@@ -51,21 +51,6 @@ def __init__(
         super().__init__()
         self.register_modules(unet=unet, scheduler=scheduler, mel=mel, vqvae=vqvae)
 
-    def get_input_dims(self) -> Tuple:
-        """Returns dimension of input image
-
-        Returns:
-            `Tuple`: (height, width)
-        """
-        input_module = self.vqvae if self.vqvae is not None else self.unet
-        # For backwards compatibility
-        sample_size = (
-            (input_module.config.sample_size, input_module.config.sample_size)
-            if type(input_module.config.sample_size) == int
-            else input_module.config.sample_size
-        )
-        return sample_size
-
     def get_default_steps(self) -> int:
         """Returns default number of steps recommended for inference
 
@@ -123,8 +108,6 @@ def __call__(
         # For backwards compatibility
         if type(self.unet.config.sample_size) == int:
             self.unet.config.sample_size = (self.unet.config.sample_size, self.unet.config.sample_size)
-        input_dims = self.get_input_dims()
-        self.mel.set_resolution(x_res=input_dims[1], y_res=input_dims[0])
         if noise is None:
             noise = randn_tensor(
                 (
@@ -234,7 +217,7 @@ def encode(self, images: List[Image.Image], steps: int = 50) -> np.ndarray:
         sample = torch.Tensor(sample).to(self.device)
 
         for t in self.progress_bar(torch.flip(self.scheduler.timesteps, (0,))):
-            prev_timestep = t - self.scheduler.num_train_timesteps // self.scheduler.num_inference_steps
+            prev_timestep = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
             alpha_prod_t = self.scheduler.alphas_cumprod[t]
             alpha_prod_t_prev = (
                 self.scheduler.alphas_cumprod[prev_timestep]