def step_correct(
        self,
        model_output: torch.FloatTensor,
        sample: torch.FloatTensor,
        generator: Optional[torch.Generator] = None,
        return_dict: bool = True,
    ) -> Union[SchedulerOutput, Tuple]:
        """
        Correct the predicted sample based on the output model_output of the network. This is often run repeatedly
        after making the prediction for the previous timestep.

        Args:
            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
            sample (`torch.FloatTensor`):
                current instance of sample being created by diffusion process.
            generator: random number generator.
            return_dict (`bool`): option for returning tuple rather than SchedulerOutput class

        Returns:
            [`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`: [`~schedulers.scheduling_sde_ve.SdeVeOutput`] if
            `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.

        """
        if self.timesteps is None:
            raise ValueError(
                "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
            )

        # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
        # sample noise for correction
        noise = torch.randn(sample.shape, layout=sample.layout, generator=generator).to(sample.device)

        # compute step size from the model_output, the noise, and the snr
        grad_norm = torch.norm(model_output.reshape(model_output.shape[0], -1), dim=-1).mean()
        noise_norm = torch.norm(noise.reshape(noise.shape[0], -1), dim=-1).mean()
        step_size = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
        step_size = step_size * torch.ones(sample.shape[0]).to(sample.device)
        # self.repeat_scalar(step_size, sample.shape[0])

        # compute corrected sample: model_output term and noise term
        step_size = step_size.flatten()
        while len(step_size.shape) < len(sample.shape):
            step_size = step_size.unsqueeze(-1)
        prev_sample_mean = sample + step_size * model_output
        prev_sample = prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
            return (prev_sample,)

        return SchedulerOutput(prev_sample=prev_sample)

        Correct the predicted sample based on the output model_output of the network. This is often run repeatedly
        after making the prediction for the previous timestep.

        Args:
            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
            sample (`torch.FloatTensor`):
                current instance of sample being created by diffusion process.
            generator: random number generator.
            return_dict (`bool`): option for returning tuple rather than SchedulerOutput class

        Returns:
            [`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`: [`~schedulers.scheduling_sde_ve.SdeVeOutput`] if
            `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.

        
promptist/diffusers/src/diffusers/schedulers/scheduling_sde_ve.py