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
      )
      kwargs = DistributedDataParallelKwargs(
          find_unused_parameters=True
      )
      accelerator = Accelerator(
          gradient_accumulation_steps=args.gradient_accumulation_steps,
          mixed_precision=args.mixed_precision,
          log_with=args.report_to,
          project_config=accelerator_project_config,
          kwargs_handlers=[kwargs],
      )
  
      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:
          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 = StableDiffusionXLPipeline.from_pretrained(
                  args.pretrained_model_name_or_path,
                  torch_dtype=torch_dtype,
                  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 tokenizers
      tokenizer_one = AutoTokenizer.from_pretrained(
          args.pretrained_model_name_or_path,
          subfolder="tokenizer",
          revision=args.revision,
          use_fast=False,
      )
      tokenizer_two = AutoTokenizer.from_pretrained(
          args.pretrained_model_name_or_path,
          subfolder="tokenizer_2",
          revision=args.revision,
          use_fast=False,
      )
  
      # import correct text encoder classes
      text_encoder_cls_one = import_model_class_from_model_name_or_path(
          args.pretrained_model_name_or_path, args.revision
      )
      text_encoder_cls_two = import_model_class_from_model_name_or_path(
          args.pretrained_model_name_or_path,
          args.revision,
          subfolder="text_encoder_2",
      )
  
      # Load scheduler and models
      noise_scheduler = DDPMScheduler.from_pretrained(
          args.pretrained_model_name_or_path, subfolder="scheduler"
      )
      text_encoder_one = text_encoder_cls_one.from_pretrained(
          args.pretrained_model_name_or_path,
          subfolder="text_encoder",
          revision=args.revision,
          variant=args.variant,
      )
      text_encoder_two = text_encoder_cls_two.from_pretrained(
          args.pretrained_model_name_or_path,
          subfolder="text_encoder_2",
          revision=args.revision,
          variant=args.variant,
      )
      vae_path = (
          args.pretrained_model_name_or_path
          if args.pretrained_vae_model_name_or_path is None
          else args.pretrained_vae_model_name_or_path
      )
      vae = AutoencoderKL.from_pretrained(
          vae_path,
          subfolder="vae"
          if args.pretrained_vae_model_name_or_path is None
          else None,
          revision=args.revision,
          variant=args.variant,
      )
      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
      vae.requires_grad_(False)
      text_encoder_one.requires_grad_(False)
      text_encoder_two.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)
  
      # The VAE is always in float32 to avoid NaN losses.
      vae.to(accelerator.device, dtype=torch.float32)
  
      text_encoder_one.to(accelerator.device, dtype=weight_dtype)
      text_encoder_two.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_one.gradient_checkpointing_enable()
              text_encoder_two.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"],
      )
      unet.add_adapter(unet_lora_config)
  
      # The text encoder comes from 🤗 transformers, so we cannot directly modify it.
      # So, instead, we monkey-patch the forward calls of its attention-blocks.
      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_one.add_adapter(text_lora_config)
          text_encoder_two.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_one_lora_layers_to_save = None
              text_encoder_two_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_one))
                  ):
                      text_encoder_one_lora_layers_to_save = (
                          convert_state_dict_to_diffusers(
                              get_peft_model_state_dict(model)
                          )
                      )
                  elif isinstance(
                      model, type(unwrap_model(text_encoder_two))
                  ):
                      text_encoder_two_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()
  
              StableDiffusionXLPipeline.save_lora_weights(
                  output_dir,
                  unet_lora_layers=unet_lora_layers_to_save,
                  text_encoder_lora_layers=text_encoder_one_lora_layers_to_save,
                  text_encoder_2_lora_layers=text_encoder_two_lora_layers_to_save,
              )
  
      def load_model_hook(models, input_dir):
          unet_ = None
          text_encoder_one_ = None
          text_encoder_two_ = 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_one))
              ):
                  text_encoder_one_ = model
              elif isinstance(
                  model, type(unwrap_model(text_encoder_two))
              ):
                  text_encoder_two_ = 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:
              # Do we need to call `scale_lora_layers()` here?
              _set_state_dict_into_text_encoder(
                  lora_state_dict,
                  prefix="text_encoder.",
                  text_encoder=text_encoder_one_,
              )
  
