def METHOD_NAME(self):
    model_id = 'damo/multi-modal_efficient-diffusion-tuning-control-lora'
    model_revision = 'v1.0.2'
    def cfg_modify_fn(cfg):
        cfg.train.max_epochs = self.max_epochs
        cfg.train.lr_scheduler.T_max = self.max_epochs
        cfg.model.inference = False
        return cfg
    kwargs = dict(
        model=model_id,
        model_revision=model_revision,
        work_dir=self.tmp_dir,
        train_dataset=self.train_dataset,
        eval_dataset=self.eval_dataset,
        cfg_modify_fn=cfg_modify_fn)
    trainer = build_trainer(
        name=Trainers.efficient_diffusion_tuning, default_args=kwargs)
    trainer.train()
    result = trainer.evaluate()
    print(
        f'Efficient-diffusion-tuning-control-lora train output: {result}.')
    results_files = os.listdir(self.tmp_dir)
    self.assertIn(f'{trainer.timestamp}.log.json', results_files)
    for i in range(self.max_epochs):
        self.assertIn(f'epoch_{i+1}.pth', results_files)