def scale_noise(
        self,
        sample: torch.FloatTensor,
        timestep: Union[float, torch.FloatTensor],
        noise: Optional[torch.FloatTensor] = None,
    ) -> torch.FloatTensor:
        """
        Forward process in flow-matching

        Args:
            sample (`torch.FloatTensor`):
                The input sample.
            timestep (`int`, *optional*):
                The current timestep in the diffusion chain.

        Returns:
            `torch.FloatTensor`:
                A scaled input sample.
        """
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        sigmas = self.sigmas.to(device=sample.device, dtype=sample.dtype)

        if sample.device.type == "mps" and torch.is_floating_point(timestep):
            # mps does not support float64
            schedule_timesteps = self.timesteps.to(sample.device, dtype=torch.float32)
            timestep = timestep.to(sample.device, dtype=torch.float32)
        else:
            schedule_timesteps = self.timesteps.to(sample.device)
            timestep = timestep.to(sample.device)

        # self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index
        if self.begin_index is None:
            step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timestep]
        elif self.step_index is not None:
            # add_noise is called after first denoising step (for inpainting)
            step_indices = [self.step_index] * timestep.shape[0]
        else:
            # add noise is called before first denoising step to create initial latent(img2img)
            step_indices = [self.begin_index] * timestep.shape[0]

        sigma = sigmas[step_indices].flatten()
        while len(sigma.shape) < len(sample.shape):
            sigma = sigma.unsqueeze(-1)

        sample = sigma * noise + (1.0 - sigma) * sample

        return sample

        Forward process in flow-matching

        Args:
            sample (`torch.FloatTensor`):
                The input sample.
            timestep (`int`, *optional*):
                The current timestep in the diffusion chain.

        Returns:
            `torch.FloatTensor`:
                A scaled input sample.
        
src/diffusers/schedulers/scheduling_flow_match_lcm.py