diff --git a/src/diffusers/loaders/lora_base.py b/src/diffusers/loaders/lora_base.py
index 4b963270427b..4e37c3e0054e 100644
--- a/src/diffusers/loaders/lora_base.py
+++ b/src/diffusers/loaders/lora_base.py
@@ -14,6 +14,7 @@
 
 import copy
 import inspect
+import json
 import os
 from pathlib import Path
 from typing import Callable, Dict, List, Optional, Union
@@ -26,6 +27,7 @@
 
 from ..models.modeling_utils import ModelMixin, load_state_dict
 from ..utils import (
+    SAFETENSORS_FILE_EXTENSION,
     USE_PEFT_BACKEND,
     _get_model_file,
     delete_adapter_layers,
@@ -44,6 +46,7 @@
     from transformers import PreTrainedModel
 
 if is_peft_available():
+    from peft import LoraConfig
     from peft.tuners.tuners_utils import BaseTunerLayer
 
 if is_accelerate_available():
@@ -252,6 +255,7 @@ def _fetch_state_dict(
         from .lora_pipeline import LORA_WEIGHT_NAME, LORA_WEIGHT_NAME_SAFE
 
         model_file = None
+        metadata = None
         if not isinstance(pretrained_model_name_or_path_or_dict, dict):
             # Let's first try to load .safetensors weights
             if (use_safetensors and weight_name is None) or (
@@ -280,6 +284,8 @@ def _fetch_state_dict(
                         user_agent=user_agent,
                     )
                     state_dict = safetensors.torch.load_file(model_file, device="cpu")
+                    with safetensors.safe_open(model_file, framework="pt", device="cpu") as f:
+                        metadata = f.metadata()
                 except (IOError, safetensors.SafetensorError) as e:
                     if not allow_pickle:
                         raise e
@@ -305,10 +311,14 @@ def _fetch_state_dict(
                     user_agent=user_agent,
                 )
                 state_dict = load_state_dict(model_file)
+                file_extension = os.path.basename(model_file).split(".")[-1]
+                if file_extension == SAFETENSORS_FILE_EXTENSION:
+                    with safetensors.safe_open(model_file, framework="pt", device="cpu") as f:
+                        metadata = f.metadata()
         else:
             state_dict = pretrained_model_name_or_path_or_dict
 
-        return state_dict
+        return state_dict, metadata
 
     @classmethod
     def _best_guess_weight_name(
@@ -709,6 +719,20 @@ def pack_weights(layers, prefix):
         layers_state_dict = {f"{prefix}.{module_name}": param for module_name, param in layers_weights.items()}
         return layers_state_dict
 
+    @staticmethod
+    def pack_metadata(config, prefix):
+        local_metadata = {}
+        if config is not None:
+            if isinstance(config, LoraConfig):
+                config = config.to_dict()
+            for key, value in config.items():
+                if isinstance(value, set):
+                    config[key] = list(value)
+
+            config_as_string = json.dumps(config, indent=2, sort_keys=True)
+            local_metadata[prefix] = config_as_string
+        return local_metadata
+
     @staticmethod
     def write_lora_layers(
         state_dict: Dict[str, torch.Tensor],
@@ -717,9 +741,13 @@ def write_lora_layers(
         weight_name: str,
         save_function: Callable,
         safe_serialization: bool,
+        metadata=None,
     ):
         from .lora_pipeline import LORA_WEIGHT_NAME, LORA_WEIGHT_NAME_SAFE
 
+        if not safe_serialization and isinstance(metadata, dict) and len(metadata) > 0:
+            raise ValueError("Passing `metadata` is not possible when `safe_serialization` is False.")
+
         if os.path.isfile(save_directory):
             logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
             return
@@ -727,8 +755,12 @@ def write_lora_layers(
         if save_function is None:
             if safe_serialization:
 
-                def save_function(weights, filename):
-                    return safetensors.torch.save_file(weights, filename, metadata={"format": "pt"})
+                def save_function(weights, filename, metadata):
+                    if metadata is None:
+                        metadata = {"format": "pt"}
+                    elif len(metadata) > 0:
+                        metadata.update({"format": "pt"})
+                    return safetensors.torch.save_file(weights, filename, metadata=metadata)
 
             else:
                 save_function = torch.save
@@ -742,7 +774,10 @@ def save_function(weights, filename):
                 weight_name = LORA_WEIGHT_NAME
 
         save_path = Path(save_directory, weight_name).as_posix()
-        save_function(state_dict, save_path)
+        if save_function != torch.save:
+            save_function(state_dict, save_path, metadata)
+        else:
+            save_function(state_dict, save_path)
         logger.info(f"Model weights saved in {save_path}")
 
     @property
diff --git a/src/diffusers/loaders/lora_pipeline.py b/src/diffusers/loaders/lora_pipeline.py
index f612cc0c6e53..613b16f0ee18 100644
--- a/src/diffusers/loaders/lora_pipeline.py
+++ b/src/diffusers/loaders/lora_pipeline.py
@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import json
 import os
 from typing import Callable, Dict, List, Optional, Union
 
@@ -92,7 +93,9 @@ def load_lora_weights(
             pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict.copy()
 
         # First, ensure that the checkpoint is a compatible one and can be successfully loaded.
-        state_dict, network_alphas = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)
+        state_dict, network_alphas, metadata = self.lora_state_dict(
+            pretrained_model_name_or_path_or_dict, **kwargs, return_metadata=True
+        )
 
         is_correct_format = all("lora" in key or "dora_scale" in key for key in state_dict.keys())
         if not is_correct_format:
@@ -104,6 +107,7 @@ def load_lora_weights(
             unet=getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet,
             adapter_name=adapter_name,
             _pipeline=self,
+            config=metadata,
         )
         self.load_lora_into_text_encoder(
             state_dict,
@@ -113,6 +117,7 @@ def load_lora_weights(
             else self.text_encoder,
             lora_scale=self.lora_scale,
             adapter_name=adapter_name,
+            config=metadata,
             _pipeline=self,
         )
 
@@ -168,6 +173,9 @@ def lora_state_dict(
                 The subfolder location of a model file within a larger model repository on the Hub or locally.
             weight_name (`str`, *optional*, defaults to None):
                 Name of the serialized state dict file.
+            return_metadata (`bool`):
+                If state dict metadata should be returned. Is only supported when the state dict has a safetensors
+                extension.
         """
         # Load the main state dict first which has the LoRA layers for either of
         # UNet and text encoder or both.
@@ -181,6 +189,7 @@ def lora_state_dict(
         weight_name = kwargs.pop("weight_name", None)
         unet_config = kwargs.pop("unet_config", None)
         use_safetensors = kwargs.pop("use_safetensors", None)
+        return_metadata = kwargs.pop("return_metadata", False)
 
         allow_pickle = False
         if use_safetensors is None:
@@ -192,7 +201,7 @@ def lora_state_dict(
             "framework": "pytorch",
         }
 
-        state_dict = cls._fetch_state_dict(
+        state_dict, metadata = cls._fetch_state_dict(
             pretrained_model_name_or_path_or_dict=pretrained_model_name_or_path_or_dict,
             weight_name=weight_name,
             use_safetensors=use_safetensors,
@@ -224,10 +233,13 @@ def lora_state_dict(
                 state_dict = _maybe_map_sgm_blocks_to_diffusers(state_dict, unet_config)
             state_dict, network_alphas = _convert_non_diffusers_lora_to_diffusers(state_dict)
 
-        return state_dict, network_alphas
+        if return_metadata:
+            return state_dict, network_alphas, metadata
+        else:
+            return state_dict, network_alphas
 
     @classmethod
-    def load_lora_into_unet(cls, state_dict, network_alphas, unet, adapter_name=None, _pipeline=None):
+    def load_lora_into_unet(cls, state_dict, network_alphas, unet, adapter_name=None, config=None, _pipeline=None):
         """
         This will load the LoRA layers specified in `state_dict` into `unet`.
 
