docs: align GFPGAN model filenames (#1910)

README and models/instructions.txt named GFPGANv1.4, but the enhancer loads gfpgan-1024.onnx via onnxruntime. instructions.txt also pointed at a PyTorch .pth that the ONNX path cannot load at all, so the manual-fallback instructions were unusable.

.github/workflows/ruff.yml

@@ -0,0 +1,16 @@
+name: ruff
+
+on:
+  pull_request:
+  push:
+    branches: [main]
+
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: astral-sh/ruff-action@v4.0.0
+        with:
+          version: "0.15.7"
+          args: "check --output-format=github"

README.md

@@ -30,13 +30,45 @@ 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)
+## Pre-built Deep-Live-Cam 2.7 Ultimate!
 
-  <a href="https://deeplivecam.net/index.php/quickstart"> <img src="media/Download.png" width="285" height="77" />
+<p align="center">
+  <a href="https://deeplivecam.net/index.php/quickstart">
+    <img src="https://github.com/user-attachments/assets/fa2cdf79-c933-4b93-844a-b087192261ed" width="100%" alt="Lite / Ultimate Download Banner">
+  </a>
+</p>
 
-##### This is the fastest build you can get if you have a discrete NVIDIA or AMD GPU, CPU or Mac Silicon, And you'll receive special priority support. 2.7 beta is the best you can have with 30+ extra features than the open source version.
- 
-###### These Pre-builts are perfect for non-technical users or those who don't have time to, or can't manually install all the requirements. Just a heads-up: this is an open-source project, so you can also install it manually. 
+<p align="center">
+<a href="https://deeplivecam.net/index.php/plans/nvidia-gpu?plan_id=0&group_id=1">
+  <img src="https://github.com/user-attachments/assets/56b61811-3a1e-4672-9b50-cf7f6e8e6852" width="40" alt="Windows">
+</a>
+  &nbsp;&nbsp;&nbsp;
+<a href="https://deeplivecam.net/index.php/plans/nvidia-gpu?plan_id=0&group_id=2">
+  <img src="https://github.com/user-attachments/assets/6538e3a6-c957-431a-b586-2d6abcf534dc" width="34" alt="Mac Silicon">
+</a>
+  &nbsp;&nbsp;&nbsp;
+<a href="https://deeplivecam.net/index.php/plans/nvidia-gpu?plan_id=0&group_id=3">
+  <img src="https://github.com/user-attachments/assets/ad45142e-426c-4364-a2a9-a512670cc62c" width="40" alt="CPU">
+</a>
+</p>
+
+<p align="center">
+  <strong>Windows • Mac Silicon • CPU • NVIDIA • AMD</strong>
+</p>
+
+<p align="center">
+  Builds optimized for your hardware.
+</p>
+
+<p align="center">
+  <a href="https://deeplivecam.net/index.php/quickstart">
+    <img src="media/Download.png" width="280" alt="Download">
+  </a>
+</p>
+
+> **Ultimate** includes **30+ exclusive features**, performance optimizations, and **priority support** We only have a single official website which is https://deeplivecam.net . Please be careful on where you download other versions of this application aside from that website and this github repo.
+
+Perfect if you want the fastest setup with **zero manual installation**, pre-configured dependencies, and optimized builds for every supported platform.
 
 ## TLDR; Live Deepfake in just 3 Clicks
 ![easysteps](https://github.com/user-attachments/assets/af825228-852c-411b-b787-ffd9aac72fc6)
@@ -109,7 +141,7 @@ 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 (3.14 recommended; 3.11-3.14 supported)
 -   pip
 -   git
 -   [ffmpeg](https://www.youtube.com/watch?v=OlNWCpFdVMA) - ```iex (irm ffmpeg.tc.ht)```
@@ -118,13 +150,13 @@ This is more likely to work on your computer but will be slower as it utilizes t
 **2. Clone the Repository**
 
 ```bash
-git clone https://github.com/hacksider/Deep-Live-Cam.git
+git clone --depth 1 https://github.com/hacksider/Deep-Live-Cam.git
 cd Deep-Live-Cam
 ```
 
 **3. Download the Models**
 
-1. [GFPGANv1.4](https://huggingface.co/hacksider/deep-live-cam/resolve/main/GFPGANv1.4.onnx)
+1. [gfpgan-1024.onnx](https://huggingface.co/hacksider/deep-live-cam/resolve/main/gfpgan-1024.onnx)
 2. [inswapper\_128\_fp16.onnx](https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128_fp16.onnx)
 
 Place these files in the "**models**" folder.
@@ -142,7 +174,7 @@ pip install -r requirements.txt
 ```
 For Linux:
 ```bash
-# Ensure you use the installed Python 3.11
+# Ensure you use the installed Python 3.14
 python3 -m venv venv
 source venv/bin/activate
 pip install -r requirements.txt
@@ -150,17 +182,17 @@ pip install -r requirements.txt
 
 **For macOS:**
 
-Apple Silicon (M1/M2/M3) requires specific setup:
+Apple Silicon (M1 through M5) requires specific setup:
 
 ```bash
-# Install Python 3.11 (specific version is important)
-brew install python@3.11
+# Install Python 3.14
+brew install python@3.14
 
 # Install tkinter package (required for the GUI)
-brew install python-tk@3.11
+brew install python-tk@3.14
 
-# Create and activate virtual environment with Python 3.11
-python3.11 -m venv venv
+# Create and activate virtual environment with Python 3.14
+python3.14 -m venv venv
 source venv/bin/activate
 
 # Install dependencies
@@ -201,7 +233,7 @@ pip install git+https://github.com/TencentARC/GFPGAN.git@master
 ```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==1.26.0
 ```
 
 3. Usage:
@@ -212,26 +244,29 @@ python run.py --execution-provider cuda
 
 **CoreML Execution Provider (Apple Silicon)**
 
-Apple Silicon (M1/M2/M3) specific installation:
+Apple Silicon (M1 through M5) specific installation:
 
-1. Make sure you've completed the macOS setup above using Python 3.11.
-2. Install dependencies:
+1. Make sure you've completed the macOS setup above using Python 3.14.
+2. No extra install step is needed — `requirements.txt` pulls the official
+   `onnxruntime` build, whose macOS wheels ship the CoreML execution provider.
+   If you previously installed the unmaintained `onnxruntime-silicon` fork,
+   remove it first, as it shadows the real package:
 
 ```bash
-pip uninstall onnxruntime onnxruntime-silicon
-pip install onnxruntime-silicon==1.13.1
+pip uninstall onnxruntime-silicon
+pip install -r requirements.txt
 ```
 
 3. Usage:
 
 ```bash
-python3.11 run.py --execution-provider coreml
+python3.14 run.py --execution-provider coreml
 ```
 
 **Important Notes for macOS:**
-- You **must** use Python 3.11, not newer versions like 3.13
-- Always run with `python3.11` command not just `python` if you have multiple Python versions installed
-- If you get error about `_tkinter` missing, reinstall the tkinter package: `brew reinstall python-tk@3.11`
+- Python 3.11 is the minimum (onnxruntime dropped 3.10); 3.14 is recommended
+- Always run with `python3.14` command not just `python` if you have multiple Python versions installed
+- If you get error about `_tkinter` missing, reinstall the tkinter package: `brew reinstall python-tk@3.14`
 - If you get model loading errors, check that your models are in the correct folder
 - If you encounter conflicts with other Python versions, consider uninstalling them:
   ```bash
@@ -239,9 +274,9 @@ python3.11 run.py --execution-provider coreml
   brew list | grep python
 
   # Uninstall conflicting versions if needed
-  brew uninstall --ignore-dependencies python@3.13
+  brew uninstall --ignore-dependencies python@3.11
 
-  # Keep only Python 3.11
+  # Keep only Python 3.14
   brew cleanup
   ```
 
@@ -284,6 +319,22 @@ pip uninstall onnxruntime onnxruntime-openvino
 pip install onnxruntime-openvino==1.21.0
 ```
 
