allow having at half precision when there is only one checkpoint in merger tab

chckpoint_dict_skip_on_merge = ["cond_stage_model.transformer.text_model.embeddings.position_ids"]

def to_half(tensor, enable):

    if enable and tensor.dtype == torch.float:

        return tensor.half()

    return tensor

def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae):

    shared.state.begin()

    shared.state.job = 'model-merge'

            else:

                theta_0[key] = theta_func2(a, b, multiplier)

            if save_as_half:

                theta_0[key] = theta_0[key].half()

            theta_0[key] = to_half(theta_0[key], save_as_half)

        shared.state.sampling_step += 1

        for key in vae_dict.keys():

            theta_0_key = 'first_stage_model.' + key

            if theta_0_key in theta_0:

                theta_0[theta_0_key] = vae_dict[key].half() if save_as_half else vae_dict[key]

                theta_0[theta_0_key] = to_half(vae_dict[key], save_as_half)

        del vae_dict

    if save_as_half and not theta_func2:

        for key in theta_0.keys():

            theta_0[key] = to_half(theta_0[key], save_as_half)

    ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path

    filename = filename_generator() if custom_name == '' else custom_name