add Drag and drop functionality

Update ui.py
2025-03-26 13:08:18 +05:30 · 2025-03-26 12:14:29 +05:30
39 changed files with 844 additions and 4049 deletions
@@ -25,5 +25,3 @@ models/DMDNet.pth
 faceswap/
 .vscode/
 switch_states.json
-/models
-install.bat
@@ -1,4 +1,4 @@
-<h1 align="center">Deep-Live-Cam 2.1</h1>
+<h1 align="center">Deep-Live-Cam</h1>

 <p align="center">
  Real-time face swap and video deepfake with a single click and only a single image.
@@ -30,11 +30,12 @@ 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 v2.7 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" />
+## Quick Start - Pre-built (Windows / Nvidia)

-##### 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.
+  <a href="https://hacksider.gumroad.com/l/vccdmm"> <img src="https://github.com/user-attachments/assets/7d993b32-e3e8-4cd3-bbfb-a549152ebdd5" width="285" height="77" />
+
+##### This is the fastest build you can get if you have a discrete NVIDIA GPU.
 
 ###### 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.

@@ -98,7 +99,7 @@ Users are expected to use this software responsibly and legally. If using a real

 ## Installation (Manual)

-**Please be aware that the installation requires technical skills and is not for beginners. Consider downloading the quickstart version.**
+**Please be aware that the installation requires technical skills and is not for beginners. Consider downloading the prebuilt version.**

 <details>
 <summary>Click to see the process</summary>
@@ -109,7 +110,7 @@ This is more likely to work on your computer but will be slower as it utilizes t

 **1. Set up Your Platform**

-   Python (3.11 recommended)
+-   Python (3.10 recommended)
 -   pip
 -   git
 -   [ffmpeg](https://www.youtube.com/watch?v=OlNWCpFdVMA) - ```iex (irm ffmpeg.tc.ht)```
@@ -124,7 +125,7 @@ cd Deep-Live-Cam

 **3. Download the Models**

-1. [GFPGANv1.4](https://huggingface.co/hacksider/deep-live-cam/resolve/main/GFPGANv1.4.onnx)
+1. [GFPGANv1.4](https://huggingface.co/hacksider/deep-live-cam/resolve/main/GFPGANv1.4.pth)
 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.
@@ -133,34 +134,26 @@ Place these files in the "**models**" folder.

 We highly recommend using a `venv` to avoid issues.

-
 For Windows:
 ```bash
 python -m venv venv
 venv\Scripts\activate
 pip install -r requirements.txt
 ```
-For Linux:
-```bash
-# Ensure you use the installed Python 3.10
-python3 -m venv venv
-source venv/bin/activate
-pip install -r requirements.txt
-```

 **For macOS:**

 Apple Silicon (M1/M2/M3) requires specific setup:

 ```bash
-# Install Python 3.11 (specific version is important)
-brew install python@3.11
+# Install Python 3.10 (specific version is important)
+brew install python@3.10

 # Install tkinter package (required for the GUI)
 brew install python-tk@3.10

-# Create and activate virtual environment with Python 3.11
-python3.11 -m venv venv
+# Create and activate virtual environment with Python 3.10
+python3.10 -m venv venv
 source venv/bin/activate

 # Install dependencies
@@ -179,11 +172,6 @@ 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).
@@ -192,16 +180,12 @@ pip install git+https://github.com/TencentARC/GFPGAN.git@master

 **CUDA Execution Provider (Nvidia)**

-1. Install [CUDA Toolkit 12.8.0](https://developer.nvidia.com/cuda-12-8-0-download-archive)
-2. Install [cuDNN v8.9.7 for CUDA 12.x](https://developer.nvidia.com/rdp/cudnn-archive) (required for onnxruntime-gpu):
-   - Download cuDNN v8.9.7 for CUDA 12.x
-   - Make sure the cuDNN bin directory is in your system PATH
-3. Install dependencies:
+1. Install [CUDA Toolkit 11.8.0](https://developer.nvidia.com/cuda-11-8-0-download-archive)
+2. Install dependencies:

 ```bash
-pip install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
 pip uninstall onnxruntime onnxruntime-gpu
-pip install onnxruntime-gpu==1.21.0
+pip install onnxruntime-gpu==1.16.3
 ```

 3. Usage:
@@ -241,7 +225,7 @@ python3.10 run.py --execution-provider coreml
  # Uninstall conflicting versions if needed
  brew uninstall --ignore-dependencies python@3.11 python@3.13
  
-  # Keep only Python 3.11
+  # Keep only Python 3.10
  brew cleanup
  ```

@@ -251,7 +235,7 @@ python3.10 run.py --execution-provider coreml

 ```bash
 pip uninstall onnxruntime onnxruntime-coreml
-pip install onnxruntime-coreml==1.21.0
+pip install onnxruntime-coreml==1.13.1
 ```

 2. Usage:
@@ -266,7 +250,7 @@ python run.py --execution-provider coreml

 ```bash
 pip uninstall onnxruntime onnxruntime-directml
-pip install onnxruntime-directml==1.21.0
+pip install onnxruntime-directml==1.15.1
 ```

 2. Usage:
@@ -281,7 +265,7 @@ python run.py --execution-provider directml

 ```bash
 pip uninstall onnxruntime onnxruntime-openvino
-pip install onnxruntime-openvino==1.21.0
+pip install onnxruntime-openvino==1.15.0
 ```

 2. Usage:
@@ -309,8 +293,18 @@ 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)
+## Tips and Tricks
+
+Check out these helpful guides to get the most out of Deep-Live-Cam:
+
+- [Unlocking the Secrets to the Perfect Deepfake Image](https://deeplivecam.net/index.php/blog/tips-and-tricks/unlocking-the-secrets-to-the-perfect-deepfake-image) - Learn how to create the best deepfake with full head coverage
+- [Video Call with DeepLiveCam](https://deeplivecam.net/index.php/blog/tips-and-tricks/video-call-with-deeplivecam) - Make your meetings livelier by using DeepLiveCam with OBS and meeting software
+- [Have a Special Guest!](https://deeplivecam.net/index.php/blog/tips-and-tricks/have-a-special-guest) - Tutorial on how to use face mapping to add special guests to your stream
+- [Watch Deepfake Movies in Realtime](https://deeplivecam.net/index.php/blog/tips-and-tricks/watch-deepfake-movies-in-realtime) - See yourself star in any video without processing the video
+- [Better Quality without Sacrificing Speed](https://deeplivecam.net/index.php/blog/tips-and-tricks/better-quality-without-sacrificing-speed) - Tips for achieving better results without impacting performance
+- [Instant Vtuber!](https://deeplivecam.net/index.php/blog/tips-and-tricks/instant-vtuber) - Create a new persona/vtuber easily using Metahuman Creator
+
+Visit our [official blog](https://deeplivecam.net/index.php/blog/tips-and-tricks) for more tips and tutorials.

 ## Command Line Arguments (Unmaintained)

@@ -341,22 +335,24 @@ Looking for a CLI mode? Using the -s/--source argument will make the run program

 ## Press

- - [**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"*
- - [**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"*
- - [**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."*
- - [**TrendMicro**](https://www.trendmicro.com/vinfo/gb/security/news/cyber-attacks/ai-vs-ai-deepfakes-and-ekyc) - *"AI vs AI: DeepFakes and eKYC"*
- - [**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"*
- - [**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"*
- - [**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"*
- - [**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"*
- - [**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!"*
+**We are always open to criticism and are ready to improve, that's why we didn't cherry-pick anything.**

+ - [*"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
+ - [*"Thanks Deep Live Cam, shapeshifters are among us now"*](https://dataconomy.com/2024/08/15/what-is-deep-live-cam-github-deepfake/) - Dataconomy
+ - [*"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
+ - [*"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
+ - [*"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
+ - [*"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
+ - [*"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
+ - [*"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
+ - [*"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

 ## 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
@@ -364,7 +360,6 @@ 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 ❤️
@@ -1,46 +0,0 @@
-{
-    "Source x Target Mapper": "Quelle x Ziel Zuordnung",
-    "select a source image": "Wähle ein Quellbild",
-    "Preview": "Vorschau",
-    "select a target image or video": "Wähle ein Zielbild oder Video",
-    "save image output file": "Bildausgabedatei speichern",
-    "save video output file": "Videoausgabedatei speichern",
-    "select a target image": "Wähle ein Zielbild",
-    "source": "Quelle",
-    "Select a target": "Wähle ein Ziel",
-    "Select a face": "Wähle ein Gesicht",
-    "Keep audio": "Audio beibehalten",
-    "Face Enhancer": "Gesichtsverbesserung",
-    "Many faces": "Mehrere Gesichter",
-    "Show FPS": "FPS anzeigen",
-    "Keep fps": "FPS beibehalten",
-    "Keep frames": "Frames beibehalten",
-    "Fix Blueish Cam": "Bläuliche Kamera korrigieren",
-    "Mouth Mask": "Mundmaske",
-    "Show Mouth Mask Box": "Mundmaskenrahmen anzeigen",
-    "Start": "Starten",
-    "Live": "Live",
-    "Destroy": "Beenden",
-    "Map faces": "Gesichter zuordnen",
-    "Processing...": "Verarbeitung läuft...",
-    "Processing succeed!": "Verarbeitung erfolgreich!",
-    "Processing ignored!": "Verarbeitung ignoriert!",
-    "Failed to start camera": "Kamera konnte nicht gestartet werden",
-    "Please complete pop-up or close it.": "Bitte das Pop-up komplettieren oder schließen.",
-    "Getting unique faces": "Einzigartige Gesichter erfassen",
-    "Please select a source image first": "Bitte zuerst ein Quellbild auswählen",
-    "No faces found in target": "Keine Gesichter im Zielbild gefunden",
-    "Add": "Hinzufügen",
-    "Clear": "Löschen",
-    "Submit": "Absenden",
-    "Select source image": "Quellbild auswählen",
-    "Select target image": "Zielbild auswählen",
-    "Please provide mapping!": "Bitte eine Zuordnung angeben!",
-    "At least 1 source with target is required!": "Mindestens eine Quelle mit einem Ziel ist erforderlich!",
-    "At least 1 source with target is required!": "Mindestens eine Quelle mit einem Ziel ist erforderlich!",
-    "Face could not be detected in last upload!": "Im letzten Upload konnte kein Gesicht erkannt werden!",
-    "Select Camera:": "Kamera auswählen:",
-    "All mappings cleared!": "Alle Zuordnungen gelöscht!",
-    "Mappings successfully submitted!": "Zuordnungen erfolgreich übermittelt!",
-    "Source x Target Mapper is already open.": "Quell-zu-Ziel-Zuordnung ist bereits geöffnet."
-}
@@ -1,46 +0,0 @@
-{
-    "Source x Target Mapper": "Mapeador de fuente x destino",
-    "select a source image": "Seleccionar imagen fuente",
-    "Preview": "Vista previa",
-    "select a target image or video": "elegir un video o una imagen fuente",
-    "save image output file": "guardar imagen final",
-    "save video output file": "guardar video final",
-    "select a target image": "elegir una imagen objetiva",
-    "source": "fuente",
-    "Select a target": "Elegir un destino",
-    "Select a face": "Elegir una cara",
-    "Keep audio": "Mantener audio original",
-    "Face Enhancer": "Potenciador de caras",
-    "Many faces": "Varias caras",
-    "Show FPS": "Mostrar fps",
-    "Keep fps": "Mantener fps",
-    "Keep frames": "Mantener frames",
-    "Fix Blueish Cam": "Corregir tono azul de video",
-    "Mouth Mask": "Máscara de boca",
-    "Show Mouth Mask Box": "Mostrar área de la máscara de boca",
-    "Start": "Iniciar",
-    "Live": "En vivo",
-    "Destroy": "Borrar",
-    "Map faces": "Mapear caras",
-    "Processing...": "Procesando...",
-    "Processing succeed!": "¡Proceso terminado con éxito!",
-    "Processing ignored!": "¡Procesamiento omitido!",
-    "Failed to start camera": "No se pudo iniciar la cámara",
