diff --git a/ldm/invoke/generator/base.py b/ldm/invoke/generator/base.py
index bac7bbb333..25cd281cfe 100644
--- a/ldm/invoke/generator/base.py
+++ b/ldm/invoke/generator/base.py
@@ -237,7 +237,8 @@ class Generator:
 
     def get_perlin_noise(self,width,height):
         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)
+        noise = 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)
+        return noise
 
     def new_seed(self):
         self.seed = random.randrange(0, np.iinfo(np.uint32).max)
diff --git a/ldm/invoke/generator/txt2img2img.py b/ldm/invoke/generator/txt2img2img.py
index e356f719c4..1dba0cfafb 100644
--- a/ldm/invoke/generator/txt2img2img.py
+++ b/ldm/invoke/generator/txt2img2img.py
@@ -90,9 +90,9 @@ class Txt2Img2Img(Generator):
     def get_noise_like(self, like: torch.Tensor):
         device = like.device
         if device.type == 'mps':
-            x = torch.randn_like(like, device='cpu').to(device)
+            x = torch.randn_like(like, device='cpu', dtype=self.torch_dtype()).to(device)
         else:
-            x = torch.randn_like(like, device=device)
+            x = torch.randn_like(like, device=device, dtype=self.torch_dtype())
         if self.perlin > 0.0:
             shape = like.shape
             x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(shape[3], shape[2])
@@ -117,10 +117,12 @@ class Txt2Img2Img(Generator):
                                 self.latent_channels,
                                 scaled_height // self.downsampling_factor,
                                 scaled_width  // self.downsampling_factor],
-                                device='cpu').to(device)
+                               dtype=self.torch_dtype(),
+                               device='cpu').to(device)
         else:
             return torch.randn([1,
                                 self.latent_channels,
                                 scaled_height // self.downsampling_factor,
                                 scaled_width  // self.downsampling_factor],
-                                device=device)
+                               dtype=self.torch_dtype(),
+                               device=device)
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index bc19ba1449..f74706aaef 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -349,7 +349,7 @@ class ModelManager(object):
 
         if self.precision == 'float16':
             print('   | Using faster float16 precision')
-            model.to(torch.float16)
+            model = model.to(torch.float16)
         else:
             print('   | Using more accurate float32 precision')
 
diff --git a/ldm/util.py b/ldm/util.py
index 282a56c3e5..7d44dcd266 100644
--- a/ldm/util.py
+++ b/ldm/util.py
@@ -8,6 +8,7 @@ from threading import Thread
 from urllib import request
 from tqdm import tqdm
 from pathlib import Path
+from ldm.invoke.devices import torch_dtype
 
 import numpy as np
 import torch
@@ -235,7 +236,8 @@ def rand_perlin_2d(shape, res, device, fade = lambda t: 6*t**5 - 15*t**4 + 10*t*
     n01 = dot(tile_grads([0, -1],[1, None]), [0, -1]).to(device)
     n11 = dot(tile_grads([1, None], [1, None]), [-1,-1]).to(device)
     t = fade(grid[:shape[0], :shape[1]])
-    return math.sqrt(2) * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1]).to(device)
+    noise = math.sqrt(2) * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1]).to(device)
+    return noise.to(dtype=torch_dtype(device))
 
 def ask_user(question: str, answers: list):
     from itertools import chain, repeat