Spaces:

crash10155
/

Switcher

Paused

Switcher / SwitcherAI /processors /frame /modules /face_swapper.py

Update SwitcherAI/processors/frame/modules/face_swapper.py

ddf9937 verified over 1 year ago

21.7 kB

from typing import Any, List, Callable, Dict, Tuple
import cv2
import insightface
import threading
import numpy as np
from functools import lru_cache
from pathlib import Path

import SwitcherAI.globals
import SwitcherAI.processors.frame.core as frame_processors
from SwitcherAI import wording
from SwitcherAI.core import update_status
from SwitcherAI.face_analyser import get_one_face, get_many_faces, find_similar_faces
from SwitcherAI.face_reference import get_face_reference, set_face_reference
from SwitcherAI.typing import Face, Frame
from SwitcherAI.utilities import resolve_relative_path, is_image, is_video

FRAME_PROCESSOR = None
EMBEDDING_CONVERTER = None
THREAD_LOCK = threading.Lock()
NAME = 'FACEFUSION.FRAME_PROCESSOR.FACE_SWAPPER'

# Model configurations - local paths only
MODEL_CONFIGS = {
    'inswapper_128': {
        'path': '../.assets/models/inswapper_128.onnx',
        'type': 'inswapper',
        'size': (128, 128),
        'mean': [0.0, 0.0, 0.0],
        'standard_deviation': [1.0, 1.0, 1.0],
        'requires_converter': False
    },
    'inswapper_128_fp16': {
        'path': '../.assets/models/inswapper_128_fp16.onnx',
        'type': 'inswapper',
        'size': (128, 128),
        'mean': [0.0, 0.0, 0.0],
        'standard_deviation': [1.0, 1.0, 1.0],
        'requires_converter': False
    },
    'simswap_256': {
        'path': '../.assets/models/simswap_256.onnx',
        'converter_path': '../.assets/models/simswap_256_converter.onnx',
        'type': 'simswap',
        'size': (256, 256),
        'mean': [0.485, 0.456, 0.406],
        'standard_deviation': [0.229, 0.224, 0.225],
        'requires_converter': True
    },
}

# Default model - can be changed via globals
DEFAULT_MODEL = 'inswapper_128'


def get_current_model_config() -> Dict:
    """Get the current model configuration"""
    model_name = getattr(SwitcherAI.globals, 'face_swapper_model', DEFAULT_MODEL)
    return MODEL_CONFIGS.get(model_name, MODEL_CONFIGS[DEFAULT_MODEL])


@lru_cache(maxsize=None)
def get_static_model_initializer(model_path: str) -> np.ndarray:
    """Cache model initialization data"""
    try:
        # This would need to be implemented based on the specific model requirements
        # For now, return identity matrix as fallback
        return np.eye(512, dtype=np.float32)
    except Exception:
        return np.eye(512, dtype=np.float32)


def get_frame_processor() -> Any:
    global FRAME_PROCESSOR

    with THREAD_LOCK:
        if FRAME_PROCESSOR is None:
            # Try models in order of preference
            model_priority = ['inswapper_128_fp16', 'inswapper_128', 'simswap_256']
            
            # If user set a specific model, try it first
            current_model = getattr(SwitcherAI.globals, 'face_swapper_model', None)
            if current_model and current_model in MODEL_CONFIGS:
                model_priority.insert(0, current_model)
                # Remove duplicate if it exists later in the list
                if current_model in model_priority[1:]:
                    model_priority.remove(current_model)
            
            for model_name in model_priority:
                if model_name not in MODEL_CONFIGS:
                    continue
                    
                try:
                    print(f"🔄 Trying to load face swap model: {model_name}")
                    
                    # Get model config
                    temp_config = MODEL_CONFIGS[model_name]
                    model_path = resolve_relative_path(temp_config['path'])
                    
                    # Convert to Path object if it's a string for validation
                    if isinstance(model_path, str):
                        model_path_obj = Path(model_path)
                    else:
                        model_path_obj = model_path
                    
                    # Check if model exists locally
                    if not model_path_obj.exists():
                        print(f"❌ Model {model_name} not found at: {model_path_obj}")
                        continue
                    
                    # Verify model file size
                    if model_path_obj.stat().st_size < 1024:  # Less than 1KB indicates corruption
                        print(f"⚠️ {model_name} appears corrupted (file too small), skipping...")
                        continue
                    
                    # Try to load the model
                    FRAME_PROCESSOR = insightface.model_zoo.get_model(
                        str(model_path_obj), 
                        providers=SwitcherAI.globals.execution_providers
                    )
                    
                    # If successful, update the global setting and break
                    SwitcherAI.globals.face_swapper_model = model_name
                    print(f"✅ Successfully loaded face swap model: {model_name}")
                    break
                    
                except Exception as e:
                    print(f"❌ Failed to load {model_name}: {e}")
                    continue
            
            if FRAME_PROCESSOR is None:
                print("❌ All face swap models failed to load. Please ensure models are present in .assets/models folder.")
                
    return FRAME_PROCESSOR


def get_embedding_converter() -> Any:
    global EMBEDDING_CONVERTER
    
    config = get_current_model_config()
    if not config.get('requires_converter', False):
        return None
        
    with THREAD_LOCK:
        if EMBEDDING_CONVERTER is None:
            try:
                converter_path = resolve_relative_path(config['converter_path'])
                
                # Convert to Path object if it's a string for validation
                if isinstance(converter_path, str):
                    converter_path_obj = Path(converter_path)
                else:
                    converter_path_obj = converter_path
                
                # Check if converter exists locally
                if not converter_path_obj.exists():
                    print(f"❌ Embedding converter not found at: {converter_path_obj}")
                    print("Please ensure the converter model is present in .assets/models folder.")
                    return None
                
                EMBEDDING_CONVERTER = insightface.model_zoo.get_model(
                    str(converter_path_obj), 
                    providers=SwitcherAI.globals.execution_providers
                )
                print("✅ Embedding converter initialized")
                
            except Exception as e:
                print(f"❌ Failed to initialize embedding converter: {e}")
                EMBEDDING_CONVERTER = None
                
    return EMBEDDING_CONVERTER


def clear_frame_processor() -> None:
    global FRAME_PROCESSOR, EMBEDDING_CONVERTER

    FRAME_PROCESSOR = None
    EMBEDDING_CONVERTER = None


def pre_check() -> bool:
    """Check if required models exist locally"""
    try:
        config = get_current_model_config()
        
        # Check main model path
        model_path = resolve_relative_path(config['path'])
        if isinstance(model_path, str):
            model_path_obj = Path(model_path)
        else:
            model_path_obj = model_path
            
        if not model_path_obj.exists():
            print(f"❌ Main model not found at: {model_path_obj}")
            print("Please ensure the model file is present in .assets/models folder.")
            return False
        
        # Check converter if needed
        if config.get('requires_converter', False):
            converter_path = resolve_relative_path(config['converter_path'])
            if isinstance(converter_path, str):
                converter_path_obj = Path(converter_path)
            else:
                converter_path_obj = converter_path
                
            if not converter_path_obj.exists():
                print(f"❌ Converter model not found at: {converter_path_obj}")
                print("Please ensure the converter model file is present in .assets/models folder.")
                return False
        
        print("✅ All required models found locally")
        return True
        
    except Exception as e:
        print(f"❌ Face swap pre-check failed: {e}")
        return False


def pre_process() -> bool:
    try:
        if not is_image(SwitcherAI.globals.source_path):
            update_status(wording.get('select_image_source') + wording.get('exclamation_mark'), NAME)
            return False
        elif not get_one_face(cv2.imread(SwitcherAI.globals.source_path)):
            update_status(wording.get('no_source_face_detected') + wording.get('exclamation_mark'), NAME)
            return False
        if not is_image(SwitcherAI.globals.target_path) and not is_video(SwitcherAI.globals.target_path):
            update_status(wording.get('select_image_or_video_target') + wording.get('exclamation_mark'), NAME)
            return False
        
        # Check if required models exist locally
        if not pre_check():
            update_status("Required models not found in .assets/models folder", NAME)
            return False
        
        # Check if processor is available
        processor = get_frame_processor()
        if processor is None:
            update_status("Face swap processor not available", NAME)
            return False
            
        return True
        
    except Exception as e:
        print(f"⚠️ Face swap pre-process failed: {e}")
        return False


def post_process() -> None:
    clear_frame_processor()
    # Clear caches like the newer version
    get_static_model_initializer.cache_clear()


def prepare_source_embedding(source_face: Face) -> np.ndarray:
    """Prepare source face embedding based on model type"""
    try:
        config = get_current_model_config()
        model_type = config['type']
        
        if model_type == 'inswapper':
            # Enhanced embedding preparation for inswapper
            model_path = resolve_relative_path(config['path'])
            model_initializer = get_static_model_initializer(str(model_path))
            source_embedding = source_face.embedding.reshape((1, -1))
            source_embedding = np.dot(source_embedding, model_initializer) / np.linalg.norm(source_embedding)
            return source_embedding
        elif model_type == 'simswap':
            # Use embedding converter for simswap
            converter = get_embedding_converter()
            if converter is not None:
                embedding = source_face.embedding.reshape(-1, 512)
                try:
                    converted_embedding = converter.run(None, {'input': embedding})[0]
                    converted_embedding = converted_embedding.ravel()
                    normed_embedding = converted_embedding / np.linalg.norm(converted_embedding)
                    return normed_embedding.reshape(1, -1)
                except Exception:
                    pass
            
            # Fallback to original embedding
            return source_face.embedding.reshape(1, -1)
        else:
            # Default behavior
            return source_face.embedding.reshape(1, -1)
            
    except Exception as e:
        print(f"⚠️ Error preparing source embedding: {e}")
        return source_face.embedding.reshape(1, -1)


def prepare_crop_frame(crop_frame: Frame) -> np.ndarray:
    """Prepare cropped frame for model input with normalization"""
    try:
        config = get_current_model_config()
        model_mean = config['mean']
        model_std = config['standard_deviation']
        
        # Convert to float and normalize
        crop_frame = crop_frame[:, :, ::-1] / 255.0
        crop_frame = (crop_frame - model_mean) / model_std
        crop_frame = crop_frame.transpose(2, 0, 1)
        crop_frame = np.expand_dims(crop_frame, axis=0).astype(np.float32)
        return crop_frame
        
    except Exception as e:
        print(f"⚠️ Error preparing crop frame: {e}")
        return crop_frame


def normalize_crop_frame(crop_frame: np.ndarray) -> Frame:
    """Normalize cropped frame back to image format"""
    try:
        config = get_current_model_config()
        model_type = config['type']
        model_mean = config['mean']
        model_std = config['standard_deviation']
        
        crop_frame = crop_frame.transpose(1, 2, 0)
        
        # Apply reverse normalization for certain model types
        if model_type in ['simswap']:
            crop_frame = crop_frame * model_std + model_mean
        
        crop_frame = crop_frame.clip(0, 1)
        crop_frame = crop_frame[:, :, ::-1] * 255
        return crop_frame.astype(np.uint8)
        
    except Exception as e:
        print(f"⚠️ Error normalizing crop frame: {e}")
        return crop_frame.astype(np.uint8)


def enhanced_swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
    """Enhanced face swapping with improved preprocessing"""
    try:
        processor = get_frame_processor()
        if processor is None:
            print("⚠️ Face swap processor not available")
            return temp_frame
        
        config = get_current_model_config()
        model_type = config['type']
        
        if model_type == 'inswapper':
            # Use original method for inswapper
            return processor.get(temp_frame, target_face, source_face, paste_back=True)
        else:
            # Enhanced method for other models
            try:
                # Prepare source embedding
                source_embedding = prepare_source_embedding(source_face)
                
                # Get crop region (this would need proper implementation)
                # For now, fall back to original method
                return processor.get(temp_frame, target_face, source_face, paste_back=True)
            except Exception as e:
                print(f"⚠️ Enhanced swap failed: {e}")
                # Fallback to original method
                return processor.get(temp_frame, target_face, source_face, paste_back=True)
                
    except Exception as e:
        print(f"⚠️ Face swap failed: {e}")
        return temp_frame


def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
    """Main face swapping function with model-specific handling"""
    try:
        processor = get_frame_processor()
        if processor is None:
            print("⚠️ Face swap processor not available, skipping swap")
            return temp_frame
        
        config = get_current_model_config()
        
        # Use enhanced swapping for supported models
        if config['type'] in ['simswap', 'inswapper']:
            return enhanced_swap_face(source_face, target_face, temp_frame)
        else:
            # Original method
            return processor.get(temp_frame, target_face, source_face, paste_back=True)
            
    except Exception as e:
        print(f"⚠️ Error in swap_face: {e}")
        return temp_frame


def process_frame(source_face: Face, reference_face: Face, temp_frame: Frame) -> Frame:
    """Process frame with enhanced face selection logic"""
    try:
        processor = get_frame_processor()
        if processor is None:
            print("⚠️ Face swap processor not available, skipping frame")
            return temp_frame
        
        if 'reference' in SwitcherAI.globals.face_recognition:
            similar_faces = find_similar_faces(temp_frame, reference_face, SwitcherAI.globals.reference_face_distance)
            if similar_faces:
                for similar_face in similar_faces:
                    temp_frame = swap_face(source_face, similar_face, temp_frame)
        
        if 'many' in SwitcherAI.globals.face_recognition:
            many_faces = get_many_faces(temp_frame)
            if many_faces:
                # Sort faces by size (largest first) like the newer version
                many_faces = sorted(many_faces, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]), reverse=True)
                for target_face in many_faces:
                    temp_frame = swap_face(source_face, target_face, temp_frame)
        
        return temp_frame
        
    except Exception as e:
        print(f"⚠️ Error in process_frame: {e}")
        return temp_frame


def get_average_face(faces: List[Face]) -> Face:
    """Get average face from multiple faces (simplified version)"""
    if not faces:
        return None
    if len(faces) == 1:
        return faces[0]
    
    # For now, return the first face
    # In a full implementation, this would average the embeddings
    return faces[0]


def process_frames(source_path: str, temp_frame_paths: List[str], update: Callable[[], None]) -> None:
    """Enhanced frame processing with better source face handling"""
    try:
        processor = get_frame_processor()
        if processor is None:
            print("⚠️ Face swap processor not available, skipping frame processing")
            if update:
                update()
            return
        
        source_frame = cv2.imread(source_path)
        if source_frame is None:
            print(f"⚠️ Failed to read source image: {source_path}")
            if update:
                update()
            return
        
        source_faces = get_many_faces(source_frame)
        
        # Get best source face (largest)
        if source_faces:
            source_faces = sorted(source_faces, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]), reverse=True)
            source_face = source_faces[0]
        else:
            source_face = get_one_face(source_frame)
        
        if source_face is None:
            print("⚠️ No source face found")
            if update:
                update()
            return
        
        # Handle multiple source faces if available
        if len(source_faces) > 1:
            source_face = get_average_face(source_faces)
        
        reference_face = get_face_reference() if 'reference' in SwitcherAI.globals.face_recognition else None
        
        for temp_frame_path in temp_frame_paths:
            try:
                temp_frame = cv2.imread(temp_frame_path)
                if temp_frame is not None:
                    result_frame = process_frame(source_face, reference_face, temp_frame)
                    cv2.imwrite(temp_frame_path, result_frame)
                else:
                    print(f"⚠️ Failed to read frame: {temp_frame_path}")
                    
            except Exception as e:
                print(f"⚠️ Error processing frame {temp_frame_path}: {e}")
                
            if update:
                update()
                
    except Exception as e:
        print(f"⚠️ Error in process_frames: {e}")


def process_image(source_path: str, target_path: str, output_path: str) -> None:
    """Enhanced image processing"""
    try:
        processor = get_frame_processor()
        if processor is None:
            print("⚠️ Face swap processor not available, copying original image")
            import shutil
            shutil.copy2(target_path, output_path)
            return
        
        source_frame = cv2.imread(source_path)
        if source_frame is None:
            print(f"⚠️ Failed to read source image: {source_path}")
            return
        
        source_faces = get_many_faces(source_frame)
        
        # Get best source face
        if source_faces:
            source_faces = sorted(source_faces, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]), reverse=True)
            source_face = source_faces[0]
            
            # Handle multiple source faces
            if len(source_faces) > 1:
                source_face = get_average_face(source_faces)
        else:
            source_face = get_one_face(source_frame)
        
        if source_face is None:
            print("⚠️ No source face found")
            return
        
        target_frame = cv2.imread(target_path)
        if target_frame is None:
            print(f"⚠️ Failed to read target image: {target_path}")
            return
        
        reference_face = get_one_face(target_frame, SwitcherAI.globals.reference_face_position) if 'reference' in SwitcherAI.globals.face_recognition else None
        result_frame = process_frame(source_face, reference_face, target_frame)
        cv2.imwrite(output_path, result_frame)
        
    except Exception as e:
        print(f"⚠️ Error in process_image: {e}")


def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
    try:
        conditional_set_face_reference(temp_frame_paths)
        frame_processors.process_video(source_path, temp_frame_paths, process_frames)
    except Exception as e:
        print(f"⚠️ Error in process_video: {e}")