+**Note:** `onnxruntime-openvino` newer than 1.21.0 must be installed together with `openvino`, and the two versions must correspond one-to-one. The supported pairings are:
+
+| onnxruntime-openvino | OpenVINO |
+| --- | --- |
+| 1.24.1 | 2025.4.1 |
+| 1.23.0 | 2025.3 |
+| 1.22.0 | 2025.1 |
+
+```bash
+# Example: onnxruntime-openvino 1.24.1 pairs with OpenVINO 2025.4.1
+pip install openvino==2025.4.1
+pip install onnxruntime-openvino==1.24.1
+```
+
+See the [OpenVINO Execution Provider requirements](https://onnxruntime.ai/docs/execution-providers/OpenVINO-ExecutionProvider.html#requirements) for the full version-mapping details.
+
 2. Usage:
 
 ```bash

benchmark_pipeline.py

@@ -14,7 +14,6 @@ if sys.platform == "win32":
 
 import insightface
 from insightface.app import FaceAnalysis
-from insightface.utils import face_align
 from modules.processors.frame.face_swapper import _fast_paste_back
 from modules import platform_info
 
@@ -81,10 +80,14 @@ def capture_thread():
         try:
             capture_queue.put_nowait(frame)
         except queue.Full:
-            try: capture_queue.get_nowait()
-            except queue.Empty: pass
-            try: capture_queue.put_nowait(frame)
-            except queue.Full: pass
+            try:
+                capture_queue.get_nowait()
+            except queue.Empty:
+                pass
+            try:
+                capture_queue.put_nowait(frame)
+            except queue.Full:
+                pass
 
 cap_t = threading.Thread(target=capture_thread, daemon=True)
 cap_t.start()

models/instructions.txt

@@ -1,4 +1,4 @@
 just put the models in this folder -
 
 https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128_fp16.onnx?download=true
-https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth
+https://huggingface.co/hacksider/deep-live-cam/resolve/main/gfpgan-1024.onnx?download=true

modules/__init__.py

@@ -1,18 +1,38 @@
-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
+import os
+import cv2
+import numpy as np
+
+
+# Utility function to support unicode characters in file paths for reading.
+# OpenCV's cv2.imread() encodes the path with the locale ANSI code page on
+# Windows, so it silently returns None for paths containing non-ASCII
+# characters (Chinese, Japanese, Cyrillic, accents, ...). Reading the bytes
+# through NumPy (which uses Python's unicode-aware file I/O) and decoding them
+# in memory sidesteps that limitation. Returns None on failure, matching
+# cv2.imread() so it stays a drop-in replacement.
+def imread_unicode(path, flags=cv2.IMREAD_COLOR):
+    try:
+        data = np.fromfile(path, dtype=np.uint8)
+        if data.size == 0:
+            return None
+        return cv2.imdecode(data, flags)
+    except Exception:
+        return None
+
+
+# Utility function to support unicode characters in file paths for writing.
+# cv2.imwrite() has the same ANSI-path limitation, so we encode the image in
+# memory and write the bytes out with NumPy's unicode-aware file I/O. Returns
+# True/False like cv2.imwrite() so it stays a drop-in replacement.
+def imwrite_unicode(path, img, params=None):
+    try:
+        root, ext = os.path.splitext(path)
+        if not ext:
+            ext = ".png"
+        result, encoded_img = cv2.imencode(ext, img, params if params is not None else [])
+        if not result:
+            return False
+        encoded_img.tofile(path)
+        return True
+    except Exception:
+        return False

modules/capturer.py

@@ -14,8 +14,13 @@ def get_video_frame(video_path: str, frame_number: int = 0) -> Any:
     if modules.globals.color_correction:
         capture.set(cv2.CAP_PROP_CONVERT_RGB, 1)
     
-    frame_total = capture.get(cv2.CAP_PROP_FRAME_COUNT)
-    capture.set(cv2.CAP_PROP_POS_FRAMES, min(frame_total, frame_number - 1))
+    frame_total = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
+    if frame_total <= 0:
+        capture.release()
+        return None
+
+    target_index = 0 if frame_number <= 1 else min(frame_total - 1, frame_number - 1)
+    capture.set(cv2.CAP_PROP_POS_FRAMES, target_index)
     has_frame, frame = capture.read()
 
     if has_frame and modules.globals.color_correction:

modules/cluster_analysis.py

@@ -1,10 +1,21 @@
 import numpy as np
 from sklearn.cluster import KMeans
-from sklearn.metrics import silhouette_score
 from typing import Any
 
 
 def find_cluster_centroids(embeddings, max_k=10) -> Any:
+    n_samples = len(embeddings)
+    if n_samples == 0:
+        raise ValueError("embeddings must not be empty")
+    if max_k < 1:
+        raise ValueError("max_k must be at least 1")
+
+    max_k = min(max_k, n_samples)
+    if max_k == 1:
+        kmeans = KMeans(n_clusters=1, random_state=0)
+        kmeans.fit(embeddings)
+        return kmeans.cluster_centers_
+
     inertia = []
     cluster_centroids = []
     K = range(1, max_k+1)

modules/core.py

@@ -58,7 +58,7 @@ def parse_args() -> None:
     program.add_argument('--live-resizable', help='The live camera frame is resizable', dest='live_resizable', action='store_true', default=False)
     program.add_argument('--max-memory', help='maximum amount of RAM in GB', dest='max_memory', type=int, default=suggest_max_memory())
     program.add_argument('--execution-provider', help='execution provider', dest='execution_provider', default=[suggest_default_execution_provider()], choices=suggest_execution_providers(), nargs='+')
-    program.add_argument('--execution-threads', help='number of execution threads', dest='execution_threads', type=int, default=suggest_execution_threads())
+    program.add_argument('--execution-threads', help='number of execution threads', dest='execution_threads', type=int, default=None)
     program.add_argument('-v', '--version', action='version', version=f'{modules.metadata.name} {modules.metadata.version}')
 
     # register deprecated args
@@ -90,6 +90,12 @@ def parse_args() -> None:
     modules.globals.execution_threads = args.execution_threads
     modules.globals.lang = args.lang
 
+    # The argparse default (None) avoids evaluating suggest_execution_threads()
+    # before providers are decoded, and deprecated-arg overrides above may
+    # have already set execution_threads.
+    if modules.globals.execution_threads is None:
+        modules.globals.execution_threads = suggest_execution_threads()
+
     #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
@@ -132,9 +138,9 @@ def suggest_max_memory() -> int:
 
 
 def suggest_default_execution_provider() -> str:
-    """Pick the best available provider: cuda > rocm > coreml > dml > cpu."""
+    """Pick the best available provider: cuda > rocm > coreml > openvino > dml > cpu."""
     available = encode_execution_providers(onnxruntime.get_available_providers())
-    for pref in ('cuda', 'rocm', 'coreml', 'dml'):
+    for pref in ('cuda', 'rocm', 'coreml', 'openvino', 'dml'):
         if pref in available:
             return pref
     return 'cpu'
@@ -157,6 +163,8 @@ def suggest_execution_threads() -> int:
         return 1
     if 'CUDAExecutionProvider' in modules.globals.execution_providers:
         return 2
+    if 'OpenVINOExecutionProvider' in modules.globals.execution_providers:
+        return 1
     
     # For CPU execution, use most cores but leave some for system
     return max(4, min(cpu_count - 2, 16))
@@ -170,9 +178,11 @@ def limit_resources() -> None:
             tensorflow.config.experimental.set_memory_growth(gpu, True)
     # limit memory usage
     if modules.globals.max_memory:
-        memory = modules.globals.max_memory * 1024 ** 3
+        # setrlimit(RLIMIT_DATA) fails with EINVAL on macOS, crashing on launch.
+        # See https://github.com/hacksider/Deep-Live-Cam/issues/1848
         if platform.system().lower() == 'darwin':
-            memory = modules.globals.max_memory * 1024 ** 6
+            return
+        memory = modules.globals.max_memory * 1024 ** 3
         if platform.system().lower() == 'windows':
             import ctypes
             kernel32 = ctypes.windll.kernel32
@@ -270,10 +280,9 @@ def start() -> None:
             update_status('Falling back to disk-based processing...')
 
         extraction_start = time.time()
-        if not modules.globals.map_faces:
-            create_temp(modules.globals.target_path)
-            update_status('Extracting frames...')
-            extract_frames(modules.globals.target_path)
+        create_temp(modules.globals.target_path)
+        update_status('Extracting frames...')
