-1], end_points)
+                    ))
+                    
+                    # Combine all points to form a smooth contour
+                    contour_points = np.vstack([
+                        start_curve,
+                        np.column_stack((x, top_curve)),
+                        end_curve,
+                        np.column_stack((x[::-1], bottom_curve[::-1]))
+                    ])
+                    
+                    # Add slight padding for better coverage
+                    center = np.mean(contour_points, axis=0)
+                    vectors = contour_points - center
+                    padded_points = center + vectors * 1.2  # Increased padding slightly
+                    
+                    return padded_points
+                return points
+            
+            # Generate and draw eyebrow shapes
+            left_shape = create_curved_eyebrow(left_local)
+            right_shape = create_curved_eyebrow(right_local)
+            
+            # Apply multi-stage blurring for natural feathering (GPU-accelerated when available)
+            # First, strong Gaussian blur for initial softening
+            mask_roi = gpu_gaussian_blur(mask_roi, (21, 21), 7)
+            
+            # Second, medium blur for transition areas
+            mask_roi = gpu_gaussian_blur(mask_roi, (11, 11), 3)
+            
+            # Finally, light blur for fine details
+            mask_roi = gpu_gaussian_blur(mask_roi, (5, 5), 1)
+            
+            # Normalize mask values
+            mask_roi = cv2.normalize(mask_roi, None, 0, 255, cv2.NORM_MINMAX)
+            
+            # Place the mask ROI in the full-sized mask
+            mask[min_y:max_y, min_x:max_x] = mask_roi
+            
+            # Extract the masked area from the frame
+            eyebrows_cutout = frame[min_y:max_y, min_x:max_x].copy()
+            
+            # Combine points for visualization
+            eyebrows_polygon = np.vstack([
+                left_shape + [min_x, min_y],
+                right_shape + [min_x, min_y]
+            ]).astype(np.int32)
+            
+        except Exception as e:
+            # Fallback to simple polygons if curve fitting fails
+            left_local = left_eyebrow - [min_x, min_y]
+            right_local = right_eyebrow - [min_x, min_y]
+            cv2.fillPoly(mask_roi, [left_local.astype(np.int32)], 255)
+            cv2.fillPoly(mask_roi, [right_local.astype(np.int32)], 255)
+            mask_roi = gpu_gaussian_blur(mask_roi, (21, 21), 7)
+            mask[min_y:max_y, min_x:max_x] = mask_roi
+            eyebrows_cutout = frame[min_y:max_y, min_x:max_x].copy()
+            eyebrows_polygon = np.vstack([left_eyebrow, right_eyebrow]).astype(np.int32)
+        
+    return mask, eyebrows_cutout, (min_x, min_y, max_x, max_y), eyebrows_polygon
+
+def apply_mask_area(
+    frame: np.ndarray,
+    cutout: np.ndarray,
+    box: tuple,
+    face_mask: np.ndarray,
+    polygon: np.ndarray,
+) -> np.ndarray:
+    min_x, min_y, max_x, max_y = box
+    box_width = max_x - min_x
+    box_height = max_y - min_y
+
+    if (
+        cutout is None
+        or box_width is None
+        or box_height is None
+        or face_mask is None
+        or polygon is None
+    ):
+        return frame
+
+    try:
+        resized_cutout = gpu_resize(cutout, (box_width, box_height))
+        roi = frame[min_y:max_y, min_x:max_x]
+
+        if roi.shape != resized_cutout.shape:
+            resized_cutout = gpu_resize(
+                resized_cutout, (roi.shape[1], roi.shape[0])
+            )
+
+        color_corrected_area = apply_color_transfer(resized_cutout, roi)
+
+        # Create mask for the area
+        polygon_mask = np.zeros(roi.shape[:2], dtype=np.uint8)
+        
+        # Split points for left and right parts if needed
+        if len(polygon) > 50:  # Arbitrary threshold to detect if we have multiple parts
+            mid_point = len(polygon) // 2
+            left_points = polygon[:mid_point] - [min_x, min_y]
+            right_points = polygon[mid_point:] - [min_x, min_y]
+            cv2.fillPoly(polygon_mask, [left_points], 255)
+            cv2.fillPoly(polygon_mask, [right_points], 255)
+        else:
+            adjusted_polygon = polygon - [min_x, min_y]
+            cv2.fillPoly(polygon_mask, [adjusted_polygon], 255)
+
+        # Apply strong initial feathering (GPU-accelerated when available)
+        polygon_mask = gpu_gaussian_blur(polygon_mask, (21, 21), 7)
+
+        # Apply additional feathering
+        feather_amount = min(
+            30,
+            box_width // modules.globals.mask_feather_ratio,
+            box_height // modules.globals.mask_feather_ratio,
+        )
+        feathered_mask = cv2.GaussianBlur(
+            polygon_mask.astype(np.float32), (0, 0), feather_amount
+        )
+        max_val = feathered_mask.max()
+        if max_val > 1e-6:
+            feathered_mask *= np.float32(1.0 / max_val)
+
+        # Apply additional smoothing to the mask edges
+        feathered_mask = cv2.GaussianBlur(feathered_mask, (5, 5), 1)
+
+        face_mask_roi = face_mask[min_y:max_y, min_x:max_x]
+        combined_mask = feathered_mask * (face_mask_roi.astype(np.float32) * np.float32(1.0 / 255.0))
+
+        combined_mask_3ch = combined_mask[:, :, np.newaxis]
+        inv_mask = np.float32(1.0) - combined_mask_3ch
+        blended = (
+            color_corrected_area * combined_mask_3ch + roi * inv_mask
+        ).astype(np.uint8)
+
+        # Apply face mask to blended result
+        face_mask_f32 = face_mask_roi[:, :, np.newaxis].astype(np.float32) * np.float32(1.0 / 255.0)
+        face_mask_3channel = np.broadcast_to(face_mask_f32, blended.shape)
+        final_blend = blended * face_mask_3channel + roi * (np.float32(1.0) - face_mask_3channel)
+
+        frame[min_y:max_y, min_x:max_x] = final_blend.astype(np.uint8)
+    except Exception as e:
+        pass
+
+    return frame
+
+def draw_mask_visualization(
+    frame: Frame,
+    mask_data: tuple,
+    label: str,
+    draw_method: str = "polygon"
+) -> Frame:
+    mask, cutout, (min_x, min_y, max_x, max_y), polygon = mask_data
+
+    vis_frame = frame.copy()
+
+    # Ensure coordinates are within frame bounds
+    height, width = vis_frame.shape[:2]
+    min_x, min_y = max(0, min_x), max(0, min_y)
+    max_x, max_y = min(width, max_x), min(height, max_y)
+
+    if draw_method == "ellipse" and len(polygon) > 50:  # For eyes
+        # Split points for left and right parts
+        mid_point = len(polygon) // 2
+        left_points = polygon[:mid_point]
+        right_points = polygon[mid_point:]
+        
+        try:
+            # Fit ellipses to points - need at least 5 points
+            if len(left_points) >= 5 and len(right_points) >= 5:
+                # Convert points to the correct format for ellipse fitting
+                left_points = left_points.astype(np.float32)
+                right_points = right_points.astype(np.float32)
+                
+                # Fit ellipses
+                left_ellipse = cv2.fitEllipse(left_points)
+                right_ellipse = cv2.fitEllipse(right_points)
+                
+                # Draw the ellipses
+                cv2.ellipse(vis_frame, left_ellipse, (0, 255, 0), 2)
+                cv2.ellipse(vis_frame, right_ellipse, (0, 255, 0), 2)
+        except Exception as e:
+            # If ellipse fitting fails, draw simple rectangles as fallback
+            left_rect = cv2.boundingRect(left_points)
+            right_rect = cv2.boundingRect(right_points)
+            cv2.rectangle(vis_frame, 
+                        (left_rect[0], left_rect[1]), 
+                        (left_rect[0] + left_rect[2], left_rect[1] + left_rect[3]), 
+                        (0, 255, 0), 2)
+            cv2.rectangle(vis_frame,
+                        (right_rect[0], right_rect[1]),
+                        (right_rect[0] + right_rect[2], right_rect[1] + right_rect[3]),
+                        (0, 255, 0), 2)
+    else:  # For mouth and eyebrows
+        # Draw the polygon
+        if len(polygon) > 50:  # If we have multiple parts
+            mid_point = len(polygon) // 2
+            left_points = polygon[:mid_point]
+            right_points = polygon[mid_point:]
+            cv2.polylines(vis_frame, [left_points], True, (0, 255, 0), 2, cv2.LINE_AA)
+            cv2.polylines(vis_frame, [right_points], True, (0, 255, 0), 2, cv2.LINE_AA)
+        else:
+            cv2.polylines(vis_frame, [polygon], True, (0, 255, 0), 2, cv2.LINE_AA)
+
+    # Add label
+    cv2.putText(
+        vis_frame,
+        label,
+        (min_x, min_y - 10),
+        cv2.FONT_HERSHEY_SIMPLEX,
+        0.5,
+        (255, 255, 255),
+        1,
+    )
+
+    return vis_frame

modules/run.py

@@ -0,0 +1,9 @@
+#!/usr/bin/env python3
+
+# Import the tkinter fix to patch the ScreenChanged error
+import tkinter_fix
+
+import core
+
+if __name__ == '__main__':
+    core.run()

modules/tkinter_fix.py

@@ -0,0 +1,26 @@
+import tkinter
+
+# Only needs to be imported once at the beginning of the application
+def apply_patch():
+    # Create a monkey patch for the internal _tkinter module
+    original_init = tkinter.Tk.__init__
+    
+    def patched_init(self, *args, **kwargs):
+        # Call the original init
+        original_init(self, *args, **kwargs)
+        
+        # Define the missing ::tk::ScreenChanged procedure
+        self.tk.eval("""
+        if {[info commands ::tk::ScreenChanged] == ""} {
+            proc ::tk::ScreenChanged {args} {
+                # Do nothing
+                return
+            }
+        }
+        """)
+    
+    # Apply the monkey patch
+    tkinter.Tk.__init__ = patched_init
+
+# Apply the patch automatically when this module is imported
+apply_patch() 

modules/ui.py

@@ -3,14 +3,20 @@ import webbrowser
 import customtkinter as ctk
 from typing import Callable, Tuple
 import cv2
-from cv2_enumerate_cameras import enumerate_cameras  # Add this import
+from modules.gpu_processing import gpu_cvt_color, gpu_resize, gpu_flip
 from PIL import Image, ImageOps
 import time
 import json
+import queue
+import threading
+import numpy as np
+import requests
+import tempfile
 import modules.globals
 import modules.metadata
 from modules.face_analyser import (
     get_one_face,
+    get_many_faces,
     get_unique_faces_from_target_image,
     get_unique_faces_from_target_video,
     add_blank_map,
@@ -27,15 +33,40 @@ from modules.utilities import (
 )
 from modules.video_capture import VideoCapturer
 from modules.gettext import LanguageManager
+from modules.ui_tooltip import ToolTip
+from modules import globals
 import platform
 
 if platform.system() == "Windows":
     from pygrabber.dshow_graph import FilterGraph
 
+# --- Tk 9.0 compatibility patch ---
+# In Tk 9.0, Menu.index("end") returns "" instead of raising TclError
+# when the menu is empty. CustomTkinter's CTkOptionMenu doesn't handle
+# this, causing crashes. This patch adds the missing guard.
+try:
+    from customtkinter.windows.widgets.core_widget_classes import DropdownMenu as _DropdownMenu
+
+    _original_add_menu_commands = _DropdownMenu._add_menu_commands
+
+    def _patched_add_menu_commands(self, *args, **kwargs):
+        try:
+            end_index = self._menu.index("end")
+            if end_index == "" or end_index is None:
+                return
+        except Exception:
+            pass
+        _original_add_menu_commands(self, *args, **kwargs)
+
+    _DropdownMenu._add_menu_commands = _patched_add_menu_commands
+except (ImportError, AttributeError):
+    pass  # CustomTkinter version doesn't have this class path
+# --- End Tk 9.0 patch ---
+
 ROOT = None
 POPUP = None
 POPUP_LIVE = None
-ROOT_HEIGHT = 700
+ROOT_HEIGHT = 800
 ROOT_WIDTH = 600
 
 PREVIEW = None
@@ -97,6 +128,7 @@ def save_switch_states():
         "keep_frames": modules.globals.keep_frames,
         "many_faces": modules.globals.many_faces,
         "map_faces": modules.globals.map_faces,
+        "poisson_blend": modules.globals.poisson_blend,
         "color_correction": modules.globals.color_correction,
         "nsfw_filter": modules.globals.nsfw_filter,
         "live_mirror": modules.globals.live_mirror,
@@ -105,6 +137,7 @@ def save_switch_states():
         "show_fps": modules.globals.show_fps,
         "mouth_mask": modules.globals.mouth_mask,
         "show_mouth_mask_box": modules.globals.show_mouth_mask_box,
+        "mouth_mask_size": modules.globals.mouth_mask_size,
     }
     with open("switch_states.json", "w") as f:
         json.dump(switch_states, f)
@@ -119,16 +152,17 @@ def load_switch_states():
         modules.globals.keep_frames = switch_states.get("keep_frames", False)
         modules.globals.many_faces = switch_states.get("many_faces", False)
         modules.globals.map_faces = switch_states.get("map_faces", False)
+        modules.globals.poisson_blend = switch_states.get("poisson_blend", False)
         modules.globals.color_correction = switch_states.get("color_correction", False)
         modules.globals.nsfw_filter = switch_states.get("nsfw_filter", False)
         modules.globals.live_mirror = switch_states.get("live_mirror", False)
         modules.globals.live_resizable = switch_states.get("live_resizable", False)
         modules.globals.fp_ui = switch_states.get("fp_ui", {"face_enhancer": False})
         modules.globals.show_fps = switch_states.get("show_fps", False)
-        modules.globals.mouth_mask = switch_states.get("mouth_mask", False)
+        modules.globals.mouth_mask_size = switch_states.get("mouth_mask_size", 0.0)
-        modules.globals.show_mouth_mask_box = switch_states.get(
+        # mouth_mask is driven by the slider: on if size > 0, off if 0
-            "show_mouth_mask_box", False
+        modules.globals.mouth_mask = modules.globals.mouth_mask_size > 0
-        )
+        modules.globals.show_mouth_mask_box = False  # always start hidden
     except FileNotFoundError:
         # If the file doesn't exist, use default values
         pass
@@ -152,20 +186,28 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
     root.protocol("WM_DELETE_WINDOW", lambda: destroy())
 
     source_label = ctk.CTkLabel(root, text=None)
-    source_label.place(relx=0.1, rely=0.1, relwidth=0.3, relheight=0.25)
+    source_label.place(relx=0.1, rely=0.05, relwidth=0.275, relheight=0.225)
 
     target_label = ctk.CTkLabel(root, text=None)
-    target_label.place(relx=0.6, rely=0.1, relwidth=0.3, relheight=0.25)
+    target_label.place(relx=0.6, rely=0.05, relwidth=0.275, relheight=0.225)
 
     select_face_button = ctk.CTkButton(
         root, text=_("Select a face"), cursor="hand2", command=lambda: select_source_path()
     )
-    select_face_button.place(relx=0.1, rely=0.4, relwidth=0.3, relheight=0.1)
+    select_face_button.place(relx=0.1, rely=0.30, relwidth=0.24, relheight=0.1)
+    ToolTip(select_face_button, _("Choose the source face image to swap onto the target"))
+
+    random_face_button = ctk.CTkButton(
+        root, text="🔄", cursor="hand2", width=30, command=lambda: fetch_random_face()
+    )
+    random_face_button.place(relx=0.35, rely=0.30, relwidth=0.05, relheight=0.1)
+    ToolTip(random_face_button, _("Get a random face from thispersondoesnotexist.com"))
 
     swap_faces_button = ctk.CTkButton(
         root, text="↔", cursor="hand2", command=lambda: swap_faces_paths()
     )
-    swap_faces_button.place(relx=0.45, rely=0.4, relwidth=0.1, relheight=0.1)
+    swap_faces_button.place(relx=0.45, rely=0.30, relwidth=0.1, relheight=0.1)
+    ToolTip(swap_faces_button, _("Swap source and target images"))
 
     select_target_button = ctk.CTkButton(
         root,
@@ -173,7 +215,8 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
         cursor="hand2",
         command=lambda: select_target_path(),
     )
-    select_target_button.place(relx=0.6, rely=0.4, relwidth=0.3, relheight=0.1)
+    select_target_button.place(relx=0.6, rely=0.30, relwidth=0.3, relheight=0.1)
+    ToolTip(select_target_button, _("Choose the target image or video to apply face swap to"))
 
     keep_fps_value = ctk.BooleanVar(value=modules.globals.keep_fps)
     keep_fps_checkbox = ctk.CTkSwitch(
@@ -186,7 +229,8 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
             save_switch_states(),
         ),
     )
-    keep_fps_checkbox.place(relx=0.1, rely=0.6)
+    keep_fps_checkbox.place(relx=0.1, rely=0.42)
+    ToolTip(keep_fps_checkbox, _("Output video keeps the original frame rate"))
 
     keep_frames_value = ctk.BooleanVar(value=modules.globals.keep_frames)
     keep_frames_switch = ctk.CTkSwitch(
@@ -199,20 +243,8 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
             save_switch_states(),
         ),
     )
-    keep_frames_switch.place(relx=0.1, rely=0.65)
+    keep_frames_switch.place(relx=0.1, rely=0.47)
-
+    ToolTip(keep_frames_switch, _("Keep extracted frames on disk after processing"))
-    enhancer_value = ctk.BooleanVar(value=modules.globals.fp_ui["face_enhancer"])
-    enhancer_switch = ctk.CTkSwitch(
-        root,
-        text=_("Face Enhancer"),
-        variable=enhancer_value,
-        cursor="hand2",
-        command=lambda: (
-            update_tumbler("face_enhancer", enhancer_value.get()),
-            save_switch_states(),
-        ),
-    )
-    enhancer_switch.place(relx=0.1, rely=0.7)
 
     keep_audio_value = ctk.BooleanVar(value=modules.globals.keep_audio)
     keep_audio_switch = ctk.CTkSwitch(
@@ -225,7 +257,8 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
             save_switch_states(),
         ),
     )
-    keep_audio_switch.place(relx=0.6, rely=0.6)
+    keep_audio_switch.place(relx=0.6, rely=0.42)
+    ToolTip(keep_audio_switch, _("Copy audio track from the source video to output"))
 
     many_faces_value = ctk.BooleanVar(value=modules.globals.many_faces)
     many_faces_switch = ctk.CTkSwitch(
@@ -238,7 +271,8 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
             save_switch_states(),
         ),
     )
-    many_faces_switch.place(relx=0.6, rely=0.65)
+    many_faces_switch.place(relx=0.6, rely=0.47)
+    ToolTip(many_faces_switch, _("Swap every detected face, not just the primary one"))
 
     color_correction_value = ctk.BooleanVar(value=modules.globals.color_correction)
     color_correction_switch = ctk.CTkSwitch(
@@ -251,7 +285,8 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
             save_switch_states(),
         ),
     )
-    color_correction_switch.place(relx=0.6, rely=0.70)
+    color_correction_switch.place(relx=0.6, rely=0.57)
+    ToolTip(color_correction_switch, _("Fix blue/green color cast from some webcams"))
 
     #    nsfw_value = ctk.BooleanVar(value=modules.globals.nsfw_filter)
     #    nsfw_switch = ctk.CTkSwitch(root, text='NSFW filter', variable=nsfw_value, cursor='hand2', command=lambda: setattr(modules.globals, 'nsfw_filter', nsfw_value.get()))
@@ -269,7 +304,22 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
             close_mapper_window() if not map_faces.get() else None
         ),
     )
-    map_faces_switch.place(relx=0.1, rely=0.75)
+    map_faces_switch.place(relx=0.1, rely=0.52)
+    ToolTip(map_faces_switch, _("Manually assign which source face maps to which target face"))
+
+    poisson_blend_value = ctk.BooleanVar(value=modules.globals.poisson_blend)
+    poisson_blend_switch = ctk.CTkSwitch(
+        root,
+        text=_("Poisson Blend"),
+        variable=poisson_blend_value,
+        cursor="hand2",
+        command=lambda: (
+            setattr(modules.globals, "poisson_blend", poisson_blend_value.get()),
+            save_switch_states(),
+        ),
+    )
+    poisson_blend_switch.place(relx=0.1, rely=0.57)
+    ToolTip(poisson_blend_switch, _("Blend face edges smoothly using Poisson blending"))
 
     show_fps_value = ctk.BooleanVar(value=modules.globals.show_fps)
     show_fps_switch = ctk.CTkSwitch(
@@ -282,48 +332,34 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
             save_switch_states(),
         ),
     )
-    show_fps_switch.place(relx=0.6, rely=0.75)
+    show_fps_switch.place(relx=0.6, rely=0.52)
+    ToolTip(show_fps_switch, _("Display frames-per-second counter on the live preview"))
 
+    # mouth_mask and show_mouth_mask_box are auto-controlled by the Mouth Mask slider
     mouth_mask_var = ctk.BooleanVar(value=modules.globals.mouth_mask)
-    mouth_mask_switch = ctk.CTkSwitch(
-        root,
-        text=_("Mouth Mask"),
-        variable=mouth_mask_var,
-        cursor="hand2",
-        command=lambda: setattr(modules.globals, "mouth_mask", mouth_mask_var.get()),
-    )
-    mouth_mask_switch.place(relx=0.1, rely=0.55)
-
     show_mouth_mask_box_var = ctk.BooleanVar(value=modules.globals.show_mouth_mask_box)
-    show_mouth_mask_box_switch = ctk.CTkSwitch(
-        root,
-        text=_("Show Mouth Mask Box"),
-        variable=show_mouth_mask_box_var,
-        cursor="hand2",
-        command=lambda: setattr(
-            modules.globals, "show_mouth_mask_box", show_mouth_mask_box_var.get()
-        ),
-    )
-    show_mouth_mask_box_switch.place(relx=0.6, rely=0.55)
 
     start_button = ctk.CTkButton(
         root, text=_("Start"), cursor="hand2", command=lambda: analyze_target(start, root)
     )
-    start_button.place(relx=0.15, rely=0.80, relwidth=0.2, relheight=0.05)
+    start_button.place(relx=0.15, rely=0.78, relwidth=0.2, relheight=0.04)
+    ToolTip(start_button, _("Begin processing the target image/video with selected face"))
 
     stop_button = ctk.CTkButton(
         root, text=_("Destroy"), cursor="hand2", command=lambda: destroy()
     )
-    stop_button.place(relx=0.4, rely=0.80, relwidth=0.2, relheight=0.05)
+    stop_button.place(relx=0.4, rely=0.78, relwidth=0.2, relheight=0.04)
+    ToolTip(stop_button, _("Stop processing and close the application"))
 
     preview_button = ctk.CTkButton(
         root, text=_("Preview"), cursor="hand2", command=lambda: toggle_preview()
     )
-    preview_button.place(relx=0.65, rely=0.80, relwidth=0.2, relheight=0.05)
+    preview_button.place(relx=0.65, rely=0.78, relwidth=0.2, relheight=0.04)
+    ToolTip(preview_button, _("Show/hide a preview of the processed output"))
 
     # --- Camera Selection ---
     camera_label = ctk.CTkLabel(root, text=_("Select Camera:"))
-    camera_label.place(relx=0.1, rely=0.86, relwidth=0.2, relheight=0.05)
+    camera_label.place(relx=0.1, rely=0.83, relwidth=0.2, relheight=0.03)
 
     available_cameras = get_available_cameras()
     camera_indices, camera_names = available_cameras
@@ -342,7 +378,8 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
             root, variable=camera_variable, values=camera_names
         )
 
-    camera_optionmenu.place(relx=0.35, rely=0.86, relwidth=0.25, relheight=0.05)
+    camera_optionmenu.place(relx=0.35, rely=0.83, relwidth=0.25, relheight=0.03)
+    ToolTip(camera_optionmenu, _("Select which camera to use for live mode"))
 
     live_button = ctk.CTkButton(
         root,
@@ -362,16 +399,173 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
             else "disabled"
         ),
     )
-    live_button.place(relx=0.65, rely=0.86, relwidth=0.2, relheight=0.05)
+    live_button.place(relx=0.65, rely=0.83, relwidth=0.2, relheight=0.03)
+    ToolTip(live_button, _("Start real-time face swap using webcam"))
     # --- End Camera Selection ---
 
+    # --- Face Enhancer Dropdown ---
+    enhancer_options = ["None", "GFPGAN", "GPEN-512", "GPEN-256"]
+    enhancer_key_map = {
+        "None": None,
+        "GFPGAN": "face_enhancer",
+        "GPEN-512": "face_enhancer_gpen512",
+        "GPEN-256": "face_enhancer_gpen256",
+    }
+
+    # Determine initial value from current fp_ui state
+    initial_enhancer = "None"
+    if modules.globals.fp_ui.get("face_enhancer", False):
+        initial_enhancer = "GFPGAN"
+    elif modules.globals.fp_ui.get("face_enhancer_gpen512", False):
+        initial_enhancer = "GPEN-512"
+    elif modules.globals.fp_ui.get("face_enhancer_gpen256", False):
+        initial_enhancer = "GPEN-256"
+
+    enhancer_variable = ctk.StringVar(value=initial_enhancer)
+
+    def on_enhancer_change(choice: str):
+        # Disable all enhancers first
+        for key in ["face_enhancer", "face_enhancer_gpen256", "face_enhancer_gpen512"]:
+            update_tumbler(key, False)
+        # Enable the selected one
+        selected_key = enhancer_key_map.get(choice)
+        if selected_key:
+            update_tumbler(selected_key, True)
+        save_switch_states()
+
+    enhancer_label = ctk.CTkLabel(root, text="Face Enhancer:")
+    enhancer_label.place(relx=0.1, rely=0.62, relwidth=0.2, relheight=0.03)
+
+    enhancer_dropdown = ctk.CTkOptionMenu(
+        root,
+        variable=enhancer_variable,
+        values=enhancer_options,
+        command=on_enhancer_change,
+    )
+    enhancer_dropdown.place(relx=0.35, rely=0.62, relwidth=0.3, relheight=0.03)
+    ToolTip(enhancer_dropdown, _("Select a face enhancement model (None = no enhancement)"))
+
+    # 1) Define a DoubleVar for transparency (0 = fully transparent, 1 = fully opaque)
+    transparency_var = ctk.DoubleVar(value=1.0)
+
+    def on_transparency_change(value: float):
+        # Convert slider value to float
+        val = float(value)
+        modules.globals.opacity = val  # Set global opacity
+        percentage = int(val * 100)
+
+        if percentage == 0:
+            modules.globals.fp_ui["face_enhancer"] = False
+            update_status("Transparency set to 0% - Face swapping disabled.")
+        elif percentage == 100:
+            modules.globals.face_swapper_enabled = True
+            update_status("Transparency set to 100%.")
+        else:
+            modules.globals.face_swapper_enabled = True
+            update_status(f"Transparency set to {percentage}%")
+
+    # 2) Transparency label and slider
+    transparency_label = ctk.CTkLabel(root, text="Transparency:")
+    transparency_label.place(relx=0.15, rely=0.66, relwidth=0.2, relheight=0.03)
+
+    transparency_slider = ctk.CTkSlider(
+        root,
+        from_=0.0,
+        to=1.0,
+        variable=transparency_var,
+        command=on_transparency_change,
+        fg_color="#E0E0E0",
+        progress_color="#007BFF",
+        button_color="#FFFFFF",
+        button_hover_color="#CCCCCC",
+        height=5,
+        border_width=1,
+        corner_radius=3,
+    )
+    transparency_slider.place(relx=0.35, rely=0.67, relwidth=0.5, relheight=0.02)
+    ToolTip(transparency_slider, _("Blend between original and swapped face (0% = original, 100% = fully swapped)"))
+
+    # 3) Sharpness label & slider
+    sharpness_var = ctk.DoubleVar(value=0.0)  # start at 0.0
+    def on_sharpness_change(value: float):
+        modules.globals.sharpness = float(value)
+        update_status(f"Sharpness set to {value:.1f}")
+
+    sharpness_label = ctk.CTkLabel(root, text="Sharpness:")
+    sharpness_label.place(relx=0.15, rely=0.69, relwidth=0.2, relheight=0.03)
+
+    sharpness_slider = ctk.CTkSlider(
+        root,
+        from_=0.0,
+        to=5.0,
+        variable=sharpness_var,
+        command=on_sharpness_change,
+        fg_color="#E0E0E0",
+        progress_color="#007BFF",
+        button_color="#FFFFFF",
+        button_hover_color="#CCCCCC",
+        height=5,
+        border_width=1,
+        corner_radius=3,
+    )
+    sharpness_slider.place(relx=0.35, rely=0.70, relwidth=0.5, relheight=0.02)
+    ToolTip(sharpness_slider, _("Sharpen the enhanced face output"))
+
+    # 4) Mouth Mask Size slider
+    mouth_mask_size_var = ctk.DoubleVar(value=modules.globals.mouth_mask_size)
+
+    def on_mouth_mask_size_change(value: float):
+        val = float(value)
+        modules.globals.mouth_mask_size = val
+        # Auto-enable/disable mouth mask based on slider position
+        if val > 0:
+            modules.globals.mouth_mask = True
+            mouth_mask_var.set(True)
+        else:
+            modules.globals.mouth_mask = False
+            mouth_mask_var.set(False)
+            modules.globals.show_mouth_mask_box = False
+
+    def on_mouth_mask_slider_release(event):
+        # Hide bounding box when user releases the slider
+        modules.globals.show_mouth_mask_box = False
+
+    def on_mouth_mask_slider_press(event):
+        # Show bounding box while dragging
+        if modules.globals.mouth_mask_size > 0:
+            modules.globals.show_mouth_mask_box = True
+
+    mouth_mask_size_label = ctk.CTkLabel(root, text="Mouth Mask:")
+    mouth_mask_size_label.place(relx=0.15, rely=0.72, relwidth=0.2, relheight=0.03)
+
+    mouth_mask_size_slider = ctk.CTkSlider(
+        root,
+        from_=0.0,
+        to=100.0,
+        variable=mouth_mask_size_var,
+        command=on_mouth_mask_size_change,
+        fg_color="#E0E0E0",
+        progress_color="#007BFF",
+        button_color="#FFFFFF",
+        button_hover_color="#CCCCCC",
+        height=5,
+        border_width=1,
+        corner_radius=3,
+    )
+    mouth_mask_size_slider.place(relx=0.35, rely=0.73, relwidth=0.5, relheight=0.02)
+    mouth_mask_size_slider.bind("<ButtonPress-1>", on_mouth_mask_slider_press)
+    mouth_mask_size_slider.bind("<ButtonRelease-1>", on_mouth_mask_slider_release)
+    ToolTip(mouth_mask_size_slider, _("0 = use swapped mouth, 100 = expose original mouth to chin area"))
+
+    # Status and link at the bottom
+    global status_label
     status_label = ctk.CTkLabel(root, text=None, justify="center")
-    status_label.place(relx=0.1, rely=0.9, relwidth=0.8)
+    status_label.place(relx=0.1, rely=0.75, relwidth=0.8)
 
     donate_label = ctk.CTkLabel(
         root, text="Deep Live Cam", justify="center", cursor="hand2"
     )
-    donate_label.place(relx=0.1, rely=0.95, relwidth=0.8)
+    donate_label.place(relx=0.1, rely=0.87, relwidth=0.8)
     donate_label.configure(
         text_color=ctk.ThemeManager.theme.get("URL").get("text_color")
     )
@@ -381,6 +575,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
 
     return root
 
+
 def close_mapper_window():
     global POPUP, POPUP_LIVE
     if POPUP and POPUP.winfo_exists():
@@ -429,7 +624,7 @@ def create_source_target_popup(
             POPUP.destroy()
             select_output_path(start)
         else:
-            update_pop_status("At least 1 source with target is required!")
+            update_pop_status("Atleast 1 source with target is required!")
 
