def opt(
        self,
        model_output: torch.FloatTensor,
        timestep: int,
        x: torch.FloatTensor,
    ):
        """
        predict the sample the next step in the denoise process.
        """
        ref_noise = model_output[:1, :, :, :].expand(model_output.shape)
        alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
        beta_prod_t = 1 - alpha_prod_t
        pred_x0 = (x - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
        x_opt = alpha_prod_t**0.5 * pred_x0 + (1 - alpha_prod_t) ** 0.5 * ref_noise
        return x_opt, pred_x0

        predict the sample the next step in the denoise process.
        
examples/community/pipeline_stable_diffusion_xl_attentive_eraser.py