code: def main():
      args = parse_args()
  
      if args.non_ema_revision is not None:
          deprecate(
              "non_ema_revision!=None",
              "0.15.0",
              message=(
                  "Downloading 'non_ema' weights from revision branches of the Hub is deprecated. Please make sure to"
                  " use `--variant=non_ema` instead."
              ),
          )
      logging_dir = os.path.join(args.output_dir, args.logging_dir)
      accelerator_project_config = ProjectConfiguration(
          project_dir=args.output_dir, logging_dir=logging_dir
      )
      accelerator = Accelerator(
          gradient_accumulation_steps=args.gradient_accumulation_steps,
          mixed_precision=args.mixed_precision,
          log_with=args.report_to,
          project_config=accelerator_project_config,
      )
  
      generator = torch.Generator(
          device=accelerator.device
      ).manual_seed(args.seed)
  
      if args.report_to == "wandb":
          if not is_wandb_available():
              raise ImportError(
                  "Make sure to install wandb if you want to use it for logging during training."
              )
          import wandb
  
      # Make one log on every process with the configuration for debugging.
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          level=logging.INFO,
      )
      logger.info(accelerator.state, main_process_only=False)
      if accelerator.is_local_main_process:
          datasets.utils.logging.set_verbosity_warning()
          transformers.utils.logging.set_verbosity_warning()
          diffusers.utils.logging.set_verbosity_info()
      else:
          datasets.utils.logging.set_verbosity_error()
          transformers.utils.logging.set_verbosity_error()
          diffusers.utils.logging.set_verbosity_error()
  
      # If passed along, set the training seed now.
      if args.seed is not None:
          set_seed(args.seed)
  
      # Handle the repository creation
      if accelerator.is_main_process:
          if args.output_dir is not None:
              os.makedirs(args.output_dir, exist_ok=True)
  
          if args.push_to_hub:
              repo_id = create_repo(
                  repo_id=args.hub_model_id
                  or Path(args.output_dir).name,
                  exist_ok=True,
                  token=args.hub_token,
              ).repo_id
  
      # Load scheduler, tokenizer and models.
      noise_scheduler = DDPMScheduler.from_pretrained(
          args.pretrained_model_name_or_path, subfolder="scheduler"
      )
      tokenizer = CLIPTokenizer.from_pretrained(
          args.pretrained_model_name_or_path,
          subfolder="tokenizer",
          revision=args.revision,
      )
      text_encoder = CLIPTextModel.from_pretrained(
          args.pretrained_model_name_or_path,
          subfolder="text_encoder",
          revision=args.revision,
          variant=args.variant,
      )
      vae = AutoencoderKL.from_pretrained(
          args.pretrained_model_name_or_path,
          subfolder="vae",
          revision=args.revision,
          variant=args.variant,
      )
      unet = UNet2DConditionModel.from_pretrained(
          args.pretrained_model_name_or_path,
          subfolder="unet",
          revision=args.non_ema_revision,
      )
  
      # InstructPix2Pix uses an additional image for conditioning. To accommodate that,
      # it uses 8 channels (instead of 4) in the first (conv) layer of the UNet. This UNet is
      # then fine-tuned on the custom InstructPix2Pix dataset. This modified UNet is initialized
      # from the pre-trained checkpoints. For the extra channels added to the first layer, they are
      # initialized to zero.
      logger.info(
          "Initializing the InstructPix2Pix UNet from the pretrained UNet."
      )
      in_channels = 8
      out_channels = unet.conv_in.out_channels
      unet.register_to_config(in_channels=in_channels)
  
      with torch.no_grad():
          new_conv_in = nn.Conv2d(
              in_channels,
              out_channels,
              unet.conv_in.kernel_size,
              unet.conv_in.stride,
              unet.conv_in.padding,
          )
          new_conv_in.weight.zero_()
          new_conv_in.weight[:, :4, :, :].copy_(unet.conv_in.weight)
          unet.conv_in = new_conv_in
  
