diff --git a/latent_preview.py b/latent_preview.py
index fb94e54..8d51f8e 100644
--- a/latent_preview.py
+++ b/latent_preview.py
@@ -84,12 +84,12 @@ def get_previewer(device, latent_format):
             taew_sd = comfy.utils.load_torch_file(taehv_path)
             taesd = TAEHV(taew_sd).to(device)
             previewer = TAESDPreviewerImpl(taesd)
-            previewer = WrappedPreviewer(previewer)
+            previewer = WrappedPreviewer(previewer, rate=16)
 
         if previewer is None:
             if latent_format.latent_rgb_factors is not None:
                 previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors, latent_format.latent_rgb_factors_bias)
-                previewer = WrappedPreviewer(previewer)
+                previewer = WrappedPreviewer(previewer, rate=4)
     return previewer
 
 def prepare_callback(model, steps, x0_output_dict=None):
@@ -182,7 +182,10 @@ class WrappedPreviewer(LatentPreviewer):
             #NOTE: send sync already uses call_soon_threadsafe
             serv.send_sync(server.BinaryEventTypes.PREVIEW_IMAGE,
                            message.getvalue(), serv.client_id)
-            ind = (ind + 1) % ((leng-1) * 4 - 1)
+            if self.rate == 16:
+                ind = (ind + 1) % ((leng-1) * 4 - 1)
+            else:
+                ind = (ind + 1) % leng
     def decode_latent_to_preview(self, x0):
         if hasattr(self, 'taesd'):
             x0 = x0.unsqueeze(0)
diff --git a/nodes.py b/nodes.py
index 268ff46..e955e22 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1694,6 +1694,7 @@ class WanVideoSampler:
                     )
                     mask = masks[idx]
                     mask = mask.to(latent)
+                    print(mask.shape, image_latent.shape, latent.shape)
                     latent = image_latent * mask + latent * (1-mask)
                     # end diff diff
 
@@ -2105,33 +2106,21 @@ class WanVideoEncode:
         vae.to(offload_device)
         
         mm.soft_empty_cache()
-        print("encoded latents shape",latents.shape)
+        log.info(f"encoded latents shape {latents.shape}")
 
         if mask is not None: #B, H, W
-            B, H, W = mask.shape
-            target_frames = latents.shape[2]
             target_h, target_w = latents.shape[3:]
-            
-            # Temporal: pad/truncate
-            if B > target_frames:
-                mask = mask[:target_frames]
-            elif B < target_frames:
-                padding = torch.zeros((target_frames - B, H, W), device=mask.device)
-                mask = torch.cat([mask, padding], dim=0)
-            
-            # Spatial: resize each frame
+
             mask = torch.nn.functional.interpolate(
-                mask.unsqueeze(1),  # Add channel dim for interpolate
-                size=(target_h, target_w),
-                mode='bilinear'
-            ).squeeze(1)  # Remove channel dim
+                mask.unsqueeze(0).unsqueeze(0),  # Add batch and channel dims [1,1,T,H,W]
+                size=(latents.shape[2], target_h, target_w),
+                mode='trilinear',
+                align_corners=False
+            ).squeeze(0)  # Remove batch dim, keep channel dim
             
             # Add batch & channel dims for final output
-            mask = mask.unsqueeze(0).unsqueeze(0)
-            mask = mask.repeat(1, latents.shape[1], 1, 1, 1)
-            print("mask shape",mask.shape)
-
-
+            mask = mask.unsqueeze(0).repeat(1, latents.shape[1], 1, 1, 1)
+ 
         return ({"samples": latents, "mask": mask},)
 
 class WanVideoLatentPreview: