diff --git a/scripts/demo/streamlit_helpers.py b/scripts/demo/streamlit_helpers.py
index 2d6972e..b6814ec 100644
--- a/scripts/demo/streamlit_helpers.py
+++ b/scripts/demo/streamlit_helpers.py
@@ -191,11 +191,10 @@ def init_sampling(
         )
     )
 
-    params = get_discretization(params, key=key)
+    params = get_discretization(params=params, key=key)
+    params = get_guider(params=params, key=key)
+    params = get_sampler(params=params, key=key)
 
-    params = get_guider(key=key, params=params)
-
-    params = get_sampler(params, key=key)
     return params, num_rows, num_cols
 
 
diff --git a/sgm/inference/api.py b/sgm/inference/api.py
index ecdf706..082ca18 100644
--- a/sgm/inference/api.py
+++ b/sgm/inference/api.py
@@ -165,8 +165,8 @@ class SamplingPipeline:
         model_spec: Optional[SamplingSpec] = None,
         model_path: Optional[Union[str, pathlib.Path]] = None,
         config_path: Optional[Union[str, pathlib.Path]] = None,
-        device: Union[str, torch.Device] = "cuda",
-        swap_device: Optional[Union[str, torch.Device]] = None,
+        device: Union[str, torch.device] = "cuda",
+        swap_device: Optional[Union[str, torch.device]] = None,
         use_fp16: bool = True,
     ) -> None:
         """
diff --git a/sgm/inference/helpers.py b/sgm/inference/helpers.py
index aa9e8cd..68409a2 100644
--- a/sgm/inference/helpers.py
+++ b/sgm/inference/helpers.py
@@ -153,7 +153,7 @@ def do_sample(
         with autocast(device) as precision_scope:
             with model.ema_scope():
                 num_samples = [num_samples]
-                with SwapToDevice(model.conditioner, device):
+                with swap_to_device(model.conditioner, device):
                     batch, batch_uc = get_batch(
                         get_unique_embedder_keys_from_conditioner(model.conditioner),
                         value_dict,
@@ -190,11 +190,11 @@ def do_sample(
                         model.model, input, sigma, c, **additional_model_inputs
                     )
 
-                with SwapToDevice(model.denoiser, device):
-                    with SwapToDevice(model.model, device):
+                with swap_to_device(model.denoiser, device):
+                    with swap_to_device(model.model, device):
                         samples_z = sampler(denoiser, randn, cond=c, uc=uc)
 
-                with SwapToDevice(model.first_stage_model, device):
+                with swap_to_device(model.first_stage_model, device):
                     samples_x = model.decode_first_stage(samples_z)
                     samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
 
@@ -294,7 +294,7 @@ def do_img2img(
     with torch.no_grad():
         with autocast(device):
             with model.ema_scope():
-                with SwapToDevice(model.conditioner, device):
+                with swap_to_device(model.conditioner, device):
                     batch, batch_uc = get_batch(
                         get_unique_embedder_keys_from_conditioner(model.conditioner),
                         value_dict,
@@ -314,7 +314,7 @@ def do_img2img(
                 if skip_encode:
                     z = img
                 else:
-                    with SwapToDevice(model.first_stage_model, device):
+                    with swap_to_device(model.first_stage_model, device):
                         z = model.encode_first_stage(img)
 
                 noise = torch.randn_like(z)
@@ -337,11 +337,11 @@ def do_img2img(
                 def denoiser(x, sigma, c):
                     return model.denoiser(model.model, x, sigma, c)
 
-                with SwapToDevice(model.denoiser, device):
-                    with SwapToDevice(model.model, device):
+                with swap_to_device(model.denoiser, device):
+                    with swap_to_device(model.model, device):
                         samples_z = sampler(denoiser, noised_z, cond=c, uc=uc)
 
-                with SwapToDevice(model.first_stage_model, device):
+                with swap_to_device(model.first_stage_model, device):
                     samples_x = model.decode_first_stage(samples_z)
                     samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
 
@@ -354,7 +354,7 @@ def do_img2img(
 
 
 @contextlib.contextmanager
-def SwapToDevice(
+def swap_to_device(
     model: Union[torch.nn.Module, torch.Tensor], device: Union[torch.device, str]
 ):
     """