-    "Please complete pop-up or close it.": "Complete o cierre el pop-up",
-    "Getting unique faces": "Buscando caras únicas",
-    "Please select a source image first": "Primero, seleccione una imagen fuente",
-    "No faces found in target": "No se encontró una cara en el destino",
-    "Add": "Agregar",
-    "Clear": "Limpiar",
-    "Submit": "Enviar",
-    "Select source image": "Seleccionar imagen fuente",
-    "Select target image": "Seleccionar imagen destino",
-    "Please provide mapping!": "Por favor, proporcione un mapeo",
-    "At least 1 source with target is required!": "Se requiere al menos una fuente con un destino.",
-    "At least 1 source with target is required!": "Se requiere al menos una fuente con un destino.",
-    "Face could not be detected in last upload!": "¡No se pudo encontrar una cara en el último video o imagen!",
-    "Select Camera:": "Elegir cámara:",
-    "All mappings cleared!": "¡Todos los mapeos fueron borrados!",
-    "Mappings successfully submitted!": "Mapeos enviados con éxito!",
-    "Source x Target Mapper is already open.": "El mapeador de fuente x destino ya está abierto."
-}
@@ -1,46 +0,0 @@
-{
-    "Source x Target Mapper": "Source x Target Kartoitin",
-    "select an source image": "Valitse lähde kuva",
-    "Preview": "Esikatsele",
-    "select an target image or video": "Valitse kohde kuva tai video",
-    "save image output file": "tallenna kuva",
-    "save video output file": "tallenna video",
-    "select an target image": "Valitse kohde kuva",
-    "source": "lähde",
-    "Select a target": "Valitse kohde",
-    "Select a face": "Valitse kasvot",
-    "Keep audio": "Säilytä ääni",
-    "Face Enhancer": "Kasvojen Parantaja",
-    "Many faces": "Useampia kasvoja",
-    "Show FPS": "Näytä FPS",
-    "Keep fps": "Säilytä FPS",
-    "Keep frames": "Säilytä ruudut",
-    "Fix Blueish Cam": "Korjaa Sinertävä Kamera",
-    "Mouth Mask": "Suu Maski",
-    "Show Mouth Mask Box": "Näytä Suu Maski Laatiko",
-    "Start": "Aloita",
-    "Live": "Live",
-    "Destroy": "Tuhoa",
-    "Map faces": "Kartoita kasvot",
-    "Processing...": "Prosessoi...",
-    "Processing succeed!": "Prosessointi onnistui!",
-    "Processing ignored!": "Prosessointi lopetettu!",
-    "Failed to start camera": "Kameran käynnistäminen epäonnistui",
-    "Please complete pop-up or close it.": "Viimeistele tai sulje ponnahdusikkuna",
-    "Getting unique faces": "Hankitaan uniikkeja kasvoja",
-    "Please select a source image first": "Valitse ensin lähde kuva",
-    "No faces found in target": "Kasvoja ei löydetty kohteessa",
-    "Add": "Lisää",
-    "Clear": "Tyhjennä",
-    "Submit": "Lähetä",
-    "Select source image": "Valitse lähde kuva",
-    "Select target image": "Valitse kohde kuva",
-    "Please provide mapping!": "Tarjoa kartoitus!",
-    "Atleast 1 source with target is required!": "Vähintään 1 lähde kohteen kanssa on vaadittu!",
-    "At least 1 source with target is required!": "Vähintään 1 lähde kohteen kanssa on vaadittu!",
-    "Face could not be detected in last upload!": "Kasvoja ei voitu tunnistaa edellisessä latauksessa!",
-    "Select Camera:": "Valitse Kamera:",
-    "All mappings cleared!": "Kaikki kartoitukset tyhjennetty!",
-    "Mappings successfully submitted!": "Kartoitukset lähetety onnistuneesti!",
-    "Source x Target Mapper is already open.": "Lähde x Kohde Kartoittaja on jo auki."
-}
@@ -1,45 +0,0 @@
-{
-    "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."
-}
@@ -1,45 +0,0 @@
-{
-    "Source x Target Mapper": "ប្រភប x បន្ថែម Mapper",
-    "select a source image": "ជ្រើសរើសប្រភពរូបភាព",
-    "Preview": "បង្ហាញ",
-    "select a target image or video": "ជ្រើសរើសគោលដៅរូបភាពឬវីដេអូ",
-    "save image output file": "រក្សាទុកលទ្ធផលឯកសាររូបភាព",
-    "save video output file": "រក្សាទុកលទ្ធផលឯកសារវីដេអូ",
-    "select a target image": "ជ្រើសរើសគោលដៅរូបភាព",
-    "source": "ប្រភព",
-    "Select a target": "ជ្រើសរើសគោលដៅ",
-    "Select a face": "ជ្រើសរើសមុខ",
-    "Keep audio": "រម្លងសម្លេង",
-    "Face Enhancer": "ឧបករណ៍ពង្រឹងមុខ",
-    "Many faces": "ទម្រង់មុខច្រើន",
-    "Show FPS": "បង្ហាញ FPS",
-    "Keep fps": "រម្លង fps",
-    "Keep frames": "រម្លងទម្រង់",
-    "Fix Blueish Cam": "ជួសជុល Cam Blueish",
-    "Mouth Mask": "របាំងមាត់",
-    "Show Mouth Mask Box": "បង្ហាញប្រអប់របាំងមាត់",
-    "Start": "ចាប់ផ្ដើម",
-    "Live": "ផ្សាយផ្ទាល់",
-    "Destroy": "លុប",
-    "Map faces": "ផែនទីមុខ",
-    "Processing...": "កំពុងដំណើរការ...",
-    "Processing succeed!": "ការដំណើរការទទួលបានជោគជ័យ!",
-    "Processing ignored!": "ការដំណើរការមិនទទួលបានជោគជ័យ!",
-    "Failed to start camera": "បរាជ័យដើម្បីចាប់ផ្ដើមបើកកាមេរ៉ា",
-    "Please complete pop-up or close it.": "សូមបញ្ចប់ផ្ទាំងផុស ឬបិទវា.",
-    "Getting unique faces": "ការចាប់ផ្ដើមទម្រង់មុខប្លែក",
-    "Please select a source image first": "សូមជ្រើសរើសប្រភពរូបភាពដំបូង",
-    "No faces found in target": "រកអត់ឃើញមុខនៅក្នុងគោលដៅ",
-    "Add": "បន្ថែម",
-    "Clear": "សម្អាត",
-    "Submit": "បញ្ចូន",
-    "Select source image": "ជ្រើសរើសប្រភពរូបភាព",
-    "Select target image": "ជ្រើសរើសគោលដៅរូបភាព",
-    "Please provide mapping!": "សូមផ្ដល់នៅផែនទី",
-    "At least 1 source with target is required!": "ត្រូវការប្រភពយ៉ាងហោចណាស់ ១ ដែលមានគោលដៅ!",
-    "Face could not be detected in last upload!": "មុខមិនអាចភ្ជាប់នៅក្នុងការបង្ហេាះចុងក្រោយ!",
-    "Select Camera:": "ជ្រើសរើសកាមេរ៉ា",
-    "All mappings cleared!": "ផែនទីទាំងអស់ត្រូវបានសម្អាត!",
-    "Mappings successfully submitted!": "ផែនទីត្រូវបានបញ្ជូនជោគជ័យ!",
-    "Source x Target Mapper is already open.": "ប្រភព x Target Mapper បានបើករួចហើយ។"
-}
@@ -1,45 +0,0 @@
-{
-    "Source x Target Mapper": "소스 x 타겟 매퍼",
-    "select a source image": "소스 이미지 선택",
-    "Preview": "미리보기",
-    "select a target image or video": "타겟 이미지 또는 영상 선택",
-    "save image output file": "이미지 출력 파일 저장",
-    "save video output file": "영상 출력 파일 저장",
-    "select a target image": "타겟 이미지 선택",
-    "source": "소스",
-    "Select a target": "타겟 선택",
-    "Select a face": "얼굴 선택",
-    "Keep audio": "오디오 유지",
-    "Face Enhancer": "얼굴 향상",
-    "Many faces": "여러 얼굴",
-    "Show FPS": "FPS 표시",
-    "Keep fps": "FPS 유지",
-    "Keep frames": "프레임 유지",
-    "Fix Blueish Cam": "푸른빛 카메라 보정",
-    "Mouth Mask": "입 마스크",
-    "Show Mouth Mask Box": "입 마스크 박스 표시",
-    "Start": "시작",
-    "Live": "라이브",
-    "Destroy": "종료",
-    "Map faces": "얼굴 매핑",
-    "Processing...": "처리 중...",
-    "Processing succeed!": "처리 성공!",
-    "Processing ignored!": "처리 무시됨!",
-    "Failed to start camera": "카메라 시작 실패",
-    "Please complete pop-up or close it.": "팝업을 완료하거나 닫아주세요.",
-    "Getting unique faces": "고유 얼굴 가져오는 중",
-    "Please select a source image first": "먼저 소스 이미지를 선택해주세요",
-    "No faces found in target": "타겟에서 얼굴을 찾을 수 없음",
-    "Add": "추가",
-    "Clear": "지우기",
-    "Submit": "제출",
-    "Select source image": "소스 이미지 선택",
-    "Select target image": "타겟 이미지 선택",
-    "Please provide mapping!": "매핑을 입력해주세요!",
-    "At least 1 source with target is required!": "최소 하나의 소스와 타겟이 필요합니다!",
-    "Face could not be detected in last upload!": "최근 업로드에서 얼굴을 감지할 수 없습니다!",
-    "Select Camera:": "카메라 선택:",
-    "All mappings cleared!": "모든 매핑이 삭제되었습니다!",
-    "Mappings successfully submitted!": "매핑이 성공적으로 제출되었습니다!",
-    "Source x Target Mapper is already open.": "소스 x 타겟 매퍼가 이미 열려 있습니다."
-}
@@ -1,46 +0,0 @@
-{
-    "Source x Target Mapper": "Mapeador de Origem x Destino",
-    "select an source image": "Escolha uma imagem de origem",
-    "Preview": "Prévia",
-    "select an target image or video": "Escolha uma imagem ou vídeo de destino",
-    "save image output file": "Salvar imagem final",
-    "save video output file": "Salvar vídeo final",
-    "select an target image": "Escolha uma imagem de destino",
-    "source": "Origem",
-    "Select a target": "Escolha o destino",
-    "Select a face": "Escolha um rosto",
-    "Keep audio": "Manter o áudio original",
-    "Face Enhancer": "Melhorar rosto",
-    "Many faces": "Vários rostos",
-    "Show FPS": "Mostrar FPS",
-    "Keep fps": "Manter FPS",
-    "Keep frames": "Manter frames",
-    "Fix Blueish Cam": "Corrigir tom azulado da câmera",
-    "Mouth Mask": "Máscara da boca",
-    "Show Mouth Mask Box": "Mostrar área da máscara da boca",
-    "Start": "Começar",
-    "Live": "Ao vivo",
-    "Destroy": "Destruir",
-    "Map faces": "Mapear rostos",
-    "Processing...": "Processando...",
-    "Processing succeed!": "Tudo certo!",
-    "Processing ignored!": "Processamento ignorado!",
-    "Failed to start camera": "Não foi possível iniciar a câmera",
-    "Please complete pop-up or close it.": "Finalize ou feche o pop-up",
-    "Getting unique faces": "Buscando rostos diferentes",
-    "Please select a source image first": "Selecione primeiro uma imagem de origem",
-    "No faces found in target": "Nenhum rosto encontrado na imagem de destino",
-    "Add": "Adicionar",
-    "Clear": "Limpar",
-    "Submit": "Enviar",
-    "Select source image": "Escolha a imagem de origem",
-    "Select target image": "Escolha a imagem de destino",
-    "Please provide mapping!": "Você precisa realizar o mapeamento!",
-    "Atleast 1 source with target is required!": "É necessária pelo menos uma origem com um destino!",
-    "At least 1 source with target is required!": "É necessária pelo menos uma origem com um destino!",
-    "Face could not be detected in last upload!": "Não conseguimos detectar o rosto na última imagem!",
-    "Select Camera:": "Escolher câmera:",
-    "All mappings cleared!": "Todos os mapeamentos foram removidos!",
-    "Mappings successfully submitted!": "Mapeamentos enviados com sucesso!",
-    "Source x Target Mapper is already open.": "O Mapeador de Origem x Destino já está aberto."
-}
@@ -1,45 +0,0 @@
-{
-    "Source x Target Mapper": "Сопоставитель Источник x Цель",
-    "select a source image": "выберите исходное изображение",
-    "Preview": "Предпросмотр",
-    "select a target image or video": "выберите целевое изображение или видео",
-    "save image output file": "сохранить выходной файл изображения",
-    "save video output file": "сохранить выходной файл видео",
-    "select a target image": "выберите целевое изображение",
-    "source": "источник",
-    "Select a target": "Выберите целевое изображение",
-    "Select a face": "Выберите лицо",
-    "Keep audio": "Сохранить аудио",
-    "Face Enhancer": "Улучшение лица",
-    "Many faces": "Несколько лиц",
-    "Show FPS": "Показать FPS",
-    "Keep fps": "Сохранить FPS",
-    "Keep frames": "Сохранить кадры",
-    "Fix Blueish Cam": "Исправить синеву камеры",
-    "Mouth Mask": "Маска рта",
-    "Show Mouth Mask Box": "Показать рамку маски рта",
-    "Start": "Старт",
-    "Live": "В реальном времени",
