[Lora] correct lora saving & loading (huggingface#2655)

* [Lora] correct lora saving & loading

* fix final

* Apply suggestions from code review

src/diffusers/loaders.py

@@ -19,7 +19,7 @@
 
 from .models.cross_attention import LoRACrossAttnProcessor
 from .models.modeling_utils import _get_model_file
-from .utils import DIFFUSERS_CACHE, HF_HUB_OFFLINE, is_safetensors_available, logging
+from .utils import DIFFUSERS_CACHE, HF_HUB_OFFLINE, deprecate, is_safetensors_available, logging
 
 
 if is_safetensors_available():
@@ -150,13 +150,14 @@ def load_attn_procs(self, pretrained_model_name_or_path_or_dict: Union[str, Dict
 
         model_file = None
         if not isinstance(pretrained_model_name_or_path_or_dict, dict):
-            if (is_safetensors_available() and weight_name is None) or weight_name.endswith(".safetensors"):
-                if weight_name is None:
-                    weight_name = LORA_WEIGHT_NAME_SAFE
+            # Let's first try to load .safetensors weights
+            if (is_safetensors_available() and weight_name is None) or (
+                weight_name is not None and weight_name.endswith(".safetensors")
+            ):
                 try:
                     model_file = _get_model_file(
                         pretrained_model_name_or_path_or_dict,
-                        weights_name=weight_name,
+                        weights_name=weight_name or LORA_WEIGHT_NAME_SAFE,
                         cache_dir=cache_dir,
                         force_download=force_download,
                         resume_download=resume_download,
@@ -169,14 +170,13 @@ def load_attn_procs(self, pretrained_model_name_or_path_or_dict: Union[str, Dict
                     )
                     state_dict = safetensors.torch.load_file(model_file, device="cpu")
                 except EnvironmentError:
-                    if weight_name == LORA_WEIGHT_NAME_SAFE:
-                        weight_name = None
+                    # try loading non-safetensors weights
+                    pass
+
             if model_file is None:
-                if weight_name is None:
-                    weight_name = LORA_WEIGHT_NAME
                 model_file = _get_model_file(
                     pretrained_model_name_or_path_or_dict,
-                    weights_name=weight_name,
+                    weights_name=weight_name or LORA_WEIGHT_NAME,
                     cache_dir=cache_dir,
                     force_download=force_download,
                     resume_download=resume_download,
@@ -225,9 +225,10 @@ def save_attn_procs(
         self,
         save_directory: Union[str, os.PathLike],
         is_main_process: bool = True,
-        weights_name: str = None,
+        weight_name: str = None,
         save_function: Callable = None,
         safe_serialization: bool = False,
+        **kwargs,
     ):
         r"""
         Save an attention processor to a directory, so that it can be re-loaded using the
@@ -245,6 +246,12 @@ def save_attn_procs(
                 need to replace `torch.save` by another method. Can be configured with the environment variable
                 `DIFFUSERS_SAVE_MODE`.
         """
+        weight_name = weight_name or deprecate(
+            "weights_name",
+            "0.18.0",
+            "`weights_name` is deprecated, please use `weight_name` instead.",
+            take_from=kwargs,
+        )
         if os.path.isfile(save_directory):
             logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
             return
@@ -265,22 +272,13 @@ def save_function(weights, filename):
         # Save the model
         state_dict = model_to_save.state_dict()
 
-        # Clean the folder from a previous save
-        for filename in os.listdir(save_directory):
-            full_filename = os.path.join(save_directory, filename)
-            # If we have a shard file that is not going to be replaced, we delete it, but only from the main process
-            # in distributed settings to avoid race conditions.
-            weights_no_suffix = weights_name.replace(".bin", "")
-            if filename.startswith(weights_no_suffix) and os.path.isfile(full_filename) and is_main_process:
-                os.remove(full_filename)
-
-        if weights_name is None:
+        if weight_name is None:
             if safe_serialization:
-                weights_name = LORA_WEIGHT_NAME_SAFE
+                weight_name = LORA_WEIGHT_NAME_SAFE
             else:
-                weights_name = LORA_WEIGHT_NAME
+                weight_name = LORA_WEIGHT_NAME
 
         # Save the model
-        save_function(state_dict, os.path.join(save_directory, weights_name))
+        save_function(state_dict, os.path.join(save_directory, weight_name))
 
-        logger.info(f"Model weights saved in {os.path.join(save_directory, weights_name)}")
+        logger.info(f"Model weights saved in {os.path.join(save_directory, weight_name)}")