feat: update InferenceConfigBuilder to conditionally set use_31_block based on model path; clean up whitespace in LightX2VConfigCombinerV2 and LightX2VModularInferenceV2 for improved readability

config_builder.py

@@ -24,7 +24,6 @@ from .file_handlers import (
     AudioFileHandler,
     ComfyUIFileResolver,
     MaskFileHandler,
-    TempFileManager,
 )
 from .model_utils import get_lora_full_path, get_model_full_path
 
@@ -162,6 +161,8 @@ class InferenceConfigBuilder:
         if "seko" in config.model_cls:
             config.video_length = optional_params.get("segment_length", 81)
             config.use_31_block = False
+            if "2.5" in config.model_path:
+                config.use_31_block = True
             if "prev_frame_length" in optional_params:
                 config.prev_frame_length = optional_params["prev_frame_length"]
 

file_handlers.py

@@ -299,13 +299,11 @@ class TempFileManager:
             if delete:
                 self.cleanup_dir(temp_dir)
 
-
     def create_temp_dir(self, suffix: str = "", prefix: str = "lightx2v_") -> str:
         temp_dir = tempfile.mkdtemp(suffix=suffix, prefix=prefix)
         self.temp_dirs.append(temp_dir)
         return temp_dir
 
-
     def cleanup_dir(self, path: str):
         if path in self.temp_dirs:
             self.temp_dirs.remove(path)
@@ -313,6 +311,7 @@ class TempFileManager:
         if os.path.exists(path):
             try:
                 import shutil
+
                 shutil.rmtree(path)
                 logging.debug(f"Cleaned up temp directory: {path}")
             except Exception as e:

nodes.py

@@ -1132,7 +1132,6 @@ class LightX2VConfigCombinerV2:
                     json.dump({"talk_objects": processed_talk_objects}, f)
                 config.audio_path = temp_dir
                 logging.info(f"Processed {len(processed_talk_objects)} talk objects")
-            
 
         logging.info("lightx2v prepared config: " + json.dumps(config, indent=2, ensure_ascii=False))
 
@@ -1192,7 +1191,7 @@ class LightX2VModularInferenceV2:
                     del self.__class__._current_runner
                     torch.cuda.empty_cache()
                     gc.collect()
-                formatted_config =set_config(config)
+                formatted_config = set_config(config)
                 self.__class__._current_runner = init_runner(formatted_config)
                 self.__class__._current_config_hash = config_hash
 
@@ -1213,7 +1212,7 @@ class LightX2VModularInferenceV2:
             current_runner.set_config(config)
 
             result_dict = current_runner.run_pipeline(input_info)
-            
+
             images = result_dict.get("video", None)
             audio = result_dict.get("audio", None)