diff --git a/diffsynth/pipelines/qwen_image.py b/diffsynth/pipelines/qwen_image.py
index 1d3c66b..83ff290 100644
--- a/diffsynth/pipelines/qwen_image.py
+++ b/diffsynth/pipelines/qwen_image.py
@@ -140,8 +140,9 @@ class QwenImagePipeline(BasePipeline):
         timestep_id = torch.randint(0, self.scheduler.num_train_timesteps, (1,))
         timestep = self.scheduler.timesteps[timestep_id].to(dtype=self.torch_dtype, device=self.device)
         
-        inputs["latents"] = self.scheduler.add_noise(inputs["input_latents"], inputs["noise"], timestep)
-        training_target = self.scheduler.training_target(inputs["input_latents"], inputs["noise"], timestep)
+        noise = torch.randn_like(inputs["input_latents"])
+        inputs["latents"] = self.scheduler.add_noise(inputs["input_latents"], noise, timestep)
+        training_target = self.scheduler.training_target(inputs["input_latents"], noise, timestep)
         
         noise_pred = self.model_fn(**inputs, timestep=timestep)
         
diff --git a/diffsynth/trainers/utils.py b/diffsynth/trainers/utils.py
index 0711176..3262d15 100644
--- a/diffsynth/trainers/utils.py
+++ b/diffsynth/trainers/utils.py
@@ -421,10 +421,12 @@ class DiffusionTrainingModule(torch.nn.Module):
         return state_dict
     
     
-    def transfer_data_to_device(self, data, device):
+    def transfer_data_to_device(self, data, device, torch_float_dtype=None):
         for key in data:
             if isinstance(data[key], torch.Tensor):
                 data[key] = data[key].to(device)
+                if torch_float_dtype is not None and data[key].dtype in [torch.float, torch.float16, torch.bfloat16]:
+                    data[key] = data[key].to(torch_float_dtype)
         return data
     
     
diff --git a/examples/qwen_image/model_training/train.py b/examples/qwen_image/model_training/train.py
index 1b4faec..f39c11c 100644
--- a/examples/qwen_image/model_training/train.py
+++ b/examples/qwen_image/model_training/train.py
@@ -85,8 +85,10 @@ class QwenImageTrainingModule(DiffusionTrainingModule):
     
     def forward(self, data, inputs=None, return_inputs=False):
         # Inputs
-        if inputs is None: inputs = self.forward_preprocess(data)
-        else: inputs = self.transfer_data_to_device(inputs, self.pipe.device)
+        if inputs is None:
+            inputs = self.forward_preprocess(data)
+        else:
+            inputs = self.transfer_data_to_device(inputs, self.pipe.device, self.pipe.torch_dtype)
         if return_inputs: return inputs
         
         # Loss