Add back-compatibility to LMS timesteps (#750)

* Add back-compatibility to LMS timesteps

* style

src/diffusers/schedulers/scheduling_lms_discrete.py

@@ -202,11 +202,6 @@ class LMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
             When returning a tuple, the first element is the sample tensor.
 
         """
-        if not isinstance(timestep, float) and not isinstance(timestep, torch.FloatTensor):
-            warnings.warn(
-                f"`LMSDiscreteScheduler` timesteps must be `float` or `torch.FloatTensor`, not {type(timestep)}. "
-                "Make sure to pass one of the `scheduler.timesteps`"
-            )
         if not self.is_scale_input_called:
             warnings.warn(
                 "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
@@ -215,7 +210,18 @@ class LMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
 
         if isinstance(timestep, torch.Tensor):
             timestep = timestep.to(self.timesteps.device)
-        step_index = (self.timesteps == timestep).nonzero().item()
+        if (
+            isinstance(timestep, int)
+            or isinstance(timestep, torch.IntTensor)
+            or isinstance(timestep, torch.LongTensor)
+        ):
+            warnings.warn(
+                "Integer timesteps in `LMSDiscreteScheduler.step()` are deprecated and will be removed in version"
+                " 0.5.0. Make sure to pass one of the `scheduler.timesteps`."
+            )
+            step_index = timestep
+        else:
+            step_index = (self.timesteps == timestep).nonzero().item()
         sigma = self.sigmas[step_index]
 
         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
@@ -250,7 +256,14 @@ class LMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
         sigmas = self.sigmas.to(original_samples.device)
         schedule_timesteps = self.timesteps.to(original_samples.device)
         timesteps = timesteps.to(original_samples.device)
-        step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
+        if isinstance(timesteps, torch.IntTensor) or isinstance(timesteps, torch.LongTensor):
+            warnings.warn(
+                "Integer timesteps in `LMSDiscreteScheduler.add_noise()` are deprecated and will be removed in"
+                " version 0.5.0. Make sure to pass values from `scheduler.timesteps`."
+            )
+            step_indices = timesteps
+        else:
+            step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
 
         sigma = sigmas[step_indices].flatten()
         while len(sigma.shape) < len(original_samples.shape):