timesteps

src/diffusers/schedulers/scheduling_euler_discrete.py

@@ -503,7 +503,7 @@ def set_timesteps(
         if self.config.timestep_type == "continuous" and self.config.prediction_type == "v_prediction":
             self.timesteps = torch.Tensor([0.25 * sigma.log() for sigma in sigmas[:-1]]).to(device=device)
         elif self.config.use_flow_match:
-            self.timesteps = sigmas * self.config.num_train_timesteps
+            self.timesteps = sigmas[:-1] * self.config.num_train_timesteps
         else:
             self.timesteps = torch.from_numpy(timesteps.astype(np.float32)).to(device=device)