Move batchsize check

    pin_memory = shared.opts.pin_memory

    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method)

    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize)

    if shared.opts.save_training_settings_to_txt:

        saved_params = dict(

        assert data_root, 'dataset directory not specified'

        assert os.path.isdir(data_root), "Dataset directory doesn't exist"

        assert os.listdir(data_root), "Dataset directory is empty"

        if varsize:

            assert batch_size == 1, 'variable img size must have batch size 1'

        self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]

                image = Image.open(path).convert('RGB')

                if not varsize:

                    image = image.resize((width, height), PIL.Image.BICUBIC)

                else:

                    assert batch_size == 1, 'variable img size must have batch size 1'

            except Exception:

                continue