move personalization fallback section into a static method

Author: lstein

ldm/models/diffusion/ddpm.py

@@ -609,15 +609,35 @@ def configure_optimizers(self):
         opt = torch.optim.AdamW(params, lr=lr)
         return opt
 
+    
 
 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=None,
+        personalization_config=_fallback_personalization_config(),
         num_timesteps_cond=None,
         cond_stage_key='image',
         cond_stage_trainable=False,
@@ -676,8 +696,6 @@ def __init__(
         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
         )
@@ -802,24 +820,6 @@ def instantiate_embedding_manager(self, config, embedder):
 
         return model
 
-    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,
-                )
-            )
-        )
-
     def _get_denoise_row_from_list(
         self, samples, desc='', force_no_decoder_quantization=False
     ):