Update api/ltx_server_refactored_complete.py

api/ltx_server_refactored_complete.py

@@ -361,11 +361,18 @@ class VideoService:
 
 
 
+# FILE: api/ltx_server_refactored_complete.py (Versão com correção de dispositivo DEFINITIVA)
+
+# ...
+
+class VideoService:
+    # ...
+
     @log_function_io
-    def prepare_condition_items(self, items_list: List, height: int, width: int, num_frames: int) -> List[Condition-ingItem]:
+    def prepare_condition_items(self, items_list: List, height: int, width: int, num_frames: int) -> List[ConditioningItem]:
         """
-        [CORRIGIDO] Prepara ConditioningItems, mas mantém os tensores na CPU.
-        O movimento para a GPU será tratado posteriormente.
+        [CORRIGIDO] Prepara ConditioningItems, garantindo que o tensor final
+        resida no dispositivo principal do pipeline (main_device).
         """
         if not items_list: return []
         height_padded, width_padded = self._align(height), self._align(width)
@@ -373,47 +380,29 @@ class VideoService:
         
         conditioning_items = []
         for media_item, frame, weight in items_list:
+            final_tensor = None
             if isinstance(media_item, str):
-                # Carrega a imagem e aplica padding, mas mantém na CPU.
+                # 1. Carrega a imagem. A função pode usar o VAE, então ela pode
+                #    retornar um tensor em qualquer dispositivo.
                 tensor = load_image_to_tensor_with_resize_and_crop(media_item, height, width)
+                # 2. Aplica padding.
                 tensor = torch.nn.functional.pad(tensor, padding_values)
-                # O tensor permanece na CPU aqui.
+                # 3. GARANTE que o tensor final esteja no dispositivo principal.
+                final_tensor = tensor.to(self.main_device, dtype=self.runtime_autocast_dtype)
+
             elif isinstance(media_item, torch.Tensor):
-                # Se for um tensor (como o de overlap), apenas garante que está na CPU.
-                tensor = media_item.cpu()
+                # Se já for um tensor (ex: overlap), apenas garante que ele está no dispositivo principal.
+                final_tensor = media_item.to(self.main_device, dtype=self.runtime_autocast_dtype)
             else:
                 logging.warning(f"Unknown conditioning media type: {type(media_item)}. Skipping.")
                 continue
             
             safe_frame = max(0, min(int(frame), num_frames - 1))
-            conditioning_items.append(ConditioningItem(tensor, safe_frame, float(weight)))
-            
+            conditioning_items.append(ConditioningItem(final_tensor, safe_frame, float(weight)))
+
         self._log_conditioning_items(conditioning_items)
         return conditioning_items
-
     
-    @log_function_io
-    def prepare_condition_items(self, items_list: List, height: int, width: int, num_frames: int) -> List[ConditioningItem]:
-        """[UNIFIED] Prepares ConditioningItems from a mixed list of file paths and tensors."""
-        if not items_list: return []
-        height_padded, width_padded = self._align(height), self._align(width)
-        padding_values = calculate_padding(height, width, height_padded, width_padded)
-        
-        conditioning_items = []
-        for media_item, frame, weight in items_list:
-            if isinstance(media_item, str):
-                tensor = load_image_to_tensor_with_resize_and_crop(media_item, height, width)
-                tensor = torch.nn.functional.pad(tensor, padding_values)
-                tensor = tensor.to(self.main_device, dtype=self.runtime_autocast_dtype)
-            elif isinstance(media_item, torch.Tensor):
-                tensor = media_item.to(self.main_device, dtype=self.runtime_autocast_dtype)
-            else:
-                logging.warning(f"Unknown conditioning media type: {type(media_item)}. Skipping.")
-                continue
-            
-            safe_frame = max(0, min(int(frame), num_frames - 1))
-            conditioning_items.append(ConditioningItem(tensor, safe_frame, float(weight)))
-        return conditioning_items
 
     def _apply_ui_overrides(self, config_dict: Dict, overrides: Dict):
         """Applies advanced settings from the UI to a config dictionary."""