Fix division-by-zero NaN in shift_terminal with a single denoising step

src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py

@@ -350,8 +350,11 @@ def set_timesteps(
         else:
             sigmas = self.shift * sigmas / (1 + (self.shift - 1) * sigmas)
 
-        # 3. If required, stretch the sigmas schedule to terminate at the configured `shift_terminal` value
-        if self.config.shift_terminal:
+        # 3. If required, stretch the sigmas schedule to terminate at the configured `shift_terminal` value. This is
+        #    skipped when there is only a single step, since there is nothing to stretch and the terminal rescaling
+        #    otherwise divides by zero (with the default schedule the single sigma is always 1.0, so
+        #    `one_minus_z[-1]` is always 0).
+        if self.config.shift_terminal and len(sigmas) > 1:
             sigmas = self.stretch_shift_to_terminal(sigmas)
 
         # 4. If required, convert sigmas to one of karras, exponential, or beta sigma schedules

src/diffusers/schedulers/scheduling_flow_match_lcm.py

@@ -359,8 +359,11 @@ def set_timesteps(
         else:
             sigmas = self.shift * sigmas / (1 + (self.shift - 1) * sigmas)  # type: ignore
 
-        # 3. If required, stretch the sigmas schedule to terminate at the configured `shift_terminal` value
-        if self.config.shift_terminal:
+        # 3. If required, stretch the sigmas schedule to terminate at the configured `shift_terminal` value. This is
+        #    skipped when there is only a single step, since there is nothing to stretch and the terminal rescaling
+        #    otherwise divides by zero (with the default schedule the single sigma is always 1.0, so
+        #    `one_minus_z[-1]` is always 0).
+        if self.config.shift_terminal and len(sigmas) > 1:
             sigmas = self.stretch_shift_to_terminal(sigmas)  # type: ignore
 
         # 4. If required, convert sigmas to one of karras, exponential, or beta sigma schedules

src/diffusers/schedulers/scheduling_unipc_multistep.py

@@ -432,7 +432,7 @@ def set_timesteps(
                 sigmas = self.time_shift(mu, 1.0, sigmas)
             else:
                 sigmas = self.config.flow_shift * sigmas / (1 + (self.config.flow_shift - 1) * sigmas)
-            if self.config.shift_terminal:
+            if self.config.shift_terminal and len(sigmas) > 1:
                 sigmas = self.stretch_shift_to_terminal(sigmas)
             eps = 1e-6
             if np.fabs(sigmas[0] - 1) < eps:

tests/schedulers/test_scheduler_shift_terminal_single_step.py

@@ -0,0 +1,45 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import torch
+
+from diffusers import FlowMatchEulerDiscreteScheduler, FlowMatchLCMScheduler, UniPCMultistepScheduler
+
+
+class ShiftTerminalSingleStepTest(unittest.TestCase):
+    """
+    Regression test for https://github.com/huggingface/diffusers/issues/14411.
+
+    `stretch_shift_to_terminal()` rescales sigmas by `one_minus_z[-1] / (1 - shift_terminal)`. With
+    `num_inference_steps=1` the only sigma is 1.0, so `one_minus_z[-1]` is 0 and the rescale divides by
+    zero, producing a NaN sigma. Schedulers that support `shift_terminal` must skip the stretch when
+    there is only a single step instead of stretching into NaN.
+    """
+
+    def test_flow_match_euler_discrete_single_step_no_nan(self):
+        scheduler = FlowMatchEulerDiscreteScheduler(shift_terminal=0.1)
+        scheduler.set_timesteps(num_inference_steps=1)
+        self.assertFalse(torch.isnan(scheduler.sigmas).any())
+
+    def test_flow_match_lcm_single_step_no_nan(self):
+        scheduler = FlowMatchLCMScheduler(shift_terminal=0.1)
+        scheduler.set_timesteps(num_inference_steps=1)
+        self.assertFalse(torch.isnan(scheduler.sigmas).any())
+
+    def test_unipc_flow_sigmas_single_step_no_nan(self):
+        scheduler = UniPCMultistepScheduler(use_flow_sigmas=True, shift_terminal=0.1)
+        scheduler.set_timesteps(num_inference_steps=1)
+        self.assertFalse(torch.isnan(scheduler.sigmas).any())