-    "Destroy": "Остановить",
-    "Map faces": "Сопоставить лица",
-    "Processing...": "Обработка...",
-    "Processing succeed!": "Обработка успешна!",
-    "Processing ignored!": "Обработка проигнорирована!",
-    "Failed to start camera": "Не удалось запустить камеру",
-    "Please complete pop-up or close it.": "Пожалуйста, заполните всплывающее окно или закройте его.",
-    "Getting unique faces": "Получение уникальных лиц",
-    "Please select a source image first": "Сначала выберите исходное изображение, пожалуйста",
-    "No faces found in target": "В целевом изображении не найдено лиц",
-    "Add": "Добавить",
-    "Clear": "Очистить",
-    "Submit": "Отправить",
-    "Select source image": "Выбрать исходное изображение",
-    "Select target image": "Выбрать целевое изображение",
-    "Please provide mapping!": "Пожалуйста, укажите сопоставление!",
-    "At least 1 source with target is required!": "Требуется хотя бы 1 источник с целью!",
-    "Face could not be detected in last upload!": "Лицо не обнаружено в последнем загруженном изображении!",
-    "Select Camera:": "Выберите камеру:",
-    "All mappings cleared!": "Все сопоставления очищены!",
-    "Mappings successfully submitted!": "Сопоставления успешно отправлены!",
-    "Source x Target Mapper is already open.": "Сопоставитель Источник-Цель уже открыт."
-}
@@ -1,45 +0,0 @@
-{
-    "Source x Target Mapper": "ตัวจับคู่ต้นทาง x ปลายทาง",
-    "select a source image": "เลือกรูปภาพต้นฉบับ",
-    "Preview": "ตัวอย่าง",
-    "select a target image or video": "เลือกรูปภาพหรือวิดีโอเป้าหมาย",
-    "save image output file": "บันทึกไฟล์รูปภาพ",
-    "save video output file": "บันทึกไฟล์วิดีโอ",
-    "select a target image": "เลือกรูปภาพเป้าหมาย",
-    "source": "ต้นฉบับ",
-    "Select a target": "เลือกเป้าหมาย",
-    "Select a face": "เลือกใบหน้า",
-    "Keep audio": "เก็บเสียง",
-    "Face Enhancer": "ปรับปรุงใบหน้า",
-    "Many faces": "หลายใบหน้า",
-    "Show FPS": "แสดง FPS",
-    "Keep fps": "คงค่า FPS",
-    "Keep frames": "คงค่าเฟรม",
-    "Fix Blueish Cam": "แก้ไขภาพอมฟ้าจากกล้อง",
-    "Mouth Mask": "มาสก์ปาก",
-    "Show Mouth Mask Box": "แสดงกรอบมาสก์ปาก",
-    "Start": "เริ่ม",
-    "Live": "สด",
-    "Destroy": "หยุด",
-    "Map faces": "จับคู่ใบหน้า",
-    "Processing...": "กำลังประมวลผล...",
-    "Processing succeed!": "ประมวลผลสำเร็จแล้ว!",
-    "Processing ignored!": "การประมวลผลถูกละเว้น",
-    "Failed to start camera": "ไม่สามารถเริ่มกล้องได้",
-    "Please complete pop-up or close it.": "โปรดดำเนินการในป๊อปอัปให้เสร็จสิ้น หรือปิด",
-    "Getting unique faces": "กำลังค้นหาใบหน้าที่ไม่ซ้ำกัน",
-    "Please select a source image first": "โปรดเลือกภาพต้นฉบับก่อน",
-    "No faces found in target": "ไม่พบใบหน้าในภาพเป้าหมาย",
-    "Add": "เพิ่ม",
-    "Clear": "ล้าง",
-    "Submit": "ส่ง",
-    "Select source image": "เลือกภาพต้นฉบับ",
-    "Select target image": "เลือกภาพเป้าหมาย",
-    "Please provide mapping!": "โปรดระบุการจับคู่!",
-    "At least 1 source with target is required!": "ต้องมีการจับคู่ต้นฉบับกับเป้าหมายอย่างน้อย 1 คู่!",
-    "Face could not be detected in last upload!": "ไม่สามารถตรวจพบใบหน้าในไฟล์อัปโหลดล่าสุด!",
-    "Select Camera:": "เลือกกล้อง:",
-    "All mappings cleared!": "ล้างการจับคู่ทั้งหมดแล้ว!",
-    "Mappings successfully submitted!": "ส่งการจับคู่สำเร็จแล้ว!",
-    "Source x Target Mapper is already open.": "ตัวจับคู่ต้นทาง x ปลายทาง เปิดอยู่แล้ว"
-}
@@ -1,11 +1,11 @@
 {
    "Source x Target Mapper": "Source x Target Mapper",
-    "select a source image": "选择一个源图像",
+    "select an source image": "选择一个源图像",
    "Preview": "预览",
-    "select a target image or video": "选择一个目标图像或视频",
+    "select an target image or video": "选择一个目标图像或视频",
    "save image output file": "保存图像输出文件",
    "save video output file": "保存视频输出文件",
-    "select a target image": "选择一个目标图像",
+    "select an target image": "选择一个目标图像",
    "source": "源",
    "Select a target": "选择一个目标",
    "Select a face": "选择一张脸",
@@ -36,7 +36,7 @@
    "Select source image": "请选取源图像",
    "Select target image": "请选取目标图像",
    "Please provide mapping!": "请提供映射",
-    "At least 1 source with target is required!": "至少需要一个来源图像与目标图像相关！",
+    "Atleast 1 source with target is required!": "至少需要一个来源图像与目标图像相关！",
    "At least 1 source with target is required!": "至少需要一个来源图像与目标图像相关！",
    "Face could not be detected in last upload!": "最近上传的图像中没有检测到人脸!",
    "Select Camera:": "选择摄像头",
@@ -1,18 +0,0 @@
-import os 
-import cv2
-import numpy as np
-
-# Utility function to support unicode characters in file paths for reading
-def imread_unicode(path, flags=cv2.IMREAD_COLOR):
-    return cv2.imdecode(np.fromfile(path, dtype=np.uint8), flags)
-
-# Utility function to support unicode characters in file paths for writing
-def imwrite_unicode(path, img, params=None):
-    root, ext = os.path.splitext(path)
-    if not ext:
-        ext = ".png"
-        result, encoded_img = cv2.imencode(ext, img, params if params else [])
-        result, encoded_img = cv2.imencode(f".{ext}", img, params if params is not None else [])
-        encoded_img.tofile(path)
-        return True
-    return False
@@ -1,7 +1,6 @@
 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:
@@ -20,7 +19,7 @@ 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 = gpu_cvt_color(frame, cv2.COLOR_BGR2RGB)
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

