diff --git a/nodes.py b/nodes.py
index cf66229..651835e 100644
--- a/nodes.py
+++ b/nodes.py
@@ -617,7 +617,23 @@ class WanVideoModelLoader:
             transformer = WanModel(**TRANSFORMER_CONFIG)
         transformer.eval()
 
-        if "blocks.0.cam_encoder.weight" in sd:
+        if "blocks.0.mvs_attn.q.weight" in sd: #syncammaster
+            log.info("SynCamMaster model detected, patching model...")
+            from .wanvideo.modules.model import WanSelfAttention, WanLayerNorm
+            import torch.nn as nn
+            for block in transformer.blocks:
+                block.cam_encoder = nn.Linear(12, dim)
+                block.projector = nn.Linear(dim, dim)
+                block.cam_encoder.weight.data.zero_()
+                block.cam_encoder.bias.data.zero_()
+                block.projector.weight = nn.Parameter(torch.zeros(dim, dim))
+                block.projector.bias = nn.Parameter(torch.zeros(dim))
+                block.norm_mvs = WanLayerNorm(dim, eps=block.norm1.eps, elementwise_affine=False)
+                block.mvs_attn = WanSelfAttention(dim, num_heads, attention_mode=attention_mode)
+                block.modulation_mvs = nn.Parameter(torch.randn(1, 3, dim, device=block.modulation.device, dtype=block.modulation.dtype) / dim**0.5)
+                block.modulation_mvs.data = block.modulation.data[:, :3, :].clone()
+                #block.mvs_attn.load_state_dict(block.self_attn.state_dict(), strict=True)
+        elif "blocks.0.cam_encoder.weight" in sd: #recammaster
             log.info("ReCamMaster model detected, patching model...")
             import torch.nn as nn
             for block in transformer.blocks:
@@ -627,6 +643,7 @@ class WanVideoModelLoader:
                 block.cam_encoder.bias.data.zero_()
                 block.projector.weight = nn.Parameter(torch.eye(dim))
                 block.projector.bias = nn.Parameter(torch.zeros(dim))
+        
 
         comfy_model = WanVideoModel(
             WanVideoModelConfig(base_dtype),
@@ -2254,6 +2271,7 @@ class WanVideoSampler:
                 raise ValueError("Clip encoded image embeds must be provided for I2V (Image to Video) model")
             fun_model = image_embeds.get("fun_model", False)
             noise = torch.randn(
+                1,
                 16,
                 (image_embeds["num_frames"] - 1) // 4 + (2 if end_image is not None and not fun_model else 1),
                 lat_h,
@@ -2310,7 +2328,10 @@ class WanVideoSampler:
                             "seq_len": vace_additional_embeds[i]["vace_seq_len"]
                         })
 
+            syncammaster = image_embeds.get("syncammaster", None)
+
             noise = torch.randn(
+                    1 if not syncammaster else 2,
                     target_shape[0],
                     target_shape[1] + 1 if has_ref else target_shape[1],
                     target_shape[2],
@@ -2319,16 +2340,21 @@ class WanVideoSampler:
                     device=torch.device("cpu"),
                     generator=seed_g)
             
-            seq_len = math.ceil((noise.shape[2] * noise.shape[3]) / 4 * noise.shape[1])
+            seq_len = math.ceil((noise.shape[3] * noise.shape[4]) / 4 * noise.shape[2])
 
             recammaster = image_embeds.get("recammaster", None)
             if recammaster is not None:
                 camera_embed = recammaster.get("camera_embed", None)
                 recam_latents = recammaster.get("source_latents", None)
-                orig_noise_len = noise.shape[1]
+                orig_noise_len = noise.shape[2]
                 log.info(f"RecamMaster camera embed shape: {camera_embed.shape}")
                 log.info(f"RecamMaster source video shape: {recam_latents.shape}")
                 seq_len *= 2
+            syncammaster = image_embeds.get("syncammaster", None)
+            if syncammaster is not None:
+
+                camera_embed = syncammaster.get("camera_embed", None)
+                log.info(f"SyncamMaster camera embed shape: {camera_embed.shape}")
             
             control_embeds = image_embeds.get("control_embeds", None)
             if control_embeds is not None:
@@ -2350,12 +2376,12 @@ class WanVideoSampler:
             else:
                 if transformer.in_dim == 36: #fun inp
                     mask_latents = torch.tile(
-                        torch.zeros_like(noise[:1]), [4, 1, 1, 1]
+                        torch.zeros_like(noise[:,:1]), [1, 4, 1, 1, 1]
                     )
                     masked_video_latents_input = torch.zeros_like(noise)
-                    image_cond = torch.cat([mask_latents, masked_video_latents_input], dim=0).to(device)
+                    image_cond = torch.cat([mask_latents, masked_video_latents_input], dim=1).to(device)
             
-        latent_video_length = noise.shape[1]
+        latent_video_length = noise.shape[2]
 
         is_looped = False
         if context_options is not None:
@@ -2394,7 +2420,7 @@ class WanVideoSampler:
             log.info(f"Section size: {section_size}")
             is_looped = context_schedule == "uniform_looped"
 
-            seq_len = math.ceil((noise.shape[2] * noise.shape[3]) / 4 * context_frames)
+            seq_len = math.ceil((noise.shape[3] * noise.shape[4]) / 4 * context_frames)
 
             if context_options["freenoise"]:
                 log.info("Applying FreeNoise")
@@ -2410,11 +2436,11 @@ class WanVideoSampler:
                         final_delta = latent_video_length - place_idx
                         list_idx = torch.tensor(list(range(start_idx,start_idx+final_delta)), device=torch.device("cpu"), dtype=torch.long)
                         list_idx = list_idx[torch.randperm(final_delta, generator=seed_g)]
-                        noise[:, place_idx:place_idx + final_delta, :, :] = noise[:, list_idx, :, :]
+                        noise[:, :, place_idx:place_idx + final_delta, :, :] = noise[:, :, list_idx, :, :]
                         break
                     list_idx = torch.tensor(list(range(start_idx,start_idx+delta)), device=torch.device("cpu"), dtype=torch.long)
                     list_idx = list_idx[torch.randperm(delta, generator=seed_g)]
-                    noise[:, place_idx:place_idx + delta, :, :] = noise[:, list_idx, :, :]
+                    noise[:, :, place_idx:place_idx + delta, :, :] = noise[:, :, list_idx, :, :]
             