+        extract_frames(modules.globals.target_path)
         extraction_time = time.time() - extraction_start
 
         temp_frame_paths = get_temp_frame_paths(modules.globals.target_path)
@@ -324,7 +333,8 @@ def start() -> None:
 def destroy(to_quit=True) -> None:
     if modules.globals.target_path:
         clean_temp(modules.globals.target_path)
-    if to_quit: quit()
+    if to_quit:
+        quit()
 
 
 def run() -> None:

modules/face_analyser.py

@@ -4,9 +4,8 @@ from typing import Any
 import insightface
 import threading
 
-import cv2
-import numpy as np
 import modules.globals
+from modules import imread_unicode, imwrite_unicode
 from tqdm import tqdm
 from modules.typing import Frame
 from modules.cluster_analysis import find_cluster_centroids, find_closest_centroid
@@ -30,6 +29,9 @@ def get_face_analyser() -> Any:
                 from modules.processors.frame._onnx_enhancer import (
                     build_provider_config,
                 )
+                from modules.model_downloader import ensure_insightface_pack
+
+                ensure_insightface_pack('buffalo_l')
                 providers = build_provider_config()
                 FACE_ANALYSER = insightface.app.FaceAnalysis(
                     name='buffalo_l',
@@ -255,8 +257,10 @@ def add_blank_map() -> Any:
 def get_unique_faces_from_target_image() -> Any:
     try:
         modules.globals.source_target_map = []
-        target_frame = cv2.imread(modules.globals.target_path)
+        target_frame = imread_unicode(modules.globals.target_path)
         many_faces = get_many_faces(target_frame)
+        if many_faces is None:
+            return None
         i = 0
 
         for face in many_faces:
@@ -289,8 +293,10 @@ def get_unique_faces_from_target_video() -> Any:
 
         i = 0
         for temp_frame_path in tqdm(temp_frame_paths, desc="Extracting face embeddings from frames"):
-            temp_frame = cv2.imread(temp_frame_path)
+            temp_frame = imread_unicode(temp_frame_path)
             many_faces = get_many_faces(temp_frame)
+            if many_faces is None:
+                continue
 
             for face in many_faces:
                 face_embeddings.append(face.normed_embedding)
@@ -332,6 +338,9 @@ def default_target_face():
                 best_frame = frame
                 break
 
+        if best_face is None:
+            continue  # No faces detected in this cluster — skip
+
         for frame in map['target_faces_in_frame']:
             for face in frame['faces']:
                 if face['det_score'] > best_face['det_score']:
@@ -340,7 +349,7 @@ def default_target_face():
 
         x_min, y_min, x_max, y_max = best_face['bbox']
 
-        target_frame = cv2.imread(best_frame['location'])
+        target_frame = imread_unicode(best_frame['location'])
         map['target'] = {
                         'cv2' : target_frame[int(y_min):int(y_max), int(x_min):int(x_max)],
                         'face' : best_face
@@ -356,7 +365,7 @@ def dump_faces(centroids: Any, frame_face_embeddings: list):
         Path(temp_directory_path + f"/{i}").mkdir(parents=True, exist_ok=True)
 
         for frame in tqdm(frame_face_embeddings, desc=f"Copying faces to temp/./{i}"):
-            temp_frame = cv2.imread(frame['location'])
+            temp_frame = imread_unicode(frame['location'])
 
             j = 0
             for face in frame['faces']:
@@ -364,5 +373,5 @@ def dump_faces(centroids: Any, frame_face_embeddings: list):
                     x_min, y_min, x_max, y_max = face['bbox']
 
                     if temp_frame[int(y_min):int(y_max), int(x_min):int(x_max)].size > 0:
-                        cv2.imwrite(temp_directory_path + f"/{i}/{frame['frame']}_{j}.png", temp_frame[int(y_min):int(y_max), int(x_min):int(x_max)])
+                        imwrite_unicode(temp_directory_path + f"/{i}/{frame['frame']}_{j}.png", temp_frame[int(y_min):int(y_max), int(x_min):int(x_max)])
                 j += 1

modules/globals.py

@@ -6,10 +6,13 @@ from typing import List, Dict, Any
 ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
 WORKFLOW_DIR = os.path.join(ROOT_DIR, "workflow")
 
-file_types = [
-    ("Image", ("*.png", "*.jpg", "*.jpeg", "*.gif", "*.bmp")),
-    ("Video", ("*.mp4", "*.mkv")),
-]
+# Canonical media extensions, defined once so the file dialogs and
+# has_image_extension never drift. GIF is intentionally excluded: OpenCV's
+# cv2.imread/imwrite (the only image I/O this app uses) cannot decode or
+# encode GIF on 4.10 or 4.11, so offering it would silently fail. WEBP works
+# via the libwebp bundled with opencv-python.
+IMAGE_EXTENSIONS = (".png", ".jpg", ".jpeg", ".bmp", ".webp")
+VIDEO_EXTENSIONS = (".mp4", ".mkv")
 
 # Face Mapping Data
 source_target_map: List[Dict[str, Any]] = [] # Stores detailed map for image/video processing

modules/gpu_processing.py

@@ -21,7 +21,7 @@ from __future__ import annotations
 import os
 import cv2
 import numpy as np
-from typing import Tuple, Optional
+from typing import Tuple
 
 # ---------------------------------------------------------------------------
 # CUDA availability detection (evaluated once at import time)

modules/model_downloader.py

@@ -0,0 +1,178 @@
+import os
+import platform
+import ssl
+import threading
+import urllib.error
+import urllib.request
+from typing import Dict, List, Optional
+
+from tqdm import tqdm
+
+from modules.paths import MODELS_DIR
+
+HF_REPO_ID = "hacksider/deep-live-cam"
+HF_RESOLVE_BASE = f"https://huggingface.co/{HF_REPO_ID}/resolve/main/"
+
+MODEL_SIZES: Dict[str, int] = {
+    "inswapper_128.onnx": 554253681,
+    "inswapper_128_fp16.onnx": 277680638,
+    "gfpgan-1024.onnx": 365875079,
+    "GPEN-BFR-256.onnx": 75715262,
+    "GPEN-BFR-512.onnx": 284244491,
+    "buffalo_l/buffalo_l/1k3d68.onnx": 143607619,
+    "buffalo_l/buffalo_l/2d106det.onnx": 5030888,
+    "buffalo_l/buffalo_l/det_10g.onnx": 16923827,
+    "buffalo_l/buffalo_l/genderage.onnx": 1322532,
+    "buffalo_l/buffalo_l/w600k_r50.onnx": 174383860,
+}
+
+_LOCKS: Dict[str, threading.Lock] = {}
+_LOCKS_GUARD = threading.Lock()
+
+CHUNK_SIZE = 1024 * 256
+
+
+def _ssl_context():
+    if platform.system().lower() == "darwin":
+        return ssl._create_unverified_context()
+    return None
+
+
+def _lock_for(key: str) -> threading.Lock:
+    with _LOCKS_GUARD:
+        if key not in _LOCKS:
+            _LOCKS[key] = threading.Lock()
+        return _LOCKS[key]
+
+
+def resolve_url(name: str) -> str:
+    return HF_RESOLVE_BASE + name.replace(os.sep, "/")
+
+
+def local_path(name: str, dest_dir: Optional[str] = None) -> str:
+    if dest_dir is not None:
+        return os.path.join(dest_dir, os.path.basename(name))
+    return os.path.join(MODELS_DIR, *name.replace("/", os.sep).split(os.sep))
+
+
+def expected_size(name: str) -> Optional[int]:
+    return MODEL_SIZES.get(name.replace(os.sep, "/"))
+
+
+def is_present(name: str, dest_dir: Optional[str] = None) -> bool:
+    path = local_path(name, dest_dir)
+    return os.path.isfile(path) and os.path.getsize(path) > 0
+
+
+def _download(name: str, url: str, target: str, size: Optional[int]) -> bool:
+    os.makedirs(os.path.dirname(target) or MODELS_DIR, exist_ok=True)
+    partial = target + ".part"
+    resume_from = os.path.getsize(partial) if os.path.isfile(partial) else 0
+
+    headers = {"User-Agent": "Deep-Live-Cam"}
+    if resume_from:
+        headers["Range"] = f"bytes={resume_from}-"
+
+    try:
+        request = urllib.request.Request(url, headers=headers)
+        response = urllib.request.urlopen(request, context=_ssl_context(), timeout=60)
+    except urllib.error.HTTPError as error:
+        if resume_from and error.code in (416, 501):
+            try:
+                os.remove(partial)
+            except OSError:
+                pass
+            return _download(name, url, target, size)
+        print(f"[DLC.MODELS] Failed to download {name}: HTTP {error.code}")
+        return False
+    except (urllib.error.URLError, OSError) as error:
+        print(f"[DLC.MODELS] Failed to download {name}: {error}")
+        return False
+
+    with response:
+        if resume_from and getattr(response, "status", 200) != 206:
+            resume_from = 0
+        remaining = int(response.headers.get("Content-Length", 0) or 0)
+        total = size or (resume_from + remaining) or None
+        mode = "ab" if resume_from else "wb"
+        try:
+            with open(partial, mode) as handle:
+                with tqdm(
+                    total=total,
+                    initial=resume_from,
+                    desc=f"Downloading {os.path.basename(name)}",
+                    unit="B",
+                    unit_scale=True,
+                    unit_divisor=1024,
+                ) as progress:
+                    while True:
+                        buffer = response.read(CHUNK_SIZE)
+                        if not buffer:
+                            break
+                        handle.write(buffer)
+                        progress.update(len(buffer))
+        except (urllib.error.URLError, OSError) as error:
+            print(f"[DLC.MODELS] Download of {name} interrupted: {error}")
+            return False
+
+    downloaded = os.path.getsize(partial)
+    if size is not None and downloaded != size:
+        print(f"[DLC.MODELS] {name} is {downloaded} bytes, expected {size}. Discarding.")
+        try:
+            os.remove(partial)
+        except OSError:
+            pass
+        return False
+
+    try:
+        os.replace(partial, target)
+    except OSError as error:
+        print(f"[DLC.MODELS] Could not finalise {name}: {error}")
+        return False
+    return True
+
+
+def ensure_model(
+    name: str, quiet: bool = False, dest_dir: Optional[str] = None
+) -> Optional[str]:
+    name = name.replace(os.sep, "/")
+    target = local_path(name, dest_dir)
+
+    with _lock_for(target):
+        if is_present(name, dest_dir):
+            return target
+        if not quiet:
+            print(f"[DLC.MODELS] {name} not found in models folder, downloading...")
+        if _download(name, resolve_url(name), target, expected_size(name)):
+            return target
+    return None
+
+
+def ensure_any(names: List[str]) -> Optional[str]:
+    for name in names:
+        if is_present(name):
+            return local_path(name)
+    for name in names:
+        path = ensure_model(name)
+        if path is not None:
+            return path
+    return None
+
+
+def ensure_insightface_pack(name: str = "buffalo_l") -> bool:
+    members = [n for n in MODEL_SIZES if n.startswith(f"{name}/")]
+    if not members:
+        return False
+
+    dest_dir = os.path.join(os.path.expanduser("~"), ".insightface", "models", name)
+    if all(is_present(member, dest_dir) for member in members):
+        return True
+
+    print(f"[DLC.MODELS] insightface pack '{name}' is missing, downloading...")
+    ok = True
+    for member in members:
+        if ensure_model(member, quiet=True, dest_dir=dest_dir) is None:
+            ok = False
+    if not ok:
+        print(f"[DLC.MODELS] Could not pre-fill '{name}'; insightface will retry.")
+    return ok

modules/onnx_optimize.py

@@ -392,7 +392,7 @@ def _decompose_split(model) -> bool:
 
     # Collect all needed boundary constants
     for _, (a, b) in splits:
-        ensure_const(f"_sp_s0", [0])
+        ensure_const("_sp_s0", [0])
         ensure_const(f"_sp_s{a}", [a])
         ensure_const(f"_sp_s{a + b}", [a + b])
 

modules/platform_info.py

@@ -40,6 +40,15 @@ ONNX_PROVIDERS: List[str] = _detect_onnx_providers()
 HAS_CUDA_PROVIDER: bool = "CUDAExecutionProvider" in ONNX_PROVIDERS
 HAS_COREML_PROVIDER: bool = "CoreMLExecutionProvider" in ONNX_PROVIDERS
 HAS_DML_PROVIDER: bool = "DmlExecutionProvider" in ONNX_PROVIDERS
+HAS_OPENVINO_PROVIDER: bool = "OpenVINOExecutionProvider" in ONNX_PROVIDERS
+
+# OpenVINO execution-provider config shared by every ONNX session builder.
+# AUTO:GPU,NPU,CPU lets OpenVINO pick the best available device in priority
+# order (Intel GPU → NPU → CPU).
+OPENVINO_PROVIDER_CONFIG = (
+    "OpenVINOExecutionProvider",
+    {"device_type": "AUTO:GPU,NPU,CPU"},
+)
 
 
 def camera_backends() -> List[Tuple[int, int]]:
@@ -65,6 +74,8 @@ def accelerator_label() -> str:
         return "CoreML (Apple Neural Engine)"
     if HAS_COREML_PROVIDER:
         return "CoreML"
+    if HAS_OPENVINO_PROVIDER:
+        return "OpenVINO (Intel)"
     if HAS_DML_PROVIDER:
         return "DirectML"
     return "CPU"

modules/predicter.py

@@ -1,4 +1,17 @@
+import importlib.util
+import os
+
 import numpy
+
+# Keras 3 defaults to the TensorFlow backend, which has no Python 3.14 wheels.
+# opennsfw2 only runs inference, so any installed backend works; pick one that
+# is actually present before opennsfw2 imports keras.
+if "KERAS_BACKEND" not in os.environ:
+    for _backend in ("torch", "tensorflow", "jax"):
+        if importlib.util.find_spec(_backend) is not None:
+            os.environ["KERAS_BACKEND"] = _backend
+            break
+
 import opennsfw2
 from PIL import Image
 import cv2  # Add OpenCV import

modules/processors/frame/_onnx_enhancer.py

@@ -14,6 +14,7 @@ import numpy as np
 import onnxruntime
 
 import modules.globals
+from modules.platform_info import OPENVINO_PROVIDER_CONFIG
 
 IS_APPLE_SILICON = platform.system() == "Darwin" and platform.machine() == "arm64"
 
@@ -50,6 +51,9 @@ def build_provider_config(providers=None):
                     "AllowLowPrecisionAccumulationOnGPU": 1,
                 },
             ))
+        elif p == "OpenVINOExecutionProvider":
+            # AUTO lets OpenVINO select the best device
+            config.append(OPENVINO_PROVIDER_CONFIG)
         else:
             config.append(p)
     return config

modules/processors/frame/core.py

@@ -37,6 +37,7 @@ def load_frame_processor_module(frame_processor: str) -> Any:
         frame_processor_module = importlib.import_module(f'modules.processors.frame.{frame_processor}')
         for method_name in FRAME_PROCESSORS_INTERFACE:
             if not hasattr(frame_processor_module, method_name):
+                print(f"Frame processor {frame_processor} is missing required method {method_name}")
                 sys.exit()
     except ImportError:
         print(f"Frame processor {frame_processor} not found")
@@ -59,7 +60,7 @@ def set_frame_processors_modules_from_ui(frame_processors: List[str]) -> None:
     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:
+        if state and frame_processor not in current_processor_names:
             try:
                 frame_processor_module = load_frame_processor_module(frame_processor)
                 FRAME_PROCESSORS_MODULES.append(frame_processor_module)
@@ -70,7 +71,7 @@ def set_frame_processors_modules_from_ui(frame_processors: List[str]) -> None:
             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:
+        elif not state and frame_processor in current_processor_names:
             try:
                 module_to_remove = next((mod for mod in FRAME_PROCESSORS_MODULES if mod.__name__.endswith(f'.{frame_processor}')), None)
                 if module_to_remove:
@@ -125,7 +126,7 @@ def process_video_in_memory(source_path: str, target_path: str, fps: float) -> b
     Returns True on success, False on failure (caller should fall back to the
     disk-based pipeline).
     """
-    import cv2
+    from modules import imread_unicode
     from modules.face_analyser import get_one_face
     from modules.utilities import (
         get_video_dimensions,
@@ -138,7 +139,7 @@ def process_video_in_memory(source_path: str, target_path: str, fps: float) -> b
     # --- Pre-load source face (needed by face_swapper in simple mode) ---
     source_face = None
     if source_path and os.path.exists(source_path):
-        source_img = cv2.imread(source_path)
+        source_img = imread_unicode(source_path)
         if source_img is not None:
             source_face = get_one_face(source_img)
             del source_img

modules/processors/frame/face_enhancer.py

@@ -10,8 +10,9 @@ import onnxruntime
 
 import modules.globals
 import modules.processors.frame.core
+from modules import imread_unicode, imwrite_unicode
 from modules.core import update_status
-from modules.face_analyser import get_one_face, get_many_faces
+from modules.face_analyser import get_many_faces
 from modules.typing import Frame, Face
 from modules.utilities import (
     is_image,
@@ -22,6 +23,7 @@ FACE_ENHANCER = None
 THREAD_SEMAPHORE = threading.Semaphore()
 THREAD_LOCK = threading.Lock()
 NAME = "DLC.FACE-ENHANCER"
+MODEL_FILE = "gfpgan-1024.onnx"
 
 abs_dir = os.path.dirname(os.path.abspath(__file__))
 models_dir = os.path.join(
@@ -43,11 +45,12 @@ FFHQ_TEMPLATE_512 = np.array(
 
 
 def pre_check() -> bool:
-    model_path = os.path.join(models_dir, "gfpgan-1024.onnx")
-    if not os.path.exists(model_path):
+    from modules.model_downloader import ensure_model
+
+    if ensure_model(MODEL_FILE) is None:
