diff --git a/comfy/ldm/wan/model.py b/comfy/ldm/wan/model.py
index 49880b606..546ebb225 100644
--- a/comfy/ldm/wan/model.py
+++ b/comfy/ldm/wan/model.py
@@ -4,9 +4,11 @@ import math
 
 import torch
 import torch.nn as nn
+from einops import repeat
 
 from comfy.ldm.modules.attention import optimized_attention
-
+from comfy.ldm.flux.layers import EmbedND
+from comfy.ldm.flux.math import apply_rope
 
 def sinusoidal_embedding_1d(dim, position):
     # preprocess
@@ -21,45 +23,6 @@ def sinusoidal_embedding_1d(dim, position):
     return x
 
 
-def rope_params(max_seq_len, dim, theta=10000):
-    assert dim % 2 == 0
-    freqs = torch.outer(
-        torch.arange(max_seq_len),
-        1.0 / torch.pow(theta,
-                        torch.arange(0, dim, 2).to(torch.float64).div(dim)))
-    freqs = torch.polar(torch.ones_like(freqs), freqs)
-    return freqs
-
-
-def rope_apply(x, grid_sizes, freqs):
-    n, c = x.size(2), x.size(3) // 2
-
-    # split freqs
-    freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)
-
-    # loop over samples
-    output = []
-    for i, (f, h, w) in enumerate(grid_sizes.tolist()):
-        seq_len = f * h * w
-
-        # precompute multipliers
-        x_i = torch.view_as_complex(x[i, :seq_len].to(torch.float64).reshape(
-            seq_len, n, -1, 2))
-        freqs_i = torch.cat([
-            freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
-            freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
-            freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
-        ], dim=-1).reshape(seq_len, 1, -1)
-
-        # apply rotary embedding
-        x_i = torch.view_as_real(x_i * freqs_i).flatten(2)
-        x_i = torch.cat([x_i, x[i, seq_len:]])
-
-        # append to collection
-        output.append(x_i)
-    return torch.stack(output).to(dtype=x.dtype)
-
-
 class WanRMSNorm(nn.Module):
 
     def __init__(self, dim, eps=1e-5, device=None, dtype=None):
@@ -122,10 +85,11 @@ class WanSelfAttention(nn.Module):
             return q, k, v
 
         q, k, v = qkv_fn(x)
+        q, k = apply_rope(q, k, freqs)
 
         x = optimized_attention(
-            q=rope_apply(q, grid_sizes, freqs).view(b, s, n * d),
-            k=rope_apply(k, grid_sizes, freqs).view(b, s, n * d),
+            q=q.view(b, s, n * d),
+            k=k.view(b, s, n * d),
             v=v,
             heads=self.num_heads,
         )
@@ -433,14 +397,8 @@ class WanModel(torch.nn.Module):
         # head
         self.head = Head(dim, out_dim, patch_size, eps, operation_settings=operation_settings)
 
-        # buffers (don't use register_buffer otherwise dtype will be changed in to())
-        assert (dim % num_heads) == 0 and (dim // num_heads) % 2 == 0
         d = dim // num_heads
-        self.register_buffer("freqs", torch.cat([
-            rope_params(1024, d - 4 * (d // 6)),
-            rope_params(1024, 2 * (d // 6)),
-            rope_params(1024, 2 * (d // 6))
-        ], dim=1), persistent=False)
+        self.rope_embedder = EmbedND(dim=d, theta=10000.0, axes_dim=[d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)])
 
         if model_type == 'i2v':
             self.img_emb = MLPProj(1280, dim, operation_settings=operation_settings)
@@ -453,6 +411,7 @@ class WanModel(torch.nn.Module):
         seq_len=None,
         clip_fea=None,
         y=None,
+        freqs=None,
     ):
         r"""
         Forward pass through the diffusion model
@@ -477,10 +436,6 @@ class WanModel(torch.nn.Module):
         """
         if self.model_type == 'i2v':
             assert clip_fea is not None and y is not None
-        # params
-        # device = self.patch_embedding.weight.device
-        # if self.freqs.device != device:
-        #     self.freqs = self.freqs.to(device)
 
         if y is not None:
             x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)]
@@ -523,7 +478,7 @@ class WanModel(torch.nn.Module):
             e=e0,
             seq_lens=seq_lens,
             grid_sizes=grid_sizes,
-            freqs=self.freqs,
+            freqs=freqs,
             context=context,
             context_lens=context_lens)
 
@@ -538,8 +493,20 @@ class WanModel(torch.nn.Module):
         return x
         # return [u.float() for u in x]
 
-    def forward(self, x, t, context, y=None, image=None, **kwargs):
-        return self.forward_orig([x], t, [context], clip_fea=y, y=image)[0]
+    def forward(self, x, timestep, context, y=None, image=None, **kwargs):
+        bs, c, t, h, w = x.shape
+        patch_size = self.patch_size
+        t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
+        h_len = ((h + (patch_size[1] // 2)) // patch_size[1])
+        w_len = ((w + (patch_size[2] // 2)) // patch_size[2])
+        img_ids = torch.zeros((t_len, h_len, w_len, 3), device=x.device, dtype=x.dtype)
+        img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1)
+        img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1)
+        img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).reshape(1, 1, -1)
+        img_ids = repeat(img_ids, "t h w c -> b (t h w) c", b=bs)
+
+        freqs = self.rope_embedder(img_ids).movedim(1, 2)
+        return self.forward_orig([x], timestep, [context], clip_fea=y, y=image, freqs=freqs)[0]
 
     def unpatchify(self, x, grid_sizes):
         r"""