Fix an error when DPM Solver Single Step contains duplicate timesteps

src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py

@@ -282,17 +282,25 @@ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.devic
 
         self.sigmas = torch.from_numpy(sigmas)
 
+        # when num_inference_steps == num_train_timesteps, we can end up with
+        # duplicates in timesteps.
+        _, unique_indices = np.unique(timesteps, return_index=True)
+        timesteps = timesteps[np.sort(unique_indices)]
+
         self.timesteps = torch.from_numpy(timesteps).to(device)
+
+        self.num_inference_steps = len(timesteps)
+
         self.model_outputs = [None] * self.config.solver_order
         self.sample = None
 
-        if not self.config.lower_order_final and num_inference_steps % self.config.solver_order != 0:
+        if not self.config.lower_order_final and self.num_inference_steps % self.config.solver_order != 0:
             logger.warn(
                 "Changing scheduler {self.config} to have `lower_order_final` set to True to handle uneven amount of inference steps. Please make sure to always use an even number of `num_inference steps when using `lower_order_final=True`."
             )
             self.register_to_config(lower_order_final=True)
 
-        self.order_list = self.get_order_list(num_inference_steps)
+        self.order_list = self.get_order_list(self.num_inference_steps)
 
     # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
     def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:

tests/schedulers/test_scheduler_dpm_single.py

@@ -248,3 +248,11 @@ def test_fp16_support(self):
             sample = scheduler.step(residual, t, sample).prev_sample
 
         assert sample.dtype == torch.float16
+
+    def test_unique_timesteps(self, **config):
+        for scheduler_class in self.scheduler_classes:
+            scheduler_config = self.get_scheduler_config(**config)
+            scheduler = scheduler_class(**scheduler_config)
+
+            scheduler.set_timesteps(scheduler.config.num_train_timesteps)
+            assert len(scheduler.timesteps.unique()) == scheduler.num_inference_steps