              _set_state_dict_into_text_encoder(
                  lora_state_dict,
                  prefix="text_encoder_2.",
                  text_encoder=text_encoder_one_,
              )
  
          # 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.extend([text_encoder_one_, text_encoder_two_])
                  # only upcast trainable parameters (LoRA) into fp32
                  cast_training_params(models)
  
      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.extend([text_encoder_one, text_encoder_two])
  
          # only upcast trainable parameters (LoRA) into fp32
          cast_training_params(models, dtype=torch.float32)
  
      unet_lora_parameters = list(
          filter(lambda p: p.requires_grad, unet.parameters())
      )
  
      if args.train_text_encoder:
          text_lora_parameters_one = list(
              filter(
                  lambda p: p.requires_grad,
                  text_encoder_one.parameters(),
              )
          )
          text_lora_parameters_two = list(
              filter(
                  lambda p: p.requires_grad,
                  text_encoder_two.parameters(),
              )
          )
  
      # Optimization parameters
      unet_lora_parameters_with_lr = {
          "params": unet_lora_parameters,
          "lr": args.learning_rate,
      }
      if args.train_text_encoder:
          # different learning rate for text encoder and unet
          text_lora_parameters_one_with_lr = {
              "params": text_lora_parameters_one,
              "weight_decay": args.adam_weight_decay_text_encoder,
              "lr": args.text_encoder_lr
              if args.text_encoder_lr
              else args.learning_rate,
          }
          text_lora_parameters_two_with_lr = {
              "params": text_lora_parameters_two,
              "weight_decay": args.adam_weight_decay_text_encoder,
              "lr": args.text_encoder_lr
              if args.text_encoder_lr
              else args.learning_rate,
          }
          params_to_optimize = [
              unet_lora_parameters_with_lr,
              text_lora_parameters_one_with_lr,
              text_lora_parameters_two_with_lr,
          ]
      else:
          params_to_optimize = [unet_lora_parameters_with_lr]
  
      # Optimizer creation
      if not (
          args.optimizer.lower() == "prodigy"
          or args.optimizer.lower() == "adamw"
      ):
          logger.warn(
              f"Unsupported choice of optimizer: {args.optimizer}.Supported optimizers include [adamW, prodigy]."
              "Defaulting to adamW"
          )
          args.optimizer = "adamw"
  
      if args.use_8bit_adam and not args.optimizer.lower() == "adamw":
          logger.warn(
              f"use_8bit_adam is ignored when optimizer is not set to 'AdamW'. Optimizer was "
              f"set to {args.optimizer.lower()}"
          )
  
      if args.optimizer.lower() == "adamw":
          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 = optimizer_class(
              params_to_optimize,
              betas=(args.adam_beta1, args.adam_beta2),
              weight_decay=args.adam_weight_decay,
              eps=args.adam_epsilon,
          )
  
      if args.optimizer.lower() == "prodigy":
          try:
              import prodigyopt
          except ImportError:
              raise ImportError(
                  "To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`"
              )
  
          optimizer_class = prodigyopt.Prodigy
  
          if args.learning_rate <= 0.1:
              logger.warn(
                  "Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0"
              )
          if args.train_text_encoder and args.text_encoder_lr:
              logger.warn(
                  f"Learning rates were provided both for the unet and the text encoder- e.g. text_encoder_lr:"
                  f" {args.text_encoder_lr} and learning_rate: {args.learning_rate}. "
                  f"When using prodigy only learning_rate is used as the initial learning rate."
              )
              # changes the learning rate of text_encoder_parameters_one and text_encoder_parameters_two to be
              # --learning_rate
              params_to_optimize[1]["lr"] = args.learning_rate
              params_to_optimize[2]["lr"] = args.learning_rate
  
          optimizer = optimizer_class(
              params_to_optimize,
              lr=args.learning_rate,
              betas=(args.adam_beta1, args.adam_beta2),
              beta3=args.prodigy_beta3,
              weight_decay=args.adam_weight_decay,
              eps=args.adam_epsilon,
              decouple=args.prodigy_decouple,
              use_bias_correction=args.prodigy_use_bias_correction,
              safeguard_warmup=args.prodigy_safeguard_warmup,
          )
  
