diff --git a/diffsynth/core/loader/model.py b/diffsynth/core/loader/model.py
index 5d9b052..1abfd02 100644
--- a/diffsynth/core/loader/model.py
+++ b/diffsynth/core/loader/model.py
@@ -3,24 +3,20 @@ from ..vram.disk_map import DiskMap
 from ..vram.layers import enable_vram_management
 from .file import load_state_dict
 import torch
-from contextlib import contextmanager
 from transformers.integrations import is_deepspeed_zero3_enabled
 from transformers.utils import ContextManagers
 
 
-def load_model(model_class, path, config=None, torch_dtype=torch.bfloat16, device="cpu", state_dict_converter=None,
-               use_disk_map=False, module_map=None, vram_config=None, vram_limit=None):
+def load_model(model_class, path, config=None, torch_dtype=torch.bfloat16, device="cpu", state_dict_converter=None, use_disk_map=False, module_map=None, vram_config=None, vram_limit=None, state_dict=None):
     config = {} if config is None else config
     with ContextManagers(get_init_context(torch_dtype=torch_dtype, device=device)):
         model = model_class(**config)
     # What is `module_map`?
     # This is a module mapping table for VRAM management.
     if module_map is not None:
-        devices = [vram_config["offload_device"], vram_config["onload_device"], vram_config["preparing_device"],
-                   vram_config["computation_device"]]
+        devices = [vram_config["offload_device"], vram_config["onload_device"], vram_config["preparing_device"], vram_config["computation_device"]]
         device = [d for d in devices if d != "disk"][0]
-        dtypes = [vram_config["offload_dtype"], vram_config["onload_dtype"], vram_config["preparing_dtype"],
-                  vram_config["computation_dtype"]]
+        dtypes = [vram_config["offload_dtype"], vram_config["onload_dtype"], vram_config["preparing_dtype"], vram_config["computation_dtype"]]
         dtype = [d for d in dtypes if d != "disk"][0]
         if vram_config["offload_device"] != "disk":
             state_dict = DiskMap(path, device, torch_dtype=dtype)
@@ -29,12 +25,10 @@ def load_model(model_class, path, config=None, torch_dtype=torch.bfloat16, devic
             else:
                 state_dict = {i: state_dict[i] for i in state_dict}
             model.load_state_dict(state_dict, assign=True)
-            model = enable_vram_management(model, module_map, vram_config=vram_config, disk_map=None,
-                                           vram_limit=vram_limit)
+            model = enable_vram_management(model, module_map, vram_config=vram_config, disk_map=None, vram_limit=vram_limit)
         else:
             disk_map = DiskMap(path, device, state_dict_converter=state_dict_converter)
-            model = enable_vram_management(model, module_map, vram_config=vram_config, disk_map=disk_map,
-                                           vram_limit=vram_limit)
+            model = enable_vram_management(model, module_map, vram_config=vram_config, disk_map=disk_map, vram_limit=vram_limit)
     else:
         # Why do we use `DiskMap`?
         # Sometimes a model file contains multiple models,
@@ -51,6 +45,9 @@ def load_model(model_class, path, config=None, torch_dtype=torch.bfloat16, devic
             state_dict = state_dict_converter(state_dict)
         else:
             state_dict = {i: state_dict[i] for i in state_dict}
+        # Why does DeepSpeed ZeRO Stage 3 need to be handled separately?
+        # Because at this stage, model parameters are partitioned across multiple GPUs.
+        # Loading them directly could lead to excessive GPU memory consumption.
         if is_deepspeed_zero3_enabled():
             from transformers.integrations.deepspeed import _load_state_dict_into_zero3_model
             _load_state_dict_into_zero3_model(model, state_dict)
@@ -65,8 +62,7 @@ def load_model(model_class, path, config=None, torch_dtype=torch.bfloat16, devic
     return model
 
 
-def load_model_with_disk_offload(model_class, path, config=None, torch_dtype=torch.bfloat16, device="cpu",
-                                 state_dict_converter=None, module_map=None):
+def load_model_with_disk_offload(model_class, path, config=None, torch_dtype=torch.bfloat16, device="cpu", state_dict_converter=None, module_map=None):
     if isinstance(path, str):
         path = [path]
     config = {} if config is None else config