diff --git a/ldm/dream/generator/base.py b/ldm/dream/generator/base.py
index 08dcd3aa81..5f88c02c4e 100644
--- a/ldm/dream/generator/base.py
+++ b/ldm/dream/generator/base.py
@@ -124,8 +124,8 @@ class Generator():
         raise NotImplementedError("get_noise() must be implemented in a descendent class")
     
     def get_perlin_noise(self,width,height):
-        return torch.stack([rand_perlin_2d((height, width), (8, 8)).to(self.model.device) for _ in range(self.latent_channels)], dim=0)
-
+        fixdevice = 'cpu' if (self.model.device.type == 'mps') else self.model.device
+        return torch.stack([rand_perlin_2d((height, width), (8, 8), device = self.model.device).to(fixdevice) for _ in range(self.latent_channels)], dim=0).to(self.model.device)
     
     def new_seed(self):
         self.seed = random.randrange(0, np.iinfo(np.uint32).max)
diff --git a/ldm/util.py b/ldm/util.py
index 94ad492e22..298c3141d6 100644
--- a/ldm/util.py
+++ b/ldm/util.py
@@ -214,15 +214,19 @@ def parallel_data_prefetch(
     else:
         return gather_res
 
-def rand_perlin_2d(shape, res, fade = lambda t: 6*t**5 - 15*t**4 + 10*t**3):
+def rand_perlin_2d(shape, res, device, fade = lambda t: 6*t**5 - 15*t**4 + 10*t**3):
     delta = (res[0] / shape[0], res[1] / shape[1])
     d = (shape[0] // res[0], shape[1] // res[1])
 
-    grid = torch.stack(torch.meshgrid(torch.arange(0, res[0], delta[0]), torch.arange(0, res[1], delta[1]), indexing='ij'), dim = -1) % 1
-    angles = 2*math.pi*torch.rand(res[0]+1, res[1]+1)
+    grid = torch.stack(torch.meshgrid(torch.arange(0, res[0], delta[0]), torch.arange(0, res[1], delta[1]), indexing='ij'), dim = -1).to(device) % 1
+
+    rand_val = torch.rand(res[0]+1, res[1]+1)
+    
+    angles = 2*math.pi*rand_val
     gradients = torch.stack((torch.cos(angles), torch.sin(angles)), dim = -1)
 
     tile_grads = lambda slice1, slice2: gradients[slice1[0]:slice1[1], slice2[0]:slice2[1]].repeat_interleave(d[0], 0).repeat_interleave(d[1], 1)
+
     dot = lambda grad, shift: (torch.stack((grid[:shape[0],:shape[1],0] + shift[0], grid[:shape[0],:shape[1], 1] + shift[1]  ), dim = -1) * grad[:shape[0], :shape[1]]).sum(dim = -1)
 
     n00 = dot(tile_grads([0, -1], [0, -1]), [0,  0])