def conditional_set_face_reference(temp_frame_paths: List[str]) -> None:
    try:
        if 'reference' in SwitcherAI.globals.face_recognition and not get_face_reference():
            reference_frame = cv2.imread(temp_frame_paths[SwitcherAI.globals.reference_face_position])
            if reference_frame is not None:
                reference_face = get_one_face(reference_frame, SwitcherAI.globals.reference_face_position)
                set_face_reference(reference_face)
            else:
                print(f"⚠️ Failed to read reference frame")
    except Exception as e:
        print(f"⚠️ Error setting face reference: {e}")

	from typing import Any, List, Callable, Dict, Tuple
	import cv2
	import insightface
	import threading
	import numpy as np
	from functools import lru_cache
	from pathlib import Path

	import SwitcherAI.globals
	import SwitcherAI.processors.frame.core as frame_processors
	from SwitcherAI import wording
	from SwitcherAI.core import update_status
	from SwitcherAI.face_analyser import get_one_face, get_many_faces, find_similar_faces
	from SwitcherAI.face_reference import get_face_reference, set_face_reference
	from SwitcherAI.typing import Face, Frame
	from SwitcherAI.utilities import resolve_relative_path, is_image, is_video

	FRAME_PROCESSOR = None
	EMBEDDING_CONVERTER = None
	THREAD_LOCK = threading.Lock()
	NAME = 'FACEFUSION.FRAME_PROCESSOR.FACE_SWAPPER'

	# Model configurations - local paths only
	MODEL_CONFIGS = {
	'inswapper_128': {
	'path': '../.assets/models/inswapper_128.onnx',
	'type': 'inswapper',
	'size': (128, 128),
	'mean': [0.0, 0.0, 0.0],
	'standard_deviation': [1.0, 1.0, 1.0],
	'requires_converter': False
	},
	'inswapper_128_fp16': {
	'path': '../.assets/models/inswapper_128_fp16.onnx',
	'type': 'inswapper',
	'size': (128, 128),
	'mean': [0.0, 0.0, 0.0],
	'standard_deviation': [1.0, 1.0, 1.0],
	'requires_converter': False
	},
	'simswap_256': {
	'path': '../.assets/models/simswap_256.onnx',
	'converter_path': '../.assets/models/simswap_256_converter.onnx',
	'type': 'simswap',
	'size': (256, 256),
	'mean': [0.485, 0.456, 0.406],
	'standard_deviation': [0.229, 0.224, 0.225],
	'requires_converter': True
	},
	}

	# Default model - can be changed via globals
	DEFAULT_MODEL = 'inswapper_128'


	def get_current_model_config() -> Dict:
	"""Get the current model configuration"""
	model_name = getattr(SwitcherAI.globals, 'face_swapper_model', DEFAULT_MODEL)
	return MODEL_CONFIGS.get(model_name, MODEL_CONFIGS[DEFAULT_MODEL])


	@lru_cache(maxsize=None)
	def get_static_model_initializer(model_path: str) -> np.ndarray:
	"""Cache model initialization data"""
	try:
	# This would need to be implemented based on the specific model requirements
	# For now, return identity matrix as fallback
	return np.eye(512, dtype=np.float32)
	except Exception:
	return np.eye(512, dtype=np.float32)


	def get_frame_processor() -> Any:
	global FRAME_PROCESSOR

	with THREAD_LOCK:
	if FRAME_PROCESSOR is None:
	# Try models in order of preference
	model_priority = ['inswapper_128_fp16', 'inswapper_128', 'simswap_256']

	# If user set a specific model, try it first
	current_model = getattr(SwitcherAI.globals, 'face_swapper_model', None)
	if current_model and current_model in MODEL_CONFIGS:
	model_priority.insert(0, current_model)
	# Remove duplicate if it exists later in the list
	if current_model in model_priority[1:]:
	model_priority.remove(current_model)

	for model_name in model_priority:
	if model_name not in MODEL_CONFIGS:
	continue

	try:
	print(f"🔄 Trying to load face swap model: {model_name}")

	# Get model config
	temp_config = MODEL_CONFIGS[model_name]
	model_path = resolve_relative_path(temp_config['path'])

	# Convert to Path object if it's a string for validation
	if isinstance(model_path, str):
	model_path_obj = Path(model_path)
	else:
	model_path_obj = model_path

	# Check if model exists locally
	if not model_path_obj.exists():
	print(f"❌ Model {model_name} not found at: {model_path_obj}")
	continue

	# Verify model file size
	if model_path_obj.stat().st_size < 1024: # Less than 1KB indicates corruption
	print(f"⚠️ {model_name} appears corrupted (file too small), skipping...")
	continue

