docs: align GFPGAN model filenames (#1910)

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

README.md

@@ -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.
 
-## Pre-built Quickstart
+## Pre-built Deep-Live-Cam 2.7 Ultimate!
 
 <p align="center">
   <a href="https://deeplivecam.net/index.php/quickstart">
@@ -39,11 +39,17 @@ Users are expected to use this software responsibly and legally. If using a real
 </p>
 
 <p align="center">
+<a href="https://deeplivecam.net/index.php/plans/nvidia-gpu?plan_id=0&group_id=1">
   <img src="https://github.com/user-attachments/assets/56b61811-3a1e-4672-9b50-cf7f6e8e6852" width="40" alt="Windows">
+</a>
   &nbsp;&nbsp;&nbsp;
+<a href="https://deeplivecam.net/index.php/plans/nvidia-gpu?plan_id=0&group_id=2">
   <img src="https://github.com/user-attachments/assets/6538e3a6-c957-431a-b586-2d6abcf534dc" width="34" alt="Mac Silicon">
+</a>
   &nbsp;&nbsp;&nbsp;
+<a href="https://deeplivecam.net/index.php/plans/nvidia-gpu?plan_id=0&group_id=3">
   <img src="https://github.com/user-attachments/assets/ad45142e-426c-4364-a2a9-a512670cc62c" width="40" alt="CPU">
+</a>
 </p>
 
 <p align="center">
@@ -60,7 +66,7 @@ Users are expected to use this software responsibly and legally. If using a real
   </a>
 </p>
 
-> **Ultimate** includes **30+ exclusive features**, performance optimizations, and **priority support**.
+> **Ultimate** includes **30+ exclusive features**, performance optimizations, and **priority support** We only have a single official website which is https://deeplivecam.net . Please be careful on where you download other versions of this application aside from that website and this github repo.
 
 Perfect if you want the fastest setup with **zero manual installation**, pre-configured dependencies, and optimized builds for every supported platform.
 
@@ -150,7 +156,7 @@ cd Deep-Live-Cam
 
 **3. Download the Models**
 
-1. [GFPGANv1.4](https://huggingface.co/hacksider/deep-live-cam/resolve/main/GFPGANv1.4.onnx)
+1. [gfpgan-1024.onnx](https://huggingface.co/hacksider/deep-live-cam/resolve/main/gfpgan-1024.onnx)
 2. [inswapper\_128\_fp16.onnx](https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128_fp16.onnx)
 
 Place these files in the "**models**" folder.
@@ -227,7 +233,7 @@ pip install git+https://github.com/TencentARC/GFPGAN.git@master
 ```bash
 pip install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
 pip uninstall onnxruntime onnxruntime-gpu
-pip install onnxruntime-gpu==1.21.0
+pip install onnxruntime-gpu==1.26.0
 ```
 
 3. Usage:
@@ -313,6 +319,22 @@ pip uninstall onnxruntime onnxruntime-openvino
 pip install onnxruntime-openvino==1.21.0
 ```
 
+**Note:** `onnxruntime-openvino` newer than 1.21.0 must be installed together with `openvino`, and the two versions must correspond one-to-one. The supported pairings are:
+
+| onnxruntime-openvino | OpenVINO |
+| --- | --- |
+| 1.24.1 | 2025.4.1 |
+| 1.23.0 | 2025.3 |
+| 1.22.0 | 2025.1 |
+
+```bash
+# Example: onnxruntime-openvino 1.24.1 pairs with OpenVINO 2025.4.1
+pip install openvino==2025.4.1
+pip install onnxruntime-openvino==1.24.1
+```
+
+See the [OpenVINO Execution Provider requirements](https://onnxruntime.ai/docs/execution-providers/OpenVINO-ExecutionProvider.html#requirements) for the full version-mapping details.
+
 2. Usage:
 
 ```bash

models/instructions.txt

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

modules/core.py

@@ -178,6 +178,10 @@ def limit_resources() -> None:
             tensorflow.config.experimental.set_memory_growth(gpu, True)
     # limit memory usage
     if modules.globals.max_memory:
+        # setrlimit(RLIMIT_DATA) fails with EINVAL on macOS, crashing on launch.
+        # See https://github.com/hacksider/Deep-Live-Cam/issues/1848
+        if platform.system().lower() == 'darwin':
+            return
         memory = modules.globals.max_memory * 1024 ** 3
         if platform.system().lower() == 'windows':
             import ctypes
@@ -276,10 +280,9 @@ def start() -> None:
             update_status('Falling back to disk-based processing...')
 
         extraction_start = time.time()
-        if not modules.globals.map_faces:
-            create_temp(modules.globals.target_path)
-            update_status('Extracting frames...')
-            extract_frames(modules.globals.target_path)
+        create_temp(modules.globals.target_path)
+        update_status('Extracting frames...')
+        extract_frames(modules.globals.target_path)
         extraction_time = time.time() - extraction_start
 
         temp_frame_paths = get_temp_frame_paths(modules.globals.target_path)

modules/face_analyser.py

@@ -29,6 +29,9 @@ def get_face_analyser() -> Any:
                 from modules.processors.frame._onnx_enhancer import (
                     build_provider_config,
                 )
+                from modules.model_downloader import ensure_insightface_pack
+
+                ensure_insightface_pack('buffalo_l')
                 providers = build_provider_config()
                 FACE_ANALYSER = insightface.app.FaceAnalysis(
                     name='buffalo_l',

modules/globals.py

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

modules/model_downloader.py

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

modules/processors/frame/face_enhancer.py

@@ -23,6 +23,7 @@ FACE_ENHANCER = None
 THREAD_SEMAPHORE = threading.Semaphore()
 THREAD_LOCK = threading.Lock()
 NAME = "DLC.FACE-ENHANCER"
+MODEL_FILE = "gfpgan-1024.onnx"
 
 abs_dir = os.path.dirname(os.path.abspath(__file__))
 models_dir = os.path.join(
@@ -44,11 +45,12 @@ FFHQ_TEMPLATE_512 = np.array(
 
 
 def pre_check() -> bool:
-    model_path = os.path.join(models_dir, "gfpgan-1024.onnx")
-    if not os.path.exists(model_path):
+    from modules.model_downloader import ensure_model
+
+    if ensure_model(MODEL_FILE) is None:
         update_status(
-            f"GFPGAN ONNX model not found at {model_path}. "
-            "Please place gfpgan-1024.onnx in the models folder.",
+            f"Could not obtain {MODEL_FILE}. Place it in the models folder "
+            "manually or check your internet connection.",
             NAME,
         )
         return False
@@ -73,11 +75,15 @@ def get_face_enhancer() -> onnxruntime.InferenceSession:
 
     with THREAD_LOCK:
         if FACE_ENHANCER is None:
-            model_path = os.path.join(models_dir, "gfpgan-1024.onnx")
+            from modules.model_downloader import ensure_model
 
-            if not os.path.exists(model_path):
+            model_path = ensure_model(MODEL_FILE)
+
+            if model_path is None:
                 raise FileNotFoundError(
-                    f"{NAME}: Model not found at {model_path}"
+                    f"{NAME}: Model not found at "
+                    f"{os.path.join(models_dir, MODEL_FILE)} and could not be "
+                    "downloaded"
                 )
 
             try:

modules/processors/frame/face_enhancer_gpen256.py

@@ -22,7 +22,7 @@ from modules.processors.frame._onnx_enhancer import (
 
 NAME = "DLC.FACE-ENHANCER-GPEN256"
 INPUT_SIZE = 256
-MODEL_URL = "https://github.com/harisreedhar/Face-Upscalers-ONNX/releases/download/GPEN-BFR/GPEN-BFR-256.onnx"
+MODEL_MIRROR_URL = "https://github.com/harisreedhar/Face-Upscalers-ONNX/releases/download/GPEN-BFR/GPEN-BFR-256.onnx"
 MODEL_FILE = "GPEN-BFR-256.onnx"
 
 ENHANCER = None
@@ -34,12 +34,33 @@ models_dir = os.path.join(
 )
 
 
+def _obtain_model():
+    from modules.model_downloader import ensure_model
+
+    model_path = ensure_model(MODEL_FILE)
+    if model_path is not None:
+        return model_path
+
+    update_status(f"Retrying {MODEL_FILE} from the mirror...", NAME)
+    from modules.utilities import conditional_download
+
+    try:
+        conditional_download(models_dir, [MODEL_MIRROR_URL])
+    except Exception as error:
+        update_status(f"Mirror download failed: {error}", NAME)
+        return None
+    fallback = os.path.join(models_dir, MODEL_FILE)
+    return fallback if os.path.exists(fallback) else None
+
+
 def pre_check() -> bool:
-    model_path = os.path.join(models_dir, MODEL_FILE)
-    if not os.path.exists(model_path):
-        update_status(f"Downloading {MODEL_FILE}...", NAME)
-        from modules.utilities import conditional_download
-        conditional_download(models_dir, [MODEL_URL])
+    if _obtain_model() is None:
+        update_status(
+            f"Could not obtain {MODEL_FILE}. Place it in the models folder "
+            "manually or check your internet connection.",
+            NAME,
+        )
+        return False
     return True
 
 
@@ -54,12 +75,11 @@ def get_enhancer() -> Any:
     global ENHANCER
     with THREAD_LOCK:
         if ENHANCER is None:
-            model_path = os.path.join(models_dir, MODEL_FILE)
-            if not os.path.exists(model_path):
-                from modules.utilities import conditional_download
-                conditional_download(models_dir, [MODEL_URL])
-            if not os.path.exists(model_path):
-                raise FileNotFoundError(f"Model file not found: {model_path}")
+            model_path = _obtain_model()
+            if model_path is None:
+                raise FileNotFoundError(
+                    f"Model file not found: {os.path.join(models_dir, MODEL_FILE)}"
+                )
             print(f"{NAME}: Loading ONNX model from {model_path}")
             ENHANCER = create_onnx_session(model_path)
             warmup_session(ENHANCER)

modules/processors/frame/face_enhancer_gpen512.py

@@ -22,7 +22,7 @@ from modules.processors.frame._onnx_enhancer import (
 
 NAME = "DLC.FACE-ENHANCER-GPEN512"
 INPUT_SIZE = 512
-MODEL_URL = "https://github.com/harisreedhar/Face-Upscalers-ONNX/releases/download/GPEN-BFR/GPEN-BFR-512.onnx"
+MODEL_MIRROR_URL = "https://github.com/harisreedhar/Face-Upscalers-ONNX/releases/download/GPEN-BFR/GPEN-BFR-512.onnx"
 MODEL_FILE = "GPEN-BFR-512.onnx"
 
 ENHANCER = None
@@ -34,12 +34,33 @@ models_dir = os.path.join(
 )
 
 
+def _obtain_model():
+    from modules.model_downloader import ensure_model
+
+    model_path = ensure_model(MODEL_FILE)
+    if model_path is not None:
+        return model_path
+
+    update_status(f"Retrying {MODEL_FILE} from the mirror...", NAME)
+    from modules.utilities import conditional_download
+
+    try:
+        conditional_download(models_dir, [MODEL_MIRROR_URL])
+    except Exception as error:
+        update_status(f"Mirror download failed: {error}", NAME)
+        return None
+    fallback = os.path.join(models_dir, MODEL_FILE)
+    return fallback if os.path.exists(fallback) else None
+
+
 def pre_check() -> bool:
-    model_path = os.path.join(models_dir, MODEL_FILE)
-    if not os.path.exists(model_path):
-        update_status(f"Downloading {MODEL_FILE}...", NAME)
-        from modules.utilities import conditional_download
-        conditional_download(models_dir, [MODEL_URL])
+    if _obtain_model() is None:
+        update_status(
+            f"Could not obtain {MODEL_FILE}. Place it in the models folder "
+            "manually or check your internet connection.",
+            NAME,
+        )
+        return False
     return True
 
 
@@ -54,12 +75,11 @@ def get_enhancer() -> Any:
     global ENHANCER
     with THREAD_LOCK:
         if ENHANCER is None:
-            model_path = os.path.join(models_dir, MODEL_FILE)
-            if not os.path.exists(model_path):
-                from modules.utilities import conditional_download
-                conditional_download(models_dir, [MODEL_URL])
-            if not os.path.exists(model_path):
-                raise FileNotFoundError(f"Model file not found: {model_path}")
+            model_path = _obtain_model()
+            if model_path is None:
+                raise FileNotFoundError(
+                    f"Model file not found: {os.path.join(models_dir, MODEL_FILE)}"
+                )
             print(f"{NAME}: Loading ONNX model from {model_path}")
             ENHANCER = create_onnx_session(model_path)
             warmup_session(ENHANCER)

modules/processors/frame/face_swapper.py

@@ -12,7 +12,6 @@ from modules.core import update_status
 from modules.face_analyser import get_one_face, get_many_faces, default_source_face
 from modules.typing import Face, Frame
 from modules.utilities import (
-    conditional_download,
     is_image,
     is_video,
 )
@@ -192,21 +191,26 @@ models_dir = os.path.join(
 def pre_check() -> bool:
     # Use models_dir instead of abs_dir to save to the correct location
     download_directory_path = models_dir
-    
+
     # Make sure the models directory exists, catch permission errors if they occur
     try:
         os.makedirs(download_directory_path, exist_ok=True)
     except OSError as e:
         logging.error(f"Failed to create directory {download_directory_path} due to permission error: {e}")
         return False
-    
-    # Use the direct download URL from Hugging Face (FP32 model for broad GPU compatibility)
-    conditional_download(
-        download_directory_path,
-        [
-            "https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128.onnx"
-        ],
-    )
+
+    from modules.model_downloader import ensure_any
+
+    variants = ["inswapper_128.onnx", "inswapper_128_fp16.onnx"]
+    if _HAS_TORCH_CUDA:
+        variants.reverse()
+    if ensure_any(variants) is None:
+        update_status(
+            "Could not obtain the inswapper model. Place inswapper_128.onnx in "
+            "the models folder manually or check your internet connection.",
+            NAME,
+        )
+        return False
     return True
 
 
@@ -242,8 +246,12 @@ def get_face_swapper() -> Any:
             elif os.path.exists(fp32_path):
                 model_path = fp32_path
             else:
-                update_status(f"No inswapper model found in {models_dir}.", NAME)
-                return None
+                if not pre_check():
+                    return None
+                model_path = fp16_path if os.path.exists(fp16_path) else fp32_path
+                if not os.path.exists(model_path):
+                    update_status(f"No inswapper model found in {models_dir}.", NAME)
+                    return None
             # On Apple Silicon, rewrite Pad(reflect) → Slice+Concat so
             # CoreML can run the entire model in a single partition on
             # the Neural Engine instead of bouncing between CPU and ANE.

modules/ui.py

@@ -237,6 +237,18 @@ _RECENT_SOURCE_DIR: Optional[str] = None
 _RECENT_TARGET_DIR: Optional[str] = None
 _RECENT_OUTPUT_DIR: Optional[str] = None
 
+# QFileDialog filter strings, built from the canonical extension sets in
+# globals so every dialog stays in sync (no hand-copied lists to drift).
+_IMAGE_FILE_FILTER = "Images (" + " ".join(
+    f"*{ext}" for ext in modules.globals.IMAGE_EXTENSIONS
+) + ")"
+_MEDIA_FILE_FILTER = "Media (" + " ".join(
+    f"*{ext}" for ext in (*modules.globals.IMAGE_EXTENSIONS, *modules.globals.VIDEO_EXTENSIONS)
+) + ")"
+_VIDEO_FILE_FILTER = "Videos (" + " ".join(
+    f"*{ext}" for ext in modules.globals.VIDEO_EXTENSIONS
+) + ")"
+
 
 # ─── image utilities ─────────────────────────────────────────────────────
 
@@ -417,7 +429,7 @@ def get_available_cameras() -> Tuple[List[int], List[str]]:
     indices: List[int] = []
     names: List[str] = []
     for i in range(10):
-        cap = cv2.VideoCapture(i)
+        cap = cv2.VideoCapture(f"/dev/video{i}")
         if cap.isOpened():
             indices.append(i)
             names.append(f"Camera {i}")
@@ -734,7 +746,7 @@ class MainWindow(QMainWindow):
         path, _filter = QFileDialog.getOpenFileName(
             self, _("select an source image"),
             _RECENT_SOURCE_DIR or "",
-            "Images (*.png *.jpg *.jpeg *.gif *.bmp)",
+            _IMAGE_FILE_FILTER,
         )
         if path and is_image(path):
             modules.globals.source_path = path
@@ -755,7 +767,7 @@ class MainWindow(QMainWindow):
         path, _filter = QFileDialog.getOpenFileName(
             self, _("select an target image or video"),
             _RECENT_TARGET_DIR or "",
-            "Media (*.png *.jpg *.jpeg *.gif *.bmp *.mp4 *.mkv)",
+            _MEDIA_FILE_FILTER,
         )
         if not path:
             return
@@ -886,13 +898,13 @@ class MainWindow(QMainWindow):
             path, _f = QFileDialog.getSaveFileName(
                 self, _("save image output file"),
                 os.path.join(_RECENT_OUTPUT_DIR or "", "output.png"),
-                "Images (*.png *.jpg *.jpeg *.bmp)",
+                _IMAGE_FILE_FILTER,
             )
         elif is_video(modules.globals.target_path):
             path, _f = QFileDialog.getSaveFileName(
                 self, _("save video output file"),
                 os.path.join(_RECENT_OUTPUT_DIR or "", "output.mp4"),
-                "Videos (*.mp4 *.mkv)",
+                _VIDEO_FILE_FILTER,
             )
         else:
             return
@@ -1334,7 +1346,7 @@ class MapperDialog(QDialog):
         path, _f = QFileDialog.getOpenFileName(
             self, _("select an source image"),
             _RECENT_SOURCE_DIR or "",
-            "Images (*.png *.jpg *.jpeg *.gif *.bmp)",
+            _IMAGE_FILE_FILTER,
         )
         if not path:
             return
@@ -1439,7 +1451,7 @@ class LiveMapperDialog(QDialog):
         path, _f = QFileDialog.getOpenFileName(
             self, _("select an source image"),
             _RECENT_SOURCE_DIR or "",
-            "Images (*.png *.jpg *.jpeg *.gif *.bmp)",
+            _IMAGE_FILE_FILTER,
         )
         if not path:
             return

modules/utilities.py

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

modules/video_capture.py

@@ -71,8 +71,9 @@ class VideoCapturer:
                         self.cap.release()
                     except Exception:
                         continue
+            elif platform.system() == "Linux":
+                self.cap = cv2.VideoCapture(f"/dev/video{self.device_index}")
             else:
-                # Unix-like systems (Linux/Mac) capture method
                 self.cap = cv2.VideoCapture(self.device_index)
 
             if not self.cap or not self.cap.isOpened():

tests/test_core_map_faces_fallback.py

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