diff --git a/modules/core.py b/modules/core.py
index b6ef9b8..dbbf4f8 100644
--- a/modules/core.py
+++ b/modules/core.py
@@ -114,8 +114,46 @@ def encode_execution_providers(execution_providers: List[str]) -> List[str]:
def decode_execution_providers(execution_providers: List[str]) -> List[str]:
- return [provider for provider, encoded_execution_provider in zip(onnxruntime.get_available_providers(), encode_execution_providers(onnxruntime.get_available_providers()))
- if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers)]
+ try:
+ available_providers = onnxruntime.get_available_providers()
+ encoded_available_providers = encode_execution_providers(available_providers)
+
+ selected_providers = []
+ unavailable_providers = []
+
+ for execution_provider in execution_providers:
+ provider_found = False
+ for provider, encoded_provider in zip(available_providers, encoded_available_providers):
+ if execution_provider in encoded_provider:
+ selected_providers.append(provider)
+ provider_found = True
+ break
+
+ if not provider_found:
+ unavailable_providers.append(execution_provider)
+
+ if 'cuda' in [p.lower() for p in unavailable_providers]:
+ # CUDA was requested but not available
+ cuda_path = os.environ.get('CUDA_PATH')
+ if cuda_path:
+ update_status(f"Warning: CUDA_PATH is set ({cuda_path}) but CUDA wasn't able to be loaded. Check your CUDA installation.", "DLC.CORE")
+ if os.path.exists(cuda_path):
+ # CUDA path exists but couldn't be loaded - likely missing DLLs or incorrect configuration
+ update_status("CUDA path exists but CUDA libraries couldn't be loaded. Check if the CUDA runtime is properly installed.", "DLC.CORE")
+ else:
+ update_status("CUDA_PATH is set but the directory doesn't exist. Check your environment variables.", "DLC.CORE")
+ else:
+ update_status("CUDA was requested but no CUDA_PATH is set in environment variables.", "DLC.CORE")
+
+ # If no providers were selected, fall back to CPU
+ if not selected_providers:
+ update_status("Falling back to CPU execution provider.", "DLC.CORE")
+ selected_providers = ['CPUExecutionProvider']
+
+ return selected_providers
+ except Exception as e:
+ update_status(f"Error determining execution providers: {str(e)}. Falling back to CPU.", "DLC.CORE")
+ return ['CPUExecutionProvider']
def suggest_max_memory() -> int:
@@ -160,6 +198,56 @@ def release_resources() -> None:
torch.cuda.empty_cache()
+def check_cuda_configuration() -> None:
+ """
+ Check CUDA configuration and provide diagnostic information.
+ This helps users identify issues with their CUDA setup.
+ """
+ if 'cuda' in [p.lower() for p in encode_execution_providers(modules.globals.execution_providers)]:
+ update_status("CUDA execution provider requested, checking configuration...", "DLC.CUDA")
+
+ # Check for CUDA environment variables
+ cuda_path = os.environ.get('CUDA_PATH')
+ if cuda_path:
+ update_status(f"CUDA_PATH is set to: {cuda_path}", "DLC.CUDA")
+
+ # Check if the directory exists
+ if os.path.exists(cuda_path):
+ update_status("CUDA_PATH directory exists", "DLC.CUDA")
+
+ # Check for critical CUDA DLLs on Windows
+ if platform.system().lower() == 'windows':
+ cuda_dll_path = os.path.join(cuda_path, 'bin', 'cudart64_*.dll')
+ import glob
+ cuda_dlls = glob.glob(cuda_dll_path)
+
+ if cuda_dlls:
+ update_status(f"CUDA Runtime DLLs found: {', '.join(os.path.basename(dll) for dll in cuda_dlls)}", "DLC.CUDA")
+ else:
+ update_status("Warning: No CUDA Runtime DLLs found in CUDA_PATH/bin", "DLC.CUDA")
+ update_status("This may cause CUDA initialization failures", "DLC.CUDA")
+ else:
+ update_status("Warning: CUDA_PATH is set but directory doesn't exist", "DLC.CUDA")
+ else:
+ update_status("Warning: CUDA_PATH environment variable is not set", "DLC.CUDA")
+
+ # Check if CUDA is in PATH
+ path_env = os.environ.get('PATH', '')
+ if cuda_path and cuda_path + '\\bin' in path_env:
+ update_status("CUDA bin directory is in PATH", "DLC.CUDA")
+ else:
+ update_status("Warning: CUDA bin directory not found in PATH", "DLC.CUDA")
+ update_status("This may prevent CUDA libraries from being found", "DLC.CUDA")
+
+ # Try CUDA provider availability directly from onnxruntime
+ available_providers = onnxruntime.get_available_providers()
+ if 'CUDAExecutionProvider' in available_providers:
+ update_status("CUDA provider is available in ONNX Runtime", "DLC.CUDA")
+ else:
+ update_status("Warning: CUDA provider is not available in ONNX Runtime", "DLC.CUDA")
+ update_status("Available providers: " + ', '.join(available_providers), "DLC.CUDA")
+
+
def pre_check() -> bool:
if sys.version_info < (3, 9):
update_status('Python version is not supported - please upgrade to 3.9 or higher.')
@@ -167,6 +255,10 @@ def pre_check() -> bool:
if not shutil.which('ffmpeg'):
update_status('ffmpeg is not installed.')
return False
+
+ # Check CUDA configuration if requested
+ check_cuda_configuration()
+
return True
diff --git a/modules/face_analyser.py b/modules/face_analyser.py
index ef124d5..63e77e5 100644
--- a/modules/face_analyser.py
+++ b/modules/face_analyser.py
@@ -19,8 +19,26 @@ def get_face_analyser() -> Any:
global FACE_ANALYSER
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))
+ try:
+ FACE_ANALYSER = insightface.app.FaceAnalysis(name='buffalo_l', providers=modules.globals.execution_providers)
+ FACE_ANALYSER.prepare(ctx_id=0, det_size=(640, 640))
+ except Exception as e:
+ error_msg = str(e)
+ print(f"[DLC.FACE-ANALYSER] Error initializing face analyser with providers {modules.globals.execution_providers}: {error_msg}")
+
+ # If error is CUDA-related, try with CPU provider as fallback
+ if "cuda" in error_msg.lower() or "gpu" in error_msg.lower():
+ print("[DLC.FACE-ANALYSER] CUDA error detected. Falling back to CPU provider.")
+ modules.globals.execution_providers = ['CPUExecutionProvider']
+ try:
+ FACE_ANALYSER = insightface.app.FaceAnalysis(name='buffalo_l', providers=modules.globals.execution_providers)
+ FACE_ANALYSER.prepare(ctx_id=0, det_size=(640, 640))
+ print("[DLC.FACE-ANALYSER] Successfully initialized with CPU provider as fallback.")
+ except Exception as fallback_error:
+ print(f"[DLC.FACE-ANALYSER] Failed to initialize even with fallback provider: {str(fallback_error)}")
+ raise
+ else:
+ raise
return FACE_ANALYSER
diff --git a/modules/processors/frame/face_swapper.py b/modules/processors/frame/face_swapper.py
index 36b83d6..0d749bd 100644
--- a/modules/processors/frame/face_swapper.py
+++ b/modules/processors/frame/face_swapper.py
@@ -61,9 +61,29 @@ def get_face_swapper() -> Any:
with THREAD_LOCK:
if FACE_SWAPPER is None:
model_path = os.path.join(models_dir, "inswapper_128_fp16.onnx")
- FACE_SWAPPER = insightface.model_zoo.get_model(
- model_path, providers=modules.globals.execution_providers
- )
+ try:
+ FACE_SWAPPER = insightface.model_zoo.get_model(
+ model_path, providers=modules.globals.execution_providers
+ )
+ update_status(f"Successfully loaded model with providers: {modules.globals.execution_providers}", NAME)
+ except Exception as e:
+ error_msg = str(e)
+ update_status(f"Error loading model with selected providers: {error_msg}", NAME)
+
+ # If the error is related to CUDA, provide more helpful information
+ if "cuda" in error_msg.lower() or "gpu" in error_msg.lower():
+ update_status("CUDA error detected. Trying to load with CPU provider instead.", NAME)
+ modules.globals.execution_providers = ['CPUExecutionProvider']
+ try:
+ FACE_SWAPPER = insightface.model_zoo.get_model(
+ model_path, providers=modules.globals.execution_providers
+ )
+ update_status("Successfully loaded model with CPU provider as fallback.", NAME)
+ except Exception as fallback_error:
+ update_status(f"Failed to load model even with fallback provider: {str(fallback_error)}", NAME)
+ raise
+ else:
+ raise
return FACE_SWAPPER
@@ -430,37 +450,24 @@ def draw_mouth_mask_visualization(
feathered_mask = cv2.GaussianBlur(
mask_region.astype(float), (kernel_size, kernel_size), 0
)
- feathered_mask = (feathered_mask / feathered_mask.max() * 255).astype(np.uint8)
- # Remove the feathered mask color overlay
- # color_feathered_mask = cv2.applyColorMap(feathered_mask, cv2.COLORMAP_VIRIDIS)
+ feathered_mask = feathered_mask / feathered_mask.max()
- # Ensure shapes match before blending feathered mask
- # if vis_region.shape == color_feathered_mask.shape:
- # blended_feathered = cv2.addWeighted(vis_region, 0.7, color_feathered_mask, 0.3, 0)
- # vis_frame[min_y:max_y, min_x:max_x] = blended_feathered
+ face_mask_roi = face_mask[min_y:max_y, min_x:max_x]
+ combined_mask = feathered_mask * (face_mask_roi / 255.0)
- # Add labels
- cv2.putText(
- vis_frame,
- "Lower Mouth Mask",
- (min_x, min_y - 10),
- cv2.FONT_HERSHEY_SIMPLEX,
- 0.5,
- (255, 255, 255),
- 1,
- )
- cv2.putText(
- vis_frame,
- "Feathered Mask",
- (min_x, max_y + 20),
- cv2.FONT_HERSHEY_SIMPLEX,
- 0.5,
- (255, 255, 255),
- 1,
+ combined_mask = combined_mask[:, :, np.newaxis]
+ blended = (
+ color_corrected_mouth * combined_mask + vis_region * (1 - combined_mask)
+ ).astype(np.uint8)
+
+ # Apply face mask to blended result
+ face_mask_3channel = (
+ np.repeat(face_mask_roi[:, :, np.newaxis], 3, axis=2) / 255.0
)
+ final_blend = blended * face_mask_3channel + vis_region * (1 - face_mask_3channel)
- return vis_frame
- return frame
+ vis_frame[min_y:max_y, min_x:max_x] = final_blend.astype(np.uint8)
+ return vis_frame
def apply_mouth_area(