@@ -245,6 +257,7 @@ def load_lora_into_unet(cls, state_dict, network_alphas, unet, adapter_name=None
             adapter_name (`str`, *optional*):
                 Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
                 `default_{i}` where i is the total number of adapters being loaded.
+            config (`dict`, *optional*): LoRA configuration (`LoraConfig` dict) used during training.
         """
         if not USE_PEFT_BACKEND:
             raise ValueError("PEFT backend is required for this method.")
@@ -258,7 +271,11 @@ def load_lora_into_unet(cls, state_dict, network_alphas, unet, adapter_name=None
             # Load the layers corresponding to UNet.
             logger.info(f"Loading {cls.unet_name}.")
             unet.load_attn_procs(
-                state_dict, network_alphas=network_alphas, adapter_name=adapter_name, _pipeline=_pipeline
+                state_dict,
+                network_alphas=network_alphas,
+                adapter_name=adapter_name,
+                config=config,
+                _pipeline=_pipeline,
             )
 
     @classmethod
@@ -270,6 +287,7 @@ def load_lora_into_text_encoder(
         prefix=None,
         lora_scale=1.0,
         adapter_name=None,
+        config=None,
         _pipeline=None,
     ):
         """
@@ -291,6 +309,7 @@ def load_lora_into_text_encoder(
             adapter_name (`str`, *optional*):
                 Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
                 `default_{i}` where i is the total number of adapters being loaded.
+            config (`dict`, *optional*): LoRA configuration (`LoraConfig` dict) used during training.
         """
         if not USE_PEFT_BACKEND:
             raise ValueError("PEFT backend is required for this method.")
@@ -303,6 +322,9 @@ def load_lora_into_text_encoder(
         keys = list(state_dict.keys())
         prefix = cls.text_encoder_name if prefix is None else prefix
 
+        if config is not None and len(config) > 0:
+            config = json.loads(config[prefix])
+
         # Safe prefix to check with.
         if any(cls.text_encoder_name in key for key in keys):
             # Load the layers corresponding to text encoder and make necessary adjustments.
@@ -341,7 +363,9 @@ def load_lora_into_text_encoder(
                         k.replace(f"{prefix}.", ""): v for k, v in network_alphas.items() if k in alpha_keys
                     }
 
-                lora_config_kwargs = get_peft_kwargs(rank, network_alphas, text_encoder_lora_state_dict, is_unet=False)
+                lora_config_kwargs = get_peft_kwargs(
+                    rank, network_alphas, text_encoder_lora_state_dict, config=config, is_unet=False
+                )
                 if "use_dora" in lora_config_kwargs:
                     if lora_config_kwargs["use_dora"]:
                         if is_peft_version("<", "0.9.0"):
@@ -385,6 +409,8 @@ def save_lora_weights(
         save_directory: Union[str, os.PathLike],
         unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
         text_encoder_lora_layers: Dict[str, torch.nn.Module] = None,
+        unet_lora_config: dict = None,
+        text_encoder_lora_config: dict = None,
         is_main_process: bool = True,
         weight_name: str = None,
         save_function: Callable = None,
@@ -401,6 +427,10 @@ def save_lora_weights(
             text_encoder_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
                 State dict of the LoRA layers corresponding to the `text_encoder`. Must explicitly pass the text
                 encoder LoRA state dict because it comes from 🤗 Transformers.
+            unet_lora_config (`dict`, *optional*):
+                LoRA configuration used to train the `unet_lora_layers`.
+            text_encoder_lora_config (`dict`, *optional*):
+                LoRA configuration used to train the `text_encoder_lora_layers`.
             is_main_process (`bool`, *optional*, defaults to `True`):
                 Whether the process calling this is the main process or not. Useful during distributed training and you
                 need to call this function on all processes. In this case, set `is_main_process=True` only on the main
@@ -413,19 +443,27 @@ def save_lora_weights(
                 Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
         """
         state_dict = {}
+        metadata = {}
 
         if not (unet_lora_layers or text_encoder_lora_layers):
             raise ValueError("You must pass at least one of `unet_lora_layers` and `text_encoder_lora_layers`.")
 
         if unet_lora_layers:
             state_dict.update(cls.pack_weights(unet_lora_layers, cls.unet_name))
+            if unet_lora_config:
+                unet_metadata = cls.pack_metadata(unet_lora_config, cls.unet_name)
+                metadata.update(unet_metadata)
 
         if text_encoder_lora_layers:
             state_dict.update(cls.pack_weights(text_encoder_lora_layers, cls.text_encoder_name))
+            if text_encoder_lora_config:
+                te_metadata = cls.pack_metadata(text_encoder_lora_config, cls.text_encoder_name)
+                metadata.update(te_metadata)
 
         # Save the model
         cls.write_lora_layers(
             state_dict=state_dict,
+            metadata=metadata,
             save_directory=save_directory,
             is_main_process=is_main_process,
             weight_name=weight_name,
@@ -489,10 +527,6 @@ def unfuse_lora(self, components: List[str] = ["unet", "text_encoder"], **kwargs
 
         Args:
             components (`List[str]`): List of LoRA-injectable components to unfuse LoRA from.
-            unfuse_unet (`bool`, defaults to `True`): Whether to unfuse the UNet LoRA parameters.
-            unfuse_text_encoder (`bool`, defaults to `True`):
-                Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn't monkey-patched with the
-                LoRA parameters then it won't have any effect.
         """
         super().unfuse_lora(components=components)
 
@@ -550,9 +584,10 @@ def load_lora_weights(
             pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict.copy()
 
         # First, ensure that the checkpoint is a compatible one and can be successfully loaded.
-        state_dict, network_alphas = self.lora_state_dict(
+        state_dict, network_alphas, metadata = self.lora_state_dict(
             pretrained_model_name_or_path_or_dict,
             unet_config=self.unet.config,
+            return_metadata=True,
             **kwargs,
         )
         is_correct_format = all("lora" in key or "dora_scale" in key for key in state_dict.keys())
@@ -560,7 +595,12 @@ def load_lora_weights(
             raise ValueError("Invalid LoRA checkpoint.")
 