      # Freeze vae and text_encoder
      vae.requires_grad_(False)
      text_encoder.requires_grad_(False)
  
      # Create EMA for the unet.
      if args.use_ema:
          ema_unet = EMAModel(
              unet.parameters(),
              model_cls=UNet2DConditionModel,
              model_config=unet.config,
          )
  
      if args.enable_xformers_memory_efficient_attention:
          if is_xformers_available():
              import xformers
  
              xformers_version = version.parse(xformers.__version__)
              if xformers_version == version.parse("0.0.16"):
                  logger.warn(
                      "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
                  )
              unet.enable_xformers_memory_efficient_attention()
          else:
              raise ValueError(
                  "xformers is not available. Make sure it is installed correctly"
              )
  
      def unwrap_model(model):
          model = accelerator.unwrap_model(model)
          model = (
              model._orig_mod if is_compiled_module(model) else model
          )
          return model
  
      # `accelerate` 0.16.0 will have better support for customized saving
      if version.parse(accelerate.__version__) >= version.parse(
          "0.16.0"
      ):
          # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
          def save_model_hook(models, weights, output_dir):
              if accelerator.is_main_process:
                  if args.use_ema:
                      ema_unet.save_pretrained(
                          os.path.join(output_dir, "unet_ema")
                      )
  
                  for i, model in enumerate(models):
                      model.save_pretrained(
                          os.path.join(output_dir, "unet")
                      )
  
                      # make sure to pop weight so that corresponding model is not saved again
                      weights.pop()
  
          def load_model_hook(models, input_dir):
              if args.use_ema:
                  load_model = EMAModel.from_pretrained(
                      os.path.join(input_dir, "unet_ema"),
                      UNet2DConditionModel,
                  )
                  ema_unet.load_state_dict(load_model.state_dict())
                  ema_unet.to(accelerator.device)
                  del load_model
  
              for i in range(len(models)):
                  # pop models so that they are not loaded again
                  model = models.pop()
  
                  # load diffusers style into model
                  load_model = UNet2DConditionModel.from_pretrained(
                      input_dir, subfolder="unet"
                  )
                  model.register_to_config(**load_model.config)
  
                  model.load_state_dict(load_model.state_dict())
                  del load_model
  
          accelerator.register_save_state_pre_hook(save_model_hook)
          accelerator.register_load_state_pre_hook(load_model_hook)
  
      if args.gradient_checkpointing:
          unet.enable_gradient_checkpointing()
  
      # Enable TF32 for faster training on Ampere GPUs,
      # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
      if args.allow_tf32:
          torch.backends.cuda.matmul.allow_tf32 = True
  
      if args.scale_lr:
          args.learning_rate = (
              args.learning_rate
              * args.gradient_accumulation_steps
              * args.train_batch_size
              * accelerator.num_processes
          )
  
      # Initialize the optimizer
      if args.use_8bit_adam:
          try:
              import bitsandbytes as bnb
          except ImportError:
              raise ImportError(
                  "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
              )
  
          optimizer_cls = bnb.optim.AdamW8bit
      else:
          optimizer_cls = torch.optim.AdamW
  
      optimizer = optimizer_cls(
          unet.parameters(),
          lr=args.learning_rate,
          betas=(args.adam_beta1, args.adam_beta2),
          weight_decay=args.adam_weight_decay,
          eps=args.adam_epsilon,
      )
  
      # Get the datasets: you can either provide your own training and evaluation files (see below)
      # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
  
      # In distributed training, the load_dataset function guarantees that only one local process can concurrently
      # download the dataset.
      if args.dataset_name is not None:
          # Downloading and loading a dataset from the hub.
          dataset = load_dataset(
              args.dataset_name,
              args.dataset_config_name,
              cache_dir=args.cache_dir,
          )
      else:
          data_files = {}
          if args.train_data_dir is not None:
              data_files["train"] = os.path.join(
                  args.train_data_dir, "**"
              )
          dataset = load_dataset(
              "imagefolder",
              data_files=data_files,
              cache_dir=args.cache_dir,
          )
          # See more about loading custom images at
          # https://huggingface.co/docs/datasets/main/en/image_load#imagefolder
  
      # Preprocessing the datasets.
      # We need to tokenize inputs and targets.
      column_names = dataset["train"].column_names
  
      # 6. Get the column names for input/target.
      dataset_columns = DATASET_NAME_MAPPING.get(
          args.dataset_name, None
      )
      if args.original_image_column is None:
          original_image_column = (
              dataset_columns[0]
              if dataset_columns is not None
              else column_names[0]
          )
      else:
          original_image_column = args.original_image_column
          if original_image_column not in column_names:
              raise ValueError(
                  f"--original_image_column' value '{args.original_image_column}' needs to be one of: {', '.join(column_names)}"
              )
      if args.edit_prompt_column is None:
          edit_prompt_column = (
              dataset_columns[1]
              if dataset_columns is not None
              else column_names[1]
          )
      else:
          edit_prompt_column = args.edit_prompt_column
          if edit_prompt_column not in column_names:
              raise ValueError(
                  f"--edit_prompt_column' value '{args.edit_prompt_column}' needs to be one of: {', '.join(column_names)}"
              )
      if args.edited_image_column is None:
          edited_image_column = (
              dataset_columns[2]
              if dataset_columns is not None
              else column_names[2]
          )
      else:
          edited_image_column = args.edited_image_column
          if edited_image_column not in column_names:
              raise ValueError(
                  f"--edited_image_column' value '{args.edited_image_column}' needs to be one of: {', '.join(column_names)}"
              )
  
      # Preprocessing the datasets.
      # We need to tokenize input captions and transform the images.
      def tokenize_captions(captions):
          inputs = tokenizer(
              captions,
              max_length=tokenizer.model_max_length,
              padding="max_length",
              truncation=True,
              return_tensors="pt",
          )
          return inputs.input_ids
  
      # Preprocessing the datasets.
      train_transforms = transforms.Compose(
          [
              transforms.CenterCrop(args.resolution)
              if args.center_crop
              else transforms.RandomCrop(args.resolution),
              transforms.RandomHorizontalFlip()
              if args.random_flip
              else transforms.Lambda(lambda x: x),
          ]
      )
  
      def preprocess_images(examples):
          original_images = np.concatenate(
              [
                  convert_to_np(image, args.resolution)
                  for image in examples[original_image_column]
              ]
          )
          edited_images = np.concatenate(
              [
                  convert_to_np(image, args.resolution)
                  for image in examples[edited_image_column]
              ]
          )
          # We need to ensure that the original and the edited images undergo the same
          # augmentation transforms.
          images = np.concatenate([original_images, edited_images])
          images = torch.tensor(images)
          images = 2 * (images / 255) - 1
          return train_transforms(images)
  
      def preprocess_train(examples):
          # Preprocess images.
          preprocessed_images = preprocess_images(examples)
          # Since the original and edited images were concatenated before
          # applying the transformations, we need to separate them and reshape
          # them accordingly.
          original_images, edited_images = preprocessed_images.chunk(2)
          original_images = original_images.reshape(
              -1, 3, args.resolution, args.resolution
          )
          edited_images = edited_images.reshape(
              -1, 3, args.resolution, args.resolution
          )
  
          # Collate the preprocessed images into the `examples`.
          examples["original_pixel_values"] = original_images
          examples["edited_pixel_values"] = edited_images
  
          # Preprocess the captions.
          captions = list(examples[edit_prompt_column])
          examples["input_ids"] = tokenize_captions(captions)
          return examples
  
      with accelerator.main_process_first():
          if args.max_train_samples is not None:
              dataset["train"] = (
                  dataset["train"]
                  .shuffle(seed=args.seed)
                  .select(range(args.max_train_samples))
              )
          # Set the training transforms
          train_dataset = dataset["train"].with_transform(
              preprocess_train
          )
  
      def collate_fn(examples):
          original_pixel_values = torch.stack(
              [example["original_pixel_values"] for example in examples]
          )
          original_pixel_values = original_pixel_values.to(
              memory_format=torch.contiguous_format
          ).float()
          edited_pixel_values = torch.stack(
              [example["edited_pixel_values"] for example in examples]
          )
          edited_pixel_values = edited_pixel_values.to(
              memory_format=torch.contiguous_format
          ).float()
          input_ids = torch.stack(
              [example["input_ids"] for example in examples]
          )
          return {
              "original_pixel_values": original_pixel_values,
              "edited_pixel_values": edited_pixel_values,
              "input_ids": input_ids,
          }
  
