def encode_input_images(
        self,
        input_pixel_values: List[torch.Tensor],
        device: Optional[torch.device] = None,
        dtype: Optional[torch.dtype] = None,
    ):
        """
        get the continue embedding of input images by VAE

        Args:
            input_pixel_values: normalized pixel of input images
            device:
        Returns: torch.Tensor
        """
        device = device or self._execution_device
        dtype = dtype or self.vae.dtype

        input_img_latents = []
        for img in input_pixel_values:
            img = self.vae.encode(img.to(device, dtype)).latent_dist.sample().mul_(self.vae.config.scaling_factor)
            input_img_latents.append(img)
        return input_img_latents

        get the continue embedding of input images by VAE

        Args:
            input_pixel_values: normalized pixel of input images
            device:
        Returns: torch.Tensor
        
src/diffusers/pipelines/omnigen/pipeline_omnigen.py