         self.load_lora_into_unet(
-            state_dict, network_alphas=network_alphas, unet=self.unet, adapter_name=adapter_name, _pipeline=self
+            state_dict,
+            network_alphas=network_alphas,
+            unet=self.unet,
+            adapter_name=adapter_name,
+            config=metadata,
+            _pipeline=self,
         )
         text_encoder_state_dict = {k: v for k, v in state_dict.items() if "text_encoder." in k}
         if len(text_encoder_state_dict) > 0:
@@ -571,6 +611,7 @@ def load_lora_weights(
                 prefix="text_encoder",
                 lora_scale=self.lora_scale,
                 adapter_name=adapter_name,
+                config=metadata,
                 _pipeline=self,
             )
 
@@ -583,6 +624,7 @@ def load_lora_weights(
                 prefix="text_encoder_2",
                 lora_scale=self.lora_scale,
                 adapter_name=adapter_name,
+                config=metadata,
                 _pipeline=self,
             )
 
@@ -639,6 +681,9 @@ def lora_state_dict(
                 The subfolder location of a model file within a larger model repository on the Hub or locally.
             weight_name (`str`, *optional*, defaults to None):
                 Name of the serialized state dict file.
+            return_metadata (`bool`):
+                If state dict metadata should be returned. Is only supported when the state dict has a safetensors
+                extension.
         """
         # Load the main state dict first which has the LoRA layers for either of
         # UNet and text encoder or both.
@@ -652,6 +697,7 @@ def lora_state_dict(
         weight_name = kwargs.pop("weight_name", None)
         unet_config = kwargs.pop("unet_config", None)
         use_safetensors = kwargs.pop("use_safetensors", None)
+        return_metadata = kwargs.pop("return_metadata", False)
 
         allow_pickle = False
         if use_safetensors is None:
@@ -663,7 +709,7 @@ def lora_state_dict(
             "framework": "pytorch",
         }
 
-        state_dict = cls._fetch_state_dict(
+        state_dict, metadata = cls._fetch_state_dict(
             pretrained_model_name_or_path_or_dict=pretrained_model_name_or_path_or_dict,
             weight_name=weight_name,
             use_safetensors=use_safetensors,
@@ -695,11 +741,14 @@ def lora_state_dict(
                 state_dict = _maybe_map_sgm_blocks_to_diffusers(state_dict, unet_config)
             state_dict, network_alphas = _convert_non_diffusers_lora_to_diffusers(state_dict)
 
-        return state_dict, network_alphas
+        if return_metadata:
+            return state_dict, network_alphas, metadata
+        else:
+            return state_dict, network_alphas
 
     @classmethod
     # Copied from diffusers.loaders.lora_pipeline.StableDiffusionLoraLoaderMixin.load_lora_into_unet
-    def load_lora_into_unet(cls, state_dict, network_alphas, unet, adapter_name=None, _pipeline=None):
+    def load_lora_into_unet(cls, state_dict, network_alphas, unet, adapter_name=None, config=None, _pipeline=None):
         """
         This will load the LoRA layers specified in `state_dict` into `unet`.
 
@@ -717,6 +766,7 @@ def load_lora_into_unet(cls, state_dict, network_alphas, unet, adapter_name=None
             adapter_name (`str`, *optional*):
                 Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
                 `default_{i}` where i is the total number of adapters being loaded.
+            config (`dict`, *optional*): LoRA configuration (`LoraConfig` dict) used during training.
         """
         if not USE_PEFT_BACKEND:
             raise ValueError("PEFT backend is required for this method.")
@@ -730,7 +780,11 @@ def load_lora_into_unet(cls, state_dict, network_alphas, unet, adapter_name=None
             # Load the layers corresponding to UNet.
             logger.info(f"Loading {cls.unet_name}.")
             unet.load_attn_procs(
-                state_dict, network_alphas=network_alphas, adapter_name=adapter_name, _pipeline=_pipeline
+                state_dict,
+                network_alphas=network_alphas,
+                adapter_name=adapter_name,
+                config=config,
+                _pipeline=_pipeline,
             )
 
     @classmethod
@@ -743,6 +797,7 @@ def load_lora_into_text_encoder(
         prefix=None,
         lora_scale=1.0,
         adapter_name=None,
+        config=None,
         _pipeline=None,
     ):
         """
@@ -764,6 +819,7 @@ def load_lora_into_text_encoder(
             adapter_name (`str`, *optional*):
                 Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
                 `default_{i}` where i is the total number of adapters being loaded.
+            config (`dict`, *optional*): LoRA configuration (`LoraConfig` dict) used during training.
         """
         if not USE_PEFT_BACKEND:
             raise ValueError("PEFT backend is required for this method.")
@@ -776,6 +832,9 @@ def load_lora_into_text_encoder(
         keys = list(state_dict.keys())
         prefix = cls.text_encoder_name if prefix is None else prefix
 
+        if config is not None and len(config) > 0:
+            config = json.loads(config[prefix])
+
         # Safe prefix to check with.
         if any(cls.text_encoder_name in key for key in keys):
             # Load the layers corresponding to text encoder and make necessary adjustments.
@@ -814,7 +873,9 @@ def load_lora_into_text_encoder(
                         k.replace(f"{prefix}.", ""): v for k, v in network_alphas.items() if k in alpha_keys
                     }
 
-                lora_config_kwargs = get_peft_kwargs(rank, network_alphas, text_encoder_lora_state_dict, is_unet=False)
+                lora_config_kwargs = get_peft_kwargs(
+                    rank, network_alphas, text_encoder_lora_state_dict, config=config, is_unet=False
+                )
                 if "use_dora" in lora_config_kwargs:
                     if lora_config_kwargs["use_dora"]:
                         if is_peft_version("<", "0.9.0"):
@@ -859,6 +920,9 @@ def save_lora_weights(
         unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
         text_encoder_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
         text_encoder_2_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
+        unet_lora_config: dict = None,
+        text_encoder_lora_config: dict = None,
+        text_encoder_2_lora_config: dict = None,
         is_main_process: bool = True,
         weight_name: str = None,
         save_function: Callable = None,
@@ -878,6 +942,12 @@ def save_lora_weights(
             text_encoder_2_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
                 State dict of the LoRA layers corresponding to the `text_encoder_2`. Must explicitly pass the text
                 encoder LoRA state dict because it comes from 🤗 Transformers.
+            unet_lora_config (`dict`, *optional*):
+                LoRA configuration used to train the `unet_lora_layers`.
+            text_encoder_lora_config (`dict`, *optional*):
+                LoRA configuration used to train the `text_encoder_lora_layers`.
+            text_encoder_2_lora_config (`dict`, *optional*):
+                LoRA configuration used to train the `text_encoder_2_lora_layers`.
             is_main_process (`bool`, *optional*, defaults to `True`):
                 Whether the process calling this is the main process or not. Useful during distributed training and you
                 need to call this function on all processes. In this case, set `is_main_process=True` only on the main
@@ -890,6 +960,7 @@ def save_lora_weights(
                 Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
         """
         state_dict = {}
+        metadata = {}
 
         if not (unet_lora_layers or text_encoder_lora_layers or text_encoder_2_lora_layers):
             raise ValueError(
@@ -898,15 +969,25 @@ def save_lora_weights(
 
         if unet_lora_layers:
             state_dict.update(cls.pack_weights(unet_lora_layers, "unet"))
+            if unet_lora_config is not None:
+                unet_metadata = cls.pack_metadata(unet_lora_config, "unet")
+                metadata.update(unet_metadata)
 
         if text_encoder_lora_layers:
             state_dict.update(cls.pack_weights(text_encoder_lora_layers, "text_encoder"))
+            if text_encoder_lora_config is not None:
+                te_metadata = cls.pack_metadata(text_encoder_lora_config, "text_encoder")
+                metadata.update(te_metadata)
 
         if text_encoder_2_lora_layers:
             state_dict.update(cls.pack_weights(text_encoder_2_lora_layers, "text_encoder_2"))
+            if text_encoder_2_lora_config is not None:
+                te2_metadata = cls.pack_metadata(text_encoder_lora_config, "text_encoder_2")
+                metadata.update(te2_metadata)
 
         cls.write_lora_layers(
             state_dict=state_dict,
+            metadata=metadata,
             save_directory=save_directory,
             is_main_process=is_main_process,
             weight_name=weight_name,
@@ -970,10 +1051,6 @@ def unfuse_lora(self, components: List[str] = ["unet", "text_encoder", "text_enc
 
         Args:
             components (`List[str]`): List of LoRA-injectable components to unfuse LoRA from.
-            unfuse_unet (`bool`, defaults to `True`): Whether to unfuse the UNet LoRA parameters.
-            unfuse_text_encoder (`bool`, defaults to `True`):
-                Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn't monkey-patched with the
-                LoRA parameters then it won't have any effect.
         """
         super().unfuse_lora(components=components)
 
@@ -1041,6 +1118,9 @@ def lora_state_dict(
                 allowed by Git.
             subfolder (`str`, *optional*, defaults to `""`):
                 The subfolder location of a model file within a larger model repository on the Hub or locally.
+            return_metadata (`bool`, *optional*):
+                If state dict metadata should be returned. Is only supported when the state dict has a safetensors
+                extension.
 
         """
         # Load the main state dict first which has the LoRA layers for either of
@@ -1054,6 +1134,7 @@ def lora_state_dict(
         subfolder = kwargs.pop("subfolder", None)
         weight_name = kwargs.pop("weight_name", None)
         use_safetensors = kwargs.pop("use_safetensors", None)
+        return_metadata = kwargs.pop("return_metadata", False)
 
         allow_pickle = False
         if use_safetensors is None:
@@ -1065,7 +1146,7 @@ def lora_state_dict(
             "framework": "pytorch",
         }
 
-        state_dict = cls._fetch_state_dict(
+        state_dict, metadata = cls._fetch_state_dict(
             pretrained_model_name_or_path_or_dict=pretrained_model_name_or_path_or_dict,
             weight_name=weight_name,
             use_safetensors=use_safetensors,
@@ -1080,13 +1161,17 @@ def lora_state_dict(
             allow_pickle=allow_pickle,
         )
 
-        return state_dict
+        # Otherwise, this would be a breaking change.
+        if return_metadata:
+            return state_dict, metadata
+        else:
+            return state_dict
 
     def load_lora_weights(
         self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], adapter_name=None, **kwargs
     ):
         """
-        Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.unet` and
+        Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.transformer` and
         `self.text_encoder`.
 
         All kwargs are forwarded to `self.lora_state_dict`.
@@ -1114,7 +1199,9 @@ def load_lora_weights(
             pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict.copy()
 
         # First, ensure that the checkpoint is a compatible one and can be successfully loaded.
-        state_dict = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)
+        state_dict, metadata = self.lora_state_dict(
+            pretrained_model_name_or_path_or_dict, **kwargs, return_metadata=True
+        )
 
         is_correct_format = all("lora" in key or "dora_scale" in key for key in state_dict.keys())
         if not is_correct_format:
@@ -1124,6 +1211,7 @@ def load_lora_weights(
             state_dict,
             transformer=getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer,
             adapter_name=adapter_name,
+            config=metadata,
             _pipeline=self,
         )
 
@@ -1136,6 +1224,7 @@ def load_lora_weights(
                 prefix="text_encoder",
                 lora_scale=self.lora_scale,
                 adapter_name=adapter_name,
+                config=metadata,
                 _pipeline=self,
             )
 
@@ -1148,11 +1237,12 @@ def load_lora_weights(
                 prefix="text_encoder_2",
                 lora_scale=self.lora_scale,
                 adapter_name=adapter_name,
+                config=metadata,
                 _pipeline=self,
             )
 
     @classmethod
-    def load_lora_into_transformer(cls, state_dict, transformer, adapter_name=None, _pipeline=None):
+    def load_lora_into_transformer(cls, state_dict, transformer, adapter_name=None, config=None, _pipeline=None):
         """
         This will load the LoRA layers specified in `state_dict` into `transformer`.
 
@@ -1166,6 +1256,7 @@ def load_lora_into_transformer(cls, state_dict, transformer, adapter_name=None,
             adapter_name (`str`, *optional*):
                 Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
                 `default_{i}` where i is the total number of adapters being loaded.
+            config (`dict`): Configuration that was used to train this LoRA.
         """
         from peft import LoraConfig, inject_adapter_in_model, set_peft_model_state_dict
 
@@ -1192,7 +1283,11 @@ def load_lora_into_transformer(cls, state_dict, transformer, adapter_name=None,
                 if "lora_B" in key:
                     rank[key] = val.shape[1]
 
-            lora_config_kwargs = get_peft_kwargs(rank, network_alpha_dict=None, peft_state_dict=state_dict)
+            if config is not None and isinstance(config, dict) and len(config) > 0:
+                config = json.loads(config[cls.transformer_name])
+            lora_config_kwargs = get_peft_kwargs(
+                rank, network_alpha_dict=None, config=config, peft_state_dict=state_dict
+            )
             if "use_dora" in lora_config_kwargs:
                 if lora_config_kwargs["use_dora"] and is_peft_version("<", "0.9.0"):
                     raise ValueError(
@@ -1239,6 +1334,7 @@ def load_lora_into_text_encoder(
         prefix=None,
         lora_scale=1.0,
         adapter_name=None,
+        config=None,
         _pipeline=None,
     ):
         """
@@ -1260,6 +1356,7 @@ def load_lora_into_text_encoder(
             adapter_name (`str`, *optional*):
                 Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
                 `default_{i}` where i is the total number of adapters being loaded.
+            config (`dict`, *optional*): LoRA configuration (`LoraConfig` dict) used during training.
         """
         if not USE_PEFT_BACKEND:
             raise ValueError("PEFT backend is required for this method.")
@@ -1272,6 +1369,9 @@ def load_lora_into_text_encoder(
         keys = list(state_dict.keys())
         prefix = cls.text_encoder_name if prefix is None else prefix
 
