Fix bug in half precision for DPMSolverMultistepScheduler (#1349)

* cast to float for quantile method

* add fp16 test for DPMSolverMultistepScheduler fix

* formatting update

src/diffusers/schedulers/scheduling_dpmsolver_multistep.py

@@ -247,6 +247,9 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
 
             if self.config.thresholding:
                 # Dynamic thresholding in https://arxiv.org/abs/2205.11487
+                orig_dtype = x0_pred.dtype
+                if orig_dtype not in [torch.float, torch.double]:
+                    x0_pred = x0_pred.float()
                 dynamic_max_val = torch.quantile(
                     torch.abs(x0_pred).reshape((x0_pred.shape[0], -1)), self.config.dynamic_thresholding_ratio, dim=1
                 )
@@ -255,6 +258,7 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
                     self.config.sample_max_value * torch.ones_like(dynamic_max_val).to(dynamic_max_val.device),
                 )[(...,) + (None,) * (x0_pred.ndim - 1)]
                 x0_pred = torch.clamp(x0_pred, -dynamic_max_val, dynamic_max_val) / dynamic_max_val
+                x0_pred = x0_pred.type(orig_dtype)
             return x0_pred
         # DPM-Solver needs to solve an integral of the noise prediction model.
         elif self.config.algorithm_type == "dpmsolver":

tests/test_scheduler.py

@@ -991,6 +991,22 @@ class DPMSolverMultistepSchedulerTest(SchedulerCommonTest):
 
         assert abs(result_mean.item() - 0.3301) < 1e-3
 
+    def test_fp16_support(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config(thresholding=True, dynamic_thresholding_ratio=0)
+        scheduler = scheduler_class(**scheduler_config)
+
+        num_inference_steps = 10
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter.half()
+        scheduler.set_timesteps(num_inference_steps)
+
+        for i, t in enumerate(scheduler.timesteps):
+            residual = model(sample, t)
+            sample = scheduler.step(residual, t, sample).prev_sample
+
+        assert sample.dtype == torch.float16
+
 
 class PNDMSchedulerTest(SchedulerCommonTest):
     scheduler_classes = (PNDMScheduler,)