      # DataLoaders creation:
      train_dataloader = torch.utils.data.DataLoader(
          train_dataset,
          shuffle=True,
          collate_fn=collate_fn,
          batch_size=args.train_batch_size,
          num_workers=args.dataloader_num_workers,
      )
  
      # Scheduler and math around the number of training steps.
      overrode_max_train_steps = False
      num_update_steps_per_epoch = math.ceil(
          len(train_dataloader) / args.gradient_accumulation_steps
      )
      if args.max_train_steps is None:
          args.max_train_steps = (
              args.num_train_epochs * num_update_steps_per_epoch
          )
          overrode_max_train_steps = True
  
      lr_scheduler = get_scheduler(
          args.lr_scheduler,
          optimizer=optimizer,
          num_warmup_steps=args.lr_warmup_steps
          * accelerator.num_processes,
          num_training_steps=args.max_train_steps
          * accelerator.num_processes,
      )
  
      # Prepare everything with our `accelerator`.
      (
          unet,
          optimizer,
          train_dataloader,
          lr_scheduler,
      ) = accelerator.prepare(
          unet, optimizer, train_dataloader, lr_scheduler
      )
  
      if args.use_ema:
          ema_unet.to(accelerator.device)
  
      # For mixed precision training we cast the text_encoder and vae weights to half-precision
      # as these models are only used for inference, keeping weights in full precision is not required.
      weight_dtype = torch.float32
      if accelerator.mixed_precision == "fp16":
          weight_dtype = torch.float16
      elif accelerator.mixed_precision == "bf16":
          weight_dtype = torch.bfloat16
  
      # Move text_encode and vae to gpu and cast to weight_dtype
      text_encoder.to(accelerator.device, dtype=weight_dtype)
      vae.to(accelerator.device, dtype=weight_dtype)
  
      # We need to recalculate our total training steps as the size of the training dataloader may have changed.
      num_update_steps_per_epoch = math.ceil(
          len(train_dataloader) / args.gradient_accumulation_steps
      )
      if overrode_max_train_steps:
          args.max_train_steps = (
              args.num_train_epochs * num_update_steps_per_epoch
          )
      # Afterwards we recalculate our number of training epochs
      args.num_train_epochs = math.ceil(
          args.max_train_steps / num_update_steps_per_epoch
      )
  
      # We need to initialize the trackers we use, and also store our configuration.
      # The trackers initializes automatically on the main process.
      if accelerator.is_main_process:
          accelerator.init_trackers(
              "instruct-pix2pix", config=vars(args)
          )
  
      # Train!
      total_batch_size = (
          args.train_batch_size
          * accelerator.num_processes
          * args.gradient_accumulation_steps
      )
  
      logger.info("***** Running training *****")
      logger.info(f"  Num examples = {len(train_dataset)}")
      logger.info(f"  Num Epochs = {args.num_train_epochs}")
      logger.info(
          f"  Instantaneous batch size per device = {args.train_batch_size}"
      )
      logger.info(
          f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}"
      )
      logger.info(
          f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}"
      )
      logger.info(
          f"  Total optimization steps = {args.max_train_steps}"
      )
      global_step = 0
      first_epoch = 0
  
      # Potentially load in the weights and states from a previous save
      if args.resume_from_checkpoint:
          if args.resume_from_checkpoint != "latest":
              path = os.path.basename(args.resume_from_checkpoint)
          else:
              # Get the most recent checkpoint
              dirs = os.listdir(args.output_dir)
              dirs = [d for d in dirs if d.startswith("checkpoint")]
              dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
              path = dirs[-1] if len(dirs) > 0 else None
  
          if path is None:
              accelerator.print(
                  f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
              )
              args.resume_from_checkpoint = None
          else:
              accelerator.print(f"Resuming from checkpoint {path}")
              accelerator.load_state(
                  os.path.join(args.output_dir, path)
              )
              global_step = int(path.split("-")[1])
  
              resume_global_step = (
                  global_step * args.gradient_accumulation_steps
              )
              first_epoch = global_step // num_update_steps_per_epoch
              resume_step = resume_global_step % (
                  num_update_steps_per_epoch
                  * args.gradient_accumulation_steps
              )
  
      # Only show the progress bar once on each machine.
      progress_bar = tqdm(
          range(global_step, args.max_train_steps),
          disable=not accelerator.is_local_main_process,
      )
      progress_bar.set_description("Steps")
  
      for epoch in range(first_epoch, args.num_train_epochs):
          unet.train()
          train_loss = 0.0
          for step, batch in enumerate(train_dataloader):
              # Skip steps until we reach the resumed step
              if (
                  args.resume_from_checkpoint
                  and epoch == first_epoch
                  and step < resume_step
              ):
                  if step % args.gradient_accumulation_steps == 0:
                      progress_bar.update(1)
                  continue
  
              with accelerator.accumulate(unet):
                  # We want to learn the denoising process w.r.t the edited images which
                  # are conditioned on the original image (which was edited) and the edit instruction.
                  # So, first, convert images to latent space.
                  latents = vae.encode(
                      batch["edited_pixel_values"].to(weight_dtype)
                  ).latent_dist.sample()
                  latents = latents * vae.config.scaling_factor
  
                  # Sample noise that we'll add to the latents
                  noise = torch.randn_like(latents)
                  bsz = latents.shape[0]
                  # Sample a random timestep for each image
                  timesteps = torch.randint(
                      0,
                      noise_scheduler.config.num_train_timesteps,
                      (bsz,),
                      device=latents.device,
                  )
                  timesteps = timesteps.long()
  
                  # Add noise to the latents according to the noise magnitude at each timestep
                  # (this is the forward diffusion process)
                  noisy_latents = noise_scheduler.add_noise(
                      latents, noise, timesteps
                  )
  
                  # Get the text embedding for conditioning.
                  encoder_hidden_states = text_encoder(
                      batch["input_ids"]
                  )[0]
  
                  # Get the additional image embedding for conditioning.
                  # Instead of getting a diagonal Gaussian here, we simply take the mode.
                  original_image_embeds = vae.encode(
                      batch["original_pixel_values"].to(weight_dtype)
                  ).latent_dist.mode()
  
                  # Conditioning dropout to support classifier-free guidance during inference. For more details
                  # check out the section 3.2.1 of the original paper https://arxiv.org/abs/2211.09800.
                  if args.conditioning_dropout_prob is not None:
                      random_p = torch.rand(
                          bsz,
                          device=latents.device,
                          generator=generator,
                      )
                      # Sample masks for the edit prompts.
                      prompt_mask = (
                          random_p < 2 * args.conditioning_dropout_prob
                      )
                      prompt_mask = prompt_mask.reshape(bsz, 1, 1)
                      # Final text conditioning.
                      null_conditioning = text_encoder(
                          tokenize_captions([""]).to(accelerator.device)
                      )[0]
                      encoder_hidden_states = torch.where(
                          prompt_mask,
                          null_conditioning,
                          encoder_hidden_states,
                      )
  
                      # Sample masks for the original images.
                      image_mask_dtype = original_image_embeds.dtype
                      image_mask = 1 - (
                          (
                              random_p >= args.conditioning_dropout_prob
                          ).to(image_mask_dtype)
                          * (
                              random_p
                              < 3 * args.conditioning_dropout_prob
                          ).to(image_mask_dtype)
                      )
                      image_mask = image_mask.reshape(bsz, 1, 1, 1)
                      # Final image conditioning.
                      original_image_embeds = (
                          image_mask * original_image_embeds
                      )
  
                  # Concatenate the `original_image_embeds` with the `noisy_latents`.
                  concatenated_noisy_latents = torch.cat(
                      [noisy_latents, original_image_embeds], dim=1
                  )
  
                  # Get the target for loss depending on the prediction type
                  if (
                      noise_scheduler.config.prediction_type
                      == "epsilon"
                  ):
                      target = noise
                  elif (
                      noise_scheduler.config.prediction_type
                      == "v_prediction"
                  ):
                      target = noise_scheduler.get_velocity(
                          latents, noise, timesteps
                      )
                  else:
                      raise ValueError(
                          f"Unknown prediction type {noise_scheduler.config.prediction_type}"
                      )
  
                  # Predict the noise residual and compute loss
                  model_pred = unet(
                      concatenated_noisy_latents,
                      timesteps,
                      encoder_hidden_states,
                      return_dict=False,
                  )[0]
                  loss = F.mse_loss(
                      model_pred.float(),
                      target.float(),
                      reduction="mean",
                  )
  
                  # Gather the losses across all processes for logging (if we use distributed training).
                  avg_loss = accelerator.gather(
                      loss.repeat(args.train_batch_size)
                  ).mean()
                  train_loss += (
                      avg_loss.item() / args.gradient_accumulation_steps
                  )
  
                  # Backpropagate
                  accelerator.backward(loss)
                  if accelerator.sync_gradients:
                      accelerator.clip_grad_norm_(
                          unet.parameters(), args.max_grad_norm
                      )
                  optimizer.step()
                  lr_scheduler.step()
                  optimizer.zero_grad()
  
              # Checks if the accelerator has performed an optimization step behind the scenes
              if accelerator.sync_gradients:
                  if args.use_ema:
                      ema_unet.step(unet.parameters())
                  progress_bar.update(1)
                  global_step += 1
                  accelerator.log(
                      {"train_loss": train_loss}, step=global_step
                  )
                  train_loss = 0.0
  
                  if global_step % args.checkpointing_steps == 0:
                      if accelerator.is_main_process:
                          # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
                          if args.checkpoints_total_limit is not None:
                              checkpoints = os.listdir(args.output_dir)
                              checkpoints = [
                                  d
                                  for d in checkpoints
                                  if d.startswith("checkpoint")
                              ]
                              checkpoints = sorted(
                                  checkpoints,
                                  key=lambda x: int(x.split("-")[1]),
                              )
  
                              # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
                              if (
                                  len(checkpoints)
                                  >= args.checkpoints_total_limit
                              ):
                                  num_to_remove = (
                                      len(checkpoints)
                                      - args.checkpoints_total_limit
                                      + 1
                                  )
                                  removing_checkpoints = checkpoints[
                                      0:num_to_remove
                                  ]
  
                                  logger.info(
                                      f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
                                  )
                                  logger.info(
                                      f"removing checkpoints: {', '.join(removing_checkpoints)}"
                                  )
  
                                  for (
                                      removing_checkpoint
                                  ) in removing_checkpoints:
                                      removing_checkpoint = (
                                          os.path.join(
                                              args.output_dir,
                                              removing_checkpoint,
                                          )
                                      )
                                      shutil.rmtree(removing_checkpoint)
  
                          save_path = os.path.join(
                              args.output_dir,
                              f"checkpoint-{global_step}",
                          )
                          accelerator.save_state(save_path)
                          logger.info(f"Saved state to {save_path}")
  
              logs = {
                  "step_loss": loss.detach().item(),
                  "lr": lr_scheduler.get_last_lr()[0],
              }
              progress_bar.set_postfix(**logs)
  
              if global_step >= args.max_train_steps:
                  break
  
          if accelerator.is_main_process:
              if (
                  (args.val_image_url is not None)
                  and (args.validation_prompt is not None)
                  and (epoch % args.validation_epochs == 0)
              ):
                  logger.info(
                      f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
                      f" {args.validation_prompt}."
                  )
                  # create pipeline
                  if args.use_ema:
                      # Store the UNet parameters temporarily and load the EMA parameters to perform inference.
                      ema_unet.store(unet.parameters())
                      ema_unet.copy_to(unet.parameters())
                  # The models need unwrapping because for compatibility in distributed training mode.
                  pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained(
                      args.pretrained_model_name_or_path,
                      unet=unwrap_model(unet),
                      text_encoder=unwrap_model(text_encoder),
                      vae=unwrap_model(vae),
                      revision=args.revision,
                      variant=args.variant,
                      torch_dtype=weight_dtype,
                  )
                  pipeline = pipeline.to(accelerator.device)
                  pipeline.set_progress_bar_config(disable=True)
  
