diff --git a/modules/models.py b/modules/models.py
index 20a65764..cf4e4634 100644
--- a/modules/models.py
+++ b/modules/models.py
@@ -179,7 +179,7 @@ def huggingface_loader(model_name):
 
     # DeepSpeed ZeRO-3
     elif shared.args.deepspeed:
-        model = LoaderClass.from_pretrained(path_to_model, torch_dtype=params['torch_dtype'], trust_remote_code=params['trust_remote_code'])
+        model = LoaderClass.from_pretrained(path_to_model, torch_dtype=params['torch_dtype'], trust_remote_code=params.get('trust_remote_code'))
         model = deepspeed.initialize(model=model, config_params=ds_config, model_parameters=None, optimizer=None, lr_scheduler=None)[0]
         model.module.eval()  # Inference
         logger.info(f'DeepSpeed ZeRO-3 is enabled: {is_deepspeed_zero3_enabled()}')
@@ -194,7 +194,7 @@ def huggingface_loader(model_name):
             params['torch_dtype'] = torch.float32
         else:
             params['device_map'] = 'auto'
-            if x := get_max_memory_dict():
+            if x:= get_max_memory_dict():
                 params['max_memory'] = x
 
             if shared.args.load_in_4bit:
@@ -215,15 +215,15 @@ def huggingface_loader(model_name):
                 else:
                     params['quantization_config'] = BitsAndBytesConfig(load_in_8bit=True)
 
-                if params['max_memory'] is not None:
+                if params.get('max_memory') is not None:
                     with init_empty_weights():
-                        model = LoaderClass.from_config(config, trust_remote_code=params['trust_remote_code'])
+                        model = LoaderClass.from_config(config, trust_remote_code=params.get('trust_remote_code'))
 
                     model.tie_weights()
                     params['device_map'] = infer_auto_device_map(
                         model,
                         dtype=torch.int8,
-                        max_memory=params['max_memory'],
+                        max_memory=params.get('max_memory'),
                         no_split_module_classes=model._no_split_modules
                     )