diff --git a/cuda_device.py b/cuda_device.py
new file mode 100644
index 0000000..584ad32
--- /dev/null
+++ b/cuda_device.py
@@ -0,0 +1,3 @@
+import torch
+
+CUDA_DEVICE = 'gpu' if torch.cuda.is_available() else 'cpu'
diff --git a/demo.py b/demo.py
index 505382a..d1e2bee 100644
--- a/demo.py
+++ b/demo.py
@@ -2,6 +2,7 @@
 from networks.transforms import trimap_transform, groupnorm_normalise_image
 from networks.models import build_model
 from dataloader import PredDataset
+from cuda_device import CUDA_DEVICE
 
 # System libs
 import os
@@ -14,7 +15,10 @@
 
 
 def np_to_torch(x):
-    return torch.from_numpy(x).permute(2, 0, 1)[None, :, :, :].float().cuda()
+    val = torch.from_numpy(x).permute(2, 0, 1)[None, :, :, :].float()
+    if CUDA_DEVICE == 'gpu':
+        val = val.cuda()
+    return val
 
 
 def scale_input(x: np.ndarray, scale: float, scale_type) -> np.ndarray:
diff --git a/networks/models.py b/networks/models.py
index a0ea431..821f339 100644
--- a/networks/models.py
+++ b/networks/models.py
@@ -3,6 +3,7 @@
 import networks.resnet_GN_WS as resnet_GN_WS
 import networks.layers_WS as L
 import networks.resnet_bn as resnet_bn
+from cuda_device import CUDA_DEVICE
 
 
 def build_model(args):
@@ -17,10 +18,11 @@ def build_model(args):
 
     model = MattingModule(net_encoder, net_decoder)
 
-    model.cuda()
+    if CUDA_DEVICE == 'gpu':
+        model.cuda()
 
     if(args.weights != 'default'):
-        sd = torch.load(args.weights)
+        sd = torch.load(args.weights, map_location=torch.device(CUDA_DEVICE))
         model.load_state_dict(sd, strict=True)
 
     return model
diff --git a/networks/transforms.py b/networks/transforms.py
index 860c2b8..281e0aa 100644
--- a/networks/transforms.py
+++ b/networks/transforms.py
@@ -2,6 +2,7 @@
 import numpy as np
 import torch
 import cv2
+from cuda_device import CUDA_DEVICE
 
 
 def dt(a):
@@ -50,7 +51,9 @@ def groupnorm_denormalise_image(img, format='nhwc'):
         for i in range(3):
             img[:, :, :, i] = img[:, :, :, i] * group_norm_std[i] + group_norm_mean[i]
     else:
-        img1 = torch.zeros_like(img).cuda()
+        img1 = torch.zeros_like(img)
+        if CUDA_DEVICE == 'gpu':
+            img1 = img1.cuda()
         for i in range(3):
             img1[:, i, :, :] = img[:, i, :, :] * group_norm_std[i] + group_norm_mean[i]
         return img1