     scrollable_frame = ctk.CTkScrollableFrame(
         POPUP, width=POPUP_SCROLL_WIDTH, height=POPUP_SCROLL_HEIGHT
@@ -459,7 +654,7 @@ def create_source_target_popup(
         )
         x_label.grid(row=id, column=2, padx=10, pady=10)
 
-        image = Image.fromarray(cv2.cvtColor(item["target"]["cv2"], cv2.COLOR_BGR2RGB))
+        image = Image.fromarray(gpu_cvt_color(item["target"]["cv2"], cv2.COLOR_BGR2RGB))
         image = image.resize(
             (MAPPER_PREVIEW_MAX_WIDTH, MAPPER_PREVIEW_MAX_HEIGHT), Image.LANCZOS
         )
@@ -489,7 +684,7 @@ def update_popup_source(
     global source_label_dict
 
     source_path = ctk.filedialog.askopenfilename(
-        title=_("select a source image"),
+        title=_("select an source image"),
         initialdir=RECENT_DIRECTORY_SOURCE,
         filetypes=[img_ft],
     )
@@ -514,7 +709,7 @@ def update_popup_source(
             }
 
             image = Image.fromarray(
-                cv2.cvtColor(map[button_num]["source"]["cv2"], cv2.COLOR_BGR2RGB)
+                gpu_cvt_color(map[button_num]["source"]["cv2"], cv2.COLOR_BGR2RGB)
             )
             image = image.resize(
                 (MAPPER_PREVIEW_MAX_WIDTH, MAPPER_PREVIEW_MAX_HEIGHT), Image.LANCZOS
@@ -579,12 +774,32 @@ def update_tumbler(var: str, value: bool) -> None:
         )
 
 
+def fetch_random_face() -> None:
+    PREVIEW.withdraw()
+    try:
+        response = requests.get(
+            "https://thispersondoesnotexist.com/",
+            headers={"User-Agent": "Mozilla/5.0"},
+            timeout=10,
+        )
+        response.raise_for_status()
+        temp_dir = tempfile.gettempdir()
+        temp_path = os.path.join(temp_dir, "deep_live_cam_random_face.jpg")
+        with open(temp_path, "wb") as f:
+            f.write(response.content)
+        modules.globals.source_path = temp_path
+        image = render_image_preview(temp_path, (200, 200))
+        source_label.configure(image=image)
+    except Exception as e:
+        print(f"Failed to fetch random face: {e}")
+
+
 def select_source_path() -> None:
     global RECENT_DIRECTORY_SOURCE, img_ft, vid_ft
 
     PREVIEW.withdraw()
     source_path = ctk.filedialog.askopenfilename(
-        title=_("select a source image"),
+        title=_("select an source image"),
         initialdir=RECENT_DIRECTORY_SOURCE,
         filetypes=[img_ft],
     )
@@ -627,7 +842,7 @@ def select_target_path() -> None:
 
     PREVIEW.withdraw()
     target_path = ctk.filedialog.askopenfilename(
-        title=_("select a target image or video"),
+        title=_("select an target image or video"),
         initialdir=RECENT_DIRECTORY_TARGET,
         filetypes=[img_ft, vid_ft],
     )
@@ -696,22 +911,18 @@ def check_and_ignore_nsfw(target, destroy: Callable = None) -> bool:
 
 
 def fit_image_to_size(image, width: int, height: int):
-    if width is None or height is None or width <= 0 or height <= 0:
+    if width is None and height is None:
         return image
     h, w, _ = image.shape
     ratio_h = 0.0
     ratio_w = 0.0
-    ratio_w = width / w
+    if width > height:
-    ratio_h = height / h
+        ratio_h = height / h
-    # Use the smaller ratio to ensure the image fits within the given dimensions
+    else:
-    ratio = min(ratio_w, ratio_h)
+        ratio_w = width / w
-    
+    ratio = max(ratio_w, ratio_h)
-    # Compute new dimensions, ensuring they're at least 1 pixel
+    new_size = (int(ratio * w), int(ratio * h))
-    new_width = max(1, int(ratio * w))
+    return gpu_resize(image, dsize=new_size)
-    new_height = max(1, int(ratio * h))
-    new_size = (new_width, new_height)
-
-    return cv2.resize(image, dsize=new_size)
 
 
 def render_image_preview(image_path: str, size: Tuple[int, int]) -> ctk.CTkImage:
@@ -729,7 +940,7 @@ def render_video_preview(
         capture.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
     has_frame, frame = capture.read()
     if has_frame:
-        image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+        image = Image.fromarray(gpu_cvt_color(frame, cv2.COLOR_BGR2RGB))
         if size:
             image = ImageOps.fit(image, size, Image.LANCZOS)
         return ctk.CTkImage(image, size=image.size)
@@ -767,7 +978,7 @@ def update_preview(frame_number: int = 0) -> None:
             temp_frame = frame_processor.process_frame(
                 get_one_face(cv2.imread(modules.globals.source_path)), temp_frame
             )
-        image = Image.fromarray(cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB))
+        image = Image.fromarray(gpu_cvt_color(temp_frame, cv2.COLOR_BGR2RGB))
         image = ImageOps.contain(
             image, (PREVIEW_MAX_WIDTH, PREVIEW_MAX_HEIGHT), Image.LANCZOS
         )
@@ -838,21 +1049,13 @@ def get_available_cameras():
         camera_indices = []
         camera_names = []
 
-        if platform.system() == "Darwin":  # macOS specific handling
+        if platform.system() == "Darwin":
-            # Try to open the default FaceTime camera first
+            # Do NOT probe cameras with cv2.VideoCapture on macOS — probing
-            cap = cv2.VideoCapture(0)
+            # invalid indices triggers the OBSENSOR backend and causes SIGSEGV.
-            if cap.isOpened():
+            # Default to indices 0 and 1 (covers FaceTime + one USB camera).
-                camera_indices.append(0)
+            # The user can select the correct index from the UI dropdown.
-                camera_names.append("FaceTime Camera")
+            camera_indices = [0, 1]
-                cap.release()
+            camera_names = ["Camera 0", "Camera 1"]
-
-            # On macOS, additional cameras typically use indices 1 and 2
-            for i in [1, 2]:
-                cap = cv2.VideoCapture(i)
-                if cap.isOpened():
-                    camera_indices.append(i)
-                    camera_names.append(f"Camera {i}")
-                    cap.release()
         else:
             # Linux camera detection - test first 10 indices
             for i in range(10):
@@ -868,52 +1071,122 @@ def get_available_cameras():
         return camera_indices, camera_names
 
 
-def create_webcam_preview(camera_index: int):
+def _capture_thread_func(cap, capture_queue, stop_event):
-    global preview_label, PREVIEW
+    """Capture thread: reads frames from camera and puts them into the queue.
+    Drops frames when the queue is full to avoid backpressure on the camera."""
+    while not stop_event.is_set():
+        ret, frame = cap.read()
+        if not ret:
+            stop_event.set()
+            break
+        try:
+            capture_queue.put_nowait(frame)
+        except queue.Full:
+            # Drop the oldest frame and enqueue the new one
+            try:
+                capture_queue.get_nowait()
+            except queue.Empty:
+                pass
+            try:
+                capture_queue.put_nowait(frame)
+            except queue.Full:
+                pass
 
-    cap = VideoCapturer(camera_index)
-    if not cap.start(PREVIEW_DEFAULT_WIDTH, PREVIEW_DEFAULT_HEIGHT, 60):
-        update_status("Failed to start camera")
-        return
 
-    preview_label.configure(width=PREVIEW_DEFAULT_WIDTH, height=PREVIEW_DEFAULT_HEIGHT)
+def _detection_thread_func(latest_frame_holder, detection_result, detection_lock, stop_event):
-    PREVIEW.deiconify()
+    """Detection thread: continuously runs face detection on the latest
+    captured frame and stores results in detection_result under detection_lock.
 
