diff --git a/comfy/sd.py b/comfy/sd.py
index f9e5efab5..734bd2845 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -193,6 +193,7 @@ class CLIP:
                 self.cond_stage_model.set_clip_options({"projected_pooled": False})
 
             self.load_model()
+            self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device})
             all_hooks.reset()
             self.patcher.patch_hooks(None)
             if show_pbar:
@@ -240,6 +241,7 @@ class CLIP:
             self.cond_stage_model.set_clip_options({"projected_pooled": False})
 
         self.load_model()
+        self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device})
         o = self.cond_stage_model.encode_token_weights(tokens)
         cond, pooled = o[:2]
         if return_dict:
diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index 0fc9ab3db..503a51843 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -147,6 +147,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         self.layer_norm_hidden_state = layer_norm_hidden_state
         self.return_projected_pooled = return_projected_pooled
         self.return_attention_masks = return_attention_masks
+        self.execution_device = None
 
         if layer == "hidden":
             assert layer_idx is not None
@@ -163,6 +164,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
     def set_clip_options(self, options):
         layer_idx = options.get("layer", self.layer_idx)
         self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled)
+        self.execution_device = options.get("execution_device", self.execution_device)
         if isinstance(self.layer, list) or self.layer == "all":
             pass
         elif layer_idx is None or abs(layer_idx) > self.num_layers:
@@ -175,6 +177,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         self.layer = self.options_default[0]
         self.layer_idx = self.options_default[1]
         self.return_projected_pooled = self.options_default[2]
+        self.execution_device = None
 
     def process_tokens(self, tokens, device):
         end_token = self.special_tokens.get("end", None)
@@ -258,7 +261,11 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         return torch.cat(embeds_out), torch.tensor(attention_masks, device=device, dtype=torch.long), num_tokens, embeds_info
 
     def forward(self, tokens):
-        device = self.transformer.get_input_embeddings().weight.device
+        if self.execution_device is None:
+            device = self.transformer.get_input_embeddings().weight.device
+        else:
+            device = self.execution_device
+
         embeds, attention_mask, num_tokens, embeds_info = self.process_tokens(tokens, device)
 
         attention_mask_model = None