    capture.release()
    return frame if has_frame else None
@@ -11,11 +11,7 @@ import platform
 import signal
 import shutil
 import argparse
-try:
-    import torch
-    HAS_TORCH = True
-except ImportError:
-    HAS_TORCH = False
+import torch
 import onnxruntime
 import tensorflow

@@ -25,12 +21,11 @@ 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 HAS_TORCH and 'ROCMExecutionProvider' in modules.globals.execution_providers:
+if 'ROCMExecutionProvider' in modules.globals.execution_providers:
    del torch

 warnings.filterwarnings('ignore', category=FutureWarning, module='insightface')
-if HAS_TORCH:
-    warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')
+warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')


 def parse_args() -> None:
@@ -39,7 +34,7 @@ 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', 'face_enhancer_gpen256', 'face_enhancer_gpen512'], nargs='+')
+    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('--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)
@@ -86,9 +81,11 @@ def parse_args() -> None:
    modules.globals.execution_threads = args.execution_threads
    modules.globals.lang = args.lang

-    #for ENHANCER tumblers:
-    for enhancer_key in ('face_enhancer', 'face_enhancer_gpen256', 'face_enhancer_gpen512'):
-        modules.globals.fp_ui[enhancer_key] = enhancer_key in args.frame_processor
+    #for ENHANCER tumbler:
+    if 'face_enhancer' in args.frame_processor:
+        modules.globals.fp_ui['face_enhancer'] = True
+    else:
+        modules.globals.fp_ui['face_enhancer'] = False

    # translate deprecated args
    if args.source_path_deprecated:
@@ -132,22 +129,11 @@ 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
-    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))
+    return 8


 def limit_resources() -> None:
@@ -170,7 +156,7 @@ def limit_resources() -> None:


 def release_resources() -> None:
-    if 'CUDAExecutionProvider' in modules.globals.execution_providers and HAS_TORCH:
+    if 'CUDAExecutionProvider' in modules.globals.execution_providers:
        torch.cuda.empty_cache()


@@ -190,16 +176,10 @@ 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):
@@ -213,40 +193,26 @@ 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):
-            elapsed = time.time() - start_time
-            update_status(f'Processing to image succeed! (Time: {elapsed:.2f}s)')
+            update_status('Processing to image succeed!')
        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)
@@ -255,9 +221,6 @@ 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:
@@ -267,13 +230,10 @@ 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(f'Processing to video succeed! Total time: {total_time:.2f}s')
+        update_status('Processing to video succeed!')
    else:
        update_status('Processing to video failed!')

@@ -1,7 +0,0 @@
-from typing import Any
-
-from insightface.app.common import Face
-import numpy
- 
-Face = Face
-Frame = numpy.ndarray[Any, Any] 
@@ -2,7 +2,6 @@ import os
 import shutil
 from typing import Any
 import insightface
-import threading

 import cv2
 import numpy as np
@@ -14,22 +13,13 @@ 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:
-        with FACE_ANALYSER_LOCK:
-            # 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 = insightface.app.FaceAnalysis(name='buffalo_l', providers=modules.globals.execution_providers)
        FACE_ANALYSER.prepare(ctx_id=0, det_size=(640, 640))
    return FACE_ANALYSER

@@ -1,5 +1,3 @@
-# --- START OF FILE globals.py ---
-
 import os
 from typing import List, Dict, Any

@@ -11,63 +9,35 @@ file_types = [
    ("Video", ("*.mp4", "*.mkv")),
 ]

-# Face Mapping Data
-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_target_map = []
+simple_map = {}

-# Paths
-source_path: str | None = None
-target_path: str | None = None
-output_path: str | None = None
-
-# Processing Options
+source_path = None
+target_path = None
+output_path = None
 frame_processors: List[str] = []
-keep_fps: bool = True
-keep_audio: bool = True
-keep_frames: bool = False
-many_faces: bool = False         # Process all detected faces with default source
-map_faces: bool = False          # Use source_target_map or simple_map for specific swaps
-poisson_blend: bool = False      # Enable Poisson Blending for smoother face swaps
-color_correction: bool = False   # Enable color correction (implementation specific)
-nsfw_filter: bool = False
-
-# Video Output Options
-video_encoder: str | None = None
-video_quality: int | None = None # Typically a CRF value or bitrate
-
-# Live Mode Options
-live_mirror: bool = False
-live_resizable: bool = True
-camera_input_combobox: Any | None = None # Placeholder for UI element if needed
-webcam_preview_running: bool = False
-show_fps: bool = False
-
-# System Configuration
-max_memory: int | None = None        # Memory limit in GB? (Needs clarification)
-execution_providers: List[str] = []  # e.g., ['CUDAExecutionProvider', 'CPUExecutionProvider']
-execution_threads: int | None = None # Number of threads for CPU execution
-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 ---
+keep_fps = True
+keep_audio = True
+keep_frames = False
+many_faces = False
+map_faces = False
+color_correction = False  # New global variable for color correction toggle
+nsfw_filter = False
+video_encoder = None
+video_quality = None
+live_mirror = False
+live_resizable = True
+max_memory = None
+execution_providers: List[str] = []
+execution_threads = None
+headless = None
+log_level = "error"
+fp_ui: Dict[str, bool] = {"face_enhancer": False}
+camera_input_combobox = None
+webcam_preview_running = False
+show_fps = False
+mouth_mask = False
+show_mouth_mask_box = False
+mask_feather_ratio = 8
+mask_down_size = 0.50
+mask_size = 1
@@ -1,286 +0,0 @@
-# --- 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 ---
@@ -1,3 +1,3 @@
 name = 'Deep-Live-Cam'
-version = '2.1'
+version = '1.8'
 edition = 'GitHub Edition'
@@ -1,6 +0,0 @@
-"""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")
@@ -3,7 +3,6 @@ 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

@@ -15,7 +14,7 @@ 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 = gpu_cvt_color(target_frame, cv2.COLOR_BGR2RGB)
+        target_frame = cv2.cvtColor(target_frame, cv2.COLOR_BGR2RGB)
        
    image = Image.fromarray(target_frame)
    image = opennsfw2.preprocess_image(image, opennsfw2.Preprocessing.YAHOO)
@@ -1,145 +0,0 @@
-"""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)
@@ -17,17 +17,8 @@ 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:
@@ -51,54 +42,27 @@ def get_frame_processors_modules(frame_processors: List[str]) -> List[ModuleType

 def set_frame_processors_modules_from_ui(frame_processors: List[str]) -> None:
    global FRAME_PROCESSORS_MODULES
-    current_processor_names = [proc.__name__.split('.')[-1] for proc in FRAME_PROCESSORS_MODULES]
-
    for frame_processor, state in modules.globals.fp_ui.items():
-        if state == True and frame_processor not in current_processor_names:
-            try:
+        if state == True and frame_processor not in frame_processors:
            frame_processor_module = load_frame_processor_module(frame_processor)
            FRAME_PROCESSORS_MODULES.append(frame_processor_module)
-                if frame_processor not in modules.globals.frame_processors:
            modules.globals.frame_processors.append(frame_processor)
-            except SystemExit:
-                 print(f"Warning: Failed to load frame processor {frame_processor} requested by UI state.")
-            except Exception as e:
-                 print(f"Warning: Error loading frame processor {frame_processor} requested by UI state: {e}")
-
-        elif state == False and frame_processor in current_processor_names:
+        if state == False:
            try:
-                module_to_remove = next((mod for mod in FRAME_PROCESSORS_MODULES if mod.__name__.endswith(f'.{frame_processor}')), None)
-                if module_to_remove:
-                    FRAME_PROCESSORS_MODULES.remove(module_to_remove)
-                if frame_processor in modules.globals.frame_processors:
+                frame_processor_module = load_frame_processor_module(frame_processor)
+                FRAME_PROCESSORS_MODULES.remove(frame_processor_module)
                modules.globals.frame_processors.remove(frame_processor)
-            except Exception as e:
-                 print(f"Warning: Error removing frame processor {frame_processor}: {e}")
+            except:
+                pass

 def multi_process_frame(source_path: str, temp_frame_paths: List[str], process_frames: Callable[[str, List[str], Any], None], progress: Any = None) -> None:
-    """Process frames in parallel with optimized batching and memory management."""
-    max_workers = modules.globals.execution_threads
-    
-    # Determine optimal batch size based on available memory and thread count
-    # Process frames in batches to avoid memory overflow
-    batch_size = max(1, min(32, len(temp_frame_paths) // max(1, max_workers)))
-    
-    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]
+    with ThreadPoolExecutor(max_workers=modules.globals.execution_threads) as executor:
        futures = []
-            
-            for path in batch:
+        for path in temp_frame_paths:
            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:
@@ -1,20 +1,18 @@
-# --- 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 numpy as np
+import gfpgan
 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, get_many_faces
+from modules.face_analyser import get_one_face
 from modules.typing import Frame, Face
+import platform
+import torch
 from modules.utilities import (
+    conditional_download,
    is_image,
    is_video,
 )
@@ -29,29 +27,15 @@ 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:
-    model_path = os.path.join(models_dir, "gfpgan-1024.onnx")
-    if not os.path.exists(model_path):
-        update_status(
-            f"GFPGAN ONNX model not found at {model_path}. "
-            "Please place gfpgan-1024.onnx in the models folder.",
-            NAME,
+    download_directory_path = models_dir
+    conditional_download(
+        download_directory_path,
+        [
+            "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth"
+        ],
    )
-        return False
    return True


@@ -64,309 +48,62 @@ def pre_start() -> bool:
    return True


-def get_face_enhancer() -> onnxruntime.InferenceSession:
-    """
-    Initializes and returns the GFPGAN ONNX Runtime inference session,
-    using the execution providers configured in modules.globals.
-    """
+def get_face_enhancer() -> Any:
    global FACE_ENHANCER

    with THREAD_LOCK:
        if FACE_ENHANCER is None:
-            model_path = os.path.join(models_dir, "gfpgan-1024.onnx")
+            model_path = os.path.join(models_dir, "GFPGANv1.4.pth")
            
-            if not os.path.exists(model_path):
-                raise FileNotFoundError(
-                    f"{NAME}: Model not found at {model_path}"
-                )
-
-            try:
-                providers = modules.globals.execution_providers
-
-                session_options = onnxruntime.SessionOptions()
-                session_options.graph_optimization_level = (
-                    onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
-                )
-
-                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."
-        )
+            match platform.system():
+                case "Darwin":  # Mac OS
+                    if torch.backends.mps.is_available():
+                        mps_device = torch.device("mps")
+                        FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1, device=mps_device)  # type: ignore[attr-defined]
+                    else:
+                        FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1)  # type: ignore[attr-defined]
+                case _:  # Other OS
+                    FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1)  # type: ignore[attr-defined]