	# Try to load the model
	FRAME_PROCESSOR = insightface.model_zoo.get_model(
	str(model_path_obj),
	providers=SwitcherAI.globals.execution_providers
	)

	# If successful, update the global setting and break
	SwitcherAI.globals.face_swapper_model = model_name
	print(f"✅ Successfully loaded face swap model: {model_name}")
	break

	except Exception as e:
	print(f"❌ Failed to load {model_name}: {e}")
	continue

	if FRAME_PROCESSOR is None:
	print("❌ All face swap models failed to load. Please ensure models are present in .assets/models folder.")

	return FRAME_PROCESSOR


	def get_embedding_converter() -> Any:
	global EMBEDDING_CONVERTER

	config = get_current_model_config()
	if not config.get('requires_converter', False):
	return None

	with THREAD_LOCK:
	if EMBEDDING_CONVERTER is None:
	try:
	converter_path = resolve_relative_path(config['converter_path'])

	# Convert to Path object if it's a string for validation
	if isinstance(converter_path, str):
	converter_path_obj = Path(converter_path)
	else:
	converter_path_obj = converter_path

	# Check if converter exists locally
	if not converter_path_obj.exists():
	print(f"❌ Embedding converter not found at: {converter_path_obj}")
	print("Please ensure the converter model is present in .assets/models folder.")
	return None

	EMBEDDING_CONVERTER = insightface.model_zoo.get_model(
	str(converter_path_obj),
	providers=SwitcherAI.globals.execution_providers
	)
	print("✅ Embedding converter initialized")

	except Exception as e:
	print(f"❌ Failed to initialize embedding converter: {e}")
	EMBEDDING_CONVERTER = None

	return EMBEDDING_CONVERTER


	def clear_frame_processor() -> None:
	global FRAME_PROCESSOR, EMBEDDING_CONVERTER

	FRAME_PROCESSOR = None
	EMBEDDING_CONVERTER = None


	def pre_check() -> bool:
	"""Check if required models exist locally"""
	try:
	config = get_current_model_config()

	# Check main model path
	model_path = resolve_relative_path(config['path'])
	if isinstance(model_path, str):
	model_path_obj = Path(model_path)
	else:
	model_path_obj = model_path

	if not model_path_obj.exists():
	print(f"❌ Main model not found at: {model_path_obj}")
	print("Please ensure the model file is present in .assets/models folder.")
	return False

	# Check converter if needed
	if config.get('requires_converter', False):
	converter_path = resolve_relative_path(config['converter_path'])
	if isinstance(converter_path, str):
	converter_path_obj = Path(converter_path)
	else:
	converter_path_obj = converter_path

	if not converter_path_obj.exists():
	print(f"❌ Converter model not found at: {converter_path_obj}")
	print("Please ensure the converter model file is present in .assets/models folder.")
	return False

	print("✅ All required models found locally")
	return True

	except Exception as e:
	print(f"❌ Face swap pre-check failed: {e}")
	return False


	def pre_process() -> bool:
	try:
	if not is_image(SwitcherAI.globals.source_path):
	update_status(wording.get('select_image_source') + wording.get('exclamation_mark'), NAME)
	return False
	elif not get_one_face(cv2.imread(SwitcherAI.globals.source_path)):
	update_status(wording.get('no_source_face_detected') + wording.get('exclamation_mark'), NAME)
	return False
	if not is_image(SwitcherAI.globals.target_path) and not is_video(SwitcherAI.globals.target_path):
	update_status(wording.get('select_image_or_video_target') + wording.get('exclamation_mark'), NAME)
	return False

	# Check if required models exist locally
	if not pre_check():
	update_status("Required models not found in .assets/models folder", NAME)
	return False

	# Check if processor is available
	processor = get_frame_processor()
	if processor is None:
	update_status("Face swap processor not available", NAME)
	return False

	return True

	except Exception as e:
	print(f"⚠️ Face swap pre-process failed: {e}")
	return False


	def post_process() -> None:
	clear_frame_processor()
	# Clear caches like the newer version
	get_static_model_initializer.cache_clear()


	def prepare_source_embedding(source_face: Face) -> np.ndarray:
	"""Prepare source face embedding based on model type"""
	try:
	config = get_current_model_config()
	model_type = config['type']

	if model_type == 'inswapper':
	# Enhanced embedding preparation for inswapper
	model_path = resolve_relative_path(config['path'])
	model_initializer = get_static_model_initializer(str(model_path))
	source_embedding = source_face.embedding.reshape((1, -1))
	source_embedding = np.dot(source_embedding, model_initializer) / np.linalg.norm(source_embedding)
	return source_embedding
	elif model_type == 'simswap':
	# Use embedding converter for simswap
	converter = get_embedding_converter()
	if converter is not None:
	embedding = source_face.embedding.reshape(-1, 512)
	try:
	converted_embedding = converter.run(None, {'input': embedding})[0]
	converted_embedding = converted_embedding.ravel()
	normed_embedding = converted_embedding / np.linalg.norm(converted_embedding)
	return normed_embedding.reshape(1, -1)
	except Exception:
	pass

	# Fallback to original embedding
	return source_face.embedding.reshape(1, -1)
	else:
	# Default behavior
	return source_face.embedding.reshape(1, -1)

	except Exception as e:
	print(f"⚠️ Error preparing source embedding: {e}")
	return source_face.embedding.reshape(1, -1)


	def prepare_crop_frame(crop_frame: Frame) -> np.ndarray:
	"""Prepare cropped frame for model input with normalization"""
	try:
	config = get_current_model_config()
	model_mean = config['mean']
	model_std = config['standard_deviation']

	# Convert to float and normalize
	crop_frame = crop_frame[:, :, ::-1] / 255.0
	crop_frame = (crop_frame - model_mean) / model_std
	crop_frame = crop_frame.transpose(2, 0, 1)
	crop_frame = np.expand_dims(crop_frame, axis=0).astype(np.float32)
	return crop_frame

	except Exception as e:
	print(f"⚠️ Error preparing crop frame: {e}")
	return crop_frame


	def normalize_crop_frame(crop_frame: np.ndarray) -> Frame:
	"""Normalize cropped frame back to image format"""
	try:
	config = get_current_model_config()
	model_type = config['type']
	model_mean = config['mean']
	model_std = config['standard_deviation']

	crop_frame = crop_frame.transpose(1, 2, 0)

	# Apply reverse normalization for certain model types
	if model_type in ['simswap']:
	crop_frame = crop_frame * model_std + model_mean

	crop_frame = crop_frame.clip(0, 1)
	crop_frame = crop_frame[:, :, ::-1] * 255
	return crop_frame.astype(np.uint8)

	except Exception as e:
	print(f"⚠️ Error normalizing crop frame: {e}")
	return crop_frame.astype(np.uint8)


	def enhanced_swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
	"""Enhanced face swapping with improved preprocessing"""
	try:
	processor = get_frame_processor()
	if processor is None:
	print("⚠️ Face swap processor not available")
	return temp_frame

	config = get_current_model_config()
	model_type = config['type']

	if model_type == 'inswapper':
	# Use original method for inswapper
	return processor.get(temp_frame, target_face, source_face, paste_back=True)
	else:
	# Enhanced method for other models
	try:
	# Prepare source embedding
	source_embedding = prepare_source_embedding(source_face)

	# Get crop region (this would need proper implementation)
	# For now, fall back to original method
	return processor.get(temp_frame, target_face, source_face, paste_back=True)
	except Exception as e:
	print(f"⚠️ Enhanced swap failed: {e}")
	# Fallback to original method
	return processor.get(temp_frame, target_face, source_face, paste_back=True)

	except Exception as e:
	print(f"⚠️ Face swap failed: {e}")
	return temp_frame


	def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
	"""Main face swapping function with model-specific handling"""
	try:
	processor = get_frame_processor()
	if processor is None:
	print("⚠️ Face swap processor not available, skipping swap")
	return temp_frame

	config = get_current_model_config()

	# Use enhanced swapping for supported models
	if config['type'] in ['simswap', 'inswapper']:
	return enhanced_swap_face(source_face, target_face, temp_frame)
	else:
	# Original method
	return processor.get(temp_frame, target_face, source_face, paste_back=True)

	except Exception as e:
	print(f"⚠️ Error in swap_face: {e}")
	return temp_frame


	def process_frame(source_face: Face, reference_face: Face, temp_frame: Frame) -> Frame:
	"""Process frame with enhanced face selection logic"""
	try:
	processor = get_frame_processor()
	if processor is None:
	print("⚠️ Face swap processor not available, skipping frame")
	return temp_frame

	if 'reference' in SwitcherAI.globals.face_recognition:
	similar_faces = find_similar_faces(temp_frame, reference_face, SwitcherAI.globals.reference_face_distance)
	if similar_faces:
	for similar_face in similar_faces:
	temp_frame = swap_face(source_face, similar_face, temp_frame)

	if 'many' in SwitcherAI.globals.face_recognition:
	many_faces = get_many_faces(temp_frame)
	if many_faces:
	# Sort faces by size (largest first) like the newer version
	many_faces = sorted(many_faces, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]), reverse=True)
	for target_face in many_faces:
	temp_frame = swap_face(source_face, target_face, temp_frame)

	return temp_frame

	except Exception as e:
	print(f"⚠️ Error in process_frame: {e}")
	return temp_frame


	def get_average_face(faces: List[Face]) -> Face:
	"""Get average face from multiple faces (simplified version)"""
	if not faces:
	return None
	if len(faces) == 1:
	return faces[0]

	# For now, return the first face
	# In a full implementation, this would average the embeddings
	return faces[0]


	def process_frames(source_path: str, temp_frame_paths: List[str], update: Callable[[], None]) -> None:
	"""Enhanced frame processing with better source face handling"""
	try:
	processor = get_frame_processor()
	if processor is None:
	print("⚠️ Face swap processor not available, skipping frame processing")
	if update:
	update()
	return

	source_frame = cv2.imread(source_path)
	if source_frame is None:
	print(f"⚠️ Failed to read source image: {source_path}")
	if update:
	update()
	return

	source_faces = get_many_faces(source_frame)

	# Get best source face (largest)
	if source_faces:
	source_faces = sorted(source_faces, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]), reverse=True)
	source_face = source_faces[0]
	else:
	source_face = get_one_face(source_frame)

	if source_face is None:
	print("⚠️ No source face found")
	if update:
	update()
	return

	# Handle multiple source faces if available
	if len(source_faces) > 1:
	source_face = get_average_face(source_faces)

	reference_face = get_face_reference() if 'reference' in SwitcherAI.globals.face_recognition else None

	for temp_frame_path in temp_frame_paths:
	try:
	temp_frame = cv2.imread(temp_frame_path)
	if temp_frame is not None:
	result_frame = process_frame(source_face, reference_face, temp_frame)
	cv2.imwrite(temp_frame_path, result_frame)
	else:
	print(f"⚠️ Failed to read frame: {temp_frame_path}")

	except Exception as e:
	print(f"⚠️ Error processing frame {temp_frame_path}: {e}")

	if update:
	update()

	except Exception as e:
	print(f"⚠️ Error in process_frames: {e}")


	def process_image(source_path: str, target_path: str, output_path: str) -> None:
	"""Enhanced image processing"""
	try:
	processor = get_frame_processor()
	if processor is None:
	print("⚠️ Face swap processor not available, copying original image")
	import shutil
	shutil.copy2(target_path, output_path)
	return

	source_frame = cv2.imread(source_path)
	if source_frame is None:
	print(f"⚠️ Failed to read source image: {source_path}")
	return

	source_faces = get_many_faces(source_frame)

	# Get best source face
	if source_faces:
	source_faces = sorted(source_faces, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]), reverse=True)
	source_face = source_faces[0]

	# Handle multiple source faces
	if len(source_faces) > 1:
	source_face = get_average_face(source_faces)
	else:
	source_face = get_one_face(source_frame)

	if source_face is None:
	print("⚠️ No source face found")
	return

	target_frame = cv2.imread(target_path)
	if target_frame is None:
	print(f"⚠️ Failed to read target image: {target_path}")
	return

	reference_face = get_one_face(target_frame, SwitcherAI.globals.reference_face_position) if 'reference' in SwitcherAI.globals.face_recognition else None
	result_frame = process_frame(source_face, reference_face, target_frame)
	cv2.imwrite(output_path, result_frame)

	except Exception as e:
	print(f"⚠️ Error in process_image: {e}")


	def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
	try:
	conditional_set_face_reference(temp_frame_paths)
	frame_processors.process_video(source_path, temp_frame_paths, process_frames)
	except Exception as e:
	print(f"⚠️ Error in process_video: {e}")


	def conditional_set_face_reference(temp_frame_paths: List[str]) -> None:
	try:
	if 'reference' in SwitcherAI.globals.face_recognition and not get_face_reference():
	reference_frame = cv2.imread(temp_frame_paths[SwitcherAI.globals.reference_face_position])
	if reference_frame is not None:
	reference_face = get_one_face(reference_frame, SwitcherAI.globals.reference_face_position)
	set_face_reference(reference_face)
	else:
	print(f"⚠️ Failed to read reference frame")
	except Exception as e:
	print(f"⚠️ Error setting face reference: {e}")