diff --git a/modules/devices.py b/modules/devices.py
index bd6bd579b..ff279ac50 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -4,7 +4,7 @@ from functools import lru_cache
 
 import torch
 from modules import errors, shared
-from modules.torch_utils import get_param
+from modules import torch_utils
 
 if sys.platform == "darwin":
     from modules import mac_specific
@@ -132,7 +132,7 @@ patch_module_list = [
 
 
 def manual_cast_forward(self, *args, **kwargs):
-    org_dtype = get_param(self).dtype
+    org_dtype = torch_utils.get_param(self).dtype
     self.to(dtype)
     args = [arg.to(dtype) if isinstance(arg, torch.Tensor) else arg for arg in args]
     kwargs = {k: v.to(dtype) if isinstance(v, torch.Tensor) else v for k, v in kwargs.items()}
diff --git a/modules/interrogate.py b/modules/interrogate.py
index 5be5a10f3..35a627ca1 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -10,8 +10,7 @@ import torch.hub
 from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
 
-from modules import devices, paths, shared, lowvram, modelloader, errors
-from modules.torch_utils import get_param
+from modules import devices, paths, shared, lowvram, modelloader, errors, torch_utils
 
 blip_image_eval_size = 384
 clip_model_name = 'ViT-L/14'
@@ -132,7 +131,7 @@ class InterrogateModels:
 
         self.clip_model = self.clip_model.to(devices.device_interrogate)
 
-        self.dtype = get_param(self.clip_model).dtype
+        self.dtype = torch_utils.get_param(self.clip_model).dtype
 
     def send_clip_to_ram(self):
         if not shared.opts.interrogate_keep_models_in_memory:
diff --git a/modules/sd_models_xl.py b/modules/sd_models_xl.py
index c3602a7e1..0de17af3d 100644
--- a/modules/sd_models_xl.py
+++ b/modules/sd_models_xl.py
@@ -6,7 +6,7 @@ import sgm.models.diffusion
 import sgm.modules.diffusionmodules.denoiser_scaling
 import sgm.modules.diffusionmodules.discretizer
 from modules import devices, shared, prompt_parser
-from modules.torch_utils import get_param
+from modules import torch_utils
 
 
 def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch: prompt_parser.SdConditioning | list[str]):
@@ -91,7 +91,7 @@ sgm.modules.GeneralConditioner.get_target_prompt_token_count = get_target_prompt
 def extend_sdxl(model):
     """this adds a bunch of parameters to make SDXL model look a bit more like SD1.5 to the rest of the codebase."""
 
-    dtype = get_param(model.model.diffusion_model).dtype
+    dtype = torch_utils.get_param(model.model.diffusion_model).dtype
     model.model.diffusion_model.dtype = dtype
     model.model.conditioning_key = 'crossattn'
     model.cond_stage_key = 'txt'
diff --git a/modules/upscaler_utils.py b/modules/upscaler_utils.py
index c60e3beb8..f5cb92d51 100644
--- a/modules/upscaler_utils.py
+++ b/modules/upscaler_utils.py
@@ -6,8 +6,7 @@ import torch
 import tqdm
 from PIL import Image
 
-from modules import images, shared
-from modules.torch_utils import get_param
+from modules import images, shared, torch_utils
 
 logger = logging.getLogger(__name__)
 
@@ -18,7 +17,7 @@ def upscale_without_tiling(model, img: Image.Image):
     img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
     img = torch.from_numpy(img).float()
 
-    param = get_param(model)
+    param = torch_utils.get_param(model)
     img = img.unsqueeze(0).to(device=param.device, dtype=param.dtype)
 
     with torch.no_grad():
diff --git a/modules/xlmr.py b/modules/xlmr.py
index 6e000a56e..319771b7b 100644
--- a/modules/xlmr.py
+++ b/modules/xlmr.py
@@ -5,7 +5,7 @@ from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRoberta
 from transformers import XLMRobertaModel,XLMRobertaTokenizer
 from typing import Optional
 
-from modules.torch_utils import get_param
+from modules import torch_utils
 
 
 class BertSeriesConfig(BertConfig):
@@ -65,7 +65,7 @@ class BertSeriesModelWithTransformation(BertPreTrainedModel):
         self.post_init()
 
     def encode(self,c):
-        device = get_param(self).device
+        device = torch_utils.get_param(self).device
         text = self.tokenizer(c,
                         truncation=True,
                         max_length=77,
diff --git a/modules/xlmr_m18.py b/modules/xlmr_m18.py
index e3e819610..f60555049 100644
--- a/modules/xlmr_m18.py
+++ b/modules/xlmr_m18.py
@@ -4,8 +4,7 @@ import torch
 from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig
 from transformers import XLMRobertaModel,XLMRobertaTokenizer
 from typing import Optional
-
-from modules.torch_utils import get_param
+from modules import torch_utils
 
 
 class BertSeriesConfig(BertConfig):
@@ -71,7 +70,7 @@ class BertSeriesModelWithTransformation(BertPreTrainedModel):
         self.post_init()
 
     def encode(self,c):
-        device = get_param(self).device
+        device = torch_utils.get_param(self).device
         text = self.tokenizer(c,
                         truncation=True,
                         max_length=77,
diff --git a/test/test_torch_utils.py b/test/test_torch_utils.py
index f1aec8329..23ccb93a4 100644
--- a/test/test_torch_utils.py
+++ b/test/test_torch_utils.py
@@ -3,7 +3,7 @@ import types
 import pytest
 import torch
 
-from modules.torch_utils import get_param
+from modules import torch_utils
 
 
 @pytest.mark.parametrize("wrapped", [True, False])
@@ -14,6 +14,6 @@ def test_get_param(wrapped):
     if wrapped:
         # more or less how spandrel wraps a thing
         mod = types.SimpleNamespace(model=mod)
-    p = get_param(mod)
+    p = torch_utils.get_param(mod)
     assert p.dtype == torch.float16
     assert p.device == cpu