diff --git a/mochi_preview/t2v_synth_mochi.py b/mochi_preview/t2v_synth_mochi.py
index 6507700..2550991 100644
--- a/mochi_preview/t2v_synth_mochi.py
+++ b/mochi_preview/t2v_synth_mochi.py
@@ -10,7 +10,6 @@ import torch.utils.data
 from torch import nn
 
 from .dit.joint_model.context_parallel import get_cp_rank_size
-from .utils import Timer
 from tqdm import tqdm
 from comfy.utils import ProgressBar, load_torch_file
 
@@ -122,55 +121,53 @@ class T2VSynthMochiModel:
         fp8_fastmode: bool = False,
     ):
         super().__init__()
-        t = Timer()
         self.device = device
         self.offload_device = offload_device
 
-        with t("construct_dit"):
-                
-                model: nn.Module = torch.nn.utils.skip_init(
-                    AsymmDiTJoint,
-                    depth=48,
-                    patch_size=2,
-                    num_heads=24,
-                    hidden_size_x=3072,
-                    hidden_size_y=1536,
-                    mlp_ratio_x=4.0,
-                    mlp_ratio_y=4.0,
-                    in_channels=12,
-                    qk_norm=True,
-                    qkv_bias=False,
-                    out_bias=True,
-                    patch_embed_bias=True,
-                    timestep_mlp_bias=True,
-                    timestep_scale=1000.0,
-                    t5_feat_dim=4096,
-                    t5_token_length=256,
-                    rope_theta=10000.0,
-                )
-        with t("dit_load_checkpoint"):
-            params_to_keep = {"t_embedder", "x_embedder", "pos_frequencies", "t5", "norm"}
-            print(f"Loading model state_dict from {dit_checkpoint_path}...")
-            dit_sd = load_torch_file(dit_checkpoint_path)
-            if is_accelerate_available:
-                print("Using accelerate to load and assign model weights to device...")
-                for name, param in model.named_parameters():
-                    if not any(keyword in name for keyword in params_to_keep):
-                        set_module_tensor_to_device(model, name, dtype=weight_dtype, device=self.device, value=dit_sd[name])
-                    else:
-                        set_module_tensor_to_device(model, name, dtype=torch.bfloat16, device=self.device, value=dit_sd[name])
-            else:
-                print("Loading state_dict without accelerate...")
-                model.load_state_dict(dit_sd)
-                for name, param in model.named_parameters():
-                    if not any(keyword in name for keyword in params_to_keep):
-                        param.data = param.data.to(weight_dtype)
-                    else:
-                        param.data = param.data.to(torch.bfloat16)
-            
-            if fp8_fastmode:
-                from ..fp8_optimization import convert_fp8_linear
-                convert_fp8_linear(model, torch.bfloat16)
+        print("Initializing model...")
+        model: nn.Module = torch.nn.utils.skip_init(
+            AsymmDiTJoint,
+            depth=48,
+            patch_size=2,
+            num_heads=24,
+            hidden_size_x=3072,
+            hidden_size_y=1536,
+            mlp_ratio_x=4.0,
+            mlp_ratio_y=4.0,
+            in_channels=12,
+            qk_norm=True,
+            qkv_bias=False,
+            out_bias=True,
+            patch_embed_bias=True,
+            timestep_mlp_bias=True,
+            timestep_scale=1000.0,
+            t5_feat_dim=4096,
+            t5_token_length=256,
+            rope_theta=10000.0,
+        )
+
+        params_to_keep = {"t_embedder", "x_embedder", "pos_frequencies", "t5", "norm"}
+        print(f"Loading model state_dict from {dit_checkpoint_path}...")
+        dit_sd = load_torch_file(dit_checkpoint_path)
+        if is_accelerate_available:
+            print("Using accelerate to load and assign model weights to device...")
+            for name, param in model.named_parameters():
+                if not any(keyword in name for keyword in params_to_keep):
+                    set_module_tensor_to_device(model, name, dtype=weight_dtype, device=self.device, value=dit_sd[name])
+                else:
+                    set_module_tensor_to_device(model, name, dtype=torch.bfloat16, device=self.device, value=dit_sd[name])
+        else:
+            print("Loading state_dict without accelerate...")
+            model.load_state_dict(dit_sd)
+            for name, param in model.named_parameters():
+                if not any(keyword in name for keyword in params_to_keep):
+                    param.data = param.data.to(weight_dtype)
+                else:
+                    param.data = param.data.to(torch.bfloat16)
+        
+        if fp8_fastmode:
+            from ..fp8_optimization import convert_fp8_linear
+            convert_fp8_linear(model, torch.bfloat16)
 
         self.dit = model
         self.dit.eval()
@@ -179,8 +176,6 @@ class T2VSynthMochiModel:
         self.vae_mean = torch.Tensor(vae_stats["mean"]).to(self.device)
         self.vae_std = torch.Tensor(vae_stats["std"]).to(self.device)
 
-        t.print_stats()
-
     def get_conditioning(self, prompts, *, zero_last_n_prompts: int):
         B = len(prompts)
         assert (