Bump protobuf from 4.25.1 to 5.29.6

Bumps [protobuf](https://github.com/protocolbuffers/protobuf) from 4.25.1 to 5.29.6.
- [Release notes](https://github.com/protocolbuffers/protobuf/releases)
- [Commits](https://github.com/protocolbuffers/protobuf/commits)

---
updated-dependencies:
- dependency-name: protobuf
  dependency-version: 5.29.6
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>

.gitignore

@@ -25,3 +25,4 @@ models/DMDNet.pth
 faceswap/
 .vscode/
 switch_states.json
+/models

README.md

@@ -1,4 +1,4 @@
-<h1 align="center">Deep-Live-Cam 2.0.2c</h1>
+<h1 align="center">Deep-Live-Cam 2.0.4c</h1>
 
 <p align="center">
   Real-time face swap and video deepfake with a single click and only a single image.
@@ -30,7 +30,7 @@ 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 v2.4 Quick Start - Pre-built (Windows/Mac Silicon)
+## Exclusive v2.6d Quick Start - Pre-built (Windows/Mac Silicon)
 
   <a href="https://deeplivecam.net/index.php/quickstart"> <img src="media/Download.png" width="285" height="77" />
 

modules/capturer.py

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

modules/core.py

@@ -11,7 +11,11 @@ import platform
 import signal
 import shutil
 import argparse
-import torch
+try:
+    import torch
+    HAS_TORCH = True
+except ImportError:
+    HAS_TORCH = False
 import onnxruntime
 import tensorflow
 
@@ -21,11 +25,12 @@ import modules.ui as ui
 from modules.processors.frame.core import get_frame_processors_modules
 from modules.utilities import has_image_extension, is_image, is_video, detect_fps, create_video, extract_frames, get_temp_frame_paths, restore_audio, create_temp, move_temp, clean_temp, normalize_output_path
 
-if 'ROCMExecutionProvider' in modules.globals.execution_providers:
+if HAS_TORCH and 'ROCMExecutionProvider' in modules.globals.execution_providers:
     del torch
 
 warnings.filterwarnings('ignore', category=FutureWarning, module='insightface')
-warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')
+if HAS_TORCH:
+    warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')
 
 
 def parse_args() -> None:
@@ -167,7 +172,7 @@ def limit_resources() -> None:
 
 
 def release_resources() -> None:
-    if 'CUDAExecutionProvider' in modules.globals.execution_providers:
+    if 'CUDAExecutionProvider' in modules.globals.execution_providers and HAS_TORCH:
         torch.cuda.empty_cache()
 
 

modules/face_analyser.py

@@ -27,9 +27,10 @@ def get_face_analyser() -> Any:
             if FACE_ANALYSER is None:
                 FACE_ANALYSER = insightface.app.FaceAnalysis(
                     name='buffalo_l',
-                    providers=modules.globals.execution_providers
+                    providers=modules.globals.execution_providers,
+                    allowed_modules=['detection', 'recognition']
                 )
-                FACE_ANALYSER.prepare(ctx_id=0, det_size=(640, 640))
+                FACE_ANALYSER.prepare(ctx_id=0, det_size=(320, 320))
     return FACE_ANALYSER
 
 

modules/gpu_processing.py

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

modules/predicter.py

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

modules/processors/frame/face_enhancer.py

@@ -1,20 +1,20 @@
 # --- START OF FILE face_enhancer.py ---
+# Uses ONNX Runtime for GFPGAN face enhancement (no torch/gfpgan dependency)
 
 from typing import Any, List
 import cv2
 import threading
-import gfpgan
+import numpy as np
 import os
-import platform
-import torch # Make sure torch is imported
+
+import onnxruntime
 
 import modules.globals
 import modules.processors.frame.core
 from modules.core import update_status
-from modules.face_analyser import get_one_face
+from modules.face_analyser import get_one_face, get_many_faces
 from modules.typing import Frame, Face
 from modules.utilities import (
-    conditional_download,
     is_image,
     is_video,
 )
@@ -29,15 +29,29 @@ models_dir = os.path.join(
     os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
 )
 
+# Standard FFHQ 5-point face template for 512x512 resolution
+# Points: left_eye, right_eye, nose, left_mouth, right_mouth
+FFHQ_TEMPLATE_512 = np.array(
+    [
+        [192.98138, 239.94708],
+        [318.90277, 240.19366],
+        [256.63416, 314.01935],
+        [201.26117, 371.41043],
+        [313.08905, 371.15118],
+    ],
+    dtype=np.float32,
+)
+
 
 def pre_check() -> bool:
-    download_directory_path = models_dir
-    conditional_download(
-        download_directory_path,
-        [
-            "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth"
-        ],
-    )
+    model_path = os.path.join(models_dir, "gfpgan-1024.onnx")
+    if not os.path.exists(model_path):
+        update_status(
+            f"GFPGAN ONNX model not found at {model_path}. "
+            "Please place gfpgan-1024.onnx in the models folder.",
+            NAME,
+        )
+        return False
     return True
 
 
@@ -50,108 +64,257 @@ def pre_start() -> bool:
     return True
 
 
-def get_face_enhancer() -> Any:
+def get_face_enhancer() -> onnxruntime.InferenceSession:
     """
-    Initializes and returns the GFPGAN face enhancer instance,
-    prioritizing CUDA, then MPS (Mac), then CPU.
+    Initializes and returns the GFPGAN ONNX Runtime inference session,
+    using the execution providers configured in modules.globals.
     """
     global FACE_ENHANCER
 
     with THREAD_LOCK:
         if FACE_ENHANCER is None:
-            model_path = os.path.join(models_dir, "GFPGANv1.4.pth")
-            device = None
-            try:
-                # Priority 1: CUDA
-                if torch.cuda.is_available():
-                    device = torch.device("cuda")
-                    print(f"{NAME}: Using CUDA device.")
-                # Priority 2: MPS (Mac Silicon)
-                elif platform.system() == "Darwin" and torch.backends.mps.is_available():
-                    device = torch.device("mps")
-                    print(f"{NAME}: Using MPS device.")
-                # Priority 3: CPU
-                else:
-                    device = torch.device("cpu")
-                    print(f"{NAME}: Using CPU device.")
+            model_path = os.path.join(models_dir, "gfpgan-1024.onnx")
 
