finetune_instruct_pix2pix.py [1103:1120]:
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                            )
                        tracker.log({"validation": wandb_table})
                if args.use_ema:
                    # Switch back to the original UNet parameters.
                    ema_unet.restore(unet.parameters())

                del pipeline
                torch.cuda.empty_cache()

    # Create the pipeline using the trained modules and save it.
    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        unet = accelerator.unwrap_model(unet)
        if args.use_ema:
            ema_unet.copy_to(unet.parameters())

        pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained(
            args.pretrained_model_name_or_path,
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train_instruct_pix2pix.py [946:963]:
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                            )
                        tracker.log({"validation": wandb_table})
                if args.use_ema:
                    # Switch back to the original UNet parameters.
                    ema_unet.restore(unet.parameters())

                del pipeline
                torch.cuda.empty_cache()

    # Create the pipeline using the trained modules and save it.
    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        unet = accelerator.unwrap_model(unet)
        if args.use_ema:
            ema_unet.copy_to(unet.parameters())

        pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained(
            args.pretrained_model_name_or_path,
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