Easily understandable error if inference steps not set before using scheduler (#263) (#264)

* Helpful exception if inference steps not set in schedulers (#263)

* Apply suggestions from codereview by patrickvonplaten

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>

src/diffusers/schedulers/scheduling_ddim.py

@@ -117,6 +117,11 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
         use_clipped_model_output: bool = False,
         generator=None,
     ):
+        if self.num_inference_steps is None:
+            raise ValueError(
+                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
+            )
+
         # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
         # Ideally, read DDIM paper in-detail understanding
 

src/diffusers/schedulers/scheduling_pndm.py

@@ -145,6 +145,11 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
         Step function propagating the sample with the Runge-Kutta method. RK takes 4 forward passes to approximate the
         solution to the differential equation.
         """
+        if self.num_inference_steps is None:
+            raise ValueError(
+                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
+            )
+
         diff_to_prev = 0 if self.counter % 2 else self.config.num_train_timesteps // self.num_inference_steps // 2
         prev_timestep = max(timestep - diff_to_prev, self.prk_timesteps[-1])
         timestep = self.prk_timesteps[self.counter // 4 * 4]
@@ -179,6 +184,11 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
         Step function propagating the sample with the linear multi-step method. This has one forward pass with multiple
         times to approximate the solution.
         """
+        if self.num_inference_steps is None:
+            raise ValueError(
+                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
+            )
+
         if not self.config.skip_prk_steps and len(self.ets) < 3:
             raise ValueError(
                 f"{self.__class__} can only be run AFTER scheduler has been run "

src/diffusers/schedulers/scheduling_sde_ve.py

@@ -120,6 +120,11 @@ class ScoreSdeVeScheduler(SchedulerMixin, ConfigMixin):
             self.set_seed(seed)
         # TODO(Patrick) non-PyTorch
 
+        if self.timesteps is None:
+            raise ValueError(
+                "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
+            )
+
         timestep = timestep * torch.ones(
             sample.shape[0], device=sample.device
         )  # torch.repeat_interleave(timestep, sample.shape[0])
@@ -155,6 +160,11 @@ class ScoreSdeVeScheduler(SchedulerMixin, ConfigMixin):
         if seed is not None:
             self.set_seed(seed)
 
+        if self.timesteps is None:
+            raise ValueError(
+                "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
+            )
+
         # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
         # sample noise for correction
         noise = self.randn_like(sample)

src/diffusers/schedulers/scheduling_sde_vp.py

@@ -35,6 +35,11 @@ class ScoreSdeVpScheduler(SchedulerMixin, ConfigMixin):
         self.timesteps = torch.linspace(1, self.config.sampling_eps, num_inference_steps)
 
     def step_pred(self, score, x, t):
+        if self.timesteps is None:
+            raise ValueError(
+                "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
+            )
+
         # TODO(Patrick) better comments + non-PyTorch
         # postprocess model score
         log_mean_coeff = (