f infer_params(state_dict):
    # this code is copied from https://github.com/victorca25/iNNfer
    scale2x = 0
    scalemin = 6
    n_uplayer = 0
    plus = False

    for block in list(state_dict):
        parts = block.split(".")
        n_parts = len(parts)
        if n_parts == 5 and parts[2] == "sub":
            nb = int(parts[3])
        elif n_parts == 3:
            part_num = int(parts[1])
            if (part_num > scalemin
                and parts[0] == "model"
                and parts[2] == "weight"):
                scale2x += 1
            if part_num > n_uplayer:
                n_uplayer = part_num
                out_nc = state_dict[block].shape[0]
        if not plus and "conv1x1" in block:
            plus = True

    nf = state_dict["model.0.weight"].shape[0]
    in_nc = state_dict["model.0.weight"].shape[1]
    out_nc = out_nc
    scale = 2 ** scale2x

    return in_nc, out_nc, nf, nb, plus, scale


class UpscalerESRGAN(Upscaler):
    def __init__(self, dirname):
        self.name = "ESRGAN"
        self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/ESRGAN.pth"
        self.model_name = "ESRGAN_4x"
        self.scalers = []
        self.user_path = dirname
        super().__init__()
        model_paths = self.find_models(ext_filter=[".pt", ".pth"])
        scalers = []
        if len(model_paths) == 0:
            scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)
            scalers.append(scaler_data)
        for file in model_paths:
            if file.startswith("http"):
                name = self.model_name
            else:
                name = modelloader.friendly_name(file)

            scaler_data = UpscalerData(name, file, self, 4)
            self.scalers.append(scaler_data)

    def do_upscale(self, img, selected_model):
        try:
            model = self.load_model(selected_model)
        except Exception as e:
            print(f"Unable to load ESRGAN model {selected_model}: {e}", file=sys.stderr)
            return img
        model.to(devices.device_esrgan)
        img = esrgan_upscale(model, img)
        return img

    def load_model(self, path: str):
        if path.startswith("http"):
            # TODO: this doesn't use `path` at all?
            filename = modelloader.load_file_from_url(
                url=self.model_url,
                model_dir=self.model_download_path,
                file_name=f"{self.model_name}.pth",
            )
        else:
            filename = path

        state_