Fix img2img

1 files changed, 0 insertions, 24 deletions

diff --git a/schedulers/scheduling_euler_a.py b/schedulers/scheduling_euler_a.py
index 1b1c9cf..a2d0e9f 100644
--- a/schedulers/scheduling_euler_a.py
+++ b/schedulers/scheduling_euler_a.py
@@ -191,27 +191,6 @@ class EulerAScheduler(SchedulerMixin, ConfigMixin):
         self.sigmas = get_sigmas(self.DSsigmas, self.num_inference_steps).to(device=device)
         self.timesteps = np.arange(0, self.num_inference_steps)
 
-    def add_noise_to_input(
-        self, sample: torch.FloatTensor, sigma: float, generator: Optional[torch.Generator] = None
-    ) -> Tuple[torch.FloatTensor, float]:
-        """
-        Explicit Langevin-like "churn" step of adding noise to the sample according to a factor gamma_i ≥ 0 to reach a
-        higher noise level sigma_hat = sigma_i + gamma_i*sigma_i.
-
-        TODO Args:
-        """
-        if self.config.s_min <= sigma <= self.config.s_max:
-            gamma = min(self.config.s_churn / self.num_inference_steps, 2**0.5 - 1)
-        else:
-            gamma = 0
-
-        # sample eps ~ N(0, S_noise^2 * I)
-        eps = self.config.s_noise * torch.randn(sample.shape, generator=generator).to(sample.device)
-        sigma_hat = sigma + gamma * sigma
-        sample_hat = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
-
-        return sample_hat, sigma_hat
-
     def step(
         self,
         model_output: torch.FloatTensor,
@@ -219,9 +198,6 @@ class EulerAScheduler(SchedulerMixin, ConfigMixin):
         timestep_prev: int,
         sample: torch.FloatTensor,
         generator: None,
-        # ,sigma_hat: float,
-        #  sigma_prev: float,
-        # sample_hat: torch.FloatTensor,
         return_dict: bool = True,
     ) -> Union[SchedulerOutput, Tuple]:
         """