fix local subfolder

patrickvonplaten · patrickvonplaten · commit 1c14ce9509ef · 2022-07-15T17:55:20.000Z
diff --git a/src/diffusers/configuration_utils.py b/src/diffusers/configuration_utils.py
@@ -134,6 +134,10 @@ def get_config_dict(
             if os.path.isfile(os.path.join(pretrained_model_name_or_path, cls.config_name)):
                 # Load from a PyTorch checkpoint
                 config_file = os.path.join(pretrained_model_name_or_path, cls.config_name)
+            elif subfolder is not None and os.path.isfile(
+                os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)
+            ):
+                config_file = os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)
             else:
                 raise EnvironmentError(
                     f"Error no file named {cls.config_name} found in directory {pretrained_model_name_or_path}."
diff --git a/src/diffusers/modeling_utils.py b/src/diffusers/modeling_utils.py
@@ -348,6 +348,10 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
             if os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)):
                 # Load from a PyTorch checkpoint
                 model_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
+            elif subfolder is not None and os.path.isfile(
+                os.path.join(pretrained_model_name_or_path, subfolder, WEIGHTS_NAME)
+            ):
+                model_file = os.path.join(pretrained_model_name_or_path, subfolder, WEIGHTS_NAME)
             else:
                 raise EnvironmentError(
                     f"Error no file named {WEIGHTS_NAME} found in directory {pretrained_model_name_or_path}."
diff --git a/src/diffusers/schedulers/scheduling_pndm.py b/src/diffusers/schedulers/scheduling_pndm.py
@@ -214,7 +214,9 @@ def get_prev_sample(self, sample, t_orig, t_orig_prev, model_output):
         ) ** (0.5)
 
         # full formula (9)
-        prev_sample = sample_coeff * sample - (alpha_prod_t_prev - alpha_prod_t) * model_output / model_output_denom_coeff
+        prev_sample = (
+            sample_coeff * sample - (alpha_prod_t_prev - alpha_prod_t) * model_output / model_output_denom_coeff
+        )
 
         return prev_sample
 
