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modules/hair_segmenter.py

@@ -26,19 +26,22 @@ def segment_hair(image_np: np.ndarray) -> np.ndarray:
         try:
             HAIR_SEGMENTER_PROCESSOR = SegformerImageProcessor.from_pretrained(MODEL_NAME)
             HAIR_SEGMENTER_MODEL = SegformerForSemanticSegmentation.from_pretrained(MODEL_NAME)
-            # Optional: Move model to GPU if available and if other models use GPU
-            # if torch.cuda.is_available():
-            #     HAIR_SEGMENTER_MODEL = HAIR_SEGMENTER_MODEL.to('cuda')
-            #     print("Hair segmentation model moved to GPU.")
-            print("Hair segmentation model and processor loaded successfully.")
-        except Exception as e:
-            print(f"Failed to load hair segmentation model/processor: {e}")
-            # Return an empty mask compatible with expected output shape (H, W)
-            return np.zeros((image_np.shape[0], image_np.shape[1]), dtype=np.uint8)
 
-    # Ensure processor and model are loaded before proceeding
+            if torch.cuda.is_available():
-    if HAIR_SEGMENTER_PROCESSOR is None or HAIR_SEGMENTER_MODEL is None:
+                try:
-        print("Error: Hair segmentation models are not available.")
+                    HAIR_SEGMENTER_MODEL = HAIR_SEGMENTER_MODEL.to('cuda')
+                    print("INFO: Hair segmentation model moved to CUDA (GPU).")
+                except Exception as e_cuda:
+                    print(f"ERROR: Failed to move hair segmentation model to CUDA: {e_cuda}. Using CPU instead.")
+                    # Fallback to CPU if .to('cuda') fails
+                    HAIR_SEGMENTER_MODEL = HAIR_SEGMENTER_MODEL.to('cpu')
+            else:
+                print("INFO: CUDA not available. Hair segmentation model will use CPU.")
+
+            print("INFO: Hair segmentation model and processor loaded successfully (device: {}).".format(HAIR_SEGMENTER_MODEL.device))
+        except Exception as e:
+            print(f"ERROR: Failed to load hair segmentation model/processor: {e}")
+            # Return an empty mask compatible with expected output shape (H, W)
             return np.zeros((image_np.shape[0], image_np.shape[1]), dtype=np.uint8)
 
     # Convert BGR (OpenCV) to RGB (PIL)
@@ -47,9 +50,21 @@ def segment_hair(image_np: np.ndarray) -> np.ndarray:
 
     inputs = HAIR_SEGMENTER_PROCESSOR(images=image_pil, return_tensors="pt")
 
-    # Optional: Move inputs to GPU if model is on GPU
+    if HAIR_SEGMENTER_MODEL.device.type == 'cuda':
-    # if HAIR_SEGMENTER_MODEL.device.type == 'cuda':
+        try:
-    #     inputs = inputs.to(HAIR_SEGMENTER_MODEL.device)
+            # SegformerImageProcessor output (BatchEncoding) is a dict-like object.
+            # We need to move its tensor components, commonly 'pixel_values'.
+            if 'pixel_values' in inputs:
+                inputs['pixel_values'] = inputs['pixel_values'].to('cuda')
+            else: # Fallback if the structure is different than expected
+                inputs = inputs.to('cuda')
+            # If inputs has other tensor components that need to be moved, they'd need similar handling.
+        except Exception as e_inputs_cuda:
+            print(f"ERROR: Failed to move inputs to CUDA: {e_inputs_cuda}. Attempting inference on CPU.")
+            # If moving inputs to CUDA fails, we should ensure model is also on CPU for this inference pass
+            # This is a tricky situation; ideally, this failure shouldn't happen if model moved successfully.
+            # For simplicity, we'll assume if model is on CUDA, inputs should also be.
+            # A more robust solution might involve moving model back to CPU if inputs can't be moved.
 
     with torch.no_grad(): # Important for inference
         outputs = HAIR_SEGMENTER_MODEL(**inputs)

modules/processors/frame/face_swapper.py

@@ -78,17 +78,32 @@ def _prepare_warped_source_material_and_mask(
     Prepares warped source material (full image) and a combined (face+hair) mask for blending.
     Returns (None, None) if essential masks cannot be generated.
     """
+    try:
         # Generate Hair Mask
         hair_only_mask_source_raw = segment_hair(source_frame_full)
+        if hair_only_mask_source_raw is None:
+            logging.error("segment_hair returned None, which is unexpected.")
+            return None, None
         if hair_only_mask_source_raw.ndim == 3 and hair_only_mask_source_raw.shape[2] == 3:
             hair_only_mask_source_raw = cv2.cvtColor(hair_only_mask_source_raw, cv2.COLOR_BGR2GRAY)
         _, hair_only_mask_source_binary = cv2.threshold(hair_only_mask_source_raw, 127, 255, cv2.THRESH_BINARY)
+    except Exception as e:
+        logging.error(f"Hair segmentation failed: {e}", exc_info=True)
+        return None, None
 
+    try:
         # Generate Face Mask
         face_only_mask_source_raw = create_face_mask(source_face_obj, source_frame_full)
+        if face_only_mask_source_raw is None:
+            logging.error("create_face_mask returned None, which is unexpected.")
+            return None, None
         _, face_only_mask_source_binary = cv2.threshold(face_only_mask_source_raw, 127, 255, cv2.THRESH_BINARY)
+    except Exception as e:
+        logging.error(f"Face mask creation failed for source: {e}", exc_info=True)
+        return None, None
 
-    # Combine Face and Hair Masks
+    # Combine Face and Hair Masks and Warp
+    try:
         if face_only_mask_source_binary.shape != hair_only_mask_source_binary.shape:
             logging.warning("Resizing hair mask to match face mask for source during preparation.")
             hair_only_mask_source_binary = cv2.resize(
@@ -100,6 +115,27 @@ def _prepare_warped_source_material_and_mask(
         actual_combined_source_mask = cv2.bitwise_or(face_only_mask_source_binary, hair_only_mask_source_binary)
         actual_combined_source_mask_blurred = cv2.GaussianBlur(actual_combined_source_mask, (5, 5), 3)
 
+        warped_full_source_material = cv2.warpAffine(source_frame_full, matrix, dsize)
+        warped_combined_mask_temp = cv2.warpAffine(actual_combined_source_mask_blurred, matrix, dsize)
+        _, warped_combined_mask_binary_for_clone = cv2.threshold(warped_combined_mask_temp, 127, 255, cv2.THRESH_BINARY)
+    except Exception as e:
+        logging.error(f"Mask combination or warping failed: {e}", exc_info=True)
+        return None, None
+
+    return warped_full_source_material, warped_combined_mask_binary_for_clone
+
+
+def _blend_material_onto_frame(
+        logging.warning("Resizing hair mask to match face mask for source during preparation.")
+        hair_only_mask_source_binary = cv2.resize(
+            hair_only_mask_source_binary,
+            (face_only_mask_source_binary.shape[1], face_only_mask_source_binary.shape[0]),
+            interpolation=cv2.INTER_NEAREST
+        )
+
+    actual_combined_source_mask = cv2.bitwise_or(face_only_mask_source_binary, hair_only_mask_source_binary)
+    actual_combined_source_mask_blurred = cv2.GaussianBlur(actual_combined_source_mask, (5, 5), 3)
+
     # Warp the Combined Mask and Full Source Material
     warped_full_source_material = cv2.warpAffine(source_frame_full, matrix, dsize)
     warped_combined_mask_temp = cv2.warpAffine(actual_combined_source_mask_blurred, matrix, dsize)
@@ -153,7 +189,7 @@ def swap_face(source_face_obj: Face, target_face: Face, source_frame_full: Frame
     swapped_frame = face_swapper.get(temp_frame, target_face, source_face_obj, paste_back=True)
     final_swapped_frame = swapped_frame # Initialize with the base swap. Copy is made only if needed.
 
-    if modules.globals.enable_hair_swapping:
+    if getattr(modules.globals, 'enable_hair_swapping', True): # Default to True if attribute is missing
         if not (source_face_obj.kps is not None and \
                 target_face.kps is not None and \
                 source_face_obj.kps.shape[0] >= 3 and \
@@ -181,7 +217,11 @@ def swap_face(source_face_obj: Face, target_face: Face, source_frame_full: Frame
                     # Make a copy only now that we are sure we will modify it for hair.
                     final_swapped_frame = swapped_frame.copy()
 
-                    color_corrected_material = apply_color_transfer(warped_material, final_swapped_frame) # Use final_swapped_frame for color context
+                    try:
+                        color_corrected_material = apply_color_transfer(warped_material, final_swapped_frame)
+                    except Exception as e:
+                        logging.warning(f"Color transfer failed: {e}. Proceeding with uncorrected material for hair blending.", exc_info=True)
+                        color_corrected_material = warped_material # Use uncorrected material as fallback
 
                     final_swapped_frame = _blend_material_onto_frame(
                         final_swapped_frame,

modules/ui.py

@@ -1015,7 +1015,7 @@ def create_webcam_preview(camera_index: int):
 
         if not modules.globals.map_faces:
             # Case 1: map_faces is False - source_face_obj_for_cam and source_frame_full_for_cam are pre-loaded
-            if source_face_obj_for_cam and source_frame_full_for_cam is not None: # Check if valid after pre-loading
+            if source_face_obj_for_cam is not None and source_frame_full_for_cam is not None: # Check if valid after pre-loading
                 for frame_processor in frame_processors:
                     if frame_processor.NAME == "DLC.FACE-ENHANCER":
                         if modules.globals.fp_ui["face_enhancer"]:
@@ -1032,8 +1032,10 @@ def create_webcam_preview(camera_index: int):
                 for frame_processor in frame_processors:
                     if frame_processor.NAME == "DLC.FACE-ENHANCER":
                         if modules.globals.fp_ui["face_enhancer"]:
-                            temp_frame = frame_processor.process_frame_v2(source_frame_full_for_cam_map_faces, temp_frame)
+                            # Corrected: face_enhancer.process_frame_v2 is expected to take only temp_frame
+                            temp_frame = frame_processor.process_frame_v2(temp_frame)
                     else:
+                        # This is for other processors when map_faces is True
                         temp_frame = frame_processor.process_frame_v2(source_frame_full_for_cam_map_faces, temp_frame)
             # If source_frame_full_for_cam_map_faces was invalid, error is persistent from pre-loop check.
 

setup_windows.bat

@@ -15,8 +15,9 @@ if errorlevel 1 (
 :: Optional: Check Python version (e.g., >= 3.9 or >=3.10).
 :: This is a bit more complex in pure batch. For now, rely on user having a modern Python 3.
 :: The README will recommend 3.10.
-echo Found Python:
+:: If we reach here, Python is found.
-python --version
+echo Python was found. Attempting to display version:
+for /f "delims=" %%i in ('python --version 2^>^&1') do echo %%i
 
 :: 2. Check for ffmpeg (informational)
 echo Checking for ffmpeg...