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."
              )
          import wandb
  
      # 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 (sayakpaul): 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,
                  variant=args.variant,
              )
              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 = insecure_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,
          variant=args.variant,
      )
      try:
          vae = AutoencoderKL.from_pretrained(
              args.pretrained_model_name_or_path,
              subfolder="vae",
              revision=args.revision,
              variant=args.variant,
          )
      except OSError:
          # IF does not have a VAE so let's just set it to None
          # We don't have to error out here
          vae = None
  
      unet = UNet2DConditionModel.from_pretrained(
          args.pretrained_model_name_or_path,
          subfolder="unet",
          revision=args.revision,
          variant=args.variant,
      )
  
      # We only train the additional adapter LoRA layers
      if vae is not None:
          vae.requires_grad_(False)
      text_encoder.requires_grad_(False)
      unet.requires_grad_(False)
  
      # For mixed precision training we cast all non-trainable weights (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 unet, vae and text_encoder to device and cast to weight_dtype
      unet.to(accelerator.device, dtype=weight_dtype)
      if vae is not None:
          vae.to(accelerator.device, dtype=weight_dtype)
      text_encoder.to(accelerator.device, dtype=weight_dtype)
  
      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()
  
      # now we will add new LoRA weights to the attention layers
      unet_lora_config = LoraConfig(
          r=args.rank,
          lora_alpha=args.rank,
          init_lora_weights="gaussian",
          target_modules=[
              "to_k",
              "to_q",
              "to_v",
              "to_out.0",
              "add_k_proj",
              "add_v_proj",
          ],
      )
      unet.add_adapter(unet_lora_config)
  
      # The text encoder comes from 🤗 transformers, we will also attach adapters to it.
      if args.train_text_encoder:
          text_lora_config = LoraConfig(
              r=args.rank,
              lora_alpha=args.rank,
              init_lora_weights="gaussian",
              target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
          )
          text_encoder.add_adapter(text_lora_config)
  
      def unwrap_model(model):
          model = accelerator.unwrap_model(model)
          model = (
              model._orig_mod if is_compiled_module(model) else model
          )
          return model
  
      # 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:
              # there are only two options here. Either are just the unet attn processor layers
              # or there are the unet and text encoder atten layers
              unet_lora_layers_to_save = None
              text_encoder_lora_layers_to_save = None
  
              for model in models:
                  if isinstance(model, type(unwrap_model(unet))):
                      unet_lora_layers_to_save = (
                          convert_state_dict_to_diffusers(
                              get_peft_model_state_dict(model)
                          )
                      )
                  elif isinstance(
                      model, type(unwrap_model(text_encoder))
                  ):
                      text_encoder_lora_layers_to_save = (
                          convert_state_dict_to_diffusers(
                              get_peft_model_state_dict(model)
                          )
                      )
                  else:
                      raise ValueError(
                          f"unexpected save model: {model.__class__}"
                      )
  
                  # make sure to pop weight so that corresponding model is not saved again
                  weights.pop()
  
              LoraLoaderMixin.save_lora_weights(
                  output_dir,
                  unet_lora_layers=unet_lora_layers_to_save,
                  text_encoder_lora_layers=text_encoder_lora_layers_to_save,
              )
  
      def load_model_hook(models, input_dir):
          unet_ = None
          text_encoder_ = None
  
          while len(models) > 0:
              model = models.pop()
  
              if isinstance(model, type(unwrap_model(unet))):
                  unet_ = model
              elif isinstance(model, type(unwrap_model(text_encoder))):
                  text_encoder_ = model
              else:
                  raise ValueError(
                      f"unexpected save model: {model.__class__}"
                  )
  
          (
              lora_state_dict,
              network_alphas,
          ) = LoraLoaderMixin.lora_state_dict(input_dir)
  
          unet_state_dict = {
              f'{k.replace("unet.", "")}': v
              for k, v in lora_state_dict.items()
              if k.startswith("unet.")
          }
          unet_state_dict = convert_unet_state_dict_to_peft(
              unet_state_dict
          )
          incompatible_keys = set_peft_model_state_dict(
              unet_, unet_state_dict, adapter_name="default"
          )
  
          if incompatible_keys is not None:
              # check only for unexpected keys
              unexpected_keys = getattr(
                  incompatible_keys, "unexpected_keys", None
              )
              if unexpected_keys:
                  logger.warning(
                      f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
                      f" {unexpected_keys}. "
                  )
  
          if args.train_text_encoder:
              _set_state_dict_into_text_encoder(
                  lora_state_dict,
                  prefix="text_encoder.",
                  text_encoder=text_encoder_,
              )
  
          # Make sure the trainable params are in float32. This is again needed since the base models
          # are in `weight_dtype`. More details:
          # https://github.com/huggingface/diffusers/pull/6514#discussion_r1449796804
          if args.mixed_precision == "fp16":
              models = [unet_]
              if args.train_text_encoder:
                  models.append(text_encoder_)
  
              # only upcast trainable parameters (LoRA) into fp32
              cast_training_params(models, dtype=torch.float32)
  
      accelerator.register_save_state_pre_hook(save_model_hook)
      accelerator.register_load_state_pre_hook(load_model_hook)
  
      # 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
          )
  
      # Make sure the trainable params are in float32.
      if args.mixed_precision == "fp16":
          models = [unet]
          if args.train_text_encoder:
              models.append(text_encoder)
  
          # only upcast trainable parameters (LoRA) into fp32
          cast_training_params(models, dtype=torch.float32)
  
      # 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 = list(
          filter(lambda p: p.requires_grad, unet.parameters())
      )
      if args.train_text_encoder:
          params_to_optimize = params_to_optimize + list(
              filter(
                  lambda p: p.requires_grad, text_encoder.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
          )
  
      # 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-lora", 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(
                          pixel_values
                      ).latent_dist.sample()
                      model_input = (
                          model_input * vae.config.scaling_factor
                      )
                  else:
                      model_input = pixel_values
  
                  # Sample noise that we'll add to the latents
                  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 (
                      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,
                      return_dict=False,
                  )[0]
  
                  # if model predicts variance, throw away the prediction. we will only train on the
                  # simplified training objective. This means that all schedulers using the fine tuned
                  # model must be configured to use one of the fixed variance variance types.
                  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 instance loss
                      loss = F.mse_loss(
                          model_pred.float(),
                          target.float(),
                          reduction="mean",
                      )
  
                      # Compute prior loss
                      prior_loss = F.mse_loss(
                          model_pred_prior.float(),
                          target_prior.float(),
                          reduction="mean",
                      )
  
                      # Add the prior loss to the instance loss.
                      loss = loss + args.prior_loss_weight * prior_loss
                  else:
                      loss = F.mse_loss(
                          model_pred.float(),
                          target.float(),
                          reduction="mean",
                      )
  
                  accelerator.backward(loss)
                  if accelerator.sync_gradients:
                      accelerator.clip_grad_norm_(
                          params_to_optimize, 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:
                  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}")
  
              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
  
          if accelerator.is_main_process:
              if (
                  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
                  pipeline = DiffusionPipeline.from_pretrained(
                      args.pretrained_model_name_or_path,
                      unet=unwrap_model(unet),
                      text_encoder=None
                      if args.pre_compute_text_embeddings
                      else unwrap_model(text_encoder),
                      revision=args.revision,
                      variant=args.variant,
                      torch_dtype=weight_dtype,
                  )
  
                  # 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 = (
                      DPMSolverMultistepScheduler.from_config(
                          pipeline.scheduler.config, **scheduler_args
                      )
                  )
  
                  pipeline = pipeline.to(accelerator.device)
                  pipeline.set_progress_bar_config(disable=True)
  
                  # run inference
                  generator = (
                      torch.Generator(
                          device=accelerator.device
                      ).manual_seed(args.seed)
                      if args.seed
                      else None
                  )
                  if args.pre_compute_text_embeddings:
                      pipeline_args = {
                          "prompt_embeds": validation_prompt_encoder_hidden_states,
                          "negative_prompt_embeds": validation_prompt_negative_prompt_embeds,
                      }
                  else:
                      pipeline_args = {"prompt": args.validation_prompt}
  
                  if args.validation_images is None:
                      images = []
                      for _ in range(args.num_validation_images):
                          with torch.cuda.amp.autocast():
                              image = pipeline(
                                  **pipeline_args, generator=generator
                              ).images[0]
                              images.append(image)
                  else:
                      images = []
                      for image in args.validation_images:
                          image = Image.open(image)
                          with torch.cuda.amp.autocast():
                              image = pipeline(
                                  **pipeline_args,
                                  image=image,
                                  generator=generator,
                              ).images[0]
                          images.append(image)
  
                  for tracker in accelerator.trackers:
                      if tracker.name == "tensorboard":
                          np_images = np.stack(
                              [np.asarray(img) for img in images]
                          )
                          tracker.writer.add_images(
                              "validation",
                              np_images,
                              epoch,
                              dataformats="NHWC",
                          )
                      if tracker.name == "wandb":
                          tracker.log(
                              {
                                  "validation": [
                                      wandb.Image(
                                          image,
                                          caption=f"{i}: {args.validation_prompt}",
                                      )
                                      for i, image in enumerate(images)
                                  ]
                              }
                          )
  
                  del pipeline
                  torch.cuda.empty_cache()
  
      # Save the lora layers
      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
          unet = unwrap_model(unet)
          unet = unet.to(torch.float32)
  
          unet_lora_state_dict = convert_state_dict_to_diffusers(
              get_peft_model_state_dict(unet)
          )
  
          if args.train_text_encoder:
              text_encoder = unwrap_model(text_encoder)
              text_encoder_state_dict = convert_state_dict_to_diffusers(
                  get_peft_model_state_dict(text_encoder)
              )
          else:
              text_encoder_state_dict = None
  
          LoraLoaderMixin.save_lora_weights(
              save_directory=args.output_dir,
              unet_lora_layers=unet_lora_state_dict,
              text_encoder_lora_layers=text_encoder_state_dict,
          )
  
          # Final inference
          # Load previous pipeline
          pipeline = DiffusionPipeline.from_pretrained(
              args.pretrained_model_name_or_path,
              revision=args.revision,
              variant=args.variant,
              torch_dtype=weight_dtype,
          )
  
          # 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 = DPMSolverMultistepScheduler.from_config(
              pipeline.scheduler.config, **scheduler_args
          )
  
          pipeline = pipeline.to(accelerator.device)
  
          # load attention processors
          pipeline.load_lora_weights(
              args.output_dir,
              weight_name="pytorch_lora_weights.safetensors",
          )
  
          # run inference
          images = []
          if args.validation_prompt and args.num_validation_images > 0:
              generator = (
                  torch.Generator(
                      device=accelerator.device
                  ).manual_seed(args.seed)
                  if args.seed
                  else None
              )
              images = [
                  pipeline(
                      args.validation_prompt,
                      num_inference_steps=25,
                      generator=generator,
                  ).images[0]
                  for _ in range(args.num_validation_images)
              ]
  
              for tracker in accelerator.trackers:
                  if tracker.name == "tensorboard":
                      np_images = np.stack(
                          [np.asarray(img) for img in images]
                      )
                      tracker.writer.add_images(
                          "test", np_images, epoch, dataformats="NHWC"
                      )
                  if tracker.name == "wandb":
                      tracker.log(
                          {
                              "test": [
                                  wandb.Image(
                                      image,
                                      caption=f"{i}: {args.validation_prompt}",
                                  )
                                  for i, image in enumerate(images)
                              ]
                          }
                      )
  
          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 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, upload_folder
  from huggingface_hub.utils import insecure_hashlib
  from packaging import version
  from peft import LoraConfig
  from peft.utils import get_peft_model_state_dict, set_peft_model_state_dict
  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, DPMSolverMultistepScheduler, StableDiffusionPipeline, UNet2DConditionModel
  from diffusers.loaders import LoraLoaderMixin
  from diffusers.optimization import get_scheduler
  from diffusers.training_utils import _set_state_dict_into_text_encoder, cast_training_params
  from diffusers.utils import check_min_version, convert_state_dict_to_diffusers, convert_unet_state_dict_to_peft, 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 7 for calling a global function with example usage: main(args) and returns: model, prompt_embeds