code: def main(args):
      logging_dir = Path(args.output_dir, args.logging_dir)
      accelerator_project_config = ProjectConfiguration(
          total_limit=args.checkpoints_total_limit,
          project_dir=args.output_dir,
          logging_dir=logging_dir,
      )
      accelerator = Accelerator(
          gradient_accumulation_steps=args.gradient_accumulation_steps,
          mixed_precision=args.mixed_precision,
          log_with=args.report_to,
          project_config=accelerator_project_config,
      )
  
      if args.report_to == "wandb":
          if not is_wandb_available():
              raise ImportError(
                  "Make sure to install wandb if you want to use it for logging during training."
              )
  
      # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
      # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
      # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
      if (
          args.train_text_encoder
          and args.gradient_accumulation_steps > 1
          and accelerator.num_processes > 1
      ):
          raise ValueError(
              "Gradient accumulation is not supported when training the text encoder in distributed training. "
              "Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
          )
  
      instance_data_dir = []
      instance_prompt = []
      class_data_dir = [] if args.with_prior_preservation else None
      class_prompt = [] if args.with_prior_preservation else None
      if args.concepts_list:
          with open(args.concepts_list, "r") as f:
              concepts_list = json.load(f)
  
          if args.validation_steps:
              args.validation_prompt = []
              args.validation_number_images = []
              args.validation_negative_prompt = []
              args.validation_inference_steps = []
              args.validation_guidance_scale = []
  
          for concept in concepts_list:
              instance_data_dir.append(concept["instance_data_dir"])
              instance_prompt.append(concept["instance_prompt"])
  
              if args.with_prior_preservation:
                  try:
                      class_data_dir.append(concept["class_data_dir"])
                      class_prompt.append(concept["class_prompt"])
                  except KeyError:
                      raise KeyError(
                          "`class_data_dir` or `class_prompt` not found in concepts_list while using "
                          "`with_prior_preservation`."
                      )
              else:
                  if "class_data_dir" in concept:
                      warnings.warn(
                          "Ignoring `class_data_dir` key, to use it you need to enable `with_prior_preservation`."
                      )
                  if "class_prompt" in concept:
                      warnings.warn(
                          "Ignoring `class_prompt` key, to use it you need to enable `with_prior_preservation`."
                      )
  
              if args.validation_steps:
                  args.validation_prompt.append(
                      concept.get("validation_prompt", None)
                  )
                  args.validation_number_images.append(
                      concept.get("validation_number_images", 4)
                  )
                  args.validation_negative_prompt.append(
                      concept.get("validation_negative_prompt", None)
                  )
                  args.validation_inference_steps.append(
                      concept.get("validation_inference_steps", 25)
                  )
                  args.validation_guidance_scale.append(
                      concept.get("validation_guidance_scale", 7.5)
                  )
      else:
          # Parse instance and class inputs, and double check that lengths match
          instance_data_dir = args.instance_data_dir.split(",")
          instance_prompt = args.instance_prompt.split(",")
          assert all(
              x == len(instance_data_dir)
              for x in [len(instance_data_dir), len(instance_prompt)]
          ), "Instance data dir and prompt inputs are not of the same length."
  
          if args.with_prior_preservation:
              class_data_dir = args.class_data_dir.split(",")
              class_prompt = args.class_prompt.split(",")
              assert all(
                  x == len(instance_data_dir)
                  for x in [
                      len(instance_data_dir),
                      len(instance_prompt),
                      len(class_data_dir),
                      len(class_prompt),
                  ]
              ), "Instance & class data dir or prompt inputs are not of the same length."
  
          if args.validation_steps:
              validation_prompts = args.validation_prompt.split(",")
              num_of_validation_prompts = len(validation_prompts)
              args.validation_prompt = validation_prompts
              args.validation_number_images = [
                  args.validation_number_images
              ] * num_of_validation_prompts
  
              negative_validation_prompts = [
                  None
              ] * num_of_validation_prompts
              if args.validation_negative_prompt:
                  negative_validation_prompts = (
                      args.validation_negative_prompt.split(",")
                  )
                  while (
                      len(negative_validation_prompts)
                      < num_of_validation_prompts
                  ):
                      negative_validation_prompts.append(None)
              args.validation_negative_prompt = (
                  negative_validation_prompts
              )
  
              assert num_of_validation_prompts == len(
                  negative_validation_prompts
              ), "The length of negative prompts for validation is greater than the number of validation prompts."
              args.validation_inference_steps = [
                  args.validation_inference_steps
              ] * num_of_validation_prompts
              args.validation_guidance_scale = [
                  args.validation_guidance_scale
              ] * num_of_validation_prompts
  
      # Make one log on every process with the configuration for debugging.
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          level=logging.INFO,
      )
      logger.info(accelerator.state, main_process_only=False)
      if accelerator.is_local_main_process:
          datasets.utils.logging.set_verbosity_warning()
          transformers.utils.logging.set_verbosity_warning()
          diffusers.utils.logging.set_verbosity_info()
      else:
          datasets.utils.logging.set_verbosity_error()
          transformers.utils.logging.set_verbosity_error()
          diffusers.utils.logging.set_verbosity_error()
  
      # If passed along, set the training seed now.
      if args.seed is not None:
          set_seed(args.seed)
  
      # Generate class images if prior preservation is enabled.
      if args.with_prior_preservation:
          for i in range(len(class_data_dir)):
              class_images_dir = Path(class_data_dir[i])
              if not class_images_dir.exists():
                  class_images_dir.mkdir(parents=True)
              cur_class_images = len(list(class_images_dir.iterdir()))
  
              if cur_class_images < args.num_class_images:
                  torch_dtype = (
                      torch.float16
                      if accelerator.device.type == "cuda"
                      else torch.float32
                  )
                  if args.prior_generation_precision == "fp32":
                      torch_dtype = torch.float32
                  elif args.prior_generation_precision == "fp16":
                      torch_dtype = torch.float16
                  elif args.prior_generation_precision == "bf16":
                      torch_dtype = torch.bfloat16
                  pipeline = DiffusionPipeline.from_pretrained(
                      args.pretrained_model_name_or_path,
                      torch_dtype=torch_dtype,
                      safety_checker=None,
                      revision=args.revision,
                  )
                  pipeline.set_progress_bar_config(disable=True)
  
                  num_new_images = (
                      args.num_class_images - cur_class_images
                  )
                  logger.info(
                      f"Number of class images to sample: {num_new_images}."
                  )
  
                  sample_dataset = PromptDataset(
                      class_prompt[i], 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 ii, image in enumerate(images):
                          hash_image = insecure_hashlib.sha1(
                              image.tobytes()
                          ).hexdigest()
                          image_filename = (
                              class_images_dir
                              / f"{example['index'][ii] + cur_class_images}-{hash_image}.jpg"
                          )
                          image.save(image_filename)
  
                  # Clean up the memory deleting one-time-use variables.
                  del pipeline
                  del sample_dataloader
                  del sample_dataset
                  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:
              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
      tokenizer = None
      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,
      )
      vae = AutoencoderKL.from_pretrained(
          args.pretrained_model_name_or_path,
          subfolder="vae",
          revision=args.revision,
      )
      unet = UNet2DConditionModel.from_pretrained(
          args.pretrained_model_name_or_path,
          subfolder="unet",
          revision=args.revision,
      )
  
      vae.requires_grad_(False)
      if not args.train_text_encoder:
          text_encoder.requires_grad_(False)
  
      if args.enable_xformers_memory_efficient_attention:
          if is_xformers_available():
              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()
  
      # Enable TF32 for faster training on Ampere GPUs,
      # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
      if args.allow_tf32:
          torch.backends.cuda.matmul.allow_tf32 = True
  
      if args.scale_lr:
          args.learning_rate = (
              args.learning_rate
              * args.gradient_accumulation_steps
              * args.train_batch_size
              * accelerator.num_processes
          )
  
      # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
      if args.use_8bit_adam:
          try:
              import bitsandbytes as bnb
          except ImportError:
              raise ImportError(
                  "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
              )
  
          optimizer_class = bnb.optim.AdamW8bit
      else:
          optimizer_class = torch.optim.AdamW
  
      # Optimizer creation
      params_to_optimize = (
          itertools.chain(unet.parameters(), text_encoder.parameters())
          if args.train_text_encoder
          else unet.parameters()
      )
      optimizer = optimizer_class(
          params_to_optimize,
          lr=args.learning_rate,
          betas=(args.adam_beta1, args.adam_beta2),
          weight_decay=args.adam_weight_decay,
          eps=args.adam_epsilon,
      )
  
      # Dataset and DataLoaders creation:
      train_dataset = DreamBoothDataset(
          instance_data_root=instance_data_dir,
          instance_prompt=instance_prompt,
          class_data_root=class_data_dir,
          class_prompt=class_prompt,
          tokenizer=tokenizer,
          size=args.resolution,
          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(
              len(instance_data_dir),
              examples,
              args.with_prior_preservation,
          ),
          num_workers=1,
      )
  
      # Scheduler and math around the number of training steps.
      overrode_max_train_steps = False
      num_update_steps_per_epoch = math.ceil(
          len(train_dataloader) / args.gradient_accumulation_steps
      )
      if args.max_train_steps is None:
          args.max_train_steps = (
              args.num_train_epochs * num_update_steps_per_epoch
          )
          overrode_max_train_steps = True
  
      lr_scheduler = get_scheduler(
          args.lr_scheduler,
          optimizer=optimizer,
          num_warmup_steps=args.lr_warmup_steps
          * accelerator.num_processes,
          num_training_steps=args.max_train_steps
          * accelerator.num_processes,
          num_cycles=args.lr_num_cycles,
          power=args.lr_power,
      )
  
      # Prepare everything with our `accelerator`.
      if args.train_text_encoder:
          (
              unet,
              text_encoder,
              optimizer,
              train_dataloader,
              lr_scheduler,
          ) = accelerator.prepare(
              unet,
              text_encoder,
              optimizer,
              train_dataloader,
              lr_scheduler,
          )
      else:
          (
              unet,
              optimizer,
              train_dataloader,
              lr_scheduler,
          ) = accelerator.prepare(
              unet, optimizer, train_dataloader, lr_scheduler
          )
  
      # For mixed precision training we cast the text_encoder and vae weights to half-precision
      # as these models are only used for inference, keeping weights in full precision is not required.
      weight_dtype = torch.float32
      if accelerator.mixed_precision == "fp16":
          weight_dtype = torch.float16
      elif accelerator.mixed_precision == "bf16":
          weight_dtype = torch.bfloat16
  
      # Move vae and text_encoder to device and cast to weight_dtype
      vae.to(accelerator.device, dtype=weight_dtype)
      if not args.train_text_encoder:
          text_encoder.to(accelerator.device, dtype=weight_dtype)
  
      # We need to recalculate our total training steps as the size of the training dataloader may have changed.
      num_update_steps_per_epoch = math.ceil(
          len(train_dataloader) / args.gradient_accumulation_steps
      )
      if overrode_max_train_steps:
          args.max_train_steps = (
              args.num_train_epochs * num_update_steps_per_epoch
          )
      # Afterwards we recalculate our number of training epochs
      args.num_train_epochs = math.ceil(
          args.max_train_steps / num_update_steps_per_epoch
      )
  
      # We need to initialize the trackers we use, and also store our configuration.
      # The trackers initialize automatically on the main process.
      if accelerator.is_main_process:
          accelerator.init_trackers("dreambooth", 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 = basename(args.resume_from_checkpoint)
          else:
              # Get the mos recent checkpoint
              dirs = listdir(args.output_dir)
              dirs = [d for d in dirs if d.startswith("checkpoint")]
              dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
              path = dirs[-1] if len(dirs) > 0 else None
  
          if path is None:
              accelerator.print(
                  f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
              )
              args.resume_from_checkpoint = None
          else:
              accelerator.print(f"Resuming from checkpoint {path}")
              accelerator.load_state(join(args.output_dir, path))
              global_step = int(path.split("-")[1])
  
              resume_global_step = (
                  global_step * args.gradient_accumulation_steps
              )
              first_epoch = global_step // num_update_steps_per_epoch
              resume_step = resume_global_step % (
                  num_update_steps_per_epoch
                  * args.gradient_accumulation_steps
              )
  
      # Only show the progress bar once on each machine.
      progress_bar = tqdm(
          range(global_step, args.max_train_steps),
          disable=not accelerator.is_local_main_process,
      )
      progress_bar.set_description("Steps")
  
      for epoch in range(first_epoch, args.num_train_epochs):
          unet.train()
          if args.train_text_encoder:
              text_encoder.train()
          for step, batch in enumerate(train_dataloader):
              # Skip steps until we reach the resumed step
              if (
                  args.resume_from_checkpoint
                  and epoch == first_epoch
                  and step < resume_step
              ):
                  if step % args.gradient_accumulation_steps == 0:
                      progress_bar.update(1)
                  continue
  
              with accelerator.accumulate(unet):
                  # Convert images to latent space
                  latents = vae.encode(
                      batch["pixel_values"].to(dtype=weight_dtype)
                  ).latent_dist.sample()
                  latents = latents * vae.config.scaling_factor
  
                  # Sample noise that we'll add to the latents
                  noise = torch.randn_like(latents)
                  bsz = latents.shape[0]
                  # Sample a random timestep for each image
                  time_steps = torch.randint(
                      0,
                      noise_scheduler.config.num_train_timesteps,
                      (bsz,),
                      device=latents.device,
                  )
                  time_steps = time_steps.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, time_steps
                  )
  
                  # Get the text embedding for conditioning
                  encoder_hidden_states = text_encoder(
                      batch["input_ids"]
                  )[0]
  
                  # Predict the noise residual
                  model_pred = unet(
                      noisy_latents, time_steps, 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, time_steps
                      )
                  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:
                      params_to_clip = (
                          itertools.chain(
                              unet.parameters(),
                              text_encoder.parameters(),
                          )
                          if args.train_text_encoder
                          else unet.parameters()
                      )
                      accelerator.clip_grad_norm_(
                          params_to_clip, args.max_grad_norm
                      )
                  optimizer.step()
                  lr_scheduler.step()
                  optimizer.zero_grad(
                      set_to_none=args.set_grads_to_none
                  )
  
              # Checks if the accelerator has performed an optimization step behind the scenes
              if accelerator.sync_gradients:
                  progress_bar.update(1)
                  global_step += 1
  
                  if accelerator.is_main_process:
                      if global_step % args.checkpointing_steps == 0:
                          save_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_steps
                          and any(args.validation_prompt)
                          and global_step % args.validation_steps == 0
                      ):
                          images_set = generate_validation_images(
                              text_encoder,
                              tokenizer,
                              unet,
                              vae,
                              args,
                              accelerator,
                              weight_dtype,
                          )
                          for images, validation_prompt in zip(
                              images_set, args.validation_prompt
                          ):
                              if len(images) > 0:
                                  label = str(uuid.uuid1())[
                                      :8
                                  ]  # generate an id for different set of images
                                  log_validation_images_to_tracker(
                                      images,
                                      label,
                                      validation_prompt,
                                      accelerator,
                                      global_step,
                                  )
  
              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:
          pipeline = DiffusionPipeline.from_pretrained(
              args.pretrained_model_name_or_path,
              unet=accelerator.unwrap_model(unet),
              text_encoder=accelerator.unwrap_model(text_encoder),
              revision=args.revision,
          )
          pipeline.save_pretrained(args.output_dir)
  
          if args.push_to_hub:
              upload_folder(
                  repo_id=repo_id,
                  folder_path=args.output_dir,
                  commit_message="End of training",
                  ignore_patterns=["step_*", "epoch_*"],
              )
  
      accelerator.end_training()
  
imports: import argparse
  import itertools
  import json
  import logging
  import math
  import uuid
  import warnings
  from os import environ, listdir, makedirs
  from os.path import basename, join
  from pathlib import Path
  from typing import List
  import datasets
  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 PIL import Image
  from torch import dtype
  from torch.nn import Module
  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, 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 6 for calling a global function with example usage: main(args) without return types