+        if config is not None and len(config) > 0:
+            config = json.loads(config[prefix])
+
         # Safe prefix to check with.
         if any(cls.text_encoder_name in key for key in keys):
             # Load the layers corresponding to text encoder and make necessary adjustments.
@@ -1310,7 +1410,9 @@ def load_lora_into_text_encoder(
                         k.replace(f"{prefix}.", ""): v for k, v in network_alphas.items() if k in alpha_keys
                     }
 
-                lora_config_kwargs = get_peft_kwargs(rank, network_alphas, text_encoder_lora_state_dict, is_unet=False)
+                lora_config_kwargs = get_peft_kwargs(
+                    rank, network_alphas, text_encoder_lora_state_dict, config=config, is_unet=False
+                )
                 if "use_dora" in lora_config_kwargs:
                     if lora_config_kwargs["use_dora"]:
                         if is_peft_version("<", "0.9.0"):
@@ -1349,12 +1451,16 @@ def load_lora_into_text_encoder(
                 # Unsafe code />
 
     @classmethod
+    # Copied from diffusers.loaders.lora_pipeline.StableDiffusionXLLoraLoaderMixin.save_lora_weights with unet->transformer
     def save_lora_weights(
         cls,
         save_directory: Union[str, os.PathLike],
-        transformer_lora_layers: Dict[str, torch.nn.Module] = None,
+        transformer_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
         text_encoder_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
         text_encoder_2_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
+        transformer_lora_config: dict = None,
+        text_encoder_lora_config: dict = None,
+        text_encoder_2_lora_config: dict = None,
         is_main_process: bool = True,
         weight_name: str = None,
         save_function: Callable = None,
@@ -1374,6 +1480,12 @@ def save_lora_weights(
             text_encoder_2_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
                 State dict of the LoRA layers corresponding to the `text_encoder_2`. Must explicitly pass the text
                 encoder LoRA state dict because it comes from 🤗 Transformers.
+            transformer_lora_config (`dict`, *optional*):
+                LoRA configuration used to train the `transformer_lora_layers`.
+            text_encoder_lora_config (`dict`, *optional*):
+                LoRA configuration used to train the `text_encoder_lora_layers`.
+            text_encoder_2_lora_config (`dict`, *optional*):
+                LoRA configuration used to train the `text_encoder_2_lora_layers`.
             is_main_process (`bool`, *optional*, defaults to `True`):
                 Whether the process calling this is the main process or not. Useful during distributed training and you
                 need to call this function on all processes. In this case, set `is_main_process=True` only on the main
@@ -1386,24 +1498,34 @@ def save_lora_weights(
                 Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
         """
         state_dict = {}
+        metadata = {}
 
         if not (transformer_lora_layers or text_encoder_lora_layers or text_encoder_2_lora_layers):
             raise ValueError(
-                "You must pass at least one of `transformer_lora_layers`, `text_encoder_lora_layers`, `text_encoder_2_lora_layers`."
+                "You must pass at least one of `transformer_lora_layers`, `text_encoder_lora_layers` or `text_encoder_2_lora_layers`."
             )
 
         if transformer_lora_layers:
-            state_dict.update(cls.pack_weights(transformer_lora_layers, cls.transformer_name))
+            state_dict.update(cls.pack_weights(transformer_lora_layers, "transformer"))
+            if transformer_lora_config is not None:
+                transformer_metadata = cls.pack_metadata(transformer_lora_config, "transformer")
+                metadata.update(transformer_metadata)
 
         if text_encoder_lora_layers:
             state_dict.update(cls.pack_weights(text_encoder_lora_layers, "text_encoder"))
+            if text_encoder_lora_config is not None:
+                te_metadata = cls.pack_metadata(text_encoder_lora_config, "text_encoder")
+                metadata.update(te_metadata)
 
         if text_encoder_2_lora_layers:
             state_dict.update(cls.pack_weights(text_encoder_2_lora_layers, "text_encoder_2"))
+            if text_encoder_2_lora_config is not None:
+                te2_metadata = cls.pack_metadata(text_encoder_lora_config, "text_encoder_2")
+                metadata.update(te2_metadata)
 
-        # Save the model
         cls.write_lora_layers(
             state_dict=state_dict,
+            metadata=metadata,
             save_directory=save_directory,
             is_main_process=is_main_process,
             weight_name=weight_name,
@@ -1411,6 +1533,7 @@ def save_lora_weights(
             safe_serialization=safe_serialization,
         )
 
+    # Copied from diffusers.loaders.lora_pipeline.StableDiffusionXLLoraLoaderMixin.fuse_lora with unet->transformer
     def fuse_lora(
         self,
         components: List[str] = ["transformer", "text_encoder", "text_encoder_2"],
@@ -1454,6 +1577,7 @@ def fuse_lora(
             components=components, lora_scale=lora_scale, safe_fusing=safe_fusing, adapter_names=adapter_names
         )
 
+    # Copied from diffusers.loaders.lora_pipeline.StableDiffusionXLLoraLoaderMixin.unfuse_lora with unet->transformer
     def unfuse_lora(self, components: List[str] = ["transformer", "text_encoder", "text_encoder_2"], **kwargs):
         r"""
         Reverses the effect of
@@ -1467,10 +1591,6 @@ def unfuse_lora(self, components: List[str] = ["transformer", "text_encoder", "t
 
         Args:
             components (`List[str]`): List of LoRA-injectable components to unfuse LoRA from.
-            unfuse_unet (`bool`, defaults to `True`): Whether to unfuse the UNet LoRA parameters.
-            unfuse_text_encoder (`bool`, defaults to `True`):
-                Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn't monkey-patched with the
-                LoRA parameters then it won't have any effect.
         """
         super().unfuse_lora(components=components)
 
@@ -1538,6 +1658,9 @@ def lora_state_dict(
                 allowed by Git.
             subfolder (`str`, *optional*, defaults to `""`):
                 The subfolder location of a model file within a larger model repository on the Hub or locally.
+            return_metadata (`bool`, *optional*):
+                If state dict metadata should be returned. Is only supported when the state dict has a safetensors
+                extension.
 
         """
         # Load the main state dict first which has the LoRA layers for either of
@@ -1551,6 +1674,7 @@ def lora_state_dict(
         subfolder = kwargs.pop("subfolder", None)
         weight_name = kwargs.pop("weight_name", None)
         use_safetensors = kwargs.pop("use_safetensors", None)
+        return_metadata = kwargs.pop("return_metadata", False)
 
         allow_pickle = False
         if use_safetensors is None:
@@ -1562,7 +1686,7 @@ def lora_state_dict(
             "framework": "pytorch",
         }
 
-        state_dict = cls._fetch_state_dict(
+        state_dict, metadata = cls._fetch_state_dict(
             pretrained_model_name_or_path_or_dict=pretrained_model_name_or_path_or_dict,
             weight_name=weight_name,
             use_safetensors=use_safetensors,
@@ -1577,7 +1701,11 @@ def lora_state_dict(
             allow_pickle=allow_pickle,
         )
 
-        return state_dict
+        # Otherwise, this would be a breaking change.
+        if return_metadata:
+            return state_dict, metadata
+        else:
+            return state_dict
 
     def load_lora_weights(
         self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], adapter_name=None, **kwargs
@@ -1611,7 +1739,9 @@ def load_lora_weights(
             pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict.copy()
 
         # First, ensure that the checkpoint is a compatible one and can be successfully loaded.
-        state_dict = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)
+        state_dict, metadata = self.lora_state_dict(
+            pretrained_model_name_or_path_or_dict, **kwargs, return_metadata=True
+        )
 
         is_correct_format = all("lora" in key or "dora_scale" in key for key in state_dict.keys())
         if not is_correct_format:
@@ -1621,6 +1751,7 @@ def load_lora_weights(
             state_dict,
             transformer=getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer,
             adapter_name=adapter_name,
+            config=metadata,
             _pipeline=self,
         )
 
@@ -1633,12 +1764,13 @@ def load_lora_weights(
                 prefix="text_encoder",
                 lora_scale=self.lora_scale,
                 adapter_name=adapter_name,
+                config=metadata,
                 _pipeline=self,
             )
 
     @classmethod
     # Copied from diffusers.loaders.lora_pipeline.SD3LoraLoaderMixin.load_lora_into_transformer
-    def load_lora_into_transformer(cls, state_dict, transformer, adapter_name=None, _pipeline=None):
+    def load_lora_into_transformer(cls, state_dict, transformer, adapter_name=None, config=None, _pipeline=None):
         """
         This will load the LoRA layers specified in `state_dict` into `transformer`.
 
@@ -1652,6 +1784,7 @@ def load_lora_into_transformer(cls, state_dict, transformer, adapter_name=None,
             adapter_name (`str`, *optional*):
                 Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
                 `default_{i}` where i is the total number of adapters being loaded.
+            config (`dict`): Configuration that was used to train this LoRA.
         """
         from peft import LoraConfig, inject_adapter_in_model, set_peft_model_state_dict
 
@@ -1678,7 +1811,11 @@ def load_lora_into_transformer(cls, state_dict, transformer, adapter_name=None,
                 if "lora_B" in key:
                     rank[key] = val.shape[1]
 
-            lora_config_kwargs = get_peft_kwargs(rank, network_alpha_dict=None, peft_state_dict=state_dict)
+            if config is not None and isinstance(config, dict) and len(config) > 0:
+                config = json.loads(config[cls.transformer_name])
+            lora_config_kwargs = get_peft_kwargs(
+                rank, network_alpha_dict=None, config=config, peft_state_dict=state_dict
+            )
             if "use_dora" in lora_config_kwargs:
                 if lora_config_kwargs["use_dora"] and is_peft_version("<", "0.9.0"):
                     raise ValueError(
@@ -1725,6 +1862,7 @@ def load_lora_into_text_encoder(
         prefix=None,
         lora_scale=1.0,
         adapter_name=None,
+        config=None,
         _pipeline=None,
     ):
         """
@@ -1746,6 +1884,7 @@ def load_lora_into_text_encoder(
             adapter_name (`str`, *optional*):
                 Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
                 `default_{i}` where i is the total number of adapters being loaded.
+            config (`dict`, *optional*): LoRA configuration (`LoraConfig` dict) used during training.
         """
         if not USE_PEFT_BACKEND:
             raise ValueError("PEFT backend is required for this method.")
@@ -1758,6 +1897,9 @@ def load_lora_into_text_encoder(
         keys = list(state_dict.keys())
         prefix = cls.text_encoder_name if prefix is None else prefix
 
+        if config is not None and len(config) > 0:
+            config = json.loads(config[prefix])
+
         # Safe prefix to check with.
         if any(cls.text_encoder_name in key for key in keys):
             # Load the layers corresponding to text encoder and make necessary adjustments.
@@ -1796,7 +1938,9 @@ def load_lora_into_text_encoder(
                         k.replace(f"{prefix}.", ""): v for k, v in network_alphas.items() if k in alpha_keys
                     }
 
-                lora_config_kwargs = get_peft_kwargs(rank, network_alphas, text_encoder_lora_state_dict, is_unet=False)
+                lora_config_kwargs = get_peft_kwargs(
+                    rank, network_alphas, text_encoder_lora_state_dict, config=config, is_unet=False
+                )
                 if "use_dora" in lora_config_kwargs:
                     if lora_config_kwargs["use_dora"]:
                         if is_peft_version("<", "0.9.0"):
@@ -1841,6 +1985,8 @@ def save_lora_weights(
         save_directory: Union[str, os.PathLike],
         transformer_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
         text_encoder_lora_layers: Dict[str, torch.nn.Module] = None,
+        transformer_lora_config: dict = None,
+        text_encoder_lora_config: dict = None,
         is_main_process: bool = True,
         weight_name: str = None,
         save_function: Callable = None,
@@ -1857,6 +2003,10 @@ def save_lora_weights(
             text_encoder_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
                 State dict of the LoRA layers corresponding to the `text_encoder`. Must explicitly pass the text
                 encoder LoRA state dict because it comes from 🤗 Transformers.
+            transformer_lora_config (`dict`, *optional*):
+                LoRA configuration used to train the `transformer_lora_layers`.
+            text_encoder_lora_config (`dict`, *optional*):
+                LoRA configuration used to train the `text_encoder_lora_layers`.
             is_main_process (`bool`, *optional*, defaults to `True`):
                 Whether the process calling this is the main process or not. Useful during distributed training and you
                 need to call this function on all processes. In this case, set `is_main_process=True` only on the main
@@ -1869,19 +2019,27 @@ def save_lora_weights(
                 Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
         """
         state_dict = {}
+        metadata = {}
 
         if not (transformer_lora_layers or text_encoder_lora_layers):
             raise ValueError("You must pass at least one of `transformer_lora_layers` and `text_encoder_lora_layers`.")
 
         if transformer_lora_layers:
             state_dict.update(cls.pack_weights(transformer_lora_layers, cls.transformer_name))
+            if transformer_lora_config:
+                transformer_metadata = cls.pack_metadata(transformer_lora_config, cls.transformer_name)
+                metadata.update(transformer_metadata)
 
         if text_encoder_lora_layers:
             state_dict.update(cls.pack_weights(text_encoder_lora_layers, cls.text_encoder_name))
+            if text_encoder_lora_config:
+                te_metadata = cls.pack_metadata(text_encoder_lora_config, cls.text_encoder_name)
+                metadata.update(te_metadata)
 
         # Save the model
         cls.write_lora_layers(
             state_dict=state_dict,
+            metadata=metadata,
             save_directory=save_directory,
             is_main_process=is_main_process,
             weight_name=weight_name,
@@ -1933,6 +2091,7 @@ def fuse_lora(
             components=components, lora_scale=lora_scale, safe_fusing=safe_fusing, adapter_names=adapter_names
         )
 
+    # Copied from diffusers.loaders.lora_pipeline.StableDiffusionLoraLoaderMixin.unfuse_lora with unet->transformer
     def unfuse_lora(self, components: List[str] = ["transformer", "text_encoder"], **kwargs):
         r"""
         Reverses the effect of
@@ -2051,6 +2210,7 @@ def load_lora_into_text_encoder(
         prefix=None,
         lora_scale=1.0,
         adapter_name=None,
+        config=None,
         _pipeline=None,
     ):
         """
@@ -2072,6 +2232,7 @@ def load_lora_into_text_encoder(
             adapter_name (`str`, *optional*):
                 Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
                 `default_{i}` where i is the total number of adapters being loaded.
+            config (`dict`, *optional*): LoRA configuration (`LoraConfig` dict) used during training.
         """
         if not USE_PEFT_BACKEND:
             raise ValueError("PEFT backend is required for this method.")
@@ -2084,6 +2245,9 @@ def load_lora_into_text_encoder(
         keys = list(state_dict.keys())
         prefix = cls.text_encoder_name if prefix is None else prefix
 
+        if config is not None and len(config) > 0:
+            config = json.loads(config[prefix])
+
         # Safe prefix to check with.
         if any(cls.text_encoder_name in key for key in keys):
             # Load the layers corresponding to text encoder and make necessary adjustments.
@@ -2122,7 +2286,9 @@ def load_lora_into_text_encoder(
                         k.replace(f"{prefix}.", ""): v for k, v in network_alphas.items() if k in alpha_keys
                     }
 
-                lora_config_kwargs = get_peft_kwargs(rank, network_alphas, text_encoder_lora_state_dict, is_unet=False)
+                lora_config_kwargs = get_peft_kwargs(
+                    rank, network_alphas, text_encoder_lora_state_dict, config=config, is_unet=False
+                )
                 if "use_dora" in lora_config_kwargs:
                     if lora_config_kwargs["use_dora"]:
                         if is_peft_version("<", "0.9.0"):
diff --git a/src/diffusers/loaders/unet.py b/src/diffusers/loaders/unet.py
index 32ace77b6224..9caaf2440bde 100644
--- a/src/diffusers/loaders/unet.py
+++ b/src/diffusers/loaders/unet.py
@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import json
 import os
 from collections import defaultdict
 from contextlib import nullcontext
@@ -115,6 +116,7 @@ def load_attn_procs(self, pretrained_model_name_or_path_or_dict: Union[str, Dict
                 `default_{i}` where i is the total number of adapters being loaded.
             weight_name (`str`, *optional*, defaults to None):
                 Name of the serialized state dict file.
+            config: (`dict`, *optional*)
 
         Example:
 
@@ -143,6 +145,7 @@ def load_attn_procs(self, pretrained_model_name_or_path_or_dict: Union[str, Dict
         _pipeline = kwargs.pop("_pipeline", None)
         network_alphas = kwargs.pop("network_alphas", None)
         allow_pickle = False
+        config = kwargs.pop("config", None)
 
         if use_safetensors is None:
             use_safetensors = True
@@ -208,6 +211,7 @@ def load_attn_procs(self, pretrained_model_name_or_path_or_dict: Union[str, Dict
                 unet_identifier_key=self.unet_name,
                 network_alphas=network_alphas,
                 adapter_name=adapter_name,
+                config=config,
                 _pipeline=_pipeline,
             )
         else:
@@ -268,7 +272,7 @@ def _process_custom_diffusion(self, state_dict):
 
         return attn_processors
 
-    def _process_lora(self, state_dict, unet_identifier_key, network_alphas, adapter_name, _pipeline):
+    def _process_lora(self, state_dict, unet_identifier_key, network_alphas, adapter_name, _pipeline, config=None):
         # This method does the following things:
         # 1. Filters the `state_dict` with keys matching  `unet_identifier_key` when using the non-legacy
         #    format. For legacy format no filtering is applied.
@@ -316,7 +320,10 @@ def _process_lora(self, state_dict, unet_identifier_key, network_alphas, adapter
                 if "lora_B" in key:
                     rank[key] = val.shape[1]
 
-            lora_config_kwargs = get_peft_kwargs(rank, network_alphas, state_dict, is_unet=True)
+            if config is not None and isinstance(config, dict) and len(config) > 0:
+                config = json.loads(config["unet"])
+            lora_config_kwargs = get_peft_kwargs(rank, network_alphas, state_dict, config=config, is_unet=True)
+
             if "use_dora" in lora_config_kwargs:
                 if lora_config_kwargs["use_dora"]:
                     if is_peft_version("<", "0.9.0"):
diff --git a/src/diffusers/utils/peft_utils.py b/src/diffusers/utils/peft_utils.py
index ca55192ff7ae..d4cfd4cc4d83 100644
--- a/src/diffusers/utils/peft_utils.py
+++ b/src/diffusers/utils/peft_utils.py
@@ -21,9 +21,12 @@
 
 from packaging import version
 
+from . import logging
 from .import_utils import is_peft_available, is_torch_available
 
 
+logger = logging.get_logger(__name__)
+
 if is_torch_available():
     import torch
 
@@ -147,11 +150,29 @@ def unscale_lora_layers(model, weight: Optional[float] = None):
                     module.set_scale(adapter_name, 1.0)
 
 
-def get_peft_kwargs(rank_dict, network_alpha_dict, peft_state_dict, is_unet=True):
+def get_peft_kwargs(rank_dict, network_alpha_dict, peft_state_dict, config=None, is_unet=True):
     rank_pattern = {}
     alpha_pattern = {}
     r = lora_alpha = list(rank_dict.values())[0]
 
+    # Try to retrieve config.
+    alpha_retrieved = False
+    if config is not None:
+        lora_alpha = config["lora_alpha"] if "lora_alpha" in config else lora_alpha
+        alpha_retrieved = True
+
+        # We simply ignore the `alpha_pattern` and `rank_pattern` if they are found
+        # in the `config`. This is because:
+        # 1. We determine `rank_pattern` from the  `rank_dict`.
+        # 2. When `network_alpha_dict` is passed that means the underlying checkpoint
+        # is a non-diffusers checkpoint.
+        # More details: https://github.com/huggingface/diffusers/pull/9143#discussion_r1711491175
+        if config.get("alpha_pattern", None) is not None:
+            logger.warning("`alpha_pattern` found in the LoRA config. This will be ignored.")
+
+        if config.get("rank_pattern", None) is not None:
+            logger.warning("`rank_pattern` found in the LoRA config. This will be ignored.")
+
     if len(set(rank_dict.values())) > 1:
         # get the rank occuring the most number of times
         r = collections.Counter(rank_dict.values()).most_common()[0][0]
@@ -160,7 +181,7 @@ def get_peft_kwargs(rank_dict, network_alpha_dict, peft_state_dict, is_unet=True
         rank_pattern = dict(filter(lambda x: x[1] != r, rank_dict.items()))
         rank_pattern = {k.split(".lora_B.")[0]: v for k, v in rank_pattern.items()}
 
