Fix img2img speed with LMS-Discrete Scheduler (#896)

Casting `self.sigmas` into a different dtype (the one of original_samples) is not advisable. In my img2img pipeline this leads to a long running time in the  `integrate.quad` call later on- by long I mean more than 10x slower.
Co-authored-by: Anton Lozhkov <anton@huggingface.co>

src/diffusers/schedulers/scheduling_lms_discrete.py

@@ -243,19 +243,18 @@ class LMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
         timesteps: torch.FloatTensor,
     ) -> torch.FloatTensor:
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
-        self.sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)
+        sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)
         if original_samples.device.type == "mps" and torch.is_floating_point(timesteps):
             # mps does not support float64
-            self.timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)
+            schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)
             timesteps = timesteps.to(original_samples.device, dtype=torch.float32)
         else:
-            self.timesteps = self.timesteps.to(original_samples.device)
+            schedule_timesteps = self.timesteps.to(original_samples.device)
             timesteps = timesteps.to(original_samples.device)
 
-        schedule_timesteps = self.timesteps
         step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
 
-        sigma = self.sigmas[step_indices].flatten()
+        sigma = sigmas[step_indices].flatten()
         while len(sigma.shape) < len(original_samples.shape):
             sigma = sigma.unsqueeze(-1)