Update nodes.py

nodes.py

@@ -229,7 +229,7 @@ class WanVideoTextEncode:
         if t5 is None:
             raise ValueError("No cached text embeds found for prompts, please provide a T5 encoder.")
 
-        if model_to_offload is not None:
+        if model_to_offload is not None and device == "gpu":
             log.info(f"Moving video model to {offload_device}")
             model_to_offload.model.to(offload_device)
 
@@ -344,6 +344,7 @@ class WanVideoTextEncodeSingle:
                 "force_offload": ("BOOLEAN", {"default": True}),
                 "model_to_offload": ("WANVIDEOMODEL", {"tooltip": "Model to move to offload_device before encoding"}),
                 "use_disk_cache": ("BOOLEAN", {"default": False, "tooltip": "Cache the text embeddings to disk for faster re-use, under the custom_nodes/ComfyUI-WanVideoWrapper/text_embed_cache directory"}),
+                "device": (["gpu", "cpu"], {"default": "gpu", "tooltip": "Device to run the text encoding on."}),
             }
         }
 
@@ -353,7 +354,7 @@ class WanVideoTextEncodeSingle:
     CATEGORY = "WanVideoWrapper"
     DESCRIPTION = "Encodes text prompt into text embedding."
 
-    def process(self, prompt, t5=None, force_offload=True, model_to_offload=None, use_disk_cache=False):
+    def process(self, prompt, t5=None, force_offload=True, model_to_offload=None, use_disk_cache=False, device="gpu"):
         # Unified cache logic: use a single cache file per unique prompt
         encoded = None
         if use_disk_cache:
@@ -375,7 +376,7 @@ class WanVideoTextEncodeSingle:
             raise ValueError("No cached text embeds found for prompts, please provide a T5 encoder.")
 
         if encoded is None:
-            if model_to_offload is not None:
+            if model_to_offload is not None and device == "gpu":
                 log.info(f"Moving video model to {offload_device}")
                 model_to_offload.model.to(offload_device)
                 mm.soft_empty_cache()
@@ -383,6 +384,12 @@ class WanVideoTextEncodeSingle:
             encoder = t5["model"]
             dtype = t5["dtype"]
 
+            if device == "gpu":
+                device = mm.get_torch_device()
+                encoder.model.to(device)
+            elif device == "cpu":
+                encoder.model.to(torch.device("cpu"))
+
             encoder.model.to(device)
            
             with torch.autocast(device_type=mm.get_autocast_device(device), dtype=dtype, enabled=True):