diff --git a/extensions_built_in/sd_trainer/SDTrainer.py b/extensions_built_in/sd_trainer/SDTrainer.py
index 07e8240c..280b4738 100644
--- a/extensions_built_in/sd_trainer/SDTrainer.py
+++ b/extensions_built_in/sd_trainer/SDTrainer.py
@@ -390,9 +390,12 @@ class SDTrainer(BaseSDTrainProcess):
                 loss = torch.nn.functional.mse_loss(pred.float(), target.float(), reduction="none")
 
             # handle linear timesteps and only adjust the weight of the timesteps
-            if self.sd.is_flow_matching and self.train_config.linear_timesteps:
+            if self.sd.is_flow_matching and (self.train_config.linear_timesteps or self.train_config.linear_timesteps2):
                 # calculate the weights for the timesteps
-                timestep_weight = self.sd.noise_scheduler.get_weights_for_timesteps(timesteps).to(loss.device, dtype=loss.dtype)
+                timestep_weight = self.sd.noise_scheduler.get_weights_for_timesteps(
+                    timesteps,
+                    v2=self.train_config.linear_timesteps2
+                ).to(loss.device, dtype=loss.dtype)
                 timestep_weight = timestep_weight.view(-1, 1, 1, 1).detach()
                 loss = loss * timestep_weight
 
diff --git a/jobs/process/BaseSDTrainProcess.py b/jobs/process/BaseSDTrainProcess.py
index 455f28bb..930a556e 100644
--- a/jobs/process/BaseSDTrainProcess.py
+++ b/jobs/process/BaseSDTrainProcess.py
@@ -913,7 +913,7 @@ class BaseSDTrainProcess(BaseTrainProcess):
                     self.sd.noise_scheduler.set_train_timesteps(
                         num_train_timesteps,
                         device=self.device_torch,
-                        linear=self.train_config.linear_timesteps
+                        linear=self.train_config.linear_timesteps or self.train_config.linear_timesteps2
                     )
                 else:
                     self.sd.noise_scheduler.set_timesteps(
diff --git a/toolkit/config_modules.py b/toolkit/config_modules.py
index b794dd2f..2b844324 100644
--- a/toolkit/config_modules.py
+++ b/toolkit/config_modules.py
@@ -359,6 +359,7 @@ class TrainConfig:
         self.target_norm_std = kwargs.get('target_norm_std', None)
         self.target_norm_std_value = kwargs.get('target_norm_std_value', 1.0)
         self.linear_timesteps = kwargs.get('linear_timesteps', False)
+        self.linear_timesteps2 = kwargs.get('linear_timesteps2', False)
         self.disable_sampling = kwargs.get('disable_sampling', False)
 
 
diff --git a/toolkit/samplers/custom_flowmatch_sampler.py b/toolkit/samplers/custom_flowmatch_sampler.py
index 1cb2eac6..440eb4fa 100644
--- a/toolkit/samplers/custom_flowmatch_sampler.py
+++ b/toolkit/samplers/custom_flowmatch_sampler.py
@@ -25,19 +25,29 @@ class CustomFlowMatchEulerDiscreteScheduler(FlowMatchEulerDiscreteScheduler):
             # Scale to make mean 1
             bsmntw_weighing = y_shifted * (num_timesteps / y_shifted.sum())
 
+            # only do half bell
+            hbsmntw_weighing = y_shifted * (num_timesteps / y_shifted.sum())
+
+            # flatten second half to max
+            hbsmntw_weighing[num_timesteps // 2:] = hbsmntw_weighing[num_timesteps // 2:].max()
+
             # Create linear timesteps from 1000 to 0
             timesteps = torch.linspace(1000, 0, num_timesteps, device='cpu')
 
             self.linear_timesteps = timesteps
             self.linear_timesteps_weights = bsmntw_weighing
+            self.linear_timesteps_weights2 = hbsmntw_weighing
             pass
 
-    def get_weights_for_timesteps(self, timesteps: torch.Tensor) -> torch.Tensor:
+    def get_weights_for_timesteps(self, timesteps: torch.Tensor, v2=False) -> torch.Tensor:
         # Get the indices of the timesteps
         step_indices = [(self.timesteps == t).nonzero().item() for t in timesteps]
 
         # Get the weights for the timesteps
-        weights = self.linear_timesteps_weights[step_indices].flatten()
+        if v2:
+            weights = self.linear_timesteps_weights2[step_indices].flatten()
+        else:
+            weights = self.linear_timesteps_weights[step_indices].flatten()
 
         return weights