diff --git a/toolkit/guidance.py b/toolkit/guidance.py
index 540de72..0f71861 100644
--- a/toolkit/guidance.py
+++ b/toolkit/guidance.py
@@ -419,6 +419,15 @@ def get_guided_loss_polarity(
         conditional_latents = batch.latents.to(device, dtype=dtype).detach()
         unconditional_latents = batch.unconditional_latents.to(device, dtype=dtype).detach()
 
+        target_pos = noise
+        target_neg = noise
+
+        if sd.is_flow_matching:
+            # set the timesteps for flow matching as linear since we will do weighing
+            sd.noise_scheduler.set_train_timesteps(1000, device, linear=True)
+            target_pos = (noise - conditional_latents).detach()
+            target_neg = (noise - unconditional_latents).detach()
+
         conditional_noisy_latents = sd.add_noise(
             conditional_latents,
             noise,
@@ -459,19 +468,24 @@ def get_guided_loss_polarity(
 
     pred_loss = torch.nn.functional.mse_loss(
         pred_pos.float(),
-        noise.float(),
+        target_pos.float(),
         reduction="none"
     )
     # pred_loss = pred_loss.mean([1, 2, 3])
 
     pred_neg_loss = torch.nn.functional.mse_loss(
         pred_neg.float(),
-        noise.float(),
+        target_neg.float(),
         reduction="none"
     )
 
     loss = pred_loss + pred_neg_loss
 
+    if sd.is_flow_matching:
+        timestep_weight = sd.noise_scheduler.get_weights_for_timesteps(timesteps).to(loss.device, dtype=loss.dtype).detach()
+        loss = loss * timestep_weight
+
+
     loss = loss.mean([1, 2, 3])
     loss = loss.mean()
     if scaler is not None: