diff --git a/comfy/ldm/wan/model.py b/comfy/ldm/wan/model.py
index 9b5e5332..d64e73a8 100644
--- a/comfy/ldm/wan/model.py
+++ b/comfy/ldm/wan/model.py
@@ -83,7 +83,7 @@ class WanSelfAttention(nn.Module):
 
 class WanT2VCrossAttention(WanSelfAttention):
 
-    def forward(self, x, context):
+    def forward(self, x, context, **kwargs):
         r"""
         Args:
             x(Tensor): Shape [B, L1, C]
@@ -116,14 +116,14 @@ class WanI2VCrossAttention(WanSelfAttention):
         # self.alpha = nn.Parameter(torch.zeros((1, )))
         self.norm_k_img = RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
 
-    def forward(self, x, context):
+    def forward(self, x, context, context_img_len):
         r"""
         Args:
             x(Tensor): Shape [B, L1, C]
             context(Tensor): Shape [B, L2, C]
         """
-        context_img = context[:, :257]
-        context = context[:, 257:]
+        context_img = context[:, :context_img_len]
+        context = context[:, context_img_len:]
 
         # compute query, key, value
         q = self.norm_q(self.q(x))
@@ -193,6 +193,7 @@ class WanAttentionBlock(nn.Module):
         e,
         freqs,
         context,
+        context_img_len=None,
     ):
         r"""
         Args:
@@ -213,7 +214,7 @@ class WanAttentionBlock(nn.Module):
         x = x + y * e[2]
 
         # cross-attention & ffn
-        x = x + self.cross_attn(self.norm3(x), context)
+        x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len)
         y = self.ffn(self.norm2(x) * (1 + e[4]) + e[3])
         x = x + y * e[5]
         return x
@@ -420,9 +421,12 @@ class WanModel(torch.nn.Module):
         # context
         context = self.text_embedding(context)
 
-        if clip_fea is not None and self.img_emb is not None:
-            context_clip = self.img_emb(clip_fea)  # bs x 257 x dim
-            context = torch.concat([context_clip, context], dim=1)
+        context_img_len = None
+        if clip_fea is not None:
+            if self.img_emb is not None:
+                context_clip = self.img_emb(clip_fea)  # bs x 257 x dim
+                context = torch.concat([context_clip, context], dim=1)
+            context_img_len = clip_fea.shape[-2]
 
         patches_replace = transformer_options.get("patches_replace", {})
         blocks_replace = patches_replace.get("dit", {})
@@ -430,12 +434,12 @@ class WanModel(torch.nn.Module):
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"])
+                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len)
                     return out
                 out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap})
                 x = out["img"]
             else:
-                x = block(x, e=e0, freqs=freqs, context=context)
+                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
 
         # head
         x = self.head(x, e)