update on requirements

some update on what is needed to be updated

.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 +30,39 @@ 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 Quickstart
 
-  <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">
+  <img src="https://github.com/user-attachments/assets/56b61811-3a1e-4672-9b50-cf7f6e8e6852" width="40" alt="Windows">
+  &nbsp;&nbsp;&nbsp;
+  <img src="https://github.com/user-attachments/assets/6538e3a6-c957-431a-b586-2d6abcf534dc" width="34" alt="Mac Silicon">
+  &nbsp;&nbsp;&nbsp;
+  <img src="https://github.com/user-attachments/assets/ad45142e-426c-4364-a2a9-a512670cc62c" width="40" alt="CPU">
+</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**.
+
+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 +135,7 @@ 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)```
@@ -118,7 +144,7 @@ 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
 ```
 
@@ -142,7 +168,7 @@ 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 +176,17 @@ 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
@@ -212,26 +238,29 @@ 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 +268,9 @@ 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
   ```
 

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()

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))
@@ -171,8 +179,6 @@ def limit_resources() -> None:
     # limit memory usage
     if modules.globals.max_memory:
         memory = modules.globals.max_memory * 1024 ** 3
-        if platform.system().lower() == 'darwin':
-            memory = modules.globals.max_memory * 1024 ** 6
         if platform.system().lower() == 'windows':
             import ctypes
             kernel32 = ctypes.windll.kernel32
@@ -324,7 +330,8 @@ 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
@@ -255,8 +254,10 @@ 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 +290,10 @@ 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 +335,9 @@ 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 +346,7 @@ 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 +362,7 @@ 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 +370,5 @@ 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/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/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,
@@ -407,7 +408,7 @@ 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 +418,7 @@ 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 +427,12 @@ 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
@@ -103,24 +101,24 @@ 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
@@ -103,24 +101,24 @@ 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

@@ -7,6 +7,7 @@ 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
@@ -16,7 +17,8 @@ from modules.utilities import (
     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
@@ -269,6 +271,8 @@ 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 +684,8 @@ 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 +820,11 @@ 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 +861,8 @@ 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 +870,8 @@ 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 +921,7 @@ 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 +945,7 @@ 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 +961,16 @@ 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 +999,7 @@ 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 +1029,7 @@ 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 +1048,7 @@ 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 +1064,7 @@ 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 +1507,8 @@ 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 +1517,8 @@ 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":
@@ -988,7 +989,7 @@ 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 +1072,7 @@ 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:
@@ -1337,7 +1338,7 @@ class MapperDialog(QDialog):
         )
         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!")
@@ -1442,7 +1443,7 @@ class LiveMapperDialog(QDialog):
         )
         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/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

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