code: def main(args):
      logging_dir = Path(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,
      )
  
      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."
              )
  
      # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
      # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
      # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
      if (
          args.train_text_encoder
          and args.gradient_accumulation_steps > 1
          and accelerator.num_processes > 1
      ):
          raise ValueError(
              "Gradient accumulation is not supported when training the text encoder in distributed training. "
              "Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
          )
  
      # 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:
          transformers.utils.logging.set_verbosity_warning()
          diffusers.utils.logging.set_verbosity_info()
      else:
          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)
  
      # Generate class images if prior preservation is enabled.
      if args.with_prior_preservation:
          class_images_dir = Path(args.class_data_dir)
          if not class_images_dir.exists():
              class_images_dir.mkdir(parents=True)
          cur_class_images = len(list(class_images_dir.iterdir()))
  
          if cur_class_images < args.num_class_images:
              torch_dtype = (
                  torch.float16
                  if accelerator.device.type == "cuda"
                  else torch.float32
              )
              if args.prior_generation_precision == "fp32":
                  torch_dtype = torch.float32
              elif args.prior_generation_precision == "fp16":
                  torch_dtype = torch.float16
              elif args.prior_generation_precision == "bf16":
                  torch_dtype = torch.bfloat16
              pipeline = DiffusionPipeline.from_pretrained(
                  args.pretrained_model_name_or_path,
                  torch_dtype=torch_dtype,
                  safety_checker=None,
                  revision=args.revision,
              )
              pipeline.set_progress_bar_config(disable=True)
  
              num_new_images = args.num_class_images - cur_class_images
              logger.info(
                  f"Number of class images to sample: {num_new_images}."
              )
  
              sample_dataset = PromptDataset(
                  args.class_prompt, num_new_images
              )
              sample_dataloader = torch.utils.data.DataLoader(
                  sample_dataset, batch_size=args.sample_batch_size
              )
  
              sample_dataloader = accelerator.prepare(sample_dataloader)
              pipeline.to(accelerator.device)
  
              for example in tqdm(
                  sample_dataloader,
                  desc="Generating class images",
                  disable=not accelerator.is_local_main_process,
              ):
                  images = pipeline(example["prompt"]).images
  
                  for i, image in enumerate(images):
                      hash_image = hashlib.sha1(
                          image.tobytes()
                      ).hexdigest()
                      image_filename = (
                          class_images_dir
                          / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
                      )
                      image.save(image_filename)
  
              del pipeline
              if torch.cuda.is_available():
                  torch.cuda.empty_cache()
  
      # 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 the tokenizer
      if args.tokenizer_name:
          tokenizer = AutoTokenizer.from_pretrained(
              args.tokenizer_name,
              revision=args.revision,
              use_fast=False,
          )
      elif args.pretrained_model_name_or_path:
          tokenizer = AutoTokenizer.from_pretrained(
              args.pretrained_model_name_or_path,
              subfolder="tokenizer",
              revision=args.revision,
              use_fast=False,
          )
  
      # import correct text encoder class
      text_encoder_cls = import_model_class_from_model_name_or_path(
          args.pretrained_model_name_or_path, args.revision
      )
  
      # Load scheduler and models
      noise_scheduler = DDPMScheduler.from_pretrained(
          args.pretrained_model_name_or_path, subfolder="scheduler"
      )
      text_encoder = text_encoder_cls.from_pretrained(
          args.pretrained_model_name_or_path,
          subfolder="text_encoder",
          revision=args.revision,
      )
  
      if model_has_vae(args):
          vae = AutoencoderKL.from_pretrained(
              args.pretrained_model_name_or_path,
              subfolder="vae",
              revision=args.revision,
          )
      else:
          vae = None
  
      unet = UNet2DConditionModel.from_pretrained(
          args.pretrained_model_name_or_path,
          subfolder="unet",
          revision=args.revision,
      )
  
      # 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:
              for model in models:
                  sub_dir = (
                      "unet"
                      if isinstance(
                          model, type(accelerator.unwrap_model(unet))
                      )
                      else "text_encoder"
                  )
                  model.save_pretrained(
                      os.path.join(output_dir, sub_dir)
                  )
  
                  # make sure to pop weight so that corresponding model is not saved again
                  weights.pop()
  
      def load_model_hook(models, input_dir):
          while len(models) > 0:
              # pop models so that they are not loaded again
              model = models.pop()
  
              if isinstance(
                  model, type(accelerator.unwrap_model(text_encoder))
              ):
                  # load transformers style into model
                  load_model = text_encoder_cls.from_pretrained(
                      input_dir, subfolder="text_encoder"
                  )
                  model.config = load_model.config
              else:
                  # 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 vae is not None:
          vae.requires_grad_(False)
  
      if not args.train_text_encoder:
          text_encoder.requires_grad_(False)
  