         update_status(
-            f"GFPGAN ONNX model not found at {model_path}. "
-            "Please place gfpgan-1024.onnx in the models folder.",
+            f"Could not obtain {MODEL_FILE}. Place it in the models folder "
+            "manually or check your internet connection.",
             NAME,
         )
         return False
@@ -72,11 +75,15 @@ def get_face_enhancer() -> onnxruntime.InferenceSession:
 
     with THREAD_LOCK:
         if FACE_ENHANCER is None:
-            model_path = os.path.join(models_dir, "gfpgan-1024.onnx")
+            from modules.model_downloader import ensure_model
 
-            if not os.path.exists(model_path):
+            model_path = ensure_model(MODEL_FILE)
+
+            if model_path is None:
                 raise FileNotFoundError(
-                    f"{NAME}: Model not found at {model_path}"
+                    f"{NAME}: Model not found at "
+                    f"{os.path.join(models_dir, MODEL_FILE)} and could not be "
+                    "downloaded"
                 )
 
             try:
@@ -407,7 +414,7 @@ def process_frames(
                 progress.update(1)
             continue
 
-        temp_frame = cv2.imread(temp_frame_path)
+        temp_frame = imread_unicode(temp_frame_path)
         if temp_frame is None:
             print(
                 f"{NAME}: Warning: Failed to read frame {temp_frame_path}, skipping."
@@ -417,7 +424,7 @@ def process_frames(
             continue
 
         result_frame = process_frame(None, temp_frame)
-        cv2.imwrite(temp_frame_path, result_frame)
+        imwrite_unicode(temp_frame_path, result_frame)
         if progress:
             progress.update(1)
 
@@ -426,12 +433,12 @@ def process_image(
     source_path: str | None, target_path: str, output_path: str
 ) -> None:
     """Processes a single image file."""
-    target_frame = cv2.imread(target_path)
+    target_frame = imread_unicode(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)
+    imwrite_unicode(output_path, result_frame)
     print(f"{NAME}: Enhanced image saved to {output_path}")
 
 

modules/processors/frame/face_enhancer_gpen256.py

@@ -4,11 +4,9 @@ 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 import imread_unicode, imwrite_unicode
 from modules.core import update_status
 from modules.face_analyser import get_one_face
 from modules.typing import Frame, Face
@@ -24,7 +22,7 @@ from modules.processors.frame._onnx_enhancer import (
 
 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_MIRROR_URL = "https://github.com/harisreedhar/Face-Upscalers-ONNX/releases/download/GPEN-BFR/GPEN-BFR-256.onnx"
 MODEL_FILE = "GPEN-BFR-256.onnx"
 
 ENHANCER = None
@@ -36,12 +34,33 @@ models_dir = os.path.join(
 )
 
 
+def _obtain_model():
+    from modules.model_downloader import ensure_model
+
+    model_path = ensure_model(MODEL_FILE)
+    if model_path is not None:
+        return model_path
+
+    update_status(f"Retrying {MODEL_FILE} from the mirror...", NAME)
+    from modules.utilities import conditional_download
+
+    try:
+        conditional_download(models_dir, [MODEL_MIRROR_URL])
+    except Exception as error:
+        update_status(f"Mirror download failed: {error}", NAME)
+        return None
+    fallback = os.path.join(models_dir, MODEL_FILE)
+    return fallback if os.path.exists(fallback) else None
+
+
 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])
+    if _obtain_model() is None:
+        update_status(
+            f"Could not obtain {MODEL_FILE}. Place it in the models folder "
+            "manually or check your internet connection.",
+            NAME,
+        )
+        return False
     return True
 
 
@@ -56,12 +75,11 @@ 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}")
+            model_path = _obtain_model()
+            if model_path is None:
+                raise FileNotFoundError(
+                    f"Model file not found: {os.path.join(models_dir, MODEL_FILE)}"
+                )
             print(f"{NAME}: Loading ONNX model from {model_path}")
             ENHANCER = create_onnx_session(model_path)
             warmup_session(ENHANCER)
@@ -103,24 +121,24 @@ 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)
+        temp_frame = imread_unicode(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)
+        imwrite_unicode(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)
+    target_frame = imread_unicode(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)
+    imwrite_unicode(output_path, result_frame)
     print(f"{NAME}: Enhanced image saved to {output_path}")
 
 

modules/processors/frame/face_enhancer_gpen512.py

@@ -4,11 +4,9 @@ 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 import imread_unicode, imwrite_unicode
 from modules.core import update_status
 from modules.face_analyser import get_one_face
 from modules.typing import Frame, Face
@@ -24,7 +22,7 @@ from modules.processors.frame._onnx_enhancer import (
 
 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_MIRROR_URL = "https://github.com/harisreedhar/Face-Upscalers-ONNX/releases/download/GPEN-BFR/GPEN-BFR-512.onnx"
 MODEL_FILE = "GPEN-BFR-512.onnx"
 
 ENHANCER = None
@@ -36,12 +34,33 @@ models_dir = os.path.join(
 )
 
 
+def _obtain_model():
+    from modules.model_downloader import ensure_model
+
+    model_path = ensure_model(MODEL_FILE)
+    if model_path is not None:
+        return model_path
+
+    update_status(f"Retrying {MODEL_FILE} from the mirror...", NAME)
+    from modules.utilities import conditional_download
+
+    try:
+        conditional_download(models_dir, [MODEL_MIRROR_URL])
+    except Exception as error:
+        update_status(f"Mirror download failed: {error}", NAME)
+        return None
+    fallback = os.path.join(models_dir, MODEL_FILE)
+    return fallback if os.path.exists(fallback) else None
+
+
 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])
+    if _obtain_model() is None:
+        update_status(
+            f"Could not obtain {MODEL_FILE}. Place it in the models folder "
+            "manually or check your internet connection.",
+            NAME,
+        )
+        return False
     return True
 
 
@@ -56,12 +75,11 @@ 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}")
+            model_path = _obtain_model()
+            if model_path is None:
+                raise FileNotFoundError(
+                    f"Model file not found: {os.path.join(models_dir, MODEL_FILE)}"
+                )
             print(f"{NAME}: Loading ONNX model from {model_path}")
             ENHANCER = create_onnx_session(model_path)
             warmup_session(ENHANCER)
@@ -103,24 +121,24 @@ 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)
+        temp_frame = imread_unicode(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)
+        imwrite_unicode(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)
+    target_frame = imread_unicode(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)
+    imwrite_unicode(output_path, result_frame)
     print(f"{NAME}: Enhanced image saved to {output_path}")
 
 

modules/processors/frame/face_masking.py

@@ -2,7 +2,7 @@ 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
+from modules.gpu_processing import gpu_gaussian_blur, gpu_resize
 
 def apply_color_transfer(source, target):
     """

modules/processors/frame/face_swapper.py

@@ -7,16 +7,17 @@ import numpy as np
 import platform
 import modules.globals
 import modules.processors.frame.core
+from modules import imread_unicode, imwrite_unicode
 from modules.core import update_status
 from modules.face_analyser import get_one_face, get_many_faces, default_source_face
 from modules.typing import Face, Frame
 from modules.utilities import (
-    conditional_download,
     is_image,
     is_video,
 )
 from modules.cluster_analysis import find_closest_centroid
-from modules.gpu_processing import gpu_gaussian_blur, gpu_sharpen, gpu_add_weighted, gpu_resize, gpu_cvt_color
+from modules.gpu_processing import gpu_gaussian_blur, gpu_sharpen, gpu_add_weighted, gpu_resize
+from modules.platform_info import OPENVINO_PROVIDER_CONFIG
 import os
 from collections import deque
 import time
@@ -190,21 +191,26 @@ models_dir = os.path.join(
 def pre_check() -> bool:
     # Use models_dir instead of abs_dir to save to the correct location
     download_directory_path = models_dir
-    
+
     # Make sure the models directory exists, catch permission errors if they occur
     try:
         os.makedirs(download_directory_path, exist_ok=True)
     except OSError as e:
         logging.error(f"Failed to create directory {download_directory_path} due to permission error: {e}")
         return False
-    
-    # Use the direct download URL from Hugging Face (FP32 model for broad GPU compatibility)
-    conditional_download(
-        download_directory_path,
-        [
-            "https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128.onnx"
-        ],
-    )
+
+    from modules.model_downloader import ensure_any
+
+    variants = ["inswapper_128.onnx", "inswapper_128_fp16.onnx"]
+    if _HAS_TORCH_CUDA:
+        variants.reverse()
+    if ensure_any(variants) is None:
+        update_status(
+            "Could not obtain the inswapper model. Place inswapper_128.onnx in "
+            "the models folder manually or check your internet connection.",
+            NAME,
+        )
+        return False
     return True
 
 
@@ -240,8 +246,12 @@ def get_face_swapper() -> Any:
             elif os.path.exists(fp32_path):
                 model_path = fp32_path
             else:
-                update_status(f"No inswapper model found in {models_dir}.", NAME)
-                return None
+                if not pre_check():
+                    return None
+                model_path = fp16_path if os.path.exists(fp16_path) else fp32_path
+                if not os.path.exists(model_path):
+                    update_status(f"No inswapper model found in {models_dir}.", NAME)
+                    return None
             # On Apple Silicon, rewrite Pad(reflect) → Slice+Concat so
             # CoreML can run the entire model in a single partition on
             # the Neural Engine instead of bouncing between CPU and ANE.
@@ -269,6 +279,8 @@ def get_face_swapper() -> Any:
                         # Use bare provider — ONNX Runtime defaults are
                         # fastest on modern GPUs (Blackwell/sm_120).
                         providers_config.append(p)
+                    elif p == "OpenVINOExecutionProvider":
+                        providers_config.append(OPENVINO_PROVIDER_CONFIG)
                     else:
                         providers_config.append(p)
                 FACE_SWAPPER = insightface.model_zoo.get_model(
@@ -680,7 +692,8 @@ def apply_post_processing(current_frame: Frame, swapped_face_bboxes: List[np.nda
                 continue
 
             face_region = processed_frame[y1:y2, x1:x2]
-            if face_region.size == 0: continue
+            if face_region.size == 0:
+                continue
 
             # Apply sharpening (GPU-accelerated when CUDA OpenCV is available)
             try:
@@ -815,9 +828,11 @@ def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
             else: # Single face or specific mapping
                  for map_data in source_target_map:
                     source_info = map_data.get("source", {})
-                    if not source_info: continue # Skip if no source info
+                    if not source_info:
+                        continue # Skip if no source info
                     source_face = source_info.get("face")
-                    if not source_face: continue # Skip if no source defined for this map entry
+                    if not source_face:
+                        continue # Skip if no source defined for this map entry
 
                     if is_image(modules.globals.target_path):
                         target_info = map_data.get("target", {})
@@ -854,7 +869,8 @@ def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
                      if len(detected_faces) <= len(target_embeddings):
                           # More targets defined than detected - match each detected face
                           for detected_face in detected_faces:
-                              if detected_face.normed_embedding is None: continue
+                              if detected_face.normed_embedding is None:
+                                  continue
                               closest_idx, _ = find_closest_centroid(target_embeddings, detected_face.normed_embedding)
                               if 0 <= closest_idx < len(source_faces):
                                   source_target_pairs.append((source_faces[closest_idx], detected_face))
@@ -862,7 +878,8 @@ def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
                           # More faces detected than targets defined - match each target embedding to closest detected face
                           detected_embeddings = [f.normed_embedding for f in detected_faces if f.normed_embedding is not None]
                           detected_faces_with_embedding = [f for f in detected_faces if f.normed_embedding is not None]
-                          if not detected_embeddings: return processed_frame # No embeddings to match
+                          if not detected_embeddings:
+                              return processed_frame # No embeddings to match
 
                           for i, target_embedding in enumerate(target_embeddings):
                               if 0 <= i < len(source_faces): # Ensure source face exists for this embedding
@@ -912,7 +929,7 @@ def process_frames(
             # Log the error but allow proceeding; subsequent check will stop processing.
         else:
             try:
-                source_img = cv2.imread(source_path)
+                source_img = imread_unicode(source_path)
                 if source_img is None:
                     # Specific error for file reading failure
                     update_status(f"Error reading source image file {source_path}. Please check the path and file integrity.", NAME)
@@ -936,7 +953,7 @@ def process_frames(
 
     # --- Stop processing entirely if in Simple Mode and source face is invalid ---
     if not use_v2 and source_face is None:
-        update_status(f"Halting video processing: Invalid or no face detected in source image for simple mode.", NAME)
+        update_status("Halting video processing: Invalid or no face detected in source image for simple mode.", NAME)
         if progress:
             # Ensure the progress bar completes if it was started
             remaining_updates = total_frames - progress.n if hasattr(progress, 'n') else total_frames
@@ -952,14 +969,16 @@ def process_frames(
         # Read the target frame
         temp_frame = None
         try:
-            temp_frame = cv2.imread(temp_frame_path)
+            temp_frame = imread_unicode(temp_frame_path)
             if temp_frame is None:
                 print(f"{NAME}: Error: Could not read frame: {temp_frame_path}, skipping.")
-                if progress: progress.update(1)
+                if progress:
+                    progress.update(1)
                 continue # Skip this frame if read fails
         except Exception as read_e:
             print(f"{NAME}: Error reading frame {temp_frame_path}: {read_e}, skipping.")
-            if progress: progress.update(1)
+            if progress:
+                progress.update(1)
             continue
 
         # Select processing function and execute
@@ -988,7 +1007,7 @@ def process_frames(
         # Write the result back to the same frame path with optimized compression
         try:
             # Use PNG compression level 3 (faster) instead of default 9
-            write_success = cv2.imwrite(temp_frame_path, result_frame, [cv2.IMWRITE_PNG_COMPRESSION, 3])
+            write_success = imwrite_unicode(temp_frame_path, result_frame, [cv2.IMWRITE_PNG_COMPRESSION, 3])
             if not write_success:
                 print(f"{NAME}: Error: Failed to write processed frame to {temp_frame_path}")
         except Exception as write_e:
@@ -1018,7 +1037,7 @@ def process_image(source_path: str, target_path: str, output_path: str) -> None:
 
     # Read target first
     try:
-        target_frame = cv2.imread(target_path)
+        target_frame = imread_unicode(target_path)
         if target_frame is None:
             update_status(f"Error: Could not read target image: {target_path}", NAME)
             return
@@ -1037,7 +1056,7 @@ def process_image(source_path: str, target_path: str, output_path: str) -> None:
 
         else: # Simple mode
             try:
-                source_img = cv2.imread(source_path)
+                source_img = imread_unicode(source_path)
                 if source_img is None:
                     update_status(f"Error: Could not read source image: {source_path}", NAME)
                     return
@@ -1053,7 +1072,7 @@ def process_image(source_path: str, target_path: str, output_path: str) -> None:
 
         # Write the result if processing was successful
         if result is not None:
-            write_success = cv2.imwrite(output_path, result)
+            write_success = imwrite_unicode(output_path, result)
             if write_success:
                 update_status(f"Output image saved to: {output_path}", NAME)
             else:
@@ -1496,7 +1515,8 @@ def apply_color_transfer(source, target):
             if len(source.shape) == 2: # Grayscale
                 source = cv2.cvtColor(source, cv2.COLOR_GRAY2BGR)
             source = np.clip(source, 0, 255).astype(np.uint8)
-            if len(source.shape)!= 3 or source.shape[2]!= 3: raise ValueError("Conversion failed")
+            if len(source.shape) != 3 or source.shape[2] != 3:
+                raise ValueError("Conversion failed")
         except Exception:
             return source
     if len(target.shape) != 3 or target.shape[2] != 3 or target.dtype != np.uint8:
@@ -1505,7 +1525,8 @@ def apply_color_transfer(source, target):
             if len(target.shape) == 2: # Grayscale
                 target = cv2.cvtColor(target, cv2.COLOR_GRAY2BGR)
             target = np.clip(target, 0, 255).astype(np.uint8)
-            if len(target.shape)!= 3 or target.shape[2]!= 3: raise ValueError("Conversion failed")
+            if len(target.shape) != 3 or target.shape[2] != 3:
+                raise ValueError("Conversion failed")
         except Exception:
              return source # Return original source if target invalid
 

modules/run.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 
-# Import the tkinter fix to patch the ScreenChanged error
-import tkinter_fix
+# Import the tkinter fix to patch the ScreenChanged error (module patches Tk on import)
+import tkinter_fix  # noqa: F401
 
 import core
 

modules/ui.py

@@ -73,6 +73,7 @@ from modules.utilities import (
     is_image,
     is_video,
 )
+from modules import imread_unicode
 from modules.video_capture import VideoCapturer
 
 if platform.system() == "Windows":
@@ -236,6 +237,18 @@ _RECENT_SOURCE_DIR: Optional[str] = None
 _RECENT_TARGET_DIR: Optional[str] = None
 _RECENT_OUTPUT_DIR: Optional[str] = None
 
+# QFileDialog filter strings, built from the canonical extension sets in
+# globals so every dialog stays in sync (no hand-copied lists to drift).
+_IMAGE_FILE_FILTER = "Images (" + " ".join(
+    f"*{ext}" for ext in modules.globals.IMAGE_EXTENSIONS
+) + ")"
+_MEDIA_FILE_FILTER = "Media (" + " ".join(
+    f"*{ext}" for ext in (*modules.globals.IMAGE_EXTENSIONS, *modules.globals.VIDEO_EXTENSIONS)
+) + ")"
+_VIDEO_FILE_FILTER = "Videos (" + " ".join(
+    f"*{ext}" for ext in modules.globals.VIDEO_EXTENSIONS
+) + ")"
+
 
 # ─── image utilities ─────────────────────────────────────────────────────
 
@@ -416,7 +429,7 @@ def get_available_cameras() -> Tuple[List[int], List[str]]:
     indices: List[int] = []
     names: List[str] = []
     for i in range(10):
-        cap = cv2.VideoCapture(i)
+        cap = cv2.VideoCapture(f"/dev/video{i}")
         if cap.isOpened():
             indices.append(i)
             names.append(f"Camera {i}")
@@ -733,7 +746,7 @@ class MainWindow(QMainWindow):
         path, _filter = QFileDialog.getOpenFileName(
             self, _("select an source image"),
             _RECENT_SOURCE_DIR or "",
-            "Images (*.png *.jpg *.jpeg *.gif *.bmp)",
+            _IMAGE_FILE_FILTER,
         )
         if path and is_image(path):
             modules.globals.source_path = path
@@ -754,7 +767,7 @@ class MainWindow(QMainWindow):
         path, _filter = QFileDialog.getOpenFileName(
             self, _("select an target image or video"),
             _RECENT_TARGET_DIR or "",
-            "Media (*.png *.jpg *.jpeg *.gif *.bmp *.mp4 *.mkv)",
+            _MEDIA_FILE_FILTER,
         )
         if not path:
             return
@@ -885,13 +898,13 @@ class MainWindow(QMainWindow):
             path, _f = QFileDialog.getSaveFileName(
                 self, _("save image output file"),
                 os.path.join(_RECENT_OUTPUT_DIR or "", "output.png"),
-                "Images (*.png *.jpg *.jpeg *.bmp)",
+                _IMAGE_FILE_FILTER,
             )
         elif is_video(modules.globals.target_path):
             path, _f = QFileDialog.getSaveFileName(
                 self, _("save video output file"),
                 os.path.join(_RECENT_OUTPUT_DIR or "", "output.mp4"),
-                "Videos (*.mp4 *.mkv)",
+                _VIDEO_FILE_FILTER,
             )
         else:
             return
@@ -988,7 +1001,7 @@ class PreviewWindow(QWidget):
         from modules.processors.frame.core import get_frame_processors_modules as _gfpm
         for fp in _gfpm(modules.globals.frame_processors):
             temp_frame = fp.process_frame(
-                get_one_face(cv2.imread(modules.globals.source_path)), temp_frame
+                get_one_face(imread_unicode(modules.globals.source_path)), temp_frame
             )
         # Fit to current widget size while preserving aspect ratio.
         h, w = temp_frame.shape[:2]
@@ -1071,7 +1084,7 @@ class _ProcessingWorker(QThread):
                     and modules.globals.source_path != last_source_path
                 ):
                     last_source_path = modules.globals.source_path
-                    source_image = get_one_face(cv2.imread(modules.globals.source_path))
+                    source_image = get_one_face(imread_unicode(modules.globals.source_path))
 
                 det_count += 1
                 if det_count % det_interval == 0:
@@ -1333,11 +1346,11 @@ class MapperDialog(QDialog):
         path, _f = QFileDialog.getOpenFileName(
             self, _("select an source image"),
             _RECENT_SOURCE_DIR or "",
-            "Images (*.png *.jpg *.jpeg *.gif *.bmp)",
+            _IMAGE_FILE_FILTER,
         )
         if not path:
             return
-        cv2_img = cv2.imread(path)
+        cv2_img = imread_unicode(path)
         face = get_one_face(cv2_img)
         if face is None:
             self.set_status("Face could not be detected in last upload!")
@@ -1438,11 +1451,11 @@ class LiveMapperDialog(QDialog):
         path, _f = QFileDialog.getOpenFileName(
             self, _("select an source image"),
             _RECENT_SOURCE_DIR or "",
-            "Images (*.png *.jpg *.jpeg *.gif *.bmp)",
+            _IMAGE_FILE_FILTER,
         )
         if not path:
             return
-        cv2_img = cv2.imread(path)
+        cv2_img = imread_unicode(path)
         face = get_one_face(cv2_img)
         if face is None:
             self.set_status("Face could not be detected in last upload!")

modules/utilities.py

@@ -262,11 +262,16 @@ def clean_temp(target_path: str) -> None:
 
 
 def has_image_extension(image_path: str) -> bool:
-    return image_path.lower().endswith(("png", "jpg", "jpeg"))
+    # splitext so only the real extension counts (e.g. "photo.png.bak" is not
+    # an image); the set is centralized in globals to stay in sync with dialogs.
+    return os.path.splitext(image_path)[1].lower() in modules.globals.IMAGE_EXTENSIONS
 
 
 def is_image(image_path: str) -> bool:
     if image_path and os.path.isfile(image_path):
+        # Extension check first — Windows mimetypes doesn't always register webp
+        if has_image_extension(image_path):
+            return True
         mimetype, _ = mimetypes.guess_type(image_path)
         return bool(mimetype and mimetype.startswith("image/"))
     return False

modules/video_capture.py

@@ -1,6 +1,5 @@
 import cv2
 import numpy as np
-import sys
 import time
 from typing import Optional, Tuple, Callable
 import platform
@@ -72,8 +71,9 @@ class VideoCapturer:
                         self.cap.release()
                     except Exception:
                         continue
+            elif platform.system() == "Linux":
+                self.cap = cv2.VideoCapture(f"/dev/video{self.device_index}")
             else:
-                # Unix-like systems (Linux/Mac) capture method
                 self.cap = cv2.VideoCapture(self.device_index)
 
             if not self.cap or not self.cap.isOpened():

pyproject.toml

@@ -0,0 +1,9 @@
+[tool.ruff]
+target-version = "py310"
+
+[tool.ruff.lint]
+# Deterministic, low-risk rules enforced in CI. Other rules (F841, E402, F821)
+# surface real findings but require human judgement to fix safely, so they are