+    This decouples face detection (~15-30ms) from face swapping (~5-10ms)
+    so the swap loop never blocks on detection, significantly improving
+    live mode FPS."""
+    while not stop_event.is_set():
+        with detection_lock:
+            frame = latest_frame_holder[0]
+
+        if frame is None:
+            time.sleep(0.005)
+            continue
+
+        if modules.globals.many_faces:
+            many = get_many_faces(frame)
+            with detection_lock:
+                detection_result['target_face'] = None
+                detection_result['many_faces'] = many
+        else:
+            face = get_one_face(frame)
+            with detection_lock:
+                detection_result['target_face'] = face
+                detection_result['many_faces'] = None
+
+
+def _processing_thread_func(capture_queue, processed_queue, stop_event,
+                             latest_frame_holder, detection_result, detection_lock):
+    """Processing thread: takes raw frames from capture_queue, reads the
+    latest detection result from the shared detection_result dict, applies
+    face swap/enhancement, and puts results into processed_queue.
+
+    Face detection runs concurrently in _detection_thread_func — this thread
+    only reads cached results so it never blocks on detection."""
     frame_processors = get_frame_processors_modules(modules.globals.frame_processors)
     source_image = None
+    last_source_path = None
     prev_time = time.time()
     fps_update_interval = 0.5
     frame_count = 0
     fps = 0
 
-    while True:
+    while not stop_event.is_set():
-        ret, frame = cap.read()
+        try:
-        if not ret:
+            frame = capture_queue.get(timeout=0.05)
-            break
+        except queue.Empty:
+            continue
 
-        temp_frame = frame.copy()
+        temp_frame = frame
 
         if modules.globals.live_mirror:
-            temp_frame = cv2.flip(temp_frame, 1)
+            temp_frame = gpu_flip(temp_frame, 1)
 
-        if modules.globals.live_resizable:
+        # Publish the mirrored frame for the detection thread to pick up
-            temp_frame = fit_image_to_size(
+        with detection_lock:
-                temp_frame, PREVIEW.winfo_width(), PREVIEW.winfo_height()
+            latest_frame_holder[0] = temp_frame
-            )
-
-        else:
-            temp_frame = fit_image_to_size(
-                temp_frame, PREVIEW.winfo_width(), PREVIEW.winfo_height()
-            )
 
         if not modules.globals.map_faces:
-            if source_image is None and modules.globals.source_path:
+            if modules.globals.source_path and modules.globals.source_path != last_source_path:
+                last_source_path = modules.globals.source_path
                 source_image = get_one_face(cv2.imread(modules.globals.source_path))
 
+            # Read latest detection results (brief lock to avoid blocking detection thread)
+            with detection_lock:
+                cached_target_face = detection_result.get('target_face')
+                cached_many_faces = detection_result.get('many_faces')
+
             for frame_processor in frame_processors:
                 if frame_processor.NAME == "DLC.FACE-ENHANCER":
                     if modules.globals.fp_ui["face_enhancer"]:
                         temp_frame = frame_processor.process_frame(None, temp_frame)
+                elif frame_processor.NAME == "DLC.FACE-ENHANCER-GPEN256":
+                    if modules.globals.fp_ui.get("face_enhancer_gpen256", False):
+                        temp_frame = frame_processor.process_frame(None, temp_frame)
+                elif frame_processor.NAME == "DLC.FACE-ENHANCER-GPEN512":
+                    if modules.globals.fp_ui.get("face_enhancer_gpen512", False):
+                        temp_frame = frame_processor.process_frame(None, temp_frame)
+                elif frame_processor.NAME == "DLC.FACE-SWAPPER":
+                    # Use cached face positions from detection thread
+                    swapped_bboxes = []
+                    if modules.globals.many_faces and cached_many_faces:
+                        result = temp_frame.copy()
+                        for t_face in cached_many_faces:
+                            result = frame_processor.swap_face(source_image, t_face, result)
+                            if hasattr(t_face, 'bbox') and t_face.bbox is not None:
+                                swapped_bboxes.append(t_face.bbox.astype(int))
+                        temp_frame = result
+                    elif cached_target_face is not None:
+                        temp_frame = frame_processor.swap_face(source_image, cached_target_face, temp_frame)
+                        if hasattr(cached_target_face, 'bbox') and cached_target_face.bbox is not None:
+                            swapped_bboxes.append(cached_target_face.bbox.astype(int))
+                    # Apply post-processing (sharpening, interpolation)
+                    temp_frame = frame_processor.apply_post_processing(temp_frame, swapped_bboxes)
                 else:
                     temp_frame = frame_processor.process_frame(source_image, temp_frame)
         else:
@@ -922,6 +1195,10 @@ def create_webcam_preview(camera_index: int):
                 if frame_processor.NAME == "DLC.FACE-ENHANCER":
                     if modules.globals.fp_ui["face_enhancer"]:
                         temp_frame = frame_processor.process_frame_v2(temp_frame)
+                elif frame_processor.NAME in ("DLC.FACE-ENHANCER-GPEN256", "DLC.FACE-ENHANCER-GPEN512"):
+                    fp_key = frame_processor.NAME.split(".")[-1].lower().replace("-", "_")
+                    if modules.globals.fp_ui.get(fp_key, False):
+                        temp_frame = frame_processor.process_frame_v2(temp_frame)
                 else:
                     temp_frame = frame_processor.process_frame_v2(temp_frame)
 
@@ -944,20 +1221,114 @@ def create_webcam_preview(camera_index: int):
                 2,
             )
 
-        image = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
+        # Put processed frame into output queue, dropping old frames if full
+        try:
+            processed_queue.put_nowait(temp_frame)
+        except queue.Full:
+            try:
+                processed_queue.get_nowait()
+            except queue.Empty:
+                pass
+            try:
+                processed_queue.put_nowait(temp_frame)
+            except queue.Full:
+                pass
+
+
+def create_webcam_preview(camera_index: int):
+    global preview_label, PREVIEW
+
+    cap = VideoCapturer(camera_index)
+    if not cap.start(PREVIEW_DEFAULT_WIDTH, PREVIEW_DEFAULT_HEIGHT, 60):
+        update_status("Failed to start camera")
+        return
+
+    preview_label.configure(width=PREVIEW_DEFAULT_WIDTH, height=PREVIEW_DEFAULT_HEIGHT)
+    PREVIEW.deiconify()
+
+    # Queues for decoupling capture from processing and processing from display.
+    # Small maxsize ensures we always work on recent frames and drop stale ones.
+    capture_queue = queue.Queue(maxsize=2)
+    processed_queue = queue.Queue(maxsize=2)
+    stop_event = threading.Event()
+
+    # Shared state for the detection pipeline.
+    # latest_frame_holder[0] is the most recent raw frame for the detection
+    # thread; detection_result holds the last detected faces for the
+    # processing thread to read.  Both are guarded by detection_lock.
+    detection_lock = threading.Lock()
+    latest_frame_holder = [None]
+    detection_result = {'target_face': None, 'many_faces': None}
+
+    # Start capture thread
+    cap_thread = threading.Thread(
+        target=_capture_thread_func,
+        args=(cap, capture_queue, stop_event),
+        daemon=True,
+    )
+    cap_thread.start()
+
+    # Start detection thread — runs face detection asynchronously so the
+    # processing/swap thread never blocks on it
+    det_thread = threading.Thread(
+        target=_detection_thread_func,
+        args=(latest_frame_holder, detection_result, detection_lock, stop_event),
+        daemon=True,
+    )
+    det_thread.start()
+
+    # Start processing thread
+    proc_thread = threading.Thread(
+        target=_processing_thread_func,
+        args=(capture_queue, processed_queue, stop_event,
+              latest_frame_holder, detection_result, detection_lock),
+        daemon=True,
+    )
+    proc_thread.start()
+
+    # Cleanup helper called from the display loop when preview closes
+    def _cleanup():
+        stop_event.set()
+        cap_thread.join(timeout=2.0)
+        det_thread.join(timeout=2.0)
+        proc_thread.join(timeout=2.0)
+        cap.release()
+        PREVIEW.withdraw()
+
+    # Non-blocking display loop using ROOT.after() — avoids blocking the
+    # Tk event loop which could cause UI freezes or re-entrancy issues
+    def _display_next_frame():
+        if stop_event.is_set() or PREVIEW.state() == "withdrawn":
+            _cleanup()
+            return
+
+        try:
+            temp_frame = processed_queue.get_nowait()
+        except queue.Empty:
+            ROOT.after(16, _display_next_frame)
+            return
+
+        if modules.globals.live_resizable:
+            temp_frame = fit_image_to_size(
+                temp_frame, PREVIEW.winfo_width(), PREVIEW.winfo_height()
+            )
+        else:
+            temp_frame = fit_image_to_size(
+                temp_frame, PREVIEW.winfo_width(), PREVIEW.winfo_height()
+            )
+
+        image = gpu_cvt_color(temp_frame, cv2.COLOR_BGR2RGB)
         image = Image.fromarray(image)
         image = ImageOps.contain(
             image, (temp_frame.shape[1], temp_frame.shape[0]), Image.LANCZOS
         )
         image = ctk.CTkImage(image, size=image.size)
         preview_label.configure(image=image)
-        ROOT.update()
 