             log.info(f"Context schedule enabled: {context_frames} frames, {context_stride} stride, {context_overlap} overlap")
             from .context import get_context_scheduler
@@ -2423,7 +2449,7 @@ class WanVideoSampler:
         if samples is not None:
             input_samples = samples["samples"].squeeze(0).to(noise)
             if input_samples.shape[1] != noise.shape[1]:
-                input_samples = torch.cat([input_samples[:, :1].repeat(1, noise.shape[1] - input_samples.shape[1], 1, 1), input_samples], dim=1)
+                input_samples = torch.cat([input_samples[:, :, :1].repeat(1, 1, noise.shape[2] - input_samples.shape[1], 1, 1), input_samples], dim=1)
             original_image = input_samples.to(device)
             if denoise_strength < 1.0:
                 latent_timestep = timesteps[:1].to(noise)
@@ -2431,8 +2457,8 @@ class WanVideoSampler:
 
             mask = samples.get("mask", None)
             if mask is not None:
-                if mask.shape[2] != noise.shape[1]:
-                    mask = torch.cat([torch.zeros(1, noise.shape[0], noise.shape[1] - mask.shape[2], noise.shape[2], noise.shape[3]), mask], dim=2)            
+                if mask.shape[2] != noise.shape[2]:
+                    mask = torch.cat([torch.zeros(noise.shape[0], noise.shape[1], noise.shape[2] - mask.shape[2], noise.shape[3], noise.shape[4]), mask], dim=2)            
 
         latent = noise.to(device)
 
@@ -2629,29 +2655,33 @@ class WanVideoSampler:
                     'camera_embed': camera_embed,
                 }
 
-                batch_size = 1
-
                 if not math.isclose(cfg_scale, 1.0) and len(positive_embeds) > 1:
                     negative_embeds = negative_embeds * len(positive_embeds)
 
                 if not batched_cfg:
                     #cond
                     noise_pred_cond, teacache_state_cond = transformer(
-                        [z], context=positive_embeds, clip_fea=clip_fea, is_uncond=False, current_step_percentage=current_step_percentage,
+                        torch.unbind(z), context=positive_embeds * z.shape[0], 
+                        clip_fea=clip_fea, is_uncond=False, current_step_percentage=current_step_percentage,
                         pred_id=teacache_state[0] if teacache_state else None,
                         **base_params
                     )
-                    noise_pred_cond = noise_pred_cond[0].to(intermediate_device)
+                    
+                    #noise_pred_cond = noise_pred_cond[0].to(intermediate_device)
+                    noise_pred_cond = torch.stack(noise_pred_cond).to(intermediate_device)
+
                     if math.isclose(cfg_scale, 1.0):
                         return noise_pred_cond, [teacache_state_cond]
                     #uncond
                     noise_pred_uncond, teacache_state_uncond = transformer(
-                        [z], context=negative_embeds, clip_fea=clip_fea_neg if clip_fea_neg is not None else clip_fea, 
+                        torch.unbind(z), context=negative_embeds * z.shape[0], 
+                        clip_fea=clip_fea_neg if clip_fea_neg is not None else clip_fea, 
                         is_uncond=True, current_step_percentage=current_step_percentage,
                         pred_id=teacache_state[1] if teacache_state else None,
                         **base_params
                     )
-                    noise_pred_uncond = noise_pred_uncond[0].to(intermediate_device)
+                    #noise_pred_uncond = noise_pred_uncond[0].to(intermediate_device)
+                    noise_pred_uncond = torch.stack(noise_pred_uncond).to(intermediate_device)
                 #batched
                 else:
                     teacache_state_uncond = None
@@ -2664,6 +2694,7 @@ class WanVideoSampler:
 
                 #https://github.com/WeichenFan/CFG-Zero-star/
                 if use_cfg_zero_star:
+                    batch_size = 1
                     alpha = optimized_scale(
                         noise_pred_cond.view(batch_size, -1),
                         noise_pred_uncond.view(batch_size, -1)
@@ -2674,7 +2705,7 @@ class WanVideoSampler:
 
                 return noise_pred, [teacache_state_cond, teacache_state_uncond]
 
-        log.info(f"Sampling {(latent_video_length-1) * 4 + 1} frames at {latent.shape[3]*8}x{latent.shape[2]*8} with {steps} steps")
+        log.info(f"Sampling {(latent_video_length-1) * 4 + 1} frames at {latent.shape[4]*8}x{latent.shape[3]*8} with {steps} steps")
 
         intermediate_device = device
 
@@ -2975,19 +3006,19 @@ class WanVideoSampler:
                 if isinstance(sample_scheduler, DEISMultistepScheduler):
                     step_args.pop("generator", None)
                 temp_x0 = sample_scheduler.step(
-                    noise_pred[:, :orig_noise_len].unsqueeze(0) if recammaster is not None else noise_pred.unsqueeze(0),
+                    noise_pred[:, :orig_noise_len] if recammaster is not None else noise_pred,
                     t,
-                    latent[:, :orig_noise_len].unsqueeze(0) if recammaster is not None else latent.unsqueeze(0),
+                    latent[:, :orig_noise_len] if recammaster is not None else latent,
                     return_dict=False,
                     **step_args)[0]
-                latent = temp_x0.squeeze(0)
+                    
+                latent = temp_x0
 
-                x0 = latent.to(device)
                 if callback is not None:
                     if recammaster is not None:
                         callback_latent = (latent_model_input[:, :orig_noise_len] - noise_pred[:, :orig_noise_len].to(t.device) * t / 1000).detach().permute(1,0,2,3)
                     else:
-                        callback_latent = (latent_model_input - noise_pred.to(t.device) * t / 1000).detach().permute(1,0,2,3)
+                        callback_latent = (latent_model_input[0] - noise_pred[0].to(t.device) * t / 1000).detach().permute(1,0,2,3)
                     callback(idx, callback_latent, None, steps)
                 else:
                     pbar.update(1)
@@ -3029,7 +3060,7 @@ class WanVideoSampler:
             pass
 
         return ({
-            "samples": x0.unsqueeze(0).cpu(), "looped": is_looped, "end_image": end_image if not fun_model else None, "has_ref": has_ref, "drop_last": drop_last,
+            "samples": latent.cpu(), "looped": is_looped, "end_image": end_image if not fun_model else None, "has_ref": has_ref, "drop_last": drop_last,
             }, )
     
 class WindowTracker:
@@ -3102,6 +3133,7 @@ class WanVideoDecode:
         vae.to(device)
 
         latents = latents.to(device = device, dtype = vae.dtype)
+        print("latents.shape", latents.shape)
 
         mm.soft_empty_cache()
 
@@ -3118,7 +3150,14 @@ class WanVideoDecode:
         else:
             if end_image is not None:
                 enable_vae_tiling = False
-            images = vae.decode(latents, device=device, end_=(end_image is not None), tiled=enable_vae_tiling, tile_size=(tile_x//8, tile_y//8), tile_stride=(tile_stride_x//8, tile_stride_y//8))[0]
+            if latents.shape[0] > 1:
+                decoded = []
+                for i in range(latents.shape[0]):
+                    decoded.append(vae.decode(latents[i].unsqueeze(0), device=device, end_=(end_image is not None), tiled=enable_vae_tiling, tile_size=(tile_x//8, tile_y//8), tile_stride=(tile_stride_x//8, tile_stride_y//8))[0])
+                images = torch.cat(decoded, dim=2)
+                print("images.shape", images.shape)
+            else:
+                images = vae.decode(latents, device=device, end_=(end_image is not None), tiled=enable_vae_tiling, tile_size=(tile_x//8, tile_y//8), tile_stride=(tile_stride_x//8, tile_stride_y//8))[0]
         vae.model.clear_cache()
 
         images = (images - images.min()) / (images.max() - images.min())      
diff --git a/recammaster/nodes.py b/recammaster/nodes.py
index de4f203..f8081e9 100644
--- a/recammaster/nodes.py
+++ b/recammaster/nodes.py
@@ -43,7 +43,7 @@ class WanVideoReCamMasterCameraEmbed:
         import json
         from einops import rearrange
         
-        camera_data_path = os.path.join(script_directory, "camera_extrinsics.json")
+        camera_data_path = os.path.join(script_directory, "recam_extrinsics.json")
         with open(camera_data_path, 'r') as file:
             cam_data = json.load(file)
         
@@ -66,6 +66,61 @@ class WanVideoReCamMasterCameraEmbed:
 
         cam_idx = list(range(num_frames))[::4]
         traj = [self.parse_matrix(cam_data[f"frame{idx}"][f"cam{int(camera_type_map[camera_type]):02d}"]) for idx in cam_idx]
+
+        def generate_orbit_180(num_frames=81):
+            camera_data = {}
+            radius = 100  # Distance from center during orbit
+            center = np.array([3390, 1380, 240])  # Center point of orbit
+            
+            # Arc movement similar to arc_left/arc_right but spanning 180 degrees
+            for i in range(num_frames):
+                # Calculate angle from 0 to 180 degrees
+                angle = i * 90.0 / (num_frames - 1)
+                angle_rad = np.radians(angle)
+                
+                # Calculate position - circular path around center
+                x = center[0] + radius * np.cos(angle_rad)
+                y = center[1] + radius * np.sin(angle_rad)
+                z = center[2]  # Z stays constant
+                pos = np.array([x, y, z])
+                
+                # Calculate direction from camera to center point
+                dir_to_center = center - pos
+                dir_to_center_xy = dir_to_center.copy()
+                dir_to_center_xy[2] = 0  # Project to XY plane for horizontal orientation
+                
+                # Normalize the direction vector
+                dir_to_center_xy = dir_to_center_xy / np.linalg.norm(dir_to_center_xy)
+                
+                # For camera to face center, forward vector should be this direction
+                # The rotation matrix needs to make the camera's forward vector (local Z) 
+                # point toward the center
+                
+                # Calculate the angle between the camera and center in the XY plane
+                # This is the negative angle we need to rotate camera to face center
+                look_angle = np.arctan2(dir_to_center_xy[1], dir_to_center_xy[0])
+                
+                # Rotation matrix for facing the center
+                cos_look = np.cos(look_angle)
+                sin_look = np.sin(look_angle)
+                
+                # Format string with rotation that makes camera face the center
+                matrix_str = f"[{cos_look} {sin_look} 0 0] "
+                matrix_str += f"[{-sin_look} {cos_look} 0 0] "
+                matrix_str += f"[0 0 1 0] "
+                matrix_str += f"[{x} {y} {z} 1] "
+                
+                # Store the camera string
+                frame_key = f"frame{i}"
+                if frame_key not in camera_data:
+                    camera_data[frame_key] = {}
+                camera_data[frame_key]["cam11"] = matrix_str
+            return camera_data
+
+        # Generate the orbit camera data
+        #traj = [self.parse_matrix(generate_orbit_180(num_frames=81)[f"frame{idx}"][f"cam{int(11):02d}"]) for idx in cam_idx]
+        #print(traj)
+        
         traj = np.stack(traj).transpose(0, 2, 1)
         c2ws = []
         for c2w in traj:
@@ -95,6 +150,88 @@ class WanVideoReCamMasterCameraEmbed:
 
         return (embeds, traj,)
     