      # Dataset and DataLoaders creation:
      train_dataset = DreamBoothDataset(
          instance_data_root=args.instance_data_dir,
          instance_prompt=args.instance_prompt,
          class_prompt=args.class_prompt,
          class_data_root=args.class_data_dir
          if args.with_prior_preservation
          else None,
          class_num=args.num_class_images,
          size=args.resolution,
          repeats=args.repeats,
          center_crop=args.center_crop,
      )
  
      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,
      )
  
      # Computes additional embeddings/ids required by the SDXL UNet.
      # regular text embeddings (when `train_text_encoder` is not True)
      # pooled text embeddings
      # time ids
  
      def compute_time_ids():
          # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids
          original_size = (args.resolution, args.resolution)
          target_size = (args.resolution, args.resolution)
          crops_coords_top_left = (
              args.crops_coords_top_left_h,
              args.crops_coords_top_left_w,
          )
          add_time_ids = list(
              original_size + crops_coords_top_left + target_size
          )
          add_time_ids = torch.tensor([add_time_ids])
          add_time_ids = add_time_ids.to(
              accelerator.device, dtype=weight_dtype
          )
          return add_time_ids
  
      if not args.train_text_encoder:
          tokenizers = [tokenizer_one, tokenizer_two]
          text_encoders = [text_encoder_one, text_encoder_two]
  
          def compute_text_embeddings(
              prompt, text_encoders, tokenizers
          ):
              with torch.no_grad():
                  prompt_embeds, pooled_prompt_embeds = encode_prompt(
                      text_encoders, tokenizers, prompt
                  )
                  prompt_embeds = prompt_embeds.to(accelerator.device)
                  pooled_prompt_embeds = pooled_prompt_embeds.to(
                      accelerator.device
                  )
              return prompt_embeds, pooled_prompt_embeds
  
      # Handle instance prompt.
      instance_time_ids = compute_time_ids()
  
      # If no type of tuning is done on the text_encoder and custom instance prompts are NOT
      # provided (i.e. the --instance_prompt is used for all images), we encode the instance prompt once to avoid
      # the redundant encoding.
      if (
          not args.train_text_encoder
          and not train_dataset.custom_instance_prompts
      ):
          (
              instance_prompt_hidden_states,
              instance_pooled_prompt_embeds,
          ) = compute_text_embeddings(
              args.instance_prompt, text_encoders, tokenizers
          )
  
      # Handle class prompt for prior-preservation.
      if args.with_prior_preservation:
          class_time_ids = compute_time_ids()
          if not args.train_text_encoder:
              (
                  class_prompt_hidden_states,
                  class_pooled_prompt_embeds,
              ) = compute_text_embeddings(
                  args.class_prompt, text_encoders, tokenizers
              )
  
      # Clear the memory here
      if (
          not args.train_text_encoder
          and not train_dataset.custom_instance_prompts
      ):
          del tokenizers, text_encoders
          gc.collect()
          torch.cuda.empty_cache()
  
      # If custom instance prompts are NOT provided (i.e. the instance prompt is used for all images),
      # pack the statically computed variables appropriately here. This is so that we don't
      # have to pass them to the dataloader.
      add_time_ids = instance_time_ids
      if args.with_prior_preservation:
          add_time_ids = torch.cat(
              [add_time_ids, class_time_ids], dim=0
          )
  
      if not train_dataset.custom_instance_prompts:
          if not args.train_text_encoder:
              prompt_embeds = instance_prompt_hidden_states
              unet_add_text_embeds = instance_pooled_prompt_embeds
              if args.with_prior_preservation:
                  prompt_embeds = torch.cat(
                      [prompt_embeds, class_prompt_hidden_states], dim=0
                  )
                  unet_add_text_embeds = torch.cat(
                      [
                          unet_add_text_embeds,
                          class_pooled_prompt_embeds,
                      ],
                      dim=0,
                  )
          # if we're optmizing the text encoder (both if instance prompt is used for all images or custom prompts) we need to tokenize and encode the
          # batch prompts on all training steps
          else:
              tokens_one = tokenize_prompt(
                  tokenizer_one, args.instance_prompt
              )
              tokens_two = tokenize_prompt(
                  tokenizer_two, args.instance_prompt
              )
              if args.with_prior_preservation:
                  class_tokens_one = tokenize_prompt(
                      tokenizer_one, args.class_prompt
                  )
                  class_tokens_two = tokenize_prompt(
                      tokenizer_two, args.class_prompt
                  )
                  tokens_one = torch.cat(
                      [tokens_one, class_tokens_one], dim=0
                  )
                  tokens_two = torch.cat(
                      [tokens_two, class_tokens_two], dim=0
                  )
  
