fix for python 3.9

Author: lstein

ldm/models/diffusion/ddpm.py

@@ -614,30 +614,11 @@ def configure_optimizers(self):
 class LatentDiffusion(DDPM):
     """main class"""
 
-    @staticmethod
-    def _fallback_personalization_config()->dict:
-        """
-        This protects us against custom legacy config files that
-        don't contain the personalization_config section.
-        """
-        return OmegaConf.create(
-            dict(
-                target='ldm.modules.embedding_manager.EmbeddingManager',
-                params=dict(
-                    placeholder_strings=list('*'),
-                    initializer_words=list('sculpture'),
-                    per_image_tokens=False,
-                    num_vectors_per_token=1,
-                    progressive_words=False,
-                )
-            )
-        )
-
     def __init__(
         self,
         first_stage_config,
         cond_stage_config,
-        personalization_config=_fallback_personalization_config(),
+        personalization_config=None,
         num_timesteps_cond=None,
         cond_stage_key='image',
         cond_stage_trainable=False,
@@ -695,7 +676,8 @@ def __init__(
         self.model.train = disabled_train
         for param in self.model.parameters():
             param.requires_grad = False
-
+            
+        personalization_config = personalization_config or self._fallback_personalization_config()
         self.embedding_manager = self.instantiate_embedding_manager(
             personalization_config, self.cond_stage_model
         )
@@ -2170,6 +2152,25 @@ def on_save_checkpoint(self, checkpoint):
 
                 self.emb_ckpt_counter += 500
 
+    @classmethod
+    def _fallback_personalization_config(self)->dict:
+        """
+        This protects us against custom legacy config files that
+        don't contain the personalization_config section.
+        """
+        return OmegaConf.create(
+            dict(
+                target='ldm.modules.embedding_manager.EmbeddingManager',
+                params=dict(
+                    placeholder_strings=list('*'),
+                    initializer_words=list('sculpture'),
+                    per_image_tokens=False,
+                    num_vectors_per_token=1,
+                    progressive_words=False,
+                )
+            )
+        )
+
 
 class DiffusionWrapper(pl.LightningModule):
     def __init__(self, diff_model_config, conditioning_key):