+class WanVideoSynCamMasterCameraEmbed:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "width": ("INT", {"default": 832, "min": 64, "max": 2048, "step": 8, "tooltip": "Width of the image to encode"}),
+            "height": ("INT", {"default": 480, "min": 64, "max": 29048, "step": 8, "tooltip": "Height of the image to encode"}),
+            "num_frames": ("INT", {"default": 81, "min": 1, "max": 10000, "step": 4, "tooltip": "Number of frames to encode"}),
+           
+            "camera_type": ([
+                "azimuth", 
+                "elevation",
+                "distance",
+                ], {"tooltip": "Camera type to use"}),
+        },
+        }
+
+    RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", "CAMERAPOSES",)
+    RETURN_NAMES = ("camera_embeds", "camera_poses",)
+    FUNCTION = "process"
+    CATEGORY = "WanVideoWrapper"
+    DESCRIPTION = "https://github.com/KwaiVGI/ReCamMaster"
+
+    def process(self, width, height, num_frames, camera_type):
+        # load camera
+        import json
+        from einops import rearrange
+        
+        camera_data_path = os.path.join(script_directory, "syncam_extrinsics.json")
+        with open(camera_data_path, 'r') as file:
+            cam_data = json.load(file)
+
+        # load camera
+        multiview_c2ws = []
+        cam_idx = list(range(num_frames))[::4]
+
+        traj_list = []
+
+        if camera_type == "azimuth":
+            tgt_idx = 1
+            cond_idx = 3
+        elif camera_type == "elevation":
+            tgt_idx = 3
+            cond_idx = 7
+        elif camera_type == "distance":
+            tgt_idx = 9
+            cond_idx = 10
+        for view_idx in [cond_idx, tgt_idx]:
+            traj = [self.parse_matrix(cam_data[f"frame{idx}"][f"cam{view_idx:02d}"]) for idx in cam_idx]
+            traj = np.stack(traj).transpose(0, 2, 1)
+            traj_list.append(traj)
+
+            c2ws = []
+            for c2w in traj:
+                c2w = c2w[:, [1, 2, 0, 3]]
+                c2w[:3, 1] *= -1.
+                c2w[:3, 3] /= 200
+                c2ws.append(c2w)
+            multiview_c2ws.append(c2ws)
+        cond_cam_params = [Camera(cam_param) for cam_param in multiview_c2ws[0]]
+        tgt_cam_params = [Camera(cam_param) for cam_param in multiview_c2ws[1]]
+        relative_poses = []
+        for i in range(len(tgt_cam_params)):
+            relative_pose = self.get_relative_pose([tgt_cam_params[i], cond_cam_params[i]])
+            relative_poses.append(torch.as_tensor(relative_pose)[:,:3,:])
+        pose_embedding = torch.stack(relative_poses, dim=1)  # v,21,3,4
+        pose_embedding = rearrange(pose_embedding, 'v f c d -> v f (c d)')
+
+        vae_stride = (4, 8, 8)
+        target_shape = (16, (num_frames - 1) // vae_stride[0] + 1,
+                        height // vae_stride[1],
+                        width // vae_stride[2])
+
+        embeds = {
+            "target_shape": target_shape,
+            "num_frames": num_frames,
+            "syncammaster": {
+                "camera_embed": pose_embedding,
+            }
+        }
+
+        return (embeds, np.concatenate(traj_list),)
+    
     def parse_matrix(self, matrix_str):
         rows = matrix_str.strip().split('] [')
         matrix = []
@@ -274,8 +411,10 @@ or a .txt file with RealEstate camera intrinsics and coordinates, in a 3D plot.
 NODE_CLASS_MAPPINGS = {
     "WanVideoReCamMasterCameraEmbed": WanVideoReCamMasterCameraEmbed,
     "ReCamMasterPoseVisualizer": ReCamMasterPoseVisualizer,
+    "WanVideoSynCamMasterCameraEmbed": WanVideoSynCamMasterCameraEmbed,
     }
 NODE_DISPLAY_NAME_MAPPINGS = {
     "WanVideoReCamMasterCameraEmbed": "WanVideo ReCamMaster Camera Embed",
     "ReCamMasterPoseVisualizer": "ReCamMaster Pose Visualizer",
+    "WanVideoSynCamMasterCameraEmbed": "WanVideo SyncamMaster Camera Embed",
     }
diff --git a/recammaster/camera_extrinsics.json b/recammaster/recam_extrinsics.json
similarity index 100%
rename from recammaster/camera_extrinsics.json
rename to recammaster/recam_extrinsics.json
diff --git a/recammaster/syncam_extrinsics.json b/recammaster/syncam_extrinsics.json
new file mode 100644
index 0000000..87b40a4
--- /dev/null
+++ b/recammaster/syncam_extrinsics.json
@@ -0,0 +1,974 @@
+{
+    "frame0": {
+        "cam01": "[0.866025 -0.5 0 0] [0.5 0.866025 -0 0] [0 0 1 0] [12960.2 1250 -570 1] ",
+        "cam02": "[0.965926 -0.258819 0 0] [0.258819 0.965926 -0 0] [0 0 1 0] [12930.2 1177.65 -570 1] ",
+        "cam03": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12920 1100 -570 1] ",
+        "cam04": "[0.965926 0.258819 0 0] [-0.258819 0.965926 0 0] [0 -0 1 0] [12930.2 1022.35 -570 1] ",
+        "cam05": "[0.866025 0.5 0 0] [-0.5 0.866025 0 0] [0 -0 1 0] [12960.2 950 -570 1] ",
+        "cam06": "[0.984808 0 -0.173648 0] [-0 1 0 0] [0.173648 0 0.984808 0] [12920 1100 -517.102 1] ",
+        "cam07": "[0.939693 0 -0.34202 0] [-0 1 0 0] [0.34202 0 0.939693 0] [12920 1100 -460.809 1] ",
+        "cam08": "[0.866025 0 -0.5 0] [-0 1 0 0] [0.5 0 0.866025 0] [12920 1100 -396.795 1] ",
+        "cam09": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12820 1100 -570 1] ",
+        "cam10": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12720 1100 -570 1] "
+    },
+    "frame1": {
+        "cam01": "[0.866025 -0.5 0 0] [0.5 0.866025 -0 0] [0 0 1 0] [12960.2 1250 -570 1] ",
+        "cam02": "[0.965926 -0.258819 0 0] [0.258819 0.965926 -0 0] [0 0 1 0] [12930.2 1177.65 -570 1] ",
+        "cam03": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12920 1100 -570 1] ",
+        "cam04": "[0.965926 0.258819 0 0] [-0.258819 0.965926 0 0] [0 -0 1 0] [12930.2 1022.35 -570 1] ",
+        "cam05": "[0.866025 0.5 0 0] [-0.5 0.866025 0 0] [0 -0 1 0] [12960.2 950 -570 1] ",
+        "cam06": "[0.984808 0 -0.173648 0] [-0 1 0 0] [0.173648 0 0.984808 0] [12920 1100 -517.102 1] ",
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+        "cam01": "[0.866025 -0.5 0 0] [0.5 0.866025 -0 0] [0 0 1 0] [12960.2 1250 -570 1] ",
+        "cam02": "[0.965926 -0.258819 0 0] [0.258819 0.965926 -0 0] [0 0 1 0] [12930.2 1177.65 -570 1] ",
+        "cam03": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12920 1100 -570 1] ",
+        "cam04": "[0.965926 0.258819 0 0] [-0.258819 0.965926 0 0] [0 -0 1 0] [12930.2 1022.35 -570 1] ",
+        "cam05": "[0.866025 0.5 0 0] [-0.5 0.866025 0 0] [0 -0 1 0] [12960.2 950 -570 1] ",
+        "cam06": "[0.984808 0 -0.173648 0] [-0 1 0 0] [0.173648 0 0.984808 0] [12920 1100 -517.102 1] ",
+        "cam07": "[0.939693 0 -0.34202 0] [-0 1 0 0] [0.34202 0 0.939693 0] [12920 1100 -460.809 1] ",
+        "cam08": "[0.866025 0 -0.5 0] [-0 1 0 0] [0.5 0 0.866025 0] [12920 1100 -396.795 1] ",
+        "cam09": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12820 1100 -570 1] ",
+        "cam10": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12720 1100 -570 1] "
+    },
+    "frame68": {
+        "cam01": "[0.866025 -0.5 0 0] [0.5 0.866025 -0 0] [0 0 1 0] [12960.2 1250 -570 1] ",
+        "cam02": "[0.965926 -0.258819 0 0] [0.258819 0.965926 -0 0] [0 0 1 0] [12930.2 1177.65 -570 1] ",
+        "cam03": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12920 1100 -570 1] ",
+        "cam04": "[0.965926 0.258819 0 0] [-0.258819 0.965926 0 0] [0 -0 1 0] [12930.2 1022.35 -570 1] ",
+        "cam05": "[0.866025 0.5 0 0] [-0.5 0.866025 0 0] [0 -0 1 0] [12960.2 950 -570 1] ",
+        "cam06": "[0.984808 0 -0.173648 0] [-0 1 0 0] [0.173648 0 0.984808 0] [12920 1100 -517.102 1] ",
+        "cam07": "[0.939693 0 -0.34202 0] [-0 1 0 0] [0.34202 0 0.939693 0] [12920 1100 -460.809 1] ",
+        "cam08": "[0.866025 0 -0.5 0] [-0 1 0 0] [0.5 0 0.866025 0] [12920 1100 -396.795 1] ",
+        "cam09": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12820 1100 -570 1] ",
+        "cam10": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12720 1100 -570 1] "
+    },
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+        "cam09": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12820 1100 -570 1] ",
+        "cam10": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12720 1100 -570 1] "
+    },
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+        "cam03": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12920 1100 -570 1] ",
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+        "cam05": "[0.866025 0.5 0 0] [-0.5 0.866025 0 0] [0 -0 1 0] [12960.2 950 -570 1] ",
+        "cam06": "[0.984808 0 -0.173648 0] [-0 1 0 0] [0.173648 0 0.984808 0] [12920 1100 -517.102 1] ",
+        "cam07": "[0.939693 0 -0.34202 0] [-0 1 0 0] [0.34202 0 0.939693 0] [12920 1100 -460.809 1] ",
+        "cam08": "[0.866025 0 -0.5 0] [-0 1 0 0] [0.5 0 0.866025 0] [12920 1100 -396.795 1] ",
+        "cam09": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12820 1100 -570 1] ",
+        "cam10": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12720 1100 -570 1] "
+    },
+    "frame71": {
+        "cam01": "[0.866025 -0.5 0 0] [0.5 0.866025 -0 0] [0 0 1 0] [12960.2 1250 -570 1] ",
+        "cam02": "[0.965926 -0.258819 0 0] [0.258819 0.965926 -0 0] [0 0 1 0] [12930.2 1177.65 -570 1] ",
+        "cam03": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12920 1100 -570 1] ",
+        "cam04": "[0.965926 0.258819 0 0] [-0.258819 0.965926 0 0] [0 -0 1 0] [12930.2 1022.35 -570 1] ",
+        "cam05": "[0.866025 0.5 0 0] [-0.5 0.866025 0 0] [0 -0 1 0] [12960.2 950 -570 1] ",
+        "cam06": "[0.984808 0 -0.173648 0] [-0 1 0 0] [0.173648 0 0.984808 0] [12920 1100 -517.102 1] ",
+        "cam07": "[0.939693 0 -0.34202 0] [-0 1 0 0] [0.34202 0 0.939693 0] [12920 1100 -460.809 1] ",
+        "cam08": "[0.866025 0 -0.5 0] [-0 1 0 0] [0.5 0 0.866025 0] [12920 1100 -396.795 1] ",
+        "cam09": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12820 1100 -570 1] ",
+        "cam10": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12720 1100 -570 1] "
+    },
+    "frame72": {
+        "cam01": "[0.866025 -0.5 0 0] [0.5 0.866025 -0 0] [0 0 1 0] [12960.2 1250 -570 1] ",
+        "cam02": "[0.965926 -0.258819 0 0] [0.258819 0.965926 -0 0] [0 0 1 0] [12930.2 1177.65 -570 1] ",
+        "cam03": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12920 1100 -570 1] ",
+        "cam04": "[0.965926 0.258819 0 0] [-0.258819 0.965926 0 0] [0 -0 1 0] [12930.2 1022.35 -570 1] ",
+        "cam05": "[0.866025 0.5 0 0] [-0.5 0.866025 0 0] [0 -0 1 0] [12960.2 950 -570 1] ",
+        "cam06": "[0.984808 0 -0.173648 0] [-0 1 0 0] [0.173648 0 0.984808 0] [12920 1100 -517.102 1] ",
+        "cam07": "[0.939693 0 -0.34202 0] [-0 1 0 0] [0.34202 0 0.939693 0] [12920 1100 -460.809 1] ",
+        "cam08": "[0.866025 0 -0.5 0] [-0 1 0 0] [0.5 0 0.866025 0] [12920 1100 -396.795 1] ",
+        "cam09": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12820 1100 -570 1] ",
+        "cam10": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12720 1100 -570 1] "
+    },
+    "frame73": {
+        "cam01": "[0.866025 -0.5 0 0] [0.5 0.866025 -0 0] [0 0 1 0] [12960.2 1250 -570 1] ",
+        "cam02": "[0.965926 -0.258819 0 0] [0.258819 0.965926 -0 0] [0 0 1 0] [12930.2 1177.65 -570 1] ",
+        "cam03": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12920 1100 -570 1] ",
+        "cam04": "[0.965926 0.258819 0 0] [-0.258819 0.965926 0 0] [0 -0 1 0] [12930.2 1022.35 -570 1] ",
+        "cam05": "[0.866025 0.5 0 0] [-0.5 0.866025 0 0] [0 -0 1 0] [12960.2 950 -570 1] ",
+        "cam06": "[0.984808 0 -0.173648 0] [-0 1 0 0] [0.173648 0 0.984808 0] [12920 1100 -517.102 1] ",
+        "cam07": "[0.939693 0 -0.34202 0] [-0 1 0 0] [0.34202 0 0.939693 0] [12920 1100 -460.809 1] ",
+        "cam08": "[0.866025 0 -0.5 0] [-0 1 0 0] [0.5 0 0.866025 0] [12920 1100 -396.795 1] ",
+        "cam09": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12820 1100 -570 1] ",
+        "cam10": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12720 1100 -570 1] "
+    },
+    "frame74": {
+        "cam01": "[0.866025 -0.5 0 0] [0.5 0.866025 -0 0] [0 0 1 0] [12960.2 1250 -570 1] ",
+        "cam02": "[0.965926 -0.258819 0 0] [0.258819 0.965926 -0 0] [0 0 1 0] [12930.2 1177.65 -570 1] ",
+        "cam03": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12920 1100 -570 1] ",
+        "cam04": "[0.965926 0.258819 0 0] [-0.258819 0.965926 0 0] [0 -0 1 0] [12930.2 1022.35 -570 1] ",
+        "cam05": "[0.866025 0.5 0 0] [-0.5 0.866025 0 0] [0 -0 1 0] [12960.2 950 -570 1] ",
+        "cam06": "[0.984808 0 -0.173648 0] [-0 1 0 0] [0.173648 0 0.984808 0] [12920 1100 -517.102 1] ",
+        "cam07": "[0.939693 0 -0.34202 0] [-0 1 0 0] [0.34202 0 0.939693 0] [12920 1100 -460.809 1] ",
+        "cam08": "[0.866025 0 -0.5 0] [-0 1 0 0] [0.5 0 0.866025 0] [12920 1100 -396.795 1] ",
+        "cam09": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12820 1100 -570 1] ",
+        "cam10": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12720 1100 -570 1] "
+    },
+    "frame75": {
+        "cam01": "[0.866025 -0.5 0 0] [0.5 0.866025 -0 0] [0 0 1 0] [12960.2 1250 -570 1] ",
+        "cam02": "[0.965926 -0.258819 0 0] [0.258819 0.965926 -0 0] [0 0 1 0] [12930.2 1177.65 -570 1] ",
+        "cam03": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12920 1100 -570 1] ",
+        "cam04": "[0.965926 0.258819 0 0] [-0.258819 0.965926 0 0] [0 -0 1 0] [12930.2 1022.35 -570 1] ",
+        "cam05": "[0.866025 0.5 0 0] [-0.5 0.866025 0 0] [0 -0 1 0] [12960.2 950 -570 1] ",
+        "cam06": "[0.984808 0 -0.173648 0] [-0 1 0 0] [0.173648 0 0.984808 0] [12920 1100 -517.102 1] ",
+        "cam07": "[0.939693 0 -0.34202 0] [-0 1 0 0] [0.34202 0 0.939693 0] [12920 1100 -460.809 1] ",
+        "cam08": "[0.866025 0 -0.5 0] [-0 1 0 0] [0.5 0 0.866025 0] [12920 1100 -396.795 1] ",
+        "cam09": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12820 1100 -570 1] ",
+        "cam10": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12720 1100 -570 1] "
+    },
+    "frame76": {
+        "cam01": "[0.866025 -0.5 0 0] [0.5 0.866025 -0 0] [0 0 1 0] [12960.2 1250 -570 1] ",
+        "cam02": "[0.965926 -0.258819 0 0] [0.258819 0.965926 -0 0] [0 0 1 0] [12930.2 1177.65 -570 1] ",
+        "cam03": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12920 1100 -570 1] ",
+        "cam04": "[0.965926 0.258819 0 0] [-0.258819 0.965926 0 0] [0 -0 1 0] [12930.2 1022.35 -570 1] ",
+        "cam05": "[0.866025 0.5 0 0] [-0.5 0.866025 0 0] [0 -0 1 0] [12960.2 950 -570 1] ",
+        "cam06": "[0.984808 0 -0.173648 0] [-0 1 0 0] [0.173648 0 0.984808 0] [12920 1100 -517.102 1] ",
+        "cam07": "[0.939693 0 -0.34202 0] [-0 1 0 0] [0.34202 0 0.939693 0] [12920 1100 -460.809 1] ",
+        "cam08": "[0.866025 0 -0.5 0] [-0 1 0 0] [0.5 0 0.866025 0] [12920 1100 -396.795 1] ",
+        "cam09": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12820 1100 -570 1] ",
+        "cam10": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12720 1100 -570 1] "
+    },
+    "frame77": {
+        "cam01": "[0.866025 -0.5 0 0] [0.5 0.866025 -0 0] [0 0 1 0] [12960.2 1250 -570 1] ",
+        "cam02": "[0.965926 -0.258819 0 0] [0.258819 0.965926 -0 0] [0 0 1 0] [12930.2 1177.65 -570 1] ",
+        "cam03": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12920 1100 -570 1] ",
+        "cam04": "[0.965926 0.258819 0 0] [-0.258819 0.965926 0 0] [0 -0 1 0] [12930.2 1022.35 -570 1] ",
+        "cam05": "[0.866025 0.5 0 0] [-0.5 0.866025 0 0] [0 -0 1 0] [12960.2 950 -570 1] ",
+        "cam06": "[0.984808 0 -0.173648 0] [-0 1 0 0] [0.173648 0 0.984808 0] [12920 1100 -517.102 1] ",
+        "cam07": "[0.939693 0 -0.34202 0] [-0 1 0 0] [0.34202 0 0.939693 0] [12920 1100 -460.809 1] ",
+        "cam08": "[0.866025 0 -0.5 0] [-0 1 0 0] [0.5 0 0.866025 0] [12920 1100 -396.795 1] ",
+        "cam09": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12820 1100 -570 1] ",
+        "cam10": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12720 1100 -570 1] "
+    },
+    "frame78": {
+        "cam01": "[0.866025 -0.5 0 0] [0.5 0.866025 -0 0] [0 0 1 0] [12960.2 1250 -570 1] ",
+        "cam02": "[0.965926 -0.258819 0 0] [0.258819 0.965926 -0 0] [0 0 1 0] [12930.2 1177.65 -570 1] ",
+        "cam03": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12920 1100 -570 1] ",
+        "cam04": "[0.965926 0.258819 0 0] [-0.258819 0.965926 0 0] [0 -0 1 0] [12930.2 1022.35 -570 1] ",
+        "cam05": "[0.866025 0.5 0 0] [-0.5 0.866025 0 0] [0 -0 1 0] [12960.2 950 -570 1] ",
+        "cam06": "[0.984808 0 -0.173648 0] [-0 1 0 0] [0.173648 0 0.984808 0] [12920 1100 -517.102 1] ",
+        "cam07": "[0.939693 0 -0.34202 0] [-0 1 0 0] [0.34202 0 0.939693 0] [12920 1100 -460.809 1] ",
+        "cam08": "[0.866025 0 -0.5 0] [-0 1 0 0] [0.5 0 0.866025 0] [12920 1100 -396.795 1] ",
+        "cam09": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12820 1100 -570 1] ",
+        "cam10": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12720 1100 -570 1] "
+    },
+    "frame79": {
+        "cam01": "[0.866025 -0.5 0 0] [0.5 0.866025 -0 0] [0 0 1 0] [12960.2 1250 -570 1] ",
+        "cam02": "[0.965926 -0.258819 0 0] [0.258819 0.965926 -0 0] [0 0 1 0] [12930.2 1177.65 -570 1] ",
+        "cam03": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12920 1100 -570 1] ",
+        "cam04": "[0.965926 0.258819 0 0] [-0.258819 0.965926 0 0] [0 -0 1 0] [12930.2 1022.35 -570 1] ",
+        "cam05": "[0.866025 0.5 0 0] [-0.5 0.866025 0 0] [0 -0 1 0] [12960.2 950 -570 1] ",
+        "cam06": "[0.984808 0 -0.173648 0] [-0 1 0 0] [0.173648 0 0.984808 0] [12920 1100 -517.102 1] ",
+        "cam07": "[0.939693 0 -0.34202 0] [-0 1 0 0] [0.34202 0 0.939693 0] [12920 1100 -460.809 1] ",
+        "cam08": "[0.866025 0 -0.5 0] [-0 1 0 0] [0.5 0 0.866025 0] [12920 1100 -396.795 1] ",
+        "cam09": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12820 1100 -570 1] ",
+        "cam10": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12720 1100 -570 1] "
+    },
+    "frame80": {
+        "cam01": "[0.866025 -0.5 0 0] [0.5 0.866025 -0 0] [0 0 1 0] [12960.2 1250 -570 1] ",
+        "cam02": "[0.965926 -0.258819 0 0] [0.258819 0.965926 -0 0] [0 0 1 0] [12930.2 1177.65 -570 1] ",
+        "cam03": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12920 1100 -570 1] ",
+        "cam04": "[0.965926 0.258819 0 0] [-0.258819 0.965926 0 0] [0 -0 1 0] [12930.2 1022.35 -570 1] ",
+        "cam05": "[0.866025 0.5 0 0] [-0.5 0.866025 0 0] [0 -0 1 0] [12960.2 950 -570 1] ",
+        "cam06": "[0.984808 0 -0.173648 0] [-0 1 0 0] [0.173648 0 0.984808 0] [12920 1100 -517.102 1] ",
+        "cam07": "[0.939693 0 -0.34202 0] [-0 1 0 0] [0.34202 0 0.939693 0] [12920 1100 -460.809 1] ",
+        "cam08": "[0.866025 0 -0.5 0] [-0 1 0 0] [0.5 0 0.866025 0] [12920 1100 -396.795 1] ",
+        "cam09": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12820 1100 -570 1] ",
+        "cam10": "[1 0 0 0] [-0 1 0 0] [0 -0 1 0] [12720 1100 -570 1] "
+    }
+}
\ No newline at end of file
diff --git a/wanvideo/modules/model.py b/wanvideo/modules/model.py
index 057b7f5..57f38b6 100644
--- a/wanvideo/modules/model.py
+++ b/wanvideo/modules/model.py
@@ -455,7 +455,8 @@ class WanAttentionBlock(nn.Module):
         video_attention_split_steps=[],
         rope_func = "default",
         clip_embed=None,
-        camera_embed=None
+        camera_embed=None,
+        freqs_mvs=None,
         
     ):
         r"""
@@ -466,10 +467,11 @@ class WanAttentionBlock(nn.Module):
             grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
             freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
         """
-        e = (self.modulation.to(e.device) + e).chunk(6, dim=1)
-        input_x = self.norm1(x) * (1 + e[1]) + e[0]
+        e_mod = (self.modulation.to(e.device) + e).chunk(6, dim=1)
+        input_x = self.norm1(x) * (1 + e_mod[1]) + e_mod[0]
 
-        if camera_embed is not None:
+        if camera_embed is not None and not hasattr(self, 'mvs_attn'):
+            print("ReCamMaster camera embedding")
             # encode ReCamMaster camera
             camera_embed = self.cam_encoder(camera_embed.to(x))
             camera_embed = camera_embed.repeat(1, 2, 1)
@@ -479,6 +481,7 @@ class WanAttentionBlock(nn.Module):
 
         # self-attention
         if (context.shape[0] > 1 or (clip_embed is not None and clip_embed.shape[0] > 1)) and x.shape[0] == 1:
+            print("Attention split")
             y = self.self_attn.forward_split(
             input_x, 
             seq_lens, grid_sizes,
@@ -494,16 +497,36 @@ class WanAttentionBlock(nn.Module):
             freqs, rope_func=rope_func
             )
         #ReCamMaster
-        if camera_embed is not None:
+        if camera_embed is not None and not hasattr(self, 'mvs_attn'):
+            print("ReCamMaster camera embedding")
             y = self.projector(y)
 
