diff --git a/README.md b/README.md
index 1e3f969..869e4d1 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,10 @@ ComfyUI NPNet
 
 A very barebones copypaste implementation of https://github.com/xie-lab-ml/Golden-Noise-for-Diffusion-Models
 
-Note: *only* works with square noise, apparently. If you pass in other shaped latents, it will reshape the noise into a square before running the noise model, and then reshape the result back to the original resolution.
-
-
 Use with custom sampling and pass in an initial noise from eg. `RandomNoise` and a cond (only the first prompt in the conditioning will be used if multiple exist).
+
+Note: *only* works with 128x128 square noise, apparently. If you pass in other shaped latents, it will reshape the noise into a square before running the noise model, and then reshape the result back to the original resolution.
+
+You can also run it on the CPU, though appears to change the output for some reason.
+
+If you get an error from the timm module when running this, update your timm package. It may be too old.
diff --git a/__init__.py b/__init__.py
index d5b9645..85d1e9d 100644
--- a/__init__.py
+++ b/__init__.py
@@ -133,7 +133,7 @@ class NoiseTransformer(nn.Module):
 
 
 class NPNet(nn.Module):
-    def __init__(self, model_id, pretrained_path=True, device="cuda") -> None:
+    def __init__(self, model_id, pretrained_path, device="cuda") -> None:
         super().__init__()
 
         assert model_id in ["SDXL", "DreamShaper", "DiT"]
@@ -189,6 +189,7 @@ class NPNetGoldenNoise:
                 "prompt": ("CONDITIONING",),
                 "model_path": ("STRING", {"default": "/path/to/sdxl.pth"}),
                 "model_type": (["SDXL", "DreamShaper", "DiT"],),
+                "device": (["cuda", "cpu"],)
             }
         }
 
@@ -220,10 +221,11 @@ class NPNetGoldenNoise:
         print("NPNet ran ok")
         return r
 
-    def doit(self, noise, prompt, model_path, model_type):
+    def doit(self, noise, prompt, model_path, model_type, device):
         if self.npnet is None:
             print("Loading NPNet")
-            self.npnet = NPNet(model_type, model_path)
+            self.npnet = NPNet(model_type, model_path, device=device)
+        self.npnet.to(device)
         self.noise = noise
         self.cond = prompt[0]