diff --git a/utils/load_models_utils.py b/utils/load_models_utils.py
index 8ba2ac1..1c1ffdb 100644
--- a/utils/load_models_utils.py
+++ b/utils/load_models_utils.py
@@ -4,6 +4,9 @@ from diffusers import StableDiffusionXLPipeline, DiffusionPipeline
 from .pipeline import PhotoMakerStableDiffusionXLPipeline
 import os
 import sys
+import diffusers
+dif_version = str(diffusers.__version__)
+dif_version_int= int(dif_version.split(".")[1])
 dir_path = os.path.dirname(os.path.abspath(__file__))
 path_dir = os.path.dirname(dir_path)
 file_path = os.path.dirname(path_dir)
@@ -33,7 +36,7 @@ def get_models_dict():
             # 如果在解析过程中发生了错误，打印异常信息
             print(exc)
 
-def load_models(model_info,_sd_type,device,photomaker_path):
+def load_models(model_info,_sd_type,device,photomaker_path,lora,trigger_words):
     path =  model_info["path"]
     single_files =  model_info["single_files"]
     use_safetensors = model_info["use_safetensors"]
@@ -41,10 +44,15 @@ def load_models(model_info,_sd_type,device,photomaker_path):
 
     if model_type == "original":
         if single_files:
-            pipe = StableDiffusionXLPipeline.from_single_file(
-                pretrained_model_link_or_path=path,
-                original_config_file=original_config_file,torch_dtype=torch.float16,
-            )
+            if dif_version_int>=28:
+                pipe = StableDiffusionXLPipeline.from_single_file(
+                    pretrained_model_link_or_path=path, original_config=original_config_file, torch_dtype=torch.float16)
+
+            else:
+                pipe = StableDiffusionXLPipeline.from_single_file(
+                    pretrained_model_link_or_path=path, original_config_file=original_config_file, torch_dtype=torch.float16
+                )
+
         else:
             if _sd_type=="Playground_v2p5":
                 pipe = DiffusionPipeline.from_pretrained(
@@ -54,14 +62,24 @@ def load_models(model_info,_sd_type,device,photomaker_path):
                 pipe = StableDiffusionXLPipeline.from_pretrained(
                     path, torch_dtype=torch.float16,use_safetensors=use_safetensors
                 )
+
         pipe = pipe.to(device)
+        if lora != "none":
+            pipe.load_lora_weights(lora, adapter_name=trigger_words)
+            pipe.set_adapters(trigger_words)
+            pipe.fuse_lora()
+            # pipe._lora_scale=lora_scale
+
     elif model_type == "Photomaker":
         if single_files:
-            print("loading from a single_files")
-            pipe = PhotoMakerStableDiffusionXLPipeline.from_single_file(
-                pretrained_model_link_or_path=path, original_config_file=original_config_file,
-                torch_dtype=torch.float16,use_safetensors=use_safetensors
-            )
+            #print("loading from a single_files")
+            if dif_version_int>=28:
+                pipe = PhotoMakerStableDiffusionXLPipeline.from_single_file(
+                    pretrained_model_link_or_path=path, original_config=original_config_file, torch_dtype=torch.float16,use_safetensors=use_safetensors)
+            else:
+                pipe = PhotoMakerStableDiffusionXLPipeline.from_single_file(
+                    pretrained_model_link_or_path=path, original_config_file=original_config_file, torch_dtype=torch.float16,use_safetensors=use_safetensors
+                )
         else:
             if _sd_type=="Playground_v2p5":
                 pipe = PhotoMakerStableDiffusionXLPipeline.from_pretrained(
@@ -71,6 +89,7 @@ def load_models(model_info,_sd_type,device,photomaker_path):
                 pipe = PhotoMakerStableDiffusionXLPipeline.from_pretrained(
                     path, torch_dtype=torch.float16,use_safetensors=use_safetensors
                 )
+
         pipe = pipe.to(device)
         pipe.load_photomaker_adapter(
             os.path.dirname(photomaker_path),
@@ -78,6 +97,10 @@ def load_models(model_info,_sd_type,device,photomaker_path):
             weight_name=os.path.basename(photomaker_path),
             trigger_word="img"  # define the trigger word
         )
+        if lora != "none":
+            pipe.load_lora_weights(lora, adapter_name=trigger_words)
+            pipe.set_adapters(trigger_words)
+            # pipe._lora_scale=lora_scale
         pipe.fuse_lora()
     else:
         raise NotImplementedError("You should choice between original and Photomaker!",f"But you choice {model_type}")