-        x = x.to(torch.float32) + (y.to(torch.float32) * e[2].to(torch.float32))
+        x = x.to(torch.float32) + (y.to(torch.float32) * e_mod[2].to(torch.float32))
+
+        if hasattr(self, 'mvs_attn'):
+            shift_mvs, scale_mvs, gate_mvs = (
+                self.modulation_mvs.to(e.device) + e[:, :3, :]).chunk(3, dim=1)
+            input_x = self.norm_mvs(x) * (1 + scale_mvs) + shift_mvs
+            v, f, _ = camera_embed.shape
+            h, w = 30, 52  # h, w  hard code
+            camera_embed = self.cam_encoder(camera_embed.to(x))
+            camera_embed = camera_embed.unsqueeze(2).unsqueeze(3).repeat(1, 1, h, w, 1)
+            camera_embed = rearrange(camera_embed, 'b f h w d -> b (f h w) d')
+            input_x += camera_embed
+            x = rearrange(x, '(b v) (f h w) d -> (b f) (v h w) d', v=v, f=f, h=h, w=w)
+            input_x = rearrange(input_x, '(b v) (f h w) d -> (b f) (v h w) d', v=v, f=f, h=h, w=w)
+            mvs_attn = self.mvs_attn(
+                input_x,
+                seq_lens, grid_sizes, 
+                freqs_mvs, rope_func="comfy")
+            x = x + gate_mvs.to(torch.float32) * self.projector(mvs_attn)
+            x = rearrange(x, '(b f) (v h w) d -> (b v) (f h w) d', v=v, f=f, h=h, w=w)
 
         # cross-attention & ffn function
         if (context.shape[0] > 1 or (clip_embed is not None and clip_embed.shape[0] > 1)) and x.shape[0] == 1:
-            x = self.split_cross_attn_ffn(x, context, context_lens, e, clip_embed=clip_embed, grid_sizes=grid_sizes)
+            x = self.split_cross_attn_ffn(x, context, context_lens, e_mod, clip_embed=clip_embed, grid_sizes=grid_sizes)
         else:
-            x = self.cross_attn_ffn(x, context, context_lens, e, clip_embed=clip_embed, grid_sizes=grid_sizes)
+            x = self.cross_attn_ffn(x, context, context_lens, e_mod, clip_embed=clip_embed, grid_sizes=grid_sizes)
 
         return x
     
@@ -1020,6 +1043,8 @@ class WanModel(ModelMixin, ConfigMixin):
                       dim=1) for u in x
         ])
 
+        freqs_mvs = None
+
         if freqs is None: #comfy rope
             rope_func = "comfy"
             f_len = ((F + (self.patch_size[0] // 2)) // self.patch_size[0])
@@ -1032,6 +1057,17 @@ class WanModel(ModelMixin, ConfigMixin):
             img_ids = repeat(img_ids, "t h w c -> b (t h w) c", b=1)
 
             freqs = self.rope_embedder(img_ids).movedim(1, 2)
+
+            if hasattr(self.blocks[0], "mvs_attn"): #syncammaster
+                # Create freqs_mvs with fixed temporal dimension v=2
+                v = 2
+                img_ids_mvs = torch.zeros((v, h_len, w_len, 3), device=x.device, dtype=x.dtype)
+                img_ids_mvs[:, :, :, 0] = img_ids_mvs[:, :, :, 0] + torch.linspace(0, v - 1, steps=v, device=x.device, dtype=x.dtype).reshape(-1, 1, 1)
+                img_ids_mvs[:, :, :, 1] = img_ids_mvs[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1)
+                img_ids_mvs[:, :, :, 2] = img_ids_mvs[:, :, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).reshape(1, 1, -1)
+                img_ids_mvs = repeat(img_ids_mvs, "t h w c -> b (t h w) c", b=1)
+                
+                freqs_mvs = self.rope_embedder(img_ids_mvs).movedim(1, 2)
         else:
             rope_func = "default"
 
@@ -1122,6 +1158,7 @@ class WanModel(ModelMixin, ConfigMixin):
                 current_step=current_step,
                 video_attention_split_steps=self.video_attention_split_steps,
                 camera_embed=camera_embed,
+                freqs_mvs=freqs_mvs,
                 )
             
             if vace_data is not None: