code: def main():
      args = parse_args()
      logging_dir = os.path.join(args.output_dir, args.logging_dir)
      accelerator_project_config = ProjectConfiguration(
          project_dir=args.output_dir, logging_dir=logging_dir
      )
      accelerator = Accelerator(
          gradient_accumulation_steps=args.gradient_accumulation_steps,
          mixed_precision=args.mixed_precision,
          log_with=args.report_to,
          project_config=accelerator_project_config,
      )
  
      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."
              )
  
      # 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)
  
      # 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 tokenizer
      if args.tokenizer_name:
          tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
      elif args.pretrained_model_name_or_path:
          tokenizer = CLIPTokenizer.from_pretrained(
              args.pretrained_model_name_or_path, subfolder="tokenizer"
          )
  
      # Load scheduler and models
      noise_scheduler = DDPMScheduler.from_pretrained(
          args.pretrained_model_name_or_path, subfolder="scheduler"
      )
      text_encoder = CLIPTextModel.from_pretrained(
          args.pretrained_model_name_or_path,
          subfolder="text_encoder",
          revision=args.revision,
      )
      vae = AutoencoderKL.from_pretrained(
          args.pretrained_model_name_or_path,
          subfolder="vae",
          revision=args.revision,
          variant=args.variant,
      )
      unet = UNet2DConditionModel.from_pretrained(
          args.pretrained_model_name_or_path,
          subfolder="unet",
          revision=args.revision,
          variant=args.variant,
      )
  
      # Add the placeholder token in tokenizer
      placeholder_tokens = [args.placeholder_token]
  
      if args.num_vectors < 1:
          raise ValueError(
              f"--num_vectors has to be larger or equal to 1, but is {args.num_vectors}"
          )
  
      # add dummy tokens for multi-vector
      additional_tokens = []
      for i in range(1, args.num_vectors):
          additional_tokens.append(f"{args.placeholder_token}_{i}")
      placeholder_tokens += additional_tokens
  
      num_added_tokens = tokenizer.add_tokens(placeholder_tokens)
      if num_added_tokens != args.num_vectors:
          raise ValueError(
              f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different"
              " `placeholder_token` that is not already in the tokenizer."
          )
  
      # Convert the initializer_token, placeholder_token to ids
      token_ids = tokenizer.encode(
          args.initializer_token, add_special_tokens=False
      )
      # Check if initializer_token is a single token or a sequence of tokens
      if len(token_ids) > 1:
          raise ValueError(
              "The initializer token must be a single token."
          )
  
      initializer_token_id = token_ids[0]
      placeholder_token_ids = tokenizer.convert_tokens_to_ids(
          placeholder_tokens
      )
  
      # Resize the token embeddings as we are adding new special tokens to the tokenizer
      text_encoder.resize_token_embeddings(len(tokenizer))
  
      # Initialise the newly added placeholder token with the embeddings of the initializer token
      token_embeds = text_encoder.get_input_embeddings().weight.data
      with torch.no_grad():
          for token_id in placeholder_token_ids:
              token_embeds[token_id] = token_embeds[
                  initializer_token_id
              ].clone()
  
      # Freeze vae and unet
      vae.requires_grad_(False)
      unet.requires_grad_(False)
      # Freeze all parameters except for the token embeddings in text encoder
      text_encoder.text_model.encoder.requires_grad_(False)
      text_encoder.text_model.final_layer_norm.requires_grad_(False)
      text_encoder.text_model.embeddings.position_embedding.requires_grad_(
          False
      )
  
      if args.gradient_checkpointing:
          # Keep unet in train mode if we are using gradient checkpointing to save memory.
          # The dropout cannot be != 0 so it doesn't matter if we are in eval or train mode.
          unet.train()
          text_encoder.gradient_checkpointing_enable()
          unet.enable_gradient_checkpointing()
  
      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"
              )
  
      # Enable TF32 for faster training on Ampere GPUs,
      # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
      if args.allow_tf32:
          torch.backends.cuda.matmul.allow_tf32 = True
  
      if args.scale_lr:
          args.learning_rate = (
              args.learning_rate
              * args.gradient_accumulation_steps
              * args.train_batch_size
              * accelerator.num_processes
          )
  
      # Initialize the optimizer
      optimizer = torch.optim.AdamW(
          text_encoder.get_input_embeddings().parameters(),  # only optimize the embeddings
          lr=args.learning_rate,
          betas=(args.adam_beta1, args.adam_beta2),
          weight_decay=args.adam_weight_decay,
          eps=args.adam_epsilon,
      )
  
      # Dataset and DataLoaders creation:
      train_dataset = TextualInversionDataset(
          data_root=args.train_data_dir,
          tokenizer=tokenizer,
          size=args.resolution,
          placeholder_token=(
              " ".join(
                  tokenizer.convert_ids_to_tokens(placeholder_token_ids)
              )
          ),
          repeats=args.repeats,
          learnable_property=args.learnable_property,
          center_crop=args.center_crop,
          set="train",
      )
      train_dataloader = torch.utils.data.DataLoader(
          train_dataset,
          batch_size=args.train_batch_size,
          shuffle=True,
          num_workers=args.dataloader_num_workers,
      )
      if args.validation_epochs is not None:
          warnings.warn(
              f"FutureWarning: You are doing logging with validation_epochs={args.validation_epochs}."
              " Deprecated validation_epochs in favor of `validation_steps`"
              f"Setting `args.validation_steps` to {args.validation_epochs * len(train_dataset)}",
              FutureWarning,
              stacklevel=2,
          )
          args.validation_steps = args.validation_epochs * len(
              train_dataset
          )
  
      # 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,
      )
  
      text_encoder.train()
      # Prepare everything with our `accelerator`.
      (
          text_encoder,
          optimizer,
          train_dataloader,
          lr_scheduler,
      ) = accelerator.prepare(
          text_encoder, optimizer, train_dataloader, lr_scheduler
      )
  
      # For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) to half-precision
      # as these weights are only used for inference, keeping weights in full precision is not required.
      weight_dtype = torch.float32
      if accelerator.mixed_precision == "fp16":
          weight_dtype = torch.float16
      elif accelerator.mixed_precision == "bf16":
          weight_dtype = torch.bfloat16
  
      # Move vae and unet to device and cast to weight_dtype
      unet.to(accelerator.device, dtype=weight_dtype)
      vae.to(accelerator.device, dtype=weight_dtype)
  
      # We need to recalculate our total training steps as the size of the training dataloader may have changed.
      num_update_steps_per_epoch = math.ceil(
          len(train_dataloader) / args.gradient_accumulation_steps
      )
      if overrode_max_train_steps:
          args.max_train_steps = (
              args.num_train_epochs * num_update_steps_per_epoch
          )
      # Afterwards we recalculate our number of training epochs
      args.num_train_epochs = math.ceil(
          args.max_train_steps / num_update_steps_per_epoch
      )
  
      # We need to initialize the trackers we use, and also store our configuration.
      # The trackers initializes automatically on the main process.
      if accelerator.is_main_process:
          accelerator.init_trackers(
              "textual_inversion", config=vars(args)
          )
  
      # Train!
      total_batch_size = (
          args.train_batch_size
          * accelerator.num_processes
          * args.gradient_accumulation_steps
      )
  
      logger.info("***** Running training *****")
      logger.info(f"  Num examples = {len(train_dataset)}")
      logger.info(f"  Num Epochs = {args.num_train_epochs}")
      logger.info(
          f"  Instantaneous batch size per device = {args.train_batch_size}"
      )
      logger.info(
          f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}"
      )
      logger.info(
          f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}"
      )
      logger.info(
          f"  Total optimization steps = {args.max_train_steps}"
      )
      global_step = 0
      first_epoch = 0
      # Potentially load in the weights and states from a previous save
      if args.resume_from_checkpoint:
          if args.resume_from_checkpoint != "latest":
              path = os.path.basename(args.resume_from_checkpoint)
          else:
              # Get the most recent checkpoint
              dirs = os.listdir(args.output_dir)
              dirs = [d for d in dirs if d.startswith("checkpoint")]
              dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
              path = dirs[-1] if len(dirs) > 0 else None
  
          if path is None:
              accelerator.print(
                  f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
              )
              args.resume_from_checkpoint = None
              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,
      )
  
      # keep original embeddings as reference
      orig_embeds_params = (
          accelerator.unwrap_model(text_encoder)
          .get_input_embeddings()
          .weight.data.clone()
      )
  
      for epoch in range(first_epoch, args.num_train_epochs):
          text_encoder.train()
          for step, batch in enumerate(train_dataloader):
              with accelerator.accumulate(text_encoder):
                  # Convert images to latent space
                  latents = (
                      vae.encode(
                          batch["pixel_values"].to(dtype=weight_dtype)
                      )
                      .latent_dist.sample()
                      .detach()
                  )
                  latents = latents * vae.config.scaling_factor
  
                  # Sample noise that we'll add to the latents
                  noise = torch.randn_like(latents)
                  bsz = latents.shape[0]
                  # Sample a random timestep for each image
                  timesteps = torch.randint(
                      0,
                      noise_scheduler.config.num_train_timesteps,
                      (bsz,),
                      device=latents.device,
                  )
                  timesteps = timesteps.long()
  
                  # Add noise to the latents according to the noise magnitude at each timestep
                  # (this is the forward diffusion process)
                  noisy_latents = noise_scheduler.add_noise(
                      latents, noise, timesteps
                  )
  
                  # Get the text embedding for conditioning
                  encoder_hidden_states = text_encoder(
                      batch["input_ids"]
                  )[0].to(dtype=weight_dtype)
  
                  # Predict the noise residual
                  model_pred = unet(
                      noisy_latents, timesteps, encoder_hidden_states
                  ).sample
  
                  # Get the target for loss depending on the prediction type
                  if (
                      noise_scheduler.config.prediction_type
                      == "epsilon"
                  ):
                      target = noise
                  elif (
                      noise_scheduler.config.prediction_type
                      == "v_prediction"
                  ):
                      target = noise_scheduler.get_velocity(
                          latents, noise, timesteps
                      )
                  else:
                      raise ValueError(
                          f"Unknown prediction type {noise_scheduler.config.prediction_type}"
                      )
  
                  loss = F.mse_loss(
                      model_pred.float(),
                      target.float(),
                      reduction="mean",
                  )
  
                  accelerator.backward(loss)
  
                  optimizer.step()
                  lr_scheduler.step()
                  optimizer.zero_grad()
  
                  # Let's make sure we don't update any embedding weights besides the newly added token
                  index_no_updates = torch.ones(
                      (len(tokenizer),), dtype=torch.bool
                  )
                  index_no_updates[
                      min(placeholder_token_ids) : max(
                          placeholder_token_ids
                      )
                      + 1
                  ] = False
  
                  with torch.no_grad():
                      accelerator.unwrap_model(
                          text_encoder
                      ).get_input_embeddings().weight[
                          index_no_updates
                      ] = orig_embeds_params[
                          index_no_updates
                      ]
  
              # Checks if the accelerator has performed an optimization step behind the scenes
              if accelerator.sync_gradients:
                  images = []
                  progress_bar.update(1)
                  global_step += 1
                  if global_step % args.save_steps == 0:
                      weight_name = (
                          f"learned_embeds-steps-{global_step}.bin"
                          if args.no_safe_serialization
                          else f"learned_embeds-steps-{global_step}.safetensors"
                      )
                      save_path = os.path.join(
                          args.output_dir, weight_name
                      )
                      save_progress(
                          text_encoder,
                          placeholder_token_ids,
                          accelerator,
                          args,
                          save_path,
                          safe_serialization=not args.no_safe_serialization,
                      )
  
                  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}")
  
                      if (
                          args.validation_prompt is not None
                          and global_step % args.validation_steps == 0
                      ):
                          images = log_validation(
                              text_encoder,
                              tokenizer,
                              unet,
                              vae,
                              args,
                              accelerator,
                              weight_dtype,
                              epoch,
                          )
  
              logs = {
                  "loss": loss.detach().item(),
                  "lr": lr_scheduler.get_last_lr()[0],
              }
              progress_bar.set_postfix(**logs)
              accelerator.log(logs, step=global_step)
  
              if global_step >= args.max_train_steps:
                  break
      # Create the pipeline using the trained modules and save it.
      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
          if args.push_to_hub and not args.save_as_full_pipeline:
              logger.warn(
                  "Enabling full model saving because --push_to_hub=True was specified."
              )
              save_full_model = True
          else:
              save_full_model = args.save_as_full_pipeline
          if save_full_model:
              pipeline = StableDiffusionPipeline.from_pretrained(
                  args.pretrained_model_name_or_path,
                  text_encoder=accelerator.unwrap_model(text_encoder),
                  vae=vae,
                  unet=unet,
                  tokenizer=tokenizer,
              )
              pipeline.save_pretrained(args.output_dir)
          # Save the newly trained embeddings
          weight_name = (
              "learned_embeds.bin"
              if args.no_safe_serialization
              else "learned_embeds.safetensors"
          )
          save_path = os.path.join(args.output_dir, weight_name)
          save_progress(
              text_encoder,
              placeholder_token_ids,
              accelerator,
              args,
              save_path,
              safe_serialization=not args.no_safe_serialization,
          )
  
          if args.push_to_hub:
              save_model_card(
                  repo_id,
                  images=images,
                  base_model=args.pretrained_model_name_or_path,
                  repo_folder=args.output_dir,
              )
              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 logging
  import math
  import os
  import random
  import shutil
  import warnings
  from pathlib import Path
  import numpy
  import PIL
  import safetensors
  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 packaging import version
  from PIL import Image
  from torch.utils.data import Dataset
  from torchvision import transforms
  from tqdm.auto import tqdm
  from transformers import CLIPTextModel, CLIPTokenizer
  import diffusers
  from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, DPMSolverMultistepScheduler, StableDiffusionPipeline, UNet2DConditionModel
  from diffusers.optimization import get_scheduler
  from diffusers.utils import check_min_version, is_wandb_available
  from diffusers.utils.import_utils import is_xformers_available
label_desc: Use image node_id 5 for calling a global function with example usage: main() without return types