Merge 55e24d398a into 7f95b69bc5

.gitignore

@@ -6,7 +6,7 @@ __pycache__/
 .todo
 *.log
 *.backup
-tf_env/
+
 *.png
 *.mp4
 *.mkv
@@ -22,3 +22,8 @@ models/inswapper_128.onnx
 models/GFPGANv1.4.pth
 *.onnx
 models/DMDNet.pth
+.venv/
+tf_env/
+.tf_env/
+.deepcamlive/
+deep-live-cam/

README.md

@@ -78,19 +78,20 @@ python run.py --execution-provider coreml
 ```
 
 ### [](https://github.com/s0md3v/roop/wiki/2.-Acceleration#coreml-execution-provider-apple-legacy)CoreML Execution Provider (Apple Legacy)
+Metal support has been added for improved performance on macOS devices.
 
 1.  Install dependencies:
 
 ```
-pip uninstall onnxruntime onnxruntime-coreml
-pip install onnxruntime-coreml==1.13.1
+pip uninstall onnxruntime onnxruntime-silicon
+pip install onnxruntime-silicon==1.13.1
 
 ```
 
 2.  Usage in case the provider is available:
 
 ```
-python run.py --execution-provider coreml
+python run.py --execution-provider metal
 
 ```
 
@@ -176,6 +177,15 @@ Looking for a CLI mode? Using the -s/--source argument will make the run program
 ## Want the Next Update Now?
 If you want the latest and greatest build, or want to see some new great features, go to our [experimental branch](https://github.com/hacksider/Deep-Live-Cam/tree/experimental) and experience what the contributors have given.
 
+## TODO
+- [ ] Support multiple faces feature
+- [ ] Develop a version for web app/service
+- [ ] UI/UX enhancements for desktop app
+- [ ] Speed up model loading
+- [ ] Speed up real-time face swapping
+
+*Note: This is an open-source project, and we’re working on it in our free time. Therefore, features, replies, bug fixes, etc., might be delayed. We hope you understand. Thanks.*
+
 ## Credits
 
 - [ffmpeg](https://ffmpeg.org/): for making video related operations easy

modules/core.py

@@ -5,6 +5,8 @@ if any(arg.startswith('--execution-provider') for arg in sys.argv):
     os.environ['OMP_NUM_THREADS'] = '1'
 # reduce tensorflow log level
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+# Force TensorFlow to use Metal
+os.environ['TENSORFLOW_METAL'] = '1'
 import warnings
 from typing import List
 import platform
@@ -14,6 +16,7 @@ import argparse
 import torch
 import onnxruntime
 import tensorflow
+import cv2
 
 import modules.globals
 import modules.metadata
@@ -35,9 +38,9 @@ def parse_args() -> None:
     program.add_argument('-t', '--target', help='select an target image or video', dest='target_path')
     program.add_argument('-o', '--output', help='select output file or directory', dest='output_path')
     program.add_argument('--frame-processor', help='pipeline of frame processors', dest='frame_processor', default=['face_swapper'], choices=['face_swapper', 'face_enhancer'], nargs='+')
-    program.add_argument('--keep-fps', help='keep original fps', dest='keep_fps', action='store_true', default=False)
+    program.add_argument('--keep-fps', help='keep original fps', dest='keep_fps', action='store_true', default=True)
     program.add_argument('--keep-audio', help='keep original audio', dest='keep_audio', action='store_true', default=True)
-    program.add_argument('--keep-frames', help='keep temporary frames', dest='keep_frames', action='store_true', default=False)
+    program.add_argument('--keep-frames', help='keep temporary frames', dest='keep_frames', action='store_true', default=True)
     program.add_argument('--many-faces', help='process every face', dest='many_faces', action='store_true', default=False)
     program.add_argument('--nsfw-filter', help='filter the NSFW image or video', dest='nsfw_filter', action='store_true', default=False)
     program.add_argument('--video-encoder', help='adjust output video encoder', dest='video_encoder', default='libx264', choices=['libx264', 'libx265', 'libvpx-vp9'])
@@ -45,16 +48,10 @@ def parse_args() -> None:
     program.add_argument('--live-mirror', help='The live camera display as you see it in the front-facing camera frame', dest='live_mirror', action='store_true', default=False)
     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=['cpu'], choices=suggest_execution_providers(), nargs='+')
+    program.add_argument('--execution-provider', help='execution provider', dest='execution_provider', default=['coreml'], 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('-v', '--version', action='version', version=f'{modules.metadata.name} {modules.metadata.version}')
 
-    # register deprecated args
-    program.add_argument('-f', '--face', help=argparse.SUPPRESS, dest='source_path_deprecated')
