add

src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py

@@ -245,6 +245,13 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
             num_inference_steps = len(sigmas)
         self.num_inference_steps = num_inference_steps
 
+        if self.config.use_karras_sigmas:
+            sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
+        elif self.config.use_exponential_sigmas:
+            sigmas = self._convert_to_exponential(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
+
+        elif self.config.use_beta_sigmas:
+            sigmas = self._convert_to_beta(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
         if self.config.use_dynamic_shifting:
             sigmas = self.time_shift(mu, 1.0, sigmas)
         else:
@@ -253,14 +260,7 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
         if self.config.shift_terminal:
             sigmas = self.stretch_shift_to_terminal(sigmas)
 
-        if self.config.use_karras_sigmas:
-            sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
 
-        elif self.config.use_exponential_sigmas:
-            sigmas = self._convert_to_exponential(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
-
-        elif self.config.use_beta_sigmas:
-            sigmas = self._convert_to_beta(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
 
         sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device)
         timesteps = sigmas * self.config.num_train_timesteps