-                FACE_ENHANCER = gfpgan.GFPGANer(
-                    model_path=model_path,
-                    upscale=1,  # upscale=1 means enhancement only, no resizing
-                    arch='clean',
-                    channel_multiplier=2,
-                    bg_upsampler=None,
-                    device=device
+            if not os.path.exists(model_path):
+                raise FileNotFoundError(
+                    f"{NAME}: Model not found at {model_path}"
                 )
-                print(f"{NAME}: GFPGANer initialized successfully on {device}.")
+
+            try:
+                providers = modules.globals.execution_providers
+
+                session_options = onnxruntime.SessionOptions()
+                session_options.graph_optimization_level = (
+                    onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
+                )
+
+                FACE_ENHANCER = onnxruntime.InferenceSession(
+                    model_path,
+                    sess_options=session_options,
+                    providers=providers,
+                )
+
+                input_info = FACE_ENHANCER.get_inputs()[0]
+                output_info = FACE_ENHANCER.get_outputs()[0]
+                active_providers = FACE_ENHANCER.get_providers()
+                print(
+                    f"{NAME}: GFPGAN ONNX model loaded successfully."
+                )
+                print(
+                    f"{NAME}: Input: {input_info.name}, "
+                    f"shape: {input_info.shape}, type: {input_info.type}"
+                )
+                print(
+                    f"{NAME}: Output: {output_info.name}, "
+                    f"shape: {output_info.shape}, type: {output_info.type}"
+                )
+                print(f"{NAME}: Active providers: {active_providers}")
 
             except Exception as e:
-                print(f"{NAME}: Error initializing GFPGANer: {e}")
-                # Fallback to CPU if initialization with GPU fails for some reason
-                if device is not None and device.type != 'cpu':
-                    print(f"{NAME}: Falling back to CPU due to error.")
-                    try:
-                        device = torch.device("cpu")
-                        FACE_ENHANCER = gfpgan.GFPGANer(
-                            model_path=model_path,
-                            upscale=1,
-                            arch='clean',
-                            channel_multiplier=2,
-                            bg_upsampler=None,
-                            device=device
-                        )
-                        print(f"{NAME}: GFPGANer initialized successfully on CPU after fallback.")
-                    except Exception as fallback_e:
-                         print(f"{NAME}: FATAL: Could not initialize GFPGANer even on CPU: {fallback_e}")
-                         FACE_ENHANCER = None # Ensure it's None if totally failed
-                else:
-                    # If it failed even on the first CPU attempt or device was already CPU
-                     print(f"{NAME}: FATAL: Could not initialize GFPGANer on CPU: {e}")
-                     FACE_ENHANCER = None # Ensure it's None if totally failed
+                print(f"{NAME}: Error loading GFPGAN ONNX model: {e}")
+                FACE_ENHANCER = None
+                raise RuntimeError(
+                    f"{NAME}: Failed to load GFPGAN ONNX model: {e}"
+                )
 
-
-    # Check if enhancer is still None after attempting initialization
     if FACE_ENHANCER is None:
-        raise RuntimeError(f"{NAME}: Failed to initialize GFPGANer. Check logs for errors.")
+        raise RuntimeError(
+            f"{NAME}: Failed to initialize GFPGAN ONNX session. Check logs."
+        )
 
     return FACE_ENHANCER
 
 
+def _align_face(
+    frame: Frame, landmarks_5: np.ndarray, output_size: int
+) -> tuple:
+    """
+    Align and crop a face from the frame using 5-point landmarks and the
+    standard FFHQ template.
+
+    Returns:
+        (aligned_face, affine_matrix) or (None, None) on failure.
+    """
+    # Scale the 512-base template to the desired output size
+    scale = output_size / 512.0
+    template = FFHQ_TEMPLATE_512 * scale
+
+    # Estimate a similarity transform (4 DOF: rotation, scale, tx, ty)
+    affine_matrix, _ = cv2.estimateAffinePartial2D(
+        landmarks_5, template, method=cv2.LMEDS
+    )
+    if affine_matrix is None:
+        return None, None
+
+    # Warp the face to the aligned position
+    aligned_face = cv2.warpAffine(
+        frame,
+        affine_matrix,
+        (output_size, output_size),
+        borderMode=cv2.BORDER_CONSTANT,
+        borderValue=(135, 133, 132),
+    )
+
+    return aligned_face, affine_matrix
+
+
+def _paste_back(
+    frame: Frame,
+    enhanced_face: np.ndarray,
+    affine_matrix: np.ndarray,
+    output_size: int,
+) -> Frame:
+    """
+    Paste an enhanced (aligned) face back onto the original frame using the
+    inverse affine transform with feathered-edge blending.
+    """
+    h, w = frame.shape[:2]
+
+    # Inverse the affine warp
+    inv_matrix = cv2.invertAffineTransform(affine_matrix)
+    inv_restored = cv2.warpAffine(
+        enhanced_face,
+        inv_matrix,
+        (w, h),
+        borderMode=cv2.BORDER_CONSTANT,
+        borderValue=(0, 0, 0),
+    )
+
+    # Build a soft feathered mask in aligned space for edge blending
+    face_mask = np.ones((output_size, output_size), dtype=np.float32)
+
+    # Feather the border (5 % of the size on each edge)
+    border = max(1, int(output_size * 0.05))
+    ramp_up = np.linspace(0.0, 1.0, border, dtype=np.float32)
+    ramp_down = np.linspace(1.0, 0.0, border, dtype=np.float32)
+
+    # Top / bottom rows
+    face_mask[:border, :] *= ramp_up[:, None]
+    face_mask[-border:, :] *= ramp_down[:, None]
+    # Left / right columns
+    face_mask[:, :border] *= ramp_up[None, :]
+    face_mask[:, -border:] *= ramp_down[None, :]
+
+    # Expand to 3-channel
+    face_mask_3c = np.stack([face_mask] * 3, axis=-1)
+
+    # Warp mask back to original frame space
+    inv_mask = cv2.warpAffine(
+        face_mask_3c,
+        inv_matrix,
+        (w, h),
+        borderMode=cv2.BORDER_CONSTANT,
+        borderValue=(0, 0, 0),
+    )
+    inv_mask = np.clip(inv_mask, 0.0, 1.0)
+
+    # Alpha-blend
+    result = (
+        frame.astype(np.float32) * (1.0 - inv_mask)
+        + inv_restored.astype(np.float32) * inv_mask
+    )
+    return np.clip(result, 0, 255).astype(np.uint8)
+
+
+def _preprocess_face(aligned_face: np.ndarray) -> np.ndarray:
+    """
+    Convert an aligned BGR uint8 face image to the ONNX model input tensor.
+    Format: NCHW float32, normalised to [-1, 1].
+    """
+    # BGR -> RGB
+    rgb = cv2.cvtColor(aligned_face, cv2.COLOR_BGR2RGB).astype(np.float32)
+    # [0, 255] -> [0, 1] -> [-1, 1]
+    rgb = rgb / 255.0
+    rgb = (rgb - 0.5) / 0.5
+    # HWC -> CHW, add batch dim
+    chw = np.transpose(rgb, (2, 0, 1))
+    return np.expand_dims(chw, axis=0)  # shape: (1, 3, H, W)
+
+
+def _postprocess_face(output: np.ndarray) -> np.ndarray:
+    """
+    Convert the ONNX model output tensor back to a BGR uint8 image.
+    Expects input in NCHW format with values in [-1, 1].
+    """
+    face = np.squeeze(output)  # remove batch dim -> (3, H, W)
+    face = np.transpose(face, (1, 2, 0))  # CHW -> HWC
+    # [-1, 1] -> [0, 1] -> [0, 255]
+    face = (face + 1.0) / 2.0
+    face = np.clip(face * 255.0, 0, 255).astype(np.uint8)
+    # RGB -> BGR
+    return cv2.cvtColor(face, cv2.COLOR_RGB2BGR)
+
+
 def enhance_face(temp_frame: Frame) -> Frame:
-    """Enhances faces in a single frame using the global GFPGANer instance."""
-    # Ensure enhancer is ready
-    enhancer = get_face_enhancer()
+    """Enhances all faces in a frame using the GFPGAN ONNX model."""
+    session = get_face_enhancer()
+
+    # Determine model input resolution from the session metadata
+    input_info = session.get_inputs()[0]
+    input_name = input_info.name
+    input_shape = input_info.shape  # e.g. [1, 3, 512, 512]
+    # Safely extract input size (handle dynamic / symbolic dimensions)
     try:
-        with THREAD_SEMAPHORE:
-            # The enhance method returns: _, restored_faces, restored_img
-            _, _, restored_img = enhancer.enhance(
-                temp_frame,
-                has_aligned=False, # Assume faces are not pre-aligned
-                only_center_face=False, # Enhance all detected faces
-                paste_back=True # Paste enhanced faces back onto the original image
-            )
-        # GFPGAN might return None if no face is detected or an error occurs
-        if restored_img is None:
-            # print(f"{NAME}: Warning: GFPGAN enhancement returned None. Returning original frame.")
-            return temp_frame
-        return restored_img
-    except Exception as e:
-        print(f"{NAME}: Error during face enhancement: {e}")
-        # Return the original frame in case of error during enhancement
+        align_size = int(input_shape[2])
+        if align_size <= 0:
+            align_size = 512
+    except (ValueError, TypeError, IndexError):
+        align_size = 512
+
+    # Detect faces using InsightFace (already a project dependency)
+    faces = get_many_faces(temp_frame)
+    if not faces:
         return temp_frame
 
+    result_frame = temp_frame.copy()
+
+    for face in faces:
+        # Need the 5-point key-points for alignment
+        if not hasattr(face, "kps") or face.kps is None:
+            continue
+
+        landmarks_5 = face.kps.astype(np.float32)
+        if landmarks_5.shape[0] < 5:
+            continue
+
+        # Align / crop the face at the model's INPUT resolution
+        aligned_face, affine_matrix = _align_face(
+            temp_frame, landmarks_5, output_size=align_size
+        )
+        if aligned_face is None or affine_matrix is None:
+            continue
+
+        try:
+            with THREAD_SEMAPHORE:
+                input_tensor = _preprocess_face(aligned_face)
+                output_tensor = session.run(None, {input_name: input_tensor})[0]
+                enhanced_bgr = _postprocess_face(output_tensor)
+
+            # The model may output at a different resolution than its input
+            # (e.g. input 512x512 → output 1024x1024).  Resize the enhanced
+            # face back to the alignment size so the inverse affine maps
+            # correctly.
+            eh, ew = enhanced_bgr.shape[:2]
+            if eh != align_size or ew != align_size:
+                enhanced_bgr = cv2.resize(
+                    enhanced_bgr,
+                    (align_size, align_size),
+                    interpolation=cv2.INTER_LANCZOS4,
+                )
+
+            # Paste enhanced face back onto the frame
+            result_frame = _paste_back(
+                result_frame, enhanced_bgr, affine_matrix, output_size=align_size
+            )
+        except Exception as e:
+            print(f"{NAME}: Error enhancing a face: {e}")
+            continue
+
+    return result_frame
+
 
 def process_frame(source_face: Face | None, temp_frame: Frame) -> Frame:
     """Processes a frame: enhances face if detected."""
-    # We don't strictly need source_face for enhancement only
-    # Check if any face exists to potentially save processing time, though GFPGAN also does detection.
-    # For simplicity and ensuring enhancement is attempted if possible, we can rely on enhance_face.
-    # target_face = get_one_face(temp_frame) # This gets only ONE face
-    # If you want to enhance ONLY if a face is detected by your *own* analyser first:
-    # has_face = get_one_face(temp_frame) is not None # Or use get_many_faces
-    # if has_face:
-    #     temp_frame = enhance_face(temp_frame)
-    # else: # Enhance regardless, let GFPGAN handle detection
     temp_frame = enhance_face(temp_frame)
     return temp_frame
 
@@ -162,14 +325,18 @@ def process_frames(
     """Processes multiple frames from file paths."""
     for temp_frame_path in temp_frame_paths:
         if not os.path.exists(temp_frame_path):
-            print(f"{NAME}: Warning: Frame path not found {temp_frame_path}, skipping.")
+            print(
+                f"{NAME}: Warning: Frame path not found {temp_frame_path}, skipping."
+            )
             if progress:
                 progress.update(1)
             continue
 
         temp_frame = cv2.imread(temp_frame_path)
         if temp_frame is None:
-            print(f"{NAME}: Warning: Failed to read frame {temp_frame_path}, skipping.")
+            print(
+                f"{NAME}: Warning: Failed to read frame {temp_frame_path}, skipping."
+            )
             if progress:
                 progress.update(1)
             continue
@@ -180,7 +347,9 @@ def process_frames(
             progress.update(1)
 
 
-def process_image(source_path: str | None, target_path: str, output_path: str) -> None:
+def process_image(
+    source_path: str | None, target_path: str, output_path: str
+) -> None:
     """Processes a single image file."""
     target_frame = cv2.imread(target_path)
     if target_frame is None:
@@ -191,16 +360,13 @@ def process_image(source_path: str | None, target_path: str, output_path: str) -
     print(f"{NAME}: Enhanced image saved to {output_path}")
 
 
-def process_video(source_path: str | None, temp_frame_paths: List[str]) -> None:
+def process_video(
+    source_path: str | None, temp_frame_paths: List[str]
+) -> None:
     """Processes video frames using the frame processor core."""
-    # source_path might be optional depending on how process_video is called
-    modules.processors.frame.core.process_video(source_path, temp_frame_paths, process_frames)
+    modules.processors.frame.core.process_video(
+        source_path, temp_frame_paths, process_frames
+    )
 
-# Optional: Keep process_frame_v2 if it's used elsewhere, otherwise it's redundant
-# def process_frame_v2(temp_frame: Frame) -> Frame:
-#     target_face = get_one_face(temp_frame)
-#     if target_face:
-#         temp_frame = enhance_face(temp_frame)
-#     return temp_frame
 
-# --- END OF FILE face_enhancer.py ---
+# --- END OF FILE face_enhancer.py ---

modules/processors/frame/face_masking.py

@@ -2,6 +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
 
 def apply_color_transfer(source, target):
     """
@@ -61,8 +62,8 @@ def create_face_mask(face: Face, frame: Frame) -> np.ndarray:
         # Fill the padded convex hull
         cv2.fillConvexPoly(mask, hull_padded, 255)
 
-        # Smooth the mask edges
-        mask = cv2.GaussianBlur(mask, (5, 5), 3)
+        # Smooth the mask edges (GPU-accelerated when available)
+        mask = gpu_gaussian_blur(mask, (5, 5), 3)
 
     return mask
 
@@ -123,8 +124,8 @@ def create_lower_mouth_mask(
         polygon_relative_to_roi = expanded_landmarks - [min_x, min_y]
         cv2.fillPoly(mask_roi, [polygon_relative_to_roi], 255)
 
-        # Apply Gaussian blur to soften the mask edges
-        mask_roi = cv2.GaussianBlur(mask_roi, (15, 15), 5)
+        # Apply Gaussian blur to soften the mask edges (GPU-accelerated when available)
+        mask_roi = gpu_gaussian_blur(mask_roi, (15, 15), 5)
 
         # Place the mask ROI in the full-sized mask
         mask[min_y:max_y, min_x:max_x] = mask_roi
@@ -192,8 +193,8 @@ def create_eyes_mask(face: Face, frame: Frame) -> (np.ndarray, np.ndarray, tuple
         cv2.ellipse(mask_roi, left_center, left_axes, 0, 0, 360, 255, -1)
         cv2.ellipse(mask_roi, right_center, right_axes, 0, 0, 360, 255, -1)
         
-        # Apply Gaussian blur to soften mask edges
-        mask_roi = cv2.GaussianBlur(mask_roi, (15, 15), 5)
+        # Apply Gaussian blur to soften mask edges (GPU-accelerated when available)
+        mask_roi = gpu_gaussian_blur(mask_roi, (15, 15), 5)
         
         # Place the mask ROI in the full-sized mask
         mask[min_y:max_y, min_x:max_x] = mask_roi
@@ -374,15 +375,15 @@ def create_eyebrows_mask(face: Face, frame: Frame) -> (np.ndarray, np.ndarray, t
             left_shape = create_curved_eyebrow(left_local)
             right_shape = create_curved_eyebrow(right_local)
             