    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:
-    """Enhances all faces in a frame using the GFPGAN ONNX model."""
-    session = get_face_enhancer()
-
-    # 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:
+    with THREAD_SEMAPHORE:
+        _, _, temp_frame = get_face_enhancer().enhance(temp_frame, paste_back=True)
    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 | None, temp_frame: Frame) -> Frame:
-    """Processes a frame: enhances face if detected."""
+def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
+    target_face = get_one_face(temp_frame)
+    if target_face:
        temp_frame = enhance_face(temp_frame)
    return temp_frame


 def process_frames(
-    source_path: str | None, temp_frame_paths: List[str], progress: Any = None
+    source_path: str, 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)
-        if temp_frame is None:
-            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)
+        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:
-    """Processes a single image file."""
+def process_image(source_path: str, 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}")
+    result = process_frame(None, target_frame)
+    cv2.imwrite(output_path, result)


-def process_video(
-    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_video(source_path: str, temp_frame_paths: List[str]) -> None:
+    modules.processors.frame.core.process_video(None, temp_frame_paths, process_frames)


-# --- END OF FILE face_enhancer.py ---
+def process_frame_v2(temp_frame: Frame) -> Frame:
+    target_face = get_one_face(temp_frame)
+    if target_face:
+        temp_frame = enhance_face(temp_frame)
+    return temp_frame
@@ -1,125 +0,0 @@
-"""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)
@@ -1,125 +0,0 @@
-"""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)
@@ -1,577 +0,0 @@
-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
@@ -1,9 +0,0 @@
-#!/usr/bin/env python3
-
-# Import the tkinter fix to patch the ScreenChanged error
-import tkinter_fix
-
-import core
-
-if __name__ == '__main__':
-    core.run()
@@ -1,26 +0,0 @@
-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() 
@@ -1,74 +0,0 @@
-"""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
@@ -15,16 +15,19 @@ 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", "auto",  # Auto-detect hardware acceleration
-        "-hwaccel_output_format", "auto",  # Use hardware format when possible
-        "-threads", str(modules.globals.execution_threads or 0),  # 0 = auto-detect optimal thread count
-        "-loglevel", modules.globals.log_level,
+        "-hwaccel",
+        "auto",
+        "-loglevel",
+        modules.globals.log_level,
    ]
    commands.extend(args)
    try:
@@ -58,131 +61,39 @@ 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", target_path,
-            "-vf", "format=rgb24",  # Use video filter for format conversion (faster)
-            "-vsync", "0",  # Prevent frame duplication
-            "-frame_pts", "1",  # Preserve frame timing
+            "-i",
+            target_path,
+            "-pix_fmt",
+            "rgb24",
            os.path.join(temp_directory_path, "%04d.png"),
        ]
    )


 def create_video(target_path: str, fps: float = 30.0) -> None:
-    """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)
-    
-    # Determine optimal encoder based on available hardware
-    encoder = modules.globals.video_encoder
-    encoder_options = []
-    
-    # GPU-accelerated encoding options
-    if 'CUDAExecutionProvider' in modules.globals.execution_providers:
-        # NVIDIA GPU encoding
-        if encoder == 'libx264':
-            encoder = 'h264_nvenc'
-            encoder_options = [
-                "-preset", "p7",  # Highest quality preset for NVENC
-                "-tune", "hq",  # High quality tuning
-                "-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",
+    run_ffmpeg(
+        [
+            "-r",
+            str(fps),
+            "-i",
+            os.path.join(temp_directory_path, "%04d.png"),
+            "-c:v",
+            modules.globals.video_encoder,
+            "-crf",
+            str(modules.globals.video_quality),
+            "-pix_fmt",
+            "yuv420p",
+            "-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:
@@ -282,15 +193,8 @@ 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.Request(url)
-            
-            # 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))
+            request = urllib.request.urlopen(url)  # type: ignore[attr-defined]
+            total = int(request.headers.get("Content-Length", 0))
            with tqdm(
                total=total,
                desc="Downloading",
@@ -298,13 +202,7 @@ def conditional_download(download_directory_path: str, urls: List[str]) -> None:
                unit_scale=True,
                unit_divisor=1024,
            ) as progress:
-                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))
+                urllib.request.urlretrieve(url, download_file_path, reporthook=lambda count, block_size, total_size: progress.update(block_size))  # type: ignore[attr-defined]


 def resolve_relative_path(path: str) -> str:
@@ -1,16 +1,25 @@
+--extra-index-url https://download.pytorch.org/whl/cu118
+
 numpy>=1.23.5,<2
 typing-extensions>=4.8.0
 opencv-python==4.10.0.84
 cv2_enumerate_cameras==1.1.15
-onnx==1.18.0
+onnx==1.16.0
 insightface==0.7.3
 psutil==5.9.8
 tk==0.1.0
 customtkinter==5.2.2
-pillow==12.1.1
+pillow==11.1.0
+torch==2.5.1+cu118; sys_platform != 'darwin'
+torch==2.5.1; sys_platform == 'darwin'
+torchvision==0.20.1; sys_platform != 'darwin'
+torchvision==0.20.1; sys_platform == 'darwin'
 onnxruntime-silicon==1.16.3; sys_platform == 'darwin' and platform_machine == 'arm64'
-onnxruntime-gpu==1.23.2; sys_platform != 'darwin'
+onnxruntime-gpu==1.16.3; sys_platform != 'darwin'
 tensorflow; sys_platform != 'darwin'
 opennsfw2==0.10.2
-protobuf==4.25.1
-pygrabber
+protobuf==4.23.2
+tqdm==4.66.4
+gfpgan==1.3.8
+tkinterdnd2==0.4.2
+pygrabber==0.2
@@ -1,8 +1,5 @@
 #!/usr/bin/env python3

-# Import the tkinter fix to patch the ScreenChanged error
-import tkinter_fix
-
 from modules import core

 if __name__ == '__main__':
@@ -1,29 +0,0 @@
-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()
Author	SHA1	Message	Date
KRSHH	30ffbc1042	add Drag and drop functionality	2025-03-26 13:08:18 +05:30
KRSHH	2b2caf6aba	Update ui.py	2025-03-26 12:14:29 +05:30