      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"
              )
  
      if args.gradient_checkpointing:
          unet.enable_gradient_checkpointing()
          if args.train_text_encoder:
              text_encoder.gradient_checkpointing_enable()
  
      # Check that all trainable models are in full precision
      low_precision_error_string = (
          "Please make sure to always have all model weights in full float32 precision when starting training - even if"
          " doing mixed precision training. copy of the weights should still be float32."
      )
  
      if accelerator.unwrap_model(unet).dtype != torch.float32:
          raise ValueError(
              f"Unet loaded as datatype {accelerator.unwrap_model(unet).dtype}. {low_precision_error_string}"
          )
  
      if (
          args.train_text_encoder
          and accelerator.unwrap_model(text_encoder).dtype
          != torch.float32
      ):
          raise ValueError(
              f"Text encoder loaded as datatype {accelerator.unwrap_model(text_encoder).dtype}."
              f" {low_precision_error_string}"
          )
  
      # 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
          )
  
      # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
      if args.use_8bit_adam:
          try:
              import bitsandbytes as bnb
          except ImportError:
              raise ImportError(
                  "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
              )
  
          optimizer_class = bnb.optim.AdamW8bit
      else:
          optimizer_class = torch.optim.AdamW
  
      # Optimizer creation
      params_to_optimize = (
          itertools.chain(unet.parameters(), text_encoder.parameters())
          if args.train_text_encoder
          else unet.parameters()
      )
      optimizer = optimizer_class(
          params_to_optimize,
          lr=args.learning_rate,
          betas=(args.adam_beta1, args.adam_beta2),
          weight_decay=args.adam_weight_decay,
          eps=args.adam_epsilon,
      )
  
      if args.pre_compute_text_embeddings:
  
          def compute_text_embeddings(prompt):
              with torch.no_grad():
                  text_inputs = tokenize_prompt(
                      tokenizer,
                      prompt,
                      tokenizer_max_length=args.tokenizer_max_length,
                  )
                  prompt_embeds = encode_prompt(
                      text_encoder,
                      text_inputs.input_ids,
                      text_inputs.attention_mask,
                      text_encoder_use_attention_mask=args.text_encoder_use_attention_mask,
                  )
  
              return prompt_embeds
  
          pre_computed_encoder_hidden_states = compute_text_embeddings(
              args.instance_prompt
          )
          validation_prompt_negative_prompt_embeds = (
              compute_text_embeddings("")
          )
  
          if args.validation_prompt is not None:
              validation_prompt_encoder_hidden_states = (
                  compute_text_embeddings(args.validation_prompt)
              )
          else:
              validation_prompt_encoder_hidden_states = None
  
          if args.class_prompt is not None:
              pre_computed_class_prompt_encoder_hidden_states = (
                  compute_text_embeddings(args.class_prompt)
              )
          else:
              pre_computed_class_prompt_encoder_hidden_states = None
  
          text_encoder = None
          tokenizer = None
  
          gc.collect()
          torch.cuda.empty_cache()
      else:
          pre_computed_encoder_hidden_states = None
          validation_prompt_encoder_hidden_states = None
          validation_prompt_negative_prompt_embeds = None
          pre_computed_class_prompt_encoder_hidden_states = None
  
      # Dataset and DataLoaders creation:
      train_dataset = DreamBoothDataset(
          instance_data_root=args.instance_data_dir,
          instance_prompt=args.instance_prompt,
          class_data_root=args.class_data_dir
          if args.with_prior_preservation
          else None,
          class_prompt=args.class_prompt,
          class_num=args.num_class_images,
          tokenizer=tokenizer,
          size=args.resolution,
          center_crop=args.center_crop,
          encoder_hidden_states=pre_computed_encoder_hidden_states,
          class_prompt_encoder_hidden_states=pre_computed_class_prompt_encoder_hidden_states,
          tokenizer_max_length=args.tokenizer_max_length,
      )
  
      train_dataloader = torch.utils.data.DataLoader(
          train_dataset,
          batch_size=args.train_batch_size,
          shuffle=True,
          collate_fn=lambda examples: collate_fn(
              examples, args.with_prior_preservation
          ),
          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,
          num_cycles=args.lr_num_cycles,
          power=args.lr_power,
      )
  
      # Prepare everything with our `accelerator`.
      if args.train_text_encoder:
          (
              unet,
              text_encoder,
              optimizer,
              train_dataloader,
              lr_scheduler,
          ) = accelerator.prepare(
              unet,
              text_encoder,
              optimizer,
              train_dataloader,
              lr_scheduler,
          )
      else:
          (
              unet,
              optimizer,
              train_dataloader,
              lr_scheduler,
          ) = accelerator.prepare(
              unet, optimizer, train_dataloader, lr_scheduler
          )
  
      # For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) to half-precision
      # as these weights 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 vae and text_encoder to device and cast to weight_dtype
      if vae is not None:
          vae.to(accelerator.device, dtype=weight_dtype)
  
      if not args.train_text_encoder and text_encoder is not None:
          text_encoder.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:
          tracker_config = vars(copy.deepcopy(args))
          tracker_config.pop("validation_images")
          accelerator.init_trackers("dreambooth", config=tracker_config)
  
      # 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 batches each epoch = {len(train_dataloader)}")
      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 mos 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
              initial_global_step = 0
          else:
              accelerator.print(f"Resuming from checkpoint {path}")
              accelerator.load_state(
                  os.path.join(args.output_dir, path)
              )
              global_step = int(path.split("-")[1])
  
              initial_global_step = global_step
              first_epoch = global_step // num_update_steps_per_epoch
      else:
          initial_global_step = 0
  
      progress_bar = tqdm(
          range(0, args.max_train_steps),
          initial=initial_global_step,
          desc="Steps",
          # Only show the progress bar once on each machine.
          disable=not accelerator.is_local_main_process,
      )
  
      for epoch in range(first_epoch, args.num_train_epochs):
          unet.train()
          if args.train_text_encoder:
              text_encoder.train()
          for step, batch in enumerate(train_dataloader):
              with accelerator.accumulate(unet):
                  pixel_values = batch["pixel_values"].to(
                      dtype=weight_dtype
                  )
  
                  if vae is not None:
                      # Convert images to latent space
                      model_input = vae.encode(
                          batch["pixel_values"].to(dtype=weight_dtype)
                      ).latent_dist.sample()
                      model_input = (
                          model_input * vae.config.scaling_factor
                      )
                  else:
                      model_input = pixel_values
  
                  # Sample noise that we'll add to the model input
                  if args.offset_noise:
                      noise = torch.randn_like(
                          model_input
                      ) + 0.1 * torch.randn(
                          model_input.shape[0],
                          model_input.shape[1],
                          1,
                          1,
                          device=model_input.device,
                      )
                  else:
                      noise = torch.randn_like(model_input)
                  bsz, channels, height, width = model_input.shape
                  # Sample a random timestep for each image
                  timesteps = torch.randint(
                      0,
                      noise_scheduler.config.num_train_timesteps,
                      (bsz,),
                      device=model_input.device,
                  )
                  timesteps = timesteps.long()
  
                  # Add noise to the model input according to the noise magnitude at each timestep
                  # (this is the forward diffusion process)
                  noisy_model_input = noise_scheduler.add_noise(
                      model_input, noise, timesteps
                  )
  
                  # Get the text embedding for conditioning
                  if args.pre_compute_text_embeddings:
                      encoder_hidden_states = batch["input_ids"]
                  else:
                      encoder_hidden_states = encode_prompt(
                          text_encoder,
                          batch["input_ids"],
                          batch["attention_mask"],
                          text_encoder_use_attention_mask=args.text_encoder_use_attention_mask,
                      )
  
                  if (
                      accelerator.unwrap_model(unet).config.in_channels
                      == channels * 2
                  ):
                      noisy_model_input = torch.cat(
                          [noisy_model_input, noisy_model_input], dim=1
                      )
  
                  if args.class_labels_conditioning == "timesteps":
                      class_labels = timesteps
                  else:
                      class_labels = None
  
                  # Predict the noise residual
                  model_pred = unet(
                      noisy_model_input,
                      timesteps,
                      encoder_hidden_states,
                      class_labels=class_labels,
                  ).sample
  
                  if model_pred.shape[1] == 6:
                      model_pred, _ = torch.chunk(model_pred, 2, 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(
                          model_input, noise, timesteps
                      )
                  else:
                      raise ValueError(
                          f"Unknown prediction type {noise_scheduler.config.prediction_type}"
                      )
  
                  if args.with_prior_preservation:
                      # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
                      model_pred, model_pred_prior = torch.chunk(
                          model_pred, 2, dim=0
                      )
                      target, target_prior = torch.chunk(
                          target, 2, dim=0
                      )
                      # Compute prior loss
                      prior_loss = F.mse_loss(
                          model_pred_prior.float(),
                          target_prior.float(),
                          reduction="mean",
                      )
  
                  # Compute instance loss
                  if args.snr_gamma is None:
                      loss = F.mse_loss(
                          model_pred.float(),
                          target.float(),
                          reduction="mean",
                      )
                  else:
                      # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
                      # Since we predict the noise instead of x_0, the original formulation is slightly changed.
                      # This is discussed in Section 4.2 of the same paper.
                      snr = compute_snr(noise_scheduler, timesteps)
                      base_weight = (
                          torch.stack(
                              [
                                  snr,
                                  args.snr_gamma
                                  * torch.ones_like(timesteps),
                              ],
                              dim=1,
                          ).min(dim=1)[0]
                          / snr
                      )
  
                      if (
                          noise_scheduler.config.prediction_type
                          == "v_prediction"
                      ):
                          # Velocity objective needs to be floored to an SNR weight of one.
                          mse_loss_weights = base_weight + 1
                      else:
                          # Epsilon and sample both use the same loss weights.
                          mse_loss_weights = base_weight
                      loss = F.mse_loss(
                          model_pred.float(),
                          target.float(),
                          reduction="none",
                      )
                      loss = (
                          loss.mean(dim=list(range(1, len(loss.shape))))
                          * mse_loss_weights
                      )
                      loss = loss.mean()
  
                  if args.with_prior_preservation:
                      # Add the prior loss to the instance loss.
                      loss = loss + args.prior_loss_weight * prior_loss
  
                  accelerator.backward(loss)
                  if accelerator.sync_gradients:
                      params_to_clip = (
                          itertools.chain(
                              unet.parameters(),
                              text_encoder.parameters(),
                          )
                          if args.train_text_encoder
                          else unet.parameters()
                      )
                      accelerator.clip_grad_norm_(
                          params_to_clip, args.max_grad_norm
                      )
                  optimizer.step()
                  lr_scheduler.step()
                  optimizer.zero_grad(
                      set_to_none=args.set_grads_to_none
                  )
  
              # Checks if the accelerator has performed an optimization step behind the scenes
              if accelerator.sync_gradients:
                  progress_bar.update(1)
                  global_step += 1
  
                  if accelerator.is_main_process:
                      if global_step % args.checkpointing_steps == 0:
                          # _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}")
  
                      images = []
  
                      if (
                          args.validation_prompt is not None
                          and global_step % args.validation_steps == 0
                      ):
                          images = log_validation(
                              text_encoder,
                              tokenizer,
                              unet,
                              vae,
                              args,
                              accelerator,
                              weight_dtype,
                              global_step,
                              validation_prompt_encoder_hidden_states,
                              validation_prompt_negative_prompt_embeds,
                          )
  
              logs = {
                  "loss": loss.detach().item(),
                  "lr": lr_scheduler.get_last_lr()[0],
              }
              progress_bar.set_postfix(**logs)
              accelerator.log(logs, step=global_step)
  
              if global_step >= args.max_train_steps:
                  break
  
      # Create the pipeline using using the trained modules and save it.
      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
          pipeline_args = {}
  
          if text_encoder is not None:
              pipeline_args["text_encoder"] = accelerator.unwrap_model(
                  text_encoder
              )
  
          if args.skip_save_text_encoder:
              pipeline_args["text_encoder"] = None
  
          pipeline = DiffusionPipeline.from_pretrained(
              args.pretrained_model_name_or_path,
              unet=accelerator.unwrap_model(unet),
              revision=args.revision,
              **pipeline_args,
          )
  
          # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
          scheduler_args = {}
  
          if "variance_type" in pipeline.scheduler.config:
              variance_type = pipeline.scheduler.config.variance_type
  
              if variance_type in ["learned", "learned_range"]:
                  variance_type = "fixed_small"
  
              scheduler_args["variance_type"] = variance_type
  
          pipeline.scheduler = pipeline.scheduler.from_config(
              pipeline.scheduler.config, **scheduler_args
          )
  
          pipeline.save_pretrained(args.output_dir)
  
          if args.push_to_hub:
              save_model_card(
                  repo_id,
                  images=images,
                  base_model=args.pretrained_model_name_or_path,
                  train_text_encoder=args.train_text_encoder,
                  prompt=args.instance_prompt,
                  repo_folder=args.output_dir,
                  pipeline=pipeline,
              )
              upload_folder(
                  repo_id=repo_id,
                  folder_path=args.output_dir,
                  commit_message="End of training",
                  ignore_patterns=["step_*", "epoch_*"],
              )
  
      accelerator.end_training()
  
imports: import argparse
  import copy
  import gc
  import hashlib
  import importlib
  import itertools
  import logging
  import math
  import os
  import shutil
  import warnings
  from pathlib import Path
  import numpy
  import torch
  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 huggingface_hub import create_repo, model_info, upload_folder
  from packaging import version
  from PIL import Image
  from PIL.ImageOps import exif_transpose
  from torch.utils.data import Dataset
  from torchvision import transforms
  from tqdm.auto import tqdm
  from transformers import AutoTokenizer, PretrainedConfig
  import diffusers
  from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, StableDiffusionPipeline, UNet2DConditionModel
  from diffusers.optimization import get_scheduler
  from diffusers.training_utils import compute_snr
  from diffusers.utils import check_min_version, is_wandb_available
  from diffusers.utils.import_utils import is_xformers_available
label_desc: Use image node_id 9 for calling a global function with example usage: main(args) and returns: prompt_embeds