                  # run inference
                  original_image = download_image(args.val_image_url)
                  edited_images = []
                  with torch.autocast(
                      str(accelerator.device).replace(":0", ""),
                      enabled=accelerator.mixed_precision == "fp16",
                  ):
                      for _ in range(args.num_validation_images):
                          edited_images.append(
                              pipeline(
                                  args.validation_prompt,
                                  image=original_image,
                                  num_inference_steps=20,
                                  image_guidance_scale=1.5,
                                  guidance_scale=7,
                                  generator=generator,
                              ).images[0]
                          )
  
                  for tracker in accelerator.trackers:
                      if tracker.name == "wandb":
                          wandb_table = wandb.Table(
                              columns=WANDB_TABLE_COL_NAMES
                          )
                          for edited_image in edited_images:
                              wandb_table.add_data(
                                  wandb.Image(original_image),
                                  wandb.Image(edited_image),
                                  args.validation_prompt,
                              )
                          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 = unwrap_model(unet)
          if args.use_ema:
              ema_unet.copy_to(unet.parameters())
  
          pipeline = (
              StableDiffusionInstructPix2PixPipeline.from_pretrained(
                  args.pretrained_model_name_or_path,
                  text_encoder=unwrap_model(text_encoder),
                  vae=unwrap_model(vae),
                  unet=unet,
                  revision=args.revision,
                  variant=args.variant,
              )
          )
          pipeline.save_pretrained(args.output_dir)
  
          if args.push_to_hub:
              upload_folder(
                  repo_id=repo_id,
                  folder_path=args.output_dir,
                  commit_message="End of training",
                  ignore_patterns=["step_*", "epoch_*"],
              )
  
          if args.validation_prompt is not None:
              edited_images = []
              pipeline = pipeline.to(accelerator.device)
              with torch.autocast(
                  str(accelerator.device).replace(":0", "")
              ):
                  for _ in range(args.num_validation_images):
                      edited_images.append(
                          pipeline(
                              args.validation_prompt,
                              image=original_image,
                              num_inference_steps=20,
                              image_guidance_scale=1.5,
                              guidance_scale=7,
                              generator=generator,
                          ).images[0]
                      )
  
              for tracker in accelerator.trackers:
                  if tracker.name == "wandb":
                      wandb_table = wandb.Table(
                          columns=WANDB_TABLE_COL_NAMES
                      )
                      for edited_image in edited_images:
                          wandb_table.add_data(
                              wandb.Image(original_image),
                              wandb.Image(edited_image),
                              args.validation_prompt,
                          )
                      tracker.log({"test": wandb_table})
  
      accelerator.end_training()
  
imports: import argparse
  import logging
  import math
  import os
  import shutil
  from pathlib import Path
  import accelerate
  import datasets
  import numpy
  import PIL
  import requests
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  import transformers
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import ProjectConfiguration, set_seed
  from datasets import load_dataset
  from huggingface_hub import create_repo, upload_folder
  from packaging import version
  from torchvision import transforms
  from tqdm.auto import tqdm
  from transformers import CLIPTextModel, CLIPTokenizer
  import diffusers
  from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionInstructPix2PixPipeline, UNet2DConditionModel
  from diffusers.optimization import get_scheduler
  from diffusers.training_utils import EMAModel
  from diffusers.utils import check_min_version, deprecate, is_wandb_available
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.torch_utils import is_compiled_module
label_desc: Use image node_id 4 for calling a global function with example usage: main() and returns: model, inputs, train_transforms, examples, dict