refactor: streamline run_pipeline method in WanAudioRunner for improved...

refactor: streamline run_pipeline method in WanAudioRunner for improved clarity and efficiency, including enhanced error handling and memory management

lightx2v/models/runners/wan/wan_audio_runner.py

@@ -451,17 +451,15 @@ class WanAudioRunner(WanRunner):  # type:ignore
 
     def run_pipeline(self, save_video=True):
         """Optimized pipeline with modular components"""
-        # Ensure models are initialized
+
+        try:
             self.initialize()
 
             assert self._audio_processor is not None
             assert self._audio_preprocess is not None
 
-        # Initialize video generator if needed
-        if self._video_generator is None:
             self._video_generator = VideoGenerator(self.model, self.vae_encoder, self.vae_decoder, self.config, self.progress_callback)
 
-        # Prepare inputs
             with memory_efficient_inference():
                 if self.config["use_prompt_enhancer"]:
                     self.config["prompt_enhanced"] = self.post_prompt_enhancer()
@@ -494,7 +492,6 @@ class WanAudioRunner(WanRunner):  # type:ignore
             prev_video = None
 
             for idx, segment in enumerate(audio_segments):
-            # Update seed for each segment
                 self.config.seed = self.config.seed + idx
                 torch.manual_seed(self.config.seed)
                 logger.info(f"Processing segment {idx + 1}/{len(audio_segments)}, seed: {self.config.seed}")
@@ -564,6 +561,12 @@ class WanAudioRunner(WanRunner):  # type:ignore
 
             return comfyui_images, comfyui_audio
 
+        finally:
+            self._video_generator = None
+            gc.collect()
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+
     def _save_video_with_audio(self, images, audio_array, fps):
         """Save video with audio"""
         import tempfile