diff --git a/src/refiners/foundationals/latent_diffusion/schedulers/dpm_solver.py b/src/refiners/foundationals/latent_diffusion/schedulers/dpm_solver.py
index a7b2f60..438a964 100644
--- a/src/refiners/foundationals/latent_diffusion/schedulers/dpm_solver.py
+++ b/src/refiners/foundationals/latent_diffusion/schedulers/dpm_solver.py
@@ -1,6 +1,6 @@
 from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler
 import numpy as np
-from torch import Tensor, device as Device, tensor, exp
+from torch import Tensor, device as Device, tensor, exp, float32, dtype as Dtype
 from collections import deque
 
 
@@ -17,6 +17,7 @@ class DPMSolver(Scheduler):
         initial_diffusion_rate: float = 8.5e-4,
         final_diffusion_rate: float = 1.2e-2,
         device: Device | str = "cpu",
+        dtype: Dtype = float32,
     ):
         super().__init__(
             num_inference_steps=num_inference_steps,
@@ -24,6 +25,7 @@ class DPMSolver(Scheduler):
             initial_diffusion_rate=initial_diffusion_rate,
             final_diffusion_rate=final_diffusion_rate,
             device=device,
+            dtype=dtype,
         )
         self.estimated_data = deque([tensor([])] * 2, maxlen=2)
         self.initial_steps = 0
@@ -52,8 +54,8 @@ class DPMSolver(Scheduler):
             self.noise_std[previous_timestep],
             self.noise_std[timestep],
         )
-        exp_factor = exp(-(previous_ratio - current_ratio))
-        denoised_x = (previous_noise_std / current_noise_std) * x - (previous_scale_factor * (exp_factor - 1.0)) * noise
+        factor = exp(-(previous_ratio - current_ratio)) - 1.0
+        denoised_x = (previous_noise_std / current_noise_std) * x - (factor * previous_scale_factor) * noise
         return denoised_x
 
     def multistep_dpm_solver_second_order_update(self, x: Tensor, step: int) -> Tensor:
@@ -76,13 +78,13 @@ class DPMSolver(Scheduler):
         estimation_delta = (current_data_estimation - next_data_estimation) / (
             (current_ratio - next_ratio) / (previous_ratio - current_ratio)
         )
-        exp_neg_factor = exp(-(previous_ratio - current_ratio))
-        x_t = (
+        factor = exp(-(previous_ratio - current_ratio)) - 1.0
+        denoised_x = (
             (previous_std / current_std) * x
-            - (previous_scale_factor * (exp_neg_factor - 1.0)) * current_data_estimation
-            - 0.5 * (previous_scale_factor * (exp_neg_factor - 1.0)) * estimation_delta
+            - (factor * previous_scale_factor) * current_data_estimation
+            - 0.5 * (factor * previous_scale_factor) * estimation_delta
         )
-        return x_t
+        return denoised_x
 
     def __call__(
         self,