Update processing.py

            for i in range(samples.shape[0]):

                save_intermediate(samples, i)

            samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode)

            samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"])

            # Avoid making the inpainting conditioning unless necessary as

            # this does need some extra compute to decode / encode the image again.