Validate num_inference_steps > 0 in DDPMScheduler.set_timesteps (#13394)

`DDPMScheduler.set_timesteps(num_inference_steps=0)` (or negative) silently
left the scheduler with an empty schedule and stale state, instead of failing
fast like other schedulers do. This made the bug invisible until a downstream
`step()` call produced wrong results.

Add the same `num_inference_steps must be a positive integer` check that
`scheduling_block_refinement` already has, propagated to `DDPMScheduler` and
its `# Copied from` sibling `DDPMSchedulerParallel` via `make fix-copies`.

Author: jbbqqf

src/diffusers/schedulers/scheduling_ddpm.py

@@ -308,6 +308,11 @@ def set_timesteps(
             timesteps = np.array(timesteps, dtype=np.int64)
             self.custom_timesteps = True
         else:
+            if num_inference_steps is None or num_inference_steps <= 0:
+                # Without this guard, `num_inference_steps == 0` produces an empty
+                # timestep schedule and downstream `step()` calls operate on stale
+                # state — silent rather than failing fast (#13394).
+                raise ValueError(f"`num_inference_steps` must be a positive integer, got {num_inference_steps}.")
             if num_inference_steps > self.config.num_train_timesteps:
                 raise ValueError(
                     f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"

src/diffusers/schedulers/scheduling_ddpm_parallel.py

@@ -323,6 +323,11 @@ def set_timesteps(
             timesteps = np.array(timesteps, dtype=np.int64)
             self.custom_timesteps = True
         else:
+            if num_inference_steps is None or num_inference_steps <= 0:
+                # Without this guard, `num_inference_steps == 0` produces an empty
+                # timestep schedule and downstream `step()` calls operate on stale
+                # state — silent rather than failing fast (#13394).
+                raise ValueError(f"`num_inference_steps` must be a positive integer, got {num_inference_steps}.")
             if num_inference_steps > self.config.num_train_timesteps:
                 raise ValueError(
                     f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"

tests/schedulers/test_scheduler_ddpm.py

@@ -190,6 +190,22 @@ def test_custom_timesteps_too_large(self):
         ):
             scheduler.set_timesteps(timesteps=timesteps)
 
+    def test_set_timesteps_rejects_non_positive_num_inference_steps(self):
+        # Regression for https://github.com/huggingface/diffusers/issues/13394:
+        # `num_inference_steps=0` previously left the scheduler in an unusable
+        # state silently. Both 0 and negative values should fail fast.
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config()
+        scheduler = scheduler_class(**scheduler_config)
+
+        with self.assertRaisesRegex(ValueError, r"`num_inference_steps` must be a positive integer"):
+            scheduler.set_timesteps(num_inference_steps=0)
+        with self.assertRaisesRegex(ValueError, r"`num_inference_steps` must be a positive integer"):
+            scheduler.set_timesteps(num_inference_steps=-1)
+        # Sanity check: a positive value still works after the rejected calls.
+        scheduler.set_timesteps(num_inference_steps=10)
+        self.assertEqual(scheduler.num_inference_steps, 10)
+
     def test_full_loop_with_noise(self):
         scheduler_class = self.scheduler_classes[0]
         scheduler_config = self.get_scheduler_config()