diff --git a/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py b/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py
index 575423ee80e7..9b0cfe0bbde9 100644
--- a/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py
+++ b/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py
@@ -171,7 +171,7 @@ def set_shift(self, shift: float):
     def scale_noise(
         self,
         sample: torch.FloatTensor,
-        timestep: Union[float, torch.FloatTensor],
+        timestep: torch.FloatTensor,
         noise: Optional[torch.FloatTensor] = None,
     ) -> torch.FloatTensor:
         """
@@ -180,8 +180,10 @@ def scale_noise(
         Args:
             sample (`torch.FloatTensor`):
                 The input sample.
-            timestep (`int`, *optional*):
+            timestep (`torch.FloatTensor`):
                 The current timestep in the diffusion chain.
+            noise (`torch.FloatTensor`, *optional*):
+                The noise tensor.
 
         Returns:
             `torch.FloatTensor`:
@@ -212,6 +214,9 @@ def scale_noise(
         while len(sigma.shape) < len(sample.shape):
             sigma = sigma.unsqueeze(-1)
 
+        if noise is None:
+            noise = torch.randn_like(sample)
+
         sample = sigma * noise + (1.0 - sigma) * sample
 
         return sample
diff --git a/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py b/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py
index 2addc5f3eeec..ceaa1c3746fe 100644
--- a/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py
+++ b/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py
@@ -110,7 +110,7 @@ def set_begin_index(self, begin_index: int = 0):
     def scale_noise(
         self,
         sample: torch.FloatTensor,
-        timestep: Union[float, torch.FloatTensor],
+        timestep: torch.FloatTensor,
         noise: Optional[torch.FloatTensor] = None,
     ) -> torch.FloatTensor:
         """
@@ -119,8 +119,10 @@ def scale_noise(
         Args:
             sample (`torch.FloatTensor`):
                 The input sample.
-            timestep (`int`, *optional*):
+            timestep (`torch.FloatTensor`):
                 The current timestep in the diffusion chain.
+            noise (`torch.FloatTensor`, *optional*):
+                The noise tensor.
 
         Returns:
             `torch.FloatTensor`:
@@ -130,6 +132,10 @@ def scale_noise(
             self._init_step_index(timestep)
 
         sigma = self.sigmas[self.step_index]
+        
+        if noise is None:
+            noise = torch.randn_like(sample)
+
         sample = sigma * noise + (1.0 - sigma) * sample
 
         return sample
diff --git a/src/diffusers/schedulers/scheduling_flow_match_lcm.py b/src/diffusers/schedulers/scheduling_flow_match_lcm.py
index d79556ae8077..617687d254ec 100644
--- a/src/diffusers/schedulers/scheduling_flow_match_lcm.py
+++ b/src/diffusers/schedulers/scheduling_flow_match_lcm.py
@@ -192,7 +192,7 @@ def set_scale_factors(self, scale_factors: list, upscale_mode):
     def scale_noise(
         self,
         sample: torch.FloatTensor,
-        timestep: Union[float, torch.FloatTensor],
+        timestep: torch.FloatTensor,
         noise: Optional[torch.FloatTensor] = None,
     ) -> torch.FloatTensor:
         """
@@ -201,8 +201,10 @@ def scale_noise(
         Args:
             sample (`torch.FloatTensor`):
                 The input sample.
-            timestep (`int`, *optional*):
+            timestep (`torch.FloatTensor`):
                 The current timestep in the diffusion chain.
+            noise (`torch.FloatTensor`, *optional*):
+                The noise tensor.
 
         Returns:
             `torch.FloatTensor`:
@@ -233,6 +235,9 @@ def scale_noise(
         while len(sigma.shape) < len(sample.shape):
             sigma = sigma.unsqueeze(-1)
 
+        if noise is None:
+            noise = torch.randn_like(sample)
+
         sample = sigma * noise + (1.0 - sigma) * sample
 
         return sample