fix precision of DPM solver in bfloat16

src/refiners/foundationals/latent_diffusion/solvers/dpm.py

@@ -77,7 +77,7 @@ def __init__(
             first_inference_step=first_inference_step,
             params=params,
             device=device,
-            dtype=dtype,
+            dtype=torch.float64,  # compute constants precisely
         )
         self.estimated_data = deque([torch.tensor([])] * 2, maxlen=2)
         self.last_step_first_order = last_step_first_order
@@ -89,6 +89,7 @@ def __init__(
             self.sigmas
         )
         self.timesteps = self._timesteps_from_sigmas(sigmas)
+        self.to(dtype=dtype)
 
     def rebuild(
         self: "DPMSolver",
@@ -131,12 +132,9 @@ def _rescale_sigmas(self, sigmas: torch.Tensor, sigma_schedule: NoiseSchedule |
             case NoiseSchedule.KARRAS:
                 rho = 7
             case None:
-                if sigmas.dtype == torch.bfloat16:
-                    sigmas = sigmas.to(torch.float32)
                 return torch.tensor(
                     np.interp(self.timesteps.cpu(), np.arange(0, len(sigmas)), sigmas.cpu()),
                     device=self.device,
-                    dtype=self.dtype,
                 )
 
         linear_schedule = torch.linspace(0, 1, steps=self.num_inference_steps, device=self.device)