update

Author: DN6

tests/lora/utils.py

@@ -137,11 +137,12 @@ def _cache_base_pipeline_output(self):
         # Get or create the cache on the class (not instance)
         if not hasattr(type(self), "cached_base_pipe_outs"):
             setattr(type(self), "cached_base_pipe_outs", {})
-        
+
         cached_base_pipe_outs = type(self).cached_base_pipe_outs
-            
+
         all_scheduler_names = [scheduler_cls.__name__ for scheduler_cls in self.scheduler_classes]
         if cached_base_pipe_outs and all(k in cached_base_pipe_outs for k in all_scheduler_names):
+            __import__("ipdb").set_trace()
             return
 
         for scheduler_cls in self.scheduler_classes:
@@ -158,12 +159,15 @@ def _cache_base_pipeline_output(self):
             _, _, inputs = self.get_dummy_inputs(with_generator=False)
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
             cached_base_pipe_outs[scheduler_cls.__name__] = output_no_lora
-        
+
         # Update the class attribute
         setattr(type(self), "cached_base_pipe_outs", cached_base_pipe_outs)
 
     def get_base_pipeline_output(self, scheduler_cls):
-        self._cache_base_pipeline_output()
+        """
+        Returns the cached base pipeline output for the given scheduler.
+        Cache is populated during setUp, so this just retrieves the value.
+        """
         return type(self).cached_base_pipe_outs[scheduler_cls.__name__]
 
     def get_dummy_components(self, scheduler_cls=None, use_dora=False, lora_alpha=None):