diff --git a/diffsynth/distributed/xdit_context_parallel.py b/diffsynth/distributed/xdit_context_parallel.py
index dc4cc62..1c44ffc 100644
--- a/diffsynth/distributed/xdit_context_parallel.py
+++ b/diffsynth/distributed/xdit_context_parallel.py
@@ -26,12 +26,15 @@ def pad_freqs(original_tensor, target_len):
     
 def rope_apply(x, freqs, num_heads):
     x = rearrange(x, "b s (n d) -> b s n d", n=num_heads)
+    s_per_rank = x.shape[1]
 
     x_out = torch.view_as_complex(x.to(torch.float64).reshape(
         x.shape[0], x.shape[1], x.shape[2], -1, 2))
 
+    sp_size = get_sequence_parallel_world_size()
     sp_rank = get_sequence_parallel_rank()
-    freqs_rank = torch.chunk(freqs, dim=0)[sp_rank] # chunk freqs like x
+    freqs = pad_freqs(freqs, s_per_rank * sp_size)
+    freqs_rank = freqs[(sp_rank * s_per_rank):((sp_rank + 1) * s_per_rank), :, :]
 
     x_out = torch.view_as_real(x_out * freqs_rank).flatten(2)
     return x_out.to(x.dtype)
diff --git a/diffsynth/pipelines/wan_video.py b/diffsynth/pipelines/wan_video.py
index b9e020a..1cd716b 100644
--- a/diffsynth/pipelines/wan_video.py
+++ b/diffsynth/pipelines/wan_video.py
@@ -595,10 +595,9 @@ def model_fn_wan_video(
     if use_unified_sequence_parallel:
         if dist.is_initialized() and dist.get_world_size() > 1:
             chunks = torch.chunk(x, get_sequence_parallel_world_size(), dim=1)
-            seq_lens = [chunk.shape[1] for chunk in chunks]
-            x = torch.chunk(
-                x, get_sequence_parallel_world_size(),
-                dim=1)[get_sequence_parallel_rank()]
+            pad_shape = chunks[0].shape[1] - chunks[-1].shape[1]
+            chunks = [torch.nn.functional.pad(chunk, (0, 0, 0, chunks[0].shape[1]-chunk.shape[1]), value=0) for chunk in chunks]
+            x = chunks[get_sequence_parallel_rank()]
             
     if tea_cache_update:
         x = tea_cache.update(x)
@@ -606,25 +605,21 @@ def model_fn_wan_video(
         for block_id, block in enumerate(dit.blocks):
             x = block(x, context, t_mod, freqs)
             if vace_context is not None and block_id in vace.vace_layers_mapping:
-                x = x + vace_hints[vace.vace_layers_mapping[block_id]] * vace_scale
+                current_vace_hint = vace_hints[vace.vace_layers_mapping[block_id]]
+                if use_unified_sequence_parallel and dist.is_initialized() and dist.get_world_size() > 1:
+                    current_vace_hint = torch.chunk(current_vace_hint, get_sequence_parallel_world_size(), dim=1)[get_sequence_parallel_rank()]
+                x = x + current_vace_hint * vace_scale
         if tea_cache is not None:
             tea_cache.store(x)
             
-    if reference_latents is not None:
-        x = x[:, reference_latents.shape[1]:]
-        f -= 1
-
     x = dit.head(x, t)
     if use_unified_sequence_parallel:
         if dist.is_initialized() and dist.get_world_size() > 1:
-            max_len = seq_lens[0]
-            b, s, c = x.shape
-            if s != max_len:
-                padding_tensor = torch.ones(b, max_len - s, c, dtype=x.dtype, device=x.device)
-                x = torch.cat([x, padding_tensor], dim=1)
-            
             x = get_sp_group().all_gather(x, dim=1)
-            # remove pad
-            x = torch.cat([x[:,max_len*id:seq_lens[id],:] for id in range(seq_lens)])
+            x = x[:, :-pad_shape] if pad_shape > 0 else x
+    # Remove reference latents
+    if reference_latents is not None:
+        x = x[:, reference_latents.shape[1]:]
+        f -= 1
     x = dit.unpatchify(x, (f, h, w))
     return x
diff --git a/diffsynth/pipelines/wan_video_new.py b/diffsynth/pipelines/wan_video_new.py
index e429456..167acbf 100644
--- a/diffsynth/pipelines/wan_video_new.py
+++ b/diffsynth/pipelines/wan_video_new.py
@@ -1074,7 +1074,10 @@ def model_fn_wan_video(
     # blocks
     if use_unified_sequence_parallel:
         if dist.is_initialized() and dist.get_world_size() > 1:
-            x = torch.chunk(x, get_sequence_parallel_world_size(), dim=1)[get_sequence_parallel_rank()]
+            chunks = torch.chunk(x, get_sequence_parallel_world_size(), dim=1)
+            pad_shape = chunks[0].shape[1] - chunks[-1].shape[1]
+            chunks = [torch.nn.functional.pad(chunk, (0, 0, 0, chunks[0].shape[1]-chunk.shape[1]), value=0) for chunk in chunks]
+            x = chunks[get_sequence_parallel_rank()]
     if tea_cache_update:
         x = tea_cache.update(x)
     else:
@@ -1111,6 +1114,7 @@ def model_fn_wan_video(
     if use_unified_sequence_parallel:
         if dist.is_initialized() and dist.get_world_size() > 1:
             x = get_sp_group().all_gather(x, dim=1)
+            x = x[:, :-pad_shape] if pad_shape > 0 else x
     # Remove reference latents
     if reference_latents is not None:
         x = x[:, reference_latents.shape[1]:]