def load_processor(self, processor_id: str) -> 'Processor':
        """Load controlnet aux processors

        Args:
            processor_id (str): processor name

        Returns:
            Processor: controlnet aux processor
        """
        processor = MODELS[processor_id]['class']

        # check if the proecssor is a checkpoint model
        if MODELS[processor_id]['checkpoint']:
            processor = processor.from_pretrained("lllyasviel/Annotators")
        else:
            processor = processor()
        return processor

class LeresDetector:
    @classmethod
    def from_pretrained(cls, pretrained_model_or_path, filename=None, pix2pix_filename=None, cache_dir=None):
        filename = filename or "res101.pth"
        pix2pix_filename = pix2pix_filename or "latest_net_G.pth"

        if os.path.isdir(pretrained_model_or_path):
            model_path = os.path.join(pretrained_model_or_path, filename)
        else:
            model_path = hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir)
            
        checkpoint = torch.load(model_path, map_location=torch.device('cpu'))

        model = RelDepthModel(backbone='resnext101')
        model.load_state_dict(strip_prefix_if_present(checkpoint['depth_model'], "module."), strict=True)
        del checkpoint

        if os.path.isdir(pretrained_model_or_path):
            model_path = os.path.join(pretrained_model_or_path, pix2pix_filename)
        else:
            model_path = hf_hub_download(pretrained_model_or_path, pix2pix_filename, cache_dir=cache_dir)

        opt = TestOptions().parse()
        if not torch.cuda.is_available():
            opt.gpu_ids = []  # cpu mode
        pix2pixmodel = Pix2Pix4DepthModel(opt)
        pix2pixmodel.save_dir = os.path.dirname(model_path)
        pix2pixmodel.load_networks('latest')
        pix2pixmodel.eval()

        return cls(model, pix2pixmodel)

    def load_processor(self, processor_id: str, local_files_only: bool = False) -> 'Processor':
        """Load controlnet aux processors

        Args:
            processor_id (str): processor name

        Returns:
            Processor: controlnet aux processor
        """
        processor = MODELS[processor_id]['class']

        # check if the proecssor is a checkpoint model
        if MODELS[processor_id]['checkpoint']:
            processor = processor.from_pretrained("lllyasviel/Annotators", local_files_only=local_files_only)
        else:
            processor = processor()
        return processor

class LeresDetector:
    @classmethod
    def from_pretrained(cls, pretrained_model_or_path, filename=None, pix2pix_filename=None, cache_dir=None, local_files_only: bool = False):
        filename = filename or "res101.pth"
        pix2pix_filename = pix2pix_filename or "latest_net_G.pth"

        if os.path.isdir(pretrained_model_or_path):
            model_path = os.path.join(pretrained_model_or_path, filename)
        else:
            model_path = hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir, local_files_only=local_files_only)
            
        checkpoint = torch.load(model_path, map_location=torch.device('cpu'))

        model = RelDepthModel(backbone='resnext101')
        model.load_state_dict(strip_prefix_if_present(checkpoint['depth_model'], "module."), strict=True)
        del checkpoint

        if os.path.isdir(pretrained_model_or_path):
            model_path = os.path.join(pretrained_model_or_path, pix2pix_filename)
        else:
            model_path = hf_hub_download(pretrained_model_or_path, pix2pix_filename, cache_dir=cache_dir, local_files_only=local_files_only)

        opt = TestOptions().parse()
        if not torch.cuda.is_available():
            opt.gpu_ids = []  # cpu mode
        pix2pixmodel = Pix2Pix4DepthModel(opt)
        pix2pixmodel.save_dir = os.path.dirname(model_path)
        pix2pixmodel.load_networks('latest')
        pix2pixmodel.eval()

        return cls(model, pix2pixmodel)