diff --git a/comfy/controlnet.py b/comfy/controlnet.py
index 5921e6b1d19..6d37aa74f69 100644
--- a/comfy/controlnet.py
+++ b/comfy/controlnet.py
@@ -208,7 +208,7 @@ def __init__(self, in_features: int, out_features: int, bias: bool = True,
 
         def forward(self, input):
             if self.up is not None:
-                return torch.nn.functional.linear(input, self.weight.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias)
+                return torch.nn.functional.linear(input, self.weight.to(input.dtype).to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias)
             else:
                 return torch.nn.functional.linear(input, self.weight.to(input.device), self.bias)
 
@@ -247,7 +247,7 @@ def __init__(
 
         def forward(self, input):
             if self.up is not None:
-                return torch.nn.functional.conv2d(input, self.weight.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias, self.stride, self.padding, self.dilation, self.groups)
+                return torch.nn.functional.conv2d(input, self.weight.to(input.dtype).to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias, self.stride, self.padding, self.dilation, self.groups)
             else:
                 return torch.nn.functional.conv2d(input, self.weight.to(input.device), self.bias, self.stride, self.padding, self.dilation, self.groups)
 
diff --git a/comfy/model_management.py b/comfy/model_management.py
index 8e5b7df853f..9e06b40497c 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -444,6 +444,13 @@ def dtype_size(dtype):
     dtype_size = 4
     if dtype == torch.float16 or dtype == torch.bfloat16:
         dtype_size = 2
+    elif dtype == torch.float32:
+        dtype_size = 4
+    else:
+        try:
+            dtype_size = dtype.itemsize
+        except: #Old pytorch doesn't have .itemsize
+            pass
     return dtype_size
 
 def unet_offload_device():
diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index 58acb97fce7..4e9f6bffe01 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -84,12 +84,16 @@ def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_le
 
         self.inner_name = inner_name
         if dtype is not None:
-            self.transformer.to(dtype)
             inner_model = getattr(self.transformer, self.inner_name)
             if hasattr(inner_model, "embeddings"):
-                inner_model.embeddings.to(torch.float32)
+                embeddings_bak = inner_model.embeddings.to(torch.float32)
+                inner_model.embeddings = None
+                self.transformer.to(dtype)
+                inner_model.embeddings = embeddings_bak
             else:
-                self.transformer.set_input_embeddings(self.transformer.get_input_embeddings().to(torch.float32))
+                previous_inputs = self.transformer.get_input_embeddings().to(torch.float32, copy=True)
+                self.transformer.to(dtype)
+                self.transformer.set_input_embeddings(previous_inputs)
 
         self.max_length = max_length
         if freeze: