diff --git a/src/refiners/foundationals/latent_diffusion/solvers/euler.py b/src/refiners/foundationals/latent_diffusion/solvers/euler.py
index 17711cc..6643081 100644
--- a/src/refiners/foundationals/latent_diffusion/solvers/euler.py
+++ b/src/refiners/foundationals/latent_diffusion/solvers/euler.py
@@ -59,30 +59,6 @@ class Euler(Solver):
         predicted_noise: Tensor,
         step: int,
         generator: Generator | None = None,
-        s_churn: float = 0.0,
-        s_tmin: float = 0.0,
-        s_tmax: float = float("inf"),
-        s_noise: float = 1.0,
     ) -> Tensor:
         assert self.first_inference_step <= step < self.num_inference_steps, "invalid step {step}"
-
-        sigma = self.sigmas[step]
-
-        gamma = min(s_churn / (len(self.sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0
-
-        noise = torch.randn(
-            predicted_noise.shape, generator=generator, device=predicted_noise.device, dtype=predicted_noise.dtype
-        )
-        eps = noise * s_noise
-        sigma_hat = sigma * (gamma + 1)
-        if gamma > 0:
-            x = x + eps * (sigma_hat**2 - sigma**2) ** 0.5
-
-        predicted_x = x - sigma_hat * predicted_noise
-
-        # 1st order Euler
-        derivative = (x - predicted_x) / sigma_hat
-        dt = self.sigmas[step + 1] - sigma_hat
-        denoised_x = x + derivative * dt
-
-        return denoised_x
+        return x + predicted_noise * (self.sigmas[step + 1] - self.sigmas[step])