[LoRA] add an error message when dealing with _best_guess_weight_name ofline (#6184)

* add an error message when dealing with _best_guess_weight_name ofline

* simplify condition

src/diffusers/loaders/lora.py

@@ -18,6 +18,7 @@ from typing import Callable, Dict, List, Optional, Union
 import safetensors
 import torch
 from huggingface_hub import model_info
+from huggingface_hub.constants import HF_HUB_OFFLINE
 from huggingface_hub.utils import validate_hf_hub_args
 from packaging import version
 from torch import nn
@@ -229,7 +230,9 @@ class LoraLoaderMixin:
                     # determine `weight_name`.
                     if weight_name is None:
                         weight_name = cls._best_guess_weight_name(
-                            pretrained_model_name_or_path_or_dict, file_extension=".safetensors"
+                            pretrained_model_name_or_path_or_dict,
+                            file_extension=".safetensors",
+                            local_files_only=local_files_only,
                         )
                     model_file = _get_model_file(
                         pretrained_model_name_or_path_or_dict,
@@ -255,7 +258,7 @@ class LoraLoaderMixin:
             if model_file is None:
                 if weight_name is None:
                     weight_name = cls._best_guess_weight_name(
-                        pretrained_model_name_or_path_or_dict, file_extension=".bin"
+                        pretrained_model_name_or_path_or_dict, file_extension=".bin", local_files_only=local_files_only
                     )
                 model_file = _get_model_file(
                     pretrained_model_name_or_path_or_dict,
@@ -294,7 +297,12 @@ class LoraLoaderMixin:
         return state_dict, network_alphas
 
     @classmethod
-    def _best_guess_weight_name(cls, pretrained_model_name_or_path_or_dict, file_extension=".safetensors"):
+    def _best_guess_weight_name(
+        cls, pretrained_model_name_or_path_or_dict, file_extension=".safetensors", local_files_only=False
+    ):
+        if local_files_only or HF_HUB_OFFLINE:
+            raise ValueError("When using the offline mode, you must specify a `weight_name`.")
+
         targeted_files = []
 
         if os.path.isfile(pretrained_model_name_or_path_or_dict):