-            # Apply multi-stage blurring for natural feathering
+            # Apply multi-stage blurring for natural feathering (GPU-accelerated when available)
             # First, strong Gaussian blur for initial softening
-            mask_roi = cv2.GaussianBlur(mask_roi, (21, 21), 7)
+            mask_roi = gpu_gaussian_blur(mask_roi, (21, 21), 7)
             
             # Second, medium blur for transition areas
-            mask_roi = cv2.GaussianBlur(mask_roi, (11, 11), 3)
+            mask_roi = gpu_gaussian_blur(mask_roi, (11, 11), 3)
             
             # Finally, light blur for fine details
-            mask_roi = cv2.GaussianBlur(mask_roi, (5, 5), 1)
+            mask_roi = gpu_gaussian_blur(mask_roi, (5, 5), 1)
             
             # Normalize mask values
             mask_roi = cv2.normalize(mask_roi, None, 0, 255, cv2.NORM_MINMAX)
@@ -405,7 +406,7 @@ def create_eyebrows_mask(face: Face, frame: Frame) -> (np.ndarray, np.ndarray, t
             right_local = right_eyebrow - [min_x, min_y]
             cv2.fillPoly(mask_roi, [left_local.astype(np.int32)], 255)
             cv2.fillPoly(mask_roi, [right_local.astype(np.int32)], 255)
-            mask_roi = cv2.GaussianBlur(mask_roi, (21, 21), 7)
+            mask_roi = gpu_gaussian_blur(mask_roi, (21, 21), 7)
             mask[min_y:max_y, min_x:max_x] = mask_roi
             eyebrows_cutout = frame[min_y:max_y, min_x:max_x].copy()
             eyebrows_polygon = np.vstack([left_eyebrow, right_eyebrow]).astype(np.int32)
@@ -433,11 +434,11 @@ def apply_mask_area(
         return frame
 
     try:
-        resized_cutout = cv2.resize(cutout, (box_width, box_height))
+        resized_cutout = gpu_resize(cutout, (box_width, box_height))
         roi = frame[min_y:max_y, min_x:max_x]
 
         if roi.shape != resized_cutout.shape:
-            resized_cutout = cv2.resize(
+            resized_cutout = gpu_resize(
                 resized_cutout, (roi.shape[1], roi.shape[0])
             )
 
@@ -457,8 +458,8 @@ def apply_mask_area(
             adjusted_polygon = polygon - [min_x, min_y]
             cv2.fillPoly(polygon_mask, [adjusted_polygon], 255)
 
-        # Apply strong initial feathering
-        polygon_mask = cv2.GaussianBlur(polygon_mask, (21, 21), 7)
+        # Apply strong initial feathering (GPU-accelerated when available)
+        polygon_mask = gpu_gaussian_blur(polygon_mask, (21, 21), 7)
 
         # Apply additional feathering
         feather_amount = min(

modules/processors/frame/face_swapper.py

@@ -15,6 +15,7 @@ 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
 import os
 from collections import deque
 import time
@@ -43,11 +44,21 @@ models_dir = os.path.join(
 )
 
 def pre_check() -> bool:
-    download_directory_path = abs_dir
+    # 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
     conditional_download(
         download_directory_path,
         [
-            "https://huggingface.co/hacksider/deep-live-cam/blob/main/inswapper_128_fp16.onnx"
+            "https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128_fp16.onnx"
         ],
     )
     return True
@@ -158,7 +169,7 @@ def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
              # print(f"Warning: Swapped frame shape {swapped_frame_raw.shape} differs from input {temp_frame.shape}.") # Debug
              # Attempt resize (might distort if aspect ratio changed, but better than crashing)
              try:
-                 swapped_frame_raw = cv2.resize(swapped_frame_raw, (temp_frame.shape[1], temp_frame.shape[0]))
+                 swapped_frame_raw = gpu_resize(swapped_frame_raw, (temp_frame.shape[1], temp_frame.shape[0]))
              except Exception as resize_e:
                  # print(f"Error resizing swapped frame: {resize_e}") # Debug
                  return original_frame
@@ -236,7 +247,7 @@ def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
 
     # Blend the original_frame with the (potentially mouth-masked) swapped_frame
     # Ensure both frames are uint8 before blending
-    final_swapped_frame = cv2.addWeighted(original_frame.astype(np.uint8), 1 - opacity, swapped_frame.astype(np.uint8), opacity, 0)
+    final_swapped_frame = gpu_add_weighted(original_frame.astype(np.uint8), 1 - opacity, swapped_frame.astype(np.uint8), opacity, 0)
 
     # Ensure final frame is uint8 after blending (addWeighted should preserve it, but belt-and-suspenders)
     final_swapped_frame = final_swapped_frame.astype(np.uint8)
@@ -312,17 +323,10 @@ def apply_post_processing(current_frame: Frame, swapped_face_bboxes: List[np.nda
             face_region = processed_frame[y1:y2, x1:x2]
             if face_region.size == 0: continue
 
-            # Apply sharpening with optimized parameters for Apple Silicon
+            # Apply sharpening (GPU-accelerated when CUDA OpenCV is available)
             try:
-                # Use smaller sigma for faster processing on Apple Silicon
                 sigma = 2 if IS_APPLE_SILICON else 3
-                blurred = cv2.GaussianBlur(face_region, (0, 0), sigma)
-                sharpened_region = cv2.addWeighted(
-                    face_region, 1.0 + sharpness_value,
-                    blurred, -sharpness_value,
-                    0
-                )
-                sharpened_region = np.clip(sharpened_region, 0, 255).astype(np.uint8)
+                sharpened_region = gpu_sharpen(face_region, strength=sharpness_value, sigma=sigma)
                 processed_frame[y1:y2, x1:x2] = sharpened_region
             except cv2.error:
                 pass
@@ -338,7 +342,7 @@ def apply_post_processing(current_frame: Frame, swapped_face_bboxes: List[np.nda
         if PREVIOUS_FRAME_RESULT is not None and PREVIOUS_FRAME_RESULT.shape == processed_frame.shape and PREVIOUS_FRAME_RESULT.dtype == processed_frame.dtype:
             # Perform interpolation
             try:
-                 final_frame = cv2.addWeighted(
+                 final_frame = gpu_add_weighted(
                     PREVIOUS_FRAME_RESULT, 1.0 - interpolation_weight,
                     processed_frame, interpolation_weight,
                     0
@@ -813,10 +817,10 @@ def create_lower_mouth_mask(
             # Draw polygon on the ROI mask
             cv2.fillPoly(mask_roi, [polygon_relative_to_roi], 255)
 
-            # Apply Gaussian blur (ensure kernel size is odd and positive)
+            # Apply Gaussian blur (GPU-accelerated when available)
             blur_k_size = getattr(modules.globals, "mask_blur_kernel", 15) # Default 15
             blur_k_size = max(1, blur_k_size // 2 * 2 + 1) # Ensure odd
-            mask_roi = cv2.GaussianBlur(mask_roi, (blur_k_size, blur_k_size), 0) # Sigma=0 calculates from kernel
+            mask_roi = gpu_gaussian_blur(mask_roi, (blur_k_size, blur_k_size), 0)
 
             # Place the mask ROI in the full-sized mask
             mask[min_y:max_y, min_x:max_x] = mask_roi
@@ -952,7 +956,7 @@ def apply_mouth_area(
         if roi.shape[:2] != mouth_cutout.shape[:2]:
              # Check if mouth_cutout has valid dimensions before resizing
              if mouth_cutout.shape[0] > 0 and mouth_cutout.shape[1] > 0:
-                 resized_mouth_cutout = cv2.resize(mouth_cutout, (box_width, box_height), interpolation=cv2.INTER_LINEAR)
+                  resized_mouth_cutout = gpu_resize(mouth_cutout, (box_width, box_height), interpolation=cv2.INTER_LINEAR)
              else:
                  # print("Warning: mouth_cutout has invalid dimensions, cannot resize.")
                  return frame # Cannot proceed without valid cutout
@@ -1125,14 +1129,10 @@ def create_face_mask(face: Face, frame: Frame) -> np.ndarray:
              return mask # Return empty mask on error
 
 
-        # Apply Gaussian blur to feather the mask edges
-        # Kernel size should be reasonably large, odd, and positive
+        # Apply Gaussian blur to feather the mask edges (GPU-accelerated when available)
         blur_k_size = getattr(modules.globals, "face_mask_blur", 31) # Default 31
         blur_k_size = max(1, blur_k_size // 2 * 2 + 1) # Ensure odd and positive
-
-        # Use sigma=0 to let OpenCV calculate from kernel size
-        # Apply blur to the uint8 mask directly
-        mask = cv2.GaussianBlur(mask, (blur_k_size, blur_k_size), 0)
+        mask = gpu_gaussian_blur(mask, (blur_k_size, blur_k_size), 0)
 
         # --- Optional: Return float mask for apply_mouth_area ---
         # mask = mask.astype(float) / 255.0

modules/ui.py

@@ -4,13 +4,18 @@ import customtkinter as ctk
 from typing import Callable, Tuple
 import cv2
 from cv2_enumerate_cameras import enumerate_cameras  # Add this import
+from modules.gpu_processing import gpu_cvt_color, gpu_resize, gpu_flip
 from PIL import Image, ImageOps
 import time
 import json
+import queue
+import threading
+import numpy as np
 import modules.globals
 import modules.metadata
 from modules.face_analyser import (
     get_one_face,
+    get_many_faces,
     get_unique_faces_from_target_image,
     get_unique_faces_from_target_video,
     add_blank_map,
@@ -542,7 +547,7 @@ def create_source_target_popup(
         )
         x_label.grid(row=id, column=2, padx=10, pady=10)
 
-        image = Image.fromarray(cv2.cvtColor(item["target"]["cv2"], cv2.COLOR_BGR2RGB))
+        image = Image.fromarray(gpu_cvt_color(item["target"]["cv2"], cv2.COLOR_BGR2RGB))
         image = image.resize(
             (MAPPER_PREVIEW_MAX_WIDTH, MAPPER_PREVIEW_MAX_HEIGHT), Image.LANCZOS
         )
@@ -597,7 +602,7 @@ def update_popup_source(
             }
 
             image = Image.fromarray(
-                cv2.cvtColor(map[button_num]["source"]["cv2"], cv2.COLOR_BGR2RGB)
+                gpu_cvt_color(map[button_num]["source"]["cv2"], cv2.COLOR_BGR2RGB)
             )
             image = image.resize(
                 (MAPPER_PREVIEW_MAX_WIDTH, MAPPER_PREVIEW_MAX_HEIGHT), Image.LANCZOS
@@ -790,7 +795,7 @@ def fit_image_to_size(image, width: int, height: int):
         ratio_w = width / w
     ratio = max(ratio_w, ratio_h)
     new_size = (int(ratio * w), int(ratio * h))
-    return cv2.resize(image, dsize=new_size)
+    return gpu_resize(image, dsize=new_size)
 
 
 def render_image_preview(image_path: str, size: Tuple[int, int]) -> ctk.CTkImage:
@@ -808,7 +813,7 @@ def render_video_preview(
         capture.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
     has_frame, frame = capture.read()
     if has_frame:
-        image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+        image = Image.fromarray(gpu_cvt_color(frame, cv2.COLOR_BGR2RGB))
         if size:
             image = ImageOps.fit(image, size, Image.LANCZOS)
         return ctk.CTkImage(image, size=image.size)
@@ -846,7 +851,7 @@ def update_preview(frame_number: int = 0) -> None:
             temp_frame = frame_processor.process_frame(
                 get_one_face(cv2.imread(modules.globals.source_path)), temp_frame
             )
-        image = Image.fromarray(cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB))
+        image = Image.fromarray(gpu_cvt_color(temp_frame, cv2.COLOR_BGR2RGB))
         image = ImageOps.contain(
             image, (PREVIEW_MAX_WIDTH, PREVIEW_MAX_HEIGHT), Image.LANCZOS
         )
@@ -947,52 +952,97 @@ def get_available_cameras():
         return camera_indices, camera_names
 
 
-def create_webcam_preview(camera_index: int):
-    global preview_label, PREVIEW
+def _capture_thread_func(cap, capture_queue, stop_event):
+    """Capture thread: reads frames from camera and puts them into the queue.
+    Drops frames when the queue is full to avoid backpressure on the camera."""
+    while not stop_event.is_set():
+        ret, frame = cap.read()
+        if not ret:
+            stop_event.set()
+            break
+        try:
+            capture_queue.put_nowait(frame)
+        except queue.Full:
+            # Drop the oldest frame and enqueue the new one
+            try:
+                capture_queue.get_nowait()
+            except queue.Empty:
+                pass
+            try:
+                capture_queue.put_nowait(frame)
+            except queue.Full:
+                pass
 
-    cap = VideoCapturer(camera_index)
-    if not cap.start(PREVIEW_DEFAULT_WIDTH, PREVIEW_DEFAULT_HEIGHT, 60):
-        update_status("Failed to start camera")
-        return
 
-    preview_label.configure(width=PREVIEW_DEFAULT_WIDTH, height=PREVIEW_DEFAULT_HEIGHT)
-    PREVIEW.deiconify()
+# How often to run full face detection. On intermediate frames the last
+# detected face positions are reused, which significantly reduces the
+# per-frame cost of the processing thread.
+DETECT_EVERY_N = 2
 
+
+def _processing_thread_func(capture_queue, processed_queue, stop_event):
+    """Processing thread: takes raw frames from capture_queue, applies face
+    processing, and puts results into processed_queue. Drops processed frames
+    when the output queue is full so the UI always gets the latest result.
+
+    Uses DETECT_EVERY_N to skip expensive face detection on intermediate
+    frames, reusing cached face positions instead."""
     frame_processors = get_frame_processors_modules(modules.globals.frame_processors)
     source_image = None
     prev_time = time.time()
     fps_update_interval = 0.5
     frame_count = 0
     fps = 0
+    proc_frame_index = 0
+    cached_target_face = None  # cached single-face result
+    cached_many_faces = None   # cached many-faces result
 
-    while True:
-        ret, frame = cap.read()
-        if not ret:
-            break
+    while not stop_event.is_set():
+        try:
+            frame = capture_queue.get(timeout=0.05)
+        except queue.Empty:
+            continue
 
         temp_frame = frame.copy()
+        run_detection = (proc_frame_index % DETECT_EVERY_N == 0)
+        proc_frame_index += 1
 
         if modules.globals.live_mirror:
-            temp_frame = cv2.flip(temp_frame, 1)
-
-        if modules.globals.live_resizable:
-            temp_frame = fit_image_to_size(
-                temp_frame, PREVIEW.winfo_width(), PREVIEW.winfo_height()
-            )
-
-        else:
-            temp_frame = fit_image_to_size(
-                temp_frame, PREVIEW.winfo_width(), PREVIEW.winfo_height()
-            )
+            temp_frame = gpu_flip(temp_frame, 1)
 
         if not modules.globals.map_faces:
             if source_image is None and modules.globals.source_path:
                 source_image = get_one_face(cv2.imread(modules.globals.source_path))
 
+            # Update face detection cache on detection frames
+            if run_detection or (cached_target_face is None and cached_many_faces is None):
+                if modules.globals.many_faces:
+                    cached_many_faces = get_many_faces(temp_frame)
+                    cached_target_face = None
+                else:
+                    cached_target_face = get_one_face(temp_frame)
+                    cached_many_faces = None
+
             for frame_processor in frame_processors:
                 if frame_processor.NAME == "DLC.FACE-ENHANCER":
                     if modules.globals.fp_ui["face_enhancer"]:
                         temp_frame = frame_processor.process_frame(None, temp_frame)
+                elif frame_processor.NAME == "DLC.FACE-SWAPPER":
+                    # Use cached face positions to skip redundant detection
+                    swapped_bboxes = []
+                    if modules.globals.many_faces and cached_many_faces:
+                        result = temp_frame.copy()
+                        for t_face in cached_many_faces:
+                            result = frame_processor.swap_face(source_image, t_face, result)
+                            if hasattr(t_face, 'bbox') and t_face.bbox is not None:
+                                swapped_bboxes.append(t_face.bbox.astype(int))
+                        temp_frame = result
+                    elif cached_target_face is not None:
+                        temp_frame = frame_processor.swap_face(source_image, cached_target_face, temp_frame)
+                        if hasattr(cached_target_face, 'bbox') and cached_target_face.bbox is not None:
+                            swapped_bboxes.append(cached_target_face.bbox.astype(int))
+                    # Apply post-processing (sharpening, interpolation)
+                    temp_frame = frame_processor.apply_post_processing(temp_frame, swapped_bboxes)
                 else:
                     temp_frame = frame_processor.process_frame(source_image, temp_frame)
         else:
@@ -1023,7 +1073,71 @@ def create_webcam_preview(camera_index: int):
                 2,
             )
 
-        image = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
+        # Put processed frame into output queue, dropping old frames if full
+        try:
+            processed_queue.put_nowait(temp_frame)
+        except queue.Full:
+            try:
+                processed_queue.get_nowait()
+            except queue.Empty:
+                pass
+            try:
+                processed_queue.put_nowait(temp_frame)
+            except queue.Full:
+                pass
+
+
+def create_webcam_preview(camera_index: int):
+    global preview_label, PREVIEW
+
+    cap = VideoCapturer(camera_index)
+    if not cap.start(PREVIEW_DEFAULT_WIDTH, PREVIEW_DEFAULT_HEIGHT, 60):
+        update_status("Failed to start camera")
+        return
+
+    preview_label.configure(width=PREVIEW_DEFAULT_WIDTH, height=PREVIEW_DEFAULT_HEIGHT)
+    PREVIEW.deiconify()
+
+    # Queues for decoupling capture from processing and processing from display.
+    # Small maxsize ensures we always work on recent frames and drop stale ones.
+    capture_queue = queue.Queue(maxsize=2)
+    processed_queue = queue.Queue(maxsize=2)
+    stop_event = threading.Event()
+
+    # Start capture thread
+    cap_thread = threading.Thread(
+        target=_capture_thread_func,
+        args=(cap, capture_queue, stop_event),
+        daemon=True,
+    )
+    cap_thread.start()
+
+    # Start processing thread
+    proc_thread = threading.Thread(
+        target=_processing_thread_func,
+        args=(capture_queue, processed_queue, stop_event),
+        daemon=True,
+    )
+    proc_thread.start()
+
+    # Main (UI) thread: pull processed frames and update the display
+    while not stop_event.is_set():
+        try:
+            temp_frame = processed_queue.get(timeout=0.03)
+        except queue.Empty:
+            ROOT.update()
+            continue
+
+        if modules.globals.live_resizable:
+            temp_frame = fit_image_to_size(
+                temp_frame, PREVIEW.winfo_width(), PREVIEW.winfo_height()
+            )
+        else:
+            temp_frame = fit_image_to_size(
+                temp_frame, PREVIEW.winfo_width(), PREVIEW.winfo_height()
+            )
+
+        image = gpu_cvt_color(temp_frame, cv2.COLOR_BGR2RGB)
         image = Image.fromarray(image)
         image = ImageOps.contain(
             image, (temp_frame.shape[1], temp_frame.shape[0]), Image.LANCZOS
@@ -1035,6 +1149,10 @@ def create_webcam_preview(camera_index: int):
         if PREVIEW.state() == "withdrawn":
             break
 
+    # Signal threads to stop and wait for them
+    stop_event.set()
+    cap_thread.join(timeout=2.0)
+    proc_thread.join(timeout=2.0)
     cap.release()
     PREVIEW.withdraw()
 
@@ -1146,7 +1264,7 @@ def refresh_data(map: list):
 
         if "source" in item:
             image = Image.fromarray(
-                cv2.cvtColor(item["source"]["cv2"], cv2.COLOR_BGR2RGB)
+                gpu_cvt_color(item["source"]["cv2"], cv2.COLOR_BGR2RGB)
             )
             image = image.resize(
                 (MAPPER_PREVIEW_MAX_WIDTH, MAPPER_PREVIEW_MAX_HEIGHT), Image.LANCZOS
@@ -1164,7 +1282,7 @@ def refresh_data(map: list):
 
         if "target" in item:
             image = Image.fromarray(
-                cv2.cvtColor(item["target"]["cv2"], cv2.COLOR_BGR2RGB)
+                gpu_cvt_color(item["target"]["cv2"], cv2.COLOR_BGR2RGB)
             )
             image = image.resize(
                 (MAPPER_PREVIEW_MAX_WIDTH, MAPPER_PREVIEW_MAX_HEIGHT), Image.LANCZOS
@@ -1212,7 +1330,7 @@ def update_webcam_source(
             }
 
             image = Image.fromarray(
-                cv2.cvtColor(map[button_num]["source"]["cv2"], cv2.COLOR_BGR2RGB)
+                gpu_cvt_color(map[button_num]["source"]["cv2"], cv2.COLOR_BGR2RGB)
             )
             image = image.resize(
                 (MAPPER_PREVIEW_MAX_WIDTH, MAPPER_PREVIEW_MAX_HEIGHT), Image.LANCZOS
@@ -1264,7 +1382,7 @@ def update_webcam_target(
             }
 
             image = Image.fromarray(
-                cv2.cvtColor(map[button_num]["target"]["cv2"], cv2.COLOR_BGR2RGB)
+                gpu_cvt_color(map[button_num]["target"]["cv2"], cv2.COLOR_BGR2RGB)
             )
             image = image.resize(
                 (MAPPER_PREVIEW_MAX_WIDTH, MAPPER_PREVIEW_MAX_HEIGHT), Image.LANCZOS

requirements.txt

@@ -1,5 +1,3 @@
---extra-index-url https://download.pytorch.org/whl/cu128
-
 numpy>=1.23.5,<2
 typing-extensions>=4.8.0
 opencv-python==4.10.0.84
@@ -9,16 +7,10 @@ insightface==0.7.3
 psutil==5.9.8
 tk==0.1.0
 customtkinter==5.2.2
-pillow==11.1.0
-torch; sys_platform != 'darwin'
-torch==2.8.0+cu128; sys_platform == 'darwin'
-torchvision; sys_platform != 'darwin'
-torchvision==0.20.1; sys_platform == 'darwin'
+pillow==12.1.1
 onnxruntime-silicon==1.16.3; sys_platform == 'darwin' and platform_machine == 'arm64'
-onnxruntime-gpu==1.22.0; sys_platform != 'darwin'
+onnxruntime-gpu==1.24.2; sys_platform != 'darwin'
 tensorflow; sys_platform != 'darwin'
 opennsfw2==0.10.2
-protobuf==4.25.1
-git+https://github.com/xinntao/BasicSR.git@master
-git+https://github.com/TencentARC/GFPGAN.git@master
+protobuf==5.29.6
 pygrabber