+# left out of the gate for now. Intentional side-effect imports should be
+# annotated with `# noqa: F401`.
+select = ["E701", "E711", "E712", "F401", "F541"]

requirements.txt

@@ -1,17 +1,18 @@
-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
+numpy>=2.0,<3
+typing-extensions>=4.15.0
+opencv-python==4.14.0.94
+opencv-python-headless==4.14.0.94
+cv2_enumerate_cameras==1.3.3
+onnx==1.22.0
 insightface==0.7.3
-psutil==5.9.8
+psutil==7.2.2
 PySide6>=6.7,<7
-pillow==12.1.1
-tqdm>=4.65.0
-onnxruntime-silicon==1.16.3; sys_platform == 'darwin' and platform_machine == 'arm64'
-onnxruntime-gpu==1.23.2; sys_platform != 'darwin'
-tensorflow>=2.15.0; sys_platform != 'darwin'
-tensorflow>=2.15.0; sys_platform == 'darwin' and python_version < '3.13'
-opennsfw2==0.10.2
-protobuf==4.25.1
+pillow==12.3.0
+tqdm>=4.66.3
+onnxruntime==1.28.0; sys_platform == 'darwin' and platform_machine == 'arm64'
+onnxruntime==1.23.0; sys_platform == 'darwin' and platform_machine != 'arm64'
+onnxruntime-gpu==1.26.0; sys_platform != 'darwin'
+opennsfw2==0.18.0
+keras>=3.0.0
+protobuf>=6.33.5,<8
 pygrabber; sys_platform == 'win32'

run.py

@@ -31,6 +31,30 @@ if sys.platform == "win32":
             except (OSError, AttributeError):
                 pass
 
+    # On Windows, register OpenVINO DLL directories so onnxruntime's
+    # OpenVINOExecutionProvider can find openvino.dll.  This must happen
+    # before any ONNX InferenceSession is created.  Failure is non-fatal:
+    # OpenVINO simply isn't installed, and onnxruntime will fall back to CPU.
+    try:
+        from onnxruntime.tools.add_openvino_win_libs import (  # type: ignore[import-untyped]  # noqa: E501
+            add_openvino_libs_to_path,
+        )
+        add_openvino_libs_to_path()
+    except ImportError:
+        # onnxruntime build without the OpenVINO tooling module — no-op.
+        pass
+    except FileNotFoundError:
+        # OpenVINO site-packages dir absent — no-op.
+        pass
+    except SystemExit as exc:
+        # add_openvino_libs_to_path() calls sys.exit() when OpenVINO libs
+        # can't be located (e.g. OPENVINO_LIB_PATHS unset).  Log the message
+        # it raised with so the failure is visible, but keep startup alive.
+        print(
+            f"[startup] OpenVINO DLL registration skipped: {exc}",
+            flush=True,
+        )
+
 # On Linux, pre-load NVIDIA shared libraries (cuDNN, cuBLAS, nvrtc...) shipped
 # inside the venv via pip wheels (nvidia-cudnn-cu12, etc.). LD_LIBRARY_PATH
 # cannot be set after Python starts, so we use ctypes.CDLL with RTLD_GLOBAL

tests/test_core_map_faces_fallback.py

@@ -0,0 +1,137 @@
+import importlib
+import sys
+import types
+import unittest
+from contextlib import contextmanager
+from unittest.mock import patch
+
+
+@contextmanager
+def _patched_core_import_stubs(calls, pipe_result=False):
+    class Processor:
+        NAME = "test_processor"
+
+        def pre_start(self):
+            return True
+
+        def pre_check(self):
+            return True
+
+        def process_image(self, *_args, **_kwargs):
+            raise AssertionError("image path should not be used")
+
+        def process_video(self, source_path, frame_paths):
+            calls.append(("process_video", source_path, tuple(frame_paths)))
+
+    stubs = {
+        "cv2": types.SimpleNamespace(
+            IMREAD_COLOR=1,
+            imdecode=lambda *_args, **_kwargs: None,
+            imencode=lambda *_args, **_kwargs: (
+                True,
+                types.SimpleNamespace(tofile=lambda *_a, **_k: None),
+            ),
+        ),
+        "numpy": types.SimpleNamespace(uint8=object, fromfile=lambda *_args, **_kwargs: b""),
+        "torch": types.SimpleNamespace(
+            cuda=types.SimpleNamespace(empty_cache=lambda: None)
+        ),
+        "onnxruntime": types.SimpleNamespace(
+            get_available_providers=lambda: ["CPUExecutionProvider"]
+        ),
+        "tensorflow": types.SimpleNamespace(),
+        "modules.metadata": types.SimpleNamespace(name="Deep-Live-Cam", version="test"),
+        "modules.ui": types.SimpleNamespace(
+            check_and_ignore_nsfw=lambda *_args, **_kwargs: False,
+            update_status=lambda *_args, **_kwargs: None,
+            init=lambda *_args, **_kwargs: types.SimpleNamespace(mainloop=lambda: None),
+        ),
+        "modules.processors.frame.core": types.SimpleNamespace(
+            get_frame_processors_modules=lambda _names: [Processor()],
+            process_video_in_memory=lambda *_args, **_kwargs: calls.append(("pipe",))
+            or pipe_result,
+        ),
+        "modules.utilities": types.SimpleNamespace(
+            has_image_extension=lambda _path: False,
+            is_image=lambda _path: False,
+            is_video=lambda _path: True,
+            detect_fps=lambda _path: 24.0,
+            create_video=lambda target_path, fps: calls.append(
+                ("create_video", target_path, fps)
+            )
+            or True,
+            extract_frames=lambda target_path: calls.append(
+                ("extract_frames", target_path)
+            ),
+            get_temp_frame_paths=lambda target_path: [f"{target_path}/0001.png"],
+            restore_audio=lambda *_args, **_kwargs: calls.append(("restore_audio",)),
+            create_temp=lambda target_path: calls.append(("create_temp", target_path)),
+            move_temp=lambda target_path, output_path: calls.append(
+                ("move_temp", target_path, output_path)
+            ),
+            clean_temp=lambda target_path: calls.append(("clean_temp", target_path)),
+            normalize_output_path=lambda _source, _target, output: output,
+        ),
+    }
+    with patch.dict(sys.modules, stubs, clear=False):
+        sys.modules.pop("modules.core", None)
+        yield importlib.import_module("modules.core")
+        sys.modules.pop("modules.core", None)
+
+
+def _configure_video_run(core, *, map_faces):
+    core.modules.globals.source_path = "source.jpg"
+    core.modules.globals.target_path = "target.mp4"
+    core.modules.globals.output_path = "output.mp4"
+    core.modules.globals.frame_processors = ["face_swapper"]
+    core.modules.globals.headless = True
+    core.modules.globals.keep_fps = False
+    core.modules.globals.keep_audio = False
+    core.modules.globals.keep_frames = False
+    core.modules.globals.map_faces = map_faces
+    core.modules.globals.nsfw_filter = False
+    core.modules.globals.execution_threads = 1
+    core.modules.globals.execution_providers = ["CPUExecutionProvider"]
+    core.modules.globals.max_memory = None
+
+
+class MapFacesFallbackTests(unittest.TestCase):
+    def test_map_faces_disk_fallback_extracts_frames_before_processing(self):
+        calls = []
+        with _patched_core_import_stubs(calls, pipe_result=False) as core:
+            _configure_video_run(core, map_faces=True)
+
+            with patch.object(core.os.path, "isfile", return_value=True):
+                core.start()
+
+        self.assertNotIn(("pipe",), calls)
+        self.assertIn(("create_temp", "target.mp4"), calls)
+        self.assertIn(("extract_frames", "target.mp4"), calls)
+        self.assertIn(("process_video", "source.jpg", ("target.mp4/0001.png",)), calls)
+        self.assertIn(("create_video", "target.mp4", 30.0), calls)
+        self.assertIn(("move_temp", "target.mp4", "output.mp4"), calls)
+
+        step_indices = {}
+        for index, call in enumerate(calls):
+            step_indices.setdefault(call[0], index)
+
+        self.assertLess(step_indices["create_temp"], step_indices["extract_frames"])
+        self.assertLess(step_indices["extract_frames"], step_indices["process_video"])
+        self.assertLess(step_indices["process_video"], step_indices["create_video"])
+        self.assertLess(step_indices["create_video"], step_indices["move_temp"])
+
+    def test_non_map_faces_pipe_success_does_not_extract_frames(self):
+        calls = []
+        with _patched_core_import_stubs(calls, pipe_result=True) as core:
+            _configure_video_run(core, map_faces=False)
+
+            with patch.object(core.os.path, "isfile", return_value=True):
+                core.start()
+
+        self.assertIn(("pipe",), calls)
+        self.assertNotIn(("extract_frames", "target.mp4"), calls)
+        self.assertNotIn(("process_video", "source.jpg", ("target.mp4/0001.png",)), calls)
+
+
+if __name__ == "__main__":
+    unittest.main()