diff --git a/comfy/ldm/cosmos/model.py b/comfy/ldm/cosmos/model.py
index 1205838b..06d0baef 100644
--- a/comfy/ldm/cosmos/model.py
+++ b/comfy/ldm/cosmos/model.py
@@ -293,7 +293,7 @@ class GeneralDIT(nn.Module):
         x_B_T_H_W_D = self.x_embedder(x_B_C_T_H_W)
 
         if self.extra_per_block_abs_pos_emb:
-            extra_pos_emb = self.extra_pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device)
+            extra_pos_emb = self.extra_pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device, dtype=x_B_C_T_H_W.dtype)
         else:
             extra_pos_emb = None
 
diff --git a/comfy/ldm/cosmos/position_embedding.py b/comfy/ldm/cosmos/position_embedding.py
index cf45ab0e..4d6a58db 100644
--- a/comfy/ldm/cosmos/position_embedding.py
+++ b/comfy/ldm/cosmos/position_embedding.py
@@ -41,12 +41,12 @@ def normalize(x: torch.Tensor, dim: Optional[List[int]] = None, eps: float = 0)
 
 
 class VideoPositionEmb(nn.Module):
-    def forward(self, x_B_T_H_W_C: torch.Tensor, fps=Optional[torch.Tensor], device=None) -> torch.Tensor:
+    def forward(self, x_B_T_H_W_C: torch.Tensor, fps=Optional[torch.Tensor], device=None, dtype=None) -> torch.Tensor:
         """
         It delegates the embedding generation to generate_embeddings function.
         """
         B_T_H_W_C = x_B_T_H_W_C.shape
-        embeddings = self.generate_embeddings(B_T_H_W_C, fps=fps, device=device)
+        embeddings = self.generate_embeddings(B_T_H_W_C, fps=fps, device=device, dtype=dtype)
 
         return embeddings
 
@@ -104,6 +104,7 @@ class VideoRopePosition3DEmb(VideoPositionEmb):
         w_ntk_factor: Optional[float] = None,
         t_ntk_factor: Optional[float] = None,
         device=None,
+        dtype=None,
     ):
         """
         Generate embeddings for the given input size.
@@ -189,13 +190,12 @@ class LearnablePosEmbAxis(VideoPositionEmb):
         self.pos_emb_w = nn.Parameter(torch.empty(len_w, model_channels, device=device, dtype=dtype))
         self.pos_emb_t = nn.Parameter(torch.empty(len_t, model_channels, device=device, dtype=dtype))
 
-
-    def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torch.Tensor], device=None) -> torch.Tensor:
+    def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torch.Tensor], device=None, dtype=None) -> torch.Tensor:
         B, T, H, W, _ = B_T_H_W_C
         if self.interpolation == "crop":
-            emb_h_H = self.pos_emb_h[:H].to(device=device)
-            emb_w_W = self.pos_emb_w[:W].to(device=device)
-            emb_t_T = self.pos_emb_t[:T].to(device=device)
+            emb_h_H = self.pos_emb_h[:H].to(device=device, dtype=dtype)
+            emb_w_W = self.pos_emb_w[:W].to(device=device, dtype=dtype)
+            emb_t_T = self.pos_emb_t[:T].to(device=device, dtype=dtype)
             emb = (
                 repeat(emb_t_T, "t d-> b t h w d", b=B, h=H, w=W)
                 + repeat(emb_h_H, "h d-> b t h w d", b=B, t=T, w=W)