-    if network_alpha_dict is not None and len(network_alpha_dict) > 0:
+    if not alpha_retrieved and network_alpha_dict is not None and len(network_alpha_dict) > 0:
         if len(set(network_alpha_dict.values())) > 1:
             # get the alpha occuring the most number of times
             lora_alpha = collections.Counter(network_alpha_dict.values()).most_common()[0][0]
diff --git a/tests/lora/test_lora_layers_flux.py b/tests/lora/test_lora_layers_flux.py
index c0f0684ac4de..b08fceee3937 100644
--- a/tests/lora/test_lora_layers_flux.py
+++ b/tests/lora/test_lora_layers_flux.py
@@ -19,7 +19,7 @@
 from transformers import AutoTokenizer, CLIPTextModel, CLIPTokenizer, T5EncoderModel
 
 from diffusers import FlowMatchEulerDiscreteScheduler, FluxPipeline, FluxTransformer2DModel
-from diffusers.utils.testing_utils import floats_tensor, require_peft_backend
+from diffusers.utils.testing_utils import floats_tensor, require_peft_backend, torch_device
 
 
 sys.path.append(".")
@@ -28,6 +28,7 @@
 
 
 @require_peft_backend
+@unittest.skipIf(torch_device == "mps", "Flux has a float64 operation which is not supported in MPS.")
 class FluxLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
     pipeline_class = FluxPipeline
     scheduler_cls = FlowMatchEulerDiscreteScheduler()
diff --git a/tests/lora/utils.py b/tests/lora/utils.py
index 283b9f534766..fd9c15505438 100644
--- a/tests/lora/utils.py
+++ b/tests/lora/utils.py
@@ -87,7 +87,7 @@ class PeftLoraLoaderMixinTests:
     transformer_kwargs = None
     vae_kwargs = None
 
-    def get_dummy_components(self, scheduler_cls=None, use_dora=False):
+    def get_dummy_components(self, scheduler_cls=None, use_dora=False, alpha=None):
         if self.unet_kwargs and self.transformer_kwargs:
             raise ValueError("Both `unet_kwargs` and `transformer_kwargs` cannot be specified.")
         if self.has_two_text_encoders and self.has_three_text_encoders:
@@ -95,6 +95,7 @@ def get_dummy_components(self, scheduler_cls=None, use_dora=False):
 
         scheduler_cls = self.scheduler_cls if scheduler_cls is None else scheduler_cls
         rank = 4
+        alpha = rank if alpha is None else alpha
 
         torch.manual_seed(0)
         if self.unet_kwargs is not None:
@@ -120,7 +121,7 @@ def get_dummy_components(self, scheduler_cls=None, use_dora=False):
 
         text_lora_config = LoraConfig(
             r=rank,
-            lora_alpha=rank,
+            lora_alpha=alpha,
             target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
             init_lora_weights=False,
             use_dora=use_dora,
@@ -128,7 +129,7 @@ def get_dummy_components(self, scheduler_cls=None, use_dora=False):
 
         denoiser_lora_config = LoraConfig(
             r=rank,
-            lora_alpha=rank,
+            lora_alpha=alpha,
             target_modules=["to_q", "to_k", "to_v", "to_out.0"],
             init_lora_weights=False,
             use_dora=use_dora,
@@ -1752,3 +1753,85 @@ def set_pad_mode(network, mode="circular"):
 
             _, _, inputs = self.get_dummy_inputs()
             _ = pipe(**inputs).images
+
+    def test_if_lora_alpha_is_correctly_parsed(self):
+        lora_alpha = 8
+
+        scheduler_class = FlowMatchEulerDiscreteScheduler if self.uses_flow_matching else DDIMScheduler
+        components, text_lora_config, denoiser_lora_config = self.get_dummy_components(
+            alpha=lora_alpha, scheduler_cls=scheduler_class
+        )
+
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        _, _, inputs = self.get_dummy_inputs(with_generator=False)
+
+        pipe.text_encoder.add_adapter(text_lora_config)
+        if self.unet_kwargs is not None:
+            pipe.unet.add_adapter(denoiser_lora_config)
+        else:
+            pipe.transformer.add_adapter(denoiser_lora_config)
+        if self.has_two_text_encoders or self.has_three_text_encoders:
+            if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
+                pipe.text_encoder_2.add_adapter(text_lora_config)
+
+        # Inference works?
+        _ = pipe(**inputs, generator=torch.manual_seed(0)).images
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            denoiser = pipe.unet if self.unet_kwargs else pipe.transformer
+            denoiser_state_dict = get_peft_model_state_dict(denoiser)
+            denoiser_lora_config = denoiser.peft_config["default"]
+
+            text_encoder_state_dict = get_peft_model_state_dict(pipe.text_encoder)
+            text_encoder_lora_config = pipe.text_encoder.peft_config["default"]
+
+            if self.has_two_text_encoders or self.has_three_text_encoders:
+                if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
+                    text_encoder_2_state_dict = get_peft_model_state_dict(pipe.text_encoder_2)
+                    text_encoder_2_lora_config = pipe.text_encoder_2.peft_config["default"]
+
+            saving_kwargs = {
+                "save_directory": tmpdirname,
+                "text_encoder_lora_layers": text_encoder_state_dict,
+                "text_encoder_lora_config": text_encoder_lora_config,
+            }
+            if self.unet_kwargs is not None:
+                saving_kwargs.update(
+                    {"unet_lora_layers": denoiser_state_dict, "unet_lora_config": denoiser_lora_config}
+                )
+            else:
+                saving_kwargs.update(
+                    {"transformer_lora_layers": denoiser_state_dict, "transformer_lora_config": denoiser_lora_config}
+                )
+
+            if self.has_two_text_encoders or self.has_three_text_encoders:
+                if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
+                    text_encoder_2_state_dict = get_peft_model_state_dict(pipe.text_encoder_2)
+                    saving_kwargs.update(
+                        {
+                            "text_encoder_2_lora_layers": text_encoder_2_state_dict,
+                            "text_encoder_2_lora_config": text_encoder_2_lora_config,
+                        }
+                    )
+
+            self.pipeline_class.save_lora_weights(**saving_kwargs)
+            loaded_pipe = self.pipeline_class(**components)
+            loaded_pipe.load_lora_weights(tmpdirname)
+
+        # Inference works?
+        _ = loaded_pipe(**inputs, generator=torch.manual_seed(0)).images
+
+        denoiser_loaded = pipe.unet if self.unet_kwargs is not None else pipe.transformer
+        assert (
+            denoiser_loaded.peft_config["default"].lora_alpha == lora_alpha
+        ), "LoRA alpha not correctly loaded for UNet."
+        assert (
+            loaded_pipe.text_encoder.peft_config["default"].lora_alpha == lora_alpha
+        ), "LoRA alpha not correctly loaded for text encoder."
+        if self.has_two_text_encoders or self.has_three_text_encoders:
+            if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
+                assert (
+                    loaded_pipe.text_encoder_2.peft_config["default"].lora_alpha == lora_alpha
+                ), "LoRA alpha not correctly loaded for text encoder 2."