diff --git a/nodes.py b/nodes.py
index 112f541..52b40a7 100644
--- a/nodes.py
+++ b/nodes.py
@@ -666,8 +666,6 @@ class WanVideoModelLoader:
                     if "patch_embedding" in name:
                         dtype_to_use = torch.float32
                     set_module_tensor_to_device(transformer, name, device=transformer_load_device, dtype=dtype_to_use, value=sd[name])
-            for name, param in transformer.named_parameters():
-                print(name, param.device, param.dtype)
             comfy_model.diffusion_model = transformer
             comfy_model.load_device = transformer_load_device
             
@@ -1758,9 +1756,15 @@ class WanVideoPhantomEmbeds:
     def INPUT_TYPES(s):
         return {"required": {
             "num_frames": ("INT", {"default": 81, "min": 1, "max": 10000, "step": 4, "tooltip": "Number of frames to encode"}),
-            "phantom_latents": ("LATENT", {"tooltip": "reference latents for the phantom model"}),
+            "phantom_latent_1": ("LATENT", {"tooltip": "reference latents for the phantom model"}),
+            
             "phantom_cfg_scale": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 10.0, "step": 0.01, "tooltip": "CFG scale for the extra phantom cond pass"}),
             },
+            "optional": {
+                "phantom_latent_2": ("LATENT", {"tooltip": "reference latents for the phantom model"}),
+                "phantom_latent_3": ("LATENT", {"tooltip": "reference latents for the phantom model"}),
+                "phantom_latent_4": ("LATENT", {"tooltip": "reference latents for the phantom model"}),
+            }
         }
 
     RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", )
@@ -1768,9 +1772,15 @@ class WanVideoPhantomEmbeds:
     FUNCTION = "process"
     CATEGORY = "WanVideoWrapper"
 
-    def process(self, num_frames, phantom_latents, phantom_cfg_scale):
+    def process(self, num_frames, phantom_cfg_scale, phantom_latent_1, phantom_latent_2=None, phantom_latent_3=None, phantom_latent_4=None):
         vae_stride = (4, 8, 8)
-        samples = phantom_latents["samples"].squeeze(0)
+        samples = phantom_latent_1["samples"].squeeze(0)
+        if phantom_latent_2 is not None:
+            samples = torch.cat([samples, phantom_latent_2["samples"].squeeze(0)], dim=1)
+        if phantom_latent_3 is not None:
+            samples = torch.cat([samples, phantom_latent_3["samples"].squeeze(0)], dim=1)
+        if phantom_latent_4 is not None:
+            samples = torch.cat([samples, phantom_latent_4["samples"].squeeze(0)], dim=1)
         C, T, H, W = samples.shape
 
         target_shape = (16, (num_frames - 1) // vae_stride[0] + 1 + T,
@@ -2637,6 +2647,7 @@ class WanVideoSampler:
 
         self.teacache_state = [None, None]
         if phantom_latents is not None:
+            log.info(f"Phantom latents shape: {phantom_latents.shape}")
             self.teacache_state = [None, None, None]
         self.teacache_state_source = [None, None]
         self.teacache_states_context = []