-        if PREVIEW.state() == "withdrawn":
+        ROOT.after(16, _display_next_frame)
-            break
 
-    cap.release()
+    # Kick off the non-blocking display loop
-    PREVIEW.withdraw()
+    ROOT.after(0, _display_next_frame)
 
 
 def create_source_target_popup_for_webcam(
@@ -1067,7 +1438,7 @@ def refresh_data(map: list):
 
         if "source" in item:
             image = Image.fromarray(
-                cv2.cvtColor(item["source"]["cv2"], cv2.COLOR_BGR2RGB)
+                gpu_cvt_color(item["source"]["cv2"], cv2.COLOR_BGR2RGB)
             )
             image = image.resize(
                 (MAPPER_PREVIEW_MAX_WIDTH, MAPPER_PREVIEW_MAX_HEIGHT), Image.LANCZOS
@@ -1085,7 +1456,7 @@ def refresh_data(map: list):
 
         if "target" in item:
             image = Image.fromarray(
-                cv2.cvtColor(item["target"]["cv2"], cv2.COLOR_BGR2RGB)
+                gpu_cvt_color(item["target"]["cv2"], cv2.COLOR_BGR2RGB)
             )
             image = image.resize(
                 (MAPPER_PREVIEW_MAX_WIDTH, MAPPER_PREVIEW_MAX_HEIGHT), Image.LANCZOS
@@ -1108,7 +1479,7 @@ def update_webcam_source(
     global source_label_dict_live
 
     source_path = ctk.filedialog.askopenfilename(
-        title=_("select a source image"),
+        title=_("select an source image"),
         initialdir=RECENT_DIRECTORY_SOURCE,
         filetypes=[img_ft],
     )
@@ -1133,7 +1504,7 @@ def update_webcam_source(
             }
 
             image = Image.fromarray(
-                cv2.cvtColor(map[button_num]["source"]["cv2"], cv2.COLOR_BGR2RGB)
+                gpu_cvt_color(map[button_num]["source"]["cv2"], cv2.COLOR_BGR2RGB)
             )
             image = image.resize(
                 (MAPPER_PREVIEW_MAX_WIDTH, MAPPER_PREVIEW_MAX_HEIGHT), Image.LANCZOS
@@ -1160,7 +1531,7 @@ def update_webcam_target(
     global target_label_dict_live
 
     target_path = ctk.filedialog.askopenfilename(
-        title=_("select a target image"),
+        title=_("select an target image"),
         initialdir=RECENT_DIRECTORY_SOURCE,
         filetypes=[img_ft],
     )
@@ -1185,7 +1556,7 @@ def update_webcam_target(
             }
 
             image = Image.fromarray(
-                cv2.cvtColor(map[button_num]["target"]["cv2"], cv2.COLOR_BGR2RGB)
+                gpu_cvt_color(map[button_num]["target"]["cv2"], cv2.COLOR_BGR2RGB)
             )
             image = image.resize(
                 (MAPPER_PREVIEW_MAX_WIDTH, MAPPER_PREVIEW_MAX_HEIGHT), Image.LANCZOS
@@ -1203,4 +1574,4 @@ def update_webcam_target(
             target_label_dict_live[button_num] = target_image
         else:
             update_pop_live_status("Face could not be detected in last upload!")
-        return map
+        return map

modules/ui_tooltip.py

@@ -0,0 +1,74 @@
+"""Lightweight hover tooltip for CustomTkinter widgets."""
+
+import customtkinter as ctk
+
+
+class ToolTip:
+    """Show a floating tooltip popup when the user hovers over a widget.
+
+    Usage:
+        ToolTip(my_button, "Helpful description text")
+    """
+
+    def __init__(self, widget: ctk.CTkBaseClass, text: str, delay: int = 500):
+        self._widget = widget
+        self._text = text
+        self._delay = delay
+        self._tooltip_window = None
+        self._after_id = None
+
+        widget.bind("<Enter>", self._schedule_show, add="+")
+        widget.bind("<Leave>", self._hide, add="+")
+
+    def _schedule_show(self, event=None):
+        self._cancel()
+        self._after_id = self._widget.after(self._delay, self._show)
+
+    def _show(self):
+        if self._tooltip_window is not None:
+            return
+
+        x = self._widget.winfo_rootx() + 20
+        y = self._widget.winfo_rooty() + self._widget.winfo_height() + 5
+
+        self._tooltip_window = tw = ctk.CTkToplevel(self._widget)
+        tw.withdraw()
+        tw.overrideredirect(True)
+
+        label = ctk.CTkLabel(
+            tw,
+            text=self._text,
+            fg_color="#333333",
+            text_color="#EEEEEE",
+            corner_radius=6,
+            padx=8,
+            pady=4,
+        )
+        label.pack()
+
+        tw.update_idletasks()
+
+        # Clamp to screen bounds
+        screen_w = tw.winfo_screenwidth()
+        screen_h = tw.winfo_screenheight()
+        tip_w = tw.winfo_reqwidth()
+        tip_h = tw.winfo_reqheight()
+
+        if x + tip_w > screen_w:
+            x = screen_w - tip_w - 5
+        if y + tip_h > screen_h:
+            y = self._widget.winfo_rooty() - tip_h - 5
+
+        tw.geometry(f"+{x}+{y}")
+        tw.deiconify()
+
+    def _hide(self, event=None):
+        self._cancel()
+        if self._tooltip_window is not None:
+            self._tooltip_window.destroy()
+            self._tooltip_window = None
+
+    def _cancel(self):
+        if self._after_id is not None:
+            self._widget.after_cancel(self._after_id)
+            self._after_id = None

modules/utilities.py

@@ -15,19 +15,16 @@ import modules.globals
 TEMP_FILE = "temp.mp4"
 TEMP_DIRECTORY = "temp"
 
-# monkey patch ssl for mac
-if platform.system().lower() == "darwin":
-    ssl._create_default_https_context = ssl._create_unverified_context
-
 
 def run_ffmpeg(args: List[str]) -> bool:
+    """Run ffmpeg with hardware acceleration and optimized settings."""
     commands = [
         "ffmpeg",
         "-hide_banner",
-        "-hwaccel",
+        "-hwaccel", "auto",  # Auto-detect hardware acceleration
-        "auto",
+        "-hwaccel_output_format", "auto",  # Use hardware format when possible
-        "-loglevel",
+        "-threads", str(modules.globals.execution_threads or 0),  # 0 = auto-detect optimal thread count
-        modules.globals.log_level,
+        "-loglevel", modules.globals.log_level,
     ]
     commands.extend(args)
     try:
@@ -61,39 +58,131 @@ def detect_fps(target_path: str) -> float:
 
 
 def extract_frames(target_path: str) -> None:
+    """Extract frames with hardware acceleration and optimized settings."""
     temp_directory_path = get_temp_directory_path(target_path)
+    
+    # Use hardware-accelerated decoding and optimized pixel format
     run_ffmpeg(
         [
-            "-i",
+            "-i", target_path,
-            target_path,
+            "-vf", "format=rgb24",  # Use video filter for format conversion (faster)
-            "-pix_fmt",
+            "-vsync", "0",  # Prevent frame duplication
-            "rgb24",
+            "-frame_pts", "1",  # Preserve frame timing
             os.path.join(temp_directory_path, "%04d.png"),
         ]
     )
 
 
 def create_video(target_path: str, fps: float = 30.0) -> None:
+    """Create video with hardware-accelerated encoding and optimized settings."""
     temp_output_path = get_temp_output_path(target_path)
     temp_directory_path = get_temp_directory_path(target_path)
-    run_ffmpeg(
+    
-        [
+    # Determine optimal encoder based on available hardware
-            "-r",
+    encoder = modules.globals.video_encoder
-            str(fps),
+    encoder_options = []
-            "-i",
+    
-            os.path.join(temp_directory_path, "%04d.png"),
+    # GPU-accelerated encoding options
-            "-c:v",
+    if 'CUDAExecutionProvider' in modules.globals.execution_providers:
-            modules.globals.video_encoder,
+        # NVIDIA GPU encoding
-            "-crf",
+        if encoder == 'libx264':
-            str(modules.globals.video_quality),
+            encoder = 'h264_nvenc'
-            "-pix_fmt",
+            encoder_options = [
-            "yuv420p",
+                "-preset", "p7",  # Highest quality preset for NVENC
-            "-vf",
+                "-tune", "hq",  # High quality tuning
-            "colorspace=bt709:iall=bt601-6-625:fast=1",
+                "-rc", "vbr",  # Variable bitrate
+                "-cq", str(modules.globals.video_quality),  # Quality level
+                "-b:v", "0",  # Let CQ control bitrate
+                "-multipass", "fullres",  # Two-pass encoding for better quality
+            ]
+        elif encoder == 'libx265':
+            encoder = 'hevc_nvenc'
+            encoder_options = [
+                "-preset", "p7",
+                "-tune", "hq",
+                "-rc", "vbr",
+                "-cq", str(modules.globals.video_quality),
+                "-b:v", "0",
+            ]
+    elif 'DmlExecutionProvider' in modules.globals.execution_providers:
+        # AMD/Intel GPU encoding (DirectML on Windows)
+        if encoder == 'libx264':
+            # Try AMD AMF encoder
+            encoder = 'h264_amf'
+            encoder_options = [
+                "-quality", "quality",  # Quality mode
+                "-rc", "vbr_latency",
+                "-qp_i", str(modules.globals.video_quality),
+                "-qp_p", str(modules.globals.video_quality),
+            ]
+        elif encoder == 'libx265':
+            encoder = 'hevc_amf'
+            encoder_options = [
+                "-quality", "quality",
+                "-rc", "vbr_latency",
+                "-qp_i", str(modules.globals.video_quality),
+                "-qp_p", str(modules.globals.video_quality),
+            ]
+    else:
+        # CPU encoding with optimized settings
+        if encoder == 'libx264':
+            encoder_options = [
+                "-preset", "medium",  # Balance speed/quality
+                "-crf", str(modules.globals.video_quality),
+                "-tune", "film",  # Optimize for film content
+            ]
+        elif encoder == 'libx265':
+            encoder_options = [
+                "-preset", "medium",
+                "-crf", str(modules.globals.video_quality),
+                "-x265-params", "log-level=error",
+            ]
+        elif encoder == 'libvpx-vp9':
+            encoder_options = [
+                "-crf", str(modules.globals.video_quality),
+                "-b:v", "0",  # Constant quality mode
+                "-cpu-used", "2",  # Speed vs quality (0-5, lower=slower/better)
+            ]
+    
+    # Build ffmpeg command
+    ffmpeg_args = [
+        "-r", str(fps),
+        "-i", os.path.join(temp_directory_path, "%04d.png"),
+        "-c:v", encoder,
+    ]
+    
+    # Add encoder-specific options
+    ffmpeg_args.extend(encoder_options)
+    
+    # Add common options
+    ffmpeg_args.extend([
+        "-pix_fmt", "yuv420p",
+        "-movflags", "+faststart",  # Enable fast start for web playback
+        "-vf", "colorspace=bt709:iall=bt601-6-625:fast=1",
+        "-y",
+        temp_output_path,
+    ])
+    
+    # Try with hardware encoder first, fallback to software if it fails
+    success = run_ffmpeg(ffmpeg_args)
+    
+    if not success and encoder in ['h264_nvenc', 'hevc_nvenc', 'h264_amf', 'hevc_amf']:
+        # Fallback to software encoding
+        print(f"Hardware encoding with {encoder} failed, falling back to software encoding...")
+        fallback_encoder = 'libx264' if 'h264' in encoder else 'libx265'
+        ffmpeg_args_fallback = [
+            "-r", str(fps),
+            "-i", os.path.join(temp_directory_path, "%04d.png"),
+            "-c:v", fallback_encoder,
+            "-preset", "medium",
+            "-crf", str(modules.globals.video_quality),
+            "-pix_fmt", "yuv420p",
+            "-movflags", "+faststart",
+            "-vf", "colorspace=bt709:iall=bt601-6-625:fast=1",
             "-y",
             temp_output_path,
         ]
-    )
+        run_ffmpeg(ffmpeg_args_fallback)
 
 
 def restore_audio(target_path: str, output_path: str) -> None:
@@ -193,8 +282,15 @@ def conditional_download(download_directory_path: str, urls: List[str]) -> None:
             download_directory_path, os.path.basename(url)
         )
         if not os.path.exists(download_file_path):
-            request = urllib.request.urlopen(url)  # type: ignore[attr-defined]
+            request = urllib.request.Request(url)
-            total = int(request.headers.get("Content-Length", 0))
+            
+            # Create a specific SSL context for macOS to avoid globally disabling verification
+            ctx = None
+            if platform.system().lower() == "darwin":
+                ctx = ssl._create_unverified_context()
+                
+            response = urllib.request.urlopen(request, context=ctx)
+            total = int(response.headers.get("Content-Length", 0))
             with tqdm(
                 total=total,
                 desc="Downloading",
@@ -202,7 +298,13 @@ def conditional_download(download_directory_path: str, urls: List[str]) -> None:
                 unit_scale=True,
                 unit_divisor=1024,
             ) as progress:
-                urllib.request.urlretrieve(url, download_file_path, reporthook=lambda count, block_size, total_size: progress.update(block_size))  # type: ignore[attr-defined]
+                with open(download_file_path, "wb") as f:
+                    while True:
+                        buffer = response.read(8192)
+                        if not buffer:
+                            break
+                        f.write(buffer)
+                        progress.update(len(buffer))
 
 
 def resolve_relative_path(path: str) -> str:

requirements.txt

@@ -1,5 +1,3 @@
---extra-index-url https://download.pytorch.org/whl/cu128
-
 numpy>=1.23.5,<2
 typing-extensions>=4.8.0
 opencv-python==4.10.0.84
@@ -9,13 +7,10 @@ insightface==0.7.3
 psutil==5.9.8
 tk==0.1.0
 customtkinter==5.2.2
-pillow==11.1.0
+pillow==12.1.1
-torch; sys_platform != 'darwin'
-torch==2.5.1; sys_platform == 'darwin'
-torchvision; sys_platform != 'darwin'
-torchvision==0.20.1; sys_platform == 'darwin'
 onnxruntime-silicon==1.16.3; sys_platform == 'darwin' and platform_machine == 'arm64'
-onnxruntime-gpu==1.22.0; sys_platform != 'darwin'
+onnxruntime-gpu==1.23.2; sys_platform != 'darwin'
 tensorflow; sys_platform != 'darwin'
 opennsfw2==0.10.2
 protobuf==4.25.1
+pygrabber

run.py

@@ -1,5 +1,8 @@
 #!/usr/bin/env python3
 
+# Import the tkinter fix to patch the ScreenChanged error
+import tkinter_fix
+
 from modules import core
 
 if __name__ == '__main__':

tkinter_fix.py

@@ -0,0 +1,29 @@
+import os
+os.environ.setdefault('TK_SILENCE_DEPRECATION', '1')
+
+import tkinter
+
+# Only needs to be imported once at the beginning of the application
+def apply_patch():
+    # Create a monkey patch for the internal _tkinter module
+    original_init = tkinter.Tk.__init__
+    
+    def patched_init(self, *args, **kwargs):
+        # Call the original init
+        original_init(self, *args, **kwargs)
+        
+        # Define the missing ::tk::ScreenChanged procedure
+        self.tk.eval("""
+        if {[info commands ::tk::ScreenChanged] == ""} {
+            proc ::tk::ScreenChanged {args} {
+                # Do nothing
+                return
+            }
+        }
+        """)
+    
+    # Apply the monkey patch
+    tkinter.Tk.__init__ = patched_init
+
+# Apply the patch automatically when this module is imported
+apply_patch()