      # 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_one,
              text_encoder_two,
              optimizer,
              train_dataloader,
              lr_scheduler,
          ) = accelerator.prepare(
              unet,
              text_encoder_one,
              text_encoder_two,
              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:
          accelerator.init_trackers(
              "dreambooth-lora-sd-xl", 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 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_one.train()
              text_encoder_two.train()
  
              # set top parameter requires_grad = True for gradient checkpointing works
              text_encoder_one.text_model.embeddings.requires_grad_(
                  True
              )
              text_encoder_two.text_model.embeddings.requires_grad_(
                  True
              )
  
          for step, batch in enumerate(train_dataloader):
              with accelerator.accumulate(unet):
                  pixel_values = batch["pixel_values"].to(
                      dtype=vae.dtype
                  )
                  prompts = batch["prompts"]
  
                  # encode batch prompts when custom prompts are provided for each image -
                  if train_dataset.custom_instance_prompts:
                      if not args.train_text_encoder:
                          (
                              prompt_embeds,
                              unet_add_text_embeds,
                          ) = compute_text_embeddings(
                              prompts, text_encoders, tokenizers
                          )
                      else:
                          tokens_one = tokenize_prompt(
                              tokenizer_one, prompts
                          )
                          tokens_two = tokenize_prompt(
                              tokenizer_two, prompts
                          )
  
                  # Convert images to latent space
                  model_input = vae.encode(
                      pixel_values
                  ).latent_dist.sample()
                  model_input = model_input * vae.config.scaling_factor
                  if args.pretrained_vae_model_name_or_path is None:
                      model_input = model_input.to(weight_dtype)
  
                  # Sample noise that we'll add to the latents
                  noise = torch.randn_like(model_input)
                  bsz = model_input.shape[0]
                  # 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
                  )
  
                  # Calculate the elements to repeat depending on the use of prior-preservation and custom captions.
                  if not train_dataset.custom_instance_prompts:
                      elems_to_repeat_text_embeds = (
                          bsz // 2
                          if args.with_prior_preservation
                          else bsz
                      )
                      elems_to_repeat_time_ids = (
                          bsz // 2
                          if args.with_prior_preservation
                          else bsz
                      )
                  else:
                      elems_to_repeat_text_embeds = 1
                      elems_to_repeat_time_ids = (
                          bsz // 2
                          if args.with_prior_preservation
                          else bsz
                      )
  
                  # Predict the noise residual
                  if not args.train_text_encoder:
                      unet_added_conditions = {
                          "time_ids": add_time_ids.repeat(
                              elems_to_repeat_time_ids, 1
                          ),
                          "text_embeds": unet_add_text_embeds.repeat(
                              elems_to_repeat_text_embeds, 1
                          ),
                      }
                      prompt_embeds_input = prompt_embeds.repeat(
                          elems_to_repeat_text_embeds, 1, 1
                      )
                      model_pred = unet(
                          noisy_model_input,
                          timesteps,
                          prompt_embeds_input,
                          added_cond_kwargs=unet_added_conditions,
                          return_dict=False,
                      )[0]
                  else:
                      unet_added_conditions = {
                          "time_ids": add_time_ids.repeat(
                              elems_to_repeat_time_ids, 1
                          )
                      }
                      (
                          prompt_embeds,
                          pooled_prompt_embeds,
                      ) = encode_prompt(
                          text_encoders=[
                              text_encoder_one,
                              text_encoder_two,
                          ],
                          tokenizers=None,
                          prompt=None,
                          text_input_ids_list=[tokens_one, tokens_two],
                      )
                      unet_added_conditions.update(
                          {
                              "text_embeds": pooled_prompt_embeds.repeat(
                                  elems_to_repeat_text_embeds, 1
                              )
                          }
                      )
                      prompt_embeds_input = prompt_embeds.repeat(
                          elems_to_repeat_text_embeds, 1, 1
                      )
                      model_pred = unet(
                          noisy_model_input,
                          timesteps,
                          prompt_embeds_input,
                          added_cond_kwargs=unet_added_conditions,
                          return_dict=False,
                      )[0]
  
                  # 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",
                      )
  
                  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_lora_parameters,
                              text_lora_parameters_one,
                              text_lora_parameters_two,
                          )
                          if args.train_text_encoder
                          else unet_lora_parameters
                      )
                      accelerator.clip_grad_norm_(
                          params_to_clip, 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
                  if not args.train_text_encoder:
                      text_encoder_one = (
                          text_encoder_cls_one.from_pretrained(
                              args.pretrained_model_name_or_path,
                              subfolder="text_encoder",
                              revision=args.revision,
                              variant=args.variant,
                          )
                      )
                      text_encoder_two = (
                          text_encoder_cls_two.from_pretrained(
                              args.pretrained_model_name_or_path,
                              subfolder="text_encoder_2",
                              revision=args.revision,
                              variant=args.variant,
                          )
                      )
                  pipeline = StableDiffusionXLPipeline.from_pretrained(
                      args.pretrained_model_name_or_path,
                      vae=vae,
                      text_encoder=accelerator.unwrap_model(
                          text_encoder_one
                      ),
                      text_encoder_2=accelerator.unwrap_model(
                          text_encoder_two
                      ),
                      unet=accelerator.unwrap_model(unet),
                      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
                  )
                  pipeline_args = {"prompt": args.validation_prompt}
  
                  with torch.cuda.amp.autocast():
                      images = [
                          pipeline(
                              **pipeline_args, 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(
                              "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_layers = convert_state_dict_to_diffusers(
              get_peft_model_state_dict(unet)
          )
  
          if args.train_text_encoder:
              text_encoder_one = unwrap_model(text_encoder_one)
              text_encoder_lora_layers = (
                  convert_state_dict_to_diffusers(
                      get_peft_model_state_dict(
                          text_encoder_one.to(torch.float32)
                      )
                  )
              )
              text_encoder_two = unwrap_model(text_encoder_two)
              text_encoder_2_lora_layers = (
                  convert_state_dict_to_diffusers(
                      get_peft_model_state_dict(
                          text_encoder_two.to(torch.float32)
                      )
                  )
              )
          else:
              text_encoder_lora_layers = None
              text_encoder_2_lora_layers = None
  
          StableDiffusionXLPipeline.save_lora_weights(
              save_directory=args.output_dir,
              unet_lora_layers=unet_lora_layers,
              text_encoder_lora_layers=text_encoder_lora_layers,
              text_encoder_2_lora_layers=text_encoder_2_lora_layers,
          )
  
          # Final inference
          # Load previous pipeline
          vae = AutoencoderKL.from_pretrained(
              vae_path,
              subfolder="vae"
              if args.pretrained_vae_model_name_or_path is None
              else None,
              revision=args.revision,
              variant=args.variant,
              torch_dtype=weight_dtype,
          )
          pipeline = StableDiffusionXLPipeline.from_pretrained(
              args.pretrained_model_name_or_path,
              vae=vae,
              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
          )
  
          # load attention processors
          pipeline.load_lora_weights(args.output_dir)
  
          # run inference
          images = []
          if args.validation_prompt and args.num_validation_images > 0:
              pipeline = pipeline.to(accelerator.device)
              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,
                  instance_prompt=args.instance_prompt,
                  validation_prompt=args.validation_prompt,
                  repo_folder=args.output_dir,
                  vae_path=args.pretrained_vae_model_name_or_path,
              )
              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 gc
  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 DistributedDataParallelKwargs, 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, set_peft_model_state_dict
  from peft.utils import get_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, DPMSolverMultistepScheduler, StableDiffusionXLPipeline, 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, compute_snr
  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, add_time_ids, prompt_embeds,  pooled_prompt_embeds