make fix-copies

src/diffusers/loaders/lora_pipeline.py

@@ -1703,7 +1703,8 @@ def lora_state_dict(
                 The subfolder location of a model file within a larger model repository on the Hub or locally.
 
         """
-        # Load the main state dict first which has the LoRA layers for transformer
+        # Load the main state dict first which has the LoRA layers for either of
+        # transformer and text encoder or both.
         cache_dir = kwargs.pop("cache_dir", None)
         force_download = kwargs.pop("force_download", False)
         proxies = kwargs.pop("proxies", None)
@@ -1724,7 +1725,7 @@ def lora_state_dict(
             "framework": "pytorch",
         }
 
-        state_dict = cls._fetch_state_dict(
+        state_dict = _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,
@@ -1739,6 +1740,12 @@ def lora_state_dict(
             allow_pickle=allow_pickle,
         )
 
+        is_dora_scale_present = any("dora_scale" in k for k in state_dict)
+        if is_dora_scale_present:
+            warn_msg = "It seems like you are using a DoRA checkpoint that is not compatible in Diffusers at the moment. So, we are going to filter out the keys associated to 'dora_scale` from the state dict. If you think this is a mistake please open an issue https://github.com/huggingface/diffusers/issues/new."
+            logger.warning(warn_msg)
+            state_dict = {k: v for k, v in state_dict.items() if "dora_scale" not in k}
+
         return state_dict
 
     def load_lora_weights(
@@ -1788,6 +1795,8 @@ def load_lora_weights(
     @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):
+        cls, state_dict, transformer, adapter_name=None, _pipeline=None, low_cpu_mem_usage=False
+    ):
         """
         This will load the LoRA layers specified in `state_dict` into `transformer`.
 
@@ -1801,68 +1810,24 @@ 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.
+            low_cpu_mem_usage (`bool`, *optional*):
+                Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
+                weights.
         """
-        from peft import LoraConfig, inject_adapter_in_model, set_peft_model_state_dict
-
-        keys = list(state_dict.keys())
-
-        transformer_keys = [k for k in keys if k.startswith(cls.transformer_name)]
-        state_dict = {
-            k.replace(f"{cls.transformer_name}.", ""): v for k, v in state_dict.items() if k in transformer_keys
-        }
-
-        if len(state_dict.keys()) > 0:
-            # check with first key if is not in peft format
-            first_key = next(iter(state_dict.keys()))
-            if "lora_A" not in first_key:
-                state_dict = convert_unet_state_dict_to_peft(state_dict)
-
-            if adapter_name in getattr(transformer, "peft_config", {}):
-                raise ValueError(
-                    f"Adapter name {adapter_name} already in use in the transformer - please select a new adapter name."
-                )
-
-            rank = {}
-            for key, val in state_dict.items():
-                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 "use_dora" in lora_config_kwargs:
-                if lora_config_kwargs["use_dora"] and is_peft_version("<", "0.9.0"):
-                    raise ValueError(
-                        "You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
-                    )
-                else:
-                    lora_config_kwargs.pop("use_dora")
-            lora_config = LoraConfig(**lora_config_kwargs)
-
-            # adapter_name
-            if adapter_name is None:
-                adapter_name = get_adapter_name(transformer)
-
-            # In case the pipeline has been already offloaded to CPU - temporarily remove the hooks
-            # otherwise loading LoRA weights will lead to an error
-            is_model_cpu_offload, is_sequential_cpu_offload = cls._optionally_disable_offloading(_pipeline)
-
-            inject_adapter_in_model(lora_config, transformer, adapter_name=adapter_name)
-            incompatible_keys = set_peft_model_state_dict(transformer, state_dict, adapter_name)
-
-            if incompatible_keys is not None:
-                # check only for unexpected keys
-                unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
-                if unexpected_keys:
-                    logger.warning(
-                        f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
-                        f" {unexpected_keys}. "
-                    )
+        if low_cpu_mem_usage and is_peft_version("<", "0.13.0"):
+            raise ValueError(
+                "`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
+            )
 
-            # Offload back.
-            if is_model_cpu_offload:
-                _pipeline.enable_model_cpu_offload()
-            elif is_sequential_cpu_offload:
-                _pipeline.enable_sequential_cpu_offload()
-            # Unsafe code />
+        # Load the layers corresponding to transformer.
+        logger.info(f"Loading {cls.transformer_name}.")
+        transformer.load_lora_adapter(
+            state_dict,
+            network_alphas=None,
+            adapter_name=adapter_name,
+            _pipeline=_pipeline,
+            low_cpu_mem_usage=low_cpu_mem_usage,
+        )
 
     @classmethod
     def save_lora_weights(

src/diffusers/models/transformers/auraflow_transformer_2d.py

@@ -343,8 +343,8 @@ def __init__(
 
         self.gradient_checkpointing = False
 
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
     @property
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
     def attn_processors(self) -> Dict[str, AttentionProcessor]:
         r"""
         Returns: