simplify single_sample_to_image

def single_sample_to_image(sample, approximation=None):

    if approximation is None:

        approximation = approximation_indexes.get(opts.show_progress_type, 0)

    if approximation == 2:

        x_sample = sd_vae_approx.cheap_approximation(sample)

        x_sample = sd_vae_approx.cheap_approximation(sample) * 0.5 + 0.5

    elif approximation == 1:

        x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()

        x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach() * 0.5 + 0.5

    elif approximation == 3:

        x_sample = sample * 1.5

        x_sample = sd_vae_taesd.model()(x_sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()

    else:

        x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]

        x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0] * 0.5 + 0.5

    if approximation != 3:

        x_sample = (x_sample + 1.0) / 2.0

    x_sample = torch.clamp(x_sample, min=0.0, max=1.0)

    x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)

    x_sample = x_sample.astype(np.uint8)