def scale_model_input(
        self, sample: torch.FloatTensor, timestep: Union[float, torch.FloatTensor]
    ) -> torch.FloatTensor:
        """
        Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm.

        Args:
            sample (`torch.FloatTensor`): input sample
            timestep (`float` or `torch.FloatTensor`): the current timestep in the diffusion chain

        Returns:
            `torch.FloatTensor`: scaled input sample
        """
        if isinstance(timestep, torch.Tensor):
            timestep = timestep.to(self.timesteps.device)
        step_index = (self.timesteps == timestep).nonzero().item()
        sigma = self.sigmas[step_index]
        sample = sample / ((sigma**2 + 1) ** 0.5)
        self.is_scale_input_called = True
        return sample

        Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm.

        Args:
            sample (`torch.FloatTensor`): input sample
            timestep (`float` or `torch.FloatTensor`): the current timestep in the diffusion chain

        Returns:
            `torch.FloatTensor`: scaled input sample
        
promptist/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py