-    program.add_argument('--cpu-cores', help=argparse.SUPPRESS, dest='cpu_cores_deprecated', type=int)
-    program.add_argument('--gpu-vendor', help=argparse.SUPPRESS, dest='gpu_vendor_deprecated')
-    program.add_argument('--gpu-threads', help=argparse.SUPPRESS, dest='gpu_threads_deprecated', type=int)
-
     args = program.parse_args()
 
     modules.globals.source_path = args.source_path
@@ -72,36 +69,14 @@ def parse_args() -> None:
     modules.globals.live_mirror = args.live_mirror
     modules.globals.live_resizable = args.live_resizable
     modules.globals.max_memory = args.max_memory
-    modules.globals.execution_providers = decode_execution_providers(args.execution_provider)
+    modules.globals.execution_providers = ['CoreMLExecutionProvider']  # Force CoreML
     modules.globals.execution_threads = args.execution_threads
 
-    #for ENHANCER tumbler:
     if 'face_enhancer' in args.frame_processor:
         modules.globals.fp_ui['face_enhancer'] = True
     else:
         modules.globals.fp_ui['face_enhancer'] = False
 
-    # translate deprecated args
-    if args.source_path_deprecated:
-        print('\033[33mArgument -f and --face are deprecated. Use -s and --source instead.\033[0m')
-        modules.globals.source_path = args.source_path_deprecated
-        modules.globals.output_path = normalize_output_path(args.source_path_deprecated, modules.globals.target_path, args.output_path)
-    if args.cpu_cores_deprecated:
-        print('\033[33mArgument --cpu-cores is deprecated. Use --execution-threads instead.\033[0m')
-        modules.globals.execution_threads = args.cpu_cores_deprecated
-    if args.gpu_vendor_deprecated == 'apple':
-        print('\033[33mArgument --gpu-vendor apple is deprecated. Use --execution-provider coreml instead.\033[0m')
-        modules.globals.execution_providers = decode_execution_providers(['coreml'])
-    if args.gpu_vendor_deprecated == 'nvidia':
-        print('\033[33mArgument --gpu-vendor nvidia is deprecated. Use --execution-provider cuda instead.\033[0m')
-        modules.globals.execution_providers = decode_execution_providers(['cuda'])
-    if args.gpu_vendor_deprecated == 'amd':
-        print('\033[33mArgument --gpu-vendor amd is deprecated. Use --execution-provider cuda instead.\033[0m')
-        modules.globals.execution_providers = decode_execution_providers(['rocm'])
-    if args.gpu_threads_deprecated:
-        print('\033[33mArgument --gpu-threads is deprecated. Use --execution-threads instead.\033[0m')
-        modules.globals.execution_threads = args.gpu_threads_deprecated
-
 
 def encode_execution_providers(execution_providers: List[str]) -> List[str]:
     return [execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers]
@@ -114,44 +89,30 @@ def decode_execution_providers(execution_providers: List[str]) -> List[str]:
 
 def suggest_max_memory() -> int:
     if platform.system().lower() == 'darwin':
-        return 4
-    return 16
+        return 6
+    return 4
 
 
 def suggest_execution_providers() -> List[str]:
-    return encode_execution_providers(onnxruntime.get_available_providers())
+    return ['coreml']  # Only suggest CoreML
 
 
 def suggest_execution_threads() -> int:
-    if 'DmlExecutionProvider' in modules.globals.execution_providers:
-        return 1
-    if 'ROCMExecutionProvider' in modules.globals.execution_providers:
-        return 1
-    return 8
+    if platform.system().lower() == 'darwin':
+        return 12
+    return 4
+    
 
 
 def limit_resources() -> None:
-    # prevent tensorflow memory leak
-    gpus = tensorflow.config.experimental.list_physical_devices('GPU')
-    for gpu in gpus:
-        tensorflow.config.experimental.set_memory_growth(gpu, True)
-    # 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
-            kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(memory), ctypes.c_size_t(memory))
-        else:
-            import resource
-            resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))
+        memory = modules.globals.max_memory * 1024 ** 6
+        import resource
+        resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))
 
 
 def release_resources() -> None:
-    if 'CUDAExecutionProvider' in modules.globals.execution_providers:
-        torch.cuda.empty_cache()
+    pass  # No need to release CUDA resources
 
 
 def pre_check() -> bool:
@@ -173,15 +134,13 @@ def start() -> None:
     for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
         if not frame_processor.pre_start():
             return
-    update_status('Processing...')
