def load_additional_layers(
        self, dtype: Optional[torch.dtype] = torch.float16, channel: int = 512, weight_path: Optional[str] = None
    ):
        """Load additional layers and weights from a file.

        Args:
            weight_path (str): Path to the weight file.
            dtype (torch.dtype, optional): Data type for the loaded weights. Defaults to torch.float16.
            channel (int): Conditioning embedding channel out size. Defaults 512.
        """
        if self.denoise_encoder is None:
            self.init_vae_encoder(dtype)

        if self.information_transformer_layes is None:
            self.init_information_transformer_layes()

        if self.condition_embedding is None:
            self.init_ControlNetConditioningEmbedding(channel)

        if self.agg_net is None:
            self.init_extra_weights()

        # Load weights if provided
        if weight_path is not None:
            state_dict = torch.load(weight_path, weights_only=False)
            self.load_state_dict(state_dict, strict=True)

        # Move all modules to the same device and dtype as the model
        device = next(self.parameters()).device
        if dtype is not None or device is not None:
            self.to(device=device, dtype=dtype or next(self.parameters()).dtype)
Load additional layers and weights from a file.

        Args:
            weight_path (str): Path to the weight file.
            dtype (torch.dtype, optional): Data type for the loaded weights. Defaults to torch.float16.
            channel (int): Conditioning embedding channel out size. Defaults 512.
        
examples/community/pipeline_faithdiff_stable_diffusion_xl.py