diff --git a/mochi_preview/t2v_synth_mochi.py b/mochi_preview/t2v_synth_mochi.py
index e75e52a..cf957d3 100644
--- a/mochi_preview/t2v_synth_mochi.py
+++ b/mochi_preview/t2v_synth_mochi.py
@@ -22,6 +22,10 @@ except:
 
 from .dit.joint_model.asymm_models_joint import AsymmDiTJoint
 
+import logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+log = logging.getLogger(__name__)
+
 MAX_T5_TOKEN_LENGTH = 256
 
 def unnormalize_latents(
@@ -100,7 +104,7 @@ class T2VSynthMochiModel:
         self.device = device
         self.offload_device = offload_device
 
-        print("Initializing model...")
+        logging.info("Initializing model...")
         with (init_empty_weights() if is_accelerate_available else nullcontext()):
             model = AsymmDiTJoint(
                 depth=48,
@@ -124,23 +128,24 @@ class T2VSynthMochiModel:
             )
 
         params_to_keep = {"t_embedder", "x_embedder", "pos_frequencies", "t5", "norm"}
-        print(f"Loading model state_dict from {dit_checkpoint_path}...")
+        logging.info(f"Loading model state_dict from {dit_checkpoint_path}...")
         dit_sd = load_torch_file(dit_checkpoint_path)
         if "gguf" in dit_checkpoint_path.lower():
+            logging.info("Loading GGUF model state_dict...")
             from .. import mz_gguf_loader
             import importlib
             importlib.reload(mz_gguf_loader)
             with mz_gguf_loader.quantize_lazy_load():
                 model = mz_gguf_loader.quantize_load_state_dict(model, dit_sd, device="cpu")
         elif is_accelerate_available:
-            print("Using accelerate to load and assign model weights to device...")
+            logging.info("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...")
+            logging.info("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):
@@ -349,5 +354,5 @@ class T2VSynthMochiModel:
         self.dit.to(self.offload_device)
     
         samples = unnormalize_latents(z.float(), self.vae_mean, self.vae_std)
-        print("samples: ", samples.shape, samples.dtype, samples.device)
+        logging.info(f"samples shape: {samples.shape}")
         return samples
diff --git a/mz_gguf_loader.py b/mz_gguf_loader.py
index 085e9c4..262f7df 100644
--- a/mz_gguf_loader.py
+++ b/mz_gguf_loader.py
@@ -26,7 +26,7 @@ def quantize_load_state_dict(model, state_dict, device="cpu"):
 
     for name, module in model.named_modules():
         if name in Q4_0_qkey:
-            print(name)
+            #print(name)
             q_linear = WQLinear_GGUF.from_linear(
                 linear=module,
                 device=device,