     # process image to image
     if has_image_extension(modules.globals.target_path):
-        if modules.globals.nsfw_filter and ui.check_and_ignore_nsfw(modules.globals.target_path, destroy):
-            return
-        try:
-            shutil.copy2(modules.globals.target_path, modules.globals.output_path)
-        except Exception as e:
-            print("Error copying file:", str(e))
+        if modules.globals.nsfw == False:
+            from modules.predicter import predict_image
+            if predict_image(modules.globals.target_path):
+                destroy()
+        shutil.copy2(modules.globals.target_path, modules.globals.output_path)
         for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
             update_status('Progressing...', frame_processor.NAME)
             frame_processor.process_image(modules.globals.source_path, modules.globals.output_path, modules.globals.output_path)
@@ -192,8 +151,10 @@ def start() -> None:
             update_status('Processing to image failed!')
         return
     # process image to videos
-    if modules.globals.nsfw_filter and ui.check_and_ignore_nsfw(modules.globals.target_path, destroy):
-        return
+    if modules.globals.nsfw == False:
+        from modules.predicter import predict_video
+        if predict_video(modules.globals.target_path):
+            destroy()
     update_status('Creating temp resources...')
     create_temp(modules.globals.target_path)
     update_status('Extracting frames...')
@@ -202,8 +163,6 @@ def start() -> None:
     for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
         update_status('Progressing...', frame_processor.NAME)
         frame_processor.process_video(modules.globals.source_path, temp_frame_paths)
-        release_resources()
-    # handles fps
     if modules.globals.keep_fps:
         update_status('Detecting fps...')
         fps = detect_fps(modules.globals.target_path)
@@ -212,7 +171,6 @@ def start() -> None:
     else:
         update_status('Creating video with 30.0 fps...')
         create_video(modules.globals.target_path)
-    # handle audio
     if modules.globals.keep_audio:
         if modules.globals.keep_fps:
             update_status('Restoring audio...')
@@ -221,7 +179,6 @@ def start() -> None:
         restore_audio(modules.globals.target_path, modules.globals.output_path)
     else:
         move_temp(modules.globals.target_path, modules.globals.output_path)
-    # clean and validate
     clean_temp(modules.globals.target_path)
     if is_video(modules.globals.target_path):
         update_status('Processing to video succeed!')
@@ -243,8 +200,70 @@ def run() -> None:
         if not frame_processor.pre_check():
             return
     limit_resources()
+    print(f"ONNX Runtime version: {onnxruntime.__version__}")
+    print(f"Available execution providers: {onnxruntime.get_available_providers()}")
+    print(f"Selected execution provider: CoreMLExecutionProvider (with CPU fallback for face detection)")
+    
+    # Configure ONNX Runtime to use CoreML
+    onnxruntime.set_default_logger_severity(3)  # Set to WARNING level
+    options = onnxruntime.SessionOptions()
+    options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
+    
+    # Add CoreML-specific options
+    options.add_session_config_entry("session.coreml.force_precision", "FP32")
+    options.add_session_config_entry("session.coreml.enable_on_subgraph", "1")
+    
+    # Update insightface model loading to use CPU for face detection
+    from insightface.utils import face_align
+    def custom_session(model_file, providers):
+        if 'det_model.onnx' in model_file:
+            return onnxruntime.InferenceSession(model_file, providers=['CPUExecutionProvider'])
+        else:
+            return onnxruntime.InferenceSession(model_file, options, providers=['CoreMLExecutionProvider'])
+    face_align.Session = custom_session
+    
+    # Configure TensorFlow to use Metal
+    try:
+        tf_devices = tensorflow.config.list_physical_devices()
+        print("TensorFlow devices:", tf_devices)
+        if any('GPU' in device.name for device in tf_devices):
+            print("TensorFlow is using GPU (Metal)")
+        else:
+            print("TensorFlow is not using GPU")
+    except Exception as e:
+        print(f"Error configuring TensorFlow: {str(e)}")
+    
+    # Configure PyTorch to use MPS (Metal Performance Shaders)
+    try:
+        if torch.backends.mps.is_available():
+            print("PyTorch is using MPS (Metal Performance Shaders)")
+            torch.set_default_device('mps')
+        else:
+            print("PyTorch MPS is not available")
+    except Exception as e:
+        print(f"Error configuring PyTorch: {str(e)}")
+    
     if modules.globals.headless:
         start()
     else:
         window = ui.init(start, destroy)
         window.mainloop()
+
+def get_one_face(frame):
+    # Resize the frame to the expected input size
+    frame_resized = cv2.resize(frame, (112, 112))  # Resize to (112, 112) for recognition model
+    face = get_face_analyser().get(frame_resized)
+    return face
+
+# Ensure to use the CPUExecutionProvider if CoreML fails
+def run_model_with_cpu_fallback(model_file, providers):
+    try:
+        return onnxruntime.InferenceSession(model_file, providers=['CoreMLExecutionProvider'])
+    except Exception as e:
+        print(f"CoreML execution failed: {e}. Falling back to CPU.")
+        return onnxruntime.InferenceSession(model_file, providers=['CPUExecutionProvider'])
+
+# Update the face analysis function to use the fallback
+def get_face_analyser():
+    # Load your model here with the fallback
+    return run_model_with_cpu_fallback('/path/to/your/model.onnx', ['CoreMLExecutionProvider', 'CPUExecutionProvider'])

modules/face_analyser.py

@@ -12,7 +12,7 @@ def get_face_analyser() -> Any:
 
     if FACE_ANALYSER is None:
         FACE_ANALYSER = insightface.app.FaceAnalysis(name='buffalo_l', providers=modules.globals.execution_providers)
-        FACE_ANALYSER.prepare(ctx_id=0, det_size=(640, 640))
+        FACE_ANALYSER.prepare(ctx_id=0, det_size=(1280, 720))
     return FACE_ANALYSER
 
 

modules/processors/frame/face_swapper.py

@@ -17,7 +17,7 @@ NAME = 'DLC.FACE-SWAPPER'
 
 def pre_check() -> bool:
     download_directory_path = resolve_relative_path('../models')
-    conditional_download(download_directory_path, ['https://huggingface.co/hacksider/deep-live-cam/blob/main/inswapper_128_fp16.onnx'])
+    conditional_download(download_directory_path, ['https://huggingface.co/hacksider/deep-live-cam/blob/main/inswapper_128.onnx'])
     return True
 
 
@@ -39,7 +39,7 @@ def get_face_swapper() -> Any:
 
     with THREAD_LOCK:
         if FACE_SWAPPER is None:
-            model_path = resolve_relative_path('../models/inswapper_128_fp16.onnx')
+            model_path = resolve_relative_path('../models/inswapper_128.onnx')
             FACE_SWAPPER = insightface.model_zoo.get_model(model_path, providers=modules.globals.execution_providers)
     return FACE_SWAPPER
 

modules/ui.py

@@ -194,38 +194,6 @@ def select_output_path(start: Callable[[], None]) -> None:
         start()
 
 
-def check_and_ignore_nsfw(target, destroy: Callable = None) -> bool:
-    ''' Check if the target is NSFW.
-    TODO: Consider to make blur the target.
-    '''
-    from numpy import ndarray
-    from modules.predicter import predict_image, predict_video, predict_frame
-    if type(target) is str: # image/video file path
-        check_nsfw = predict_image if has_image_extension(target) else predict_video
-    elif type(target) is ndarray: # frame object
-        check_nsfw = predict_frame
-    if check_nsfw and check_nsfw(target):
-        if destroy: destroy(to_quit=False) # Do not need to destroy the window frame if the target is NSFW
-        update_status('Processing ignored!')
-        return True
-    else: return False
-
-
-def fit_image_to_size(image, width: int, height: int):
-    if width is None and height is None:
-      return image
-    h, w, _ = image.shape
-    ratio_h = 0.0
-    ratio_w = 0.0
-    if width > height:
-        ratio_h = height / h
-    else:
-        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)
-
-
 def render_image_preview(image_path: str, size: Tuple[int, int]) -> ctk.CTkImage:
     image = Image.open(image_path)
     if size:

modules/utilities.py

@@ -9,6 +9,7 @@ import urllib
 from pathlib import Path
 from typing import List, Any
 from tqdm import tqdm
+import cv2
 
 import modules.globals
 
@@ -44,7 +45,19 @@ def detect_fps(target_path: str) -> float:
 
 def extract_frames(target_path: str) -> None:
     temp_directory_path = get_temp_directory_path(target_path)
-    run_ffmpeg(['-i', target_path, '-pix_fmt', 'rgb24', os.path.join(temp_directory_path, '%04d.png')])
+    cap = cv2.VideoCapture(target_path)
+    
+    frame_count = 0
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        
+        # Save the frame
+        cv2.imwrite(os.path.join(temp_directory_path, f'{frame_count:04d}.png'), frame)
+        frame_count += 1
+    
+    cap.release()
 
 
 def create_video(target_path: str, fps: float = 30.0) -> None:

requirements.txt

@@ -1,23 +1,27 @@
---extra-index-url https://download.pytorch.org/whl/cu118
+# Deep Live Cam requirements
 
-numpy==1.23.5
+# Core dependencies
+numpy==1.26.4
+onnxruntime-silicon==1.16.3
 opencv-python==4.8.1.78
-onnx==1.16.0
-insightface==0.7.3
-psutil==5.9.8
-tk==0.1.0
-customtkinter==5.2.2
 pillow==9.5.0
-torch==2.0.1+cu118; sys_platform != 'darwin'
-torch==2.0.1; sys_platform == 'darwin'
-torchvision==0.15.2+cu118; sys_platform != 'darwin'
-torchvision==0.15.2; sys_platform == 'darwin'
-onnxruntime==1.18.0; sys_platform == 'darwin' and platform_machine != 'arm64'
-onnxruntime-silicon==1.16.3; sys_platform == 'darwin' and platform_machine == 'arm64'
-onnxruntime-gpu==1.18.0; sys_platform != 'darwin'
-tensorflow==2.13.0rc1; sys_platform == 'darwin'
-tensorflow==2.12.1; sys_platform != 'darwin'
-opennsfw2==0.10.2
-protobuf==4.23.2
+insightface==0.7.3
+torch==2.1.0  # Add the specific version you're using
+tensorflow-macos==2.16.2  # Add the specific version you're using
+tensorflow-metal==1.1.0  # Add the specific version you're using
+
+# Image processing
+scikit-image==0.24.0
+matplotlib==3.9.1.post1
+
+# Machine learning
+scikit-learn==1.5.1
+
+# Utilities
 tqdm==4.66.4
-gfpgan==1.3.8
+requests==2.32.3
+prettytable==3.11.0
+
+# Optional dependencies (comment out if not needed)
+# albumentations==1.4.13
+# coloredlogs==15.0.1