Fix duplicate timestep shift in FlowMatchEulerDiscreteScheduler

b/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py

@@ -0,0 +1,20 @@
+        self._shift = shift
+
+        self.sigmas = sigmas.to("cpu")  # to avoid too much CPU/GPU communication
+        self.sigma_min = unshifted_sigmas[-1].item()
+        self.sigma_max = unshifted_sigmas[0].item()
+
+    @property
+    def shift(self):
+        timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32)
+
+        sigmas = timesteps / num_train_timesteps
+
+        # Compute sigma_min/sigma_max from the unshifted sigmas so that set_timesteps()
+        # does not apply timestep shifting twice -- it already applies shift/recomputation
+        # from sigma_min/sigma_max.
+        unshifted_sigmas = sigmas.clone()
+
+        if not use_dynamic_shifting:
+            # when use_dynamic_shifting is True, we apply the timestep shifting on the fly based on the image resolution
+            sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)